64 lines
1.9 KiB
Markdown
64 lines
1.9 KiB
Markdown
# Docker images
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## Build images from 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 https://github.com/Lightning-AI/lightning.git
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# build with the default arguments
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docker image build -t pytorch-lightning:latest -f dockers/base-cuda/Dockerfile .
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# build with specific arguments
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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 .
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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 your 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`. For example,
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```
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docker run --rm -it --gpus all pytorchlightning/pytorch_lightning:base-cuda-py3.9-torch1.12-cuda11.7.1
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```
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## Run Jupyter server
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1. Build the docker image:
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```bash
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docker image build -t pytorch-lightning:v1.6.5 -f dockers/nvidia/Dockerfile --build-arg LIGHTNING_VERSION=1.6.5 .
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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 --gpus=all -p 8888:8888 pytorch-lightning:v1.6.5
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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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