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Make packaged kedro project return session.run() output (#4139)
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* Made run command return output

Signed-off-by: Elena Khaustova <[email protected]>

* Updated docs

Signed-off-by: Elena Khaustova <[email protected]>

* Updated release notes

Signed-off-by: Elena Khaustova <[email protected]>

* Updated empty project template

Signed-off-by: Elena Khaustova <[email protected]>

* Update RELEASE.md

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Co-authored-by: Merel Theisen <[email protected]>
Signed-off-by: ElenaKhaustova <[email protected]>

* Update docs/source/tutorial/package_a_project.md

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Co-authored-by: Merel Theisen <[email protected]>
Signed-off-by: ElenaKhaustova <[email protected]>

* Update docs/source/tutorial/package_a_project.md

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Co-authored-by: Merel Theisen <[email protected]>
Signed-off-by: ElenaKhaustova <[email protected]>

* Updated release notes

Signed-off-by: Elena Khaustova <[email protected]>

* Updated outdated prefer docs refs

Signed-off-by: Elena Khaustova <[email protected]>

* Updated outdated prefer docs refs

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* Updated outdated prefer docs refs

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---------

Signed-off-by: Elena Khaustova <[email protected]>
Signed-off-by: ElenaKhaustova <[email protected]>
Co-authored-by: Merel Theisen <[email protected]>
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ElenaKhaustova and merelcht authored Sep 5, 2024
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1 change: 1 addition & 0 deletions RELEASE.md
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# Upcoming Release

## Major features and improvements
* Made packaged Kedro projects return `session.run()` output to be used when running it in the interactive environment.
* Enhanced `OmegaConfigLoader` configuration validation to detect duplicate keys at all parameter levels, ensuring comprehensive nested key checking.
## Bug fixes and other changes
* Fixed bug where using dataset factories breaks with `ThreadRunner`.
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10 changes: 5 additions & 5 deletions docs/source/deployment/prefect.md
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Expand Up @@ -2,7 +2,7 @@

This page explains how to run your Kedro pipeline using [Prefect 2.0](https://www.prefect.io/opensource), an open-source workflow management system.

The scope of this documentation is the deployment to a self hosted [Prefect Server](https://docs.prefect.io/2.10.17/host/), which is an open-source backend that makes it easy to monitor and execute your Prefect flows and automatically extends Prefect 2.0. We will use an [Agent that dequeues submitted flow runs from a Work Queue](https://docs.prefect.io/2.10.17/tutorial/deployments/#why-workpools-and-workers).
The scope of this documentation is the deployment to a self hosted [Prefect Server](https://docs-2.prefect.io/latest/guides/host/), which is an open-source backend that makes it easy to monitor and execute your Prefect flows and automatically extends Prefect 2.0. We will use an [Agent that dequeues submitted flow runs from a Work Queue](https://docs-2.prefect.io/latest/concepts/deployments/#workers-and-work-pools).

```{note}
This deployment has been tested using Kedro 0.18.10 with Prefect version 2.10.17. If you want to deploy with Prefect 1.0, we recommend you review [earlier versions of Kedro's Prefect deployment documentation](https://docs.kedro.org/en/0.18.9/deployment/prefect.html).
Expand All @@ -12,7 +12,7 @@ This deployment has been tested using Kedro 0.18.10 with Prefect version 2.10.17

To use Prefect 2.0 and Prefect Server, ensure you have the following prerequisites in place:

- [Prefect 2.0 is installed](https://docs.prefect.io/2.10.17/getting-started/installation/#installing-the-latest-version) on your machine
- [Prefect 2.0 is installed](https://docs-2.prefect.io/latest/getting-started/installation/#installing-the-prefect-2-latest-version) on your machine

## Setup

Expand Down Expand Up @@ -40,7 +40,7 @@ Run a Prefect Server instance:
prefect server start
```

In a separate terminal, [create a work pool](https://docs.prefect.io/2.10.17/concepts/work-pools/#work-pool-configuration) to organise the work and [create a work queue](https://docs.prefect.io/2.10.17/concepts/work-pools/#work-queues) for your agent to pull from:
In a separate terminal, [create a work pool](https://docs-2.prefect.io/latest/concepts/work-pools/#work-pool-configuration) to organise the work and [create a work queue](https://docs-2.prefect.io/latest/concepts/work-pools/#work-queues) for your agent to pull from:

```bash
prefect work-pool create --type prefect-agent <work_pool_name>
Expand All @@ -57,7 +57,7 @@ prefect agent start --pool <work_pool_name> --work-queue <work_queue_name>

### Convert your Kedro pipeline to Prefect 2.0 flow

To build a [Prefect flow](https://docs.prefect.io/core/concepts/flows.html) for your Kedro pipeline programmatically and register it with the Prefect API, use the following Python script, which should be stored in your project’s **root directory**:
To build a [Prefect flow](https://docs-2.prefect.io/latest/concepts/flows/) for your Kedro pipeline programmatically and register it with the Prefect API, use the following Python script, which should be stored in your project’s **root directory**:

```python
# <project_root>/register_prefect_flow.py
Expand Down Expand Up @@ -250,7 +250,7 @@ Be sure that your Prefect Server is up and running. Verify that the deployment s

### Run Prefect flow

Now, having the flow registered, you can use [Prefect Server UI](https://docs.prefect.io/2.10.17/host/) to orchestrate and monitor it.
Now, having the flow registered, you can use [Prefect Server UI](https://docs-2.prefect.io/latest/guides/host/) to orchestrate and monitor it.

Navigate to http://localhost:4200/deployments to see your registered flow.

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13 changes: 13 additions & 0 deletions docs/source/tutorial/package_a_project.md
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Expand Up @@ -144,6 +144,19 @@ main(

This is equivalent to `python -m <package_name>` at the command line, and you can pass in all the arguments that correspond to the options described by `python -m <package_name> --help`.

```{note}
If you run the packaged project in the interactive environment like IPython or Databricks you can also consume the output of the `main()`
which returns the `session.run()` output.
```

```python
from spaceflights.__main__ import main

def run_kedro_pipeline():
result = main(pipeline_name=<pipeline>)
do_something_with(<result>)
```

### Docker, Airflow and other deployment targets

There are various methods to deploy packaged pipelines via Kedro plugins:
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4 changes: 2 additions & 2 deletions kedro/framework/cli/project.py
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Expand Up @@ -215,7 +215,7 @@ def run( # noqa: PLR0913
conf_source: str,
params: dict[str, Any],
namespace: str,
) -> None:
) -> dict[str, Any]:
"""Run the pipeline."""

runner_obj = load_obj(runner or "SequentialRunner", "kedro.runner")
Expand All @@ -225,7 +225,7 @@ def run( # noqa: PLR0913
with KedroSession.create(
env=env, conf_source=conf_source, extra_params=params
) as session:
session.run(
return session.run(
tags=tuple_tags,
runner=runner_obj(is_async=is_async),
node_names=tuple_node_names,
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Expand Up @@ -3,20 +3,21 @@
"""
import sys
from pathlib import Path
from typing import Any

from kedro.framework.cli.utils import find_run_command
from kedro.framework.project import configure_project


def main(*args, **kwargs):
def main(*args, **kwargs) -> Any:
package_name = Path(__file__).parent.name
configure_project(package_name)

interactive = hasattr(sys, 'ps1')
kwargs["standalone_mode"] = not interactive

run = find_run_command(package_name)
run(*args, **kwargs)
return run(*args, **kwargs)


if __name__ == "__main__":
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