89 lines
2.3 KiB
Markdown
89 lines
2.3 KiB
Markdown
# Docker images
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## Builds images form attached Dockerfiles
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You can build it on your own, note it takes lots of time, be prepared.
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```bash
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git clone <git-repository>
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docker image build -t pytorch-lightning:latest -f dockers/conda/Dockerfile .
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```
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or with specific arguments
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```bash
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git clone <git-repository>
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docker image build \
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-t pytorch-lightning:base-cuda-py3.7-pt1.8 \
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-f dockers/base-cuda/Dockerfile \
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--build-arg PYTHON_VERSION=3.7 \
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--build-arg PYTORCH_VERSION=1.8 \
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.
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```
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or nightly version from Conda
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```bash
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git clone <git-repository>
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docker image build \
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-t pytorch-lightning:base-conda-py3.9-pt1.11 \
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-f dockers/base-conda/Dockerfile \
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--build-arg PYTHON_VERSION=3.9 \
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--build-arg PYTORCH_VERSION=1.11 \
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.
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```
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To run your docker use
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```bash
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docker image list
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docker run --rm -it pytorch-lightning:latest bash
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```
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and if you do not need it anymore, just clean it:
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```bash
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docker image list
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docker image rm pytorch-lightning:latest
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```
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## Run docker image with GPUs
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To run docker image with access to you GPUs you need to install
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```bash
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# Add the package repositories
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distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
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curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
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curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
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sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
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sudo systemctl restart docker
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```
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and later run the docker image with `--gpus all` so for example
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```
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docker run --rm -it --gpus all pytorchlightning/pytorch_lightning:base-cuda-py3.7-torch1.6
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```
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## Run Jupyter server
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Inspiration comes from https://u.group/thinking/how-to-put-jupyter-notebooks-in-a-dockerfile
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1. Build the docker image:
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```bash
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docker image build \
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-t pytorch-lightning:v1.3.1 \
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-f dockers/nvidia/Dockerfile \
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--build-arg LIGHTNING_VERSION=1.3.1 \
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.
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```
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1. start the server and map ports:
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```bash
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docker run --rm -it --runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=all -p 8888:8888 pytorch-lightning:v1.3.1
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```
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1. Connect in local browser:
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- copy the generated path e.g. `http://hostname:8888/?token=0719fa7e1729778b0cec363541a608d5003e26d4910983c6`
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- replace the `hostname` by `localhost`
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