Installing Ray


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Ray currently supports MacOS and Linux. Windows wheels are now available, but Windows support is experimental and under development.

Latest stable version

You can install the latest stable version of Ray as follows.

pip install -U ray  # also recommended: ray[debug]

Note for Windows Users: To use Ray on Windows, Visual C++ runtime must be installed (see Windows Dependencies section). If you run into any issues, please see the Windows Support section.

Latest Snapshots (Nightlies)

You can install the latest Ray wheels via the following command:

pip install -U ray
ray install-nightly


ray install-nightly may not capture updated library dependencies. After running ray install-nightly, consider running pip install ray[<library>] without upgrading (via -U) to update dependencies.

Alternatively, here are the links to the latest wheels (which are built for each commit on the master branch). To install these wheels, use the following pip command and wheels instead of the ones above:

pip install -U [link to wheel]



Windows (experimental)

Linux Python 3.8

MacOS Python 3.8

Windows Python 3.8

Linux Python 3.7

MacOS Python 3.7

Windows Python 3.7

Linux Python 3.6

MacOS Python 3.6

Windows Python 3.6

Installing from a specific commit

You can install the Ray wheels of any particular commit on master with the following template. You need to specify the commit hash, Ray version, Operating System, and Python version:


For example, here are the Ray 1.0.0 wheels for Python 3.5, MacOS for commit a0ba4499ac645c9d3e82e68f3a281e48ad57f873:

pip install

Install Ray With Maven

The latest Ray Java release can be found in central repository. To use the latest Ray Java release in your application, add the following entries in your pom.xml:


The latest Ray Java snapshot can be found in sonatype repository. To use the latest Ray Java snapshot in your application, add the following entries in your pom.xml:

<!-- only needed for snapshot version of ray -->



When you run pip install to install Ray, Java jars are installed as well. The above dependencies are only used to build your Java code and to run your code in local or single machine mode.

If you want to run your Java code in a multi-node Ray cluster, it’s better to exclude Ray jars when packaging your code to avoid jar conficts if the versions (installed Ray with pip install and maven dependencies) don’t match.

Windows Support

Windows support is currently limited and “alpha” quality. Bugs, process/resource leaks, or other incompatibilities may exist under various scenarios. Unusual, unattended, or production usage is not recommended.

To use Ray on Windows, the Visual C++ runtime must be installed (see Windows Dependencies section).

If you encounter any issues, please try the following:

If your issue has not yet been addressed, comment on the Windows Known Issues page.

Windows Dependencies

For Windows, ensure the latest Visual C++ runtime (install link) is installed before using Ray.

Otherwise, you may receive an error similar to the following when Ray fails to find the runtime library files (e.g. VCRUNTIME140_1.dll):

FileNotFoundError: Could not find module '_raylet.pyd' (or one of its dependencies).

Installing Ray on Arch Linux

Note: Installing Ray on Arch Linux is not tested by the Project Ray developers.

Ray is available on Arch Linux via the Arch User Repository (AUR) as python-ray.

You can manually install the package by following the instructions on the Arch Wiki or use an AUR helper like yay (recommended for ease of install) as follows:

yay -S python-ray

To discuss any issues related to this package refer to the comments section on the AUR page of python-ray here.

Installing Ray with Anaconda

If you use Anaconda and want to use Ray in a defined environment, e.g, ray, use these commands:

conda create --name ray
conda activate ray
conda install --name ray pip
pip install ray

Use pip list to confirm that ray is installed.

Building Ray from Source

Installing from pip should be sufficient for most Ray users.

However, should you need to build from source, follow these instructions for building Ray.

Docker Source Images

Most users should pull a Docker image from the Ray Docker Hub.

  • The rayproject/ray image has ray and all required dependencies. It comes with anaconda and Python 3.7.

  • The rayproject/autoscaler image has the above features as well as many additional libraries.

  • The rayproject/base-deps and rayproject/ray-deps are for the linux and python dependencies respectively.

These images are tagged by their release number (or commit hash for nightlies) as well as a "-gpu" if they are GPU compatible.

If you want to tweak some aspect of these images and build them locally, refer to the following script:

cd ray

Beyond creating the above Docker images, this script can also produce the following two images.

  • The rayproject/development image has the ray source code included and is setup for development.

  • The rayproject/examples image adds additional libraries for running examples.

Review images by listing them:

docker images

Output should look something like the following:

REPOSITORY                          TAG                 IMAGE ID            CREATED             SIZE
rayproject/ray                      latest              7243a11ac068        2 days ago          1.11 GB
rayproject/ray-deps                 latest              b6b39d979d73        8 days ago          996  MB
rayproject/base-deps                latest              5606591eeab9        8 days ago          512  MB
ubuntu                              focal               1e4467b07108        3 weeks ago         73.9 MB

Launch Ray in Docker

Start out by launching the deployment container.

docker run --shm-size=<shm-size> -t -i ray-project/ray

Replace <shm-size> with a limit appropriate for your system, for example 512M or 2G. The -t and -i options here are required to support interactive use of the container.

Note: Ray requires a large amount of shared memory because each object store keeps all of its objects in shared memory, so the amount of shared memory will limit the size of the object store.

You should now see a prompt that looks something like:


Test if the installation succeeded

To test if the installation was successful, try running some tests. This assumes that you’ve cloned the git repository.

python -m pytest -v python/ray/tests/