8440595b26
* Move to CUDA image * Remove deepspeed install as deepspeed now in the cuda image * Remove path setting, as ninja should be in the container now |
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|---|---|---|
| .. | ||
| base-conda | ||
| base-cuda | ||
| base-xla | ||
| nvidia | ||
| release | ||
| tpu-tests | ||
| README.md | ||
README.md
Docker images
Builds images form attached Dockerfiles
You can build it on your own, note it takes lots of time, be prepared.
git clone <git-repository>
docker image build -t pytorch-lightning:latest -f dockers/conda/Dockerfile .
or with specific arguments
git clone <git-repository>
docker image build \
-t pytorch-lightning:py3.8-pt1.6 \
-f dockers/base-cuda/Dockerfile \
--build-arg PYTHON_VERSION=3.8 \
--build-arg PYTORCH_VERSION=1.6 \
.
or nightly version from Coda
git clone <git-repository>
docker image build \
-t pytorch-lightning:py3.7-pt1.8 \
-f dockers/base-conda/Dockerfile \
--build-arg PYTHON_VERSION=3.7 \
--build-arg PYTORCH_VERSION=1.8 \
.
To run your docker use
docker image list
docker run --rm -it pytorch-lightning:latest bash
and if you do not need it anymore, just clean it:
docker image list
docker image rm pytorch-lightning:latest
Run docker image with GPUs
To run docker image with access to you GPUs you need to install
# Add the package repositories
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
sudo systemctl restart docker
and later run the docker image with --gpus all so for example
docker run --rm -it --gpus all pytorchlightning/pytorch_lightning:base-cuda-py3.7-torch1.6