Useful docker images for deep learning and machine learning in general.
Current version: Pytorch, tensorflow-gpu, tensorboardx and usual useful python libraries (numpy, pandas, dask, sklearn, matplotlib, seaborn)
To build images (don't forget the dot at the ends):
docker build -f ./Dockerfile -t mayhem-pegasus:<TAG> .
To run a jupyter lab session and mounting a shared folder (host: ./mnt will be paired with /io in the container) :
docker run -u $(id -u):$(id -g) -v $PWD/mnt:/io -p 0.0.0.0:<local jupyter port>:8888 -p 0.0.0.0:<local tensorboard port>:6888 -it --runtime=nvidia --rm mayhem-pegasus:<TAG>
that is
docker run -u $(id -u):$(id -g) -v $PWD/mnt:/io -p 0.0.0.0:8888:8888 -p 0.0.0.0:6888:6888 -it --runtime=nvidia --rm mayhem-pegasus:<TAG>
Or, with new nvidia-docker command,
docker run -u $(id -u):$(id -g) -v $PWD/mnt:/io -p 0.0.0.0:<local jupyter port>:8888 -p 0.0.0.0:<local tensorboard port>:6006 -it --gpus <device> --rm mayhem-pegasus:<TAG>
where <device> is the device number. If you want to use multiple devices, see usage examples.
--gpus 1 to use only first one GPU
--gpus '"device=1,2"' to use devices 1 and 2
--gpus all to use all devices
that is
docker run -u $(id -u):$(id -g) -v $PWD/mnt:/io -p 0.0.0.0:8888:8888 -p 0.0.0.0:6888:6006 -it --gpus '"device=1,2"'` --rm mayhem-pegasus:<TAG>
The provider docker file / container suppose NVidia GPU usage. You need to install nvidia-docker2 to be able to use --runtime=nvidia, see the following installation instructions.
Autocompletion in Jupyter might be veeeery slow or inactive. This might be cause by issues with jedi (see many issues in jupyter repo on this matter). To deactivate jedi and get autocompletion in the notebooks, you can run the following magic:
%config Completer.use_jedi = False
- to get a bash shell in the container
docker exec -it <container name> /bin/bash