Ray

🧪 Experimental Version

This provider is an experimental alpha containing necessary components to orchestrate and schedule Ray tasks using Airflow. It is actively maintained and being developed to bring production-ready workflows to Ray using Airflow. Thie release contains everything needed to begin building these workflows using the Airlfow taskflow API.

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Current Release: 0.2.1

Requirements

Visit the Ray Project page for more info on Ray.

⚠️ The server version and client version (build) of Ray MUST be

the same.

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- Python Version >= 3.7
- Airflow Version >= 2.0.0
- Ray Version == 1.3.0
- Filelock >= 3.0.0

Modules

• Ray XCom Backend: Custom XCom backend

to assist operators in moving data between tasks using the Ray API with its internal Plasma store, thereby allowing for in-memory distributed processing and handling of large data objects.

• Ray Hook: Extension of Http hook

that uses the Ray client to provide connections to the Ray Server.

• Ray Decorator: Task decorator

to be used with the task flow API, combining wrapping the existing airflow @task decorate with ray.remote functionality, thereby executing each task on the ray cluster.

Configuration and Usage

1. Add the provider package wheel file to the root directory of your Airflow project.

2. In your Airflow Dockerfile, you will need to add an environment variable to

specify your custom backend, along with the provider wheel install. Add the following:

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FROM quay.io/astronomer/ap-airflow:2.0.2-1-buster-onbuild
USER root
RUN pip uninstall astronomer-airflow-version-check -y
USER astro

> Check ap-airflow version, if unsure, change to `ap-airflow:latest-onbuild`

3. We are using a Ray 1.3.0 and python version 3.7. To get a bleeding edge

version of Ray, you can to follow this format to build the wheel url in your requirements.txt file:

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pip install airflow-provider-ray

4. Configure Ray Locally. To run ray locally, you'll need a minimum 6GB of free

memory.To start, in your environment with ray installed, run:

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(venv)$ ray start --num-cpus=8 --object-store-memory=7000000000 --head

If you have extra resources, you can bump the memory up.

You should now be able to open the ray dashboard at [http://127.0.0.1:8265/](http://127.0.0.1:8265/).

6. Start your Airflow environment and open the UI.

7. In the Airflow UI, add an Airflow Pool with the following:

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   Pool (name): ray_worker_pool
   Slots: 25

8. In the Airflow UI, add an Airflow Connection with the following:

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   Conn Id: ray_cluster_connection
   Conn Type: HTTP
   Host: Cluster IP Address, with basic Auth params if needed
   Port: 10001

9. Using the taskflow API, your airflow task should now use the

@ray_task decorator for any ray task and add the ray_conn_id, parameter as task_args, like:

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from ray_provider.decorators import ray_task

default_args = {"owner": "airflow"}
task_args = {"ray_conn_id": "ray_cluster_connection"}
.
.
.
@dag(
    default_args=default_args,
    .
    .
)
def ray_example_dag():

    @ray_task(**task_args)
    def sum_cols(df: pd.DataFrame) -> pd.DataFrame:
        return pd.DataFrame(df.sum()).T

Project Contributors and Maintainers

This project is built in collaboration between Astronomer and Anyscale, with active contributions from:

• Pete DeJoy
• Daniel Imberman
• Rob Deeb
• Richard Liaw
• Charles Greer
• Will Drevo

This project is formatted via black:

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pip install black
black .

Connections

TBD - [Info on building a connection to Ray]