Update model file docs to be more accurate

examples/README.md

@@ -110,7 +110,7 @@ Following are the steps to create a torch-model-archive (.mar) to execute an eag
 
 * Pre-requisites to create a torch model archive (.mar) :
     * serialized-file (.pt) : This file represents the `state_dict` in case of eager mode model.
-    * model-file (.py) : This file contains model class extended from `torch nn`.modules representing the model architecture. This parameter is mandatory for eager mode models. This file must contain only one class definition extended from torch.nn.modules
+    * model-file (.py) : This file contains model class extended from `torch nn`.modules representing the model architecture. This parameter is mandatory for eager mode models. This file must contain only one class definition extended from [torch.nn.Module](https://pytorch.org/docs/stable/generated/torch.nn.Module.html).
     * index_to_name.json : This file contains the mapping of predicted index to class. The default TorchServe handles returns the predicted index and probability. This file can be passed to model archiver using --extra-files parameter.
     * version : Model's version.
     * handler : TorchServe default handler's name or path to custom inference handler(.py)

examples/image_classifier/mnist/README.md

@@ -16,7 +16,7 @@ https://github.com/pytorch/examples/tree/master/mnist
 
 Run the commands given in following steps from the parent directory of the root of the repository. For example, if you cloned the repository into /home/my_path/serve, run the steps from /home/my_path
 
- * Step - 1: Create a new model architecture file which contains model class extended from torch.nn.modules. In this example we have created [mnist model file](mnist.py).
+ * Step - 1: Create a new model architecture file which contains model class extended from [torch.nn.Module](https://pytorch.org/docs/stable/generated/torch.nn.Module.html). In this example we have created [mnist model file](mnist.py).
  * Step - 2: Train a MNIST digit recognition model using https://github.com/pytorch/examples/blob/master/mnist/main.py and save the state dict of model. We have added the pre-created [state dict](mnist_cnn.pt) of this model.
  * Step - 3: Write a custom handler to run the inference on your model. In this example, we have added a [custom_handler](mnist_handler.py) which runs the inference on the input grayscale images using the above model and recognizes the digit in the image.
  * Step - 4: Create a torch model archive using the torch-model-archiver utility to archive the above files.

model-archiver/README.md

@@ -78,7 +78,7 @@ optional arguments:
                         Path to python file containing model architecture.
                         This parameter is mandatory for eager mode models.
                         The model architecture file must contain only one
-                        class definition extended from torch.nn.modules.
+                        class definition extended from torch.nn.Module.
   --handler HANDLER     TorchServe's default handler name  or handler python
                         file path to handle custom TorchServe inference logic.
   --extra-files EXTRA_FILES
@@ -132,6 +132,9 @@ A valid model name must begin with a letter of the alphabet and can only contain
 
 A model file should contain the model architecture. This file is mandatory in case of eager mode models.
 
+This file should contain a single class that inherits from
+[torch.nn.Module](https://pytorch.org/docs/stable/generated/torch.nn.Module.html).
+
 ### Serialized file
 
 A serialized file (.pt or .pth) should be a checkpoint in case of torchscript and state_dict in case of eager mode.

model-archiver/model_archiver/arg_parser.py

@@ -54,7 +54,7 @@ def export_model_args_parser():
             help="Path to python file containing model architecture.\n"
             "This parameter is mandatory for eager mode models.\n"
             "The model architecture file must contain only one\n"
-            "class definition extended from torch.nn.modules.",
+            "class definition extended from torch.nn.Module.",
         )
 
         parser_export.add_argument(