FlyteOperator Example#

This tutorial will walk you through constructing an Airflow DAG using the FlyteOperator.


The provider source code can be found in the airflow-provider-flyte repository.

Watch a demo of this provider below! It demonstrates an example of pulling NYC taxi data from S3, uploading it to CrateDB, and building an XGBoost model. The extract and load steps are handled by Airflow and the machine learning task is offloaded to Flyte using the Flyte Airflow Provider.

The Airflow DAG demonstrated in the video is available here.

Environment Setup#


Astronomer’s CLI is the fastest and easiest way to set up Airflow.

Download the Astro CLI and then initialize a new astro project.

mkdir flyte-astro-project
cd flyte-astro-project
astro dev init

The directory structure of flyte-astro-project would look as follows:

├── Dockerfile
├── airflow_settings.yaml
├── dags
│   ├──
│   └──
├── include
├── packages.txt
├── plugins
├── requirements.txt
└── tests
    └── dags


The getting started tutorial should help you with setting up Flyte.

Create an Airflow Connection#

Hit http://localhost:8080/, give the credentials (default username and password: admin), navigate to Connections and create a Flyte connection.

Airflow Connection

Click Save in the end.


Use external IP as the Flyte Host. You can deploy Flyte on an on-prem machine or on cloud.

Register Flyte Code#

At the Flyte end, we’ll train an XGBoost model on Pima Indians Diabetes Dataset. The source code is available here.

Register the example on the Flyte backend before proceeding with running the Airflow DAG.

  • Configure flytectl config at ~/.flyte/config.yaml to point to the relevant endpoint.

      endpoint: dns:///<your-endpoint>
      insecure: true # Set to false to enable TLS/SSL connection.
      authType: Pkce # authType: Pkce # if using authentication or just drop this.
  • Clone the flytesnacks repository and go into the cookbook/case_studies directory.

    git clone
    cd flytesnacks/cookbook/case_studies
  • Serialize the workflow.

    pyflyte --pkgs ml_training.pima_diabetes package --image "" -f
  • Register the workflow.

    flytectl register files --project flytesnacks --domain development --archive flyte-package.tgz --version v1

Create an Airflow DAG#

Place the following file under the dags/ directory. You can name it

from datetime import datetime, timedelta

from airflow import DAG

from flyte_provider.operators.flyte import FlyteOperator
from flyte_provider.sensors.flyte import FlyteSensor

with DAG(
   start_date=datetime(2022, 1, 1),
) as dag:
   task = FlyteOperator(
       inputs={"test_split_ratio": 0.66, "seed": 5},

   sensor = FlyteSensor(

   task >> sensor

Also, add airflow-provider-flyte package to requirements.txt under the astro project.

Run the Workflow#

  • Run the command astro dev start.

  • Trigger the Airflow DAG by clicking the “Trigger DAG” button on the Airflow UI.

  • Verify if Flyte execution got triggered on the FlyteConsole by going to http://<path> and navigating to the workflow page.

That’s about it! With the Flyte Airflow Provider, you get to reap the benefits of Flyte, a full-fledged machine learning orchestration service, as an extension to Airflow. For more example DAGs, refer to this folder.

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