Merge pull request #107950 from likebupt/change-run-to-submit

ttorble · web-flow · commit 473c67d05191 · 2020-03-17T08:10:07.000Z
change Run to Submit
diff --git a/articles/machine-learning/algorithm-module-reference/add-columns.md b/articles/machine-learning/algorithm-module-reference/add-columns.md
@@ -29,7 +29,7 @@ Use this module to concatenate two datasets. You combine all columns from the tw
 
     - You cannot choose individual columns to add. All the columns from each dataset are concatenated when you use **Add Columns**. Therefore, if you want to add only a subset of the columns, use Select Columns in Dataset to create a dataset with the columns you want.
 
-3. Run the pipeline.
+3. Submit the pipeline.
 
 ### Results
 After the pipeline has run:
diff --git a/articles/machine-learning/algorithm-module-reference/add-rows.md b/articles/machine-learning/algorithm-module-reference/add-rows.md
@@ -32,7 +32,7 @@ To concatenate rows from two datasets, the rows must have exactly  the same sche
 
 2. Connect the datasets to the two input ports. The dataset that you want to append should be connected to the second (right) port. 
   
-3.  Run the pipeline. The number of rows in the output dataset should equal the sum of the rows of both input datasets.
+3.  Submit the pipeline. The number of rows in the output dataset should equal the sum of the rows of both input datasets.
 
     If you add the same dataset to both inputs of the **Add Rows** module, the dataset is duplicated. 
 
diff --git a/articles/machine-learning/algorithm-module-reference/apply-math-operation.md b/articles/machine-learning/algorithm-module-reference/apply-math-operation.md
@@ -82,7 +82,7 @@ Each instance of this module can perform only one type of operation at a time. T
     - **Inplace**. The values in the columns used as inputs are replaced with the new calculated values. 
     - **ResultOnly**. A single column is returned containing the results of the math operation.
   
-1.  Run the pipeline.  
+1.  Submit the pipeline.  
   
 ## Results
 
diff --git a/articles/machine-learning/algorithm-module-reference/assign-data-to-clusters.md b/articles/machine-learning/algorithm-module-reference/assign-data-to-clusters.md
@@ -38,7 +38,7 @@ The Assign Data to Clusters module returns a dataset that contains the probable
   
     If you clear this check box, only the results are returned. This option might be useful when you create predictions as part of a web service.
   
-5.  Run the pipeline.  
+5.  Submit the pipeline.  
   
 ### Results
 
diff --git a/articles/machine-learning/algorithm-module-reference/boosted-decision-tree-regression.md b/articles/machine-learning/algorithm-module-reference/boosted-decision-tree-regression.md
@@ -80,7 +80,7 @@ The gradient boosting method can also be used for classification problems by red
   
     
 
-10. Run the pipeline.  
+10. Submit the pipeline.  
   
 ## Results
 
diff --git a/articles/machine-learning/algorithm-module-reference/clean-missing-data.md b/articles/machine-learning/algorithm-module-reference/clean-missing-data.md
@@ -106,7 +106,7 @@ Each time that you apply the  [Clean Missing Data](./clean-missing-data.md) modu
   
 7. **Generate missing value indicator column**: Select this option if you want to output some indication of whether the values in the column met the criteria for missing value cleaning. This option is particularly useful when you are setting up a new cleaning operation and want to make sure it works as designed.
   
-8. Run the pipeline.
+8. Submit the pipeline.
 
 ### Results
 
@@ -134,7 +134,7 @@ If you need to repeat cleaning operations often, we recommend that you save your
 
     However, suppose you created a transformation on a subset of numeric columns. You can apply this transformation to a dataset of mixed column types without raising an error, because the missing values are changed only in the matching numeric columns.
 
-6.  Run the pipeline.  
+6.  Submit the pipeline.  
 
 ## Next steps
 
diff --git a/articles/machine-learning/algorithm-module-reference/clip-values.md b/articles/machine-learning/algorithm-module-reference/clip-values.md
@@ -80,7 +80,7 @@ If you need to apply clipping methods or different criteria to some columns, you
   
     To keep the original column and add a new column with the clipped values, deselect this option.  
   
-1.  Run the pipeline.  
+1.  Submit the pipeline.  
   
     Right-click the **Clip Values** module and select **Visualize** or select the module and switch to the **Outputs** tab in the right panel, click on the histogram icon in the **Port outputs**, to review the values and make sure the clipping operation met your expectations.  
  
diff --git a/articles/machine-learning/algorithm-module-reference/convert-to-csv.md b/articles/machine-learning/algorithm-module-reference/convert-to-csv.md
@@ -37,7 +37,7 @@ When you convert a dataset to CSV, the csv is saved in your Azure ML workspace.
 
 2. Connect it to any module that outputs a dataset.   
   
-3.  Run the pipeline.
+3.  Submit the pipeline.
 
 ### Results
   
diff --git a/articles/machine-learning/algorithm-module-reference/convert-to-dataset.md b/articles/machine-learning/algorithm-module-reference/convert-to-dataset.md
@@ -47,7 +47,7 @@ We recommend that you use the [Edit Metadata](edit-metadata.md) module to prepar
     Note that the **ReplaceValues** operation applies only to exact matches. For example, these strings would not be affected: `obs.`, `obsolete`.  
  
   
-5.  Run the pipeline.  
+5.  Submit the pipeline.  
 
 ## Results
 
diff --git a/articles/machine-learning/algorithm-module-reference/convert-to-indicator-values.md b/articles/machine-learning/algorithm-module-reference/convert-to-indicator-values.md
@@ -36,7 +36,7 @@ This module also outputs a definition of the transformation used to convert to i
     >  If you choose the option to overwrite, the source column is not actually deleted or modified. Instead, the new columns are generated and presented in the output dataset, and the source column remains available in the workspace. 
     > If you need to see the original data, you can use the [Add Columns](add-columns.md) module at any time to add the source column back in.
 
-1. Run the pipeline.
+1. Submit the pipeline.
 
 ## Results
 
@@ -85,7 +85,7 @@ If you need to repeat indicator values operations often, you can save your data
 
    When you apply a saved transformation, you cannot select which columns to transform. This is because the transformation has been defined and applies automatically to the data types specified in the original operation.
 
-1. Run the pipeline.
+1. Submit the pipeline.
  
 ## Technical notes  
 
diff --git a/articles/machine-learning/algorithm-module-reference/cross-validate-model.md b/articles/machine-learning/algorithm-module-reference/cross-validate-model.md
@@ -67,7 +67,7 @@ In this scenario, you both train and test the model by using Cross Validate Mode
 
 5. Set a value for the **Random seed** parameter if you want to repeat the results of cross-validation across successive runs on the same data.  
 
-6. Run the pipeline.
+6. Submit the pipeline.
 
 7. See the [Results](#results) section for a description of the reports.
 
diff --git a/articles/machine-learning/algorithm-module-reference/decision-forest-regression.md b/articles/machine-learning/algorithm-module-reference/decision-forest-regression.md
@@ -80,7 +80,7 @@ For more information about the theoretical framework for this algorithm and its
 
    
 
-10. Run the pipeline.
+10. Submit the pipeline.
 
 ### Results
 
diff --git a/articles/machine-learning/algorithm-module-reference/edit-metadata.md b/articles/machine-learning/algorithm-module-reference/edit-metadata.md
@@ -89,7 +89,7 @@ Typical metadata changes might include:
   
     + All selected columns must be renamed. You can't omit or skip columns.  
   
-1. Run the pipeline.  
+1. Submit the pipeline.  
 
 ## Next steps
 
diff --git a/articles/machine-learning/algorithm-module-reference/evaluate-model.md b/articles/machine-learning/algorithm-module-reference/evaluate-model.md
@@ -57,7 +57,7 @@ This feature is useful because you can easily compare results from two different
 
 1. Connect the **Scored dataset** output of the [Score Model](score-model.md) to the input of **Evaluate Model**. 
 2. Connect the output of the Score Model module for the second model to the right-hand input of **Evaluate Model**.
-3. Run the pipeline.
+3. Submit the pipeline.
 
