update samples from Release-131 as a part of 1.41.0 SDK stable release

2022-04-25 16:41:55 +00:00
27 changed files with 31 additions and 54 deletions
--- a/how-to-use-azureml/automated-machine-learning/automl_env.yml
+++ b/how-to-use-azureml/automated-machine-learning/automl_env.yml
@@ -15,9 +15,8 @@ dependencies:
 - cudatoolkit=10.1.243
 - scipy==1.5.2
 - notebook
- pywin32==227
+- pywin32==225
 - PySocks==1.7.1
- Pygments==2.11.2
 - conda-forge::pyqt==5.12.3

 - pip:
--- a/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb
@@ -90,7 +90,6 @@
        "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"
--- a/how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.ipynb
@@ -101,7 +101,6 @@
        "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"
--- a/how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb
@@ -102,7 +102,6 @@
        "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"
--- a/how-to-use-azureml/automated-machine-learning/forecasting-backtest-many-models/assets/score.py
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-backtest-many-models/assets/score.py
@@ -5,7 +5,6 @@ import json
 import os
 import re

-import numpy as np
 import pandas as pd

 from matplotlib import pyplot as plt
@@ -147,9 +146,6 @@ 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)

--- a/how-to-use-azureml/automated-machine-learning/forecasting-backtest-many-models/auto-ml-forecasting-backtest-many-models.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-backtest-many-models/auto-ml-forecasting-backtest-many-models.ipynb
@@ -86,7 +86,6 @@
        "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"
--- a/how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/assets/score.py
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/assets/score.py
@@ -5,7 +5,6 @@ import json
 import os
 import re

-import numpy as np
 import pandas as pd

 from matplotlib import pyplot as plt
@@ -147,9 +146,6 @@ 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)

