diff --git a/configuration.ipynb b/configuration.ipynb index 022db8a5..4152578c 100644 --- a/configuration.ipynb +++ b/configuration.ipynb @@ -103,7 +103,7 @@ "source": [ "import azureml.core\n", "\n", - "print(\"This notebook was created using version 1.45.0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.46.0 of the Azure ML SDK\")\n", "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" ] }, @@ -367,9 +367,9 @@ } ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/contrib/RAPIDS/azure-ml-with-nvidia-rapids.ipynb b/contrib/RAPIDS/azure-ml-with-nvidia-rapids.ipynb index 5d45bbf4..b7bcb839 100644 --- a/contrib/RAPIDS/azure-ml-with-nvidia-rapids.ipynb +++ b/contrib/RAPIDS/azure-ml-with-nvidia-rapids.ipynb @@ -525,9 +525,9 @@ } ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/contrib/fairness/fairlearn-azureml-mitigation.ipynb b/contrib/fairness/fairlearn-azureml-mitigation.ipynb index 68040ed5..83cf336c 100644 --- a/contrib/fairness/fairlearn-azureml-mitigation.ipynb +++ b/contrib/fairness/fairlearn-azureml-mitigation.ipynb @@ -599,9 +599,9 @@ } ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/contrib/fairness/fairlearn-azureml-mitigation.yml b/contrib/fairness/fairlearn-azureml-mitigation.yml index 9e7198f1..98c1b8b8 100644 --- a/contrib/fairness/fairlearn-azureml-mitigation.yml +++ b/contrib/fairness/fairlearn-azureml-mitigation.yml @@ -6,7 +6,7 @@ dependencies: - fairlearn>=0.6.2 - joblib - liac-arff - - raiwidgets~=0.21.0 + - raiwidgets~=0.22.0 - itsdangerous==2.0.1 - markupsafe<2.1.0 - protobuf==3.20.0 diff --git a/contrib/fairness/upload-fairness-dashboard.ipynb b/contrib/fairness/upload-fairness-dashboard.ipynb index 35623f15..5f204d21 100644 --- a/contrib/fairness/upload-fairness-dashboard.ipynb +++ b/contrib/fairness/upload-fairness-dashboard.ipynb @@ -523,9 +523,9 @@ } ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/contrib/fairness/upload-fairness-dashboard.yml b/contrib/fairness/upload-fairness-dashboard.yml index 713f941e..64bc1a35 100644 --- a/contrib/fairness/upload-fairness-dashboard.yml +++ b/contrib/fairness/upload-fairness-dashboard.yml @@ -6,7 +6,7 @@ dependencies: - fairlearn>=0.6.2 - joblib - liac-arff - - raiwidgets~=0.21.0 + - raiwidgets~=0.22.0 - itsdangerous==2.0.1 - markupsafe<2.1.0 - protobuf==3.20.0 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 3008f483..0835839e 100644 --- a/how-to-use-azureml/automated-machine-learning/automl_env.yml +++ b/how-to-use-azureml/automated-machine-learning/automl_env.yml @@ -18,19 +18,19 @@ dependencies: - pywin32==227 - PySocks==1.7.1 - conda-forge::pyqt==5.12.3 -- jsonschema==4.15.0 - jinja2<=2.11.2 - markupsafe<2.1.0 - tqdm==4.64.0 +- jsonschema==4.15.0 - pip: # Required packages for AzureML execution, history, and data preparation. - - azureml-widgets~=1.45.0 - - azureml-defaults~=1.45.0 + - azureml-widgets~=1.46.0 + - azureml-defaults~=1.46.0 - pytorch-transformers==1.0.0 - spacy==2.2.4 - pystan==2.19.1.1 - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz - - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.45.0/validated_win32_requirements.txt [--no-deps] + - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.46.0/validated_win32_requirements.txt [--no-deps] - arch==4.14 - wasabi==0.9.1 diff --git a/how-to-use-azureml/automated-machine-learning/automl_env_linux.yml b/how-to-use-azureml/automated-machine-learning/automl_env_linux.yml index 8b2b1b45..ded19faf 100644 --- a/how-to-use-azureml/automated-machine-learning/automl_env_linux.yml +++ b/how-to-use-azureml/automated-machine-learning/automl_env_linux.yml @@ -23,14 +23,15 @@ dependencies: - cudatoolkit=10.1.243 - jinja2<=2.11.2 - markupsafe<2.1.0 +- jsonschema==4.15.0 - pip: # Required packages for AzureML execution, history, and data preparation. - - azureml-widgets~=1.45.0 - - azureml-defaults~=1.45.0 + - azureml-widgets~=1.46.0 + - azureml-defaults~=1.46.0 - pytorch-transformers==1.0.0 - spacy==2.2.4 - pystan==2.19.1.1 - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz - - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.45.0/validated_linux_requirements.txt [--no-deps] + - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.46.0/validated_linux_requirements.txt [--no-deps] - arch==4.14 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 83c4d151..205f3150 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 @@ -24,14 +24,15 @@ dependencies: - cudatoolkit=9.0 - jinja2<=2.11.2 - markupsafe<2.1.0 +- jsonschema==4.15.0 - pip: # Required packages for AzureML execution, history, and data preparation. - - azureml-widgets~=1.45.0 - - azureml-defaults~=1.45.0 + - azureml-widgets~=1.46.0 + - azureml-defaults~=1.46.0 - pytorch-transformers==1.0.0 - spacy==2.2.4 - pystan==2.19.1.1 - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz - - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.45.0/validated_darwin_requirements.txt [--no-deps] + - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.46.0/validated_darwin_requirements.txt [--no-deps] - arch==4.14 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 092e3595..aafcdf52 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 @@ -1060,9 +1060,9 @@ "name": "python3-azureml" }, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { 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 ba861161..a310c7be 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 @@ -456,9 +456,9 @@ "friendly_name": "Classification of credit card fraudulent transactions using Automated ML", "index_order": 5, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { 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 84336089..d941e095 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 @@ -567,9 +567,9 @@ "friendly_name": "DNN Text Featurization", "index_order": 2, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { 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 da471818..90f51993 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 @@ -564,9 +564,9 @@ } ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/automated-machine-learning/experimental/autofeaturization-codegen/codegen-for-autofeaturization.ipynb b/how-to-use-azureml/automated-machine-learning/experimental/autofeaturization-codegen/codegen-for-autofeaturization.ipynb index dd90ef9e..11b807a6 100644 --- a/how-to-use-azureml/automated-machine-learning/experimental/autofeaturization-codegen/codegen-for-autofeaturization.ipynb +++ b/how-to-use-azureml/automated-machine-learning/experimental/autofeaturization-codegen/codegen-for-autofeaturization.ipynb @@ -97,7 +97,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(\"This notebook was created using version 1.45.0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.46.0 of the Azure ML SDK\")\n", "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" ] }, @@ -324,9 +324,9 @@ "hash": "adb464b67752e4577e3dc163235ced27038d19b7d88def00d75d1975bde5d9ab" }, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/automated-machine-learning/experimental/autofeaturization-custom-model-training/custom-model-training-from-autofeaturization-run.ipynb b/how-to-use-azureml/automated-machine-learning/experimental/autofeaturization-custom-model-training/custom-model-training-from-autofeaturization-run.ipynb index 6c97d076..5ac3e430 100644 --- a/how-to-use-azureml/automated-machine-learning/experimental/autofeaturization-custom-model-training/custom-model-training-from-autofeaturization-run.ipynb +++ b/how-to-use-azureml/automated-machine-learning/experimental/autofeaturization-custom-model-training/custom-model-training-from-autofeaturization-run.ipynb @@ -97,7 +97,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(\"This notebook was created using version 1.45.0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.46.0 of the Azure ML SDK\")\n", "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" ] }, @@ -713,9 +713,9 @@ "hash": "adb464b67752e4577e3dc163235ced27038d19b7d88def00d75d1975bde5d9ab" }, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/automated-machine-learning/experimental/automl_thin_client_env.yml b/how-to-use-azureml/automated-machine-learning/experimental/automl_thin_client_env.yml index 879a8782..c7d165e9 100644 --- a/how-to-use-azureml/automated-machine-learning/experimental/automl_thin_client_env.yml +++ b/how-to-use-azureml/automated-machine-learning/experimental/automl_thin_client_env.yml @@ -7,7 +7,7 @@ dependencies: - cython==0.29.14 - urllib3==1.26.7 - PyJWT < 2.0.0 -- numpy==1.21.6 +- numpy==1.22.3 - pywin32==227 - cryptography<37.0.0 @@ -21,3 +21,4 @@ dependencies: - azureml-mlflow - pandas - mlflow + - docker<6.0.0 diff --git a/how-to-use-azureml/automated-machine-learning/experimental/classification-credit-card-fraud-local-managed/auto-ml-classification-credit-card-fraud-local-managed.ipynb b/how-to-use-azureml/automated-machine-learning/experimental/classification-credit-card-fraud-local-managed/auto-ml-classification-credit-card-fraud-local-managed.ipynb index 58fe9c3a..2344885c 100644 --- a/how-to-use-azureml/automated-machine-learning/experimental/classification-credit-card-fraud-local-managed/auto-ml-classification-credit-card-fraud-local-managed.ipynb +++ b/how-to-use-azureml/automated-machine-learning/experimental/classification-credit-card-fraud-local-managed/auto-ml-classification-credit-card-fraud-local-managed.ipynb @@ -92,7 +92,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(\"This notebook was created using version 1.45.0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.46.0 of the Azure ML SDK\")\n", "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" ] }, @@ -354,7 +354,7 @@ "This Credit Card fraud Detection dataset is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/ and is available at: https://www.kaggle.com/mlg-ulb/creditcardfraud\n", "\n", "\n", - "The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Universit\u00c3\u0192\u00c2\u00a9 Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on https://www.researchgate.net/project/Fraud-detection-5 and the page of the DefeatFraud project\n", + "The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Universit\u00c3\u0192\u00c2\u00a9 Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on https://www.researchgate.net and the page of the DefeatFraud project\n", "Please cite the following works: \n", "\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00a2\tAndrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015\n", "\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00a2\tDal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-Ael; Waterschoot, Serge; Bontempi, Gianluca. Learned lessons in credit card fraud detection from a practitioner perspective, Expert systems with applications,41,10,4915-4928,2014, Pergamon\n", @@ -389,9 +389,9 @@ "friendly_name": "Classification of credit card fraudulent transactions using Automated ML", "index_order": 5, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.ipynb b/how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.ipynb index 4bb84353..91bc487d 100644 --- a/how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.ipynb +++ b/how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.ipynb @@ -91,7 +91,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(\"This notebook was created using version 1.45.0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.46.0 of the Azure ML SDK\")\n", "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" ] }, @@ -448,9 +448,9 @@ "automated-machine-learning" ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git 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 index f0b09739..355e2921 100644 --- 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 @@ -706,9 +706,9 @@ "automated-machine-learning" ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git 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 index 0bc5465b..3109bc54 100644 --- 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 @@ -700,9 +700,9 @@ "Azure ML AutoML" ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { 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 2bdc794d..2d2d4043 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 @@ -697,9 +697,9 @@ "friendly_name": "Forecasting BikeShare Demand", "index_order": 1, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { 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 6292b44c..665fcc33 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 @@ -767,9 +767,9 @@ "automated-machine-learning" ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { 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 186b4b63..e7ce0272 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 @@ -866,9 +866,9 @@ "friendly_name": "Forecasting away from training data", "index_order": 3, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git 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 index 80594004..00db9508 100644 --- 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 @@ -681,9 +681,9 @@ ], "hide_code_all_hidden": false, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git 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 index 840c1cc2..303df879 100644 --- 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 @@ -620,9 +620,9 @@ "automated-machine-learning" ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git 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 index f889609d..5f2c16dc 100644 --- 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 @@ -837,9 +837,9 @@ "automated-machine-learning" ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { 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 4aeaa581..fe569859 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 @@ -821,9 +821,9 @@ "friendly_name": "Forecasting orange juice sales with deployment", "index_order": 1, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-pipelines/auto-ml-forecasting-pipelines.ipynb b/how-to-use-azureml/automated-machine-learning/forecasting-pipelines/auto-ml-forecasting-pipelines.ipynb index 89bce95d..14803431 100644 --- a/how-to-use-azureml/automated-machine-learning/forecasting-pipelines/auto-ml-forecasting-pipelines.ipynb +++ b/how-to-use-azureml/automated-machine-learning/forecasting-pipelines/auto-ml-forecasting-pipelines.ipynb @@ -799,9 +799,9 @@ "friendly_name": "Forecasting orange juice sales with deployment", "index_order": 1, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/auto-ml-forecasting-univariate-recipe-experiment-settings.ipynb b/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/auto-ml-forecasting-univariate-recipe-experiment-settings.ipynb index 2e773fbd..d73121a2 100644 --- a/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/auto-ml-forecasting-univariate-recipe-experiment-settings.ipynb +++ b/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/auto-ml-forecasting-univariate-recipe-experiment-settings.ipynb @@ -472,9 +472,9 @@ } ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/auto-ml-forecasting-univariate-recipe-run-experiment.ipynb b/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/auto-ml-forecasting-univariate-recipe-run-experiment.ipynb index b0cc9070..1fcca49f 100644 --- a/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/auto-ml-forecasting-univariate-recipe-run-experiment.ipynb +++ b/how-to-use-azureml/automated-machine-learning/forecasting-recipes-univariate/auto-ml-forecasting-univariate-recipe-run-experiment.ipynb @@ -572,9 +572,9 @@ } ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { 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 ccf908a3..6f857d91 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 @@ -870,9 +870,9 @@ "friendly_name": "Classification of credit card fraudulent transactions using Automated ML", "index_order": 5, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { 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 063c8254..3377db8c 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 @@ -895,9 +895,9 @@ "friendly_name": "Automated ML run with featurization and model explainability.", "index_order": 5, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { 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 e168f124..d71b909a 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 @@ -449,9 +449,9 @@ "automated-machine-learning" ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/azure-databricks/automl/automl-databricks-local-01.ipynb b/how-to-use-azureml/azure-databricks/automl/automl-databricks-local-01.ipynb index 10ccdecb..1f9f0439 100644 --- a/how-to-use-azureml/azure-databricks/automl/automl-databricks-local-01.ipynb +++ b/how-to-use-azureml/azure-databricks/automl/automl-databricks-local-01.ipynb @@ -429,9 +429,9 @@ } ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/azure-databricks/automl/automl-databricks-local-with-deployment.ipynb b/how-to-use-azureml/azure-databricks/automl/automl-databricks-local-with-deployment.ipynb index 5865afc1..d927849b 100644 --- a/how-to-use-azureml/azure-databricks/automl/automl-databricks-local-with-deployment.ipynb +++ b/how-to-use-azureml/azure-databricks/automl/automl-databricks-local-with-deployment.ipynb @@ -557,9 +557,9 @@ } ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/azure-synapse/Synapse_Job_Scala_Support.ipynb b/how-to-use-azureml/azure-synapse/Synapse_Job_Scala_Support.ipynb index 520958c9..a5116d68 100644 --- a/how-to-use-azureml/azure-synapse/Synapse_Job_Scala_Support.ipynb +++ b/how-to-use-azureml/azure-synapse/Synapse_Job_Scala_Support.ipynb @@ -161,9 +161,9 @@ } ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/azure-synapse/Synapse_Session_Scala_Support.ipynb b/how-to-use-azureml/azure-synapse/Synapse_Session_Scala_Support.ipynb index a08e0121..09c23af8 100644 --- a/how-to-use-azureml/azure-synapse/Synapse_Session_Scala_Support.ipynb +++ b/how-to-use-azureml/azure-synapse/Synapse_Session_Scala_Support.ipynb @@ -215,9 +215,9 @@ } ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/azure-synapse/spark_job_on_synapse_spark_pool.ipynb b/how-to-use-azureml/azure-synapse/spark_job_on_synapse_spark_pool.ipynb index fe2f74cb..12b44207 100644 --- a/how-to-use-azureml/azure-synapse/spark_job_on_synapse_spark_pool.ipynb +++ b/how-to-use-azureml/azure-synapse/spark_job_on_synapse_spark_pool.ipynb @@ -482,9 +482,9 @@ } ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/azure-synapse/spark_session_on_synapse_spark_pool.ipynb b/how-to-use-azureml/azure-synapse/spark_session_on_synapse_spark_pool.ipynb index 1eebb8a4..ee3d4160 