From c1e70d6c2acf549dbc3b7e5622abe739e04b4a81 Mon Sep 17 00:00:00 2001 From: W1y1r <2730956796@qq.com> Date: Fri, 4 Jul 2025 22:43:02 +0800 Subject: [PATCH] Modification of all documents in the dual model of AINode tree table --- src/.vuepress/public/img/ETTh1-tab.csv | 17421 ++++++++++++++++ src/.vuepress/public/img/model01.png | Bin 0 -> 186709 bytes src/.vuepress/public/img/model02.png | Bin 0 -> 359527 bytes src/.vuepress/public/img/model03.png | Bin 0 -> 132745 bytes src/.vuepress/sidebar/V2.0.x/en-Table.ts | 10 + src/.vuepress/sidebar/V2.0.x/zh-Table.ts | 10 + .../sidebar_timecho/V2.0.x/en-Table.ts | 10 + .../sidebar_timecho/V2.0.x/zh-Table.ts | 10 + .../Table/AI-capability/AINode_apache.md | 381 + .../Table/AI-capability/AINode_timecho.md | 447 + .../AI-capability/TimeSeries-Large-Model.md | 113 + .../AINode_Deployment_apache.md | 266 + .../AINode_Deployment_timecho.md | 266 + .../Tree/AI-capability/AINode_apache.md | 221 +- .../Tree/AI-capability/AINode_timecho.md | 292 +- 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.../AINode_Deployment_apache.md | 313 +- .../AINode_Deployment_timecho.md | 347 +- .../AI-capability/AINode_apache.md | 379 + .../AI-capability/AINode_timecho.md | 444 + .../AI-capability/TimeSeries-Large-Model.md | 112 + .../AINode_Deployment_apache.md | 267 + .../AINode_Deployment_timecho.md | 14 + .../latest/AI-capability/AINode_apache.md | 137 +- .../latest/AI-capability/AINode_timecho.md | 207 +- .../AI-capability/TimeSeries-Large-Model.md | 37 +- .../AINode_Deployment_apache.md | 313 +- .../AINode_Deployment_timecho.md | 550 +- 48 files changed, 23971 insertions(+), 3608 deletions(-) create mode 100644 src/.vuepress/public/img/ETTh1-tab.csv create mode 100644 src/.vuepress/public/img/model01.png create mode 100644 src/.vuepress/public/img/model02.png create mode 100644 src/.vuepress/public/img/model03.png create mode 100644 src/UserGuide/Master/Table/AI-capability/AINode_apache.md create mode 100644 src/UserGuide/Master/Table/AI-capability/AINode_timecho.md create mode 100644 src/UserGuide/Master/Table/AI-capability/TimeSeries-Large-Model.md create mode 100644 src/UserGuide/Master/Table/Deployment-and-Maintenance/AINode_Deployment_apache.md create mode 100644 src/UserGuide/Master/Table/Deployment-and-Maintenance/AINode_Deployment_timecho.md create mode 100644 src/UserGuide/latest-Table/AI-capability/AINode_apache.md create mode 100644 src/UserGuide/latest-Table/AI-capability/AINode_timecho.md create mode 100644 src/UserGuide/latest-Table/AI-capability/TimeSeries-Large-Model.md create mode 100644 src/UserGuide/latest-Table/Deployment-and-Maintenance/AINode_Deployment_apache.md create mode 100644 src/UserGuide/latest-Table/Deployment-and-Maintenance/AINode_Deployment_timecho.md create mode 100644 src/zh/UserGuide/Master/Table/AI-capability/AINode_apache.md create mode 100644 src/zh/UserGuide/Master/Table/AI-capability/AINode_timecho.md create mode 100644 src/zh/UserGuide/Master/Table/AI-capability/TimeSeries-Large-Model.md create mode 100644 src/zh/UserGuide/Master/Table/Deployment-and-Maintenance/AINode_Deployment_apache.md create mode 100644 src/zh/UserGuide/Master/Table/Deployment-and-Maintenance/AINode_Deployment_timecho.md create mode 100644 src/zh/UserGuide/latest-Table/AI-capability/AINode_apache.md create mode 100644 src/zh/UserGuide/latest-Table/AI-capability/AINode_timecho.md create mode 100644 src/zh/UserGuide/latest-Table/AI-capability/TimeSeries-Large-Model.md create mode 100644 src/zh/UserGuide/latest-Table/Deployment-and-Maintenance/AINode_Deployment_apache.md create mode 100644 src/zh/UserGuide/latest-Table/Deployment-and-Maintenance/AINode_Deployment_timecho.md diff --git a/src/.vuepress/public/img/ETTh1-tab.csv b/src/.vuepress/public/img/ETTh1-tab.csv new file mode 100644 index 000000000..790748afb --- /dev/null +++ b/src/.vuepress/public/img/ETTh1-tab.csv @@ -0,0 +1,17421 @@ +time,hufl,hull,mufl,mull,lufl,lull,ot +2016-07-01 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zQTdaHX~01F{8wt6!w1JMFwP!UJnR;hE&ggq^lhYJ>ie+pc?bRCD6zd|i)&JOKmB5> zeT(glrJ^p=T0c`FaatTwIq8>G?Y1}ced|-&G%?}29jXsb88K`N7tl<1$q=e%^0KqL z4G-t;<}dRiCOg$A(08)D7A8ffW+W_Q`NQz6#o5W4Cn}TwF@LNU#lHvnbO{X0x|8Q@ zIH`^Pn*KFiiTKB|7DM-wNeIZd7qvR}%K*!9PXFAsp-sf_z&Jy|?mqEF&@l^2&twS(HhtpUR%~=Ou9#rD_X$x{blEka> z5m-&WKw|ME^9HEHIE;0;Mhg8MVzVzd78FP44q+h=|##wDO zq7Nm4dw)FzC3BwWvBO?GdKc1<`s*Al>aBe? zbVufDhK#a_oZOkxOuc^4RJ(iBoF(M|ad$Ds^RoKV>v|3=^NwrFMQ}9ya%(*LYbZWq zhhDnNswj%vvvC_W)62p;QdyoXd}QW1ncL@mI(S6LjO4=cvH2~(VuMMdm>#XZITPzW z^5l`7u4kO+<%$nybJ=>I+bc(rZrd=G^!fhWR$1a3^V8nY(p+pb_-)+0a!exPzv8xm zhT??(v9@ + +# AINode + +AINode is an IoTDB native node designed to support the registration, management, and invocation of large-scale time series models. It comes with industry-leading proprietary time series models such as Timer and Sundial. These models can be invoked through standard SQL statements, enabling real-time inference of time series data at the millisecond level, and supporting application scenarios such as trend forecasting, missing value imputation, and anomaly detection for time series data. + +The system architecture is shown below: +::: center + +::: +The responsibilities of the three nodes are as follows: + +- **ConfigNode**: responsible for storing and managing the meta-information of the model; responsible for distributed node management. +- **DataNode**: responsible for receiving and parsing SQL requests from users; responsible for storing time-series data; responsible for preprocessing computation of data. +- **AINode**: responsible for model file import creation and model inference. + +## 1. Advantageous features + +Compared with building a machine learning service alone, it has the following advantages: + +- **Simple and easy to use**: no need to use Python or Java programming, the complete process of machine learning model management and inference can be completed using SQL statements. Creating a model can be done using the CREATE MODEL statement, and using a model for inference can be done using the SELECT * FROM FORECAST (...) statement, making it simpler and more convenient to use. + +- **Avoid Data Migration**: With IoTDB native machine learning, data stored in IoTDB can be directly applied to the inference of machine learning models without having to move the data to a separate machine learning service platform, which accelerates data processing, improves security, and reduces costs. + +![](/img/AInode1.png) + +- **Built-in Advanced Algorithms**: supports industry-leading machine learning analytics algorithms covering typical timing analysis tasks, empowering the timing database with native data analysis capabilities. Such as: + - **Time Series Forecasting**: learns patterns of change from past time series; thus outputs the most likely prediction of future series based on observations at a given past time. + - **Anomaly Detection for Time Series**: detects and identifies outliers in a given time series data, helping to discover anomalous behaviour in the time series. + - **Annotation for Time Series (Time Series Annotation)**: Adds additional information or markers, such as event occurrence, outliers, trend changes, etc., to each data point or specific time period to better understand and analyse the data. + + +## 2. Basic Concepts + +- **Model**: A machine learning model that takes time series data as input and outputs analysis results or decisions. Models are the basic management units of AINode, supporting model operations such as creation (registration), deletion, query, and inference. +- **Create**: Load externally designed or trained model files/algorithms into AINode for unified management and usage by IoTDB. +- **Inference**: Use the created model to complete time series analysis tasks applicable to the model on specified time series data. +- **Built-in Capabilities**: AINode comes with machine learning algorithms or self-developed models for common time series analysis scenarios (e.g., forecasting and anomaly detection). +![](/img/h3.png) + +## 3. Installation and Deployment + +The deployment of AINode can be found in the document [Deployment Guidelines](../Deployment-and-Maintenance/AINode_Deployment_apache.md#ainode-deployment) . + + +## 4. Usage Guide + +AINode provides model creation and deletion functions for time series models. Built-in models do not require creation and can be used directly. + +### 4.1 Registering Models + +Trained deep learning models can be registered by specifying their input and output vector dimensions for inference. + +Models that meet the following criteria can be registered with AINode: + +1. AINode currently supports models trained with PyTorch 2.4.0. Features above version 2.4.0 should be avoided. +2. AINode supports models stored using PyTorch JIT (`model.pt`), which must include both the model structure and weights. +3. The model input sequence can include single or multiple columns. If multi-column, it must match the model capabilities and configuration file. +4. Model configuration parameters must be clearly defined in the `config.yaml` file. When using the model, the input and output dimensions defined in `config.yaml` must be strictly followed. Mismatches with the configuration file will cause errors. + +The SQL syntax for model registration is defined as follows: + +```SQL +create model using uri +``` + +Detailed meanings of SQL parameters: + +- **model_name**: The global unique identifier for the model, non-repeating. Model names have the following constraints: + - Allowed characters: [0-9 a-z A-Z _] (letters, numbers, underscores) + - Length: 2-64 characters + - Case-sensitive +- **uri**: The resource path of the model registration files, which should include the **model structure and weight file `model.pt` and the model configuration file `config.yaml`** + + - **Model structure and weight file**: The weight file generated after model training, currently supporting `.pt` files from PyTorch training. + + - **Model configuration file**: Parameters related to the model structure provided during registration, which must include input and output dimensions for inference: + + | **Parameter Name** | **Description** | **Example** | + | ------------ | ---------------------------- | -------- | + | input_shape | Rows and columns of model input | [96,2] | + | output_shape | Rows and columns of model output | [48,2] | + + In addition to inference, data types of input and output can also be specified: + + | **Parameter Name** | **Description** | **Example** | + | ------------------ | ------------------------- | ---------------------- | + | input_type | Data type of model input | ['float32', 'float32'] | + | output_type | Data type of model output | ['float32', 'float32'] | + + Additional notes can be specified for model management display: + + | **Parameter Name** | **Description** | **Example** | + | ------------------ | --------------------------------------------- | -------------------------------------------- | + | attributes | Optional notes set by users for model display | 'model_type': 'dlinear', 'kernel_size': '25' | + +In addition to registering local model files, remote resource paths can be specified via URIs for registration, using open-source model repositories (e.g., HuggingFace). + +#### Example + +The current example folder contains model.pt (trained model) and config.yaml with the following content: + +```YAML +configs: + # Required + input_shape: [96, 2] # Model accepts 96 rows x 2 columns of data + output_shape: [48, 2] # Model outputs 48 rows x 2 columns of data + + # Optional (default to all float32, column count matches shape) + input_type: ["int64", "int64"] # Data types of inputs, must match input column count + output_type: ["text", "int64"] # Data types of outputs, must match output column count + +attributes: # Optional user-defined notes + 'model_type': 'dlinear' + 'kernel_size': '25' +``` + +Register the model by specifying this folder as the loading path: + +```SQL +IoTDB> create model dlinear_example using uri "file://./example" +``` + +Models can also be downloaded from HuggingFace for registration: + +```SQL +IoTDB> create model dlinear_example using uri "https://huggingface.co/google/timesfm-2.0-500m-pytorch" +``` + +After SQL execution, registration proceeds asynchronously. The registration status can be checked via model display (see Model Display section). The registration success time mainly depends on the model file size. + +Once registered, the model can be invoked for inference through normal query syntax. + +### 4.2 Viewing Models + +Registered models can be queried using the `show models` command. The SQL definitions are: + +```SQL +show models + +show models +``` + +In addition to displaying all models, specifying a `model_id` shows details of a specific model. The display includes: + +| **ModelId** | **State** | **Configs** | **Attributes** | +| ----------- | --------------------------------------------------------- | ------------------------------------------------ | -------------- | +| Unique ID | Registration status (INACTIVE, LOADING, ACTIVE, DROPPING) | InputShape, outputShape, inputTypes, outputTypes | User notes | + +**State descriptions:** + +- **INACTIVE**: Model is unavailable. +- **LOADING**: Model is being loaded. +- **ACTIVE**: Model is available. +- **DROPPING**: Model is being deleted. + +#### Example + +```SQL +IoTDB> show models + ++---------------------+--------------------+--------+--------+ +| ModelId| ModelType|Category| State| ++---------------------+--------------------+--------+--------+ +| arima| Arima|BUILT-IN| ACTIVE| +| holtwinters| HoltWinters|BUILT-IN| ACTIVE| +|exponential_smoothing|ExponentialSmoothing|BUILT-IN| ACTIVE| +| naive_forecaster| NaiveForecaster|BUILT-IN| ACTIVE| +| stl_forecaster| StlForecaster|BUILT-IN| ACTIVE| +| gaussian_hmm| GaussianHmm|BUILT-IN| ACTIVE| +| gmm_hmm| GmmHmm|BUILT-IN| ACTIVE| +| stray| Stray|BUILT-IN| ACTIVE| +| timer_xl| Timer-XL|BUILT-IN| ACTIVE| +| sundial| Timer-Sundial|BUILT-IN| ACTIVE| ++---------------------+--------------------+--------+--------+ +``` + +### 4.3 Deleting Models + +Registered models can be deleted via SQL, which removes all related files under AINode: + +```SQL +drop model +``` + +Specify the registered `model_id` to delete the model. Since deletion involves data cleanup, the operation is not immediate, and the model state becomes `DROPPING`, during which it cannot be used for inference. **Note:** Built-in models cannot be deleted. + +### 4.4 Inference with Built-in Models + +SQL syntax: + + +```SQL +SELECT * FROM forecast( + input, + model_id, + [output_length, + output_start_time, + output_interval, + timecol, + preserve_input, + model_options]? +) +``` +Built-in models do not require prior registration for inference. Simply use the `forecast` function and specify the `model_id` to invoke the model's inference capabilities. + + - **Note**: Inference with built-in time series large models requires local availability of model weights in the directory `/IOTDB_AINODE_HOME/data/ainode/models/weights/model_id/`. If weights are missing, they will be automatically downloaded from HuggingFace. Ensure direct network access to HuggingFace. + +Parameter descriptions: + + +| Parameter | Type | Attribute | Description | Required | Notes | +| ----------------- | ------------- | -------------------------------------------------------- | ------------------------------------------------------------ | -------- | ------------------------------------------------------------ | +| input | Table | SET SEMANTIC | Input data for forecasting | Yes | | +| model_id | String | Scalar | Name of the model to use | Yes | Must be non-empty and a built-in model; otherwise, errors like "MODEL_ID cannot be null" occur. | +| output_length | INT32 | Scalar (default: 96) | Size of the output forecast window | No | Must be > 0. | +| output_start_time | Timestamp | Scalar (default: last input timestamp + output_interval) | Start timestamp of the forecast results | No | Can be negative (before 1970-01-01). | +| output_interval | Time interval | Scalar (default: inferred from input) | Time interval between forecast points (supports ns, us, ms, s, m, h, d, w) | No | If > 0, uses user-specified interval; else, infers from input. | +| timecol | String | Scalar (default: "time") | Name of the timestamp column | No | Must exist in `input` and be of TIMESTAMP type; otherwise, errors occur. | +| preserve_input | Boolean | Scalar (default: false) | Retain all input rows in the output | No | | +| model_options | String | Scalar (default: empty) | Model-specific key-value pairs (e.g., normalization) | No | Unsupported parameters are ignored. See appendix for built-in model parameters. | + +**Notes:** + +1. The `forecast` function predicts all columns in the input table by default (excluding the time column and columns specified in `partition by`). +2. The `forecast` function does not require the input data to be in any specific order. It sorts the input data in ascending order by the timestamp (specified by the `TIMECOL` parameter) before invoking the model for prediction. +3. Different models have different requirements for the number of input data rows: + - If the input data has fewer rows than the minimum requirement, an error will be thrown. + - If the input data exceeds the maximum row limit, the last rows that meet the requirement will be automatically truncated for processing. + - Among the currently built-in models in AINode, only `sundial` has a row limit. It supports a maximum of 2880 input rows. If exceeded, the last 2880 rows will be automatically used. +4. The result columns of the `forecast` function include all input columns from the input table, with their original data types preserved. If `preserve_input = true`, an additional `is_input` column will be included to indicate whether a row is from the input data. + - Currently, only columns of type INT32, INT64, FLOAT, or DOUBLE are supported for prediction. Otherwise, an error will occur: "The type of the column [%s] is [%s], only INT32, INT64, FLOAT, DOUBLE is allowed." +5. `output_start_time` and `output_interval` only affect the generation of the timestamp column in the output results. Both are optional parameters: + - `output_start_time` defaults to the last timestamp of the input data plus `output_interval`. + - `output_interval` defaults to the sampling interval of the input data, calculated as: (last timestamp - first timestamp) / (number of rows - 1). + - The timestamp of the Nth output row is calculated as: `output_start_time + (N - 1) * output_interval`. + +#### Example + +Using the ETTh1-tab dataset:[ETTh1-tab](/img/ETTh1-tab.csv)。 + +**View supported models** + +```Bash +IoTDB:etth> show models ++---------------------+--------------------+--------+------+ +| ModelId| ModelType|Category| State| ++---------------------+--------------------+--------+------+ +| arima| Arima|BUILT-IN|ACTIVE| +| holtwinters| HoltWinters|BUILT-IN|ACTIVE| +|exponential_smoothing|ExponentialSmoothing|BUILT-IN|ACTIVE| +| naive_forecaster| NaiveForecaster|BUILT-IN|ACTIVE| +| stl_forecaster| StlForecaster|BUILT-IN|ACTIVE| +| gaussian_hmm| GaussianHmm|BUILT-IN|ACTIVE| +| gmm_hmm| GmmHmm|BUILT-IN|ACTIVE| +| stray| Stray|BUILT-IN|ACTIVE| +| sundial| Timer-Sundial|BUILT-IN|ACTIVE| +| timer_xl| Timer-XL|BUILT-IN|ACTIVE| ++---------------------+--------------------+--------+------+ +Total line number = 10 +It costs 0.004s +``` + +**Inference with sundial model:** + +```Bash +IoTDB:etth> select Time, HUFL,HULL,MUFL,MULL,LUFL,LULL,OT from eg LIMIT 96 ++-----------------------------+------+-----+-----+-----+-----+-----+------+ +| Time| HUFL| HULL| MUFL| MULL| LUFL| LULL| OT| ++-----------------------------+------+-----+-----+-----+-----+-----+------+ +|2016-07-01T00:00:00.000+08:00| 5.827|2.009|1.599|0.462|4.203| 1.34|30.531| +|2016-07-01T01:00:00.000+08:00| 5.693|2.076|1.492|0.426|4.142|1.371|27.787| +|2016-07-01T02:00:00.000+08:00| 5.157|1.741|1.279|0.355|3.777|1.218|27.787| +|2016-07-01T03:00:00.000+08:00| 5.09|1.942|1.279|0.391|3.807|1.279|25.044| +...... +Total line number = 96 +It costs 0.119s + +IoTDB:etth> select * from forecast( + model_id => 'sundial', + input => (select Time, ot from etth.eg where time >= 2016-08-07T18:00:00.000+08:00 limit 1440) order BY time, + output_length => 96 +) ++-----------------------------+---------+ +| time| ot| ++-----------------------------+---------+ +|2016-10-06T18:00:00.000+08:00|20.781654| +|2016-10-06T19:00:00.000+08:00|20.252121| +|2016-10-06T20:00:00.000+08:00|19.960138| +|2016-10-06T21:00:00.000+08:00|19.662334| +...... +Total line number = 96 +It costs 1.615s +``` + +### 4.5 Time Series Large Model Import Steps + +AINode supports multiple time series large models. For deployment, refer to [Time Series Large Model](https://timecho.com/docs/zh/UserGuide/latest/AI-capability/TimeSeries-Large-Model.html) + + +## 5 Permission Management + +AINode uses IoTDB's authentication for permission management. Users need `USE_MODEL` permission for model management and `READ_DATA` permission for inference (to access input data sources). + +| **Permission** | **Scope** | **Admin (ROOT)** | **Regular User** | **Path-Related** | +| -------------- | ------------------------ | ---------------- | ---------------- | ---------------- | +| USE_MODEL | Create/Show/Drop models | ✔️ | ✔️ | ❌ | +| READ_DATA | Call inference functions | ✔️ | ✔️ | ✔️ | + + +## 6 Appendix + +**Arima** + +| Parameter | Description | Default | +| ----------------------- | ------------------------------------------------------------ | --------- | +| order | ARIMA order `(p, d, q)`: p=autoregressive, d=differencing, q=moving average. | (1,0,0) | +| seasonal_order | Seasonal ARIMA order `(P, D, Q, s)`: seasonal AR, differencing, MA orders, and season length (e.g., 12 for monthly data). | (0,0,0,0) | +| method | Optimizer: 'newton', 'nm', 'bfgs', 'lbfgs', 'powell', 'cg', 'ncg', 'basinhopping'. | 'lbfgs' | +| maxiter | Maximum iterations/function evaluations. | 50 | +| out_of_sample_size | Number of tail samples for validation (not used in fitting). | 0 | +| scoring | Scoring function for validation (sklearn metric or custom). | 'mse' | +| trend | Trend term configuration. If `with_intercept=True` and None, defaults to 'c' (constant). | None | +| with_intercept | Include intercept term. | True | +| time_varying_regression | Allow regression coefficients to vary over time. | False | +| enforce_stationarity | Enforce stationarity of AR components. | True | +| enforce_invertibility | Enforce invertibility of MA components. | True | +| simple_differencing | Use differenced data for estimation (sacrifices first rows). | False | +| measurement_error | Assume observation errors. | False | +| mle_regression | Use maximum likelihood for regression (must be False if `time_varying_regression=True`). | True | +| hamilton_representation | Use Hamilton representation (default is Harvey). | False | +| concentrate_scale | Exclude scale parameter from likelihood (reduces parameters). | False | + +**NaiveForecaster** + +| Parameter | Description | Default | +| --------- | ------------------------------------------------------------ | ------- | +| strategy | Forecasting strategy: - `"last"`: Use last training value (seasonal if `sp`>1). - `"mean"`: Use mean of last window (seasonal if `sp`>1). - `"drift"`: Fit line through last window and extrapolate (non-robust to NaN). | "last" | +| sp | Seasonal period. `None` or 1 means no seasonality; 12 means monthly. | 1 | + +**STLForecaster** + +| Parameter | Description | Default | +| ------------- | ---------------------------------------------------------- | ------- | +| sp | Seasonal period (units). Passed to statsmodels' STL. | 2 | +| seasonal | Seasonal smoothing window (odd ≥3, typically ≥7). | 7 | +| seasonal_deg | LOESS polynomial degree for season (0=constant, 1=linear). | 1 | +| trend_deg | LOESS polynomial degree for trend (0 or 1). | 1 | +| low_pass_deg | LOESS polynomial degree for low-pass (0 or 1). | 1 | +| seasonal_jump | Interpolation step for season LOESS (larger = faster). | 1 | +| trend_jump | Interpolation step for trend LOESS (larger = faster). | 1 | +| low_pass_jump | Interpolation step for low-pass LOESS. | 1 | + +**ExponentialSmoothing (HoltWinters)** + +| Parameter | Description | Default | +| --------------------- | ------------------------------------------------------------ | ----------- | +| damped_trend | Use damped trend (trend flattens instead of growing infinitely). | True | +| initialization_method | Initialization method: - `"estimated"`: Fit to estimate initial states - `"heuristic"`: Use heuristic for initial level/trend/season - `"known"`: User-provided initial values - `"legacy-heuristic"`: Legacy compatibility | "estimated" | +| optimized | Optimize parameters via maximum likelihood. | True | +| remove_bias | Remove bias to make residuals' mean zero. | False | +| use_brute | Use brute-force grid search for initial parameters. | | \ No newline at end of file diff --git a/src/UserGuide/Master/Table/AI-capability/AINode_timecho.md b/src/UserGuide/Master/Table/AI-capability/AINode_timecho.md new file mode 100644 index 000000000..034a54971 --- /dev/null +++ b/src/UserGuide/Master/Table/AI-capability/AINode_timecho.md @@ -0,0 +1,447 @@ + + +# AINode + +AINode is an IoTDB native node designed to support the registration, management, and invocation of large-scale time series models. It comes with industry-leading proprietary time series models such as Timer and Sundial. These models can be invoked through standard SQL statements, enabling real-time inference of time series data at the millisecond level, and supporting application scenarios such as trend forecasting, missing value imputation, and anomaly detection for time series data. + +The system architecture is shown below: +::: center + +::: +The responsibilities of the three nodes are as follows: + +- **ConfigNode**: responsible for storing and managing the meta-information of the model; responsible for distributed node management. +- **DataNode**: responsible for receiving and parsing SQL requests from users; responsible for storing time-series data; responsible for preprocessing computation of data. +- **AINode**: responsible for model file import creation and model inference. + +## 1. Advantageous features + +Compared with building a machine learning service alone, it has the following advantages: + +- **Simple and easy to use**: no need to use Python or Java programming, the complete process of machine learning model management and inference can be completed using SQL statements. Creating a model can be done using the CREATE MODEL statement, and using a model for inference can be done using the SELECT * FROM FORECAST (...) statement, making it simpler and more convenient to use. + +- **Avoid Data Migration**: With IoTDB native machine learning, data stored in IoTDB can be directly applied to the inference of machine learning models without having to move the data to a separate machine learning service platform, which accelerates data processing, improves security, and reduces costs. + +![](/img/AInode1.png) + +- **Built-in Advanced Algorithms**: supports industry-leading machine learning analytics algorithms covering typical timing analysis tasks, empowering the timing database with native data analysis capabilities. Such as: + - **Time Series Forecasting**: learns patterns of change from past time series; thus outputs the most likely prediction of future series based on observations at a given past time. + - **Anomaly Detection for Time Series**: detects and identifies outliers in a given time series data, helping to discover anomalous behaviour in the time series. + - **Annotation for Time Series (Time Series Annotation)**: Adds additional information or markers, such as event occurrence, outliers, trend changes, etc., to each data point or specific time period to better understand and analyse the data. + + +## 2. Basic Concepts + +- **Model**: A machine learning model takes time series data as input and outputs analysis task results or decisions. Models are the basic management units of AINode, supporting model operations such as creation (registration), deletion, query, modification (fine-tuning), and usage (inference). +- **Create**: Load externally designed or trained model files/algorithms into AINode for unified management and usage by IoTDB. +- **Inference**: Use the created model to complete time series analysis tasks applicable to the model on specified time series data. +- **Built-in Capabilities**: AINode comes with machine learning algorithms or self-developed models for common time series analysis scenarios (e.g., forecasting and anomaly detection). +![](/img/h3.png) + +## 3. Installation and Deployment + +The deployment of AINode can be found in the document [Deployment Guidelines](../Deployment-and-Maintenance/AINode_Deployment_timecho.md#ainode-deployment) . + + +## 4. Usage Guide + +AINode provides model creation and deletion functions for time series models. Built-in models do not require creation and can be used directly. + +### 4.1 Registering Models + +Trained deep learning models can be registered by specifying their input and output vector dimensions for inference. + +Models that meet the following criteria can be registered with AINode: + +1. AINode currently supports models trained with PyTorch 2.4.0. Features above version 2.4.0 should be avoided. +2. AINode supports models stored using PyTorch JIT (`model.pt`), which must include both the model structure and weights. +3. The model input sequence can include single or multiple columns. If multi-column, it must match the model capabilities and configuration file. +4. Model configuration parameters must be clearly defined in the `config.yaml` file. When using the model, the input and output dimensions defined in `config.yaml` must be strictly followed. Mismatches with the configuration file will cause errors. + +The SQL syntax for model registration is defined as follows: + +```SQL +create model using uri +``` + +Detailed meanings of SQL parameters: + +- **model_name**: The global unique identifier for the model, non-repeating. Model names have the following constraints: + - Allowed characters: [0-9 a-z A-Z _] (letters, numbers, underscores) + - Length: 2-64 characters + - Case-sensitive +- **uri**: The resource path of the model registration files, which should include the **model structure and weight file `model.pt` and the model configuration file `config.yaml`** + + - **Model structure and weight file**: The weight file generated after model training, currently supporting `.pt` files from PyTorch training. + + - **Model configuration file**: Parameters related to the model structure provided during registration, which must include input and output dimensions for inference: + + | **Parameter Name** | **Description** | **Example** | + | ------------ | ---------------------------- | -------- | + | input_shape | Rows and columns of model input | [96,2] | + | output_shape | Rows and columns of model output | [48,2] | + + In addition to inference, data types of input and output can also be specified: + + | **Parameter Name** | **Description** | **Example** | + | ------------------ | ------------------------- | ---------------------- | + | input_type | Data type of model input | ['float32', 'float32'] | + | output_type | Data type of model output | ['float32', 'float32'] | + + Additional notes can be specified for model management display: + + | **Parameter Name** | **Description** | **Example** | + | ------------------ | --------------------------------------------- | -------------------------------------------- | + | attributes | Optional notes set by users for model display | 'model_type': 'dlinear', 'kernel_size': '25' | + +In addition to registering local model files, remote resource paths can be specified via URIs for registration, using open-source model repositories (e.g., HuggingFace). + +#### Example + +The current example folder contains model.pt (trained model) and config.yaml with the following content: + +```YAML +configs: + # Required + input_shape: [96, 2] # Model accepts 96 rows x 2 columns of data + output_shape: [48, 2] # Model outputs 48 rows x 2 columns of data + + # Optional (default to all float32, column count matches shape) + input_type: ["int64", "int64"] # Data types of inputs, must match input column count + output_type: ["text", "int64"] # Data types of outputs, must match output column count + +attributes: # Optional user-defined notes + 'model_type': 'dlinear' + 'kernel_size': '25' +``` + +Register the model by specifying this folder as the loading path: + +```SQL +IoTDB> create model dlinear_example using uri "file://./example" +``` + +Models can also be downloaded from HuggingFace for registration: + +```SQL +IoTDB> create model dlinear_example using uri "https://huggingface.co/google/timesfm-2.0-500m-pytorch" +``` + +After SQL execution, registration proceeds asynchronously. The registration status can be checked via model display (see Model Display section). The registration success time mainly depends on the model file size. + +Once registered, the model can be invoked for inference through normal query syntax. + +### 4.2 Viewing Models + +Registered models can be queried using the `show models` command. The SQL definitions are: + +```SQL +show models + +show models +``` + +In addition to displaying all models, specifying a `model_id` shows details of a specific model. The display includes: + +| **ModelId** | **State** | **Configs** | **Attributes** | +| ----------- | --------------------------------------------------------- | ------------------------------------------------ | -------------- | +| Unique ID | Registration status (INACTIVE, LOADING, ACTIVE,TRAINING,FAILED, DROPPING) | InputShape, outputShape, inputTypes, outputTypes | User notes | + +**State descriptions:** + +- **INACTIVE**: The model is in an unavailable state. +- **LOADING**: The model is being loaded. +- **ACTIVE**: The model is in an available state. +- **TRAINING**: The model is in the fine-tuning state. +- **FAILED**: The model fine-tuning failed. +- **DROPPING**: The model is being deleted. + +#### Example + +```SQL +IoTDB> show models + ++---------------------+--------------------+--------+--------+ +| ModelId| ModelType|Category| State| ++---------------------+--------------------+--------+--------+ +| arima| Arima|BUILT-IN| ACTIVE| +| holtwinters| HoltWinters|BUILT-IN| ACTIVE| +|exponential_smoothing|ExponentialSmoothing|BUILT-IN| ACTIVE| +| naive_forecaster| NaiveForecaster|BUILT-IN| ACTIVE| +| stl_forecaster| StlForecaster|BUILT-IN| ACTIVE| +| gaussian_hmm| GaussianHmm|BUILT-IN| ACTIVE| +| gmm_hmm| GmmHmm|BUILT-IN| ACTIVE| +| stray| Stray|BUILT-IN| ACTIVE| +| timer_xl| Timer-XL|BUILT-IN| ACTIVE| +| sundial| Timer-Sundial|BUILT-IN| ACTIVE| ++---------------------+--------------------+--------+--------+ +``` + +### 4.3 Deleting Models + +Registered models can be deleted via SQL, which removes all related files under AINode: + +```SQL +drop model +``` + +Specify the registered `model_id` to delete the model. Since deletion involves data cleanup, the operation is not immediate, and the model state becomes `DROPPING`, during which it cannot be used for inference. **Note:** Built-in models cannot be deleted. + +### 4.4 Inference with Built-in Models + +SQL syntax: + + +```SQL +SELECT * FROM forecast( + input, + model_id, + [output_length, + output_start_time, + output_interval, + timecol, + preserve_input, + model_options]? +) +``` + + +Built-in models do not require prior registration for inference. Simply use the `forecast` function and specify the `model_id` to invoke the model's inference capabilities. + + - **Note**: Inference with built-in time series large models requires local availability of model weights in the directory `/IOTDB_AINODE_HOME/data/ainode/models/weights/model_id/`. If weights are missing, they will be automatically downloaded from HuggingFace. Ensure direct network access to HuggingFace. + +Parameter descriptions: + +| Parameter | Type | Attribute | Description | Required | Notes | +| ----------------- | ------------- | -------------------------------------------------------- | ------------------------------------------------------------ | -------- | ------------------------------------------------------------ | +| input | Table | SET SEMANTIC | Input data for forecasting | Yes | | +| model_id | String | Scalar | Name of the model to use | Yes | Must be non-empty and a built-in model; otherwise, errors like "MODEL_ID cannot be null" occur. | +| output_length | INT32 | Scalar (default: 96) | Size of the output forecast window | No | Must be > 0. | +| output_start_time | Timestamp | Scalar (default: last input timestamp + output_interval) | Start timestamp of the forecast results | No | Can be negative (before 1970-01-01). | +| output_interval | Time interval | Scalar (default: inferred from input) | Time interval between forecast points (supports ns, us, ms, s, m, h, d, w) | No | If > 0, uses user-specified interval; else, infers from input. | +| timecol | String | Scalar (default: "time") | Name of the timestamp column | No | Must exist in `input` and be of TIMESTAMP type; otherwise, errors occur. | +| preserve_input | Boolean | Scalar (default: false) | Retain all input rows in the output | No | | +| model_options | String | Scalar (default: empty) | Model-specific key-value pairs (e.g., normalization) | No | Unsupported parameters are ignored. See appendix for built-in model parameters. | + +**Notes:** + +1. The `forecast` function predicts all columns in the input table by default (excluding the time column and columns specified in `partition by`). +2. The `forecast` function does not require the input data to be in any specific order. It sorts the input data in ascending order by the timestamp (specified by the `TIMECOL` parameter) before invoking the model for prediction. +3. Different models have different requirements for the number of input data rows: + - If the input data has fewer rows than the minimum requirement, an error will be thrown. + - If the input data exceeds the maximum row limit, the last rows that meet the requirement will be automatically truncated for processing. + - Among the currently built-in models in AINode, only `sundial` has a row limit. It supports a maximum of 2880 input rows. If exceeded, the last 2880 rows will be automatically used. +4. The result columns of the `forecast` function include all input columns from the input table, with their original data types preserved. If `preserve_input = true`, an additional `is_input` column will be included to indicate whether a row is from the input data. + - Currently, only columns of type INT32, INT64, FLOAT, or DOUBLE are supported for prediction. Otherwise, an error will occur: "The type of the column [%s] is [%s], only INT32, INT64, FLOAT, DOUBLE is allowed." +5. `output_start_time` and `output_interval` only affect the generation of the timestamp column in the output results. Both are optional parameters: + - `output_start_time` defaults to the last timestamp of the input data plus `output_interval`. + - `output_interval` defaults to the sampling interval of the input data, calculated as: (last timestamp - first timestamp) / (number of rows - 1). + - The timestamp of the Nth output row is calculated as: `output_start_time + (N - 1) * output_interval`. + +#### Example + +Using the ETTh1-tab dataset:[ETTh1-tab](/img/ETTh1-tab.csv)。 + +**View supported models** + +```Bash +IoTDB:etth> show models ++---------------------+--------------------+--------+------+ +| ModelId| ModelType|Category| State| ++---------------------+--------------------+--------+------+ +| arima| Arima|BUILT-IN|ACTIVE| +| holtwinters| HoltWinters|BUILT-IN|ACTIVE| +|exponential_smoothing|ExponentialSmoothing|BUILT-IN|ACTIVE| +| naive_forecaster| NaiveForecaster|BUILT-IN|ACTIVE| +| stl_forecaster| StlForecaster|BUILT-IN|ACTIVE| +| gaussian_hmm| GaussianHmm|BUILT-IN|ACTIVE| +| gmm_hmm| GmmHmm|BUILT-IN|ACTIVE| +| stray| Stray|BUILT-IN|ACTIVE| +| sundial| Timer-Sundial|BUILT-IN|ACTIVE| +| timer_xl| Timer-XL|BUILT-IN|ACTIVE| ++---------------------+--------------------+--------+------+ +Total line number = 10 +It costs 0.004s +``` + +**Inference with sundial model:** + +```Bash +IoTDB:etth> select Time, HUFL,HULL,MUFL,MULL,LUFL,LULL,OT from eg LIMIT 96 ++-----------------------------+------+-----+-----+-----+-----+-----+------+ +| Time| HUFL| HULL| MUFL| MULL| LUFL| LULL| OT| ++-----------------------------+------+-----+-----+-----+-----+-----+------+ +|2016-07-01T00:00:00.000+08:00| 5.827|2.009|1.599|0.462|4.203| 1.34|30.531| +|2016-07-01T01:00:00.000+08:00| 5.693|2.076|1.492|0.426|4.142|1.371|27.787| +|2016-07-01T02:00:00.000+08:00| 5.157|1.741|1.279|0.355|3.777|1.218|27.787| +|2016-07-01T03:00:00.000+08:00| 5.09|1.942|1.279|0.391|3.807|1.279|25.044| +...... +Total line number = 96 +It costs 0.119s + +IoTDB:etth> select * from forecast( + model_id => 'sundial', + input => (select Time, ot from etth.eg where time >= 2016-08-07T18:00:00.000+08:00 limit 1440) order BY time, + output_length => 96 +) ++-----------------------------+---------+ +| time| ot| ++-----------------------------+---------+ +|2016-10-06T18:00:00.000+08:00|20.781654| +|2016-10-06T19:00:00.000+08:00|20.252121| +|2016-10-06T20:00:00.000+08:00|19.960138| +|2016-10-06T21:00:00.000+08:00|19.662334| +...... +Total line number = 96 +It costs 1.615s +``` +### 4.5 Fine-tuning Built-in Models +> Only Timer-XL and Timer-Sundial support fine-tuning. + + +The SQL syntax is as follows: + + +```SQL +create model (with hyperparameters +(=(, =)*))? +from model +on dataset (inputSql) +``` + +#### Example + +1. Select the first 80% of data from the measurement `ot` as the fine-tuning dataset, and create the model `sundialv3` based on `sundial`. + +```SQL +IoTDB> set sql_dialect=table +Msg: The statement is executed successfully. +IoTDB> CREATE MODEL sundialv3 FROM MODEL sundial ON DATASET ('SELECT time, ot from etth.eg where 1467302400000 <= time and time < 1517468400001') +Msg: The statement is executed successfully. +IoTDB> show models ++---------------------+--------------------+----------+--------+ +| ModelId| ModelType| Category| State| ++---------------------+--------------------+----------+--------+ +| arima| Arima| BUILT-IN| ACTIVE| +| holtwinters| HoltWinters| BUILT-IN| ACTIVE| +|exponential_smoothing|ExponentialSmoothing| BUILT-IN| ACTIVE| +| naive_forecaster| NaiveForecaster| BUILT-IN| ACTIVE| +| stl_forecaster| StlForecaster| BUILT-IN| ACTIVE| +| gaussian_hmm| GaussianHmm| BUILT-IN| ACTIVE| +| gmm_hmm| GmmHmm| BUILT-IN| ACTIVE| +| stray| Stray| BUILT-IN| ACTIVE| +| sundial| Timer-Sundial| BUILT-IN| ACTIVE| +| timer_xl| Timer-XL| BUILT-IN| ACTIVE| +| sundialv2| Timer-Sundial|FINE-TUNED| ACTIVE| +| sundialv3| Timer-Sundial|FINE-TUNED|TRAINING| ++---------------------+--------------------+----------+--------+ +``` + +2. The fine-tuning task starts asynchronously in the background, and logs can be viewed in the AINode process. After fine-tuning is complete, query and use the new model + +```SQL +IoTDB> show models ++---------------------+--------------------+----------+------+ +| ModelId| ModelType| Category| State| ++---------------------+--------------------+----------+------+ +| arima| Arima| BUILT-IN|ACTIVE| +| holtwinters| HoltWinters| BUILT-IN|ACTIVE| +|exponential_smoothing|ExponentialSmoothing| BUILT-IN|ACTIVE| +| naive_forecaster| NaiveForecaster| BUILT-IN|ACTIVE| +| stl_forecaster| StlForecaster| BUILT-IN|ACTIVE| +| gaussian_hmm| GaussianHmm| BUILT-IN|ACTIVE| +| gmm_hmm| GmmHmm| BUILT-IN|ACTIVE| +| stray| Stray| BUILT-IN|ACTIVE| +| sundial| Timer-Sundial| BUILT-IN|ACTIVE| +| timer_xl| Timer-XL| BUILT-IN|ACTIVE| +| sundialv2| Timer-Sundial|FINE-TUNED|ACTIVE| +| sundialv3| Timer-Sundial|FINE-TUNED|ACTIVE| ++---------------------+--------------------+----------+------+ +``` + +### 4.6 Time Series Large Model Import Steps + +AINode supports multiple time series large models. For deployment, refer to [Time Series Large Model](https://timecho.com/docs/zh/UserGuide/latest/AI-capability/TimeSeries-Large-Model.html) + + +## 5 Permission Management + +AINode uses IoTDB's authentication for permission management. Users need `USE_MODEL` permission for model management and `READ_DATA` permission for inference (to access input data sources). + +| **Permission** | **Scope** | **Admin (ROOT)** | **Regular User** | **Path-Related** | +| -------------- | ------------------------ | ---------------- | ---------------- | ---------------- | +| USE_MODEL | Create/Show/Drop models | ✔️ | ✔️ | ❌ | +| READ_DATA | Call inference functions | ✔️ | ✔️ | ✔️ | + + +## 6 Appendix + +**Arima** + +| Parameter | Description | Default | +| ----------------------- | ------------------------------------------------------------ | --------- | +| order | ARIMA order `(p, d, q)`: p=autoregressive, d=differencing, q=moving average. | (1,0,0) | +| seasonal_order | Seasonal ARIMA order `(P, D, Q, s)`: seasonal AR, differencing, MA orders, and season length (e.g., 12 for monthly data). | (0,0,0,0) | +| method | Optimizer: 'newton', 'nm', 'bfgs', 'lbfgs', 'powell', 'cg', 'ncg', 'basinhopping'. | 'lbfgs' | +| maxiter | Maximum iterations/function evaluations. | 50 | +| out_of_sample_size | Number of tail samples for validation (not used in fitting). | 0 | +| scoring | Scoring function for validation (sklearn metric or custom). | 'mse' | +| trend | Trend term configuration. If `with_intercept=True` and None, defaults to 'c' (constant). | None | +| with_intercept | Include intercept term. | True | +| time_varying_regression | Allow regression coefficients to vary over time. | False | +| enforce_stationarity | Enforce stationarity of AR components. | True | +| enforce_invertibility | Enforce invertibility of MA components. | True | +| simple_differencing | Use differenced data for estimation (sacrifices first rows). | False | +| measurement_error | Assume observation errors. | False | +| mle_regression | Use maximum likelihood for regression (must be False if `time_varying_regression=True`). | True | +| hamilton_representation | Use Hamilton representation (default is Harvey). | False | +| concentrate_scale | Exclude scale parameter from likelihood (reduces parameters). | False | + +**NaiveForecaster** + +| Parameter | Description | Default | +| --------- | ------------------------------------------------------------ | ------- | +| strategy | Forecasting strategy: - `"last"`: Use last training value (seasonal if `sp`>1). - `"mean"`: Use mean of last window (seasonal if `sp`>1). - `"drift"`: Fit line through last window and extrapolate (non-robust to NaN). | "last" | +| sp | Seasonal period. `None` or 1 means no seasonality; 12 means monthly. | 1 | + +**STLForecaster** + +| Parameter | Description | Default | +| ------------- | ---------------------------------------------------------- | ------- | +| sp | Seasonal period (units). Passed to statsmodels' STL. | 2 | +| seasonal | Seasonal smoothing window (odd ≥3, typically ≥7). | 7 | +| seasonal_deg | LOESS polynomial degree for season (0=constant, 1=linear). | 1 | +| trend_deg | LOESS polynomial degree for trend (0 or 1). | 1 | +| low_pass_deg | LOESS polynomial degree for low-pass (0 or 1). | 1 | +| seasonal_jump | Interpolation step for season LOESS (larger = faster). | 1 | +| trend_jump | Interpolation step for trend LOESS (larger = faster). | 1 | +| low_pass_jump | Interpolation step for low-pass LOESS. | 1 | + +**ExponentialSmoothing (HoltWinters)** + +| Parameter | Description | Default | +| --------------------- | ------------------------------------------------------------ | ----------- | +| damped_trend | Use damped trend (trend flattens instead of growing infinitely). | True | +| initialization_method | Initialization method: - `"estimated"`: Fit to estimate initial states - `"heuristic"`: Use heuristic for initial level/trend/season - `"known"`: User-provided initial values - `"legacy-heuristic"`: Legacy compatibility | "estimated" | +| optimized | Optimize parameters via maximum likelihood. | True | +| remove_bias | Remove bias to make residuals' mean zero. | False | +| use_brute | Use brute-force grid search for initial parameters. | | \ No newline at end of file diff --git a/src/UserGuide/Master/Table/AI-capability/TimeSeries-Large-Model.md b/src/UserGuide/Master/Table/AI-capability/TimeSeries-Large-Model.md new file mode 100644 index 000000000..c678ad28c --- /dev/null +++ b/src/UserGuide/Master/Table/AI-capability/TimeSeries-Large-Model.md @@ -0,0 +1,113 @@ + + +# TimeSeries Large Model + +## Introduction + +A time series large model is a foundational model specifically designed for time series analysis. The IoTDB team has independently developed time series large models, which are pre-trained on massive time series data using technologies such as transformer structures. These models can understand and generate time series data across various domains and are applicable to applications like time series forecasting, anomaly detection, and time series imputation. Unlike traditional time series analysis techniques, time series large models possess the capability to extract universal features and provide technical services based on zero-shot analysis and fine-tuning for a wide range of analytical tasks. + +The team's related technologies of time series large models have been published in top international machine learning conferences. + +## Application Scenarios + +- **Time Series Forecasting**: Provides forecasting services for time series data in industrial production, natural environments, and other areas, helping users to understand future trends in advance. +- **Data Imputation**: For missing segments in time series, perform context imputation to enhance the continuity and completeness of the dataset. +- **Anomaly Detection**: Utilizing regression analysis technology, monitor time series data in real-time and provide timely warnings for potential anomalies. + +![](/img/LargeModel10.png) + +## Timer Model + +The Timer model not only demonstrates excellent few-shot generalization and multi-task adaptation capabilities but also gains a rich knowledge base through pre-training, endowing it with the universal capability to handle a variety of downstream tasks, featuring the following: + +- **Generalization**: The model can be fine-tuned using a small number of samples to achieve leading predictive performance in the industry. +- **Versatility**: The model is designed flexibly to adapt to various task requirements and supports variable input and output lengths, enabling it to play a role in various application scenarios. +- **Scalability**: As the number of model parameters increases or the scale of pre-training data expands, the model's performance continues to improve, ensuring the model can optimize its predictive effects with the growth of time and data volume. + +![](/img/model01.png) + +## Timer-XL Model + +Timer-XL is an upgraded version of Timer that further extends the network structure and achieves comprehensive breakthroughs in multiple dimensions: + +- **Ultra-long Context Support**: This model breaks through the limitations of traditional time series forecasting models, supporting inputs of thousands of Tokens (equivalent to tens of thousands of time points), effectively solving the context length bottleneck problem. +- **Multi-variable Prediction Scenarios Coverage**: Supports various forecasting scenarios, including non-stationary time series forecasting, multi-variable forecasting tasks, and forecasting with covariates, meeting diverse business needs. +- **Large-scale Industrial Time Series Dataset**: Pre-trained on a trillion-scale industrial IoT time series dataset, which has the important characteristics of huge volume, excellent quality, and rich domains, covering energy, aerospace, steel, transportation, and other fields. + +![](/img/model02.png) + +## Timer-Sundial Model + +Timer-Sundial is a series of generative foundational models focused on time series forecasting. The basic version has 128 million parameters and has undergone large-scale pre-training on 1 trillion time points. Its core features include: + +- **Powerful Generalization Performance**: Possesses zero-shot forecasting capabilities, supporting both point forecasting and probabilistic forecasting simultaneously. +- **Flexible Forecasting Distribution Analysis**: Can not only forecast mean values or quantiles but also evaluate any statistical characteristics of the forecasting distribution through the original samples generated by the model. +- **Innovative Generative Architecture**: Adopts a "Transformer + TimeFlow" collaborative architecture—Transformer learns the autoregressive representation of time segments, and the TimeFlow module converts random noise into diversified forecasting trajectories based on the Flow-Matching framework, achieving efficient non-deterministic sample generation. + +![](/img/model03.png) + +## Effect Demonstration + +Time series large models can adapt to real-time series data in various domains and scenarios, demonstrating excellent processing effects on various tasks. The following are real-world performances on different datasets: + +**Time Series Forecasting:** + +Utilizing the forecasting capability of time series large models, the future change trends of time series can be accurately predicted. In the figure below, the blue curve represents the forecast trend, and the red curve represents the actual trend, with a high degree of 吻合 (coincidence) between the two curves. + +![](/img/LargeModel03.png) + +**Data Imputation:** + +Utilizing time series large models to perform predictive imputation on missing data segments. + +![](/img/timeseries-large-model-data-imputation.png) + + +**Anomaly Detection:** + +Utilizing time series large models to accurately identify outliers that deviate significantly from normal trends. + +![](/img/LargeModel05.png) + +## Deployment Usage + +1. Open the IoTDB CLI console and verify that the ConfigNode, DataNode, and AINode statuses are all ​Running. + +Check command: + +```sql +show cluster +``` + +![](/img/ainode-timer-1.png) + +2. When the AINode is started for the first time in a networked environment, the Timer-XL and Sundial models will be automatically pulled. + +3. Verify model registration success + +Check command: + +```sql +show models +``` + +![](/img/LargeModel06.png) diff --git a/src/UserGuide/Master/Table/Deployment-and-Maintenance/AINode_Deployment_apache.md b/src/UserGuide/Master/Table/Deployment-and-Maintenance/AINode_Deployment_apache.md new file mode 100644 index 000000000..50c532376 --- /dev/null +++ b/src/UserGuide/Master/Table/Deployment-and-Maintenance/AINode_Deployment_apache.md @@ -0,0 +1,266 @@ + +# AINode Deployment + +## 1. AINode Introduction + +### 1.1 Capability Introduction + + AINode is the third type of endogenous node provided by IoTDB after the Configurable Node and DataNode. This node extends its ability to perform machine learning analysis on time series by interacting with the DataNode and Configurable Node of the IoTDB cluster. It supports the introduction of existing machine learning models from external sources for registration and the use of registered models to complete time series analysis tasks on specified time series data through simple SQL statements. The creation, management, and inference of models are integrated into the database engine. Currently, machine learning algorithms or self-developed models are available for common time series analysis scenarios, such as prediction and anomaly detection. + +### 1.2 Delivery Method + It is an additional package outside the IoTDB cluster, with independent installation. + +### 1.3 Deployment mode +

+ + +
+ +## 2. Installation preparation + +### 2.1 Get installation package + + Unzip and install the package + `(apache-iotdb--ainode-bin.zip)`， The directory structure after unpacking the installation package is as follows: +| **Catalogue** | **Type** | **Explain** | +| ------------ | -------- | ------------------------------------------------ | +| lib | folder | AINode compiled binary executable files and related code dependencies | +| sbin | folder | The running script of AINode can start, remove, and stop AINode | +| conf | folder | Contains configuration items for AINode, specifically including the following configuration items | +| LICENSE | file | Certificate | +| NOTICE | file | Tips | +| README_ZH.md | file | Explanation of the Chinese version of the markdown format | +| `README.md` | file | Instructions | + +### 2.2 Environmental Preparation + +1. Recommended operating systems: Ubuntu, CentOS, MacOS +2. IoTDB version: >= V 2.0.5.1 +3. Runtime environment + - Python version between 3.9 and 3.12, with pip and venv tools installed; + +## 3. Installation steps + +### 3.1 Install AINode + + + 1. Ensure Python version is between 3.9 and 3.12: +```shell + python --version +# or +python3 --version +``` + + 2. Download and import AINode into a dedicated folder, switch to the folder, and unzip the package: +```shell + unzip timechodb-2.0.5.1-ainode-bin.zip + ``` +3. Create a virtual environment (execute in the ainode directory): + + ```shell + python3 -m venv venv + ``` + +4. Activate the virtual environment: + + ```shell + source venv/bin/activate + ``` +5. Update pip and install AINode dependencies: + ```shell + python -m pip install --upgrade pip + poetry lock + poetry install + ``` + +### 3.2 Configuration item modification + +AINode supports modifying some necessary parameters. You can find the following parameters in the `conf/iotdb-ainode.properties` file and make persistent modifications to them: +| **Name** | **Description** | **Type** | **Default Value** | +| ------------------------------ | ------------------------------------------------------------ | -------- | ------------------ | +| cluster_name | Identifier of the cluster AINode joins | string | defaultCluster | +| ain_seed_config_node | Address of the ConfigNode registered when AINode starts | String | 127.0.0.1:10710 | +| ain_cluster_ingress_address | RPC address of the DataNode for AINode to pull data | String | 127.0.0.1 | +| ain_cluster_ingress_port | RPC port of the DataNode for AINode to pull data | Integer | 6667 | +| ain_cluster_ingress_username | Client username for AINode to pull data from the DataNode | String | root | +| ain_cluster_ingress_password | Client password for AINode to pull data from the DataNode | String | root | +| ain_cluster_ingress_time_zone | Client time zone for AINode to pull data from the DataNode | String | UTC+8 | +| ain_inference_rpc_address | Address for AINode to provide services and communication (internal interface) | String | 127.0.0.1 | +| ain_inference_rpc_port | Port for AINode to provide services and communication | String | 10810 | +| ain_system_dir | Metadata storage path for AINode (relative path starts from OS-dependent directory; absolute path is recommended) | String | data/AINode/system | +| ain_models_dir | Path to store model files for AINode (relative path starts from OS-dependent directory; absolute path is recommended) | String | data/AINode/models | +| ain_thrift_compression_enabled | Whether to enable Thrift compression for AINode (0=disabled, 1=enabled) | Boolean | 0 | + +### 3.3 Start AINode + + After completing the deployment of Seed Config Node, the registration and inference functions of the model can be supported by adding AINode nodes. After specifying the information of the IoTDB cluster in the configuration file, the corresponding instruction can be executed to start AINode and join the IoTDB cluster。 + +#### Networking environment startup + +##### Start command + +```shell + # Start command + # Linux and MacOS systems + bash sbin/start-ainode.sh + + # Windows systems + sbin\start-ainode.bat + + # Backend startup command (recommended for long-term running) + # Linux and MacOS systems + nohup bash sbin/start-ainode.sh > myout.file 2>& 1 & + + # Windows systems + nohup bash sbin\start-ainode.bat > myout.file 2>& 1 & + ``` + + +#### Example + +```shell + # Start command + # Linux and MacOS systems + bash sbin/start-ainode.sh + # Windows systems + sbin\start-ainode.bat + + + # Backend startup command (recommended for long-term running) + # Linux and MacOS systems + nohup bash sbin/start-ainode.sh > myout.file 2>& 1 & + # Windows systems + nohup bash sbin\start-ainode.bat > myout.file 2>& 1 & + ``` + +### 3.4 Detecting the status of AINode nodes + +During the startup process of AINode, the new AINode will be automatically added to the IoTDB cluster. After starting AINode, you can enter SQL in the command line to query. If you see an AINode node in the cluster and its running status is Running (as shown below), it indicates successful joining. + + +```shell +IoTDB> show cluster ++------+----------+-------+---------------+------------+-------+-----------+ +|NodeID| NodeType| Status|InternalAddress|InternalPort|Version| BuildInfo| ++------+----------+-------+---------------+------------+-------+-----------+ +| 0|ConfigNode|Running| 127.0.0.1| 10710|UNKNOWN|190e303-dev| +| 1| DataNode|Running| 127.0.0.1| 10730|UNKNOWN|190e303-dev| +| 2| AINode|Running| 127.0.0.1| 10810|UNKNOWN|190e303-dev| ++------+----------+-------+---------------+------------+-------+-----------+ +``` + +### 3.5 Stop AINode + +If you need to stop a running AINode node, execute the corresponding shutdown script. + +#### Stop command + +```shell + # Linux / MacOS + bash sbin/stop-ainode.sh + + #Windows + sbin\stop-ainode.bat + ``` + +#### Example + +```shell + # Linux / MacOS + bash sbin/stop-ainode.sh + + # Windows + sbin\stop-ainode.bat + ``` +After stopping AINode, you can still see AINode nodes in the cluster, whose running status is UNKNOWN (as shown below), and the AINode function cannot be used at this time. + + ```shell +IoTDB> show cluster ++------+----------+-------+---------------+------------+-------+-----------+ +|NodeID| NodeType| Status|InternalAddress|InternalPort|Version| BuildInfo| ++------+----------+-------+---------------+------------+-------+-----------+ +| 0|ConfigNode|Running| 127.0.0.1| 10710|UNKNOWN|190e303-dev| +| 1| DataNode|Running| 127.0.0.1| 10730|UNKNOWN|190e303-dev| +| 2| AINode|UNKNOWN| 127.0.0.1| 10790|UNKNOWN|190e303-dev| ++------+----------+-------+---------------+------------+-------+-----------+ +``` +If you need to restart the node, you need to execute the startup script again. + +## 4. common problem + +### 4.1 An error occurs when starting AINode stating that the venv module cannot be found + + When starting AINode using the default method, a Python virtual environment will be created in the installation package directory and dependencies will be installed, so it is required to install the venv module. Generally speaking, Python 3.10 and above versions come with built-in VenV, but for some systems with built-in Python environments, this requirement may not be met. There are two solutions when this error occurs (choose one or the other): + + To install the Venv module locally, taking Ubuntu as an example, you can run the following command to install the built-in Venv module in Python. Or install a Python version with built-in Venv from the Python official website. + + ```shell +apt-get install python3.10-venv +``` +Install version 3.10.0 of venv into AINode in the AINode path. + + ```shell +../Python-3.10.0/python -m venv venv(Folder Name） +``` + When running the startup script, use ` -i ` to specify an existing Python interpreter path as the running environment for AINode, eliminating the need to create a new virtual environment. + + ### 4.2 The SSL module in Python is not properly installed and configured to handle HTTPS resources +WARNING: pip is configured with locations that require TLS/SSL, however the ssl module in Python is not available. +You can install OpenSSLS and then rebuild Python to solve this problem +> Currently Python versions 3.6 to 3.9 are compatible with OpenSSL 1.0.2, 1.1.0, and 1.1.1. + + Python requires OpenSSL to be installed on our system, the specific installation method can be found in [link](https://stackoverflow.com/questions/56552390/how-to-fix-ssl-module-in-python-is-not-available-in-centos) + + ```shell +sudo apt-get install build-essential libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm libncurses5-dev libncursesw5-dev xz-utils tk-dev libffi-dev liblzma-dev uuid-dev lzma-dev liblzma-dev +sudo -E ./configure --with-ssl +make +sudo make install +``` + + ### 4.3 Pip version is lower + + A compilation issue similar to "error: Microsoft Visual C++14.0 or greater is required..." appears on Windows + +The corresponding error occurs during installation and compilation, usually due to insufficient C++version or Setup tools version. You can check it in + + ```shell +./python -m pip install --upgrade pip +./python -m pip install --upgrade setuptools +``` + + + ### 4.4 Install and compile Python + + Use the following instructions to download the installation package from the official website and extract it: + ```shell +.wget https://www.python.org/ftp/python/3.10.0/Python-3.10.0.tar.xz +tar Jxf Python-3.10.0.tar.xz +``` + Compile and install the corresponding Python package: + ```shell +cd Python-3.10.0 +./configure prefix=/usr/local/python3 +make +sudo make install +python3 --version +``` \ No newline at end of file diff --git a/src/UserGuide/Master/Table/Deployment-and-Maintenance/AINode_Deployment_timecho.md b/src/UserGuide/Master/Table/Deployment-and-Maintenance/AINode_Deployment_timecho.md new file mode 100644 index 000000000..50c532376 --- /dev/null +++ b/src/UserGuide/Master/Table/Deployment-and-Maintenance/AINode_Deployment_timecho.md @@ -0,0 +1,266 @@ + +# AINode Deployment + +## 1. AINode Introduction + +### 1.1 Capability Introduction + + AINode is the third type of endogenous node provided by IoTDB after the Configurable Node and DataNode. This node extends its ability to perform machine learning analysis on time series by interacting with the DataNode and Configurable Node of the IoTDB cluster. It supports the introduction of existing machine learning models from external sources for registration and the use of registered models to complete time series analysis tasks on specified time series data through simple SQL statements. The creation, management, and inference of models are integrated into the database engine. Currently, machine learning algorithms or self-developed models are available for common time series analysis scenarios, such as prediction and anomaly detection. + +### 1.2 Delivery Method + It is an additional package outside the IoTDB cluster, with independent installation. + +### 1.3 Deployment mode +
