Bitnami package for Ray

What is Ray?

Ray is a Python library for scaling AI and Python applications. Provides an API and consists of a core distributed runtime and a set of AI libraries for simplifying ML compute

Overview of Ray Trademarks: This software listing is packaged by Bitnami. The respective trademarks mentioned in the offering are owned by the respective companies, and use of them does not imply any affiliation or endorsement.

TL;DR

docker run -it --name ray bitnami/ray

Why use Bitnami Images?

• Bitnami closely tracks upstream source changes and promptly publishes new versions of this image using our automated systems.
• With Bitnami images the latest bug fixes and features are available as soon as possible.
• Bitnami containers, virtual machines and cloud images use the same components and configuration approach - making it easy to switch between formats based on your project needs.
• All our images are based on minideb -a minimalist Debian based container image that gives you a small base container image and the familiarity of a leading Linux distribution- or scratch -an explicitly empty image-.
• All Bitnami images available in Docker Hub are signed with Notation. Check this post to know how to verify the integrity of the images.
• Bitnami container images are released on a regular basis with the latest distribution packages available.

Looking to use Ray in production? Try VMware Tanzu Application Catalog, the commercial edition of the Bitnami catalog.

Why use a non-root container?

Non-root container images add an extra layer of security and are generally recommended for production environments. However, because they run as a non-root user, privileged tasks are typically off-limits. Learn more about non-root containers in our docs.

Supported tags and respective Dockerfile links

Learn more about the Bitnami tagging policy and the difference between rolling tags and immutable tags in our documentation page.

You can see the equivalence between the different tags by taking a look at the tags-info.yaml file present in the branch folder, i.e bitnami/ASSET/BRANCH/DISTRO/tags-info.yaml.

Subscribe to project updates by watching the bitnami/containers GitHub repo.

Get this image

The recommended way to get the Bitnami Ray Docker Image is to pull the prebuilt image from the Docker Hub Registry.

docker pull bitnami/ray:latest

To use a specific version, you can pull a versioned tag. You can view the list of available versions in the Docker Hub Registry.

docker pull bitnami/ray:[TAG]

If you wish, you can also build the image yourself by cloning the repository, changing to the directory containing the Dockerfile and executing the docker build command. Remember to replace the APP, VERSION and OPERATING-SYSTEM path placeholders in the example command below with the correct values.

git clone https://github.com/bitnami/containers.git
cd bitnami/APP/VERSION/OPERATING-SYSTEM
docker build -t bitnami/APP:latest .

Entering the REPL

By default, running this image will drop you into the Python REPL, where you can interactively test and try things out with Ray in Python.

docker run -it --name ray bitnami/ray

Configuration

Running your Ray app

The default work directory for the Ray image is /app. You can mount a folder from your host here that includes your Ray script, and run it normally using the python command.

docker run -it --name ray -v /path/to/app:/app bitnami/ray \
  python script.py

Running a Ray app with package dependencies

If your Ray app has a requirements.txt defining your app's dependencies, you can install the dependencies before running your app.

docker run -it --name ray -v /path/to/app:/app bitnami/ray \
  sh -c "pip install --file requirements.txt && python script.py"

Further Reading:

• ray documentation

Maintenance

Upgrade this image

Bitnami provides up-to-date versions of Ray, including security patches, soon after they are made upstream. We recommend that you follow these steps to upgrade your container.

Step 1: Get the updated image

docker pull bitnami/ray:latest

Step 2: Remove the currently running container

docker rm -v ray

Step 3: Run the new image

Re-create your container from the new image.

docker run --name ray bitnami/ray:latest

Notable Changes

Starting January 16, 2024

• The docker-compose.yaml file has been removed, as it was solely intended for internal testing purposes.

Contributing

We'd love for you to contribute to this Docker image. You can request new features by creating an issue or submitting a pull request with your contribution.

Issues

If you encountered a problem running this container, you can file an issue. For us to provide better support, be sure to fill the issue template.

License

Copyright © 2024 Broadcom. The term "Broadcom" refers to Broadcom Inc. and/or its subsidiaries.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.