updates

.github/workflows/docs.yml

@@ -2,7 +2,7 @@ name: Deploy Sphinx documentation to Pages
 
 on:
   push:
-    branches: [main]
+    branches: ["main","documentation"]
 
 permissions:
   contents: read

docs/getting_started/code_samples.rst

@@ -17,66 +17,6 @@ If you already have a Ray cluster set up, you can use the ``SubmitRayJob`` opera
 
 In the below example(``ray_taskflow_example.py``), the ``@task.ray`` decorator is used to define a task that will be executed on the Ray cluster.
 
-.. code-block:: python
-
-   from airflow.decorators import dag, task as airflow_task
-   from datetime import datetime, timedelta
-   from ray_provider.decorators.ray import task
-
-   RAY_TASK_CONFIG = {
-       "conn_id": "ray_conn",
-       "runtime_env": {
-           "working_dir": "/usr/local/airflow/dags/ray_scripts",
-           "pip": ["numpy"],
-       },
-       "num_cpus": 1,
-       "num_gpus": 0,
-       "memory": 0,
-       "poll_interval": 5,
-   }
-
-
-   @dag(
-       dag_id="ray_taskflow_example",
-       start_date=datetime(2023, 1, 1),
-       schedule_interval=timedelta(days=1),
-       catchup=False,
-       default_args={
-           "owner": "airflow",
-           "retries": 1,
-           "retry_delay": timedelta(minutes=5),
-       },
-       tags=["ray", "example"],
-   )
-   def ray_taskflow_dag():
-
-       @airflow_task
-       def generate_data():
-           import numpy as np
-
-           return np.random.rand(100).tolist()
-
-       @task.ray(config=RAY_TASK_CONFIG)
-       def process_data_with_ray(data):
-           import ray
-           import numpy as np
-
-           @ray.remote
-           def square(x):
-               return x**2
-
-           ray.init()
-           data = np.array(data)
-           futures = [square.remote(x) for x in data]
-           results = ray.get(futures)
-           mean = np.mean(results)
-           print(f"Mean of squared values: {mean}")
-           return mean
-
-       data = generate_data()
-       process_data_with_ray(data)
-
-
-   ray_example_dag = ray_taskflow_dag()
+.. literalinclude:: ../../example_dags/ray_taskflow_example.py
 
 Remember to adjust file paths, connection IDs, and other specifics according to your setup.