Upload llms-full.txt with huggingface_hub

llms-full.txt

@@ -1,5 +1,5 @@
 This file is a merged representation of the entire codebase, combining all repository files into a single document.
-Generated by Repomix on: 2025-01-27T15:50:56.709Z
+Generated by Repomix on: 2025-01-30T10:25:41.954Z
 
 ================================================================
 File Summary
@@ -285,6 +285,7 @@ docs/
           control-caching-behavior.md
           control-execution-order-of-steps.md
           delete-a-pipeline.md
+          fan-in-fan-out.md
           fetching-pipelines.md
           get-past-pipeline-step-runs.md
           hyper-parameter-tuning.md
@@ -315,12 +316,6 @@ docs/
         training-with-gpus/
           accelerate-distributed-training.md
           README.md
-        trigger-pipelines/
-          README.md
-          use-templates-cli.md
-          use-templates-dashboard.md
-          use-templates-python.md
-          use-templates-rest-api.md
         use-configuration-files/
           autogenerate-a-template-yaml-file.md
           configuration-hierarchy.md
@@ -354,6 +349,12 @@ docs/
           set-up-repository.md
         interact-with-secrets.md
         README.md
+      trigger-pipelines/
+        README.md
+        use-templates-cli.md
+        use-templates-dashboard.md
+        use-templates-python.md
+        use-templates-rest-api.md
       debug-and-solve-issues.md
     reference/
       api-reference.md
@@ -16272,7 +16273,7 @@ The image above shows the hierarchy of concepts in ZenML Pro.
 - [**Teams**](./teams.md) are groups of users within an organization. They help in organizing users and managing access to resources.
 - **Users** are single individual accounts on a ZenML Pro instance.
 - [**Roles**](./roles.md) are used to control what actions users can perform within a tenant or inside an organization.
-- [**Templates**](../../how-to/pipeline-development/trigger-pipelines/README.md) are pipeline runs that can be re-run with a different configuration.
+- [**Templates**](../../how-to/trigger-pipelines/README.md) are pipeline runs that can be re-run with a different configuration.
 
 More details about each of these concepts are available in their linked pages below:
 
@@ -16408,7 +16409,7 @@ that expand the functionality of the Open Source product. ZenML Pro adds a manag
 - **User management with teams**: Create [organizations](./organization.md) and [teams](./teams.md) to easily manage users at scale.
 - **Role-based access control and permissions**: Implement fine-grained access control using customizable [roles](./roles.md) to ensure secure and efficient resource management.
 - **Enhanced model and artifact control plane**: Leverage the [Model Control Plane](../../user-guide/starter-guide/track-ml-models.md) and [Artifact Control Plane](../../user-guide/starter-guide/manage-artifacts.md) for improved tracking and management of your ML assets.
-- **Triggers and run templates**: ZenML Pro enables you to [create and run templates](../../how-to/pipeline-development/trigger-pipelines/README.md#run-templates). This way, you can use the dashboard or our Client/REST API to run a pipeline with updated configuration, allowing you to iterate quickly with minimal friction.
+- **Triggers and run templates**: ZenML Pro enables you to [create and run templates](../../how-to/trigger-pipelines/README.md#run-templates). This way, you can use the dashboard or our Client/REST API to run a pipeline with updated configuration, allowing you to iterate quickly with minimal friction.
 - **Early-access features**: Get early access to pro-specific features such as triggers, filters, sorting, generating usage reports, and more.
 
 Learn more about ZenML Pro on the [ZenML website](https://zenml.io/pro).
@@ -16764,8 +16765,8 @@ Some Pro-only features that you can leverage in your tenant are as follows:
 
 - [Model Control Plane](../../../../docs/book/how-to/model-management-metrics/model-control-plane/register-a-model.md)
 - [Artifact Control Plane](../../how-to/data-artifact-management/handle-data-artifacts/README.md)
-- [Ability to run pipelines from the Dashboard](../../../../docs/book/how-to/pipeline-development/trigger-pipelines/use-templates-rest-api.md), 
-- [Create templates out of your pipeline runs](../../../../docs/book/how-to/pipeline-development/trigger-pipelines/use-templates-rest-api.md)
+- [Ability to run pipelines from the Dashboard](../../../../docs/book/how-to/trigger-pipelines/use-templates-rest-api.md), 
+- [Create templates out of your pipeline runs](../../../../docs/book/how-to/trigger-pipelines/use-templates-rest-api.md)
 
 and [more](https://zenml.io/pro)!
 
@@ -19506,7 +19507,7 @@ def example_pipeline():
 example_pipeline()
 ```
 
-You can see another example of using an `UnmaterializedArtifact` when triggering a [pipeline from another](../../pipeline-development/trigger-pipelines/use-templates-python.md#advanced-usage-run-a-template-from-another-pipeline).
+You can see another example of using an `UnmaterializedArtifact` when triggering a [pipeline from another](../../trigger-pipelines/use-templates-python.md#advanced-usage-run-a-template-from-another-pipeline).
 
 <figure><img src="https://static.scarf.sh/a.png?x-pxid=f0b4f458-0a54-4fcd-aa95-d5ee424815bc" alt="ZenML Scarf"><figcaption></figcaption></figure>
 
@@ -34950,7 +34951,7 @@ def training_pipeline():
 ```
 
 {% hint style="info" %}
-Here we are calling one pipeline from within another pipeline, so functionally the `data_loading_pipeline` is functioning as a step within the `training_pipeline`, i.e. the steps of the former are added to the latter. Only the parent pipeline will be visible in the dashboard. In order to actually trigger a pipeline from another, see [here](../../pipeline-development/trigger-pipelines/use-templates-python.md#advanced-usage-run-a-template-from-another-pipeline)
+Here we are calling one pipeline from within another pipeline, so functionally the `data_loading_pipeline` is functioning as a step within the `training_pipeline`, i.e. the steps of the former are added to the latter. Only the parent pipeline will be visible in the dashboard. In order to actually trigger a pipeline from another, see [here](../../trigger-pipelines/use-templates-python.md#advanced-usage-run-a-template-from-another-pipeline)
 {% endhint %}
 
 <table data-view="cards"><thead><tr><th></th><th></th><th></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td>Learn more about orchestrators here</td><td></td><td></td><td><a href="../../../component-guide/orchestrators/orchestrators.md">orchestrators.md</a></td></tr></tbody></table>
@@ -34977,9 +34978,9 @@ You can learn more about these options [here](../../pipeline-development/use-con
 
 However, there is one exception: if you would like to trigger a pipeline from the client
 or another pipeline, you would need to pass the `PipelineRunConfiguration` object.
-Learn more about this [here](../../pipeline-development/trigger-pipelines/use-templates-python.md#advanced-usage-run-a-template-from-another-pipeline).
+Learn more about this [here](../../trigger-pipelines/use-templates-python.md#advanced-usage-run-a-template-from-another-pipeline).
 
