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windmill/docker/DockerfileCuda
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FROM ghcr.io/windmill-labs/windmill-ee:dev
# Install any necessary dependencies for managing repositories and packages
RUN apt-get update && apt-get install -y curl gnupg2
RUN curl "https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.0-1_all.deb" -o cuda.deb && \
dpkg -i cuda.deb && rm cuda.deb
# NVIDIA's CUDA apt repo signing key carries a SHA1 self-binding signature,
# which the Debian trixie base image's Sequoia-based apt verifier (sqv) rejects
# as of 2026-02-01, leaving the repo treated as unsigned. Re-enable SHA1 via a
# scoped crypto policy applied only to the apt runs that touch the CUDA repo.
RUN printf '[hash_algorithms.sha1]\ncollision_resistance = "always"\nsecond_preimage_resistance = "always"\n' > /etc/apt-nvidia-sqv-policy.toml
RUN export SEQUOIA_CRYPTO_POLICY=/etc/apt-nvidia-sqv-policy.toml && \
apt-get update -y && \
apt-get install -y --no-install-recommends \
cuda-cudart-12-2 cuda-nvcc-12-2 cuda-nvrtc-12-2 \
libcudnn8 libcublas-12-2 && \
rm -rf /var/lib/apt/lists/*
# Install FFmpeg if needed
RUN export SEQUOIA_CRYPTO_POLICY=/etc/apt-nvidia-sqv-policy.toml && \
apt-get update && \
apt-get install -y ffmpeg && \
rm -rf /var/lib/apt/lists/*
# Make sure the PyTorch version is compatible with the installed CUDA version
# Set necessary environment variables for CUDA libraries (if needed)
ENV PATH=/usr/local/nvidia/bin:/usr/local/cuda/bin:${PATH}
ENV LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64
ENV NVIDIA_VISIBLE_DEVICES=all
ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility