README for serving models using TorchServe Docker Container (#2118)

* Added an example serving models using TorchServe Docker Container

* Updated examples README with a link to the new readme

* added link to example in docker readme

docker/README.md

@@ -303,3 +303,7 @@ docker run --rm --shm-size=1g \
         -p7071:7071 \
         --mount type=bind,source=/path/to/model/store,target=/tmp/models <container> torchserve --model-store=/tmp/models
 ```
+
+# Example showing serving model using Docker container
+
+[This](../examples/image_classifier/mnist/Docker.md) is an example showing serving MNIST model using Docker.

examples/README.md

@@ -5,6 +5,7 @@
 * [Creating mar file for an eager mode model](#creating-mar-file-for-eager-mode-model)
 * [Creating mar file for torchscript mode model](#creating-mar-file-for-torchscript-mode-model)
 * [Serving custom model with custom service handler](#serving-custom-model-with-custom-service-handler)
+* [Serving model using Docker Container](image_classifier/mnist/Docker.md)
 * [Creating a Workflow](Workflows/dog_breed_classification)
 * [Custom Metrics](custom_metrics)
 * [Dynamic Batch Processing](image_classifier/resnet_152_batch)

examples/image_classifier/mnist/Docker.md

@@ -0,0 +1,54 @@
+# Digit recognition model with MNIST dataset using Docker container
+
+In this example, we show how to use a pre-trained custom MNIST model to performing real time Digit recognition with TorchServe.
+We will be serving the model using a Docker container.
+
+The inference service would return the digit inferred by the model in the input image.
+
+We used the following pytorch example to train the basic MNIST model for digit recognition :
+https://github.com/pytorch/examples/tree/master/mnist
+
+## Serve an MNIST model on TorchServe docker container
+
+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/serve
+
+ ### Create a torch model archive using the torch-model-archiver utility to archive the above files.
+
+    ```bash
+    torch-model-archiver --model-name mnist --version 1.0 --model-file examples/image_classifier/mnist/mnist.py --serialized-file examples/image_classifier/mnist/mnist_cnn.pt --handler  examples/image_classifier/mnist/mnist_handler.py
+    ```
+
+  ### Move .mar file into model_store directory
+
+    ```bash
+    mkdir model_store
+    mv mnist.mar model_store/
+    ```
+
+  ### Start a docker container with torchserve
+
+  ```bash
+  docker run --rm -it -p 8080:8080 -p 8081:8081 -p 8082:8082 -v $(pwd)/model_store:/home/model-server/model-store pytorch/torchserve:latest-cpu
+  ```
+
+  ### Register the model on TorchServe using the above model archive file
+
+  ```bash
+  curl -X POST "localhost:8081/models?model_name=mnist&url=mnist.mar&initial_workers=4"
+  ```
+
+  If this succeeeds, you will see a message like below
+
+  ```bash
+  {
+  "status": "Model \"mnist\" Version: 1.0 registered with 4 initial workers"
+  }
+  ```
+
+  ### Run digit recognition inference outside the container
+
+    ```bash
+    curl http://127.0.0.1:8080/predictions/mnist -T examples/image_classifier/mnist/test_data/0.png
+    ```
+
+   The output in this case will be a `0`