-<table data-view="cards"><thead><tr><th></th><th></th><th></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td>Using config files</td><td></td><td></td><td><a href="../../pipeline-development/use-configuration-files/README.md">../../pipeline-development/use-configuration-files/README.md</a></td></tr></tbody></table>
+<table data-view="cards"><thead><tr><th></th><th></th><th></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td>Using config files</td><td></td><td></td><td><a href="../../use-configuration-files/README.md">../../pipeline-development/use-configuration-files/README.md</a></td></tr></tbody></table>
 
 <!-- For scarf -->
 <figure><img alt="ZenML Scarf" referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=f0b4f458-0a54-4fcd-aa95-d5ee424815bc" /></figure>
@@ -35147,6 +35148,87 @@ Client().delete_pipeline_run(<RUN_NAME_OR_ID>)
 {% endtab %}
 {% endtabs %}
 
+<figure><img src="https://static.scarf.sh/a.png?x-pxid=f0b4f458-0a54-4fcd-aa95-d5ee424815bc" alt="ZenML Scarf"><figcaption></figcaption></figure>
+
+================
+File: docs/book/how-to/pipeline-development/build-pipelines/fan-in-fan-out.md
+================
+---
+description: Running steps in parallel.
+---
+
+# Fan-in and Fan-out Patterns
+
+The fan-out/fan-in pattern is a common pipeline architecture where a single step splits into multiple parallel operations (fan-out) and then consolidates the results back into a single step (fan-in). This pattern is particularly useful for parallel processing, distributed workloads, or when you need to process data through different transformations and then aggregate the results. For example, you might want to process different chunks of data in parallel and then aggregate the results:
+
+```python
+from zenml import step, get_step_context, pipeline
+from zenml.client import Client
+
+
+@step
+def load_step() -> str:
+    return "Hello from ZenML!"
+
+
+@step
+def process_step(input_data: str) -> str:
+    return input_data
+
+
+@step
+def combine_step(step_prefix: str, output_name: str) -> None:
+    run_name = get_step_context().pipeline_run.name
+    run = Client().get_pipeline_run(run_name)
+
+    # Fetch all results from parallel processing steps
+    processed_results = {}
+    for step_name, step_info in run.steps.items():
+        if step_name.startswith(step_prefix):
+            output = step_info.outputs[output_name][0]
+            processed_results[step_info.name] = output.load()
+
+    # Combine all results
+    print(",".join([f"{k}: {v}" for k, v in processed_results.items()]))
+
+
+@pipeline(enable_cache=False)
+def fan_out_fan_in_pipeline(parallel_count: int) -> None:
+    # Initial step (source)
+    input_data = load_step()
+
+    # Fan out: Process data in parallel branches
+    after = []
+    for i in range(parallel_count):
+        _ = process_step(input_data, id=f"process_{i}")
+        after.append(f"process_{i}")
+
+    # Fan in: Combine results from all parallel branches
+    combine_step(step_prefix="process_", output_name="output", after=after)
+
+
+fan_out_fan_in_pipeline(parallel_count=8)
+```
+
+The fan-out pattern allows for parallel processing and better resource utilization, while the fan-in pattern enables aggregation and consolidation of results. This is particularly useful for:
+
+- Parallel data processing
+- Distributed model training
+- Ensemble methods
+- Batch processing
+- Data validation across multiple sources
+- [Hyperparameter tuning](./hyper-parameter-tuning.md)
+
+Note that when implementing the fan-in step, you'll need to use the ZenML Client to query the results from previous parallel steps, as shown in the example above, and you can't pass in the result directly.
+
+{% hint style="warning" %}
+The fan-in, fan-out method has the following limitations:
+
+1. Steps run sequentially rather than in parallel if the underlying orchestrator does not support parallel step runs (e.g. with the local orchestrator)
+2. The number of steps need to be known ahead-of-time, and ZenML does not yet support the ability to dynamically create steps on the fly.
+{% endhint %}
+
+
 <figure><img src="https://static.scarf.sh/a.png?x-pxid=f0b4f458-0a54-4fcd-aa95-d5ee424815bc" alt="ZenML Scarf"><figcaption></figcaption></figure>
 
 ================
@@ -35561,98 +35643,83 @@ description: Running a hyperparameter tuning trial with ZenML.
 
 # Hyperparameter tuning
 
-{% hint style="warning" %}
-Hyperparameter tuning is not yet a first-class citizen in ZenML, but it is [(high up) on our roadmap of features](https://zenml.featureos.app/p/enable-hyper-parameter-tuning) and will likely receive first-class ZenML support soon. In the meanwhile, the following example shows how hyperparameter tuning can currently be implemented within a ZenML run.
-{% endhint %}
-
-A basic iteration through a number of hyperparameters can be achieved with ZenML by using a simple pipeline like this:
+A basic iteration through a number of hyperparameters can be achieved with 
+ZenML by using a simple pipeline. The following example showcases an 
+implementation of a basic grid search (across a single dimension) 
+that would allow for a different learning rate to be used across the 
+same `train_step`. Once that step has been run for all the different 
+learning rates, the `selection_step` finds which hyperparameters gave the 
+best results or performance. It utilizes the [fan-in, fan-out method of 
+building a pipeline.](./fan-in-fan-out.md)
 
 ```python
-@pipeline
-def my_pipeline(step_count: int) -> None:
-    data = load_data_step()
-    after = []
-    for i in range(step_count):
-        train_step(data, learning_rate=i * 0.0001, id=f"train_step_{i}")
-        after.append(f"train_step_{i}")
-    model = select_model_step(..., after=after)
-```
-
-This is an implementation of a basic grid search (across a single dimension) that would allow for a different learning rate to be used across the same `train_step`. Once that step has been run for all the different learning rates, the `select_model_step` finds which hyperparameters gave the best results or performance.
+from typing import Annotated
 
-<details>
+from sklearn.base import ClassifierMixin
 
-<summary>See it in action with the E2E example</summary>
+from zenml import step, pipeline, get_step_context
+from zenml.client import Client
 
-_To set up the local environment used below, follow the recommendations from the_ [_Project templates_](../../../how-to/project-setup-and-management/collaborate-with-team/project-templates/README.md)_._
+model_output_name = "my_model"
 
-In [`pipelines/training.py`](../../../../examples/e2e/pipelines/training.py), you will find a training pipeline with a `Hyperparameter tuning stage` section. It contains a `for` loop that runs the `hp_tuning_single_search` over the configured model search spaces, followed by the `hp_tuning_select_best_model` being executed after all search steps are completed. As a result, we are getting `best_model_config` to be used to train the best possible model later on.
 
-```python
-...
-########## Hyperparameter tuning stage ##########
-after = []
-search_steps_prefix = "hp_tuning_search_"
-for i, model_search_configuration in enumerate(
-    MetaConfig.model_search_space
-):
-    step_name = f"{search_steps_prefix}{i}"
-    hp_tuning_single_search(
-        model_metadata=ExternalArtifact(
-            value=model_search_configuration,
-        ),
-        id=step_name,
-        dataset_trn=dataset_trn,
-        dataset_tst=dataset_tst,
-        target=target,
-    )
-    after.append(step_name)
-best_model_config = hp_tuning_select_best_model(
-    search_steps_prefix=search_steps_prefix, after=after
-)
-...
-```
-
-</details>
-
-The main challenge of this implementation is that it is currently not possible to pass a variable number of artifacts into a step programmatically, so the `select_model_step` needs to query all artifacts produced by the previous steps via the ZenML Client instead:
+@step
+def train_step(
+    learning_rate: float
+) -> Annotated[ClassifierMixin, model_output_name]:
+    return ...  # Train a model with the learning rate and return it here. 
 
-```python
-from zenml import step, get_step_context
-from zenml.client import Client
 
 @step
-def select_model_step():
+def selection_step(step_prefix: str, output_name: str) -> None:
     run_name = get_step_context().pipeline_run.name
     run = Client().get_pipeline_run(run_name)
 
-    # Fetch all models trained by a 'train_step' before
     trained_models_by_lr = {}
-    for step_name, step in run.steps.items():
-        if step_name.startswith("train_step"):
-            for output_name, output in step.outputs.items():
-                if output_name == "<NAME_OF_MODEL_OUTPUT_IN_TRAIN_STEP>":
-                    model = output.load()
-                    lr = step.config.parameters["learning_rate"]
-                    trained_models_by_lr[lr] = model
-    
-    # Evaluate the models to find the best one
+    for step_name, step_info in run.steps.items():
+        if step_name.startswith(step_prefix):
+            model = step_info.outputs[output_name][0].load()
+            lr = step_info.config.parameters["learning_rate"]
+            trained_models_by_lr[lr] = model
+
     for lr, model in trained_models_by_lr.items():
-        ...
-```
+        ...  # Evaluate the models to find the best one
 
-<details>
 
-<summary>See it in action with the E2E example</summary>
+@pipeline
+def my_pipeline(step_count: int) -> None:
+    after = []
+    for i in range(step_count):
+        train_step(learning_rate=i * 0.0001, id=f"train_step_{i}")
+        after.append(f"train_step_{i}")
 
-_To set up the local environment used below, follow the recommendations from the_ [_Project templates_](../../../how-to/project-setup-and-management/collaborate-with-team/project-templates/README.md)_._
+    selection_step(
+        step_prefix="train_step_",
+        output_name=model_output_name,
+        after=after
+    )
 
-In the `steps/hp_tuning` folder, you will find two step files, which can be used as a starting point for building your own hyperparameter search tailored specifically to your use case:
 
-* [`hp_tuning_single_search(...)`](../../../../examples/e2e/steps/hp_tuning/hp_tuning_single_search.py) is performing a randomized search for the best model hyperparameters in a configured space.
-* [`hp_tuning_select_best_model(...)`](../../../../examples/e2e/steps/hp_tuning/hp_tuning_select_best_model.py) is searching for the best hyperparameters, looping other results of previous random searches to find the best model according to a defined metric.
+my_pipeline(step_count=4)
+```
 
-</details>
+{% hint style="warning" %}
+The main challenge of this implementation is that it is currently not 
+possible to pass a variable number of artifacts into a step programmatically, 
+so the `selection_step` needs to query all artifacts produced by the previous 
+steps via the ZenML Client instead.
+{% endhint %}
+
+{% hint style="info" %}
