flytekit.extend.TaskPlugins

class flytekit.extend.TaskPlugins

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 ::.