diff --git a/Classification/cnns/README.md b/ComputerVision/cnns/README.md similarity index 100% rename from Classification/cnns/README.md rename to ComputerVision/cnns/README.md diff --git a/Classification/cnns/alexnet_model.py b/ComputerVision/cnns/alexnet_model.py similarity index 100% rename from Classification/cnns/alexnet_model.py rename to ComputerVision/cnns/alexnet_model.py diff --git a/Classification/cnns/config.py b/ComputerVision/cnns/config.py similarity index 98% rename from Classification/cnns/config.py rename to ComputerVision/cnns/config.py index a87b6e9..0b41a20 100755 --- a/Classification/cnns/config.py +++ b/ComputerVision/cnns/config.py @@ -53,7 +53,7 @@ def str2bool(v): parser.add_argument('--node_ips', type=str_list, default=['192.168.1.13', '192.168.1.14'], help='nodes ip list for training, devided by ",", length >= num_nodes') - parser.add_argument("--model", type=str, default="resnet50", + parser.add_argument("--model", type=str, default="resnext50", help="resnet50") parser.add_argument( '--use_fp16', diff --git a/Classification/cnns/data/ILSVRC2012_val_00020287.JPEG b/ComputerVision/cnns/data/ILSVRC2012_val_00020287.JPEG similarity index 100% rename from Classification/cnns/data/ILSVRC2012_val_00020287.JPEG rename to ComputerVision/cnns/data/ILSVRC2012_val_00020287.JPEG diff --git a/Classification/cnns/data/fish.jpg b/ComputerVision/cnns/data/fish.jpg similarity index 100% rename from Classification/cnns/data/fish.jpg rename to ComputerVision/cnns/data/fish.jpg diff --git a/Classification/cnns/data/tiger.jpg b/ComputerVision/cnns/data/tiger.jpg similarity index 100% rename from Classification/cnns/data/tiger.jpg rename to ComputerVision/cnns/data/tiger.jpg diff --git a/Classification/cnns/docs/resnet50_lr_schedule.png b/ComputerVision/cnns/docs/resnet50_lr_schedule.png similarity index 100% rename from Classification/cnns/docs/resnet50_lr_schedule.png rename to ComputerVision/cnns/docs/resnet50_lr_schedule.png diff --git a/Classification/cnns/docs/resnet50_validation_acuracy.png b/ComputerVision/cnns/docs/resnet50_validation_acuracy.png similarity index 100% rename from Classification/cnns/docs/resnet50_validation_acuracy.png rename to ComputerVision/cnns/docs/resnet50_validation_acuracy.png diff --git a/Classification/cnns/evaluate.sh b/ComputerVision/cnns/evaluate.sh similarity index 100% rename from Classification/cnns/evaluate.sh rename to ComputerVision/cnns/evaluate.sh diff --git a/Classification/cnns/imagenet1000_clsidx_to_labels.py b/ComputerVision/cnns/imagenet1000_clsidx_to_labels.py similarity index 100% rename from Classification/cnns/imagenet1000_clsidx_to_labels.py rename to ComputerVision/cnns/imagenet1000_clsidx_to_labels.py diff --git a/Classification/cnns/inception_model.py b/ComputerVision/cnns/inception_model.py similarity index 100% rename from Classification/cnns/inception_model.py rename to ComputerVision/cnns/inception_model.py diff --git a/Classification/cnns/inference.sh b/ComputerVision/cnns/inference.sh similarity index 100% rename from Classification/cnns/inference.sh rename to ComputerVision/cnns/inference.sh diff --git a/Classification/cnns/job_function_util.py b/ComputerVision/cnns/job_function_util.py similarity index 100% rename from Classification/cnns/job_function_util.py rename to ComputerVision/cnns/job_function_util.py diff --git a/Classification/cnns/mobilenet_v2_model.py b/ComputerVision/cnns/mobilenet_v2_model.py similarity index 100% rename from Classification/cnns/mobilenet_v2_model.py rename to ComputerVision/cnns/mobilenet_v2_model.py diff --git a/Classification/cnns/of_cnn_evaluate.py b/ComputerVision/cnns/of_cnn_evaluate.py similarity index 100% rename from Classification/cnns/of_cnn_evaluate.py rename to ComputerVision/cnns/of_cnn_evaluate.py diff --git a/Classification/cnns/of_cnn_inference.py b/ComputerVision/cnns/of_cnn_inference.py similarity index 100% rename