+You can also see this in action with the [E2E example](https://github.com/zenml-io/zenml/tree/main/examples/e2e).
+
+In the `steps/hp_tuning` folder, you will find two step files, that can be 
+used as a starting point for building your own hyperparameter search tailored 
+specifically to your use case:
+
+* [`hp_tuning_single_search(...)`](https://github.com/zenml-io/zenml/blob/main/examples/e2e/steps/hp_tuning/hp_tuning_single_search.py) is performing a randomized search for the best model hyperparameters in a configured space.
+* [`hp_tuning_select_best_model(...)`](https://github.com/zenml-io/zenml/blob/main/examples/e2e/steps/hp_tuning/hp_tuning_select_best_model.py) is searching for the best hyperparameters, looping other results of previous random searches to find the best model according to a defined metric.
+{% endhint %}
 
 <figure><img src="https://static.scarf.sh/a.png?x-pxid=f0b4f458-0a54-4fcd-aa95-d5ee424815bc" alt="ZenML Scarf"><figcaption></figcaption></figure>
 
@@ -35752,7 +35819,7 @@ locally or remotely. See our documentation on this [here](../../../getting-start
 
 Check below for more advanced ways to build and interact with your pipeline.
 
-<table data-view="cards"><thead><tr><th></th><th></th><th></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td>Configure pipeline/step parameters</td><td></td><td></td><td><a href="use-pipeline-step-parameters.md">use-pipeline-step-parameters.md</a></td></tr><tr><td>Name and annotate step outputs</td><td></td><td></td><td><a href="step-output-typing-and-annotation.md">step-output-typing-and-annotation.md</a></td></tr><tr><td>Control caching behavior</td><td></td><td></td><td><a href="control-caching-behavior.md">control-caching-behavior.md</a></td></tr><tr><td>Run pipeline from a pipeline</td><td></td><td></td><td><a href="../trigger-pipelines/README.md">README.md</a></td></tr><tr><td>Control the execution order of steps</td><td></td><td></td><td><a href="control-execution-order-of-steps.md">control-execution-order-of-steps.md</a></td></tr><tr><td>Customize the step invocation ids</td><td></td><td></td><td><a href="using-a-custom-step-invocation-id.md">using-a-custom-step-invocation-id.md</a></td></tr><tr><td>Name your pipeline runs</td><td></td><td></td><td><a href="name-your-pipeline-runs.md">name-your-pipeline-runs.md</a></td></tr><tr><td>Use failure/success hooks</td><td></td><td></td><td><a href="use-failure-success-hooks.md">use-failure-success-hooks.md</a></td></tr><tr><td>Hyperparameter tuning</td><td></td><td></td><td><a href="hyper-parameter-tuning.md">hyper-parameter-tuning.md</a></td></tr><tr><td>Attach metadata to a step</td><td></td><td></td><td><a href="../../model-management-metrics/track-metrics-metadata/attach-metadata-to-a-step.md">attach-metadata-to-a-step.md</a></td></tr><tr><td>Fetch metadata within steps</td><td></td><td></td><td><a href="../../model-management-metrics/track-metrics-metadata/fetch-metadata-within-steps.md">fetch-metadata-within-steps.md</a></td></tr><tr><td>Fetch metadata during pipeline composition</td><td></td><td></td><td><a href="../../model-management-metrics/track-metrics-metadata/fetch-metadata-within-pipeline.md">fetch-metadata-within-pipeline.md</a></td></tr><tr><td>Enable or disable logs storing</td><td></td><td></td><td><a href="../../control-logging/enable-or-disable-logs-storing.md">enable-or-disable-logs-storing.md</a></td></tr><tr><td>Special Metadata Types</td><td></td><td></td><td><a href="../../model-management-metrics/track-metrics-metadata/logging-metadata.md">logging-metadata.md</a></td></tr><tr><td>Access secrets in a step</td><td></td><td></td><td><a href="access-secrets-in-a-step.md">access-secrets-in-a-step.md</a></td></tr></tbody></table>
+<table data-view="cards"><thead><tr><th></th><th></th><th></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td>Configure pipeline/step parameters</td><td></td><td></td><td><a href="use-pipeline-step-parameters.md">use-pipeline-step-parameters.md</a></td></tr><tr><td>Name and annotate step outputs</td><td></td><td></td><td><a href="step-output-typing-and-annotation.md">step-output-typing-and-annotation.md</a></td></tr><tr><td>Control caching behavior</td><td></td><td></td><td><a href="control-caching-behavior.md">control-caching-behavior.md</a></td></tr><tr><td>Customize the step invocation ids</td><td></td><td></td><td><a href="using-a-custom-step-invocation-id.md">using-a-custom-step-invocation-id.md</a></td></tr><tr><td>Name your pipeline runs</td><td></td><td></td><td><a href="name-your-pipeline-runs.md">name-your-pipeline-runs.md</a></td></tr><tr><td>Use failure/success hooks</td><td></td><td></td><td><a href="use-failure-success-hooks.md">use-failure-success-hooks.md</a></td></tr><tr><td>Hyperparameter tuning</td><td></td><td></td><td><a href="hyper-parameter-tuning.md">hyper-parameter-tuning.md</a></td></tr><tr><td>Attach metadata to a step</td><td></td><td></td><td><a href="../../model-management-metrics/track-metrics-metadata/attach-metadata-to-a-step.md">attach-metadata-to-a-step.md</a></td></tr><tr><td>Fetch metadata within steps</td><td></td><td></td><td><a href="../../model-management-metrics/track-metrics-metadata/fetch-metadata-within-steps.md">fetch-metadata-within-steps.md</a></td></tr><tr><td>Fetch metadata during pipeline composition</td><td></td><td></td><td><a href="../../model-management-metrics/track-metrics-metadata/fetch-metadata-within-pipeline.md">fetch-metadata-within-pipeline.md</a></td></tr><tr><td>Enable or disable logs storing</td><td></td><td></td><td><a href="../../control-logging/enable-or-disable-logs-storing.md">enable-or-disable-logs-storing.md</a></td></tr><tr><td>Special Metadata Types</td><td></td><td></td><td><a href="../../model-management-metrics/track-metrics-metadata/logging-metadata.md">logging-metadata.md</a></td></tr><tr><td>Access secrets in a step</td><td></td><td></td><td><a href="access-secrets-in-a-step.md">access-secrets-in-a-step.md</a></td></tr></tbody></table>
 
 <figure><img src="https://static.scarf.sh/a.png?x-pxid=f0b4f458-0a54-4fcd-aa95-d5ee424815bc" alt="ZenML Scarf"><figcaption></figcaption></figure>
 
@@ -37471,347 +37538,6 @@ to help you out.
 
 <figure><img src="https://static.scarf.sh/a.png?x-pxid=f0b4f458-0a54-4fcd-aa95-d5ee424815bc" alt="ZenML Scarf"><figcaption></figcaption></figure>
 
-================
-File: docs/book/how-to/pipeline-development/trigger-pipelines/README.md
-================
----
-icon: bell-concierge
-description: There are numerous ways to trigger a pipeline
----
-
-# Trigger a pipeline
-
-In ZenML, the simplest way to execute a run is to use your pipeline function:
-
-```python
-from zenml import step, pipeline
-
-
-@step  # Just add this decorator
