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. .. code:: python 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 labels={"exampleLabel": "example", "secondLabel": "example"}, annotations={"key1":"value1", "key2":"value2"}, volumes=[], # See Custom Volumes/Volume Mounts volume_mounts=[], # See Custom Volumes/Volume Mounts )) .. note:: The default images used by the CodeFlare SDK for creating a RayCluster resource depend on the installed Python version: - For Python 3.11: `quay.io/modh/ray:2.52.1-py311-cu121` If you prefer to use a custom Ray image that better suits your needs, you can specify it in the image field to override the default. If you are using ROCm compatible GPUs you can use `quay.io/modh/ray:2.52.1-py311-rocm62`. You can also find documentation on building a custom image `here `__. Ray Usage Statistics ------------------- By default, Ray usage statistics collection is **disabled** in Ray Clusters created with the Codeflare SDK. This prevents statistics from being captured and sent externally. If you want to enable usage statistics collection, you can simply set the ``enable_usage_stats`` parameter to ``True`` in your cluster configuration: .. code:: python from codeflare_sdk import Cluster, ClusterConfiguration cluster = Cluster(ClusterConfiguration( name='ray-example', namespace='default', enable_usage_stats=True )) This will automatically set the ``RAY_USAGE_STATS_ENABLED`` environment variable to ``1`` for all Ray pods in the cluster. If you do not set this parameter, usage statistics will remain disabled (``RAY_USAGE_STATS_ENABLED=0``). In-tree Autoscaling ------------------- Ray cluster autoscaling allows worker nodes to scale up when workload demand exceeds current capacity, and scale down when workers are idle. Enable it on ``ClusterConfiguration`` with ``enable_autoscaling=True`` and set the worker bounds with ``min_workers`` and ``max_workers``. When autoscaling is enabled, the SDK sets ``enableInTreeAutoscaling`` on the RayCluster and maps ``min_workers`` / ``max_workers`` to ``workerGroupSpecs.minReplicas`` and ``workerGroupSpecs.maxReplicas``. The ``num_workers`` parameter is not used in this mode. .. list-table:: :header-rows: 1 :widths: auto * - Parameter - Description * - ``enable_autoscaling`` - Boolean to enable Ray in-tree autoscaling (default ``False``) * - ``min_workers`` - Minimum number of worker nodes (required when autoscaling is enabled) * - ``max_workers`` - Maximum number of worker nodes (required when autoscaling is enabled; must be >= ``min_workers``) Example configuration: .. code:: python from codeflare_sdk import Cluster, ClusterConfiguration cluster = Cluster(ClusterConfiguration( name='autoscale-ray', namespace='default', enable_autoscaling=True, min_workers=1, max_workers=4, head_cpu_requests=1, head_cpu_limits=1, worker_cpu_requests=1, worker_cpu_limits=1, )) .. note:: Autoscaling is **not supported when Kueue manages Ray clusters in your namespace**. If you set ``local_queue`` or your namespace has a default Kueue LocalQueue, the SDK raises an error when ``enable_autoscaling=True``. Elastic Ray jobs with Kueue are tracked in `RHAIRFE-909 `__. For a step-by-step example that demonstrates scale-up and scale-down, see the ``6_autoscaling.ipynb`` guided demo in ``demo-notebooks/guided-demos/``. 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.apply()`` and ``cluster.down()`` to respectively create or remove the Ray Cluster. Custom Volumes/Volume Mounts ---------------------------- | To add custom Volumes and Volume Mounts to your Ray Cluster you need to create two lists ``volumes`` and ``volume_mounts``. The lists consist of ``V1Volume`` and ``V1VolumeMount`` objects respectively. | Populating these parameters will create Volumes and Volume Mounts for the head and each worker pod. .. code:: python from kubernetes.client import V1Volume, V1VolumeMount, V1EmptyDirVolumeSource, V1ConfigMapVolumeSource, V1KeyToPath, V1SecretVolumeSource # In this example we are using the Config Map, EmptyDir and Secret Volume types volume_mounts_list = [ V1VolumeMount( mount_path="/home/ray/test1", name = "test" ), V1VolumeMount( mount_path = "/home/ray/test2", name = "test2", ), V1VolumeMount( mount_path = "/home/ray/test3", name = "test3", ) ] volumes_list = [ V1Volume( name="test", empty_dir=V1EmptyDirVolumeSource(size_limit="2Gi"), ), V1Volume( name="test2", config_map=V1ConfigMapVolumeSource( name="test-config-map", items=[V1KeyToPath(key="test", path="data.txt")] ) ), V1Volume( name="test3", secret=V1SecretVolumeSource( secret_name="test-secret" ) ) ] | For more information on creating Volumes and Volume Mounts with Python check out the Python Kubernetes docs (`Volumes `__, `Volume Mounts `__). | You can also find further information on Volumes and Volume Mounts by visiting the Kubernetes `documentation `__. GCS Fault Tolerance ------------------ By default, the state of the Ray cluster is transient to the head Pod. Whatever triggers a restart of the head Pod results in losing that state, including Ray Cluster history. To make Ray cluster state persistent you can enable Global Control Service (GCS) fault tolerance with an external Redis storage. To configure GCS fault tolerance you need to set the following parameters: .. list-table:: :header-rows: 1 :widths: auto * - Parameter - Description * - ``enable_gcs_ft`` - Boolean to enable GCS fault tolerance * - ``redis_address`` - Address of the external Redis service, ex: "redis:6379" * - ``redis_password_secret`` - Dictionary with 'name' and 'key' fields specifying the Kubernetes secret for Redis password * - ``external_storage_namespace`` - Custom storage namespace for GCS fault tolerance (by default, KubeRay sets it to the RayCluster's UID) Example configuration: .. code:: python from codeflare_sdk import Cluster, ClusterConfiguration cluster = Cluster(ClusterConfiguration( name='ray-cluster-with-persistence', num_workers=2, enable_gcs_ft=True, redis_address="redis:6379", redis_password_secret={ "name": "redis-password-secret", "key": "password" }, # external_storage_namespace="my-custom-namespace" # Optional: Custom namespace for GCS data in Redis )) .. note:: You need to have a Redis instance deployed in your Kubernetes cluster before using this feature.