+ + +
+ +## 2. Installation preparation + +### 2.1 Get installation package + + Unzip and install the package + `(apache-iotdb--ainode-bin.zip)`， The directory structure after unpacking the installation package is as follows: +| **Catalogue** | **Type** | **Explain** | +| ------------ | -------- | ------------------------------------------------ | +| lib | folder | AINode compiled binary executable files and related code dependencies | +| sbin | folder | The running script of AINode can start, remove, and stop AINode | +| conf | folder | Contains configuration items for AINode, specifically including the following configuration items | +| LICENSE | file | Certificate | +| NOTICE | file | Tips | +| README_ZH.md | file | Explanation of the Chinese version of the markdown format | +| `README.md` | file | Instructions | + +### 2.2 Environmental Preparation + +1. Recommended operating systems: Ubuntu, CentOS, MacOS +2. IoTDB version: >= V 2.0.5.1 +3. Runtime environment + - Python version between 3.9 and 3.12, with pip and venv tools installed; + +## 3. Installation steps + +### 3.1 Install AINode + + + 1. Ensure Python version is between 3.9 and 3.12: +```shell + python --version +# or +python3 --version +``` + + 2. Download and import AINode into a dedicated folder, switch to the folder, and unzip the package: +```shell + unzip timechodb-2.0.5.1-ainode-bin.zip + ``` +3. Create a virtual environment (execute in the ainode directory): + + ```shell + python3 -m venv venv + ``` + +4. Activate the virtual environment: + + ```shell + source venv/bin/activate + ``` +5. Update pip and install AINode dependencies: + ```shell + python -m pip install --upgrade pip + poetry lock + poetry install + ``` + +### 3.2 Configuration item modification + +AINode supports modifying some necessary parameters. You can find the following parameters in the `conf/iotdb-ainode.properties` file and make persistent modifications to them: +| **Name** | **Description** | **Type** | **Default Value** | +| ------------------------------ | ------------------------------------------------------------ | -------- | ------------------ | +| cluster_name | Identifier of the cluster AINode joins | string | defaultCluster | +| ain_seed_config_node | Address of the ConfigNode registered when AINode starts | String | 127.0.0.1:10710 | +| ain_cluster_ingress_address | RPC address of the DataNode for AINode to pull data | String | 127.0.0.1 | +| ain_cluster_ingress_port | RPC port of the DataNode for AINode to pull data | Integer | 6667 | +| ain_cluster_ingress_username | Client username for AINode to pull data from the DataNode | String | root | +| ain_cluster_ingress_password | Client password for AINode to pull data from the DataNode | String | root | +| ain_cluster_ingress_time_zone | Client time zone for AINode to pull data from the DataNode | String | UTC+8 | +| ain_inference_rpc_address | Address for AINode to provide services and communication (internal interface) | String | 127.0.0.1 | +| ain_inference_rpc_port | Port for AINode to provide services and communication | String | 10810 | +| ain_system_dir | Metadata storage path for AINode (relative path starts from OS-dependent directory; absolute path is recommended) | String | data/AINode/system | +| ain_models_dir | Path to store model files for AINode (relative path starts from OS-dependent directory; absolute path is recommended) | String | data/AINode/models | +| ain_thrift_compression_enabled | Whether to enable Thrift compression for AINode (0=disabled, 1=enabled) | Boolean | 0 | + +### 3.3 Start AINode + + After completing the deployment of Seed Config Node, the registration and inference functions of the model can be supported by adding AINode nodes. After specifying the information of the IoTDB cluster in the configuration file, the corresponding instruction can be executed to start AINode and join the IoTDB cluster。 + +#### Networking environment startup + +##### Start command + +```shell + # Start command + # Linux and MacOS systems + bash sbin/start-ainode.sh + + # Windows systems + sbin\start-ainode.bat + + # Backend startup command (recommended for long-term running) + # Linux and MacOS systems + nohup bash sbin/start-ainode.sh > myout.file 2>& 1 & + + # Windows systems + nohup bash sbin\start-ainode.bat > myout.file 2>& 1 & + ``` + + +#### Example + +```shell + # Start command + # Linux and MacOS systems + bash sbin/start-ainode.sh + # Windows systems + sbin\start-ainode.bat + + + # Backend startup command (recommended for long-term running) + # Linux and MacOS systems + nohup bash sbin/start-ainode.sh > myout.file 2>& 1 & + # Windows systems + nohup bash sbin\start-ainode.bat > myout.file 2>& 1 & + ``` + +### 3.4 Detecting the status of AINode nodes + +During the startup process of AINode, the new AINode will be automatically added to the IoTDB cluster. After starting AINode, you can enter SQL in the command line to query. If you see an AINode node in the cluster and its running status is Running (as shown below), it indicates successful joining. + + +```shell +IoTDB> show cluster ++------+----------+-------+---------------+------------+-------+-----------+ +|NodeID| NodeType| Status|InternalAddress|InternalPort|Version| BuildInfo| ++------+----------+-------+---------------+------------+-------+-----------+ +| 0|ConfigNode|Running| 127.0.0.1| 10710|UNKNOWN|190e303-dev| +| 1| DataNode|Running| 127.0.0.1| 10730|UNKNOWN|190e303-dev| +| 2| AINode|Running| 127.0.0.1| 10810|UNKNOWN|190e303-dev| ++------+----------+-------+---------------+------------+-------+-----------+ +``` + +### 3.5 Stop AINode + +If you need to stop a running AINode node, execute the corresponding shutdown script. + +#### Stop command + +```shell + # Linux / MacOS + bash sbin/stop-ainode.sh + + #Windows + sbin\stop-ainode.bat + ``` + +#### Example + +```shell + # Linux / MacOS + bash sbin/stop-ainode.sh + + # Windows + sbin\stop-ainode.bat + ``` +After stopping AINode, you can still see AINode nodes in the cluster, whose running status is UNKNOWN (as shown below), and the AINode function cannot be used at this time. + + ```shell +IoTDB> show cluster ++------+----------+-------+---------------+------------+-------+-----------+ +|NodeID| NodeType| Status|InternalAddress|InternalPort|Version| BuildInfo| ++------+----------+-------+---------------+------------+-------+-----------+ +| 0|ConfigNode|Running| 127.0.0.1| 10710|UNKNOWN|190e303-dev| +| 1| DataNode|Running| 127.0.0.1| 10730|UNKNOWN|190e303-dev| +| 2| AINode|UNKNOWN| 127.0.0.1| 10790|UNKNOWN|190e303-dev| ++------+----------+-------+---------------+------------+-------+-----------+ +``` +If you need to restart the node, you need to execute the startup script again. + +## 4. common problem + +### 4.1 An error occurs when starting AINode stating that the venv module cannot be found + + When starting AINode using the default method, a Python virtual environment will be created in the installation package directory and dependencies will be installed, so it is required to install the venv module. Generally speaking, Python 3.10 and above versions come with built-in VenV, but for some systems with built-in Python environments, this requirement may not be met. There are two solutions when this error occurs (choose one or the other): + + To install the Venv module locally, taking Ubuntu as an example, you can run the following command to install the built-in Venv module in Python. Or install a Python version with built-in Venv from the Python official website. + + ```shell +apt-get install python3.10-venv +``` +Install version 3.10.0 of venv into AINode in the AINode path. + + ```shell +../Python-3.10.0/python -m venv venv(Folder Name） +``` + When running the startup script, use ` -i ` to specify an existing Python interpreter path as the running environment for AINode, eliminating the need to create a new virtual environment. + + ### 4.2 The SSL module in Python is not properly installed and configured to handle HTTPS resources +WARNING: pip is configured with locations that require TLS/SSL, however the ssl module in Python is not available. +You can install OpenSSLS and then rebuild Python to solve this problem +> Currently Python versions 3.6 to 3.9 are compatible with OpenSSL 1.0.2, 1.1.0, and 1.1.1. + + Python requires OpenSSL to be installed on our system, the specific installation method can be found in [link](https://stackoverflow.com/questions/56552390/how-to-fix-ssl-module-in-python-is-not-available-in-centos) + + ```shell +sudo apt-get install build-essential libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm libncurses5-dev libncursesw5-dev xz-utils tk-dev libffi-dev liblzma-dev uuid-dev lzma-dev liblzma-dev +sudo -E ./configure --with-ssl +make +sudo make install +``` + + ### 4.3 Pip version is lower + + A compilation issue similar to "error: Microsoft Visual C++14.0 or greater is required..." appears on Windows + +The corresponding error occurs during installation and compilation, usually due to insufficient C++version or Setup tools version. You can check it in + + ```shell +./python -m pip install --upgrade pip +./python -m pip install --upgrade setuptools +``` + + + ### 4.4 Install and compile Python + + Use the following instructions to download the installation package from the official website and extract it: + ```shell +.wget https://www.python.org/ftp/python/3.10.0/Python-3.10.0.tar.xz +tar Jxf Python-3.10.0.tar.xz +``` + Compile and install the corresponding Python package: + ```shell +cd Python-3.10.0 +./configure prefix=/usr/local/python3 +make +sudo make install +python3 --version +``` \ No newline at end of file diff --git a/src/UserGuide/Master/Tree/AI-capability/AINode_apache.md b/src/UserGuide/Master/Tree/AI-capability/AINode_apache.md index 61006e644..bae4ce651 100644 --- a/src/UserGuide/Master/Tree/AI-capability/AINode_apache.md +++ b/src/UserGuide/Master/Tree/AI-capability/AINode_apache.md @@ -52,7 +52,7 @@ Compared with building a machine learning service alone, it has the following ad ## 2. Basic Concepts -- **Model**: a machine learning model that takes time-series data as input and outputs the results or decisions of an analysis task. Model is the basic management unit of AINode, which supports adding (registration), deleting, checking, and using (inference) of models. +- **Model**: A machine learning model that takes time series data as input and outputs analysis results or decisions. Models are the basic management units of AINode, supporting model operations such as creation (registration), deletion, query, and inference. - **Create**: Load externally designed or trained model files or algorithms into MLNode for unified management and use by IoTDB. - **Inference**: The process of using the created model to complete the timing analysis task applicable to the model on the specified timing data. - **Built-in capabilities**: AINode comes with machine learning algorithms or home-grown models for common timing analysis scenarios (e.g., prediction and anomaly detection). @@ -68,93 +68,91 @@ The deployment of AINode can be found in the document [Deployment Guidelines](.. ## 4. Usage Guidelines -AINode provides model creation and deletion process for deep learning models related to timing data. Built-in models do not need to be created and deleted, they can be used directly, and the built-in model instances created after inference is completed will be destroyed automatically. +AINode provides model creation and deletion functions for time series models. Built-in models do not require creation and can be used directly. ### 4.1 Registering Models -A trained deep learning model can be registered by specifying the vector dimensions of the model's inputs and outputs, which can be used for model inference. +Trained deep learning models can be registered by specifying their input and output vector dimensions for inference. -Models that meet the following criteria can be registered in AINode: -1. Models trained on PyTorch 2.1.0 and 2.2.0 versions supported by AINode should avoid using features from versions 2.2.0 and above. -2. AINode supports models stored using PyTorch JIT, and the model file needs to include the parameters and structure of the model. -3. The input sequence of the model can contain one or more columns, and if there are multiple columns, they need to correspond to the model capability and model configuration file. -4. The input and output dimensions of the model must be clearly defined in the `config.yaml` configuration file. When using the model, it is necessary to strictly follow the input-output dimensions defined in the `config.yaml` configuration file. If the number of input and output columns does not match the configuration file, it will result in errors. +Models that meet the following criteria can be registered with AINode: -The following is the SQL syntax definition for model registration. +1. AINode currently supports models trained with PyTorch 2.4.0. Features above version 2.4.0 should be avoided. +2. AINode supports models stored using PyTorch JIT (`model.pt`), which must include both the model structure and weights. +3. The model input sequence can include single or multiple columns. If multi-column, it must match the model capabilities and configuration file. +4. Model configuration parameters must be clearly defined in the `config.yaml` file. When using the model, the input and output dimensions defined in `config.yaml` must be strictly followed. Mismatches with the configuration file will cause errors. + +The SQL syntax for model registration is defined as follows: ```SQL create model using uri ``` -The specific meanings of the parameters in the SQL are as follows: - -- model_name: a globally unique identifier for the model, which cannot be repeated. The model name has the following constraints: - - - Identifiers [ 0-9 a-z A-Z _ ] (letters, numbers, underscores) are allowed. - - Length is limited to 2-64 characters - - Case sensitive - -- uri: resource path to the model registration file, which should contain the **model weights model.pt file and the model's metadata description file config.yaml**. +Detailed meanings of SQL parameters: - - Model weight file: the weight file obtained after the training of the deep learning model is completed, currently supporting pytorch training of the .pt file +- **model_name**: The global unique identifier for the model, non-repeating. Model names have the following constraints: + - Allowed characters: [0-9 a-z A-Z _] (letters, numbers, underscores) + - Length: 2-64 characters + - Case-sensitive +- **uri**: The resource path of the model registration files, which should include the **model structure and weight file `model.pt` and the model configuration file `config.yaml`** - - yaml metadata description file: parameters related to the model structure that need to be provided when the model is registered, which must contain the input and output dimensions of the model for model inference: + - **Model structure and weight file**: The weight file generated after model training, currently supporting `.pt` files from PyTorch training. - - | **Parameter name** | **Parameter description** | **Example** | - | ------------ | ---------------------------- | -------- | - | input_shape | Rows and columns of model inputs for model inference | [96,2] | - | output_shape | rows and columns of model outputs, for model inference | [48,2] | + - **Model configuration file**: Parameters related to the model structure provided during registration, which must include input and output dimensions for inference: - - In addition to model inference, the data types of model input and output can be specified: + | **Parameter Name** | **Description** | **Example** | + | ------------ | ---------------------------- | -------- | + | input_shape | Rows and columns of model input | [96,2] | + | output_shape | Rows and columns of model output | [48,2] | + + In addition to inference, data types of input and output can also be specified: - - | **Parameter name** | **Parameter description** | **Example** | - | ----------- | ------------------ | --------------------- | - | input_type | model input data type | ['float32','float32'] | - | output_type | data type of the model output | ['float32','float32'] | + | **Parameter Name** | **Description** | **Example** | + | ------------------ | ------------------------- | ---------------------- | + | input_type | Data type of model input | ['float32', 'float32'] | + | output_type | Data type of model output | ['float32', 'float32'] | - - In addition to this, additional notes can be specified for display during model management + Additional notes can be specified for model management display: - - | **Parameter name** | **Parameter description** | **Examples** | - | ---------- | ---------------------------------------------- | ------------------------------------------- | - | attributes | optional, user-defined model notes for model display | 'model_type': 'dlinear','kernel_size': '25' | + | **Parameter Name** | **Description** | **Example** | + | ------------------ | --------------------------------------------- | -------------------------------------------- | + | attributes | Optional notes set by users for model display | 'model_type': 'dlinear', 'kernel_size': '25' | - -In addition to registration of local model files, registration can also be done by specifying remote resource paths via URIs, using open source model repositories (e.g. HuggingFace). +In addition to registering local model files, remote resource paths can be specified via URIs for registration, using open-source model repositories (e.g., HuggingFace). #### Example -In the current example folder, it contains model.pt and config.yaml files, model.pt is the training get, and the content of config.yaml is as follows: +The current example folder contains model.pt (trained model) and config.yaml with the following content: ```YAML -configs. - # Required options - input_shape: [96, 2] # The model receives data in 96 rows x 2 columns. - output_shape: [48, 2] # Indicates that the model outputs 48 rows x 2 columns. +configs: + # Required + input_shape: [96, 2] # Model accepts 96 rows x 2 columns of data + output_shape: [48, 2] # Model outputs 48 rows x 2 columns of data - # Optional Default is all float32 and the number of columns is the number of columns in the shape. - input_type: ["int64", "int64"] # Input data type, need to match the number of columns. - output_type: ["text", "int64"] #Output data type, need to match the number of columns. + # Optional (default to all float32, column count matches shape) + input_type: ["int64", "int64"] # Data types of inputs, must match input column count + output_type: ["text", "int64"] # Data types of outputs, must match output column count -attributes: # Optional user-defined notes for the input. +attributes: # Optional user-defined notes 'model_type': 'dlinear' 'kernel_size': '25' ``` -Specify this folder as the load path to register the model. +Register the model by specifying this folder as the loading path: ```SQL -IoTDB> create model dlinear_example using uri "file://. /example" +IoTDB> create model dlinear_example using uri "file://./example" ``` -Alternatively, you can download the corresponding model file from huggingFace and register it. +Models can also be downloaded from HuggingFace for registration: ```SQL -IoTDB> create model dlinear_example using uri "https://huggingface.com/IoTDBML/dlinear/" +IoTDB> create model dlinear_example using uri "https://huggingface.co/google/timesfm-2.0-500m-pytorch" ``` -After the SQL is executed, the registration process will be carried out asynchronously, and you can view the registration status of the model through the model showcase (see the Model Showcase section), and the time consumed for successful registration is mainly affected by the size of the model file. +After SQL execution, registration proceeds asynchronously. The registration status can be checked via model display (see Model Display section). The registration success time mainly depends on the model file size. -Once the model registration is complete, you can call specific functions and perform model inference by using normal queries. +Once registered, the model can be invoked for inference through normal query syntax. ### 4.2 Viewing Models @@ -166,61 +164,61 @@ show models show models ``` -In addition to displaying information about all models directly, you can specify a model id to view information about a specific model. The results of the model show contain the following information: +In addition to displaying all models, specifying a `model_id` shows details of a specific model. The display includes: -| **ModelId** | **State** | **Configs** | **Attributes** | -| ------------ | ------------------------------------- | ---------------------------------------------- | -------------- | -| Model Unique Identifier | Model Registration Status (LOADING, ACTIVE, DROPPING) | InputShape, outputShapeInputTypes, outputTypes | Model Notes | +| **ModelId** | **State** | **Configs** | **Attributes** | +| ----------- | --------------------------------------------------------- | ------------------------------------------------ | -------------- | +| Unique ID | Registration status (INACTIVE, LOADING, ACTIVE, DROPPING) | InputShape, outputShape, inputTypes, outputTypes | User notes | -State is used to show the current state of model registration, which consists of the following three stages +**State descriptions:** -- **LOADING**: The corresponding model meta information has been added to the configNode, and the model file is being transferred to the AINode node. -- **ACTIVE**: The model has been set up and the model is in the available state -- **DROPPING**: Model deletion is in progress, model related information is being deleted from configNode and AINode. -- **UNAVAILABLE**: Model creation failed, you can delete the failed model_name by drop model. +- **INACTIVE**: Model is unavailable. +- **LOADING**: Model is being loaded. +- **ACTIVE**: Model is available. +- **DROPPING**: Model is being deleted. #### Example ```SQL IoTDB> show models - -+---------------------+--------------------------+-----------+----------------------------+-----------------------+ -| ModelId| ModelType| State| Configs| Notes| -+---------------------+--------------------------+-----------+----------------------------+-----------------------+ -| dlinear_example| USER_DEFINED| ACTIVE| inputShape:[96,2]| | -| | | | outputShape:[48,2]| | -| | | | inputDataType:[float,float]| | -| | | |outputDataType:[float,float]| | -| _STLForecaster| BUILT_IN_FORECAST| ACTIVE| |Built-in model in IoTDB| -| _NaiveForecaster| BUILT_IN_FORECAST| ACTIVE| |Built-in model in IoTDB| -| _ARIMA| BUILT_IN_FORECAST| ACTIVE| |Built-in model in IoTDB| -|_ExponentialSmoothing| BUILT_IN_FORECAST| ACTIVE| |Built-in model in IoTDB| -| _GaussianHMM|BUILT_IN_ANOMALY_DETECTION| ACTIVE| |Built-in model in IoTDB| -| _GMMHMM|BUILT_IN_ANOMALY_DETECTION| ACTIVE| |Built-in model in IoTDB| -| _Stray|BUILT_IN_ANOMALY_DETECTION| ACTIVE| |Built-in model in IoTDB| -+---------------------+--------------------------+-----------+------------------------------------------------------------+-----------------------+ ++---------------------+--------------------+--------+--------+ +| ModelId| ModelType|Category| State| ++---------------------+--------------------+--------+--------+ +| arima| Arima|BUILT-IN| ACTIVE| +| holtwinters| HoltWinters|BUILT-IN| ACTIVE| +|exponential_smoothing|ExponentialSmoothing|BUILT-IN| ACTIVE| +| naive_forecaster| NaiveForecaster|BUILT-IN| ACTIVE| +| stl_forecaster| StlForecaster|BUILT-IN| ACTIVE| +| gaussian_hmm| GaussianHmm|BUILT-IN| ACTIVE| +| gmm_hmm| GmmHmm|BUILT-IN| ACTIVE| +| stray| Stray|BUILT-IN| ACTIVE| +| timer_xl| Timer-XL|BUILT-IN| ACTIVE| +| sundial| Timer-Sundial|BUILT-IN| ACTIVE| ++---------------------+--------------------+--------+--------+ ``` +### 4.3 Deleting Models -We have registered the corresponding model earlier, you can view the model status through the corresponding designation, active indicates that the model is successfully registered and can be used for inference. - -### 4.3 Delete Model - -For a successfully registered model, the user can delete it via SQL. In addition to deleting the meta information on the configNode, this operation also deletes all the related model files under the AINode. The SQL is as follows: +Registered models can be deleted via SQL, which removes all related files under AINode: ```SQL -drop model +drop model ``` -You need to specify the model model_name that has been successfully registered to delete the corresponding model. Since model deletion involves the deletion of data on multiple nodes, the operation will not be completed immediately, and the state of the model at this time is DROPPING, and the model in this state cannot be used for model inference. +Specify the registered `model_id` to delete the model. Since deletion involves data cleanup, the operation is not immediate, and the model state becomes `DROPPING`, during which it cannot be used for inference. **Note:** Built-in models cannot be deleted. ### 4.4 Using Built-in Model Reasoning -The SQL syntax is as follows: +SQL syntax: ```SQL -call inference(,sql[,=]) +call inference(,inputSql,(=)*) + +window_function: + head(window_size) + tail(window_size) + count(window_size,sliding_step) ``` Built-in model inference does not require a registration process, the inference function can be used by calling the inference function through the call keyword, and its corresponding parameters are described as follows: @@ -229,6 +227,9 @@ Built-in model inference does not require a registration process, the inference - **parameterName**: parameter name - **parameterValue**: parameter value +- **Note**: To use a built-in time series large model for inference, the corresponding model weights must be stored locally in the directory `/IOTDB_AINODE_HOME/data/ainode/models/weights/model_id/`. If the weights are not present locally, they will be automatically downloaded from HuggingFace. Ensure your environment has direct access to HuggingFace. + + #### Built-in Models and Parameter Descriptions The following machine learning models are currently built-in, please refer to the following links for detailed parameter descriptions. @@ -243,59 +244,7 @@ The following machine learning models are currently built-in, please refer to th | GMMHMM | _GMMHMM | Annotation | [GMMHMM Parameter description](https://www.sktime.net/en/latest/api_reference/auto_generated/sktime.detection.hmm_learn.gmm.GMMHMM.html) | | Stray | _Stray | Anomaly detection | [Stray Parameter description](https://www.sktime.net/en/latest/api_reference/auto_generated/sktime.detection.stray.STRAY.html) | -#### Example -The following is an example of an operation using built-in model inference. The built-in Stray model is used for anomaly detection algorithm. The input is `[144,1]` and the output is `[144,1]`. We use it for reasoning through SQL. - -```SQL -IoTDB> select * from root.eg.airline -+-----------------------------+------------------+ -| Time|root.eg.airline.s0| -+-----------------------------+------------------+ -|1949-01-31T00:00:00.000+08:00| 224.0| -|1949-02-28T00:00:00.000+08:00| 118.0| -|1949-03-31T00:00:00.000+08:00| 132.0| -|1949-04-30T00:00:00.000+08:00| 129.0| -...... -|1960-09-30T00:00:00.000+08:00| 508.0| -|1960-10-31T00:00:00.000+08:00| 461.0| -|1960-11-30T00:00:00.000+08:00| 390.0| -|1960-12-31T00:00:00.000+08:00| 432.0| -+-----------------------------+------------------+ -Total line number = 144 - -IoTDB> call inference(_Stray, "select s0 from root.eg.airline", generateTime=True, k=2) -+-----------------------------+-------+ -| Time|output0| -+-----------------------------+-------+ -|1960-12-31T00:00:00.000+08:00| 0| -|1961-01-31T08:00:00.000+08:00| 1| -|1961-02-28T08:00:00.000+08:00| 0| -|1961-03-31T08:00:00.000+08:00| 0| -...... -|1972-06-30T08:00:00.000+08:00| 0| -|1972-07-31T08:00:00.000+08:00| 1| -|1972-08-31T08:00:00.000+08:00| 0| -|1972-09-30T08:00:00.000+08:00| 0| -|1972-10-31T08:00:00.000+08:00| 0| -|1972-11-30T08:00:00.000+08:00| 0| -+-----------------------------+-------+ -Total line number = 144 -``` - -### 4.5 Reasoning with Deep Learning Models - -The SQL syntax is as follows: - -```SQL -call inference(,sql[,window=]) - - -window_function: - head(window_size) - tail(window_size) - count(window_size,sliding_step) -``` After completing the registration of the model, the inference function can be used by calling the inference function through the call keyword, and its corresponding parameters are described as follows: diff --git a/src/UserGuide/Master/Tree/AI-capability/AINode_timecho.md b/src/UserGuide/Master/Tree/AI-capability/AINode_timecho.md index 6954358ac..da8fc5728 100644 --- a/src/UserGuide/Master/Tree/AI-capability/AINode_timecho.md +++ b/src/UserGuide/Master/Tree/AI-capability/AINode_timecho.md @@ -53,10 +53,10 @@ Compared with building a machine learning service alone, it has the following ad ## 2. Basic Concepts -- **Model**: a machine learning model that takes time-series data as input and outputs the results or decisions of an analysis task. Model is the basic management unit of AINode, which supports adding (registration), deleting, checking, and using (inference) of models. -- **Create**: Load externally designed or trained model files or algorithms into MLNode for unified management and use by IoTDB. -- **Inference**: The process of using the created model to complete the timing analysis task applicable to the model on the specified timing data. -- **Built-in capabilities**: AINode comes with machine learning algorithms or home-grown models for common timing analysis scenarios (e.g., prediction and anomaly detection). +- **Model**: A machine learning model takes time series data as input and outputs analysis task results or decisions. Models are the basic management units of AINode, supporting model operations such as creation (registration), deletion, query, modification (fine-tuning), and usage (inference). +- **Create**: Load externally designed or trained model files/algorithms into AINode for unified management and usage by IoTDB. +- **Inference**: Use the created model to complete time series analysis tasks applicable to the model on specified time series data. +- **Built-in Capabilities**: AINode comes with machine learning algorithms or self-developed models for common time series analysis scenarios (e.g., forecasting and anomaly detection). ::: center @@ -64,102 +64,102 @@ Compared with building a machine learning service alone, it has the following ad ## 3. Installation and Deployment -The deployment of AINode can be found in the document [Deployment Guidelines](../Deployment-and-Maintenance/AINode_Deployment_timecho.md#AINode-部署) . - +The deployment of AINode can be found in the document [Deployment Guidelines](../Deployment-and-Maintenance/AINode_Deployment_timecho.md#ainode-deployment) . ## 4. Usage Guidelines -AINode provides model creation and deletion process for deep learning models related to timing data. Built-in models do not need to be created and deleted, they can be used directly, and the built-in model instances created after inference is completed will be destroyed automatically. +AINode provides model creation and deletion functions for time series models. Built-in models do not require creation and can be used directly. + ### 4.1 Registering Models -A trained deep learning model can be registered by specifying the vector dimensions of the model's inputs and outputs, which can be used for model inference. -Models that meet the following criteria can be registered in AINode: -1. Models trained on PyTorch 2.1.0 and 2.2.0 versions supported by AINode should avoid using features from versions 2.2.0 and above. -2. AINode supports models stored using PyTorch JIT, and the model file needs to include the parameters and structure of the model. -3. The input sequence of the model can contain one or more columns, and if there are multiple columns, they need to correspond to the model capability and model configuration file. -4. The input and output dimensions of the model must be clearly defined in the `config.yaml` configuration file. When using the model, it is necessary to strictly follow the input-output dimensions defined in the `config.yaml` configuration file. If the number of input and output columns does not match the configuration file, it will result in errors. +Trained deep learning models can be registered by specifying their input and output vector dimensions for inference. + +Models that meet the following criteria can be registered with AINode: -The following is the SQL syntax definition for model registration. +1. AINode currently supports models trained with PyTorch 2.4.0. Features above version 2.4.0 should be avoided. +2. AINode supports models stored using PyTorch JIT (`model.pt`), which must include both the model structure and weights. +3. The model input sequence can include single or multiple columns. If multi-column, it must match the model capabilities and configuration file. +4. Model configuration parameters must be clearly defined in the `config.yaml` file. When using the model, the input and output dimensions defined in `config.yaml` must be strictly followed. Mismatches with the configuration file will cause errors. + +The SQL syntax for model registration is defined as follows: ```SQL create model using uri ``` -The specific meanings of the parameters in the SQL are as follows: - -- model_name: a globally unique identifier for the model, which cannot be repeated. The model name has the following constraints: - - - Identifiers [ 0-9 a-z A-Z _ ] (letters, numbers, underscores) are allowed. - - Length is limited to 2-64 characters - - Case sensitive +Detailed meanings of SQL parameters: -- uri: resource path to the model registration file, which should contain the **model weights model.pt file and the model's metadata description file config.yaml**. +- **model_name**: The global unique identifier for the model, non-repeating. Model names have the following constraints: + - Allowed characters: [0-9 a-z A-Z _] (letters, numbers, underscores) + - Length: 2-64 characters + - Case-sensitive +- **uri**: The resource path of the model registration files, which should include the **model structure and weight file `model.pt` and the model configuration file `config.yaml`** - - Model weight file: the weight file obtained after the training of the deep learning model is completed, currently supporting pytorch training of the .pt file + - **Model structure and weight file**: The weight file generated after model training, currently supporting `.pt` files from PyTorch training. - - yaml metadata description file: parameters related to the model structure that need to be provided when the model is registered, which must contain the input and output dimensions of the model for model inference: + - **Model configuration file**: Parameters related to the model structure provided during registration, which must include input and output dimensions for inference: - - | **Parameter name** | **Parameter description** | **Example** | - | ------------ | ---------------------------- | -------- | - | input_shape | Rows and columns of model inputs for model inference | [96,2] | - | output_shape | rows and columns of model outputs, for model inference | [48,2] | + | **Parameter Name** | **Description** | **Example** | + | ------------ | ---------------------------- | -------- | + | input_shape | Rows and columns of model input | [96,2] | + | output_shape | Rows and columns of model output | [48,2] | + + In addition to inference, data types of input and output can also be specified: - - In addition to model inference, the data types of model input and output can be specified: + | **Parameter Name** | **Description** | **Example** | + | ------------------ | ------------------------- | ---------------------- | + | input_type | Data type of model input | ['float32', 'float32'] | + | output_type | Data type of model output | ['float32', 'float32'] | - - | **Parameter name** | **Parameter description** | **Example** | - | ----------- | ------------------ | --------------------- | - | input_type | model input data type | ['float32','float32'] | - | output_type | data type of the model output | ['float32','float32'] | + Additional notes can be specified for model management display: - - In addition to this, additional notes can be specified for display during model management + | **Parameter Name** | **Description** | **Example** | + | ------------------ | --------------------------------------------- | -------------------------------------------- | + | attributes | Optional notes set by users for model display | 'model_type': 'dlinear', 'kernel_size': '25' | - - | **Parameter name** | **Parameter description** | **Examples** | - | ---------- | ---------------------------------------------- | ------------------------------------------- | - | attributes | optional, user-defined model notes for model display | 'model_type': 'dlinear','kernel_size': '25' | +In addition to registering local model files, remote resource paths can be specified via URIs for registration, using open-source model repositories (e.g., HuggingFace). -In addition to registration of local model files, registration can also be done by specifying remote resource paths via URIs, using open source model repositories (e.g. HuggingFace). - #### Example -In the current example folder, it contains model.pt and config.yaml files, model.pt is the training get, and the content of config.yaml is as follows: +The current example folder contains model.pt (trained model) and config.yaml with the following content: ```YAML -configs. - # Required options - input_shape: [96, 2] # The model receives data in 96 rows x 2 columns. - output_shape: [48, 2] # Indicates that the model outputs 48 rows x 2 columns. +configs: + # Required + input_shape: [96, 2] # Model accepts 96 rows x 2 columns of data + output_shape: [48, 2] # Model outputs 48 rows x 2 columns of data - # Optional Default is all float32 and the number of columns is the number of columns in the shape. - input_type: ["int64", "int64"] # Input data type, need to match the number of columns. - output_type: ["text", "int64"] #Output data type, need to match the number of columns. + # Optional (default to all float32, column count matches shape) + input_type: ["int64", "int64"] # Data types of inputs, must match input column count + output_type: ["text", "int64"] # Data types of outputs, must match output column count -attributes: # Optional user-defined notes for the input. +attributes: # Optional user-defined notes 'model_type': 'dlinear' 'kernel_size': '25' ``` -Specify this folder as the load path to register the model. +Register the model by specifying this folder as the loading path: ```SQL -IoTDB> create model dlinear_example using uri "file://. /example" +IoTDB> create model dlinear_example using uri "file://./example" ``` -Alternatively, you can download the corresponding model file from huggingFace and register it. +Models can also be downloaded from HuggingFace for registration: ```SQL -IoTDB> create model dlinear_example using uri "https://huggingface.com/IoTDBML/dlinear/" +IoTDB> create model dlinear_example using uri "https://huggingface.co/google/timesfm-2.0-500m-pytorch" ``` -After the SQL is executed, the registration process will be carried out asynchronously, and you can view the registration status of the model through the model showcase (see the Model Showcase section), and the time consumed for successful registration is mainly affected by the size of the model file. +After SQL execution, registration proceeds asynchronously. The registration status can be checked via model display (see Model Display section). The registration success time mainly depends on the model file size. -Once the model registration is complete, you can call specific functions and perform model inference by using normal queries. +Once registered, the model can be invoked for inference through normal query syntax. ### 4.2 Viewing Models -Successfully registered models can be queried for model-specific information through the show models command. The SQL definition is as follows: +Registered models can be queried using the `show models` command. The SQL definitions are: ```SQL show models @@ -167,53 +167,51 @@ show models show models ``` -In addition to displaying information about all models directly, you can specify a model id to view information about a specific model. The results of the model show contain the following information: +In addition to displaying all models, specifying a `model_id` shows details of a specific model. The display includes: -| **ModelId** | **State** | **Configs** | **Attributes** | -| ------------ | ------------------------------------- | ---------------------------------------------- | -------------- | -| Model Unique Identifier | Model Registration Status (LOADING, ACTIVE, DROPPING) | InputShape, outputShapeInputTypes, outputTypes | Model Notes | +| **ModelId** | **State** | **Configs** | **Attributes** | +| ----------- | --------------------------------------------------------- | ------------------------------------------------ | -------------- | +| Unique ID | Registration status (INACTIVE, LOADING, ACTIVE,TRAINING,FAILED, DROPPING) | InputShape, outputShape, inputTypes, outputTypes | User notes | -State is used to show the current state of model registration, which consists of the following three stages +**State descriptions:** -- **LOADING**: The corresponding model meta information has been added to the configNode, and the model file is being transferred to the AINode node. -- **ACTIVE**: The model has been set up and the model is in the available state -- **DROPPING**: Model deletion is in progress, model related information is being deleted from configNode and AINode. -- **UNAVAILABLE**: Model creation failed, you can delete the failed model_name by drop model. +- **INACTIVE**: The model is in an unavailable state. +- **LOADING**: The model is being loaded. +- **ACTIVE**: The model is in an available state. +- **TRAINING**: The model is in the fine-tuning state. +- **FAILED**: The model fine-tuning failed. +- **DROPPING**: The model is being deleted. #### Example ```SQL IoTDB> show models - -+---------------------+--------------------------+-----------+----------------------------+-----------------------+ -| ModelId| ModelType| State| Configs| Notes| -+---------------------+--------------------------+-----------+----------------------------+-----------------------+ -| dlinear_example| USER_DEFINED| ACTIVE| inputShape:[96,2]| | -| | | | outputShape:[48,2]| | -| | | | inputDataType:[float,float]| | -| | | |outputDataType:[float,float]| | -| _STLForecaster| BUILT_IN_FORECAST| ACTIVE| |Built-in model in IoTDB| -| _NaiveForecaster| BUILT_IN_FORECAST| ACTIVE| |Built-in model in IoTDB| -| _ARIMA| BUILT_IN_FORECAST| ACTIVE| |Built-in model in IoTDB| -|_ExponentialSmoothing| BUILT_IN_FORECAST| ACTIVE| |Built-in model in IoTDB| -| _GaussianHMM|BUILT_IN_ANOMALY_DETECTION| ACTIVE| |Built-in model in IoTDB| -| _GMMHMM|BUILT_IN_ANOMALY_DETECTION| ACTIVE| |Built-in model in IoTDB| -| _Stray|BUILT_IN_ANOMALY_DETECTION| ACTIVE| |Built-in model in IoTDB| -+---------------------+--------------------------+-----------+------------------------------------------------------------+-----------------------+ ++---------------------+--------------------+--------+--------+ +| ModelId| ModelType|Category| State| ++---------------------+--------------------+--------+--------+ +| arima| Arima|BUILT-IN| ACTIVE| +| holtwinters| HoltWinters|BUILT-IN| ACTIVE| +|exponential_smoothing|ExponentialSmoothing|BUILT-IN| ACTIVE| +| naive_forecaster| NaiveForecaster|BUILT-IN| ACTIVE| +| stl_forecaster| StlForecaster|BUILT-IN| ACTIVE| +| gaussian_hmm| GaussianHmm|BUILT-IN| ACTIVE| +| gmm_hmm| GmmHmm|BUILT-IN| ACTIVE| +| stray| Stray|BUILT-IN| ACTIVE| +| timer_xl| Timer-XL|BUILT-IN| ACTIVE| +| sundial| Timer-Sundial|BUILT-IN| ACTIVE| ++---------------------+--------------------+--------+--------+ ``` -We have registered the corresponding model earlier, you can view the model status through the corresponding designation, active indicates that the model is successfully registered and can be used for inference. - -### 4.3 Delete Model +### 4.3 Deleting Models -For a successfully registered model, the user can delete it via SQL. In addition to deleting the meta information on the configNode, this operation also deletes all the related model files under the AINode. The SQL is as follows: +Registered models can be deleted via SQL, which removes all related files under AINode: ```SQL -drop model +drop model ``` -You need to specify the model model_name that has been successfully registered to delete the corresponding model. Since model deletion involves the deletion of data on multiple nodes, the operation will not be completed immediately, and the state of the model at this time is DROPPING, and the model in this state cannot be used for model inference. +Specify the registered `model_id` to delete the model. Since deletion involves data cleanup, the operation is not immediate, and the model state becomes `DROPPING`, during which it cannot be used for inference. **Note:** Built-in models cannot be deleted. ### 4.4 Using Built-in Model Reasoning @@ -221,7 +219,12 @@ The SQL syntax is as follows: ```SQL -call inference(,sql[,=]) +call inference(,inputSql,(=)*) + +window_function: + head(window_size) + tail(window_size) + count(window_size,sliding_step) ``` Built-in model inference does not require a registration process, the inference function can be used by calling the inference function through the call keyword, and its corresponding parameters are described as follows: @@ -230,6 +233,8 @@ Built-in model inference does not require a registration process, the inference - **parameterName**: parameter name - **parameterValue**: parameter value +- **Note**: To use a built-in time series large model for inference, the corresponding model weights must be stored locally in the directory `/IOTDB_AINODE_HOME/data/ainode/models/weights/model_id/`. If the weights are not present locally, they will be automatically downloaded from HuggingFace. Ensure your environment has direct access to HuggingFace. + #### Built-in Models and Parameter Descriptions The following machine learning models are currently built-in, please refer to the following links for detailed parameter descriptions. @@ -245,60 +250,6 @@ The following machine learning models are currently built-in, please refer to th | Stray | _Stray | Anomaly detection | [Stray Parameter description](https://www.sktime.net/en/latest/api_reference/auto_generated/sktime.detection.stray.STRAY.html) | -#### Example - -The following is an example of an operation using built-in model inference. The built-in Stray model is used for anomaly detection algorithm. The input is `[144,1]` and the output is `[144,1]`. We use it for reasoning through SQL. - -```SQL -IoTDB> select * from root.eg.airline -+-----------------------------+------------------+ -| Time|root.eg.airline.s0| -+-----------------------------+------------------+ -|1949-01-31T00:00:00.000+08:00| 224.0| -|1949-02-28T00:00:00.000+08:00| 118.0| -|1949-03-31T00:00:00.000+08:00| 132.0| -|1949-04-30T00:00:00.000+08:00| 129.0| -...... -|1960-09-30T00:00:00.000+08:00| 508.0| -|1960-10-31T00:00:00.000+08:00| 461.0| -|1960-11-30T00:00:00.000+08:00| 390.0| -|1960-12-31T00:00:00.000+08:00| 432.0| -+-----------------------------+------------------+ -Total line number = 144 - -IoTDB> call inference(_Stray, "select s0 from root.eg.airline", generateTime=True, k=2) -+-----------------------------+-------+ -| Time|output0| -+-----------------------------+-------+ -|1960-12-31T00:00:00.000+08:00| 0| -|1961-01-31T08:00:00.000+08:00| 1| -|1961-02-28T08:00:00.000+08:00| 0| -|1961-03-31T08:00:00.000+08:00| 0| -...... -|1972-06-30T08:00:00.000+08:00| 0| -|1972-07-31T08:00:00.000+08:00| 1| -|1972-08-31T08:00:00.000+08:00| 0| -|1972-09-30T08:00:00.000+08:00| 0| -|1972-10-31T08:00:00.000+08:00| 0| -|1972-11-30T08:00:00.000+08:00| 0| -+-----------------------------+-------+ -Total line number = 144 -``` - -### 4.5 Reasoning with Deep Learning Models - -The SQL syntax is as follows: - -```SQL -call inference(,sql[,window=]) - - -window_function: - head(window_size) - tail(window_size) - count(window_size,sliding_step) -``` - After completing the registration of the model, the inference function can be used by calling the inference function through the call keyword, and its corresponding parameters are described as follows: - **model_name**: corresponds to a registered model @@ -445,6 +396,65 @@ Total line number = 4 In the result set, each row's label corresponds to the output of the anomaly detection model after inputting each group of 24 rows of data. +### 4.5 Fine-tuning Built-in Models +> Only Timer-XL and Timer-Sundial support fine-tuning. + + +The SQL syntax is as follows: + + +```SQL +create model (with hyperparameters +(=(, =)*))? +from model +on dataset (PATH ([timeRange])?) +``` + +#### Examples + +1. Select the first 80% of data from the measurement point `root.db.etth.ot` as the fine-tuning dataset, and create the model `sundialv2` based on `sundial`. + +```SQL +IoTDB> CREATE MODEL sundialv2 FROM MODEL sundial ON DATASET (PATH root.db.etth.OT([1467302400000, 1517468400001))) +Msg: The statement is executed successfully. +IoTDB> show models ++---------------------+--------------------+----------+--------+ +| ModelId| ModelType| Category| State| ++---------------------+--------------------+----------+--------+ +| arima| Arima| BUILT-IN| ACTIVE| +| holtwinters| HoltWinters| BUILT-IN| ACTIVE| +|exponential_smoothing|ExponentialSmoothing| BUILT-IN| ACTIVE| +| naive_forecaster| NaiveForecaster| BUILT-IN| ACTIVE| +| stl_forecaster| StlForecaster| BUILT-IN| ACTIVE| +| gaussian_hmm| GaussianHmm| BUILT-IN| ACTIVE| +| gmm_hmm| GmmHmm| BUILT-IN| ACTIVE| +| stray| Stray| BUILT-IN| ACTIVE| +| sundial| Timer-Sundial| BUILT-IN| ACTIVE| +| timer_xl| Timer-XL| BUILT-IN| ACTIVE| +| sundialv2| Timer-Sundial|FINE-TUNED|TRAINING| ++---------------------+--------------------+----------+--------+ +``` + +2. The fine-tuning task starts asynchronously in the background, and logs can be viewed in the AINode process. After fine-tuning is complete, query and use the new model + +```SQL +IoTDB> show models ++---------------------+--------------------+----------+------+ +| ModelId| ModelType| Category| State| ++---------------------+--------------------+----------+------+ +| arima| Arima| BUILT-IN|ACTIVE| +| holtwinters| HoltWinters| BUILT-IN|ACTIVE| +|exponential_smoothing|ExponentialSmoothing| BUILT-IN|ACTIVE| +| naive_forecaster| NaiveForecaster| BUILT-IN|ACTIVE| +| stl_forecaster| StlForecaster| BUILT-IN|ACTIVE| +| gaussian_hmm| GaussianHmm| BUILT-IN|ACTIVE| +| gmm_hmm| GmmHmm| BUILT-IN|ACTIVE| +| stray| Stray| BUILT-IN|ACTIVE| +| sundial| Timer-Sundial| BUILT-IN|ACTIVE| +| timer_xl| Timer-XL| BUILT-IN|ACTIVE| +| sundialv2| Timer-Sundial|FINE-TUNED|ACTIVE| ++---------------------+--------------------+----------+------+ +``` ### 4.6 TimeSeries Large Models Import Steps diff --git a/src/UserGuide/Master/Tree/AI-capability/TimeSeries-Large-Model.md b/src/UserGuide/Master/Tree/AI-capability/TimeSeries-Large-Model.md index 7743ff545..c678ad28c 100644 --- a/src/UserGuide/Master/Tree/AI-capability/TimeSeries-Large-Model.md +++ b/src/UserGuide/Master/Tree/AI-capability/TimeSeries-Large-Model.md @@ -43,37 +43,48 @@ The Timer model not only demonstrates excellent few-shot generalization and mult - **Versatility**: The model is designed flexibly to adapt to various task requirements and supports variable input and output lengths, enabling it to play a role in various application scenarios. - **Scalability**: As the number of model parameters increases or the scale of pre-training data expands, the model's performance continues to improve, ensuring the model can optimize its predictive effects with the growth of time and data volume. -![](/img/LargeModel02.png) +![](/img/model01.png) ## Timer-XL Model Timer-XL is an upgraded version of Timer that further extends the network structure and achieves comprehensive breakthroughs in multiple dimensions: -- **Long Context Support**: This model breaks through the limitations of traditional time series forecasting models, supporting the processing of thousands of tokens (equivalent to tens of thousands of time points) of input, effectively addressing the bottleneck of context length. -- **Multi-variable Forecasting Scenario Coverage**: Supports a variety of forecasting scenarios, including non-stationary time series forecasting, multi-variable prediction tasks, and predictions involving covariates, meeting diverse business needs. -- **Large-scale Industrial Time Series Dataset**: Pre-trained using a massive industrial IoT time series dataset that has a large volume, excellent quality, and rich domain characteristics, covering energy, aerospace, steel, transportation, and more. +- **Ultra-long Context Support**: This model breaks through the limitations of traditional time series forecasting models, supporting inputs of thousands of Tokens (equivalent to tens of thousands of time points), effectively solving the context length bottleneck problem. +- **Multi-variable Prediction Scenarios Coverage**: Supports various forecasting scenarios, including non-stationary time series forecasting, multi-variable forecasting tasks, and forecasting with covariates, meeting diverse business needs. +- **Large-scale Industrial Time Series Dataset**: Pre-trained on a trillion-scale industrial IoT time series dataset, which has the important characteristics of huge volume, excellent quality, and rich domains, covering energy, aerospace, steel, transportation, and other fields. +![](/img/model02.png) + +## Timer-Sundial Model + +Timer-Sundial is a series of generative foundational models focused on time series forecasting. The basic version has 128 million parameters and has undergone large-scale pre-training on 1 trillion time points. Its core features include: + +- **Powerful Generalization Performance**: Possesses zero-shot forecasting capabilities, supporting both point forecasting and probabilistic forecasting simultaneously. +- **Flexible Forecasting Distribution Analysis**: Can not only forecast mean values or quantiles but also evaluate any statistical characteristics of the forecasting distribution through the original samples generated by the model. +- **Innovative Generative Architecture**: Adopts a "Transformer + TimeFlow" collaborative architecture—Transformer learns the autoregressive representation of time segments, and the TimeFlow module converts random noise into diversified forecasting trajectories based on the Flow-Matching framework, achieving efficient non-deterministic sample generation. + +![](/img/model03.png) ## Effect Demonstration -Time series large models can adapt to real time series data from various fields and scenarios, showing excellent processing effects in various tasks. Here are the real performances on different data: +Time series large models can adapt to real-time series data in various domains and scenarios, demonstrating excellent processing effects on various tasks. The following are real-world performances on different datasets: **Time Series Forecasting:** -Utilizing the predictive capabilities of the time series large model, it can accurately predict the future trend of time series. As shown in the figure, the blue curve represents the predicted trend, and the red curve represents the actual trend, with the two curves highly matching. +Utilizing the forecasting capability of time series large models, the future change trends of time series can be accurately predicted. In the figure below, the blue curve represents the forecast trend, and the red curve represents the actual trend, with a high degree of 吻合 (coincidence) between the two curves. ![](/img/LargeModel03.png) **Data Imputation:** -Using the time series large model to perform predictive imputation for missing data segments. +Utilizing time series large models to perform predictive imputation on missing data segments. ![](/img/timeseries-large-model-data-imputation.png) **Anomaly Detection:** -Utilizing the time series large model to accurately identify anomalies that deviate significantly from the normal trend. +Utilizing time series large models to accurately identify outliers that deviate significantly from normal trends. ![](/img/LargeModel05.png) @@ -89,24 +100,9 @@ show cluster ![](/img/ainode-timer-1.png) -2. Model file storage path: It is recommended to place the model files in the same directory as the AINode installation package. - You may create a new folder to store model files. - -3. Register the model - -Use the following SQL statement: - -```sql -create model using uri -``` - -Example (for the Timer model): - -```sql -create model Timer using uri -``` +2. When the AINode is started for the first time in a networked environment, the Timer-XL and Sundial models will be automatically pulled. -4. Verify model registration success +3. Verify model registration success Check command: diff --git a/src/UserGuide/Master/Tree/Deployment-and-Maintenance/AINode_Deployment_apache.md b/src/UserGuide/Master/Tree/Deployment-and-Maintenance/AINode_Deployment_apache.md index 451921216..50c532376 100644 --- a/src/UserGuide/Master/Tree/Deployment-and-Maintenance/AINode_Deployment_apache.md +++ b/src/UserGuide/Master/Tree/Deployment-and-Maintenance/AINode_Deployment_apache.md @@ -39,8 +39,6 @@ ### 2.1 Get installation package - Users can download the software installation package for AINode, download and unzip it to complete the installation of AINode. - Unzip and install the package `(apache-iotdb--ainode-bin.zip)`， The directory structure after unpacking the installation package is as follows: | **Catalogue** | **Type** | **Explain** | @@ -53,97 +51,64 @@ | README_ZH.md | file | Explanation of the Chinese version of the markdown format | | `README.md` | file | Instructions | -### 2.2 Environment preparation -- Suggested operating environment：Ubuntu, CentOS, MacOS - -- Runtime Environment - - Python >= 3.10 and Python <= 3.12 is sufficient, and comes with pip and venv tools; For non networked environments, download the zip package for the corresponding operating system from [here](https://cloud.tsinghua.edu.cn/d/4c1342f6c272439aa96c/?p=%2Flibs&mode=list) (Note that when downloading dependencies, you need to select the zip file in the libs folder, as shown in the following figure). Copy all files in the folder to the `lib` folder in the `apache-iotdb--ainode-bin` folder, and follow the steps below to start AINode. - - +### 2.2 Environmental Preparation - - There must be a Python interpreter in the environment variables that can be directly called through the `python` instruction. - - It is recommended to create a Python interpreter venv virtual environment in the `apache-iotdb--ainode-bin` folder. If installing version 3.10.0 virtual environment, the statement is as follows: - ```shell - # Install version 3.10.0 of Venv , Create a virtual environment with the folder name `venv`. - ../Python-3.10.0/python -m venv `venv` - ``` +1. Recommended operating systems: Ubuntu, CentOS, MacOS +2. IoTDB version: >= V 2.0.5.1 +3. Runtime environment + - Python version between 3.9 and 3.12, with pip and venv tools installed; ## 3. Installation steps ### 3.1 Install AINode - 1. Check the kernel architecture of Linux + 1. Ensure Python version is between 3.9 and 3.12: ```shell - uname -m - ``` - - 2. Import Python environment [Download](https://repo.anaconda.com/miniconda/) - - Recommend downloading the py311 version application and importing it into the iotdb dedicated folder in the user's root directory - -3. Verify Python version + python --version +# or +python3 --version +``` + 2. Download and import AINode into a dedicated folder, switch to the folder, and unzip the package: ```shell - python --version + unzip timechodb-2.0.5.1-ainode-bin.zip ``` -4. Create a virtual environment (execute in the ainode directory) +3. Create a virtual environment (execute in the ainode directory): ```shell - python -m venv venv + python3 -m venv venv ``` -5. Activate the virtual environment +4. Activate the virtual environment: ```shell source venv/bin/activate ``` - - - 6. Download and import AINode to a dedicated folder, switch to the dedicated folder and extract the installation package - -```shell - unzip apache-iotdb-1.3.3-ainode-bin.zip - ``` - - 7. Configuration item modification - -```shell - vi apache-iotdb-1.3.3-ainode-bin/conf/iotdb-ainode.properties - ``` - Configuration item modification：[detailed information](#configuration-item-modification) - -> ain_seed_config_node=iotdb-1:10710 (Cluster communication node IP: communication node port)
