flytekitplugins.papermill.task

Directory

Classes

Methods

Variables

Methods

load_flytedirectory()

def load_flytedirectory(
    path: str,
) -> ~T

Loads a FlyteDirectory from a file.

load_flytefile()

def load_flytefile(
    path: str,
) -> ~T

Loads a FlyteFile from a file.

load_python_val_from_file()

def load_python_val_from_file(
    path: str,
    dtype: ~T,
) -> ~T

Loads a python value from a Flyte literal saved to a local file.

If the path matches the type, it is returned as is. This enables reusing the parameters cell for local development.

load_structureddataset()

def load_structureddataset(
    path: str,
) -> ~T

Loads a StructuredDataset from a file.

record_outputs()

def record_outputs(
    kwargs,
) -> str

Use this method to record outputs from a notebook. It will convert all outputs to a Flyte understandable format. For Files, Directories, please use FlyteFile or FlyteDirectory, or wrap up your paths in these decorators.

save_python_val_to_file()

def save_python_val_to_file(
    input: typing.Any,
) -> str

Save a python value to a local file as a Flyte literal.

flytekitplugins.papermill.task.NotebookTask

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 :py:func:record_outputs in your notebook after the outputs are ready and pass all outputs.

Usage:

.. code-block:: python

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 :py:class:NotebookTask as follows

Usage:

.. code-block:: python

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,
    enable_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. If render_deck=True is passed, this html content will be inserted into a deck.

.. note:

Users can access these notebooks after execution of the task locally or from remote servers.

.. note:

By default, print statements in your notebook won't be transmitted to the pod logs/stdout. If you would
like to have logs forwarded as the notebook executes, pass the stream_logs argument. Note that notebook
logs can be quite verbose, so ensure you are prepared for any downstream log ingestion costs
(e.g., cloudwatch)

.. todo:

Implicit extraction of SparkConfiguration from the notebook is not supported.

.. todo:

Support for remote notebook execution, we can create a custom metadata field that is read by a propeller plugin
or just passed down back into the container, so no need to rebuild the container.

.. note:

Some input types are not permitted by papermill. Types that cannot be passed directly into the cell are not
supported - Only supported types are
str, int, float, bool
Most output types are supported as long as FlyteFile etc is used.

class NotebookTask(
    name: str,
    notebook_path: str,
    render_deck: bool,
    stream_logs: bool,
    task_config: ~T,
    inputs: typing.Optional[typing.Dict[str, typing.Type]],
    outputs: typing.Optional[typing.Dict[str, typing.Type]],
    output_notebooks: typing.Optional[bool],
    kwargs,
)

Please see class level documentation.

Methods

compile()

def compile(
    ctx: flytekit.core.context_manager.FlyteContext,
    args,
    kwargs,
) -> typing.Union[typing.Tuple[flytekit.core.promise.Promise], flytekit.core.promise.Promise, flytekit.core.promise.VoidPromise, NoneType]

Generates a node that encapsulates this task in a workflow definition.

construct_node_metadata()

def construct_node_metadata()

Used when constructing the node that encapsulates this task as part of a broader workflow definition.

dispatch_execute()

def dispatch_execute(
    ctx: flytekit.core.context_manager.FlyteContext,
    input_literal_map: flytekit.models.literals.LiteralMap,
) -> typing.Union[flytekit.models.literals.LiteralMap, flytekit.models.dynamic_job.DynamicJobSpec, typing.Coroutine]

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 none
• DynamicJobSpec is returned when a dynamic workflow is executed

execute()

def execute(
    kwargs,
) -> typing.Any

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

extract_outputs()

def extract_outputs(
    nb: str,
) -> flytekit.models.literals.LiteralMap

Parse Outputs from Notebook. This looks for a cell, with the tag “outputs” to be present.

find_lhs()

def find_lhs()

get_command()

def get_command(
    settings: SerializationSettings,
) -> List[str]

Returns the command which should be used in the container definition for the serialized version of this task registered on a hosted Flyte platform.

get_config()

def get_config(
    settings: flytekit.configuration.SerializationSettings,
) -> typing.Dict[str, str]

Returns the task config as a serializable dictionary. This task config consists of metadata about the custom defined for this task.

get_container()

def get_container(
    settings: flytekit.configuration.SerializationSettings,
) -> flytekit.models.task.Container

Returns the container definition (if any) that is used to run the task on hosted Flyte.

get_custom()

def get_custom(
    settings: flytekit.configuration.SerializationSettings,
) -> typing.Optional[typing.Dict[str, typing.Any]]

Return additional plugin-specific custom data (if any) as a serializable dictionary.

get_default_command()

def get_default_command(
    settings: SerializationSettings,
) -> List[str]

Returns the default pyflyte-execute command used to run this on hosted Flyte platforms.

get_extended_resources()

def get_extended_resources(
    settings: SerializationSettings,
) -> Optional[tasks_pb2.ExtendedResources]

Returns the extended resources to allocate to the task on hosted Flyte.

get_image()

def get_image(
    settings: SerializationSettings,
) -> str

Update image spec based on fast registration usage, and return string representing the image

get_input_types()

def get_input_types()

Returns the names and python types as a dictionary for the inputs of this task.

get_k8s_pod()

def get_k8s_pod(
    settings: flytekit.configuration.SerializationSettings,
) -> flytekit.models.task.K8sPod

Returns the kubernetes pod definition (if any) that is used to run the task on hosted Flyte.

get_sql()

def get_sql(
    settings: flytekit.configuration.SerializationSettings,
) -> typing.Optional[flytekit.models.task.Sql]

Returns the Sql definition (if any) that is used to run the task on hosted Flyte.

get_type_for_input_var()

def get_type_for_input_var(
    k: str,
    v: typing.Any,
) -> typing.Type[typing.Any]

Returns the python type for an input variable by name.

get_type_for_output_var()

def get_type_for_output_var(
    k: str,
    v: typing.Any,
) -> typing.Type[typing.Any]

Returns the python type for the specified output variable by name.

local_execute()

def local_execute(
    ctx: flytekit.core.context_manager.FlyteContext,
    kwargs,
) -> typing.Union[typing.Tuple[flytekit.core.promise.Promise], flytekit.core.promise.Promise, flytekit.core.promise.VoidPromise, typing.Coroutine, NoneType]

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).

local_execution_mode()

def local_execution_mode()

post_execute()

def post_execute(
    user_params: flytekit.core.context_manager.ExecutionParameters,
    rval: typing.Any,
) -> typing.Any

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

pre_execute()

def pre_execute(
    user_params: flytekit.core.context_manager.ExecutionParameters,
) -> flytekit.core.context_manager.ExecutionParameters

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

render_nb_html()

def render_nb_html(
    from_nb: str,
    to: str,
)

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()

def 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()

def sandbox_execute(
    ctx: flytekit.core.context_manager.FlyteContext,
    input_literal_map: flytekit.models.literals.LiteralMap,
) -> flytekit.models.literals.LiteralMap

Call dispatch_execute, in the context of a local sandbox execution. Not invoked during runtime.

set_command_fn()

def set_command_fn(
    get_command_fn: Optional[Callable[[SerializationSettings], List[str]]],
)

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.

set_resolver()

def set_resolver(
    resolver: TaskResolverMixin,
)

By default, flytekit uses the DefaultTaskResolver to resolve the task. This method allows the user to set a custom task resolver. It can be useful to override the task resolver for specific cases like running tasks in the jupyter notebook.

Properties