Add support for dynamic pipelines to the Vertex orchestrator

Author: schustmi

docs/book/how-to/steps-pipelines/dynamic_pipelines.md

@@ -5,7 +5,7 @@ description: Write dynamic pipelines
 # Dynamic Pipelines (Experimental)
 
 {% hint style="warning" %}
-**Experimental Feature**: Dynamic pipelines are currently an experimental feature. There are known issues and limitations, and the interface is subject to change. This feature is only supported by the `local` and `kubernetes` orchestrators. If you encounter any issues or have feedback, please let us know at [https://github.com/zenml-io/zenml/issues](https://github.com/zenml-io/zenml/issues).
+**Experimental Feature**: Dynamic pipelines are currently an experimental feature. There are known issues and limitations, and the interface is subject to change. This feature is only supported by the `local`, `kubernetes`, `sagemaker` and `vertex` orchestrators. If you encounter any issues or have feedback, please let us know at [https://github.com/zenml-io/zenml/issues](https://github.com/zenml-io/zenml/issues).
 {% endhint %}
 
 {% hint style="info" %}
@@ -265,26 +265,11 @@ When running multiple steps concurrently using `step.submit()`, a failure in one
 Dynamic pipelines are currently only supported by:
 - `local` orchestrator
 - `kubernetes` orchestrator
+- `sagemaker` orchestrator
+- `vertex` orchestrator
 
 Other orchestrators will raise an error if you try to run a dynamic pipeline with them.
 
-### Remote Execution Requirement
-
-When running dynamic pipelines remotely (e.g., with the `kubernetes` orchestrator), you **must** include `depends_on` for at least one step in your pipeline definition. This is currently required due to a bug in remote execution.
-
-{% hint style="warning" %}
-**Required for Remote Execution**: Without `depends_on`, remote execution will fail. This requirement does not apply when running locally with the `local` orchestrator.
-{% endhint %}
-
-For example:
-
-```python
-@pipeline(dynamic=True, depends_on=[some_step])
-def dynamic_pipeline():
-    some_step()
-    # ... rest of your pipeline
-```
-
 ### Artifact Loading
 
 When you call `.load()` on an artifact in a dynamic pipeline, it synchronously loads the data. For large artifacts or when you want to maintain parallelism, consider passing the step outputs (future or artifact) directly to downstream steps instead of loading them.

src/zenml/integrations/aws/orchestrators/sagemaker_orchestrator.py

@@ -1050,10 +1050,10 @@ def _wait_for_completion() -> None:
             metadata=metadata,
         )
 
-    def launch_dynamic_step(
+    def run_isolated_step(
         self, step_run_info: "StepRunInfo", environment: Dict[str, str]
     ) -> None:
-        """Launch a dynamic step.
+        """Runs an isolated step on Sagemaker.
 
         Args:
             step_run_info: The step run information.