training

Confidential Training

Fine-tune LLMs on sensitive data using JupyterLab inside a TEE. Upload your dataset through the browser, run training cells, download your model.

Quick Start

phala auth login
phala deploy -n my-training -c docker-compose.yaml \
  --instance-type h200.small \
  -e HF_TOKEN=hf_xxxxx \
  -e JUPYTER_PASSWORD=your-secret

Open https://<endpoint>:8888 and log in with your password.

Workflow

graph LR
    You -->|upload data| Jupyter
    subgraph TEE[Confidential VM]
        Jupyter[JupyterLab]
        Jupyter --> GPU[Confidential GPU]
    end
    Jupyter -->|download model| You

Loading

1. Use the pre-installed notebooks in /workspace/unsloth-notebooks/ or upload finetune.ipynb from this repo
2. Upload your training data (JSONL with instruction and response fields)
3. Run the cells to train
4. Download the output folder with your fine-tuned weights

Your data and model weights stay encrypted in GPU memory throughout training.

Cleanup

phala cvms delete my-training --force

Further Reading

• Confidential AI Guide
• Unsloth for the training library