-def load_data() -> dict:
-    training_data = [[1, 2], [3, 4], [5, 6]]
-    labels = [0, 1, 0]
-    return {'features': training_data, 'labels': labels}
-
-
-@step
-def train_model(data: dict) -> None:
-    total_features = sum(map(sum, data['features']))
-    total_labels = sum(data['labels'])
-
-    # Train some model here...
-
-    print(
-        f"Trained model using {len(data['features'])} data points. "
-        f"Feature sum is {total_features}, label sum is {total_labels}."
-    )
-
-
-@pipeline  # This function combines steps together 
-def simple_ml_pipeline():
-    dataset = load_data()
-    train_model(dataset)
-
-
-if __name__ == "__main__":
-    simple_ml_pipeline()
-```
-
-However, there are other ways to trigger a pipeline, specifically a pipeline 
-with a remote stack (remote orchestrator, artifact store, and container 
-registry).
-
-## Run Templates
-
-**Run Templates** are pre-defined, parameterized configurations for your ZenML 
-pipelines that can be easily executed from the ZenML dashboard or via our 
-Client/REST API. Think of them as blueprints for your pipeline runs, ready 
-to be customized on the fly.
-
-{% hint style="success" %}
-This is a [ZenML Pro](https://zenml.io/pro)-only feature. Please
-[sign up here](https://cloud.zenml.io) to get access.
-{% endhint %}
-
-![Working with Templates](../../../.gitbook/assets/run-templates.gif)
-
-<table data-view="cards"><thead><tr><th></th><th></th><th></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td>Use templates: Python SDK</td><td></td><td></td><td><a href="use-templates-python.md">use-templates-python.md</a></td></tr><tr><td>Use templates: CLI</td><td></td><td></td><td><a href="use-templates-cli.md">use-templates-cli.md</a></td></tr><tr><td>Use templates: Dashboard</td><td></td><td></td><td><a href="use-templates-dashboard.md">use-templates-dashboard.md</a></td></tr><tr><td>Use templates: Rest API</td><td></td><td></td><td><a href="use-templates-rest-api.md">use-templates-rest-api.md</a></td></tr></tbody></table>
-<figure><img src="https://static.scarf.sh/a.png?x-pxid=f0b4f458-0a54-4fcd-aa95-d5ee424815bc" alt="ZenML Scarf"><figcaption></figcaption></figure>
-
-================
-File: docs/book/how-to/pipeline-development/trigger-pipelines/use-templates-cli.md
-================
----
-description: Create a template using the ZenML CLI
----
-
-{% hint style="success" %}
-This is a [ZenML Pro](https://zenml.io/pro)-only feature. Please
-[sign up here](https://cloud.zenml.io) to get access.
-{% endhint %}
-
-## Create a template
-
-You can use the ZenML CLI to create a run template:
-
-```bash
-# The <PIPELINE_SOURCE_PATH> will be `run.my_pipeline` if you defined a
-# pipeline with name `my_pipeline` in a file called `run.py`
-zenml pipeline create-run-template <PIPELINE_SOURCE_PATH> --name=<TEMPLATE_NAME>
-```
-
-{% hint style="warning" %}
-You need to have an active **remote stack** while running this command or you can specify
-one with the `--stack` option.
-{% endhint %}
-
-
-<!-- For scarf -->
-<figure><img alt="ZenML Scarf" referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=f0b4f458-0a54-4fcd-aa95-d5ee424815bc" /></figure>
-
-================
-File: docs/book/how-to/pipeline-development/trigger-pipelines/use-templates-dashboard.md
-================
----
-description: Create and run a template over the ZenML Dashboard
----
-
-{% hint style="success" %}
-This is a [ZenML Pro](https://zenml.io/pro)-only feature. Please
-[sign up here](https://cloud.zenml.io) to get access.
-{% endhint %}
-
-## Create a template
-
-In order to create a template over the dashboard, go a pipeline run that you 
-executed on a remote stack (i.e. at least a remote orchestrator, artifact 
-store, and container registry):
-
-![Create Templates on the dashboard](../../../.gitbook/assets/run-templates-create-1.png)
-
-Click on `+ New Template`, give it a name and click `Create`:
-
-![Template Details](../../../.gitbook/assets/run-templates-create-2.png)
-
-## Run a template using the dashboard
-
-In order to run a template from the dashboard:
-
-- You can either click `Run a Pipeline` on the main `Pipelines` page, or
-- You can go to a specific template page and click on `Run Template`.
-
-Either way, you will be forwarded to a page where you will see the 
-`Run Details`. Here, you have the option to upload a `.yaml` [configurations file](../../pipeline-development/use-configuration-files/README.md)
-or change the configuration on the go by using our editor.
-
-![Run Details](../../../.gitbook/assets/run-templates-run-1.png)
-
-Once you run the template, a new run will be executed on the same stack as 
-the original run was executed on.
-
-<!-- For scarf -->
-<figure><img alt="ZenML Scarf" referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=f0b4f458-0a54-4fcd-aa95-d5ee424815bc" /></figure>
-
-================
-File: docs/book/how-to/pipeline-development/trigger-pipelines/use-templates-python.md
-================
----
-description: Create and run a template using the ZenML Python SDK
----
-
-{% hint style="success" %}
-This is a [ZenML Pro](https://zenml.io/pro)-only feature. Please
-[sign up here](https://cloud.zenml.io) to get access.
-{% endhint %}
-
-## Create a template
-
-You can use the ZenML client to create a run template:
-
-```python
-from zenml.client import Client
-
-run = Client().get_pipeline_run(<RUN_NAME_OR_ID>)
-
-Client().create_run_template(
-    name=<TEMPLATE_NAME>,
-    deployment_id=run.deployment_id
-)
-```
-
-{% hint style="warning" %}
-You need to select **a pipeline run that was executed on a remote stack** 
-(i.e. at least a remote orchestrator, artifact store, and container registry)
-{% endhint %}
-
-
-You can also create a template directly from your pipeline definition by running the
-following code while having a **remote stack** active:
-```python
-from zenml import pipeline
-
-@pipeline
-def my_pipeline():
-    ...
-
-template = my_pipeline.create_run_template(name=<TEMPLATE_NAME>)
-```
-
-## Run a template 
-
-You can use the ZenML client to run a template:
-
-```python
-from zenml.client import Client
-
-template = Client().get_run_template(<TEMPLATE_NAME>)
-
-config = template.config_template
-
-# [OPTIONAL] ---- modify the config here ----
-
-Client().trigger_pipeline(
-    template_id=template.id,
-    run_configuration=config,
-)
-```
-
-Once you trigger the template, a new run will be executed on the same stack as 
-the original run was executed on.
-
-## Advanced Usage: Run a template from another pipeline
-
-It is also possible to use the same logic to run a pipeline within another 
-pipeline:
-
-```python
-import pandas as pd
-
-from zenml import pipeline, step
-from zenml.artifacts.unmaterialized_artifact import UnmaterializedArtifact
-from zenml.artifacts.utils import load_artifact
-from zenml.client import Client
-from zenml.config.pipeline_run_configuration import PipelineRunConfiguration
-
-
-@step
-def trainer(data_artifact_id: str):
-    df = load_artifact(data_artifact_id)
-
-
-@pipeline
-def training_pipeline():
-    trainer()
-
-
-@step
-def load_data() -> pd.Dataframe:
-    ...
-
-
-@step
-def trigger_pipeline(df: UnmaterializedArtifact):
-    # By using UnmaterializedArtifact we can get the ID of the artifact
-    run_config = PipelineRunConfiguration(
-        steps={"trainer": {"parameters": {"data_artifact_id": df.id}}}
-    )
-
-    Client().trigger_pipeline("training_pipeline", run_configuration=run_config)
-
-
-@pipeline
-def loads_data_and_triggers_training():
-    df = load_data()
-    trigger_pipeline(df)  # Will trigger the other pipeline
-```
-
-Read more about the [PipelineRunConfiguration](https://sdkdocs.zenml.io/latest/core_code_docs/core-config/#zenml.config.pipeline_run_configuration.PipelineRunConfiguration) and [`trigger_pipeline`](https://sdkdocs.zenml.io/latest/core_code_docs/core-client/#zenml.client.Client) function object in the [SDK Docs](https://sdkdocs.zenml.io/).
-
-Read more about Unmaterialized Artifacts [here](../../data-artifact-management/complex-usecases/unmaterialized-artifacts.md).
-
-<!-- For scarf -->
-<figure><img alt="ZenML Scarf" referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=f0b4f458-0a54-4fcd-aa95-d5ee424815bc" /></figure>
-
-================
-File: docs/book/how-to/pipeline-development/trigger-pipelines/use-templates-rest-api.md
-================
----
-description: Create and run a template over the ZenML Rest API
----
-
-{% hint style="success" %}
-This is a [ZenML Pro](https://zenml.io/pro)-only feature. Please
-[sign up here](https://cloud.zenml.io) to get access.
-{% endhint %}
-
-## Run a template 
-
-Triggering a pipeline from the REST API **only** works if you've created at 
-least one run template for that pipeline.
-
-As a pre-requisite, you need a pipeline name. After you have it, there are 
-three calls that need to be made in order to trigger a pipeline from the 
-REST API:
-
-1. `GET /pipelines?name=<PIPELINE_NAME>` -> This returns a response, where a <PIPELINE_ID> can be copied
-2. `GET /run_templates?pipeline_id=<PIPELINE_ID>` -> This returns a list of responses where a <TEMPLATE_ID> can be chosen
-3. `POST /run_templates/<TEMPLATE_ID>/runs` -> This runs the pipeline. You can pass the [PipelineRunConfiguration](https://sdkdocs.zenml.io/latest/core_code_docs/core-config/#zenml.config.pipeline_run_configuration.PipelineRunConfiguration) in the body
-
-## A working example
-
-{% hint style="info" %}
-Learn how to get a bearer token for the curl commands 
-[here](../../../reference/api-reference.md#using-a-bearer-token-to-access-the-api-programmatically).
-{% endhint %}
-
-Here is an example. Let's say would we like to re-run a pipeline called 
-`training`. We first query the `/pipelines` endpoint:
-
-```shell
-curl -X 'GET' \
-  '<YOUR_ZENML_SERVER_URL>/api/v1/pipelines?hydrate=false&name=training' \
-  -H 'accept: application/json' \
-  -H 'Authorization: Bearer <YOUR_TOKEN>'
-```
-
-<figure><img src="../../../.gitbook/assets/rest_api_step_1.png" alt=""><figcaption><p>Identifying the pipeline ID</p></figcaption></figure>
-
-We can take the ID from any object in the list of responses. In this case, 
-the <PIPELINE_ID> is `c953985e-650a-4cbf-a03a-e49463f58473` in the response.
-
-After this, we take the pipeline ID and call the`/run_templates?pipeline_id=<PIPELINE_ID>` API:
-
-```shell
-curl -X 'GET' \
-  '<YOUR_ZENML_SERVER_URL>/api/v1/run_templates?hydrate=false&logical_operator=and&page=1&size=20&pipeline_id=b826b714-a9b3-461c-9a6e-1bde3df3241d' \
-  -H 'accept: application/json' \
-  -H 'Authorization: Bearer <YOUR_TOKEN>'
-```
-
-We can now take the <TEMPLATE_ID> from this response. Here it is `b826b714-a9b3-461c-9a6e-1bde3df3241d`.
-
-<figure><img src="../../../.gitbook/assets/rest_api_step_2.png" alt=""><figcaption><p>Identifying the template ID</p></figcaption></figure>
-
-Finally, we can use the template ID to trigger the pipeline with a different 
-configuration:
-
-```shell
-curl -X 'POST' \
-  '<YOUR_ZENML_SERVER_URL>/api/v1/run_templates/b826b714-a9b3-461c-9a6e-1bde3df3241d/runs' \
-  -H 'accept: application/json' \
-  -H 'Content-Type: application/json' \
-  -H 'Authorization: Bearer <YOUR_TOKEN>' \
-  -d '{
-  "steps": {"model_trainer": {"parameters": {"model_type": "rf"}}}
-}'
-```
-
-A positive response means your pipeline has been re-triggered with a 
-different config!
-
-<!-- For scarf -->
-<figure><img alt="ZenML Scarf" referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=f0b4f458-0a54-4fcd-aa95-d5ee424815bc" /></figure>
-
 ================
 File: docs/book/how-to/pipeline-development/use-configuration-files/autogenerate-a-template-yaml-file.md
 ================
@@ -40802,6 +40528,347 @@ icon: building-columns
 
 This section covers all aspects of setting up and managing ZenML projects.
 
+================
+File: docs/book/how-to/trigger-pipelines/README.md
+================
+---
+icon: bell-concierge
+description: There are numerous ways to trigger a pipeline
+---
+
+# Trigger a pipeline (Run Templates)
+
+In ZenML, the simplest way to execute a run is to use your pipeline function:
+
+```python
+from zenml import step, pipeline
+
+
+@step  # Just add this decorator
+def load_data() -> dict:
+    training_data = [[1, 2], [3, 4], [5, 6]]
+    labels = [0, 1, 0]
+    return {'features': training_data, 'labels': labels}
+
+
+@step
+def train_model(data: dict) -> None:
+    total_features = sum(map(sum, data['features']))
+    total_labels = sum(data['labels'])
+
+    # Train some model here...
+
+    print(
+        f"Trained model using {len(data['features'])} data points. "
+        f"Feature sum is {total_features}, label sum is {total_labels}."
+    )
+
+
+@pipeline  # This function combines steps together 
+def simple_ml_pipeline():
+    dataset = load_data()
+    train_model(dataset)
+
+
+if __name__ == "__main__":
+    simple_ml_pipeline()
+```
+
+However, there are other ways to trigger a pipeline, specifically a pipeline 
+with a remote stack (remote orchestrator, artifact store, and container 
+registry).
+
+## Run Templates
+
+**Run Templates** are pre-defined, parameterized configurations for your ZenML 
+pipelines that can be easily executed from the ZenML dashboard or via our 
+Client/REST API. Think of them as blueprints for your pipeline runs, ready 
+to be customized on the fly.
+
+{% hint style="success" %}
