Alkanso/python client

clients/python-client/.gitignore

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+
+
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+
+# Distribution / packaging
+bin/
+build/
+develop-eggs/
+dist/
+eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+.tox/
+htmlcov
+.coverage
+.cache
+nosetests.xml
+coverage.xml

clients/python-client/README.md

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+# 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.](https://github.com/ray-project/kuberay#quick-start)
+
+## 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
+
+```python
+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
+
+```python
+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.
+
+```python
+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
+    ├── README.md
+    ├── examples
+    │   ├── complete-example.py
+    │   ├── use-builder.py
+    │   ├── use-director.py
+    │   ├── use-raw-with-api.py
+    │   └── use-utils.py
+    ├── python_client
+    │   ├── LICENSE
+    │   ├── __init__.py
+    │   ├── constants.py
+    │   ├── kuberay_cluster_api.py
+    │   ├── pyproject.toml
+    │   ├── setup.cfg
+    │   └── utils
+    │       ├── __init__.py
+    │       ├── kuberay_cluster_builder.py
+    │       └── kuberay_cluster_utils.py
+    └── python_client_test
+        ├── README.md
+        ├── test_director.py
+        └── test_utils.py
+
+## 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/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_test/'`

clients/python-client/examples/complete-example.py

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+import sys
+import os
+from os import path
+
+
+""" 
+in case you are working directly with the source, and don't wish to 
+install the module with pip install, you can directly import the packages by uncommenting the following code.
+"""
+
+"""
+sys.path.append(path.dirname(path.dirname(path.abspath(__file__))))
+
+current_dir = os.path.dirname(os.path.abspath(__file__))
+parent_dir = os.path.abspath(os.path.join(current_dir, os.pardir))
+sibling_dirs = [
+    d for d in os.listdir(parent_dir) if os.path.isdir(os.path.join(parent_dir, d))
+]
+for sibling_dir in sibling_dirs:
+    sys.path.append(os.path.join(parent_dir, sibling_dir))
+"""
+
+import kuberay_cluster_api
+
+from utils import kuberay_cluster_utils, kuberay_cluster_builder
+
+
+def main():
+
+    print("starting cluster handler...")
+    my_kuberay_api = kuberay_cluster_api.RayClusterApi() # this is the main api object
+
+    my_cluster_director = kuberay_cluster_builder.Director() # this is the director object, to create a cluster with a single line of code
+
+    my_cluster_builder = kuberay_cluster_builder.ClusterBuilder() # this is the builder object, to create a cluster with a more granular control
+
+    my_Cluster_utils = kuberay_cluster_utils.ClusterUtils() # this is the utils object, to perform operations on a cluster
+
+    cluster0 = my_cluster_director.build_small_cluster(name="new-cluster0", labels={'demo-cluster':'yes'}) # this is the cluster object, it is a dict
+
+    if cluster0:
+        my_kuberay_api.create_ray_cluster(body=cluster0) # this is the api call to create the cluster0 in k8s
+
+    cluster1 = (
+        my_cluster_builder.build_meta(name="new-cluster1",labels={'demo-cluster':'yes'})
+        .build_head()
+        .build_worker(group_name="workers")
+        .get_cluster()
+    )
+
+    if not my_cluster_builder.succeeded:
+        print("error building the cluster, aborting...")
+        return
+    my_kuberay_api.create_ray_cluster(body=cluster1) # this is the api call to create the cluster1 in k8s
+
+    cluster2 = (
+        my_cluster_builder.build_meta(name="new-cluster2", labels={'demo-cluster':'yes'})
+        .build_head()
+        .build_worker(group_name="workers")
+        .get_cluster()
+    )
+
+    if not my_cluster_builder.succeeded:
+        print("error building the cluster, aborting...")
+        return
+
+    my_kuberay_api.create_ray_cluster(body=cluster2) # this is the api call to create the cluster2 in k8s
+
+    # modifying the number of replicas in the workergroup
+    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:
+        print(
+            "trying to patch raycluster = {}".format(
+                cluster_to_patch["metadata"]["name"]
+            )
+        )
+        my_kuberay_api.patch_ray_cluster(
+            name=cluster_to_patch["metadata"]["name"], ray_patch=cluster_to_patch
+        ) # this is the api call to patch the cluster2 in k8s
+
+    cluster_to_patch, succeeded = my_Cluster_utils.duplicate_worker_group(
+        cluster1, group_name="workers", new_group_name="new-workers"
+    ) # this is the call to duplicate the worker group in cluster1
+    if succeeded:
+        print(
+            "trying to patch raycluster = {}".format(
+                cluster_to_patch["metadata"]["name"]
+            )
+        )
+        my_kuberay_api.patch_ray_cluster(
+            name=cluster_to_patch["metadata"]["name"], ray_patch=cluster_to_patch
+        ) # this is the api call to patch the cluster1 in k8s
+
+    kube_ray_list = my_kuberay_api.list_ray_clusters(k8s_namespace="default", label_selector='demo-cluster=yes') # this is the api call to list all the clusters in k8s
+    if "items" in kube_ray_list:
+        line = "-" * 72
+        print(line)
+        print("{:<63s}{:>2s}".format("Name", "Namespace"))
+        print(line)
+        for cluster in kube_ray_list["items"]:
+            print(
+                "{:<63s}{:>2s}".format(
+                    cluster["metadata"]["name"],
+                    cluster["metadata"]["namespace"],
+                )
+            )
+    print(line)
+
+    if "items" in kube_ray_list:
+        for cluster in kube_ray_list["items"]:
+            print("deleting raycluster = {}".format(cluster["metadata"]["name"]))
+            my_kuberay_api.delete_ray_cluster(
+                name=cluster["metadata"]["name"],
+                k8s_namespace=cluster["metadata"]["namespace"],
+            ) # this is the api call to delete the cluster in k8s
+
+
+if __name__ == "__main__":
+    main()

