ClearML/clearml-docs
1
0
Fork 0
mirror of https://github.com/clearml/clearml-docs synced 2025-02-07 13:21:46 +00:00
Code Issues Actions 1 Packages Projects Releases Wiki Activity

Add multi-file pipeline implementation clarification (#561)

Browse Source
This commit is contained in:
pollfly 2023-05-16 14:27:19 +03:00 committed by GitHub
parent e93f0718fb
commit 7f4fd856c5
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 10 additions and 1 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

11
docs/pipelines/pipelines_sdk_function_decorators.md
View File

@ -12,7 +12,14 @@ for your main pipeline execution logic function.
## @PipelineDecorator.pipeline
Using the [`@PipelineDecorator.pipeline`](../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorpipeline)
decorator transforms the function which implements your pipeline's execution logic to a ClearML pipeline controller
decorator transforms the function which implements your pipeline's execution logic to a ClearML pipeline controller. Since
the function is transformed into an independently executed task, it needs to be self-contained. To facilitate this,
all package imports inside the function are automatically logged as required packages for the pipeline controller.
:::tip Multi-file Pipeline Implementation
In the case your pipeline is implemented across multiple files, make sure the pipeline step implementation (files containing
functions decorated with `@PipelineDecorator.component`) is imported before `@PipelineDecorator.pipeline`.
:::
```python
@PipelineDecorator.pipeline(
@ -27,6 +34,7 @@ def main(pickle_url, mock_parameter='mock'):
print(f"Accuracy={accuracy}%")
```
### Arguments
* `name` - The name for the pipeline controller task
@ -84,6 +92,7 @@ def step_one(pickle_data_url: str, extra: int = 43):
data_frame['target'] = iris['target']
return data_frame
```
### Arguments
* `return_values` - The artifact names for the step’s corresponding ClearML task to store the step’s returned objects.
In the example above, a single object is returned and stored as an artifact named `data_frame`