from Classification/cnns/of_cnn_inference.py rename to ComputerVision/cnns/of_cnn_inference.py diff --git a/Classification/cnns/of_cnn_train_val.py b/ComputerVision/cnns/of_cnn_train_val.py similarity index 100% rename from Classification/cnns/of_cnn_train_val.py rename to ComputerVision/cnns/of_cnn_train_val.py diff --git a/Classification/cnns/ofrecord_util.py b/ComputerVision/cnns/ofrecord_util.py similarity index 100% rename from Classification/cnns/ofrecord_util.py rename to ComputerVision/cnns/ofrecord_util.py diff --git a/Classification/cnns/optimizer_util.py b/ComputerVision/cnns/optimizer_util.py similarity index 100% rename from Classification/cnns/optimizer_util.py rename to ComputerVision/cnns/optimizer_util.py diff --git a/Classification/cnns/resnet_model.py b/ComputerVision/cnns/resnet_model.py similarity index 100% rename from Classification/cnns/resnet_model.py rename to ComputerVision/cnns/resnet_model.py diff --git a/Classification/cnns/resnet_to_onnx.py b/ComputerVision/cnns/resnet_to_onnx.py similarity index 100% rename from Classification/cnns/resnet_to_onnx.py rename to ComputerVision/cnns/resnet_to_onnx.py diff --git a/Classification/cnns/resnext_model.py b/ComputerVision/cnns/resnext_model.py similarity index 100% rename from Classification/cnns/resnext_model.py rename to ComputerVision/cnns/resnext_model.py diff --git a/Classification/cnns/tools/README.md b/ComputerVision/cnns/tools/README.md similarity index 100% rename from Classification/cnns/tools/README.md rename to ComputerVision/cnns/tools/README.md diff --git a/Classification/cnns/tools/extract_trainval.sh b/ComputerVision/cnns/tools/extract_trainval.sh similarity index 100% rename from Classification/cnns/tools/extract_trainval.sh rename to ComputerVision/cnns/tools/extract_trainval.sh diff --git a/Classification/cnns/tools/imagenet_2012_validation_synset_labels.txt b/ComputerVision/cnns/tools/imagenet_2012_validation_synset_labels.txt similarity index 100% rename from Classification/cnns/tools/imagenet_2012_validation_synset_labels.txt rename to ComputerVision/cnns/tools/imagenet_2012_validation_synset_labels.txt diff --git a/Classification/cnns/tools/imagenet_lsvrc_2015_synsets.txt b/ComputerVision/cnns/tools/imagenet_lsvrc_2015_synsets.txt similarity index 100% rename from Classification/cnns/tools/imagenet_lsvrc_2015_synsets.txt rename to ComputerVision/cnns/tools/imagenet_lsvrc_2015_synsets.txt diff --git a/Classification/cnns/tools/imagenet_metadata.txt b/ComputerVision/cnns/tools/imagenet_metadata.txt similarity index 100% rename from Classification/cnns/tools/imagenet_metadata.txt rename to ComputerVision/cnns/tools/imagenet_metadata.txt diff --git a/Classification/cnns/tools/imagenet_ofrecord.py b/ComputerVision/cnns/tools/imagenet_ofrecord.py similarity index 100% rename from Classification/cnns/tools/imagenet_ofrecord.py rename to ComputerVision/cnns/tools/imagenet_ofrecord.py diff --git a/Classification/cnns/tools/preprocess_imagenet_validation_data.py 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mode 100755 new mode 100644 index e279278..a9756a0 --- a/README.md +++ b/README.md @@ -1,24 +1,171 @@ # OneFlow Deep Learning Benchmarks -## Introduction -This repository provides OneFlow deep learning benchmark examples for CV, CTR and NLP, and more models are on the way and will be provided here when ready. - -## [Convolutional Networks](./Classification/cnns) for Computer Vision Classification -- [ResNet-50](./Classification/cnns) -- [ResNeXt-50-32*4d](./Classification/cnns) -- [VGG-16](./Classification/cnns) -- [Inception-V3](./Classification/cnns) -- [AlexNet](./Classification/cnns) -- [MobileNet-V2](./Classification/cnns) - -## [Wide Deep Learning](./ClickThroughRate/WideDeepLearning) for Click-Through-Rate (CTR) Recommender Systems -- [OneFlow-WDL](./ClickThroughRate/WideDeepLearning) - -## [BERT](./LanguageModeling/BERT) for Nature Language