docker-python
Kaggle 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 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
- GPU: 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.
If you the first step above doesn't work for your use case, open an issue or a pull request.
Opening a pull request
- Edit the Dockerfile.
- Follow the instructions below to build a new image.
- Add tests for your new package. See this example.
- Follow the instructions below to test the new image.
- Open a PR on this repo and you are all set!
Building a new image
./build
Flags:
--gputo build an image for GPU.--use-cachefor 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:
./test
Flags:
--gputo test the GPU image.--pattern test_keras.pyor-p test_keras.pyto run a single test--image gcr.io/kaggle-images/python:ci-pretestor-i gcr.io/kaggle-images/python:ci-pretestto test against a specific image
Running the image
For the CPU-only image:
# 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:
# 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.