diff --git a/configuration.ipynb b/configuration.ipynb index fcef9920..9375f51f 100644 --- a/configuration.ipynb +++ b/configuration.ipynb @@ -103,7 +103,7 @@ "source": [ "import azureml.core\n", "\n", - "print(\"This notebook was created using version 1.9.0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.10.0 of the Azure ML SDK\")\n", "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" ] }, diff --git a/contrib/fairness/fairlearn-azureml-mitigation.ipynb b/contrib/fairness/fairlearn-azureml-mitigation.ipynb index d9d0c210..ac6559ce 100644 --- a/contrib/fairness/fairlearn-azureml-mitigation.ipynb +++ b/contrib/fairness/fairlearn-azureml-mitigation.ipynb @@ -50,7 +50,22 @@ "* `joblib`\n", "* `shap`\n", "\n", - "\n", + "Fairlearn relies on features introduced in v0.22.1 of `scikit-learn`. If you have an older version already installed, please uncomment and run the following cell:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# !pip install --upgrade scikit-learn>=0.22.1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "\n", "## Loading the Data\n", "We use the well-known `adult` census dataset, which we load using `shap` (for convenience). We start with a fairly unremarkable set of imports:" diff --git a/contrib/fairness/fairlearn-azureml-mitigation.yml b/contrib/fairness/fairlearn-azureml-mitigation.yml deleted file mode 100644 index 0e23d674..00000000 --- a/contrib/fairness/fairlearn-azureml-mitigation.yml +++ /dev/null @@ -1,8 +0,0 @@ -name: fairlearn-azureml-mitigation -dependencies: -- pip: - - azureml-sdk - - azureml-contrib-fairness - - fairlearn==0.4.6 - - joblib - - shap diff --git a/contrib/fairness/upload-fairness-dashboard.ipynb b/contrib/fairness/upload-fairness-dashboard.ipynb index 0e56e677..5090785a 100644 --- a/contrib/fairness/upload-fairness-dashboard.ipynb +++ b/contrib/fairness/upload-fairness-dashboard.ipynb @@ -52,9 +52,22 @@ "* `joblib`\n", "* `shap`\n", "\n", - "\n", - "\n", - "\n", + "Fairlearn relies on features introduced in v0.22.1 of `scikit-learn`. If you have an older version already installed, please uncomment and run the following cell:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# !pip install --upgrade scikit-learn>=0.22.1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "\n", "## Loading the Data\n", "We use the well-known `adult` census dataset, which we load using `shap` (for convenience). We start with a fairly unremarkable set of imports:" diff --git a/contrib/fairness/upload-fairness-dashboard.yml b/contrib/fairness/upload-fairness-dashboard.yml deleted file mode 100644 index 8317e795..00000000 --- a/contrib/fairness/upload-fairness-dashboard.yml +++ /dev/null @@ -1,8 +0,0 @@ -name: upload-fairness-dashboard -dependencies: -- pip: - - azureml-sdk - - azureml-contrib-fairness - - fairlearn==0.4.6 - - joblib - - shap diff --git a/how-to-use-azureml/automated-machine-learning/README.md b/how-to-use-azureml/automated-machine-learning/README.md index cdccd095..05c61f27 100644 --- a/how-to-use-azureml/automated-machine-learning/README.md +++ b/how-to-use-azureml/automated-machine-learning/README.md @@ -230,15 +230,6 @@ You may check the version of tensorflow and uninstall as follows 2) enter `pip freeze` and look for `tensorflow` , if found, the version listed should be < 1.13 3) If the listed version is a not a supported version, `pip uninstall tensorflow` in the command shell and enter y for confirmation. -## KeyError: 'brand' when running AutoML on local compute or Azure Databricks cluster** -If a new environment was created after 10 June 2020 using SDK 1.7.0 or lower, training may fail with the above error due to an update in the py-cpuinfo package. (Environments created on or before 10 June 2020 are unaffected, as well as experiments run on remote compute as cached training images are used.) To work around this issue, either of the two following steps can be taken: - -1) Update the SDK version to 1.8.0 or higher (this will also downgrade py-cpuinfo to 5.0.0): -`pip install --upgrade azureml-sdk[automl]` - -2) Downgrade the installed version of py-cpuinfo to 5.0.0: -`pip install py-cpuinfo==5.0.0` - ## Remote run: DsvmCompute.create fails There are several reasons why the DsvmCompute.create can fail. The reason is usually in the error message but you have to look at the end of the error message for the detailed reason. Some common reasons are: 1) `Compute name is invalid, it should start with a letter, be between 2 and 16 character, and only include letters (a-zA-Z), numbers (0-9) and \'-\'.` Note that underscore is not allowed in the name. diff --git a/how-to-use-azureml/automated-machine-learning/automl_env.yml b/how-to-use-azureml/automated-machine-learning/automl_env.yml index f1da6ce1..db4c8811 100644 --- a/how-to-use-azureml/automated-machine-learning/automl_env.yml +++ b/how-to-use-azureml/automated-machine-learning/automl_env.yml @@ -6,7 +6,7 @@ dependencies: - python>=3.5.2,<3.6.8 - nb_conda - matplotlib==2.1.0 -- numpy>=1.16.0,<=1.16.2 +- numpy~=1.16.0 - cython - urllib3<1.24 - scipy==1.4.1 diff --git a/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml b/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml index 7f086895..3057cf6e 100644 --- a/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml +++ b/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml @@ -7,7 +7,7 @@ dependencies: - python>=3.5.2,<3.6.8 - nb_conda - matplotlib==2.1.0 -- numpy>=1.16.0,<=1.16.2 +- numpy~=1.16.0 - cython - urllib3<1.24 - scipy==1.4.1 diff --git a/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb b/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb index 5b609cf8..d59323b6 100644 --- a/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb +++ b/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb @@ -57,7 +57,7 @@ "9. Test the ACI service.\n", "\n", "In addition this notebook showcases the following features\n", - "- **Blacklisting** certain pipelines\n", + "- **Blocking** certain pipelines\n", "- Specifying **target metrics** to indicate stopping criteria\n", "- Handling **missing data** in the input" ] @@ -105,7 +105,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(\"This notebook was created using version 1.9.0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.10.0 of the Azure ML SDK\")\n", "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" ] }, @@ -314,8 +314,8 @@ "|**task**|classification or regression or forecasting|\n", "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics:
accuracy
AUC_weighted
average_precision_score_weighted
norm_macro_recall
precision_score_weighted|\n", "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n", - "|**blacklist_models** | *List* of *strings* indicating machine learning algorithms for AutoML to avoid in this run.

