ploomber.tasks.ScriptRunner

class ploomber.tasks.ScriptRunner(source, product, dag, name=None, params=None, ext_in=None, static_analysis='regular', local_execution=False)

Similar to NotebookRunner, except it uses python to run the code, instead of papermill, hence, it doesn’t generate an output notebook. But it also works by injecting a cell into the source code. Source can be a .py script or an .ipynb notebook. Does not support magics.

Examples

Spec API:

tasks:
  - source: script.py
    class: ScriptRunner
    product:
        data: data.csv
        another: another.csv

Python API:

>>> from pathlib import Path
>>> from ploomber import DAG
>>> from ploomber.tasks import ScriptRunner
>>> from ploomber.products import File
>>> dag = DAG()
>>> product = {'data': File('data.csv'), 'another': File('another.csv')}
>>> _ = ScriptRunner(Path('script.py'), product, dag=dag)
>>> _ = dag.build()

Methods

build([force, catch_exceptions])

Build a single task

debug([kind])

Opens the notebook (with injected parameters) in debug mode in a temporary location

develop([app, args])

Opens the rendered notebook (with injected parameters) and adds a “debugging-settings” cell to the that changes directory to the current active directory.

load([key])

Load task as pandas.DataFrame.

render([force, outdated_by_code, remote])

Renders code and product, all upstream tasks must have been rendered first, for that reason, this method will usually not be called directly but via DAG.render(), which renders in the right order.

run()

This is the only required method Task subclasses must implement

set_upstream(other[, group_name])

status([return_code_diff, sections])

Prints the current task status

build(force=False, catch_exceptions=True)

Build a single task

Although Tasks are primarily designed to execute via DAG.build(), it is possible to do so in isolation. However, this only works if the task does not have any unrendered upstream dependencies, if that’s the case, you should call DAG.render() before calling Task.build()

Returns

A dictionary with keys ‘run’ and ‘elapsed’

Return type

dict

Raises
  • TaskBuildError – If the error failed to build because it has upstream dependencies, the build itself failed or build succeded but on_finish hook failed

  • DAGBuildEarlyStop – If any task or on_finish hook raises a DAGBuildEarlyStop error

debug(kind='ipdb')

Opens the notebook (with injected parameters) in debug mode in a temporary location

Parameters

kind (str, default='ipdb') – Debugger to use, ‘ipdb’ to use line-by-line IPython debugger, ‘pdb’ to use line-by-line Python debugger or ‘pm’ to to post-portem debugging using IPython

Notes

Be careful when debugging tasks. If the task has run successfully, you overwrite products but don’t save the updated source code, your DAG will enter an inconsistent state where the metadata won’t match the overwritten product.

develop(app='notebook', args=None)

Opens the rendered notebook (with injected parameters) and adds a “debugging-settings” cell to the that changes directory to the current active directory. This will reflect conditions when callign DAG.build(). This modified notebook is saved in the same location as the source with a “-tmp” added to the filename. Changes to this notebook can be exported to the original notebook after the notebook process is shut down. The “injected-parameters” and “debugging-settings” cells are deleted before saving.

Parameters
  • app ({'notebook', 'lab'}, default: 'notebook') – Which Jupyter application to use

  • args (str) – Extra parameters passed to the jupyter application

Notes

Be careful when developing tasks interacively. If the task has run successfully, you overwrite products but don’t save the updated source code, your DAG will enter an inconsistent state where the metadata won’t match the overwritten product.

If you modify the source code and call develop again, the source code will be updated only if the hot_reload option is turned on. See ploomber.DAGConfigurator for details.

load(key=None, **kwargs)

Load task as pandas.DataFrame. Only implemented in certain tasks

render(force=False, outdated_by_code=True, remote=False)

Renders code and product, all upstream tasks must have been rendered first, for that reason, this method will usually not be called directly but via DAG.render(), which renders in the right order.

Render fully determines whether a task should run or not.

Parameters
  • force (bool, default=False) – If True, mark status as WaitingExecution/WaitingUpstream even if the task is up-to-date (if there are any File(s) with clients, this also ignores the status of the remote copy), otherwise, the normal process follows and only up-to-date tasks are marked as Skipped.

  • outdated_by_code (bool, default=True) – Factors to determine if Task.product is marked outdated when source code changes. Otherwise just the upstream timestamps are used.

  • remote (bool, default=False) – Use remote metadata to determine status

Notes

This method tries to avoid calls to check for product status whenever possible, since checking product’s metadata can be a slow operation (e.g. if metadata is stored in a remote database)

When passing force=True, product’s status checking is skipped altogether, this can be useful when we only want to quickly get a rendered DAG object to interact with it

run()

This is the only required method Task subclasses must implement

set_upstream(other, group_name=None)
status(return_code_diff=False, sections=None)

Prints the current task status

Parameters

sections (list, optional) – Sections to include. Defaults to “name”, “last_run”, “oudated”, “product”, “doc”, “location”

Attributes

PRODUCT_CLASSES_ALLOWED

client

exec_status

name

A str that represents the name of the task, you can access tasks in a dag using dag[‘some_name’]

on_failure

Callable to be executed if task fails (passes Task as first parameter and the exception as second parameter)

on_finish

Callable to be executed after this task is built successfully (passes Task as first parameter)

on_render

params

dict that holds the parameter that will be passed to the task upon execution.

product

The product this task will create upon execution

source

Source is used by the task to compute its output, for most cases this is source code, for example PythonCallable takes a function as source and SQLScript takes a string with SQL code as source.

static_analysis

upstream

A mapping for upstream dependencies {task name} -> [task object]