https://github.com/mpolinowski/pytorch-jupyter

Develop your PyTorch models inside the official PyTorch image container with Jupyter Notebooks.
https://github.com/mpolinowski/pytorch-jupyter

docker-image jupyter-notebook pytorch

Last synced: about 2 months ago
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Develop your PyTorch models inside the official PyTorch image container with Jupyter Notebooks.

• Host: GitHub
• URL: https://github.com/mpolinowski/pytorch-jupyter
• Owner: mpolinowski
• Created: 2023-08-21T09:03:16.000Z (over 1 year ago)
• Default Branch: master
• Last Pushed: 2024-03-09T18:07:53.000Z (10 months ago)
• Last Synced: 2024-04-21T02:03:16.299Z (9 months ago)
• Topics: docker-image, jupyter-notebook, pytorch
• Language: Jupyter Notebook
• Homepage: https://mpolinowski.github.io/docs/IoT-and-Machine-Learning/ML/2023-08-21--pytorch-development-in-docker/2023-08-21
• Size: 44 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Containerized PyTorch Dev Workflow 

Develop your PyTorch models inside the official PyTorch container image __with__ Jupyter Notebooks.

__Variations__:

* Vanilla

* [pytorch-jupyter with mlflow](https://github.com/mpolinowski/pytorch-jupyter/tree/mlflow)

## Running the Container

Let's build this custom image with:

```bash

docker build -t pytorch-jupyter . -f Dockerfile

```

I can now create the container and mount my working directory into the container WORKDIR to get started:

```bash

docker run --gpus all -ti --rm \

    -v $(pwd):/opt/app -p 8888:8888 \

    --name pytorch-jupyter \

    pytorch-jupyter:latest

```

```bash

[C 2023-08-21 08:47:56.598 ServerApp] 

    

    To access the server, open this file in a browser:

        file:///root/.local/share/jupyter/runtime/jpserver-7-open.html

    Or copy and paste one of these URLs:

        http://e7f849cdd75e:8888/tree?token=8d72a759100e2c2971c4266bbcb8c6da5f743015eecd5255

        http://127.0.0.1:8888/tree?token=8d72a759100e2c2971c4266bbcb8c6da5f743015eecd5255

```

### Verify PyTorch

![Containerized PyTorch Dev Workflow](./notebooks/assets/PyTorch_Jupyter_Notebook_in_Docker_01.png)

### Troubleshooting

> `ERROR: Unexpected bus error encountered in worker. This might be caused by insufficient shared memory (shm).`

Please note that PyTorch uses shared memory to share data between processes, so if torch multiprocessing is used (e.g. for multithreaded data loaders) the default shared memory segment size that container runs with is not enough, and you should [increase shared memory size](https://github.com/pytorch/pytorch#using-pre-built-images) either with --ipc=host or --shm-size command line options to nvidia-docker run.

```bash

docker run --ipc=host --gpus all -ti --rm \

    -v $(pwd):/opt/app -p 8888:8888 \

    --name pytorch-jupyter \

    pytorch-jupyter:latest

```