flytekitplugins.papermill.NotebookTask#
- class flytekitplugins.papermill.NotebookTask(*args, **kwargs)[source]#
Simple Papermill based input output handling for a Python Jupyter notebook. This task should be used to wrap a Notebook that has 2 properties
Property 1: One of the cells (usually the first) should be marked as the parameters cell. This task will inject inputs after this cell. The task will inject the outputs observed from Flyte
Property 2: For a notebook that produces outputs, that should be consumed by a subsequent notebook, use the method
record_outputs()
in your notebook after the outputs are ready and pass all outputs.Usage:
val_x = 10 val_y = "hello" ... # cell begin from flytekitplugins.papermill import record_outputs record_outputs(x=val_x, y=val_y) #cell end
Step 2: Wrap in a task Now point to the notebook and create an instance of
NotebookTask
as followsUsage:
nb = NotebookTask( name="modulename.my_notebook_task", # the name should be unique within all your tasks, usually it is a good # idea to use the modulename notebook_path="../path/to/my_notebook", render_deck=True, inputs=kwtypes(v=int), outputs=kwtypes(x=int, y=str), metadata=TaskMetadata(retries=3, cache=True, cache_version="1.0"), )
Step 3: Task can be executed as usual
The Task produces 2 implicit outputs.
It captures the executed notebook in its entirety and is available from Flyte with the name
out_nb
.It also converts the captured notebook into an
html
page, which the FlyteConsole will render called -out_rendered_nb
. Ifrender_deck=True
is passed, this html content will be inserted into a deck.
Please see class level documentation.
Methods
- compile(ctx, *args, **kwargs)#
Generates a node that encapsulates this task in a workflow definition.
- Parameters
- Return type
Optional[Union[Tuple[flytekit.core.promise.Promise], flytekit.core.promise.Promise, flytekit.core.promise.VoidPromise]]
- construct_node_metadata()#
Used when constructing the node that encapsulates this task as part of a broader workflow definition.
- Return type
flytekit.models.core.workflow.NodeMetadata
- dispatch_execute(ctx, input_literal_map)#
This method translates Flyte’s Type system based input values and invokes the actual call to the executor This method is also invoked during runtime.
VoidPromise
is returned in the case when the task itself declares no outputs.Literal Map
is returned when the task returns either one more outputs in the declaration. Individual outputs may be noneDynamicJobSpec
is returned when a dynamic workflow is executed
- execute(**kwargs)[source]#
- TODO: Figure out how to share FlyteContext ExecutionParameters with the notebook kernel (as notebook kernel
is executed in a separate python process)
For Spark, the notebooks today need to use the new_session or just getOrCreate session and get a handle to the singleton
- Return type
- static extract_outputs(nb)[source]#
Parse Outputs from Notebook. This looks for a cell, with the tag “outputs” to be present.
- Parameters
nb (str) –
- Return type
flytekit.models.literals.LiteralMap
- get_command(settings)#
Returns the command which should be used in the container definition for the serialized version of this task registered on a hosted Flyte platform.
- Parameters
settings (flytekit.configuration.SerializationSettings) –
- Return type
- get_config(settings)[source]#
Returns the task config as a serializable dictionary. This task config consists of metadata about the custom defined for this task.
- Parameters
settings (flytekit.configuration.SerializationSettings) –
- Return type
- get_container(settings)[source]#
Returns the container definition (if any) that is used to run the task on hosted Flyte.
- Parameters
settings (flytekit.configuration.SerializationSettings) –
- Return type
flytekit.models.task.Container
- get_custom(settings)#
Return additional plugin-specific custom data (if any) as a serializable dictionary.
- Parameters
settings (flytekit.configuration.SerializationSettings) –
- Return type
- get_default_command(settings)#
Returns the default pyflyte-execute command used to run this on hosted Flyte platforms.
- Parameters
settings (flytekit.configuration.SerializationSettings) –
- Return type
- get_extended_resources(settings)#
Returns the extended resources to allocate to the task on hosted Flyte.
- Parameters
settings (flytekit.configuration.SerializationSettings) –
- Return type
Optional[flyteidl.core.tasks_pb2.ExtendedResources]
- get_input_types()#
Returns the names and python types as a dictionary for the inputs of this task.
- get_k8s_pod(settings)[source]#
Returns the kubernetes pod definition (if any) that is used to run the task on hosted Flyte.
- Parameters
settings (flytekit.configuration.SerializationSettings) –
- Return type
flytekit.models.task.K8sPod
- get_sql(settings)#
Returns the Sql definition (if any) that is used to run the task on hosted Flyte.
- Parameters
settings (flytekit.configuration.SerializationSettings) –
- Return type
Optional[flytekit.models.task.Sql]
- get_type_for_input_var(k, v)#
Returns the python type for an input variable by name.
- get_type_for_output_var(k, v)#
Returns the python type for the specified output variable by name.
- local_execute(ctx, **kwargs)#
This function is used only in the local execution path and is responsible for calling dispatch execute. Use this function when calling a task with native values (or Promises containing Flyte literals derived from Python native values).
- Parameters
- Return type
Optional[Union[Tuple[flytekit.core.promise.Promise], flytekit.core.promise.Promise, flytekit.core.promise.VoidPromise, Coroutine]]
- local_execution_mode()#
- post_execute(user_params, rval)[source]#
Post execute is called after the execution has completed, with the user_params and can be used to clean-up, or alter the outputs to match the intended tasks outputs. If not overridden, then this function is a No-op
- Parameters
execute (rval is returned value from call to) –
user_params (flytekit.core.context_manager.ExecutionParameters) – are the modified user params as created during the pre_execute step
rval (Any) –
- Return type
- pre_execute(user_params)[source]#
This is the method that will be invoked directly before executing the task method and before all the inputs are converted. One particular case where this is useful is if the context is to be modified for the user process to get some user space parameters. This also ensures that things like SparkSession are already correctly setup before the type transformers are called
This should return either the same context of the mutated context
- Parameters
user_params (flytekit.core.context_manager.ExecutionParameters) –
- Return type
- static render_nb_html(from_nb, to)[source]#
render output notebook to html We are using nbconvert htmlexporter and its classic template later about how to customize the exporter further.
- reset_command_fn()#
Resets the command which should be used in the container definition of this task to the default arguments. This is useful when the command line is overridden at serialization time.
- sandbox_execute(ctx, input_literal_map)#
Call dispatch_execute, in the context of a local sandbox execution. Not invoked during runtime.
- Parameters
input_literal_map (flytekit.models.literals.LiteralMap) –
- Return type
flytekit.models.literals.LiteralMap
- set_command_fn(get_command_fn=None)#
By default, the task will run on the Flyte platform using the pyflyte-execute command. However, it can be useful to update the command with which the task is serialized for specific cases like running map tasks (“pyflyte-map-execute”) or for fast-executed tasks.
- Parameters
get_command_fn (Optional[Callable[[flytekit.configuration.SerializationSettings], List[str]]]) –
Attributes
- container_image
- disable_deck
If true, this task will not output deck html file
- docs
- environment
Any environment variables that supplied during the execution of the task.
- instantiated_in
- interface
- lhs
- location
- metadata
- name
- notebook_path
- output_notebook_path
- python_interface
Returns this task’s python interface.
- rendered_output_path
- resources
- security_context
- task_config
Returns the user-specified task config which is used for plugin-specific handling of the task.
- task_resolver
- task_type
- task_type_version