100644 --- a/how-to-use-azureml/azure-synapse/spark_session_on_synapse_spark_pool.ipynb +++ b/how-to-use-azureml/azure-synapse/spark_session_on_synapse_spark_pool.ipynb @@ -302,9 +302,9 @@ } ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/deployment/deploy-multi-model/multi-model-register-and-deploy.ipynb b/how-to-use-azureml/deployment/deploy-multi-model/multi-model-register-and-deploy.ipynb index 6a8b1514..871cfd10 100644 --- a/how-to-use-azureml/deployment/deploy-multi-model/multi-model-register-and-deploy.ipynb +++ b/how-to-use-azureml/deployment/deploy-multi-model/multi-model-register-and-deploy.ipynb @@ -86,7 +86,7 @@ "source": [ "In this example, we will be using and registering two models. \n", "\n", - "First we will train two simple models on the [diabetes dataset](https://scikit-learn.org/stable/datasets/index.html#diabetes-dataset) included with scikit-learn, serializing them to files in the current directory." + "First we will train two simple models on the [diabetes dataset](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset) included with scikit-learn, serializing them to files in the current directory." ] }, { @@ -373,9 +373,9 @@ } ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git 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 index e2062c19..6bc9b916 100644 --- 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 @@ -541,7 +541,7 @@ " - To run a local web service, see the [notebook on deployment to a local Docker container](../deploy-to-local/register-model-deploy-local.ipynb).\n", " - For more information on datasets, see the [notebook on training with datasets](../../work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.ipynb).\n", " - For more information on environments, see the [notebook on using environments](../../training/using-environments/using-environments.ipynb).\n", - " - For information on all the available deployment targets, see [“How and where to deploy models”](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-and-where#choose-a-compute-target)." + " - For information on all the available deployment targets, see [“How and where to deploy models”](https://docs.microsoft.com/azure/machine-learning/v1/how-to-deploy-and-where#choose-a-compute-target)." ] } ], @@ -568,9 +568,9 @@ "friendly_name": "Register model and deploy as webservice", "index_order": 3, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local-advanced.ipynb b/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local-advanced.ipynb index 9095363a..e41859ce 100644 --- a/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local-advanced.ipynb +++ b/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local-advanced.ipynb @@ -473,9 +473,9 @@ } ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.ipynb b/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.ipynb index 33e5eadc..20959cdf 100644 --- a/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.ipynb +++ b/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.ipynb @@ -529,9 +529,9 @@ "friendly_name": "Register a model and deploy locally", "index_order": 1, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git 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 index 5b1e4763..26031a08 100644 --- 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 @@ -344,9 +344,9 @@ "friendly_name": "Deploy models to AKS using controlled roll out", "index_order": 3, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb b/how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb index 466da238..2dbfa989 100644 --- a/how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb +++ b/how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb @@ -476,9 +476,9 @@ } ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb b/how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb index bcf50701..79cc3c9f 100644 --- a/how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb +++ b/how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb @@ -405,9 +405,9 @@ "friendly_name": "Convert and deploy TinyYolo with ONNX Runtime", "index_order": 5, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb b/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb index 9d9e2590..3448643f 100644 --- a/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb +++ b/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb @@ -773,9 +773,9 @@ "friendly_name": "Deploy Facial Expression Recognition (FER+) with ONNX Runtime", "index_order": 2, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb b/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb index 67466c82..129e0e0f 100644 --- a/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb +++ b/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb @@ -750,9 +750,9 @@ "friendly_name": "Deploy MNIST digit recognition with ONNX Runtime", "index_order": 1, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/deployment/onnx/onnx-model-register-and-deploy.ipynb b/how-to-use-azureml/deployment/onnx/onnx-model-register-and-deploy.ipynb index 42fdb6d8..d21fba44 100644 --- a/how-to-use-azureml/deployment/onnx/onnx-model-register-and-deploy.ipynb +++ b/how-to-use-azureml/deployment/onnx/onnx-model-register-and-deploy.ipynb @@ -206,9 +206,9 @@ } ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb b/how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb index fb408032..524d6e67 100644 --- a/how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb +++ b/how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb @@ -389,9 +389,9 @@ "friendly_name": "Deploy ResNet50 with ONNX Runtime", "index_order": 4, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb b/how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb index 92d8ef5e..00693646 100644 --- a/how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb +++ b/how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb @@ -564,9 +564,9 @@ "friendly_name": "Train MNIST in PyTorch, convert, and deploy with ONNX Runtime", "index_order": 3, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/deployment/production-deploy-to-aks-gpu/production-deploy-to-aks-gpu.ipynb b/how-to-use-azureml/deployment/production-deploy-to-aks-gpu/production-deploy-to-aks-gpu.ipynb index 838aa996..f48c4fca 100644 --- a/how-to-use-azureml/deployment/production-deploy-to-aks-gpu/production-deploy-to-aks-gpu.ipynb +++ b/how-to-use-azureml/deployment/production-deploy-to-aks-gpu/production-deploy-to-aks-gpu.ipynb @@ -329,9 +329,9 @@ } ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { 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 baec6484..2cb8fe0f 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 @@ -213,7 +213,7 @@ "\n", "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", "\n", - "See code snippet below. Check the documentation [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-secure-web-service) for more details" + "See code snippet below. Check the documentation [here](https://docs.microsoft.com/azure/machine-learning/v1/how-to-secure-web-service) for more details" ] }, { @@ -334,9 +334,9 @@ } ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { 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 59270d81..63b89c4e 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 @@ -366,7 +366,7 @@ "metadata": {}, "source": [ "# Create AKS Cluster in an existing virtual network (optional)\n", - "See code snippet below. Check the documentation [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-enable-virtual-network#use-azure-kubernetes-service) for more details." + "See code snippet below. Check the documentation [here](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-network-security-overview) for more details." ] }, { @@ -397,7 +397,7 @@ "metadata": {}, "source": [ "# Enable SSL on the AKS Cluster (optional)\n", - "See code snippet below. Check the documentation [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-secure-web-service) for more details" + "See code snippet below. Check the documentation [here](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-network-security-overview#secure-the-inferencing-environment-v1) for more details" ] }, { @@ -603,9 +603,9 @@ } ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/deployment/spark/model-register-and-deploy-spark.ipynb b/how-to-use-azureml/deployment/spark/model-register-and-deploy-spark.ipynb index fd2f73b8..e5f27da7 100644 --- a/how-to-use-azureml/deployment/spark/model-register-and-deploy-spark.ipynb +++ b/how-to-use-azureml/deployment/spark/model-register-and-deploy-spark.ipynb @@ -327,9 +327,9 @@ ], "friendly_name": "Register Spark model and deploy as webservice", "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/explain-model/azure-integration/gpu-explanation/train-explain-model-gpu-tree-explainer.ipynb b/how-to-use-azureml/explain-model/azure-integration/gpu-explanation/train-explain-model-gpu-tree-explainer.ipynb index 5f10d3af..0d314018 100644 --- a/how-to-use-azureml/explain-model/azure-integration/gpu-explanation/train-explain-model-gpu-tree-explainer.ipynb +++ b/how-to-use-azureml/explain-model/azure-integration/gpu-explanation/train-explain-model-gpu-tree-explainer.ipynb @@ -106,7 +106,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(\"This