https://github.com/kaggle/docker-python

Kaggle Python docker image
https://github.com/kaggle/docker-python

Last synced: about 2 months ago
JSON representation

Kaggle Python docker image

• Host: GitHub
• URL: https://github.com/kaggle/docker-python
• Owner: Kaggle
• License: apache-2.0
• Created: 2015-04-14T01:45:38.000Z (about 9 years ago)
• Default Branch: main
• Last Pushed: 2024-05-03T22:39:19.000Z (about 2 months ago)
• Last Synced: 2024-05-04T17:52:49.895Z (about 2 months ago)
• Language: Python
• Size: 13.3 MB
• Stars: 2,349
• Watchers: 91
• Forks: 916
• Open Issues: 36
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Lists

• awesome-jupyter - kaggle/docker-python - Kaggle Python docker image that includes datasets and packages. (Runtimes/Frontends)
• -awesome-jupyter - kaggle/docker-python - Kaggle Python docker image that includes datasets and packages. (Runtimes/Frontends)

README

        
# docker-python

[Kaggle Notebooks](https://www.kaggle.com/notebooks) allow users to run a Python Notebook in the cloud against our competitions and datasets without having to download data or set up their environment.

This repository includes the [Dockerfile](Dockerfile.tmpl) for building the CPU-only and GPU image that runs Python Notebooks on Kaggle.

Our Python Docker images are stored on the Google Container Registry at:

* CPU-only: [gcr.io/kaggle-images/python](https://gcr.io/kaggle-images/python)

* GPU: [gcr.io/kaggle-gpu-images/python](https://gcr.io/kaggle-gpu-images/python)

## Requesting new packages

First, evaluate whether installing the package yourself in your own notebooks suits your needs. See [guide](https://github.com/Kaggle/docker-python/wiki/Missing-Packages).

If you the first step above doesn't work for your use case, [open an issue](https://github.com/Kaggle/docker-python/issues/new) or a [pull request](https://github.com/Kaggle/docker-python/pulls).

## Opening a pull request

1. Edit the [Dockerfile](Dockerfile.tmpl).

1. Follow the instructions below to build a new image.

1. Add tests for your new package. See this [example](https://github.com/Kaggle/docker-python/blob/main/tests/test_fastai.py).

1. Follow the instructions below to test the new image.

1. Open a PR on this repo and you are all set!

## Building a new image

```sh

./build

```

Flags:

* `--gpu` to build an image for GPU.

* `--use-cache` for faster iterative builds.

## Testing a new image

A suite of tests can be found under the `/tests` folder. You can run the test using this command:

```sh

./test

```

Flags:

* `--gpu` to test the GPU image.

## Running the image

For the CPU-only image:

```sh

# Run the image built locally:

docker run --rm -it kaggle/python-build /bin/bash

# Run the pre-built image from gcr.io

docker run --rm -it gcr.io/kaggle-images/python /bin/bash

```

For the GPU image:

```sh

# Run the image built locally:

docker run --runtime nvidia --rm -it kaggle/python-gpu-build /bin/bash

# Run the image pre-built image from gcr.io

docker run --runtime nvidia --rm -it gcr.io/kaggle-gpu-images/python /bin/bash

```

To ensure your container can access the GPU, follow the instructions posted [here](https://github.com/Kaggle/docker-python/issues/361#issuecomment-448093930).