 ## Results
 
diff --git a/articles/machine-learning/algorithm-module-reference/execute-python-script.md b/articles/machine-learning/algorithm-module-reference/execute-python-script.md
@@ -146,7 +146,7 @@ The **Execute Python Script** module contains sample Python code that you can us
 
     + Two datasets can be returned to the designer, which must be a sequence of type `pandas.DataFrame`. You can create other outputs in your Python code and write them directly to Azure storage.
 
-6. Run the pipeline, or select the module and click **Run selected** to run just the Python script.
+6. Submit the pipeline, or select the module and click **Run selected** to run just the Python script.
 
     All of the data and code is loaded into a virtual machine, and run using the specified Python environment.
 
diff --git a/articles/machine-learning/algorithm-module-reference/execute-r-script.md b/articles/machine-learning/algorithm-module-reference/execute-r-script.md
@@ -154,7 +154,7 @@ Datasets stored in the designer are automatically converted to an R data frame w
 
 1.  **Random Seed**: Type a value to use inside the R environment as the random seed value. This parameter is equivalent to calling `set.seed(value)` in R code.  
 
-1. Run the pipeline.  
+1. Submit the pipeline.  
 
 ## Results
 
diff --git a/articles/machine-learning/algorithm-module-reference/export-data.md b/articles/machine-learning/algorithm-module-reference/export-data.md
@@ -47,7 +47,7 @@ Before exporting your data, you need to first register a datastore in your Azure
 
 1. For **File format**, select the format in which data should be stored.
  
-1. Run the pipeline.
+1. Submit the pipeline.
 
 ## Next steps
 
diff --git a/articles/machine-learning/algorithm-module-reference/extract-n-gram-features-from-text.md b/articles/machine-learning/algorithm-module-reference/extract-n-gram-features-from-text.md
@@ -68,7 +68,7 @@ The module supports the following scenarios for using an n-gram dictionary:
 
 1. Select the option **Normalize n-gram feature vectors** to normalize the feature vectors. If this option is enabled, each n-gram feature vector is divided by its L2 norm.
 
-1. Run the pipeline.
+1. Submit the pipeline.
 
 ### Use an existing n-gram dictionary
 
@@ -87,7 +87,7 @@ The module supports the following scenarios for using an n-gram dictionary:
 
 1.  For all other options, see the property descriptions in the [previous section](#create-a-new-n-gram-dictionary).
 
-1.  Run the pipeline.
+1.  Submit the pipeline.
 
 ### Score or publish a model that uses n-grams
 
diff --git a/articles/machine-learning/algorithm-module-reference/feature-hashing.md b/articles/machine-learning/algorithm-module-reference/feature-hashing.md
@@ -89,7 +89,7 @@ Numeric outputs also make it possible to use common machine learning methods, in
 
     For example, if you enter 3, unigrams, bigrams, and trigrams will be created.
 
-1. Run the pipeline.
+1. Submit the pipeline.
 
 ## Results
 
diff --git a/articles/machine-learning/algorithm-module-reference/filter-based-feature-selection.md b/articles/machine-learning/algorithm-module-reference/filter-based-feature-selection.md
@@ -85,7 +85,7 @@ You choose a standard statistical metric. The module computes the correlation be
 
     - If you specify fewer result columns than there are feature columns, the features are ranked by descending score. Only the top features are returned. 
 
-7.  Run the pipeline, or select the Filter Based Feature Selection module and then select **Run selected**.
+7.  Submit the pipeline, or select the Filter Based Feature Selection module and then select **Run selected**.
 
 
 ## Results
diff --git a/articles/machine-learning/algorithm-module-reference/import-data.md b/articles/machine-learning/algorithm-module-reference/import-data.md
@@ -61,7 +61,7 @@ If your source data changes, you can refresh the dataset and add new data by rer
     If it is selected, the system will execute the module again to regenerate output. So select this option when underlying data in storage is updated, it can help to get the latest data.
 
