update samples from Release-92 as a part of 1.31.0 SDK stable release

configuration.ipynb

@@ -103,7 +103,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.30.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.31.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

how-to-use-azureml/automated-machine-learning/automl_env.yml

@@ -21,8 +21,8 @@ dependencies:
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.30.0
+  - azureml-widgets~=1.31.0
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-  - -r https://automlresources-prod.azureedge.net/validated-requirements/1.30.0/validated_win32_requirements.txt [--no-deps]
+  - -r https://automlresources-prod.azureedge.net/validated-requirements/1.31.0/validated_win32_requirements.txt [--no-deps]

how-to-use-azureml/automated-machine-learning/automl_env_linux.yml

@@ -21,8 +21,8 @@ dependencies:
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.30.0
+  - azureml-widgets~=1.31.0
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-  - -r https://automlresources-prod.azureedge.net/validated-requirements/1.30.0/validated_linux_requirements.txt [--no-deps]
+  - -r https://automlresources-prod.azureedge.net/validated-requirements/1.31.0/validated_linux_requirements.txt [--no-deps]

how-to-use-azureml/automated-machine-learning/automl_env_mac.yml

@@ -22,8 +22,8 @@ dependencies:
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.30.0
+  - azureml-widgets~=1.31.0
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-  - -r https://automlresources-prod.azureedge.net/validated-requirements/1.30.0/validated_darwin_requirements.txt [--no-deps]
+  - -r https://automlresources-prod.azureedge.net/validated-requirements/1.31.0/validated_darwin_requirements.txt [--no-deps]

how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb

@@ -105,7 +105,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.30.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.31.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb

@@ -93,7 +93,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.30.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.31.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.ipynb

@@ -96,7 +96,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.30.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.31.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb

@@ -81,7 +81,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.30.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.31.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

how-to-use-azureml/automated-machine-learning/experimental/classification-credit-card-fraud-local-managed/auto-ml-classification-credit-card-fraud-local-managed.ipynb

@@ -92,7 +92,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.30.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.31.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.ipynb

@@ -91,7 +91,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.30.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.31.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb

@@ -113,7 +113,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.30.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.31.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb

@@ -87,7 +87,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.30.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.31.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb

@@ -97,7 +97,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.30.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.31.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/auto-ml-forecasting-function.ipynb

@@ -94,7 +94,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.30.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.31.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb

@@ -82,7 +82,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.30.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.31.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -728,7 +728,7 @@
         "X_query[time_column_name] = X_query[time_column_name].astype(str)\n",
         "# The Service object accept the complex dictionary, which is internally converted to JSON string.\n",
         "# The section 'data' contains the data frame in the form of dictionary.\n",
-        "test_sample = json.dumps({'data': X_query.to_dict(orient='records')})\n",
+        "test_sample = json.dumps({\"data\": json.loads(X_query.to_json(orient=\"records\"))})\n",
         "response = aci_service.run(input_data = test_sample)\n",
         "# translate from networkese to datascientese\n",
         "try: \n",

how-to-use-azureml/automated-machine-learning/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.ipynb

@@ -96,7 +96,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.30.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.31.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -436,7 +436,8 @@
         "\n",
         "automl_explainer_setup_obj = automl_setup_model_explanations(fitted_model, X=X_train, \n",
         "                                                             X_test=X_test, y=y_train, \n",
-        "                                                             task='classification')"
+        "                                                             task='classification',\n",
+        "                                                             automl_run=automl_run)"
       ]
     },
     {
@@ -453,11 +454,10 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from interpret.ext.glassbox import LGBMExplainableModel\n",
         "from azureml.interpret.mimic_wrapper import MimicWrapper\n",
         "explainer = MimicWrapper(ws, automl_explainer_setup_obj.automl_estimator,\n",
         "                         explainable_model=automl_explainer_setup_obj.surrogate_model, \n",
-        "                         init_dataset=automl_explainer_setup_obj.X_transform, run=automl_run,\n",
+        "                         init_dataset=automl_explainer_setup_obj.X_transform, run=automl_explainer_setup_obj.automl_run,\n",
         "                         features=automl_explainer_setup_obj.engineered_feature_names, \n",
         "                         feature_maps=[automl_explainer_setup_obj.feature_map],\n",
         "                         classes=automl_explainer_setup_obj.classes,\n",

how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/auto-ml-regression-explanation-featurization.ipynb

@@ -96,7 +96,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.30.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.31.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/train_explainer.py

