updated doc

README.md

@@ -101,7 +101,6 @@ A sample file can be found in this repository in the folder `.cloud/.azure`. The
 | Parameter               | Required | Allowed Values | Default    | Description |
 | ----------------------- | -------- | -------------- | ---------- | ----------- |
 | model_file_name         |          | str            | `"model.pkl"` | The file name for the model asset. You only have to specify the name of the model file (e.g. (`"model.pkl"`)) and not the path (e.g. `"outputs/model.pkl"`). The can take care of the path that was used to store the file. |
-| pipeline_child_run_name |  | str   | `"model_training"` | If you provided a run ID of a pipeline to this GitHub Action, you have to specify the name of the step that produced the model. Without providing the name of the step that produced the model, the Action does not know where to look for the model file. The step in the pipeline with the provided name can be of any type (HyperDriveStep, PythonScriptStep, etc.). There are no limitations on the step type.  |
 | model_name              |          | str            | <REPO_NAME>-<BRANCH_NAME> |The name to register the model with. |
 | model_framework         |          | str: `"scikitlearn"`, `"onnx"`, `"tensorflow"`, `"keras"`, `"custom"` | `"custom"` | The framework of the registered model. | 
 | model_framework_version |          | str      | null     | The framework version of the registered model. |
@@ -111,6 +110,7 @@ A sample file can be found in this repository in the folder `.cloud/.azure`. The
 | datasets                |          | list  | null | A list of dataset names that are regstered in your workspace that should be assigned to the registered model. |
 | sample_input_dataset    |          | str   | null | Name of a sample input dataset that is regstered in your workspace for the registered model. |
 | sample_output_dataset   |          | str   | null | Name of a sample output dataset that is regstered in your workspace for the registered model. |
+| pipeline_child_run_name |  | str   | `"model_training"` | If you provided a run ID of a pipeline to this GitHub Action, you have to specify the name of the step that produced the model. Without providing the name of the step that produced the model, the Action does not know where to look for the model file. The step in the pipeline with the provided name can be of any type (HyperDriveStep, PythonScriptStep, etc.). There are no limitations on the step type. |
 | cpu                     |          | float | null | The number of CPU cores to allocate for this resource. Can be a decimal. You have to specify `cpu` and `memory` to register the model with a resource configuration. If you do not specify both parameters the model will be registered without a resource configuration. |
 | memory                  |          | float | null | The amount of memory (in GB) to allocate for this resource. Can be a decimal. You have to specify `cpu` and `memory` to register the model with a resource configuration. If you do not specify both parameters the model will be registered without a resource configuration. |
 | metrics_max             |          | list  | null | List of metrics names that must be maximized. The action compares the metrics of the provided run with the linked run of the latest model with the same name that is registered in the model registry. The action fails if any of the specified metrics are lower than the metrics of the latest model in your model registry. If a model with the same name cannot be found or if the latest model in your model registry is not linked to a run in Azure Machine Learning, it will register the model without comparing any metrics. |