Allowed values for **Classification**
LogisticRegression
SGD
MultinomialNaiveBayes
BernoulliNaiveBayes
SVM
LinearSVM
KNN
DecisionTree
RandomForest
ExtremeRandomTrees
LightGBM
GradientBoosting
TensorFlowDNN
TensorFlowLinearClassifier

Allowed values for **Regression**
ElasticNet
GradientBoosting
DecisionTree
KNN
LassoLars
SGD
RandomForest
ExtremeRandomTrees
LightGBM
TensorFlowLinearRegressor
TensorFlowDNN

Allowed values for **Forecasting**
ElasticNet
GradientBoosting
DecisionTree
KNN
LassoLars
SGD
RandomForest
ExtremeRandomTrees
LightGBM
TensorFlowLinearRegressor
TensorFlowDNN
Arima
Prophet|\n", - "| **whitelist_models** | *List* of *strings* indicating machine learning algorithms for AutoML to use in this run. Same values listed above for **blacklist_models** allowed for **whitelist_models**.|\n", + "|**blocked_models** | *List* of *strings* indicating machine learning algorithms for AutoML to avoid in this run.

Allowed values for **Classification**
LogisticRegression
SGD
MultinomialNaiveBayes
BernoulliNaiveBayes
SVM
LinearSVM
KNN
DecisionTree
RandomForest
ExtremeRandomTrees
LightGBM
GradientBoosting
TensorFlowDNN
TensorFlowLinearClassifier

Allowed values for **Regression**
ElasticNet
GradientBoosting
DecisionTree
KNN
LassoLars
SGD
RandomForest
ExtremeRandomTrees
LightGBM
TensorFlowLinearRegressor
TensorFlowDNN

Allowed values for **Forecasting**
ElasticNet
GradientBoosting
DecisionTree
KNN
LassoLars
SGD
RandomForest
ExtremeRandomTrees
LightGBM
TensorFlowLinearRegressor
TensorFlowDNN
Arima
Prophet|\n", + "|**allowed_models** | *List* of *strings* indicating machine learning algorithms for AutoML to use in this run. Same values listed above for **blocked_models** allowed for **allowed_models**.|\n", "|**experiment_exit_score**| Value indicating the target for *primary_metric*.
Once the target is surpassed the run terminates.|\n", "|**experiment_timeout_hours**| Maximum amount of time in hours that all iterations combined can take before the experiment terminates.|\n", "|**enable_early_stopping**| Flag to enble early termination if the score is not improving in the short term.|\n", @@ -349,7 +349,7 @@ " debug_log = 'automl_errors.log',\n", " compute_target=compute_target,\n", " experiment_exit_score = 0.9984,\n", - " blacklist_models = ['KNN','LinearSVM'],\n", + " blocked_models = ['KNN','LinearSVM'],\n", " enable_onnx_compatible_models=True,\n", " training_data = train_data,\n", " label_column_name = label,\n", diff --git 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 index 80e51c79..b1075d29 100644 --- 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 @@ -93,7 +93,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(\"This notebook was created using version 1.9.0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.10.0 of the Azure ML SDK\")\n", "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" ] }, diff --git 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 index e5ce6556..62e5e1d8 100644 --- 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 @@ -97,7 +97,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(\"This notebook was created using version 1.9.0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.10.0 of the Azure ML SDK\")\n", "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" ] }, diff --git 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 index 92e893a0..7f13a096 100644 --- 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 @@ -88,7 +88,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(\"This notebook was created using version 1.9.0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.10.0 of the Azure ML SDK\")\n", "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" ] }, diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb b/how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb index 8d24e995..0a611995 100644 --- a/how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb +++ b/how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb @@ -114,7 +114,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(\"This notebook was created using version 1.9.0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.10.0 of the Azure ML SDK\")\n", "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" ] }, @@ -352,8 +352,6 @@ "|**label_column_name**|The name of the label column.|\n", "|**enable_dnn**|Enable Forecasting DNNs|\n", "\n", - "This notebook uses the blacklist_models parameter to exclude some models that take a longer time to train on this dataset. You can choose to remove models from the blacklist_models list but you may need to increase the iteration_timeout_minutes parameter value to get results.\n", - "\n", "This step requires an Enterprise workspace to gain access to this feature. To learn more about creating an Enterprise workspace or upgrading to an Enterprise workspace from the Azure portal, please visit our [Workspace page.](https://docs.microsoft.com/azure/machine-learning/service/concept-workspace#upgrade)." ] }, diff --git 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 index 497f495b..659a444c 100644 --- 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 @@ -87,7 +87,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(\"This notebook was created using version 1.9.0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.10.0 of the Azure ML SDK\")\n", "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" ] }, @@ -250,7 +250,7 @@ "|-|-|\n", "|**task**|forecasting|\n", "|**primary_metric**|This is the metric that you want to optimize.
Forecasting supports the following primary metrics
spearman_correlation
normalized_root_mean_squared_error
r2_score
normalized_mean_absolute_error\n", - "|**blacklist_models**|Models in blacklist won't be used by AutoML. All supported models can be found at [here](https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.forecasting?view=azure-ml-py).|\n", + "|**blocked_models**|Models in blocked_models won't be used by AutoML. All supported models can be found at [here](https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.forecasting?view=azure-ml-py).|\n", "|**experiment_timeout_hours**|Experimentation timeout in hours.|\n", "|**training_data**|Input dataset, containing both features and label column.|\n", "|**label_column_name**|The name of the label column.|\n", @@ -263,7 +263,7 @@ "|**target_lags**|The target_lags specifies how far back we will construct the lags of the target variable.|\n", "|**drop_column_names**|Name(s) of columns to drop prior to modeling|\n", "\n", - "This notebook uses the blacklist_models parameter to exclude some models that take a longer time to train on this dataset. You can choose to remove models from the blacklist_models list but you may need to increase the experiment_timeout_hours parameter value to get results." + "This notebook uses the blocked_models parameter to exclude some models that take a longer time to train on this dataset. You can choose to remove models from the blocked_models list but you may need to increase the experiment_timeout_hours parameter value to get results." ] }, { @@ -307,7 +307,7 @@ "\n", "automl_config = AutoMLConfig(task='forecasting', \n", " primary_metric='normalized_root_mean_squared_error',\n", - " blacklist_models = ['ExtremeRandomTrees'], \n", + " blocked_models = ['ExtremeRandomTrees'], \n", " experiment_timeout_hours=0.3,\n", " training_data=train,\n", " label_column_name=target_column_name,\n", diff --git 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 index f4cd5e78..ee135a58 100644 --- 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 @@ -97,7 +97,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(\"This notebook was created using version 1.9.0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.10.0 of the Azure ML SDK\")\n", "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" ] }, @@ -303,7 +303,7 @@ "|-|-|\n", "|**task**|forecasting|\n", "|**primary_metric**|This is the metric that you want to optimize.
