Overview

This python client library provide APIs to handle raycluster from your python application.

Prerequisites

It is assumed that your k8s cluster in already setup. Your kubectl configuration is expected to be in ~/.kube/config if you are running the code directly from you terminal.

It is also expected that the kuberay operator is installed. Installation instructions are here.

Usage

There are multiple levels of using the api with increasing levels of complexity.

director

This is the easiest form of using the api to create rayclusters with predefined cluster sizes

my_kuberay_api = kuberay_cluster_api.RayClusterApi()

my_cluster_director = kuberay_cluster_builder.Director()

cluster0 = my_cluster_director.build_small_cluster(name="new-cluster0")

if cluster0:
    my_kuberay_api.create_ray_cluster(body=cluster0)

the director create the custer definition, and the custer_api acts as the http client sending the create (post) request to the k8s api-server

cluster_builder

The builder allows you to build the cluster piece by piece, you are can customize more the values of the cluster definition

cluster1 = (
        my_cluster_builder.build_meta(name="new-cluster1")
        .build_head()
        .build_worker(group_name="workers", replicas=3)
        .get_cluster()
    )

if not my_cluster_builder.succeeded:
    return

my_kuberay_api.create_ray_cluster(body=cluster1)

cluster_utils

the cluster_utils gives you even more options to modify your cluster definition, add/remove worker groups, change replicas in a worker group, duplicate a worker group, etc.

my_Cluster_utils = kuberay_cluster_utils.ClusterUtils()

cluster_to_patch, succeeded = my_Cluster_utils.update_worker_group_replicas(
        cluster2, group_name="workers", max_replicas=4, min_replicas=1, replicas=2
    )

if succeeded:
        my_kuberay_api.patch_ray_cluster(
            name=cluster_to_patch["metadata"]["name"], ray_patch=cluster_to_patch
        )

cluster_api

Finally, the cluster_api is the one you always use to implement your cluster change in k8s. You can use it with raw JSON if you wish. The director/cluster_builder/cluster_utils are just tools to shield the user from using raw JSON.

Code Organization

clients/
└── python-client
    ├── LICENSE
    ├── README.md
    ├── examples
    │   ├── complete-example.py
    │   ├── use-builder.py
    │   ├── use-director.py
    │   ├── use-raw-config_map_with-api.py
    │   ├── use-raw-with-api.py
    │   └── use-utils.py
    ├── pyproject.toml
    ├── python_client
    │   ├── __init__.py
    │   ├── constants.py
    │   ├── kuberay_cluster_api.py
    │   └── utils
    │       ├── __init__.py
    │       ├── kuberay_cluster_builder.py
    │       └── kuberay_cluster_utils.py
    ├── python_client_test
    │   ├── README.md
    │   ├── test_api.py
    │   ├── test_director.py
    │   └── test_utils.py
    └── setup.cfg

For developers

make sure you have installed setuptool

pip install -U pip setuptools

run the pip command

from the directory path/to/kuberay/clients/python-client

pip install -e .

to uninstall the module run

pip uninstall python-client

For testing run

python -m unittest discover 'path/to/kuberay/clients/python-client/python_client_test/'