Automatic Cluster Setup

Ray comes with a built-in autoscaler that makes deploying a Ray cluster simple, just run ray up from your local machine to start or update a cluster in the cloud or on an on-premise cluster. Once the Ray cluster is running, you can manually SSH into it or use provided commands like ray attach, ray rsync-up, and ray-exec to access it and run Ray programs.

Setup

This section provides instructions for configuring the autoscaler to launch a Ray cluster on AWS/GCP, an existing Kubernetes cluster, or on a private cluster of host machines.

Once you have finished configuring the autoscaler to create a cluster, see the Quickstart guide below for more details on how to get started running Ray programs on it.

AWS

First, install boto (pip install boto3) and configure your AWS credentials in ~/.aws/credentials, as described in the boto docs.

Once boto is configured to manage resources on your AWS account, you should be ready to run the autoscaler. The provided ray/python/ray/autoscaler/aws/example-full.yaml cluster config file will create a small cluster with an m5.large head node (on-demand) configured to autoscale up to two m5.large spot workers.

Test that it works by running the following commands from your local machine:

# 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/aws/example-full.yaml

# Get a remote screen on the head node.
$ ray attach ray/python/ray/autoscaler/aws/example-full.yaml
$ source activate tensorflow_p36
$ # Try running a Ray program with 'ray.init(address="auto")'.

# Tear down the cluster.
$ ray down ray/python/ray/autoscaler/aws/example-full.yaml

GCP

First, install the Google API client (pip install google-api-python-client), set up your GCP credentials, and create a new GCP project.

Once the API client is configured to manage resources on your GCP account, you should be ready to run the autoscaler. The provided ray/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. Note that you’ll need to fill in your project id in those templates.

Test that it works by running the following commands from your local machine:

# 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/gcp/example-full.yaml

# Get a remote screen on the head node.
$ ray attach ray/python/ray/autoscaler/gcp/example-full.yaml
$ source activate tensorflow_p36
$ # Try running a Ray program with 'ray.init(address="auto")'.

# Tear down the cluster.
$ ray down ray/python/ray/autoscaler/gcp/example-full.yaml

Kubernetes

The autoscaler can also be used to start Ray clusters on an existing Kubernetes cluster. First, install the Kubernetes API client (pip install kubernetes), then make sure your Kubernetes credentials are set up properly to access the cluster (if a command like kubectl get pods succeeds, you should be good to go).

Once you have kubectl configured locally to access the remote cluster, you should be ready to run the autoscaler. The provided ray/python/ray/autoscaler/kubernetes/example-full.yaml cluster config file will create a small cluster of one pod for the head node configured to autoscale up to two worker node pods, with all pods requiring 1 CPU and 0.5GiB of memory.

Test that it works by running the following commands from your local machine:

# Create or update the cluster. When the command finishes, it will print
# out the command that can be used to get a remote shell into the head node.
$ ray up ray/python/ray/autoscaler/kubernetes/example-full.yaml

# List the pods running in the cluster. You shoud only see one head node
# until you start running an application, at which point worker nodes
# should be started. Don't forget to include the Ray namespace in your
# 'kubectl' commands ('ray' by default).
$ kubectl -n ray get pods

# Get a remote screen on the head node.
$ ray attach ray/python/ray/autoscaler/kubernetes/example-full.yaml
$ # Try running a Ray program with 'ray.init(address="auto")'.

# Tear down the cluster
$ ray down ray/python/ray/autoscaler/kubernetes/example-full.yaml

Private Cluster

The autoscaler can also be used to run a Ray cluster on a private cluster of hosts, specified as a list of machine IP addresses to connect to. You can get started by filling out the fields in the provided ray/python/ray/autoscaler/local/example-full.yaml. Be sure to specify the proper head_ip, list of worker_ips, and the ssh_user field.

Test that it works by running the following commands from your local machine:

# Create or update the cluster. When the command finishes, it will print
# out the command that can be used to get a remote shell into the head node.
$ ray up ray/python/ray/autoscaler/local/example-full.yaml

# Get a remote screen on the head node.
$ ray attach ray/python/ray/autoscaler/local/example-full.yaml
$ # Try running a Ray program with 'ray.init(address="auto")'.

# Tear down the cluster
$ ray down ray/python/ray/autoscaler/local/example-full.yaml

External Node Provider

Ray also supports external node providers (check node_provider.py implementation). You can specify the external node provider using the yaml config:

provider:
    type: external
    module: mypackage.myclass

The module needs to be in the format package.provider_class or package.sub_package.provider_class.

Additional Cloud Providers

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. Contributions are welcome!

Quickstart

Starting and updating a cluster

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).

If you don’t want the update to restart services (e.g., because the changes don’t require a restart), pass --no-restart to the update call.

# Replace '<your_backend>' with one of: 'aws', 'gcp', 'kubernetes', or 'local'.
$ BACKEND=<your_backend>

# Create or update the cluster.
$ ray up ray/python/ray/autoscaler/$BACKEND/example-full.yaml

# Reconfigure autoscaling behavior without interrupting running jobs.
$ ray up ray/python/ray/autoscaler/$BACKEND/example-full.yaml \
    --max-workers=N --no-restart

# Tear down the cluster.
$ ray down ray/python/ray/autoscaler/$BACKEND/example-full.yaml

Running commands on new and existing clusters

You can use ray exec to conveniently run commands on clusters. Note that scripts you run should connect to Ray via ray.init(address="auto").

