Local Deployer implementation (#4085)

* First draft implementation for local deployer

* Tested and fixed preliminary issues

* Add default deployer to default stack

* Apply code review suggestions

* Small fixes and doc updates

* Fix docstrings

* More minor doc updates

* Fix linter errors on windows and add docs and logo to the flavor

* Fix remaining linter errors on windows

* Update src/zenml/deployers/local/local_deployer.py

Co-authored-by: graphite-app[bot] <96075541+graphite-app[bot]@users.noreply.github.com>

* Update src/zenml/utils/daemon.py

Co-authored-by: graphite-app[bot] <96075541+graphite-app[bot]@users.noreply.github.com>

* Update src/zenml/utils/daemon.py

Co-authored-by: graphite-app[bot] <96075541+graphite-app[bot]@users.noreply.github.com>

* Fix doc links and apply some code-review suggestions

* Fix linter errors again

* Remove PID file use and address code review comments.

---------

Co-authored-by: graphite-app[bot] <96075541+graphite-app[bot]@users.noreply.github.com>

Author: stefannica

docs/book/component-guide/deployers/README.md

@@ -25,10 +25,11 @@ Use deployers when you need request-response patterns, and orchestrators for sch
 
 ### Deployer Flavors
 
-ZenML provides deployer implementations for different deployment environments:
+Out of the box, ZenML comes with a `local` deployer already part of the default stack that deploys pipelines on your local machine in the form of background processes. Additional Deployers are provided by integrations:
 
 | Deployer                           | Flavor    | Integration   | Notes                                                                        |
 |------------------------------------|-----------|---------------|------------------------------------------------------------------------------|
+| [Local](local.md)                  | `local`  | _built-in_  | This is the default Deployer. It deploys pipelines on your local machine in the form of background processes. Should be used only for running ZenML locally. |
 | [Docker](docker.md)                | `docker`   | Built-in      | Deploys pipelines as locally running Docker containers                                |
 | [GCP Cloud Run](gcp-cloud-run.md)            | `gcp`     | `gcp`         | Deploys pipelines to Google Cloud Run for serverless execution             |
 | [AWS App Runner](aws-app-runner.md)           | `aws`     | `aws`         | Deploys pipelines to AWS App Runner for serverless execution                       |
@@ -43,9 +44,15 @@ zenml deployer flavor list
 
 You don't need to directly interact with the ZenML deployer stack component in your code. As long as the deployer that you want to use is part of your active [ZenML stack](../../user-guide/production-guide/understand-stacks.md), you can simply deploy a pipeline or snapshot using the ZenML CLI or the ZenML SDK. The resulting deployment can be managed using the ZenML CLI or the ZenML SDK.
 
-Example:
+Examples:
 
-* set up a stack with a deployer:
+* just use the default stack - it has a default local deployer that will deploy the pipeline on your local machine in the form of a background process:
+
+```bash
+zenml stack set default
+```
+
+* or set up a new stack with a deployer in it:
 
 ```bash
 zenml deployer register docker --flavor=local
@@ -129,7 +136,7 @@ def weather_pipeline(city: str = "Paris", temperature: float = 20.0) -> str:
     return analysis
 ```
 
-For more information, see the [Deployable Pipeline Requirements](../../how-to/deployment/deployment.md#deployable-pipeline-requirements) section of the tutorial.
+For more information, see the [Deployable Pipeline Requirements](https://docs.zenml.io/concepts/deployment#deployable-pipeline-requirements) section of the tutorial.
 
 #### Deployment Lifecycle Management
 

docs/book/component-guide/deployers/aws-app-runner.md

@@ -138,7 +138,7 @@ ZenML will build a Docker image called `<CONTAINER_REGISTRY_URI>/zenml:<PIPELINE
 You can now [deploy any ZenML pipeline](https://docs.zenml.io/concepts/deployment) using the AWS App Runner deployer:
 
 ```shell
-zenml pipeline deploy my_module.my_pipeline
+zenml pipeline deploy --name my_deployment my_module.my_pipeline
 ```
 
 ### Additional configuration

docs/book/component-guide/deployers/docker.md

@@ -25,22 +25,22 @@ To use the Docker deployer, you can register it and use it in your active stack:
 ```shell
 zenml deployer register docker --flavor=docker
 
-# Register and activate a stack with the new orchestrator
+# Register and activate a stack with the new deployer
 zenml stack register docker-deployer -D docker -o default -a default --set
 ```
 {% hint style="info" %}
 ZenML will build a local Docker image called `zenml:<PIPELINE_NAME>` and use it to deploy your pipeline as a Docker container. Check out [this page](https://docs.zenml.io/how-to/customize-docker-builds/) if you want to learn more about how ZenML builds these images and how you can customize them.
 {% endhint %}
 
-You can now deploy your ZenML pipeline using the Docker deployer:
+You can now [deploy any ZenML pipeline](https://docs.zenml.io/concepts/deployment) using the Docker deployer:
 
 ```shell
 zenml pipeline deploy my_module.my_pipeline
 ```
 
 ### Additional configuration
 
-For additional configuration of the Local Docker orchestrator, you can pass the following `DockerDeployerSettings` attributes defined in the `zenml.deployers.docker.docker_deployer` module when configuring the deployer or defining or deploying your pipeline:
+For additional configuration of the Docker deployer, you can pass the following `DockerDeployerSettings` attributes defined in the `zenml.deployers.docker.docker_deployer` module when configuring the deployer or defining or deploying your pipeline:
 
 * Basic settings common to all Deployers:
 
@@ -71,7 +71,7 @@ def greet(name: str) -> str:
 
 settings = {
     "deployer": DockerDeployerSettings(
-        run_args={"port": 8000}
+        port=8000
     )
 }
 

docs/book/component-guide/deployers/gcp-cloud-run.md

@@ -114,7 +114,7 @@ ZenML will build a Docker image called `<CONTAINER_REGISTRY_URI>/zenml:<PIPELINE
 You can now [deploy any ZenML pipeline](https://docs.zenml.io/concepts/deployment) using the GCP Cloud Run deployer:
 
 ```shell
-zenml pipeline deploy my_module.my_pipeline
+zenml pipeline deploy --name my_deployment my_module.my_pipeline
 ```
 
 ### Additional configuration

docs/book/component-guide/deployers/local.md

@@ -0,0 +1,79 @@
+---
+description: Deploying pipelines on your local machine as background processes.
+---
+
+# Local Deployer
+
+The local deployer is a [deployer](./) flavor that comes built-in with ZenML and deploys pipelines on your local machine as background processes.
+
+### When to use it
+
+The local deployer is part of your default stack when you're first getting started with ZenML. Due to it running locally on your machine, it requires no additional setup and is easy to use and debug.
+
+You should use the local deployer if:
+
+* you're just getting started with ZenML and want to deploy pipelines without setting up any cloud infrastructure.
+* you're writing a new pipeline and want to experiment and debug quickly
+
+### How to deploy it
+
+The local deployer comes with ZenML and works without any additional setup.
+
+### How to use it
+
+To use the local deployer, you can register it and use it in your active stack:
+
+```shell
+zenml deployer register <DEPLOYER_NAME> --flavor=local
+
+# Register and activate a stack with the new deployer
+zenml stack register <STACK_NAME> -D <DEPLOYER_NAME> ... --set
+```
+
+You can now [deploy any ZenML pipeline](https://docs.zenml.io/concepts/deployment) using the local deployer:
+
+```shell
+zenml pipeline deploy --name my_deployment my_module.my_pipeline
+```
+### Additional configuration
+
+For additional configuration of the Local deployer, you can pass the following `LocalDeployerSettings` attributes defined in the `zenml.deployers.local.local_deployer` module when configuring the deployer or defining or deploying your pipeline:
+
+* Basic settings common to all Deployers:
+
+  * `auth_key`: A user-defined authentication key to use to authenticate with deployment API calls.
+  * `generate_auth_key`: Whether to generate and use a random authentication key instead of the user-defined one.
+  * `lcm_timeout`: The maximum time in seconds to wait for the deployment lifecycle management to complete.
+
+* Settings specific to the Local deployer:
+
+  * `port`: A custom port that the deployment server will listen on. Not set by default.
+  * `allocate_port_if_busy`: If True, allocate a free port if the configured `port` is busy or not set. Defaults to True.
+  * `port_range`: The range of ports to search for a free port. Defaults to `(8000, 65535)`.
+  * `address`: The address that the deployment server will listen on. Defaults to `127.0.0.1`.
+  * `blocking`: Whether to run the deployment in the current process instead of running it as a daemon process. Defaults to False. Use this if you want to debug issues with the deployment ASGI application itself.
+
+Check out [this docs page](https://docs.zenml.io/concepts/steps_and_pipelines/configuration) for more information on how to specify settings.
+
+For example, if you wanted to specify the port to use for the deployment, you would configure settings as follows:
+
+```python
+from zenml import step, pipeline
+from zenml.deployers.local.local_deployer import LocalDeployerSettings
+
+
+@step
+def greet(name: str) -> str:
+    return f"Hello {name}!"
+
+
+settings = {
+    "deployer": LocalDeployerSettings(
+        port=8000
+    )
+}
+
+@pipeline(settings=settings)
+def greet_pipeline(name: str = "John"):
+    greet(name=name)
+```

