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

2025-12-19 17:17:04 -05:00 · 2022-05-04 23:01:56 +00:00
parent 5928ba83bb
commit b0aa91acce
19 changed files with 30 additions and 1 deletions
--- 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
@@ -90,6 +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"
--- 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
@@ -101,6 +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"
--- 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
@@ -102,6 +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"
--- 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
@@ -5,6 +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)

--- 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
@@ -86,6 +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"
--- 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
@@ -5,6 +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)

--- 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
@@ -100,6 +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"
--- 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
@@ -119,6 +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"
--- 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
@@ -132,6 +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"
--- 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
@@ -121,6 +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"
--- 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
@@ -147,6 +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"
--- 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
@@ -78,6 +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"
--- 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
@@ -78,6 +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"
--- 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
@@ -112,6 +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"
--- 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
@@ -93,6 +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"
--- 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
@@ -93,6 +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"
--- 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
@@ -89,6 +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"
--- 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
@@ -1,5 +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 \
--- 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)"
      ]
    },
    {