notebook was created using version 1.45.0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.46.0 of the Azure ML SDK\")\n", "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" ] }, @@ -481,9 +481,9 @@ } ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/explain-model/azure-integration/gpu-explanation/train-explain-model-gpu-tree-explainer.yml b/how-to-use-azureml/explain-model/azure-integration/gpu-explanation/train-explain-model-gpu-tree-explainer.yml index bc0820e3..919a43fd 100644 --- a/how-to-use-azureml/explain-model/azure-integration/gpu-explanation/train-explain-model-gpu-tree-explainer.yml +++ b/how-to-use-azureml/explain-model/azure-integration/gpu-explanation/train-explain-model-gpu-tree-explainer.yml @@ -10,7 +10,7 @@ dependencies: - ipython - matplotlib - ipywidgets - - raiwidgets~=0.21.0 + - raiwidgets~=0.22.0 - itsdangerous==2.0.1 - markupsafe<2.1.0 - scipy>=1.5.3 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 835eb18e..861d32c6 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 @@ -496,9 +496,9 @@ } ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.yml b/how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.yml index cf4fd24e..35147848 100644 --- a/how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.yml +++ b/how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.yml @@ -10,7 +10,7 @@ dependencies: - matplotlib - azureml-dataset-runtime - ipywidgets - - raiwidgets~=0.21.0 + - raiwidgets~=0.22.0 - itsdangerous==2.0.1 - markupsafe<2.1.0 - scipy>=1.5.3 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 b9cb9cb2..4d306e44 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 @@ -595,9 +595,9 @@ } ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.yml b/how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.yml index ffd96693..109aea0a 100644 --- a/how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.yml +++ b/how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.yml @@ -9,7 +9,7 @@ dependencies: - ipython - matplotlib - ipywidgets - - raiwidgets~=0.21.0 + - raiwidgets~=0.22.0 - packaging>=20.9 - itsdangerous==2.0.1 - markupsafe<2.1.0 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 25b77f0d..d1c9f432 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 @@ -516,9 +516,9 @@ } ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.yml b/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.yml index 34a03028..440d35ef 100644 --- a/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.yml +++ b/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.yml @@ -9,7 +9,7 @@ dependencies: - ipython - matplotlib - ipywidgets - - raiwidgets~=0.21.0 + - raiwidgets~=0.22.0 - packaging>=20.9 - itsdangerous==2.0.1 - markupsafe<2.1.0 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 0f08f370..adcf2463 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 @@ -576,9 +576,9 @@ } ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.yml b/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.yml index c370b9f2..d369d23f 100644 --- a/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.yml +++ b/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.yml @@ -11,7 +11,7 @@ dependencies: - azureml-dataset-runtime - azureml-core - ipywidgets - - raiwidgets~=0.21.0 + - raiwidgets~=0.22.0 - itsdangerous==2.0.1 - markupsafe<2.1.0 - scipy>=1.5.3 diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-data-transfer.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-data-transfer.ipynb index 8221f22d..3869028d 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-data-transfer.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-data-transfer.ipynb @@ -579,9 +579,9 @@ ], "friendly_name": "Azure Machine Learning Pipeline with DataTranferStep", "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.ipynb index 63233de1..c5a2a47a 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.ipynb @@ -632,9 +632,9 @@ ], "friendly_name": "Getting Started with Azure Machine Learning Pipelines", "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-azurebatch-to-run-a-windows-executable.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-azurebatch-to-run-a-windows-executable.ipynb index f9f2d4c9..3c935a0f 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-azurebatch-to-run-a-windows-executable.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-azurebatch-to-run-a-windows-executable.ipynb @@ -384,9 +384,9 @@ ], "friendly_name": "Azure Machine Learning Pipeline with AzureBatchStep", "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-modulestep.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-modulestep.ipynb index d349b5b1..9d0ea83b 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-modulestep.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-modulestep.ipynb @@ -470,9 +470,9 @@ ], "friendly_name": "How to use ModuleStep with AML Pipelines", "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-pipeline-drafts.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-pipeline-drafts.ipynb index 3b07b1d3..d5d26de1 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-pipeline-drafts.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-how-to-use-pipeline-drafts.ipynb @@ -261,9 +261,9 @@ ], "friendly_name": "How to use Pipeline Drafts to create a Published Pipeline", "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-parameter-tuning-with-hyperdrive.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-parameter-tuning-with-hyperdrive.ipynb index 7a617663..2085c59a 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-parameter-tuning-with-hyperdrive.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-parameter-tuning-with-hyperdrive.ipynb @@ -292,7 +292,7 @@ "metadata": {}, "outputs": [], "source": [ - "tf_env = Environment.get(ws, name='AzureML-TensorFlow-2.0-GPU')" + "tf_env = Environment.get(ws, name='AzureML-tensorflow-2.6-ubuntu20.04-py38-cuda11-gpu')" ] }, { @@ -595,9 +595,9 @@ ], "friendly_name": "Azure Machine Learning Pipeline with HyperDriveStep", "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-publish-and-run-using-rest-endpoint.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-publish-and-run-using-rest-endpoint.ipynb index 816e7f47..34069a7d 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-publish-and-run-using-rest-endpoint.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-publish-and-run-using-rest-endpoint.ipynb @@ -443,9 +443,9 @@ ], "friendly_name": "How to Publish a Pipeline and Invoke the REST endpoint", "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-schedule-for-a-published-pipeline.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-schedule-for-a-published-pipeline.ipynb index 2acb6e06..2e51e0d8 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-schedule-for-a-published-pipeline.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-schedule-for-a-published-pipeline.ipynb @@ -432,7 +432,7 @@ "This schedule will run when additions or modifications are made to Blobs in the Datastore.\n", "By default, the Datastore container is monitored for changes. Use the path_on_datastore parameter to instead specify a path on the Datastore to monitor for changes. Note: the path_on_datastore will be under the container for the datastore, so the actual path monitored will be container/path_on_datastore. Changes made to subfolders in the container/path will not trigger the schedule.\n", "Note: Only Blob Datastores are supported.\n", - "Note: Not supported for CMK workspaces. Please review these [instructions](https://docs.microsoft.com/azure/machine-learning/how-to-trigger-published-pipeline) in order to setup a blob trigger submission schedule with CMK enabled. Also see those instructions to bring your own LogicApp to avoid the schedule triggers per month limit." + "Note: Not supported for CMK workspaces. Please review these [instructions](https://docs.microsoft.com/azure/machine-learning/v1/how-to-trigger-published-pipeline) in order to setup a blob trigger submission schedule with CMK enabled. Also see those instructions to bring your own LogicApp to avoid the schedule triggers per month limit." ] }, { @@ -637,9 +637,9 @@ ], "friendly_name": "How to Setup a Schedule for a Published Pipeline or Pipeline Endpoint", "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-versioned-pipeline-endpoints.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-versioned-pipeline-endpoints.ipynb index aefaf8f7..f1f468fd 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-versioned-pipeline-endpoints.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-versioned-pipeline-endpoints.ipynb @@ -581,9 +581,9 @@ ], "friendly_name": "How to setup a versioned Pipeline Endpoint", "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-showcasing-datapath-and-pipelineparameter.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-showcasing-datapath-and-pipelineparameter.ipynb index ff01d3a0..40abdc1f 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-showcasing-datapath-and-pipelineparameter.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-showcasing-datapath-and-pipelineparameter.ipynb @@ -500,9 +500,9 @@ ], "friendly_name": "How to use DataPath as a PipelineParameter", "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-showcasing-dataset-and-pipelineparameter.