Add autoclass sections

docs/model-dev-guide/model-usage/batch-process-api-ug.rst

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+:orphan:
+
+.. _torch_batch_processing_ug:
+
+############################
+ Torch Batch Processing API
+############################
+
+.. meta::
+   :description: Learn how to use the Torch Batch Processing API.
+
+In this guide, you'll learn about the :ref:`torch_batch_process_api_ref` and how to perform batch
+inference (also known as offline inference).
+
++---------------------------------------------------------------------+
+| Visit the API reference                                             |
++=====================================================================+
+| :ref:`torch_batch_process_api_ref`                                  |
++---------------------------------------------------------------------+
+
+.. caution::
+
+   This is an experimental API and may change at any time.
+
+**********
+ Overview
+**********
+
+The Torch Batch Processing API takes in (1) a dataset and (2) a user-defined processor class and
+runs distributed data processing.
+
+With this API, you can perform the following tasks:
+
+-  shard a dataset by number of workers available
+-  apply user-defined logic to each batch of data
+-  handle synchronization between workers
+-  track job progress to enable preemption and resumption of trial
+
+This is a flexible API that can be used for many different tasks, including batch (offline)
+inference.
+
+*******
+ Usage
+*******
+
+The main arguments to torch_batch_process are processor class and dataset.
+
+.. code:: python
+
+   torch_batch_process(
+       batch_processor_cls=MyProcessor
+       dataset=dataset
+   )
+
+``TorchBatchProcessorContext``
+==============================
+
+:class:`~determined.pytorch.experimental.TorchBatchProcessorContext`
+ should be a subclass of :class:`~determined.pytorch.experimental.TorchBatchProcessor`. 
+ The two functions you must implement are the
+``__init__`` and ``process_batch``. The other lifecycle functions are optional.
+
+``TorchBatchProcessor``
+=======================
+
+During ``__init__`` of :class:`~determined.pytorch.experimental.TorchBatchProcessor`, 
+we pass in a :class:`~determined.pytorch.experimental.TorchBatchProcessorContext` object,
+which contains useful methods that can be used within the ``TorchBatchProcessor`` class.
+
+******************************************
+ How To Perform Batch (Offline) Inference
+******************************************
+
+In this section, we'll learn how to perform batch inference using the Torch Batch Processing API.
+For more information about this use case or to obtain the tutorial files, visit this [placeholder
+URL].
+
+Step 1: Define a InferenceProcessor
+===================================
+
+The first step is to define an InferenceProcessor. You should initialize your model in the ``__init__``
+function of the InferenceProcessor.
+
+.. code:: python
+
+   """
+   Define custom processor class
+   """
+   class InferenceProcessor(TorchBatchProcessor):
+       def __init__(self, context):
+           self.context = context
+           self.model = context.prepare_model_for_inference(get_model())
+           self.output = []
+           self.last_index = 0
+
+       def process_batch(self, batch, batch_idx) -> None:
+           model_input = batch[0]
+           model_input = self.context.to_device(model_input)
+
+           with torch.no_grad():
+               with self.profiler as p:
+                   pred = self.model(model_input)
+                   p.step()
+                   output = {"predictions": pred, "input": batch}
+                   self.output.append(output)
+
+           self.last_index = batch_idx
+
+       def on_checkpoint_start(self):
+           # During checkpoint, we persist prediction result
+           if len(self.output) == 0:
+               return
+           file_name = f"prediction_output_{self.last_index}"
+           with self.context.upload_path() as path:
+               file_path = pathlib.Path(path, file_name)
+               torch.save(self.output, file_path)
+
+           self.output = []
+
+Step 2: Initialize the Dataset
+==============================
+
+Initialize the dataset you want to process.
+
+.. code:: python
+
+   """
+   Initialize dataset
+   """
+   transform = transforms.Compose(
+       [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
+   )
+   with filelock.FileLock(os.path.join("/tmp", "inference.lock")):
+       inference_data = tv.datasets.CIFAR10(
+           root="/data", train=False, download=True, transform=transform
+       )
+
+Step 3: Pass the InferenceProcessor Class and Dataset
+=====================================================
+
+Finally, pass the InferenceProcessor class and the dataset to ``torch_batch_process``.
+
+.. code:: python
+
+   """
+   Pass processor class and dataset to torch_batch_process
+   """
+   torch_batch_process(
+           InferenceProcessor,
+           dataset,
+           batch_size=64,
+           checkpoint_interval=10
+       )

docs/reference/batch-processing/api-torch-batch-process-reference.rst

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+:orphan:
+
+.. _torch_batch_process_api_ref:
+
+#######################################
+ ``Torch Batch Process`` API Reference
+#######################################
+
+.. meta::
+   :description: Familiarize yourself with the Torch Batch Process API.
+
++--------------------------------------------+
+| User Guide                                 |
++============================================+
+| :ref:`torch_batch_processing_ug`           |
++--------------------------------------------+
+
+.. caution::
+
+   This is an experimental API and may change at any time.
+
+The main arguments to torch_batch_process is processor class and dataset.
+
+.. code:: python
+
+   torch_batch_process(
+       batch_processor_cls=MyProcessor
+       dataset=dataset
+   )
+
+***************************************************
+ ``determined.pytorch.TorchBatchProcessorContext``
+***************************************************
+
+.. autoclass:: determined.pytorch.experimental.TorchBatchProcessorContext
+   :members:
+   :member-order: bysource
+
+********************************************
+ ``determined.pytorch.TorchBatchProcessor``
+********************************************
+
+.. autoclass:: determined.pytorch.experimental.TorchBatchProcessor
+   :members:
+   :member-order: bysource