Integrations

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

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.

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 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.

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.

Snowflake

Run Snowflake 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

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, defined as spark.

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.