AutoPilot Setup for VS Code

- 3 mins read

In this post, I’m going to demonstrate the setup of Continue, an open-source autopilot designed for VS Code.

Getting Started

Once you’ve installed the plugin from the market place, let’s proceed by adding Continue to the right sidebar of VS Code, as recommended.

Provider - LM Studio

First, on my Windows machine, I’ll execute LM Studio and download Google’s Gemma 2B Instruct model.

autopilot-gemma-2b-it-gguf

Provider - OpenAI-compatible

Next, let’s configure another LLM in our Home Lab. Here’s the Dockerfile from my previous post:

FROM python:3-slim-bullseye

ENV model sample_model

# We need to set the host to 0.0.0.0 to allow outside access
ENV HOST 0.0.0.0

# Install the package
RUN apt update && apt install -y libopenblas-dev ninja-build build-essential pkg-config
RUN pip install --upgrade pip
RUN python -m pip install  --no-cache-dir --upgrade pip pytest cmake scikit-build setuptools fastapi uvicorn sse-starlette pydantic-settings starlette-context

RUN CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install --no-cache-dir --force-reinstall llama_cpp_python==0.2.27 --verbose

# Run the server
CMD ["sh", "-c", "python3 -m llama_cpp.server --model /models/\"$model\""]

Now, let’s build it:

# Build the image
docker build . -t llama-cpp-python:0.2.27

# Tag it
docker tag llama-cpp-python:0.2.27 registry.local:5000/llama-cpp-python:0.2.27

# Push to the registry
docker push registry.local:5000/llama-cpp-python:0.2.27

In this post, I’ll utilize the Deepseek-Coder model. Here’s the deploy.yaml file:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: deepseek-coder-instruct
  namespace: llm
spec:
  replicas: 1
  selector:
    matchLabels:
      app: deepseek-coder-instruct
  template:
    metadata:
      labels:
        app: deepseek-coder-instruct
    spec:
      containers:
      - name: deepseek-coder-instruct
        image: registry.local:5000/llama-cpp-python:0.2.27
        imagePullPolicy: IfNotPresent
        ports:
        - containerPort: 8000
        securityContext:
          capabilities:
            add:
            - IPC_LOCK
        volumeMounts:
        - name: models-store
          mountPath: /models
          subPath: ./llama-models
        env:
        - name: model
          value: deepseek-coder-6.7b-instruct.Q4_K_M.gguf
        resources:
          requests:
            memory: "6Gi"
          limits:
            memory: "6Gi"
      imagePullSecrets:
      - name: regcred
      volumes:
      - name: models-store
        persistentVolumeClaim:
            claimName: nfs

Here’s the svc.yaml file:

apiVersion: v1
kind: Service
metadata:
  name: deepseek-coder-instruct-svc
  namespace: llm
spec:
  selector:
    app: deepseek-coder-instruct
  type: ClusterIP
  ports:
    - name: http
      protocol: TCP
      port: 80
      targetPort: 8000

Depending on your Home Lab configuration, I’m utilizing Kong Manager. Here’s my Gateway Service setup:

autopilot-gateway-service-deepseek-coder

And here’s the Route setup:

autopilot-route-deepseek-coder

Additionally, here’s the Application Log for the deployed pod:

autopilot-portainer-deepseek-coder-deployed

Configuration

From the Continue sidebar, click on the Plus icon at the bottom, then scroll all the way down and click on the Open config.json button.

autopilot-continue-sidebar-config

Since I’ve set up 2 models locally, here are the corresponding config values:

{
  "models": [
    {
      "title": "LM Studio",
      "provider": "lmstudio",
      "model": "gemma-2b-it"
    },
    {
      "title": "llama-cpp-python",
      "provider": "openai",
      "model": "deepseek-coder-6.7b-instruct.Q4_K_M.gguf",
      "apiKey": "EMPTY",
      "apiBase": "http://kong.local:8000/deepseek/v1"
    }
  ],
  ...

For the tabAutocompleteModel, I am using:

  "tabAutocompleteModel": {
    "title": "llama-cpp-python",
    "provider": "openai",
    "model": "deepseek-coder-6.7b-instruct.Q4_K_M.gguf",
    "apiKey": "EMPTY",
    "apiBase": "http://kong.local:8000/deepseek/v1"
  },
  "allowAnonymousTelemetry": false,
  ...
}

Putting it into Action

Let’s test it out by inserting the dockerfile block into this post. After selecting the block of code, press CTRL-L, and it will appear on the right.

LM Studio

Let’s experiment with the LM Studio model:

autopilot-lm-studio-in-action

OpenAI-compatible

Now, let’s experiment with the llama-cpp-python model:

autopilot-llama-cpp-in-action

That’s all there is to it! We now have a locally enabled autopilot for code completion!