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pollfly
2023-02-16 12:17:53 +02:00
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27 changed files with 64 additions and 64 deletions
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2
docs/pipelines/pipelines.md
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@@ -50,7 +50,7 @@ Each pipeline step can log additional artifacts and metrics on the step task wit
Additionally, pipeline steps can directly report metrics or upload artifacts / models to the pipeline using these
PipelineController and PipelineDecorator class methods: `get_logger`, `upload_model`, `upload_artifact`.
The pipeline controller also offers automation for logging step metrics / artifacts / models on the pipeline task itself.
The pipeline controller also supports automation for logging step metrics / artifacts / models on the pipeline task itself.
Each pipeline step can specify metrics / artifacts / models to also automatically log to the pipeline Task. The idea is
that pipeline steps report metrics internally while the pipeline automatically collects them into a unified view on the
pipeline Task. To enable the automatic logging, use the `monitor_metrics`, `monitor_artifacts`, `monitor_models` arguments

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docs/pipelines/pipelines_sdk_function_decorators.md
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@@ -108,7 +108,7 @@ def step_one(pickle_data_url: str, extra: int = 43):
* `repo_branch` (Optional) - Specify the remote repository branch (Ignored, if local repo path is used)
* `repo_commit` (Optional) - Specify the repository commit ID (Ignored, if local repo path is used)
* `helper_functions` (Optional) - A list of helper functions to make available for the standalone pipeline step. By default, the pipeline step function has no access to any of the other functions, by specifying additional functions here, the remote pipeline step could call the additional functions.
Example, assuming we have two functions, `parse_data()` and `load_data()`: `[parse_data, load_data]`
Example, assuming you have two functions, `parse_data()` and `load_data()`: `[parse_data, load_data]`
* `parents` – Optional list of parent steps in the pipeline. The current step in the pipeline will be sent for execution only after all the parent steps have been executed successfully.
Additionally, you can enable automatic logging of a step’s metrics / artifacts / models to the pipeline task using the

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docs/pipelines/pipelines_sdk_tasks.md
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@@ -175,7 +175,7 @@ def step_created_callback(
pass
```
A `post_execute_callback` function is called when a step is completed. It allows you to modify the step’s status after completion.
A `post_execute_callback` function is called when a step is completed. It lets you modify the step’s status after completion.
```python
def step_completed_callback(