usrbinkat · September 13, 2023 19:15 · Sep 13, 2023
diff --git a/how-to.md b/how-to.md
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+## How To
+
+1. We’ll make a directory
+
+2. And create a new project from pulumi templates
+
+```bash
+mkdir ai && cd ai
+pulumi new sagemaker-aws-python
+```
+
+a. Give the project a name    
+b. Name our stack    
+c. Choose your aws region    
+d. Now Pulumi will setup our python virtual environment   
+
+## It's that easy let's deploy!
+
+3. Run the pulumi up command to deploy
+
+```bash
+pulumi up
+```
+
+There it is! Our very own Large Language Model endpoint!
+
+## Now let's try a prompt with python:
+
+1. be sure to activate your virtual environment
+
+```bash
+source venv/bin/activate.fish
+```
+
+2. Create a small test python script
+
+```python
+import json, boto3, argparse
+
+def main(endpoint_name):
+    client = boto3.client('sagemaker-runtime', region_name='us-east-1')
+    payload = json.dumps({"inputs": "In 3 words, name the biggest mountain on earth?"})
+    response = client.invoke_endpoint(EndpointName=endpoint_name, ContentType="application/json", Body=payload)
+    print("Response:", json.loads(response['Body'].read().decode()))
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("endpoint_name")
+    main(parser.parse_args().endpoint_name)
+```
+
+3. Send the test python prompt!
+
+```bash
+python3 test.py (pulumi stack output EndpointName)
+```
+
+Like and subscribe for more Machine Learning content. Go make stuff, break stuff, and automate it all with Pulumi.
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