Spaces:
Paused
Paused
File size: 2,196 Bytes
a90eb0c d7c58f8 ed51e31 d7c58f8 a90eb0c d7c58f8 a90eb0c fcfee49 a90eb0c fcfee49 a90eb0c d7c58f8 d6e6068 d7c58f8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 |
# Use an official PyTorch image with CUDA support as the base image
FROM pytorch/pytorch:2.0.0-cuda11.7-cudnn8-devel
# Add NVIDIA CUDA GPG key
# Add NVIDIA CUDA GPG key using a different key server
RUN apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv-key A4B469963BF863CC
ENV DEBIAN_FRONTEND=noninteractive
# Set the MKL_THREADING_LAYER environment variable to GNU
ENV MKL_THREADING_LAYER=GNU
# Install Git, OpenGL libraries, and libglib2.0
RUN apt-get update && apt-get install -y git libgl1-mesa-glx libglib2.0-0
RUN apt-get update && apt-get install -y ninja-build
# Install necessary dependencies, including CMake, a C++ compiler, and others
RUN apt-get update && apt-get install -y unzip ffmpeg cmake g++ build-essential aria2
# Install the packaging module
RUN pip install packaging
# Set up a new user named "user" with user ID 1000
RUN useradd -m -u 1000 user
# Switch to the "user" user
USER user
# Set environment variables
ENV HOME=/home/user \
CUDA_HOME=/usr/local/cuda \
PATH=/home/user/.local/bin:$PATH \
LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${LD_LIBRARY_PATH} \
LIBRARY_PATH=${CUDA_HOME}/lib64/stubs:${LIBRARY_PATH} \
PYTHONPATH=$HOME/app \
PYTHONUNBUFFERED=1 \
GRADIO_ALLOW_FLAGGING=never \
GRADIO_NUM_PORTS=1 \
GRADIO_SERVER_NAME=0.0.0.0 \
GRADIO_THEME=huggingface \
GRADIO_SHARE=False \
SYSTEM=spaces
# Set the environment variable to specify the GPU device
ENV CUDA_DEVICE_ORDER=PCI_BUS_ID
ENV CUDA_VISIBLE_DEVICES=0
# Set the working directory to the user's home directory
WORKDIR $HOME/app
# Clone your repository or add your code to the container
RUN git clone -b main https://github.com/fffiloni/MiniGPT4-video $HOME/app
# Install dependencies
#COPY requirements.txt $HOME/app/requirements.txt
RUN pip install --no-cache-dir -r requirements.txt
RUN mkdir checkpoints
# Download checkpoint files using aria2
RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/Vision-CAIR/MiniGPT4-Video/resolve/main/checkpoints/video_llama_checkpoint_last.pth -d $HOME/app/checkpoints -o video_llama_checkpoint_last.pth
# Run your app.py script
CMD ["python", "minigpt4_video_demo.py"] |