flytekit.extend.TaskPlugins

class flytekit.extend.TaskPlugins[source]

This is the TaskPlugins factory for task types that are derivative of PythonFunctionTask. Every task that the user wishes to use should be available in this factory. Usage

TaskPlugins.register_pythontask_plugin(config_object_type, plugin_object_type)
# config_object_type is any class that will be passed to the plugin_object as task_config
# Plugin_object_type is a derivative of ``PythonFunctionTask``

Examples of available task plugins include different query-based plugins such as flytekitplugins.athena.task.AthenaTask and flytekitplugins.hive.task.HiveTask, ML tools like plugins.awssagemaker.flytekitplugins.awssagemaker.training.SagemakerBuiltinAlgorithmsTask, kubeflow operators like plugins.kfpytorch.flytekitplugins.kfpytorch.task.PyTorchFunctionTask and plugins.kftensorflow.flytekitplugins.kftensorflow.task.TensorflowFunctionTask, and generic plugins like flytekitplugins.pod.task.PodFunctionTask which doesn’t integrate with third party tools or services.

The task_config is different for every task plugin type. This is filled out by users when they define a task to specify plugin-specific behavior and features. For example, with a query type task plugin, the config might store information related to which database to query.

The plugin_object_type can be used to customize execution behavior and task serialization properties in tandem with the task_config.

__init__()

Methods

__init__()

find_pythontask_plugin(plugin_config_type)

Returns a PluginObjectType if found or returns the base PythonFunctionTask

register_pythontask_plugin(...)

Use this method to register a new plugin into Flytekit. Usage ::.