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-showcasing-dataset-and-pipelineparameter.ipynb index 08490e40..226ac0bc 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-showcasing-dataset-and-pipelineparameter.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-showcasing-dataset-and-pipelineparameter.ipynb @@ -496,9 +496,9 @@ ], "friendly_name": "How to use Dataset as a PipelineParameter", "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-adla-as-compute-target.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-adla-as-compute-target.ipynb index 8c51bc3d..4c2ff478 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-adla-as-compute-target.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-adla-as-compute-target.ipynb @@ -377,9 +377,9 @@ ], "friendly_name": "How to use AdlaStep with AML Pipelines", "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-databricks-as-compute-target.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-databricks-as-compute-target.ipynb index 60c72fe4..9a6f67ed 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-databricks-as-compute-target.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-databricks-as-compute-target.ipynb @@ -20,7 +20,7 @@ "metadata": {}, "source": [ "# Using Databricks as a Compute Target from Azure Machine Learning Pipeline\n", - "To use Databricks as a compute target from [Azure Machine Learning Pipeline](https://aka.ms/pl-concept), a [DatabricksStep](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.databricks_step.databricksstep?view=azure-ml-py) is used. This notebook demonstrates the use of DatabricksStep in Azure Machine Learning Pipeline.\n", + "To use Databricks as a compute target from [Azure Machine Learning Pipeline](https://aka.ms/pl-concept), a [DatabricksStep](https://docs.microsoft.com/python/api/azureml-pipeline-steps/azureml.pipeline.steps.databricks_step.databricksstep?view=azure-ml-py) is used. This notebook demonstrates the use of DatabricksStep in Azure Machine Learning Pipeline.\n", "\n", "The notebook will show:\n", "1. Running an arbitrary Databricks notebook that the customer has in Databricks workspace\n", @@ -180,10 +180,9 @@ "metadata": {}, "source": [ "## Data Connections with Inputs and Outputs\n", - "The DatabricksStep supports DBFS, Azure Blob and ADLS for inputs and outputs. You also will need to define a [Secrets](https://docs.azuredatabricks.net/user-guide/secrets/index.html) scope to enable authentication to external data sources such as Blob and ADLS from Databricks.\n", + "The DatabricksStep supports DBFS, Azure Blob and ADLS for inputs and outputs. You also will need to define a [Secrets](https://docs.microsoft.com/azure/databricks/security/access-control/secret-acl) scope to enable authentication to external data sources such as Blob and ADLS from Databricks.\n", "\n", - "- Databricks documentation on [Azure Blob](https://docs.azuredatabricks.net/spark/latest/data-sources/azure/azure-storage.html)\n", - "- Databricks documentation on [ADLS](https://docs.databricks.com/spark/latest/data-sources/azure/azure-datalake.html)\n", + "- Databricks documentation on [Azure Storage](https://docs.microsoft.com/azure/databricks/data/data-sources/azure/azure-storage)\n", "\n", "### Type of Data Access\n", "Databricks allows to interact with Azure Blob and ADLS in two ways.\n", @@ -415,7 +414,7 @@ "### 1. Running the demo notebook already added to the Databricks workspace\n", "Create a notebook in the Azure Databricks workspace, and provide the path to that notebook as the value associated with the environment variable \"DATABRICKS_NOTEBOOK_PATH\". This will then set the variable\u00c2\u00a0notebook_path\u00c2\u00a0when you run the code cell below:\n", "\n", - "your notebook's path in Azure Databricks UI by hovering over to notebook's title. A typical path of notebook looks like this `/Users/example@databricks.com/example`. See [Databricks Workspace](https://docs.azuredatabricks.net/user-guide/workspace.html) to learn about the folder structure.\n", + "your notebook's path in Azure Databricks UI by hovering over to notebook's title. A typical path of notebook looks like this `/Users/example@databricks.com/example`. See [Databricks Workspace](https://docs.microsoft.com/azure/databricks/workspace) to learn about the folder structure.\n", "\n", "Note: DataPath `PipelineParameter` should be provided in list of inputs. Such parameters can be accessed by the datapath `name`." ] @@ -487,7 +486,7 @@ "### 2. Running a Python script from DBFS\n", "This shows how to run a Python script in DBFS. \n", "\n", - "To complete this, you will need to first upload the Python script in your local machine to DBFS using the [CLI](https://docs.azuredatabricks.net/user-guide/dbfs-databricks-file-system.html). The CLI command is given below:\n", + "To complete this, you will need to first upload the Python script in your local machine to DBFS using the [CLI](https://docs.microsoft.com/azure/databricks/dbfs). The CLI command is given below:\n", "\n", "```\n", "dbfs cp ./train-db-dbfs.py dbfs:/train-db-dbfs.py\n", @@ -630,7 +629,7 @@ "metadata": {}, "source": [ "### 4. Running a JAR job that is alreay added in DBFS\n", - "To run a JAR job that is already uploaded to DBFS, follow the instructions below. You will first upload the JAR file to DBFS using the [CLI](https://docs.azuredatabricks.net/user-guide/dbfs-databricks-file-system.html).\n", + "To run a JAR job that is already uploaded to DBFS, follow the instructions below. You will first upload the JAR file to DBFS using the [CLI](https://docs.microsoft.com/azure/databricks/dbfs).\n", "\n", "The commented out code in the below cell assumes that you have uploaded `train-db-dbfs.jar` to the root folder in DBFS. You can upload `train-db-dbfs.jar` to the root folder in DBFS using this commandline so you can use `jar_library_dbfs_path = \"dbfs:/train-db-dbfs.jar\"`:\n", "\n", @@ -704,7 +703,7 @@ "metadata": {}, "source": [ "### 5. Running demo notebook already added to the Databricks workspace using existing cluster\n", - "First you need register DBFS datastore and make sure path_on_datastore does exist in databricks file system, you can browser the files by refering [this](https://docs.azuredatabricks.net/user-guide/dbfs-databricks-file-system.html).\n", + "First you need register DBFS datastore and make sure path_on_datastore does exist in databricks file system, you can browser the files by refering [this](https://docs.microsoft.com/azure/databricks/dbfs).\n", "\n", "Find existing_cluster_id by opeing Azure Databricks UI with Clusters page and in url you will find a string connected with '-' right after \"clusters/\"." ] @@ -941,9 +940,9 @@ ], "friendly_name": "How to use DatabricksStep with AML Pipelines", "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-kusto-as-compute-target.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-kusto-as-compute-target.ipynb index 3959bd04..4976b70e 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-kusto-as-compute-target.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-kusto-as-compute-target.ipynb @@ -244,9 +244,9 @@ ], "friendly_name": "How to use KustoStep with AML Pipelines", "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-automated-machine-learning-step.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-automated-machine-learning-step.ipynb index 6683fab9..f54e0e3e 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-automated-machine-learning-step.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-automated-machine-learning-step.ipynb @@ -498,9 +498,9 @@ ], "friendly_name": "How to use AutoMLStep with AML Pipelines", "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-commandstep-r.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-commandstep-r.ipynb index 4d7a3af5..2b2cff79 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-commandstep-r.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-commandstep-r.ipynb @@ -315,9 +315,9 @@ ], "friendly_name": "Azure Machine Learning Pipeline with CommandStep for R", "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-commandstep.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-commandstep.ipynb index fd719bb6..7c787163 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-commandstep.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-commandstep.ipynb @@ -278,9 +278,9 @@ ], "friendly_name": "Azure Machine Learning Pipeline with CommandStep", "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-data-dependency-steps.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-data-dependency-steps.ipynb index fa89e5c3..ba864e60 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-data-dependency-steps.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-data-dependency-steps.ipynb @@ -545,9 +545,9 @@ ], "friendly_name": "Azure Machine Learning Pipelines with Data Dependency", "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-notebook-runner-step.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-notebook-runner-step.ipynb index 2d17c662..7f724338 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-notebook-runner-step.