-> ain_inference_rpc_address=iotdb-3 (IP address of the server running AINode) - - 8. Replace Python source - -```shell - pip config set global.index-url https://mirrors.aliyun.com/pypi/simple/ - ``` - - 9. Start the AINode node - -```shell - nohup bash apache-iotdb-1.3.3-ainode-bin/sbin/start-ainode.sh > myout.file 2>& 1 & - ``` -> Return to the default environment of the system: conda deactivate +5. Update pip and install AINode dependencies: + ```shell + python -m pip install --upgrade pip + poetry lock + poetry install + ``` ### 3.2 Configuration item modification AINode supports modifying some necessary parameters. You can find the following parameters in the `conf/iotdb-ainode.properties` file and make persistent modifications to them: -： - -| **Name** | **Describe** | **Type** | **Default value** | **Effective method after modification** | -| :----------------------------- | ------------------------------------------------------------ | ------- | ------------------ | ---------------------------- | -| cluster_name | The identifier for AINode to join the cluster | string | defaultCluster | Only allow modifications before the first service startup | -| ain_seed_config_node | The Configurable Node address registered during AINode startup | String | 127.0.0.1:10710 | Only allow modifications before the first service startup | -| ain_inference_rpc_address | AINode provides service and communication addresses , Internal Service Communication Interface | String | 127.0.0.1 | Only allow modifications before the first service startup | -| ain_inference_rpc_port | AINode provides ports for services and communication | String | 10810 | Only allow modifications before the first service startup | -| ain_system_dir | AINode metadata storage path, the starting directory of the relative path is related to the operating system, and it is recommended to use an absolute path | String | data/AINode/system | Only allow modifications before the first service startup | -| ain_models_dir | AINode stores the path of the model file, and the starting directory of the relative path is related to the operating system. It is recommended to use an absolute path | String | data/AINode/models | Only allow modifications before the first service startup | -| ain_logs_dir | The path where AINode stores logs, the starting directory of the relative path is related to the operating system, and it is recommended to use an absolute path | String | logs/AINode | Effective after restart | -| ain_thrift_compression_enabled | Does AINode enable Thrift's compression mechanism , 0-Do not start, 1-Start | Boolean | 0 | Effective after restart | +| **Name** | **Description** | **Type** | **Default Value** | +| ------------------------------ | ------------------------------------------------------------ | -------- | ------------------ | +| cluster_name | Identifier of the cluster AINode joins | string | defaultCluster | +| ain_seed_config_node | Address of the ConfigNode registered when AINode starts | String | 127.0.0.1:10710 | +| ain_cluster_ingress_address | RPC address of the DataNode for AINode to pull data | String | 127.0.0.1 | +| ain_cluster_ingress_port | RPC port of the DataNode for AINode to pull data | Integer | 6667 | +| ain_cluster_ingress_username | Client username for AINode to pull data from the DataNode | String | root | +| ain_cluster_ingress_password | Client password for AINode to pull data from the DataNode | String | root | +| ain_cluster_ingress_time_zone | Client time zone for AINode to pull data from the DataNode | String | UTC+8 | +| ain_inference_rpc_address | Address for AINode to provide services and communication (internal interface) | String | 127.0.0.1 | +| ain_inference_rpc_port | Port for AINode to provide services and communication | String | 10810 | +| ain_system_dir | Metadata storage path for AINode (relative path starts from OS-dependent directory; absolute path is recommended) | String | data/AINode/system | +| ain_models_dir | Path to store model files for AINode (relative path starts from OS-dependent directory; absolute path is recommended) | String | data/AINode/models | +| ain_thrift_compression_enabled | Whether to enable Thrift compression for AINode (0=disabled, 1=enabled) | Boolean | 0 | ### 3.3 Start AINode @@ -169,43 +134,8 @@ AINode supports modifying some necessary parameters. You can find the following nohup bash sbin\start-ainode.bat > myout.file 2>& 1 & ``` -#### Detailed Syntax - -```shell - # Start command - # Linux and MacOS systems - bash sbin/start-ainode.sh -i -r -n - - # Windows systems - sbin\start-ainode.bat -i -r -n - ``` - -##### Parameter introduction: - -| **Name** | **Label** | **Describe** | **Is it mandatory** | **Type** | **Default value** | **Input method** | -| ------------------- | ---- | ------------------------------------------------------------ | -------- | ------ | ---------------- | ---------------------- | -| ain_interpreter_dir | -i | The interpreter path of the virtual environment where AINode is installed requires the use of an absolute path. | no | String | Default reading of environment variables | Input or persist modifications during invocation | -| ain_force_reinstall | -r | Does this script check the version when checking the installation status of AINode. If it does, it will force the installation of the whl package in lib if the version is incorrect. | no | Bool | false | Input when calling | -| ain_no_dependencies | -n | Specify whether to install dependencies when installing AINode, and if so, only install the AINode main program without installing dependencies. | no | Bool | false | Input when calling | - - If you don't want to specify the corresponding parameters every time you start, you can also persistently modify the parameters in the `ainode-env.sh` and `ainode-env.bat` scripts in the `conf` folder (currently supporting persistent modification of the ain_interpreter-dir parameter). - - `ainode-env.sh` : - ```shell - # The defaulte venv environment is used if ain_interpreter_dir is not set. Please use absolute path without quotation mark - # ain_interpreter_dir= - ``` - `ainode-env.bat` : -```shell - @REM The defaulte venv environment is used if ain_interpreter_dir is not set. Please use absolute path without quotation mark - @REM set ain_interpreter_dir= - ``` - After writing the parameter value, uncomment the corresponding line and save it to take effect on the next script execution. - #### Example - -##### Directly start: ```shell # Start command @@ -215,82 +145,6 @@ AINode supports modifying some necessary parameters. You can find the following sbin\start-ainode.bat - # Backend startup command (recommended for long-term running) - # Linux and MacOS systems - nohup bash sbin/start-ainode.sh > myout.file 2>& 1 & - # Windows systems - nohup bash sbin\start-ainode.bat > myout.file 2>& 1 & - ``` - -##### Update Start: -If the version of AINode has been updated (such as updating the `lib` folder), this command can be used. Firstly, it is necessary to ensure that AINode has stopped running, and then restart it using the `-r` parameter, which will reinstall AINode based on the files under `lib`. - - -```shell - # Update startup command - # Linux and MacOS systems - bash sbin/start-ainode.sh -r - # Windows systems - sbin\start-ainode.bat -r - - - # Backend startup command (recommended for long-term running) - # Linux and MacOS systems - nohup bash sbin/start-ainode.sh -r > myout.file 2>& 1 & - # Windows c - nohup bash sbin\start-ainode.bat -r > myout.file 2>& 1 & - ``` -#### Non networked environment startup - -##### Start command - -```shell - # Start command - # Linux and MacOS systems - bash sbin/start-ainode.sh - - # Windows systems - sbin\start-ainode.bat - - # Backend startup command (recommended for long-term running) - # Linux and MacOS systems - nohup bash sbin/start-ainode.sh > myout.file 2>& 1 & - - # Windows systems - nohup bash sbin\start-ainode.bat > myout.file 2>& 1 & - ``` - -#### Detailed Syntax - -```shell - # Start command - # Linux and MacOS systems - bash sbin/start-ainode.sh -i -r -n - - # Windows systems - sbin\start-ainode.bat -i -r -n - ``` - -##### Parameter introduction: - -| **Name** | **Label** | **Describe** | **Is it mandatory** | **Type** | **Default value** | **Input method** | -| ------------------- | ---- | ------------------------------------------------------------ | -------- | ------ | ---------------- | ---------------------- | -| ain_interpreter_dir | -i | The interpreter path of the virtual environment where AINode is installed requires the use of an absolute path | no | String | Default reading of environment variables | Input or persist modifications during invocation | -| ain_force_reinstall | -r | Does this script check the version when checking the installation status of AINode. If it does, it will force the installation of the whl package in lib if the version is incorrect | no | Bool | false | Input when calling | - -> Attention: When installation fails in a non networked environment, first check if the installation package corresponding to the platform is selected, and then confirm the Python version (due to the limitations of the downloaded installation package on Python versions, 3.7, 3.9, and others are not allowed) - -#### Example - -##### Directly start: - -```shell - # Start command - # Linux and MacOS systems - bash sbin/start-ainode.sh - # Windows systems - sbin\start-ainode.bat - # Backend startup command (recommended for long-term running) # Linux and MacOS systems nohup bash sbin/start-ainode.sh > myout.file 2>& 1 & @@ -328,23 +182,6 @@ If you need to stop a running AINode node, execute the corresponding shutdown sc sbin\stop-ainode.bat ``` - -#### Detailed Syntax - -```shell - # Linux / MacOS - bash sbin/stop-ainode.sh -t - - #Windows - sbin\stop-ainode.bat -t - ``` - -##### Parameter introduction: - -| **Name** | **Label** | **Describe** | **Is it mandatory** | **Type** | **Default value** | **Input method** | -| ----------------- | ---- | ------------------------------------------------------------ | -------- | ------ | ------ | ---------- | -| ain_remove_target | -t | When closing AINode, you can specify the Node ID, address, and port number of the target AINode to be removed, in the format of `` | no | String | nothing | Input when calling | - #### Example ```shell @@ -368,88 +205,6 @@ IoTDB> show cluster ``` If you need to restart the node, you need to execute the startup script again. -### 3.6 Remove AINode - -When it is necessary to remove an AINode node from the cluster, a removal script can be executed. The difference between removing and stopping scripts is that stopping retains the AINode node in the cluster but stops the AINode service, while removing removes the AINode node from the cluster. - -#### Remove command - - -```shell - # Linux / MacOS - bash sbin/remove-ainode.sh - - # Windows - sbin\remove-ainode.bat - ``` - -#### Detailed Syntax - -```shell - # Linux / MacOS - bash sbin/remove-ainode.sh -i -t/: -r -n - - # Windows - sbin\remove-ainode.bat -i -t/: -r -n - ``` - -##### Parameter introduction: - - | **Name** | **Label** | **Describe** | **Is it mandatory** | **Type** | **Default value** | **Input method** | -| ------------------- | ---- | ------------------------------------------------------------ | -------- | ------ | ---------------- | --------------------- | -| ain_interpreter_dir | -i | The interpreter path of the virtual environment where AINode is installed requires the use of an absolute path | no | String | Default reading of environment variables | Input+persistent modification during invocation | -| ain_remove_target | -t | When closing AINode, you can specify the Node ID, address, and port number of the target AINode to be removed, in the format of `` | no | String | nothing | Input when calling | -| ain_force_reinstall | -r | Does this script check the version when checking the installation status of AINode. If it does, it will force the installation of the whl package in lib if the version is incorrect | no | Bool | false | Input when calling | -| ain_no_dependencies | -n | Specify whether to install dependencies when installing AINode, and if so, only install the AINode main program without installing dependencies | no | Bool | false | Input when calling | - - If you don't want to specify the corresponding parameters every time you start, you can also persistently modify the parameters in the `ainode-env.sh` and `ainode-env.bat` scripts in the `conf` folder (currently supporting persistent modification of the ain_interpreter-dir parameter). - - `ainode-env.sh` : - ```shell - # The defaulte venv environment is used if ain_interpreter_dir is not set. Please use absolute path without quotation mark - # ain_interpreter_dir= - ``` - `ainode-env.bat` : -```shell - @REM The defaulte venv environment is used if ain_interpreter_dir is not set. Please use absolute path without quotation mark - @REM set ain_interpreter_dir= - ``` - After writing the parameter value, uncomment the corresponding line and save it to take effect on the next script execution. - -#### Example - -##### Directly remove: - - ```shell - # Linux / MacOS - bash sbin/remove-ainode.sh - - # Windows - sbin\remove-ainode.bat - ``` - After removing the node, relevant information about the node cannot be queried. - - ```shell -IoTDB> show cluster -+------+----------+-------+---------------+------------+-------+-----------+ -|NodeID| NodeType| Status|InternalAddress|InternalPort|Version| BuildInfo| -+------+----------+-------+---------------+------------+-------+-----------+ -| 0|ConfigNode|Running| 127.0.0.1| 10710|UNKNOWN|190e303-dev| -| 1| DataNode|Running| 127.0.0.1| 10730|UNKNOWN|190e303-dev| -+------+----------+-------+---------------+------------+-------+-----------+ -``` -##### Specify removal: - -If the user loses files in the data folder, AINode may not be able to actively remove them locally. The user needs to specify the node number, address, and port number for removal. In this case, we support users to input parameters according to the following methods for deletion. - - ```shell - # Linux / MacOS - bash sbin/remove-ainode.sh -t /: - - # Windows - sbin\remove-ainode.bat -t /: - ``` - ## 4. common problem ### 4.1 An error occurs when starting AINode stating that the venv module cannot be found diff --git a/src/UserGuide/Master/Tree/Deployment-and-Maintenance/AINode_Deployment_timecho.md b/src/UserGuide/Master/Tree/Deployment-and-Maintenance/AINode_Deployment_timecho.md index ac7c278ca..50c532376 100644 --- a/src/UserGuide/Master/Tree/Deployment-and-Maintenance/AINode_Deployment_timecho.md +++ b/src/UserGuide/Master/Tree/Deployment-and-Maintenance/AINode_Deployment_timecho.md @@ -35,12 +35,10 @@ -## 2. Installation preparation +## 2. Installation preparation ### 2.1 Get installation package - Users can download the software installation package for AINode, download and unzip it to complete the installation of AINode. - Unzip and install the package `(apache-iotdb--ainode-bin.zip)`， The directory structure after unpacking the installation package is as follows: | **Catalogue** | **Type** | **Explain** | @@ -53,130 +51,64 @@ | README_ZH.md | file | Explanation of the Chinese version of the markdown format | | `README.md` | file | Instructions | -### 2.2 Environment preparation -- Suggested operating environment：Ubuntu, CentOS, MacOS - -- Runtime Environment - - Python >= 3.10 and Python <= 3.12 is sufficient, and comes with pip and venv tools; For non networked environments, download the zip package for the corresponding operating system from [here](https://cloud.tsinghua.edu.cn/d/4c1342f6c272439aa96c/?p=%2Flibs&mode=list) (Note that when downloading dependencies, you need to select the zip file in the libs folder, as shown in the following figure). Copy all files in the folder to the `lib` folder in the `apache-iotdb--ainode-bin` folder, and follow the steps below to start AINode. - - +### 2.2 Environmental Preparation - - There must be a Python interpreter in the environment variables that can be directly called through the `python` instruction. - - It is recommended to create a Python interpreter venv virtual environment in the `apache-iotdb--ainode-bin` folder. If installing version 3.10.0 virtual environment, the statement is as follows: - ```shell - # Install version 3.10.0 of Venv , Create a virtual environment with the folder name `venv`. - ../Python-3.10.0/python -m venv `venv` - ``` +1. Recommended operating systems: Ubuntu, CentOS, MacOS +2. IoTDB version: >= V 2.0.5.1 +3. Runtime environment + - Python version between 3.9 and 3.12, with pip and venv tools installed; ## 3. Installation steps ### 3.1 Install AINode -1. AINode activation - - Require IoTDB to be in normal operation and have AINode module authorization in the license. - - The authorization method for activating the AINode module is as follows： - - Method 1: Activate file copy activation - - After restarting the confignode node, enter the activation folder, copy the system_info file to the Timecho staff, and inform them to apply for independent authorization for AINode; - - Received the license file returned by the staff; - - Put the license file into the activation folder of the corresponding node; - -- Method 2: Activate Script Activation - - Obtain the required machine code for activation, enter the `sbin` directory of the installation directory, and execute the activation script： - ```shell - cd sbin - ./start-activate.sh - ``` - - The following information is displayed. Please copy the machine code (i.e. this string of characters) to the Timecho staff and inform them to apply for independent authorization of AINode： - ```shell - Please copy the system_info's content and send it to Timecho: - Y17hFA0xRCE1TmkVxILuCIEPc7uJcr5bzlXWiptw8uZTmTX5aThfypQdLUIhMljw075hNRSicyvyJR9JM7QaNm1gcFZPHVRWVXIiY5IlZkXdxCVc1erXMsbCqUYsR2R2Mw4PSpFJsUF5jHWSoFIIjQ2bmJFW5P52KCccFMVeHTc= - Please enter license: - ``` - - Enter the activation code returned by the staff into the `Please enter license:` command prompt in the previous step, as shown below： - ```shell - Please enter license: - Jw+MmF+AtexsfgNGOFgTm83BgXbq0zT1+fOfPvQsLlj6ZsooHFU6HycUSEGC78eT1g67KPvkcLCUIsz2QpbyVmPLr9x1+kVjBubZPYlVpsGYLqLFc8kgpb5vIrPLd3hGLbJ5Ks8fV1WOVrDDVQq89YF2atQa2EaB9EAeTWd0bRMZ+s9ffjc/1Zmh9NSP/T3VCfJcJQyi7YpXWy5nMtcW0gSV+S6fS5r7a96PjbtE0zXNjnEhqgRzdU+mfO8gVuUNaIy9l375cp1GLpeCh6m6pF+APW1CiXLTSijK9Qh3nsL5bAOXNeob5l+HO5fEMgzrW8OJPh26Vl6ljKUpCvpTiw== - License has been stored to sbin/../activation/license - Import completed. Please start cluster and excute 'show cluster' to verify activation status - ``` -- After updating the license, restart the DataNode node and enter the sbin directory of IoTDB to start the datanode： - ```shell - cd sbin - ./start-datanode.sh -d #The parameter'd 'will be started in the background - ``` - -2. Check the kernel architecture of Linux - ```shell - uname -m - ``` - -3. Import Python environment [Download](https://repo.anaconda.com/miniconda/) - - Recommend downloading the py311 version application and importing it into the iotdb dedicated folder in the user's root directory - -4. Verify Python version + 1. Ensure Python version is between 3.9 and 3.12: ```shell python --version +# or +python3 --version +``` + + 2. Download and import AINode into a dedicated folder, switch to the folder, and unzip the package: +```shell + unzip timechodb-2.0.5.1-ainode-bin.zip ``` -5. Create a virtual environment (execute in the ainode directory) +3. Create a virtual environment (execute in the ainode directory): ```shell - python -m venv venv + python3 -m venv venv ``` -6. Activate the virtual environment +4. Activate the virtual environment: ```shell source venv/bin/activate ``` - -7. Download and import AINode to a dedicated folder, switch to the dedicated folder and extract the installation package - +5. Update pip and install AINode dependencies: ```shell - unzip iotdb-enterprise-ainode-1.3.3.2.zip - ``` - -8. Configuration item modification - - ```shell - vi iotdb-enterprise-ainode-1.3.3.2/conf/iotdb-ainode.properties - ``` - Configuration item modification：[detailed information](#configuration-item-modification) - - > ain_seed_config_node=iotdb-1:10710 (Cluster communication node IP: communication node port)
- > ain_inference_rpc_address=iotdb-3 (IP address of the server running AINode) - -9. Replace Python source - - ```shell - pip config set global.index-url https://mirrors.aliyun.com/pypi/simple/ - ``` - -10. Start the AINode node - - ```shell - nohup bash iotdb-enterprise-ainode-1.3.3.2/sbin/start-ainode.sh > myout.file 2>& 1 & - ``` - > Return to the default environment of the system: conda deactivate + python -m pip install --upgrade pip + poetry lock + poetry install + ``` - ### 3.2 Configuration item modification +### 3.2 Configuration item modification AINode supports modifying some necessary parameters. You can find the following parameters in the `conf/iotdb-ainode.properties` file and make persistent modifications to them: -： - -| **Name** | **Describe** | **Type** | **Default value** | **Effective method after modification** | -| :----------------------------- | ------------------------------------------------------------ | ------- | ------------------ | ---------------------------- | -| cluster_name | The identifier for AINode to join the cluster | string | defaultCluster | Only allow modifications before the first service startup | -| ain_seed_config_node | The Configurable Node address registered during AINode startup | String | 127.0.0.1:10710 | Only allow modifications before the first service startup | -| ain_inference_rpc_address | AINode provides service and communication addresses , Internal Service Communication Interface | String | 127.0.0.1 | Only allow modifications before the first service startup | -| ain_inference_rpc_port | AINode provides ports for services and communication | String | 10810 | Only allow modifications before the first service startup | -| ain_system_dir | AINode metadata storage path, the starting directory of the relative path is related to the operating system, and it is recommended to use an absolute path | String | data/AINode/system | Only allow modifications before the first service startup | -| ain_models_dir | AINode stores the path of the model file, and the starting directory of the relative path is related to the operating system. It is recommended to use an absolute path | String | data/AINode/models | Only allow modifications before the first service startup | -| ain_logs_dir | The path where AINode stores logs, the starting directory of the relative path is related to the operating system, and it is recommended to use an absolute path | String | logs/AINode | Effective after restart | -| ain_thrift_compression_enabled | Does AINode enable Thrift's compression mechanism , 0-Do not start, 1-Start | Boolean | 0 | Effective after restart | +| **Name** | **Description** | **Type** | **Default Value** | +| ------------------------------ | ------------------------------------------------------------ | -------- | ------------------ | +| cluster_name | Identifier of the cluster AINode joins | string | defaultCluster | +| ain_seed_config_node | Address of the ConfigNode registered when AINode starts | String | 127.0.0.1:10710 | +| ain_cluster_ingress_address | RPC address of the DataNode for AINode to pull data | String | 127.0.0.1 | +| ain_cluster_ingress_port | RPC port of the DataNode for AINode to pull data | Integer | 6667 | +| ain_cluster_ingress_username | Client username for AINode to pull data from the DataNode | String | root | +| ain_cluster_ingress_password | Client password for AINode to pull data from the DataNode | String | root | +| ain_cluster_ingress_time_zone | Client time zone for AINode to pull data from the DataNode | String | UTC+8 | +| ain_inference_rpc_address | Address for AINode to provide services and communication (internal interface) | String | 127.0.0.1 | +| ain_inference_rpc_port | Port for AINode to provide services and communication | String | 10810 | +| ain_system_dir | Metadata storage path for AINode (relative path starts from OS-dependent directory; absolute path is recommended) | String | data/AINode/system | +| ain_models_dir | Path to store model files for AINode (relative path starts from OS-dependent directory; absolute path is recommended) | String | data/AINode/models | +| ain_thrift_compression_enabled | Whether to enable Thrift compression for AINode (0=disabled, 1=enabled) | Boolean | 0 | ### 3.3 Start AINode @@ -202,43 +134,8 @@ AINode supports modifying some necessary parameters. You can find the following nohup bash sbin\start-ainode.bat > myout.file 2>& 1 & ``` -#### Detailed Syntax -```shell - # Start command - # Linux and MacOS systems - bash sbin/start-ainode.sh -i -r -n - - # Windows systems - sbin\start-ainode.bat -i -r -n - ``` - -##### Parameter introduction: - -| **Name** | **Label** | **Describe** | **Is it mandatory** | **Type** | **Default value** | **Input method** | -| ------------------- | ---- | ------------------------------------------------------------ | -------- | ------ | ---------------- | ---------------------- | -| ain_interpreter_dir | -i | The interpreter path of the virtual environment where AINode is installed requires the use of an absolute path. | no | String | Default reading of environment variables | Input or persist modifications during invocation | -| ain_force_reinstall | -r | Does this script check the version when checking the installation status of AINode. If it does, it will force the installation of the whl package in lib if the version is incorrect. | no | Bool | false | Input when calling | -| ain_no_dependencies | -n | Specify whether to install dependencies when installing AINode, and if so, only install the AINode main program without installing dependencies. | no | Bool | false | Input when calling | - - If you don't want to specify the corresponding parameters every time you start, you can also persistently modify the parameters in the `ainode-env.sh` and `ainode-env.bat` scripts in the `conf` folder (currently supporting persistent modification of the ain_interpreter-dir parameter). - - `ainode-env.sh` : - ```shell - # The defaulte venv environment is used if ain_interpreter_dir is not set. Please use absolute path without quotation mark - # ain_interpreter_dir= - ``` - `ainode-env.bat` : -```shell - @REM The defaulte venv environment is used if ain_interpreter_dir is not set. Please use absolute path without quotation mark - @REM set ain_interpreter_dir= - ``` - After writing the parameter value, uncomment the corresponding line and save it to take effect on the next script execution. - - -#### Example - -##### Directly start: +#### Example ```shell # Start command @@ -248,82 +145,6 @@ AINode supports modifying some necessary parameters. You can find the following sbin\start-ainode.bat - # Backend startup command (recommended for long-term running) - # Linux and MacOS systems - nohup bash sbin/start-ainode.sh > myout.file 2>& 1 & - # Windows systems - nohup bash sbin\start-ainode.bat > myout.file 2>& 1 & - ``` - -##### Update Start: -If the version of AINode has been updated (such as updating the `lib` folder), this command can be used. Firstly, it is necessary to ensure that AINode has stopped running, and then restart it using the `-r` parameter, which will reinstall AINode based on the files under `lib`. - - -```shell - # Update startup command - # Linux and MacOS systems - bash sbin/start-ainode.sh -r - # Windows systems - sbin\start-ainode.bat -r - - - # Backend startup command (recommended for long-term running) - # Linux and MacOS systems - nohup bash sbin/start-ainode.sh -r > myout.file 2>& 1 & - # Windows c - nohup bash sbin\start-ainode.bat -r > myout.file 2>& 1 & - ``` -#### Non networked environment startup - -##### Start command - -```shell - # Start command - # Linux and MacOS systems - bash sbin/start-ainode.sh - - # Windows systems - sbin\start-ainode.bat - - # Backend startup command (recommended for long-term running) - # Linux and MacOS systems - nohup bash sbin/start-ainode.sh > myout.file 2>& 1 & - - # Windows systems - nohup bash sbin\start-ainode.bat > myout.file 2>& 1 & - ``` - -#### Detailed Syntax - -```shell - # Start command - # Linux and MacOS systems - bash sbin/start-ainode.sh -i -r -n - - # Windows systems - sbin\start-ainode.bat -i -r -n - ``` - -##### Parameter introduction: - -| **Name** | **Label** | **Describe** | **Is it mandatory** | **Type** | **Default value** | **Input method** | -| ------------------- | ---- | ------------------------------------------------------------ | -------- | ------ | ---------------- | ---------------------- | -| ain_interpreter_dir | -i | The interpreter path of the virtual environment where AINode is installed requires the use of an absolute path | no | String | Default reading of environment variables | Input or persist modifications during invocation | -| ain_force_reinstall | -r | Does this script check the version when checking the installation status of AINode. If it does, it will force the installation of the whl package in lib if the version is incorrect | no | Bool | false | Input when calling | - -> Attention: When installation fails in a non networked environment, first check if the installation package corresponding to the platform is selected, and then confirm the Python version (due to the limitations of the downloaded installation package on Python versions, 3.7, 3.9, and others are not allowed) - -#### Example - -##### Directly start: - -```shell - # Start command - # Linux and MacOS systems - bash sbin/start-ainode.sh - # Windows systems - sbin\start-ainode.bat - # Backend startup command (recommended for long-term running) # Linux and MacOS systems nohup bash sbin/start-ainode.sh > myout.file 2>& 1 & @@ -361,23 +182,6 @@ If you need to stop a running AINode node, execute the corresponding shutdown sc sbin\stop-ainode.bat ``` - -#### Detailed Syntax - -```shell - # Linux / MacOS - bash sbin/stop-ainode.sh -t - - #Windows - sbin\stop-ainode.bat -t - ``` - -##### Parameter introduction: - -| **Name** | **Label** | **Describe** | **Is it mandatory** | **Type** | **Default value** | **Input method** | -| ----------------- | ---- | ------------------------------------------------------------ | -------- | ------ | ------ | ---------- | -| ain_remove_target | -t | When closing AINode, you can specify the Node ID, address, and port number of the target AINode to be removed, in the format of `` | no | String | nothing | Input when calling | - #### Example ```shell @@ -401,88 +205,6 @@ IoTDB> show cluster ``` If you need to restart the node, you need to execute the startup script again. -### 3.6 Remove AINode - -When it is necessary to remove an AINode node from the cluster, a removal script can be executed. The difference between removing and stopping scripts is that stopping retains the AINode node in the cluster but stops the AINode service, while removing removes the AINode node from the cluster. - -#### Remove command - - -```shell - # Linux / MacOS - bash sbin/remove-ainode.sh - - # Windows - sbin\remove-ainode.bat - ``` - -#### Detailed Syntax - -```shell - # Linux / MacOS - bash sbin/remove-ainode.sh -i -t/: -r -n - - # Windows - sbin\remove-ainode.bat -i -t/: -r -n - ``` - -##### Parameter introduction: - - | **Name** | **Label** | **Describe** | **Is it mandatory** | **Type** | **Default value** | **Input method** | -| ------------------- | ---- | ------------------------------------------------------------ | -------- | ------ | ---------------- | --------------------- | -| ain_interpreter_dir | -i | The interpreter path of the virtual environment where AINode is installed requires the use of an absolute path | no | String | Default reading of environment variables | Input+persistent modification during invocation | -| ain_remove_target | -t | When closing AINode, you can specify the Node ID, address, and port number of the target AINode to be removed, in the format of `` | no | String | nothing | Input when calling | -| ain_force_reinstall | -r | Does this script check the version when checking the installation status of AINode. If it does, it will force the installation of the whl package in lib if the version is incorrect | no | Bool | false | Input when calling | -| ain_no_dependencies | -n | Specify whether to install dependencies when installing AINode, and if so, only install the AINode main program without installing dependencies | no | Bool | false | Input when calling | - - If you don't want to specify the corresponding parameters every time you start, you can also persistently modify the parameters in the `ainode-env.sh` and `ainode-env.bat` scripts in the `conf` folder (currently supporting persistent modification of the ain_interpreter-dir parameter). - - `ainode-env.sh` : - ```shell - # The defaulte venv environment is used if ain_interpreter_dir is not set. Please use absolute path without quotation mark - # ain_interpreter_dir= - ``` - `ainode-env.bat` : -```shell - @REM The defaulte venv environment is used if ain_interpreter_dir is not set. Please use absolute path without quotation mark - @REM set ain_interpreter_dir= - ``` - After writing the parameter value, uncomment the corresponding line and save it to take effect on the next script execution. - -#### Example - -##### Directly remove: - - ```shell - # Linux / MacOS - bash sbin/remove-ainode.sh - - # Windows - sbin\remove-ainode.bat - ``` - After removing the node, relevant information about the node cannot be queried. - - ```shell -IoTDB> show cluster -+------+----------+-------+---------------+------------+-------+-----------+ -|NodeID| NodeType| Status|InternalAddress|InternalPort|Version| BuildInfo| -+------+----------+-------+---------------+------------+-------+-----------+ -| 0|ConfigNode|Running| 127.0.0.1| 10710|UNKNOWN|190e303-dev| -| 1| DataNode|Running| 127.0.0.1| 10730|UNKNOWN|190e303-dev| -+------+----------+-------+---------------+------------+-------+-----------+ -``` -##### Specify removal: - -If the user loses files in the data folder, AINode may not be able to actively remove them locally. The user needs to specify the node number, address, and port number for removal. In this case, we support users to input parameters according to the following methods for deletion. - - ```shell - # Linux / MacOS - bash sbin/remove-ainode.sh -t /: - - # Windows - sbin\remove-ainode.bat -t /: - ``` - ## 4. common problem ### 4.1 An error occurs when starting AINode stating that the venv module cannot be found diff --git a/src/UserGuide/latest-Table/AI-capability/AINode_apache.md b/src/UserGuide/latest-Table/AI-capability/AINode_apache.md new file mode 100644 index 000000000..58aab926b --- /dev/null +++ b/src/UserGuide/latest-Table/AI-capability/AINode_apache.md @@ -0,0 +1,381 @@ + + +# AINode + +AINode is an IoTDB native node designed to support the registration, management, and invocation of large-scale time series models. It comes with industry-leading proprietary time series models such as Timer and Sundial. These models can be invoked through standard SQL statements, enabling real-time inference of time series data at the millisecond level, and supporting application scenarios such as trend forecasting, missing value imputation, and anomaly detection for time series data. + +The system architecture is shown below: +::: center + +::: +The responsibilities of the three nodes are as follows: + +- **ConfigNode**: responsible for storing and managing the meta-information of the model; responsible for distributed node management. +- **DataNode**: responsible for receiving and parsing SQL requests from users; responsible for storing time-series data; responsible for preprocessing computation of data. +- **AINode**: responsible for model file import creation and model inference. + +## 1. Advantageous features + +Compared with building a machine learning service alone, it has the following advantages: + +- **Simple and easy to use**: no need to use Python or Java programming, the complete process of machine learning model management and inference can be completed using SQL statements. Creating a model can be done using the CREATE MODEL statement, and using a model for inference can be done using the SELECT * FROM FORECAST (...) statement, making it simpler and more convenient to use. + +- **Avoid Data Migration**: With IoTDB native machine learning, data stored in IoTDB can be directly applied to the inference of machine learning models without having to move the data to a separate machine learning service platform, which accelerates data processing, improves security, and reduces costs. + +![](/img/AInode1.png) + +- **Built-in Advanced Algorithms**: supports industry-leading machine learning analytics algorithms covering typical timing analysis tasks, empowering the timing database with native data analysis capabilities. Such as: + - **Time Series Forecasting**: learns patterns of change from past time series; thus outputs the most likely prediction of future series based on observations at a given past time. + - **Anomaly Detection for Time Series**: detects and identifies outliers in a given time series data, helping to discover anomalous behaviour in the time series. + - **Annotation for Time Series (Time Series Annotation)**: Adds additional information or markers, such as event occurrence, outliers, trend changes, etc., to each data point or specific time period to better understand and analyse the data. + + +## 2. Basic Concepts + +- **Model**: A machine learning model that takes time series data as input and outputs analysis results or decisions. Models are the basic management units of AINode, supporting model operations such as creation (registration), deletion, query, and inference. +- **Create**: Load externally designed or trained model files/algorithms into AINode for unified management and usage by IoTDB. +- **Inference**: Use the created model to complete time series analysis tasks applicable to the model on specified time series data. +- **Built-in Capabilities**: AINode comes with machine learning algorithms or self-developed models for common time series analysis scenarios (e.g., forecasting and anomaly detection). +![](/img/h3.png) + +## 3. Installation and Deployment + +The deployment of AINode can be found in the document [Deployment Guidelines](../Deployment-and-Maintenance/AINode_Deployment_apache.md#ainode-deployment) . + + +## 4. Usage Guide + +AINode provides model creation and deletion functions for time series models. Built-in models do not require creation and can be used directly. + +### 4.1 Registering Models + +Trained deep learning models can be registered by specifying their input and output vector dimensions for inference. + +Models that meet the following criteria can be registered with AINode: + +1. AINode currently supports models trained with PyTorch 2.4.0. Features above version 2.4.0 should be avoided. +2. AINode supports models stored using PyTorch JIT (`model.pt`), which must include both the model structure and weights. +3. The model input sequence can include single or multiple columns. If multi-column, it must match the model capabilities and configuration file. +4. Model configuration parameters must be clearly defined in the `config.yaml` file. When using the model, the input and output dimensions defined in `config.yaml` must be strictly followed. Mismatches with the configuration file will cause errors. + +The SQL syntax for model registration is defined as follows: + +```SQL +create model using uri +``` + +Detailed meanings of SQL parameters: + +- **model_name**: The global unique identifier for the model, non-repeating. Model names have the following constraints: + - Allowed characters: [0-9 a-z A-Z _] (letters, numbers, underscores) + - Length: 2-64 characters + - Case-sensitive +- **uri**: The resource path of the model registration files, which should include the **model structure and weight file `model.pt` and the model configuration file `config.yaml`** + + - **Model structure and weight file**: The weight file generated after model training, currently supporting `.pt` files from PyTorch training. + + - **Model configuration file**: Parameters related to the model structure provided during registration, which must include input and output dimensions for inference: + + | **Parameter Name** | **Description** | **Example** | + | ------------ | ---------------------------- | -------- | + | input_shape | Rows and columns of model input | [96,2] | + | output_shape | Rows and columns of model output | [48,2] | + + In addition to inference, data types of input and output can also be specified: + + | **Parameter Name** | **Description** | **Example** | + | ------------------ | ------------------------- | ---------------------- | + | input_type | Data type of model input | ['float32', 'float32'] | + | output_type | Data type of model output | ['float32', 'float32'] | + + Additional notes can be specified for model management display: + + | **Parameter Name** | **Description** | **Example** | + | ------------------ | --------------------------------------------- | -------------------------------------------- | + | attributes | Optional notes set by users for model display | 'model_type': 'dlinear', 'kernel_size': '25' | + +In addition to registering local model files, remote resource paths can be specified via URIs for registration, using open-source model repositories (e.g., HuggingFace). + +#### Example + +The current example folder contains model.pt (trained model) and config.yaml with the following content: + +```YAML +configs: + # Required + input_shape: [96, 2] # Model accepts 96 rows x 2 columns of data + output_shape: [48, 2] # Model outputs 48 rows x 2 columns of data + + # Optional (default to all float32, column count matches shape) + input_type: ["int64", "int64"] # Data types of inputs, must match input column count + output_type: ["text", "int64"] # Data types of outputs, must match output column count + +attributes: # Optional user-defined notes + 'model_type': 'dlinear' + 'kernel_size': '25' +``` + +Register the model by specifying this folder as the loading path: + +```SQL +IoTDB> create model dlinear_example using uri "file://./example" +``` + +Models can also be downloaded from HuggingFace for registration: + +```SQL +IoTDB> create model dlinear_example using uri "https://huggingface.co/google/timesfm-2.0-500m-pytorch" +``` + +After SQL execution, registration proceeds asynchronously. The registration status can be checked via model display (see Model Display section). The registration success time mainly depends on the model file size. + +Once registered, the model can be invoked for inference through normal query syntax. + +### 4.2 Viewing Models + +Registered models can be queried using the `show models` command. The SQL definitions are: + +```SQL +show models + +show models +``` + +In addition to displaying all models, specifying a `model_id` shows details of a specific model. The display includes: + +| **ModelId** | **State** | **Configs** | **Attributes** | +| ----------- | --------------------------------------------------------- | ------------------------------------------------ | -------------- | +| Unique ID | Registration status (INACTIVE, LOADING, ACTIVE, DROPPING) | InputShape, outputShape, inputTypes, outputTypes | User notes | + +**State descriptions:** + +- **INACTIVE**: Model is unavailable. +- **LOADING**: Model is being loaded. +- **ACTIVE**: Model is available. +- **DROPPING**: Model is being deleted. + +#### Example + +```SQL +IoTDB> show models + ++---------------------+--------------------+--------+--------+ +| ModelId| ModelType|Category| State| ++---------------------+--------------------+--------+--------+ +| arima| Arima|BUILT-IN| ACTIVE| +| holtwinters| HoltWinters|BUILT-IN| ACTIVE| +|exponential_smoothing|ExponentialSmoothing|BUILT-IN| ACTIVE| +| naive_forecaster| NaiveForecaster|BUILT-IN| ACTIVE| +| stl_forecaster| StlForecaster|BUILT-IN| ACTIVE| +| gaussian_hmm| GaussianHmm|BUILT-IN| ACTIVE| +| gmm_hmm| GmmHmm|BUILT-IN| ACTIVE| +| stray| Stray|BUILT-IN| ACTIVE| +| timer_xl| Timer-XL|BUILT-IN| ACTIVE| +| sundial| Timer-Sundial|BUILT-IN| ACTIVE| ++---------------------+--------------------+--------+--------+ +``` + +### 4.3 Deleting Models + +Registered models can be deleted via SQL, which removes all related files under AINode: + +```SQL +drop model +``` + +Specify the registered `model_id` to delete the model. Since deletion involves data cleanup, the operation is not immediate, and the model state becomes `DROPPING`, during which it cannot be used for inference. **Note:** Built-in models cannot be deleted. + +### 4.4 Inference with Built-in Models + +SQL syntax: + + +```SQL +SELECT * FROM forecast( + input, + model_id, + [output_length, + output_start_time, + output_interval, + timecol, + preserve_input, + model_options]? +) +``` +Built-in models do not require prior registration for inference. Simply use the `forecast` function and specify the `model_id` to invoke the model's inference capabilities. + + - **Note**: Inference with built-in time series large models requires local availability of model weights in the directory `/IOTDB_AINODE_HOME/data/ainode/models/weights/model_id/`. If weights are missing, they will be automatically downloaded from HuggingFace. Ensure direct network access to HuggingFace. + +Parameter descriptions: + + +| Parameter | Type | Attribute | Description | Required | Notes | +| ----------------- | ------------- | -------------------------------------------------------- | ------------------------------------------------------------ | -------- | ------------------------------------------------------------ | +| input | Table | SET SEMANTIC | Input data for forecasting | Yes | | +| model_id | String | Scalar | Name of the model to use | Yes | Must be non-empty and a built-in model; otherwise, errors like "MODEL_ID cannot be null" occur. | +| output_length | INT32 | Scalar (default: 96) | Size of the output forecast window | No | Must be > 0. | +| output_start_time | Timestamp | Scalar (default: last input timestamp + output_interval) | Start timestamp of the forecast results | No | Can be negative (before 1970-01-01). | +| output_interval | Time interval | Scalar (default: inferred from input) | Time interval between forecast points (supports ns, us, ms, s, m, h, d, w) | No | If > 0, uses user-specified interval; else, infers from input. | +| timecol | String | Scalar (default: "time") | Name of the timestamp column | No | Must exist in `input` and be of TIMESTAMP type; otherwise, errors occur. | +| preserve_input | Boolean | Scalar (default: false) | Retain all input rows in the output | No | | +| model_options | String | Scalar (default: empty) | Model-specific key-value pairs (e.g., normalization) | No | Unsupported parameters are ignored. See appendix for built-in model parameters. | + +**Notes:** + +1. The `forecast` function predicts all columns in the input table by default (excluding the time column and columns specified in `partition by`). +2. The `forecast` function does not require the input data to be in any specific order. It sorts the input data in ascending order by the timestamp (specified by the `TIMECOL` parameter) before invoking the model for prediction. +3. Different models have different requirements for the number of input data rows: + - If the input data has fewer rows than the minimum requirement, an error will be thrown. + - If the input data exceeds the maximum row limit, the last rows that meet the requirement will be automatically truncated for processing. + - Among the currently built-in models in AINode, only `sundial` has a row limit. It supports a maximum of 2880 input rows. If exceeded, the last 2880 rows will be automatically used. +4. The result columns of the `forecast` function include all input columns from the input table, with their original data types preserved. If `preserve_input = true`, an additional `is_input` column will be included to indicate whether a row is from the input data. + - Currently, only columns of type INT32, INT64, FLOAT, or DOUBLE are supported for prediction. Otherwise, an error will occur: "The type of the column [%s] is [%s], only INT32, INT64, FLOAT, DOUBLE is allowed." +5. `output_start_time` and `output_interval` only affect the generation of the timestamp column in the output results. Both are optional parameters: + - `output_start_time` defaults to the last timestamp of the input data plus `output_interval`. + - `output_interval` defaults to the sampling interval of the input data, calculated as: (last timestamp - first timestamp) / (number of rows - 1). + - The timestamp of the Nth output row is calculated as: `output_start_time + (N - 1) * output_interval`. + +#### Example + +Using the ETTh1-tab dataset:[ETTh1-tab](/img/ETTh1-tab.csv)。 + +**View supported models** + +```Bash +IoTDB:etth> show models ++---------------------+--------------------+--------+------+ +| ModelId| ModelType|Category| State| ++---------------------+--------------------+--------+------+ +| arima| Arima|BUILT-IN|ACTIVE| +| holtwinters| HoltWinters|BUILT-IN|ACTIVE| +|exponential_smoothing|ExponentialSmoothing|BUILT-IN|ACTIVE| +| naive_forecaster| NaiveForecaster|BUILT-IN|ACTIVE| +| stl_forecaster| StlForecaster|BUILT-IN|ACTIVE| +| gaussian_hmm| GaussianHmm|BUILT-IN|ACTIVE| +| gmm_hmm| GmmHmm|BUILT-IN|ACTIVE| +| stray| Stray|BUILT-IN|ACTIVE| +| sundial| Timer-Sundial|BUILT-IN|ACTIVE| +| timer_xl| Timer-XL|BUILT-IN|ACTIVE| ++---------------------+--------------------+--------+------+ +Total line number = 10 +It costs 0.004s +``` + +**Inference with sundial model:** + +```Bash +IoTDB:etth> select Time, HUFL,HULL,MUFL,MULL,LUFL,LULL,OT from eg LIMIT 96 ++-----------------------------+------+-----+-----+-----+-----+-----+------+ +| Time| HUFL| HULL| MUFL| MULL| LUFL| LULL| OT| ++-----------------------------+------+-----+-----+-----+-----+-----+------+ +|2016-07-01T00:00:00.000+08:00| 5.827|2.009|1.599|0.462|4.203| 1.34|30.531| +|2016-07-01T01:00:00.000+08:00| 5.693|2.076|1.492|0.426|4.142|1.371|27.787| +|2016-07-01T02:00:00.000+08:00| 5.157|1.741|1.279|0.355|3.777|1.218|27.787| +|2016-07-01T03:00:00.000+08:00| 5.09|1.942|1.279|0.391|3.807|1.279|25.044| +...... +Total line number = 96 +It costs 0.119s + +IoTDB:etth> select * from forecast( + model_id => 'sundial', + input => (select Time, ot from etth.eg where time >= 2016-08-07T18:00:00.000+08:00 limit 1440) order BY time, + output_length => 96 +) ++-----------------------------+---------+ +| time| ot| ++-----------------------------+---------+ +|2016-10-06T18:00:00.000+08:00|20.781654| +|2016-10-06T19:00:00.000+08:00|20.252121| +|2016-10-06T20:00:00.000+08:00|19.960138| +|2016-10-06T21:00:00.000+08:00|19.662334| +...... +Total line number = 96 +It costs 1.615s +``` + +### 4.5 Time Series Large Model Import Steps + +AINode supports multiple time series large models. For deployment, refer to [Time Series Large Model](https://timecho.com/docs/zh/UserGuide/latest/AI-capability/TimeSeries-Large-Model.html) + + +## 5 Permission Management + +AINode uses IoTDB's authentication for permission management. Users need `USE_MODEL` permission for model management and `READ_DATA` permission for inference (to access input data sources). + +| **Permission** | **Scope** | **Admin (ROOT)** | **Regular User** | **Path-Related** | +| -------------- | ------------------------ | ---------------- | ---------------- | ---------------- | +| USE_MODEL | Create/Show/Drop models | ✔️ | ✔️ | ❌ | +| READ_DATA | Call inference functions | ✔️ | ✔️ | ✔️ | + + +## 6 Appendix + +**Arima** + +| Parameter | Description | Default | +| ----------------------- | ------------------------------------------------------------ | --------- | +| order | ARIMA order `(p, d, q)`: p=autoregressive, d=differencing, q=moving average. | (1,0,0) | +| seasonal_order | Seasonal ARIMA order `(P, D, Q, s)`: seasonal AR, differencing, MA orders, and season length (e.g., 12 for monthly data). | (0,0,0,0) | +| method | Optimizer: 'newton', 'nm', 'bfgs', 'lbfgs', 'powell', 'cg', 'ncg', 'basinhopping'. | 'lbfgs' | +| maxiter | Maximum iterations/function evaluations. | 50 | +| out_of_sample_size | Number of tail samples for validation (not used in fitting). | 0 | +| scoring | Scoring function for validation (sklearn metric or custom). | 'mse' | +| trend | Trend term configuration. If `with_intercept=True` and None, defaults to 'c' (constant). | None | +| with_intercept | Include intercept term. | True | +| time_varying_regression | Allow regression coefficients to vary over time. | False | +| enforce_stationarity | Enforce stationarity of AR components. | True | +| enforce_invertibility | Enforce invertibility of MA components. | True | +| simple_differencing | Use differenced data for estimation (sacrifices first rows). | False | +| measurement_error | Assume observation errors. | False | +| mle_regression | Use maximum likelihood for regression (must be False if `time_varying_regression=True`). | True | +| hamilton_representation | Use Hamilton representation (default is Harvey). | False | +| concentrate_scale | Exclude scale parameter from likelihood (reduces parameters). | False | + +**NaiveForecaster** + +| Parameter | Description | Default | +| --------- | ------------------------------------------------------------ | ------- | +| strategy | Forecasting strategy: - `"last"`: Use last training value (seasonal if `sp`>1). - `"mean"`: Use mean of last window (seasonal if `sp`>1). - `"drift"`: Fit line through last window and extrapolate (non-robust to NaN). | "last" | +| sp | Seasonal period. `None` or 1 means no seasonality; 12 means monthly. | 1 | + +**STLForecaster** + +| Parameter | Description | Default | +| ------------- | ---------------------------------------------------------- | ------- | +| sp | Seasonal period (units). Passed to statsmodels' STL. | 2 | +| seasonal | Seasonal smoothing window (odd ≥3, typically ≥7). | 7 | +| seasonal_deg | LOESS polynomial degree for season (0=constant, 1=linear). | 1 | +| trend_deg | LOESS polynomial degree for trend (0 or 1). | 1 | +| low_pass_deg | LOESS polynomial degree for low-pass (0 or 1). | 1 | +| seasonal_jump | Interpolation step for season LOESS (larger = faster). | 1 | +| trend_jump | Interpolation step for trend LOESS (larger = faster). | 1 | +| low_pass_jump | Interpolation step for low-pass LOESS. | 1 | + +**ExponentialSmoothing (HoltWinters)** + +| Parameter | Description | Default | +| --------------------- | ------------------------------------------------------------ | ----------- | +| damped_trend | Use damped trend (trend flattens instead of growing infinitely). | True | +| initialization_method | Initialization method: - `"estimated"`: Fit to estimate initial states - `"heuristic"`: Use heuristic for initial level/trend/season - `"known"`: User-provided initial values - `"legacy-heuristic"`: Legacy compatibility | "estimated" | +| optimized | Optimize parameters via maximum likelihood. | True | +| remove_bias | Remove bias to make residuals' mean zero. | False | +| use_brute | Use brute-force grid search for initial parameters. | | \ No newline at end of file diff --git a/src/UserGuide/latest-Table/AI-capability/AINode_timecho.md b/src/UserGuide/latest-Table/AI-capability/AINode_timecho.md new file mode 100644 index 000000000..034a54971 --- /dev/null +++ b/src/UserGuide/latest-Table/AI-capability/AINode_timecho.md @@ -0,0 +1,447 @@ + + +# AINode + +AINode is an IoTDB native node designed to support the registration, management, and invocation of large-scale time series models. It comes with industry-leading proprietary time series models such as Timer and Sundial. These models can be invoked through standard SQL statements, enabling real-time inference of time series data at the millisecond level, and supporting application scenarios such as trend forecasting, missing value imputation, and anomaly detection for time series data. + +The system architecture is shown below: +::: center + +::: +The responsibilities of the three nodes are as follows: + +- **ConfigNode**: responsible for storing and managing the meta-information of the model; responsible for distributed node management. +- **DataNode**: responsible for receiving and parsing SQL requests from users; responsible for storing time-series data; responsible for preprocessing computation of data. +- **AINode**: responsible for model file import creation and model inference. + +## 1. Advantageous features + +Compared with building a machine learning service alone, it has the following advantages: + +- **Simple and easy to use**: no need to use Python or Java programming, the complete process of machine learning model management and inference can be completed using SQL statements. Creating a model can be done using the CREATE MODEL statement, and using a model for inference can be done using the SELECT * FROM FORECAST (...) statement, making it simpler and more convenient to use. + +- **Avoid Data Migration**: With IoTDB native machine learning, data stored in IoTDB can be directly applied to the inference of machine learning models without having to move the data to a separate machine learning service platform, which accelerates data processing, improves security, and reduces costs. + +![](/img/AInode1.png) + +- **Built-in Advanced Algorithms**: supports industry-leading machine learning analytics algorithms covering typical timing analysis tasks, empowering the timing database with native data analysis capabilities. Such as: + - **Time Series Forecasting**: learns patterns of change from past time series; thus outputs the most likely prediction of future series based on observations at a given past time. + - **Anomaly Detection for Time Series**: detects and identifies outliers in a given time series data, helping to discover anomalous behaviour in the time series. + - **Annotation for Time Series (Time Series Annotation)**: Adds additional information or markers, such as event occurrence, outliers, trend changes, etc., to each data point or specific time period to better understand and analyse the data. + + +## 2. Basic Concepts + +- **Model**: A machine learning model takes time series data as input and outputs analysis task results or decisions. Models are the basic management units of AINode, supporting model operations such as creation (registration), deletion, query, modification (fine-tuning), and usage (inference). +- **Create**: Load externally designed or trained model files/algorithms into AINode for unified management and usage by IoTDB. +- **Inference**: Use the created model to complete time series analysis tasks applicable to the model on specified time series data. +- **Built-in Capabilities**: AINode comes with machine learning algorithms or self-developed models for common time series analysis scenarios (e.g., forecasting and anomaly detection). +![](/img/h3.png) + +## 3. Installation and Deployment + +The deployment of AINode can be found in the document [Deployment Guidelines](../Deployment-and-Maintenance/AINode_Deployment_timecho.md#ainode-deployment) . + + +## 4. Usage Guide + +AINode provides model creation and deletion functions for time series models. Built-in models do not require creation and can be used directly. + +### 4.1 Registering Models + +Trained deep learning models can be registered by specifying their input and output vector dimensions for inference. + +Models that meet the following criteria can be registered with AINode: + +1. AINode currently supports models trained with PyTorch 2.4.0. Features above version 2.4.0 should be avoided. +2. AINode supports models stored using PyTorch JIT (`model.pt`), which must include both the model structure and weights. +3. The model input sequence can include single or multiple columns. If multi-column, it must match the model capabilities and configuration file. +4. Model configuration parameters must be clearly defined in the `config.yaml` file. When using the model, the input and output dimensions defined in `config.yaml` must be strictly followed. Mismatches with the configuration file will cause errors. + +The SQL syntax for model registration is defined as follows: + +```SQL +create model using uri +``` + +Detailed meanings of SQL parameters: + +- **model_name**: The global unique identifier for the model, non-repeating. Model names have the following constraints: + - Allowed characters: [0-9 a-z A-Z _] (letters, numbers, underscores) + - Length: 2-64 characters + - Case-sensitive +- **uri**: The resource path of the model registration files, which should include the **model structure and weight file `model.pt` and the model configuration file `config.yaml`** + + - **Model structure and weight file**: The weight file generated after model training, currently supporting `.pt` files from PyTorch training. + + - **Model configuration file**: Parameters related to the model structure provided during registration, which must include input and output dimensions for inference: + + | **Parameter Name** | **Description** | **Example** | + | ------------ | ---------------------------- | -------- | + | input_shape | Rows and columns of model input | [96,2] | + | output_shape | Rows and columns of model output | [48,2] | + + In addition to inference, data types of input and output can also be specified: + + | **Parameter Name** | **Description** | **Example** | + | ------------------ | ------------------------- | ---------------------- | + | input_type | Data type of model input | ['float32', 'float32'] | + | output_type | Data type of model output | ['float32', 'float32'] | + + Additional notes can be specified for model management display: + + | **Parameter Name** | **Description** | **Example** | + | ------------------ | --------------------------------------------- | -------------------------------------------- | + | attributes | Optional notes set by users for model display | 'model_type': 'dlinear', 'kernel_size': '25' | + +In addition to registering local model files, remote resource paths can be specified via URIs for registration, using open-source model repositories (e.g., HuggingFace). + +#### Example + +The current example folder contains model.pt (trained model) and config.yaml with the following content: + +```YAML +configs: + # Required + input_shape: [96, 2] # Model accepts 96 rows x 2 columns of data + output_shape: [48, 2] # Model outputs 48 rows x 2 columns of data + + # Optional (default to all float32, column count matches shape) + input_type: ["int64", "int64"] # Data types of inputs, must match input column count + output_type: ["text", "int64"] # Data types of outputs, must match output column count + +attributes: # Optional user-defined notes + 'model_type': 'dlinear' + 'kernel_size': '25' +``` + +Register the model by specifying this folder as the loading path: + +```SQL +IoTDB> create model dlinear_example using uri "file://./example" +``` + +Models can also be downloaded from HuggingFace for registration: + +```SQL +IoTDB> create model dlinear_example using uri "https://huggingface.co/google/timesfm-2.0-500m-pytorch" +``` + +After SQL execution, registration proceeds asynchronously. The registration status can be checked via model display (see Model Display section). The registration success time mainly depends on the model file size. + +Once registered, the model can be invoked for inference through normal query syntax. + +### 4.2 Viewing Models + +Registered models can be queried using the `show models` command. The SQL definitions are: + +```SQL +show models + +show models +``` + +In addition to displaying all models, specifying a `model_id` shows details of a specific model. The display includes: + +| **ModelId** | **State** | **Configs** | **Attributes** | +| ----------- | --------------------------------------------------------- | ------------------------------------------------ | -------------- | +| Unique ID | Registration status (INACTIVE, LOADING, ACTIVE,TRAINING,FAILED, DROPPING) | InputShape, outputShape, inputTypes, outputTypes | User notes | + +**State descriptions:** + +- **INACTIVE**: The model is in an unavailable state. +- **LOADING**: The model is being loaded. +- **ACTIVE**: The model is in an available state. +- **TRAINING**: The model is in the fine-tuning state. +- **FAILED**: The model fine-tuning failed. +- **DROPPING**: The model is being deleted. + +#### Example + +```SQL +IoTDB> show models + ++---------------------+--------------------+--------+--------+ +| ModelId| ModelType|Category| State| ++---------------------+--------------------+--------+--------+ +| arima| Arima|BUILT-IN| ACTIVE| +| holtwinters| HoltWinters|BUILT-IN| ACTIVE| +|exponential_smoothing|ExponentialSmoothing|BUILT-IN| ACTIVE| +| naive_forecaster| NaiveForecaster|BUILT-IN| ACTIVE| +| stl_forecaster| StlForecaster|BUILT-IN| ACTIVE| +| gaussian_hmm| GaussianHmm|BUILT-IN| ACTIVE| +| gmm_hmm| GmmHmm|BUILT-IN| ACTIVE| +| stray| Stray|BUILT-IN| ACTIVE| +| timer_xl| Timer-XL|BUILT-IN| ACTIVE| +| sundial| Timer-Sundial|BUILT-IN| ACTIVE| ++---------------------+--------------------+--------+--------+ +``` + +### 4.3 Deleting Models + +Registered models can be deleted via SQL, which removes all related files under AINode: + +```SQL +drop model +``` + +Specify the registered `model_id` to delete the model. Since deletion involves data cleanup, the operation is not immediate, and the model state becomes `DROPPING`, during which it cannot be used for inference. **Note:** Built-in models cannot be deleted. + +### 4.4 Inference with Built-in Models + +SQL syntax: + + +```SQL +SELECT * FROM forecast( + input, + model_id, + [output_length, + output_start_time, + output_interval, + timecol, + preserve_input, + model_options]? +) +``` + + +Built-in models do not require prior registration for inference. Simply use the `forecast` function and specify the `model_id` to invoke the model's inference capabilities. + + - **Note**: Inference with built-in time series large models requires local availability of model weights in the directory `/IOTDB_AINODE_HOME/data/ainode/models/weights/model_id/`. If weights are missing, they will be automatically downloaded from HuggingFace. Ensure direct network access to HuggingFace. + +Parameter descriptions: + +| Parameter | Type | Attribute | Description | Required | Notes | +| ----------------- | ------------- | -------------------------------------------------------- | ------------------------------------------------------------ | -------- | ------------------------------------------------------------ | +| input | Table | SET SEMANTIC | Input data for forecasting | Yes | | +| model_id | String | Scalar | Name of the model to use | Yes | Must be non-empty and a built-in model; otherwise, errors like "MODEL_ID cannot be null" occur. | +| output_length | INT32 | Scalar (default: 96) | Size of the output forecast window | No | Must be > 0. | +| output_start_time | Timestamp | Scalar (default: last input timestamp + output_interval) | Start timestamp of the forecast results | No | Can be negative (before 1970-01-01). | +| output_interval | Time interval | Scalar (default: inferred from input) | Time interval between forecast points (supports ns, us, ms, s, m, h, d, w) | No | If > 0, uses user-specified interval; else, infers from input. | +| timecol | String | Scalar (default: "time") | Name of the timestamp column | No | Must exist in `input` and be of TIMESTAMP type; otherwise, errors occur. | +| preserve_input | Boolean | Scalar (default: false) | Retain all input rows in the output | No | | +| model_options | String | Scalar (default: empty) | Model-specific key-value pairs (e.g., normalization) | No | Unsupported parameters are ignored. See appendix for built-in model parameters. | + +**Notes:** + +1. The `forecast` function predicts all columns in the input table by default (excluding the time column and columns specified in `partition by`). +2. The `forecast` function does not require the input data to be in any specific order. It sorts the input data in ascending order by the timestamp (specified by the `TIMECOL` parameter) before invoking the model for prediction. +3. Different models have different requirements for the number of input data rows: + - If the input data has fewer rows than the minimum requirement, an error will be thrown. + - If the input data exceeds the maximum row limit, the last rows that meet the requirement will be automatically truncated for processing. + - Among the currently built-in models in AINode, only `sundial` has a row limit. It supports a maximum of 2880 input rows. If exceeded, the last 2880 rows will be automatically used. +4. The result columns of the `forecast` function include all input columns from the input table, with their original data types preserved. If `preserve_input = true`, an additional `is_input` column will be included to indicate whether a row is from the input data. + - Currently, only columns of type INT32, INT64, FLOAT, or DOUBLE are supported for prediction. Otherwise, an error will occur: "The type of the column [%s] is [%s], only INT32, INT64, FLOAT, DOUBLE is allowed." +5. `output_start_time` and `output_interval` only affect the generation of the timestamp column in the output results. Both are optional parameters: + - `output_start_time` defaults to the last timestamp of the input data plus `output_interval`. + - `output_interval` defaults to the sampling interval of the input data, calculated as: (last timestamp - first timestamp) / (number of rows - 1). + - The timestamp of the Nth output row is calculated as: `output_start_time + (N - 1) * output_interval`. + +#### Example + +Using the ETTh1-tab dataset:[ETTh1-tab](/img/ETTh1-tab.csv)。 + +**View supported models** + +```Bash +IoTDB:etth> show models ++---------------------+--------------------+--------+------+ +| ModelId| ModelType|Category| State| ++---------------------+--------------------+--------+------+ +| arima| Arima|BUILT-IN|ACTIVE| +| holtwinters| HoltWinters|BUILT-IN|ACTIVE| +|exponential_smoothing|ExponentialSmoothing|BUILT-IN|ACTIVE| +| naive_forecaster| NaiveForecaster|BUILT-IN|ACTIVE| +| stl_forecaster| StlForecaster|BUILT-IN|ACTIVE| +| gaussian_hmm| GaussianHmm|BUILT-IN|ACTIVE| +| gmm_hmm| GmmHmm|BUILT-IN|ACTIVE| +| stray| Stray|BUILT-IN|ACTIVE| +| sundial| Timer-Sundial|BUILT-IN|ACTIVE| +| timer_xl| Timer-XL|BUILT-IN|ACTIVE| ++---------------------+--------------------+--------+------+ +Total line number = 10 +It costs 0.004s +``` + +**Inference with sundial model:** + +```Bash +IoTDB:etth> select Time, HUFL,HULL,MUFL,MULL,LUFL,LULL,OT from eg LIMIT 96 ++-----------------------------+------+-----+-----+-----+-----+-----+------+ +| Time| HUFL| HULL| MUFL| MULL| LUFL| LULL| OT| ++-----------------------------+------+-----+-----+-----+-----+-----+------+ +|2016-07-01T00:00:00.000+08:00| 5.827|2.009|1.599|0.462|4.203| 1.34|30.531| +|2016-07-01T01:00:00.000+08:00| 5.693|2.076|1.492|0.426|4.142|1.371|27.787| +|2016-07-01T02:00:00.000+08:00| 5.157|1.741|1.279|0.355|3.777|1.218|27.787| +|2016-07-01T03:00:00.000+08:00| 5.09|1.942|1.279|0.391|3.807|1.279|25.044| +...... +Total line number = 96 +It costs 0.119s + +IoTDB:etth> select * from forecast( + model_id => 'sundial', + input => (select Time, ot from etth.eg where time >= 2016-08-07T18:00:00.000+08:00 limit 1440) order BY time, + output_length => 96 +) ++-----------------------------+---------+ +| time| ot| ++-----------------------------+---------+ +|2016-10-06T18:00:00.000+08:00|20.781654| +|2016-10-06T19:00:00.000+08:00|20.252121| +|2016-10-06T20:00:00.000+08:00|19.960138| +|2016-10-06T21:00:00.000+08:00|19.662334| +...... +Total line number = 96 +It costs 1.615s +``` +### 4.5 Fine-tuning Built-in Models +> Only Timer-XL and Timer-Sundial support fine-tuning. + + +The SQL syntax is as follows: + + +```SQL +create model (with hyperparameters +(=(, =)*))? +from model +on dataset (inputSql) +``` + +#### Example + +1. Select the first 80% of data from the measurement `ot` as the fine-tuning dataset, and create the model `sundialv3` based on `sundial`. + +```SQL +IoTDB> set sql_dialect=table +Msg: The statement is executed successfully. +IoTDB> CREATE MODEL sundialv3 FROM MODEL sundial ON DATASET ('SELECT time, ot from etth.eg where 1467302400000 <= time and time < 1517468400001') +Msg: The statement is executed successfully. +IoTDB> show models ++---------------------+--------------------+----------+--------+ +| ModelId| ModelType| Category| State| ++---------------------+--------------------+----------+--------+ +| arima| Arima| BUILT-IN| ACTIVE| +| holtwinters| HoltWinters| BUILT-IN| ACTIVE| +|exponential_smoothing|ExponentialSmoothing| BUILT-IN| ACTIVE| +| naive_forecaster| NaiveForecaster| BUILT-IN| ACTIVE| +| stl_forecaster| StlForecaster| BUILT-IN| ACTIVE| +| gaussian_hmm| GaussianHmm| BUILT-IN| ACTIVE| +| gmm_hmm| GmmHmm| BUILT-IN| ACTIVE| +| stray| Stray| BUILT-IN| ACTIVE| +| sundial| Timer-Sundial| BUILT-IN| ACTIVE| +| timer_xl| Timer-XL| BUILT-IN| ACTIVE| +| sundialv2| Timer-Sundial|FINE-TUNED| ACTIVE| +| sundialv3| Timer-Sundial|FINE-TUNED|TRAINING| ++---------------------+--------------------+----------+--------+ +``` + +2. The fine-tuning task starts asynchronously in the background, and logs can be viewed in the AINode process. After fine-tuning is complete, query and use the new model + +```SQL +IoTDB> show models ++---------------------+--------------------+----------+------+ +| ModelId| ModelType| Category| State| ++---------------------+--------------------+----------+------+ +| arima| Arima| BUILT-IN|ACTIVE| +| holtwinters| HoltWinters| BUILT-IN|ACTIVE| +|exponential_smoothing|ExponentialSmoothing| BUILT-IN|ACTIVE| +| naive_forecaster| NaiveForecaster| BUILT-IN|ACTIVE| +| stl_forecaster| StlForecaster| BUILT-IN|ACTIVE| +| gaussian_hmm| GaussianHmm| BUILT-IN|ACTIVE| +| gmm_hmm| GmmHmm| BUILT-IN|ACTIVE| +| stray| Stray| BUILT-IN|ACTIVE| +| sundial| Timer-Sundial| BUILT-IN|ACTIVE| +| timer_xl| Timer-XL| BUILT-IN|ACTIVE| +| sundialv2| Timer-Sundial|FINE-TUNED|ACTIVE| +| sundialv3| Timer-Sundial|FINE-TUNED|ACTIVE| ++---------------------+--------------------+----------+------+ +``` + +### 4.6 Time Series Large Model Import Steps + +AINode supports multiple time series large models. For deployment, refer to [Time Series Large Model](https://timecho.com/docs/zh/UserGuide/latest/AI-capability/TimeSeries-Large-Model.html) + + +## 5 Permission Management + +AINode uses IoTDB's authentication for permission management. Users need `USE_MODEL` permission for model management and `READ_DATA` permission for inference (to access input data sources). + +| **Permission** | **Scope** | **Admin (ROOT)** | **Regular User** | **Path-Related** | +| -------------- | ------------------------ | ---------------- | ---------------- | ---------------- | +| USE_MODEL | Create/Show/Drop models | ✔️ | ✔️ | ❌ | +| READ_DATA | Call inference functions | ✔️ | ✔️ | ✔️ | + + +## 6 Appendix + +**Arima** + +| Parameter | Description | Default | +| ----------------------- | ------------------------------------------------------------ | --------- | +| order | ARIMA order `(p, d, q)`: p=autoregressive, d=differencing, q=moving average. | (1,0,0) | +| seasonal_order | Seasonal ARIMA order `(P, D, Q, s)`: seasonal AR, differencing, MA orders, and season length (e.g., 12 for monthly data). | (0,0,0,0) | +| method | Optimizer: 'newton', 'nm', 'bfgs', 'lbfgs', 'powell', 'cg', 'ncg', 'basinhopping'. | 'lbfgs' | +| maxiter | Maximum iterations/function evaluations. | 50 | +| out_of_sample_size | Number of tail samples for validation (not used in fitting). | 0 | +| scoring | Scoring function for validation (sklearn metric or custom). | 'mse' | +| trend | Trend term configuration. If `with_intercept=True` and None, defaults to 'c' (constant). | None | +| with_intercept | Include intercept term. | True | +| time_varying_regression | Allow regression coefficients to vary over time. | False | +| enforce_stationarity | Enforce stationarity of AR components. | True | +| enforce_invertibility | Enforce invertibility of MA components. | True | +| simple_differencing | Use differenced data for estimation (sacrifices first rows). | False | +| measurement_error | Assume observation errors. | False | +| mle_regression | Use maximum likelihood for regression (must be False if `time_varying_regression=True`). | True | +| hamilton_representation | Use Hamilton representation (default is Harvey). | False | +| concentrate_scale | Exclude scale parameter from likelihood (reduces parameters). | False | + +**NaiveForecaster** + +| Parameter | Description | Default | +| --------- | ------------------------------------------------------------ | ------- | +| strategy | Forecasting strategy: - `"last"`: Use last training value (seasonal if `sp`>1). - `"mean"`: Use mean of last window (seasonal if `sp`>1). - `"drift"`: Fit line through last window and extrapolate (non-robust to NaN). | "last" | +| sp | Seasonal period. `None` or 1 means no seasonality; 12 means monthly. | 1 | + +**STLForecaster** + +| Parameter | Description | Default | +| ------------- | ---------------------------------------------------------- | ------- | +| sp | Seasonal period (units). Passed to statsmodels' STL. | 2 | +| seasonal | Seasonal smoothing window (odd ≥3, typically ≥7). | 7 | +| seasonal_deg | LOESS polynomial degree for season (0=constant, 1=linear). | 1 | +| trend_deg | LOESS polynomial degree for trend (0 or 1). | 1 | +| low_pass_deg | LOESS polynomial degree for low-pass (0 or 1). | 1 | +| seasonal_jump | Interpolation step for season LOESS (larger = faster). | 1 | +| trend_jump | Interpolation step for trend LOESS (larger = faster). | 1 | +| low_pass_jump | Interpolation step for low-pass LOESS. | 1 | + +**ExponentialSmoothing (HoltWinters)** + +| Parameter | Description | Default | +| --------------------- | ------------------------------------------------------------ | ----------- | +| damped_trend | Use damped trend (trend flattens instead of growing infinitely). | True | +| initialization_method | Initialization method: - `"estimated"`: Fit to estimate initial states - `"heuristic"`: Use heuristic for initial level/trend/season - `"known"`: User-provided initial values - `"legacy-heuristic"`: Legacy compatibility | "estimated" | +| optimized | Optimize parameters via maximum likelihood. | True | +| remove_bias | Remove bias to make residuals' mean zero. | False | +| use_brute | Use brute-force grid search for initial parameters. | | \ No newline at end of file diff --git a/src/UserGuide/latest-Table/AI-capability/TimeSeries-Large-Model.md b/src/UserGuide/latest-Table/AI-capability/TimeSeries-Large-Model.md new file mode 100644 index 000000000..c678ad28c --- /dev/null +++ b/src/UserGuide/latest-Table/AI-capability/TimeSeries-Large-Model.md @@ -0,0 +1,113 @@ + + +# TimeSeries Large Model + +## Introduction + +A time series large model is a foundational model specifically designed for time series analysis. The IoTDB team has independently developed time series large models, which are pre-trained on massive time series data using technologies such as transformer structures. These models can understand and generate time series data across various domains and are applicable to applications like time series forecasting, anomaly detection, and time series imputation. Unlike traditional time series analysis techniques, time series large models possess the capability to extract universal features and provide technical services based on zero-shot analysis and fine-tuning for a wide range of analytical tasks. + +The team's related technologies of time series large models have been published in top international machine learning conferences. + +## Application Scenarios + +- **Time Series Forecasting**: Provides forecasting services for time series data in industrial production, natural environments, and other areas, helping users to understand future trends in advance. +- **Data Imputation**: For missing segments in time series, perform context imputation to enhance the continuity and completeness of the dataset. +- **Anomaly Detection**: Utilizing regression analysis technology, monitor time series data in real-time and provide timely warnings for potential anomalies. + +![](/img/LargeModel10.png) + +## Timer Model + +The Timer model not only demonstrates excellent few-shot generalization and multi-task adaptation capabilities but also gains a rich knowledge base through pre-training, endowing it with the universal capability to handle a variety of downstream tasks, featuring the following: + +- **Generalization**: The model can be fine-tuned using a small number of samples to achieve leading predictive performance in the industry. +- **Versatility**: The model is designed flexibly to adapt to various task requirements and supports variable input and output lengths, enabling it to play a role in various application scenarios. +- **Scalability**: As the number of model parameters increases or the scale of pre-training data expands, the model's performance continues to improve, ensuring the model can optimize its predictive effects with the growth of time and data volume. + +![](/img/model01.png) + +## Timer-XL Model + +Timer-XL is an upgraded version of Timer that further extends the network structure and achieves comprehensive breakthroughs in multiple dimensions: + +- **Ultra-long Context Support**: This model breaks through the limitations of traditional time series forecasting models, supporting inputs of thousands of Tokens (equivalent to tens of thousands of time points), effectively solving the context length bottleneck problem. +- **Multi-variable Prediction Scenarios Coverage**: Supports various forecasting scenarios, including non-stationary time series forecasting, multi-variable forecasting tasks, and forecasting with covariates, meeting diverse business needs. +- **Large-scale Industrial Time Series Dataset**: Pre-trained on a trillion-scale industrial IoT time series dataset, which has the important characteristics of huge volume, excellent quality, and rich domains, covering energy, aerospace, steel, transportation, and other fields. + +![](/img/model02.png) + +## Timer-Sundial Model + +Timer-Sundial is a series of generative foundational models focused on time series forecasting. The basic version has 128 million parameters and has undergone large-scale pre-training on 1 trillion time points. Its core features include: + +- **Powerful Generalization Performance**: Possesses zero-shot forecasting capabilities, supporting both point forecasting and probabilistic forecasting simultaneously. +- **Flexible Forecasting Distribution Analysis**: Can not only forecast mean values or quantiles but also evaluate any statistical characteristics of the forecasting distribution through the original samples generated by the model. +- **Innovative Generative Architecture**: Adopts a "Transformer + TimeFlow" collaborative architecture—Transformer learns the autoregressive representation of time segments, and the TimeFlow module converts random noise into diversified forecasting trajectories based on the Flow-Matching framework, achieving efficient non-deterministic sample generation. + +![](/img/model03.png) + +## Effect Demonstration + +Time series large models can adapt to real-time series data in various domains and scenarios, demonstrating excellent processing effects on various tasks. The following are real-world performances on different datasets: + +**Time Series Forecasting:** + +Utilizing the forecasting capability of time series large models, the future change trends of time series can be accurately predicted. In the figure below, the blue curve represents the forecast trend, and the red curve represents the actual trend, with a high degree of 吻合 (coincidence) between the two curves. + +![](/img/LargeModel03.png) + +**Data Imputation:** + +Utilizing time series large models to perform predictive imputation on missing data segments. + +![](/img/timeseries-large-model-data-imputation.png) + + +**Anomaly Detection:** + +Utilizing time series large models to accurately identify outliers that deviate significantly from normal trends. + +![](/img/LargeModel05.png) + +## Deployment Usage + +1. Open the IoTDB CLI console and verify that the ConfigNode, DataNode, and AINode statuses are all ​Running. + +Check command: + +```sql +show cluster +``` + +![](/img/ainode-timer-1.png) + +2. When the AINode is started for the first time in a networked environment, the Timer-XL and Sundial models will be automatically pulled. + +3. Verify model registration success + +Check command: + +```sql +show models +``` + +![](/img/LargeModel06.png) diff --git a/src/UserGuide/latest-Table/Deployment-and-Maintenance/AINode_Deployment_apache.md b/src/UserGuide/latest-Table/Deployment-and-Maintenance/AINode_Deployment_apache.md new file mode 100644 index 000000000..50c532376 --- /dev/null +++ b/src/UserGuide/latest-Table/Deployment-and-Maintenance/AINode_Deployment_apache.md @@ -0,0 +1,266 @@ + +# AINode Deployment + +## 1. AINode Introduction + +### 1.1 Capability Introduction + + AINode is the third type of endogenous node provided by IoTDB after the Configurable Node and DataNode. This node extends its ability to perform machine learning analysis on time series by interacting with the DataNode and Configurable Node of the IoTDB cluster. It supports the introduction of existing machine learning models from external sources for registration and the use of registered models to complete time series analysis tasks on specified time series data through simple SQL statements. The creation, management, and inference of models are integrated into the database engine. Currently, machine learning algorithms or self-developed models are available for common time series analysis scenarios, such as prediction and anomaly detection. + +### 1.2 Delivery Method + It is an additional package outside the IoTDB cluster, with independent installation. + +### 1.3 Deployment mode +
+ + +
+ +## 2. Installation preparation + +### 2.1 Get installation package + + Unzip and install the package + `(apache-iotdb--ainode-bin.zip)`， The directory structure after unpacking the installation package is as follows: +| **Catalogue** | **Type** | **Explain** | +| ------------ | -------- | ------------------------------------------------ | +| lib | folder | AINode compiled binary executable files and related code dependencies | +| sbin | folder | The running script of AINode can start, remove, and stop AINode | +| conf | folder | Contains configuration items for AINode, specifically including the following configuration items | +| LICENSE | file | Certificate | +| NOTICE | file | Tips | +| README_ZH.md | file | Explanation of the Chinese version of the markdown format | +| `README.md` | file | Instructions | + +### 2.2 Environmental Preparation + +1. Recommended operating systems: Ubuntu, CentOS, MacOS +2. IoTDB version: >= V 2.0.5.1 +3. Runtime environment + - Python version between 3.9 and 3.12, with pip and venv tools installed; + +## 3. Installation steps + +### 3.1 Install AINode + + + 1. Ensure Python version is between 3.9 and 3.12: +```shell + python --version +# or +python3 --version +``` + + 2. Download and import AINode into a dedicated folder, switch to the folder, and unzip the package: +```shell + unzip timechodb-2.0.5.1-ainode-bin.zip + ``` +3. Create a virtual environment (execute in the ainode directory): + + ```shell + python3 -m venv venv + ``` + +4. Activate the virtual environment: + + ```shell + source venv/bin/activate + ``` +5. Update pip and install AINode dependencies: + ```shell + python -m pip install --upgrade pip + poetry lock + poetry install + ``` + +### 3.2 Configuration item modification + +AINode supports modifying some necessary parameters. You can find the following parameters in the `conf/iotdb-ainode.properties` file and make persistent modifications to them: +| **Name** | **Description** | **Type** | **Default Value** | +| ------------------------------ | ------------------------------------------------------------ | -------- | ------------------ | +| cluster_name | Identifier of the cluster AINode joins | string | defaultCluster | +| ain_seed_config_node | Address of the ConfigNode registered when AINode starts | String | 127.0.0.1:10710 | +| ain_cluster_ingress_address | RPC address of the DataNode for AINode to pull data | String | 127.0.0.1 | +| ain_cluster_ingress_port | RPC port of the DataNode for AINode to pull data | Integer | 6667 | +| ain_cluster_ingress_username | Client username for AINode to pull data from the DataNode | String | root | +| ain_cluster_ingress_password | Client password for AINode to pull data from the DataNode | String | root | +| ain_cluster_ingress_time_zone | Client time zone for AINode to pull data from the DataNode | String | UTC+8 | +| ain_inference_rpc_address | Address for AINode to provide services and communication (internal interface) | String | 127.0.0.1 | +| ain_inference_rpc_port | Port for AINode to provide services and communication | String | 10810 | +| ain_system_dir | Metadata storage path for AINode (relative path starts from OS-dependent directory; absolute path is recommended) | String | data/AINode/system | +| ain_models_dir | Path to store model files for AINode (relative path starts from OS-dependent directory; absolute path is recommended) | String | data/AINode/models | +| ain_thrift_compression_enabled | Whether to enable Thrift compression for AINode (0=disabled, 1=enabled) | Boolean | 0 | + +### 3.3 Start AINode + + After completing the deployment of Seed Config Node, the registration and inference functions of the model can be supported by adding AINode nodes. After specifying the information of the IoTDB cluster in the configuration file, the corresponding instruction can be executed to start AINode and join the IoTDB cluster。 + +#### Networking environment startup + +##### Start command + +```shell + # Start command + # Linux and MacOS systems + bash sbin/start-ainode.sh + + # Windows systems + sbin\start-ainode.bat + + # Backend startup command (recommended for long-term running) + # Linux and MacOS systems + nohup bash sbin/start-ainode.sh > myout.file 2>& 1 & + + # Windows systems + nohup bash sbin\start-ainode.bat > myout.file 2>& 1 & + ``` + + +#### Example + +```shell + # Start command + # Linux and MacOS systems + bash sbin/start-ainode.sh + # Windows systems + sbin\start-ainode.bat + + + # Backend startup command (recommended for long-term running) + # Linux and MacOS systems + nohup bash sbin/start-ainode.sh > myout.file 2>& 1 & + # Windows systems + nohup bash sbin\start-ainode.bat > myout.file 2>& 1 & + ``` + +### 3.4 Detecting the status of AINode nodes + +During the startup process of AINode, the new AINode will be automatically added to the IoTDB cluster. After starting AINode, you can enter SQL in the command line to query. If you see an AINode node in the cluster and its running status is Running (as shown below), it indicates successful joining. + + +```shell +IoTDB> show cluster ++------+----------+-------+---------------+------------+-------+-----------+ +|NodeID| NodeType| Status|InternalAddress|InternalPort|Version| BuildInfo| ++------+----------+-------+---------------+------------+-------+-----------+ +| 0|ConfigNode|Running| 127.0.0.1| 10710|UNKNOWN|190e303-dev| +| 1| DataNode|Running| 127.0.0.1| 10730|UNKNOWN|190e303-dev| +| 2| AINode|Running| 127.0.0.1| 10810|UNKNOWN|190e303-dev| ++------+----------+-------+---------------+------------+-------+-----------+ +``` + +### 3.5 Stop AINode + +If you need to stop a running AINode node, execute the corresponding shutdown script. + +#### Stop command + +```shell + # Linux / MacOS + bash sbin/stop-ainode.sh + + #Windows + sbin\stop-ainode.bat + ``` + +#### Example + +```shell + # Linux / MacOS + bash sbin/stop-ainode.sh + + # Windows + sbin\stop-ainode.bat + ``` +After stopping AINode, you can still see AINode nodes in the cluster, whose running status is UNKNOWN (as shown below), and the AINode function cannot be used at this time. + + ```shell +IoTDB> show cluster ++------+----------+-------+---------------+------------+-------+-----------+ +|NodeID| NodeType| Status|InternalAddress|InternalPort|Version| BuildInfo| ++------+----------+-------+---------------+------------+-------+-----------+ +| 0|ConfigNode|Running| 127.0.0.1| 10710|UNKNOWN|190e303-dev| +| 1| DataNode|Running| 127.0.0.1| 10730|UNKNOWN|190e303-dev| +| 2| AINode|UNKNOWN| 127.0.0.1| 10790|UNKNOWN|190e303-dev| ++------+----------+-------+---------------+------------+-------+-----------+ +``` +If you need to restart the node, you need to execute the startup script again. + +## 4. common problem + +### 4.1 An error occurs when starting AINode stating that the venv module cannot be found + + When starting AINode using the default method, a Python virtual environment will be created in the installation package directory and dependencies will be installed, so it is required to install the venv module. Generally speaking, Python 3.10 and above versions come with built-in VenV, but for some systems with built-in Python environments, this requirement may not be met. There are two solutions when this error occurs (choose one or the other): + + To install the Venv module locally, taking Ubuntu as an example, you can run the following command to install the built-in Venv module in Python. Or install a Python version with built-in Venv from the Python official website. + + ```shell +apt-get install python3.10-venv +``` +Install version 3.10.0 of venv into AINode in the AINode path. + + ```shell +../Python-3.10.0/python -m venv venv(Folder Name） +``` + When running the startup script, use ` -i ` to specify an existing Python interpreter path as the running environment for AINode, eliminating the need to create a new virtual environment. + + ### 4.2 The SSL module in Python is not properly installed and configured to handle HTTPS resources +WARNING: pip is configured with locations that require TLS/SSL, however the ssl module in Python is not available. +You can install OpenSSLS and then rebuild Python to solve this problem +> Currently Python versions 3.6 to 3.9 are compatible with OpenSSL 1.0.2, 1.1.0, and 1.1.1. + + Python requires OpenSSL to be installed on our system, the specific installation method can be found in [link](https://stackoverflow.com/questions/56552390/how-to-fix-ssl-module-in-python-is-not-available-in-centos) + + ```shell +sudo apt-get install build-essential libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm libncurses5-dev libncursesw5-dev xz-utils tk-dev libffi-dev liblzma-dev uuid-dev lzma-dev liblzma-dev +sudo -E ./configure --with-ssl +make +sudo make install +``` + + ### 4.3 Pip version is lower + + A compilation issue similar to "error: Microsoft Visual C++14.0 or greater is required..." appears on Windows + +The corresponding error occurs during installation and compilation, usually due to insufficient C++version or Setup tools version. You can check it in + + ```shell +./python -m pip install --upgrade pip +./python -m pip install --upgrade setuptools +``` + + + ### 4.4 Install and compile Python + + Use the following instructions to download the installation package from the official website and extract it: + ```shell +.wget https://www.python.org/ftp/python/3.10.0/Python-3.10.0.tar.xz +tar Jxf Python-3.10.0.tar.xz +``` + Compile and install the corresponding Python package: + ```shell +cd Python-3.10.0 +./configure prefix=/usr/local/python3 +make +sudo make install +python3 --version +``` \ No newline at end of file diff --git a/src/UserGuide/latest-Table/Deployment-and-Maintenance/AINode_Deployment_timecho.md b/src/UserGuide/latest-Table/Deployment-and-Maintenance/AINode_Deployment_timecho.md new file mode 100644 index 000000000..50c532376 --- /dev/null +++ b/src/UserGuide/latest-Table/Deployment-and-Maintenance/AINode_Deployment_timecho.md @@ -0,0 +1,266 @@ + +# AINode Deployment + +## 1. AINode Introduction + +### 1.1 Capability Introduction + + AINode is the third type of endogenous node provided by IoTDB after the Configurable Node and DataNode. This node extends its ability to perform machine learning analysis on time series by interacting with the DataNode and Configurable Node of the IoTDB cluster. It supports the introduction of existing machine learning models from external sources for registration and the use of registered models to complete time series analysis tasks on specified time series data through simple SQL statements. The creation, management, and inference of models are integrated into the database engine. Currently, machine learning algorithms or self-developed models are available for common time series analysis scenarios, such as prediction and anomaly detection. + +### 1.2 Delivery Method + It is an additional package outside the IoTDB cluster, with independent installation. + +### 1.3 Deployment mode +
+ + +
+ +## 2. Installation preparation + +### 2.1 Get installation package + + Unzip and install the package + `(apache-iotdb--ainode-bin.zip)`， The directory structure after unpacking the installation package is as follows: +| **Catalogue** | **Type** | **Explain** | +| ------------ | -------- | ------------------------------------------------ | +| lib | folder | AINode compiled binary executable files and related code dependencies | +| sbin | folder | The running script of AINode can start, remove, and stop AINode | +| conf | folder | Contains configuration items for AINode, specifically including the following configuration items | +| LICENSE | file | Certificate | +| NOTICE | file | Tips | +| README_ZH.md | file | Explanation of the Chinese version of the markdown format | +| `README.md` | file | Instructions | + +### 2.2 Environmental Preparation + +1. Recommended operating systems: Ubuntu, CentOS, MacOS +2. IoTDB version: >= V 2.0.5.1 +3. Runtime environment + - Python version between 3.9 and 3.12, with pip and venv tools installed; + +## 3. Installation steps + +### 3.1 Install AINode + + + 1. Ensure Python version is between 3.9 and 3.12: +```shell + python --version +# or +python3 --version +``` + + 2. Download and import AINode into a dedicated folder, switch to the folder, and unzip the package: +```shell + unzip timechodb-2.0.5.1-ainode-bin.zip + ``` +3. Create a virtual environment (execute in the ainode directory): + + ```shell + python3 -m venv venv + ``` + +4. Activate the virtual environment: + + ```shell + source venv/bin/activate + ``` +5. Update pip and install AINode dependencies: + ```shell + python -m pip install --upgrade pip + poetry lock + poetry install + ``` + +### 3.2 Configuration item modification + +AINode supports modifying some necessary parameters. You can find the following parameters in the `conf/iotdb-ainode.properties` file and make persistent modifications to them: +| **Name** | **Description** | **Type** | **Default Value** | +| ------------------------------ | ------------------------------------------------------------ | -------- | ------------------ | +| cluster_name | Identifier of the cluster AINode joins | string | defaultCluster | +| ain_seed_config_node | Address of the ConfigNode registered when AINode starts | String | 127.0.0.1:10710 | +| ain_cluster_ingress_address | RPC address of the DataNode for AINode to pull data | String | 127.0.0.1 | +| ain_cluster_ingress_port | RPC port of the DataNode for AINode to pull data | Integer | 6667 | +| ain_cluster_ingress_username | Client username for AINode to pull data from the DataNode | String | root | +| ain_cluster_ingress_password | Client password for AINode to pull data from the DataNode | String | root | +| ain_cluster_ingress_time_zone | Client time zone for AINode to pull data from the DataNode | String | UTC+8 | +| ain_inference_rpc_address | Address for AINode to provide services and communication (internal interface) | String | 127.0.0.1 | +| ain_inference_rpc_port | Port for AINode to provide services and communication | String | 10810 | +| ain_system_dir | Metadata storage path for AINode (relative path starts from OS-dependent directory; absolute path is recommended) | String | data/AINode/system | +| ain_models_dir | Path to store model files for AINode (relative path starts from OS-dependent directory; absolute path is recommended) | String | data/AINode/models | +| ain_thrift_compression_enabled | Whether to enable Thrift compression for AINode (0=disabled, 1=enabled) | Boolean | 0 | + +### 3.3 Start AINode + + After completing the deployment of Seed Config Node, the registration and inference functions of the model can be supported by adding AINode nodes. After specifying the information of the IoTDB cluster in the configuration file, the corresponding instruction can be executed to start AINode and join the IoTDB cluster。 + +#### Networking environment startup + +##### Start command + +```shell + # Start command + # Linux and MacOS systems + bash sbin/start-ainode.sh + + # Windows systems + sbin\start-ainode.bat + + # Backend startup command (recommended for long-term running) + # Linux and MacOS systems + nohup bash sbin/start-ainode.sh > myout.file 2>& 1 & + + # Windows systems + nohup bash sbin\start-ainode.bat > myout.file 2>& 1 & + ``` + + +#### Example + +```shell + # Start command + # Linux and MacOS systems + bash sbin/start-ainode.sh + # Windows systems + sbin\start-ainode.bat + + + # Backend startup command (recommended for long-term running) + # Linux and MacOS systems + nohup bash sbin/start-ainode.sh > myout.file 2>& 1 & + # Windows systems + nohup bash sbin\start-ainode.bat > myout.file 2>& 1 & + ``` + +### 3.4 Detecting the status of AINode nodes + +During the startup process of AINode, the new AINode will be automatically added to the IoTDB cluster. After starting AINode, you can enter SQL in the command line to query. If you see an AINode node in the cluster and its running status is Running (as shown below), it indicates successful joining. + + +```shell +IoTDB> show cluster ++------+----------+-------+---------------+------------+-------+-----------+ +|NodeID| NodeType| Status|InternalAddress|InternalPort|Version| BuildInfo| ++------+----------+-------+---------------+------------+-------+-----------+ +| 0|ConfigNode|Running| 127.0.0.1| 10710|UNKNOWN|190e303-dev| +| 1| DataNode|Running| 127.0.0.1| 10730|UNKNOWN|190e303-dev| +| 2| AINode|Running| 127.0.0.1| 10810|UNKNOWN|190e303-dev| ++------+----------+-------+---------------+------------+-------+-----------+ +``` + +### 3.5 Stop AINode + +If you need to stop a running AINode node, execute the corresponding shutdown script. + +#### Stop command + +```shell + # Linux / MacOS + bash sbin/stop-ainode.sh + + #Windows + sbin\stop-ainode.bat + ``` + +#### Example + +```shell + # Linux / MacOS + bash sbin/stop-ainode.sh + + # Windows + sbin\stop-ainode.bat + ``` +After stopping AINode, you can still see AINode nodes in the cluster, whose running status is UNKNOWN (as shown below), and the AINode function cannot be used at this time. + + ```shell +IoTDB> show cluster ++------+----------+-------+---------------+------------+-------+-----------+ +|NodeID| NodeType| Status|InternalAddress|InternalPort|Version| BuildInfo| ++------+----------+-------+---------------+------------+-------+-----------+ +| 0|ConfigNode|Running| 127.0.0.1| 10710|UNKNOWN|190e303-dev| +| 1| DataNode|Running| 127.0.0.1| 10730|UNKNOWN|190e303-dev| +| 2| AINode|UNKNOWN| 127.0.0.1| 10790|UNKNOWN|190e303-dev| ++------+----------+-------+---------------+------------+-------+-----------+ +``` +If you need to restart the node, you need to execute the startup script again. + +## 4. common problem + +### 4.1 An error occurs when starting AINode stating that the venv module cannot be found + + When starting AINode using the default method, a Python virtual environment will be created in the installation package directory and dependencies will be installed, so it is required to install the venv module. Generally speaking, Python 3.10 and above versions come with built-in VenV, but for some systems with built-in Python environments, this requirement may not be met. There are two solutions when this error occurs (choose one or the other): + + To install the Venv module locally, taking Ubuntu as an example, you can run the following command to install the built-in Venv module in Python. Or install a Python version with built-in Venv from the Python official website. + + ```shell +apt-get install python3.10-venv +``` +Install version 3.10.0 of venv into AINode in the AINode path. + + ```shell +../Python-3.10.0/python -m venv venv(Folder Name） +``` + When running the startup script, use ` -i ` to specify an existing Python interpreter path as the running environment for AINode, eliminating the need to create a new virtual environment. + + ### 4.2 The SSL module in Python is not properly installed and configured to handle HTTPS resources +WARNING: pip is configured with locations that require TLS/SSL, however the ssl module in Python is not available. +You can install OpenSSLS and then rebuild Python to solve this problem +> Currently Python versions 3.6 to 3.9 are compatible with OpenSSL 1.0.2, 1.1.0, and 1.1.1. + + Python requires OpenSSL to be installed on our system, the specific installation method can be found in [link](https://stackoverflow.com/questions/56552390/how-to-fix-ssl-module-in-python-is-not-available-in-centos) + + ```shell +sudo apt-get install build-essential libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm libncurses5-dev libncursesw5-dev xz-utils tk-dev libffi-dev liblzma-dev uuid-dev lzma-dev liblzma-dev +sudo -E ./configure --with-ssl +make +sudo make install +``` + + ### 4.3 Pip version is lower + + A compilation issue similar to "error: Microsoft Visual C++14.0 or greater is required..." appears on Windows + +The corresponding error occurs during installation and compilation, usually due to insufficient C++version or Setup tools version. You can check it in + + ```shell +./python -m pip install --upgrade pip +./python -m pip install --upgrade setuptools +``` + + + ### 4.4 Install and compile Python + + Use the following instructions to download the installation package from the official website and extract it: + ```shell +.wget https://www.python.org/ftp/python/3.10.0/Python-3.10.0.tar.xz +tar Jxf Python-3.10.0.tar.xz +``` + Compile and install the corresponding Python package: + ```shell +cd Python-3.10.0 +./configure prefix=/usr/local/python3 +make +sudo make install +python3 --version +``` \ No newline at end of file diff --git a/src/UserGuide/latest/AI-capability/AINode_apache.md b/src/UserGuide/latest/AI-capability/AINode_apache.md index 61006e644..bae4ce651 100644 --- a/src/UserGuide/latest/AI-capability/AINode_apache.md +++ b/src/UserGuide/latest/AI-capability/AINode_apache.md @@ -52,7 +52,7 @@ Compared with building a machine learning service alone, it has the following ad ## 2. Basic Concepts -- **Model**: a machine learning model that takes time-series data as input and outputs the results or decisions of an analysis task. Model is the basic management unit of AINode, which supports adding (registration), deleting, checking, and using (inference) of models. +- **Model**: A machine learning model that takes time series data as input and outputs analysis results or decisions. Models are the basic management units of AINode, supporting model operations such as creation (registration), deletion, query, and inference. - **Create**: Load externally designed or trained model files or algorithms into MLNode for unified management and use by IoTDB. - **Inference**: The process of using the created model to complete the timing analysis task applicable to the model on the specified timing data. - **Built-in capabilities**: AINode comes with machine learning algorithms or home-grown models for common timing analysis scenarios (e.g., prediction and anomaly detection). @@ -68,93 +68,91 @@ The deployment of AINode can be found in the document [Deployment Guidelines](.. ## 4. Usage Guidelines -AINode provides model creation and deletion process for deep learning models related to timing data. Built-in models do not need to be created and deleted, they can be used directly, and the built-in model instances created after inference is completed will be destroyed automatically. +AINode provides model creation and deletion functions for time series models. Built-in models do not require creation and can be used directly. ### 4.1 Registering Models -A trained deep learning model can be registered by specifying the vector dimensions of the model's inputs and outputs, which can be used for model inference. +Trained deep learning models can be registered by specifying their input and output vector dimensions for inference. -Models that meet the following criteria can be registered in AINode: -1. Models trained on PyTorch 2.1.0 and 2.2.0 versions supported by AINode should avoid using features from versions 2.2.0 and above. -2. AINode supports models stored using PyTorch JIT, and the model file needs to include the parameters and structure of the model. -3. The input sequence of the model can contain one or more columns, and if there are multiple columns, they need to correspond to the model capability and model configuration file. -4. The input and output dimensions of the model must be clearly defined in the `config.yaml` configuration file. When using the model, it is necessary to strictly follow the input-output dimensions defined in the `config.yaml` configuration file. If the number of input and output columns does not match the configuration file, it will result in errors. +Models that meet the following criteria can be registered with AINode: -The following is the SQL syntax definition for model registration. +1. AINode currently supports models trained with PyTorch 2.4.0. Features above version 2.4.0 should be avoided. +2. AINode supports models stored using PyTorch JIT (`model.pt`), which must include both the model structure and weights. +3. The model input sequence can include single or multiple columns. If multi-column, it must match the model capabilities and configuration file. +4. Model configuration parameters must be clearly defined in the `config.yaml` file. When using the model, the input and output dimensions defined in `config.yaml` must be strictly followed. Mismatches with the configuration file will cause errors. + +The SQL syntax for model registration is defined as follows: ```SQL create model using uri ``` -The specific meanings of the parameters in the SQL are as follows: - -- model_name: a globally unique identifier for the model, which cannot be repeated. The model name has the following constraints: - - - Identifiers [ 0-9 a-z A-Z _ ] (letters, numbers, underscores) are allowed. - - Length is limited to 2-64 characters - - Case sensitive - -- uri: resource path to the model registration file, which should contain the **model weights model.pt file and the model's metadata description file config.yaml**. +Detailed meanings of SQL parameters: - - Model weight file: the weight file obtained after the training of the deep learning model is completed, currently supporting pytorch training of the .pt file +- **model_name**: The global unique identifier for the model, non-repeating. Model names have the following constraints: + - Allowed characters: [0-9 a-z A-Z _] (letters, numbers, underscores) + - Length: 2-64 characters + - Case-sensitive +- **uri**: The resource path of the model registration files, which should include the **model structure and weight file `model.pt` and the model configuration file `config.yaml`** - - yaml metadata description file: parameters related to the model structure that need to be provided when the model is registered, which must contain the input and output dimensions of the model for model inference: + - **Model structure and weight file**: The weight file generated after model training, currently supporting `.pt` files from PyTorch training. - - | **Parameter name** | **Parameter description** | **Example** | - | ------------ | ---------------------------- | -------- | - | input_shape | Rows and columns of model inputs for model inference | [96,2] | - | output_shape | rows and columns of model outputs, for model inference | [48,2] | + - **Model configuration file**: Parameters related to the model structure provided during registration, which must include input and output dimensions for inference: - - In addition to model inference, the data types of model input and output can be specified: + | **Parameter Name** | **Description** | **Example** | + | ------------ | ---------------------------- | -------- | + | input_shape | Rows and columns of model input | [96,2] | + | output_shape | Rows and columns of model output | [48,2] | + + In addition to inference, data types of input and output can also be specified: - - | **Parameter name** | **Parameter description** | **Example** | - | ----------- | ------------------ | --------------------- | - | input_type | model input data type | ['float32','float32'] | - | output_type | data type of the model output | ['float32','float32'] | + | **Parameter Name** | **Description** | **Example** | + | ------------------ | ------------------------- | ---------------------- | + | input_type | Data type of model input | ['float32', 'float32'] | + | output_type | Data type of model output | ['float32', 'float32'] | - - In addition to this, additional notes can be specified for display during model management + Additional notes can be specified for model management display: - - | **Parameter name** | **Parameter description** | **Examples** | - | ---------- | ---------------------------------------------- | ------------------------------------------- | - | attributes | optional, user-defined model notes for model display | 'model_type': 'dlinear','kernel_size': '25' | + | **Parameter Name** | **Description** | **Example** | + | ------------------ | --------------------------------------------- | -------------------------------------------- | + | attributes | Optional notes set by users for model display | 'model_type': 'dlinear', 'kernel_size': '25' | - -In addition to registration of local model files, registration can also be done by specifying remote resource paths via URIs, using open source model repositories (e.g. HuggingFace). +In addition to registering local model files, remote resource paths can be specified via URIs for registration, using open-source model repositories (e.g., HuggingFace). #### Example -In the current example folder, it contains model.pt and config.yaml files, model.pt is the training get, and the content of config.yaml is as follows: +The current example folder contains model.pt (trained model) and config.yaml with the following content: ```YAML -configs. - # Required options - input_shape: [96, 2] # The model receives data in 96 rows x 2 columns. - output_shape: [48, 2] # Indicates that the model outputs 48 rows x 2 columns. +configs: + # Required + input_shape: [96, 2] # Model accepts 96 rows x 2 columns of data + output_shape: [48, 2] # Model outputs 48 rows x 2 columns of data - # Optional Default is all float32 and the number of columns is the number of columns in the shape. - input_type: ["int64", "int64"] # Input data type, need to match the number of columns. - output_type: ["text", "int64"] #Output data type, need to match the number of columns. + # Optional (default to all float32, column count matches shape) + input_type: ["int64", "int64"] # Data types of inputs, must match input column count + output_type: ["text", "int64"] # Data types of outputs, must match output column count -attributes: # Optional user-defined notes for the input. +attributes: # Optional user-defined notes 'model_type': 'dlinear' 'kernel_size': '25' ``` -Specify this folder as the load path to register the model. +Register the model by specifying this folder as the loading path: ```SQL -IoTDB> create model dlinear_example using uri "file://. /example" +IoTDB> create model dlinear_example using uri "file://./example" ``` -Alternatively, you can download the corresponding model file from huggingFace and register it. +Models can also be downloaded from HuggingFace for registration: ```SQL -IoTDB> create model dlinear_example using uri "https://huggingface.com/IoTDBML/dlinear/" +IoTDB> create model dlinear_example using uri "https://huggingface.co/google/timesfm-2.0-500m-pytorch" ``` -After the SQL is executed, the registration process will be carried out asynchronously, and you can view the registration status of the model through the model showcase (see the Model Showcase section), and the time consumed for successful registration is mainly affected by the size of the model file. +After SQL execution, registration proceeds asynchronously. The registration status can be checked via model display (see Model Display section). The registration success time mainly depends on the model file size. -Once the model registration is complete, you can call specific functions and perform model inference by using normal queries. +Once registered, the model can be invoked for inference through normal query syntax. ### 4.2 Viewing Models @@ -166,61 +164,61 @@ show models show models ``` -In addition to displaying information about all models directly, you can specify a model id to view information about a specific model. The results of the model show contain the following information: +In addition to displaying all models, specifying a `model_id` shows details of a specific model. The display includes: -| **ModelId** | **State** | **Configs** | **Attributes** | -| ------------ | ------------------------------------- | ---------------------------------------------- | -------------- | -| Model Unique Identifier | Model Registration Status (LOADING, ACTIVE, DROPPING) | InputShape, outputShapeInputTypes, outputTypes | Model Notes | +| **ModelId** | **State** | **Configs** | **Attributes** | +| ----------- | --------------------------------------------------------- | ------------------------------------------------ | -------------- | +| Unique ID | Registration status (INACTIVE, LOADING, ACTIVE, DROPPING) | InputShape, outputShape, inputTypes, outputTypes | User notes | -State is used to show the current state of model registration, which consists of the following three stages +**State descriptions:** -- **LOADING**: The corresponding model meta information has been added to the configNode, and the model file is being transferred to the AINode node. -- **ACTIVE**: The model has been set up and the model is in the available state -- **DROPPING**: Model deletion is in progress, model related information is being deleted from configNode and AINode. -- **UNAVAILABLE**: Model creation failed, you can delete the failed model_name by drop model. +- **INACTIVE**: Model is unavailable. +- **LOADING**: Model is being loaded. +- **ACTIVE**: Model is available. +- **DROPPING**: Model is being deleted. #### Example ```SQL IoTDB> show models - -+---------------------+--------------------------+-----------+----------------------------+-----------------------+ -| ModelId| ModelType| State| Configs| Notes| -+---------------------+--------------------------+-----------+----------------------------+-----------------------+ -| dlinear_example| USER_DEFINED| ACTIVE| inputShape:[96,2]| | -| | | | outputShape:[48,2]| | -| | | | inputDataType:[float,float]| | -| | | |outputDataType:[float,float]| | -| _STLForecaster| BUILT_IN_FORECAST| ACTIVE| |Built-in model in IoTDB| -| _NaiveForecaster| BUILT_IN_FORECAST| ACTIVE| |Built-in model in