mirror of
https://github.com/Wan-Video/Wan2.1.git
synced 2025-06-06 23:34:53 +00:00
cf8764c186
This commit introduces the necessary files and documentation to enable
deployment of the Wan video generation application to Google Cloud Platform.
Key additions:
- Dockerfile: Defines the container image, using an NVIDIA PyTorch base
image and including special handling for flash-attn installation.
It allows for configurable Gradio application startup.
- cloudbuild.yaml: Provides a Google Cloud Build configuration to automate
the Docker image build and push process to Google Artifact Registry.
- DEPLOY_GCLOUD.md: A comprehensive guide detailing the steps to:
- Set up prerequisites on GCP.
- Configure and run Cloud Build.
- Deploy the container to Cloud Run (with CPU and GPU options).
- Deploy the container to Vertex AI Endpoints.
- Troubleshoot common issues and select appropriate machine resources.
These changes aim to simplify and standardize the deployment process on
Google Cloud, addressing potential issues related to dependencies and
environment configuration.
49 lines
2.0 KiB
Docker
49 lines
2.0 KiB
Docker
# Use a PyTorch base image with CUDA support
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# Check NVIDIA's NGC catalog or Docker Hub for suitable images
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# Example: nvcr.io/nvidia/pytorch:24.03-py3 (PyTorch 2.4.0, Python 3.10, CUDA 12.1)
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# Ensure the Python version in the base image is compatible with project dependencies.
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# Python 3.10 is generally a safe bet for recent PyTorch versions.
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FROM nvcr.io/nvidia/pytorch:24.03-py3
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# Set the working directory
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WORKDIR /app
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# Install essential build tools and Python headers (if needed for some packages)
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# RUN apt-get update && apt-get install -y --no-install-recommends # build-essential # python3-dev # && rm -rf /var/lib/apt/lists/*
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# Copy the requirements file first to leverage Docker layer caching
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COPY requirements.txt .
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# Install Python dependencies
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# Upgrading pip, setuptools, and wheel first can prevent some build issues.
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RUN pip install --upgrade pip setuptools wheel
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# Special handling for flash-attn as per INSTALL.md
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# Attempting the no-build-isolation method first.
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# Ensure that the PyTorch and CUDA versions are compatible with flash-attn
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RUN pip install flash-attn --no-build-isolation
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# Install other dependencies
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# Using --no-cache-dir to reduce image size
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RUN pip install --no-cache-dir -r requirements.txt
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# Copy the rest of the application code
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COPY . .
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# Make generate.py executable (if it's intended to be run directly and has a shebang)
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# RUN chmod +x generate.py
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# Set environment variables (optional, can be overridden at runtime)
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# Example: Set a default Gradio app to run
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ENV GRADIO_APP_SCRIPT gradio/t2v_14B_singleGPU.py
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# ENV MODEL_CKPT_DIR ./Wan2.1-T2V-14B # Or a path inside the container where models will be mounted/downloaded
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# Expose the default Gradio port (usually 7860)
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EXPOSE 7860
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# Default command to run the Gradio application
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# This assumes the Gradio apps are launched with `python <script_name>`
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# Users might need to adjust this based on how they want to run their app.
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# Using `sh -c` to allow environment variable substitution in the command.
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CMD ["sh", "-c", "python $GRADIO_APP_SCRIPT"]
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