Forecasting supports the following primary metrics
spearman_correlation
normalized_root_mean_squared_error
r2_score
normalized_mean_absolute_error|\n", - "|**blacklist_models**|Models in blacklist won't be used by AutoML. All supported models can be found at [here](https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.forecasting?view=azure-ml-py).|\n", + "|**blocked_models**|Models in blocked_models won't be used by AutoML. All supported models can be found at [here](https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.constants.supportedmodels.forecasting?view=azure-ml-py).|\n", "|**experiment_timeout_hours**|Maximum amount of time in hours that the experiment take before it terminates.|\n", "|**training_data**|The training data to be used within the experiment.|\n", "|**label_column_name**|The name of the label column.|\n", @@ -318,7 +318,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This notebook uses the blacklist_models parameter to exclude some models that take a longer time to train on this dataset. You can choose to remove models from the blacklist_models list but you may need to increase the experiment_timeout_hours parameter value to get results." + "This notebook uses the blocked_models parameter to exclude some models that take a longer time to train on this dataset. You can choose to remove models from the blocked_models list but you may need to increase the experiment_timeout_hours parameter value to get results." ] }, { @@ -334,7 +334,7 @@ "\n", "automl_config = AutoMLConfig(task='forecasting', \n", " primary_metric='normalized_root_mean_squared_error',\n", - " blacklist_models = ['ExtremeRandomTrees', 'AutoArima', 'Prophet'], \n", + " blocked_models = ['ExtremeRandomTrees', 'AutoArima', 'Prophet'], \n", " experiment_timeout_hours=0.3,\n", " training_data=train,\n", " label_column_name=target_column_name,\n", @@ -560,7 +560,7 @@ "### Using lags and rolling window features\n", "Now we will configure the target lags, that is the previous values of the target variables, meaning the prediction is no longer horizon-less. We therefore must still specify the `max_horizon` that the model will learn to forecast. The `target_lags` keyword specifies how far back we will construct the lags of the target variable, and the `target_rolling_window_size` specifies the size of the rolling window over which we will generate the `max`, `min` and `sum` features.\n", "\n", - "This notebook uses the blacklist_models parameter to exclude some models that take a longer time to train on this dataset. You can choose to remove models from the blacklist_models list but you may need to increase the iteration_timeout_minutes parameter value to get results." + "This notebook uses the blocked_models parameter to exclude some models that take a longer time to train on this dataset. You can choose to remove models from the blocked_models list but you may need to increase the iteration_timeout_minutes parameter value to get results." ] }, { @@ -578,7 +578,7 @@ "\n", "automl_config = AutoMLConfig(task='forecasting', \n", " primary_metric='normalized_root_mean_squared_error',\n", - " blacklist_models = ['ElasticNet','ExtremeRandomTrees','GradientBoosting','XGBoostRegressor','ExtremeRandomTrees', 'AutoArima', 'Prophet'], #These models are blacklisted for tutorial purposes, remove this for real use cases. \n", + " blocked_models = ['ElasticNet','ExtremeRandomTrees','GradientBoosting','XGBoostRegressor','ExtremeRandomTrees', 'AutoArima', 'Prophet'], #These models are blocked for tutorial purposes, remove this for real use cases. \n", " experiment_timeout_hours=0.3,\n", " training_data=train,\n", " label_column_name=target_column_name,\n", diff --git 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 index 030bee04..c8939720 100644 --- 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 @@ -94,7 +94,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(\"This notebook was created using version 1.9.0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.10.0 of the Azure ML SDK\")\n", "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" ] }, diff --git 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 index 19fa29cb..ad5ccbc0 100644 --- 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 @@ -82,7 +82,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(\"This notebook was created using version 1.9.0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.10.0 of the Azure ML SDK\")\n", "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" ] }, diff --git 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 index 1fee1fce..a98f63dc 100644 --- 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 @@ -96,7 +96,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(\"This notebook was created using version 1.9.0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.10.0 of the Azure ML SDK\")\n", "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" ] }, diff --git 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 index c0b70f1e..34ddda9a 100644 --- 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 @@ -98,7 +98,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(\"This notebook was created using version 1.9.0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.10.0 of the Azure ML SDK\")\n", "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" ] }, diff --git 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 index 37b3075f..c1def31e 100644 --- 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 @@ -92,7 +92,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(\"This notebook was created using version 1.9.0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.10.0 of the Azure ML SDK\")\n", "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" ] }, diff --git a/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks-ssl.ipynb b/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks-ssl.ipynb index 6209701a..17eb2578 100644 --- a/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks-ssl.ipynb +++ b/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks-ssl.ipynb @@ -149,7 +149,7 @@ "metadata": {}, "outputs": [], "source": [ - "%%writefile score.py\n", + "%%writefile score_ssl.py\n", "import os\n", "import pickle\n", "import json\n", @@ -201,7 +201,7 @@ "source": [ "from azureml.core.model import InferenceConfig\n", "\n", - "inf_config = InferenceConfig(entry_script='score.py', environment=myenv)" + "inf_config = InferenceConfig(entry_script='score_ssl.py', environment=myenv)" ] }, { diff --git a/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb b/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb index 4bacb1ad..eb8e376d 100644 --- a/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb +++ b/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb @@ -109,7 +109,7 @@ "from azureml.core import Environment\n", "from azureml.core.conda_dependencies import CondaDependencies \n", "\n", - "conda_deps = CondaDependencies.create(conda_packages=['numpy','scikit-learn==0.19.1','scipy'], pip_packages=['azureml-defaults'])\n", + "conda_deps = CondaDependencies.create(conda_packages=['numpy','scikit-learn==0.19.1','scipy'], pip_packages=['azureml-defaults', 'inference-schema'])\n", "myenv = Environment(name='myenv')\n", "myenv.python.conda_dependencies = conda_deps" ] diff --git a/how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb b/how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb index b826754e..ad6acdfa 100644 --- a/how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb +++ b/how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb @@ -204,108 +204,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Option 1: Provision as a run based compute target\n", + "### Option 1: Provision a compute target (Basic)\n", "\n", - "You can provision AmlCompute as a compute target at run-time. In this case, the compute is auto-created for your run, scales up to max_nodes that you specify, and then **deleted automatically** after the run completes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.runconfig import RunConfiguration\n", - "from azureml.core.conda_dependencies import CondaDependencies\n", - "from azureml.core.runconfig import DEFAULT_CPU_IMAGE\n", - "\n", - "# create a new runconfig object\n", - "run_config = RunConfiguration()\n", - "\n", - "# signal that you want to use AmlCompute to execute script.