+This is a [ZenML Pro](https://zenml.io/pro)-only feature. Please
+[sign up here](https://cloud.zenml.io) to get access.
+{% endhint %}
+
+![Working with Templates](../../../.gitbook/assets/run-templates.gif)
+
+<table data-view="cards"><thead><tr><th></th><th></th><th></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td>Use templates: Python SDK</td><td></td><td></td><td><a href="use-templates-python.md">use-templates-python.md</a></td></tr><tr><td>Use templates: CLI</td><td></td><td></td><td><a href="use-templates-cli.md">use-templates-cli.md</a></td></tr><tr><td>Use templates: Dashboard</td><td></td><td></td><td><a href="use-templates-dashboard.md">use-templates-dashboard.md</a></td></tr><tr><td>Use templates: Rest API</td><td></td><td></td><td><a href="use-templates-rest-api.md">use-templates-rest-api.md</a></td></tr></tbody></table>
+<figure><img src="https://static.scarf.sh/a.png?x-pxid=f0b4f458-0a54-4fcd-aa95-d5ee424815bc" alt="ZenML Scarf"><figcaption></figcaption></figure>
+
+================
+File: docs/book/how-to/trigger-pipelines/use-templates-cli.md
+================
+---
+description: Create a template using the ZenML CLI
+---
+
+{% hint style="success" %}
+This is a [ZenML Pro](https://zenml.io/pro)-only feature. Please
+[sign up here](https://cloud.zenml.io) to get access.
+{% endhint %}
+
+## Create a template
+
+You can use the ZenML CLI to create a run template:
+
+```bash
+# The <PIPELINE_SOURCE_PATH> will be `run.my_pipeline` if you defined a
+# pipeline with name `my_pipeline` in a file called `run.py`
+zenml pipeline create-run-template <PIPELINE_SOURCE_PATH> --name=<TEMPLATE_NAME>
+```
+
+{% hint style="warning" %}
+You need to have an active **remote stack** while running this command or you can specify
+one with the `--stack` option.
+{% endhint %}
+
+
+<!-- For scarf -->
+<figure><img alt="ZenML Scarf" referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=f0b4f458-0a54-4fcd-aa95-d5ee424815bc" /></figure>
+
+================
+File: docs/book/how-to/trigger-pipelines/use-templates-dashboard.md
+================
+---
+description: Create and run a template over the ZenML Dashboard
+---
+
+{% hint style="success" %}
+This is a [ZenML Pro](https://zenml.io/pro)-only feature. Please
+[sign up here](https://cloud.zenml.io) to get access.
+{% endhint %}
+
+## Create a template
+
+In order to create a template over the dashboard, go a pipeline run that you 
+executed on a remote stack (i.e. at least a remote orchestrator, artifact 
+store, and container registry):
+
+![Create Templates on the dashboard](../../../.gitbook/assets/run-templates-create-1.png)
+
+Click on `+ New Template`, give it a name and click `Create`:
+
+![Template Details](../../../.gitbook/assets/run-templates-create-2.png)
+
+## Run a template using the dashboard
+
+In order to run a template from the dashboard:
+
+- You can either click `Run a Pipeline` on the main `Pipelines` page, or
+- You can go to a specific template page and click on `Run Template`.
+
+Either way, you will be forwarded to a page where you will see the 
+`Run Details`. Here, you have the option to upload a `.yaml` [configurations file](../pipeline-development/use-configuration-files/README.md)
+or change the configuration on the go by using our editor.
+
+![Run Details](../../../.gitbook/assets/run-templates-run-1.png)
+
+Once you run the template, a new run will be executed on the same stack as 
+the original run was executed on.
+
+<!-- For scarf -->
+<figure><img alt="ZenML Scarf" referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=f0b4f458-0a54-4fcd-aa95-d5ee424815bc" /></figure>
+
+================
+File: docs/book/how-to/trigger-pipelines/use-templates-python.md
+================
+---
+description: Create and run a template using the ZenML Python SDK
+---
+
+{% hint style="success" %}
+This is a [ZenML Pro](https://zenml.io/pro)-only feature. Please
+[sign up here](https://cloud.zenml.io) to get access.
+{% endhint %}
+
+## Create a template
+
+You can use the ZenML client to create a run template:
+
+```python
+from zenml.client import Client
+
+run = Client().get_pipeline_run(<RUN_NAME_OR_ID>)
+
+Client().create_run_template(
+    name=<TEMPLATE_NAME>,
+    deployment_id=run.deployment_id
+)
+```
+
+{% hint style="warning" %}
+You need to select **a pipeline run that was executed on a remote stack** 
+(i.e. at least a remote orchestrator, artifact store, and container registry)
+{% endhint %}
+
+
+You can also create a template directly from your pipeline definition by running the
+following code while having a **remote stack** active:
+```python
+from zenml import pipeline
+
+@pipeline
+def my_pipeline():
+    ...
+
+template = my_pipeline.create_run_template(name=<TEMPLATE_NAME>)
+```
+
+## Run a template 
+
+You can use the ZenML client to run a template:
+
+```python
+from zenml.client import Client
+
+template = Client().get_run_template(<TEMPLATE_NAME>)
+
+config = template.config_template
+
+# [OPTIONAL] ---- modify the config here ----
+
+Client().trigger_pipeline(
+    template_id=template.id,
+    run_configuration=config,
+)
+```
+
+Once you trigger the template, a new run will be executed on the same stack as 
+the original run was executed on.
+
+## Advanced Usage: Run a template from another pipeline
+
+It is also possible to use the same logic to run a pipeline within another 
+pipeline:
+
+```python
+import pandas as pd
+
+from zenml import pipeline, step
+from zenml.artifacts.unmaterialized_artifact import UnmaterializedArtifact
+from zenml.artifacts.utils import load_artifact
+from zenml.client import Client
+from zenml.config.pipeline_run_configuration import PipelineRunConfiguration
+
+
+@step
+def trainer(data_artifact_id: str):
+    df = load_artifact(data_artifact_id)
+
+
+@pipeline
+def training_pipeline():
+    trainer()
+
+
+@step
+def load_data() -> pd.Dataframe:
+    ...
+
+
+@step
+def trigger_pipeline(df: UnmaterializedArtifact):
+    # By using UnmaterializedArtifact we can get the ID of the artifact
+    run_config = PipelineRunConfiguration(
+        steps={"trainer": {"parameters": {"data_artifact_id": df.id}}}
+    )
+
+    Client().trigger_pipeline("training_pipeline", run_configuration=run_config)
+
+
+@pipeline
+def loads_data_and_triggers_training():
+    df = load_data()
+    trigger_pipeline(df)  # Will trigger the other pipeline
+```
+
+Read more about the [PipelineRunConfiguration](https://sdkdocs.zenml.io/latest/core_code_docs/core-config/#zenml.config.pipeline_run_configuration.PipelineRunConfiguration) and [`trigger_pipeline`](https://sdkdocs.zenml.io/latest/core_code_docs/core-client/#zenml.client.Client) function object in the [SDK Docs](https://sdkdocs.zenml.io/).