docs/book/component-guide/toc.md

@@ -21,6 +21,7 @@
   * [Lightning AI Orchestrator](orchestrators/lightning.md)
   * [Develop a custom orchestrator](orchestrators/custom.md)
 * [Deployers](deployers/README.md)
+  * [Local Deployer](deployers/local.md)
   * [Docker Deployer](deployers/docker.md)
   * [AWS App Runner Deployer](deployers/aws-app-runner.md)
   * [GCP Cloud Run Deployer](deployers/gcp-cloud-run.md)

docs/book/how-to/deployment/deployment.md

@@ -62,7 +62,13 @@ transition period, but new investments will focus on pipeline deployments.
 
 ## How Deployments Work
 
-To deploy a pipeline or snapshot, a **Deployer** stack component needs to be in your active stack:
+To deploy a pipeline or snapshot, a **Deployer** stack component needs to be in your active stack. You can use the default stack, which has a default local deployer that will deploy the pipeline directly on your local machine as a background process:
+
+```bash
+zenml stack set default
+```
+
+or set up a new stack with a deployer in it:
 
 ```bash
 zenml deployer register <DEPLOYER-NAME> --flavor=<DEPLOYER-FLAVOR>

src/zenml/deployers/__init__.py

@@ -44,6 +44,12 @@
     DockerDeployerFlavor,
     DockerDeployerSettings,
 )
+from zenml.deployers.local.local_deployer import (
+    LocalDeployer,
+    LocalDeployerConfig,
+    LocalDeployerFlavor,
+    LocalDeployerSettings,
+)
 
 __all__ = [
     "BaseDeployer",
@@ -54,4 +60,8 @@
     "DockerDeployerConfig",
     "DockerDeployerFlavor",
     "DockerDeployerSettings",
+    "LocalDeployer",
+    "LocalDeployerConfig",
+    "LocalDeployerFlavor",
+    "LocalDeployerSettings",
 ]

src/zenml/deployers/docker/docker_deployer.py

@@ -1,4 +1,4 @@
-#  Copyright (c) ZenML GmbH 2022. All Rights Reserved.
+#  Copyright (c) ZenML GmbH 2025. All Rights Reserved.
 #
 #  Licensed under the Apache License, Version 2.0 (the "License");
 #  you may not use this file except in compliance with the License.

src/zenml/deployers/local/__init__.py

@@ -0,0 +1,14 @@
+#  Copyright (c) ZenML GmbH 2025. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at:
+#
+#       https://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+#  or implied. See the License for the specific language governing
+#  permissions and limitations under the License.
+"""Local daemon deployer implementation."""