Launching Cloud Clusters

This section provides instructions for configuring the Ray Cluster Launcher to use with AWS/Azure/GCP, an existing Kubernetes cluster, or on a private cluster of host machines.

See this blog post for a step by step guide to using the Ray Cluster Launcher.

AWS (EC2)

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 launch your cluster. 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

Tip

For the AWS node configuration, you can set "ImageId: latest_dlami" to automatically use the newest Deep Learning AMI for your region. For example, head_node: {InstanceType: c5.xlarge, ImageId: latest_dlami}.

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;

Azure

First, install the Azure CLI (pip install azure-cli azure-core) then login using (az login).

Set the subscription to use from the command line (az account set -s <subscription_id>) or by modifying the provider section of the config provided e.g: ray/python/ray/autoscaler/azure/example-full.yaml

Once the Azure CLI is configured to manage resources on your Azure account, you should be ready to launch your cluster. The provided ray/python/ray/autoscaler/azure/example-full.yaml cluster config file will create a small cluster with a Standard DS2v3 head node (on-demand) configured to autoscale up to two Standard DS2v3 spot workers. Note that you’ll need to fill in your resource group and location 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/azure/example-full.yaml

# Get a remote screen on the head node.
$ ray attach ray/python/ray/autoscaler/azure/example-full.yaml
# test ray setup
# enable conda environment
$ exec bash -l
$ conda activate py37_tensorflow
$ python -c 'import ray; ray.init()'
$ exit
# Tear down the cluster.
$ ray down ray/python/ray/autoscaler/azure/example-full.yaml

Azure Portal

Alternatively, you can deploy a cluster using Azure portal directly. Please note that autoscaling is done using Azure VM Scale Sets and not through the Ray autoscaler. This will deploy Azure Data Science VMs (DSVM) for both the head node and the auto-scalable cluster managed by Azure Virtual Machine Scale Sets. The head node conveniently exposes both SSH as well as JupyterLab.

Deploy to Azure

Once the template is successfully deployed the deployment output page provides the ssh command to connect and the link to the JupyterHub on the head node (username/password as specified on the template input). Use the following code in a Jupyter notebook to connect to the Ray cluster.

import ray
ray.init(address='auto')

Note that on each node the azure-init.sh script is executed and performs the following actions:

  1. Activates one of the conda environments available on DSVM

  2. Installs Ray and any other user-specified dependencies

  3. Sets up a systemd task (/lib/systemd/system/ray.service) to start Ray in head or worker mode

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 launch your cluster. 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 cluster launcher 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 launch your cluster. 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. It’s also possible to deploy service and ingress resources for each scaled worker pod. An example is provided in ray/python/ray/autoscaler/kubernetes/example-ingress.yaml.

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

Tip

This section describes the easiest way to launch a Ray cluster on Kubernetes. See this document for advanced usage of Kubernetes with Ray.

Tip

If you would like to use Ray Tune in your Kubernetes cluster, have a look at this short guide to make it work.

Staroid

First, install the staroid client package (pip install staroid) then get access token. Once you have an access token, you should be ready to launch your cluster.

The provided ray/python/ray/autoscaler/staroid/example-full.yaml cluster config file will create a cluster with

  • a Jupyter notebook running on head node. (Staroid management console -> Kubernetes -> <your_ske_name> -> <ray_cluster_name> -> Click “notebook”)

  • a shared nfs volume across all ray nodes mounted under /nfs directory.

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

# Configure access token through environment variable.
$ export STAROID_ACCESS_TOKEN=<your access token>

# Create or update the cluster. When the command finishes,
# you can attach a screen to the head node.
$ ray up ray/python/ray/autoscaler/staroid/example-full.yaml

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

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

Local On Premise Cluster (List of nodes)

You would use this mode if you want to run distributed Ray applications on some local nodes available on premise.

The most preferable way to run a Ray cluster on a private cluster of hosts is via the Ray Cluster Launcher.

There are two ways of running private clusters:

  • Manually managed, i.e., the user explicitly specifies the head and worker ips.

  • Automatically managed, i.e., the user only specifies a coordinator address to a coordinating server that automatically coordinates its head and worker ips.

Tip

To avoid getting the password prompt when running private clusters make sure to setup your ssh keys on the private cluster as follows:

$ ssh-keygen
$ cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys

Manually Managed

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

Automatically Managed

Start by launching the coordinator server that will manage all the on prem clusters. This server also makes sure to isolate the resources between different users. The script for running the coordinator server is ray/python/ray/autoscaler/local/coordinator_server.py. To launch the coordinator server run:

$ python coordinator_server.py --ips <list_of_node_ips> --port <PORT>

where list_of_node_ips is a comma separated list of all the available nodes on the private cluster. For example, 160.24.42.48,160.24.42.49,... and <PORT> is the port that the coordinator server will listen on. After running the coordinator server it will print the address of the coordinator server. For example:

>> INFO:ray.autoscaler.local.coordinator_server:Running on prem coordinator server
      on address <Host:PORT>

Next, the user only specifies the <Host:PORT> printed above in the coordinator_address entry instead of specific head/worker ips in the provided ray/python/ray/autoscaler/local/example-full.yaml.

Now we cant 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!

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.

Configuring your Cluster

The Ray Cluster Launcher requires a cluster configuration file, which specifies some important details about the cluster. At a minimum, we need to specify:

  • the name of your cluster,

  • the number of workers in the cluster

  • the cloud provider

  • any setup commands that should run on the node upon launch.

Here is an example cluster configuration file:

# A unique identifier for this cluster.
cluster_name: basic-ray

# The maximum number of workers nodes to launch in addition to the head
# node.
max_workers: 0 # this means zero workers

# Cloud-provider specific configuration.
provider:
   type: aws
   region: us-west-2
   availability_zone: us-west-2a

# How Ray will authenticate with newly launched nodes.
auth:
   ssh_user: ubuntu

setup_commands:
  - pip install ray[all]
  # The following line demonstrate that you can specify arbitrary
  # startup scripts on the cluster.
  - touch /tmp/some_file.txt

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.

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.

Setup Commands

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.

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.

docker:
    image: rayproject/ray:0.8.7
    container_name: ray_docker

If Docker is not installed, add the following commands to initialization_commands to install it.

initialization_commands:
- curl -fsSL https://get.docker.com -o get-docker.sh
- sudo sh get-docker.sh
- sudo usermod -aG docker $USER
- sudo systemctl restart docker -f

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