45 lines
1.7 KiB
Docker
45 lines
1.7 KiB
Docker
# Use the base image with CUDA and PyTorch
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FROM kom4cr0/cuda11.7-pytorch1.13-mamba1.1.1:1.1.1
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# Install NVIDIA Container Toolkit (necessary for GPU support)
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RUN apt-get update && apt-get install -y \
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nvidia-container-runtime \
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python3 \
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python3-pip \
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&& rm -rf /var/lib/apt/lists/*
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# Install necessary dependencies and configure NVIDIA repository
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RUN apt-get update && apt-get install -y \
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curl \
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gnupg \
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lsb-release \
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sudo \
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&& curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
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&& curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
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sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
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tee /etc/apt/sources.list.d/nvidia-container-toolkit.list \
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&& sed -i -e '/experimental/ s/^#//g' /etc/apt/sources.list.d/nvidia-container-toolkit.list \
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&& apt-get update
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# Install NVIDIA Container Toolkit
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RUN apt-get install -y nvidia-container-toolkit
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# Set the environment variables for CUDA
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ENV PATH=/usr/local/cuda-11.7/bin:$PATH
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ENV LD_LIBRARY_PATH=/usr/local/cuda-11.7/lib64:$LD_LIBRARY_PATH
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# Set the runtime for GPU (requires NVIDIA runtime to be installed on the host machine)
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ENV NVIDIA_VISIBLE_DEVICES=all
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ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
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# Set working directory to /projects
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WORKDIR /project
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# Install necessary Python dependencies
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# Uncomment and modify the next lines as per your project requirements
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COPY requirements.txt requirements.txt
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RUN pip3 install -r requirements.txt
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# Run your Python script
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CMD ["python3", "main.py"]
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