lightning/docs/source-app/examples/dag/dag.rst

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######################################
Develop a Directed Acyclic Graph (DAG)
######################################
.. _dag_example:
**Audience:** Users coming from MLOps to Lightning Apps, looking for more flexibility.
A typical ML training workflow can be implemented with a simple DAG.
Below is a pseudo-code using the lightning framework that uses a LightningFlow to orchestrate the serial workflow: process data, train a model, and serve the model.
.. code-block:: python
import lightning as L
class DAGFlow(L.LightningFlow):
def __init__(self):
super().__init__()
self.processor = DataProcessorWork(...)
self.train_work = TrainingWork(...)
self.serve_work = ServeWork(...)
def run(self):
self.processor.run(...)
self.train_work.run(...)
self.serve_work.run(...)
Below is a pseudo-code to run several works in parallel using a built-in :class:`~lightning_app.structures.Dict`.
.. code-block:: python
import lightning as L
class DAGFlow(L.LightningFlow):
def __init__(self):
super().__init__()
...
self.train_works = L.structures.Dict(**{
"1": TrainingWork(..., parallel=True),
"2": TrainingWork(..., parallel=True),
"3": TrainingWork(..., parallel=True),
...
})
...
def run(self):
self.processor.run(...)
# The flow runs through them all, so we need to guard self.serve_work.run
for work in self.train_works.values():
work.run(...)
# Wait for all to have finished without errors.
if not all(w.has_succeeded for w in self.train_works):
continue
self.serve_work.run(...)
----
**********
Next Steps
**********
.. raw:: html
<div class="display-card-container">
<div class="row">
.. displayitem::
:header: Scheduled DAG with pandas and sklearn from scratch.
:description: DAG example in pure Lightning.
:col_css: col-md-4
:button_link: dag_from_scratch.html
:height: 180
:tag: intermediate