Integrations

Flyte is designed to be highly extensible and can be customized in multiple ways.

Note

Want to contribute an example? Check out the Example Contribution Guide.

Flytekit Plugins

Flytekit plugins are simple plugins that can be implemented purely in python, unit tested locally and allow extending Flytekit functionality. These plugins can be anything and for comparison can be thought of like Airflow Operators.

SQL	Execute SQL queries as tasks.
Great Expectations	Validate data with great_expectations.
Papermill	Execute Jupyter Notebooks with papermill.
Pandera	Validate pandas dataframes with pandera.
Modin	Scale pandas workflows with modin.
Dolt	Version your SQL database with dolt.
DBT	Run and test your dbt pipelines in Flyte.
WhyLogs	whylogs: the open standard for data logging.
MLFlow	mlflow: the open standard for model tracking.
ONNX	Convert ML models to ONNX models seamlessly.
DuckDB	Run analytical queries using DuckDB.

Using flytekit plugins

Data is automatically marshalled and unmarshalled in and out of the plugin. Users should mostly implement the PythonTask API defined in Flytekit.

Flytekit Plugins are lazily loaded and can be released independently like libraries. We follow a convention to name the plugin like flytekitplugins-*, where * indicates the package to be integrated into Flytekit. For example flytekitplugins-papermill enables users to author Flytekit tasks using Papermill.

You can find the plugins maintained by the core Flyte team here.

Native Backend Plugins

Native Backend Plugins are the plugins that can be executed without any external service dependencies because the compute is orchestrated by Flyte itself, within its provisioned Kubernetes clusters.

K8s Pods	Execute K8s pods for arbitrary workloads.
K8s Cluster Dask Jobs	Run Dask jobs on a K8s Cluster.
K8s Cluster Spark Jobs	Run Spark jobs on a K8s Cluster.
Kubeflow PyTorch	Run distributed PyTorch training jobs using Kubeflow.
Kubeflow TensorFlow	Run distributed TensorFlow training jobs using Kubeflow.
MPI Operator	Run distributed deep learning training jobs using Horovod and MPI.
Ray Task	Run Ray jobs on a K8s Cluster.

External Service Backend Plugins

As the term suggests, external service backend plugins relies on external services like AWS Sagemaker, Hive or Snowflake for handling the workload defined in the Flyte task that use the respective plugin.

AWS Sagemaker: Model Training	Train models with built-in or define your own custom algorithms.
AWS Sagemaker: Pytorch Training	Train Pytorch models using Sagemaker, with support for distributed training.
AWS Athena	Execute queries using AWS Athena
AWS Batch	Running tasks and workflows on AWS batch service
Hive	Run Hive jobs in your workflows.
MMCloud	Execute tasks using MemVerge Memory Machine Cloud
Snowflake	Run Snowflake jobs in your workflows.
Databricks	Run Databricks jobs in your workflows.
BigQuery	Run BigQuery jobs in your workflows.

Enabling Backend Plugins

To enable a backend plugin you have to add the ID of the plugin to the enabled plugins list. The enabled-plugins is available under the tasks > task-plugins section of FlytePropeller’s configuration. The plugin configuration structure is defined here. An example of the config follows,

tasks:
  task-plugins:
    enabled-plugins:
      - container
      - sidecar
      - k8s-array
    default-for-task-types:
      container: container
      sidecar: sidecar
      container_array: k8s-array

Finding the ID of the Backend Plugin

This is a little tricky since you have to look at the source code of the plugin to figure out the ID. In the case of Spark, for example, the value of ID is used here here, defined as spark.

Enabling a Specific Backend Plugin in Your Own Kustomize Generator

Flyte uses Kustomize to generate the the deployment configuration which can be leveraged to kustomize your own deployment.

Custom Container Tasks

Because Flyte uses executable docker containers as the smallest unit of compute, you can write custom tasks with the flytekit.ContainerTask via the flytekit SDK.

Raw Container Tasks

Execute arbitrary containers: You can write C++ code, bash scripts and any containerized program.

SDKs for Writing Tasks and Workflows

The community would love to help you with your own ideas of building a new SDK. Currently the available SDKs are:

flytekit	The Python SDK for Flyte.
flytekit-java	The Java/Scala SDK for Flyte.

Flyte Operators

Flyte can be integrated with other orchestrators to help you leverage Flyte’s constructs natively within other orchestration tools.

Airflow

Trigger Flyte executions from Airflow.