This document provides instructions for launching a Ray cluster either privately, on AWS, or on GCP.
The ``ray up`` command starts or updates a Ray cluster from your personal computer. Once the cluster is up, you can then SSH into it to run Ray programs.
Then you're ready to go. The provided `ray/python/ray/autoscaler/aws/example-full.yaml <https://github.com/ray-project/ray/tree/master/python/ray/autoscaler/aws/example-full.yaml>`__ cluster config file will create a small cluster with a m5.large head node (on-demand) configured to autoscale up to two m5.large `spot workers <https://aws.amazon.com/ec2/spot/>`__.
First, install the Google API client (``pip install google-api-python-client``), set up your GCP credentials, and create a new GCP project.
Then you're ready to go. The provided `ray/python/ray/autoscaler/gcp/example-full.yaml <https://github.com/ray-project/ray/tree/master/python/ray/autoscaler/gcp/example-full.yaml>`__ cluster config file will create a small cluster with a n1-standard-2 head node (on-demand) configured to autoscale up to two n1-standard-2 `preemptible workers <https://cloud.google.com/preemptible-vms/>`__. Note that you'll need to fill in your project id in those templates.
Try it out by running these commands from your personal computer. Once the cluster is started, you can then
SSH into the head node and then run Ray programs with ``ray.init(redis_address="localhost:6379")``.
..code-block:: bash
# Create or update the cluster. When the command finishes, it will print
# out the command that can be used to SSH into the cluster head node.
This is used when you have a list of machine IP addresses to connect in a Ray cluster. You can get started by filling out the fields in the provided `ray/python/ray/autoscaler/local/example-full.yaml <https://github.com/ray-project/ray/tree/master/python/ray/autoscaler/local/example-full.yaml>`__.
Be sure to specify the proper ``head_ip``, list of ``worker_ips``, and the ``ssh_user`` field.
Try it out by running these commands from your personal computer. Once the cluster is started, you can then
SSH into the head node and then run Ray programs with ``ray.init(redis_address="localhost:6379")``.
..code-block:: bash
# Create or update the cluster. When the command finishes, it will print
# out the command that can be used to SSH into the cluster head node.
$ ray up ray/python/ray/autoscaler/local/example-full.yaml
# Reconfigure autoscaling behavior without interrupting running jobs
$ ray up ray/python/ray/autoscaler/local/example-full.yaml \
--max-workers=N --no-restart
# Teardown the cluster
$ ray down ray/python/ray/autoscaler/local/example-full.yaml
You can use ``ray exec`` to conveniently run commands on clusters. Note that scripts you run should connect to Ray via ``ray.init(redis_address="localhost:6379")``.
..code-block:: bash
# Run a command on the cluster
$ ray exec cluster.yaml 'echo "hello world"'
# Run a command on the cluster, starting it if needed
$ ray exec cluster.yaml 'echo "hello world"' --start
# Run a command on the cluster, stopping the cluster after it finishes
$ ray exec cluster.yaml 'echo "hello world"' --stop
# Run a command on a new cluster called 'experiment-1', stopping it after
You can also use ``ray submit`` to execute Python scripts on clusters. This will ``rsync`` the designated file onto the cluster and execute it with the given arguments.
To run connect to applications running on the cluster (e.g. Jupyter notebook) using a web browser, you can use the port-forward option for ``ray exec``. The local port opened is the same as the remote port:
When you run ``ray up`` with an existing cluster, the command checks if the local configuration differs from the applied configuration of the cluster. This includes any changes to synced files specified in the ``file_mounts`` section of the config. If so, the new files and config will be uploaded to the cluster. Following that, Ray services will be restarted.
You can also run ``ray up`` to restart a cluster if it seems to be in a bad state (this will restart all Ray services even if there are no config changes).
By default, the nodes will be launched into their own security group, with traffic allowed only between nodes in the same group. A new SSH key will also be created and saved to your local machine for access to the cluster.
Ray clusters come with a load-based auto-scaler. When cluster resource usage exceeds a configurable threshold (80% by default), new nodes will be launched up the specified ``max_workers`` limit. When nodes are idle for more than a timeout, they will be removed, down to the ``min_workers`` limit. The head node is never removed.
The default idle timeout is 5 minutes. This is to prevent excessive node churn which could impact performance and increase costs (in AWS / GCP there is a minimum billing charge of 1 minute per instance, after which usage is billed by the second).
The Ray autoscaler also reports per-node status in the form of instance tags. In your cloud provider console, you can click on a Node, go the the "Tags" pane, and add the ``ray-node-status`` tag as a column. This lets you see per-node statuses at a glance:
You are encouraged to copy the example YAML file and modify it to your needs. This may include adding additional setup commands to install libraries or sync local data files.
..note:: After you have customized the nodes, it is also a good idea to create a new machine image and use that in the config file. This reduces worker setup time, improving the efficiency of auto-scaling.
The setup commands you use should ideally be *idempotent*, that is, can be run more than once. This allows Ray to update nodes after they have been created. You can usually make commands idempotent with small modifications, e.g. ``git clone foo`` can be rewritten as ``test -e foo || git clone foo`` which checks if the repo is already cloned first.
Most of the example YAML file is optional. Here is a `reference minimal YAML file <https://github.com/ray-project/ray/tree/master/python/ray/autoscaler/aws/example-minimal.yaml>`__, and you can find the defaults for `optional fields in this YAML file <https://github.com/ray-project/ray/tree/master/python/ray/autoscaler/aws/example-full.yaml>`__.
A common use case is syncing a particular local git branch to all workers of the cluster. However, if you just put a `git checkout <branch>` in the setup commands, the autoscaler won't know when to rerun the command to pull in updates. There is a nice workaround for this by including the git SHA in the input (the hash of the file will change if the branch is updated):
This tells ``ray up`` to sync the current git branch SHA from your personal computer to a temporary file on the cluster (assuming you've pushed the branch head already). Then, the setup commands read that file to figure out which SHA they should checkout on the nodes. Note that each command runs in its own session. The final workflow to update the cluster then becomes just this:
The ``example-full.yaml`` configuration is enough to get started with Ray, but for more compute intensive workloads you will want to change the instance types to e.g. use GPU or larger compute instance by editing the yaml file. Here are a few common configurations:
Ray also supports external node providers (check `node_provider.py <https://github.com/ray-project/ray/tree/master/python/ray/autoscaler/node_provider.py>`__ implementation).
You can specify the external node provider using the yaml config:
..code-block:: yaml
provider:
type: external
module: mypackage.myclass
The module needs to be in the format `package.provider_class` or `package.sub_package.provider_class`.
To use Ray autoscaling on other Cloud providers or cluster management systems, you can implement the ``NodeProvider`` interface (~100 LOC) and register it in `node_provider.py <https://github.com/ray-project/ray/tree/master/python/ray/autoscaler/node_provider.py>`__. Contributions are welcome!
