tgi example styling updates

examples/tgi-inference-aws-ec2/tgi_mistral_ec2.py

@@ -1,4 +1,4 @@
-# # Deploy Mistral's 7B TGI Model with AWS EC2
+# # Deploy Mistral's 7B Model with TGI on AWS EC2
 
 # This example demonstrates how to deploy a
 # [TGI model](https://huggingface.co/docs/text-generation-inference/messages_api) on AWS EC2 using Runhouse.
@@ -7,14 +7,13 @@
 # Zephyr is a 7B fine-tuned version of [Mistral's 7B-v0.1 model](https://huggingface.co/mistralai/Mistral-7B-v0.1).
 #
 # ## Setup credentials and dependencies
-# ```
 # Install the required dependencies:
 # ```shell
 # $ pip install -r requirements.txt
 # ```
 #
-# We'll be launching an AWS EC2 instance via SkyPilot, so we need to make sure our AWS credentials are set up
-# with SkyPilot:
+# We'll be launching an AWS EC2 instance via [SkyPilot](https://github.com/skypilot-org/skypilot), so we need to make
+# sure our AWS credentials are set up with SkyPilot:
 # ```shell
 # $ aws configure
 # $ sky check
@@ -127,19 +126,18 @@ def restart_container(self):
         # Deploy a new container
         self.deploy()
 
-    # ---------------------------------------------------
-
 
 # ## Setting up Runhouse primitives
 #
 # Now, we define the main function that will run locally when we run this script, and set up
 # our Runhouse module on a remote cluster. First, we create a cluster with the desired instance type and provider.
 # Our `instance_type` here is defined as `g5.4xlarge`, which is
 # an [AWS instance type on EC2](https://aws.amazon.com/ec2/instance-types/g5/) with a GPU.
-# (For this model we'll need a GPU and at least 16GB of RAM)
+#
+# For this model we'll need a GPU and at least 16GB of RAM
 # We also open port 8080, which is the port that the TGI model will be running on.
 #
-# Learn more in the [Runhouse docs on clusters](/docs/tutorials/api-clusters).
+# Learn more about clusters in the [Runhouse docs](/docs/tutorials/api-clusters).
 #
 # NOTE: Make sure that your code runs within a `if __name__ == "__main__":` block, as shown below. Otherwise,
 # the script code will run when Runhouse attempts to run code remotely.
@@ -173,10 +171,14 @@ def restart_container(self):
 
     # ## Sharing an inference endpoint
     # We can publish this module for others to use:
+    # ```python
     # remote_tgi_model.share(visibility="public")
+    # ```
 
     # Alternatively we can share with specific users:
+    # ```python
     # remote_tgi_model.share(["user1@gmail.com", "user2@gmail.com"], access_level="read")
+    # ```
 
     # Note: For more info on fine-grained access controls, see the
     # [Runhouse docs on sharing](https://www.run.house/docs/tutorials/quick-start-den#sharing).
@@ -185,7 +187,7 @@ def restart_container(self):
     # We can call the `deploy` method on the model class instance if it were running locally.
     # This will load and run the model on the remote cluster.
     # We only need to do this setup step once, as further calls will use the existing docker container deployed
-    # on the cluster and maintain state between calls
+    # on the cluster and maintain state between calls:
     remote_tgi_model.deploy()
 
     # ## Sending a prompt to the model
@@ -199,27 +201,27 @@ def restart_container(self):
         {"role": "user", "content": "Do you have mayonnaise recipes?"},
     ]
 
-    # We'll use the Messages API to send the prompt to the model
+    # We'll use the Messages API to send the prompt to the model.
     # See [here](https://huggingface.co/docs/text-generation-inference/messages_api#streaming) for more info
     # on the Messages API, and using the OpenAI python client
-    base_url = f"http://{cluster.address}:{port}/v1"
 
-    # Initialize the OpenAI client
+    # Initialize the OpenAI client, with the URL set to the cluster's address:
+    base_url = f"http://{cluster.address}:{port}/v1"
     client = OpenAI(base_url=base_url, api_key="-")
 
-    # Call the model with the prompt messages
+    # Call the model with the prompt messages:
     chat_completion = client.chat.completions.create(
         model="tgi", messages=prompt_messages, stream=False
     )
     print(chat_completion)
 
     # For streaming results, set `stream=True` and iterate over the results:
+    # ```python
     # for message in chat_completion:
-    #     print(message)
+    #    print(message)
+    # ```
 
-    # Alternatively, we can also call the model via HTTP
-    print("------------")
-    print("To call the model via HTTP, use the following cURL command:")
+    # Alternatively, we can also call the model via HTTP:
     print(
         f"curl http://{cluster.address}:{port}/v1/chat/completions -X POST -d '"
         '{"model": "tgi", "stream": false, "messages": [{"role": "system", "content": "You are a helpful assistant."},'