--- a/how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/auto-ml-forecasting-backtest-single-model.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/auto-ml-forecasting-backtest-single-model.ipynb
@@ -100,7 +100,6 @@
        "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"
--- a/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb
@@ -119,7 +119,6 @@
        "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"
--- a/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb
@@ -132,7 +132,6 @@
        "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"
--- a/how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/auto-ml-forecasting-function.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/auto-ml-forecasting-function.ipynb
@@ -121,7 +121,6 @@
        "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"
--- a/how-to-use-azureml/automated-machine-learning/forecasting-github-dau/auto-ml-forecasting-github-dau.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-github-dau/auto-ml-forecasting-github-dau.ipynb
@@ -147,7 +147,6 @@
        "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"
--- a/how-to-use-azureml/automated-machine-learning/forecasting-hierarchical-timeseries/auto-ml-forecasting-hierarchical-timeseries.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-hierarchical-timeseries/auto-ml-forecasting-hierarchical-timeseries.ipynb
@@ -78,7 +78,6 @@
        "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"
--- a/how-to-use-azureml/automated-machine-learning/forecasting-many-models/auto-ml-forecasting-many-models.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-many-models/auto-ml-forecasting-many-models.ipynb
@@ -78,7 +78,6 @@
        "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"
--- a/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb
@@ -112,7 +112,6 @@
        "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"
--- a/how-to-use-azureml/automated-machine-learning/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.ipynb
@@ -93,7 +93,6 @@
        "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"
--- a/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/auto-ml-regression-explanation-featurization.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/auto-ml-regression-explanation-featurization.ipynb
@@ -93,7 +93,6 @@
        "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"
--- a/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb
@@ -89,7 +89,6 @@
        "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"
--- a/how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.ipynb
+++ b/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/modules/generated/sklearn.datasets.load_diabetes.html). "
+        "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). "
      ]
    },
    {
@@ -279,9 +279,7 @@
        "\n",
        "\n",
        "environment = Environment('my-sklearn-environment')\n",
-        "environment.python.conda_dependencies = CondaDependencies.create(conda_packages=[\n",
-        "    'pip==20.2.4'],\n",
-        "    pip_packages=[\n",
+        "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n",
        "    'azureml-defaults',\n",
        "    'inference-schema[numpy-support]',\n",
        "    'joblib',\n",
@@ -480,9 +478,7 @@
        "\n",
        "\n",
        "environment = Environment('my-sklearn-environment')\n",
-        "environment.python.conda_dependencies = CondaDependencies.create(conda_packages=[\n",
-        "    'pip==20.2.4'],\n",
-        "    pip_packages=[\n",
+        "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n",
        "    'azureml-defaults',\n",
        "    'inference-schema[numpy-support]',\n",
        "    'joblib',\n",
--- a/how-to-use-azureml/deployment/deploy-with-controlled-rollout/deploy-aks-with-controlled-rollout.ipynb
+++ b/how-to-use-azureml/deployment/deploy-with-controlled-rollout/deploy-aks-with-controlled-rollout.ipynb
@@ -105,9 +105,7 @@
        "from azureml.core.conda_dependencies import CondaDependencies\n",
        "\n",
        "environment=Environment('my-sklearn-environment')\n",
-        "environment.python.conda_dependencies = CondaDependencies.create(conda_packages=[\n",
-        "    'pip==20.2.4'],\n",
-        "    pip_packages=[\n",
+        "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n",
        "    'azureml-defaults',\n",
        "    'inference-schema[numpy-support]',\n",
        "    'numpy',\n",
--- a/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb
+++ b/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb
@@ -358,7 +358,6 @@
        "# 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",
--- a/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/docker/Dockerfile-gpu
+++ b/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/docker/Dockerfile-gpu
@@ -1,11 +1,5 @@
 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 \
--- a/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/pong_rllib.ipynb
+++ b/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)"
      ]
    },
    {
@@ -242,7 +242,11 @@
        "    register(workspace=ws)\n",
        "ray_cpu_build_details = ray_cpu_env.build(workspace=ws)\n",
        "\n",
-        "ray_cpu_build_details.wait_for_completion(show_output=True)"
+        "import time\n",
+        "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}')"
      ]
    },
    {
@@ -275,7 +279,11 @@
        "    register(workspace=ws)\n",
        "ray_gpu_build_details = ray_gpu_env.build(workspace=ws)\n",
        "\n",
-        "ray_gpu_build_details.wait_for_completion(show_output=True)"
+        "import time\n",
+        "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}')"
      ]
    },
    {
--- a/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/cartpole_ci.ipynb
+++ b/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/cartpole_ci.ipynb
@@ -255,7 +255,11 @@
        "    register(workspace=ws)\n",
        "ray_env_build_details = ray_environment.build(workspace=ws)\n",
        "\n",
-        "ray_env_build_details.wait_for_completion(show_output=True)"
+        "# import time\n",
+        "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}')"
      ]
    },
    {
--- a/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/cartpole_sc.ipynb
+++ b/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/cartpole_sc.ipynb
@@ -223,7 +223,11 @@
        "    register(workspace=ws)\n",
        "ray_env_build_details = ray_environment.build(workspace=ws)\n",
        "\n",
-        "ray_env_build_details.wait_for_completion(show_output=True)"
+        "import time\n",
+        "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}')"
      ]
    },
    {
--- a/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/files/docker/Dockerfile
+++ b/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/files/docker/Dockerfile
@@ -8,8 +8,10 @@ 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=4.12.0 python=3.7 && conda clean -ay
-RUN pip install ray-on-aml==0.1.6 & \
+RUN conda install -y conda=4.7.12 python=3.7 && conda clean -ay && \
+    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] \
@@ -30,5 +32,3 @@ RUN pip install ray-on-aml==0.1.6 & \
    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
--- a/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/particle.ipynb
+++ b/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/particle.ipynb
@@ -246,9 +246,7 @@
        "ray_environment = Environment. \\\n",
        "    from_dockerfile(name=ray_environment_name, dockerfile=ray_environment_dockerfile_path). \\\n",
        "    register(workspace=ws)\n",
-        "ray_cpu_build_details = ray_environment.build(workspace=ws)\n",
-        "\n",
-        "ray_cpu_build_details.wait_for_completion(show_output=True)"
+        "ray_gpu_build_details = ray_environment.build(workspace=ws)"
      ]
    },
    {