remove request size limit in doc comments (#433)

Author: jesterhazy

src/sagemaker/chainer/README.rst

@@ -409,8 +409,7 @@ When an InvokeEndpoint operation is made against an Endpoint running a SageMaker
 the model server receives two pieces of information:
 
 -  The request Content-Type, for example "application/x-npy"
--  The request data body, a byte array which is at most 5 MB (5 \* 1024
-   \* 1024 bytes) in size.
+-  The request data body, a byte array
 
 The SageMaker Chainer model server will invoke an "input_fn" function in your hosting script,
 passing in this information. If you define an ``input_fn`` function definition,

src/sagemaker/mxnet/README.rst

@@ -403,8 +403,7 @@ Input processing
 When an InvokeEndpoint operation is made against an Endpoint running a SageMaker MXNet model server, the model server receives two pieces of information:
 
 -  The request Content-Type, for example "application/json"
--  The request data body, a byte array which is at most 5 MB (5 \* 1024
-   \* 1024 bytes) in size.
+-  The request data body, a byte array
 
 The SageMaker MXNet model server will invoke an "input_fn" function in your training script, passing in this information. If you define an ``input_fn`` function definition, it should return an object that can be passed to ``predict_fn`` and have the following signature:
 

src/sagemaker/pytorch/README.rst

@@ -442,8 +442,7 @@ When an InvokeEndpoint operation is made against an Endpoint running a SageMaker
 the model server receives two pieces of information:
 
 -  The request Content-Type, for example "application/x-npy"
--  The request data body, a byte array which is at most 5 MB (5 \* 1024
-   \* 1024 bytes) in size.
+-  The request data body, a byte array
 
 The SageMaker PyTorch model server will invoke an ``input_fn`` function in your hosting script,
 passing in this information. If you define an ``input_fn`` function definition,