Improve SageMaker inference sample ReadMe

Author: silv-io

sagemaker-inference/ReadMe.md

@@ -1,11 +1,65 @@
 # SageMaker Model Inference
 
-This is a small example about how you can use LocalStack to host your PyTorch ML models.
+This is a small example about how you can use LocalStack to host your PyTorch ML models. It does the following:
+
+* Create MNIST model in SageMaker
+* Create a SageMaker Endpoint for accessing the model
+* Invoke the endpoint
+  * directly on the container
+  * via boto
+
+## Requirements
+
+* Python 3.8+
+  * boto3
+  * numpy
+  * mypy
+* LocalStack
+* Docker
+
+## How To
+
+### Obtain Deep Learning image
 
 Before using this example you should set up your Docker Client to pull the AWS Deep Learning images ([more info here](https://github.com/aws/deep-learning-containers/blob/master/available_images.md)):
 
 ```bash
-    aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin 763104351884.dkr.ecr.us-east-1.amazonaws.com
+aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin 763104351884.dkr.ecr.us-east-1.amazonaws.com
 ```
 
-Because the images tend to be heavy (multiple GB), you might want to `docker pull` them beforehand.
+Because the images tend to be heavy (multiple GB), you might want to `docker pull` them beforehand:
+
+```bash
+docker pull 763104351884.dkr.ecr.us-east-1.amazonaws.com/pytorch-inference:1.5.0-cpu-py3
+```
+
+### Test the application
+
+Afterwards you can start localstack:
+
+```bash
+localstack start    
+```
+
+And execute the example with:
+
+```bash
+python main.py
+```
+
+You should see an output like this:
+```
+Creating bucket...
+Uploading model data to bucket...
+Creating model in SageMaker...
+Adding endpoint configuration...
+Creating endpoint...
+Checking endpoint status...
+Endpoint not ready - waiting...
+Checking endpoint status...
+Endpoint ready!
+Invoking via boto...
+Predicted digits: [7, 3]
+Invoking endpoint directly...
+Predicted digits: [2, 6]
+```