update samples from Release-140 as a part of SDK release

update samples from Release-138 as a part of  SDK release

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

@@ -90,6 +90,7 @@
         "output[\"Resource Group\"] = ws.resource_group\n",
         "output[\"Location\"] = ws.location\n",
         "output[\"Experiment Name\"] = experiment.name\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
         "pd.set_option(\"display.max_colwidth\", None)\n",
         "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
         "outputDf.T"

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

@@ -101,6 +101,7 @@
         "output[\"Resource Group\"] = ws.resource_group\n",
         "output[\"Location\"] = ws.location\n",
         "output[\"Experiment Name\"] = experiment.name\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
         "pd.set_option(\"display.max_colwidth\", None)\n",
         "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
         "outputDf.T"

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

@@ -102,6 +102,7 @@
         "output[\"Resource Group\"] = ws.resource_group\n",
         "output[\"Location\"] = ws.location\n",
         "output[\"Run History Name\"] = experiment_name\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
         "pd.set_option(\"display.max_colwidth\", None)\n",
         "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
         "outputDf.T"

how-to-use-azureml/automated-machine-learning/forecasting-backtest-many-models/assets/score.py

@@ -5,6 +5,7 @@ import json
 import os
 import re
 
+import numpy as np
 import pandas as pd
 
 from matplotlib import pyplot as plt
@@ -146,6 +147,9 @@ def calculate_scores_and_build_plots(
         _draw_one_plot(one_forecast, time_column_name, grains, pdf)
     pdf.close()
     forecast_df.to_csv(os.path.join(output_dir, FORECASTS_FILE), index=False)
+    # Remove np.NaN and np.inf from the prediction and actuals data.
+    forecast_df.replace([np.inf, -np.inf], np.nan, inplace=True)
+    forecast_df.dropna(subset=[ACTUALS, PREDICTIONS], inplace=True)
     metrics = compute_all_metrics(forecast_df, grains + [BACKTEST_ITER])
     metrics.to_csv(os.path.join(output_dir, SCORES_FILE), index=False)
 

how-to-use-azureml/automated-machine-learning/forecasting-backtest-many-models/auto-ml-forecasting-backtest-many-models.ipynb

@@ -86,6 +86,7 @@
         "output[\"Resource Group\"] = ws.resource_group\n",
         "output[\"Location\"] = ws.location\n",
         "output[\"Default datastore name\"] = dstore.name\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
         "pd.set_option(\"display.max_colwidth\", None)\n",
         "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
         "outputDf.T"

how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/assets/score.py

@@ -5,6 +5,7 @@ import json
 import os
 import re
 
+import numpy as np
 import pandas as pd
 
 from matplotlib import pyplot as plt
@@ -146,6 +147,9 @@ def calculate_scores_and_build_plots(
         _draw_one_plot(one_forecast, time_column_name, grains, pdf)
     pdf.close()
     forecast_df.to_csv(os.path.join(output_dir, FORECASTS_FILE), index=False)
+    # Remove np.NaN and np.inf from the prediction and actuals data.
+    forecast_df.replace([np.inf, -np.inf], np.nan, inplace=True)
+    forecast_df.dropna(subset=[ACTUALS, PREDICTIONS], inplace=True)
     metrics = compute_all_metrics(forecast_df, grains + [BACKTEST_ITER])
     metrics.to_csv(os.path.join(output_dir, SCORES_FILE), index=False)
 

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

@@ -100,6 +100,7 @@
         "output[\"SKU\"] = ws.sku\n",
         "output[\"Resource Group\"] = ws.resource_group\n",
         "output[\"Location\"] = ws.location\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
         "pd.set_option(\"display.max_colwidth\", None)\n",
         "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
         "outputDf.T"

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

@@ -119,6 +119,7 @@
         "output[\"Resource Group\"] = ws.resource_group\n",
         "output[\"Location\"] = ws.location\n",
         "output[\"Run History Name\"] = experiment_name\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
         "pd.set_option(\"display.max_colwidth\", None)\n",
         "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
         "outputDf.T"

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

@@ -132,6 +132,7 @@
         "output[\"Resource Group\"] = ws.resource_group\n",
         "output[\"Location\"] = ws.location\n",
         "output[\"Run History Name\"] = experiment_name\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
         "pd.set_option(\"display.max_colwidth\", None)\n",
         "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
         "outputDf.T"

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

@@ -121,6 +121,7 @@
         "output[\"Resource Group\"] = ws.resource_group\n",
         "output[\"Location\"] = ws.location\n",
         "output[\"Run History Name\"] = experiment_name\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
         "pd.set_option(\"display.max_colwidth\", None)\n",
         "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
         "outputDf.T"

how-to-use-azureml/automated-machine-learning/forecasting-github-dau/auto-ml-forecasting-github-dau.ipynb

