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Docker images

Build images from Dockerfiles

You can build it on your own, note it takes lots of time, be prepared.

git clone https://github.com/Lightning-AI/lightning.git

# build with the default arguments
docker image build -t pytorch-lightning:latest -f dockers/base-cuda/Dockerfile .

# build with specific arguments
docker image build -t pytorch-lightning:base-cuda-py3.9-torch1.13-cuda11.7.1 -f dockers/base-cuda/Dockerfile --build-arg PYTHON_VERSION=3.9 --build-arg PYTORCH_VERSION=1.13 --build-arg CUDA_VERSION=11.7.1 .

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 your 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. For example,

docker run --rm -it --gpus all pytorchlightning/pytorch_lightning:base-cuda-py3.9-torch1.12-cuda11.7.1

Run Jupyter server

  1. Build the docker image: 
    docker image build -t pytorch-lightning:v1.6.5 -f dockers/nvidia/Dockerfile --build-arg LIGHTNING_VERSION=1.6.5 .
  2. start the server and map ports: 
    docker run --rm -it --gpus=all -p 8888:8888 pytorch-lightning:v1.6.5
  3. Connect in local browser:
    • copy the generated path e.g. https://hostname:8888/?token=0719fa7e1729778b0cec363541a608d5003e26d4910983c6
    • replace the hostname by localhost