Deploy StarCoder on Your Local Machine with Docker & Hugging Face

StarCoder is a high performance AI model optimized for code generation. To run it on a local server, we will use Docker to ensure a stable and isolated environment and Hugging Face Transformers to download and execute the model.

Before starting, ensure your local server meets the following:

• Ubuntu 20.04+ or Debian-based OS
• Docker installed (if not, install it using the steps below)
• At least 16GB RAM for smooth model execution

To set up Docker on your local server run

sudo apt update && sudo apt upgrade -y
sudo apt install docker.io -y
sudo systemctl start docker
sudo systemctl enable docker

Verify the installation:

docker --version

Create a container with Python and necessary libraries:

docker run -it --name starcoder-container --rm -p 8000:8000 python:3.9 bash

Inside the container, install dependencies:

pip install torch transformers flask

Now, download the StarCoder model:

from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("bigcode/starcoder")
model = AutoModelForCausalLM.from_pretrained("bigcode/starcoder")

This fetches the model and tokenizer from Hugging Face.

Create a simple Flask server to expose StarCoder’s API:

from flask import Flask, request, jsonify

def generate_code(prompt):
    inputs = tokenizer(prompt, return_tensors="pt")
    output = model.generate(**inputs, max_length=200)
    return tokenizer.decode(output[0])
app = Flask(__name__)
@app.route("/generate", methods=["POST"])
def generate():
    data = request.json
    response = generate_code(data["prompt"])
    return jsonify({"response": response})
if __name__ == "__main__":
    app.run(host="0.0.0.0", port=8000)

Save this as server.py inside the container.

Inside the Docker container, start the server:

python server.py

Your StarCoder API is now live at:

http://localhost:8000/generate

You can send a POST request with a prompt:

{
    "prompt": "def fibonacci(n):"
}

With this setup, you have StarCoder running in a Docker container on a local server, accessible via an API. This approach ensures flexibility and easy deployment for local AI based coding assistance.

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