Adds torch.compile documentation to alexnet example readme

examples/image_classifier/alexnet/README.md

@@ -1,4 +1,4 @@
-#### Sample commands to create a alexnet eager mode model archive, register it on TorchServe and run image prediction
+### Sample commands to create a alexnet model archive, register it on TorchServe and run image prediction
 
 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
 
@@ -11,7 +11,52 @@ torchserve --start --model-store model_store --models alexnet=alexnet.mar
 curl http://127.0.0.1:8080/predictions/alexnet -T ./serve/examples/image_classifier/kitten.jpg
 ```
 
-#### TorchScript example using alexnet image classifier:
+### Serving with Torch Compile
+`torch.compile` allows for potential performance improvements when serving the model. It supports a variety of configs and the performance you get can vary based on the config. You can find the various options [here](https://pytorch.org/docs/stable/generated/torch.compile.html).
+
+Use the command below to create a `model-config.yaml` file that will be used in this example:
+
+```
+echo "pt2:
+  compile:
+    enable: True
+    backend: inductor
+    mode: reduce-overhead" > model-config.yaml
+```
+
+##### Create archive and serve model
+Ensure your current directory is `examples/image_classifier/alexnet`, then run:
+
+```
+wget https://download.pytorch.org/models/alexnet-owt-7be5be79.pth
+mkdir model_store
+torch-model-archiver --model-name alexnet --version 1.0 --model-file model.py --serialized-file alexnet-owt-7be5be79.pth --handler image_classifier --extra-files ../index_to_name.json --config-file model-config.yaml
+mv alexnet.mar model_store/
+torchserve --start --model-store model_store --models alexnet=alexnet.mar --disable-token-auth --enable-model-api
+```
+
+##### Run Inference
+```
+curl http://127.0.0.1:8080/predictions/alexnet -T ../kitten.jpg
+```
+This should output:
+```
+{
+  "tabby": 0.40966343879699707,
+  "tiger_cat": 0.346704363822937,
+  "Egyptian_cat": 0.13002890348434448,
+  "lynx": 0.023919545114040375,
+  "bucket": 0.011532172560691833
+}
+```
+If you would like to measure performance, run:
+```
+echo "handler:
+  profile: true" > model-config.yaml
+```
+to add the relevant config to your model-config.yaml. This will add inference performance metrics (marked as `[METRICS]`) to the output.
+
+### TorchScript example using alexnet image classifier:
 
 * Save the alexnet model in as an executable script module or a traced script:
 
@@ -35,8 +80,8 @@ curl http://127.0.0.1:8080/predictions/alexnet -T ./serve/examples/image_classif
    example_input = torch.rand(1, 3, 224, 224)
    traced_script_module = torch.jit.trace(model, example_input)
    traced_script_module.save("alexnet.pt")
-   ```  
- 
+   ```
+
 * Use following commands to register alexnet torchscript model on TorchServe and run image prediction
 
     ```bash