@@ -50,11 +50,13 @@ X_test = test_dataset.drop_columns(columns=['<<target_column_name>>'])
 # Setup the class for explaining the AutoML models
 automl_explainer_setup_obj = automl_setup_model_explanations(fitted_model, '<<task>>',
                                                              X=X_train, X_test=X_test,
-                                                             y=y_train)
+                                                             y=y_train,
+                                                             automl_run=automl_run)
 
 # Initialize the Mimic Explainer
 explainer = MimicWrapper(ws, automl_explainer_setup_obj.automl_estimator, LGBMExplainableModel,
-                         init_dataset=automl_explainer_setup_obj.X_transform, run=automl_run,
+                         init_dataset=automl_explainer_setup_obj.X_transform,
+                         run=automl_explainer_setup_obj.automl_run,
                          features=automl_explainer_setup_obj.engineered_feature_names,
                          feature_maps=[automl_explainer_setup_obj.feature_map],
                          classes=automl_explainer_setup_obj.classes)

how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb

@@ -92,7 +92,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.30.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.31.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

how-to-use-azureml/azure-synapse/Synapse_Job_Scala_Support.ipynb

@@ -0,0 +1,160 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved. \n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/azure-arcadia/Synapse_Job_Scala_Support.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Get AML workspace which has synapse spark pool attached"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Workspace, Experiment, Dataset, Environment\n",
+        "\n",
+        "ws = Workspace.from_config()\n",
+        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Leverage ScriptRunConfig to submit scala job to an attached synapse spark cluster"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.runconfig import RunConfiguration\n",
+        "from azureml.data import HDFSOutputDatasetConfig\n",
+        "import uuid\n",
+        "\n",
+        "run_config = RunConfiguration(framework=\"pyspark\")\n",
+        "run_config.target = \"link-pool\"\n",
+        "run_config.spark.configuration[\"spark.driver.memory\"] = \"2g\"\n",
+        "run_config.spark.configuration[\"spark.driver.cores\"] = 2\n",
+        "run_config.spark.configuration[\"spark.executor.memory\"] = \"2g\"\n",
+        "run_config.spark.configuration[\"spark.executor.cores\"] = 1\n",
+        "run_config.spark.configuration[\"spark.executor.instances\"] = 1\n",
+        "\n",
+        "run_config.spark.configuration[\"spark.yarn.dist.jars\"]=\"wasbs://synapse@azuremlexamples.blob.core.windows.net/shared/wordcount.jar\"  # this can be removed if you are using local jars in source folder\n",
+        "\n",
+        "dir_name = \"wordcount-{}\".format(str(uuid.uuid4()))\n",
+        "input = \"wasbs://synapse@azuremlexamples.blob.core.windows.net/shared/shakespeare.txt\"\n",
+        "output = HDFSOutputDatasetConfig(destination=(ws.get_default_datastore(), \"{}/result\".format(dir_name)))\n",
+        "\n",
+        "from azureml.core import ScriptRunConfig\n",
+        "args = ['--input', input, '--output', output]\n",
+        "script_run_config = ScriptRunConfig(source_directory = '.',\n",
+        "                                    script= 'start_script.py',\n",
+        "                                    arguments= args,\n",
+        "                                    run_config = run_config)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Experiment\n",
+        "exp = Experiment(workspace=ws, name='synapse-spark')\n",
+        "run = exp.submit(config=script_run_config)\n",
+        "run"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Leverage SynapseSparkStep in an AML pipeline to add dataprep step on synapse spark cluster"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.pipeline.core import Pipeline\n",
+        "from azureml.pipeline.steps import SynapseSparkStep\n",
+        "\n",
+        "configs = {}\n",
+        "configs[\"spark.yarn.dist.jars\"] = \"wasbs://synapse@azuremlexamples.blob.core.windows.net/shared/wordcount.jar\"\n",
+        "step_1 = SynapseSparkStep(name = 'synapse-spark',\n",
+        "                          file = 'start_script.py',\n",
+        "                          source_directory=\".\",\n",
+        "                          arguments = args,\n",
+        "                          compute_target = 'link-pool',\n",
+        "                          driver_memory = \"2g\",\n",
+        "                          driver_cores = 2,\n",
+        "                          executor_memory = \"2g\",\n",
+        "                          executor_cores = 1,\n",
+        "                          num_executors = 1,\n",
+        "                          conf = configs)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pipeline = Pipeline(workspace=ws, steps=[step_1])\n",
+        "pipeline_run = pipeline.submit('synapse-pipeline', regenerate_outputs=True)"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "feli1"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.5"
+    },
+    "nteract": {
+      "version": "0.28.0"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

