update samples from Release-193 as a part of 1.53.0 SDK stable release

README.md

@@ -1,8 +1,6 @@
 # Azure Machine Learning Python SDK notebooks
 
-
-** **With the introduction of AzureML SDK v2, this samples repository for the v1 SDK is now deprecated and will not be monitored or updated. Users are encouraged to visit the [v2 SDK samples repository](https://github.com/Azure/azureml-examples) instead for up-to-date and enhanced examples of how to build, train, and deploy machine learning models with AzureML's newest features.** **
-
+> a community-driven repository of examples using mlflow for tracking can be found at https://github.com/Azure/azureml-examples
 
 Welcome to the Azure Machine Learning Python SDK notebooks repository!
 

configuration.ipynb

@@ -103,7 +103,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.51.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.53.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -329,7 +329,7 @@
         "    print(\"Creating new gpu-cluster\")\n",
         "    \n",
         "    # Specify the configuration for the new cluster\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_NC6\",\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size=\"Standard_NC6s_v3\",\n",
         "                                                           min_nodes=0,\n",
         "                                                           max_nodes=4)\n",
         "    # Create the cluster with the specified name and configuration\n",

contrib/RAPIDS/azure-ml-with-nvidia-rapids.ipynb

@@ -174,7 +174,7 @@
         "else:\n",
         "    print(\"creating new cluster\")\n",
         "    # vm_size parameter below could be modified to one of the RAPIDS-supported VM types\n",
-        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"Standard_NC6s_v2\", min_nodes=1, max_nodes = 1)\n",
+        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"Standard_NC6s_v3\", min_nodes=1, max_nodes = 1)\n",
         "\n",
         "    # create the cluster\n",
         "    gpu_cluster = ComputeTarget.create(ws, gpu_cluster_name, provisioning_config)\n",

contrib/fairness/fairlearn-azureml-mitigation.yml

@@ -3,10 +3,10 @@ dependencies:
 - pip:
   - azureml-sdk
   - azureml-contrib-fairness
-  - fairlearn>=0.6.2
+  - fairlearn>=0.6.2,<=0.7.0
   - joblib
   - liac-arff
-  - raiwidgets~=0.26.0
+  - raiwidgets~=0.28.0
   - itsdangerous==2.0.1
   - markupsafe<2.1.0
   - protobuf==3.20.0

contrib/fairness/upload-fairness-dashboard.yml

@@ -3,10 +3,10 @@ dependencies:
 - pip:
   - azureml-sdk
   - azureml-contrib-fairness
-  - fairlearn>=0.6.2
+  - fairlearn>=0.6.2,<=0.7.0
   - joblib
   - liac-arff
-  - raiwidgets~=0.26.0
+  - raiwidgets~=0.28.0
   - itsdangerous==2.0.1
   - markupsafe<2.1.0
   - protobuf==3.20.0

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

@@ -7,7 +7,8 @@ dependencies:
   # The python interpreter version.
   # Azure ML only supports 3.7.0 and later.
 - pip==22.3.1
-- python>=3.7,<3.9
+- python>=3.8,<3.9
+- holidays==0.10.3
 - conda-forge::fbprophet==0.7.1
 - pandas==1.1.5
 - scipy==1.5.3
@@ -16,10 +17,10 @@ dependencies:
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.51.0
-  - azureml-defaults~=1.51.0
-  - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.51.0/validated_win32_requirements.txt [--no-deps]
-  - matplotlib==3.6.2
+  - azureml-widgets~=1.53.0
+  - azureml-defaults~=1.53.0
+  - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.53.0/validated_win32_requirements.txt [--no-deps]
+  - matplotlib==3.7.1
   - xgboost==1.3.3
   - cmdstanpy==0.9.5
   - setuptools-git==1.2

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

@@ -7,13 +7,13 @@ dependencies:
   # The python interpreter version.
   # Azure ML only supports 3.7 and later.
 - pip==22.3.1
-- python>=3.7,<3.9
-- matplotlib==3.2.1
+- python>=3.8,<3.9
+- matplotlib==3.7.1
 - numpy>=1.21.6,<=1.22.3
 - cython==0.29.14
 - urllib3==1.26.7
 - scipy>=1.4.1,<=1.5.3
-- scikit-learn==0.22.1
+- scikit-learn==1.1.0
 - py-xgboost<=1.3.3
 - holidays==0.10.3
 - conda-forge::fbprophet==0.7.1
@@ -23,10 +23,10 @@ dependencies:
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.51.0
-  - azureml-defaults~=1.51.0
+  - azureml-widgets~=1.53.0
+  - azureml-defaults~=1.53.0
   - 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/1.51.0/validated_linux_requirements.txt [--no-deps]
+  - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.53.0/validated_linux_requirements.txt [--no-deps]

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

@@ -7,26 +7,22 @@ dependencies:
   # The python interpreter version.
   # Currently Azure ML only supports 3.7 and later.
 - pip==22.3.1
-- python>=3.7,<3.9
-- matplotlib==3.2.1
+- python>=3.8,<3.9
 - numpy>=1.21.6,<=1.22.3
 - cython==0.29.14
-- urllib3==1.26.7
 - scipy>=1.4.1,<=1.5.3
-- scikit-learn==0.22.1
-- py-xgboost<=1.3.3
+- scikit-learn==1.1.0
 - holidays==0.10.3
-- pytorch::pytorch=1.11.0
-- cudatoolkit=9.0
-- notebook
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.51.0
-  - azureml-defaults~=1.51.0
+  - azureml-widgets~=1.53.0
+  - azureml-defaults~=1.53.0
   - pytorch-transformers==1.0.0
   - spacy==2.2.4
   - pystan==2.19.1.1
   - fbprophet==0.7.1
+  - xgboost==1.3.3
+  - matplotlib==3.7.1
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-  - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.51.0/validated_darwin_requirements.txt [--no-deps]
+  - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.53.0/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

