lightning/dockers
Carlos Mocholí c6a171b776
Fix requirements/adjust_versions.py (#7149)
Co-authored-by: jirka <jirka.borovec@seznam.cz>
2021-05-04 01:06:28 +02:00
..
base-conda Fix `apex` version in Docker due to broken upstream (#7146) 2021-04-21 23:58:55 +01:00
base-cuda Update DeepSpeed version requirement in Dockerfile (#7326) 2021-05-03 20:21:19 +02:00
base-xla Fix requirements/adjust_versions.py (#7149) 2021-05-04 01:06:28 +02:00
nvidia Docker/nvidia (#7109) 2021-04-27 20:29:49 +01:00
release remake nvidia docker (#6686) 2021-03-29 09:39:06 +01:00
tpu-tests
README.md Docker/nvidia (#7109) 2021-04-27 20:29:49 +01:00

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

Run Jupyter server

Inspiration comes from https://u.group/thinking/how-to-put-jupyter-notebooks-in-a-dockerfile

  1. Build the docker image:
    docker image build \
        -t pytorch-lightning:v1.2.9 \
        -f dockers/nvidia/Dockerfile \
        --build-arg LIGHTNING_VERSION=1.2.9 \
        .
    
  2. start the server and map ports:
    docker run --rm -it --runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=all -p 8888:8888 pytorch-lightning:v1.2.9
    
  3. Connect in local browser:
    • copy the generated path e.g. http://hostname:8888/?token=0719fa7e1729778b0cec363541a608d5003e26d4910983c6
    • replace the hostname by localhost