Launching Cloud Clusters¶
This section provides instructions for configuring the Ray Cluster Launcher to use with various cloud providers or on a private cluster of host machines.
See this blog post for a step by step guide to using the Ray Cluster Launcher.
To learn about deploying Ray on an existing Kubernetes cluster, refer to the guide here.
Ray with cloud providers¶
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
$ # Try running a Ray program with 'ray.init(address="auto")'.
# Tear down the cluster.
$ ray down ray/python/ray/autoscaler/aws/example-full.yaml
AWS Node Provider Maintainers (GitHub handles): pdames, Zyiqin-Miranda, DmitriGekhtman, wuisawesome
See AWS Configurations for recipes on customizing AWS clusters.
First, install the Azure CLI (pip install azure-cli azure-identity) 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
$ python -c 'import ray; ray.init(address="auto")'
$ 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.
Once the template is successfully deployed the deployment Outputs 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 (using the conda environment specified in the template input, py38_tensorflow by default) 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:
Activates one of the conda environments available on DSVM
Installs Ray and any other user-specified dependencies
Sets up a systemd task (
/lib/systemd/system/ray.service) to start Ray in head or worker mode
Azure Node Provider Maintainers (GitHub handles): gramhagen, eisber, ijrsvt .. note:: The Azure Node Provider is community-maintained. It is maintained by its authors, not the Ray team.
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
$ # Try running a Ray program with 'ray.init(address="auto")'.
# Tear down the cluster.
$ ray down ray/python/ray/autoscaler/gcp/example-full.yaml
GCP Node Provider Maintainers (GitHub handles): wuisawesome, DmitriGekhtman, ijrsvt
First, install the aliyun client package (pip install aliyun-python-sdk-core aliyun-python-sdk-ecs). Obtain the AccessKey pair of the Aliyun account as described in the docs and grant AliyunECSFullAccess/AliyunVPCFullAccess permissions to the RAM user. Finally, set the AccessKey pair in your cluster config file.
Once the above is done, you should be ready to launch your cluster. The provided aliyun/example-full.yaml cluster config file will create a small cluster with an ecs.n4.large head node (on-demand) configured to autoscale up to two ecs.n4.2xlarge nodes.
Make sure your account balance is not less than 100 RMB, otherwise you will receive a InvalidAccountStatus.NotEnoughBalance error.
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/aliyun/example-full.yaml
# Get a remote screen on the head node.
$ ray attach ray/python/ray/autoscaler/aliyun/example-full.yaml
$ # Try running a Ray program with 'ray.init(address="auto")'.
# Tear down the cluster.
$ ray down ray/python/ray/autoscaler/aliyun/example-full.yaml
Aliyun Node Provider Maintainers (GitHub handles): zhuangzhuang131419, chenk008
Note
The Aliyun Node Provider is community-maintained. It is maintained by its authors, not the Ray team.
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.
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
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
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 can 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
Manual Ray Cluster Setup¶
The most preferable way to run a Ray cluster is via the Ray Cluster Launcher. However, it is also possible to start a Ray cluster by hand.
This section assumes that you have a list of machines and that the nodes in the cluster can communicate with each other. It also assumes that Ray is installed on each machine. To install Ray, follow the installation instructions.
Starting Ray on each machine¶
On the head node (just choose one node to be the head node), run the following.
If the --port argument is omitted, Ray will choose port 6379, falling back to a
random port.
$ ray start --head --port=6379
...
Next steps
To connect to this Ray runtime from another node, run
ray start --address='<ip address>:6379'
If connection fails, check your firewall settings and network configuration.
The command will print out the address of the Ray GCS server that was started (the local node IP address plus the port number you specified).
Note
If you already has remote Redis instances, you can specify environment variable
RAY_REDIS_ADDRESS=ip1:port1,ip2:port2... to use them. The first one is
primary and rest are shards.
Then on each of the other nodes, run the following. Make sure to replace
<address> with the value printed by the command on the head node (it
should look something like 123.45.67.89:6379).
Note that if your compute nodes are on their own subnetwork with Network Address Translation, to connect from a regular machine outside that subnetwork, the command printed by the head node will not work. You need to find the address that will reach the head node from the second machine. If the head node has a domain address like compute04.berkeley.edu, you can simply use that in place of an IP address and rely on the DNS.
