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https://github.com/kaggle/docker-python
Kaggle Python docker image
https://github.com/kaggle/docker-python
Last synced: 24 days 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 (over 9 years ago)
- Default Branch: main
- Last Pushed: 2024-05-22T22:57:55.000Z (6 months ago)
- Last Synced: 2024-05-23T00:37:50.718Z (6 months ago)
- Language: Python
- Size: 13.3 MB
- Stars: 2,355
- Watchers: 90
- Forks: 919
- Open Issues: 31
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
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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.
* `--pattern test_keras.py` or `-p test_keras.py` to run a single test
* `--image gcr.io/kaggle-images/python:ci-pretest` or `-i gcr.io/kaggle-images/python:ci-pretest` to test against a specific 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).