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-notebook-runner-step.ipynb @@ -409,9 +409,9 @@ ], "friendly_name": "How to use run a notebook as a step in AML Pipelines", "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/notebook_runner/training_notebook.ipynb b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/notebook_runner/training_notebook.ipynb index db234669..c54da350 100644 --- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/notebook_runner/training_notebook.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/notebook_runner/training_notebook.ipynb @@ -84,9 +84,9 @@ } ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/nyc-taxi-data-regression-model-building.ipynb b/how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/nyc-taxi-data-regression-model-building.ipynb index 8325b4bd..75d86925 100644 --- a/how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/nyc-taxi-data-regression-model-building.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/nyc-taxi-data-regression-model-building.ipynb @@ -1046,9 +1046,9 @@ } ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-image-inference-mnist.ipynb b/how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-image-inference-mnist.ipynb index e5f60a87..d11b6ac7 100644 --- a/how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-image-inference-mnist.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-image-inference-mnist.ipynb @@ -24,7 +24,7 @@ "In this notebook, we will demonstrate how to make predictions on large quantities of data asynchronously using the ML pipelines with Azure Machine Learning. Batch inference (or batch scoring) provides cost-effective inference, with unparalleled throughput for asynchronous applications. Batch prediction pipelines can scale to perform inference on terabytes of production data. Batch prediction is optimized for high throughput, fire-and-forget predictions for a large collection of data.\n", "\n", "> **Tip**\n", - "If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/azure/machine-learning/service/how-to-consume-web-service) instead of batch prediction.\n", + "If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/azure/machine-learning/v1/how-to-consume-web-service) instead of batch prediction.\n", "\n", "In this example will be take a digit identification model already-trained on MNIST dataset using the [AzureML training with deep learning example notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb), and run that trained model on some of the MNIST test images in batch. \n", "\n", @@ -277,7 +277,7 @@ "### Register the model with Workspace\n", "A registered model is a logical container for one or more files that make up your model. For example, if you have a model that's stored in multiple files, you can register them as a single model in the workspace. After you register the files, you can then download or deploy the registered model and receive all the files that you registered.\n", "\n", - "Using tags, you can track useful information such as the name and version of the machine learning library used to train the model. Note that tags must be alphanumeric. Learn more about registering models [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-deploy-and-where#registermodel) " + "Using tags, you can track useful information such as the name and version of the machine learning library used to train the model. Note that tags must be alphanumeric. Learn more about registering models [here](https://docs.microsoft.com/azure/machine-learning/v1/how-to-deploy-and-where#registermodel) " ] }, { @@ -581,16 +581,7 @@ "metadata": { "authors": [ { - "name": "joringer" - }, - { - "name": "asraniwa" - }, - { - "name": "pansav" - }, - { - "name": "tracych" + "name": "prsbjdev" } ], "category": "Other notebooks", @@ -610,9 +601,9 @@ "friendly_name": "MNIST data inferencing using ParallelRunStep", "index_order": 1, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-partition-per-folder.ipynb b/how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-partition-per-folder.ipynb index c18ca015..aa59149a 100644 --- a/how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-partition-per-folder.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-partition-per-folder.ipynb @@ -24,7 +24,7 @@ "In this notebook, we will demonstrate how to make predictions on large quantities of data asynchronously using the ML pipelines with Azure Machine Learning. Batch inference (or batch scoring) provides cost-effective inference, with unparalleled throughput for asynchronous applications. Batch prediction pipelines can scale to perform inference on terabytes of production data. Batch prediction is optimized for high throughput, fire-and-forget predictions for a large collection of data.\n", "\n", "> **Tip**\n", - "If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-consume-web-service) instead of batch prediction.\n", + "If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/en-us/azure/machine-learning/v1/how-to-consume-web-service) instead of batch prediction.\n", "\n", "This example will create a sample dataset with nested folder structure, where the folder name corresponds to the attribute of the files inside it. The Batch Inference job would split the files inside the dataset according to their attributes, so that all files with identical value on the specified attribute will form up a single mini-batch to be processed.\n", "\n", @@ -356,13 +356,7 @@ "metadata": { "authors": [ { - "name": "pansav" - }, - { - "name": "tracych" - }, - { - "name": "migu" + "name": "prsbjdev" } ], "category": "Other notebooks", @@ -382,9 +376,9 @@ "friendly_name": "Batch inferencing file data partitioned by folder using ParallelRunStep", "index_order": 1, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-inference-iris.ipynb b/how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-inference-iris.ipynb index d60ed533..38168e1e 100644 --- a/how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-inference-iris.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-inference-iris.ipynb @@ -24,7 +24,7 @@ "In this notebook, we will demonstrate how to make predictions on large quantities of data asynchronously using the ML pipelines with Azure Machine Learning. Batch inference (or batch scoring) provides cost-effective inference, with unparalleled throughput for asynchronous applications. Batch prediction pipelines can scale to perform inference on terabytes of production data. Batch prediction is optimized for high throughput, fire-and-forget predictions for a large collection of data.\n", "\n", "> **Tip**\n", - "If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/azure/machine-learning/service/how-to-consume-web-service) instead of batch prediction.\n", + "If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/azure/machine-learning/v1/how-to-consume-web-service) instead of batch prediction.\n", "\n", "In this example we will take use a machine learning model already trained to predict different types of iris flowers and run that trained model on some of the data in a CSV file which has characteristics of different iris flowers. However, the same example can be extended to manipulating data to any embarrassingly-parallel processing through a python script.\n", "\n", @@ -487,16 +487,7 @@ "metadata": { "authors": [ { - "name": "joringer" - }, - { - "name": "asraniwa" - }, - { - "name": "pansav" - }, - { - "name": "tracych" + "name": "prsbjdev" } ], "category": "Other notebooks", @@ -516,9 +507,9 @@ "friendly_name": "IRIS data inferencing using ParallelRunStep", "index_order": 1, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-partition-per-column.ipynb b/how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-partition-per-column.ipynb index 647fcb49..74343a87 100644 --- a/how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-partition-per-column.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-partition-per-column.ipynb @@ -24,7 +24,7 @@ "In this notebook, we will demonstrate how to make predictions on large quantities of data asynchronously using the ML pipelines with Azure Machine Learning. Batch inference (or batch scoring) provides cost-effective inference, with unparalleled throughput for asynchronous applications. Batch prediction pipelines can scale to perform inference on terabytes of production data. Batch prediction is optimized for high throughput, fire-and-forget predictions for a large collection of data.\n", "\n", "> **Tip**\n", - "If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-consume-web-service) instead of batch prediction.\n", + "If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/en-us/azure/machine-learning/v1/how-to-consume-web-service) instead of batch prediction.\n", "\n", "This example will create a partitioned tabular dataset by splitting the rows in a large csv file by its value on specified column. Each partition will form up a mini-batch in the parallel processing procedure.