Merge pull request #2743 from S3Studio/DockerizeSupport
Add dockerize support
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.vscode
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.git
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.github
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.venv
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cache
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data
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examples
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.dockerignore
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.gitattributes
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.gitignore
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Dockerfile
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FROM cnstark/pytorch:2.0.1-py3.9.17-cuda11.8.0-ubuntu20.04
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WORKDIR /app
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COPY requirements.txt /app/
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RUN pip install -r requirements.txt && \
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pip install tiktoken && \
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pip install transformers_stream_generator
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COPY . /app/
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VOLUME [ "/root/.cache/huggingface/", "/app/data", "/app/output" ]
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EXPOSE 7860
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CMD [ "python", "src/train_web.py" ]
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README.md
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README.md
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> [!TIP]
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> We recommend using `--per_device_eval_batch_size=1` and `--max_target_length 128` at 4/8-bit predict.
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### Dockerize Training
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#### Get ready
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Necessary dockerized environment is needed, such as Docker or Docker Compose.
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#### Docker support
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```bash
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docker build -f ./Dockerfile -t llama-factory:latest .
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docker run --gpus=all -v ./hf_cache:/root/.cache/huggingface/ -v ./data:/app/data -v ./output:/app/output -p 7860:7860 --shm-size 16G --name llama_factory -d llama-factory:latest
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```
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#### Docker Compose support
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```bash
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docker compose -f ./docker-compose.yml up -d
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```
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> [!TIP]
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> Details about volume:
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> * hf_cache: Utilize Huggingface cache on the host machine. Reassignable if a cache already exists in a different directory.
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> * data: Place datasets on this dir of the host machine so that they can be selected on LLaMA Board GUI.
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> * output: Set export dir to this location so that the merged result can be accessed directly on the host machine.
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## Projects using LLaMA Factory
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1. Wang et al. ESRL: Efficient Sampling-based Reinforcement Learning for Sequence Generation. 2023. [[arxiv]](https://arxiv.org/abs/2308.02223)
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version: '3.8'
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services:
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llama-factory:
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build:
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dockerfile: Dockerfile
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context: .
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container_name: llama_factory
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volumes:
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- ./hf_cache:/root/.cache/huggingface/
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- ./data:/app/data
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- ./output:/app/output
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ports:
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- "7860:7860"
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shm_size: 16G
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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count: "all"
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capabilities: [gpu]
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restart: unless-stopped
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