Merge pull request #2743 from S3Studio/DockerizeSupport

Add dockerize support
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hoshi-hiyouga 2024-03-12 00:05:49 +08:00 committed by GitHub
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.dockerignore Normal file
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.vscode
.git
.github
.venv
cache
data
examples
.dockerignore
.gitattributes
.gitignore
Dockerfile

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FROM cnstark/pytorch:2.0.1-py3.9.17-cuda11.8.0-ubuntu20.04
WORKDIR /app
COPY requirements.txt /app/
RUN pip install -r requirements.txt && \
pip install tiktoken && \
pip install transformers_stream_generator
COPY . /app/
VOLUME [ "/root/.cache/huggingface/", "/app/data", "/app/output" ]
EXPOSE 7860
CMD [ "python", "src/train_web.py" ]

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@ -651,6 +651,32 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
> [!TIP]
> We recommend using `--per_device_eval_batch_size=1` and `--max_target_length 128` at 4/8-bit predict.
### Dockerize Training
#### Get ready
Necessary dockerized environment is needed, such as Docker or Docker Compose.
#### Docker support
```bash
docker build -f ./Dockerfile -t llama-factory:latest .
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
```
#### Docker Compose support
```bash
docker compose -f ./docker-compose.yml up -d
```
> [!TIP]
> Details about volume:
> * hf_cache: Utilize Huggingface cache on the host machine. Reassignable if a cache already exists in a different directory.
> * data: Place datasets on this dir of the host machine so that they can be selected on LLaMA Board GUI.
> * output: Set export dir to this location so that the merged result can be accessed directly on the host machine.
## Projects using LLaMA Factory
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'
services:
llama-factory:
build:
dockerfile: Dockerfile
context: .
container_name: llama_factory
volumes:
- ./hf_cache:/root/.cache/huggingface/
- ./data:/app/data
- ./output:/app/output
ports:
- "7860:7860"
shm_size: 16G
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: "all"
capabilities: [gpu]
restart: unless-stopped