\n", "\n", @@ -379,13 +379,7 @@ "metadata": { "authors": [ { - "name": "pansav" - }, - { - "name": "tracych" - }, - { - "name": "migu" + "name": "prsbjdev" } ], "category": "Other notebooks", @@ -405,9 +399,9 @@ "friendly_name": "Batch inferencing OJ Sales Data partitioned by column using ParallelRunStep", "index_order": 1, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/pipeline-style-transfer-parallel-run.ipynb b/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/pipeline-style-transfer-parallel-run.ipynb index 293b994b..e1aec369 100644 --- a/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/pipeline-style-transfer-parallel-run.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/pipeline-style-transfer-parallel-run.ipynb @@ -27,7 +27,7 @@ "3. Stitch the image back into a video.\n", "\n", "> **Tip**\n", - "If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-consume-web-service) instead of batch prediction." + "If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/en-us/azure/machine-learning/v1/how-to-consume-web-service) instead of batch prediction." ] }, { @@ -726,9 +726,9 @@ "friendly_name": "Style transfer using ParallelRunStep", "index_order": 1, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/manage-azureml-service/authentication-in-azureml/authentication-in-azureml.ipynb b/how-to-use-azureml/manage-azureml-service/authentication-in-azureml/authentication-in-azureml.ipynb index 9354a50b..6de3680b 100644 --- a/how-to-use-azureml/manage-azureml-service/authentication-in-azureml/authentication-in-azureml.ipynb +++ b/how-to-use-azureml/manage-azureml-service/authentication-in-azureml/authentication-in-azureml.ipynb @@ -521,9 +521,9 @@ } ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/ml-frameworks/chainer/distributed-chainer/distributed-chainer.ipynb b/how-to-use-azureml/ml-frameworks/chainer/distributed-chainer/distributed-chainer.ipynb index eb21490d..d4706d09 100644 --- a/how-to-use-azureml/ml-frameworks/chainer/distributed-chainer/distributed-chainer.ipynb +++ b/how-to-use-azureml/ml-frameworks/chainer/distributed-chainer/distributed-chainer.ipynb @@ -332,9 +332,9 @@ "friendly_name": "Distributed Training with Chainer", "index_order": 1, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/ml-frameworks/chainer/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.ipynb b/how-to-use-azureml/ml-frameworks/chainer/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.ipynb index f69fab5f..e7b7d264 100644 --- a/how-to-use-azureml/ml-frameworks/chainer/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.ipynb +++ b/how-to-use-azureml/ml-frameworks/chainer/train-hyperparameter-tune-deploy-with-chainer/train-hyperparameter-tune-deploy-with-chainer.ipynb @@ -783,9 +783,9 @@ "friendly_name": "Train a model with hyperparameter tuning", "index_order": 1, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/ml-frameworks/fastai/fastai-with-custom-docker/fastai-with-custom-docker.ipynb b/how-to-use-azureml/ml-frameworks/fastai/fastai-with-custom-docker/fastai-with-custom-docker.ipynb index d70ba867..fdcaedc5 100644 --- a/how-to-use-azureml/ml-frameworks/fastai/fastai-with-custom-docker/fastai-with-custom-docker.ipynb +++ b/how-to-use-azureml/ml-frameworks/fastai/fastai-with-custom-docker/fastai-with-custom-docker.ipynb @@ -344,9 +344,9 @@ "friendly_name": "Train a model with a custom Docker image", "index_order": 1, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb b/how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb index fe577062..0a406bc5 100644 --- a/how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb +++ b/how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb @@ -453,7 +453,7 @@ "\n", "# Specify a GPU base image\n", "keras_env.docker.enabled = True\n", - "keras_env.docker.base_image = 'mcr.microsoft.com/azureml/openmpi3.1.2-cuda10.0-cudnn7-ubuntu18.04'" + "keras_env.docker.base_image = 'mcr.microsoft.com/azureml/openmpi4.1.0-cuda11.1-cudnn8-ubuntu20.04'" ] }, { @@ -1224,9 +1224,9 @@ "friendly_name": "Train a DNN using hyperparameter tuning and deploying with Keras", "index_order": 1, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-distributeddataparallel/distributed-pytorch-with-distributeddataparallel.ipynb b/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-distributeddataparallel/distributed-pytorch-with-distributeddataparallel.ipynb index 088233e3..3d93e31f 100644 --- a/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-distributeddataparallel/distributed-pytorch-with-distributeddataparallel.ipynb +++ b/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-distributeddataparallel/distributed-pytorch-with-distributeddataparallel.ipynb @@ -471,9 +471,9 @@ "friendly_name": "Distributed training with PyTorch", "index_order": 1, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb b/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb index 00fc8ad2..91acafb9 100644 --- a/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb +++ b/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb @@ -352,9 +352,9 @@ "friendly_name": "Distributed PyTorch", "index_order": 1, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb b/how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb index b646bae1..77eb4287 100644 --- a/how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb +++ b/how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb @@ -283,7 +283,7 @@ "\n", "# Specify a GPU base image\n", "pytorch_env.docker.enabled = True\n", - "pytorch_env.docker.base_image = 'mcr.microsoft.com/azureml/openmpi4.1.0-cuda11.1-cudnn8-ubuntu18.04'" + "pytorch_env.docker.base_image = 'mcr.microsoft.com/azureml/openmpi4.1.0-cuda11.1-cudnn8-ubuntu20.04'" ] }, { @@ -736,9 +736,9 @@ "friendly_name": "Training with hyperparameter tuning using PyTorch", "index_order": 1, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/ml-frameworks/scikit-learn/train-hyperparameter-tune-deploy-with-sklearn/train-hyperparameter-tune-deploy-with-sklearn.ipynb b/how-to-use-azureml/ml-frameworks/scikit-learn/train-hyperparameter-tune-deploy-with-sklearn/train-hyperparameter-tune-deploy-with-sklearn.ipynb index 7936fb15..02788b11 100644 --- a/how-to-use-azureml/ml-frameworks/scikit-learn/train-hyperparameter-tune-deploy-with-sklearn/train-hyperparameter-tune-deploy-with-sklearn.ipynb +++ b/how-to-use-azureml/ml-frameworks/scikit-learn/train-hyperparameter-tune-deploy-with-sklearn/train-hyperparameter-tune-deploy-with-sklearn.ipynb @@ -594,9 +594,9 @@ "friendly_name": "Training and hyperparameter tuning with Scikit-learn", "index_order": 1, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/ml-frameworks/tensorflow/distributed-tensorflow-with-horovod/distributed-tensorflow-with-horovod.ipynb b/how-to-use-azureml/ml-frameworks/tensorflow/distributed-tensorflow-with-horovod/distributed-tensorflow-with-horovod.ipynb index 10139f41..38b73ef4 100644 --- a/how-to-use-azureml/ml-frameworks/tensorflow/distributed-tensorflow-with-horovod/distributed-tensorflow-with-horovod.ipynb +++ b/how-to-use-azureml/ml-frameworks/tensorflow/distributed-tensorflow-with-horovod/distributed-tensorflow-with-horovod.ipynb @@ -382,9 +382,9 @@ "friendly_name": "Distributed training using TensorFlow with Horovod", "index_order": 1, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/ml-frameworks/tensorflow/distributed-tensorflow-with-parameter-server/distributed-tensorflow-with-parameter-server.ipynb b/how-to-use-azureml/ml-frameworks/tensorflow/distributed-tensorflow-with-parameter-server/distributed-tensorflow-with-parameter-server.ipynb index db1df133..58387e97 100644 --- a/how-to-use-azureml/ml-frameworks/tensorflow/distributed-tensorflow-with-parameter-server/distributed-tensorflow-with-parameter-server.ipynb +++ b/how-to-use-azureml/ml-frameworks/tensorflow/distributed-tensorflow-with-parameter-server/distributed-tensorflow-with-parameter-server.ipynb @@ -21,7 +21,7 @@ "metadata": {}, "source": [ "# Distributed TensorFlow with parameter server\n", - "In this tutorial, you will train a TensorFlow model on the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset using native [distributed TensorFlow](https://www.tensorflow.org/deploy/distributed)." + "In this tutorial, you will train a TensorFlow model on the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset using native [distributed TensorFlow](https://www.tensorflow.org/guide/distributed_training)." ] }, { @@ -328,9 +328,9 @@ "friendly_name": "Distributed TensorFlow with parameter server", "index_order": 1, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/ml-frameworks/tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow.ipynb b/how-to-use-azureml/ml-frameworks/tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow.ipynb index f7aa0b33..01354480 100644 --- a/how-to-use-azureml/ml-frameworks/tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow.ipynb +++ b/how-to-use-azureml/ml-frameworks/tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow.ipynb @@ -424,7 +424,7 @@ "source": [ "from azureml.core import Environment\n", "\n", - "tf_env = Environment.get(ws, name='AzureML-TensorFlow-2.0-GPU')" + "tf_env = Environment.get(ws, name='AzureML-tensorflow-2.6-ubuntu20.04-py38-cuda11-gpu')" ] }, { @@ -874,9 +874,9 @@ "friendly_name": "Hyperparameter tuning and warm start using the TensorFlow estimator", "index_order": 1, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb b/how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb index 1bc22728..aaff556f 