$ ray start --address=<address>
--------------------
Ray runtime started.
--------------------
To terminate the Ray runtime, run
ray stop
If you wish to specify that a machine has 10 CPUs and 1 GPU, you can do this
with the flags --num-cpus=10 and --num-gpus=1. See the Configuration page for more information.
If you see Unable to connect to GCS at ...,
this means the head node is inaccessible at the given --address (because, for
example, the head node is not actually running, a different version of Ray is
running at the specified address, the specified address is wrong, or there are
firewall settings preventing access).
If you see Ray runtime started., then the node successfully connected to
the head node at the --address. You should now be able to connect to the
cluster with ray.init(address='auto').
If connection fails, check your firewall settings and network configuration.
If the connection fails, to check whether each port can be reached from a node,
you can use a tool such as nmap or nc.
$ nmap -sV --reason -p $PORT $HEAD_ADDRESS
Nmap scan report for compute04.berkeley.edu (123.456.78.910)
Host is up, received echo-reply ttl 60 (0.00087s latency).
rDNS record for 123.456.78.910: compute04.berkeley.edu
PORT STATE SERVICE REASON VERSION
6379/tcp open redis? syn-ack
Service detection performed. Please report any incorrect results at https://nmap.org/submit/ .
$ nc -vv -z $HEAD_ADDRESS $PORT
Connection to compute04.berkeley.edu 6379 port [tcp/*] succeeded!
If the node cannot access that port at that IP address, you might see
$ nmap -sV --reason -p $PORT $HEAD_ADDRESS
Nmap scan report for compute04.berkeley.edu (123.456.78.910)
Host is up (0.0011s latency).
rDNS record for 123.456.78.910: compute04.berkeley.edu
PORT STATE SERVICE REASON VERSION
6379/tcp closed redis reset ttl 60
Service detection performed. Please report any incorrect results at https://nmap.org/submit/ .
$ nc -vv -z $HEAD_ADDRESS $PORT
nc: connect to compute04.berkeley.edu port 6379 (tcp) failed: Connection refused
Stopping Ray¶
When you want to stop the Ray processes, run ray stop on each node.
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.
Running a Ray program on the Ray cluster¶
To run a distributed Ray program, you’ll need to execute your program on the same machine as one of the nodes.
Within your program/script, you must call ray.init and add the address parameter to ray.init (like ray.init(address=...)). This causes your script to connect to the existing Ray runtime on the cluster. For example:
ray.init(address="auto")
You need to add the ray.address parameter to your command line (like -Dray.address=...).
To connect your program to the Ray cluster, run it like this:
java -classpath <classpath> \ -Dray.address=<address> \ <classname> <args>
Note
Specifying auto as the address hasn’t been implemented in Java yet. You need to provide the actual address. You can find the address of the server from the output of the ray up command.
You need to add the RAY_ADDRESS env var to your command line (like RAY_ADDRESS=...).
To connect your program to the Ray cluster, run it like this:
RAY_ADDRESS=<address> ./<binary> <args>
Note
Specifying auto as the address hasn’t been implemented in C++ yet. You need to provide the actual address. You can find the address of the server from the output of the ray up command.
Note
A common mistake is setting the address to be a cluster node while running the script on your laptop. This will not work because the script needs to be started/executed on one of the Ray nodes.
To verify that the correct number of nodes have joined the cluster, you can run the following.
import time
@ray.remote
def f():
time.sleep(0.01)
return ray._private.services.get_node_ip_address()
# Get a list of the IP addresses of the nodes that have joined the cluster.
set(ray.get([f.remote() for _ in range(1000)]))
What’s Next?¶
Now that you have a working understanding of the cluster launcher, check out:
Ray Clusters Quick Start: A end-to-end demo to run an application that autoscales.
Cluster YAML Configuration Options: A complete reference of how to configure your Ray cluster.
Cluster Launcher Commands: A short user guide to the various cluster launcher commands.
Questions or Issues?¶
You can post questions or issues or feedback through the following channels:
Discussion Board: For questions about Ray usage or feature requests.
GitHub Issues: For bug reports.
Ray Slack: For getting in touch with Ray maintainers.
StackOverflow: Use the [ray] tag questions about Ray.