@@ -1,5 +1,21 @@
 {
   "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/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -830,9 +846,7 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {
-        "scrolled": true
-      },
+      "metadata": {},
       "outputs": [],
       "source": [
         "%matplotlib notebook\n",

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

@@ -1,5 +1,21 @@
 {
   "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/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},

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

@@ -1,5 +1,21 @@
 {
   "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/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -139,8 +155,8 @@
         "    print(\"Found existing cluster, use it.\")\n",
         "except ComputeTargetException:\n",
         "    compute_config = AmlCompute.provisioning_configuration(\n",
-        "        vm_size=\"STANDARD_NC6\",  # CPU for BiLSTM, such as \"STANDARD_D2_V2\"\n",
-        "        # To use BERT (this is recommended for best performance), select a GPU such as \"STANDARD_NC6\"\n",
+        "        vm_size=\"Standard_NC6s_v3\",  # CPU for BiLSTM, such as \"STANDARD_D2_V2\"\n",
+        "        # To use BERT (this is recommended for best performance), select a GPU such as \"Standard_NC6s_v3\"\n",
         "        # or similar GPU option\n",
         "        # available in your workspace\n",
         "        idle_seconds_before_scaledown=60,\n",
@@ -336,7 +352,7 @@
       "metadata": {},
       "source": [
         "For local inferencing, you can load the model locally via. the method `remote_run.get_output()`. For more information on the arguments expected by this method, you can run `remote_run.get_output??`.\n",
-        "Note that when the model contains BERT, this step will require pytorch and pytorch-transformers installed in your local environment. The exact versions of these packages can be found in the **automl_env.yml** file located in the local copy of your azureml-examples folder here: \"azureml-examples/python-sdk/tutorials/automl-with-azureml\""
+        "Note that when the model contains BERT, this step will require pytorch and pytorch-transformers installed in your local environment. The exact versions of these packages can be found in the **automl_env.yml** file located in the local copy of your MachineLearningNotebooks folder here: \"MachineLearningNotebooks\\how-to-use-azureml\\automated-machine-learning\""
       ]
     },
     {

how-to-use-azureml/automated-machine-learning/classification-text-dnn/helper.py

@@ -1,3 +1,4 @@
+import json
 import pandas as pd
 from azureml.core import Environment, ScriptRunConfig
 from azureml.core.run import Run
@@ -13,7 +14,16 @@ def run_inference(
     model_name,
 ):
 
-    inference_env = train_run.get_environment()
+    try:
+        inference_env = train_run.get_environment()
+    except BaseException:
+        run_details = train_run.get_details()
+        run_def = run_details.get("runDefinition")
+        env = run_def.get("environment")
+        if env is None:
+            raise
+        json.dump(env, open("azureml_environment.json", "w"))
+        inference_env = Environment.load_from_directory(".")
 
     est = ScriptRunConfig(
         source_directory=script_folder,

how-to-use-azureml/automated-machine-learning/classification-text-dnn/infer.py

@@ -3,7 +3,7 @@
 import pandas as pd
 import numpy as np
 
-from sklearn.externals import joblib
+import joblib
 
 from azureml.automl.runtime.shared.score import scoring, constants
 from azureml.core import Run, Dataset

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

@@ -1,5 +1,21 @@
 {
   "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/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},

how-to-use-azureml/automated-machine-learning/continuous-retraining/check_data.py

@@ -31,7 +31,7 @@
     model = Model(ws, args.model_name)
     last_train_time = model.created_time
     print("Model was last trained on {0}.".format(last_train_time))
-except Exception as e:
+except Exception:
     print("Could not get last model train time.")
     last_train_time = datetime.min.replace(tzinfo=pytz.UTC)
 

how-to-use-azureml/automated-machine-learning/experimental/autofeaturization-codegen/codegen-for-autofeaturization.ipynb

@@ -97,7 +97,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.51.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.53.0 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/autofeaturization-custom-model-training/custom-model-training-from-autofeaturization-run.ipynb

@@ -97,7 +97,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.51.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.53.0 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/automl_thin_client_env.yml

@@ -1,21 +1,9 @@
 name: azure_automl_experimental
 dependencies:
   # The python interpreter version.
-<<<<<<< HEAD
-  # Currently Azure ML only supports 3.6.0 and later.
-- pip<=20.2.4
-- python>=3.6.0,<3.10
-- cython==0.29.14
-- urllib3==1.26.7
-- PyJWT < 2.0.0
-- numpy==1.22.3
-- pywin32==227
-- cryptography<37.0.0
-=======
   # Currently Azure ML only supports 3.7.0 and later.
 - pip<=22.3.1
 - python>=3.7.0,<3.11
->>>>>>> 4671acd451ce979c3cebcd3917804861a333b710
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
@@ -25,4 +13,3 @@ dependencies:
   - azureml-mlflow
   - pandas
   - mlflow
-  - docker<6.0.0

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 1.51.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.53.0 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 1.51.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.53.0 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-backtest-many-models/auto-ml-forecasting-backtest-many-models.ipynb

@@ -13,7 +13,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/forecasting-hierarchical-timeseries/auto-ml-forecasting-hierarchical-timeseries.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/forecasting-backtest-many-models/auto-ml-forecasting-backtest-many-models.png)"
       ]
     },
     {

how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/auto-ml-forecasting-backtest-single-model.ipynb

@@ -7,7 +7,7 @@
         "Copyright (c) Microsoft Corporation. All rights reserved.\n",
         "\n",
         "Licensed under the MIT License.\n",
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/automl-forecasting-function.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/auto-ml-forecasting-backtest-single-model.png)"
       ]
     },
     {