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pollfly
2023-09-21 13:52:36 +03:00
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4
docs/pipelines/pipelines.md
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@@ -101,8 +101,8 @@ When you rerun the pipeline through the ClearML WebApp, the pipeline is construc
code.
To change this behavior, pass `always_create_from_code=False` when instantiating a `PipelineController`. In this case,
when rerun, the pipeline DAG will be generated from the pipeline configuration stored in the pipeline task. This allows
you to modify the pipeline configuration via the UI, without changing the original codebase.
when rerun, the pipeline DAG will be generated from the pipeline configuration stored in the pipeline task. This
lets you modify the pipeline configuration via the UI, without changing the original codebase.
### Pipeline Versions
Each pipeline must be assigned a version number to help track the evolution of your pipeline structure and parameters.

2
docs/pipelines/pipelines_sdk_tasks.md
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@@ -105,7 +105,7 @@ See [add_step](../references/sdk/automation_controller_pipelinecontroller.md#add
#### parameter_override
Use the `parameter_override` argument to modify the step’s parameter values. The `parameter_override` dictionary key is
the task parameter’s full path, which includes the parameter section's name and the parameter name separated by a slash
(e.g. `'General/dataset_url'`). Passing `"${}"` in the argument value allows you to reference input/output configurations
(e.g. `'General/dataset_url'`). Passing `"${}"` in the argument value lets you reference input/output configurations
from other pipeline steps. For example: `"${<step_name>.id}"` will be converted to the Task ID of the referenced pipeline
step.