[AIR] Add computer vision guide

doc/source/_toc.yml

@@ -2,7 +2,7 @@ format: jb-book
 root: index
 parts:
   - caption: Ray
-    chapters: 
+    chapters:
       - file: ray-overview/index
         title: "Overview"
       - file: ray-overview/getting-started
@@ -48,6 +48,7 @@ parts:
               - file: ray-air/check-ingest
               - file: ray-air/tuner
               - file: ray-air/predictors
+              - file: ray-air/computer-vision
               - file: ray-air/examples/serving_guide
               - file: ray-air/deployment
           - file: ray-air/examples/index

doc/source/ray-air/computer-vision.rst

@@ -0,0 +1,329 @@
+.. _computer-vision:
+
+Computer Vision
+===============
+
+This guide explains how to perform common computer vision tasks like:
+
+* `Reading image data`_
+* `Transforming images`_
+* `Training vision models`_
+* `Batch predicting images`_
+* `Serving vision models`_
+
+Reading image data
+------------------
+
+.. tabbed:: Raw images
+
+    Datasets like ImageNet store files like this:
+
+    .. code-block::
+
+        root/dog/xxx.png
+        root/dog/xxy.png
+        root/dog/[...]/xxz.png
+
+        root/cat/123.png
+        root/cat/nsdf3.png
+        root/cat/[...]/asd932_.png
+
+    To load images stored in this layout, read the raw images and include the
+    class names.
+
+    .. literalinclude:: ./doc_code/computer_vision.py
+        :start-after: __read_images1_start__
+        :end-before: __read_images1_stop__
+        :dedent:
+
+    Then, apply a :ref:`user-defined function <transform_datasets_writing_udfs>` to
+    encode the class names as integer targets.
+
+    .. literalinclude:: ./doc_code/computer_vision.py
+        :start-after: __read_images2_start__
+        :end-before: __read_images2_stop__
+        :dedent:
+
+    .. tip::
+
+        You can also use :class:`~ray.data.preprocessors.LabelEncoder` to encode labels.
+
+.. tabbed:: NumPy
+
+    To load NumPy arrays into a :class:`~ray.data.dataset.Dataset`, separately read the image and label arrays.
+
+    .. literalinclude:: ./doc_code/computer_vision.py
+        :start-after: __read_numpy1_start__
+        :end-before: __read_numpy1_stop__
+        :dedent:
+
+    Then, combine the datasets and rename the columns.
+
+    .. literalinclude:: ./doc_code/computer_vision.py
+        :start-after: __read_numpy2_start__
+        :end-before: __read_numpy2_stop__
+        :dedent:
+
+.. tabbed:: TFRecords
+
+    Image datasets often contain ``tf.train.Example`` messages that look like this:
+
+    .. code-block::
+
+        features {
+            feature {
+                key: "image"
+                value {
+                    bytes_list {
+                        value: ...  # Raw image bytes
+                    }
+                }
+            }
+            feature {
+                key: "label"
+                value {
+                    int64_list {
+                        value: 3
+                    }
+                }
+            }
+        }
+
+    To load examples stored in this format, read the TFRecords into a :class:`~ray.data.dataset.Dataset`.
+
+    .. literalinclude:: ./doc_code/computer_vision.py
+        :start-after: __read_tfrecords1_start__
+        :end-before: __read_tfrecords1_stop__
+        :dedent:
+
+    Then, apply a :ref:`user-defined function <transform_datasets_writing_udfs>` to
+    decode the raw image bytes.
+
+    .. literalinclude:: ./doc_code/computer_vision.py
+        :start-after: __read_tfrecords2_start__
+        :end-before: __read_tfrecords2_stop__
+        :dedent:
+
+.. tabbed:: Parquet
+
+    To load image data stored in Parquet files, call :func:`ray.data.read_parquet`.
+
+    .. literalinclude:: ./doc_code/computer_vision.py
+        :start-after: __read_parquet_start__
+        :end-before: __read_parquet_stop__
+        :dedent:
+
+
+For more information on creating datasets, see :ref:`Creating Datasets <creating_datasets>`.
+
+
+Transforming images
+-------------------
+
+To transform images, create a :class:`~ray.data.preprocessor.Preprocessor`. They're the
+standard way to preprocess data with Ray.
+
+.. tabbed:: Torch
+
+    To apply TorchVision transforms, create a :class:`~ray.data.preprocessors.TorchVisionPreprocessor`.
