Kaggle docker images come with a huge list of pre-installed packages for machine-learning, including the support of GPU computing. They run within a container as a Jupyter application accessed by users through its web interface. Running a custom image boils down to these steps
- đź’ˇ pulling the right version from the container registry
- âť— publishing with appropriate parameters (
--runtimeflag important for GPU support)
Below we can see how it looks like
(base) maciej.skorski@shared-notebooks:~$ docker pull gcr.io/kaggle-gpu-images/python:v128
v128: Pulling from kaggle-gpu-images/python
d5fd17ec1767: Pulling fs layer
...(base) maciej.skorski@shared-notebooks:~$ sudo docker run \
> --name "/payload-container" \
> --runtime "nvidia" \
> --volume "/home/jupyter:/home/jupyter" \
> --mount type=bind,source=/opt/deeplearning/jupyter/jupyter_notebook_config.py,destination=/opt/jupyter/.jupyter/jupyter_notebook_config.py,readonly \
> --log-driver "json-file" \
> --restart "always" \
> --publish "127.0.0.1:8080:8080/tcp" \
> --network "bridge" \
> --expose "8080/tcp" \
> --label "kaggle-lang"="python" \
> --detach \
> --tty \
> --entrypoint "/entrypoint.sh" \
> "gcr.io/kaggle-gpu-images/python:v128" \
> "/run_jupyter.sh"
cf1b6f63d729d357ef3a320dfab076001a3513c54344be7ae3a5af9789395e63The following test in Python shell shows that we can indeed use GPU 🙂
root@cf1b6f63d729:/# ipython
Python 3.7.12 | packaged by conda-forge | (default, Oct 26 2021, 06:08:53)
Type 'copyright', 'credits' or 'license' for more information
IPython 7.33.0 -- An enhanced Interactive Python. Type '?' for help.
In [1]: import torch
In [2]: torch.cuda.is_available()
Out[2]: True
In [3]: torch.Tensor([1,2,3]).to(0)
Out[3]: tensor([1., 2., 3.], device='cuda:0')