@@ -147,6 +147,7 @@
         "output[\"Resource Group\"] = ws.resource_group\n",
         "output[\"Location\"] = ws.location\n",
         "output[\"Run History Name\"] = experiment_name\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
         "pd.set_option(\"display.max_colwidth\", None)\n",
         "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
         "outputDf.T"

how-to-use-azureml/automated-machine-learning/forecasting-hierarchical-timeseries/auto-ml-forecasting-hierarchical-timeseries.ipynb

@@ -78,6 +78,7 @@
         "output[\"Resource Group\"] = ws.resource_group\n",
         "output[\"Location\"] = ws.location\n",
         "output[\"Default datastore name\"] = dstore.name\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
         "pd.set_option(\"display.max_colwidth\", None)\n",
         "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
         "outputDf.T"

how-to-use-azureml/automated-machine-learning/forecasting-many-models/auto-ml-forecasting-many-models.ipynb

@@ -78,6 +78,7 @@
         "output[\"Resource Group\"] = ws.resource_group\n",
         "output[\"Location\"] = ws.location\n",
         "output[\"Default datastore name\"] = dstore.name\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
         "pd.set_option(\"display.max_colwidth\", None)\n",
         "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
         "outputDf.T"

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

@@ -112,6 +112,7 @@
         "output[\"Resource Group\"] = ws.resource_group\n",
         "output[\"Location\"] = ws.location\n",
         "output[\"Run History Name\"] = experiment_name\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
         "pd.set_option(\"display.max_colwidth\", None)\n",
         "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
         "outputDf.T"

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

@@ -93,6 +93,7 @@
         "output[\"Resource Group\"] = ws.resource_group\n",
         "output[\"Location\"] = ws.location\n",
         "output[\"Experiment Name\"] = experiment.name\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
         "pd.set_option(\"display.max_colwidth\", None)\n",
         "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
         "outputDf.T"

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

@@ -93,6 +93,7 @@
         "output[\"Resource Group\"] = ws.resource_group\n",
         "output[\"Location\"] = ws.location\n",
         "output[\"Experiment Name\"] = experiment.name\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
         "pd.set_option(\"display.max_colwidth\", None)\n",
         "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
         "outputDf.T"

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

@@ -89,6 +89,7 @@
         "output[\"Resource Group\"] = ws.resource_group\n",
         "output[\"Location\"] = ws.location\n",
         "output[\"Run History Name\"] = experiment_name\n",
+        "output[\"SDK Version\"] = azureml.core.VERSION\n",
         "pd.set_option(\"display.max_colwidth\", None)\n",
         "outputDf = pd.DataFrame(data=output, index=[\"\"])\n",
         "outputDf.T"

how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.ipynb

@@ -82,7 +82,7 @@
       "source": [
         "## Create trained model\n",
         "\n",
-        "For this example, we will train a small model on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/index.html#diabetes-dataset). "
+        "For this example, we will train a small model on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html). "
       ]
     },
     {
@@ -279,7 +279,9 @@
         "\n",
         "\n",
         "environment = Environment('my-sklearn-environment')\n",
-        "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n",
+        "environment.python.conda_dependencies = CondaDependencies.create(conda_packages=[\n",
+        "    'pip==20.2.4'],\n",
+        "    pip_packages=[\n",
         "    'azureml-defaults',\n",
         "    'inference-schema[numpy-support]',\n",
         "    'joblib',\n",
@@ -478,7 +480,9 @@
         "\n",
         "\n",
         "environment = Environment('my-sklearn-environment')\n",
-        "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n",
+        "environment.python.conda_dependencies = CondaDependencies.create(conda_packages=[\n",
+        "    'pip==20.2.4'],\n",
+        "    pip_packages=[\n",
         "    'azureml-defaults',\n",
         "    'inference-schema[numpy-support]',\n",
         "    'joblib',\n",

how-to-use-azureml/deployment/deploy-with-controlled-rollout/deploy-aks-with-controlled-rollout.ipynb

@@ -105,7 +105,9 @@
         "from azureml.core.conda_dependencies import CondaDependencies\n",
         "\n",
         "environment=Environment('my-sklearn-environment')\n",
-        "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n",
+        "environment.python.conda_dependencies = CondaDependencies.create(conda_packages=[\n",
+        "    'pip==20.2.4'],\n",
+        "    pip_packages=[\n",
         "    'azureml-defaults',\n",
         "    'inference-schema[numpy-support]',\n",
         "    'numpy',\n",

how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb

@@ -358,6 +358,7 @@
         "# cause errors.  Please take extra care when specifying your dependencies in a production environment.\n",
         "myenv = CondaDependencies.create(\n",
         "    python_version=python_version,\n",
+        "    conda_packages=['pip==20.2.4'],\n",
         "    pip_packages=['pyyaml', sklearn_dep, pandas_dep] + azureml_pip_packages)\n",
         "\n",
         "with open(\"myenv.yml\",\"w\") as f:\n",

how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/docker/Dockerfile-gpu

@@ -1,5 +1,11 @@
 FROM mcr.microsoft.com/azureml/openmpi4.1.0-cuda11.0.3-cudnn8-ubuntu18.04:20211111.v1
 
