squeezenet torch.compile example (#3277)

Co-authored-by: Ankith Gunapal <agunapal@ischool.Berkeley.edu>

examples/image_classifier/squeezenet/README.md

@@ -11,6 +11,45 @@ torchserve --start --model-store model_store --models squeezenet1_1=squeezenet1_
 curl http://127.0.0.1:8080/predictions/squeezenet1_1 -T examples/image_classifier/kitten.jpg
 ```
 
+#### TorchServe inference with torch.compile of squeezenet model
+This example shows how to take eager model of `squeezenet`, configure TorchServe to use `torch.compile` and run inference using `torch.compile`.
+
+Change directory to the examples directory
+Ex:  `cd  examples/image_classifier/squeezenet`
+
+##### torch.compile config
+`torch.compile` supports a variety of config 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).
+
+In this example, we use the following config
+
+```
+echo "pt2 : {backend: inductor, mode: reduce-overhead}" > model-config.yaml
+```
+
+##### Sample commands to create a Squeezenet torch.compile model archive, register it on TorchServe and run image prediction
+
+```bash
+wget https://download.pytorch.org/models/squeezenet1_1-b8a52dc0.pth
+torch-model-archiver --model-name squeezenet --version 1.0 --model-file model.py --serialized-file squeezenet1_1-b8a52dc0.pth --handler image_classifier --extra-files ../index_to_name.json --config-file model-config.yaml
+mkdir model_store
+mv squeezenet.mar model_store/
+torchserve --start --model-store model_store --models squeezenet=squeezenet.mar
+curl http://127.0.0.1:8080/predictions/squeezenet -T ../kitten.jpg
+```
+
+produces the output
+```
+{
+  "tabby": 0.2751994729042053,
+  "lynx": 0.2546878755092621,
+  "tiger_cat": 0.2425432652235031,
+  "Egyptian_cat": 0.22137290239334106,
+  "cougar": 0.0022544628009200096
+}%                                                                                     
+```
+
+
+
 #### TorchScript example using alexnet image classifier:
 
 * Save the Squeezenet1_1 model as an executable script module or a traced script: