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updated
Browse files- Dockerfile +20 -9
Dockerfile
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@@ -2,6 +2,7 @@
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FROM pytorch/pytorch:2.0.0-cuda11.7-cudnn8-devel
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# Add NVIDIA CUDA GPG key
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RUN apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv-key A4B469963BF863CC
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# Install Git and system libraries required for OpenGL without interactive prompts
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# Install Git, OpenGL libraries, and libglib2.0
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RUN apt-get update && apt-get install -y git libgl1-mesa-glx libglib2.0-0
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# Set up a new user named "user" with user ID 1000
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RUN useradd -m -u 1000 user
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# Switch to the "user"
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USER user
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# Set environment variables
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GRADIO_SERVER_NAME=0.0.0.0 \
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GRADIO_THEME=huggingface \
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GRADIO_SHARE=True \
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SYSTEM=spaces
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CUDA_DEVICE_ORDER=PCI_BUS_ID \
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CUDA_VISIBLE_DEVICES=0
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# Set the working directory to the user's home directory
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WORKDIR $HOME/app
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# Install specific versions of PyTorch and TorchVision
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RUN pip install torch==2.0.0+cu117 torchvision==0.15.0+cu117 -f https://download.pytorch.org/whl/torch_stable.html
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# Install dependencies
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# Download checkpoint files using aria2
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RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/video-retalking/resolve/main/30_net_gen.pth -d $HOME/app/checkpoints -o 30_net_gen.pth
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RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/video-retalking/resolve/main/shape_predictor_68_face_landmarks.dat -d $HOME/app/checkpoints -o shape_predictor_68_face_landmarks.dat
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RUN unzip -d $HOME/app/checkpoints/BFM $HOME/app/checkpoints/BFM.zip
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# Ensure the compiled CUDA code can be found
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ENV LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${LD_LIBRARY_PATH}
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#
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CMD ["python", "app.py"]
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FROM pytorch/pytorch:2.0.0-cuda11.7-cudnn8-devel
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# Add NVIDIA CUDA GPG key
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# Add NVIDIA CUDA GPG key using a different key server
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RUN apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv-key A4B469963BF863CC
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# Install Git and system libraries required for OpenGL without interactive prompts
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# Install Git, OpenGL libraries, and libglib2.0
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RUN apt-get update && apt-get install -y git libgl1-mesa-glx libglib2.0-0
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RUN apt-get update && apt-get install -y ninja-build
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# Install necessary dependencies, including CMake, a C++ compiler, and others
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RUN apt-get update && apt-get install -y unzip ffmpeg cmake g++ build-essential aria2
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# Set up a new user named "user" with user ID 1000
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RUN useradd -m -u 1000 user
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# Switch to the "user" user
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USER user
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# Set environment variables
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GRADIO_SERVER_NAME=0.0.0.0 \
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GRADIO_THEME=huggingface \
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GRADIO_SHARE=True \
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SYSTEM=spaces
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# Set the working directory to the user's home directory
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WORKDIR $HOME/app
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# Install specific versions of PyTorch and TorchVision
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RUN pip install torch==2.0.0+cu117 torchvision==0.15.0+cu117 -f https://download.pytorch.org/whl/torch_stable.html
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# Install dependencies
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#COPY requirements.txt $HOME/app/requirements.txt
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RUN pip install --no-cache-dir -r requirements.txt gradio==4.36.1 moviepy
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# Download checkpoint files using aria2
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RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/video-retalking/resolve/main/30_net_gen.pth -d $HOME/app/checkpoints -o 30_net_gen.pth
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RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/video-retalking/resolve/main/shape_predictor_68_face_landmarks.dat -d $HOME/app/checkpoints -o shape_predictor_68_face_landmarks.dat
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RUN unzip -d $HOME/app/checkpoints/BFM $HOME/app/checkpoints/BFM.zip
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# Ensure the compiled CUDA code can be found
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ENV LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${LD_LIBRARY_PATH}
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# Update package lists and install other dependencies as needed
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# Ensure that CUDA components are correctly installed and configured
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# Install any other required packages
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# Set the environment variable to specify the GPU device
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ENV CUDA_DEVICE_ORDER=PCI_BUS_ID
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ENV CUDA_VISIBLE_DEVICES=0
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# Run your app.py script
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CMD ["python", "app.py"]
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