+
+    Create two :class:`TorchVisionPreprocessors <ray.data.preprocessors.TorchVisionPreprocessor>`
+    -- one to normalize images, and another to augment images. Later, you'll pass the preprocessors to :class:`Trainers <ray.train.trainer.BaseTrainer>`,
+    :class:`Predictors <ray.train.predictor.Predictor>`, and
+    :class:`PredictorDeployments <ray.serve.air_integrations.PredictorDeployment>`.
+
+    .. literalinclude:: ./doc_code/computer_vision.py
+        :start-after: __torch_preprocessors_start__
+        :end-before: __torch_preprocessors_stop__
+        :dedent:
+
+.. tabbed:: TensorFlow
+
+    To apply TorchVision transforms, create a :class:`~ray.data.preprocessors.BatchMapper`.
+
+    Create two :class:`~ray.data.preprocessors.BatchMapper` -- one to normalize images, and another to
+    augment images. Later, you'll pass the preprocessors to :class:`Trainers <ray.train.trainer.BaseTrainer>`,
+    :class:`Predictors <ray.train.predictor.Predictor>`, and
+    :class:`PredictorDeployments <ray.serve.air_integrations.PredictorDeployment>`.
+
+    .. literalinclude:: ./doc_code/computer_vision.py
+        :start-after: __tensorflow_preprocessors_start__
+        :end-before: __tensorflow_preprocessors_stop__
+        :dedent:
+
+For more information on transforming data, see
+:ref:`Using Preprocessors <air-preprocessors>` and
+:ref:`Transforming Datasets <transforming_datasets>`.
+
+Training vision models
+----------------------
+
+:class:`Trainers <ray.train.trainer.BaseTrainer>` let you train models in parallel.
+
+.. tabbed:: Torch
+
+    To train a vision model, define the training loop per worker.
+
+    .. literalinclude:: ./doc_code/computer_vision.py
+        :start-after: __torch_training_loop_start__
+        :end-before: __torch_training_loop_stop__
+        :dedent:
+
+    Then, create a :class:`~ray.train.torch.TorchTrainer` and call
+    :meth:`~ray.train.torch.TorchTrainer.fit`.
+
+    .. literalinclude:: ./doc_code/computer_vision.py
+        :start-after: __torch_trainer_start__
+        :end-before: __torch_trainer_stop__
+        :dedent:
+
+    For more in-depth examples, read :doc:`/ray-air/examples/torch_image_example` and
+    :ref:`Using Trainers <air-trainers>`.
+
+.. tabbed:: TensorFlow
+
+    To train a vision model, define the training loop per worker.
+
+    .. literalinclude:: ./doc_code/computer_vision.py
+        :start-after: __tensorflow_training_loop_start__
+        :end-before: __tensorflow_training_loop_stop__
+        :dedent:
+
+    Then, create a :class:`~ray.train.tensorflow.TensorflowTrainer` and call
+    :meth:`~ray.train.tensorflow.TensorflowTrainer.fit`.
+
+    .. literalinclude:: ./doc_code/computer_vision.py
+        :start-after: __tensorflow_trainer_start__
+        :end-before: __tensorflow_trainer_stop__
+        :dedent:
+
+    For more information, read :ref:`Using Trainers <air-trainers>`.
+
+Creating checkpoints
+--------------------
+
+:class:`Checkpoints <ray.air.checkpoint.Checkpoint>` are required for batch inference and model
+serving. They contain model state and optionally a preprocessor.
+
+If you're going from training to prediction, don't create a new checkpoint.
+:meth:`Trainer.fit() <ray.train.trainer.BaseTrainer.fit>` returns a
+:class:`~ray.air.result.Result` object. Use
+:attr:`Result.checkpoint <ray.air.result.Result.checkpoint>` instead.
+
+.. tabbed:: Torch
+
+    To create a :class:`~ray.train.torch.TorchCheckpoint`, pass a Torch model and
+    the :class:`~ray.data.preprocessor.Preprocessor` you created in `Transforming images`_
+    to :meth:`TorchCheckpoint.from_model() <ray.train.torch.TorchCheckpoint.from_model>`.
+
+    .. literalinclude:: ./doc_code/computer_vision.py
+        :start-after: __torch_checkpoint_start__
+        :end-before: __torch_checkpoint_stop__
+        :dedent:
+
+.. tabbed:: TensorFlow
+
+    To create a :class:`~ray.train.tensorflow.TensorflowCheckpoint`, pass a TensorFlow model and
+    the :class:`~ray.data.preprocessor.Preprocessor` you created in `Transforming images`_
+    to :meth:`TensorflowCheckpoint.from_model() <ray.train.tensorflow.TensorflowCheckpoint.from_model>`.
