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Ray currently supports MacOS and Linux. Windows support is planned for the future.
You can install the latest stable version of Ray as follows.
pip install -U ray # also recommended: ray[debug]
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]
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:
pip install https://ray-wheels.s3-us-west-2.amazonaws.com/master/{COMMIT_HASH}/ray-{RAY_VERSION}-{PYTHON_VERSION}-{PYTHON_VERSION}m-{OS_VERSION}_intel.whl
For example, here are the Ray 0.9.0.dev0 wheels for Python 3.5, MacOS for commit a0ba4499ac645c9d3e82e68f3a281e48ad57f873
:
pip install https://ray-wheels.s3-us-west-2.amazonaws.com/master/a0ba4499ac645c9d3e82e68f3a281e48ad57f873/ray-0.9.0.dev0-cp35-cp35m-macosx_10_13_intel.whl
Installing from 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.
For Ubuntu, run the following commands:
sudo apt-get update
sudo apt-get install -y build-essential curl unzip psmisc
pip install cython==0.29.0 pytest
For MacOS, run the following commands:
brew update
brew install wget
pip install cython==0.29.0 pytest
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.
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]
The command ray.init()
or ray start --head
will print out the address of
the dashboard. For example,
>>> import ray
>>> ray.init()
======================================================================
View the dashboard at http://127.0.0.1:8265.
Note: If Ray is running on a remote node, you will need to set up an
SSH tunnel with local port forwarding in order to access the dashboard
in your browser, e.g. by running 'ssh -L 8265:127.0.0.1:8265
<username>@<host>'. Alternatively, you can set webui_host="0.0.0.0" in
the call to ray.init() to allow direct access from external machines.
======================================================================
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.
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.
Run the script to create Docker images.
cd ray
./build-docker.sh
This script creates several Docker images:
- The
ray-project/deploy
image is a self-contained copy of code and binaries suitable for end users. - The
ray-project/examples
adds additional libraries for running examples. - The
ray-project/base-deps
image 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:
docker images
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
Start out by launching the deployment container.
docker run --shm-size=<shm-size> -t -i ray-project/deploy
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:
root@ebc78f68d100:/ray#
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
Some candidate possibilities.
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.
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.