+
+Read more about Unmaterialized Artifacts [here](../data-artifact-management/complex-usecases/unmaterialized-artifacts.md).
+
+<!-- For scarf -->
+<figure><img alt="ZenML Scarf" referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=f0b4f458-0a54-4fcd-aa95-d5ee424815bc" /></figure>
+
+================
+File: docs/book/how-to/trigger-pipelines/use-templates-rest-api.md
+================
+---
+description: Create and run a template over the ZenML Rest API
+---
+
+{% hint style="success" %}
+This is a [ZenML Pro](https://zenml.io/pro)-only feature. Please
+[sign up here](https://cloud.zenml.io) to get access.
+{% endhint %}
+
+## Run a template 
+
+Triggering a pipeline from the REST API **only** works if you've created at 
+least one run template for that pipeline.
+
+As a pre-requisite, you need a pipeline name. After you have it, there are 
+three calls that need to be made in order to trigger a pipeline from the 
+REST API:
+
+1. `GET /pipelines?name=<PIPELINE_NAME>` -> This returns a response, where a <PIPELINE_ID> can be copied
+2. `GET /run_templates?pipeline_id=<PIPELINE_ID>` -> This returns a list of responses where a <TEMPLATE_ID> can be chosen
+3. `POST /run_templates/<TEMPLATE_ID>/runs` -> This runs the pipeline. You can pass the [PipelineRunConfiguration](https://sdkdocs.zenml.io/latest/core_code_docs/core-config/#zenml.config.pipeline_run_configuration.PipelineRunConfiguration) in the body
+
+## A working example
+
+{% hint style="info" %}
+Learn how to get a bearer token for the curl commands 
+[here](../../../reference/api-reference.md#using-a-bearer-token-to-access-the-api-programmatically).
+{% endhint %}
+
+Here is an example. Let's say would we like to re-run a pipeline called 
+`training`. We first query the `/pipelines` endpoint:
+
+```shell
+curl -X 'GET' \
+  '<YOUR_ZENML_SERVER_URL>/api/v1/pipelines?hydrate=false&name=training' \
+  -H 'accept: application/json' \
+  -H 'Authorization: Bearer <YOUR_TOKEN>'
+```
+
+<figure><img src="../../../.gitbook/assets/rest_api_step_1.png" alt=""><figcaption><p>Identifying the pipeline ID</p></figcaption></figure>
+
+We can take the ID from any object in the list of responses. In this case, 
+the <PIPELINE_ID> is `c953985e-650a-4cbf-a03a-e49463f58473` in the response.
+
+After this, we take the pipeline ID and call the`/run_templates?pipeline_id=<PIPELINE_ID>` API:
+
+```shell
+curl -X 'GET' \
+  '<YOUR_ZENML_SERVER_URL>/api/v1/run_templates?hydrate=false&logical_operator=and&page=1&size=20&pipeline_id=b826b714-a9b3-461c-9a6e-1bde3df3241d' \
+  -H 'accept: application/json' \
+  -H 'Authorization: Bearer <YOUR_TOKEN>'
+```
+
+We can now take the <TEMPLATE_ID> from this response. Here it is `b826b714-a9b3-461c-9a6e-1bde3df3241d`.
+
+<figure><img src="../../../.gitbook/assets/rest_api_step_2.png" alt=""><figcaption><p>Identifying the template ID</p></figcaption></figure>
+
+Finally, we can use the template ID to trigger the pipeline with a different 
+configuration:
+
+```shell
+curl -X 'POST' \
+  '<YOUR_ZENML_SERVER_URL>/api/v1/run_templates/b826b714-a9b3-461c-9a6e-1bde3df3241d/runs' \
+  -H 'accept: application/json' \
+  -H 'Content-Type: application/json' \
+  -H 'Authorization: Bearer <YOUR_TOKEN>' \
+  -d '{
+  "steps": {"model_trainer": {"parameters": {"model_type": "rf"}}}
+}'
+```
+
+A positive response means your pipeline has been re-triggered with a 
+different config!
+
+<!-- For scarf -->
+<figure><img alt="ZenML Scarf" referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=f0b4f458-0a54-4fcd-aa95-d5ee424815bc" /></figure>
+
 ================
 File: docs/book/how-to/debug-and-solve-issues.md
 ================
@@ -48714,6 +48781,7 @@ File: docs/book/toc.md
     * [Name your pipeline runs](how-to/pipeline-development/build-pipelines/name-your-pipeline-runs.md)
     * [Tag your pipeline runs](how-to/pipeline-development/build-pipelines/tag-your-pipeline-runs.md)
     * [Use failure/success hooks](how-to/pipeline-development/build-pipelines/use-failure-success-hooks.md)
+    * [Fan in, fan out](how-to/pipeline-development/build-pipelines/fan-in-fan-out.md)
     * [Hyperparameter tuning](how-to/pipeline-development/build-pipelines/hyper-parameter-tuning.md)
     * [Access secrets in a step](how-to/pipeline-development/build-pipelines/access-secrets-in-a-step.md)
     * [Run an individual step](how-to/pipeline-development/build-pipelines/run-an-individual-step.md)
@@ -48722,11 +48790,6 @@ File: docs/book/toc.md
   * [Develop locally](how-to/pipeline-development/develop-locally/README.md)
     * [Use config files to develop locally](how-to/pipeline-development/develop-locally/local-prod-pipeline-variants.md)
     * [Keep your pipelines and dashboard clean](how-to/pipeline-development/develop-locally/keep-your-dashboard-server-clean.md)
-  * [Trigger a pipeline](how-to/pipeline-development/trigger-pipelines/README.md)
-    * [Use templates: Python SDK](how-to/pipeline-development/trigger-pipelines/use-templates-python.md)
-    * [Use templates: CLI](how-to/pipeline-development/trigger-pipelines/use-templates-cli.md)
-    * [Use templates: Dashboard](how-to/pipeline-development/trigger-pipelines/use-templates-dashboard.md)
-    * [Use templates: Rest API](how-to/pipeline-development/trigger-pipelines/use-templates-rest-api.md)
   * [Use configuration files](how-to/pipeline-development/use-configuration-files/README.md)
     * [How to configure a pipeline with a YAML](how-to/pipeline-development/use-configuration-files/how-to-use-config.md)
     * [What can be configured](how-to/pipeline-development/use-configuration-files/what-can-be-configured.md)
@@ -48742,6 +48805,11 @@ File: docs/book/toc.md
   * [Configure Python environments](how-to/pipeline-development/configure-python-environments/README.md)
     * [Handling dependencies](how-to/pipeline-development/configure-python-environments/handling-dependencies.md)
     * [Configure the server environment](how-to/pipeline-development/configure-python-environments/configure-the-server-environment.md)
+* [Trigger a pipeline](how-to/trigger-pipelines/README.md)
+  * [Use templates: Python SDK](how-to/trigger-pipelines/use-templates-python.md)
+  * [Use templates: CLI](how-to/trigger-pipelines/use-templates-cli.md)
+  * [Use templates: Dashboard](how-to/trigger-pipelines/use-templates-dashboard.md)
+  * [Use templates: Rest API](how-to/trigger-pipelines/use-templates-rest-api.md)
 * [Customize Docker builds](how-to/customize-docker-builds/README.md)
   * [Docker settings on a pipeline](how-to/customize-docker-builds/docker-settings-on-a-pipeline.md)
   * [Docker settings on a step](how-to/customize-docker-builds/docker-settings-on-a-step.md)