# 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
$ ray exec cluster.yaml 'echo "hello world"' \
    --start --stop --cluster-name experiment-1

# Run a command in a detached tmux session
$ ray exec cluster.yaml 'echo "hello world"' --tmux

# Run a command in a screen (experimental)
$ ray exec cluster.yaml 'echo "hello world"' --screen

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.

# Run a Python script in a detached tmux session
$ ray submit cluster.yaml --tmux --start --stop tune_experiment.py

Attaching to a running cluster

You can use ray attach to attach to an interactive screen session on the cluster.

# Open a screen on the cluster
$ ray attach cluster.yaml

# Open a screen on a new cluster called 'session-1'
$ ray attach cluster.yaml --start --cluster-name=session-1

# Attach to tmux session on cluster (creates a new one if none available)
$ ray attach cluster.yaml --tmux

Port-forwarding applications

If you want to run applications on the cluster that are accessible from a web browser (e.g., Jupyter notebook), you can use the --port-forward option for ray exec. The local port opened is the same as the remote port.

$ ray exec cluster.yaml --port-forward=8899 'source ~/anaconda3/bin/activate tensorflow_p36 && jupyter notebook --port=8899'

Manually synchronizing files

To download or upload files to the cluster head node, use ray rsync_down or ray rsync_up:

$ ray rsync_down cluster.yaml '/path/on/cluster' '/local/path'
$ ray rsync_up cluster.yaml '/local/path' '/path/on/cluster'

Security

On cloud providers, nodes will be launched into their own security group by default, 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.

Autoscaling

Ray clusters come with a load-based autoscaler. 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).

Monitoring cluster status

You can monitor cluster usage and auto-scaling status by tailing the autoscaling logs in /tmp/ray/session_*/logs/monitor*.

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:

_images/autoscaler-status.png

Customizing cluster setup

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 (or docker container) 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, and you can find the defaults for optional fields in this YAML file.

Syncing git branches

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):

file_mounts: {
    "/tmp/current_branch_sha": "/path/to/local/repo/.git/refs/heads/<YOUR_BRANCH_NAME>",
}

setup_commands:
    - test -e <REPO_NAME> || git clone https://github.com/<REPO_ORG>/<REPO_NAME>.git
    - cd <REPO_NAME> && git fetch && git checkout `cat /tmp/current_branch_sha`

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:

  1. Make local changes to a git branch
  2. Commit the changes with git commit and git push
  3. Update files on your Ray cluster with ray up

Using Amazon EFS

To use Amazon EFS, install some utilities and mount the EFS in setup_commands. Note that these instructions only work if you are using the AWS Autoscaler.

Note

You need to replace the {{FileSystemId}} to your own EFS ID before using the config. You may also need to set correct SecurityGroupIds for the instances in the config file.

setup_commands:
    - sudo kill -9 `sudo lsof /var/lib/dpkg/lock-frontend | awk '{print $2}' | tail -n 1`;
        sudo pkill -9 apt-get;
        sudo pkill -9 dpkg;
        sudo dpkg --configure -a;
        sudo apt-get -y install binutils;
        cd $HOME;
        git clone https://github.com/aws/efs-utils;
        cd $HOME/efs-utils;
        ./build-deb.sh;
        sudo apt-get -y install ./build/amazon-efs-utils*deb;
        cd $HOME;
        mkdir efs;
        sudo mount -t efs {{FileSystemId}}:/ efs;
        sudo chmod 777 efs;

Common cluster configurations

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:

GPU single node: use Ray on a single large GPU instance.

max_workers: 0
head_node:
    InstanceType: p2.8xlarge

Docker: Specify docker image. This executes all commands on all nodes in the docker container, and opens all the necessary ports to support the Ray cluster. It will also automatically install Docker if Docker is not installed. This currently does not have GPU support.

docker:
    image: tensorflow/tensorflow:1.5.0-py3
    container_name: ray_docker

Mixed GPU and CPU nodes: for RL applications that require proportionally more CPU than GPU resources, you can use additional CPU workers with a GPU head node.

max_workers: 10
head_node:
    InstanceType: p2.8xlarge
worker_nodes:
    InstanceType: m4.16xlarge

Autoscaling CPU cluster: use a small head node and have Ray auto-scale workers as needed. This can be a cost-efficient configuration for clusters with bursty workloads. You can also request spot workers for additional cost savings.

min_workers: 0
max_workers: 10
head_node:
    InstanceType: m4.large
worker_nodes:
    InstanceMarketOptions:
        MarketType: spot
    InstanceType: m4.16xlarge

Autoscaling GPU cluster: similar to the autoscaling CPU cluster, but with GPU worker nodes instead.

min_workers: 0  # NOTE: older Ray versions may need 1+ GPU workers (#2106)
max_workers: 10
head_node:
    InstanceType: m4.large
worker_nodes:
    InstanceMarketOptions:
        MarketType: spot
    InstanceType: p2.xlarge

Questions or Issues?

You can post questions or issues or feedback through the following channels:

  1. ray-dev@googlegroups.com: For discussions about development or any general questions and feedback.
  2. StackOverflow: For questions about how to use Ray.
  3. GitHub Issues: For bug reports and feature requests.