+# CUDA repository key rotation: https://forums.developer.nvidia.com/t/notice-cuda-linux-repository-key-rotation/212771
+RUN apt-key del 7fa2af80
+ENV distro ubuntu1804
+ENV arch x86_64
+RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/$distro/$arch/3bf863cc.pub
+
 RUN apt-get update && apt-get install -y --no-install-recommends \
     python-opengl \
     rsync \

how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/pong_rllib.ipynb

@@ -97,7 +97,7 @@
         "import azureml.core\n",
         "\n",
         "# Check core SDK version number\n",
-        "print(\"Azure Machine Learning SDK Version: \", azureml.core.VERSION)"
+        "print(\"Azure Machine Learning SDK version: \", azureml.core.VERSION)"
       ]
     },
     {
@@ -242,11 +242,7 @@
         "    register(workspace=ws)\n",
         "ray_cpu_build_details = ray_cpu_env.build(workspace=ws)\n",
         "\n",
-        "import time\n",
+        "ray_cpu_build_details.wait_for_completion(show_output=True)"
-        "while ray_cpu_build_details.status not in ['Succeeded', 'Failed']:\n",
-        "    print(f'Awaiting completion of ray CPU environment build. Current status is: {ray_cpu_build_details.status}')\n",
-        "    time.sleep(30)\n",
-        "print(f'status={ray_cpu_build_details.status}')"
       ]
     },
     {
@@ -279,11 +275,7 @@
         "    register(workspace=ws)\n",
         "ray_gpu_build_details = ray_gpu_env.build(workspace=ws)\n",
         "\n",
-        "import time\n",
+        "ray_gpu_build_details.wait_for_completion(show_output=True)"
-        "while ray_gpu_build_details.status not in ['Succeeded', 'Failed']:\n",
-        "    print(f'Awaiting completion of ray GPU environment build. Current status is: {ray_gpu_build_details.status}')\n",
-        "    time.sleep(30)\n",
-        "print(f'status={ray_gpu_build_details.status}')"
       ]
     },
     {

how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/cartpole_ci.ipynb

@@ -255,11 +255,7 @@
         "    register(workspace=ws)\n",
         "ray_env_build_details = ray_environment.build(workspace=ws)\n",
         "\n",
-        "# import time\n",
+        "ray_env_build_details.wait_for_completion(show_output=True)"
-        "while ray_env_build_details.status not in ['Succeeded', 'Failed']:\n",
-        "    print(f'Awaiting completion of environment build. Current status is: {ray_env_build_details.status}')\n",
-        "    time.sleep(30)\n",
-        "print(f'status={ray_env_build_details.status}')"
       ]
     },
     {

how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/cartpole_sc.ipynb

@@ -223,11 +223,7 @@
         "    register(workspace=ws)\n",
         "ray_env_build_details = ray_environment.build(workspace=ws)\n",
         "\n",
-        "import time\n",
+        "ray_env_build_details.wait_for_completion(show_output=True)"
-        "while ray_env_build_details.status not in ['Succeeded', 'Failed']:\n",
-        "    print(f'Awaiting completion of environment build. Current status is: {ray_env_build_details.status}')\n",
-        "    time.sleep(30)\n",
-        "print(f'status={ray_env_build_details.status}')"
       ]
     },
     {

how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/files/docker/Dockerfile

@@ -8,10 +8,8 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
     rm -rf /var/lib/apt/lists/* && \
     rm -rf /usr/share/man/*
 
-RUN conda install -y conda=4.7.12 python=3.7 && conda clean -ay && \
+RUN conda install -y conda=4.12.0 python=3.7 && conda clean -ay
-    pip install ray-on-aml==0.1.6 & \
+RUN pip install ray-on-aml==0.1.6 & \
-    pip install --upgrade ray==0.8.3 \
-    ray[rllib,dashboard,tune]==0.8.3 & \
     pip install --no-cache-dir \
     azureml-defaults \
     azureml-dataset-runtime[fuse,pandas] \
@@ -32,3 +30,5 @@ RUN conda install -y conda=4.7.12 python=3.7 && conda clean -ay && \
     conda install -y -c conda-forge x264='1!152.20180717' ffmpeg=4.0.2 && \
     conda install -c anaconda opencv
 
+RUN pip install --upgrade ray==0.8.3 \
+    ray[rllib,dashboard,tune]==0.8.3

how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/particle.ipynb

@@ -246,7 +246,9 @@
         "ray_environment = Environment. \\\n",
         "    from_dockerfile(name=ray_environment_name, dockerfile=ray_environment_dockerfile_path). \\\n",
         "    register(workspace=ws)\n",
-        "ray_gpu_build_details = ray_environment.build(workspace=ws)"
+        "ray_cpu_build_details = ray_environment.build(workspace=ws)\n",
+        "\n",
+        "ray_cpu_build_details.wait_for_completion(show_output=True)"
       ]
     },
     {