clients/python-client/examples/use-builder.py

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+import sys
+import os
+from os import path
+import json
+
+
+""" 
+in case you are working directly with the source, and don't wish to 
+install the module with pip install, you can directly import the packages by uncommenting the following code.
+"""
+
+"""
+sys.path.append(path.dirname(path.dirname(path.abspath(__file__))))
+
+current_dir = os.path.dirname(os.path.abspath(__file__))
+parent_dir = os.path.abspath(os.path.join(current_dir, os.pardir))
+sibling_dirs = [
+    d for d in os.listdir(parent_dir) if os.path.isdir(os.path.join(parent_dir, d))
+]
+for sibling_dir in sibling_dirs:
+    sys.path.append(os.path.join(parent_dir, sibling_dir))
+"""
+
+import kuberay_cluster_api
+
+from utils import kuberay_cluster_builder
+
+
+def main():
+
+    print("starting cluster handler...")
+    my_kuberay_api = kuberay_cluster_api.RayClusterApi()
+
+    my_cluster_builder = kuberay_cluster_builder.ClusterBuilder()
+
+    cluster1 = (
+        my_cluster_builder.build_meta(name="new-cluster1", labels={'demo-cluster':'yes'})
+        .build_head()
+        .build_worker(group_name="workers")
+        .get_cluster()
+    )
+
+    if not my_cluster_builder.succeeded:
+        print("error building the cluster, aborting...")
+        return
+
+    print("creating raycluster = {}".format(cluster1["metadata"]["name"]))
+    my_kuberay_api.create_ray_cluster(body=cluster1)
+
+    # the rest of the code is simply to list and cleanup the created cluster
+    kube_ray_list = my_kuberay_api.list_ray_clusters(k8s_namespace="default", label_selector='demo-cluster=yes')
+    if "items" in kube_ray_list:
+        line = "-" * 72
+        print(line)
+        print("{:<63s}{:>2s}".format("Name", "Namespace"))
+        print(line)
+        for cluster in kube_ray_list["items"]:
+            print(
+                "{:<63s}{:>2s}".format(
+                    cluster["metadata"]["name"],
+                    cluster["metadata"]["namespace"],
+                )
+            )
+    print(line)
+
+    if "items" in kube_ray_list:
+        for cluster in kube_ray_list["items"]:
+            print("deleting raycluster = {}".format(cluster["metadata"]["name"]))
+            my_kuberay_api.delete_ray_cluster(
+                name=cluster["metadata"]["name"],
+                k8s_namespace=cluster["metadata"]["namespace"],
+            )
+
+
+if __name__ == "__main__":
+    main()