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# Ignore everything | |
** | |
# Allow files and directories | |
!/audio.py | |
!/Dockerfile | |
!/hparams.py | |
!/preprocess.py | |
!/checkpoints/ | |
!/evaluation/ | |
!/hq_wav2lip_train.py | |
!/README.md | |
!/temp/ | |
!/color_syncnet_train.py | |
!/face_detection/ | |
!/inference.py | |
!/requirements.txt | |
!/filelists/ | |
!/models/ | |
!/results/ | |
!/wav2lip_train.py | |
# Ignore unnecessary files inside allowed directories | |
# This should go after the allowed directories | |
**/*~ | |
**/*.log | |
**/.DS_Store | |
**/Thumbs.db |
# 1. install a version of docker with gpu support (docker-ce >= 19.03) | |
# 2. enter the project directory and build the wav2lip image: | |
# docker build -t wav2lip . | |
# 3. allow root user to connect to the display | |
# xhost +local:root | |
# 4. instantiate the container | |
# docker run --rm --gpus all -v /tmp/.X11-unix:/tmp/.X11-unix -v $PWD:/workspace/src -e DISPLAY=$DISPLAY --device /dev/dri -ti wav2lip bash | |
# NOTES: | |
# export CUDA_VISIBLE_DEVICES="" ## force cpu only | |
# Based on https://github.com/1adrianb/face-alignment/blob/master/Dockerfile | |
FROM nvidia/cuda:10.1-cudnn7-devel-ubuntu18.04 | |
RUN export DEBIAN_FRONTEND=noninteractive RUNLEVEL=1 ; \ | |
apt-get update && apt-get install -y --no-install-recommends \ | |
build-essential cmake git curl ca-certificates \ | |
vim \ | |
python3-pip python3-dev python3-wheel \ | |
libglib2.0-0 libxrender1 python3-soundfile \ | |
ffmpeg && \ | |
rm -rf /var/lib/apt/lists/* && \ | |
pip3 install --upgrade setuptools | |
# RUN curl -o ~/miniconda.sh -O https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && \ | |
# chmod +x ~/miniconda.sh && \ | |
# ~/miniconda.sh -b -p /opt/conda && \ | |
# rm ~/miniconda.sh | |
# ENV PATH /opt/conda/bin:$PATH | |
# RUN conda config --set always_yes yes --set changeps1 no && conda update -q conda | |
# RUN conda install pytorch torchvision cudatoolkit=10.1 -c pytorch | |
# # Install Wav2Lip package | |
# # NOTE we use the git clone to install the requirements only once | |
# # (if we use COPY it will invalidate the cache and reinstall the dependencies for every change in the sources) | |
WORKDIR /workspace | |
RUN chmod -R a+w /workspace | |
RUN git clone https://github.com/Rudrabha/Wav2Lip | |
WORKDIR /workspace/Wav2Lip | |
RUN pip3 install -r requirements.txt | |
RUN mkdir -p /root/.cache/torch/checkpoints && \ | |
curl -SL -o /root/.cache/torch/checkpoints/s3fd-619a316812.pth "https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth" | |
# !!! NOTE !!! nvidia-driver version must match the version installed on the host(/docker server) | |
RUN export DEBIAN_FRONTEND=noninteractive RUNLEVEL=1 ; \ | |
apt-get update && apt-get install -y --no-install-recommends \ | |
nvidia-driver-450 mesa-utils && \ | |
rm -rf /var/lib/apt/lists/* | |
# create the working directory, to be mounted with the bind option | |
RUN mkdir /workspace/src | |
WORKDIR /workspace/src |
I had to add RUN pip3 install --upgrade pip
to the docker file just before RUN pip3 install -r requirements.txt
to get it to install the requirements correctly.
I had to add
RUN pip3 install --upgrade pip
to the docker file just beforeRUN pip3 install -r requirements.txt
to get it to install the requirements correctly.
This is important
Exactly this works for me.
Why doesnot anyone had already created the docker image share to public repo?
Why doesnot anyone had already created the docker image share to public repo?
I haven't used this image for a while, lately when I need some python-ML projects I just use Conda.
I had to add
RUN pip3 install --upgrade pip
to the docker file just beforeRUN pip3 install -r requirements.txt
to get it to install the requirements correctly.This is important
Super important, was fun wasting time today
I have been having problems running with this error python: can't open file
'inference.py': [Errno 2] No such file or directory
The base image FROM nvidia/cuda:10.1-cudnn7-devel-ubuntu18.04
seems to be deprecated. Any pre-built image ready to run available?
The base image
FROM nvidia/cuda:10.1-cudnn7-devel-ubuntu18.04
seems to be deprecated. Any pre-built image ready to run available?
Did any find a resolution for this?
I am having some issues trying to use newer base images
Having issues with the regular conda dev too. Maybe some dependency stuff?
Well, just due to time constraints I ended up installing it normally, without docker to do my tests. So there's no urgency for it to be fixed, but I wish you good luck!