Deploy Llama 3 8B Chat Model Inference on AWS EC2

This example demonstrates how to deploy a LLama 3 8B model from Hugging Face on AWS EC2 using Runhouse.

Make sure to sign the waiver on the model page so that you can access it.

Setup credentials and dependencies

Optionally, set up a virtual environment:

$ conda create -n llama3-rh python=3.9.15
$ conda activate llama3-rh

Install the required dependencies:

$ pip install "runhouse[aws]" torch

We'll be launching an AWS EC2 instance via SkyPilot, so we need to make sure our AWS credentials are set up:

$ aws configure
$ sky check

We'll be downloading the Llama 3 model from Hugging Face, so we need to set up our Hugging Face token:

$ export HF_TOKEN=<your huggingface token>

Setting up a model class

We import runhouse and torch, because that's all that's needed to run the script locally. The actual transformers imports can happen within the functions that will be sent to the Runhouse cluster; we don't need those locally.

import runhouse as rh
import torch

Next, we define a class that will hold the model and allow us to send prompts to it. We'll later wrap this with 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.

class HFChatModel:
    def __init__(self, model_id="meta-llama/Meta-Llama-3-8B-Instruct", **model_kwargs):
        super().__init__()
        self.model_id, self.model_kwargs = model_id, model_kwargs
        self.pipeline = None

    def load_model(self):
        import transformers

        self.pipeline = transformers.pipeline(
            "text-generation",
            model=self.model_id,
            model_kwargs=self.model_kwargs,
            device="cuda",
        )

    def predict(self, prompt_text, **inf_kwargs):
        if not self.pipeline:
            self.load_model()

        messages = [
            {
                "role": "system",
                "content": "You are a pirate chatbot who always responds in pirate speak!",
            },
            {"role": "user", "content": prompt_text},
        ]

        prompt = self.pipeline.tokenizer.apply_chat_template(
            messages, tokenize=False, add_generation_prompt=True
        )

        terminators = [
            self.pipeline.tokenizer.eos_token_id,
            self.pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>"),
        ]

        outputs = self.pipeline(
            prompt,
            max_new_tokens=256,
            eos_token_id=terminators,
            do_sample=True,
            temperature=0.6,
            top_p=0.9,
        )
        return outputs[0]["generated_text"][len(prompt) :]

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 A10G:1, which is the accelerator type and count that we need. We could alternatively specify a specific AWS instance type, such as p3.2xlarge or g4dn.xlarge.

Learn more in the Runhouse docs on 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__":
    gpu = rh.cluster(
        name="rh-a10x", instance_type="A10G:1", memory="32+", provider="aws"
    ).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.

Learn more in the Runhouse docs on envs.

env = rh.env(
    reqs=[
        "torch",
        "transformers",
        "accelerate",
        "bitsandbytes",
        "safetensors",
        "scipy",
    ],
    secrets=["huggingface"],  # Needed to download Llama 3 from HuggingFace
    name="llama3inference",
)

Finally, we define our module and run it on the remote cluster. We construct it normally and then call to to run it on the remote cluster. Alternatively, we could first check for an existing instance on the cluster by calling cluster.get(name="llama3-8b-model"). This would return the remote model after an initial run. If we want to update the module each time we run this script, we prefer to use to.

Note that we also pass the env object to the to method, which will ensure that the environment is set up on the remote machine before the module is run.

RemoteChatModel = rh.module(HFChatModel).to(gpu, env=env, name="HFChatModel")
remote_hf_chat_model = RemoteChatModel(
    torch_dtype=torch.bfloat16, name="llama3-8b-model"
)

Calling our remote function

We can call the predict method on the model class instance if it were running locally. This will run the function on the remote cluster and return the response to our local machine automatically. Further calls will also run on the remote machine, and maintain state that was updated between calls, like self.pipeline.

while True:
    prompt = input(
        "\n\n... Enter a prompt to chat with the model, and 'exit' to exit ...\n"
    )
    if prompt.lower().strip() == "exit":
        break
    output = remote_hf_chat_model.predict(prompt)
    print("\n\n... Model Output ...\n")
    print(output)