 
-1. Run the pipeline.
+1. Submit the pipeline.
 
     When Import Data loads the data into the designer, it infers the data type of each column based on the values it contains, either numerical or categorical.
 
diff --git a/articles/machine-learning/algorithm-module-reference/join-data.md b/articles/machine-learning/algorithm-module-reference/join-data.md
@@ -58,7 +58,7 @@ To perform a join on two datasets, they should be related by a key column. Compo
     * Select this option to view the keys from both input tables.
     * Deselect to only return the key columns from the left input.
 
-1. Run the pipeline.
+1. Submit the pipeline.
 
 1. To view the results, right-click the **Join Data** and select **Visualize**.
 
diff --git a/articles/machine-learning/algorithm-module-reference/linear-regression.md b/articles/machine-learning/algorithm-module-reference/linear-regression.md
@@ -77,7 +77,7 @@ This module supports two methods for fitting a regression model, with different
 
 7. Add the [Train Model](./train-model.md) module to your pipeline, and connect a labeled dataset.
 
-8. Run the pipeline.
+8. Submit the pipeline.
 
 ### Results for ordinary least squares model
 
@@ -126,7 +126,7 @@ After training is complete:
 
     If you are not using a parameter sweep, use the [Train Model](train-model.md) module.
 
-13. Run the pipeline.
+13. Submit the pipeline.
 
 ### Results for online gradient descent
 
diff --git a/articles/machine-learning/algorithm-module-reference/multiclass-decision-forest.md b/articles/machine-learning/algorithm-module-reference/multiclass-decision-forest.md
@@ -70,7 +70,7 @@ The decision forest classifier in Azure Machine Learning consists of an ensemble
 
     + If you set **Create trainer mode** to **Single Parameter**, use the [Train Model](./train-model.md) module.
 
-11. Run the pipeline.
+11. Submit the pipeline.
 
 
 
diff --git a/articles/machine-learning/algorithm-module-reference/multiclass-logistic-regression.md b/articles/machine-learning/algorithm-module-reference/multiclass-logistic-regression.md
@@ -53,7 +53,7 @@ In multiclass logistic regression, the classifier can be used to predict multipl
 
     + If you set **Create trainer mode** to **Single Parameter**, use the [Train Model](./train-model.md) module.
 
-9. Run the pipeline.
+9. Submit the pipeline.
 
 
 
diff --git a/articles/machine-learning/algorithm-module-reference/neural-network-regression.md b/articles/machine-learning/algorithm-module-reference/neural-network-regression.md
@@ -81,7 +81,7 @@ Neural networks can be extensively customized. This section describes how to cre
     -   If you set **Create trainer mode** to **Single Parameter**, use [Train Model](./train-model.md).  
   
    
-14. Run the pipeline.  
+14. Submit the pipeline.  
 
 ## Results
 
diff --git a/articles/machine-learning/algorithm-module-reference/normalize-data.md b/articles/machine-learning/algorithm-module-reference/normalize-data.md
@@ -87,7 +87,7 @@ You can apply only one normalization method at a time using this module. Therefo
     
       ![normalization using the tanh function](media/module/aml-normalization-tanh.png "AML_normalization-tanh")
 
-6. Run the pipeline, or double-click the **Normalize Data** module and select **Run Selected**. 
+6. Submit the pipeline, or double-click the **Normalize Data** module and select **Run Selected**. 
 
 ## Results
 
diff --git a/articles/machine-learning/algorithm-module-reference/one-vs-all-multiclass.md b/articles/machine-learning/algorithm-module-reference/one-vs-all-multiclass.md
@@ -45,7 +45,7 @@ When you combine the models, One-vs-All Multiclass creates multiple binary class
 
 4. On the other input of [Train Model](train-model.md), connect a labeled training dataset that has multiple class values.
 
-5. Run the pipeline.
+5. Submit the pipeline.
 
 ## Results
 
diff --git a/articles/machine-learning/algorithm-module-reference/partition-and-sample.md b/articles/machine-learning/algorithm-module-reference/partition-and-sample.md
@@ -56,7 +56,7 @@ Use this mode to get only the first *n* rows. This option is useful if you want
 
    The number of rows must be a non-negative integer. If the number of selected rows is larger than the number of rows in the dataset, the entire dataset is returned.
 
