Prefect

New method for generating parameter schemas without dependencies

`prefect deploy` now works even when dependencies are missing from the current environment. This can speed up deployment via CI by removing the need to install dependencies before deploying your flows.

See the following pull requests for implementation details:
- https://github.com/PrefectHQ/prefect/pull/13620
- https://github.com/PrefectHQ/prefect/pull/13315

Enhancements
- Provide URL in CLI output upon work pool creation — https://github.com/PrefectHQ/prefect/pull/13597

Fixes
- Ensure graceful cancellation of flow runs corresponding to deleted deployments — https://github.com/PrefectHQ/prefect/pull/13669

Integrations
- Add loading state to concurrency limits table in the Prefect UI — https://github.com/PrefectHQ/prefect-ui-library/pull/2483
- Remove old schema properties from deployments in the Prefect UI — https://github.com/PrefectHQ/prefect-ui-library/pull/2482
- Add handling for multi-word dbt CLI commands — https://github.com/PrefectHQ/prefect/pull/13616

**All changes**: https://github.com/PrefectHQ/prefect/compare/2.19.2...2.19.3

This release includes a few bug fixes, ensuring:
- 🛠️ runs created from the `deployments/{id}/create_flow_run` endpoint hydrates workspace variables
- 🔢 proper integer value display on the Variables page of the Prefect UI — https://github.com/PrefectHQ/prefect-ui-library/pull/2454
- ⚙️ "Run a deployment" automation action parameter display input configuration persists during editing — https://github.com/PrefectHQ/prefect-ui-library/pull/2458
- 📌 `requests` dependency pinned to `<2.32.0` in `requirements-dev.txt` - https://github.com/PrefectHQ/prefect/pull/13538
- 📄 Jinja template example is renderable in automations documentation — https://github.com/PrefectHQ/prefect/pull/13421

Enhancements to `prefect-dbt` for running `dbt-core` commands

Introducing `prefect-dbt` summary artifacts! With summary artifacts, you get a view of all of the runs that succeeded, failed, or were skipped as well as where the failed models live in your dbt project and why they failed. This exposes information about each dbt node in a succinct format that teams can use for development, debugging, insights, and so much more.

These changes are available in `prefect-dbt` version 0.5.0!

<img width="1184" alt="prefect-dbt-summary-artifact-example" src="https://github.com/PrefectHQ/prefect/assets/104510333/3868b961-5aff-4115-b409-f86d3992704d">

See the following pull requests for implementation details:
- https://github.com/PrefectHQ/prefect/pull/12964
- https://github.com/PrefectHQ/prefect/pull/13387
- https://github.com/PrefectHQ/prefect/pull/13406

Enhancements

- Enable flow run infra overrides section in the Prefect UI - https://github.com/PrefectHQ/prefect-ui-library/pull/2417

Experimental
Events and Automations

- Add ability to create and manage automations in the Prefect UI - https://github.com/PrefectHQ/prefect/pull/13342

Documentation

- Update documentation formatting for `prefect-ray` - https://github.com/PrefectHQ/prefect/pull/13385

Integrations

- Update all integrations libraries to pin `prefect<3.0.0` - https://github.com/PrefectHQ/prefect/pull/13408

Integration library releases

This release was accompanied by the following integration library releases:

- `prefect-aws` - 0.4.17
- `prefect-azure` - 0.3.10
- `prefect-bitbucket` - 0.2.5
- `prefect-dask` - 0.2.9
- `prefect-databricks` - 0.2.7
- `prefect-dbt` - 0.5.0
- `prefect-docker` - 0.5.2
- `prefect-email` - 0.3.5
- `prefect-gcp` - 0.5.12
- `prefect-github` - 0.2.5
- `prefect-gitlab` - 0.2.5
- `prefect-kubernetes` - 0.3.10
- `prefect-ray` - 0.3.6
- `prefect-shell` - 0.2.5
- `prefect-slack` - 0.2.6
- `prefect-snowflake` - 0.27.6
- `prefect-sqlalchemy` - 0.4.3

**All changes**: https://github.com/PrefectHQ/prefect/compare/2.19.0...2.19.1

✨ This release includes a number of enhancements and fixes!


Support for major infrastructure and distributed task integrations
As `prefect-dask` and other integrations have been added to the prefect codebase, this release adds these integrations as extra requirements of the prefect package, making it easier to install support for everything in your Prefect stack:

bash
pip install 'prefect[dask]'


We loved this community contribution so much, we did it for all our first-party integrations:

bash
pip install 'prefect[aws,kubernetes,dask,dbt,sqlalchemy,slack]'


You can see the full list of Prefect's extra requirements in [our setup.py](https://github.com/PrefectHQ/prefect/blob/main/setup.py#L43).

Support for timeout seconds in global concurrency context manager
You may want to fail immediately if a global concurrency slot is unavailable. Rather than block and wait, you can now specify a `timeout_seconds` argument in the global concurrency context manager and catch a `TimeoutError` if a slot is not available within the specified time.

python
flow
def fail_immediately_flow():
try:
with concurrency("there-can-be-only-one", occupy=1, timeout_seconds=0.1):
do_something_resource_intensive()
except TimeoutError:
return Cancelled(message="Another flow run is already running")


Manage global concurrency limits via the CLI
Global concurrency limits let you control how many operations can run simultaneously-- now you can create, read, edit, and delete global concurrency limits via the Prefect CLI!

To create a new concurrency limit, use the `prefect gcl create` command. You must specify a `--limit` argument, and can optionally specify a `--slot-decay-per-second` and `--disable` argument.

bash

Experimental
Engine
- Wire up new engine to deployment runs — https://github.com/PrefectHQ/prefect/pull/12914

Fixes
- Fix parameters becoming unresponsive and disappearing in Prefect UI — https://github.com/PrefectHQ/prefect-ui-library/pull/2355

**All changes**: https://github.com/PrefectHQ/prefect/compare/2.18.2...2.18.3

💡 Providing a deployment name to `flow.serve` is now optional

When running `flow.serve`, you can now omit the deployment name. If you do not provide a deployment name, the deployment name will default to the name of the flow. This change makes it easier to run flows without needing to specify a deployment name each time:

python
flow
def etl_flow():
pass

if __name__ == "__main__":
etl_flow.serve()

results in:
bash
Your flow 'etl-flow' is being served and polling for scheduled runs!

To trigger a run for this flow, use the following command:

$ prefect deployment run 'etl-flow/etl-flow'


🛠✨ We've also released a few important fixes to our deployment parameter form when creating a run in the Prefect UI! 🧑‍🎨

🚀 This release also includes a number of other fixes and in-flight feature work. See the [release notes](https://github.com/PrefectHQ/prefect/blob/main/RELEASE-NOTES.md#release-2182) for more details!