IoTDB| -| _ARIMA| BUILT_IN_FORECAST| ACTIVE| |Built-in model in IoTDB| -|_ExponentialSmoothing| BUILT_IN_FORECAST| ACTIVE| |Built-in model in IoTDB| -| _GaussianHMM|BUILT_IN_ANOMALY_DETECTION| ACTIVE| |Built-in model in IoTDB| -| _GMMHMM|BUILT_IN_ANOMALY_DETECTION| ACTIVE| |Built-in model in IoTDB| -| _Stray|BUILT_IN_ANOMALY_DETECTION| ACTIVE| |Built-in model in IoTDB| -+---------------------+--------------------------+-----------+------------------------------------------------------------+-----------------------+ ++---------------------+--------------------+--------+--------+ +| ModelId| ModelType|Category| State| ++---------------------+--------------------+--------+--------+ +| arima| Arima|BUILT-IN| ACTIVE| +| holtwinters| HoltWinters|BUILT-IN| ACTIVE| +|exponential_smoothing|ExponentialSmoothing|BUILT-IN| ACTIVE| +| naive_forecaster| NaiveForecaster|BUILT-IN| ACTIVE| +| stl_forecaster| StlForecaster|BUILT-IN| ACTIVE| +| gaussian_hmm| GaussianHmm|BUILT-IN| ACTIVE| +| gmm_hmm| GmmHmm|BUILT-IN| ACTIVE| +| stray| Stray|BUILT-IN| ACTIVE| +| timer_xl| Timer-XL|BUILT-IN| ACTIVE| +| sundial| Timer-Sundial|BUILT-IN| ACTIVE| ++---------------------+--------------------+--------+--------+ ``` +### 4.3 Deleting Models -We have registered the corresponding model earlier, you can view the model status through the corresponding designation, active indicates that the model is successfully registered and can be used for inference. - -### 4.3 Delete Model - -For a successfully registered model, the user can delete it via SQL. In addition to deleting the meta information on the configNode, this operation also deletes all the related model files under the AINode. The SQL is as follows: +Registered models can be deleted via SQL, which removes all related files under AINode: ```SQL -drop model +drop model ``` -You need to specify the model model_name that has been successfully registered to delete the corresponding model. Since model deletion involves the deletion of data on multiple nodes, the operation will not be completed immediately, and the state of the model at this time is DROPPING, and the model in this state cannot be used for model inference. +Specify the registered `model_id` to delete the model. Since deletion involves data cleanup, the operation is not immediate, and the model state becomes `DROPPING`, during which it cannot be used for inference. **Note:** Built-in models cannot be deleted. ### 4.4 Using Built-in Model Reasoning -The SQL syntax is as follows: +SQL syntax: ```SQL -call inference(,sql[,=]) +call inference(,inputSql,(=)*) + +window_function: + head(window_size) + tail(window_size) + count(window_size,sliding_step) ``` Built-in model inference does not require a registration process, the inference function can be used by calling the inference function through the call keyword, and its corresponding parameters are described as follows: @@ -229,6 +227,9 @@ Built-in model inference does not require a registration process, the inference - **parameterName**: parameter name - **parameterValue**: parameter value +- **Note**: To use a built-in time series large model for inference, the corresponding model weights must be stored locally in the directory `/IOTDB_AINODE_HOME/data/ainode/models/weights/model_id/`. If the weights are not present locally, they will be automatically downloaded from HuggingFace. Ensure your environment has direct access to HuggingFace. + + #### Built-in Models and Parameter Descriptions The following machine learning models are currently built-in, please refer to the following links for detailed parameter descriptions. @@ -243,59 +244,7 @@ The following machine learning models are currently built-in, please refer to th | GMMHMM | _GMMHMM | Annotation | [GMMHMM Parameter description](https://www.sktime.net/en/latest/api_reference/auto_generated/sktime.detection.hmm_learn.gmm.GMMHMM.html) | | Stray | _Stray | Anomaly detection | [Stray Parameter description](https://www.sktime.net/en/latest/api_reference/auto_generated/sktime.detection.stray.STRAY.html) | -#### Example -The following is an example of an operation using built-in model inference. The built-in Stray model is used for anomaly detection algorithm. The input is `[144,1]` and the output is `[144,1]`. We use it for reasoning through SQL. - -```SQL -IoTDB> select * from root.eg.airline -+-----------------------------+------------------+ -| Time|root.eg.airline.s0| -+-----------------------------+------------------+ -|1949-01-31T00:00:00.000+08:00| 224.0| -|1949-02-28T00:00:00.000+08:00| 118.0| -|1949-03-31T00:00:00.000+08:00| 132.0| -|1949-04-30T00:00:00.000+08:00| 129.0| -...... -|1960-09-30T00:00:00.000+08:00| 508.0| -|1960-10-31T00:00:00.000+08:00| 461.0| -|1960-11-30T00:00:00.000+08:00| 390.0| -|1960-12-31T00:00:00.000+08:00| 432.0| -+-----------------------------+------------------+ -Total line number = 144 - -IoTDB> call inference(_Stray, "select s0 from root.eg.airline", generateTime=True, k=2) -+-----------------------------+-------+ -| Time|output0| -+-----------------------------+-------+ -|1960-12-31T00:00:00.000+08:00| 0| -|1961-01-31T08:00:00.000+08:00| 1| -|1961-02-28T08:00:00.000+08:00| 0| -|1961-03-31T08:00:00.000+08:00| 0| -...... -|1972-06-30T08:00:00.000+08:00| 0| -|1972-07-31T08:00:00.000+08:00| 1| -|1972-08-31T08:00:00.000+08:00| 0| -|1972-09-30T08:00:00.000+08:00| 0| -|1972-10-31T08:00:00.000+08:00| 0| -|1972-11-30T08:00:00.000+08:00| 0| -+-----------------------------+-------+ -Total line number = 144 -``` - -### 4.5 Reasoning with Deep Learning Models - -The SQL syntax is as follows: - -```SQL -call inference(,sql[,window=]) - - -window_function: - head(window_size) - tail(window_size) - count(window_size,sliding_step) -``` After completing the registration of the model, the inference function can be used by calling the inference function through the call keyword, and its corresponding parameters are described as follows: diff --git a/src/UserGuide/latest/AI-capability/AINode_timecho.md b/src/UserGuide/latest/AI-capability/AINode_timecho.md index 6954358ac..da8fc5728 100644 --- a/src/UserGuide/latest/AI-capability/AINode_timecho.md +++ b/src/UserGuide/latest/AI-capability/AINode_timecho.md @@ -53,10 +53,10 @@ Compared with building a machine learning service alone, it has the following ad ## 2. Basic Concepts -- **Model**: a machine learning model that takes time-series data as input and outputs the results or decisions of an analysis task. Model is the basic management unit of AINode, which supports adding (registration), deleting, checking, and using (inference) of models. -- **Create**: Load externally designed or trained model files or algorithms into MLNode for unified management and use by IoTDB. -- **Inference**: The process of using the created model to complete the timing analysis task applicable to the model on the specified timing data. -- **Built-in capabilities**: AINode comes with machine learning algorithms or home-grown models for common timing analysis scenarios (e.g., prediction and anomaly detection). +- **Model**: A machine learning model takes time series data as input and outputs analysis task results or decisions. Models are the basic management units of AINode, supporting model operations such as creation (registration), deletion, query, modification (fine-tuning), and usage (inference). +- **Create**: Load externally designed or trained model files/algorithms into AINode for unified management and usage by IoTDB. +- **Inference**: Use the created model to complete time series analysis tasks applicable to the model on specified time series data. +- **Built-in Capabilities**: AINode comes with machine learning algorithms or self-developed models for common time series analysis scenarios (e.g., forecasting and anomaly detection). ::: center @@ -64,102 +64,102 @@ Compared with building a machine learning service alone, it has the following ad ## 3. Installation and Deployment -The deployment of AINode can be found in the document [Deployment Guidelines](../Deployment-and-Maintenance/AINode_Deployment_timecho.md#AINode-部署) . - +The deployment of AINode can be found in the document [Deployment Guidelines](../Deployment-and-Maintenance/AINode_Deployment_timecho.md#ainode-deployment) . ## 4. Usage Guidelines -AINode provides model creation and deletion process for deep learning models related to timing data. Built-in models do not need to be created and deleted, they can be used directly, and the built-in model instances created after inference is completed will be destroyed automatically. +AINode provides model creation and deletion functions for time series models. Built-in models do not require creation and can be used directly. + ### 4.1 Registering Models -A trained deep learning model can be registered by specifying the vector dimensions of the model's inputs and outputs, which can be used for model inference. -Models that meet the following criteria can be registered in AINode: -1. Models trained on PyTorch 2.1.0 and 2.2.0 versions supported by AINode should avoid using features from versions 2.2.0 and above. -2. AINode supports models stored using PyTorch JIT, and the model file needs to include the parameters and structure of the model. -3. The input sequence of the model can contain one or more columns, and if there are multiple columns, they need to correspond to the model capability and model configuration file. -4. The input and output dimensions of the model must be clearly defined in the `config.yaml` configuration file. When using the model, it is necessary to strictly follow the input-output dimensions defined in the `config.yaml` configuration file. If the number of input and output columns does not match the configuration file, it will result in errors. +Trained deep learning models can be registered by specifying their input and output vector dimensions for inference. + +Models that meet the following criteria can be registered with AINode: -The following is the SQL syntax definition for model registration. +1. AINode currently supports models trained with PyTorch 2.4.0. Features above version 2.4.0 should be avoided. +2. AINode supports models stored using PyTorch JIT (`model.pt`), which must include both the model structure and weights. +3. The model input sequence can include single or multiple columns. If multi-column, it must match the model capabilities and configuration file. +4. Model configuration parameters must be clearly defined in the `config.yaml` file. When using the model, the input and output dimensions defined in `config.yaml` must be strictly followed. Mismatches with the configuration file will cause errors. + +The SQL syntax for model registration is defined as follows: ```SQL create model using uri ``` -The specific meanings of the parameters in the SQL are as follows: - -- model_name: a globally unique identifier for the model, which cannot be repeated. The model name has the following constraints: - - - Identifiers [ 0-9 a-z A-Z _ ] (letters, numbers, underscores) are allowed. - - Length is limited to 2-64 characters - - Case sensitive +Detailed meanings of SQL parameters: -- uri: resource path to the model registration file, which should contain the **model weights model.pt file and the model's metadata description file config.yaml**. +- **model_name**: The global unique identifier for the model, non-repeating. Model names have the following constraints: + - Allowed characters: [0-9 a-z A-Z _] (letters, numbers, underscores) + - Length: 2-64 characters + - Case-sensitive +- **uri**: The resource path of the model registration files, which should include the **model structure and weight file `model.pt` and the model configuration file `config.yaml`** - - Model weight file: the weight file obtained after the training of the deep learning model is completed, currently supporting pytorch training of the .pt file + - **Model structure and weight file**: The weight file generated after model training, currently supporting `.pt` files from PyTorch training. - - yaml metadata description file: parameters related to the model structure that need to be provided when the model is registered, which must contain the input and output dimensions of the model for model inference: + - **Model configuration file**: Parameters related to the model structure provided during registration, which must include input and output dimensions for inference: - - | **Parameter name** | **Parameter description** | **Example** | - | ------------ | ---------------------------- | -------- | - | input_shape | Rows and columns of model inputs for model inference | [96,2] | - | output_shape | rows and columns of model outputs, for model inference | [48,2] | + | **Parameter Name** | **Description** | **Example** | + | ------------ | ---------------------------- | -------- | + | input_shape | Rows and columns of model input | [96,2] | + | output_shape | Rows and columns of model output | [48,2] | + + In addition to inference, data types of input and output can also be specified: - - In addition to model inference, the data types of model input and output can be specified: + | **Parameter Name** | **Description** | **Example** | + | ------------------ | ------------------------- | ---------------------- | + | input_type | Data type of model input | ['float32', 'float32'] | + | output_type | Data type of model output | ['float32', 'float32'] | - - | **Parameter name** | **Parameter description** | **Example** | - | ----------- | ------------------ | --------------------- | - | input_type | model input data type | ['float32','float32'] | - | output_type | data type of the model output | ['float32','float32'] | + Additional notes can be specified for model management display: - - In addition to this, additional notes can be specified for display during model management + | **Parameter Name** | **Description** | **Example** | + | ------------------ | --------------------------------------------- | -------------------------------------------- | + | attributes | Optional notes set by users for model display | 'model_type': 'dlinear', 'kernel_size': '25' | - - | **Parameter name** | **Parameter description** | **Examples** | - | ---------- | ---------------------------------------------- | ------------------------------------------- | - | attributes | optional, user-defined model notes for model display | 'model_type': 'dlinear','kernel_size': '25' | +In addition to registering local model files, remote resource paths can be specified via URIs for registration, using open-source model repositories (e.g., HuggingFace). -In addition to registration of local model files, registration can also be done by specifying remote resource paths via URIs, using open source model repositories (e.g. HuggingFace). - #### Example -In the current example folder, it contains model.pt and config.yaml files, model.pt is the training get, and the content of config.yaml is as follows: +The current example folder contains model.pt (trained model) and config.yaml with the following content: ```YAML -configs. - # Required options - input_shape: [96, 2] # The model receives data in 96 rows x 2 columns. - output_shape: [48, 2] # Indicates that the model outputs 48 rows x 2 columns. +configs: + # Required + input_shape: [96, 2] # Model accepts 96 rows x 2 columns of data + output_shape: [48, 2] # Model outputs 48 rows x 2 columns of data - # Optional Default is all float32 and the number of columns is the number of columns in the shape. - input_type: ["int64", "int64"] # Input data type, need to match the number of columns. - output_type: ["text", "int64"] #Output data type, need to match the number of columns. + # Optional (default to all float32, column count matches shape) + input_type: ["int64", "int64"] # Data types of inputs, must match input column count + output_type: ["text", "int64"] # Data types of outputs, must match output column count -attributes: # Optional user-defined notes for the input. +attributes: # Optional user-defined notes 'model_type': 'dlinear' 'kernel_size': '25' ``` -Specify this folder as the load path to register the model. +Register the model by specifying this folder as the loading path: ```SQL -IoTDB> create model dlinear_example using uri "file://. /example" +IoTDB> create model dlinear_example using uri "file://./example" ``` -Alternatively, you can download the corresponding model file from huggingFace and register it. +Models can also be downloaded from HuggingFace for registration: ```SQL -IoTDB> create model dlinear_example using uri "https://huggingface.com/IoTDBML/dlinear/" +IoTDB> create model dlinear_example using uri "https://huggingface.co/google/timesfm-2.0-500m-pytorch" ``` -After the SQL is executed, the registration process will be carried out asynchronously, and you can view the registration status of the model through the model showcase (see the Model Showcase section), and the time consumed for successful registration is mainly affected by the size of the model file. +After SQL execution, registration proceeds asynchronously. The registration status can be checked via model display (see Model Display section). The registration success time mainly depends on the model file size. -Once the model registration is complete, you can call specific functions and perform model inference by using normal queries. +Once registered, the model can be invoked for inference through normal query syntax. ### 4.2 Viewing Models -Successfully registered models can be queried for model-specific information through the show models command. The SQL definition is as follows: +Registered models can be queried using the `show models` command. The SQL definitions are: ```SQL show models @@ -167,53 +167,51 @@ show models show models ``` -In addition to displaying information about all models directly, you can specify a model id to view information about a specific model. The results of the model show contain the following information: +In addition to displaying all models, specifying a `model_id` shows details of a specific model. The display includes: -| **ModelId** | **State** | **Configs** | **Attributes** | -| ------------ | ------------------------------------- | ---------------------------------------------- | -------------- | -| Model Unique Identifier | Model Registration Status (LOADING, ACTIVE, DROPPING) | InputShape, outputShapeInputTypes, outputTypes | Model Notes | +| **ModelId** | **State** | **Configs** | **Attributes** | +| ----------- | --------------------------------------------------------- | ------------------------------------------------ | -------------- | +| Unique ID | Registration status (INACTIVE, LOADING, ACTIVE,TRAINING,FAILED, DROPPING) | InputShape, outputShape, inputTypes, outputTypes | User notes | -State is used to show the current state of model registration, which consists of the following three stages +**State descriptions:** -- **LOADING**: The corresponding model meta information has been added to the configNode, and the model file is being transferred to the AINode node. -- **ACTIVE**: The model has been set up and the model is in the available state -- **DROPPING**: Model deletion is in progress, model related information is being deleted from configNode and AINode. -- **UNAVAILABLE**: Model creation failed, you can delete the failed model_name by drop model. +- **INACTIVE**: The model is in an unavailable state. +- **LOADING**: The model is being loaded. +- **ACTIVE**: The model is in an available state. +- **TRAINING**: The model is in the fine-tuning state. +- **FAILED**: The model fine-tuning failed. +- **DROPPING**: The model is being deleted. #### Example ```SQL IoTDB> show models - -+---------------------+--------------------------+-----------+----------------------------+-----------------------+ -| ModelId| ModelType| State| Configs| Notes| -+---------------------+--------------------------+-----------+----------------------------+-----------------------+ -| dlinear_example| USER_DEFINED| ACTIVE| inputShape:[96,2]| | -| | | | outputShape:[48,2]| | -| | | | inputDataType:[float,float]| | -| | | |outputDataType:[float,float]| | -| _STLForecaster| BUILT_IN_FORECAST| ACTIVE| |Built-in model in IoTDB| -| _NaiveForecaster| BUILT_IN_FORECAST| ACTIVE| |Built-in model in IoTDB| -| _ARIMA| BUILT_IN_FORECAST| ACTIVE| |Built-in model in IoTDB| -|_ExponentialSmoothing| BUILT_IN_FORECAST| ACTIVE| |Built-in model in IoTDB| -| _GaussianHMM|BUILT_IN_ANOMALY_DETECTION| ACTIVE| |Built-in model in IoTDB| -| _GMMHMM|BUILT_IN_ANOMALY_DETECTION| ACTIVE| |Built-in model in IoTDB| -| _Stray|BUILT_IN_ANOMALY_DETECTION| ACTIVE| |Built-in model in IoTDB| -+---------------------+--------------------------+-----------+------------------------------------------------------------+-----------------------+ ++---------------------+--------------------+--------+--------+ +| ModelId| ModelType|Category| State| ++---------------------+--------------------+--------+--------+ +| arima| Arima|BUILT-IN| ACTIVE| +| holtwinters| HoltWinters|BUILT-IN| ACTIVE| +|exponential_smoothing|ExponentialSmoothing|BUILT-IN| ACTIVE| +| naive_forecaster| NaiveForecaster|BUILT-IN| ACTIVE| +| stl_forecaster| StlForecaster|BUILT-IN| ACTIVE| +| gaussian_hmm| GaussianHmm|BUILT-IN| ACTIVE| +| gmm_hmm| GmmHmm|BUILT-IN| ACTIVE| +| stray| Stray|BUILT-IN| ACTIVE| +| timer_xl| Timer-XL|BUILT-IN| ACTIVE| +| sundial| Timer-Sundial|BUILT-IN| ACTIVE| ++---------------------+--------------------+--------+--------+ ``` -We have registered the corresponding model earlier, you can view the model status through the corresponding designation, active indicates that the model is successfully registered and can be used for inference. - -### 4.3 Delete Model +### 4.3 Deleting Models -For a successfully registered model, the user can delete it via SQL. In addition to deleting the meta information on the configNode, this operation also deletes all the related model files under the AINode. The SQL is as follows: +Registered models can be deleted via SQL, which removes all related files under AINode: ```SQL -drop model +drop model ``` -You need to specify the model model_name that has been successfully registered to delete the corresponding model. Since model deletion involves the deletion of data on multiple nodes, the operation will not be completed immediately, and the state of the model at this time is DROPPING, and the model in this state cannot be used for model inference. +Specify the registered `model_id` to delete the model. Since deletion involves data cleanup, the operation is not immediate, and the model state becomes `DROPPING`, during which it cannot be used for inference. **Note:** Built-in models cannot be deleted. ### 4.4 Using Built-in Model Reasoning @@ -221,7 +219,12 @@ The SQL syntax is as follows: ```SQL -call inference(,sql[,=]) +call inference(,inputSql,(=)*) + +window_function: + head(window_size) + tail(window_size) + count(window_size,sliding_step) ``` Built-in model inference does not require a registration process, the inference function can be used by calling the inference function through the call keyword, and its corresponding parameters are described as follows: @@ -230,6 +233,8 @@ Built-in model inference does not require a registration process, the inference - **parameterName**: parameter name - **parameterValue**: parameter value +- **Note**: To use a built-in time series large model for inference, the corresponding model weights must be stored locally in the directory `/IOTDB_AINODE_HOME/data/ainode/models/weights/model_id/`. If the weights are not present locally, they will be automatically downloaded from HuggingFace. Ensure your environment has direct access to HuggingFace. + #### Built-in Models and Parameter Descriptions The following machine learning models are currently built-in, please refer to the following links for detailed parameter descriptions. @@ -245,60 +250,6 @@ The following machine learning models are currently built-in, please refer to th | Stray | _Stray | Anomaly detection | [Stray Parameter description](https://www.sktime.net/en/latest/api_reference/auto_generated/sktime.detection.stray.STRAY.html) | -#### Example - -The following is an example of an operation using built-in model inference. The built-in Stray model is used for anomaly detection algorithm. The input is `[144,1]` and the output is `[144,1]`. We use it for reasoning through SQL. - -```SQL -IoTDB> select * from root.eg.airline -+-----------------------------+------------------+ -| Time|root.eg.airline.s0| -+-----------------------------+------------------+ -|1949-01-31T00:00:00.000+08:00| 224.0| -|1949-02-28T00:00:00.000+08:00| 118.0| -|1949-03-31T00:00:00.000+08:00| 132.0| -|1949-04-30T00:00:00.000+08:00| 129.0| -...... -|1960-09-30T00:00:00.000+08:00| 508.0| -|1960-10-31T00:00:00.000+08:00| 461.0| -|1960-11-30T00:00:00.000+08:00| 390.0| -|1960-12-31T00:00:00.000+08:00| 432.0| -+-----------------------------+------------------+ -Total line number = 144 - -IoTDB> call inference(_Stray, "select s0 from root.eg.airline", generateTime=True, k=2) -+-----------------------------+-------+ -| Time|output0| -+-----------------------------+-------+ -|1960-12-31T00:00:00.000+08:00| 0| -|1961-01-31T08:00:00.000+08:00| 1| -|1961-02-28T08:00:00.000+08:00| 0| -|1961-03-31T08:00:00.000+08:00| 0| -...... -|1972-06-30T08:00:00.000+08:00| 0| -|1972-07-31T08:00:00.000+08:00| 1| -|1972-08-31T08:00:00.000+08:00| 0| -|1972-09-30T08:00:00.000+08:00| 0| -|1972-10-31T08:00:00.000+08:00| 0| -|1972-11-30T08:00:00.000+08:00| 0| -+-----------------------------+-------+ -Total line number = 144 -``` - -### 4.5 Reasoning with Deep Learning Models - -The SQL syntax is as follows: - -```SQL -call inference(,sql[,window=]) - - -window_function: - head(window_size) - tail(window_size) - count(window_size,sliding_step) -``` - After completing the registration of the model, the inference function can be used by calling the inference function through the call keyword, and its corresponding parameters are described as follows: - **model_name**: corresponds to a registered model @@ -445,6 +396,65 @@ Total line number = 4 In the result set, each row's label corresponds to the output of the anomaly detection model after inputting each group of 24 rows of data. +### 4.5 Fine-tuning Built-in Models +> Only Timer-XL and Timer-Sundial support fine-tuning. + + +The SQL syntax is as follows: + + +```SQL +create model (with hyperparameters +(=(, =)*))? +from model +on dataset (PATH ([timeRange])?) +``` + +#### Examples + +1. Select the first 80% of data from the measurement point `root.db.etth.ot` as the fine-tuning dataset, and create the model `sundialv2` based on `sundial`. + +```SQL +IoTDB> CREATE MODEL sundialv2 FROM MODEL sundial ON DATASET (PATH root.db.etth.OT([1467302400000, 1517468400001))) +Msg: The statement is executed successfully. +IoTDB> show models ++---------------------+--------------------+----------+--------+ +| ModelId| ModelType| Category| State| ++---------------------+--------------------+----------+--------+ +| arima| Arima| BUILT-IN| ACTIVE| +| holtwinters| HoltWinters| BUILT-IN| ACTIVE| +|exponential_smoothing|ExponentialSmoothing| BUILT-IN| ACTIVE| +| naive_forecaster| NaiveForecaster| BUILT-IN| ACTIVE| +| stl_forecaster| StlForecaster| BUILT-IN| ACTIVE| +| gaussian_hmm| GaussianHmm| BUILT-IN| ACTIVE| +| gmm_hmm| GmmHmm| BUILT-IN| ACTIVE| +| stray| Stray| BUILT-IN| ACTIVE| +| sundial| Timer-Sundial| BUILT-IN| ACTIVE| +| timer_xl| Timer-XL| BUILT-IN| ACTIVE| +| sundialv2| Timer-Sundial|FINE-TUNED|TRAINING| ++---------------------+--------------------+----------+--------+ +``` + +2. The fine-tuning task starts asynchronously in the background, and logs can be viewed in the AINode process. After fine-tuning is complete, query and use the new model + +```SQL +IoTDB> show models ++---------------------+--------------------+----------+------+ +| ModelId| ModelType| Category| State| ++---------------------+--------------------+----------+------+ +| arima| Arima| BUILT-IN|ACTIVE| +| holtwinters| HoltWinters| BUILT-IN|ACTIVE| +|exponential_smoothing|ExponentialSmoothing| BUILT-IN|ACTIVE| +| naive_forecaster| NaiveForecaster| BUILT-IN|ACTIVE| +| stl_forecaster| StlForecaster| BUILT-IN|ACTIVE| +| gaussian_hmm| GaussianHmm| BUILT-IN|ACTIVE| +| gmm_hmm| GmmHmm| BUILT-IN|ACTIVE| +| stray| Stray| BUILT-IN|ACTIVE| +| sundial| Timer-Sundial| BUILT-IN|ACTIVE| +| timer_xl| Timer-XL| BUILT-IN|ACTIVE| +| sundialv2| Timer-Sundial|FINE-TUNED|ACTIVE| ++---------------------+--------------------+----------+------+ +``` ### 4.6 TimeSeries Large Models Import Steps diff --git a/src/UserGuide/latest/AI-capability/TimeSeries-Large-Model.md b/src/UserGuide/latest/AI-capability/TimeSeries-Large-Model.md index 7743ff545..c678ad28c 100644 --- a/src/UserGuide/latest/AI-capability/TimeSeries-Large-Model.md +++ b/src/UserGuide/latest/AI-capability/TimeSeries-Large-Model.md @@ -43,37 +43,48 @@ The Timer model not only demonstrates excellent few-shot generalization and mult - **Versatility**: The model is designed flexibly to adapt to various task requirements and supports variable input and output lengths, enabling it to play a role in various application scenarios. - **Scalability**: As the number of model parameters increases or the scale of pre-training data expands, the model's performance continues to improve, ensuring the model can optimize its predictive effects with the growth of time and data volume. -![](/img/LargeModel02.png) +![](/img/model01.png) ## Timer-XL Model Timer-XL is an upgraded version of Timer that further extends the network structure and achieves comprehensive breakthroughs in multiple dimensions: -- **Long Context Support**: This model breaks through the limitations of traditional time series forecasting models, supporting the processing of thousands of tokens (equivalent to tens of thousands of time points) of input, effectively addressing the bottleneck of context length. -- **Multi-variable Forecasting Scenario Coverage**: Supports a variety of forecasting scenarios, including non-stationary time series forecasting, multi-variable prediction tasks, and predictions involving covariates, meeting diverse business needs. -- **Large-scale Industrial Time Series Dataset**: Pre-trained using a massive industrial IoT time series dataset that has a large volume, excellent quality, and rich domain characteristics, covering energy, aerospace, steel, transportation, and more. +- **Ultra-long Context Support**: This model breaks through the limitations of traditional time series forecasting models, supporting inputs of thousands of Tokens (equivalent to tens of thousands of time points), effectively solving the context length bottleneck problem. +- **Multi-variable Prediction Scenarios Coverage**: Supports various forecasting scenarios, including non-stationary time series forecasting, multi-variable forecasting tasks, and forecasting with covariates, meeting diverse business needs. +- **Large-scale Industrial Time Series Dataset**: Pre-trained on a trillion-scale industrial IoT time series dataset, which has the important characteristics of huge volume, excellent quality, and rich domains, covering energy, aerospace, steel, transportation, and other fields. +![](/img/model02.png) + +## Timer-Sundial Model + +Timer-Sundial is a series of generative foundational models focused on time series forecasting. The basic version has 128 million parameters and has undergone large-scale pre-training on 1 trillion time points. Its core features include: + +- **Powerful Generalization Performance**: Possesses zero-shot forecasting capabilities, supporting both point forecasting and probabilistic forecasting simultaneously. +- **Flexible Forecasting Distribution Analysis**: Can not only forecast mean values or quantiles but also evaluate any statistical characteristics of the forecasting distribution through the original samples generated by the model. +- **Innovative Generative Architecture**: Adopts a "Transformer + TimeFlow" collaborative architecture—Transformer learns the autoregressive representation of time segments, and the TimeFlow module converts random noise into diversified forecasting trajectories based on the Flow-Matching framework, achieving efficient non-deterministic sample generation. + +![](/img/model03.png) ## Effect Demonstration -Time series large models can adapt to real time series data from various fields and scenarios, showing excellent processing effects in various tasks. Here are the real performances on different data: +Time series large models can adapt to real-time series data in various domains and scenarios, demonstrating excellent processing effects on various tasks. The following are real-world performances on different datasets: **Time Series Forecasting:** -Utilizing the predictive capabilities of the time series large model, it can accurately predict the future trend of time series. As shown in the figure, the blue curve represents the predicted trend, and the red curve represents the actual trend, with the two curves highly matching. +Utilizing the forecasting capability of time series large models, the future change trends of time series can be accurately predicted. In the figure below, the blue curve represents the forecast trend, and the red curve represents the actual trend, with a high degree of 吻合 (coincidence) between the two curves. ![](/img/LargeModel03.png) **Data Imputation:** -Using the time series large model to perform predictive imputation for missing data segments. +Utilizing time series large models to perform predictive imputation on missing data segments. ![](/img/timeseries-large-model-data-imputation.png) **Anomaly Detection:** -Utilizing the time series large model to accurately identify anomalies that deviate significantly from the normal trend. +Utilizing time series large models to accurately identify outliers that deviate significantly from normal trends. ![](/img/LargeModel05.png) @@ -89,24 +100,9 @@ show cluster ![](/img/ainode-timer-1.png) -2. Model file storage path: It is recommended to place the model files in the same directory as the AINode installation package. - You may create a new folder to store model files. - -3. Register the model - -Use the following SQL statement: - -```sql -create model using uri -``` - -Example (for the Timer model): - -```sql -create model Timer using uri -``` +2. When the AINode is started for the first time in a networked environment, the Timer-XL and Sundial models will be automatically pulled. -4. Verify model registration success +3. Verify model registration success Check command: diff --git a/src/UserGuide/latest/Deployment-and-Maintenance/AINode_Deployment_apache.md b/src/UserGuide/latest/Deployment-and-Maintenance/AINode_Deployment_apache.md index 451921216..50c532376 100644 --- a/src/UserGuide/latest/Deployment-and-Maintenance/AINode_Deployment_apache.md +++ b/src/UserGuide/latest/Deployment-and-Maintenance/AINode_Deployment_apache.md @@ -39,8 +39,6 @@ ### 2.1 Get installation package - Users can download the software installation package for AINode, download and unzip it to complete the installation of AINode. - Unzip and install the package `(apache-iotdb--ainode-bin.zip)`， The directory structure after unpacking the installation package is as follows: | **Catalogue** | **Type** | **Explain** | @@ -53,97 +51,64 @@ | README_ZH.md | file | Explanation of the Chinese version of the markdown format | | `README.md` | file | Instructions | -### 2.2 Environment preparation -- Suggested operating environment：Ubuntu, CentOS, MacOS - -- Runtime Environment - - Python >= 3.10 and Python <= 3.12 is sufficient, and comes with pip and venv tools; For non networked environments, download the zip package for the corresponding operating system from [here](https://cloud.tsinghua.edu.cn/d/4c1342f6c272439aa96c/?p=%2Flibs&mode=list) (Note that when downloading dependencies, you need to select the zip file in the libs folder, as shown in the following figure). Copy all files in the folder to the `lib` folder in the `apache-iotdb--ainode-bin` folder, and follow the steps below to start AINode. - - +### 2.2 Environmental Preparation - - There must be a Python interpreter in the environment variables that can be directly called through the `python` instruction. - - It is recommended to create a Python interpreter venv virtual environment in the `apache-iotdb--ainode-bin` folder. If installing version 3.10.0 virtual environment, the statement is as follows: - ```shell - # Install version 3.10.0 of Venv , Create a virtual environment with the folder name `venv`. - ../Python-3.10.0/python -m venv `venv` - ``` +1. Recommended operating systems: Ubuntu, CentOS, MacOS +2. IoTDB version: >= V 2.0.5.1 +3. Runtime environment + - Python version between 3.9 and 3.12, with pip and venv tools installed; ## 3. Installation steps ### 3.1 Install AINode - 1. Check the kernel architecture of Linux + 1. Ensure Python version is between 3.9 and 3.12: ```shell - uname -m - ``` - - 2. Import Python environment [Download](https://repo.anaconda.com/miniconda/) - - Recommend downloading the py311 version application and importing it into the iotdb dedicated folder in the user's root directory - -3. Verify Python version + python --version +# or +python3 --version +``` + 2. Download and import AINode into a dedicated folder, switch to the folder, and unzip the package: ```shell - python --version + unzip timechodb-2.0.5.1-ainode-bin.zip ``` -4. Create a virtual environment (execute in the ainode directory) +3. Create a virtual environment (execute in the ainode directory): ```shell - python -m venv venv + python3 -m venv venv ``` -5. Activate the virtual environment +4. Activate the virtual environment: ```shell source venv/bin/activate ``` - - - 6. Download and import AINode to a dedicated folder, switch to the dedicated folder and extract the installation package - -```shell - unzip apache-iotdb-1.3.3-ainode-bin.zip - ``` - - 7. Configuration item modification - -```shell - vi apache-iotdb-1.3.3-ainode-bin/conf/iotdb-ainode.properties - ``` - Configuration item modification：[detailed information](#configuration-item-modification) - -> ain_seed_config_node=iotdb-1:10710 (Cluster communication node IP: communication node port)
-> ain_inference_rpc_address=iotdb-3 (IP address of the server running AINode) - - 8. Replace Python source - -```shell - pip config set global.index-url https://mirrors.aliyun.com/pypi/simple/ - ``` - - 9. Start the AINode node - -```shell - nohup bash apache-iotdb-1.3.3-ainode-bin/sbin/start-ainode.sh > myout.file 2>& 1 & - ``` -> Return to the default environment of the system: conda deactivate +5. Update pip and install AINode dependencies: + ```shell + python -m pip install --upgrade pip + poetry lock + poetry install + ``` ### 3.2 Configuration item modification AINode supports modifying some necessary parameters. You can find the following parameters in the `conf/iotdb-ainode.properties` file and make persistent modifications to them: -： - -| **Name** | **Describe** | **Type** | **Default value** | **Effective method after modification** | -| :----------------------------- | ------------------------------------------------------------ | ------- | ------------------ | ---------------------------- | -| cluster_name | The identifier for AINode to join the cluster | string | defaultCluster | Only allow modifications before the first service startup | -| ain_seed_config_node | The Configurable Node address registered during AINode startup | String | 127.0.0.1:10710 | Only allow modifications before the first service startup | -| ain_inference_rpc_address | AINode provides service and communication addresses , Internal Service Communication Interface | String | 127.0.0.1 | Only allow modifications before the first service startup | -| ain_inference_rpc_port | AINode provides ports for services and communication | String | 10810 | Only allow modifications before the first service startup | -| ain_system_dir | AINode metadata storage path, the starting directory of the relative path is related to the operating system, and it is recommended to use an absolute path | String | data/AINode/system | Only allow modifications before the first service startup | -| ain_models_dir | AINode stores the path of the model file, and the starting directory of the relative path is related to the operating system. It is recommended to use an absolute path | String | data/AINode/models | Only allow modifications before the first service startup | -| ain_logs_dir | The path where AINode stores logs, the starting directory of the relative path is related to the operating system, and it is recommended to use an absolute path | String | logs/AINode | Effective after restart | -| ain_thrift_compression_enabled | Does AINode enable Thrift's compression mechanism , 0-Do not start, 1-Start | Boolean | 0 | Effective after restart | +| **Name** | **Description** | **Type** | **Default Value** | +| ------------------------------ | ------------------------------------------------------------ | -------- | ------------------ | +| cluster_name | Identifier of the cluster AINode joins | string | defaultCluster | +| ain_seed_config_node | Address of the ConfigNode registered when AINode starts | String | 127.0.0.1:10710 | +| ain_cluster_ingress_address | RPC address of the DataNode for AINode to pull data | String | 127.0.0.1 | +| ain_cluster_ingress_port | RPC port of the DataNode for AINode to pull data | Integer | 6667 | +| ain_cluster_ingress_username | Client username for AINode to pull data from the DataNode | String | root | +| ain_cluster_ingress_password | Client password for AINode to pull data from the DataNode | String | root | +| ain_cluster_ingress_time_zone | Client time zone for AINode to pull data from the DataNode | String | UTC+8 | +| ain_inference_rpc_address | Address for AINode to provide services and communication (internal interface) | String | 127.0.0.1 | +| ain_inference_rpc_port | Port for AINode to provide services and communication | String | 10810 | +| ain_system_dir | Metadata storage path for AINode (relative path starts from OS-dependent directory; absolute path is recommended) | String | data/AINode/system | +| ain_models_dir | Path to store model files for AINode (relative path starts from OS-dependent directory; absolute path is recommended) | String | data/AINode/models | +| ain_thrift_compression_enabled | Whether to enable Thrift compression for AINode (0=disabled, 1=enabled) | Boolean | 0 | ### 3.3 Start AINode @@ -169,43 +134,8 @@ AINode supports modifying some necessary parameters. You can find the following nohup bash sbin\start-ainode.bat > myout.file 2>& 1 & ``` -#### Detailed Syntax - -```shell - # Start command - # Linux and MacOS systems - bash sbin/start-ainode.sh -i -r -n - - # Windows systems - sbin\start-ainode.bat -i -r -n - ``` - -##### Parameter introduction: - -| **Name** | **Label** | **Describe** | **Is it mandatory** | **Type** | **Default value** | **Input method** | -| ------------------- | ---- | ------------------------------------------------------------ | -------- | ------ | ---------------- | ---------------------- | -| ain_interpreter_dir | -i | The interpreter path of the virtual environment where AINode is installed requires the use of an absolute path. | no | String | Default reading of environment variables | Input or persist modifications during invocation | -| ain_force_reinstall | -r | Does this script check the version when checking the installation status of AINode. If it does, it will force the installation of the whl package in lib if the version is incorrect. | no | Bool | false | Input when calling | -| ain_no_dependencies | -n | Specify whether to install dependencies when installing AINode, and if so, only install the AINode main program without installing dependencies. | no | Bool | false | Input when calling | - - If you don't want to specify the corresponding parameters every time you start, you can also persistently modify the parameters in the `ainode-env.sh` and `ainode-env.bat` scripts in the `conf` folder (currently supporting persistent modification of the ain_interpreter-dir parameter). - - `ainode-env.sh` : - ```shell - # The defaulte venv environment is used if ain_interpreter_dir is not set. Please use absolute path without quotation mark - # ain_interpreter_dir= - ``` - `ainode-env.bat` : -```shell - @REM The defaulte venv environment is used if ain_interpreter_dir is not set. Please use absolute path without quotation mark - @REM set ain_interpreter_dir= - ``` - After writing the parameter value, uncomment the corresponding line and save it to take effect on the next script execution. - #### Example - -##### Directly start: ```shell # Start command @@ -215,82 +145,6 @@ AINode supports modifying some necessary parameters. You can find the following sbin\start-ainode.bat - # Backend startup command (recommended for long-term running) - # Linux and MacOS systems - nohup bash sbin/start-ainode.sh > myout.file 2>& 1 & - # Windows systems - nohup bash sbin\start-ainode.bat > myout.file 2>& 1 & - ``` - -##### Update Start: -If the version of AINode has been updated (such as updating the `lib` folder), this command can be used. Firstly, it is necessary to ensure that AINode has stopped running, and then restart it using the `-r` parameter, which will reinstall AINode based on the files under `lib`. - - -```shell - # Update startup command - # Linux and MacOS systems - bash sbin/start-ainode.sh -r - # Windows systems - sbin\start-ainode.bat -r - - - # Backend startup command (recommended for long-term running) - # Linux and MacOS systems - nohup bash sbin/start-ainode.sh -r > myout.file 2>& 1 & - # Windows c - nohup bash sbin\start-ainode.bat -r > myout.file 2>& 1 & - ``` -#### Non networked environment startup - -##### Start command - -```shell - # Start command - # Linux and MacOS systems - bash sbin/start-ainode.sh - - # Windows systems - sbin\start-ainode.bat - - # Backend startup command (recommended for long-term running) - # Linux and MacOS systems - nohup bash sbin/start-ainode.sh > myout.file 2>& 1 & - - # Windows systems - nohup bash sbin\start-ainode.bat > myout.file 2>& 1 & - ``` - -#### Detailed Syntax - -```shell - # Start command - # Linux and MacOS systems - bash sbin/start-ainode.sh -i -r -n - - # Windows systems - sbin\start-ainode.bat -i -r -n - ``` - -##### Parameter introduction: - -| **Name** | **Label** | **Describe** | **Is it mandatory** | **Type** | **Default value** | **Input method** | -| ------------------- | ---- | ------------------------------------------------------------ | -------- | ------ | ---------------- | ---------------------- | -| ain_interpreter_dir | -i | The interpreter path of the virtual environment where AINode is installed requires the use of an absolute path | no | String | Default reading of environment variables | Input or persist modifications during invocation | -| ain_force_reinstall | -r | Does this script check the version when checking the installation status of AINode. If it does, it will force the installation of the whl package in lib if the version is incorrect | no | Bool | false | Input when calling | - -> Attention: When installation fails in a non networked environment, first check if the installation package corresponding to the platform is selected, and then confirm the Python version (due to the limitations of the downloaded installation package on Python versions, 3.7, 3.9, and others are not allowed) - -#### Example - -##### Directly start: - -```shell - # Start command - # Linux and MacOS systems - bash sbin/start-ainode.sh - # Windows systems - sbin\start-ainode.bat - # Backend startup command (recommended for long-term running) # Linux and MacOS systems nohup bash sbin/start-ainode.sh > myout.file 2>& 1 & @@ -328,23 +182,6 @@ If you need to stop a running AINode node, execute the corresponding shutdown sc sbin\stop-ainode.bat ``` - -#### Detailed Syntax - -```shell - # Linux / MacOS - bash sbin/stop-ainode.sh -t - - #Windows - sbin\stop-ainode.bat -t - ``` - -##### Parameter introduction: - -| **Name** | **Label** | **Describe** | **Is it mandatory** | **Type** | **Default value** | **Input method** | -| ----------------- | ---- | ------------------------------------------------------------ | -------- | ------ | ------ | ---------- | -| ain_remove_target | -t | When closing AINode, you can specify the Node ID, address, and port number of the target AINode to be removed, in the format of `` | no | String | nothing | Input when calling | - #### Example ```shell @@ -368,88 +205,6 @@ IoTDB> show cluster ``` If you need to restart the node, you need to execute the startup script again. -### 3.6 Remove AINode - -When it is necessary to remove an AINode node from the cluster, a removal script can be executed. The difference between removing and stopping scripts is that stopping retains the AINode node in the cluster but stops the AINode service, while removing removes the AINode node from the cluster. - -#### Remove command - - -```shell - # Linux / MacOS - bash sbin/remove-ainode.sh - - # Windows - sbin\remove-ainode.bat - ``` - -#### Detailed Syntax - -```shell - # Linux / MacOS - bash sbin/remove-ainode.sh -i -t/: -r -n - - # Windows - sbin\remove-ainode.bat -i -t/: -r -n - ``` - -##### Parameter introduction: - - | **Name** | **Label** | **Describe** | **Is it mandatory** | **Type** | **Default value** | **Input method** | -| ------------------- | ---- | ------------------------------------------------------------ | -------- | ------ | ---------------- | --------------------- | -| ain_interpreter_dir | -i | The interpreter path of the virtual environment where AINode is installed requires the use of an absolute path | no | String | Default reading of environment variables | Input+persistent modification during invocation | -| ain_remove_target | -t | When closing AINode, you can specify the Node ID, address, and port number of the target AINode to be removed, in the format of `` | no | String | nothing | Input when calling | -| ain_force_reinstall | -r | Does this script check the version when checking the installation status of AINode. If it does, it will force the installation of the whl package in lib if the version is incorrect | no | Bool | false | Input when calling | -| ain_no_dependencies | -n | Specify whether to install dependencies when installing AINode, and if so, only install the AINode main program without installing dependencies | no | Bool | false | Input when calling | - - If you don't want to specify the corresponding parameters every time you start, you can also persistently modify the parameters in the `ainode-env.sh` and `ainode-env.bat` scripts in the `conf` folder (currently supporting persistent modification of the ain_interpreter-dir parameter). - - `ainode-env.sh` : - ```shell - # The defaulte venv environment is used if ain_interpreter_dir is not set. Please use absolute path without quotation mark - # ain_interpreter_dir= - ``` - `ainode-env.bat` : -```shell - @REM The defaulte venv environment is used if ain_interpreter_dir is not set. Please use absolute path without quotation mark - @REM set ain_interpreter_dir= - ``` - After writing the parameter value, uncomment the corresponding line and save it to take effect on the next script execution. - -#### Example - -##### Directly remove: - - ```shell - # Linux / MacOS - bash sbin/remove-ainode.sh - - # Windows - sbin\remove-ainode.bat - ``` - After removing the node, relevant information about the node cannot be queried. - - ```shell -IoTDB> show cluster -+------+----------+-------+---------------+------------+-------+-----------+ -|NodeID| NodeType| Status|InternalAddress|InternalPort|Version| BuildInfo| -+------+----------+-------+---------------+------------+-------+-----------+ -| 0|ConfigNode|Running| 127.0.0.1| 10710|UNKNOWN|190e303-dev| -| 1| DataNode|Running| 127.0.0.1| 10730|UNKNOWN|190e303-dev| -+------+----------+-------+---------------+------------+-------+-----------+ -``` -##### Specify removal: - -If the user loses files in the data folder, AINode may not be able to actively remove them locally. The user needs to specify the node number, address, and port number for removal. In this case, we support users to input parameters according to the following methods for deletion. - - ```shell - # Linux / MacOS - bash sbin/remove-ainode.sh -t /: - - # Windows - sbin\remove-ainode.bat -t /: - ``` - ## 4. common problem ### 4.1 An error occurs when starting AINode stating that the venv module cannot be found diff --git a/src/UserGuide/latest/Deployment-and-Maintenance/AINode_Deployment_timecho.md b/src/UserGuide/latest/Deployment-and-Maintenance/AINode_Deployment_timecho.md index ac7c278ca..50c532376 100644 --- a/src/UserGuide/latest/Deployment-and-Maintenance/AINode_Deployment_timecho.md +++ b/src/UserGuide/latest/Deployment-and-Maintenance/AINode_Deployment_timecho.md @@ -35,12 +35,10 @@ -## 2. Installation preparation +## 2. Installation preparation ### 2.1 Get installation package - Users can download the software installation package for AINode, download and unzip it to complete the installation of AINode. - Unzip and install the package `(apache-iotdb--ainode-bin.zip)`， The directory structure after unpacking the installation package is as follows: | **Catalogue** | **Type** | **Explain** | @@ -53,130 +51,64 @@ | README_ZH.md | file | Explanation of the Chinese version of the markdown format | | `README.md` | file | Instructions | -### 2.2 Environment preparation -- Suggested operating environment：Ubuntu, CentOS, MacOS - -- Runtime Environment - - Python >= 3.10 and Python <= 3.12 is sufficient, and comes with pip and venv tools; For non networked environments, download the zip package for the corresponding operating system from [here](https://cloud.tsinghua.edu.cn/d/4c1342f6c272439aa96c/?p=%2Flibs&mode=list) (Note that when downloading dependencies, you need to select the zip file in the libs folder, as shown in the following figure). Copy all files in the folder to the `lib` folder in the `apache-iotdb--ainode-bin` folder, and follow the steps below to start AINode. - - +### 2.2 Environmental Preparation - - There must be a Python interpreter in the environment variables that can be directly called through the `python` instruction. - - It is recommended to create a Python interpreter venv virtual environment in the `apache-iotdb--ainode-bin` folder. If installing version 3.10.0 virtual environment, the statement is as follows: - ```shell - # Install version 3.10.0 of Venv , Create a virtual environment with the folder name `venv`. - ../Python-3.10.0/python -m venv `venv` - ``` +1. Recommended operating systems: Ubuntu, CentOS, MacOS +2. IoTDB version: >= V 2.0.5.1 +3. Runtime environment + - Python version between 3.9 and 3.12, with pip and venv tools installed; ## 3. Installation steps ### 3.1 Install AINode -1. AINode activation - - Require IoTDB to be in normal operation and have AINode module authorization in the license. - - The authorization method for activating the AINode module is as follows： - - Method 1: Activate file copy activation - - After restarting the confignode node, enter the activation folder, copy the system_info file to the Timecho staff, and inform them to apply for independent authorization for AINode; - - Received the license file returned by the staff; - - Put the license file into the activation folder of the corresponding node; - -- Method 2: Activate Script Activation - - Obtain the required machine code for activation, enter the `sbin` directory of the installation directory, and execute the activation script： - ```shell - cd sbin - ./start-activate.sh - ``` - - The following information is displayed. Please copy the machine code (i.e. this string of characters) to the Timecho staff and inform them to apply for independent authorization of AINode： - ```shell - Please copy the system_info's content and send it to Timecho: - Y17hFA0xRCE1TmkVxILuCIEPc7uJcr5bzlXWiptw8uZTmTX5aThfypQdLUIhMljw075hNRSicyvyJR9JM7QaNm1gcFZPHVRWVXIiY5IlZkXdxCVc1erXMsbCqUYsR2R2Mw4PSpFJsUF5jHWSoFIIjQ2bmJFW5P52KCccFMVeHTc= - Please enter license: - ``` - - Enter the activation code returned by the staff into the `Please enter license:` command prompt in the previous step, as shown below： - ```shell - Please enter license: - Jw+MmF+AtexsfgNGOFgTm83BgXbq0zT1+fOfPvQsLlj6ZsooHFU6HycUSEGC78eT1g67KPvkcLCUIsz2QpbyVmPLr9x1+kVjBubZPYlVpsGYLqLFc8kgpb5vIrPLd3hGLbJ5Ks8fV1WOVrDDVQq89YF2atQa2EaB9EAeTWd0bRMZ+s9ffjc/1Zmh9NSP/T3VCfJcJQyi7YpXWy5nMtcW0gSV+S6fS5r7a96PjbtE0zXNjnEhqgRzdU+mfO8gVuUNaIy9l375cp1GLpeCh6m6pF+APW1CiXLTSijK9Qh3nsL5bAOXNeob5l+HO5fEMgzrW8OJPh26Vl6ljKUpCvpTiw== - License has been stored to sbin/../activation/license - Import completed. Please start cluster and excute 'show cluster' to verify activation status - ``` -- After updating the license, restart the DataNode node and enter the sbin directory of IoTDB to start the datanode： - ```shell - cd sbin - ./start-datanode.sh -d #The parameter'd 'will be started in the background - ``` - -2. Check the kernel architecture of Linux - ```shell - uname -m - ``` - -3. Import Python environment [Download](https://repo.anaconda.com/miniconda/) - - Recommend downloading the py311 version application and importing it into the iotdb dedicated folder in the user's root directory - -4. Verify Python version + 1. Ensure Python version is between 3.9 and 3.12: ```shell python --version +# or +python3 --version +``` + + 2. Download and import AINode into a dedicated folder, switch to the folder, and unzip the package: +```shell + unzip timechodb-2.0.5.1-ainode-bin.zip ``` -5. Create a virtual environment (execute in the ainode directory) +3. Create a virtual environment (execute in the ainode directory): ```shell - python -m venv venv + python3 -m venv venv ``` -6. Activate the virtual environment +4. Activate the virtual environment: ```shell source venv/bin/activate ``` - -7. Download and import AINode to a dedicated folder, switch to the dedicated folder and extract the installation package - +5. Update pip and install AINode dependencies: ```shell - unzip iotdb-enterprise-ainode-1.3.3.2.zip - ``` - -8. Configuration item modification - - ```shell - vi iotdb-enterprise-ainode-1.3.3.2/conf/iotdb-ainode.properties - ``` - Configuration item modification：[detailed information](#configuration-item-modification) - - > ain_seed_config_node=iotdb-1:10710 (Cluster communication node IP: communication node port)
