Merge pull request #107082 from likebupt/update-upload-files-in-script

update python/r script articles about uploading files using sdk

Author: ShannonLeavitt

articles/machine-learning/algorithm-module-reference/execute-python-script.md

@@ -9,7 +9,7 @@ ms.topic: reference
 
 author: likebupt
 ms.author: keli19
-ms.date: 10/22/2019
+ms.date: 03/10/2020
 ---
 # Execute Python Script module
 
@@ -70,7 +70,49 @@ import os
 os.system(f"pip install scikit-misc")
 ```
 
-## How to use
+## Upload files
+The **Execute Python Script** supports uploading files using [Azure Machine Learning Python SDK](https://docs.microsoft.com/python/api/azureml-core/azureml.core.run%28class%29?view=azure-ml-py#upload-file-name--path-or-stream-).
+
+The following example shows how to upload an image file in the **Execute Python Script** module:
+
+```Python
+
+# The script MUST contain a function named azureml_main
+# which is the entry point for this module.
+
+# imports up here can be used to
+import pandas as pd
+
+# The entry point function can contain up to two input arguments:
+#   Param<dataframe1>: a pandas.DataFrame
+#   Param<dataframe2>: a pandas.DataFrame
+def azureml_main(dataframe1 = None, dataframe2 = None):
+
+    # Execution logic goes here
+    print(f'Input pandas.DataFrame #1: {dataframe1}')
+
+    from matplotlib import pyplot as plt
+    plt.plot([1, 2, 3, 4])
+    plt.ylabel('some numbers')
+    img_file = "line.png"
+    plt.savefig(img_file)
+
+    from azureml.core import Run
+    run = Run.get_context(allow_offline=True)
+    run.upload_file(f"graphics/{img_file}", img_file)
+
+    # Return value must be of a sequence of pandas.DataFrame
+    # E.g.
+    #   -  Single return value: return dataframe1,
+    #   -  Two return values: return dataframe1, dataframe2
+    return dataframe1,
+}
+```
+
+After the pipeline is submitted successfully, you can preview the image in the right panel of the module
+![Uploaded-image](media/module/upload-image-in-python-script.png)
+
+## How to configure Execute Python Script
 
 The **Execute Python Script** module contains sample Python code that you can use as a starting point. To configure the **Execute Python Script** module, you provide a set of inputs and Python code to execute in the **Python script** text box.
 

articles/machine-learning/algorithm-module-reference/execute-r-script.md

@@ -9,7 +9,7 @@ ms.topic: reference
 
 author: likebupt
 ms.author: keli19
-ms.date: 11/19/2019
+ms.date: 03/10/2020
 ---
 
 # Execute R Script
@@ -63,11 +63,44 @@ azureml_main <- function(dataframe1, dataframe2){
  > [!NOTE]
   > Please check if the package already exists before install it to avoid repeat installing. Like `  if(!require(zoo)) install.packages("zoo",repos = "http://cran.us.r-project.org")`  in above sample code. Repeat installing may cause web service request timeout.     
 
+## Upload files
+The **Execute R Script** supports uploading files using Azure Machine Learning R SDK.
+
+The following example shows how to upload an image file in the **Execute R Script**:
+```R
+
+# R version: 3.5.1
+# The script MUST contain a function named azureml_main
+# which is the entry point for this module.
+
+# The entry point function can contain up to two input arguments:
+#   Param<dataframe1>: a R DataFrame
+#   Param<dataframe2>: a R DataFrame
+azureml_main <- function(dataframe1, dataframe2){
+  print("R script run.")
+
+  # Generate a jpeg graph
+  img_file_name <- "rect.jpg"
+  jpeg(file=img_file_name)
+  example(rect)
+  dev.off()
+
+  upload_files_to_run(names = list(file.path("graphic", img_file_name)), paths=list(img_file_name))
+
+
+  # Return datasets as a Named List
+  return(list(dataset1=dataframe1, dataset2=dataframe2))
+}
+```
+
+After the pipeline is submitted successfully, you can preview the image in the right panel of the module
+![Uploaded-image](media/module/upload-image-in-r-script.png)
+
 ## How to configure Execute R Script
 
 The **Execute R Script** module contains sample code that you can use as a starting point. To configure the **Execute R Script** module, provide a set of inputs and code to execute.
 
-![R-module](media/module/execute-r-script.png)
+![R-module](media/module/upload-image-in-r-script.png)
 
 Datasets stored in the designer are automatically converted to an R data frame when loaded with this module.
 
@@ -183,7 +216,7 @@ azureml_main <- function(dataframe1, dataframe2){
 
 This sample shows how to use a dataset in a ZIP file as an input to the **Execute R Script** module.
 
-1. Create the data file in CSV format, and name it “mydatafile.csv”.
+1. Create the data file in CSV format, and name it "mydatafile.csv".
 1. Create a ZIP file and add the CSV file to the archive.
 1. Upload the zipped file to your Azure Machine Learning workspace. 
 1. Connect the resulting dataset to the **ScriptBundle** input of your **Execute R Script** module.
@@ -219,7 +252,7 @@ azureml_main <- function(dataframe1, dataframe2){
 
 You can pass R objects between instances of the **Execute R Script** module by using the internal serialization mechanism. This example assumes that you want to move the R object named `A` between two **Execute R Script** modules.
 
-1. Add the first **Execute R Script** module to your pipeline, and type the following code in the **R Script** text box to create a serialized object `A` as a column in the module’s output Data Table:  
+1. Add the first **Execute R Script** module to your pipeline, and type the following code in the **R Script** text box to create a serialized object `A` as a column in the module's output Data Table:  
 
     ```R
     azureml_main <- function(dataframe1, dataframe2){