139 lines
3.7 KiB
Markdown
139 lines
3.7 KiB
Markdown
(developer-guide)=
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# Contributing to FastVideo
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Thank you for your interest in contributing to FastVideo. We want to make the process as smooth for you as possible and this is a guide to help get you started!
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Our community is open to everyone and welcomes any contributions no matter how large or small.
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# Developer Environment:
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Do make sure you have CUDA 12.4 installed and supported. FastVideo currently only support Linux and CUDA GPUs, but we hope to support other platforms in the future.
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We recommend using a fresh Python 3.10 Conda environment to develop FastVideo:
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Install Miniconda:
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```
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wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
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bash Miniconda3-latest-Linux-x86_64.sh
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source ~/.bashrc
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```
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Create and activate a Conda environment for FastVideo:
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```
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conda create -n fastvideo python=3.10 -y
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conda activate fastvideo
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```
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Clone the FastVideo repository and go to the FastVideo directory:
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```
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git clone https://github.com/hao-ai-lab/FastVideo.git && cd FastVideo
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```
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Now you can install FastVideo and setup git hooks for running linting. By using `pre-commit`, the linters will run and have to pass before you'll be able to make a commit.
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```bash
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pip install -e .[dev]
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# Can also install flash-attn (optional)
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pip install flash-attn==2.7.0.post2 --no-build-isolation
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# Linting, formatting and static type checking
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pre-commit install --hook-type pre-commit --hook-type commit-msg
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# You can manually run pre-commit with
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pre-commit run --all-files
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# Unit tests
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pytest tests/
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```
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---
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## 🐳 Using the FastVideo Docker Image
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If you prefer a containerized development environment or want to avoid managing dependencies manually, you can use our prebuilt Docker image:
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**Image:** [`ghcr.io/hao-ai-lab/fastvideo/fastvideo-dev:latest`](https://ghcr.io/hao-ai-lab/fastvideo/fastvideo-dev)
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### Starting the container
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```bash
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docker run --gpus all -it ghcr.io/hao-ai-lab/fastvideo/fastvideo-dev:latest
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```
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This will:
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- Start the container with GPU access
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- Drop you into a shell with the `fastvideo-dev` Conda environment preconfigured
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### Using the container
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```bash
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# Conda environment should already be active
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# FastVideo package installed in editable mode
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# Pull the latest changes from remote
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cd /FastVideo
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git pull
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# Run linters and tests
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pre-commit run --all-files
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pytest tests/
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```
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---
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## 📦 Developing FastVideo on RunPod
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You can easily use the FastVideo Docker image as a custom container on [RunPod](https://www.runpod.io) for development or experimentation.
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### Creating a new pod
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Choose a GPU that supports CUDA 12.4
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When creating your pod template, use this image:
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```
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ghcr.io/hao-ai-lab/fastvideo/fastvideo-dev:latest
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```
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Paste Container Start Command to support SSH ([RunPod Docs](https://docs.runpod.io/pods/configuration/use-ssh)):
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```bash
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bash -c "apt update;DEBIAN_FRONTEND=noninteractive apt-get install openssh-server -y;mkdir -p ~/.ssh;cd $_;chmod 700 ~/.ssh;echo \"$PUBLIC_KEY\" >> authorized_keys;chmod 700 authorized_keys;service ssh start;sleep infinity"
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```
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After deploying, the pod will take a few minutes to pull the image and start the SSH service.
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### Working with the pod
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After SSH'ing into your pod, you'll find the `fastvideo-dev` Conda environment already activated.
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To pull in the latest changes from the GitHub repo:
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```bash
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cd /FastVideo
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git pull
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```
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`If you have a persistent volume and want to keep your code changes, you can move /FastVideo to /workspace/FastVideo, or simply clone the repository there.`
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Run your development workflows as usual:
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```bash
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# Run linters
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pre-commit run --all-files
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# Run tests
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pytest tests/
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```
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