Ray Cluster Interaction๏
The CodeFlare SDK offers multiple ways to interact with Ray Clusters including the below methods.
get_cluster()๏
The get_cluster() function is used to initialise a Cluster
object from a pre-existing Ray Cluster/AppWrapper. Below is an example
of itโs usage:
from codeflare_sdk import get_cluster
cluster = get_cluster(cluster_name="raytest", namespace="example", is_appwrapper=False, write_to_file=False)
-> output: Yaml resources loaded for raytest
cluster.status()
-> output:
๐ CodeFlare Cluster Status ๐
โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ
โ Name โ
โ raytest Active โ
โ
โ โ
โ URI: ray://raytest-head-svc.example.svc:10001 โ
โ โ
โ Dashboard๐ โ
โ โ
โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
(<CodeFlareClusterStatus.READY: 1>, True)
cluster.down()
cluster.up() # This function will create an exact copy of the retrieved Ray Cluster only if the Ray Cluster has been previously deleted.
These are the parameters the
get_cluster() function accepts:cluster_name: str # Required -> The name of the Ray Cluster.namespace: str # Default: "default" -> The namespace of the Ray Cluster.is_appwrapper: bool # Default: False -> When set toTrue the function will attempt to retrieve an AppWrapper instead of a Ray Cluster.write_to_file: bool # Default: False -> When set to True the Ray Cluster/AppWrapper will be written to a file similar to how it is done in ClusterConfiguration.list_all_queued()๏
The
list_all_queued() function returns (and prints by default) a list of all currently queued-up Ray Clusters in a given namespace.It accepts the following parameters:
namespace: str # Required -> The namespace you want to retrieve the list from.print_to_console: bool # Default: True -> Allows the user to print the list to their console.appwrapper: bool # Default: False -> When set to True allows the user to list queued AppWrappers.list_all_clusters()๏
The
list_all_clusters() function will return a list of detailed descriptions of Ray Clusters to the console by default.It accepts the following parameters:
namespace: str # Required -> The namespace you want to retrieve the list from.print_to_console: bool # Default: True -> A boolean that allows the user to print the list to their console.Note
The following methods require a Cluster object to be
initialized. See Ray Cluster Configuration
cluster.up()๏
The
cluster.up() function creates a Ray Cluster in the given namespace.cluster.apply()๏
The
cluster.apply() function applies a Ray Cluster in the given namespace. If the cluster already exists, it is updated.If it does not exist it is created.
cluster.down()๏
The
cluster.down() function deletes the Ray Cluster in the given namespace.cluster.status()๏
The
cluster.status() function prints out the status of the Ray Clusterโs state with a link to the Ray Dashboard.cluster.details()๏
The
cluster.details() function prints out a detailed description of the Ray Clusterโs status, worker resources and a link to the Ray Dashboard.cluster.wait_ready()๏
The
cluster.wait_ready() function waits for the requested cluster to be ready, up to an optional timeout and checks every 5 seconds.It accepts the following parameters:
timeout: Optional[int] # Default: None -> Allows the user to define a timeout for the wait_ready() function.dashboard_check: bool # Default: True -> If enabled the wait_ready() function will wait until the Ray Dashboard is ready too.