-1. Run the pipeline.
+1. Submit the pipeline.
 
 The module outputs a single dataset that contains only the specified number of rows. The rows are always read from the top of the dataset.
 
@@ -89,7 +89,7 @@ This option supports simple random sampling or stratified random sampling. It's
    1. Each group is selectively added to the output dataset to meet the specified ratio.
 
 
-1. Run the pipeline.
+1. Submit the pipeline.
 
    With this option, the module outputs a single dataset that contains a representative sampling of the data. The remaining, unsampled portion of the dataset is not output. 
 
@@ -127,7 +127,7 @@ Use this option when you want to divide the dataset into subsets of the data. Th
 
 1. **Stratified split**: Select this option if you want the rows to be stratified when split, and then choose the _strata column_.
 
-1. Run the pipeline.
+1. Submit the pipeline.
 
    With this option, the module outputs multiple datasets. The datasets are partitioned according to the rules that you specified.
 
@@ -153,7 +153,7 @@ Use this option when you have divided a dataset into multiple partitions and now
 
    ![Partition and sample](./media/module/partition-and-sample.png)
 
-1. Run the pipeline.
+1. Submit the pipeline.
 
    With this option, the module outputs a single dataset that contains only the rows assigned to that fold.
 
diff --git a/articles/machine-learning/algorithm-module-reference/permutation-feature-importance.md b/articles/machine-learning/algorithm-module-reference/permutation-feature-importance.md
@@ -49,7 +49,7 @@ Generating a set of feature scores requires that you have an already trained mod
 
      For a more detailed description of these evaluation metrics and how they're calculated, see [Evaluate Model](evaluate-model.md).  
 
-6.  Run the pipeline.  
+6.  Submit the pipeline.  
 
 7.  The module outputs a list of feature columns and the scores associated with them. The list is ranked in descending order of the scores.  
 
diff --git a/articles/machine-learning/algorithm-module-reference/preprocess-text.md b/articles/machine-learning/algorithm-module-reference/preprocess-text.md
@@ -78,7 +78,7 @@ The **Preprocess Text** module currently only supports English.
 
     For example, the string `MS---WORD` would be separated into three tokens, `MS`, `-`, and `WORD`.
 
-1. Run the pipeline.
+1. Submit the pipeline.
 
 ## Next steps
 
diff --git a/articles/machine-learning/algorithm-module-reference/remove-duplicate-rows.md b/articles/machine-learning/algorithm-module-reference/remove-duplicate-rows.md
@@ -64,7 +64,7 @@ When you run the module, it creates a candidate dataset, and returns a set of ro
     + If selected, the first row is returned and others discarded. 
     + If you uncheck this option, the last duplicate row is kept in the results, and others are discarded. 
 
-5. Run the pipeline.
+5. Submit the pipeline.
 
 6. To review the results, right-click the module, and select **Visualize**. 
 
diff --git a/articles/machine-learning/algorithm-module-reference/score-model.md b/articles/machine-learning/algorithm-module-reference/score-model.md
@@ -25,7 +25,7 @@ Use this module to generate predictions using a trained classification or regres
 
     The data should be in a format compatible with the type of trained model you are using. The schema of the input dataset should also generally match the schema of the data used to train the model.
 
-3. Run the pipeline.
+3. Submit the pipeline.
 
 ## Results
 
diff --git a/articles/machine-learning/algorithm-module-reference/score-svd-recommender.md b/articles/machine-learning/algorithm-module-reference/score-svd-recommender.md
@@ -48,7 +48,7 @@ When you predict ratings, the model calculates how a user will react to a partic
 
    The dataset can contain an optional third column of ratings for the user-item pair in the first and second columns. But the third column will be ignored during prediction.
 
-4. Run the pipeline.
+4. Submit the pipeline.
 
