Ray currently supports MacOS and Linux. Windows support is planned for the future.
Latest stable version¶
You can install the latest stable version of Ray as follows.
pip install -U ray # also recommended: ray[debug]
Latest Snapshots (Nightlies)¶
Here are links to the latest wheels (which are built for each commit on the master branch). To install these wheels, run the following command:
pip install -U [link to wheel]
|Linux Python 3.7||MacOS Python 3.7|
|Linux Python 3.6||MacOS Python 3.6|
|Linux Python 3.5||MacOS Python 3.5|
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 0.9.0.dev0 wheels for Python 3.5, MacOS for commit
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
pip list to confirm that
ray is installed.
Building Ray from Source¶
pip should be sufficient for most Ray users.
However, should you need to build from source, follow instructions below for both Linux and MacOS.
To build Ray, first install the following dependencies. We recommend using Anaconda.
For Ubuntu, run the following commands:
sudo apt-get update sudo apt-get install -y build-essential curl unzip psmisc # If you are not using Anaconda, you need the following. sudo apt-get install python-dev # For Python 2. sudo apt-get install python3-dev # For Python 3. pip install cython==0.29.0
For MacOS, run the following commands:
brew update brew install wget pip install cython==0.29.0
If you are using Anaconda, you may also need to run the following.
conda install libgcc
Ray can be built from the repository as follows.
git clone https://github.com/ray-project/ray.git # Install Bazel. ray/ci/travis/install-bazel.sh # Optionally build the dashboard (requires Node.js, see below for more information). pushd ray/python/ray/dashboard/client npm ci npm run build popd # Install Ray. cd ray/python pip install -e . --verbose # Add --user if you see a permission denied error.
[Optional] Dashboard support¶
If you would like to use the dashboard, you will additionally need to install Node.js and build the dashboard before installing Ray. The relevant build steps are included in the installation instructions above.
The dashboard requires a few additional Python packages, which can be installed via pip.
pip install ray[dashboard]
If you are using Anaconda and have trouble installing
setproctitle, the try
conda install psutil setproctitle
ray start --head will print out the address of
the dashboard. For example,
Docker Source Images¶
Run the script to create Docker images.
cd ray ./build-docker.sh
This script creates several Docker images:
ray-project/deployimage is a self-contained copy of code and binaries suitable for end users.
ray-project/examplesadds additional libraries for running examples.
ray-project/base-depsimage builds from Ubuntu Xenial and includes Anaconda and other basic dependencies and can serve as a starting point for developers.
Review images by listing them:
Output should look something like the following:
REPOSITORY TAG IMAGE ID CREATED SIZE ray-project/examples latest 7584bde65894 4 days ago 3.257 GB ray-project/deploy latest 970966166c71 4 days ago 2.899 GB ray-project/base-deps latest f45d66963151 4 days ago 2.649 GB ubuntu xenial f49eec89601e 3 weeks ago 129.5 MB
Launch Ray in Docker¶
Start out by launching the deployment container.
docker run --shm-size=<shm-size> -t -i ray-project/deploy
<shm-size> with a limit appropriate for your system, for example
-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/test_mini.py
Troubleshooting installing Arrow¶
Some candidate possibilities.
You have a different version of Flatbuffers installed¶
Arrow pulls and builds its own copy of Flatbuffers, but if you already have
Flatbuffers installed, Arrow may find the wrong version. If a directory like
/usr/local/include/flatbuffers shows up in the output, this may be the
problem. To solve it, get rid of the old version of flatbuffers.
There is some problem with Boost¶
If a message like
Unable to find the requested Boost libraries appears when
installing Arrow, there may be a problem with Boost. This can happen if you
installed Boost using MacPorts. This is sometimes solved by using Brew instead.