- > ain_inference_rpc_address=iotdb-3 (IP address of the server running AINode) - -9. Replace Python source - - ```shell - pip config set global.index-url https://mirrors.aliyun.com/pypi/simple/ - ``` - -10. Start the AINode node - - ```shell - nohup bash iotdb-enterprise-ainode-1.3.3.2/sbin/start-ainode.sh > myout.file 2>& 1 & - ``` - > Return to the default environment of the system: conda deactivate + python -m pip install --upgrade pip + poetry lock + poetry install + ``` - ### 3.2 Configuration item modification +### 3.2 Configuration item modification AINode supports modifying some necessary parameters. You can find the following parameters in the `conf/iotdb-ainode.properties` file and make persistent modifications to them: -： - -| **Name** | **Describe** | **Type** | **Default value** | **Effective method after modification** | -| :----------------------------- | ------------------------------------------------------------ | ------- | ------------------ | ---------------------------- | -| cluster_name | The identifier for AINode to join the cluster | string | defaultCluster | Only allow modifications before the first service startup | -| ain_seed_config_node | The Configurable Node address registered during AINode startup | String | 127.0.0.1:10710 | Only allow modifications before the first service startup | -| ain_inference_rpc_address | AINode provides service and communication addresses , Internal Service Communication Interface | String | 127.0.0.1 | Only allow modifications before the first service startup | -| ain_inference_rpc_port | AINode provides ports for services and communication | String | 10810 | Only allow modifications before the first service startup | -| ain_system_dir | AINode metadata storage path, the starting directory of the relative path is related to the operating system, and it is recommended to use an absolute path | String | data/AINode/system | Only allow modifications before the first service startup | -| ain_models_dir | AINode stores the path of the model file, and the starting directory of the relative path is related to the operating system. It is recommended to use an absolute path | String | data/AINode/models | Only allow modifications before the first service startup | -| ain_logs_dir | The path where AINode stores logs, the starting directory of the relative path is related to the operating system, and it is recommended to use an absolute path | String | logs/AINode | Effective after restart | -| ain_thrift_compression_enabled | Does AINode enable Thrift's compression mechanism , 0-Do not start, 1-Start | Boolean | 0 | Effective after restart | +| **Name** | **Description** | **Type** | **Default Value** | +| ------------------------------ | ------------------------------------------------------------ | -------- | ------------------ | +| cluster_name | Identifier of the cluster AINode joins | string | defaultCluster | +| ain_seed_config_node | Address of the ConfigNode registered when AINode starts | String | 127.0.0.1:10710 | +| ain_cluster_ingress_address | RPC address of the DataNode for AINode to pull data | String | 127.0.0.1 | +| ain_cluster_ingress_port | RPC port of the DataNode for AINode to pull data | Integer | 6667 | +| ain_cluster_ingress_username | Client username for AINode to pull data from the DataNode | String | root | +| ain_cluster_ingress_password | Client password for AINode to pull data from the DataNode | String | root | +| ain_cluster_ingress_time_zone | Client time zone for AINode to pull data from the DataNode | String | UTC+8 | +| ain_inference_rpc_address | Address for AINode to provide services and communication (internal interface) | String | 127.0.0.1 | +| ain_inference_rpc_port | Port for AINode to provide services and communication | String | 10810 | +| ain_system_dir | Metadata storage path for AINode (relative path starts from OS-dependent directory; absolute path is recommended) | String | data/AINode/system | +| ain_models_dir | Path to store model files for AINode (relative path starts from OS-dependent directory; absolute path is recommended) | String | data/AINode/models | +| ain_thrift_compression_enabled | Whether to enable Thrift compression for AINode (0=disabled, 1=enabled) | Boolean | 0 | ### 3.3 Start AINode @@ -202,43 +134,8 @@ AINode supports modifying some necessary parameters. You can find the following nohup bash sbin\start-ainode.bat > myout.file 2>& 1 & ``` -#### Detailed Syntax -```shell - # Start command - # Linux and MacOS systems - bash sbin/start-ainode.sh -i -r -n - - # Windows systems - sbin\start-ainode.bat -i -r -n - ``` - -##### Parameter introduction: - -| **Name** | **Label** | **Describe** | **Is it mandatory** | **Type** | **Default value** | **Input method** | -| ------------------- | ---- | ------------------------------------------------------------ | -------- | ------ | ---------------- | ---------------------- | -| ain_interpreter_dir | -i | The interpreter path of the virtual environment where AINode is installed requires the use of an absolute path. | no | String | Default reading of environment variables | Input or persist modifications during invocation | -| ain_force_reinstall | -r | Does this script check the version when checking the installation status of AINode. If it does, it will force the installation of the whl package in lib if the version is incorrect. | no | Bool | false | Input when calling | -| ain_no_dependencies | -n | Specify whether to install dependencies when installing AINode, and if so, only install the AINode main program without installing dependencies. | no | Bool | false | Input when calling | - - If you don't want to specify the corresponding parameters every time you start, you can also persistently modify the parameters in the `ainode-env.sh` and `ainode-env.bat` scripts in the `conf` folder (currently supporting persistent modification of the ain_interpreter-dir parameter). - - `ainode-env.sh` : - ```shell - # The defaulte venv environment is used if ain_interpreter_dir is not set. Please use absolute path without quotation mark - # ain_interpreter_dir= - ``` - `ainode-env.bat` : -```shell - @REM The defaulte venv environment is used if ain_interpreter_dir is not set. Please use absolute path without quotation mark - @REM set ain_interpreter_dir= - ``` - After writing the parameter value, uncomment the corresponding line and save it to take effect on the next script execution. - - -#### Example - -##### Directly start: +#### Example ```shell # Start command @@ -248,82 +145,6 @@ AINode supports modifying some necessary parameters. You can find the following sbin\start-ainode.bat - # Backend startup command (recommended for long-term running) - # Linux and MacOS systems - nohup bash sbin/start-ainode.sh > myout.file 2>& 1 & - # Windows systems - nohup bash sbin\start-ainode.bat > myout.file 2>& 1 & - ``` - -##### Update Start: -If the version of AINode has been updated (such as updating the `lib` folder), this command can be used. Firstly, it is necessary to ensure that AINode has stopped running, and then restart it using the `-r` parameter, which will reinstall AINode based on the files under `lib`. - - -```shell - # Update startup command - # Linux and MacOS systems - bash sbin/start-ainode.sh -r - # Windows systems - sbin\start-ainode.bat -r - - - # Backend startup command (recommended for long-term running) - # Linux and MacOS systems - nohup bash sbin/start-ainode.sh -r > myout.file 2>& 1 & - # Windows c - nohup bash sbin\start-ainode.bat -r > myout.file 2>& 1 & - ``` -#### Non networked environment startup - -##### Start command - -```shell - # Start command - # Linux and MacOS systems - bash sbin/start-ainode.sh - - # Windows systems - sbin\start-ainode.bat - - # Backend startup command (recommended for long-term running) - # Linux and MacOS systems - nohup bash sbin/start-ainode.sh > myout.file 2>& 1 & - - # Windows systems - nohup bash sbin\start-ainode.bat > myout.file 2>& 1 & - ``` - -#### Detailed Syntax - -```shell - # Start command - # Linux and MacOS systems - bash sbin/start-ainode.sh -i -r -n - - # Windows systems - sbin\start-ainode.bat -i -r -n - ``` - -##### Parameter introduction: - -| **Name** | **Label** | **Describe** | **Is it mandatory** | **Type** | **Default value** | **Input method** | -| ------------------- | ---- | ------------------------------------------------------------ | -------- | ------ | ---------------- | ---------------------- | -| ain_interpreter_dir | -i | The interpreter path of the virtual environment where AINode is installed requires the use of an absolute path | no | String | Default reading of environment variables | Input or persist modifications during invocation | -| ain_force_reinstall | -r | Does this script check the version when checking the installation status of AINode. If it does, it will force the installation of the whl package in lib if the version is incorrect | no | Bool | false | Input when calling | - -> Attention: When installation fails in a non networked environment, first check if the installation package corresponding to the platform is selected, and then confirm the Python version (due to the limitations of the downloaded installation package on Python versions, 3.7, 3.9, and others are not allowed) - -#### Example - -##### Directly start: - -```shell - # Start command - # Linux and MacOS systems - bash sbin/start-ainode.sh - # Windows systems - sbin\start-ainode.bat - # Backend startup command (recommended for long-term running) # Linux and MacOS systems nohup bash sbin/start-ainode.sh > myout.file 2>& 1 & @@ -361,23 +182,6 @@ If you need to stop a running AINode node, execute the corresponding shutdown sc sbin\stop-ainode.bat ``` - -#### Detailed Syntax - -```shell - # Linux / MacOS - bash sbin/stop-ainode.sh -t - - #Windows - sbin\stop-ainode.bat -t - ``` - -##### Parameter introduction: - -| **Name** | **Label** | **Describe** | **Is it mandatory** | **Type** | **Default value** | **Input method** | -| ----------------- | ---- | ------------------------------------------------------------ | -------- | ------ | ------ | ---------- | -| ain_remove_target | -t | When closing AINode, you can specify the Node ID, address, and port number of the target AINode to be removed, in the format of `` | no | String | nothing | Input when calling | - #### Example ```shell @@ -401,88 +205,6 @@ IoTDB> show cluster ``` If you need to restart the node, you need to execute the startup script again. -### 3.6 Remove AINode - -When it is necessary to remove an AINode node from the cluster, a removal script can be executed. The difference between removing and stopping scripts is that stopping retains the AINode node in the cluster but stops the AINode service, while removing removes the AINode node from the cluster. - -#### Remove command - - -```shell - # Linux / MacOS - bash sbin/remove-ainode.sh - - # Windows - sbin\remove-ainode.bat - ``` - -#### Detailed Syntax - -```shell - # Linux / MacOS - bash sbin/remove-ainode.sh -i -t/: -r -n - - # Windows - sbin\remove-ainode.bat -i -t/: -r -n - ``` - -##### Parameter introduction: - - | **Name** | **Label** | **Describe** | **Is it mandatory** | **Type** | **Default value** | **Input method** | -| ------------------- | ---- | ------------------------------------------------------------ | -------- | ------ | ---------------- | --------------------- | -| ain_interpreter_dir | -i | The interpreter path of the virtual environment where AINode is installed requires the use of an absolute path | no | String | Default reading of environment variables | Input+persistent modification during invocation | -| ain_remove_target | -t | When closing AINode, you can specify the Node ID, address, and port number of the target AINode to be removed, in the format of `` | no | String | nothing | Input when calling | -| ain_force_reinstall | -r | Does this script check the version when checking the installation status of AINode. If it does, it will force the installation of the whl package in lib if the version is incorrect | no | Bool | false | Input when calling | -| ain_no_dependencies | -n | Specify whether to install dependencies when installing AINode, and if so, only install the AINode main program without installing dependencies | no | Bool | false | Input when calling | - - If you don't want to specify the corresponding parameters every time you start, you can also persistently modify the parameters in the `ainode-env.sh` and `ainode-env.bat` scripts in the `conf` folder (currently supporting persistent modification of the ain_interpreter-dir parameter). - - `ainode-env.sh` : - ```shell - # The defaulte venv environment is used if ain_interpreter_dir is not set. Please use absolute path without quotation mark - # ain_interpreter_dir= - ``` - `ainode-env.bat` : -```shell - @REM The defaulte venv environment is used if ain_interpreter_dir is not set. Please use absolute path without quotation mark - @REM set ain_interpreter_dir= - ``` - After writing the parameter value, uncomment the corresponding line and save it to take effect on the next script execution. - -#### Example - -##### Directly remove: - - ```shell - # Linux / MacOS - bash sbin/remove-ainode.sh - - # Windows - sbin\remove-ainode.bat - ``` - After removing the node, relevant information about the node cannot be queried. - - ```shell -IoTDB> show cluster -+------+----------+-------+---------------+------------+-------+-----------+ -|NodeID| NodeType| Status|InternalAddress|InternalPort|Version| BuildInfo| -+------+----------+-------+---------------+------------+-------+-----------+ -| 0|ConfigNode|Running| 127.0.0.1| 10710|UNKNOWN|190e303-dev| -| 1| DataNode|Running| 127.0.0.1| 10730|UNKNOWN|190e303-dev| -+------+----------+-------+---------------+------------+-------+-----------+ -``` -##### Specify removal: - -If the user loses files in the data folder, AINode may not be able to actively remove them locally. The user needs to specify the node number, address, and port number for removal. In this case, we support users to input parameters according to the following methods for deletion. - - ```shell - # Linux / MacOS - bash sbin/remove-ainode.sh -t /: - - # Windows - sbin\remove-ainode.bat -t /: - ``` - ## 4. common problem ### 4.1 An error occurs when starting AINode stating that the venv module cannot be found diff --git a/src/zh/UserGuide/Master/Table/AI-capability/AINode_apache.md b/src/zh/UserGuide/Master/Table/AI-capability/AINode_apache.md new file mode 100644 index 000000000..7e528963f --- /dev/null +++ b/src/zh/UserGuide/Master/Table/AI-capability/AINode_apache.md @@ -0,0 +1,379 @@ + + +# AINode + +AINode 是支持时序大模型注册、管理、调用的 IoTDB 原生节点，内置业界领先的自研时序大模型，如 Timer、Sundial 等，可通过标准 SQL 语句进行调用，实现时序数据的毫秒级实时推理，可支持时序趋势预测、缺失值填补、异常值检测等应用场景。 + +系统架构如下图所示： + +![](/img/h4.png) + +三种节点的职责如下： + +- **ConfigNode**：负责保存和管理模型的元信息；负责分布式节点管理。 +- **DataNode**：负责接收并解析用户的 SQL请求；负责存储时间序列数据；负责数据的预处理计算。 +- **AINode**：负责模型文件的导入创建以及模型推理。 + +## 1. 优势特点 + +与单独构建机器学习服务相比，具有以下优势： + +- **简单易用**：无需使用 Python 或 Java 编程，使用 SQL 语句即可完成机器学习模型管理与推理的完整流程。如创建模型可使用CREATE MODEL语句、使用模型进行推理可使用 SELECT * FROM FORECAST (...) 语句等，使用更加简单便捷。 + +- **避免数据迁移**：使用 IoTDB 原生机器学习可以将存储在 IoTDB 中的数据直接应用于机器学习模型的推理，无需将数据移动到单独的机器学习服务平台，从而加速数据处理、提高安全性并降低成本。 + +![](/img/h1.png) + +- **内置先进算法**：支持业内领先机器学习分析算法，覆盖典型时序分析任务，为时序数据库赋能原生数据分析能力。如： + - **时间序列预测（Time Series Forecasting）**：从过去时间序列中学习变化模式；从而根据给定过去时间的观测值，输出未来序列最可能的预测。 + - **时序异常检测（Anomaly Detection for Time Series）**：在给定的时间序列数据中检测和识别异常值，帮助发现时间序列中的异常行为。 + - **时间序列标注（Time Series Annotation）**：为每个数据点或特定时间段添加额外的信息或标记，例如事件发生、异常点、趋势变化等，以便更好地理解和分析数据。 + + +## 2. 基本概念 + +- **模型（Model）**：机器学习模型，以时序数据作为输入，输出分析任务的结果或决策。模型是 AINode 的基本管理单元，支持模型的增（注册）、删、查、用（推理）。 +- **创建（Create）**: 将外部设计或训练好的模型文件或算法加载到 AINode 中，由 IoTDB 统一管理与使用。 +- **推理（Inference）**：使用创建的模型在指定时序数据上完成该模型适用的时序分析任务。 +- **内置能力（Built-in）**：AINode 自带常见时序分析场景（例如预测与异常检测）的机器学习算法或自研模型。 + +![](/img/h3.png) + +## 3. 安装部署 + +AINode 的部署可参考文档 [部署指导](../Deployment-and-Maintenance/AINode_Deployment_apache.md#ainode-部署) 章节。 + +## 4. 使用指导 + +AINode 对时序模型提供了模型创建及删除功能，内置模型无需创建，可直接使用。 + +### 4.1 注册模型 + +通过指定模型输入输出的向量维度，可以注册训练好的深度学习模型，从而用于模型推理。 + +符合以下内容的模型可以注册到AINode中： + 1. AINode 目前支持基于 PyTorch 2.4.0 版本训练的模型，需避免使用 2.4.0 版本以上的特性。 + 2. AINode 支持使用 PyTorch JIT 存储的模型（`model.pt`），模型文件需要包含模型的结构和权重。 + 3. 模型输入序列可以包含一列或多列，若有多列，需要和模型能力、模型配置文件对应。 + 4. 模型的配置参数必须在`config.yaml`配置文件中明确定义。使用模型时，必须严格按照`config.yaml`配置文件中定义的输入输出维度。如果输入输出列数不匹配配置文件，将会导致错误。 + +下方为模型注册的SQL语法定义。 + +```SQL +create model using uri +``` + +SQL中参数的具体含义如下： + +- model_name：模型的全局唯一标识，不可重复。模型名称具备以下约束： + + - 允许出现标识符 [ 0-9 a-z A-Z _ ] （字母，数字，下划线） + - 长度限制为2-64字符 + - 大小写敏感 + +- uri：模型注册文件的资源路径，路径下应包含**模型结构及权重文件 model.pt 文件和模型配置文件 config.yaml** + + - 模型结构及权重文件：模型训练完成后得到的权重文件，目前支持 pytorch 训练得到的 .pt 文件 + + - 模型配置文件：模型注册时需要提供的与模型结构有关的参数，其中必须包含模型的输入输出维度用于模型推理： + + - | **参数名** | **参数描述** | **示例** | + | ------------ | ---------------------------- | -------- | + | input_shape | 模型输入的行列，用于模型推理 | [96,2] | + | output_shape | 模型输出的行列，用于模型推理 | [48,2] | + + - ​ 除了模型推理外，还可以指定模型输入输出的数据类型： + + - | **参数名** | **参数描述** | **示例** | + | ----------- | ------------------ | --------------------- | + | input_type | 模型输入的数据类型 | ['float32','float32'] | + | output_type | 模型输出的数据类型 | ['float32','float32'] | + + - ​ 除此之外，可以额外指定备注信息用于在模型管理时进行展示 + + - | **参数名** | **参数描述** | **示例** | + | ---------- | ---------------------------------------------- | ------------------------------------------- | + | attributes | 可选，用户自行设定的模型备注信息，用于模型展示 | 'model_type': 'dlinear','kernel_size': '25' | + + +除了本地模型文件的注册，还可以通过URI来指定远程资源路径来进行注册，使用开源的模型仓库（例如HuggingFace）。 + +#### 示例 + +在当前的example文件夹下，包含model.pt和config.yaml文件，model.pt为训练得到，config.yaml的内容如下： + +```YAML +configs: + # 必选项 + input_shape: [96, 2] # 表示模型接收的数据为96行x2列 + output_shape: [48, 2] # 表示模型输出的数据为48行x2列 + + # 可选项 默认为全部float32，列数为shape对应的列数 + input_type: ["int64","int64"] #输入对应的数据类型，需要与输入列数匹配 + output_type: ["text","int64"] #输出对应的数据类型，需要与输出列数匹配 + +attributes: # 可选项 为用户自定义的备注信息 + 'model_type': 'dlinear' + 'kernel_size': '25' +``` + +指定该文件夹作为加载路径就可以注册该模型 + +```SQL +IoTDB> create model dlinear_example using uri "file://./example" +``` + +也可以从huggingFace上下载对应的模型文件进行注册 + +```SQL +IoTDB> create model dlinear_example using uri "https://huggingface.co/google/timesfm-2.0-500m-pytorch" +``` + +SQL执行后会异步进行注册的流程，可以通过模型展示查看模型的注册状态（见模型展示章节），注册成功的耗时主要受到模型文件大小的影响。 + +模型注册完成后，就可以通过使用正常查询的方式调用具体函数，进行模型推理。 + +### 4.2 查看模型 + +注册成功的模型可以通过show models指令查询模型的具体信息。其SQL定义如下： + +```SQL +show models + +show models +``` + +除了直接展示所有模型的信息外，可以指定model id来查看某一具体模型的信息。模型展示的结果中包含如下信息： + +| **ModelId** | **State** | **Configs** | **Attributes** | +| ------------ | ------------------------------------- | ---------------------------------------------- | -------------- | +| 模型唯一标识 | 模型注册状态(INACTIVE,LOADING,ACTIVE,DROPPING) | InputShape, outputShapeInputTypes, outputTypes | 模型备注信息 | + +其中，State用于展示当前模型注册的状态，包含以下三个阶段 + +- **INACTIVE**：模型处于不可用状态 +- **LOADING**：模型加载中状态 +- **ACTIVE**：模型处于可用状态 +- **DROPPING**:模型删除中状态 + +#### 示例 + +```SQL +IoTDB> show models + ++---------------------+--------------------+--------+--------+ +| ModelId| ModelType|Category| State| ++---------------------+--------------------+--------+--------+ +| arima| Arima|BUILT-IN| ACTIVE| +| holtwinters| HoltWinters|BUILT-IN| ACTIVE| +|exponential_smoothing|ExponentialSmoothing|BUILT-IN| ACTIVE| +| naive_forecaster| NaiveForecaster|BUILT-IN| ACTIVE| +| stl_forecaster| StlForecaster|BUILT-IN| ACTIVE| +| gaussian_hmm| GaussianHmm|BUILT-IN| ACTIVE| +| gmm_hmm| GmmHmm|BUILT-IN| ACTIVE| +| stray| Stray|BUILT-IN| ACTIVE| +| timer_xl| Timer-XL|BUILT-IN| ACTIVE| +| sundial| Timer-Sundial|BUILT-IN| ACTIVE| ++---------------------+--------------------+--------+--------+ +``` + +### 4.3 删除模型 + +对于注册成功的模型，用户可以通过SQL进行删除，该操作会删除所有 AINode 下的相关模型文件，其SQL如下： + +```SQL +drop model +``` + +需要指定已经成功注册的模型 model_id 来删除对应的模型。由于模型删除涉及模型数据清理，操作不会立即完成，此时模型的状态为 DROPPING，该状态的模型不能用于模型推理。请注意，该功能不支持删除内置模型。 + +### 4.4 使用内置模型推理 + +SQL语法如下： + + +```SQL +SELECT * FROM forecast( + input, + model_id, + [output_length, + output_start_time, + output_interval, + timecol, + preserve_input, + model_options]? +) +``` + +内置模型推理无需注册流程，通过 forecast 函数，指定 model_id 就可以使用模型的推理功能 + + - 请注意，使用内置时序大模型进行推理的前提条件是本地存有对应模型权重，目录为 /IOTDB_AINODE_HOME/data/ainode/models/weights/model_id/。若本地没有模型权重，则会自动从 HuggingFace 拉取，请保证本地能直接访问 HuggingFace。 + +- 参数介绍如下： + +| 参数名 | 参数类型 | 参数属性 | 描述 | 是否必填 | 备注 | +| :---------------- | :------- | :----------------------------------------------------------- | :----------------------------------------------------------- | :------- | :----------------------------------------------------------- | +| input | 表参数 | SET SEMANTIC | 待预测的输入数据 | 是 | | +| model_id | 标量参数 | 字符串类型 | 需要选择的model名 | 是 | 只能为非空，且内置的模型，否则报错：空字符串：MODEL_ID should never be null or empty不存在的模型：model [%s] has not been created模型不可用：model [%s] is not available | +| output_length | 标量参数 | INT32类型默认值：96 | 输出窗口大小 | 否 | 必须大于 0，否则报错：OUTPUT_LENGTH should be greater than 0 | +| output_start_time | 标量参数 | 时间戳类型默认值：输入数据的最后一个时间戳加 output_interval | 输出的预测点的起始时间戳 | 否 | 可以为负数，表示1970年1月1号之前的时间戳 | +| output_interval | 标量参数 | 时间间隔类型默认值：0（输入数据的采样间隔） | 输出的预测点之间的时间间隔支持的单位是 ns、us、ms、s、m、h、d、w | 否 | 大于 0 时，采用用户指定的输出间隔；小于等于 0 时，根据输入数据自动推测 | +| timecol | 标量参数 | 字符串类型默认值：time | 时间列名 | 否 | 存在于 input 中的，数据类型为 TIMESTAMP 的列，否则报错：若数据类型不为 TIMESTAMP： The type of the column [%s] is not as expected.若列不存在：Required column [%s] not found in the source table argument. | +| preserve_input | 标量参数 | 布尔类型默认值：false | 是否在输出结果集中保留输入的所有原始行 | 否 | | +| model_options | 标量参数 | 字符串类型默认值：空字符串 | 模型相关的key-value对，比如是否需要对输入进行归一化等。不同的key-value对以';'间隔 | 否 | 指定某个模型不支持参数，并不会报错，只会被忽略；AINode 中内置的模型支持的常见参数详见文末附录说明。 | + +**说明：** + +1. forecast 函数默认对输入表中所有列进行预测（不包含time列和partition by 的列）。 +2. forecast 函数对于输入数据无顺序性要求，默认对输入数据按照时间戳（由 TIMECOL 参数指定时间戳的列名）做升序排序后，再调用模型进行预测。 +3. 不同模型对于输入数据的行数要求不同，输入数据少于最低行数要求时，则报错；输入数据高于最大行数要求时，则自动截取最后的满足要求的行数。 + - 在当前的 AINdoe 内置模型中，仅_sundial对输入行数有限制。_sundial 目前支持的最大输入行数为 2880，超过 2880 时，会自动截取输入数据的最后 2880 行。 +4. forecast 函数的返回结果列包含 input 表的所有输入列，列的数据类型与原表列的数据类型一致。若 preserve_input= true，则还包含 is_input 列（表征当前行是否为输入行） + - 目前只支持对 INT32、INT64、FLOAT、DOUBLE 进行预测，否则报错：The type of the column [%s] is [%s], only INT32, INT64, FLOAT, DOUBLE is allowed +5. output_start_time 和 output_interval 只会影响输出结果集的时间戳列生成，均为可选参数。 + - output_start_time 默认为输入数据的最后一个时间戳加 output_interval + - output_interval = (输入数据的最后一个时间戳 - 输入数据的第一个时间戳) / n - 1， 默认为输入数据的采样间隔 + - 第 N 个输出行的时间为 output_start_time + (N - 1) * output_interval + +#### 示例 + +我们所使用的的测试集的数据为[ETTh1-tab](/img/ETTh1-tab.csv)。 + +**查看当前支持的模型** + +```Bash +IoTDB:etth> show models ++---------------------+--------------------+--------+------+ +| ModelId| ModelType|Category| State| ++---------------------+--------------------+--------+------+ +| arima| Arima|BUILT-IN|ACTIVE| +| holtwinters| HoltWinters|BUILT-IN|ACTIVE| +|exponential_smoothing|ExponentialSmoothing|BUILT-IN|ACTIVE| +| naive_forecaster| NaiveForecaster|BUILT-IN|ACTIVE| +| stl_forecaster| StlForecaster|BUILT-IN|ACTIVE| +| gaussian_hmm| GaussianHmm|BUILT-IN|ACTIVE| +| gmm_hmm| GmmHmm|BUILT-IN|ACTIVE| +| stray| Stray|BUILT-IN|ACTIVE| +| sundial| Timer-Sundial|BUILT-IN|ACTIVE| +| timer_xl| Timer-XL|BUILT-IN|ACTIVE| ++---------------------+--------------------+--------+------+ +Total line number = 10 +It costs 0.004s +``` + +**表模型推理（以 sundial 为例）** + +```Bash +IoTDB:etth> select Time, HUFL,HULL,MUFL,MULL,LUFL,LULL,OT from eg LIMIT 96 ++-----------------------------+------+-----+-----+-----+-----+-----+------+ +| Time| HUFL| HULL| MUFL| MULL| LUFL| LULL| OT| ++-----------------------------+------+-----+-----+-----+-----+-----+------+ +|2016-07-01T00:00:00.000+08:00| 5.827|2.009|1.599|0.462|4.203| 1.34|30.531| +|2016-07-01T01:00:00.000+08:00| 5.693|2.076|1.492|0.426|4.142|1.371|27.787| +|2016-07-01T02:00:00.000+08:00| 5.157|1.741|1.279|0.355|3.777|1.218|27.787| +|2016-07-01T03:00:00.000+08:00| 5.09|1.942|1.279|0.391|3.807|1.279|25.044| +...... +Total line number = 96 +It costs 0.119s + +IoTDB:etth> select * from forecast( + model_id => 'sundial', + input => (select Time, ot from etth.eg where time >= 2016-08-07T18:00:00.000+08:00 limit 1440) order BY time, + output_length => 96 +) ++-----------------------------+---------+ +| time| ot| ++-----------------------------+---------+ +|2016-10-06T18:00:00.000+08:00|20.781654| +|2016-10-06T19:00:00.000+08:00|20.252121| +|2016-10-06T20:00:00.000+08:00|19.960138| +|2016-10-06T21:00:00.000+08:00|19.662334| +...... +Total line number = 96 +It costs 1.615s +``` + +### 4.5 时序大模型导入步骤 + +AINode 目前支持多种时序大模型，部署使用请参考[时序大模型](https://timecho.com/docs/zh/UserGuide/latest/AI-capability/TimeSeries-Large-Model.html) + +## 5 权限管理 + +使用AINode相关的功能时，可以使用IoTDB本身的鉴权去做一个权限管理，用户只有在具备 USE_MODEL 权限时，才可以使用模型管理的相关功能。当使用推理功能时，用户需要有访问输入模型的SQL对应的源序列的权限。 + +| **权限名称** | **权限范围** | **管理员用户（默认ROOT）** | **普通用户** | **路径相关** | +| :----------- | :-------------------------------------- | :------------------------- | :----------- | :----------- | +| USE_MODEL | create model / show models / drop model | √ | √ | x | +| READ_DATA | call inference | √ | √ | √ | + +## 6 附录 + +**Arima** + +| 支持的参数 | 含义 | 默认值 | +| :---------------------- | :----------------------------------------------------------- | :-------- | +| order | ARIMA模型的阶数 `(p, d, q)`：p是自回归阶数，d是差分阶数，q是滑动平均阶数。 | (1,0,0) | +| seansonal_order | 季节性ARIMA的阶数 `(P, D, Q, s)`：分别为季节性自回归、差分、滑动平均阶数，s是季节周期（如12代表月度数据）。 | (0,0,0,0) | +| method | 优化器选择，可选：'newton'、'nm'、'bfgs'、'lbfgs'、'powell'、'cg'、'ncg'、'basinhopping'。 | 'lbfgs' | +| maxiter | 最大迭代次数或函数评估次数。 | 50 | +| out_of_sample_size | 用于验证的时间序列尾部样本数，模型不在这些样本上拟合。 | 0 | +| scoring | 验证时使用的评分函数，字符串需为 sklearn 中可导入的评分指标，或用户自定义函数。 | 'mse' | +| trend | 趋势项配置，若 with_intercept=True 且此项为 None，则默认使用 'c'（包含常数项）。 | None | +| with_intercept | 是否包含截距项。 | True | +| time_varying_regression | 是否允许回归系数随时间变化。 | False | +| enforce_stationarity | 是否强制AR部分平稳性。 | True | +| enforce_invertibility | 是否强制MA部分可逆性。 | True | +| simple_differnecing | 是否使用差分后的数据估计（牺牲前几行数据换取更简状态空间）。 | False | +| measurement_error | 是否认为观测值中含有误差。 | False | +| mle_regression | 是否使用极大似然估计回归系数，若 `time_varying_regression=True` 则必须为 False。 | True | +| hamilton_representation | 是否使用 Hamilton 表达方式（默认用 Harvey）。 | False | +| concentrate_scale | 是否从似然函数中排除误差方差参数，减少待估参数个数（但无法获得误差项方差的标准误）。 | False | + +**NaiveForecaster** + +| 支持的参数 | 含义 | 默认值 | +| ---------- | ------------------------------------------------------------ | ------ | +| strategy | 预测策略： • `"last"`：预测训练集最后一个值；若设置了季节周期（`sp`>1），则每个季节分别预测其最后一个周期值。对 NaN 值鲁棒。 • `"mean"`：预测最后窗口中的平均值；若 `sp`>1，按每个季节分别计算均值。对 NaN 值鲁棒。 • `"drift"`：用最后窗口的首尾点拟合一条直线并外推预测。对 NaN 值不鲁棒。 | "last" | +| sp | 季节性周期。若为 `None`，等效于 `1`，表示无季节性；如果设为 12，表示每 12 个单位（如月）为一个周期。 | 1 | + +- STLForecaster + +| 支持的参数 | 含义 | 默认值 | +| :------------ | :----------------------------------------------------------- | :----- | +| sp | 季节周期长度（周期性单位数）。传入 statsmodels 的 STL 中。 | 2 | +| seasonal | 季节项平滑窗口长度，必须为 ≥3 的奇数，通常建议 ≥7。 | 7 | +| seasonal_deg | 季节项 LOESS 的多项式阶数（0 表示常数，1 表示线性）。 | 1 | +| trend_deg | 趋势项 LOESS 的多项式阶数（0 或 1）。 | 1 | +| low_pass_deg | 低通项 LOESS 的多项式阶数（0 或 1）。 | 1 | +| seasonal_jump | LOESS 拟合的插值步长（季节项），每 n 点拟合一次，中间插值。值越大，估计速度越快。 | 1 | +| trend_jump | 趋势项插值步长，越大速度越快但精度可能下降。 | 1 | +| low_pass_jump | 低通项插值步长，设置同上。 | 1 | + +**ExponentialSmoothing (HoltWinters)** + +| 支持的参数 | 含义 | 默认值 | +| :-------------------- | :----------------------------------------------------------- | :---------- | +| damped_trend | 是否使用阻尼趋势（趋势会逐渐平缓，而非无限增长）。 | True | +| initialization_method | 初始化方法： • `"estimated"`：通过拟合估计初始状态 • `"heuristic"`：使用启发式方法估计初始水平/趋势/季节 • `"known"`：用户显式提供所有初始值 • `"legacy-heuristic"`：旧版本兼容方式 | "estimated" | +| optmized | 是否通过最大化对数似然来优化参数。 | True | +| remove_bias | 是否移除偏差，使预测值和拟合值的残差平均值为0。 | False | +| use_brute | 是否使用穷举法（网格搜索）来寻找初始参数。否则使用启发式初始值。 | | \ No newline at end of file diff --git a/src/zh/UserGuide/Master/Table/AI-capability/AINode_timecho.md b/src/zh/UserGuide/Master/Table/AI-capability/AINode_timecho.md new file mode 100644 index 000000000..53370fd52 --- /dev/null +++ b/src/zh/UserGuide/Master/Table/AI-capability/AINode_timecho.md @@ -0,0 +1,444 @@ + + +# AINode + +AINode 是支持时序大模型注册、管理、调用的 IoTDB 原生节点，内置业界领先的自研时序大模型，如 Timer、Sundial 等，可通过标准 SQL 语句进行调用，实现时序数据的毫秒级实时推理，可支持时序趋势预测、缺失值填补、异常值检测等应用场景。 + +系统架构如下图所示： + +![](/img/h4.png) + +三种节点的职责如下： + +- **ConfigNode**：负责保存和管理模型的元信息；负责分布式节点管理。 +- **DataNode**：负责接收并解析用户的 SQL请求；负责存储时间序列数据；负责数据的预处理计算。 +- **AINode**：负责模型文件的导入创建以及模型推理。 + +## 1. 优势特点 + +与单独构建机器学习服务相比，具有以下优势： + +- **简单易用**：无需使用 Python 或 Java 编程，使用 SQL 语句即可完成机器学习模型管理与推理的完整流程。如创建模型可使用CREATE MODEL语句、使用模型进行推理可使用 SELECT * FROM FORECAST (...) 语句等，使用更加简单便捷。 + +- **避免数据迁移**：使用 IoTDB 原生机器学习可以将存储在 IoTDB 中的数据直接应用于机器学习模型的推理，无需将数据移动到单独的机器学习服务平台，从而加速数据处理、提高安全性并降低成本。 + +![](/img/h1.png) + +- **内置先进算法**：支持业内领先机器学习分析算法，覆盖典型时序分析任务，为时序数据库赋能原生数据分析能力。如： + - **时间序列预测（Time Series Forecasting）**：从过去时间序列中学习变化模式；从而根据给定过去时间的观测值，输出未来序列最可能的预测。 + - **时序异常检测（Anomaly Detection for Time Series）**：在给定的时间序列数据中检测和识别异常值，帮助发现时间序列中的异常行为。 + - **时间序列标注（Time Series Annotation）**：为每个数据点或特定时间段添加额外的信息或标记，例如事件发生、异常点、趋势变化等，以便更好地理解和分析数据。 + + +## 2. 基本概念 + +- **模型（Model）**：机器学习模型，以时序数据作为输入，输出分析任务的结果或决策。模型是 AINode 的基本管理单元，支持模型的增（注册）、删、查、改（微调）、用（推理）。 +- **创建（Create）**: 将外部设计或训练好的模型文件或算法加载到 AINode 中，由 IoTDB 统一管理与使用。 +- **推理（Inference）**：使用创建的模型在指定时序数据上完成该模型适用的时序分析任务。 +- **内置能力（Built-in）**：AINode 自带常见时序分析场景（例如预测与异常检测）的机器学习算法或自研模型。 + +![](/img/h3.png) + +## 3. 安装部署 + +AINode 的部署可参考文档 [部署指导](../Deployment-and-Maintenance/AINode_Deployment_apache.md#ainode-部署) 章节。 + +## 4. 使用指导 + +AINode 对时序模型提供了模型创建及删除功能，内置模型无需创建，可直接使用。 + +### 4.1 注册模型 + +通过指定模型输入输出的向量维度，可以注册训练好的深度学习模型，从而用于模型推理。 + +符合以下内容的模型可以注册到AINode中： + 1. AINode 目前支持基于 PyTorch 2.4.0 版本训练的模型，需避免使用 2.4.0 版本以上的特性。 + 2. AINode 支持使用 PyTorch JIT 存储的模型（`model.pt`），模型文件需要包含模型的结构和权重。 + 3. 模型输入序列可以包含一列或多列，若有多列，需要和模型能力、模型配置文件对应。 + 4. 模型的配置参数必须在`config.yaml`配置文件中明确定义。使用模型时，必须严格按照`config.yaml`配置文件中定义的输入输出维度。如果输入输出列数不匹配配置文件，将会导致错误。 + +下方为模型注册的SQL语法定义。 + +```SQL +create model using uri +``` + +SQL中参数的具体含义如下： + +- model_name：模型的全局唯一标识，不可重复。模型名称具备以下约束： + + - 允许出现标识符 [ 0-9 a-z A-Z _ ] （字母，数字，下划线） + - 长度限制为2-64字符 + - 大小写敏感 + +- uri：模型注册文件的资源路径，路径下应包含**模型结构及权重文件 model.pt 文件和模型配置文件 config.yaml** + + - 模型结构及权重文件：模型训练完成后得到的权重文件，目前支持 pytorch 训练得到的 .pt 文件 + + - 模型配置文件：模型注册时需要提供的与模型结构有关的参数，其中必须包含模型的输入输出维度用于模型推理： + + - | **参数名** | **参数描述** | **示例** | + | ------------ | ---------------------------- | -------- | + | input_shape | 模型输入的行列，用于模型推理 | [96,2] | + | output_shape | 模型输出的行列，用于模型推理 | [48,2] | + + - ​ 除了模型推理外，还可以指定模型输入输出的数据类型： + + - | **参数名** | **参数描述** | **示例** | + | ----------- | ------------------ | --------------------- | + | input_type | 模型输入的数据类型 | ['float32','float32'] | + | output_type | 模型输出的数据类型 | ['float32','float32'] | + + - ​ 除此之外，可以额外指定备注信息用于在模型管理时进行展示 + + - | **参数名** | **参数描述** | **示例** | + | ---------- | ---------------------------------------------- | ------------------------------------------- | + | attributes | 可选，用户自行设定的模型备注信息，用于模型展示 | 'model_type': 'dlinear','kernel_size': '25' | + + +除了本地模型文件的注册，还可以通过URI来指定远程资源路径来进行注册，使用开源的模型仓库（例如HuggingFace）。 + +#### 示例 + +在当前的example文件夹下，包含model.pt和config.yaml文件，model.pt为训练得到，config.yaml的内容如下： + +```YAML +configs: + # 必选项 + input_shape: [96, 2] # 表示模型接收的数据为96行x2列 + output_shape: [48, 2] # 表示模型输出的数据为48行x2列 + + # 可选项 默认为全部float32，列数为shape对应的列数 + input_type: ["int64","int64"] #输入对应的数据类型，需要与输入列数匹配 + output_type: ["text","int64"] #输出对应的数据类型，需要与输出列数匹配 + +attributes: # 可选项 为用户自定义的备注信息 + 'model_type': 'dlinear' + 'kernel_size': '25' +``` + +指定该文件夹作为加载路径就可以注册该模型 + +```SQL +IoTDB> create model dlinear_example using uri "file://./example" +``` + +也可以从huggingFace上下载对应的模型文件进行注册 + +```SQL +IoTDB> create model dlinear_example using uri "https://huggingface.co/google/timesfm-2.0-500m-pytorch" +``` + +SQL执行后会异步进行注册的流程，可以通过模型展示查看模型的注册状态（见模型展示章节），注册成功的耗时主要受到模型文件大小的影响。 + +模型注册完成后，就可以通过使用正常查询的方式调用具体函数，进行模型推理。 + +### 4.2 查看模型 + +注册成功的模型可以通过show models指令查询模型的具体信息。其SQL定义如下： + +```SQL +show models + +show models +``` + +除了直接展示所有模型的信息外，可以指定model id来查看某一具体模型的信息。模型展示的结果中包含如下信息： + +| **ModelId** | **State** | **Configs** | **Attributes** | +| ------------ | ------------------------------------- | ---------------------------------------------- | -------------- | +| 模型唯一标识 | 模型注册状态(INACTIVE,LOADING,ACTIVE,TRAINING,FAILED,DROPPING) | InputShape, outputShapeInputTypes, outputTypes | 模型备注信息 | + +其中，State用于展示当前模型注册的状态，包含以下三个阶段 + +- **INACTIVE**：模型处于不可用状态 +- **LOADING**：模型加载中 +- **ACTIVE**：模型处于可用状态 +- **TRAINING**：模型处于微调中状态 +- **FAILED**：模型微调失败状态 +- **DROPPING**:模型删除中状态 + +#### 示例 + +```SQL +IoTDB> show models + ++---------------------+--------------------+--------+--------+ +| ModelId| ModelType|Category| State| ++---------------------+--------------------+--------+--------+ +| arima| Arima|BUILT-IN| ACTIVE| +| holtwinters| HoltWinters|BUILT-IN| ACTIVE| +|exponential_smoothing|ExponentialSmoothing|BUILT-IN| ACTIVE| +| naive_forecaster| NaiveForecaster|BUILT-IN| ACTIVE| +| stl_forecaster| StlForecaster|BUILT-IN| ACTIVE| +| gaussian_hmm| GaussianHmm|BUILT-IN| ACTIVE| +| gmm_hmm| GmmHmm|BUILT-IN| ACTIVE| +| stray| Stray|BUILT-IN| ACTIVE| +| timer_xl| Timer-XL|BUILT-IN| ACTIVE| +| sundial| Timer-Sundial|BUILT-IN| ACTIVE| ++---------------------+--------------------+--------+--------+ +``` + +### 4.3 删除模型 + +对于注册成功的模型，用户可以通过SQL进行删除，该操作会删除所有 AINode 下的相关模型文件，其SQL如下： + +```SQL +drop model +``` + +需要指定已经成功注册的模型 model_id 来删除对应的模型。由于模型删除涉及模型数据清理，操作不会立即完成，此时模型的状态为 DROPPING，该状态的模型不能用于模型推理。请注意，该功能不支持删除内置模型。 + +### 4.4 使用内置模型推理 + +SQL语法如下： + + +```SQL +SELECT * FROM forecast( + input, + model_id, + [output_length, + output_start_time, + output_interval, + timecol, + preserve_input, + model_options]? +) +``` + +内置模型推理无需注册流程，通过 forecast 函数，指定 model_id 就可以使用模型的推理功能 + + - 请注意，使用内置时序大模型进行推理的前提条件是本地存有对应模型权重，目录为 /IOTDB_AINODE_HOME/data/ainode/models/weights/model_id/。若本地没有模型权重，则会自动从 HuggingFace 拉取，请保证本地能直接访问 HuggingFace。 + +- 参数介绍如下： + +| 参数名 | 参数类型 | 参数属性 | 描述 | 是否必填 | 备注 | +| :---------------- | :------- | :----------------------------------------------------------- | :----------------------------------------------------------- | :------- | :----------------------------------------------------------- | +| input | 表参数 | SET SEMANTIC | 待预测的输入数据 | 是 | | +| model_id | 标量参数 | 字符串类型 | 需要选择的model名 | 是 | 只能为非空，且内置的模型，否则报错：空字符串：MODEL_ID should never be null or empty不存在的模型：model [%s] has not been created模型不可用：model [%s] is not available | +| output_length | 标量参数 | INT32类型默认值：96 | 输出窗口大小 | 否 | 必须大于 0，否则报错：OUTPUT_LENGTH should be greater than 0 | +| output_start_time | 标量参数 | 时间戳类型默认值：输入数据的最后一个时间戳加 output_interval | 输出的预测点的起始时间戳 | 否 | 可以为负数，表示1970年1月1号之前的时间戳 | +| output_interval | 标量参数 | 时间间隔类型默认值：0（输入数据的采样间隔） | 输出的预测点之间的时间间隔支持的单位是 ns、us、ms、s、m、h、d、w | 否 | 大于 0 时，采用用户指定的输出间隔；小于等于 0 时，根据输入数据自动推测 | +| timecol | 标量参数 | 字符串类型默认值：time | 时间列名 | 否 | 存在于 input 中的，数据类型为 TIMESTAMP 的列，否则报错：若数据类型不为 TIMESTAMP： The type of the column [%s] is not as expected.若列不存在：Required column [%s] not found in the source table argument. | +| preserve_input | 标量参数 | 布尔类型默认值：false | 是否在输出结果集中保留输入的所有原始行 | 否 | | +| model_options | 标量参数 | 字符串类型默认值：空字符串 | 模型相关的key-value对，比如是否需要对输入进行归一化等。不同的key-value对以';'间隔 | 否 | 指定某个模型不支持参数，并不会报错，只会被忽略；AINode 中内置的模型支持的常见参数详见文末附录说明。 | + +**说明：** + +1. forecast 函数默认对输入表中所有列进行预测（不包含time列和partition by 的列）。 +2. forecast 函数对于输入数据无顺序性要求，默认对输入数据按照时间戳（由 TIMECOL 参数指定时间戳的列名）做升序排序后，再调用模型进行预测。 +3. 不同模型对于输入数据的行数要求不同，输入数据少于最低行数要求时，则报错；输入数据高于最大行数要求时，则自动截取最后的满足要求的行数。 + - 在当前的 AINdoe 内置模型中，仅_sundial对输入行数有限制。_sundial 目前支持的最大输入行数为 2880，超过 2880 时，会自动截取输入数据的最后 2880 行。 +4. forecast 函数的返回结果列包含 input 表的所有输入列，列的数据类型与原表列的数据类型一致。若 preserve_input= true，则还包含 is_input 列（表征当前行是否为输入行） + - 目前只支持对 INT32、INT64、FLOAT、DOUBLE 进行预测，否则报错：The type of the column [%s] is [%s], only INT32, INT64, FLOAT, DOUBLE is allowed +5. output_start_time 和 output_interval 只会影响输出结果集的时间戳列生成，均为可选参数。 + - output_start_time 默认为输入数据的最后一个时间戳加 output_interval + - output_interval = (输入数据的最后一个时间戳 - 输入数据的第一个时间戳) / n - 1， 默认为输入数据的采样间隔 + - 第 N 个输出行的时间为 output_start_time + (N - 1) * output_interval + +#### 示例 + +我们所使用的的测试集的数据为[ETTh1-tab](/img/ETTh1-tab.csv)。 + +**查看当前支持的模型** + +```Bash +IoTDB:etth> show models ++---------------------+--------------------+--------+------+ +| ModelId| ModelType|Category| State| ++---------------------+--------------------+--------+------+ +| arima| Arima|BUILT-IN|ACTIVE| +| holtwinters| HoltWinters|BUILT-IN|ACTIVE| +|exponential_smoothing|ExponentialSmoothing|BUILT-IN|ACTIVE| +| naive_forecaster| NaiveForecaster|BUILT-IN|ACTIVE| +| stl_forecaster| StlForecaster|BUILT-IN|ACTIVE| +| gaussian_hmm| GaussianHmm|BUILT-IN|ACTIVE| +| gmm_hmm| GmmHmm|BUILT-IN|ACTIVE| +| stray| Stray|BUILT-IN|ACTIVE| +| sundial| Timer-Sundial|BUILT-IN|ACTIVE| +| timer_xl| Timer-XL|BUILT-IN|ACTIVE| ++---------------------+--------------------+--------+------+ +Total line number = 10 +It costs 0.004s +``` + +**表模型推理（以 sundial 为例）** + +```Bash +IoTDB:etth> select Time, HUFL,HULL,MUFL,MULL,LUFL,LULL,OT from eg LIMIT 96 ++-----------------------------+------+-----+-----+-----+-----+-----+------+ +| Time| HUFL| HULL| MUFL| MULL| LUFL| LULL| OT| ++-----------------------------+------+-----+-----+-----+-----+-----+------+ +|2016-07-01T00:00:00.000+08:00| 5.827|2.009|1.599|0.462|4.203| 1.34|30.531| +|2016-07-01T01:00:00.000+08:00| 5.693|2.076|1.492|0.426|4.142|1.371|27.787| +|2016-07-01T02:00:00.000+08:00| 5.157|1.741|1.279|0.355|3.777|1.218|27.787| +|2016-07-01T03:00:00.000+08:00| 5.09|1.942|1.279|0.391|3.807|1.279|25.044| +...... +Total line number = 96 +It costs 0.119s + +IoTDB:etth> select * from forecast( + model_id => 'sundial', + input => (select Time, ot from etth.eg where time >= 2016-08-07T18:00:00.000+08:00 limit 1440) order BY time, + output_length => 96 +) ++-----------------------------+---------+ +| time| ot| ++-----------------------------+---------+ +|2016-10-06T18:00:00.000+08:00|20.781654| +|2016-10-06T19:00:00.000+08:00|20.252121| +|2016-10-06T20:00:00.000+08:00|19.960138| +|2016-10-06T21:00:00.000+08:00|19.662334| +...... +Total line number = 96 +It costs 1.615s +``` +### 4.5 使用内置模型微调 + +> 仅 Timer-XL、Timer-Sundial 可以进行微调操作。 + +SQL语法如下： + + +```SQL +create model (with hyperparameters +(=(, =)*))? +from model +on dataset (inputSql) +``` + +#### 示例 + +1. 选择测点 ot 中前 80% 数据作为微调数据集，基于 sundial 创建模型 sundialv3。 + +```SQL +IoTDB> set sql_dialect=table +Msg: The statement is executed successfully. +IoTDB> CREATE MODEL sundialv3 FROM MODEL sundial ON DATASET ('SELECT time, ot from etth.eg where 1467302400000 <= time and time < 1517468400001') +Msg: The statement is executed successfully. +IoTDB> show models ++---------------------+--------------------+----------+--------+ +| ModelId| ModelType| Category| State| ++---------------------+--------------------+----------+--------+ +| arima| Arima| BUILT-IN| ACTIVE| +| holtwinters| HoltWinters| BUILT-IN| ACTIVE| +|exponential_smoothing|ExponentialSmoothing| BUILT-IN| ACTIVE| +| naive_forecaster| NaiveForecaster| BUILT-IN| ACTIVE| +| stl_forecaster| StlForecaster| BUILT-IN| ACTIVE| +| gaussian_hmm| GaussianHmm| BUILT-IN| ACTIVE| +| gmm_hmm| GmmHmm| BUILT-IN| ACTIVE| +| stray| Stray| BUILT-IN| ACTIVE| +| sundial| Timer-Sundial| BUILT-IN| ACTIVE| +| timer_xl| Timer-XL| BUILT-IN| ACTIVE| +| sundialv2| Timer-Sundial|FINE-TUNED| ACTIVE| +| sundialv3| Timer-Sundial|FINE-TUNED|TRAINING| ++---------------------+--------------------+----------+--------+ +``` + +2. 微调任务后台异步启动，可在 AINode 进程看到 log；微调完成后，查询并使用新的模型 + +```SQL +IoTDB> show models ++---------------------+--------------------+----------+------+ +| ModelId| ModelType| Category| State| ++---------------------+--------------------+----------+------+ +| arima| Arima| BUILT-IN|ACTIVE| +| holtwinters| HoltWinters| BUILT-IN|ACTIVE| +|exponential_smoothing|ExponentialSmoothing| BUILT-IN|ACTIVE| +| naive_forecaster| NaiveForecaster| BUILT-IN|ACTIVE| +| stl_forecaster| StlForecaster| BUILT-IN|ACTIVE| +| gaussian_hmm| GaussianHmm| BUILT-IN|ACTIVE| +| gmm_hmm| GmmHmm| BUILT-IN|ACTIVE| +| stray| Stray| BUILT-IN|ACTIVE| +| sundial| Timer-Sundial| BUILT-IN|ACTIVE| +| timer_xl| Timer-XL| BUILT-IN|ACTIVE| +| sundialv2| Timer-Sundial|FINE-TUNED|ACTIVE| +| sundialv3| Timer-Sundial|FINE-TUNED|ACTIVE| ++---------------------+--------------------+----------+------+ +``` + +### 4.6 时序大模型导入步骤 + +AINode 目前支持多种时序大模型，部署使用请参考[时序大模型](https://timecho.com/docs/zh/UserGuide/latest/AI-capability/TimeSeries-Large-Model.html) + +## 5 权限管理 + +使用AINode相关的功能时，可以使用IoTDB本身的鉴权去做一个权限管理，用户只有在具备 USE_MODEL 权限时，才可以使用模型管理的相关功能。当使用推理功能时，用户需要有访问输入模型的SQL对应的源序列的权限。 + +| **权限名称** | **权限范围** | **管理员用户（默认ROOT）** | **普通用户** | **路径相关** | +| :----------- | :-------------------------------------- | :------------------------- | :----------- | :----------- | +| USE_MODEL | create model / show models / drop model | √ | √ | x | +| READ_DATA | call inference | √ | √ | √ | + +## 6 附录 + +**Arima** + +| 支持的参数 | 含义 | 默认值 | +| :---------------------- | :----------------------------------------------------------- | :-------- | +| order | ARIMA模型的阶数 `(p, d, q)`：p是自回归阶数，d是差分阶数，q是滑动平均阶数。 | (1,0,0) | +| seansonal_order | 季节性ARIMA的阶数 `(P, D, Q, s)`：分别为季节性自回归、差分、滑动平均阶数，s是季节周期（如12代表月度数据）。 | (0,0,0,0) | +| method | 优化器选择，可选：'newton'、'nm'、'bfgs'、'lbfgs'、'powell'、'cg'、'ncg'、'basinhopping'。 | 'lbfgs' | +| maxiter | 最大迭代次数或函数评估次数。 | 50 | +| out_of_sample_size | 用于验证的时间序列尾部样本数，模型不在这些样本上拟合。 | 0 | +| scoring | 验证时使用的评分函数，字符串需为 sklearn 中可导入的评分指标，或用户自定义函数。 | 'mse' | +| trend | 趋势项配置，若 with_intercept=True 且此项为 None，则默认使用 'c'（包含常数项）。 | None | +| with_intercept | 是否包含截距项。 | True | +| time_varying_regression | 是否允许回归系数随时间变化。 | False | +| enforce_stationarity | 是否强制AR部分平稳性。 | True | +| enforce_invertibility | 是否强制MA部分可逆性。 | True | +| simple_differnecing | 是否使用差分后的数据估计（牺牲前几行数据换取更简状态空间）。 | False | +| measurement_error | 是否认为观测值中含有误差。 | False | +| mle_regression | 是否使用极大似然估计回归系数，若 `time_varying_regression=True` 则必须为 False。 | True | +| hamilton_representation | 是否使用 Hamilton 表达方式（默认用 Harvey）。 | False | +| concentrate_scale | 是否从似然函数中排除误差方差参数，减少待估参数个数（但无法获得误差项方差的标准误）。 | False | + +**NaiveForecaster** + +| 支持的参数 | 含义 | 默认值 | +| ---------- | ------------------------------------------------------------ | ------ | +| strategy | 预测策略： • `"last"`：预测训练集最后一个值；若设置了季节周期（`sp`>1），则每个季节分别预测其最后一个周期值。对 NaN 值鲁棒。 • `"mean"`：预测最后窗口中的平均值；若 `sp`>1，按每个季节分别计算均值。对 NaN 值鲁棒。 • `"drift"`：用最后窗口的首尾点拟合一条直线并外推预测。对 NaN 值不鲁棒。 | "last" | +| sp | 季节性周期。若为 `None`，等效于 `1`，表示无季节性；如果设为 12，表示每 12 个单位（如月）为一个周期。 | 1 | + +- STLForecaster + +| 支持的参数 | 含义 | 默认值 | +| :------------ | :----------------------------------------------------------- | :----- | +| sp | 季节周期长度（周期性单位数）。传入 statsmodels 的 STL 中。 | 2 | +| seasonal | 季节项平滑窗口长度，必须为 ≥3 的奇数，通常建议 ≥7。 | 7 | +| seasonal_deg | 季节项 LOESS 的多项式阶数（0 表示常数，1 表示线性）。 | 1 | +| trend_deg | 趋势项 LOESS 的多项式阶数（0 或 1）。 | 1 | +| low_pass_deg | 低通项 LOESS 的多项式阶数（0 或 1）。 | 1 | +| seasonal_jump | LOESS 拟合的插值步长（季节项），每 n 点拟合一次，中间插值。值越大，估计速度越快。 | 1 | +| trend_jump | 趋势项插值步长，越大速度越快但精度可能下降。 | 1 | +| low_pass_jump | 低通项插值步长，设置同上。 | 1 | + +**ExponentialSmoothing (HoltWinters)** + +| 支持的参数 | 含义 | 默认值 | +| :-------------------- | :----------------------------------------------------------- | :---------- | +| damped_trend | 是否使用阻尼趋势（趋势会逐渐平缓，而非无限增长）。 | True | +| initialization_method | 初始化方法： • `"estimated"`：通过拟合估计初始状态 • `"heuristic"`：使用启发式方法估计初始水平/趋势/季节 • `"known"`：用户显式提供所有初始值 • `"legacy-heuristic"`：旧版本兼容方式 | "estimated" | +| optmized | 是否通过最大化对数似然来优化参数。 | True | +| remove_bias | 是否移除偏差，使预测值和拟合值的残差平均值为0。 | False | +| use_brute | 是否使用穷举法（网格搜索）来寻找初始参数。否则使用启发式初始值。 | | \ No newline at end of file diff --git a/src/zh/UserGuide/Master/Table/AI-capability/TimeSeries-Large-Model.md b/src/zh/UserGuide/Master/Table/AI-capability/TimeSeries-Large-Model.md new file mode 100644 index 000000000..a632e1dec --- /dev/null +++ b/src/zh/UserGuide/Master/Table/AI-capability/TimeSeries-Large-Model.md @@ -0,0 +1,112 @@ + + +# 时序大模型 + +## 简介 + +时序大模型是一种专为时序数据分析设计的基础模型。IoTDB 团队长期自研时序大模型，基于变换器（Transformer）结构等技术在海量时序数据上预训练，能够理解并生成多种领域的时序数据，可被应用于时序预测、异常检测、时序填补等应用场景。不同于传统时序分析技术，时序大模型具备通用特征提取能力，基于零样本分析、微调等技术服务广泛的分析任务。 + +团队所研时序大模型相关技术均发表在国际机器学习顶级会议。 + +## 应用场景 + +- **时序预测**：为工业生产、自然环境等领域提供时间序列数据的预测服务，帮助用户提前了解未来趋势。 +- **数据填补**：针对时间序列中的缺失序列段，进行上下文填补，以增强数据集的连续性和完整性。 +- **异常检测**：利用自回归分析技术，对时间序列数据进行实时监测，及时预警潜在的异常情况。 + +![](/img/LargeModel09.png) + +## Timer-1 模型 + +Timer模型不仅展现了出色的少样本泛化和多任务适配能力，还通过预训练获得了丰富的知识库，赋予了它处理多样化下游任务的通用能力，拥有以下特点： + +- **泛化性**：模型能够通过使用少量样本进行微调，达到行业内领先的深度模型预测效果。 +- **通用性**：模型设计灵活，能够适配多种不同的任务需求，并且支持变化的输入和输出长度，使其在各种应用场景中都能发挥作用。 +- **可扩展性**：随着模型参数数量的增加或预训练数据规模的扩大，模型效果会持续提升，确保模型能够随着时间和数据量的增长而不断优化其预测效果。 + +![](/img/model01.png) + +## Timer-XL 模型 + +Timer-XL 基于 Timer 进一步扩展升级了网络结构，在多个维度全面突破： + +- **超长上下文支持**：该模型突破了传统时序预测模型的限制，支持处理数千个 Token（相当于数万个时间点）的输入，有效解决了上下文长度瓶颈问题。 +- **多变量预测场景覆盖**：支持多种预测场景，包括非平稳时间序列的预测、涉及多个变量的预测任务以及包含协变量的预测，满足多样化的业务需求。 +- **大规模工业时序数据集**：采用万亿大规模工业物联网领域的时序数据集进行预训练，数据集兼有庞大的体量、卓越的质量和丰富的领域等重要特质，覆盖能源、航空航天、钢铁、交通等多领域。 + +![](/img/model02.png) + +## Timer-Sundial 模型 + +Timer-Sundial 是一个专注于时间序列预测的生成式基础模型系列，其基础版本拥有 1.28 亿参数，并在 1 万亿个时间点上进行了大规模预训练，其核心特性包括： + +- **强大的泛化性能**：具备零样本预测能力，可同时支持点预测和概率预测。 +- **灵活预测分布分析**：不仅能预测均值或分位数，还可通过模型生成的原始样本评估预测分布的任意统计特性。 +- **创新生成架构**：采用 “Transformer + TimeFlow” 协同架构——Transformer 学习时间片段的自回归表征，TimeFlow 模块基于流匹配框架 (Flow-Matching) 将随机噪声转化为多样化预测轨迹，实现高效的非确定性样本生成。 + +![](/img/model03.png) + +## 效果展示 + +时序大模型能够适应多种不同领域和场景的真实时序数据，在各种任务上拥有优异的处理效果，以下是在不同数据上的真实表现： + +**时序预测：** + +利用时序大模型的预测能力，能够准确预测时间序列的未来变化趋势，如下图蓝色曲线代表预测趋势，红色曲线为实际趋势，两曲线高度吻合。 + +![](/img/LargeModel03.png) + +**数据填补：** + +利用时序大模型对缺失数据段进行预测式填补。 + +![](/img/timeseries-large-model-data-imputation.png) + + +**异常检测：** + +利用时序大模型精准识别与正常趋势偏离过大的异常值。 + +![](/img/LargeModel05.png) + +## 部署使用 + +1. 打开 IoTDB cli 控制台，检查 ConfigNode、DataNode、AINode 节点确保均为 Running。 + +检查命令： +```sql +show cluster +``` + +![](/img/ainode-timer-1.png) + +2. 联网环境下首次启动 AINode 节点会自动拉取 Timer-XL、Sundial 模型。 + +3. 检查模型是否可用 + +检查命令： + +```sql +show models +``` + +![](/img/LargeModel06.png) diff --git a/src/zh/UserGuide/Master/Table/Deployment-and-Maintenance/AINode_Deployment_apache.md b/src/zh/UserGuide/Master/Table/Deployment-and-Maintenance/AINode_Deployment_apache.md new file mode 100644 index 000000000..052c039c2 --- /dev/null +++ b/src/zh/UserGuide/Master/Table/Deployment-and-Maintenance/AINode_Deployment_apache.md @@ -0,0 +1,267 @@ + +# AINode 部署 + +## 1. AINode介绍 + +### 1.1 能力介绍 + +AINode 是 IoTDB 在 ConfigNode、DataNode 后提供的第三种内生节点，该节点通过与 IoTDB 集群的 DataNode、ConfigNode 的交互，扩展了对时间序列进行机器学习分析的能力，支持从外部引入已有机器学习模型进行注册，并使用注册的模型在指定时序数据上通过简单 SQL 语句完成时序分析任务的过程，将模型的创建、管理及推理融合在数据库引擎中。目前已提供常见时序分析场景（例如预测与异常检测）的机器学习算法或自研模型。 + +### 1.2 交付方式 + 是 IoTDB 集群外的额外套件，独立安装包。 + +### 1.3 部署模式 +
+ + +
+ +## 2. 安装准备 + +### 2.1 安装包获取 + +AINode 安装包（`apache-iotdb--ainode-bin.zip`），安装包解压后目录结构如下： +| **目录** | **类型** | **说明** | +| ------------ | -------- | ------------------------------------------------ | +| lib | 文件夹 | AINode编译后的二进制可执行文件以及相关的代码依赖 | +| sbin | 文件夹 | AINode的运行脚本，可以启动，移除和停止AINode | +| conf | 文件夹 | 包含AINode的配置项，具体包含以下配置项 | +| LICENSE | 文件 | 证书 | +| NOTICE | 文件 | 提示 | +| README_ZH.md | 文件 | markdown格式的中文版说明 | +| `README.md` | 文件 | 使用说明 | + +### 2.2 环境准备 + +1. 建议操作环境: Ubuntu, CentOS, MacOS +2. IoTDB 版本：>= V 2.0.5.1 +3. 运行环境 + - Python 版本在 3.9 ~3.12，且带有 pip 和 venv 工具； + + +## 3. 安装部署及使用 + +### 3.1 安装 AINode + +1. 保证 Python 版本介于 3.9 ~3.12 + +```shell + python --version +# 或 +python3 --version +``` +2. 下载导入 AINode 到专用文件夹，切换到专用文件夹并解压安装包 + +```shell + unzip timechodb-2.0.5.1-ainode-bin.zip +``` + +3. 创建虚拟环境（在 ainode 目录下执行）： + + ```shell + python3 -m venv venv + ``` + +4. 激活虚拟环境： + + ```shell + source venv/bin/activate + ``` + +5. 更新 pip 并安装 AINode 依赖 + ```shell + python -m pip install --upgrade pip + poetry lock + poetry install + ``` + +### 3.2 配置项修改 +AINode 支持修改一些必要的参数。可以在 `conf/iotdb-ainode.properties` 文件中找到下列参数并进行持久化的修改： + +| **名称** | **描述** | **类型** | **默认值** | +| ------------------------------ | ------------------------------------------------------------ | -------- | ------------------ | +| cluster_name | AINode 要加入集群的标识 | string | defaultCluster | +| ain_seed_config_node | AINode 启动时注册的 ConfigNode 地址 | String | 127.0.0.1:10710 | +| ain_cluster_ingress_address | AINode 拉取数据的 DataNode 的 rpc 地址 | String | 127.0.0.1 | +| ain_cluster_ingress_port | AINode 拉取数据的 DataNode 的 rpc 端口 | Integer | 6667 | +| ain_cluster_ingress_username | AINode 拉取数据的 DataNode 的客户端用户名 | String | root | +| ain_cluster_ingress_password | AINode 拉取数据的 DataNode 的客户端密码 | String | root | +| ain_cluster_ingress_time_zone | AINode 拉取数据的 DataNode 的客户端时区 | String | UTC+8 | +| ain_inference_rpc_address | AINode 提供服务与通信的地址 ，内部服务通讯接口 | String | 127.0.0.1 | +| ain_inference_rpc_port | AINode 提供服务与通信的端口 | String | 10810 | +| ain_system_dir | AINode 元数据存储路径，相对路径的起始目录与操作系统相关，建议使用绝对路径 | String | data/AINode/system | +| ain_models_dir | AINode 存储模型文件的路径，相对路径的起始目录与操作系统相关，建议使用绝对路径 | String | data/AINode/models | +| ain_thrift_compression_enabled | AINode 是否启用 thrift 的压缩机制，0-不启动、1-启动 | Boolean | 0 | + +### 3.3 启动 AINode + + 在完成 Seed-ConfigNode 的部署后，可以通过添加 AINode 节点来支持模型的注册和推理功能。在配置项中指定 IoTDB 集群的信息后，可以执行相应的指令来启动 AINode，加入 IoTDB 集群。 + +#### 联网环境启动 + +##### 启动命令 + +```shell + # 启动命令 + # Linux 和 MacOS 系统 + bash sbin/start-ainode.sh + + # Windows 系统 + sbin\start-ainode.bat + + # 后台启动命令（长期运行推荐） + # Linux 和 MacOS 系统 + nohup bash sbin/start-ainode.sh > myout.file 2>& 1 & + + # Windows 系统 + nohup bash sbin\start-ainode.bat > myout.file 2>& 1 & + ``` + +#### 示例 + +```shell + # 启动命令 + # Linux 和 MacOS 系统 + bash sbin/start-ainode.sh + # Windows 系统 + sbin\start-ainode.bat + + + # 后台启动命令（长期运行推荐） + # Linux 和 MacOS 系统 + nohup bash sbin/start-ainode.sh > myout.file 2>& 1 & + # Windows 系统 + nohup bash sbin\start-ainode.bat > myout.file 2>& 1 & + ``` + +### 3.4 检测 AINode 节点状态 + +AINode 启动过程中会自动将新的 AINode 加入 IoTDB 集群。启动 AINode 后可以在 命令行中输入 SQL 来查询，集群中看到 AINode 节点，其运行状态为 Running（如下展示）表示加入成功。 + +```shell +IoTDB> show cluster ++------+----------+-------+---------------+------------+-------+-----------+ +|NodeID| NodeType| Status|InternalAddress|InternalPort|Version| BuildInfo| ++------+----------+-------+---------------+------------+-------+-----------+ +| 0|ConfigNode|Running| 127.0.0.1| 10710|UNKNOWN|190e303-dev| +| 1| DataNode|Running| 127.0.0.1| 10730|UNKNOWN|190e303-dev| +| 2| AINode|Running| 127.0.0.1| 10810|UNKNOWN|190e303-dev| ++------+----------+-------+---------------+------------+-------+-----------+ +``` + +### 3.5 停止 AINode + +如果需要停止正在运行的 AINode 节点，则执行相应的关闭脚本。 + +#### 停止命令 + +```shell + # Linux / MacOS + bash sbin/stop-ainode.sh + + #Windows + sbin\stop-ainode.bat + ``` + +#### 示例 +```shell + # Linux / MacOS + bash sbin/stop-ainode.sh + + # Windows + sbin\stop-ainode.bat + ``` +停止 AINode 后，还可以在集群中看到 AINode 节点，其运行状态为 UNKNOWN（如下展示），此时无法使用 AINode 功能。 + + ```shell +IoTDB> show cluster ++------+----------+-------+---------------+------------+-------+-----------+ +|NodeID| NodeType| Status|InternalAddress|InternalPort|Version| BuildInfo| ++------+----------+-------+---------------+------------+-------+-----------+ +| 0|ConfigNode|Running| 127.0.0.1| 10710|UNKNOWN|190e303-dev| +| 1| DataNode|Running| 127.0.0.1| 10730|UNKNOWN|190e303-dev| +| 2| AINode|UNKNOWN| 127.0.0.1| 10790|UNKNOWN|190e303-dev| ++------+----------+-------+---------------+------------+-------+-----------+ +``` +如果需要重新启动该节点，需重新执行启动脚本。 + + + +## 4. 常见问题 + +### 4.1 启动AINode时出现找不到venv模块的报错 + + 当使用默认方式启动 AINode 时，会在安装包目录下创建一个 python 虚拟环境并安装依赖，因此要求安装 venv 模块。通常来说 python3.10 及以上的版本会自带 venv，但对于一些系统自带的 python 环境可能并不满足这一要求。出现该报错时有两种解决方案（二选一）： + + 在本地安装 venv 模块，以 ubuntu 为例，可以通过运行以下命令来安装 python 自带的 venv 模块。或者从 python 官网安装一个自带 venv 的 python 版本。 + + ```shell +apt-get install python3.10-venv +``` + 安装 3.10.0 版本的 venv 到 AINode 里面 在 AINode 路径下 + + ```shell +../Python-3.10.0/python -m venv venv(文件夹名） +``` + 在运行启动脚本时通过 `-i` 指定已有的 python 解释器路径作为 AINode 的运行环境，这样就不再需要创建一个新的虚拟环境。 + + ### 4.2 python中的SSL模块没有被正确安装和配置，无法处理HTTPS资源 +WARNING: pip is configured with locations that require TLS/SSL, however the ssl module in Python is not available. +可以安装 OpenSSLS 后，再重新构建 python 来解决这个问题 +> Currently Python versions 3.6 to 3.9 are compatible with OpenSSL 1.0.2, 1.1.0, and 1.1.1. + + Python 要求我们的系统上安装有 OpenSSL，具体安装方法可见[链接](https://stackoverflow.com/questions/56552390/how-to-fix-ssl-module-in-python-is-not-available-in-centos) + + ```shell +sudo apt-get install build-essential libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm libncurses5-dev libncursesw5-dev xz-utils tk-dev libffi-dev liblzma-dev uuid-dev lzma-dev liblzma-dev +sudo -E ./configure --with-ssl +make +sudo make install +``` + + ### 4.3 pip版本较低 + + windows下出现类似“error：Microsoft Visual C++ 14.0 or greater is required...”的编译问题 + + 出现对应的报错，通常是 c++版本或是 setuptools 版本不足，可以在 + + ```shell +./python -m pip install --upgrade pip +./python -m pip install --upgrade setuptools +``` + + + ### 4.4 安装编译python + + 使用以下指定从官网下载安装包并解压: + ```shell +.wget https://www.python.org/ftp/python/3.10.0/Python-3.10.0.tar.xz +tar Jxf Python-3.10.0.tar.xz +``` + 编译安装对应的 python 包: + ```shell +cd Python-3.10.0 +./configure prefix=/usr/local/python3 +make +sudo make install +python3 --version +``` \ No newline at end of file diff --git a/src/zh/UserGuide/Master/Table/Deployment-and-Maintenance/AINode_Deployment_timecho.md b/src/zh/UserGuide/Master/Table/Deployment-and-Maintenance/AINode_Deployment_timecho.md new file mode 100644 index 000000000..e04378a43 --- /dev/null +++ b/src/zh/UserGuide/Master/Table/Deployment-and-Maintenance/AINode_Deployment_timecho.md @@ -0,0 +1,14 @@ +| **名称** | **描述** | **类型** | **默认值** | +| ------------------------------ | ------------------------------------------------------------ | -------- | ------------------ | +| cluster_name | AINode 要加入集群的标识 | string | defaultCluster | +| ain_seed_config_node | AINode 启动时注册的 ConfigNode 地址 | String | 127.0.0.1:10710 | +| ain_cluster_ingress_address | AINode 拉取数据的 DataNode 的 rpc 地址 | String | 127.0.0.1 | +| ain_cluster_ingress_port | AINode 拉取数据的 DataNode 的 rpc 端口 | Integer | 6667 | +| ain_cluster_ingress_username | AINode 拉取数据的 DataNode 的客户端用户名 | String | root | +| ain_cluster_ingress_password | AINode 拉取数据的 DataNode 的客户端密码 | String | root | +| ain_cluster_ingress_time_zone | AINode 拉取数据的 DataNode 的客户端时区 | String | UTC+8 | +| ain_inference_rpc_address | AINode 提供服务与通信的地址 ，内部服务通讯接口 | String | 127.0.0.1 | +| ain_inference_rpc_port | AINode 提供服务与通信的端口 | String | 10810 | +| ain_system_dir | AINode 元数据存储路径，相对路径的起始目录与操作系统相关，建议使用绝对路径 | String | data/AINode/system | +| ain_models_dir | AINode 存储模型文件的路径，相对路径的起始目录与操作系统相关，建议使用绝对路径 | String | data/AINode/models | +| ain_thrift_compression_enabled | AINode 是否启用 thrift 的压缩机制，0-不启动、1-启动 | Boolean | 0 | \ No newline at end of file diff --git a/src/zh/UserGuide/Master/Tree/AI-capability/AINode_apache.md b/src/zh/UserGuide/Master/Tree/AI-capability/AINode_apache.md index 963b4288b..d3b350672 100644 --- a/src/zh/UserGuide/Master/Tree/AI-capability/AINode_apache.md +++ b/src/zh/UserGuide/Master/Tree/AI-capability/AINode_apache.md @@ -51,9 +51,9 @@ AINode 是支持时序大模型注册、管理、调用的 IoTDB 原生节点， ## 2. 基本概念 -- **模型（Model）**：机器学习模型，以时序数据作为输入，输出分析任务的结果或决策。模型是AINode 的基本管理单元，支持模型的增（注册）、删、查、用（推理）。 -- **创建（Create）**: 将外部设计或训练好的模型文件或算法加载到MLNode中，由IoTDB统一管理与使用。 -- **推理（Inference）**：使用创建的模型在指定时序数据上完成该模型适用的时序分析任务的过程。 +- **模型（Model）**：机器学习模型，以时序数据作为输入，输出分析任务的结果或决策。模型是 AINode 的基本管理单元，支持模型的增（注册）、删、查、用（推理）。 +- **创建（Create）**: 将外部设计或训练好的模型文件或算法加载到 AINode 中，由 IoTDB 统一管理与使用。 +- **推理（Inference）**：使用创建的模型在指定时序数据上完成该模型适用的时序分析任务。 - **内置能力（Built-in）**：AINode 自带常见时序分析场景（例如预测与异常检测）的机器学习算法或自研模型。 ![](/img/h3.png) @@ -64,17 +64,17 @@ AINode 的部署可参考文档 [部署指导](../Deployment-and-Maintenance/AIN ## 4. 使用指导 -AINode 对时序数据相关的深度学习模型提供了模型创建及删除的流程，内置模型无需创建及删除，可直接使用，并且在完成推理后创建的内置模型实例将自动销毁。 +AINode 对时序模型提供了模型创建及删除功能，内置模型无需创建，可直接使用。 ### 4.1 注册模型 通过指定模型输入输出的向量维度，可以注册训练好的深度学习模型，从而用于模型推理。 符合以下内容的模型可以注册到AINode中： - 1. AINode 支持的PyTorch 2.1.0、 2.2.0版本训练的模型，需避免使用2.2.0版本以上的特性。 - 2. AINode支持使用PyTorch JIT存储的模型，模型文件需要包含模型的参数和结构。 - 3. 模型输入序列可以包含一列或多列，若有多列，需要和模型能力、模型配置文件对应。 - 4. 模型的输入输出维度必须在`config.yaml`配置文件中明确定义。使用模型时，必须严格按照`config.yaml`配置文件中定义的输入输出维度。如果输入输出列数不匹配配置文件，将会导致错误。 + 1. AINode 目前支持基于 PyTorch 2.4.0 版本训练的模型，需避免使用 2.4.0 版本以上的特性。 + 2. AINode 支持使用 PyTorch JIT 存储的模型（`model.pt`），模型文件需要包含模型的结构和权重。 + 3. 模型输入序列可以包含一列或多列，若有多列，需要和模型能力、模型配置文件对应。 + 4. 模型的配置参数必须在`config.yaml`配置文件中明确定义。使用模型时，必须严格按照`config.yaml`配置文件中定义的输入输出维度。如果输入输出列数不匹配配置文件，将会导致错误。 下方为模型注册的SQL语法定义。 @@ -90,11 +90,11 @@ SQL中参数的具体含义如下： - 长度限制为2-64字符 - 大小写敏感 -- uri：模型注册文件的资源路径，路径下应包含**模型权重model.pt文件和模型的元数据描述文件config.yaml** +- uri：模型注册文件的资源路径，路径下应包含**模型结构及权重文件 model.pt 文件和模型配置文件 config.yaml** - - 模型权重文件：深度学习模型训练完成后得到的权重文件，目前支持pytorch训练得到的.pt文件 + - 模型结构及权重文件：模型训练完成后得到的权重文件，目前支持 pytorch 训练得到的 .pt 文件 - - yaml元数据描述文件：模型注册时需要提供的与模型结构有关的参数，其中必须包含模型的输入输出维度用于模型推理： + - 模型配置文件：模型注册时需要提供的与模型结构有关的参数，其中必须包含模型的输入输出维度用于模型推理： - | **参数名** | **参数描述** | **示例** | | ------------ | ---------------------------- | -------- | @@ -145,7 +145,7 @@ IoTDB> create model dlinear_example using uri "file://./example" 也可以从huggingFace上下载对应的模型文件进行注册 ```SQL -IoTDB> create model dlinear_example using uri "https://huggingface.com/IoTDBML/dlinear/" +IoTDB> create model dlinear_example using uri "https://huggingface.co/google/timesfm-2.0-500m-pytorch" ``` SQL执行后会异步进行注册的流程，可以通过模型展示查看模型的注册状态（见模型展示章节），注册成功的耗时主要受到模型文件大小的影响。 @@ -166,49 +166,45 @@ show models | **ModelId** | **State** | **Configs** | **Attributes** | | ------------ | ------------------------------------- | ---------------------------------------------- | -------------- | -| 模型唯一标识 | 模型注册状态(LOADING,ACTIVE,DROPPING) | InputShape, outputShapeInputTypes, outputTypes | 模型备注信息 | +| 模型唯一标识 | 模型注册状态(INACTIVE,LOADING,ACTIVE,DROPPING) | InputShape, outputShapeInputTypes, outputTypes | 模型备注信息 | 其中，State用于展示当前模型注册的状态，包含以下三个阶段 -- **LOADING**：已经在configNode中添加对应的模型元信息，正将模型文件传输到AINode节点上 -- **ACTIVE:** 模型已经设置完成，模型处于可用状态 -- **DROPPING**：模型删除中，正在从configNode以及AINode处删除模型相关信息 -- **UNAVAILABLE**: 模型创建失败，可以通过drop model删除创建失败的model_name。 +- **INACTIVE**：模型处于不可用状态 +- **LOADING**：模型加载中状态 +- **ACTIVE**：模型处于可用状态 +- **DROPPING**:模型删除中状态 #### 示例 ```SQL IoTDB> show models - -+---------------------+--------------------------+-----------+----------------------------+-----------------------+ -| ModelId| ModelType| State| Configs| Notes| -+---------------------+--------------------------+-----------+----------------------------+-----------------------+ -| dlinear_example| USER_DEFINED| ACTIVE| inputShape:[96,2]| | -| | | | outputShape:[48,2]| | -| | | | inputDataType:[float,float]| | -| | | |outputDataType:[float,float]| | -| _STLForecaster| BUILT_IN_FORECAST| ACTIVE| |Built-in model in IoTDB| -| _NaiveForecaster| BUILT_IN_FORECAST| ACTIVE| |Built-in model in IoTDB| -| _ARIMA| BUILT_IN_FORECAST| ACTIVE| |Built-in model in IoTDB| -|_ExponentialSmoothing| BUILT_IN_FORECAST| ACTIVE| |Built-in model in IoTDB| -| _GaussianHMM|BUILT_IN_ANOMALY_DETECTION| ACTIVE| |Built-in model in IoTDB| -| _GMMHMM|BUILT_IN_ANOMALY_DETECTION| ACTIVE| |Built-in model in IoTDB| -| _Stray|BUILT_IN_ANOMALY_DETECTION| ACTIVE| |Built-in model in IoTDB| -+---------------------+--------------------------+-----------+------------------------------------------------------------+-----------------------+ ++---------------------+--------------------+--------+--------+ +| ModelId| ModelType|Category| State| ++---------------------+--------------------+--------+--------+ +| arima| Arima|BUILT-IN| ACTIVE| +| holtwinters| HoltWinters|BUILT-IN| ACTIVE| +|exponential_smoothing|ExponentialSmoothing|BUILT-IN| ACTIVE| +| naive_forecaster| NaiveForecaster|BUILT-IN| ACTIVE| +| stl_forecaster| StlForecaster|BUILT-IN| ACTIVE| +| gaussian_hmm| GaussianHmm|BUILT-IN| ACTIVE| +| gmm_hmm| GmmHmm|BUILT-IN| ACTIVE| +| stray| Stray|BUILT-IN| ACTIVE| +| timer_xl| Timer-XL|BUILT-IN| ACTIVE| +| sundial| Timer-Sundial|BUILT-IN| ACTIVE| ++---------------------+--------------------+--------+--------+ ``` -我们前面已经注册了对应的模型，可以通过对应的指定查看模型状态，active表明模型注册成功，可用于推理。 - ### 4.3 删除模型 -对于注册成功的模型，用户可以通过SQL进行删除。该操作除了删除configNode上的元信息外，还会删除所有AINode下的相关模型文件。其SQL如下： +对于注册成功的模型，用户可以通过SQL进行删除，该操作会删除所有 AINode 下的相关模型文件，其SQL如下： ```SQL -drop model +drop model ``` -需要指定已经成功注册的模型model_name来删除对应的模型。由于模型删除涉及多个节点上的数据删除，操作不会立即完成，此时模型的状态为DROPPING，该状态的模型不能用于模型推理。 +需要指定已经成功注册的模型 model_id 来删除对应的模型。由于模型删除涉及模型数据清理，操作不会立即完成，此时模型的状态为 DROPPING，该状态的模型不能用于模型推理。请注意，该功能不支持删除内置模型。 ### 4.4 使用内置模型推理 @@ -216,15 +212,22 @@ SQL语法如下： ```SQL -call inference(,sql[,=]) +call inference(,inputSql,(=)*) + +window_function: + head(window_size) + tail(window_size) + count(window_size,sliding_step) ``` 内置模型推理无需注册流程，通过call关键字，调用inference函数就可以使用模型的推理功能，其对应的参数介绍如下： -- **built_in_model_name:** 内置模型名称 +- **model_id:** 模型名称 - **parameterName**：参数名 - **parameterValue**：参数值 +请注意，使用内置时序大模型进行推理的前提条件是本地存有对应模型权重，目录为 /IOTDB_AINODE_HOME/data/ainode/models/weights/model_id/。若本地没有模型权重，则会自动从 HuggingFace 拉取，请保证本地能直接访问 HuggingFace。 + #### 内置模型及参数说明 目前已内置如下机器学习模型，具体参数说明请参考以下链接。 @@ -239,60 +242,6 @@ call inference(,sql[,=]) | GMMHMM | _GMMHMM | 标注 | [GMMHMM参数说明](https://www.sktime.net/en/latest/api_reference/auto_generated/sktime.detection.hmm_learn.gmm.GMMHMM.html) | | Stray | _Stray | 异常检测 | [Stray参数说明](https://www.sktime.net/en/latest/api_reference/auto_generated/sktime.detection.stray.STRAY.html) | -#### 示例 - -下面是使用内置模型推理的一个操作示例，使用内置的Stray模型进行异常检测算法，输入为`[144,1]`，输出为`[144,1]`，我们通过SQL使用其进行推理。 - -```SQL -IoTDB> select * from root.eg.airline -+-----------------------------+------------------+ -| Time|root.eg.airline.s0| -+-----------------------------+------------------+ -|1949-01-31T00:00:00.000+08:00| 224.0| -|1949-02-28T00:00:00.000+08:00| 118.0| -|1949-03-31T00:00:00.000+08:00| 132.0| -|1949-04-30T00:00:00.000+08:00| 129.0| -...... -|1960-09-30T00:00:00.000+08:00| 508.0| -|1960-10-31T00:00:00.000+08:00| 461.0| -|1960-11-30T00:00:00.000+08:00| 390.0| -|1960-12-31T00:00:00.000+08:00| 432.0| -+-----------------------------+------------------+ -Total line number = 144 - -IoTDB> call inference(_Stray, "select s0 from root.eg.airline", generateTime=True, k=2) -+-----------------------------+-------+ -| Time|output0| -+-----------------------------+-------+ -|1960-12-31T00:00:00.000+08:00| 0| -|1961-01-31T08:00:00.000+08:00| 1| -|1961-02-28T08:00:00.000+08:00| 0| -|1961-03-31T08:00:00.000+08:00| 0| -...... -|1972-06-30T08:00:00.000+08:00| 0| -|1972-07-31T08:00:00.000+08:00| 1| -|1972-08-31T08:00:00.000+08:00| 0| -|1972-09-30T08:00:00.000+08:00| 0| -|1972-10-31T08:00:00.000+08:00| 0| -|1972-11-30T08:00:00.000+08:00| 0| -+-----------------------------+-------+ -Total line number = 144 -``` - -### 4.5 使用深度学习模型推理 - -SQL语法如下： - -```SQL -call inference(,sql[,window=]) - - -window_function: - head(window_size) - tail(window_size) - count(window_size,sliding_step) -``` - 在完成模型的注册后，通过call关键字，调用inference函数就可以使用模型的推理功能，其对应的参数介绍如下： - **model_name**: 对应一个已经注册的模型 diff --git a/src/zh/UserGuide/Master/Tree/AI-capability/AINode_timecho.md b/src/zh/UserGuide/Master/Tree/AI-capability/AINode_timecho.md index f25d0ff62..ae5ddf117 100644 --- a/src/zh/UserGuide/Master/Tree/AI-capability/AINode_timecho.md +++ b/src/zh/UserGuide/Master/Tree/AI-capability/AINode_timecho.md @@ -51,9 +51,9 @@ AINode 是支持时序大模型注册、管理、调用的 IoTDB 原生节点， ## 2. 基本概念 -- **模型（Model）**：机器学习模型，以时序数据作为输入，输出分析任务的结果或决策。模型是AINode 的基本管理单元，支持模型的增（注册）、删、查、用（推理）。 -- **创建（Create）**: 将外部设计或训练好的模型文件或算法加载到MLNode中，由IoTDB统一管理与使用。 -- **推理（Inference）**：使用创建的模型在指定时序数据上完成该模型适用的时序分析任务的过程。 +- **模型（Model）**：机器学习模型，以时序数据作为输入，输出分析任务的结果或决策。模型是 AINode 的基本管理单元，支持模型的增（注册）、删、查、改（微调）、用（推理）。 +- **创建（Create）**: 将外部设计或训练好的模型文件或算法加载到 AINode 中，由 IoTDB 统一管理与使用。 +- **推理（Inference）**：使用创建的模型在指定时序数据上完成该模型适用的时序分析任务。 - **内置能力（Built-in）**：AINode 自带常见时序分析场景（例如预测与异常检测）的机器学习算法或自研模型。 ![](/img/h3.png) @@ -64,37 +64,37 @@ AINode 的部署可参考文档 [部署指导](../Deployment-and-Maintenance/AIN ## 4. 使用指导 -AINode 对时序数据相关的深度学习模型提供了模型创建及删除的流程，内置模型无需创建及删除，可直接使用，并且在完成推理后创建的内置模型实例将自动销毁。 +AINode 对时序模型提供了模型创建及删除功能，内置模型无需创建，可直接使用。 ### 4.1 注册模型 通过指定模型输入输出的向量维度，可以注册训练好的深度学习模型，从而用于模型推理。 符合以下内容的模型可以注册到AINode中： - 1. AINode 支持的PyTorch 2.1.0、 2.2.0版本训练的模型，需避免使用2.2.0版本以上的特性。 - 2. AINode支持使用PyTorch JIT存储的模型，模型文件需要包含模型的参数和结构。 - 3. 模型输入序列可以包含一列或多列，若有多列，需要和模型能力、模型配置文件对应。 - 4. 模型的输入输出维度必须在`config.yaml`配置文件中明确定义。使用模型时，必须严格按照`config.yaml`配置文件中定义的输入输出维度。如果输入输出列数不匹配配置文件，将会导致错误。 + 1. AINode 目前支持基于 PyTorch 2.4.0 版本训练的模型，需避免使用 2.4.0 版本以上的特性。 + 2. AINode 支持使用 PyTorch JIT 存储的模型（`model.pt`），模型文件需要包含模型的结构和权重。 + 3. 模型输入序列可以包含一列或多列，若有多列，需要和模型能力、模型配置文件对应。 + 4. 模型的配置参数必须在`config.yaml`配置文件中明确定义。使用模型时，必须严格按照`config.yaml`配置文件中定义的输入输出维度。如果输入输出列数不匹配配置文件，将会导致错误。 下方为模型注册的SQL语法定义。 ```SQL -create model using uri +create model using uri ``` SQL中参数的具体含义如下： -- model_name：模型的全局唯一标识，不可重复。模型名称具备以下约束： +- model_id：模型的全局唯一标识，不可重复。模型名称具备以下约束： - 允许出现标识符 [ 0-9 a-z A-Z _ ] （字母，数字，下划线） - 长度限制为2-64字符 - 大小写敏感 -- uri：模型注册文件的资源路径，路径下应包含**模型权重model.pt文件和模型的元数据描述文件config.yaml** +- uri：模型注册文件的资源路径，路径下应包含**模型结构及权重文件 model.pt 文件和模型配置文件 config.yaml** - - 模型权重文件：深度学习模型训练完成后得到的权重文件，目前支持pytorch训练得到的.pt文件 + - 模型结构及权重文件：模型训练完成后得到的权重文件，目前支持 pytorch 训练得到的 .pt 文件 - - yaml元数据描述文件：模型注册时需要提供的与模型结构有关的参数，其中必须包含模型的输入输出维度用于模型推理： + - 模型配置文件：模型注册时需要提供的与模型结构有关的参数，其中必须包含模型的输入输出维度用于模型推理： - | **参数名** | **参数描述** | **示例** | | ------------ | ---------------------------- | -------- | @@ -145,7 +145,7 @@ IoTDB> create model dlinear_example using uri "file://./example" 也可以从huggingFace上下载对应的模型文件进行注册 ```SQL -IoTDB> create model dlinear_example using uri "https://huggingface.com/IoTDBML/dlinear/" +IoTDB> create model dlinear_example using uri "https://huggingface.co/google/timesfm-2.0-500m-pytorch" ``` SQL执行后会异步进行注册的流程，可以通过模型展示查看模型的注册状态（见模型展示章节），注册成功的耗时主要受到模型文件大小的影响。 @@ -159,56 +159,54 @@ SQL执行后会异步进行注册的流程，可以通过模型展示查看模 ```SQL show models -show models +show models ``` 除了直接展示所有模型的信息外，可以指定model id来查看某一具体模型的信息。