Process -- [BERT Pretrain for Language Modeling](./LanguageModeling/BERT) -- [SQuAD for Question Answering](./LanguageModeling/BERT) -- [CoLA and MRPC of GLUE](./LanguageModeling/BERT) - -## OneFlow Benchmark Test Reports + + [![](https://img.shields.io/badge/Language-CH-red.svg)](https://github.com/Oneflow-Inc/OneFlow-Benchmark/blob/dev_sx_benchmark/README_CH.md) + +This repository provides a collection of example implementations and modeling solutions using the latest OneFlow's high-level APIs, for CV, CTR and NLP models as a benchmark. + +It aims to demonstrate the best practices for modeling so that OneFlow users can take full advantage of OneFlow for their research and product development. + + More models are coming! + +## Contents + +### Models and Implementations + +- ### Computer Vision + + - #### Image Classification + +| Model | Reference (Paper) | +| ------------------------------------------------------------ | ------------------------------------------------------------ | +| [ResNet-50](https://github.com/Oneflow-Inc/OneFlow-Benchmark/blob/master/Classification/cnns/resnet_model.py) | [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) | +| [ResNeXt](https://github.com/Oneflow-Inc/OneFlow-Benchmark/blob/master/Classification/cnns/resnext_model.py) | [Aggregated_Residual_Transformations_CVPR_2017](https://openaccess.thecvf.com/content_cvpr_2017/papers/Xie_Aggregated_Residual_Transformations_CVPR_2017_paper.pdf) | +| [VGG-16](https://github.com/Oneflow-Inc/OneFlow-Benchmark/blob/master/Classification/cnns/vgg_model.py) | [VGG16 – Convolutional Network for Classification and Detection](https://neurohive.io/en/popular-networks/vgg16/) | +| [Inception-V3](https://github.com/Oneflow-Inc/OneFlow-Benchmark/blob/master/Classification/cnns/inception_model.py) | [Inception V3 Deep Convolutional Architecture For Classifying Acute Myeloid/Lymphoblastic Leukemia](https://software.intel.com/content/www/us/en/develop/articles/inception-v3-deep-convolutional-architecture-for-classifying-acute-myeloidlymphoblastic.html) | +| [AlexNet](https://github.com/Oneflow-Inc/OneFlow-Benchmark/blob/master/Classification/cnns/alexnet_model.py) | [ImageNet Classification with Deep Convolutional Neural Networks](http://vision.stanford.edu/teaching/cs231b_spring1415/slides/alexnet_tugce_kyunghee.pdf) | +| [MobileNet-V2](https://github.com/Oneflow-Inc/OneFlow-Benchmark/blob/master/Classification/cnns/mobilenet_v2_model.py) | [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861) | + +- ## Natural Language Processing + +| Model | Reference (Paper) | +| ------------------------------------------------------------ | ------------------------------------------------------------ | +| [BERT (Bidirectional Encoder Representations from Transformers)](https://github.com/OneFlow/models/blob/master/official/nlp/bert) | [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) | +| [SQuAD for Question Answering](https://github.com/Oneflow-Inc/OneFlow-Benchmark/blob/master/LanguageModeling/BERT/run_squad.py) | [BERT-SQuAD](https://github.com/kamalkraj/BERT-SQuAD) | +| [CoLA and MRPC of GLUE](https://github.com/Oneflow-Inc/OneFlow-Benchmark/blob/master/LanguageModeling/BERT/run_classifier.py) | [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://www.aclweb.org/anthology/W18-5446.pdf) | + +- ## Click-Through-Rate + +| Model | Reference (Paper) | +| ------------------------------------------------------------ | ------------------------------------------------------------ | +| [OneFlow-WDL](https://github.com/Oneflow-Inc/OneFlow-Benchmark/blob/master/ClickThroughRate/WideDeepLearning) | [**Wide & Deep Learning for Recommender Systems**](https://arxiv.org/pdf/1606.07792.pdf) | + + + +## Get started with the models + +- The models in the master branch are developed using OneFlow [], and they target the OneFlow [nightly binaries](https://github.com/OneFlow/OneFlow#installation) built