Changed Advise of n_cross_validations In V2 Notebooks (Azure#2029)

* Update auto-ml-forecasting-bike-share.ipynb

* Update automl-forecasting-orange-juice-sales-mlflow.ipynb

* Update automl-forecasting-task-energy-demand-advanced.ipynb

* Update automl-forecasting-in-pipeline.ipynb

sdk/python/jobs/automl-standalone-jobs/automl-forecasting-orange-juice-sales/automl-forecasting-orange-juice-sales-mlflow.ipynb

@@ -366,7 +366,7 @@
     "|**primary_metric**|This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>|\n",
     "|**training_data**|The training data to be used within the experiment. You can use a registered MLTable in the workspace using the format '&lt;mltable_name&gt;:&lt;version&#47;&gt;' OR you can use a local file or folder as a MLTable. For e.g Input(mltable='my_mltable:1') OR Input(mltable=MLTable(local_path=\"./data\")) The parameter 'training_data' must always be provided.|\n",
     "|**compute**|The compute on which the AutoML job will run. In this example we are using a compute called 'oj-cluster' present in the workspace. You can replace it any other compute in the workspace.|\n",
-    "|**n_cross_validations**|Number of cross-validation folds to use for model/pipeline selection. The default value is \"auto\", in which case AutoMl determines the number of cross-validations automatically, if a validation set is not provided. Or users could specify an integer value.|\n",
+    "|**n_cross_validations**|Number of cross-validation folds to use for model/pipeline selection. This can be set to \"auto\", in which case AutoMl determines the number of cross-validations automatically, if a validation set is not provided. Or users could specify an integer value.|\n",
     "|**name**|The name of the Job/Run. This is an optional property. If not specified, a random name will be generated.\n",
     "|**experiment_name**|The name of the Experiment. An Experiment is like a folder with multiple runs in Azure ML Workspace that should be related to the same logical machine learning experiment.|\n",
     "|**enable_model_explainability**|If set to true, the explanations for the best model will be generated.|\n",

sdk/python/jobs/automl-standalone-jobs/automl-forecasting-task-bike-share/auto-ml-forecasting-bike-share.ipynb

@@ -284,7 +284,7 @@
     "|**primary_metric**|This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>|\n",
     "|**training_data**|The training data to be used for this experiment. You can use a registered MLTable in the workspace using the format `<mltable_name>:<version>` OR you can use a local file or folder as a MLTable. For e.g `Input(mltable='my_mltable:1')` OR `Input(mltable=MLTable(local_path=\"./data\"))` The parameter 'training_data' must always be provided.|\n",
     "|**compute**|The compute on which the AutoML job will run. In this example we are using a compute called 'cpu-cluster' present in the workspace. You can replace it with any other compute in the workspace.|\n",
-    "|**n_cross_validations**|Number of cross-validation folds to use for model/pipeline selection. The default value is \"auto\", in which case AutoMl determines the number of cross-validations automatically, if a validation set is not provided. Or, users could specify an integer value.|\n",
+    "|**n_cross_validations**|Number of cross-validation folds to use for model/pipeline selection. This can be set to \"auto\", in which case AutoMl determines the number of cross-validations automatically, if a validation set is not provided. Or, users could specify an integer value.|\n",
     "|**name**|The name of the Job/Run. This is an optional property. If not specified, a random name will be generated.\n",
     "|**experiment_name**|The name of the Experiment. An Experiment is like a folder with multiple runs in Azure ML Workspace that should be related to the same logical machine learning experiment. For example, if a user runs this notebook multiple times, there will be multiple runs associated with the same Experiment name.|\n",
     "|**enable_model_explainability**|If set to true, the explanations such as feature importance for the best model will be generated.|\n",
@@ -378,7 +378,7 @@
     "    training_data=my_training_data_input,\n",
     "    target_column_name=target_column_name,\n",
     "    primary_metric=\"NormalizedRootMeanSquaredError\",\n",
-    "    n_cross_validations=3,\n",
+    "    n_cross_validations=\"auto\",\n",
     "    enable_model_explainability=True,\n",
     ")\n",
     "\n",

sdk/python/jobs/automl-standalone-jobs/automl-forecasting-task-energy-demand/automl-forecasting-task-energy-demand-advanced.ipynb

@@ -272,7 +272,7 @@
     "    # validation_data = my_validation_data_input,\n",
     "    target_column_name=\"demand\",\n",
     "    primary_metric=\"NormalizedRootMeanSquaredError\",\n",
-    "    n_cross_validations=3,\n",
+    "    n_cross_validations=\"auto\",\n",
     "    enable_model_explainability=True,\n",
     "    tags={\"my_custom_tag\": \"My custom value\"},\n",
     ")\n",
@@ -402,4 +402,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

sdk/python/jobs/pipelines/1h_automl_in_pipeline/automl-forecasting-in-pipeline/automl-forecasting-in-pipeline.ipynb

@@ -342,7 +342,7 @@
     "|**primary_metric**|This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>|\n",
     "|**training_data**|The training data to be used for this experiment. You can use a registered MLTable in the workspace using the format `<mltable_name>:<version>` OR you can use a local file or folder as a MLTable. For e.g `Input(mltable='my_mltable:1')` OR `Input(mltable=MLTable(local_path=\"./data\"))` The parameter 'training_data' must always be provided.|\n",
     "|**compute**|The compute on which the AutoML job will run. In this example we are using a compute called 'cpu-cluster' present in the workspace. You can replace it with any other compute in the workspace.|\n",
-    "|**n_cross_validations**|Number of cross-validation folds to use for model/pipeline selection. The default value is \"auto\", in which case AutoMl determines the number of cross-validations automatically, if a validation set is not provided. Or, users could specify an integer value.|\n",
+    "|**n_cross_validations**|Number of cross-validation folds to use for model/pipeline selection. This can be set to \"auto\", in which case AutoMl determines the number of cross-validations automatically, if a validation set is not provided. Or, users could specify an integer value.|\n",
     "|**name**|The name of the Job/Run. This is an optional property. If not specified, a random name will be generated.\n",
     "|**experiment_name**|The name of the Experiment. An Experiment is like a folder with multiple runs in Azure ML Workspace that should be related to the same logical machine learning experiment. For example, if a user runs this notebook multiple times, there will be multiple runs associated with the same Experiment name.|\n",
     "|**enable_model_explainability**|If set to true, the explanations such as feature importance for the best model will be generated.|\n",
@@ -427,7 +427,7 @@
     "        training_data=preprocess_node.outputs.preprocessed_train_data,\n",
     "        target_column_name=target_column_name,\n",
     "        primary_metric=\"normalized_root_mean_squared_error\",\n",
-    "        n_cross_validations=2,\n",
+    "        n_cross_validations=\"auto\",\n",
     "        forecasting_settings=forecasting_settings,\n",
     "        # currently need to specify outputs \"custom_model\" explictly to reference it in following nodes\n",
     "        outputs={\"best_model\": Output(type=AssetTypes.CUSTOM_MODEL)},\n",