模型展示的结果中包含如下信息： | **ModelId** | **State** | **Configs** | **Attributes** | | ------------ | ------------------------------------- | ---------------------------------------------- | -------------- | -| 模型唯一标识 | 模型注册状态(LOADING,ACTIVE,DROPPING) | InputShape, outputShapeInputTypes, outputTypes | 模型备注信息 | +| 模型唯一标识 | 模型注册状态(INACTIVE,LOADING,ACTIVE,TRAINING,FAILED,DROPPING) | InputShape, outputShapeInputTypes, outputTypes | 模型备注信息 | 其中，State用于展示当前模型注册的状态，包含以下三个阶段 -- **LOADING**：已经在configNode中添加对应的模型元信息，正将模型文件传输到AINode节点上 -- **ACTIVE:** 模型已经设置完成，模型处于可用状态 -- **DROPPING**：模型删除中，正在从configNode以及AINode处删除模型相关信息 -- **UNAVAILABLE**: 模型创建失败，可以通过drop model删除创建失败的model_name。 +- **INACTIVE**：模型处于不可用状态 +- **LOADING**：模型加载中 +- **ACTIVE**：模型处于可用状态 +- **TRAINING**：模型处于微调中状态 +- **FAILED**：模型微调失败状态 +- **DROPPING**:模型删除中状态 #### 示例 ```SQL IoTDB> show models - -+---------------------+--------------------------+-----------+----------------------------+-----------------------+ -| ModelId| ModelType| State| Configs| Notes| -+---------------------+--------------------------+-----------+----------------------------+-----------------------+ -| dlinear_example| USER_DEFINED| ACTIVE| inputShape:[96,2]| | -| | | | outputShape:[48,2]| | -| | | | inputDataType:[float,float]| | -| | | |outputDataType:[float,float]| | -| _STLForecaster| BUILT_IN_FORECAST| ACTIVE| |Built-in model in IoTDB| -| _NaiveForecaster| BUILT_IN_FORECAST| ACTIVE| |Built-in model in IoTDB| -| _ARIMA| BUILT_IN_FORECAST| ACTIVE| |Built-in model in IoTDB| -|_ExponentialSmoothing| BUILT_IN_FORECAST| ACTIVE| |Built-in model in IoTDB| -| _GaussianHMM|BUILT_IN_ANOMALY_DETECTION| ACTIVE| |Built-in model in IoTDB| -| _GMMHMM|BUILT_IN_ANOMALY_DETECTION| ACTIVE| |Built-in model in IoTDB| -| _Stray|BUILT_IN_ANOMALY_DETECTION| ACTIVE| |Built-in model in IoTDB| -+---------------------+--------------------------+-----------+------------------------------------------------------------+-----------------------+ ++---------------------+--------------------+--------+--------+ +| ModelId| ModelType|Category| State| ++---------------------+--------------------+--------+--------+ +| arima| Arima|BUILT-IN| ACTIVE| +| holtwinters| HoltWinters|BUILT-IN| ACTIVE| +|exponential_smoothing|ExponentialSmoothing|BUILT-IN| ACTIVE| +| naive_forecaster| NaiveForecaster|BUILT-IN| ACTIVE| +| stl_forecaster| StlForecaster|BUILT-IN| ACTIVE| +| gaussian_hmm| GaussianHmm|BUILT-IN| ACTIVE| +| gmm_hmm| GmmHmm|BUILT-IN| ACTIVE| +| stray| Stray|BUILT-IN| ACTIVE| +| timer_xl| Timer-XL|BUILT-IN| ACTIVE| +| sundial| Timer-Sundial|BUILT-IN| ACTIVE| ++---------------------+--------------------+--------+--------+ ``` -我们前面已经注册了对应的模型，可以通过对应的指定查看模型状态，active表明模型注册成功，可用于推理。 - ### 4.3 删除模型 -对于注册成功的模型，用户可以通过SQL进行删除。该操作除了删除configNode上的元信息外，还会删除所有AINode下的相关模型文件。其SQL如下： +对于注册成功的模型，用户可以通过SQL进行删除，该操作会删除所有 AINode 下的相关模型文件，其SQL如下： ```SQL -drop model +drop model ``` -需要指定已经成功注册的模型model_name来删除对应的模型。由于模型删除涉及多个节点上的数据删除，操作不会立即完成，此时模型的状态为DROPPING，该状态的模型不能用于模型推理。 +需要指定已经成功注册的模型 model_id 来删除对应的模型。由于模型删除涉及模型数据清理，操作不会立即完成，此时模型的状态为 DROPPING，该状态的模型不能用于模型推理。请注意，该功能不支持删除内置模型。 ### 4.4 使用内置模型推理 @@ -216,20 +214,28 @@ SQL语法如下： ```SQL -call inference(,sql[,=]) +call inference(,inputSql,(=)*) + +window_function: + head(window_size) + tail(window_size) + count(window_size,sliding_step) ``` 内置模型推理无需注册流程，通过call关键字，调用inference函数就可以使用模型的推理功能，其对应的参数介绍如下： -- **built_in_model_name:** 内置模型名称 +- **model_id:** 模型名称 - **parameterName**：参数名 - **parameterValue**：参数值 +请注意，使用内置时序大模型进行推理的前提条件是本地存有对应模型权重，目录为 /IOTDB_AINODE_HOME/data/ainode/models/weights/model_id/。若本地没有模型权重，则会自动从 HuggingFace 拉取，请保证本地能直接访问 HuggingFace。 + + #### 内置模型及参数说明 目前已内置如下机器学习模型，具体参数说明请参考以下链接。 -| 模型 | built_in_model_name | 任务类型 | 参数说明 | +| 模型 | built_in_model_id | 任务类型 | 参数说明 | | -------------------- | --------------------- | -------- | ------------------------------------------------------------ | | Arima | _Arima | 预测 | [Arima参数说明](https://www.sktime.net/en/latest/api_reference/auto_generated/sktime.forecasting.arima.ARIMA.html?highlight=Arima) | | STLForecaster | _STLForecaster | 预测 | [STLForecaster参数说明](https://www.sktime.net/en/latest/api_reference/auto_generated/sktime.forecasting.trend.STLForecaster.html#sktime.forecasting.trend.STLForecaster) | @@ -239,63 +245,10 @@ call inference(,sql[,=]) | GMMHMM | _GMMHMM | 标注 | [GMMHMM参数说明](https://www.sktime.net/en/latest/api_reference/auto_generated/sktime.detection.hmm_learn.gmm.GMMHMM.html) | | Stray | _Stray | 异常检测 | [Stray参数说明](https://www.sktime.net/en/latest/api_reference/auto_generated/sktime.detection.stray.STRAY.html) | -#### 示例 - -下面是使用内置模型推理的一个操作示例，使用内置的Stray模型进行异常检测算法，输入为`[144,1]`，输出为`[144,1]`，我们通过SQL使用其进行推理。 - -```SQL -IoTDB> select * from root.eg.airline -+-----------------------------+------------------+ -| Time|root.eg.airline.s0| -+-----------------------------+------------------+ -|1949-01-31T00:00:00.000+08:00| 224.0| -|1949-02-28T00:00:00.000+08:00| 118.0| -|1949-03-31T00:00:00.000+08:00| 132.0| -|1949-04-30T00:00:00.000+08:00| 129.0| -...... -|1960-09-30T00:00:00.000+08:00| 508.0| -|1960-10-31T00:00:00.000+08:00| 461.0| -|1960-11-30T00:00:00.000+08:00| 390.0| -|1960-12-31T00:00:00.000+08:00| 432.0| -+-----------------------------+------------------+ -Total line number = 144 - -IoTDB> call inference(_Stray, "select s0 from root.eg.airline", generateTime=True, k=2) -+-----------------------------+-------+ -| Time|output0| -+-----------------------------+-------+ -|1960-12-31T00:00:00.000+08:00| 0| -|1961-01-31T08:00:00.000+08:00| 1| -|1961-02-28T08:00:00.000+08:00| 0| -|1961-03-31T08:00:00.000+08:00| 0| -...... -|1972-06-30T08:00:00.000+08:00| 0| -|1972-07-31T08:00:00.000+08:00| 1| -|1972-08-31T08:00:00.000+08:00| 0| -|1972-09-30T08:00:00.000+08:00| 0| -|1972-10-31T08:00:00.000+08:00| 0| -|1972-11-30T08:00:00.000+08:00| 0| -+-----------------------------+-------+ -Total line number = 144 -``` - -### 4.5 使用深度学习模型推理 - -SQL语法如下： - -```SQL -call inference(,sql[,window=]) - - -window_function: - head(window_size) - tail(window_size) - count(window_size,sliding_step) -``` 在完成模型的注册后，通过call关键字，调用inference函数就可以使用模型的推理功能，其对应的参数介绍如下： -- **model_name**: 对应一个已经注册的模型 +- **model_id**: 对应一个已经注册的模型 - **sql**：sql查询语句，查询的结果作为模型的输入进行模型推理。查询的结果中行列的维度需要与具体模型config中指定的大小相匹配。(这里的sql不建议使用`SELECT *`子句，因为在IoTDB中，`*`并不会对列进行排序，因此列的顺序是未定义的，可以使用`SELECT s0,s1`的方式确保列的顺序符合模型输入的预期) - **window_function**: 推理过程中可以使用的窗口函数，目前提供三种类型的窗口函数用于辅助模型推理： - **head(window_size)**: 获取数据中最前的window_size个点用于模型推理，该窗口可用于数据裁剪 @@ -439,6 +392,64 @@ Total line number = 4 其中结果集中每行的标签对应每24行数据为一组，输入该异常检测模型后的输出。 +### 4.5 使用内置模型微调 +> 仅 Timer-XL、Timer-Sundial 可以进行微调操作。 + +SQL语法如下： + + +```SQL +create model (with hyperparameters +(=(, =)*))? +from model +on dataset (PATH ([timeRange])?) +``` + +#### 示例 + +1. 选择测点 root.db.etth.ot 中前 80% 的数据作为微调数据集，基于 sundial 创建模型 sundialv2. + +```SQL +IoTDB> CREATE MODEL sundialv2 FROM MODEL sundial ON DATASET (PATH root.db.etth.OT([1467302400000, 1517468400001))) +Msg: The statement is executed successfully. +IoTDB> show models ++---------------------+--------------------+----------+--------+ +| ModelId| ModelType| Category| State| ++---------------------+--------------------+----------+--------+ +| arima| Arima| BUILT-IN| ACTIVE| +| holtwinters| HoltWinters| BUILT-IN| ACTIVE| +|exponential_smoothing|ExponentialSmoothing| BUILT-IN| ACTIVE| +| naive_forecaster| NaiveForecaster| BUILT-IN| ACTIVE| +| stl_forecaster| StlForecaster| BUILT-IN| ACTIVE| +| gaussian_hmm| GaussianHmm| BUILT-IN| ACTIVE| +| gmm_hmm| GmmHmm| BUILT-IN| ACTIVE| +| stray| Stray| BUILT-IN| ACTIVE| +| sundial| Timer-Sundial| BUILT-IN| ACTIVE| +| timer_xl| Timer-XL| BUILT-IN| ACTIVE| +| sundialv2| Timer-Sundial|FINE-TUNED|TRAINING| ++---------------------+--------------------+----------+--------+ +``` + +2. 微调任务后台异步启动，可在 AINode 进程看到 log；微调完成后，查询并使用新的模型 + +```SQL +IoTDB> show models ++---------------------+--------------------+----------+------+ +| ModelId| ModelType| Category| State| ++---------------------+--------------------+----------+------+ +| arima| Arima| BUILT-IN|ACTIVE| +| holtwinters| HoltWinters| BUILT-IN|ACTIVE| +|exponential_smoothing|ExponentialSmoothing| BUILT-IN|ACTIVE| +| naive_forecaster| NaiveForecaster| BUILT-IN|ACTIVE| +| stl_forecaster| StlForecaster| BUILT-IN|ACTIVE| +| gaussian_hmm| GaussianHmm| BUILT-IN|ACTIVE| +| gmm_hmm| GmmHmm| BUILT-IN|ACTIVE| +| stray| Stray| BUILT-IN|ACTIVE| +| sundial| Timer-Sundial| BUILT-IN|ACTIVE| +| timer_xl| Timer-XL| BUILT-IN|ACTIVE| +| sundialv2| Timer-Sundial|FINE-TUNED|ACTIVE| ++---------------------+--------------------+----------+------+ +``` ### 4.6 时序大模型导入步骤 diff --git a/src/zh/UserGuide/Master/Tree/AI-capability/TimeSeries-Large-Model.md b/src/zh/UserGuide/Master/Tree/AI-capability/TimeSeries-Large-Model.md index 48be16c8f..a632e1dec 100644 --- a/src/zh/UserGuide/Master/Tree/AI-capability/TimeSeries-Large-Model.md +++ b/src/zh/UserGuide/Master/Tree/AI-capability/TimeSeries-Large-Model.md @@ -35,24 +35,35 @@ ![](/img/LargeModel09.png) -## Timer 模型 +## Timer-1 模型 Timer模型不仅展现了出色的少样本泛化和多任务适配能力，还通过预训练获得了丰富的知识库，赋予了它处理多样化下游任务的通用能力，拥有以下特点： - **泛化性**：模型能够通过使用少量样本进行微调，达到行业内领先的深度模型预测效果。 - **通用性**：模型设计灵活，能够适配多种不同的任务需求，并且支持变化的输入和输出长度，使其在各种应用场景中都能发挥作用。 -- **可扩展性**：随着模型参数数量的增加或预训练数据规模的扩大，模型的性能会持续提升，确保模型能够随着时间和数据量的增长而不断优化其预测效果。 +- **可扩展性**：随着模型参数数量的增加或预训练数据规模的扩大，模型效果会持续提升，确保模型能够随着时间和数据量的增长而不断优化其预测效果。 -![](/img/LargeModel02.png) +![](/img/model01.png) ## Timer-XL 模型 -Timer-XL 基于 Timer 进一步扩展升级了网络结构，在多个维度上进行全面突破： +Timer-XL 基于 Timer 进一步扩展升级了网络结构，在多个维度全面突破： -- **超长上下文支持**：该模型突破了传统时序预测模型的限制，支持处理数千个Token（相当于数万个时间点）的输入，有效解决了上下文长度的瓶颈问题。 +- **超长上下文支持**：该模型突破了传统时序预测模型的限制，支持处理数千个 Token（相当于数万个时间点）的输入，有效解决了上下文长度瓶颈问题。 - **多变量预测场景覆盖**：支持多种预测场景，包括非平稳时间序列的预测、涉及多个变量的预测任务以及包含协变量的预测，满足多样化的业务需求。 - **大规模工业时序数据集**：采用万亿大规模工业物联网领域的时序数据集进行预训练，数据集兼有庞大的体量、卓越的质量和丰富的领域等重要特质，覆盖能源、航空航天、钢铁、交通等多领域。 +![](/img/model02.png) + +## Timer-Sundial 模型 + +Timer-Sundial 是一个专注于时间序列预测的生成式基础模型系列，其基础版本拥有 1.28 亿参数，并在 1 万亿个时间点上进行了大规模预训练，其核心特性包括： + +- **强大的泛化性能**：具备零样本预测能力，可同时支持点预测和概率预测。 +- **灵活预测分布分析**：不仅能预测均值或分位数，还可通过模型生成的原始样本评估预测分布的任意统计特性。 +- **创新生成架构**：采用 “Transformer + TimeFlow” 协同架构——Transformer 学习时间片段的自回归表征，TimeFlow 模块基于流匹配框架 (Flow-Matching) 将随机噪声转化为多样化预测轨迹，实现高效的非确定性样本生成。 + +![](/img/model03.png) ## 效果展示 @@ -88,20 +99,9 @@ show cluster ![](/img/ainode-timer-1.png) -2. 模型文件存放路径：推荐放在 AINode 安装包相同文件夹下，可新建模型文件夹存放模型文件 -3. 注册模型语句 - -```sql -create model using uri -``` - -示例： - -```sql -create model Timer-xl using uri -``` +2. 联网环境下首次启动 AINode 节点会自动拉取 Timer-XL、Sundial 模型。 -4. 检查模型是否注册成功 +3. 检查模型是否可用 检查命令： diff --git a/src/zh/UserGuide/Master/Tree/Deployment-and-Maintenance/AINode_Deployment_apache.md b/src/zh/UserGuide/Master/Tree/Deployment-and-Maintenance/AINode_Deployment_apache.md index 1092905ab..052c039c2 100644 --- a/src/zh/UserGuide/Master/Tree/Deployment-and-Maintenance/AINode_Deployment_apache.md +++ b/src/zh/UserGuide/Master/Tree/Deployment-and-Maintenance/AINode_Deployment_apache.md @@ -39,9 +39,7 @@ AINode 是 IoTDB 在 ConfigNode、DataNode 后提供的第三种内生节点， ### 2.1 安装包获取 - 用户可以下载AINode的软件安装包，下载并解压后即完成AINode的安装。 - - 解压后安装包（`apache-iotdb--ainode-bin.zip`），安装包解压后目录结构如下： +AINode 安装包（`apache-iotdb--ainode-bin.zip`），安装包解压后目录结构如下： | **目录** | **类型** | **说明** | | ------------ | -------- | ------------------------------------------------ | | lib | 文件夹 | AINode编译后的二进制可执行文件以及相关的代码依赖 | @@ -53,93 +51,67 @@ AINode 是 IoTDB 在 ConfigNode、DataNode 后提供的第三种内生节点， | `README.md` | 文件 | 使用说明 | ### 2.2 环境准备 -- 建议操作环境: Ubuntu, CentOS, MacOS -- 运行环境 - - Python 版本在 3.10~3.12 即可，且带有 pip 和 venv 工具；非联网环境下需要从 [此处](https://cloud.tsinghua.edu.cn/d/4c1342f6c272439aa96c/?p=%2Flibs&mode=list) 下载对应操作系统的zip压缩包（注意下载依赖需选择libs文件夹中的zip压缩包，如下图），并将文件夹下的所有文件拷贝到 `apache-iotdb--ainode-bin` 文件夹中 `lib` 文件夹下，并按下文步骤启动AINode。 +1. 建议操作环境: Ubuntu, CentOS, MacOS +2. IoTDB 版本：>= V 2.0.5.1 +3. 运行环境 + - Python 版本在 3.9 ~3.12，且带有 pip 和 venv 工具； + - - - - 环境变量中需存在 Python 解释器且可以通过 `python` 指令直接调用 - - 建议在 `apache-iotdb--ainode-bin` 文件夹下，新建 Python 解释器 venv 虚拟环境。如安装 3.10.0 版本虚拟环境，语句如下： - - ```shell - # 安装3.10.0版本的venv，创建虚拟环境，文件夹名为 `venv` - ../Python-3.10.0/python -m venv `venv` - ``` ## 3. 安装部署及使用 ### 3.1 安装 AINode -1. 检查Linux的内核架构 -```shell - uname -m - ``` - -2. 导入Python环境[下载](https://repo.anaconda.com/miniconda/) - -推荐下载py311版本应用，导入至用户根目录下 iotdb专用文件夹 中 - - -3. 验证Python版本 +1. 保证 Python 版本介于 3.9 ~3.12 ```shell python --version - ``` +# 或 +python3 --version +``` +2. 下载导入 AINode 到专用文件夹，切换到专用文件夹并解压安装包 + +```shell + unzip timechodb-2.0.5.1-ainode-bin.zip +``` -4. 创建虚拟环境（在 ainode 目录下执行）： +3. 创建虚拟环境（在 ainode 目录下执行）： ```shell - python -m venv venv + python3 -m venv venv ``` -5. 激活虚拟环境： +4. 激活虚拟环境： ```shell source venv/bin/activate ``` -6. 下载导入AINode到专用文件夹，切换到专用文件夹并解压安装包 - -```shell - unzip apache-iotdb-1.3.3-ainode-bin.zip - ``` - -7. 配置项修改 - -```shell - vi apache-iotdb-1.3.3-ainode-bin/conf/iotdb-ainode.properties - ``` - 配置项修改：[详细信息](#配置项修改) -> ain_seed_config_node=iotdb-1:10710（集群通讯节点IP:通讯节点端口）
-> ain_inference_rpc_address=iotdb-3（运行AINode的服务器IP） - -8. 更换Python源 - -```shell - pip config set global.index-url https://mirrors.aliyun.com/pypi/simple/ - ``` - -9. 启动AINode节点 - -```shell - nohup bash apache-iotdb-1.3.3-ainode-bin/sbin/start-ainode.sh > myout.file 2>& 1 & - ``` -> 回到系统默认环境：conda deactivate +5. 更新 pip 并安装 AINode 依赖 + ```shell + python -m pip install --upgrade pip + poetry lock + poetry install + ``` ### 3.2 配置项修改 AINode 支持修改一些必要的参数。可以在 `conf/iotdb-ainode.properties` 文件中找到下列参数并进行持久化的修改： -| **名称** | **描述** | **类型** | **默认值** | **改后生效方式** | -| :----------------------------- | ------------------------------------------------------------ | ------- | ------------------ | ---------------------------- | -| cluster_name | AINode 要加入集群的标识 | string | defaultCluster | 仅允许在第一次启动服务前修改 | -| ain_seed_config_node | AINode 启动时注册的 ConfigNode 地址 | String | 127.0.0.1:10710 | 仅允许在第一次启动服务前修改 | -| ain_inference_rpc_address | AINode 提供服务与通信的地址 ，内部服务通讯接口 | String | 127.0.0.1 | 仅允许在第一次启动服务前修改 | -| ain_inference_rpc_port | AINode 提供服务与通信的端口 | String | 10810 | 仅允许在第一次启动服务前修改 | -| ain_system_dir | AINode 元数据存储路径，相对路径的起始目录与操作系统相关，建议使用绝对路径 | String | data/AINode/system | 仅允许在第一次启动服务前修改 | -| ain_models_dir | AINode 存储模型文件的路径，相对路径的起始目录与操作系统相关，建议使用绝对路径 | String | data/AINode/models | 仅允许在第一次启动服务前修改 | -| ain_logs_dir | AINode 存储日志的路径，相对路径的起始目录与操作系统相关，建议使用绝对路径 | String | logs/AINode | 重启后生效 | -| ain_thrift_compression_enabled | AINode 是否启用 thrift 的压缩机制，0-不启动、1-启动 | Boolean | 0 | 重启后生效 | +| **名称** | **描述** | **类型** | **默认值** | +| ------------------------------ | ------------------------------------------------------------ | -------- | ------------------ | +| cluster_name | AINode 要加入集群的标识 | string | defaultCluster | +| ain_seed_config_node | AINode 启动时注册的 ConfigNode 地址 | String | 127.0.0.1:10710 | +| ain_cluster_ingress_address | AINode 拉取数据的 DataNode 的 rpc 地址 | String | 127.0.0.1 | +| ain_cluster_ingress_port | AINode 拉取数据的 DataNode 的 rpc 端口 | Integer | 6667 | +| ain_cluster_ingress_username | AINode 拉取数据的 DataNode 的客户端用户名 | String | root | +| ain_cluster_ingress_password | AINode 拉取数据的 DataNode 的客户端密码 | String | root | +| ain_cluster_ingress_time_zone | AINode 拉取数据的 DataNode 的客户端时区 | String | UTC+8 | +| ain_inference_rpc_address | AINode 提供服务与通信的地址 ，内部服务通讯接口 | String | 127.0.0.1 | +| ain_inference_rpc_port | AINode 提供服务与通信的端口 | String | 10810 | +| ain_system_dir | AINode 元数据存储路径，相对路径的起始目录与操作系统相关，建议使用绝对路径 | String | data/AINode/system | +| ain_models_dir | AINode 存储模型文件的路径，相对路径的起始目录与操作系统相关，建议使用绝对路径 | String | data/AINode/models | +| ain_thrift_compression_enabled | AINode 是否启用 thrift 的压缩机制，0-不启动、1-启动 | Boolean | 0 | + ### 3.3 启动 AINode 在完成 Seed-ConfigNode 的部署后，可以通过添加 AINode 节点来支持模型的注册和推理功能。在配置项中指定 IoTDB 集群的信息后，可以执行相应的指令来启动 AINode，加入 IoTDB 集群。 @@ -164,118 +136,7 @@ AINode 支持修改一些必要的参数。可以在 `conf/iotdb-ainode.properti nohup bash sbin\start-ainode.bat > myout.file 2>& 1 & ``` -##### 详细语法 - -```shell - # 启动命令 - # Linux 和 MacOS 系统 - bash sbin/start-ainode.sh -i -r -n - - # Windows 系统 - sbin\start-ainode.bat -i -r -n - ``` - -##### 参数介绍： - -| **名称** | **标签** | **描述** | **是否必填** | **类型** | **默认值** | **输入方式** | -| ------------------- | ---- | ------------------------------------------------------------ | -------- | ------ | ---------------- | ---------------------- | -| ain_interpreter_dir | -i | AINode 所安装在的虚拟环境的解释器路径，需要使用绝对路径 | 否 | String | 默认读取环境变量 | 调用时输入或持久化修改 | -| ain_force_reinstall | -r | 该脚本在检查 AINode 安装情况的时候是否检查版本，如果检查则在版本不对的情况下会强制安装 lib 里的 whl 安装包 | 否 | Bool | false | 调用时输入 | -| ain_no_dependencies | -n | 指定在安装 AINode 的时候是否安装依赖，如果指定则仅安装 AINode 主程序而不安装依赖。 | 否 | Bool | false | 调用时输入 | - - 如不想每次启动时指定对应参数，也可以在 `conf` 文件夹下的`ainode-env.sh` 和 `ainode-env.bat` 脚本中持久化修改参数（目前支持持久化修改 ain_interpreter_dir 参数）。 - - `ainode-env.sh` : - ```shell - # The defaulte venv environment is used if ain_interpreter_dir is not set. Please use absolute path without quotation mark - # ain_interpreter_dir= - ``` - `ainode-env.bat` : -```shell - @REM The defaulte venv environment is used if ain_interpreter_dir is not set. Please use absolute path without quotation mark - @REM set ain_interpreter_dir= - ``` - 在写入参数值的后解除对应行的注释并保存即可在下一次执行脚本时生效。 - #### 示例 - -##### 直接启动： - -```shell - # 启动命令 - # Linux 和 MacOS 系统 - bash sbin/start-ainode.sh - # Windows 系统 - sbin\start-ainode.bat - - - # 后台启动命令（长期运行推荐） - # Linux 和 MacOS 系统 - nohup bash sbin/start-ainode.sh > myout.file 2>& 1 & - # Windows 系统 - nohup bash sbin\start-ainode.bat > myout.file 2>& 1 & - ``` - -##### 更新启动： -如果 AINode 的版本进行了更新（如更新了 `lib` 文件夹），可使用此命令。首先要保证 AINode 已经停止运行，然后通过 `-r` 参数重启，该参数会根据 `lib` 下的文件重新安装 AINode。 - -```shell - # 更新启动命令 - # Linux 和 MacOS 系统 - bash sbin/start-ainode.sh -r - # Windows 系统 - sbin\start-ainode.bat -r - - - # 后台更新启动命令（长期运行推荐） - # Linux 和 MacOS 系统 - nohup bash sbin/start-ainode.sh -r > myout.file 2>& 1 & - # Windows 系统 - nohup bash sbin\start-ainode.bat -r > myout.file 2>& 1 & - ``` -#### 非联网环境启动 - -##### 启动命令 - -```shell - # 启动命令 - # Linux 和 MacOS 系统 - bash sbin/start-ainode.sh - - # Windows 系统 - sbin\start-ainode.bat - - # 后台启动命令（长期运行推荐） - # Linux 和 MacOS 系统 - nohup bash sbin/start-ainode.sh > myout.file 2>& 1 & - - # Windows 系统 - nohup bash sbin\start-ainode.bat > myout.file 2>& 1 & - ``` - -##### 详细语法 - -```shell - # 启动命令 - # Linux 和 MacOS 系统 - bash sbin/start-ainode.sh -i -r -n - - # Windows 系统 - sbin\start-ainode.bat -i -r -n - ``` - -##### 参数介绍： - -| **名称** | **标签** | **描述** | **是否必填** | **类型** | **默认值** | **输入方式** | -| ------------------- | ---- | ------------------------------------------------------------ | -------- | ------ | ---------------- | ---------------------- | -| ain_interpreter_dir | -i | AINode 所安装在的虚拟环境的解释器路径，需要使用绝对路径 | 否 | String | 默认读取环境变量 | 调用时输入或持久化修改 | -| ain_force_reinstall | -r | 该脚本在检查 AINode 安装情况的时候是否检查版本，如果检查则在版本不对的情况下会强制安装 lib 里的 whl 安装包 | 否 | Bool | false | 调用时输入 | - -> 注意：非联网环境下安装失败时，首先检查是否选择了平台对应的安装包，其次确认python版本（由于下载的安装包限制了python版本，3.7、3.9等其他都不行） - -#### 示例 - -##### 直接启动： ```shell # 启动命令 @@ -321,22 +182,6 @@ IoTDB> show cluster sbin\stop-ainode.bat ``` -##### 详细语法 - -```shell - # Linux / MacOS - bash sbin/stop-ainode.sh -t - - #Windows - sbin\stop-ainode.bat -t - ``` - -##### 参数介绍： - - | **名称** | **标签** | **描述** | **是否必填** | **类型** | **默认值** | **输入方式** | -| ----------------- | ---- | ------------------------------------------------------------ | -------- | ------ | ------ | ---------- | -| ain_remove_target | -t | AINode 关闭时可以指定待移除的目标 AINode 的 Node ID、地址和端口号，格式为`` | 否 | String | 无 | 调用时输入 | - #### 示例 ```shell # Linux / MacOS @@ -359,87 +204,7 @@ IoTDB> show cluster ``` 如果需要重新启动该节点，需重新执行启动脚本。 -### 3.6 移除 AINode - -当需要把一个 AINode 节点移出集群时，可以执行移除脚本。移除和停止脚本的差别是：停止是在集群中保留 AINode 节点但停止 AINode 服务，移除则是把 AINode 节点从集群中移除出去。 - - - #### 移除命令 - -```shell - # Linux / MacOS - bash sbin/remove-ainode.sh - - # Windows - sbin\remove-ainode.bat - ``` - -##### 详细语法 - -```shell - # Linux / MacOS - bash sbin/remove-ainode.sh -i -t -r -n - # Windows - sbin\remove-ainode.bat -i -t -r -n - ``` - -##### 参数介绍： - - | **名称** | **标签** | **描述** | **是否必填** | **类型** | **默认值** | **输入方式** | -| ------------------- | ---- | ------------------------------------------------------------ | -------- | ------ | ---------------- | --------------------- | -| ain_interpreter_dir | -i | AINode 所安装在的虚拟环境的解释器路径，需要使用绝对路径 | 否 | String | 默认读取环境变量 | 调用时输入+持久化修改 | -| ain_remove_target | -t | AINode 关闭时可以指定待移除的目标 AINode 的 Node ID、地址和端口号，格式为`` | 否 | String | 无 | 调用时输入 | -| ain_force_reinstall | -r | 该脚本在检查 AINode 安装情况的时候是否检查版本，如果检查则在版本不对的情况下会强制安装 lib 里的 whl 安装包 | 否 | Bool | false | 调用时输入 | -| ain_no_dependencies | -n | 指定在安装 AINode 的时候是否安装依赖，如果指定则仅安装 AINode 主程序而不安装依赖。 | 否 | Bool | false | 调用时输入 | - - 如不想每次启动时指定对应参数，也可以在 `conf` 文件夹下的`ainode-env.sh` 和 `ainode-env.bat` 脚本中持久化修改参数（目前支持持久化修改 ain_interpreter_dir 参数）。 - - `ainode-env.sh` : - ```shell - # The defaulte venv environment is used if ain_interpreter_dir is not set. Please use absolute path without quotation mark - # ain_interpreter_dir= - ``` - `ainode-env.bat` : -```shell - @REM The defaulte venv environment is used if ain_interpreter_dir is not set. Please use absolute path without quotation mark - @REM set ain_interpreter_dir= - ``` - 在写入参数值的后解除对应行的注释并保存即可在下一次执行脚本时生效。 - -#### 示例 - -##### 直接移除： - - ```shell - # Linux / MacOS - bash sbin/remove-ainode.sh - - # Windows - sbin\remove-ainode.bat - ``` - 移除节点后，将无法查询到节点的相关信息。 - - ```shell -IoTDB> show cluster -+------+----------+-------+---------------+------------+-------+-----------+ -|NodeID| NodeType| Status|InternalAddress|InternalPort|Version| BuildInfo| -+------+----------+-------+---------------+------------+-------+-----------+ -| 0|ConfigNode|Running| 127.0.0.1| 10710|UNKNOWN|190e303-dev| -| 1| DataNode|Running| 127.0.0.1| 10730|UNKNOWN|190e303-dev| -+------+----------+-------+---------------+------------+-------+-----------+ -``` -##### 指定移除： - -如果用户丢失了 data 文件夹下的文件，可能 AINode 本地无法主动移除自己，需要用户指定节点号、地址和端口号进行移除，此时我们支持用户按照以下方法输入参数进行删除。 - - ```shell - # Linux / MacOS - bash sbin/remove-ainode.sh -t /: - - # Windows - sbin\remove-ainode.bat -t /: - ``` ## 4. 常见问题 diff --git a/src/zh/UserGuide/Master/Tree/Deployment-and-Maintenance/AINode_Deployment_timecho.md b/src/zh/UserGuide/Master/Tree/Deployment-and-Maintenance/AINode_Deployment_timecho.md index 1e8d83e96..052c039c2 100644 --- a/src/zh/UserGuide/Master/Tree/Deployment-and-Maintenance/AINode_Deployment_timecho.md +++ b/src/zh/UserGuide/Master/Tree/Deployment-and-Maintenance/AINode_Deployment_timecho.md @@ -37,11 +37,9 @@ AINode 是 IoTDB 在 ConfigNode、DataNode 后提供的第三种内生节点， ## 2. 安装准备 -### 2.1 装包获取 +### 2.1 安装包获取 - 用户可以下载AINode的软件安装包，下载并解压后即完成AINode的安装。 - - 解压后安装包（`apache-iotdb--ainode-bin.zip`），安装包解压后目录结构如下： +AINode 安装包（`apache-iotdb--ainode-bin.zip`），安装包解压后目录结构如下： | **目录** | **类型** | **说明** | | ------------ | -------- | ------------------------------------------------ | | lib | 文件夹 | AINode编译后的二进制可执行文件以及相关的代码依赖 | @@ -53,127 +51,67 @@ AINode 是 IoTDB 在 ConfigNode、DataNode 后提供的第三种内生节点， | `README.md` | 文件 | 使用说明 | ### 2.2 环境准备 -- 建议操作环境: Ubuntu, CentOS, MacOS -- 运行环境 - - Python 版本在 3.10~3.12 即可，且带有 pip 和 venv 工具；非联网环境下需要从 [此处](https://cloud.tsinghua.edu.cn/d/4c1342f6c272439aa96c/?p=%2Flibs&mode=list) 下载对应操作系统的zip压缩包（注意下载依赖需选择libs文件夹中的zip压缩包，如下图），并将文件夹下的所有文件拷贝到 `apache-iotdb--ainode-bin` 文件夹中 `lib` 文件夹下，并按下文步骤启动AINode。 +1. 建议操作环境: Ubuntu, CentOS, MacOS +2. IoTDB 版本：>= V 2.0.5.1 +3. 运行环境 + - Python 版本在 3.9 ~3.12，且带有 pip 和 venv 工具； + - - - - 环境变量中需存在 Python 解释器且可以通过 `python` 指令直接调用 - - 建议在 `apache-iotdb--ainode-bin` 文件夹下，新建 Python 解释器 venv 虚拟环境。如安装 3.10.0 版本虚拟环境，语句如下： - - ```shell - # 安装3.10.0版本的venv，创建虚拟环境，文件夹名为 `venv` - ../Python-3.10.0/python -m venv `venv` - ``` ## 3. 安装部署及使用 ### 3.1 安装 AINode -1. AINode 激活 - - 要求 IoTDB 处于正常运行状态，且 license 中有 AINode 模块授权。 - - 激活 AINode 模块授权方式如下： - - 方式一：激活文件拷贝激活 - - 重新启动 confignode 节点后，进入 activation 文件夹， 将 system_info 文件复制给天谋工作人员，并告知工作人员申请 AINode 独立授权； - - 收到工作人员返回的 license 文件； - - 将 license 文件放入对应节点的 activation 文件夹下； -- 方式二：激活脚本激活 - - 获取激活所需机器码，进入安装目录的 `sbin` 目录，执行激活脚本： - ```shell - cd sbin - ./start-activate.sh - ``` - - 显示如下信息，请将机器码（即该串字符）复制给天谋工作人员，并告知工作人员申请 AINode 独立授权： - ```shell - Please copy the system_info's content and send it to Timecho: - 01-KU5LDFFN-PNBEHDRH - Please enter license: - ``` - - 将工作人员返回的激活码输入上一步的命令行提示处 `Please enter license:`，如下提示： - ```shell - Please enter license: - Jw+MmF+AtexsfgNGOFgTm83BgXbq0zT1+fOfPvQsLlj6ZsooHFU6HycUSEGC78eT1g67KPvkcLCUIsz2QpbyVmPLr9x1+kVjBubZPYlVpsGYLqLFc8kgpb5vIrPLd3hGLbJ5Ks8fV1WOVrDDVQq89YF2atQa2EaB9EAeTWd0bRMZ+s9ffjc/1Zmh9NSP/T3VCfJcJQyi7YpXWy5nMtcW0gSV+S6fS5r7a96PjbtE0zXNjnEhqgRzdU+mfO8gVuUNaIy9l375cp1GLpeCh6m6pF+APW1CiXLTSijK9Qh3nsL5bAOXNeob5l+HO5fEMgzrW8OJPh26Vl6ljKUpCvpTiw== - License has been stored to sbin/../activation/license - Import completed. Please start cluster and excute 'show cluster' to verify activation status - ``` -- 更新 license 后，重新启动 DataNode 节点，进入 IoTDB 的 sbin 目录下，启动 datanode： - ```shell - cd sbin - ./start-datanode.sh -d #-d参数将在后台进行启动 - ``` - -2. 检查Linux的内核架构 - ```shell - uname -m - ``` - -3. 导入Python环境[下载](https://repo.anaconda.com/miniconda/) - - 推荐下载py311版本应用，导入至用户根目录下 iotdb专用文件夹 中 - +1. 保证 Python 版本介于 3.9 ~3.12 -4. 验证Python版本 +```shell + python --version +# 或 +python3 --version +``` +2. 下载导入 AINode 到专用文件夹，切换到专用文件夹并解压安装包 - ```shell - python --version - ``` +```shell + unzip timechodb-2.0.5.1-ainode-bin.zip +``` -5. 创建虚拟环境（在 ainode 目录下执行）： +3. 创建虚拟环境（在 ainode 目录下执行）： ```shell - python -m venv venv + python3 -m venv venv ``` -6. 激活虚拟环境： +4. 激活虚拟环境： ```shell source venv/bin/activate ``` -7. 下载导入AINode到专用文件夹，切换到专用文件夹并解压安装包 - +5. 更新 pip 并安装 AINode 依赖 ```shell - unzip iotdb-enterprise-ainode-1.3.3.2.zip + python -m pip install --upgrade pip + poetry lock + poetry install ``` - -8. 配置项修改 - - ```shell - vi iotdb-enterprise-ainode-1.3.3.2/conf/iotdb-ainode.properties - ``` - 配置项修改：[详细信息](#配置项修改) - > ain_seed_config_node=iotdb-1:10710（集群通讯节点IP:通讯节点端口）
- > ain_inference_rpc_address=iotdb-3（运行AINode的服务器IP） - -9. 更换Python源 - - ```shell - pip config set global.index-url https://mirrors.aliyun.com/pypi/simple/ - ``` - -10. 启动AINode节点 - - ```shell - nohup bash iotdb-enterprise-ainode-1.3.3.2/sbin/start-ainode.sh > myout.file 2>& 1 & - ``` - > 回到系统默认环境：conda deactivate ### 3.2 配置项修改 AINode 支持修改一些必要的参数。可以在 `conf/iotdb-ainode.properties` 文件中找到下列参数并进行持久化的修改： -| **名称** | **描述** | **类型** | **默认值** | **改后生效方式** | -| :----------------------------- | ------------------------------------------------------------ | ------- | ------------------ | ---------------------------- | -| cluster_name | AINode 要加入集群的标识 | string | defaultCluster | 仅允许在第一次启动服务前修改 | -| ain_seed_config_node | AINode 启动时注册的 ConfigNode 地址 | String | 127.0.0.1:10710 | 仅允许在第一次启动服务前修改 | -| ain_inference_rpc_address | AINode 提供服务与通信的地址 ，内部服务通讯接口 | String | 127.0.0.1 | 仅允许在第一次启动服务前修改 | -| ain_inference_rpc_port | AINode 提供服务与通信的端口 | String | 10810 | 仅允许在第一次启动服务前修改 | -| ain_system_dir | AINode 元数据存储路径，相对路径的起始目录与操作系统相关，建议使用绝对路径 | String | data/AINode/system | 仅允许在第一次启动服务前修改 | -| ain_models_dir | AINode 存储模型文件的路径，相对路径的起始目录与操作系统相关，建议使用绝对路径 | String | data/AINode/models | 仅允许在第一次启动服务前修改 | -| ain_logs_dir | AINode 存储日志的路径，相对路径的起始目录与操作系统相关，建议使用绝对路径 | String | logs/AINode | 重启后生效 | -| ain_thrift_compression_enabled | AINode 是否启用 thrift 的压缩机制，0-不启动、1-启动 | Boolean | 0 | 重启后生效 | +| **名称** | **描述** | **类型** | **默认值** | +| ------------------------------ | ------------------------------------------------------------ | -------- | ------------------ | +| cluster_name | AINode 要加入集群的标识 | string | defaultCluster | +| ain_seed_config_node | AINode 启动时注册的 ConfigNode 地址 | String | 127.0.0.1:10710 | +| ain_cluster_ingress_address | AINode 拉取数据的 DataNode 的 rpc 地址 | String | 127.0.0.1 | +| ain_cluster_ingress_port | AINode 拉取数据的 DataNode 的 rpc 端口 | Integer | 6667 | +| ain_cluster_ingress_username | AINode 拉取数据的 DataNode 的客户端用户名 | String | root | +| ain_cluster_ingress_password | AINode 拉取数据的 DataNode 的客户端密码 | String | root | +| ain_cluster_ingress_time_zone | AINode 拉取数据的 DataNode 的客户端时区 | String | UTC+8 | +| ain_inference_rpc_address | AINode 提供服务与通信的地址 ，内部服务通讯接口 | String | 127.0.0.1 | +| ain_inference_rpc_port | AINode 提供服务与通信的端口 | String | 10810 | +| ain_system_dir | AINode 元数据存储路径，相对路径的起始目录与操作系统相关，建议使用绝对路径 | String | data/AINode/system | +| ain_models_dir | AINode 存储模型文件的路径，相对路径的起始目录与操作系统相关，建议使用绝对路径 | String | data/AINode/models | +| ain_thrift_compression_enabled | AINode 是否启用 thrift 的压缩机制，0-不启动、1-启动 | Boolean | 0 | + ### 3.3 启动 AINode 在完成 Seed-ConfigNode 的部署后，可以通过添加 AINode 节点来支持模型的注册和推理功能。在配置项中指定 IoTDB 集群的信息后，可以执行相应的指令来启动 AINode，加入 IoTDB 集群。 @@ -198,118 +136,7 @@ AINode 支持修改一些必要的参数。可以在 `conf/iotdb-ainode.properti nohup bash sbin\start-ainode.bat > myout.file 2>& 1 & ``` -##### 详细语法 - -```shell - # 启动命令 - # Linux 和 MacOS 系统 - bash sbin/start-ainode.sh -i -r -n - - # Windows 系统 - sbin\start-ainode.bat -i -r -n - ``` - -##### 参数介绍： - -| **名称** | **标签** | **描述** | **是否必填** | **类型** | **默认值** | **输入方式** | -| ------------------- | ---- | ------------------------------------------------------------ | -------- | ------ | ---------------- | ---------------------- | -| ain_interpreter_dir | -i | AINode 所安装在的虚拟环境的解释器路径，需要使用绝对路径 | 否 | String | 默认读取环境变量 | 调用时输入或持久化修改 | -| ain_force_reinstall | -r | 该脚本在检查 AINode 安装情况的时候是否检查版本，如果检查则在版本不对的情况下会强制安装 lib 里的 whl 安装包 | 否 | Bool | false | 调用时输入 | -| ain_no_dependencies | -n | 指定在安装 AINode 的时候是否安装依赖，如果指定则仅安装 AINode 主程序而不安装依赖。 | 否 | Bool | false | 调用时输入 | - - 如不想每次启动时指定对应参数，也可以在 `conf` 文件夹下的`ainode-env.sh` 和 `ainode-env.bat` 脚本中持久化修改参数（目前支持持久化修改 ain_interpreter_dir 参数）。 - - `ainode-env.sh` : - ```shell - # The defaulte venv environment is used if ain_interpreter_dir is not set. Please use absolute path without quotation mark - # ain_interpreter_dir= - ``` - `ainode-env.bat` : -```shell - @REM The defaulte venv environment is used if ain_interpreter_dir is not set. Please use absolute path without quotation mark - @REM set ain_interpreter_dir= - ``` - 在写入参数值的后解除对应行的注释并保存即可在下一次执行脚本时生效。 - #### 示例 - -##### 直接启动： - -```shell - # 启动命令 - # Linux 和 MacOS 系统 - bash sbin/start-ainode.sh - # Windows 系统 - sbin\start-ainode.bat - - - # 后台启动命令（长期运行推荐） - # Linux 和 MacOS 系统 - nohup bash sbin/start-ainode.sh > myout.file 2>& 1 & - # Windows 系统 - nohup bash sbin\start-ainode.bat > myout.file 2>& 1 & - ``` - -##### 更新启动： -如果 AINode 的版本进行了更新（如更新了 `lib` 文件夹），可使用此命令。首先要保证 AINode 已经停止运行，然后通过 `-r` 参数重启，该参数会根据 `lib` 下的文件重新安装 AINode。 - -```shell - # 更新启动命令 - # Linux 和 MacOS 系统 - bash sbin/start-ainode.sh -r - # Windows 系统 - sbin\start-ainode.bat -r - - - # 后台更新启动命令（长期运行推荐） - # Linux 和 MacOS 系统 - nohup bash sbin/start-ainode.sh -r > myout.file 2>& 1 & - # Windows 系统 - nohup bash sbin\start-ainode.bat -r > myout.file 2>& 1 & - ``` -#### 非联网环境启动 - -##### 启动命令 - -```shell - # 启动命令 - # Linux 和 MacOS 系统 - bash sbin/start-ainode.sh - - # Windows 系统 - sbin\start-ainode.bat - - # 后台启动命令（长期运行推荐） - # Linux 和 MacOS 系统 - nohup bash sbin/start-ainode.sh > myout.file 2>& 1 & - - # Windows 系统 - nohup bash sbin\start-ainode.bat > myout.file 2>& 1 & - ``` - -##### 详细语法 - -```shell - # 启动命令 - # Linux 和 MacOS 系统 - bash sbin/start-ainode.sh -i -r -n - - # Windows 系统 - sbin\start-ainode.bat -i -r -n - ``` - -##### 参数介绍： - -| **名称** | **标签** | **描述** | **是否必填** | **类型** | **默认值** | **输入方式** | -| ------------------- | ---- | ------------------------------------------------------------ | -------- | ------ | ---------------- | ---------------------- | -| ain_interpreter_dir | -i | AINode 所安装在的虚拟环境的解释器路径，需要使用绝对路径 | 否 | String | 默认读取环境变量 | 调用时输入或持久化修改 | -| ain_force_reinstall | -r | 该脚本在检查 AINode 安装情况的时候是否检查版本，如果检查则在版本不对的情况下会强制安装 lib 里的 whl 安装包 | 否 | Bool | false | 调用时输入 | - -> 注意：非联网环境下安装失败时，首先检查是否选择了平台对应的安装包，其次确认python版本（由于下载的安装包限制了python版本，3.7、3.9等其他都不行） - -#### 示例 - -##### 直接启动： ```shell # 启动命令 @@ -355,22 +182,6 @@ IoTDB> show cluster sbin\stop-ainode.bat ``` -##### 详细语法 - -```shell - # Linux / MacOS - bash sbin/stop-ainode.sh -t - - #Windows - sbin\stop-ainode.bat -t - ``` - -##### 参数介绍： - - | **名称** | **标签** | **描述** | **是否必填** | **类型** | **默认值** | **输入方式** | -| ----------------- | ---- | ------------------------------------------------------------ | -------- | ------ | ------ | ---------- | -| ain_remove_target | -t | AINode 关闭时可以指定待移除的目标 AINode 的 Node ID、地址和端口号，格式为`` | 否 | String | 无 | 调用时输入 | - #### 示例 ```shell # Linux / MacOS @@ -393,87 +204,7 @@ IoTDB> show cluster ``` 如果需要重新启动该节点，需重新执行启动脚本。 -### 3.6 移除 AINode - -当需要把一个 AINode 节点移出集群时，可以执行移除脚本。移除和停止脚本的差别是：停止是在集群中保留 AINode 节点但停止 AINode 服务，移除则是把 AINode 节点从集群中移除出去。 - - - #### 移除命令 -```shell - # Linux / MacOS - bash sbin/remove-ainode.sh - - # Windows - sbin\remove-ainode.bat - ``` - -##### 详细语法 - -```shell - # Linux / MacOS - bash sbin/remove-ainode.sh -i -t -r -n - - # Windows - sbin\remove-ainode.bat -i -t -r -n - ``` - -##### 参数介绍： - - | **名称** | **标签** | **描述** | **是否必填** | **类型** | **默认值** | **输入方式** | -| ------------------- | ---- | ------------------------------------------------------------ | -------- | ------ | ---------------- | --------------------- | -| ain_interpreter_dir | -i | AINode 所安装在的虚拟环境的解释器路径，需要使用绝对路径 | 否 | String | 默认读取环境变量 | 调用时输入+持久化修改 | -| ain_remove_target | -t | AINode 关闭时可以指定待移除的目标 AINode 的 Node ID、地址和端口号，格式为`` | 否 | String | 无 | 调用时输入 | -| ain_force_reinstall | -r | 该脚本在检查 AINode 安装情况的时候是否检查版本，如果检查则在版本不对的情况下会强制安装 lib 里的 whl 安装包 | 否 | Bool | false | 调用时输入 | -| ain_no_dependencies | -n | 指定在安装 AINode 的时候是否安装依赖，如果指定则仅安装 AINode 主程序而不安装依赖。 | 否 | Bool | false | 调用时输入 | - - 如不想每次启动时指定对应参数，也可以在 `conf` 文件夹下的`ainode-env.sh` 和 `ainode-env.bat` 脚本中持久化修改参数（目前支持持久化修改 ain_interpreter_dir 参数）。 - - `ainode-env.sh` : - ```shell - # The defaulte venv environment is used if ain_interpreter_dir is not set. Please use absolute path without quotation mark - # ain_interpreter_dir= - ``` - `ainode-env.bat` : -```shell - @REM The defaulte venv environment is used if ain_interpreter_dir is not set. Please use absolute path without quotation mark - @REM set ain_interpreter_dir= - ``` - 在写入参数值的后解除对应行的注释并保存即可在下一次执行脚本时生效。 - -#### 示例 - -##### 直接移除： - - ```shell - # Linux / MacOS - bash sbin/remove-ainode.sh - - # Windows - sbin\remove-ainode.bat - ``` - 移除节点后，将无法查询到节点的相关信息。 - - ```shell -IoTDB> show cluster -+------+----------+-------+---------------+------------+-------+-----------+ -|NodeID| NodeType| Status|InternalAddress|InternalPort|Version| BuildInfo| -+------+----------+-------+---------------+------------+-------+-----------+ -| 0|ConfigNode|Running| 127.0.0.1| 10710|UNKNOWN|190e303-dev| -| 1| DataNode|Running| 127.0.0.1| 10730|UNKNOWN|190e303-dev| -+------+----------+-------+---------------+------------+-------+-----------+ -``` -##### 指定移除： - -如果用户丢失了 data 文件夹下的文件，可能 AINode 本地无法主动移除自己，需要用户指定节点号、地址和端口号进行移除，此时我们支持用户按照以下方法输入参数进行删除。 - - ```shell - # Linux / MacOS - bash sbin/remove-ainode.sh -t /: - - # Windows - sbin\remove-ainode.bat -t /: - ``` ## 4. 常见问题 diff --git a/src/zh/UserGuide/latest-Table/AI-capability/AINode_apache.md b/src/zh/UserGuide/latest-Table/AI-capability/AINode_apache.md new file mode 100644 index 000000000..0f4a8b1ca --- /dev/null +++ b/src/zh/UserGuide/latest-Table/AI-capability/AINode_apache.md @@ -0,0 +1,379 @@ + + +# AINode + +AINode 是支持时序大模型注册、管理、调用的 IoTDB 原生节点，内置业界领先的自研时序大模型，如 Timer、Sundial 等，可通过标准 SQL 语句进行调用，实现时序数据的毫秒级实时推理，可支持时序趋势预测、缺失值填补、异常值检测等应用场景。 + +系统架构如下图所示： + +![](/img/h4.png) + +三种节点的职责如下： + +- **ConfigNode**：负责保存和管理模型的元信息；负责分布式节点管理。 +- **DataNode**：负责接收并解析用户的 SQL请求；负责存储时间序列数据；负责数据的预处理计算。 +- **AINode**：负责模型文件的导入创建以及模型推理。 + +## 1. 优势特点 + +与单独构建机器学习服务相比，具有以下优势： + +- **简单易用**：无需使用 Python 或 Java 编程，使用 SQL 语句即可完成机器学习模型管理与推理的完整流程。如创建模型可使用CREATE MODEL语句、使用模型进行推理可使用 SELECT * FROM FORECAST (...) 语句等，使用更加简单便捷。 + +- **避免数据迁移**：使用 IoTDB 原生机器学习可以将存储在 IoTDB 中的数据直接应用于机器学习模型的推理，无需将数据移动到单独的机器学习服务平台，从而加速数据处理、提高安全性并降低成本。 + +![](/img/h1.png) + +- **内置先进算法**：支持业内领先机器学习分析算法，覆盖典型时序分析任务，为时序数据库赋能原生数据分析能力。如： + - **时间序列预测（Time Series Forecasting）**：从过去时间序列中学习变化模式；从而根据给定过去时间的观测值，输出未来序列最可能的预测。 + - **时序异常检测（Anomaly Detection for Time Series）**：在给定的时间序列数据中检测和识别异常值，帮助发现时间序列中的异常行为。 + - **时间序列标注（Time Series Annotation）**：为每个数据点或特定时间段添加额外的信息或标记，例如事件发生、异常点、趋势变化等，以便更好地理解和分析数据。 + + +## 2. 基本概念 + +- **模型（Model）**：机器学习模型，以时序数据作为输入，输出分析任务的结果或决策。模型是 AINode 的基本管理单元，支持模型的增（注册）、删、查、用（推理）。 +- **创建（Create）**: 将外部设计或训练好的模型文件或算法加载到 AINode 中，由 IoTDB 统一管理与使用。 +- **推理（Inference）**：使用创建的模型在指定时序数据上完成该模型适用的时序分析任务。 +- **内置能力（Built-in）**：AINode 自带常见时序分析场景（例如预测与异常检测）的机器学习算法或自研模型。 + +![](/img/h3.png) + +## 3. 安装部署 + +AINode 的部署可参考文档 [部署指导](../Deployment-and-Maintenance/AINode_Deployment_apache.md#ainode-部署) 章节。 + +## 4. 使用指导 + +AINode 对时序模型提供了模型创建及删除功能，内置模型无需创建，可直接使用。 + +### 4.1 注册模型 + +通过指定模型输入输出的向量维度，可以注册训练好的深度学习模型，从而用于模型推理。 + +符合以下内容的模型可以注册到AINode中： + 1. AINode 目前支持基于 PyTorch 2.4.0 版本训练的模型，需避免使用 2.4.0 版本以上的特性。 + 2. AINode 支持使用 PyTorch JIT 存储的模型（`model.pt`），模型文件需要包含模型的结构和权重。 + 3. 模型输入序列可以包含一列或多列，若有多列，需要和模型能力、模型配置文件对应。 + 4. 模型的配置参数必须在`config.yaml`配置文件中明确定义。使用模型时，必须严格按照`config.yaml`配置文件中定义的输入输出维度。如果输入输出列数不匹配配置文件，将会导致错误。 + +下方为模型注册的SQL语法定义。 + +```SQL +create model using uri +``` + +SQL中参数的具体含义如下： + +- model_name：模型的全局唯一标识，不可重复。模型名称具备以下约束： + + - 允许出现标识符 [ 0-9 a-z A-Z _ ] （字母，数字，下划线） + - 长度限制为2-64字符 + - 大小写敏感 + +- uri：模型注册文件的资源路径，路径下应包含**模型结构及权重文件 model.pt 文件和模型配置文件 config.yaml** + + - 模型结构及权重文件：模型训练完成后得到的权重文件，目前支持 pytorch 训练得到的 .pt 文件 + + - 模型配置文件：模型注册时需要提供的与模型结构有关的参数，其中必须包含模型的输入输出维度用于模型推理： + + - | **参数名** | **参数描述** | **示例** | + | ------------ | ---------------------------- | -------- | + | input_shape | 模型输入的行列，用于模型推理 | [96,2] | + | output_shape | 模型输出的行列，用于模型推理 | [48,2] | + + 除了模型推理外，还可以指定模型输入输出的数据类型： + + - | **参数名** | **参数描述** | **示例** | + | ----------- | ------------------ | --------------------- | + | input_type | 模型输入的数据类型 | ['float32','float32'] | + | output_type | 模型输出的数据类型 | ['float32','float32'] | + + 除此之外，可以额外指定备注信息用于在模型管理时进行展示 + + - | **参数名** | **参数描述** | **示例** | + | ---------- | ---------------------------------------------- | ------------------------------------------- | + | attributes | 可选，用户自行设定的模型备注信息，用于模型展示 | 'model_type': 'dlinear','kernel_size': '25' | + + +除了本地模型文件的注册，还可以通过URI来指定远程资源路径来进行注册，使用开源的模型仓库（例如HuggingFace）。 + +#### 示例 + +在当前的example文件夹下，包含model.pt和config.yaml文件，model.pt为训练得到，config.yaml的内容如下： + +```YAML +configs: + # 必选项 + input_shape: [96, 2] # 表示模型接收的数据为96行x2列 + output_shape: [48, 2] # 表示模型输出的数据为48行x2列 + + # 可选项 默认为全部float32，列数为shape对应的列数 + input_type: ["int64","int64"] #输入对应的数据类型，需要与输入列数匹配 + output_type: ["text","int64"] #输出对应的数据类型，需要与输出列数匹配 + +attributes: # 可选项 为用户自定义的备注信息 + 'model_type': 'dlinear' + 'kernel_size': '25' +``` + +指定该文件夹作为加载路径就可以注册该模型 + +```SQL +IoTDB> create model dlinear_example using uri "file://./example" +``` + +也可以从huggingFace上下载对应的模型文件进行注册 + +```SQL +IoTDB> create model dlinear_example using uri "https://huggingface.co/google/timesfm-2.0-500m-pytorch" +``` + +SQL执行后会异步进行注册的流程，可以通过模型展示查看模型的注册状态（见模型展示章节），注册成功的耗时主要受到模型文件大小的影响。 + +模型注册完成后，就可以通过使用正常查询的方式调用具体函数，进行模型推理。 + +### 4.2 查看模型 + +注册成功的模型可以通过show models指令查询模型的具体信息。其SQL定义如下： + +```SQL +show models + +show models +``` + +除了直接展示所有模型的信息外，可以指定model id来查看某一具体模型的信息。模型展示的结果中包含如下信息： + +| **ModelId** | **State** | **Configs** | **Attributes** | +| ------------ | ------------------------------------- | ---------------------------------------------- | -------------- | +| 模型唯一标识 | 模型注册状态(INACTIVE,LOADING,ACTIVE,DROPPING) | InputShape, outputShapeInputTypes, outputTypes | 模型备注信息 | + +其中，State用于展示当前模型注册的状态，包含以下三个阶段 + +- **INACTIVE**：模型处于不可用状态 +- **LOADING**：模型加载中状态 +- **ACTIVE**：模型处于可用状态 +- **DROPPING**:模型删除中状态 + +#### 示例 + +```SQL +IoTDB> show models + ++---------------------+--------------------+--------+--------+ +| ModelId| ModelType|Category| State| ++---------------------+--------------------+--------+--------+ +| arima| Arima|BUILT-IN| ACTIVE| +| holtwinters| HoltWinters|BUILT-IN| ACTIVE| +|exponential_smoothing|ExponentialSmoothing|BUILT-IN| ACTIVE| +| naive_forecaster| NaiveForecaster|BUILT-IN| ACTIVE| +| stl_forecaster| StlForecaster|BUILT-IN| ACTIVE| +| gaussian_hmm| GaussianHmm|BUILT-IN| ACTIVE| +| gmm_hmm| GmmHmm|BUILT-IN| ACTIVE| +| stray| Stray|BUILT-IN| ACTIVE| +| timer_xl| Timer-XL|BUILT-IN| ACTIVE| +| sundial| Timer-Sundial|BUILT-IN| ACTIVE| ++---------------------+--------------------+--------+--------+ +``` + +### 4.3 删除模型 + +对于注册成功的模型，用户可以通过SQL进行删除，该操作会删除所有 AINode 下的相关模型文件，其SQL如下： + +```SQL +drop model +``` + +需要指定已经成功注册的模型 model_id 来删除对应的模型。由于模型删除涉及模型数据清理，操作不会立即完成，此时模型的状态为 DROPPING，该状态的模型不能用于模型推理。请注意，该功能不支持删除内置模型。 + +### 4.4 使用内置模型推理 + +SQL语法如下： + + +```SQL +SELECT * FROM forecast( + input, + model_id, + [output_length, + output_start_time, + output_interval, + timecol, + preserve_input, + model_options]? +) +``` + +内置模型推理无需注册流程，通过 forecast 函数，指定 model_id 就可以使用模型的推理功能 + + - 请注意，使用内置时序大模型进行推理的前提条件是本地存有对应模型权重，目录为 /IOTDB_AINODE_HOME/data/ainode/models/weights/model_id/。若本地没有模型权重，则会自动从 HuggingFace 拉取，请保证本地能直接访问 HuggingFace。 + +- 参数介绍如下： + +| 参数名 | 参数类型 | 参数属性 | 描述 | 是否必填 | 备注 | +| :---------------- | :------- | :----------------------------------------------------------- | :----------------------------------------------------------- | :------- | :----------------------------------------------------------- | +| input | 表参数 | SET SEMANTIC | 待预测的输入数据 | 是 | | +| model_id | 标量参数 | 字符串类型 | 需要选择的model名 | 是 | 只能为非空，且内置的模型，否则报错：空字符串：MODEL_ID should never be null or empty不存在的模型：model [%s] has not been created模型不可用：model [%s] is not available | +| output_length | 标量参数 | INT32类型默认值：96 | 输出窗口大小 | 否 | 必须大于 0，否则报错：OUTPUT_LENGTH should be greater than 0 | +| output_start_time | 标量参数 | 时间戳类型默认值：输入数据的最后一个时间戳加 output_interval | 输出的预测点的起始时间戳 | 否 | 可以为负数，表示1970年1月1号之前的时间戳 | +| output_interval | 标量参数 | 时间间隔类型默认值：0（输入数据的采样间隔） | 输出的预测点之间的时间间隔支持的单位是 ns、us、ms、s、m、h、d、w | 否 | 大于 0 时，采用用户指定的输出间隔；小于等于 0 时，根据输入数据自动推测 | +| timecol | 标量参数 | 字符串类型默认值：time | 时间列名 | 否 | 存在于 input 中的，数据类型为 TIMESTAMP 的列，否则报错：若数据类型不为 TIMESTAMP： The type of the column [%s] is not as expected.若列不存在：Required column [%s] not found in the source table argument. | +| preserve_input | 标量参数 | 布尔类型默认值：false | 是否在输出结果集中保留输入的所有原始行 | 否 | | +| model_options | 标量参数 | 字符串类型默认值：空字符串 | 模型相关的key-value对，比如是否需要对输入进行归一化等。不同的key-value对以';'间隔 | 否 | 指定某个模型不支持参数，并不会报错，只会被忽略；AINode 中内置的模型支持的常见参数详见文末附录说明。 | + +**说明：** + +1. forecast 函数默认对输入表中所有列进行预测（不包含time列和partition by 的列）。 +2. forecast 函数对于输入数据无顺序性要求，默认对输入数据按照时间戳（由 TIMECOL 参数指定时间戳的列名）做升序排序后，再调用模型进行预测。 +3. 不同模型对于输入数据的行数要求不同，输入数据少于最低行数要求时，则报错；输入数据高于最大行数要求时，则自动截取最后的满足要求的行数。 + - 在当前的 AINdoe 内置模型中，仅_sundial对输入行数有限制。_sundial 目前支持的最大输入行数为 2880，超过 2880 时，会自动截取输入数据的最后 2880 行。 +4. forecast 函数的返回结果列包含 input 表的所有输入列，列的数据类型与原表列的数据类型一致。若 preserve_input= true，则还包含 is_input 列（表征当前行是否为输入行） + - 目前只支持对 INT32、INT64、FLOAT、DOUBLE 进行预测，否则报错：The type of the column [%s] is [%s], only INT32, INT64, FLOAT, DOUBLE is allowed +5. output_start_time 和 output_interval 只会影响输出结果集的时间戳列生成，均为可选参数。 + - output_start_time 默认为输入数据的最后一个时间戳加 output_interval + - output_interval = (输入数据的最后一个时间戳 - 输入数据的第一个时间戳) / n - 1， 默认为输入数据的采样间隔 + - 第 N 个输出行的时间为 output_start_time + (N - 1) * output_interval + +#### 示例 + +我们所使用的的测试集的数据为[ETTh1-tab](/img/ETTh1-tab.csv)。 + +**查看当前支持的模型** + +```Bash +IoTDB:etth> show models ++---------------------+--------------------+--------+------+ +| ModelId| ModelType|Category| State| ++---------------------+--------------------+--------+------+ +| arima| Arima|BUILT-IN|ACTIVE| +| holtwinters| HoltWinters|BUILT-IN|ACTIVE| +|exponential_smoothing|ExponentialSmoothing|BUILT-IN|ACTIVE| +| naive_forecaster| NaiveForecaster|BUILT-IN|ACTIVE| +| stl_forecaster| StlForecaster|BUILT-IN|ACTIVE| +| gaussian_hmm| GaussianHmm|BUILT-IN|ACTIVE| +| gmm_hmm| GmmHmm|BUILT-IN|ACTIVE| +| stray| Stray|BUILT-IN|ACTIVE| +| sundial| Timer-Sundial|BUILT-IN|ACTIVE| +| timer_xl| Timer-XL|BUILT-IN|ACTIVE| ++---------------------+--------------------+--------+------+ +Total line number = 10 +It costs 0.004s +``` + +**表模型推理（以 sundial 为例）** + +```Bash +IoTDB:etth> select Time, HUFL,HULL,MUFL,MULL,LUFL,LULL,OT from eg LIMIT 96 ++-----------------------------+------+-----+-----+-----+-----+-----+------+ +| Time| HUFL| HULL| MUFL| MULL| LUFL| LULL| OT| ++-----------------------------+------+-----+-----+-----+-----+-----+------+ +|2016-07-01T00:00:00.000+08:00| 5.827|2.009|1.599|0.462|4.203| 1.34|30.531| +|2016-07-01T01:00:00.000+08:00| 5.693|2.076|1.492|0.426|4.142|1.371|27.787| +|2016-07-01T02:00:00.000+08:00| 5.157|1.741|1.279|0.355|3.777|1.218|27.787| +|2016-07-01T03:00:00.000+08:00| 5.09|1.942|1.279|0.391|3.807|1.279|25.044| +...... +Total line number = 96 +It costs 0.119s + +IoTDB:etth> select * from forecast( + model_id => 'sundial', + input => (select Time, ot from etth.eg where time >= 2016-08-07T18:00:00.000+08:00 limit 1440) order BY time, + output_length => 96 +) ++-----------------------------+---------+ +| time| ot| ++-----------------------------+---------+ +|2016-10-06T18:00:00.000+08:00|20.781654| +|2016-10-06T19:00:00.000+08:00|20.252121| +|2016-10-06T20:00:00.000+08:00|19.960138| +|2016-10-06T21:00:00.000+08:00|19.662334| +...... +Total line number = 96 +It costs 1.615s +``` + +### 4.5 时序大模型导入步骤 + +AINode 目前支持多种时序大模型，部署使用请参考[时序大模型](https://timecho.com/docs/zh/UserGuide/latest/AI-capability/TimeSeries-Large-Model.html) + +## 5 权限管理 + +使用AINode相关的功能时，可以使用IoTDB本身的鉴权去做一个权限管理，用户只有在具备 USE_MODEL 权限时，才可以使用模型管理的相关功能。当使用推理功能时，用户需要有访问输入模型的SQL对应的源序列的权限。 + +| **权限名称** | **权限范围** | **管理员用户（默认ROOT）** | **普通用户** | **路径相关** | +| :----------- | :-------------------------------------- | :------------------------- | :----------- | :----------- | +| USE_MODEL | create model / show models / drop model | √ | √ | x | +| READ_DATA | call inference | √ | √ | √ | + +## 6 附录 + +**Arima** + +| 支持的参数 | 含义 | 默认值 | +| :---------------------- | :----------------------------------------------------------- | :-------- | +| order | ARIMA模型的阶数 `(p, d, q)`：p是自回归阶数，d是差分阶数，q是滑动平均阶数。 | (1,0,0) | +| seansonal_order | 季节性ARIMA的阶数 `(P, D, Q, s)`：分别为季节性自回归、差分、滑动平均阶数，s是季节周期（如12代表月度数据）。 | (0,0,0,0) | +| method | 优化器选择，可选：'newton'、'nm'、'bfgs'、'lbfgs'、'powell'、'cg'、'ncg'、'basinhopping'。 | 'lbfgs' | +| maxiter | 最大迭代次数或函数评估次数。 | 50 | +| out_of_sample_size | 用于验证的时间序列尾部样本数，模型不在这些样本上拟合。 | 0 | +| scoring | 验证时使用的评分函数，字符串需为 sklearn 中可导入的评分指标，或用户自定义函数。 | 'mse' | +| trend | 趋势项配置，若 with_intercept=True 且此项为 None，则默认使用 'c'（包含常数项）。 | None | +| with_intercept | 是否包含截距项。 | True | +| time_varying_regression | 是否允许回归系数随时间变化。 | False | +| enforce_stationarity | 是否强制AR部分平稳性。 | True | +| enforce_invertibility | 是否强制MA部分可逆性。 | True | +| simple_differnecing | 是否使用差分后的数据估计（牺牲前几行数据换取更简状态空间）。 | False | +| measurement_error | 是否认为观测值中含有误差。 | False | +| mle_regression | 是否使用极大似然估计回归系数，若 `time_varying_regression=True` 则必须为 False。 | True | +| hamilton_representation | 是否使用 Hamilton 表达方式（默认用 Harvey）。 | False | +| concentrate_scale | 是否从似然函数中排除误差方差参数，减少待估参数个数（但无法获得误差项方差的标准误）。 | False | + +**NaiveForecaster** + +| 支持的参数 | 含义 | 默认值 | +| ---------- | ------------------------------------------------------------ | ------ | +| strategy | 预测策略： • `"last"`：预测训练集最后一个值；若设置了季节周期（`sp`>1），则每个季节分别预测其最后一个周期值。对 NaN 值鲁棒。 • `"mean"`：预测最后窗口中的平均值；若 `sp`>1，按每个季节分别计算均值。对 NaN 值鲁棒。 • `"drift"`：用最后窗口的首尾点拟合一条直线并外推预测。对 NaN 值不鲁棒。 | "last" | +| sp | 季节性周期。若为 `None`，等效于 `1`，表示无季节性；如果设为 12，表示每 12 个单位（如月）为一个周期。 | 1 | + +- STLForecaster + +| 支持的参数 | 含义 | 默认值 | +| :------------ | :----------------------------------------------------------- | :----- | +| sp | 季节周期长度（周期性单位数）。传入 statsmodels 的 STL 中。 | 2 | +| seasonal | 季节项平滑窗口长度，必须为 ≥3 的奇数，通常建议 ≥7。 | 7 | +| seasonal_deg | 季节项 LOESS 的多项式阶数（0 表示常数，1 表示线性）。 | 1 | +| trend_deg | 趋势项 LOESS 的多项式阶数（0 或 1）。 | 1 | +| low_pass_deg | 低通项 LOESS 的多项式阶数（0 或 1）。 | 1 | +| seasonal_jump | LOESS 拟合的插值步长（季节项），每 n 点拟合一次，中间插值。值越大，估计速度越快。 | 1 | +| trend_jump | 趋势项插值步长，越大速度越快但精度可能下降。 | 1 | +| low_pass_jump | 低通项插值步长，设置同上。 | 1 | + +**ExponentialSmoothing (HoltWinters)** + +| 支持的参数 | 含义 | 默认值 | +| :-------------------- | :----------------------------------------------------------- | :---------- | +| damped_trend | 是否使用阻尼趋势（趋势会逐渐平缓，而非无限增长）。 | True | +| initialization_method | 初始化方法： • `"estimated"`：通过拟合估计初始状态 • `"heuristic"`：使用启发式方法估计初始水平/趋势/季节 • `"known"`：用户显式提供所有初始值 • `"legacy-heuristic"`：旧版本兼容方式 | "estimated" | +| optmized | 是否通过最大化对数似然来优化参数。 | True | +| remove_bias | 是否移除偏差，使预测值和拟合值的残差平均值为0。 | False | +| use_brute | 是否使用穷举法（网格搜索）来寻找初始参数。否则使用启发式初始值。 | | \ No newline at end of file diff --git a/src/zh/UserGuide/latest-Table/AI-capability/AINode_timecho.md b/src/zh/UserGuide/latest-Table/AI-capability/AINode_timecho.md new file mode 100644 index 000000000..53370fd52 --- /dev/null +++ b/src/zh/UserGuide/latest-Table/AI-capability/AINode_timecho.md @@ -0,0 +1,444 @@ + + +# AINode + +AINode 是支持时序大模型注册、管理、调用的 IoTDB 原生节点，内置业界领先的自研时序大模型，如 Timer、Sundial 等，可通过标准 SQL 语句进行调用，实现时序数据的毫秒级实时推理，可支持时序趋势预测、缺失值填补、异常值检测等应用场景。 + +系统架构如下图所示： + +![](/img/h4.png) + +三种节点的职责如下： + +- **ConfigNode**：负责保存和管理模型的元信息；负责分布式节点管理。 +- **DataNode**：负责接收并解析用户的 SQL请求；负责存储时间序列数据；负责数据的预处理计算。 +- **AINode**：负责模型文件的导入创建以及模型推理。 + +## 1. 优势特点 + +与单独构建机器学习服务相比，具有以下优势： + +- **简单易用**：无需使用 Python 或 Java 编程，使用 SQL 语句即可完成机器学习模型管理与推理的完整流程。如创建模型可使用CREATE MODEL语句、使用模型进行推理可使用 SELECT * FROM FORECAST (...) 语句等，使用更加简单便捷。 + +- **避免数据迁移**：使用 IoTDB 原生机器学习可以将存储在 IoTDB 中的数据直接应用于机器学习模型的推理，无需将数据移动到单独的机器学习服务平台，从而加速数据处理、提高安全性并降低成本。 + +![](/img/h1.png) + +- **内置先进算法**：支持业内领先机器学习分析算法，覆盖典型时序分析任务，为时序数据库赋能原生数据分析能力。如： + - **时间序列预测（Time Series Forecasting）**：从过去时间序列中学习变化模式；从而根据给定过去时间的观测值，输出未来序列最可能的预测。 + - **时序异常检测（Anomaly Detection for Time Series）**：在给定的时间序列数据中检测和识别异常值，帮助发现时间序列中的异常行为。 + - **时间序列标注（Time Series Annotation）**：为每个数据点或特定时间段添加额外的信息或标记，例如事件发生、异常点、趋势变化等，以便更好地理解和分析数据。 + + +## 2. 基本概念 + +- **模型（Model）**：机器学习模型，以时序数据作为输入，输出分析任务的结果或决策。模型是 AINode 的基本管理单元，支持模型的增（注册）、删、查、改（微调）、用（推理）。 +- **创建（Create）**: 将外部设计或训练好的模型文件或算法加载到 AINode 中，由 IoTDB 统一管理与使用。 +- **推理（Inference）**：使用创建的模型在指定时序数据上完成该模型适用的时序分析任务。 +- **内置能力（Built-in）**：AINode 自带常见时序分析场景（例如预测与异常检测）的机器学习算法或自研模型。 + +![](/img/h3.png) + +## 3. 安装部署 + +AINode 的部署可参考文档 [部署指导](../Deployment-and-Maintenance/AINode_Deployment_apache.md#ainode-部署) 章节。 + +## 4. 使用指导 + +AINode 对时序模型提供了模型创建及删除功能，内置模型无需创建，可直接使用。 + +### 4.1 注册模型 + +通过指定模型输入输出的向量维度，可以注册训练好的深度学习模型，从而用于模型推理。 + +符合以下内容的模型可以注册到AINode中： + 1. AINode 目前支持基于 PyTorch 2.4.0 版本训练的模型，需避免使用 2.4.0 版本以上的特性。 + 2. AINode 支持使用 PyTorch JIT 存储的模型（`model.pt`），模型文件需要包含模型的结构和权重。 + 3. 模型输入序列可以包含一列或多列，若有多列，需要和模型能力、模型配置文件对应。 + 4. 模型的配置参数必须在`config.yaml`配置文件中明确定义。使用模型时，必须严格按照`config.yaml`配置文件中定义的输入输出维度。如果输入输出列数不匹配配置文件，将会导致错误。 + +下方为模型注册的SQL语法定义。 + +```SQL +create model using uri +``` + +SQL中参数的具体含义如下： + +- model_name：模型的全局唯一标识，不可重复。模型名称具备以下约束： + + - 允许出现标识符 [ 0-9 a-z A-Z _ ] （字母，数字，下划线） + - 长度限制为2-64字符 + - 大小写敏感 + +- uri：模型注册文件的资源路径，路径下应包含**模型结构及权重文件 model.pt 文件和模型配置文件 config.yaml** + + - 模型结构及权重文件：模型训练完成后得到的权重文件，目前支持 pytorch 训练得到的 .pt 文件 + + - 模型配置文件：模型注册时需要提供的与模型结构有关的参数，其中必须包含模型的输入输出维度用于模型推理： + + - | **参数名** | **参数描述** | **示例** | + | ------------ | ---------------------------- | -------- | + | input_shape | 模型输入的行列，用于模型推理 | [96,2] | + | output_shape | 模型输出的行列，用于模型推理 | [48,2] | + + - ​ 除了模型推理外，还可以指定模型输入输出的数据类型： + + - | **参数名** | **参数描述** | **示例** | + | ----------- | ------------------ | --------------------- | + | input_type | 模型输入的数据类型 | ['float32','float32'] | + | output_type | 模型输出的数据类型 | ['float32','float32'] | + + - ​ 除此之外，可以额外指定备注信息用于在模型管理时进行展示 + + - | **参数名** | **参数描述** | **示例** | + | ---------- | ---------------------------------------------- | ------------------------------------------- | + | attributes | 可选，用户自行设定的模型备注信息，用于模型展示 | 'model_type': 'dlinear','kernel_size': '25' | + + +除了本地模型文件的注册，还可以通过URI来指定远程资源路径来进行注册，使用开源的模型仓库（例如HuggingFace）。 + +#### 示例 + +在当前的example文件夹下，包含model.pt和config.yaml文件，model.pt为训练得到，config.yaml的内容如下： + +```YAML +configs: + # 必选项 + input_shape: [96, 2] # 表示模型接收的数据为96行x2列 + output_shape: [48, 2] # 表示模型输出的数据为48行x2列 + + # 可选项 默认为全部float32，列数为shape对应的列数 + input_type: ["int64","int64"] #输入对应的数据类型，需要与输入列数匹配 + output_type: ["text","int64"] #输出对应的数据类型，需要与输出列数匹配 + +attributes: # 可选项 为用户自定义的备注信息 + 'model_type': 'dlinear' + 'kernel_size': '25' +``` + +指定该文件夹作为加载路径就可以注册该模型 + +```SQL +IoTDB> create model dlinear_example using uri "file://./example" +``` + +也可以从huggingFace上下载对应的模型文件进行注册 + +```SQL +IoTDB> create model dlinear_example using uri "https://huggingface.co/google/timesfm-2.0-500m-pytorch" +``` + +SQL执行后会异步进行注册的流程，可以通过模型展示查看模型的注册状态（见模型展示章节），注册成功的耗时主要受到模型文件大小的影响。 + +模型注册完成后，就可以通过使用正常查询的方式调用具体函数，进行模型推理。 + +### 4.2 查看模型 + +注册成功的模型可以通过show models指令查询模型的具体信息。其SQL定义如下： + +```SQL +show models + +show models +``` + +除了直接展示所有模型的信息外，可以指定model id来查看某一具体模型的信息。模型展示的结果中包含如下信息： + +| **ModelId** | **State** | **Configs** | **Attributes** | +| ------------ | ------------------------------------- | ---------------------------------------------- | -------------- | +| 模型唯一标识 | 模型注册状态(INACTIVE,LOADING,ACTIVE,TRAINING,FAILED,DROPPING) | InputShape, outputShapeInputTypes, outputTypes | 模型备注信息 | + +其中，State用于展示当前模型注册的状态，包含以下三个阶段 + +- **INACTIVE**：模型处于不可用状态 +- **LOADING**：模型加载中 +- **ACTIVE**：模型处于可用状态 +- **TRAINING**：模型处于微调中状态 +- **FAILED**：模型微调失败状态 +- **DROPPING**:模型删除中状态 + +#### 示例 + +```SQL +IoTDB> show models + ++---------------------+--------------------+--------+--------+ +| ModelId| ModelType|Category| State| ++---------------------+--------------------+--------+--------+ +| arima| Arima|BUILT-IN| ACTIVE| +| holtwinters| HoltWinters|BUILT-IN| ACTIVE| +|exponential_smoothing|ExponentialSmoothing|BUILT-IN| ACTIVE| +| naive_forecaster| NaiveForecaster|BUILT-IN| ACTIVE| +| stl_forecaster| StlForecaster|BUILT-IN| ACTIVE| +| gaussian_hmm| GaussianHmm|BUILT-IN| ACTIVE| +| gmm_hmm| GmmHmm|BUILT-IN| ACTIVE| +| stray| Stray|BUILT-IN| ACTIVE| +| timer_xl| Timer-XL|BUILT-IN| ACTIVE| +| sundial| Timer-Sundial|BUILT-IN| ACTIVE| ++---------------------+--------------------+--------+--------+ +``` + +### 4.3 删除模型 + +对于注册成功的模型，用户可以通过SQL进行删除，该操作会删除所有 AINode 下的相关模型文件，其SQL如下： + +```SQL +drop model +``` + +需要指定已经成功注册的模型 model_id 来删除对应的模型。由于模型删除涉及模型数据清理，操作不会立即完成，此时模型的状态为 DROPPING，该状态的模型不能用于模型推理。请注意，该功能不支持删除内置模型。 + +### 4.4 使用内置模型推理 + +SQL语法如下： + + +```SQL +SELECT * FROM forecast( + input, + model_id, + [output_length, + output_start_time, + output_interval, + timecol, + preserve_input, + model_options]? +) +``` + +内置模型推理无需注册流程，通过 forecast 函数，指定 model_id 就可以使用模型的推理功能 + + - 请注意，使用内置时序大模型进行推理的前提条件是本地存有对应模型权重，目录为 /IOTDB_AINODE_HOME/data/ainode/models/weights/model_id/。若本地没有模型权重，则会自动从 HuggingFace 拉取，请保证本地能直接访问 HuggingFace。 + +- 参数介绍如下： + +| 参数名 | 参数类型 | 参数属性 | 描述 | 是否必填 | 备注 | +| :---------------- | :------- | :----------------------------------------------------------- | :----------------------------------------------------------- | :------- | :----------------------------------------------------------- | +| input | 表参数 | SET SEMANTIC | 待预测的输入数据 | 是 | | +| model_id | 标量参数 | 字符串类型 | 需要选择的model名 | 是 | 只能为非空，且内置的模型，否则报错：空字符串：MODEL_ID should never be null or empty不存在的模型：model [%s] has not been created模型不可用：model [%s] is not available | +| output_length | 标量参数 | INT32类型默认值：96 | 输出窗口大小 | 否 | 必须大于 0，否则报错：OUTPUT_LENGTH should be greater than 0 | +| output_start_time | 标量参数 | 时间戳类型默认值：输入数据的最后一个时间戳加 output_interval | 输出的预测点的起始时间戳 | 否 | 可以为负数，表示1970年1月1号之前的时间戳 | +| output_interval | 标量参数 | 时间间隔类型默认值：0（输入数据的采样间隔） | 输出的预测点之间的时间间隔支持的单位是 ns、us、ms、s、m、h、d、w | 否 | 大于 0 时，采用用户指定的输出间隔；小于等于 0 时，根据输入数据自动推测 | +| timecol | 标量参数 | 字符串类型默认值：time | 时间列名 | 否 | 存在于 input 中的，数据类型为 TIMESTAMP 的列，否则报错：若数据类型不为 TIMESTAMP： The type of the column [%s] is not as expected.若列不存在：Required column [%s] not found in the source table argument. | +| preserve_input | 标量参数 | 布尔类型默认值：false | 是否在输出结果集中保留输入的所有原始行 | 否 | | +| model_options | 标量参数 | 字符串类型默认值：空字符串 | 模型相关的key-value对，比如是否需要对输入进行归一化等。不同的key-value对以';'间隔 | 否 | 指定某个模型不支持参数，并不会报错，只会被忽略；AINode 中内置的模型支持的常见参数详见文末附录说明。 | + +**说明：** + +1. forecast 函数默认对输入表中所有列进行预测（不包含time列和partition by 的列）。 +2. forecast 函数对于输入数据无顺序性要求，默认对输入数据按照时间戳（由 TIMECOL 参数指定时间戳的列名）做升序排序后，再调用模型进行预测。 +3. 不同模型对于输入数据的行数要求不同，输入数据少于最低行数要求时，则报错；输入数据高于最大行数要求时，则自动截取最后的满足要求的行数。 + - 在当前的 AINdoe 内置模型中，仅_sundial对输入行数有限制。_sundial 目前支持的最大输入行数为 2880，超过 2880 时，会自动截取输入数据的最后 2880 行。 +4. forecast 函数的返回结果列包含 input 表的所有输入列，列的数据类型与原表列的数据类型一致。若 preserve_input= true，则还包含 is_input 列（表征当前行是否为输入行） + - 目前只支持对 INT32、INT64、FLOAT、DOUBLE 进行预测，否则报错：The type of the column [%s] is [%s], only INT32, INT64, FLOAT, DOUBLE is allowed +5. output_start_time 和 output_interval 只会影响输出结果集的时间戳列生成，均为可选参数。 + - output_start_time 默认为输入数据的最后一个时间戳加 output_interval + - output_interval = (输入数据的最后一个时间戳 - 输入数据的第一个时间戳) / n - 1， 默认为输入数据的采样间隔 + - 第 N 个输出行的时间为 output_start_time + (N - 1) * output_interval + +#### 示例 + +我们所使用的的测试集的数据为[ETTh1-tab](/img/ETTh1-tab.csv)。 + +**查看当前支持的模型** + +```Bash +IoTDB:etth> show models ++---------------------+--------------------+--------+------+ +| ModelId| ModelType|Category| State| ++---------------------+--------------------+--------+------+ +| arima| Arima|BUILT-IN|ACTIVE| +| holtwinters| HoltWinters|BUILT-IN|ACTIVE| +|exponential_smoothing|ExponentialSmoothing|BUILT-IN|ACTIVE| +| naive_forecaster| NaiveForecaster|BUILT-IN|ACTIVE| +| stl_forecaster| StlForecaster|BUILT-IN|ACTIVE| +| gaussian_hmm| GaussianHmm|BUILT-IN|ACTIVE| +| gmm_hmm| GmmHmm|BUILT-IN|ACTIVE| +| stray| Stray|BUILT-IN|ACTIVE| +| sundial| Timer-Sundial|BUILT-IN|ACTIVE| +| timer_xl| Timer-XL|BUILT-IN|ACTIVE| ++---------------------+--------------------+--------+------+ +Total line number = 10 +It costs 0.004s +``` + +**表模型推理（以 sundial 为例）** + +```Bash +IoTDB:etth> select Time, HUFL,HULL,MUFL,MULL,LUFL,LULL,OT from eg LIMIT 96 ++-----------------------------+------+-----+-----+-----+-----+-----+------+ +| Time| HUFL| HULL| MUFL| MULL| LUFL| LULL| OT| ++-----------------------------+------+-----+-----+-----+-----+-----+------+ +|2016-07-01T00:00:00.000+08:00| 5.827|2.009|1.599|0.462|4.203| 1.34|30.531| +|2016-07-01T01:00:00.000+08:00| 5.693|2.076|1.492|0.426|4.142|1.371|27.787| +|2016-07-01T02:00:00.000+08:00| 5.157|1.741|1.279|0.355|3.777|1.218|27.787| +|2016-07-01T03:00:00.000+08:00| 5.09|1.942|1.279|0.391|3.807|1.279|25.044| +...... +Total line number = 96 +It costs 0.119s + +IoTDB:etth> select * from forecast( + model_id => 'sundial', + input => (select Time, ot from etth.eg where time >= 2016-08-07T18:00:00.000+08:00 limit 1440) order BY time, + output_length => 96 +) ++-----------------------------+---------+ +| time| ot| ++-----------------------------+---------+ +|2016-10-06T18:00:00.000+08:00|20.781654| +|2016-10-06T19:00:00.000+08:00|20.252121| +|2016-10-06T20:00:00.000+08:00|19.960138| +|2016-10-06T21:00:00.000+08:00|19.662334| +...... +Total line number = 96 +It costs 1.615s +``` +### 4.5 使用内置模型微调 + +> 仅 Timer-XL、Timer-Sundial 可以进行微调操作。 + +SQL语法如下： + + +```SQL +create model (with hyperparameters +(=(, =)*))? +from model +on dataset (inputSql) +``` + +#### 示例 + +1. 选择测点 ot 中前 80% 数据作为微调数据集，基于 sundial 创建模型 sundialv3。 + +```SQL +IoTDB> set sql_dialect=table +Msg: The statement is executed successfully. +IoTDB> CREATE MODEL sundialv3 FROM MODEL sundial ON DATASET ('SELECT time, ot from etth.eg where 1467302400000 <= time and time < 1517468400001') +Msg: The statement is executed successfully. +IoTDB> show models ++---------------------+--------------------+----------+--------+ +| ModelId| ModelType| Category| State| ++---------------------+--------------------+----------+--------+ +| arima| Arima| BUILT-IN| ACTIVE| +| holtwinters| HoltWinters| BUILT-IN| ACTIVE| +|exponential_smoothing|ExponentialSmoothing| BUILT-IN| ACTIVE| +| naive_forecaster| NaiveForecaster| BUILT-IN| ACTIVE| +| stl_forecaster| StlForecaster| BUILT-IN| ACTIVE| +| gaussian_hmm| GaussianHmm| BUILT-IN| ACTIVE| +| gmm_hmm| GmmHmm| BUILT-IN| ACTIVE| +| stray| Stray| BUILT-IN| ACTIVE| +| sundial| Timer-Sundial| BUILT-IN| ACTIVE| +| timer_xl| Timer-XL| BUILT-IN| ACTIVE| +| sundialv2| Timer-Sundial|FINE-TUNED| ACTIVE| +| sundialv3| Timer-Sundial|FINE-TUNED|TRAINING| ++---------------------+--------------------+----------+--------+ +``` + +2. 微调任务后台异步启动，可在 AINode 进程看到 log；微调完成后，查询并使用新的模型 + +```SQL +IoTDB> show models ++---------------------+--------------------+----------+------+ +| ModelId| ModelType| Category| State| ++---------------------+--------------------+----------+------+ +| arima| Arima| BUILT-IN|ACTIVE| +| holtwinters| HoltWinters| BUILT-IN|ACTIVE| +|exponential_smoothing|ExponentialSmoothing| BUILT-IN|ACTIVE| +| naive_forecaster| NaiveForecaster| BUILT-IN|ACTIVE| +| stl_forecaster| StlForecaster| BUILT-IN|ACTIVE| +| gaussian_hmm| GaussianHmm| BUILT-IN|ACTIVE| +| gmm_hmm| GmmHmm| BUILT-IN|ACTIVE| +| stray| Stray| BUILT-IN|ACTIVE| +| sundial| Timer-Sundial| BUILT-IN|ACTIVE| +| timer_xl| Timer-XL| BUILT-IN|ACTIVE| +| sundialv2| Timer-Sundial|FINE-TUNED|ACTIVE| +| sundialv3| Timer-Sundial|FINE-TUNED|ACTIVE| ++---------------------+--------------------+----------+------+ +``` + +### 4.6 时序大模型导入步骤 + +AINode 目前支持多种时序大模型，部署使用请参考[时序大模型](https://timecho.com/docs/zh/UserGuide/latest/AI-capability/TimeSeries-Large-Model.html) + +## 5 权限管理 + +使用AINode相关的功能时，可以使用IoTDB本身的鉴权去做一个权限管理，用户只有在具备 USE_MODEL 权限时，才可以使用模型管理的相关功能。当使用推理功能时，用户需要有访问输入模型的SQL对应的源序列的权限。 + +| **权限名称** | **权限范围** | **管理员用户（默认ROOT）** | **普通用户** | **路径相关** | +| :----------- | :-------------------------------------- | :------------------------- | :----------- | :----------- | +| USE_MODEL | create model / show models / drop model | √ | √ | x | +| READ_DATA | call inference | √ | √ | √ | + +## 6 附录 + +**Arima** + +| 支持的参数 | 含义 | 默认值 | +| :---------------------- | :----------------------------------------------------------- | :-------- | +| order | ARIMA模型的阶数 `(p, d, q)`：p是自回归阶数，d是差分阶数，q是滑动平均阶数。 | (1,0,0) | +| seansonal_order | 季节性ARIMA的阶数 `(P, D, Q, s)`：分别为季节性自回归、差分、滑动平均阶数，s是季节周期（如12代表月度数据）。 | (0,0,0,0) | +| method | 优化器选择，可选：'newton'、'nm'、'bfgs'、'lbfgs'、'powell'、'cg'、'ncg'、'basinhopping'。 | 'lbfgs' | +| maxiter | 最大迭代次数或函数评估次数。 | 50 | +| out_of_sample_size | 用于验证的时间序列尾部样本数，模型不在这些样本上拟合。 | 0 | +| scoring | 验证时使用的评分函数，字符串需为 sklearn 中可导入的评分指标，或用户自定义函数。 | 'mse' | +| trend | 趋势项配置，若 with_intercept=True 且此项为 None，则默认使用 'c'（包含常数项）。 | None | +| with_intercept | 是否包含截距项。 | True | +| time_varying_regression | 是否允许回归系数随时间变化。 | False | +| enforce_stationarity | 是否强制AR部分平稳性。 | True | +| enforce_invertibility | 是否强制MA部分可逆性。 | True | +| simple_differnecing | 是否使用差分后的数据估计（牺牲前几行数据换取更简状态空间）。 | False | +| measurement_error | 是否认为观测值中含有误差。 | False | +| mle_regression | 是否使用极大似然估计回归系数，若 `time_varying_regression=True` 则必须为 False。 | True | +| hamilton_representation | 是否使用 Hamilton 表达方式（默认用 Harvey）。 | False | +| concentrate_scale | 是否从似然函数中排除误差方差参数，减少待估参数个数（但无法获得误差项方差的标准误）。 | False | + +**NaiveForecaster** + +| 支持的参数 | 含义 | 默认值 | +| ---------- | ------------------------------------------------------------ | ------ | +| strategy | 预测策略： • `"last"`：预测训练集最后一个值；若设置了季节周期（`sp`>1），则每个季节分别预测其最后一个周期值。对 NaN 值鲁棒。 • `"mean"`：预测最后窗口中的平均值；若 `sp`>1，按每个季节分别计算均值。对 NaN 值鲁棒。 • `"drift"`：用最后窗口的首尾点拟合一条直线并外推预测。对 NaN 值不鲁棒。 | "last" | +| sp | 季节性周期。若为 `None`，等效于 `1`，表示无季节性；如果设为 12，表示每 12 个单位（如月）为一个周期。 | 1 | + +- STLForecaster + +| 支持的参数 | 含义 | 默认值 | +| :------------ | :----------------------------------------------------------- | :----- | +| sp | 季节周期长度（周期性单位数）。传入 statsmodels 的 STL 中。 | 2 | +| seasonal | 季节项平滑窗口长度，必须为 ≥3 的奇数，通常建议 ≥7。 | 7 | +| seasonal_deg | 季节项 LOESS 的多项式阶数（0 表示常数，1 表示线性）。 | 1 | +| trend_deg | 趋势项 LOESS 的多项式阶数（0 或 1）。 | 1 | +| low_pass_deg | 低通项 LOESS 的多项式阶数（0 或 1）。 | 1 | +| seasonal_jump | LOESS 拟合的插值步长（季节项），每 n 点拟合一次，中间插值。值越大，估计速度越快。 | 1 | +| trend_jump | 趋势项插值步长，越大速度越快但精度可能下降。 | 1 | +| low_pass_jump | 低通项插值步长，设置同上。 | 1 | + +**ExponentialSmoothing (HoltWinters)** + +| 支持的参数 | 含义 | 默认值 | +| :-------------------- | :----------------------------------------------------------- | :---------- | +| damped_trend | 是否使用阻尼趋势（趋势会逐渐平缓，而非无限增长）。 | True | +| initialization_method | 初始化方法： • `"estimated"`：通过拟合估计初始状态 • `"heuristic"`：使用启发式方法估计初始水平/趋势/季节 • `"known"`：用户显式提供所有初始值 • `"legacy-heuristic"`：旧版本兼容方式 | "estimated" | +| optmized | 是否通过最大化对数似然来优化参数。 | True | +| remove_bias | 是否移除偏差，使预测值和拟合值的残差平均值为0。 | False | +| use_brute | 是否使用穷举法（网格搜索）来寻找初始参数。否则使用启发式初始值。 | | \ No newline at end of file diff --git a/src/zh/UserGuide/latest-Table/AI-capability/TimeSeries-Large-Model.md b/src/zh/UserGuide/latest-Table/AI-capability/TimeSeries-Large-Model.md new file mode 100644 index 000000000..a632e1dec --- /dev/null +++ b/src/zh/UserGuide/latest-Table/AI-capability/TimeSeries-Large-Model.md @@ -0,0 +1,112 @@ + + +# 时序大模型 + +## 简介 + +时序大模型是一种专为时序数据分析设计的基础模型。IoTDB 团队长期自研时序大模型，基于变换器（Transformer）结构等技术在海量时序数据上预训练，能够理解并生成多种领域的时序数据，可被应用于时序预测、异常检测、时序填补等应用场景。不同于传统时序分析技术，时序大模型具备通用特征提取能力，基于零样本分析、微调等技术服务广泛的分析任务。 + +团队所研时序大模型相关技术均发表在国际机器学习顶级会议。 + +## 应用场景 + +- **时序预测**：为工业生产、自然环境等领域提供时间序列数据的预测服务，帮助用户提前了解未来趋势。 +- **数据填补**：针对时间序列中的缺失序列段，进行上下文填补，以增强数据集的连续性和完整性。 +- **异常检测**：利用自回归分析技术，对时间序列数据进行实时监测，及时预警潜在的异常情况。 + +![](/img/LargeModel09.png) + +## Timer-1 模型 + +Timer模型不仅展现了出色的少样本泛化和多任务适配能力，还通过预训练获得了丰富的知识库，赋予了它处理多样化下游任务的通用能力，拥有以下特点： + +- **泛化性**：模型能够通过使用少量样本进行微调，达到行业内领先的深度模型预测效果。 +- **通用性**：模型设计灵活，能够适配多种不同的任务需求，并且支持变化的输入和输出长度，使其在各种应用场景中都能发挥作用。 +- **可扩展性**：随着模型参数数量的增加或预训练数据规模的扩大，模型效果会持续提升，确保模型能够随着时间和数据量的增长而不断优化其预测效果。 + +![](/img/model01.png) + +## Timer-XL 模型 + +Timer-XL 基于 Timer 进一步扩展升级了网络结构，在多个维度全面突破： + +- **超长上下文支持**：该模型突破了传统时序预测模型的限制，支持处理数千个 Token（相当于数万个时间点）的输入，有效解决了上下文长度瓶颈问题。 +- **多变量预测场景覆盖**：支持多种预测场景，包括非平稳时间序列的预测、涉及多个变量的预测任务以及包含协变量的预测，满足多样化的业务需求。 +- **大规模工业时序数据集**：采用万亿大规模工业物联网领域的时序数据集进行预训练，数据集兼有庞大的体量、卓越的质量和丰富的领域等重要特质，覆盖能源、航空航天、钢铁、交通等多领域。 + +![](/img/model02.png) + +## Timer-Sundial 模型 + +Timer-Sundial 是一个专注于时间序列预测的生成式基础模型系列，其基础版本拥有 1.28 亿参数，并在 1 万亿个时间点上进行了大规模预训练，其核心特性包括： + +- **强大的泛化性能**：具备零样本预测能力，可同时支持点预测和概率预测。 +- **灵活预测分布分析**：不仅能预测均值或分位数，还可通过模型生成的原始样本评估预测分布的任意统计特性。 +- **创新生成架构**：采用 “Transformer + TimeFlow” 协同架构——Transformer 学习时间片段的自回归表征，TimeFlow 模块基于流匹配框架 (Flow-Matching) 将随机噪声转化为多样化预测轨迹，实现高效的非确定性样本生成。 + +![](/img/model03.png) + +## 效果展示 + +时序大模型能够适应多种不同领域和场景的真实时序数据，在各种任务上拥有优异的处理效果，以下是在不同数据上的真实表现： + +**时序预测：** + +利用时序大模型的预测能力，能够准确预测时间序列的未来变化趋势，如下图蓝色曲线代表预测趋势，红色曲线为实际趋势，两曲线高度吻合。 + +![](/img/LargeModel03.png) + +**数据填补：** + +利用时序大模型对缺失数据段进行预测式填补。 + +![](/img/timeseries-large-model-data-imputation.png) + + +**异常检测：** + +利用时序大模型精准识别与正常趋势偏离过大的异常值。 + +![](/img/LargeModel05.png) + +## 部署使用 + +1. 打开 IoTDB cli 控制台，检查 ConfigNode、DataNode、AINode 节点确保均为 Running。 + +检查命令： +```sql +show cluster +``` + +![](/img/ainode-timer-1.png) + +2. 联网环境下首次启动 AINode 节点会自动拉取 Timer-XL、Sundial 模型。 + +3. 检查模型是否可用 + +检查命令： + +```sql +show models +``` + +![](/img/LargeModel06.png) diff --git a/src/zh/UserGuide/latest-Table/Deployment-and-Maintenance/AINode_Deployment_apache.md b/src/zh/UserGuide/latest-Table/Deployment-and-Maintenance/AINode_Deployment_apache.md new file mode 100644 index 000000000..052c039c2 --- /dev/null +++ b/src/zh/UserGuide/latest-Table/Deployment-and-Maintenance/AINode_Deployment_apache.md @@ -0,0 +1,267 @@ + +# AINode 部署 + +## 1. AINode介绍 + +### 1.1 能力介绍 + +AINode 是 IoTDB 在 ConfigNode、DataNode 后提供的第三种内生节点，该节点通过与 IoTDB 集群的 DataNode、ConfigNode 的交互，扩展了对时间序列进行机器学习分析的能力，支持从外部引入已有机器学习模型进行注册，并使用注册的模型在指定时序数据上通过简单 SQL 语句完成时序分析任务的过程，将模型的创建、管理及推理融合在数据库引擎中。目前已提供常见时序分析场景（例如预测与异常检测）的机器学习算法或自研模型。 + +### 1.2 交付方式 + 是 IoTDB 集群外的额外套件，独立安装包。 + +### 1.3 部署模式 +