 ### Results for rating predictions 
 
@@ -94,7 +94,7 @@ To recommend items for users, you provide a list of users and items as input. Fr
 
     To apply this filter, connect the original training dataset to the input port.
 
-8. Run the pipeline.
+8. Submit the pipeline.
 
 ### Results of item recommendation
 
diff --git a/articles/machine-learning/algorithm-module-reference/select-columns-transform.md b/articles/machine-learning/algorithm-module-reference/select-columns-transform.md
@@ -43,7 +43,7 @@ This scenario assumes that you want to use feature selection to generate a dynam
 
    > [!IMPORTANT]
    > You can't expect to apply [Filter Based Feature Selection](filter-based-feature-selection.md) to the scoring dataset and get the same results. Because feature selection is based on values, it might choose a different set of columns, which would cause the scoring operation to fail.
-7. Run the pipeline.
+7. Submit the pipeline.
 
 This process of saving and then applying a column selection ensures that the same data schema is available for training and scoring.
 
diff --git a/articles/machine-learning/algorithm-module-reference/smote.md b/articles/machine-learning/algorithm-module-reference/smote.md
@@ -75,7 +75,7 @@ To increase the number of cases, you can set the value of **SMOTE percentage**,
   
 7. Enter a value in the **Random seed** box if you want to ensure the same results over runs of the same pipeline, with the same data. Otherwise, the module generates a random seed based on processor clock values when the pipeline is deployed. The generation of a random seed can cause slightly different results over runs.
 
-8. Run the pipeline.  
+8. Submit the pipeline.  
   
    The output of the module is a dataset that contains the original rows plus a number of added rows with minority cases.  
 
diff --git a/articles/machine-learning/algorithm-module-reference/split-data.md b/articles/machine-learning/algorithm-module-reference/split-data.md
diff --git a/articles/machine-learning/algorithm-module-reference/summarize-data.md b/articles/machine-learning/algorithm-module-reference/summarize-data.md
diff --git a/articles/machine-learning/algorithm-module-reference/train-clustering-model.md b/articles/machine-learning/algorithm-module-reference/train-clustering-model.md
diff --git a/articles/machine-learning/algorithm-module-reference/train-model.md b/articles/machine-learning/algorithm-module-reference/train-model.md
diff --git a/articles/machine-learning/algorithm-module-reference/train-svd-recommender.md b/articles/machine-learning/algorithm-module-reference/train-svd-recommender.md
diff --git a/articles/machine-learning/algorithm-module-reference/tune-model-hyperparameters.md b/articles/machine-learning/algorithm-module-reference/tune-model-hyperparameters.md
diff --git a/articles/machine-learning/algorithm-module-reference/two-class-logistic-regression.md b/articles/machine-learning/algorithm-module-reference/two-class-logistic-regression.md
diff --git a/articles/machine-learning/algorithm-module-reference/two-class-neural-network.md b/articles/machine-learning/algorithm-module-reference/two-class-neural-network.md
diff --git a/articles/machine-learning/algorithm-module-reference/two-class-support-vector-machine.md b/articles/machine-learning/algorithm-module-reference/two-class-support-vector-machine.md
diff --git a/articles/machine-learning/how-to-designer-python.md b/articles/machine-learning/how-to-designer-python.md
diff --git a/articles/machine-learning/how-to-run-batch-predictions-designer.md b/articles/machine-learning/how-to-run-batch-predictions-designer.md
diff --git a/articles/machine-learning/how-to-use-parallel-run-step.md b/articles/machine-learning/how-to-use-parallel-run-step.md
diff --git a/articles/machine-learning/tutorial-designer-automobile-price-train-score.md b/articles/machine-learning/tutorial-designer-automobile-price-train-score.md
diff --git a/articles/machine-learning/tutorial-pipeline-batch-scoring-classification.md b/articles/machine-learning/tutorial-pipeline-batch-scoring-classification.md

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`@@ -80,7 +80,7 @@ The gradient boosting method can also be used for classification problems by red`
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`83`		`-10. Run the pipeline.`
	`83`	`+10. Submit the pipeline.`
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`85`	`85`	`## Results`
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