how-to-use-azureml/azure-synapse/Synapse_Session_Scala_Support.ipynb

@@ -0,0 +1,240 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved. \n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/azure-arcadia/Synapse_Session_Scala_Support.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Interactive Spark Session on Synapse Spark Pool"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "!pip install -U \"azureml-synapse\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "For JupyterLab, please additionally run:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "!jupyter lab build --minimize=False"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## PLEASE restart kernel and then refresh web page before starting spark session."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 0. Magic Usage"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2020-06-05T03:22:14.965395Z",
+          "iopub.status.busy": "2020-06-05T03:22:14.965395Z",
+          "iopub.status.idle": "2020-06-05T03:22:14.970398Z",
+          "shell.execute_reply": "2020-06-05T03:22:14.969397Z",
+          "shell.execute_reply.started": "2020-06-05T03:22:14.965395Z"
+        },
+        "gather": {
+          "logged": 1615594584642
+        }
+      },
+      "outputs": [],
+      "source": [
+        "# show help\n",
+        "%synapse ?"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 1. Start Synapse Session"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "gather": {
+          "logged": 1615577715289
+        }
+      },
+      "outputs": [],
+      "source": [
+        "%synapse start -c linktestpool --start-timeout 1000"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "source": [
+        "# 2. Use Scala"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "source": [
+        "## (1) Read Data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "jupyter": {
+          "outputs_hidden": false,
+          "source_hidden": false
+        },
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "outputs": [],
+      "source": [
+        "%%synapse scala\n",
+        "\n",
+        "var df = spark.read.option(\"header\", \"true\").csv(\"wasbs://demo@dprepdata.blob.core.windows.net/Titanic.csv\")\n",
+        "df.show(5)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## (2) Use Scala Sql"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "jupyter": {
+          "outputs_hidden": false,
+          "source_hidden": false
+        },
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "outputs": [],
+      "source": [
+        "%%synapse scala\n",
+        "\n",
+        "df.createOrReplaceTempView(\"titanic\")\n",
+        "var sqlDF = spark.sql(\"SELECT Name, Fare from titanic\")\n",
+        "sqlDF.show(5)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Stop Session"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "jupyter": {
+          "outputs_hidden": false,
+          "source_hidden": false
+        },
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "outputs": [],
+      "source": [
+        "%synapse stop"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "feli1"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.5"
+    },
+    "nteract": {
+      "version": "0.28.0"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4
+}

how-to-use-azureml/azure-synapse/start_script.py

@@ -0,0 +1,18 @@
+from pyspark.sql import SparkSession
+
+import argparse
+parser = argparse.ArgumentParser()
+parser.add_argument("--input", default="")
+parser.add_argument("--output", default="")
+
+args, unparsed = parser.parse_known_args()
+
+spark = SparkSession.builder.getOrCreate()
+sc = spark.sparkContext
+
+arr = sc._gateway.new_array(sc._jvm.java.lang.String, 2)
+arr[0] = args.input
+arr[1] = args.output
+
+obj = sc._jvm.WordCount
+obj.main(arr)

how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb

@@ -100,7 +100,7 @@
         "\n",
         "# Check core SDK version number\n",
         "\n",
-        "print(\"This notebook was created using SDK version 1.30.0, you are currently running version\", azureml.core.VERSION)"
+        "print(\"This notebook was created using SDK version 1.31.0, you are currently running version\", azureml.core.VERSION)"
       ]
     },
     {

index.md

@@ -112,6 +112,8 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an
 | [automl-databricks-local-with-deployment](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/azure-databricks/automl/automl-databricks-local-with-deployment.ipynb) |  |  |  |  |  |  |
 | [spark_job_on_synapse_spark_pool](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/azure-synapse/spark_job_on_synapse_spark_pool.ipynb) |  |  |  |  |  |  |
 | [spark_session_on_synapse_spark_pool](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/azure-synapse/spark_session_on_synapse_spark_pool.ipynb) |  |  |  |  |  |  |
+| [Synapse_Job_Scala_Support](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/azure-synapse/Synapse_Job_Scala_Support.ipynb) |  |  |  |  |  |  |
+| [Synapse_Session_Scala_Support](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/azure-synapse/Synapse_Session_Scala_Support.ipynb) |  |  |  |  |  |  |
 | [multi-model-register-and-deploy](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/deploy-multi-model/multi-model-register-and-deploy.ipynb) |  |  |  |  |  |  |
 | [register-model-deploy-local-advanced](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local-advanced.ipynb) |  |  |  |  |  |  |
 | [enable-app-insights-in-production-service](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb) |  |  |  |  |  |  |

setup-environment/configuration.ipynb

@@ -102,7 +102,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.30.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.31.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },