Ray Cluster Configuration
To create Ray Clusters using the CodeFlare SDK a cluster configuration needs to be created first. This is what a typical cluster configuration would look like; Note: The values for CPU and Memory are at the minimum requirements for creating the Ray Cluster.
from codeflare_sdk import Cluster, ClusterConfiguration
cluster = Cluster(ClusterConfiguration(
name='ray-example', # Mandatory Field
namespace='default', # Default None
head_cpu_requests=1, # Default 2
head_cpu_limits=1, # Default 2
head_memory_requests=1, # Default 8
head_memory_limits=1, # Default 8
head_extended_resource_requests={'nvidia.com/gpu':0}, # Default 0
worker_extended_resource_requests={'nvidia.com/gpu':0}, # Default 0
num_workers=1, # Default 1
worker_cpu_requests=1, # Default 1
worker_cpu_limits=1, # Default 1
worker_memory_requests=2, # Default 2
worker_memory_limits=2, # Default 2
# image="", # Optional Field
machine_types=["m5.xlarge", "g4dn.xlarge"],
labels={"exampleLabel": "example", "secondLabel": "example"},
))
Note
quay.io/modh/ray:2.35.0-py39-cu121 is the default image used by the CodeFlare SDK for creating a RayCluster resource. If you have your own Ray image which suits your purposes, specify it in image field to override the default image. If you are using ROCm compatible GPUs you can use quay.io/modh/ray:2.35.0-py39-rocm61. You can also find documentation on building a custom image here.
The labels={"exampleLabel": "example"} parameter can be used to
apply additional labels to the RayCluster resource.
After creating their cluster, a user can call cluster.up() and
cluster.down() to respectively create or remove the Ray Cluster.
Deprecating Parameters
The following parameters of the ClusterConfiguration are being
deprecated.
Deprecated Parameter |
Replaced By |
|---|---|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|