documentation: add context for pytorch

doc/frameworks/pytorch/using_pytorch.rst

@@ -415,20 +415,25 @@ Before a model can be served, it must be loaded. The SageMaker PyTorch model ser
 
 .. code:: python
 
-    def model_fn(model_dir)
+    def model_fn(model_dir, context)
+
+``context`` is an optional argument that contains additional serving information, such as the GPU ID and batch size.
+If specified in the function declaration, the context will be created and passed to the function by SageMaker.
+For more information about ``context``, see the `Serving Context class <https://github.com/pytorch/serve/blob/master/ts/context.py>`_.
 
 SageMaker will inject the directory where your model files and sub-directories, saved by ``save``, have been mounted.
 Your model function should return a model object that can be used for model serving.
 
 The following code-snippet shows an example ``model_fn`` implementation.
-It loads the model parameters from a ``model.pth`` file in the SageMaker model directory ``model_dir``.
+It loads the model parameters from a ``model.pth`` file in the SageMaker model directory ``model_dir``. As explained in the preceding example,
+``context`` is an optional argument that passes additional information.
 
 .. code:: python
 
     import torch
     import os
 
-    def model_fn(model_dir):
+    def model_fn(model_dir, context):
         model = Your_Model()
         with open(os.path.join(model_dir, 'model.pth'), 'rb') as f:
             model.load_state_dict(torch.load(f))
@@ -482,13 +487,13 @@ function in the chain. Inside the SageMaker PyTorch model server, the process lo
 .. code:: python
 
     # Deserialize the Invoke request body into an object we can perform prediction on
-    input_object = input_fn(request_body, request_content_type)
+    input_object = input_fn(request_body, request_content_type, context)
 
     # Perform prediction on the deserialized object, with the loaded model
-    prediction = predict_fn(input_object, model)
+    prediction = predict_fn(input_object, model, context)
 
     # Serialize the prediction result into the desired response content type
-    output = output_fn(prediction, response_content_type)
+    output = output_fn(prediction, response_content_type, context)
 
 The above code sample shows the three function definitions:
 
@@ -536,9 +541,13 @@ it should return an object that can be passed to ``predict_fn`` and have the fol
 
 .. code:: python
 
-    def input_fn(request_body, request_content_type)
+    def input_fn(request_body, request_content_type, context)
 
-Where ``request_body`` is a byte buffer and ``request_content_type`` is a Python string
+Where ``request_body`` is a byte buffer and ``request_content_type`` is a Python string.
+
+``context`` is an optional argument that contains additional serving information, such as the GPU ID and batch size.
+If specified in the function declaration, the context will be created and passed to the function by SageMaker.
+For more information about ``context``, see the `Serving Context class <https://github.com/pytorch/serve/blob/master/ts/context.py>`_.
 
 The SageMaker PyTorch model server provides a default implementation of ``input_fn``.
 This function deserializes JSON, CSV, or NPY encoded data into a torch.Tensor.
@@ -586,16 +595,19 @@ The ``predict_fn`` function has the following signature:
 
 .. code:: python
 
-    def predict_fn(input_object, model)
+    def predict_fn(input_object, model, context)
 
 Where ``input_object`` is the object returned from ``input_fn`` and
 ``model`` is the model loaded by ``model_fn``.
+If you are using multiple GPUs, then specify the ``context`` argument, which contains information such as the GPU ID for a dynamically-selected GPU and the batch size.
+One of the examples below demonstrates how to configure ``predict_fn`` with the ``context`` argument to handle multiple GPUs. For more information about ``context``, see the `Serving Context class <https://github.com/pytorch/serve/blob/master/ts/context.py>`_.
+If you are using CPUs or a single GPU, then you do not need to specify the ``context`` argument.
 
 The default implementation of ``predict_fn`` invokes the loaded model's ``__call__`` function on ``input_object``,
 and returns the resulting value. The return-type should be a torch.Tensor to be compatible with the default
 ``output_fn``.
 
-The example below shows an overridden ``predict_fn``:
+The following example shows an overridden ``predict_fn``:
 
 .. code:: python
 
@@ -609,6 +621,20 @@ The example below shows an overridden ``predict_fn``:
         with torch.no_grad():
             return model(input_data.to(device))
 
+The following example is for use cases with multiple GPUs and shows an overridden ``predict_fn`` that uses the ``context`` argument to dynamically select a GPU device for making predictions:
+
+.. code:: python
+
+    import torch
+    import numpy as np
+
+    def predict_fn(input_data, model):
+        device = torch.device("cuda:" + str(context.system_properties.get("gpu_id")) if torch.cuda.is_available() else "cpu")
+        model.to(device)
+        model.eval()
+        with torch.no_grad():
+            return model(input_data.to(device))
+
 If you implement your own prediction function, you should take care to ensure that:
 
 -  The first argument is expected to be the return value from input_fn.
@@ -664,11 +690,14 @@ The ``output_fn`` has the following signature:
 
 .. code:: python
 
-    def output_fn(prediction, content_type)
+    def output_fn(prediction, content_type, context)
 
 Where ``prediction`` is the result of invoking ``predict_fn`` and
-the content type for the response, as specified by the InvokeEndpoint request.
-The function should return a byte array of data serialized to content_type.
+the content type for the response, as specified by the InvokeEndpoint request. The function should return a byte array of data serialized to ``content_type``.
+
+``context`` is an optional argument that contains additional serving information, such as the GPU ID and batch size.
+If specified in the function declaration, the context will be created and passed to the function by SageMaker.
+For more information about ``context``, see the `Serving Context class <https://github.com/pytorch/serve/blob/master/ts/context.py>`_.
 
 The default implementation expects ``prediction`` to be a torch.Tensor and can serialize the result to JSON, CSV, or NPY.
 It accepts response content types of "application/json", "text/csv", and "application/x-npy".