\n", - "run_config.target = \"amlcompute\"\n", - "\n", - "# AmlCompute will be created in the same region as workspace\n", - "# Set vm size for AmlCompute\n", - "run_config.amlcompute.vm_size = 'STANDARD_D2_V2'\n", - "\n", - "# enable Docker \n", - "run_config.environment.docker.enabled = True\n", - "\n", - "# set Docker base image to the default CPU-based image\n", - "run_config.environment.docker.base_image = DEFAULT_CPU_IMAGE\n", - "\n", - "# use conda_dependencies.yml to create a conda environment in the Docker image for execution\n", - "run_config.environment.python.user_managed_dependencies = False\n", - "\n", - "azureml_pip_packages = [\n", - " 'azureml-defaults', 'azureml-contrib-interpret', 'azureml-core', 'azureml-telemetry',\n", - " 'azureml-interpret', 'sklearn-pandas', 'azureml-dataprep'\n", - "]\n", - "\n", - "# Note: this is to pin the scikit-learn and pandas versions to be same as notebook.\n", - "# In production scenario user would choose their dependencies\n", - "import pkg_resources\n", - "available_packages = pkg_resources.working_set\n", - "sklearn_ver = None\n", - "pandas_ver = None\n", - "for dist in available_packages:\n", - " if dist.key == 'scikit-learn':\n", - " sklearn_ver = dist.version\n", - " elif dist.key == 'pandas':\n", - " pandas_ver = dist.version\n", - "sklearn_dep = 'scikit-learn'\n", - "pandas_dep = 'pandas'\n", - "if sklearn_ver:\n", - " sklearn_dep = 'scikit-learn=={}'.format(sklearn_ver)\n", - "if pandas_ver:\n", - " pandas_dep = 'pandas=={}'.format(pandas_ver)\n", - "# specify CondaDependencies obj\n", - "# The CondaDependencies specifies the conda and pip packages that are installed in the environment\n", - "# the submitted job is run in. Note the remote environment(s) needs to be similar to the local\n", - "# environment, otherwise if a model is trained or deployed in a different environment this can\n", - "# cause errors. Please take extra care when specifying your dependencies in a production environment.\n", - "run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=[sklearn_dep, pandas_dep],\n", - " pip_packages=azureml_pip_packages)\n", - "\n", - "# Now submit a run on AmlCompute\n", - "from azureml.core.script_run_config import ScriptRunConfig\n", - "\n", - "script_run_config = ScriptRunConfig(source_directory=project_folder,\n", - " script='train_explain.py',\n", - " run_config=run_config)\n", - "\n", - "run = experiment.submit(script_run_config)\n", - "\n", - "# Show run details\n", - "run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note: if you need to cancel a run, you can follow [these instructions](https://aka.ms/aml-docs-cancel-run)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "# Shows output of the run on stdout.\n", - "run.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Option 2: Provision as a persistent compute target (Basic)\n", - "\n", - "You can provision a persistent AmlCompute resource by simply defining two parameters thanks to smart defaults. By default it autoscales from 0 nodes and provisions dedicated VMs to run your job in a container. This is useful when you want to continously re-use the same target, debug it between jobs or simply share the resource with other users of your workspace.\n", + "You can provision an AmlCompute resource by simply defining two parameters thanks to smart defaults. By default it autoscales from 0 nodes and provisions dedicated VMs to run your job in a container. This is useful when you want to continously re-use the same target, debug it between jobs or simply share the resource with other users of your workspace.\n", "\n", "* `vm_size`: VM family of the nodes provisioned by AmlCompute. Simply choose from the supported_vmsizes() above\n", "* `max_nodes`: Maximum nodes to autoscale to while running a job on AmlCompute" @@ -351,13 +252,13 @@ "from azureml.core.runconfig import RunConfiguration\n", "from azureml.core.conda_dependencies import CondaDependencies\n", "\n", - "# create a new RunConfig object\n", + "# Create a new RunConfig object\n", "run_config = RunConfiguration(framework=\"python\")\n", "\n", "# Set compute target to AmlCompute target created in previous step\n", "run_config.target = cpu_cluster.name\n", "\n", - "# enable Docker \n", + "# Enable Docker \n", "run_config.environment.docker.enabled = True\n", "\n", "azureml_pip_packages = [\n", @@ -382,7 +283,7 @@ " sklearn_dep = 'scikit-learn=={}'.format(sklearn_ver)\n", "if pandas_ver:\n", " pandas_dep = 'pandas=={}'.format(pandas_ver)\n", - "# specify CondaDependencies obj\n", + "# Specify CondaDependencies obj\n", "# The CondaDependencies specifies the conda and pip packages that are installed in the environment\n", "# the submitted job is run in. Note the remote environment(s) needs to be similar to the local\n", "# environment, otherwise if a model is trained or deployed in a different environment this can\n", @@ -400,6 +301,13 @@ "run" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note: if you need to cancel a run, you can follow [these instructions](https://aka.ms/aml-docs-cancel-run)." + ] + }, { "cell_type": "code", "execution_count": null, @@ -424,7 +332,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Option 3: Provision as a persistent compute target (Advanced)\n", + "### Option 2: Provision a compute target (Advanced)\n", "\n", "You can also specify additional properties or change defaults while provisioning AmlCompute using a more advanced configuration. This is useful when you want a dedicated cluster of 4 nodes (for example you can set the min_nodes and max_nodes to 4), or want the compute to be within an existing VNet in your subscription.\n", "\n", @@ -483,13 +391,13 @@ "from azureml.core.runconfig import RunConfiguration\n", "from azureml.core.conda_dependencies import CondaDependencies\n", "\n", - "# create a new RunConfig object\n", + "# Create a new RunConfig object\n", "run_config = RunConfiguration(framework=\"python\")\n", "\n", "# Set compute target to AmlCompute target created in previous step\n", "run_config.target = cpu_cluster.name\n", "\n", - "# enable Docker \n", + "# Enable Docker \n", "run_config.environment.docker.enabled = True\n", "\n", "azureml_pip_packages = [\n", @@ -516,7 +424,7 @@ " sklearn_dep = 'scikit-learn=={}'.format(sklearn_ver)\n", "if pandas_ver:\n", " pandas_dep = 'pandas=={}'.format(pandas_ver)\n", - "# specify CondaDependencies obj\n", + "# Specify CondaDependencies obj\n", "# The CondaDependencies specifies the conda and pip packages that are installed in the environment\n", "# the submitted job is run in. Note the remote environment(s) needs to be similar to the local\n", "# environment, otherwise if a model is trained or deployed in a different environment this can\n", @@ -554,19 +462,6 @@ "run.get_metrics()" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.contrib.interpret.explanation.explanation_client import ExplanationClient\n", - "\n", - "client = ExplanationClient.from_run(run)\n", - "# Get the top k (e.g., 4) most important features with their importance values\n", - "explanation = client.download_model_explanation(top_k=4)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -682,7 +577,7 @@ "metadata": {}, "outputs": [], "source": [ - "# retrieve model for visualization and deployment\n", + "# Retrieve model for visualization and deployment\n", "from azureml.core.model import Model\n", "import joblib\n", "original_model = Model(ws, 'model_explain_model_on_amlcomp')\n", @@ -703,7 +598,7 @@ "metadata": {}, "outputs": [], "source": [ - "# retrieve x_test for visualization\n", + "# Retrieve x_test for visualization\n", "import joblib\n", "x_test_path = './x_test_boston_housing.pkl'\n", "run.download_file('x_test_boston_housing.pkl', output_file_path=x_test_path)" diff --git a/how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.ipynb b/how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.ipynb index 2fc247c2..678484bf 100644 --- a/how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.ipynb +++ b/how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.ipynb @@ -122,7 +122,7 @@ "metadata": {}, "outputs": [], "source": [ - "# get the IBM employee attrition dataset\n", + "# Get the IBM employee attrition dataset\n", "outdirname = 'dataset.6.21.19'\n", "try:\n", " from urllib import urlretrieve\n", @@ -163,7 +163,7 @@ "from sklearn.model_selection import train_test_split\n", "x_train, x_test, y_train, y_test = train_test_split(attritionXData, \n", " target, \n", - " test_size = 0.2,\n", + " test_size=0.2,\n", " random_state=0,\n", " stratify=target)" ] @@ -223,7 +223,7 @@ "# Append classifier to preprocessing pipeline.\n", "# Now we have a full prediction pipeline.\n", "clf = Pipeline(steps=[('preprocessor', transformations),\n", - " ('classifier', SVC(kernel='linear', C = 1.0, probability=True))])" + " ('classifier', SVC(C=1.0, probability=True))])" ] }, { @@ -249,7 +249,7 @@ "# Append classifier to preprocessing pipeline.\n", "# Now we have a full prediction pipeline.\n", "clf = Pipeline(steps=[('preprocessor', transformations),\n", - " ('classifier', SVC(kernel='linear', C = 1.0, probability=True))]) \n", + " ('classifier', SVC(C=1.0, probability=True))]) \n", "\n", "\n", "\n", @@ -393,7 +393,7 @@ "metadata": {}, "outputs": [], "source": [ - "# feature shap values for all features and all data points in the training data\n", + "# Feature shap values for all features and all data points in the training data\n", "print('local importance values: {}'.format(global_explanation.local_importance_values))" ] }, @@ -450,8 +450,7 @@ "outputs": [], "source": [ "import azureml.core\n", - "from azureml.core import Workspace, Experiment, Run\n", - "from interpret.ext.blackbox import TabularExplainer\n", + "from azureml.core import Workspace, Experiment\n", "from azureml.contrib.interpret.explanation.explanation_client import ExplanationClient\n", "# Check core SDK version number\n", "print(\"SDK version:\", azureml.core.VERSION)" @@ -576,6 +575,23 @@ "ExplanationDashboard(downloaded_global_explanation, model, datasetX=x_test)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## End\n", + "Complete the run" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run.complete()" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git 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 index 21b0a367..268a9332 100644 --- 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 @@ -141,7 +141,7 @@ "metadata": {}, "outputs": [], "source": [ - "# get IBM attrition data\n", + "# Get IBM attrition data\n", "import os\n", "import pandas as pd\n", "\n", @@ -218,17 +218,17 @@ "from sklearn.model_selection import train_test_split\n", "x_train, x_test, y_train, y_test = train_test_split(attritionXData,\n", " target,\n", - " test_size = 0.2,\n", + " test_size=0.2,\n", " random_state=0,\n", " stratify=target)\n", "\n", - "# preprocess the data and fit the classification model\n", + "# Preprocess the data and fit the classification model\n", "clf.fit(x_train, y_train)\n", "model = clf.steps[-1][1]\n", "\n", "model_file_name = 'log_reg.pkl'\n", "\n", - "# save model in the outputs folder so it automatically get uploaded\n", + "# Save model in the outputs folder so it automatically get uploaded\n", "with open(model_file_name, 'wb') as file:\n", " joblib.dump(value=clf, filename=os.path.join('./outputs/',\n", " model_file_name))" @@ -345,7 +345,7 @@ " sklearn_dep = 'scikit-learn=={}'.format(sklearn_ver)\n", "if pandas_ver:\n", " pandas_dep = 'pandas=={}'.format(pandas_ver)\n", - "# specify CondaDependencies obj\n", + "# Specify CondaDependencies obj\n", "# The CondaDependencies specifies the conda and pip packages that are installed in the environment\n", "# the submitted job is run in. Note the remote environment(s) needs to be similar to the local\n", "# environment, otherwise if a model is trained or deployed in a different environment this can\n", @@ -368,7 +368,7 @@ "outputs": [], "source": [ "from azureml.core.model import Model\n", - "# retrieve scoring explainer for deployment\n", + "# Retrieve scoring explainer for deployment\n", "scoring_explainer_model = Model(ws, 'IBM_attrition_explainer')" ] }, @@ -416,11 +416,11 @@ "\n", "headers = {'Content-Type':'application/json'}\n", "\n", - "# send request to service\n", + "# Send request to service\n", "print(\"POST to url\", service.scoring_uri)\n", "resp = requests.post(service.scoring_uri, sample_data, headers=headers)\n", "\n", - "# can covert back to Python objects from json string if desired\n", + "# Can covert back to Python objects from json string if desired\n", "print(\"prediction:\", resp.text)\n", "result = json.loads(resp.text)" ] @@ -431,7 +431,7 @@ "metadata": {}, "outputs": [], "source": [ - "#plot the feature importance for the prediction\n", + "# Plot the feature importance for the prediction\n", "import numpy as np\n", "import matplotlib.pyplot as plt; plt.rcdefaults()\n", "\n", diff --git a/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb b/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb index 099b2a36..0f737042 100644 --- a/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb +++ b/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb @@ -156,7 +156,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Submit an AmlCompute run in a few different ways\n", + "## Submit an AmlCompute run\n", "\n", "First lets check which VM families are available in your region. Azure is a regional service and some specialized SKUs (especially GPUs) are only available in certain regions. Since AmlCompute is created in the region of your workspace, we will use the supported_vms () function to see if the VM family we want to use ('STANDARD_D2_V2') is supported.\n", "\n", @@ -202,9 +202,43 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Provision as a run based compute target\n", + "### Provision a compute target\n", "\n", - "You can provision AmlCompute as a compute target at run-time. In this case, the compute is auto-created for your run, scales up to max_nodes that you specify, and then **deleted automatically** after the run completes." + "You can provision an AmlCompute resource by simply defining two parameters thanks to smart defaults. By default it autoscales from 0 nodes and provisions dedicated VMs to run your job in a container. This is useful when you want to continously re-use the same target, debug it between jobs or simply share the resource with other users of your workspace.\n", + "\n", + "* `vm_size`: VM family of the nodes provisioned by AmlCompute. Simply choose from the supported_vmsizes() above\n", + "* `max_nodes`: Maximum nodes to autoscale to while running a job on AmlCompute" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.compute import ComputeTarget, AmlCompute\n", + "from azureml.core.compute_target import ComputeTargetException\n", + "\n", + "# Choose a name for your CPU cluster\n", + "cpu_cluster_name = \"cpu-cluster\"\n", + "\n", + "# Verify that cluster does not exist already\n", + "try:\n", + " cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n", + " print('Found existing cluster, use it.')\n", + "except ComputeTargetException:\n", + " compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n", + " max_nodes=4)\n", + " cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n", + "\n", + "cpu_cluster.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Configure & Run" ] }, { @@ -217,28 +251,21 @@ "from azureml.core.conda_dependencies import CondaDependencies\n", "from azureml.core.runconfig import DEFAULT_CPU_IMAGE\n", "\n", - "# create a new runconfig object\n", + "# Create a new runconfig object\n", "run_config = RunConfiguration()\n", "\n", - "# signal that you want to use AmlCompute to execute script.\n", - "run_config.target = \"amlcompute\"\n", + "# Set compute target to AmlCompute target created in previous step\n", + "run_config.target = cpu_cluster.name\n", "\n", - "# AmlCompute will be created in the same region as workspace\n", - "# Set vm size for AmlCompute\n", - "run_config.amlcompute.vm_size = 'STANDARD_D2_V2'\n", - "\n", - "# enable Docker \n", + "# Enable Docker \n", "run_config.environment.docker.enabled = True\n", "\n", - "# set Docker base image to the default CPU-based image\n", + "# Set Docker base image to the default CPU-based image\n", "run_config.environment.docker.base_image = DEFAULT_CPU_IMAGE\n", "\n", - "# use conda_dependencies.yml to create a conda environment in the Docker image for execution\n", + "# Use conda_dependencies.yml to create a conda environment in the Docker image for execution\n", "run_config.environment.python.user_managed_dependencies = False\n", "\n", - "# auto-prepare the Docker image when used for execution (if it is not already prepared)\n", - "run_config.auto_prepare_environment = True\n", - "\n", "azureml_pip_packages = [\n", " 'azureml-defaults', 'azureml-contrib-interpret', 'azureml-core', 'azureml-telemetry',\n", " 'azureml-interpret', 'azureml-dataprep'\n", @@ -263,7 +290,7 @@ " sklearn_dep = 'scikit-learn=={}'.format(sklearn_ver)\n", "if pandas_ver:\n", " pandas_dep = 'pandas=={}'.format(pandas_ver)\n", - "# specify CondaDependencies obj\n", + "# Specify CondaDependencies obj\n", "# The CondaDependencies specifies the conda and pip packages that are installed in the environment\n", "# the submitted job is run in. Note the remote environment(s) needs to be similar to the local\n", "# environment, otherwise if a model is trained or deployed in a different environment this can\n", @@ -327,7 +354,7 @@ "metadata": {}, "outputs": [], "source": [ - "# retrieve model for visualization and deployment\n", + "# Retrieve model for visualization and deployment\n", "from azureml.core.model import Model\n", "import joblib\n", "original_model = Model(ws, 'amlcompute_deploy_model')\n", @@ -341,7 +368,7 @@ "metadata": {}, "outputs": [], "source": [ - "# retrieve global explanation for visualization\n", + "# Retrieve global explanation for visualization\n", "from azureml.contrib.interpret.explanation.explanation_client import ExplanationClient\n", "\n", "# get model explanation data\n", @@ -355,7 +382,7 @@ "metadata": {}, "outputs": [], "source": [ - "# retrieve x_test for visualization\n", + "# Retrieve x_test for visualization\n", "import joblib\n", "x_test_path = './x_test.pkl'\n", "run.download_file('x_test_ibm.pkl', output_file_path=x_test_path)\n", @@ -435,7 +462,7 @@ " sklearn_dep = 'scikit-learn=={}'.format(sklearn_ver)\n", "if pandas_ver:\n", " pandas_dep = 'pandas=={}'.format(pandas_ver)\n", - "# specify CondaDependencies obj\n", + "# Specify CondaDependencies obj\n", "# The CondaDependencies specifies the conda and pip packages that are installed in the environment\n", "# the submitted job is run in. Note the remote environment(s) needs to be similar to the local\n", "# environment, otherwise if a model is trained or deployed in a different environment this can\n", @@ -457,7 +484,7 @@ "metadata": {}, "outputs": [], "source": [ - "# retrieve scoring explainer for deployment\n", + "# Retrieve scoring explainer for deployment\n", "scoring_explainer_model = Model(ws, 'IBM_attrition_explainer')" ] }, @@ -496,17 +523,17 @@ "source": [ "import requests\n", "\n", - "# create data to test service with\n", + "# Create data to test service with\n", "examples = x_test[:4]\n", "input_data = examples.to_json()\n", "\n", "headers = {'Content-Type':'application/json'}\n", "\n", - "# send request to service\n", + "# Send request to service\n", "print(\"POST to url\", service.scoring_uri)\n", "resp = requests.post(service.scoring_uri, input_data, headers=headers)\n", "\n", - "# can covert back to Python objects from json string if desired\n", + "# Can covert back to Python objects from json string if desired\n", "print(\"prediction:\", resp.text)" ] }, diff --git 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 index 1ca0c7fa..b4757d4b 100644 --- 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 @@ -22,7 +22,7 @@ "source": [ "# Reinforcement Learning in Azure Machine Learning - Cartpole Problem on Compute Instance\n", "\n", - "Reinforcement Learning in Azure Machine Learning is a managed service for running reinforcement learning training and simulation. With Reinforcement Learning in Azure Machine Learning, data scientists can start developing reinforcement learning systems on one machine, and scale to compute targets with 100\u00e2\u20ac\u2122s of nodes if needed.\n", + "Reinforcement Learning in Azure Machine Learning is a managed service for running reinforcement learning training and simulation. With Reinforcement Learning in Azure Machine Learning, data scientists can start developing reinforcement learning systems on one machine, and scale to compute targets with 100s of nodes if needed.\n", "\n", "This example shows how to use Reinforcement Learning in Azure Machine Learning to train a Cartpole playing agent on a compute instance." ] diff --git 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 index c0410f91..059e429f 100644 --- 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 @@ -22,7 +22,7 @@ "source": [ "# Reinforcement Learning in Azure Machine Learning - Cartpole Problem on Single Compute\n", "\n", - "Reinforcement Learning in Azure Machine Learning is a managed service for running reinforcement learning training and simulation. With Reinforcement Learning in Azure Machine Learning, data scientists can start developing reinforcement learning systems on one machine, and scale to compute targets with 100\u00e2\u20ac\u2122s of nodes if needed.\n", + "Reinforcement Learning in Azure Machine Learning is a managed service for running reinforcement learning training and simulation. With Reinforcement Learning in Azure Machine Learning, data scientists can start developing reinforcement learning systems on one machine, and scale to compute targets with 100s of nodes if needed.\n", "\n", "This example shows how to use Reinforcement Learning in Azure Machine Learning to train a Cartpole playing agent on a single compute. " ] diff --git a/how-to-use-azureml/reinforcement-learning/minecraft-on-distributed-compute/docker/cpu/patch_files/ClientStateMachine.java b/how-to-use-azureml/reinforcement-learning/minecraft-on-distributed-compute/docker/cpu/patch_files/ClientStateMachine.java index 5cdee5c3..71668152 100644 --- a/how-to-use-azureml/reinforcement-learning/minecraft-on-distributed-compute/docker/cpu/patch_files/ClientStateMachine.java +++ b/how-to-use-azureml/reinforcement-learning/minecraft-on-distributed-compute/docker/cpu/patch_files/ClientStateMachine.java @@ -1477,7 +1477,7 @@ public class ClientStateMachine extends StateMachine implements IMalmoMessageLis * Pause the old server. It's vital that we do this, otherwise it will * respond to the quit disconnect package straight away and kill the server * thread, which means there will be no server to respond to the loadWorld - * code. (This was the cause of the infamous "Holder Lookups" hang.) + * code. */ public class PauseOldServerEpisode extends ConfigAwareStateEpisode { @@ -1505,7 +1505,6 @@ public class ClientStateMachine extends StateMachine implements IMalmoMessageLis { if (!killPublicFlag(Minecraft.getMinecraft().getIntegratedServer())) { - // Can't pause, don't want to risk the hang - so bail. episodeHasCompletedWithErrors(ClientState.ERROR_CANNOT_CREATE_WORLD, "Can not pause the old server since it's open to LAN; no way to safely create new world."); } } @@ -1555,8 +1554,7 @@ public class ClientStateMachine extends StateMachine implements IMalmoMessageLis if (inAbortState()) episodeHasCompleted(ClientState.MISSION_ABORTED); - // We need to make sure that both the client and server have paused, - // otherwise we are still susceptible to the "Holder Lookups" hang. + // We need to make sure that both the client and server have paused. // Since the server sets its pause state in response to the client's pause state, // and it only performs this check once, at the top of its tick method, @@ -1615,7 +1613,7 @@ public class ClientStateMachine extends StateMachine implements IMalmoMessageLis // If the Minecraft server isn't paused at this point, // then the following line will cause the server thread to exit... Minecraft.getMinecraft().world.sendQuittingDisconnectingPacket(); - // ...in which case the next line will hang. + // ...in which case the next line will block. Minecraft.getMinecraft().loadWorld((WorldClient) null); // Must display the GUI or Minecraft will attempt to access a non-existent player in the client tick. Minecraft.getMinecraft().displayGuiScreen(new GuiMainMenu()); @@ -2135,7 +2133,6 @@ public class ClientStateMachine extends StateMachine implements IMalmoMessageLis envServer.observation(data); } } else { - // Bung the whole shebang off via TCP: if (this.observationSocket.sendTCPString(data)) { this.failedTCPObservationSendCount = 0; } else { diff --git a/how-to-use-azureml/reinforcement-learning/minecraft-on-distributed-compute/docker/gpu/patch_files/ClientStateMachine.java b/how-to-use-azureml/reinforcement-learning/minecraft-on-distributed-compute/docker/gpu/patch_files/ClientStateMachine.java index 5cdee5c3..71668152 100644 --- a/how-to-use-azureml/reinforcement-learning/minecraft-on-distributed-compute/docker/gpu/patch_files/ClientStateMachine.java +++ b/how-to-use-azureml/reinforcement-learning/minecraft-on-distributed-compute/docker/gpu/patch_files/ClientStateMachine.java @@ -1477,7 +1477,7 @@ public class ClientStateMachine extends StateMachine implements IMalmoMessageLis * Pause the old server. It's vital that we do this, otherwise it will * respond to the quit disconnect package straight away and kill the server * thread, which means there will be no server to respond to the loadWorld - * code. (This was the cause of the infamous "Holder Lookups" hang.) + * code. */ public class PauseOldServerEpisode extends ConfigAwareStateEpisode { @@ -1505,7 +1505,6 @@ public class ClientStateMachine extends StateMachine implements IMalmoMessageLis { if (!killPublicFlag(Minecraft.getMinecraft().getIntegratedServer())) { - // Can't pause, don't want to risk the hang - so bail. episodeHasCompletedWithErrors(ClientState.ERROR_CANNOT_CREATE_WORLD, "Can not pause the old server since it's open to LAN; no way to safely create new world."); } } @@ -1555,8 +1554,7 @@ public class ClientStateMachine extends StateMachine implements IMalmoMessageLis if (inAbortState()) episodeHasCompleted(ClientState.MISSION_ABORTED); - // We need to make sure that both the client and server have paused, - // otherwise we are still susceptible to the "Holder Lookups" hang. + // We need to make sure that both the client and server have paused. // Since the server sets its pause state in response to the client's pause state, // and it only performs this check once, at the top of its tick method, @@ -1615,7 +1613,7 @@ public class ClientStateMachine extends StateMachine implements IMalmoMessageLis // If the Minecraft server isn't paused at this point, // then the following line will cause the server thread to exit... Minecraft.getMinecraft().world.sendQuittingDisconnectingPacket(); - // ...in which case the next line will hang. + // ...in which case the next line will block. Minecraft.getMinecraft().loadWorld((WorldClient) null); // Must display the GUI or Minecraft will attempt to access a non-existent player in the client tick. Minecraft.getMinecraft().displayGuiScreen(new GuiMainMenu()); @@ -2135,7 +2133,6 @@ public class ClientStateMachine extends StateMachine implements IMalmoMessageLis envServer.observation(data); } } else { - // Bung the whole shebang off via TCP: if (this.observationSocket.sendTCPString(data)) { this.failedTCPObservationSendCount = 0; } else { diff --git a/how-to-use-azureml/reinforcement-learning/setup/devenv_setup.yml b/how-to-use-azureml/reinforcement-learning/setup/devenv_setup.yml index e1752561..b528812e 100644 --- a/how-to-use-azureml/reinforcement-learning/setup/devenv_setup.yml +++ b/how-to-use-azureml/reinforcement-learning/setup/devenv_setup.yml @@ -2,4 +2,3 @@ name: devenv_setup dependencies: - pip: - azureml-sdk - - azure-mgmt-network diff --git a/how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb b/how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb index fa5cbc8e..59ddef0c 100644 --- a/how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb +++ b/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 1.9.0, you are currently running version\", azureml.core.VERSION)" + "print(\"This notebook was created using SDK version 1.10.0, you are currently running version\", azureml.core.VERSION)" ] }, { diff --git a/how-to-use-azureml/track-and-monitor-experiments/manage-runs/manage-runs.ipynb b/how-to-use-azureml/track-and-monitor-experiments/manage-runs/manage-runs.ipynb index 7003c125..c80480e4 100644 --- a/how-to-use-azureml/track-and-monitor-experiments/manage-runs/manage-runs.ipynb +++ b/how-to-use-azureml/track-and-monitor-experiments/manage-runs/manage-runs.ipynb @@ -215,6 +215,24 @@ "!more hello.py" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Submitted runs take a snapshot of the *source_directory* to use when executing. You can control which files are available to the run by using an *.amlignore* file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile .amlignore\n", + "# Exclude the outputs directory automatically created by our earlier runs.