+
+    .. literalinclude:: ./doc_code/computer_vision.py
+        :start-after: __tensorflow_checkpoint_start__
+        :end-before: __tensorflow_checkpoint_stop__
+        :dedent:
+
+
+Batch predicting images
+-----------------------
+
+:class:`~ray.train.batch_predictor.BatchPredictor` lets you perform inference on large
+image datasets.
+
+.. tabbed:: Torch
+
+    To create a :class:`~ray.train.batch_predictor.BatchPredictor`, call
+    :meth:`BatchPredictor.from_checkpoint <ray.train.batch_predictor.BatchPredictor.from_checkpoint>` and pass the checkpoint
+    you created in `Creating checkpoints`_.
+
+    .. literalinclude:: ./doc_code/computer_vision.py
+        :start-after: __torch_batch_predictor_start__
+        :end-before: __torch_batch_predictor_stop__
+        :dedent:
+
+    For more in-depth examples, read :doc:`/ray-air/examples/pytorch_resnet_batch_prediction`
+    and :ref:`Using Predictors for Inference <air-predictors>`.
+
+.. tabbed:: TensorFlow
+
+    To create a :class:`~ray.train.batch_predictor.BatchPredictor`, call
+    :meth:`BatchPredictor.from_checkpoint <ray.train.batch_predictor.BatchPredictor.from_checkpoint>` and pass the checkpoint
+    you created in `Creating checkpoints`_.
+
+    .. literalinclude:: ./doc_code/computer_vision.py
+        :start-after: __tensorflow_batch_predictor_start__
+        :end-before: __tensorflow_batch_predictor_stop__
+        :dedent:
+
+    For more information, read :ref:`Using Predictors for Inference <air-predictors>`.
+
+Serving vision models
+---------------------
+
+:class:`~ray.serve.air_integrations.PredictorDeployment` lets you
+deploy a model to an endpoint and make predictions over the Internet.
+
+Deployments use :ref:`HTTP adapters <serve-http>` to define how HTTP messages are converted to model
+inputs. For example, :func:`~ray.serve.http_adapters.json_to_ndarray` converts HTTP messages like this:
+
+.. code-block::
+
+    {"array": [[1, 2], [3, 4]]}
+
+To NumPy ndarrays like this:
+
+.. code-block::
+
+    array([[1., 2.],
+            [3., 4.]])
+
+
+.. tabbed:: Torch
+
+    To deploy a Torch model to an endpoint, pass the checkpoint you created in `Creating checkpoints`_
+    to :meth:`PredictorDeployment.bind <ray.serve.air_integrations.PredictorDeployment.bind>` and specify
+    :func:`~ray.serve.http_adapters.json_to_ndarray` as the HTTP adapter.
+
+    .. literalinclude:: ./doc_code/computer_vision.py
+        :start-after: __torch_serve_start__
+        :end-before: __torch_serve_stop__
+        :dedent:
+
+    Then, make a request to classify an image.
+
+    .. literalinclude:: ./doc_code/computer_vision.py
+        :start-after: __torch_online_predict_start__
+        :end-before: __torch_online_predict_stop__
+        :dedent:
+
+    For more in-depth examples, read :doc:`/ray-air/examples/torch_image_example`
+    and :doc:`/ray-air/examples/serving_guide`.
+
+.. tabbed:: TensorFlow
+
+    To deploy a TensorFlow model to an endpoint, pass the checkpoint you created in `Creating checkpoints`_
+    to :meth:`PredictorDeployment.bind <ray.serve.air_integrations.PredictorDeployment.bind>` and specify
+    :func:`~ray.serve.http_adapters.json_to_multi_ndarray` as the HTTP adapter.
+
+    .. literalinclude:: ./doc_code/computer_vision.py
+        :start-after: __tensorflow_serve_start__
+        :end-before: __tensorflow_serve_stop__
+        :dedent:
+
+    Then, make a request to classify an image.
+
+    .. literalinclude:: ./doc_code/computer_vision.py
+        :start-after: __tensorflow_online_predict_start__
+        :end-before: __tensorflow_online_predict_stop__
+        :dedent:
+
+    For more information, read :doc:`/ray-air/examples/serving_guide`.