from the [master branch of OneFlow](https://github.com/OneFlow/OneFlow/tree/master). + +- The stable versions targeting releases of OneFlow are available as tagged branches or [downloadable releases](https://github.com/OneFlow/models/releases). + + + +Please follow the below steps before running models in this repository. + +### Requirements + +- Python >= 3.5 + +- CUDA Toolkit Linux x86_64 Driver + + | OneFlow | CUDA Driver Version | + | ------------- | ------------------- | + | oneflow_cu102 | >= 440.33 | + | oneflow_cu101 | >= 418.39 | + | oneflow_cu100 | >= 410.48 | + | oneflow_cu92 | >= 396.26 | + | oneflow_cu91 | >= 390.46 | + | oneflow_cu90 | >= 384.81 | + + - CUDA runtime is statically linked into OneFlow. OneFlow will work on a minimum supported driver, and any driver beyond. For more information, please refer to [CUDA compatibility documentation](https://docs.nvidia.com/deploy/cuda-compatibility/index.html). + - Support for latest stable version of CUDA will be prioritized. Please upgrade your Nvidia driver to version 440.33 or above and install `oneflow_cu102` if possible. + - We are sorry that due to limits on bandwidth and other resources, we could only guarantee the efficiency and stability of `oneflow_cu102`. We will improve it ASAP. + +### Installation + +#### Method 1: Install with pip package + +- To install latest release of OneFlow with CUDA support: + + ``` + python3 -m pip install --find-links https://oneflow-inc.github.io/nightly oneflow_cu102 --user + ``` + +- To install OneFlow with legacy CUDA support, run one of: + + ``` + python3 -m pip install --find-links https://oneflow-inc.github.io/nightly oneflow_cu101 --user + python3 -m pip install --find-links https://oneflow-inc.github.io/nightly oneflow_cu100 --user + python3 -m pip install --find-links https://oneflow-inc.github.io/nightly oneflow_cu92 --user + python3 -m pip install --find-links https://oneflow-inc.github.io/nightly oneflow_cu91 --user + python3 -m pip install --find-links https://oneflow-inc.github.io/nightly oneflow_cu90 --user + ``` + +- If you are in China, you could run this to have pip download packages from domestic mirror of pypi: + + ``` + python3 -m pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple + ``` + + For more information on this, please refer to [pypi 镜像使用帮助](https://mirror.tuna.tsinghua.edu.cn/help/pypi/) + +- CPU-only OneFlow is not available for now. + +- Releases are built with G++/GCC 4.8.5, cuDNN 7 and MKL 2020.0-088. + +#### Method 2: Build from source + +1. System Requirements to Build OneFlow + +- Please use a newer version of CMake to build OneFlow. You could download cmake release from [here](https://github.com/Kitware/CMake/releases/download/v3.14.0/cmake-3.14.0-Linux-x86_64.tar.gz). + +- Please make sure you have G++ and GCC >= 4.8.5 installed. Clang is not supported for now. + +- To install dependencies, run: + + ``` + yum-config-manager --add-repo https://yum.repos.intel.com/setup/intelproducts.repo && \ + rpm --import https://yum.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS-2019.PUB && \ + yum update -y && yum install -y epel-release && \ + yum install -y intel-mkl-64bit-2020.0-088 nasm swig rdma-core-devel + ``` + + On CentOS, if you have MKL installed, please update the environment variable: + + ``` + export LD_LIBRARY_PATH=/opt/intel/lib/intel64_lin:/opt/intel/mkl/lib/intel64:$LD_LIBRARY_PATH + ``` + + If you don't want to build OneFlow with MKL, you could install OpenBLAS: + + ``` + sudo yum -y install openblas-devel + ``` + +2. Clone Source Code + +Clone source code and submodules (faster, recommended) + +``` +git clone https://github.com/Oneflow-Inc/oneflow +cd oneflow +git submodule update --init --recursive +``` + +Or you could also clone the repo with `--recursive` flag to clone third_party submodules together + +``` +git clone https://github.com/Oneflow-Inc/oneflow --recursive +``` + +3. Build and Install OneFlow + +``` +cd build +cmake .. +make -j$(nproc) +make pip_install +``` + +- For pure CPU build, please add this CMake flag `-DBUILD_CUDA=OFF`. + +### More models to come + +[new models] + +## Contributions + +- How to add new models? +- How to add new framework tests? | Model | DType | XLA | Throughput | Speedup on 32 devices | | ----- | ----- | --- | ---------- | ------- | diff --git a/README_CH.md b/README_CH.md new file mode 100644 index 0000000..11d0859 --- /dev/null +++ b/README_CH.md @@ -0,0 +1,46 @@ +# OneFlow 深度学习基准 + + [![](https://img.shields.io/badge/Language-EN-blue.svg)](https://github.com/Oneflow-Inc/OneFlow-Benchmark/tree/dev_sx_benchmark) + +本仓库将提供一系列由 OneFlow 最新的高级接口实现的模型及样例网络，模型基准涉及计算机视觉（Computer Vision，CV）、点击率推荐（Click-Through-Rate，CTR）、自然语言处理（Natural Language Processing， NLP）。 + +为了能让 OneFlow 用户实现自己拓展研究和产品迭代的需求，充分利用该框架，本仓库旨在提供各个模型基于 OneFlow 的最佳实现。 + +更多新模型正在路上！ + +## 内容 + +### 模型及实现 + +- ### 计算机视觉（Computer Vision） + + - #### 图片识别（Image Classification） + +| 模型 | 参考来源（论文） | +| ------------------------------------------------------------ | ------------------------------------------------------------ | +| [ResNet-50](https://github.com/Oneflow-Inc/OneFlow-Benchmark/blob/master/Classification/cnns/resnet_model.py) | [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) | +| [ResNeXt](https://github.com/Oneflow-Inc/OneFlow-Benchmark/blob/master/Classification/cnns/resnext_model.py) | [Aggregated_Residual_Transformations_CVPR_2017](https://openaccess.thecvf.com/content_cvpr_2017/papers/Xie_Aggregated_Residual_Transformations_CVPR_2017_paper.pdf) | +| [VGG-16](https://github.com/Oneflow-Inc/OneFlow-Benchmark/blob/master/Classification/cnns/vgg_model.py) | [VGG16 – Convolutional Network for Classification and Detection](https://neurohive.io/en/popular-networks/vgg16/) | +| [Inception-V3](https://github.com/Oneflow-Inc/OneFlow-Benchmark/blob/master/Classification/cnns/inception_model.py) | [Inception V3 Deep Convolutional Architecture For Classifying Acute Myeloid/Lymphoblastic Leukemia](https://software.intel.com/content/www/us/en/develop/articles/inception-v3-deep-convolutional-architecture-for-classifying-acute-myeloidlymphoblastic.html) | +| [AlexNet](https://github.com/Oneflow-Inc/OneFlow-Benchmark/blob/master/Classification/cnns/alexnet_model.py) | [ImageNet Classification with Deep Convolutional Neural Networks](http://vision.stanford.edu/teaching/cs231b_spring1415/slides/alexnet_tugce_kyunghee.pdf) | +| [MobileNet-V2](https://github.com/Oneflow-Inc/OneFlow-Benchmark/blob/master/Classification/cnns/mobilenet_v2_model.py) | [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861) | + +- ### 自然语言处理（Natural Language Processing） + +| 模型 | 参考来源（论文） | +| ------------------------------------------------------------ | ------------------------------------------------------------ | +| [BERT (Bidirectional Encoder Representations from Transformers)](https://github.com/OneFlow/models/blob/master/official/nlp/bert) | [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) | +| [SQuAD for Question Answering](https://github.com/Oneflow-Inc/OneFlow-Benchmark/blob/master/LanguageModeling/BERT/run_squad.py) | [BERT-SQuAD](https://github.com/kamalkraj/BERT-SQuAD) | +| [CoLA and MRPC of GLUE](https://github.com/Oneflow-Inc/OneFlow-Benchmark/blob/master/LanguageModeling/BERT/run_classifier.py) | [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://www.aclweb.org/anthology/W18-5446.pdf) | + +- ### 点击率推荐（Click-Through-Rate） + + | 模型 | 参考来源（论文） | + | ------------------------------------------------------------ | ------------------------------------------------------------ | + | [OneFlow-WDL](https://github.com/Oneflow-Inc/OneFlow-Benchmark/blob/master/ClickThroughRate/WideDeepLearning) | [**Wide & Deep Learning for Recommender Systems**](https://arxiv.org/pdf/1606.07792.pdf) | + + + +### 开始使用模型 + +...