update samples from Release-134 as a part of SDK release

configuration.ipynb

@@ -103,7 +103,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.40.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version Latest 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

@@ -21,10 +21,10 @@ dependencies:
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.40.0
+  - azureml-widgets~=Latest
   - pytorch-transformers==1.0.0
   - spacy==2.2.4
   - pystan==2.19.1.1
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-  - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.40.0/validated_win32_requirements.txt [--no-deps]
+  - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/Latest/validated_win32_requirements.txt [--no-deps]
   - arch==4.14

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

@@ -24,10 +24,10 @@ dependencies:
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.40.0
+  - azureml-widgets~=Latest
   - pytorch-transformers==1.0.0
   - spacy==2.2.4
   - pystan==2.19.1.1
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-  - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.40.0/validated_linux_requirements.txt [--no-deps]
+  - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/Latest/validated_linux_requirements.txt [--no-deps]
   - arch==4.14

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

@@ -25,10 +25,10 @@ dependencies:
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.40.0
+  - azureml-widgets~=Latest
   - pytorch-transformers==1.0.0
   - spacy==2.2.4
   - pystan==2.19.1.1
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-  - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.40.0/validated_darwin_requirements.txt [--no-deps]
+  - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/Latest/validated_darwin_requirements.txt [--no-deps]
   - arch==4.14

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 1.40.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version Latest 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

@@ -75,7 +75,6 @@
         "from azureml.core.experiment import Experiment\n",
         "from azureml.core.workspace import Workspace\n",
         "from azureml.core.dataset import Dataset\n",
-        "from azureml.data.dataset_factory import TabularDatasetFactory\n",
         "from azureml.train.automl import AutoMLConfig"
       ]
     },
@@ -92,7 +91,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.40.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version Latest of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -197,10 +196,10 @@
       "source": [
         "ds = ws.get_default_datastore()\n",
         "\n",
-        "train_data = TabularDatasetFactory.register_pandas_dataframe(\n",
+        "train_data = Dataset.Tabular.register_pandas_dataframe(\n",
         "    train_data.to_pandas_dataframe(), target=(ds, \"machineTrainData\"), name=\"train_data\")\n",
         "\n",
-        "test_data = TabularDatasetFactory.register_pandas_dataframe(\n",
+        "test_data = Dataset.Tabular.register_pandas_dataframe(\n",
         "    test_data.to_pandas_dataframe(), target=(ds, \"machineTestData\"), name=\"test_data\")"
       ]
     },
@@ -328,7 +327,8 @@
       "metadata": {},
       "source": [
         "#### Show hyperparameters\n",
-        "Show the model pipeline used for the best run with its hyperparameters."
+        "Show the model pipeline used for the best run with its hyperparameters.\n",
+        "For ensemble pipelines it shows the iterations and algorithms that are ensembled."
       ]
     },
     {
@@ -337,8 +337,19 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "run_properties = json.loads(best_run.get_details()['properties']['pipeline_script'])\n",
-        "print(json.dumps(run_properties, indent = 1)) "
+        "run_properties = best_run.get_details()['properties']\n",
+        "pipeline_script = json.loads(run_properties['pipeline_script'])\n",
+        "print(json.dumps(pipeline_script, indent = 1)) \n",
+        "\n",
+        "if 'ensembled_iterations' in run_properties:\n",
+        "    print(\"\")\n",
+        "    print(\"Ensembled Iterations\")\n",
+        "    print(run_properties['ensembled_iterations'])\n",
+        "    \n",
+        "if 'ensembled_algorithms' in run_properties:\n",
+        "    print(\"\")\n",
+        "    print(\"Ensembled Algorithms\")\n",
+        "    print(run_properties['ensembled_algorithms'])"
       ]
     },
     {

how-to-use-azureml/explain-model/azure-integration/gpu-explanation/train-explain-model-gpu-tree-explainer.ipynb

@@ -106,7 +106,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.40.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version Latest of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

how-to-use-azureml/responsible-ai/auto-ml-regression-responsibleai/auto-ml-regression-responsibleai.ipynb

@@ -95,7 +95,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.40.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version Latest of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

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 1.40.0, you are currently running version\", azureml.core.VERSION)"
+        "print(\"This notebook was created using SDK version Latest, you are currently running version\", azureml.core.VERSION)"
       ]
     },
     {
@@ -363,6 +363,43 @@
         "run.log_image(name='Hyperbolic Tangent', plot=plt)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Logging for when more Metric Names are required\n",
+        "\n",
+        "Limits on logging are internally enforced to ensure a smooth experience, however these can sometimes be limiting, particularly in terms of the limit on metric names.\n",
+        "\n",
+        "The \"Logging Vectors\" or \"Logging Tables\" examples previously can be expanded upon to use up to 15 columns to increase this limit, with the information still being presented in Run Details as a chart, and being directly comparable in experiment reports.\n",
+        "\n",
+        "**Note:** see [Azure Machine Learning Limits Documentation](https://aka.ms/azure-machine-learning-limits) for more information on service limits.\n",
+        "**Note:** tables logged into the run are expected to be relatively small. Logging very large tables into Azure ML can result in reduced performance. If you need to store large amounts of data associated with the run, you can write the data to file that will be uploaded."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import random\n",
+        "metricNames = [ \"Accuracy\", \"Precision\", \"Recall\" ]\n",
+        "columnNames = [ \"expected\", \"actual\", \"calculated\", \"inferred\", \"determined\", \"predicted\", \"forecast\", \"speculated\", \"assumed\", \"required\", \"intended\", \"deduced\", \"theorized\", \"hoped\", \"hypothesized\" ]\n",
+        "\n",
+        "for step in range(1000):\n",
+        "    for metricName in metricNames:\n",
+        "\n",
+        "        metricKeyValueDictionary={}\n",
+        "        for column in columnNames:\n",
+        "            metricKeyValueDictionary[column] = random.randrange(0, step + 1)\n",
+        "\n",
+        "        run.log_row(\n",
+        "            metricName,\n",
+        "            \"Example row for metric \" + metricName,\n",
+        "            **metricKeyValueDictionary)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -498,7 +535,6 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "import os\n",
         "os.makedirs('files', exist_ok=True)\n",
         "\n",
         "for f in run.get_file_names():\n",

setup-environment/configuration.ipynb

@@ -102,7 +102,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.40.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version Latest of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },