llama2 on ec2 example (#608)

examples/tgi-llama-inference-aws-ec2/README.md

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+# Deploy Llama 2 7B Model with TGI on AWS EC2
+
+See a more [rich explanation](https://www.run.house/examples/llama-tgi-inference-on-aws-ec2)
+of this example on our site.
+
+This example demonstrates how to deploy a
+[Llama 7B model](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) using
+[TGI](https://huggingface.co/docs/text-generation-inference/messages_api) on AWS EC2 using Runhouse.
+
+## Setup credentials and dependencies
+Install the required dependencies:
+```shell
+$ pip install -r requirements.txt
+```
+
+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:
+```shell
+$ aws configure
+$ sky check
+```

examples/tgi-llama-inference-aws-ec2/requirements.txt

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+runhouse[aws]

examples/tgi-llama-inference-aws-ec2/tgi_llama_ec2.py

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+# # Deploy Llama 7B Model with TGI on AWS EC2
+
+# This example demonstrates how to deploy a [Llama 7B model](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)
+# using [TGI](https://huggingface.co/docs/text-generation-inference/messages_api) on AWS EC2 using Runhouse.
+#
+# ## Setup credentials and dependencies
+# Install the required dependencies:
+# ```shell
+# $ pip install -r requirements.txt
+# ```
+#
+# 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:
+# ```shell
+# $ aws configure
+# $ sky check
+# ```
+#
+# ## Setting up a model class
+# We import runhouse, the only required library to have installed locally:
+
+import time
+from pathlib import Path
+
+import requests
+
+import runhouse as rh
+
+# Next, we define a class that will hold the model and allow us to send prompts to it.
+# You'll notice this class inherits from `rh.Module`.
+# This is a Runhouse class that allows you to
+# run code in your class on a remote machine.
+#
+# Learn more in the [Runhouse docs on functions and modules](/docs/tutorials/api-modules).
+class TGIInference(rh.Module):
+    def __init__(
+        self,
+        model_id="meta-llama/Llama-2-7b-chat-hf",
+        image_uri="ghcr.io/huggingface/text-generation-inference:latest",
+        max_input_length=2048,
+        max_total_tokens=4096,
+        **model_kwargs,
+    ):
+        super().__init__()
+        self.docker_client = None
+
+        self.model_id = model_id
+        self.image_uri = image_uri
+        self.max_input_length = max_input_length
+        self.max_total_tokens = max_total_tokens
+        self.model_kwargs = model_kwargs
+
+        self.container_port = 8080
+        self.container_name = "text-generation-service"
+
+    def _load_docker_client(self):
+        import docker
+
+        self.docker_client = docker.from_env()
+
+    def _model_is_deployed(self):
+        if self.docker_client is None:
+            self._load_docker_client()
+
+        containers = self.docker_client.containers.list(
+            filters={"name": self.container_name}
+        )
+
+        return bool(containers)
+
+    def deploy(self):
+        # Adapted from: https://huggingface.co/docs/text-generation-inference/quicktour
+        import docker
+
+        if self._model_is_deployed():
+            return
+
+        print("Model has not yet been deployed, loading image and running container.")
+
+        home_dir = str(Path.home())
+        data_volume_path = f"{home_dir}/data"
+
+        device_request = docker.types.DeviceRequest(
+            count=-1,
+            capabilities=[["gpu"]],
+        )
+
+        start_time = time.time()
+        timeout = 600
+
+        # Load the HF token which was synced onto the cluster as part of the env setup
+        hf_secret = rh.secret(provider="huggingface")
+        hf_token = hf_secret.values.get("token")
+
+        model_cmd = (
+            f"--model-id {self.model_id} "
+            f"--max-input-length {self.max_input_length} "
+            f"--max-total-tokens {self.max_total_tokens}"
+        )
+
+        # Add any other model kwargs to the command
+        # https://github.com/huggingface/optimum-neuron/tree/main/text-generation-inference#choosing-service-parameters
+        for key, value in self.model_kwargs.items():
+            model_cmd += f" --{key} {value}"
+
+        container = self.docker_client.containers.run(
+            self.image_uri,
+            name=self.container_name,
+            detach=True,
+            ports={"80/tcp": self.container_port},
+            volumes={data_volume_path: {"bind": "/data", "mode": "rw"}},
+            command=model_cmd,
+            environment={"HF_TOKEN": hf_token},
+            device_requests=[device_request],
+            shm_size="1g",
+        )
+
+        print("Container started, waiting for model to load.")
+
+        # Wait for model to load inside the container
+        for line in container.logs(stream=True):
+            current_time = time.time()
+            elapsed_time = current_time - start_time
+
+            log_line = line.strip().decode("utf-8")
+            if "Connected" in log_line:
+                print("Finished loading model, endpoint is ready.")
+                break
+
+            if elapsed_time > timeout:
+                print(f"Failed to load model within {timeout} seconds. Exiting.")
+                break
+
+    def restart_container(self):
+        if self.docker_client is None:
+            self._load_docker_client()
+
+        try:
+            container = self.docker_client.containers.get(self.container_name)
+            container.stop()
+            container.remove()
+        except Exception as e:
+            raise RuntimeError(f"Failed to stop or remove container: {e}")
+
+        # 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
+# We also open port 8080, which is the port that the TGI model will be running on.
+#
+# 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.
+if __name__ == "__main__":
+    port = 8080
+    cluster = rh.cluster(
+        name="rh-g5-4xlarge",
+        instance_type="g5.4xlarge",
+        provider="aws",
+        open_ports=[port],
+    ).up_if_not()
+
+    # Next, we define the environment for our module. This includes the required dependencies that need
+    # to be installed on the remote machine, as well as any secrets that need to be synced up from local to remote.
+    #
+    # Passing `huggingface` to the `secrets` parameter will load the Hugging Face token we set up earlier. This is
+    # needed to download the model from the Hugging Face model hub. Runhouse will handle saving the token down
+    # on the cluster in the default Hugging Face token location (`~/.cache/huggingface/token`).
+    #
+    # Learn more in the [Runhouse docs on envs](/docs/tutorials/api-envs).
+    env = rh.env(
+        name="tgi_env",
+        reqs=["docker", "torch", "transformers"],
+        secrets=["huggingface"],
+        working_dir="./",
+    )
+
+    # Finally, we define our module and run it on the remote cluster. We construct it normally and then call
+    # `get_or_to` to run it on the remote cluster. Using `get_or_to` allows us to load the exiting Module
+    # by the name `tgi_inference` if it was already put on the cluster. If we want to update the module each
+    # time we run this script, we can use `to` instead of `get_or_to`.
+    #
+    # Note that we also pass the `env` object to the `get_or_to` method, which will ensure that the environment is
+    # set up on the remote machine before the module is run.
+    remote_tgi_model = TGIInference().get_or_to(cluster, env=env, name="tgi-inference")
+
+    # ## 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).
+
+    # ## Deploying the model
+    # 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:
+    remote_tgi_model.deploy()
+
+    # ## Sending a prompt to the model
+    prompt_message = "What is Deep Learning?"
+
+    # 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
+
+    # Call the model with the prompt messages:
+    # Note: We can also update some of the [default parameters](https://huggingface.github.io/text-generation-inference/#/Text%20Generation%20Inference/generate)
+    data = {
+        "inputs": prompt_message,
+        "parameters": {
+            "max_new_tokens": 50,
+            "temperature": 0.9,
+            "top_p": 0.92,
+            "do_sample": True,
+            "repetition_penalty": 1.2,
+            "frequency_penalty": 1.0,
+        },
+    }
+    headers = {
+        "Content-Type": "application/json",
+    }
+
+    response = requests.post(
+        f"http://{cluster.address}:{port}/generate", headers=headers, json=data
+    )
+    print(response.json())
+
+    # For streaming results, use the `/generate_stream` endpoint and iterate over the results:
+    # ```python
+    # for message in resp:
+    #    print(message)
+    # ```
+
+    # Alternatively, we can also call the model via HTTP
+    # Note: We can also use a streaming route by replacing `generate` with `generate_stream`:
+    print(
+        f"curl http://{cluster.address}:{port}/generate -X POST -d '"
+        f'{{"inputs":"{prompt_message}","parameters":{{"max_new_tokens":20}}}}'
+        "' -H 'Content-Type: application/json'"
+    )