+ + +
+ +## 2. 安装准备 + +### 2.1 安装包获取 + +AINode 安装包（`apache-iotdb--ainode-bin.zip`），安装包解压后目录结构如下： +| **目录** | **类型** | **说明** | +| ------------ | -------- | ------------------------------------------------ | +| lib | 文件夹 | AINode编译后的二进制可执行文件以及相关的代码依赖 | +| sbin | 文件夹 | AINode的运行脚本，可以启动，移除和停止AINode | +| conf | 文件夹 | 包含AINode的配置项，具体包含以下配置项 | +| LICENSE | 文件 | 证书 | +| NOTICE | 文件 | 提示 | +| README_ZH.md | 文件 | markdown格式的中文版说明 | +| `README.md` | 文件 | 使用说明 | + +### 2.2 环境准备 + +1. 建议操作环境: Ubuntu, CentOS, MacOS +2. IoTDB 版本：>= V 2.0.5.1 +3. 运行环境 + - Python 版本在 3.9 ~3.12，且带有 pip 和 venv 工具； + + +## 3. 安装部署及使用 + +### 3.1 安装 AINode + +1. 保证 Python 版本介于 3.9 ~3.12 + +```shell + python --version +# 或 +python3 --version +``` +2. 下载导入 AINode 到专用文件夹，切换到专用文件夹并解压安装包 + +```shell + unzip timechodb-2.0.5.1-ainode-bin.zip +``` + +3. 创建虚拟环境（在 ainode 目录下执行）： + + ```shell + python3 -m venv venv + ``` + +4. 激活虚拟环境： + + ```shell + source venv/bin/activate + ``` + +5. 更新 pip 并安装 AINode 依赖 + ```shell + python -m pip install --upgrade pip + poetry lock + poetry install + ``` + +### 3.2 配置项修改 +AINode 支持修改一些必要的参数。可以在 `conf/iotdb-ainode.properties` 文件中找到下列参数并进行持久化的修改： + +| **名称** | **描述** | **类型** | **默认值** | +| ------------------------------ | ------------------------------------------------------------ | -------- | ------------------ | +| cluster_name | AINode 要加入集群的标识 | string | defaultCluster | +| ain_seed_config_node | AINode 启动时注册的 ConfigNode 地址 | String | 127.0.0.1:10710 | +| ain_cluster_ingress_address | AINode 拉取数据的 DataNode 的 rpc 地址 | String | 127.0.0.1 | +| ain_cluster_ingress_port | AINode 拉取数据的 DataNode 的 rpc 端口 | Integer | 6667 | +| ain_cluster_ingress_username | AINode 拉取数据的 DataNode 的客户端用户名 | String | root | +| ain_cluster_ingress_password | AINode 拉取数据的 DataNode 的客户端密码 | String | root | +| ain_cluster_ingress_time_zone | AINode 拉取数据的 DataNode 的客户端时区 | String | UTC+8 | +| ain_inference_rpc_address | AINode 提供服务与通信的地址 ，内部服务通讯接口 | String | 127.0.0.1 | +| ain_inference_rpc_port | AINode 提供服务与通信的端口 | String | 10810 | +| ain_system_dir | AINode 元数据存储路径，相对路径的起始目录与操作系统相关，建议使用绝对路径 | String | data/AINode/system | +| ain_models_dir | AINode 存储模型文件的路径，相对路径的起始目录与操作系统相关，建议使用绝对路径 | String | data/AINode/models | +| ain_thrift_compression_enabled | AINode 是否启用 thrift 的压缩机制，0-不启动、1-启动 | Boolean | 0 | + +### 3.3 启动 AINode + + 在完成 Seed-ConfigNode 的部署后，可以通过添加 AINode 节点来支持模型的注册和推理功能。在配置项中指定 IoTDB 集群的信息后，可以执行相应的指令来启动 AINode，加入 IoTDB 集群。 + +#### 联网环境启动 + +##### 启动命令 + +```shell + # 启动命令 + # Linux 和 MacOS 系统 + bash sbin/start-ainode.sh + + # Windows 系统 + sbin\start-ainode.bat + + # 后台启动命令（长期运行推荐） + # Linux 和 MacOS 系统 + nohup bash sbin/start-ainode.sh > myout.file 2>& 1 & + + # Windows 系统 + nohup bash sbin\start-ainode.bat > myout.file 2>& 1 & + ``` + +#### 示例 + +```shell + # 启动命令 + # Linux 和 MacOS 系统 + bash sbin/start-ainode.sh + # Windows 系统 + sbin\start-ainode.bat + + + # 后台启动命令（长期运行推荐） + # Linux 和 MacOS 系统 + nohup bash sbin/start-ainode.sh > myout.file 2>& 1 & + # Windows 系统 + nohup bash sbin\start-ainode.bat > myout.file 2>& 1 & + ``` + +### 3.4 检测 AINode 节点状态 + +AINode 启动过程中会自动将新的 AINode 加入 IoTDB 集群。启动 AINode 后可以在 命令行中输入 SQL 来查询，集群中看到 AINode 节点，其运行状态为 Running（如下展示）表示加入成功。 + +```shell +IoTDB> show cluster ++------+----------+-------+---------------+------------+-------+-----------+ +|NodeID| NodeType| Status|InternalAddress|InternalPort|Version| BuildInfo| ++------+----------+-------+---------------+------------+-------+-----------+ +| 0|ConfigNode|Running| 127.0.0.1| 10710|UNKNOWN|190e303-dev| +| 1| DataNode|Running| 127.0.0.1| 10730|UNKNOWN|190e303-dev| +| 2| AINode|Running| 127.0.0.1| 10810|UNKNOWN|190e303-dev| ++------+----------+-------+---------------+------------+-------+-----------+ +``` + +### 3.5 停止 AINode + +如果需要停止正在运行的 AINode 节点，则执行相应的关闭脚本。 + +#### 停止命令 + +```shell + # Linux / MacOS + bash sbin/stop-ainode.sh + + #Windows + sbin\stop-ainode.bat + ``` + +#### 示例 +```shell + # Linux / MacOS + bash sbin/stop-ainode.sh + + # Windows + sbin\stop-ainode.bat + ``` +停止 AINode 后，还可以在集群中看到 AINode 节点，其运行状态为 UNKNOWN（如下展示），此时无法使用 AINode 功能。 + + ```shell +IoTDB> show cluster ++------+----------+-------+---------------+------------+-------+-----------+ +|NodeID| NodeType| Status|InternalAddress|InternalPort|Version| BuildInfo| ++------+----------+-------+---------------+------------+-------+-----------+ +| 0|ConfigNode|Running| 127.0.0.1| 10710|UNKNOWN|190e303-dev| +| 1| DataNode|Running| 127.0.0.1| 10730|UNKNOWN|190e303-dev| +| 2| AINode|UNKNOWN| 127.0.0.1| 10790|UNKNOWN|190e303-dev| ++------+----------+-------+---------------+------------+-------+-----------+ +``` +如果需要重新启动该节点，需重新执行启动脚本。 + + + +## 4. 常见问题 + +### 4.1 启动AINode时出现找不到venv模块的报错 + + 当使用默认方式启动 AINode 时，会在安装包目录下创建一个 python 虚拟环境并安装依赖，因此要求安装 venv 模块。通常来说 python3.10 及以上的版本会自带 venv，但对于一些系统自带的 python 环境可能并不满足这一要求。出现该报错时有两种解决方案（二选一）： + + 在本地安装 venv 模块，以 ubuntu 为例，可以通过运行以下命令来安装 python 自带的 venv 模块。或者从 python 官网安装一个自带 venv 的 python 版本。 + + ```shell +apt-get install python3.10-venv +``` + 安装 3.10.0 版本的 venv 到 AINode 里面 在 AINode 路径下 + + ```shell +../Python-3.10.0/python -m venv venv(文件夹名） +``` + 在运行启动脚本时通过 `-i` 指定已有的 python 解释器路径作为 AINode 的运行环境，这样就不再需要创建一个新的虚拟环境。 + + ### 4.2 python中的SSL模块没有被正确安装和配置，无法处理HTTPS资源 +WARNING: pip is configured with locations that require TLS/SSL, however the ssl module in Python is not available. +可以安装 OpenSSLS 后，再重新构建 python 来解决这个问题 +> Currently Python versions 3.6 to 3.9 are compatible with OpenSSL 1.0.2, 1.1.0, and 1.1.1. + + Python 要求我们的系统上安装有 OpenSSL，具体安装方法可见[链接](https://stackoverflow.com/questions/56552390/how-to-fix-ssl-module-in-python-is-not-available-in-centos) + + ```shell +sudo apt-get install build-essential libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm libncurses5-dev libncursesw5-dev xz-utils tk-dev libffi-dev liblzma-dev uuid-dev lzma-dev liblzma-dev +sudo -E ./configure --with-ssl +make +sudo make install +``` + + ### 4.3 pip版本较低 + + windows下出现类似“error：Microsoft Visual C++ 14.0 or greater is required...”的编译问题 + + 出现对应的报错，通常是 c++版本或是 setuptools 版本不足，可以在 + + ```shell +./python -m pip install --upgrade pip +./python -m pip install --upgrade setuptools +``` + + + ### 4.4 安装编译python + + 使用以下指定从官网下载安装包并解压: + ```shell +.wget https://www.python.org/ftp/python/3.10.0/Python-3.10.0.tar.xz +tar Jxf Python-3.10.0.tar.xz +``` + 编译安装对应的 python 包: + ```shell +cd Python-3.10.0 +./configure prefix=/usr/local/python3 +make +sudo make install +python3 --version +``` \ No newline at end of file diff --git a/src/zh/UserGuide/latest-Table/Deployment-and-Maintenance/AINode_Deployment_timecho.md b/src/zh/UserGuide/latest-Table/Deployment-and-Maintenance/AINode_Deployment_timecho.md new file mode 100644 index 000000000..e04378a43 --- /dev/null +++ b/src/zh/UserGuide/latest-Table/Deployment-and-Maintenance/AINode_Deployment_timecho.md @@ -0,0 +1,14 @@ +| **名称** | **描述** | **类型** | **默认值** | +| ------------------------------ | ------------------------------------------------------------ | -------- | ------------------ | +| cluster_name | AINode 要加入集群的标识 | string | defaultCluster | +| ain_seed_config_node | AINode 启动时注册的 ConfigNode 地址 | String | 127.0.0.1:10710 | +| ain_cluster_ingress_address | AINode 拉取数据的 DataNode 的 rpc 地址 | String | 127.0.0.1 | +| ain_cluster_ingress_port | AINode 拉取数据的 DataNode 的 rpc 端口 | Integer | 6667 | +| ain_cluster_ingress_username | AINode 拉取数据的 DataNode 的客户端用户名 | String | root | +| ain_cluster_ingress_password | AINode 拉取数据的 DataNode 的客户端密码 | String | root | +| ain_cluster_ingress_time_zone | AINode 拉取数据的 DataNode 的客户端时区 | String | UTC+8 | +| ain_inference_rpc_address | AINode 提供服务与通信的地址 ，内部服务通讯接口 | String | 127.0.0.1 | +| ain_inference_rpc_port | AINode 提供服务与通信的端口 | String | 10810 | +| ain_system_dir | AINode 元数据存储路径，相对路径的起始目录与操作系统相关，建议使用绝对路径 | String | data/AINode/system | +| ain_models_dir | AINode 存储模型文件的路径，相对路径的起始目录与操作系统相关，建议使用绝对路径 | String | data/AINode/models | +| ain_thrift_compression_enabled | AINode 是否启用 thrift 的压缩机制，0-不启动、1-启动 | Boolean | 0 | \ No newline at end of file diff --git a/src/zh/UserGuide/latest/AI-capability/AINode_apache.md b/src/zh/UserGuide/latest/AI-capability/AINode_apache.md index 963b4288b..d3b350672 100644 --- a/src/zh/UserGuide/latest/AI-capability/AINode_apache.md +++ b/src/zh/UserGuide/latest/AI-capability/AINode_apache.md @@ -51,9 +51,9 @@ AINode 是支持时序大模型注册、管理、调用的 IoTDB 原生节点， ## 2. 基本概念 -- **模型（Model）**：机器学习模型，以时序数据作为输入，输出分析任务的结果或决策。模型是AINode 的基本管理单元，支持模型的增（注册）、删、查、用（推理）。 -- **创建（Create）**: 将外部设计或训练好的模型文件或算法加载到MLNode中，由IoTDB统一管理与使用。 -- **推理（Inference）**：使用创建的模型在指定时序数据上完成该模型适用的时序分析任务的过程。 +- **模型（Model）**：机器学习模型，以时序数据作为输入，输出分析任务的结果或决策。模型是 AINode 的基本管理单元，支持模型的增（注册）、删、查、用（推理）。 +- **创建（Create）**: 将外部设计或训练好的模型文件或算法加载到 AINode 中，由 IoTDB 统一管理与使用。 +- **推理（Inference）**：使用创建的模型在指定时序数据上完成该模型适用的时序分析任务。 - **内置能力（Built-in）**：AINode 自带常见时序分析场景（例如预测与异常检测）的机器学习算法或自研模型。 ![](/img/h3.png) @@ -64,17 +64,17 @@ AINode 的部署可参考文档 [部署指导](../Deployment-and-Maintenance/AIN ## 4. 使用指导 -AINode 对时序数据相关的深度学习模型提供了模型创建及删除的流程，内置模型无需创建及删除，可直接使用，并且在完成推理后创建的内置模型实例将自动销毁。 +AINode 对时序模型提供了模型创建及删除功能，内置模型无需创建，可直接使用。 ### 4.1 注册模型 通过指定模型输入输出的向量维度，可以注册训练好的深度学习模型，从而用于模型推理。 符合以下内容的模型可以注册到AINode中： - 1. AINode 支持的PyTorch 2.1.0、 2.2.0版本训练的模型，需避免使用2.2.0版本以上的特性。 - 2. AINode支持使用PyTorch JIT存储的模型，模型文件需要包含模型的参数和结构。 - 3. 模型输入序列可以包含一列或多列，若有多列，需要和模型能力、模型配置文件对应。 - 4. 模型的输入输出维度必须在`config.yaml`配置文件中明确定义。使用模型时，必须严格按照`config.yaml`配置文件中定义的输入输出维度。如果输入输出列数不匹配配置文件，将会导致错误。 + 1. AINode 目前支持基于 PyTorch 2.4.0 版本训练的模型，需避免使用 2.4.0 版本以上的特性。 + 2. AINode 支持使用 PyTorch JIT 存储的模型（`model.pt`），模型文件需要包含模型的结构和权重。 + 3. 模型输入序列可以包含一列或多列，若有多列，需要和模型能力、模型配置文件对应。 + 4. 模型的配置参数必须在`config.yaml`配置文件中明确定义。使用模型时，必须严格按照`config.yaml`配置文件中定义的输入输出维度。如果输入输出列数不匹配配置文件，将会导致错误。 下方为模型注册的SQL语法定义。 @@ -90,11 +90,11 @@ SQL中参数的具体含义如下： - 长度限制为2-64字符 - 大小写敏感 -- uri：模型注册文件的资源路径，路径下应包含**模型权重model.pt文件和模型的元数据描述文件config.yaml** +- uri：模型注册文件的资源路径，路径下应包含**模型结构及权重文件 model.pt 文件和模型配置文件 config.yaml** - - 模型权重文件：深度学习模型训练完成后得到的权重文件，目前支持pytorch训练得到的.pt文件 + - 模型结构及权重文件：模型训练完成后得到的权重文件，目前支持 pytorch 训练得到的 .pt 文件 - - yaml元数据描述文件：模型注册时需要提供的与模型结构有关的参数，其中必须包含模型的输入输出维度用于模型推理： + - 模型配置文件：模型注册时需要提供的与模型结构有关的参数，其中必须包含模型的输入输出维度用于模型推理： - | **参数名** | **参数描述** | **示例** | | ------------ | ---------------------------- | -------- | @@ -145,7 +145,7 @@ IoTDB> create model dlinear_example using uri "file://./example" 也可以从huggingFace上下载对应的模型文件进行注册 ```SQL -IoTDB> create model dlinear_example using uri "https://huggingface.com/IoTDBML/dlinear/" +IoTDB> create model dlinear_example using uri "https://huggingface.co/google/timesfm-2.0-500m-pytorch" ``` SQL执行后会异步进行注册的流程，可以通过模型展示查看模型的注册状态（见模型展示章节），注册成功的耗时主要受到模型文件大小的影响。 @@ -166,49 +166,45 @@ show models | **ModelId** | **State** | **Configs** | **Attributes** | | ------------ | ------------------------------------- | ---------------------------------------------- | -------------- | -| 模型唯一标识 | 模型注册状态(LOADING,ACTIVE,DROPPING) | InputShape, outputShapeInputTypes, outputTypes | 模型备注信息 | +| 模型唯一标识 | 模型注册状态(INACTIVE,LOADING,ACTIVE,DROPPING) | InputShape, outputShapeInputTypes, outputTypes | 模型备注信息 | 其中，State用于展示当前模型注册的状态，包含以下三个阶段 -- **LOADING**：已经在configNode中添加对应的模型元信息，正将模型文件传输到AINode节点上 -- **ACTIVE:** 模型已经设置完成，模型处于可用状态 -- **DROPPING**：模型删除中，正在从configNode以及AINode处删除模型相关信息 -- **UNAVAILABLE**: 模型创建失败，可以通过drop model删除创建失败的model_name。 +- **INACTIVE**：模型处于不可用状态 +- **LOADING**：模型加载中状态 +- **ACTIVE**：模型处于可用状态 +- **DROPPING**:模型删除中状态 #### 示例 ```SQL IoTDB> show models - -+---------------------+--------------------------+-----------+----------------------------+-----------------------+ -| ModelId| ModelType| State| Configs| Notes| -+---------------------+--------------------------+-----------+----------------------------+-----------------------+ -| dlinear_example| USER_DEFINED| ACTIVE| inputShape:[96,2]| | -| | | | outputShape:[48,2]| | -| | | | inputDataType:[float,float]| | -| | | |outputDataType:[float,float]| | -| _STLForecaster| BUILT_IN_FORECAST| ACTIVE| |Built-in model in IoTDB| -| _NaiveForecaster| BUILT_IN_FORECAST| ACTIVE| |Built-in model in IoTDB| -| _ARIMA| BUILT_IN_FORECAST| ACTIVE| |Built-in model in IoTDB| -|_ExponentialSmoothing| BUILT_IN_FORECAST| ACTIVE| |Built-in model in IoTDB| -| _GaussianHMM|BUILT_IN_ANOMALY_DETECTION| ACTIVE| |Built-in model in IoTDB| -| _GMMHMM|BUILT_IN_ANOMALY_DETECTION| ACTIVE| |Built-in model in IoTDB| -| _Stray|BUILT_IN_ANOMALY_DETECTION| ACTIVE| |Built-in model in IoTDB| -+---------------------+--------------------------+-----------+------------------------------------------------------------+-----------------------+ ++---------------------+--------------------+--------+--------+ +| ModelId| ModelType|Category| State| ++---------------------+--------------------+--------+--------+ +| arima| Arima|BUILT-IN| ACTIVE| +| holtwinters| HoltWinters|BUILT-IN| ACTIVE| +|exponential_smoothing|ExponentialSmoothing|BUILT-IN| ACTIVE| +| naive_forecaster| NaiveForecaster|BUILT-IN| ACTIVE| +| stl_forecaster| StlForecaster|BUILT-IN| ACTIVE| +| gaussian_hmm| GaussianHmm|BUILT-IN| ACTIVE| +| gmm_hmm| GmmHmm|BUILT-IN| ACTIVE| +| stray| Stray|BUILT-IN| ACTIVE| +| timer_xl| Timer-XL|BUILT-IN| ACTIVE| +| sundial| Timer-Sundial|BUILT-IN| ACTIVE| ++---------------------+--------------------+--------+--------+ ``` -我们前面已经注册了对应的模型，可以通过对应的指定查看模型状态，active表明模型注册成功，可用于推理。 - ### 4.3 删除模型 -对于注册成功的模型，用户可以通过SQL进行删除。该操作除了删除configNode上的元信息外，还会删除所有AINode下的相关模型文件。其SQL如下： +对于注册成功的模型，用户可以通过SQL进行删除，该操作会删除所有 AINode 下的相关模型文件，其SQL如下： ```SQL -drop model +drop model ``` -需要指定已经成功注册的模型model_name来删除对应的模型。由于模型删除涉及多个节点上的数据删除，操作不会立即完成，此时模型的状态为DROPPING，该状态的模型不能用于模型推理。 +需要指定已经成功注册的模型 model_id 来删除对应的模型。由于模型删除涉及模型数据清理，操作不会立即完成，此时模型的状态为 DROPPING，该状态的模型不能用于模型推理。请注意，该功能不支持删除内置模型。 ### 4.4 使用内置模型推理 @@ -216,15 +212,22 @@ SQL语法如下： ```SQL -call inference(,sql[,=]) +call inference(,inputSql,(=)*) + +window_function: + head(window_size) + tail(window_size) + count(window_size,sliding_step) ``` 内置模型推理无需注册流程，通过call关键字，调用inference函数就可以使用模型的推理功能，其对应的参数介绍如下： -- **built_in_model_name:** 内置模型名称 +- **model_id:** 模型名称 - **parameterName**：参数名 - **parameterValue**：参数值 +请注意，使用内置时序大模型进行推理的前提条件是本地存有对应模型权重，目录为 /IOTDB_AINODE_HOME/data/ainode/models/weights/model_id/。若本地没有模型权重，则会自动从 HuggingFace 拉取，请保证本地能直接访问 HuggingFace。 + #### 内置模型及参数说明 目前已内置如下机器学习模型，具体参数说明请参考以下链接。 @@ -239,60 +242,6 @@ call inference(,sql[,=]) | GMMHMM | _GMMHMM | 标注 | [GMMHMM参数说明](https://www.sktime.net/en/latest/api_reference/auto_generated/sktime.detection.hmm_learn.gmm.GMMHMM.html) | | Stray | _Stray | 异常检测 | [Stray参数说明](https://www.sktime.net/en/latest/api_reference/auto_generated/sktime.detection.stray.STRAY.html) | -#### 示例 - -下面是使用内置模型推理的一个操作示例，使用内置的Stray模型进行异常检测算法，输入为`[144,1]`，输出为`[144,1]`，我们通过SQL使用其进行推理。 - -```SQL -IoTDB> select * from root.eg.airline -+-----------------------------+------------------+ -| Time|root.eg.airline.s0| -+-----------------------------+------------------+ -|1949-01-31T00:00:00.000+08:00| 224.0| -|1949-02-28T00:00:00.000+08:00| 118.0| -|1949-03-31T00:00:00.000+08:00| 132.0| -|1949-04-30T00:00:00.000+08:00| 129.0| -...... -|1960-09-30T00:00:00.000+08:00| 508.0| -|1960-10-31T00:00:00.000+08:00| 461.0| -|1960-11-30T00:00:00.000+08:00| 390.0| -|1960-12-31T00:00:00.000+08:00| 432.0| -+-----------------------------+------------------+ -Total line number = 144 - -IoTDB> call inference(_Stray, "select s0 from root.eg.airline", generateTime=True, k=2) -+-----------------------------+-------+ -| Time|output0| -+-----------------------------+-------+ -|1960-12-31T00:00:00.000+08:00| 0| -|1961-01-31T08:00:00.000+08:00| 1| -|1961-02-28T08:00:00.000+08:00| 0| -|1961-03-31T08:00:00.000+08:00| 0| -...... -|1972-06-30T08:00:00.000+08:00| 0| -|1972-07-31T08:00:00.000+08:00| 1| -|1972-08-31T08:00:00.000+08:00| 0| -|1972-09-30T08:00:00.000+08:00| 0| -|1972-10-31T08:00:00.000+08:00| 0| -|1972-11-30T08:00:00.000+08:00| 0| -+-----------------------------+-------+ -Total line number = 144 -``` - -### 4.5 使用深度学习模型推理 - -SQL语法如下： - -```SQL -call inference(,sql[,window=]) - - -window_function: - head(window_size) - tail(window_size) - count(window_size,sliding_step) -``` - 在完成模型的注册后，通过call关键字，调用inference函数就可以使用模型的推理功能，其对应的参数介绍如下： - **model_name**: 对应一个已经注册的模型 diff --git a/src/zh/UserGuide/latest/AI-capability/AINode_timecho.md b/src/zh/UserGuide/latest/AI-capability/AINode_timecho.md index f25d0ff62..ae5ddf117 100644 --- a/src/zh/UserGuide/latest/AI-capability/AINode_timecho.md +++ b/src/zh/UserGuide/latest/AI-capability/AINode_timecho.md @@ -51,9 +51,9 @@ AINode 是支持时序大模型注册、管理、调用的 IoTDB 原生节点， ## 2. 基本概念 -- **模型（Model）**：机器学习模型，以时序数据作为输入，输出分析任务的结果或决策。模型是AINode 的基本管理单元，支持模型的增（注册）、删、查、用（推理）。 -- **创建（Create）**: 将外部设计或训练好的模型文件或算法加载到MLNode中，由IoTDB统一管理与使用。 -- **推理（Inference）**：使用创建的模型在指定时序数据上完成该模型适用的时序分析任务的过程。 +- **模型（Model）**：机器学习模型，以时序数据作为输入，输出分析任务的结果或决策。模型是 AINode 的基本管理单元，支持模型的增（注册）、删、查、改（微调）、用（推理）。 +- **创建（Create）**: 将外部设计或训练好的模型文件或算法加载到 AINode 中，由 IoTDB 统一管理与使用。 +- **推理（Inference）**：使用创建的模型在指定时序数据上完成该模型适用的时序分析任务。 - **内置能力（Built-in）**：AINode 自带常见时序分析场景（例如预测与异常检测）的机器学习算法或自研模型。 ![](/img/h3.png) @@ -64,37 +64,37 @@ AINode 的部署可参考文档 [部署指导](../Deployment-and-Maintenance/AIN ## 4. 使用指导 -AINode 对时序数据相关的深度学习模型提供了模型创建及删除的流程，内置模型无需创建及删除，可直接使用，并且在完成推理后创建的内置模型实例将自动销毁。 +AINode 对时序模型提供了模型创建及删除功能，内置模型无需创建，可直接使用。 ### 4.1 注册模型 通过指定模型输入输出的向量维度，可以注册训练好的深度学习模型，从而用于模型推理。 符合以下内容的模型可以注册到AINode中： - 1. AINode 支持的PyTorch 2.1.0、 2.2.0版本训练的模型，需避免使用2.2.0版本以上的特性。 - 2. AINode支持使用PyTorch JIT存储的模型，模型文件需要包含模型的参数和结构。 - 3. 模型输入序列可以包含一列或多列，若有多列，需要和模型能力、模型配置文件对应。 - 4. 模型的输入输出维度必须在`config.yaml`配置文件中明确定义。使用模型时，必须严格按照`config.yaml`配置文件中定义的输入输出维度。如果输入输出列数不匹配配置文件，将会导致错误。 + 1. AINode 目前支持基于 PyTorch 2.4.0 版本训练的模型，需避免使用 2.4.0 版本以上的特性。 + 2. AINode 支持使用 PyTorch JIT 存储的模型（`model.pt`），模型文件需要包含模型的结构和权重。 + 3. 模型输入序列可以包含一列或多列，若有多列，需要和模型能力、模型配置文件对应。 + 4. 模型的配置参数必须在`config.yaml`配置文件中明确定义。使用模型时，必须严格按照`config.yaml`配置文件中定义的输入输出维度。如果输入输出列数不匹配配置文件，将会导致错误。 下方为模型注册的SQL语法定义。 ```SQL -create model using uri +create model using uri ``` SQL中参数的具体含义如下： -- model_name：模型的全局唯一标识，不可重复。模型名称具备以下约束： +- model_id：模型的全局唯一标识，不可重复。模型名称具备以下约束： - 允许出现标识符 [ 0-9 a-z A-Z _ ] （字母，数字，下划线） - 长度限制为2-64字符 - 大小写敏感 -- uri：模型注册文件的资源路径，路径下应包含**模型权重model.pt文件和模型的元数据描述文件config.yaml** +- uri：模型注册文件的资源路径，路径下应包含**模型结构及权重文件 model.pt 文件和模型配置文件 config.yaml** - - 模型权重文件：深度学习模型训练完成后得到的权重文件，目前支持pytorch训练得到的.pt文件 + - 模型结构及权重文件：模型训练完成后得到的权重文件，目前支持 pytorch 训练得到的 .pt 文件 - - yaml元数据描述文件：模型注册时需要提供的与模型结构有关的参数，其中必须包含模型的输入输出维度用于模型推理： + - 模型配置文件：模型注册时需要提供的与模型结构有关的参数，其中必须包含模型的输入输出维度用于模型推理： - | **参数名** | **参数描述** | **示例** | | ------------ | ---------------------------- | -------- | @@ -145,7 +145,7 @@ IoTDB> create model dlinear_example using uri "file://./example" 也可以从huggingFace上下载对应的模型文件进行注册 ```SQL -IoTDB> create model dlinear_example using uri "https://huggingface.com/IoTDBML/dlinear/" +IoTDB> create model dlinear_example using uri "https://huggingface.co/google/timesfm-2.0-500m-pytorch" ``` SQL执行后会异步进行注册的流程，可以通过模型展示查看模型的注册状态（见模型展示章节），注册成功的耗时主要受到模型文件大小的影响。 @@ -159,56 +159,54 @@ SQL执行后会异步进行注册的流程，可以通过模型展示查看模 ```SQL show models -show models +show models ``` 除了直接展示所有模型的信息外，可以指定model id来查看某一具体模型的信息。模型展示的结果中包含如下信息： | **ModelId** | **State** | **Configs** | **Attributes** | | ------------ | ------------------------------------- | ---------------------------------------------- | -------------- | -| 模型唯一标识 | 模型注册状态(LOADING,ACTIVE,DROPPING) | InputShape, outputShapeInputTypes, outputTypes | 模型备注信息 | +| 模型唯一标识 | 模型注册状态(INACTIVE,LOADING,ACTIVE,TRAINING,FAILED,DROPPING) | InputShape, outputShapeInputTypes, outputTypes | 模型备注信息 | 其中，State用于展示当前模型注册的状态，包含以下三个阶段 -- **LOADING**：已经在configNode中添加对应的模型元信息，正将模型文件传输到AINode节点上 -- **ACTIVE:** 模型已经设置完成，模型处于可用状态 -- **DROPPING**：模型删除中，正在从configNode以及AINode处删除模型相关信息 -- **UNAVAILABLE**: 模型创建失败，可以通过drop model删除创建失败的model_name。 +- **INACTIVE**：模型处于不可用状态 +- **LOADING**：模型加载中 +- **ACTIVE**：模型处于可用状态 +- **TRAINING**：模型处于微调中状态 +- **FAILED**：模型微调失败状态 +- **DROPPING**:模型删除中状态 #### 示例 ```SQL IoTDB> show models - -+---------------------+--------------------------+-----------+----------------------------+-----------------------+ -| ModelId| ModelType| State| Configs| Notes| -+---------------------+--------------------------+-----------+----------------------------+-----------------------+ -| dlinear_example| USER_DEFINED| ACTIVE| inputShape:[96,2]| | -| | | | outputShape:[48,2]| | -| | | | inputDataType:[float,float]| | -| | | |outputDataType:[float,float]| | -| _STLForecaster| BUILT_IN_FORECAST| ACTIVE| |Built-in model in IoTDB| -| _NaiveForecaster| BUILT_IN_FORECAST| ACTIVE| |Built-in model in IoTDB| -| _ARIMA| BUILT_IN_FORECAST| ACTIVE| |Built-in model in IoTDB| -|_ExponentialSmoothing| BUILT_IN_FORECAST| ACTIVE| |Built-in model in IoTDB| -| _GaussianHMM|BUILT_IN_ANOMALY_DETECTION| ACTIVE| |Built-in model in IoTDB| -| _GMMHMM|BUILT_IN_ANOMALY_DETECTION| ACTIVE| |Built-in model in IoTDB| -| _Stray|BUILT_IN_ANOMALY_DETECTION| ACTIVE| |Built-in model in IoTDB| -+---------------------+--------------------------+-----------+------------------------------------------------------------+-----------------------+ ++---------------------+--------------------+--------+--------+ +| ModelId| ModelType|Category| State| ++---------------------+--------------------+--------+--------+ +| arima| Arima|BUILT-IN| ACTIVE| +| holtwinters| HoltWinters|BUILT-IN| ACTIVE| +|exponential_smoothing|ExponentialSmoothing|BUILT-IN| ACTIVE| +| naive_forecaster| NaiveForecaster|BUILT-IN| ACTIVE| +| stl_forecaster| StlForecaster|BUILT-IN| ACTIVE| +| gaussian_hmm| GaussianHmm|BUILT-IN| ACTIVE| +| gmm_hmm| GmmHmm|BUILT-IN| ACTIVE| +| stray| Stray|BUILT-IN| ACTIVE| +| timer_xl| Timer-XL|BUILT-IN| ACTIVE| +| sundial| Timer-Sundial|BUILT-IN| ACTIVE| ++---------------------+--------------------+--------+--------+ ``` -我们前面已经注册了对应的模型，可以通过对应的指定查看模型状态，active表明模型注册成功，可用于推理。 - ### 4.3 删除模型 -对于注册成功的模型，用户可以通过SQL进行删除。该操作除了删除configNode上的元信息外，还会删除所有AINode下的相关模型文件。其SQL如下： +对于注册成功的模型，用户可以通过SQL进行删除，该操作会删除所有 AINode 下的相关模型文件，其SQL如下： ```SQL -drop model +drop model ``` -需要指定已经成功注册的模型model_name来删除对应的模型。由于模型删除涉及多个节点上的数据删除，操作不会立即完成，此时模型的状态为DROPPING，该状态的模型不能用于模型推理。 +需要指定已经成功注册的模型 model_id 来删除对应的模型。由于模型删除涉及模型数据清理，操作不会立即完成，此时模型的状态为 DROPPING，该状态的模型不能用于模型推理。请注意，该功能不支持删除内置模型。 ### 4.4 使用内置模型推理 @@ -216,20 +214,28 @@ SQL语法如下： ```SQL -call inference(,sql[,=]) +call inference(,inputSql,(=)*) + +window_function: + head(window_size) + tail(window_size) + count(window_size,sliding_step) ``` 内置模型推理无需注册流程，通过call关键字，调用inference函数就可以使用模型的推理功能，其对应的参数介绍如下： -- **built_in_model_name:** 内置模型名称 +- **model_id:** 模型名称 - **parameterName**：参数名 - **parameterValue**：参数值 +请注意，使用内置时序大模型进行推理的前提条件是本地存有对应模型权重，目录为 /IOTDB_AINODE_HOME/data/ainode/models/weights/model_id/。若本地没有模型权重，则会自动从 HuggingFace 拉取，请保证本地能直接访问 HuggingFace。 + + #### 内置模型及参数说明 目前已内置如下机器学习模型，具体参数说明请参考以下链接。 -| 模型 | built_in_model_name | 任务类型 | 参数说明 | +| 模型 | built_in_model_id | 任务类型 | 参数说明 | | -------------------- | --------------------- | -------- | ------------------------------------------------------------ | | Arima | _Arima | 预测 | [Arima参数说明](https://www.sktime.net/en/latest/api_reference/auto_generated/sktime.forecasting.arima.ARIMA.html?highlight=Arima) | | STLForecaster | _STLForecaster | 预测 | [STLForecaster参数说明](https://www.sktime.net/en/latest/api_reference/auto_generated/sktime.forecasting.trend.STLForecaster.html#sktime.forecasting.trend.STLForecaster) | @@ -239,63 +245,10 @@ call inference(,sql[,=]) | GMMHMM | _GMMHMM | 标注 | [GMMHMM参数说明](https://www.sktime.net/en/latest/api_reference/auto_generated/sktime.detection.hmm_learn.gmm.GMMHMM.html) | | Stray | _Stray | 异常检测 | [Stray参数说明](https://www.sktime.net/en/latest/api_reference/auto_generated/sktime.detection.stray.STRAY.html) | -#### 示例 - -下面是使用内置模型推理的一个操作示例，使用内置的Stray模型进行异常检测算法，输入为`[144,1]`，输出为`[144,1]`，我们通过SQL使用其进行推理。 - -```SQL -IoTDB> select * from root.eg.airline -+-----------------------------+------------------+ -| Time|root.eg.airline.s0| -+-----------------------------+------------------+ -|1949-01-31T00:00:00.000+08:00| 224.0| -|1949-02-28T00:00:00.000+08:00| 118.0| -|1949-03-31T00:00:00.000+08:00| 132.0| -|1949-04-30T00:00:00.000+08:00| 129.0| -...... -|1960-09-30T00:00:00.000+08:00| 508.0| -|1960-10-31T00:00:00.000+08:00| 461.0| -|1960-11-30T00:00:00.000+08:00| 390.0| -|1960-12-31T00:00:00.000+08:00| 432.0| -+-----------------------------+------------------+ -Total line number = 144 - -IoTDB> call inference(_Stray, "select s0 from root.eg.airline", generateTime=True, k=2) -+-----------------------------+-------+ -| Time|output0| -+-----------------------------+-------+ -|1960-12-31T00:00:00.000+08:00| 0| -|1961-01-31T08:00:00.000+08:00| 1| -|1961-02-28T08:00:00.000+08:00| 0| -|1961-03-31T08:00:00.000+08:00| 0| -...... -|1972-06-30T08:00:00.000+08:00| 0| -|1972-07-31T08:00:00.000+08:00| 1| -|1972-08-31T08:00:00.000+08:00| 0| -|1972-09-30T08:00:00.000+08:00| 0| -|1972-10-31T08:00:00.000+08:00| 0| -|1972-11-30T08:00:00.000+08:00| 0| -+-----------------------------+-------+ -Total line number = 144 -``` - -### 4.5 使用深度学习模型推理 - -SQL语法如下： - -```SQL -call inference(,sql[,window=]) - - -window_function: - head(window_size) - tail(window_size) - count(window_size,sliding_step) -``` 在完成模型的注册后，通过call关键字，调用inference函数就可以使用模型的推理功能，其对应的参数介绍如下： -- **model_name**: 对应一个已经注册的模型 +- **model_id**: 对应一个已经注册的模型 - **sql**：sql查询语句，查询的结果作为模型的输入进行模型推理。查询的结果中行列的维度需要与具体模型config中指定的大小相匹配。(这里的sql不建议使用`SELECT *`子句，因为在IoTDB中，`*`并不会对列进行排序，因此列的顺序是未定义的，可以使用`SELECT s0,s1`的方式确保列的顺序符合模型输入的预期) - **window_function**: 推理过程中可以使用的窗口函数，目前提供三种类型的窗口函数用于辅助模型推理： - **head(window_size)**: 获取数据中最前的window_size个点用于模型推理，该窗口可用于数据裁剪 @@ -439,6 +392,64 @@ Total line number = 4 其中结果集中每行的标签对应每24行数据为一组，输入该异常检测模型后的输出。 +### 4.5 使用内置模型微调 +> 仅 Timer-XL、Timer-Sundial 可以进行微调操作。 + +SQL语法如下： + + +```SQL +create model (with hyperparameters +(=(, =)*))? +from model +on dataset (PATH ([timeRange])?) +``` + +#### 示例 + +1. 选择测点 root.db.etth.ot 中前 80% 的数据作为微调数据集，基于 sundial 创建模型 sundialv2. + +```SQL +IoTDB> CREATE MODEL sundialv2 FROM MODEL sundial ON DATASET (PATH root.db.etth.OT([1467302400000, 1517468400001))) +Msg: The statement is executed successfully. +IoTDB> show models ++---------------------+--------------------+----------+--------+ +| ModelId| ModelType| Category| State| ++---------------------+--------------------+----------+--------+ +| arima| Arima| BUILT-IN| ACTIVE| +| holtwinters| HoltWinters| BUILT-IN| ACTIVE| +|exponential_smoothing|ExponentialSmoothing| BUILT-IN| ACTIVE| +| naive_forecaster| NaiveForecaster| BUILT-IN| ACTIVE| +| stl_forecaster| StlForecaster| BUILT-IN| ACTIVE| +| gaussian_hmm| GaussianHmm| BUILT-IN| ACTIVE| +| gmm_hmm| GmmHmm| BUILT-IN| ACTIVE| +| stray| Stray| BUILT-IN| ACTIVE| +| sundial| Timer-Sundial| BUILT-IN| ACTIVE| +| timer_xl| Timer-XL| BUILT-IN| ACTIVE| +| sundialv2| Timer-Sundial|FINE-TUNED|TRAINING| ++---------------------+--------------------+----------+--------+ +``` + +2. 微调任务后台异步启动，可在 AINode 进程看到 log；微调完成后，查询并使用新的模型 + +```SQL +IoTDB> show models ++---------------------+--------------------+----------+------+ +| ModelId| ModelType| Category| State| ++---------------------+--------------------+----------+------+ +| arima| Arima| BUILT-IN|ACTIVE| +| holtwinters| HoltWinters| BUILT-IN|ACTIVE| +|exponential_smoothing|ExponentialSmoothing| BUILT-IN|ACTIVE| +| naive_forecaster| NaiveForecaster| BUILT-IN|ACTIVE| +| stl_forecaster| StlForecaster| BUILT-IN|ACTIVE| +| gaussian_hmm| GaussianHmm| BUILT-IN|ACTIVE| +| gmm_hmm| GmmHmm| BUILT-IN|ACTIVE| +| stray| Stray| BUILT-IN|ACTIVE| +| sundial| Timer-Sundial| BUILT-IN|ACTIVE| +| timer_xl| Timer-XL| BUILT-IN|ACTIVE| +| sundialv2| Timer-Sundial|FINE-TUNED|ACTIVE| ++---------------------+--------------------+----------+------+ +``` ### 4.6 时序大模型导入步骤 diff --git a/src/zh/UserGuide/latest/AI-capability/TimeSeries-Large-Model.md b/src/zh/UserGuide/latest/AI-capability/TimeSeries-Large-Model.md index 79a8f6c01..a632e1dec 100644 --- a/src/zh/UserGuide/latest/AI-capability/TimeSeries-Large-Model.md +++ b/src/zh/UserGuide/latest/AI-capability/TimeSeries-Large-Model.md @@ -35,24 +35,36 @@ ![](/img/LargeModel09.png) -## Timer 模型 +## Timer-1 模型 Timer模型不仅展现了出色的少样本泛化和多任务适配能力，还通过预训练获得了丰富的知识库，赋予了它处理多样化下游任务的通用能力，拥有以下特点： - **泛化性**：模型能够通过使用少量样本进行微调，达到行业内领先的深度模型预测效果。 - **通用性**：模型设计灵活，能够适配多种不同的任务需求，并且支持变化的输入和输出长度，使其在各种应用场景中都能发挥作用。 -- **可扩展性**：随着模型参数数量的增加或预训练数据规模的扩大，模型的性能会持续提升，确保模型能够随着时间和数据量的增长而不断优化其预测效果。 +- **可扩展性**：随着模型参数数量的增加或预训练数据规模的扩大，模型效果会持续提升，确保模型能够随着时间和数据量的增长而不断优化其预测效果。 -![](/img/LargeModel02.png) +![](/img/model01.png) ## Timer-XL 模型 -Timer-XL 基于 Timer 进一步扩展升级了网络结构，在多个维度上进行全面突破： +Timer-XL 基于 Timer 进一步扩展升级了网络结构，在多个维度全面突破： -- **超长上下文支持**：该模型突破了传统时序预测模型的限制，支持处理数千个Token（相当于数万个时间点）的输入，有效解决了上下文长度的瓶颈问题。 +- **超长上下文支持**：该模型突破了传统时序预测模型的限制，支持处理数千个 Token（相当于数万个时间点）的输入，有效解决了上下文长度瓶颈问题。 - **多变量预测场景覆盖**：支持多种预测场景，包括非平稳时间序列的预测、涉及多个变量的预测任务以及包含协变量的预测，满足多样化的业务需求。 - **大规模工业时序数据集**：采用万亿大规模工业物联网领域的时序数据集进行预训练，数据集兼有庞大的体量、卓越的质量和丰富的领域等重要特质，覆盖能源、航空航天、钢铁、交通等多领域。 +![](/img/model02.png) + +## Timer-Sundial 模型 + +Timer-Sundial 是一个专注于时间序列预测的生成式基础模型系列，其基础版本拥有 1.28 亿参数，并在 1 万亿个时间点上进行了大规模预训练，其核心特性包括： + +- **强大的泛化性能**：具备零样本预测能力，可同时支持点预测和概率预测。 +- **灵活预测分布分析**：不仅能预测均值或分位数，还可通过模型生成的原始样本评估预测分布的任意统计特性。 +- **创新生成架构**：采用 “Transformer + TimeFlow” 协同架构——Transformer 学习时间片段的自回归表征，TimeFlow 模块基于流匹配框架 (Flow-Matching) 将随机噪声转化为多样化预测轨迹，实现高效的非确定性样本生成。 + +![](/img/model03.png) + ## 效果展示 时序大模型能够适应多种不同领域和场景的真实时序数据，在各种任务上拥有优异的处理效果，以下是在不同数据上的真实表现： @@ -87,20 +99,9 @@ show cluster ![](/img/ainode-timer-1.png) -2. 模型文件存放路径：推荐放在 AINode 安装包相同文件夹下，可新建模型文件夹存放模型文件 -3. 注册模型语句 - -```sql -create model using uri -``` - -示例： - -```sql -create model Timer-xl using uri -``` +2. 联网环境下首次启动 AINode 节点会自动拉取 Timer-XL、Sundial 模型。 -4. 检查模型是否注册成功 +3. 检查模型是否可用 检查命令： diff --git a/src/zh/UserGuide/latest/Deployment-and-Maintenance/AINode_Deployment_apache.md b/src/zh/UserGuide/latest/Deployment-and-Maintenance/AINode_Deployment_apache.md index 1092905ab..052c039c2 100644 --- a/src/zh/UserGuide/latest/Deployment-and-Maintenance/AINode_Deployment_apache.md +++ b/src/zh/UserGuide/latest/Deployment-and-Maintenance/AINode_Deployment_apache.md @@ -39,9 +39,7 @@ AINode 是 IoTDB 在 ConfigNode、DataNode 后提供的第三种内生节点， ### 2.1 安装包获取 - 用户可以下载AINode的软件安装包，下载并解压后即完成AINode的安装。 - - 解压后安装包（`apache-iotdb--ainode-bin.zip`），安装包解压后目录结构如下： +AINode 安装包（`apache-iotdb--ainode-bin.zip`），安装包解压后目录结构如下： | **目录** | **类型** | **说明** | | ------------ | -------- | ------------------------------------------------ | | lib | 文件夹 | AINode编译后的二进制可执行文件以及相关的代码依赖 | @@ -53,93 +51,67 @@ AINode 是 IoTDB 在 ConfigNode、DataNode 后提供的第三种内生节点， | `README.md` | 文件 | 使用说明 | ### 2.2 环境准备 -- 建议操作环境: Ubuntu, CentOS, MacOS -- 运行环境 - - Python 版本在 3.10~3.12 即可，且带有 pip 和 venv 工具；非联网环境下需要从 [此处](https://cloud.tsinghua.edu.cn/d/4c1342f6c272439aa96c/?p=%2Flibs&mode=list) 下载对应操作系统的zip压缩包（注意下载依赖需选择libs文件夹中的zip压缩包，如下图），并将文件夹下的所有文件拷贝到 `apache-iotdb--ainode-bin` 文件夹中 `lib` 文件夹下，并按下文步骤启动AINode。 +1. 建议操作环境: Ubuntu, CentOS, MacOS +2. IoTDB 版本：>= V 2.0.5.1 +3. 运行环境 + - Python 版本在 3.9 ~3.12，且带有 pip 和 venv 工具； + - - - - 环境变量中需存在 Python 解释器且可以通过 `python` 指令直接调用 - - 建议在 `apache-iotdb--ainode-bin` 文件夹下，新建 Python 解释器 venv 虚拟环境。如安装 3.10.0 版本虚拟环境，语句如下： - - ```shell - # 安装3.10.0版本的venv，创建虚拟环境，文件夹名为 `venv` - ../Python-3.10.0/python -m venv `venv` - ``` ## 3. 安装部署及使用 ### 3.1 安装 AINode -1. 检查Linux的内核架构 -```shell - uname -m - ``` - -2. 导入Python环境[下载](https://repo.anaconda.com/miniconda/) - -推荐下载py311版本应用，导入至用户根目录下 iotdb专用文件夹 中 - - -3. 验证Python版本 +1. 保证 Python 版本介于 3.9 ~3.12 ```shell python --version - ``` +# 或 +python3 --version +``` +2. 下载导入 AINode 到专用文件夹，切换到专用文件夹并解压安装包 + +```shell + unzip timechodb-2.0.5.1-ainode-bin.zip +``` -4. 创建虚拟环境（在 ainode 目录下执行）： +3. 创建虚拟环境（在 ainode 目录下执行）： ```shell - python -m venv venv + python3 -m venv venv ``` -5. 激活虚拟环境： +4. 激活虚拟环境： ```shell source venv/bin/activate ``` -6. 下载导入AINode到专用文件夹，切换到专用文件夹并解压安装包 - -```shell - unzip apache-iotdb-1.3.3-ainode-bin.zip - ``` - -7. 配置项修改 - -```shell - vi apache-iotdb-1.3.3-ainode-bin/conf/iotdb-ainode.properties - ``` - 配置项修改：[详细信息](#配置项修改) -> ain_seed_config_node=iotdb-1:10710（集群通讯节点IP:通讯节点端口）
-> ain_inference_rpc_address=iotdb-3（运行AINode的服务器IP） - -8. 更换Python源 - -```shell - pip config set global.index-url https://mirrors.aliyun.com/pypi/simple/ - ``` - -9. 启动AINode节点 - -```shell - nohup bash apache-iotdb-1.3.3-ainode-bin/sbin/start-ainode.sh > myout.file 2>& 1 & - ``` -> 回到系统默认环境：conda deactivate +5. 更新 pip 并安装 AINode 依赖 + ```shell + python -m pip install --upgrade pip + poetry lock + poetry install + ``` ### 3.2 配置项修改 AINode 支持修改一些必要的参数。可以在 `conf/iotdb-ainode.properties` 文件中找到下列参数并进行持久化的修改： -| **名称** | **描述** | **类型** | **默认值** | **改后生效方式** | -| :----------------------------- | ------------------------------------------------------------ | ------- | ------------------ | ---------------------------- | -| cluster_name | AINode 要加入集群的标识 | string | defaultCluster | 仅允许在第一次启动服务前修改 | -| ain_seed_config_node | AINode 启动时注册的 ConfigNode 地址 | String | 127.0.0.1:10710 | 仅允许在第一次启动服务前修改 | -| ain_inference_rpc_address | AINode 提供服务与通信的地址 ，内部服务通讯接口 | String | 127.0.0.1 | 仅允许在第一次启动服务前修改 | -| ain_inference_rpc_port | AINode 提供服务与通信的端口 | String | 10810 | 仅允许在第一次启动服务前修改 | -| ain_system_dir | AINode 元数据存储路径，相对路径的起始目录与操作系统相关，建议使用绝对路径 | String | data/AINode/system | 仅允许在第一次启动服务前修改 | -| ain_models_dir | AINode 存储模型文件的路径，相对路径的起始目录与操作系统相关，建议使用绝对路径 | String | data/AINode/models | 仅允许在第一次启动服务前修改 | -| ain_logs_dir | AINode 存储日志的路径，相对路径的起始目录与操作系统相关，建议使用绝对路径 | String | logs/AINode | 重启后生效 | -| ain_thrift_compression_enabled | AINode 是否启用 thrift 的压缩机制，0-不启动、1-启动 | Boolean | 0 | 重启后生效 | +| **名称** | **描述** | **类型** | **默认值** | +| ------------------------------ | ------------------------------------------------------------ | -------- | ------------------ | +| cluster_name | AINode 要加入集群的标识 | string | defaultCluster | +| ain_seed_config_node | AINode 启动时注册的 ConfigNode 地址 | String | 127.0.0.1:10710 | +| ain_cluster_ingress_address | AINode 拉取数据的 DataNode 的 rpc 地址 | String | 127.0.0.1 | +| ain_cluster_ingress_port | AINode 拉取数据的 DataNode 的 rpc 端口 | Integer | 6667 | +| ain_cluster_ingress_username | AINode 拉取数据的 DataNode 的客户端用户名 | String | root | +| ain_cluster_ingress_password | AINode 拉取数据的 DataNode 的客户端密码 | String | root | +| ain_cluster_ingress_time_zone | AINode 拉取数据的 DataNode 的客户端时区 | String | UTC+8 | +| ain_inference_rpc_address | AINode 提供服务与通信的地址 ，内部服务通讯接口 | String | 127.0.0.1 | +| ain_inference_rpc_port | AINode 提供服务与通信的端口 | String | 10810 | +| ain_system_dir | AINode 元数据存储路径，相对路径的起始目录与操作系统相关，建议使用绝对路径 | String | data/AINode/system | +| ain_models_dir | AINode 存储模型文件的路径，相对路径的起始目录与操作系统相关，建议使用绝对路径 | String | data/AINode/models | +| ain_thrift_compression_enabled | AINode 是否启用 thrift 的压缩机制，0-不启动、1-启动 | Boolean | 0 | + ### 3.3 启动 AINode 在完成 Seed-ConfigNode 的部署后，可以通过添加 AINode 节点来支持模型的注册和推理功能。在配置项中指定 IoTDB 集群的信息后，可以执行相应的指令来启动 AINode，加入 IoTDB 集群。 @@ -164,118 +136,7 @@ AINode 支持修改一些必要的参数。可以在 `conf/iotdb-ainode.properti nohup bash sbin\start-ainode.bat > myout.file 2>& 1 & ``` -##### 详细语法 - -```shell - # 启动命令 - # Linux 和 MacOS 系统 - bash sbin/start-ainode.sh -i -r -n - - # Windows 系统 - sbin\start-ainode.bat -i -r -n - ``` - -##### 参数介绍： - -| **名称** | **标签** | **描述** | **是否必填** | **类型** | **默认值** | **输入方式** | -| ------------------- | ---- | ------------------------------------------------------------ | -------- | ------ | ---------------- | ---------------------- | -| ain_interpreter_dir | -i | AINode 所安装在的虚拟环境的解释器路径，需要使用绝对路径 | 否 | String | 默认读取环境变量 | 调用时输入或持久化修改 | -| ain_force_reinstall | -r | 该脚本在检查 AINode 安装情况的时候是否检查版本，如果检查则在版本不对的情况下会强制安装 lib 里的 whl 安装包 | 否 | Bool | false | 调用时输入 | -| ain_no_dependencies | -n | 指定在安装 AINode 的时候是否安装依赖，如果指定则仅安装 AINode 主程序而不安装依赖。 | 否 | Bool | false | 调用时输入 | - - 如不想每次启动时指定对应参数，也可以在 `conf` 文件夹下的`ainode-env.sh` 和 `ainode-env.bat` 脚本中持久化修改参数（目前支持持久化修改 ain_interpreter_dir 参数）。 - - `ainode-env.sh` : - ```shell - # The defaulte venv environment is used if ain_interpreter_dir is not set. Please use absolute path without quotation mark - # ain_interpreter_dir= - ``` - `ainode-env.bat` : -```shell - @REM The defaulte venv environment is used if ain_interpreter_dir is not set. Please use absolute path without quotation mark - @REM set ain_interpreter_dir= - ``` - 在写入参数值的后解除对应行的注释并保存即可在下一次执行脚本时生效。 - #### 示例 - -##### 直接启动： - -```shell - # 启动命令 - # Linux 和 MacOS 系统 - bash sbin/start-ainode.sh - # Windows 系统 - sbin\start-ainode.bat - - - # 后台启动命令（长期运行推荐） - # Linux 和 MacOS 系统 - nohup bash sbin/start-ainode.sh > myout.file 2>& 1 & - # Windows 系统 - nohup bash sbin\start-ainode.bat > myout.file 2>& 1 & - ``` - -##### 更新启动： -如果 AINode 的版本进行了更新（如更新了 `lib` 文件夹），可使用此命令。首先要保证 AINode 已经停止运行，然后通过 `-r` 参数重启，该参数会根据 `lib` 下的文件重新安装 AINode。 - -```shell - # 更新启动命令 - # Linux 和 MacOS 系统 - bash sbin/start-ainode.sh -r - # Windows 系统 - sbin\start-ainode.bat -r - - - # 后台更新启动命令（长期运行推荐） - # Linux 和 MacOS 系统 - nohup bash sbin/start-ainode.sh -r > myout.file 2>& 1 & - # Windows 系统 - nohup bash sbin\start-ainode.bat -r > myout.file 2>& 1 & - ``` -#### 非联网环境启动 - -##### 启动命令 - -```shell - # 启动命令 - # Linux 和 MacOS 系统 - bash sbin/start-ainode.sh - - # Windows 系统 - sbin\start-ainode.bat - - # 后台启动命令（长期运行推荐） - # Linux 和 MacOS 系统 - nohup bash sbin/start-ainode.sh > myout.file 2>& 1 & - - # Windows 系统 - nohup bash sbin\start-ainode.bat > myout.file 2>& 1 & - ``` - -##### 详细语法 - -```shell - # 启动命令 - # Linux 和 MacOS 系统 - bash sbin/start-ainode.sh -i -r -n - - # Windows 系统 - sbin\start-ainode.bat -i -r -n - ``` - -##### 参数介绍： - -| **名称** | **标签** | **描述** | **是否必填** | **类型** | **默认值** | **输入方式** | -| ------------------- | ---- | ------------------------------------------------------------ | -------- | ------ | ---------------- | ---------------------- | -| ain_interpreter_dir | -i | AINode 所安装在的虚拟环境的解释器路径，需要使用绝对路径 | 否 | String | 默认读取环境变量 | 调用时输入或持久化修改 | -| ain_force_reinstall | -r | 该脚本在检查 AINode 安装情况的时候是否检查版本，如果检查则在版本不对的情况下会强制安装 lib 里的 whl 安装包 | 否 | Bool | false | 调用时输入 | - -> 注意：非联网环境下安装失败时，首先检查是否选择了平台对应的安装包，其次确认python版本（由于下载的安装包限制了python版本，3.7、3.9等其他都不行） - -#### 示例 - -##### 直接启动： ```shell # 启动命令 @@ -321,22 +182,6 @@ IoTDB> show cluster sbin\stop-ainode.bat ``` -##### 详细语法 - -```shell - # Linux / MacOS - bash sbin/stop-ainode.sh -t - - #Windows - sbin\stop-ainode.bat -t - ``` - -##### 参数介绍： - - | **名称** | **标签** | **描述** | **是否必填** | **类型** | **默认值** | **输入方式** | -| ----------------- | ---- | ------------------------------------------------------------ | -------- | ------ | ------ | ---------- | -| ain_remove_target | -t | AINode 关闭时可以指定待移除的目标 AINode 的 Node ID、地址和端口号，格式为`` | 否 | String | 无 | 调用时输入 | - #### 示例 ```shell # Linux / MacOS @@ -359,87 +204,7 @@ IoTDB> show cluster ``` 如果需要重新启动该节点，需重新执行启动脚本。 -### 3.6 移除 AINode - -当需要把一个 AINode 节点移出集群时，可以执行移除脚本。移除和停止脚本的差别是：停止是在集群中保留 AINode 节点但停止 AINode 服务，移除则是把 AINode 节点从集群中移除出去。 - - - #### 移除命令 - -```shell - # Linux / MacOS - bash sbin/remove-ainode.sh - - # Windows - sbin\remove-ainode.bat - ``` - -##### 详细语法 - -```shell - # Linux / MacOS - bash sbin/remove-ainode.sh -i -t -r -n - # Windows - sbin\remove-ainode.bat -i -t -r -n - ``` - -##### 参数介绍： - - | **名称** | **标签** | **描述** | **是否必填** | **类型** | **默认值** | **输入方式** | -| ------------------- | ---- | ------------------------------------------------------------ | -------- | ------ | ---------------- | --------------------- | -| ain_interpreter_dir | -i | AINode 所安装在的虚拟环境的解释器路径，需要使用绝对路径 | 否 | String | 默认读取环境变量 | 调用时输入+持久化修改 | -| ain_remove_target | -t | AINode 关闭时可以指定待移除的目标 AINode 的 Node ID、地址和端口号，格式为`` | 否 | String | 无 | 调用时输入 | -| ain_force_reinstall | -r | 该脚本在检查 AINode 安装情况的时候是否检查版本，如果检查则在版本不对的情况下会强制安装 lib 里的 whl 安装包 | 否 | Bool | false | 调用时输入 | -| ain_no_dependencies | -n | 指定在安装 AINode 的时候是否安装依赖，如果指定则仅安装 AINode 主程序而不安装依赖。 | 否 | Bool | false | 调用时输入 | - - 如不想每次启动时指定对应参数，也可以在 `conf` 文件夹下的`ainode-env.sh` 和 `ainode-env.bat` 脚本中持久化修改参数（目前支持持久化修改 ain_interpreter_dir 参数）。 - - `ainode-env.sh` : - ```shell - # The defaulte venv environment is used if ain_interpreter_dir is not set. Please use absolute path without quotation mark - # ain_interpreter_dir= - ``` - `ainode-env.bat` : -```shell - @REM The defaulte venv environment is used if ain_interpreter_dir is not set. Please use absolute path without quotation mark - @REM set ain_interpreter_dir= - ``` - 在写入参数值的后解除对应行的注释并保存即可在下一次执行脚本时生效。 - -#### 示例 - -##### 直接移除： - - ```shell - # Linux / MacOS - bash sbin/remove-ainode.sh - - # Windows - sbin\remove-ainode.bat - ``` - 移除节点后，将无法查询到节点的相关信息。 - - ```shell -IoTDB> show cluster -+------+----------+-------+---------------+------------+-------+-----------+ -|NodeID| NodeType| Status|InternalAddress|InternalPort|Version| BuildInfo| -+------+----------+-------+---------------+------------+-------+-----------+ -| 0|ConfigNode|Running| 127.0.0.1| 10710|UNKNOWN|190e303-dev| -| 1| DataNode|Running| 127.0.0.1| 10730|UNKNOWN|190e303-dev| -+------+----------+-------+---------------+------------+-------+-----------+ -``` -##### 指定移除： - -如果用户丢失了 data 文件夹下的文件，可能 AINode 本地无法主动移除自己，需要用户指定节点号、地址和端口号进行移除，此时我们支持用户按照以下方法输入参数进行删除。 - - ```shell - # Linux / MacOS - bash sbin/remove-ainode.sh -t /: - - # Windows - sbin\remove-ainode.bat -t /: - ``` ## 4. 常见问题 diff --git a/src/zh/UserGuide/latest/Deployment-and-Maintenance/AINode_Deployment_timecho.md b/src/zh/UserGuide/latest/Deployment-and-Maintenance/AINode_Deployment_timecho.md index 1e8d83e96..e04378a43 100644 --- a/src/zh/UserGuide/latest/Deployment-and-Maintenance/AINode_Deployment_timecho.md +++ b/src/zh/UserGuide/latest/Deployment-and-Maintenance/AINode_Deployment_timecho.md @@ -1,536 +1,14 @@ - -# AINode 部署 - -## 1. AINode介绍 - -### 1.1 能力介绍 - -AINode 是 IoTDB 在 ConfigNode、DataNode 后提供的第三种内生节点，该节点通过与 IoTDB 集群的 DataNode、ConfigNode 的交互，扩展了对时间序列进行机器学习分析的能力，支持从外部引入已有机器学习模型进行注册，并使用注册的模型在指定时序数据上通过简单 SQL 语句完成时序分析任务的过程，将模型的创建、管理及推理融合在数据库引擎中。目前已提供常见时序分析场景（例如预测与异常检测）的机器学习算法或自研模型。 - -### 1.2 交付方式 - 是 IoTDB 集群外的额外套件，独立安装包。 - -### 1.3 部署模式 -
- - -
- -## 2. 安装准备 - -### 2.1 装包获取 - - 用户可以下载AINode的软件安装包，下载并解压后即完成AINode的安装。 - - 解压后安装包（`apache-iotdb--ainode-bin.zip`），安装包解压后目录结构如下： -| **目录** | **类型** | **说明** | -| ------------ | -------- | ------------------------------------------------ | -| lib | 文件夹 | AINode编译后的二进制可执行文件以及相关的代码依赖 | -| sbin | 文件夹 | AINode的运行脚本，可以启动，移除和停止AINode | -| conf | 文件夹 | 包含AINode的配置项，具体包含以下配置项 | -| LICENSE | 文件 | 证书 | -| NOTICE | 文件 | 提示 | -| README_ZH.md | 文件 | markdown格式的中文版说明 | -| `README.md` | 文件 | 使用说明 | - -### 2.2 环境准备 -- 建议操作环境: Ubuntu, CentOS, MacOS - -- 运行环境 - - Python 版本在 3.10~3.12 即可，且带有 pip 和 venv 工具；非联网环境下需要从 [此处](https://cloud.tsinghua.edu.cn/d/4c1342f6c272439aa96c/?p=%2Flibs&mode=list) 下载对应操作系统的zip压缩包（注意下载依赖需选择libs文件夹中的zip压缩包，如下图），并将文件夹下的所有文件拷贝到 `apache-iotdb--ainode-bin` 文件夹中 `lib` 文件夹下，并按下文步骤启动AINode。 - - - - - 环境变量中需存在 Python 解释器且可以通过 `python` 指令直接调用 - - 建议在 `apache-iotdb--ainode-bin` 文件夹下，新建 Python 解释器 venv 虚拟环境。如安装 3.10.0 版本虚拟环境，语句如下： - - ```shell - # 安装3.10.0版本的venv，创建虚拟环境，文件夹名为 `venv` - ../Python-3.10.0/python -m venv `venv` - ``` -## 3. 安装部署及使用 - -### 3.1 安装 AINode - -1. AINode 激活 - - 要求 IoTDB 处于正常运行状态，且 license 中有 AINode 模块授权。 - - 激活 AINode 模块授权方式如下： - - 方式一：激活文件拷贝激活 - - 重新启动 confignode 节点后，进入 activation 文件夹， 将 system_info 文件复制给天谋工作人员，并告知工作人员申请 AINode 独立授权； - - 收到工作人员返回的 license 文件； - - 将 license 文件放入对应节点的 activation 文件夹下； -- 方式二：激活脚本激活 - - 获取激活所需机器码，进入安装目录的 `sbin` 目录，执行激活脚本： - ```shell - cd sbin - ./start-activate.sh - ``` - - 显示如下信息，请将机器码（即该串字符）复制给天谋工作人员，并告知工作人员申请 AINode 独立授权： - ```shell - Please copy the system_info's content and send it to Timecho: - 01-KU5LDFFN-PNBEHDRH - Please enter license: - ``` - - 将工作人员返回的激活码输入上一步的命令行提示处 `Please enter license:`，如下提示： - ```shell - Please enter license: - Jw+MmF+AtexsfgNGOFgTm83BgXbq0zT1+fOfPvQsLlj6ZsooHFU6HycUSEGC78eT1g67KPvkcLCUIsz2QpbyVmPLr9x1+kVjBubZPYlVpsGYLqLFc8kgpb5vIrPLd3hGLbJ5Ks8fV1WOVrDDVQq89YF2atQa2EaB9EAeTWd0bRMZ+s9ffjc/1Zmh9NSP/T3VCfJcJQyi7YpXWy5nMtcW0gSV+S6fS5r7a96PjbtE0zXNjnEhqgRzdU+mfO8gVuUNaIy9l375cp1GLpeCh6m6pF+APW1CiXLTSijK9Qh3nsL5bAOXNeob5l+HO5fEMgzrW8OJPh26Vl6ljKUpCvpTiw== - License has been stored to sbin/../activation/license - Import completed. Please start cluster and excute 'show cluster' to verify activation status - ``` -- 更新 license 后，重新启动 DataNode 节点，进入 IoTDB 的 sbin 目录下，启动 datanode： - ```shell - cd sbin - ./start-datanode.sh -d #-d参数将在后台进行启动 - ``` - -2. 检查Linux的内核架构 - ```shell - uname -m - ``` - -3. 导入Python环境[下载](https://repo.anaconda.com/miniconda/) - - 推荐下载py311版本应用，导入至用户根目录下 iotdb专用文件夹 中 - - -4. 验证Python版本 - - ```shell - python --version - ``` - -5. 创建虚拟环境（在 ainode 目录下执行）： - - ```shell - python -m venv venv - ``` - -6. 激活虚拟环境： - - ```shell - source venv/bin/activate - ``` - -7. 下载导入AINode到专用文件夹，切换到专用文件夹并解压安装包 - - ```shell - unzip iotdb-enterprise-ainode-1.3.3.2.zip - ``` - -8. 配置项修改 - - ```shell - vi iotdb-enterprise-ainode-1.3.3.2/conf/iotdb-ainode.properties - ``` - 配置项修改：[详细信息](#配置项修改) - > ain_seed_config_node=iotdb-1:10710（集群通讯节点IP:通讯节点端口）
- > ain_inference_rpc_address=iotdb-3（运行AINode的服务器IP） - -9. 更换Python源 - - ```shell - pip config set global.index-url https://mirrors.aliyun.com/pypi/simple/ - ``` - -10. 启动AINode节点 - - ```shell - nohup bash iotdb-enterprise-ainode-1.3.3.2/sbin/start-ainode.sh > myout.file 2>& 1 & - ``` - > 回到系统默认环境：conda deactivate - -### 3.2 配置项修改 -AINode 支持修改一些必要的参数。可以在 `conf/iotdb-ainode.properties` 文件中找到下列参数并进行持久化的修改： - -| **名称** | **描述** | **类型** | **默认值** | **改后生效方式** | -| :----------------------------- | ------------------------------------------------------------ | ------- | ------------------ | ---------------------------- | -| cluster_name | AINode 要加入集群的标识 | string | defaultCluster | 仅允许在第一次启动服务前修改 | -| ain_seed_config_node | AINode 启动时注册的 ConfigNode 地址 | String | 127.0.0.1:10710 | 仅允许在第一次启动服务前修改 | -| ain_inference_rpc_address | AINode 提供服务与通信的地址 ，内部服务通讯接口 | String | 127.0.0.1 | 仅允许在第一次启动服务前修改 | -| ain_inference_rpc_port | AINode 提供服务与通信的端口 | String | 10810 | 仅允许在第一次启动服务前修改 | -| ain_system_dir | AINode 元数据存储路径，相对路径的起始目录与操作系统相关，建议使用绝对路径 | String | data/AINode/system | 仅允许在第一次启动服务前修改 | -| ain_models_dir | AINode 存储模型文件的路径，相对路径的起始目录与操作系统相关，建议使用绝对路径 | String | data/AINode/models | 仅允许在第一次启动服务前修改 | -| ain_logs_dir | AINode 存储日志的路径，相对路径的起始目录与操作系统相关，建议使用绝对路径 | String | logs/AINode | 重启后生效 | -| ain_thrift_compression_enabled | AINode 是否启用 thrift 的压缩机制，0-不启动、1-启动 | Boolean | 0 | 重启后生效 | -### 3.3 启动 AINode - - 在完成 Seed-ConfigNode 的部署后，可以通过添加 AINode 节点来支持模型的注册和推理功能。在配置项中指定 IoTDB 集群的信息后，可以执行相应的指令来启动 AINode，加入 IoTDB 集群。 - -#### 联网环境启动 - -##### 启动命令 - -```shell - # 启动命令 - # Linux 和 MacOS 系统 - bash sbin/start-ainode.sh - - # Windows 系统 - sbin\start-ainode.bat - - # 后台启动命令（长期运行推荐） - # Linux 和 MacOS 系统 - nohup bash sbin/start-ainode.sh > myout.file 2>& 1 & - - # Windows 系统 - nohup bash sbin\start-ainode.bat > myout.file 2>& 1 & - ``` - -##### 详细语法 - -```shell - # 启动命令 - # Linux 和 MacOS 系统 - bash sbin/start-ainode.sh -i -r -n - - # Windows 系统 - sbin\start-ainode.bat -i -r -n - ``` - -##### 参数介绍： - -| **名称** | **标签** | **描述** | **是否必填** | **类型** | **默认值** | **输入方式** | -| ------------------- | ---- | ------------------------------------------------------------ | -------- | ------ | ---------------- | ---------------------- | -| ain_interpreter_dir | -i | AINode 所安装在的虚拟环境的解释器路径，需要使用绝对路径 | 否 | String | 默认读取环境变量 | 调用时输入或持久化修改 | -| ain_force_reinstall | -r | 该脚本在检查 AINode 安装情况的时候是否检查版本，如果检查则在版本不对的情况下会强制安装 lib 里的 whl 安装包 | 否 | Bool | false | 调用时输入 | -| ain_no_dependencies | -n | 指定在安装 AINode 的时候是否安装依赖，如果指定则仅安装 AINode 主程序而不安装依赖。 | 否 | Bool | false | 调用时输入 | - - 如不想每次启动时指定对应参数，也可以在 `conf` 文件夹下的`ainode-env.sh` 和 `ainode-env.bat` 脚本中持久化修改参数（目前支持持久化修改 ain_interpreter_dir 参数）。 - - `ainode-env.sh` : - ```shell - # The defaulte venv environment is used if ain_interpreter_dir is not set. Please use absolute path without quotation mark - # ain_interpreter_dir= - ``` - `ainode-env.bat` : -```shell - @REM The defaulte venv environment is used if ain_interpreter_dir is not set. Please use absolute path without quotation mark - @REM set ain_interpreter_dir= - ``` - 在写入参数值的后解除对应行的注释并保存即可在下一次执行脚本时生效。 - -#### 示例 - -##### 直接启动： - -```shell - # 启动命令 - # Linux 和 MacOS 系统 - bash sbin/start-ainode.sh - # Windows 系统 - sbin\start-ainode.bat - - - # 后台启动命令（长期运行推荐） - # Linux 和 MacOS 系统 - nohup bash sbin/start-ainode.sh > myout.file 2>& 1 & - # Windows 系统 - nohup bash sbin\start-ainode.bat > myout.file 2>& 1 & - ``` - -##### 更新启动： -如果 AINode 的版本进行了更新（如更新了 `lib` 文件夹），可使用此命令。首先要保证 AINode 已经停止运行，然后通过 `-r` 参数重启，该参数会根据 `lib` 下的文件重新安装 AINode。 - -```shell - # 更新启动命令 - # Linux 和 MacOS 系统 - bash sbin/start-ainode.sh -r - # Windows 系统 - sbin\start-ainode.bat -r - - - # 后台更新启动命令（长期运行推荐） - # Linux 和 MacOS 系统 - nohup bash sbin/start-ainode.sh -r > myout.file 2>& 1 & - # Windows 系统 - nohup bash sbin\start-ainode.bat -r > myout.file 2>& 1 & - ``` -#### 非联网环境启动 - -##### 启动命令 - -```shell - # 启动命令 - # Linux 和 MacOS 系统 - bash sbin/start-ainode.sh - - # Windows 系统 - sbin\start-ainode.bat - - # 后台启动命令（长期运行推荐） - # Linux 和 MacOS 系统 - nohup bash sbin/start-ainode.sh > myout.file 2>& 1 & - - # Windows 系统 - nohup bash sbin\start-ainode.bat > myout.file 2>& 1 & - ``` - -##### 详细语法 - -```shell - # 启动命令 - # Linux 和 MacOS 系统 - bash sbin/start-ainode.sh -i -r -n - - # Windows 系统 - sbin\start-ainode.bat -i -r -n - ``` - -##### 参数介绍： - -| **名称** | **标签** | **描述** | **是否必填** | **类型** | **默认值** | **输入方式** | -| ------------------- | ---- | ------------------------------------------------------------ | -------- | ------ | ---------------- | ---------------------- | -| ain_interpreter_dir | -i | AINode 所安装在的虚拟环境的解释器路径，需要使用绝对路径 | 否 | String | 默认读取环境变量 | 调用时输入或持久化修改 | -| ain_force_reinstall | -r | 该脚本在检查 AINode 安装情况的时候是否检查版本，如果检查则在版本不对的情况下会强制安装 lib 里的 whl 安装包 | 否 | Bool | false | 调用时输入 | - -> 注意：非联网环境下安装失败时，首先检查是否选择了平台对应的安装包，其次确认python版本（由于下载的安装包限制了python版本，3.7、3.9等其他都不行） - -#### 示例 - -##### 直接启动： - -```shell - # 启动命令 - # Linux 和 MacOS 系统 - bash sbin/start-ainode.sh - # Windows 系统 - sbin\start-ainode.bat - - - # 后台启动命令（长期运行推荐） - # Linux 和 MacOS 系统 - nohup bash sbin/start-ainode.sh > myout.file 2>& 1 & - # Windows 系统 - nohup bash sbin\start-ainode.bat > myout.file 2>& 1 & - ``` - -### 3.4 检测 AINode 节点状态 - -AINode 启动过程中会自动将新的 AINode 加入 IoTDB 集群。启动 AINode 后可以在 命令行中输入 SQL 来查询，集群中看到 AINode 节点，其运行状态为 Running（如下展示）表示加入成功。 - -```shell -IoTDB> show cluster -+------+----------+-------+---------------+------------+-------+-----------+ -|NodeID| NodeType| Status|InternalAddress|InternalPort|Version| BuildInfo| -+------+----------+-------+---------------+------------+-------+-----------+ -| 0|ConfigNode|Running| 127.0.0.1| 10710|UNKNOWN|190e303-dev| -| 1| DataNode|Running| 127.0.0.1| 10730|UNKNOWN|190e303-dev| -| 2| AINode|Running| 127.0.0.1| 10810|UNKNOWN|190e303-dev| -+------+----------+-------+---------------+------------+-------+-----------+ -``` - -### 3.5 停止 AINode - -如果需要停止正在运行的 AINode 节点，则执行相应的关闭脚本。 - -#### 停止命令 - -```shell - # Linux / MacOS - bash sbin/stop-ainode.sh - - #Windows - sbin\stop-ainode.bat - ``` - -##### 详细语法 - -```shell - # Linux / MacOS - bash sbin/stop-ainode.sh -t - - #Windows - sbin\stop-ainode.bat -t - ``` - -##### 参数介绍： - - | **名称** | **标签** | **描述** | **是否必填** | **类型** | **默认值** | **输入方式** | -| ----------------- | ---- | ------------------------------------------------------------ | -------- | ------ | ------ | ---------- | -| ain_remove_target | -t | AINode 关闭时可以指定待移除的目标 AINode 的 Node ID、地址和端口号，格式为`` | 否 | String | 无 | 调用时输入 | - -#### 示例 -```shell - # Linux / MacOS - bash sbin/stop-ainode.sh - - # Windows - sbin\stop-ainode.bat - ``` -停止 AINode 后，还可以在集群中看到 AINode 节点，其运行状态为 UNKNOWN（如下展示），此时无法使用 AINode 功能。 - - ```shell -IoTDB> show cluster -+------+----------+-------+---------------+------------+-------+-----------+ -|NodeID| NodeType| Status|InternalAddress|InternalPort|Version| BuildInfo| -+------+----------+-------+---------------+------------+-------+-----------+ -| 0|ConfigNode|Running| 127.0.0.1| 10710|UNKNOWN|190e303-dev| -| 1| DataNode|Running| 127.0.0.1| 10730|UNKNOWN|190e303-dev| -| 2| AINode|UNKNOWN| 127.0.0.1| 10790|UNKNOWN|190e303-dev| -+------+----------+-------+---------------+------------+-------+-----------+ -``` -如果需要重新启动该节点，需重新执行启动脚本。 - -### 3.6 移除 AINode - -当需要把一个 AINode 节点移出集群时，可以执行移除脚本。移除和停止脚本的差别是：停止是在集群中保留 AINode 节点但停止 AINode 服务，移除则是把 AINode 节点从集群中移除出去。 - - - #### 移除命令 - -```shell - # Linux / MacOS - bash sbin/remove-ainode.sh - - # Windows - sbin\remove-ainode.bat - ``` - -##### 详细语法 - -```shell - # Linux / MacOS - bash sbin/remove-ainode.sh -i -t -r -n - - # Windows - sbin\remove-ainode.bat -i -t -r -n - ``` - -##### 参数介绍： - - | **名称** | **标签** | **描述** | **是否必填** | **类型** | **默认值** | **输入方式** | -| ------------------- | ---- | ------------------------------------------------------------ | -------- | ------ | ---------------- | --------------------- | -| ain_interpreter_dir | -i | AINode 所安装在的虚拟环境的解释器路径，需要使用绝对路径 | 否 | String | 默认读取环境变量 | 调用时输入+持久化修改 | -| ain_remove_target | -t | AINode 关闭时可以指定待移除的目标 AINode 的 Node ID、地址和端口号，格式为`` | 否 | String | 无 | 调用时输入 | -| ain_force_reinstall | -r | 该脚本在检查 AINode 安装情况的时候是否检查版本，如果检查则在版本不对的情况下会强制安装 lib 里的 whl 安装包 | 否 | Bool | false | 调用时输入 | -| ain_no_dependencies | -n | 指定在安装 AINode 的时候是否安装依赖，如果指定则仅安装 AINode 主程序而不安装依赖。 | 否 | Bool | false | 调用时输入 | - - 如不想每次启动时指定对应参数，也可以在 `conf` 文件夹下的`ainode-env.sh` 和 `ainode-env.bat` 脚本中持久化修改参数（目前支持持久化修改 ain_interpreter_dir 参数）。 - - `ainode-env.sh` : - ```shell - # The defaulte venv environment is used if ain_interpreter_dir is not set. Please use absolute path without quotation mark - # ain_interpreter_dir= - ``` - `ainode-env.bat` : -```shell - @REM The defaulte venv environment is used if ain_interpreter_dir is not set. Please use absolute path without quotation mark - @REM set ain_interpreter_dir= - ``` - 在写入参数值的后解除对应行的注释并保存即可在下一次执行脚本时生效。 - -#### 示例 - -##### 直接移除： - - ```shell - # Linux / MacOS - bash sbin/remove-ainode.sh - - # Windows - sbin\remove-ainode.bat - ``` - 移除节点后，将无法查询到节点的相关信息。 - - ```shell -IoTDB> show cluster -+------+----------+-------+---------------+------------+-------+-----------+ -|NodeID| NodeType| Status|InternalAddress|InternalPort|Version| BuildInfo| -+------+----------+-------+---------------+------------+-------+-----------+ -| 0|ConfigNode|Running| 127.0.0.1| 10710|UNKNOWN|190e303-dev| -| 1| DataNode|Running| 127.0.0.1| 10730|UNKNOWN|190e303-dev| -+------+----------+-------+---------------+------------+-------+-----------+ -``` -##### 指定移除： - -如果用户丢失了 data 文件夹下的文件，可能 AINode 本地无法主动移除自己，需要用户指定节点号、地址和端口号进行移除，此时我们支持用户按照以下方法输入参数进行删除。 - - ```shell - # Linux / MacOS - bash sbin/remove-ainode.sh -t /: - - # Windows - sbin\remove-ainode.bat -t /: - ``` - -## 4. 常见问题 - -### 4.1 启动AINode时出现找不到venv模块的报错 - - 当使用默认方式启动 AINode 时，会在安装包目录下创建一个 python 虚拟环境并安装依赖，因此要求安装 venv 模块。通常来说 python3.10 及以上的版本会自带 venv，但对于一些系统自带的 python 环境可能并不满足这一要求。出现该报错时有两种解决方案（二选一）： - - 在本地安装 venv 模块，以 ubuntu 为例，可以通过运行以下命令来安装 python 自带的 venv 模块。或者从 python 官网安装一个自带 venv 的 python 版本。 - - ```shell -apt-get install python3.10-venv -``` - 安装 3.10.0 版本的 venv 到 AINode 里面 在 AINode 路径下 - - ```shell -../Python-3.10.0/python -m venv venv(文件夹名） -``` - 在运行启动脚本时通过 `-i` 指定已有的 python 解释器路径作为 AINode 的运行环境，这样就不再需要创建一个新的虚拟环境。 - - ### 4.2 python中的SSL模块没有被正确安装和配置，无法处理HTTPS资源 -WARNING: pip is configured with locations that require TLS/SSL, however the ssl module in Python is not available. -可以安装 OpenSSLS 后，再重新构建 python 来解决这个问题 -> Currently Python versions 3.6 to 3.9 are compatible with OpenSSL 1.0.2, 1.1.0, and 1.1.1. - - Python 要求我们的系统上安装有 OpenSSL，具体安装方法可见[链接](https://stackoverflow.com/questions/56552390/how-to-fix-ssl-module-in-python-is-not-available-in-centos) - - ```shell -sudo apt-get install build-essential libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm libncurses5-dev libncursesw5-dev xz-utils tk-dev libffi-dev liblzma-dev uuid-dev lzma-dev liblzma-dev -sudo -E ./configure --with-ssl -make -sudo make install -``` - - ### 4.3 pip版本较低 - - windows下出现类似“error：Microsoft Visual C++ 14.0 or greater is required...”的编译问题 - - 出现对应的报错，通常是 c++版本或是 setuptools 版本不足，可以在 - - ```shell -./python -m pip install --upgrade pip -./python -m pip install --upgrade setuptools -``` - - - ### 4.4 安装编译python - - 使用以下指定从官网下载安装包并解压: - ```shell -.wget https://www.python.org/ftp/python/3.10.0/Python-3.10.0.tar.xz -tar Jxf Python-3.10.0.tar.xz -``` - 编译安装对应的 python 包: - ```shell -cd Python-3.10.0 -./configure prefix=/usr/local/python3 -make -sudo make install -python3 --version -``` \ No newline at end of file +| **名称** | **描述** | **类型** | **默认值** | +| ------------------------------ | ------------------------------------------------------------ | -------- | ------------------ | +| cluster_name | AINode 要加入集群的标识 | string | defaultCluster | +| ain_seed_config_node | AINode 启动时注册的 ConfigNode 地址 | String | 127.0.0.1:10710 | +| ain_cluster_ingress_address | AINode 拉取数据的 DataNode 的 rpc 地址 | String | 127.0.0.1 | +| ain_cluster_ingress_port | AINode 拉取数据的 DataNode 的 rpc 端口 | Integer | 6667 | +| ain_cluster_ingress_username | AINode 拉取数据的 DataNode 的客户端用户名 | String | root | +| ain_cluster_ingress_password | AINode 拉取数据的 DataNode 的客户端密码 | String | root | +| ain_cluster_ingress_time_zone | AINode 拉取数据的 DataNode 的客户端时区 | String | UTC+8 | +| ain_inference_rpc_address | AINode 提供服务与通信的地址 ，内部服务通讯接口 | String | 127.0.0.1 | +| ain_inference_rpc_port | AINode 提供服务与通信的端口 | String | 10810 | +| ain_system_dir | AINode 元数据存储路径，相对路径的起始目录与操作系统相关，建议使用绝对路径 | String | data/AINode/system | +| ain_models_dir | AINode 存储模型文件的路径，相对路径的起始目录与操作系统相关，建议使用绝对路径 | String | data/AINode/models | +| ain_thrift_compression_enabled | AINode 是否启用 thrift 的压缩机制，0-不启动、1-启动 | Boolean | 0 | \ No newline at end of file

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