\n", + "/outputs" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -373,7 +391,7 @@ "source": [ "## Query properties and tags\n", "\n", - "You can quary runs within an experiment that match specific properties and tags. " + "You can query runs within an experiment that match specific properties and tags." ] }, { @@ -579,6 +597,22 @@ "name": "roastala" } ], + "category": "training", + "compute": [ + "Local" + ], + "datasets": [ + "None" + ], + "deployment": [ + "None" + ], + "exclude_from_index": false, + "framework": [ + "None" + ], + "friendly_name": "Managing your training runs", + "index_order": 2, "kernelspec": { "display_name": "Python 3.6", "language": "python", @@ -596,26 +630,10 @@ "pygments_lexer": "ipython3", "version": "3.6.5" }, - "friendly_name": "Managing your training runs", - "exclude_from_index": false, - "index_order": 2, - "category": "training", - "task": "Monitor and complete runs", - "datasets": [ - "None" - ], - "compute": [ - "Local" - ], - "deployment": [ - "None" - ], - "framework": [ - "None" - ], "tags": [ "None" - ] + ], + "task": "Monitor and complete runs" }, "nbformat": 4, "nbformat_minor": 2 diff --git a/how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb b/how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb index 7bca43b7..c9f79b94 100644 --- a/how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb +++ b/how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb @@ -184,7 +184,7 @@ "myenv = Environment(\"myenv\")\n", "\n", "myenv.docker.enabled = True\n", - "myenv.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])" + "myenv.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn', 'packaging'])" ] }, { diff --git a/how-to-use-azureml/training/train-on-computeinstance/train-on-computeinstance.ipynb b/how-to-use-azureml/training/train-on-computeinstance/train-on-computeinstance.ipynb index 048e4277..ec9ddf16 100644 --- a/how-to-use-azureml/training/train-on-computeinstance/train-on-computeinstance.ipynb +++ b/how-to-use-azureml/training/train-on-computeinstance/train-on-computeinstance.ipynb @@ -129,7 +129,8 @@ "from azureml.core.compute_target import ComputeTargetException\n", "\n", "# Choose a name for your instance\n", - "compute_name = \"compute-instance\"\n", + "# Compute instance name should be unique across the azure region\n", + "compute_name = \"ci{}\".format(ws._workspace_id)[:10]\n", "\n", "# Verify that instance does not exist already\n", "try:\n", @@ -182,7 +183,7 @@ "source": [ "### Create environment\n", "\n", - "Create Docker based environment with scikit-learn installed." + "Create an environment with scikit-learn installed." ] }, { @@ -195,8 +196,6 @@ "from azureml.core.conda_dependencies import CondaDependencies\n", "\n", "myenv = Environment(\"myenv\")\n", - "\n", - "myenv.docker.enabled = True\n", "myenv.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])" ] }, @@ -240,9 +239,15 @@ "metadata": {}, "outputs": [], "source": [ - "%%time\n", - "# Shows output of the run on stdout.\n", - "run.wait_for_completion(show_output=True)" + "from azureml.widgets import RunDetails\n", + "RunDetails(run).show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can use the get_active_runs() to get the currently running or queued jobs on the compute instance" ] }, { @@ -251,6 +256,33 @@ "metadata": {}, "outputs": [], "source": [ + "# wait for the run to reach Queued or Running state if it is in Preparing state\n", + "status = run.get_status()\n", + "while status not in ['Queued', 'Running', 'Completed', 'Failed', 'Canceled']:\n", + " state = run.get_status()\n", + " print('Run status: {}'.format(status))\n", + " time.sleep(10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# get active runs which are in Queued or Running state\n", + "active_runs = instance.get_active_runs()\n", + "for active_run in active_runs:\n", + " print(active_run.run_id, ',', active_run.status)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run.wait_for_completion()\n", "print(run.get_metrics())" ] }, @@ -321,7 +353,7 @@ "outputs": [], "source": [ "# delete() is used to delete the ComputeInstance target. Useful if you want to re-use the compute name \n", - "# instance.delete()" + "# instance.delete(wait_for_completion=True, show_output=True)" ] } ], diff --git a/setup-environment/configuration.ipynb b/setup-environment/configuration.ipynb index dadf83c1..7dc1e369 100644 --- a/setup-environment/configuration.ipynb +++ b/setup-environment/configuration.ipynb @@ -102,7 +102,7 @@ "source": [ "import azureml.core\n", "\n", - "print(\"This notebook was created using version 1.9.0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.10.0 of the Azure ML SDK\")\n", "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" ] }, diff --git a/tutorials/regression-automl-nyc-taxi-data/regression-automated-ml.ipynb b/tutorials/regression-automl-nyc-taxi-data/regression-automated-ml.ipynb index 9b06e729..143d3255 100644 --- a/tutorials/regression-automl-nyc-taxi-data/regression-automated-ml.ipynb +++ b/tutorials/regression-automl-nyc-taxi-data/regression-automated-ml.ipynb @@ -269,10 +269,7 @@ "source": [ "from sklearn.model_selection import train_test_split\n", "\n", - "y_df = final_df.pop(\"totalAmount\")\n", - "x_df = final_df\n", - "\n", - "x_train, x_test, y_train, y_test = train_test_split(x_df, y_df, test_size=0.2, random_state=223)" + "x_train, x_test = train_test_split(final_df, test_size=0.2, random_state=223)" ] }, { @@ -311,15 +308,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Define the experiment parameter and model settings for training. View the full list of [settings](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-auto-train). Submitting the experiment with these default settings will take approximately 5-10 min, but if you want a shorter run time, reduce the `iterations` parameter.\n", + "Define the experiment parameter and model settings for training. View the full list of [settings](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-auto-train). Submitting the experiment with these default settings will take approximately 20 minutes, but if you want a shorter run time, reduce the `experiment_timeout_hours` parameter.\n", "\n", "\n", "|Property| Value in this tutorial |Description|\n", "|----|----|---|\n", "|**iteration_timeout_minutes**|2|Time limit in minutes for each iteration. Reduce this value to decrease total runtime.|\n", - "|**iterations**|20|Number of iterations. In each iteration, a new machine learning model is trained with your data. This is the primary value that affects total run time.|\n", + "|**experiment_timeout_hours**|0.3|Maximum amount of time in hours that all iterations combined can take before the experiment terminates.|\n", + "|**enable_early_stopping**|True|Flag to enable early termination if the score is not improving in the short term.|\n", "|**primary_metric**| spearman_correlation | Metric that you want to optimize. The best-fit model will be chosen based on this metric.|\n", - "|**preprocess**| True | By using **True**, the experiment can preprocess the input data (handling missing data, converting text to numeric, etc.)|\n", + "|**featurization**| auto | By using auto, the experiment can preprocess the input data (handling missing data, converting text to numeric, etc.)|\n", "|**verbosity**| logging.INFO | Controls the level of logging.|\n", "|**n_cross_validations**|5|Number of cross-validation splits to perform when validation data is not specified.|" ] @@ -334,9 +332,10 @@ "\n", "automl_settings = {\n", " \"iteration_timeout_minutes\": 2,\n", - " \"iterations\": 20,\n", + " \"experiment_timeout_hours\": 0.3,\n", + " \"enable_early_stopping\": True,\n", " \"primary_metric\": 'spearman_correlation',\n", - " \"preprocess\": True,\n", + " \"featurization\": 'auto',\n", " \"verbosity\": logging.INFO,\n", " \"n_cross_validations\": 5\n", "}" @@ -359,8 +358,8 @@ "\n", "automl_config = AutoMLConfig(task='regression',\n", " debug_log='automated_ml_errors.log',\n", - " X=x_train.values,\n", - " y=y_train.values.flatten(),\n", + " training_data=x_train,\n", + " label_column_name=\"totalAmount\",\n", " **automl_settings)" ] }, @@ -467,7 +466,9 @@ "metadata": {}, "outputs": [], "source": [ - "y_predict = fitted_model.predict(x_test.values)\n", + "y_test = x_test.pop(\"totalAmount\")\n", + "\n", + "y_predict = fitted_model.predict(x_test)\n", "print(y_predict[:10])" ] },