100644 --- a/how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb +++ b/how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb @@ -441,7 +441,7 @@ "source": [ "from azureml.core import Environment\n", "\n", - "tf_env = Environment.get(ws, name='AzureML-TensorFlow-2.0-GPU')" + "tf_env = Environment.get(ws, name='AzureML-tensorflow-2.6-ubuntu20.04-py38-cuda11-gpu')" ] }, { @@ -1158,9 +1158,9 @@ "friendly_name": "Training and hyperparameter tuning using the TensorFlow estimator", "index_order": 1, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/ml-frameworks/tensorflow/train-tensorflow-resume-training/train-tensorflow-resume-training.ipynb b/how-to-use-azureml/ml-frameworks/tensorflow/train-tensorflow-resume-training/train-tensorflow-resume-training.ipynb index c6d097ba..bb14af03 100644 --- a/how-to-use-azureml/ml-frameworks/tensorflow/train-tensorflow-resume-training/train-tensorflow-resume-training.ipynb +++ b/how-to-use-azureml/ml-frameworks/tensorflow/train-tensorflow-resume-training/train-tensorflow-resume-training.ipynb @@ -477,9 +477,9 @@ "friendly_name": "Resuming a model", "index_order": 1, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/ml-frameworks/using-mlflow/train-and-deploy-keras-auto-logging/train-and-deploy-keras-auto-logging.ipynb b/how-to-use-azureml/ml-frameworks/using-mlflow/train-and-deploy-keras-auto-logging/train-and-deploy-keras-auto-logging.ipynb index 7d75d8fd..9e3e8110 100644 --- a/how-to-use-azureml/ml-frameworks/using-mlflow/train-and-deploy-keras-auto-logging/train-and-deploy-keras-auto-logging.ipynb +++ b/how-to-use-azureml/ml-frameworks/using-mlflow/train-and-deploy-keras-auto-logging/train-and-deploy-keras-auto-logging.ipynb @@ -153,7 +153,7 @@ "\n", "Next, let's run the same script on GPU-enabled compute for faster training. If you've completed the the [Configuration](../../../configuration.ipnyb) notebook, you should have a GPU cluster named \"gpu-cluster\" available in your workspace. Otherwise, follow the instructions in the notebook to create one. For simplicity, this example uses single process on single VM to train the model.\n", "\n", - "Clone an environment object from the Tensorflow 2.1 Azure ML curated environment. Azure ML curated environments are pre-configured environments to simplify ML setup, reference [this doc](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-environments#use-a-curated-environment) for more information. To enable MLflow tracking, add ```azureml-mlflow``` as pip package." + "Clone an environment object from the Tensorflow 2.1 Azure ML curated environment. Azure ML curated environments are pre-configured environments to simplify ML setup, reference [this doc](https://docs.microsoft.com/en-us/azure/machine-learning/v1/how-to-use-environments#use-a-curated-environment) for more information. To enable MLflow tracking, add ```azureml-mlflow``` as pip package." ] }, { @@ -254,17 +254,17 @@ "\n", "In this example, we deploy the Docker image to Azure Container Instance: a serverless compute capable of running a single container. You can tag and add descriptions to help keep track of your web service. \n", "\n", - "[Other inferencing compute choices](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-and-where) include Azure Kubernetes Service which provides scalable endpoint suitable for production use.\n", + "[Other inferencing compute choices](https://docs.microsoft.com/azure/machine-learning/v1/how-to-deploy-and-where) include Azure Kubernetes Service which provides scalable endpoint suitable for production use.\n", "\n", "Note that the service deployment can take several minutes." ] }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "First define your deployment target and customize parameters in the deployment config. Refer to [this documentation](https://docs.microsoft.com/azure/machine-learning/reference-azure-machine-learning-cli#azure-container-instance-deployment-configuration-schema) for more information. " - ], - "cell_type": "markdown", - "metadata": {} + ] }, { "cell_type": "code", @@ -360,9 +360,9 @@ ], "friendly_name": "Use MLflow with Azure Machine Learning to Train and Deploy Keras Image Classifier", "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/ml-frameworks/using-mlflow/train-and-deploy-pytorch/train-and-deploy-pytorch.ipynb b/how-to-use-azureml/ml-frameworks/using-mlflow/train-and-deploy-pytorch/train-and-deploy-pytorch.ipynb index 4d91702c..7dbe4b1c 100644 --- a/how-to-use-azureml/ml-frameworks/using-mlflow/train-and-deploy-pytorch/train-and-deploy-pytorch.ipynb +++ b/how-to-use-azureml/ml-frameworks/using-mlflow/train-and-deploy-pytorch/train-and-deploy-pytorch.ipynb @@ -153,7 +153,7 @@ "\n", "Next, let's run the same script on GPU-enabled compute for faster training. If you've completed the the [Configuration](../../../configuration.ipnyb) notebook, you should have a GPU cluster named \"gpu-cluster\" available in your workspace. Otherwise, follow the instructions in the notebook to create one. For simplicity, this example uses single process on single VM to train the model.\n", "\n", - "Clone an environment object from the PyTorch 1.4 Azure ML curated environment. Azure ML curated environments are pre-configured environments to simplify ML setup, reference [this doc](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-environments#use-a-curated-environment) for more information. To enable MLflow tracking, add ```azureml-mlflow``` as pip package." + "Clone an environment object from the PyTorch 1.4 Azure ML curated environment. Azure ML curated environments are pre-configured environments to simplify ML setup, reference [this doc](https://docs.microsoft.com/en-us/azure/machine-learning/v1/how-to-use-environments#use-a-curated-environment) for more information. To enable MLflow tracking, add ```azureml-mlflow``` as pip package." ] }, { @@ -253,17 +253,17 @@ "\n", "In this example, we deploy the Docker image to Azure Container Instance: a serverless compute capable of running a single container. You can tag and add descriptions to help keep track of your web service. \n", "\n", - "[Other inferencing compute choices](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-and-where) include Azure Kubernetes Service which provides scalable endpoint suitable for production use.\n", + "[Other inferencing compute choices](https://docs.microsoft.com/azure/machine-learning/v1/how-to-deploy-and-where) include Azure Kubernetes Service which provides scalable endpoint suitable for production use.\n", "\n", "Note that the service deployment can take several minutes." ] }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "First define your deployment target and customize parameters in the deployment config. Refer to [this documentation](https://docs.microsoft.com/azure/machine-learning/reference-azure-machine-learning-cli#azure-container-instance-deployment-configuration-schema) for more information. " - ], - "cell_type": "markdown", - "metadata": {} + ] }, { "cell_type": "code", @@ -351,9 +351,9 @@ ], "friendly_name": "Use MLflow with Azure Machine Learning to Train and Deploy PyTorch Image Classifier", "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/docker/Dockerfile-cpu b/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/docker/Dockerfile-cpu deleted file mode 100644 index 18e97c96..00000000 --- a/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/docker/Dockerfile-cpu +++ /dev/null @@ -1,19 +0,0 @@ -FROM mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04 - -USER root -RUN conda install -c anaconda python=3.7 - -RUN pip install ray-on-aml==0.1.6 -RUN pip install gym[atari]==0.19.0 -RUN pip install gym[accept-rom-license]==0.19.0 -RUN pip install ale-py==0.7.0 -RUN pip install azureml-core -RUN pip install ray==0.8.7 -RUN pip install ray[rllib,tune,serve]==0.8.7 -RUN pip install tensorflow==1.14.0 -RUN pip install 'msrest<0.7.0' -RUN pip install protobuf==3.20.0 - -RUN apt-get update -RUN apt-get install -y jq -RUN apt-get install -y rsync diff --git 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 index 6453d3ac..5fea0f50 100644 --- 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 @@ -46,7 +46,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The goal here is to train an agent to win an episode of Pong game against opponent with the score of at least 18 points. An episode in Pong runs until one of the players reaches a score of 21. Episodes are a terminology that is used across all the [OpenAI gym](https://gym.openai.com/envs/Pong-v0/) environments that contains a strictly defined task.\n", + "The goal here is to train an agent to win an episode of Pong game against opponent with the score of at least 18 points. An episode in Pong runs until one of the players reaches a score of 21. Episodes are a terminology that is used across all the [OpenAI gym](https://www.gymlibrary.dev/environments/atari/pong/) environments that contains a strictly defined task.\n", "\n", "Training a Pong agent is a compute-intensive task and this example demonstrates the use of Reinforcement Learning in Azure Machine Learning service to train an agent faster in a distributed, parallel environment. You'll learn more about using the head and the worker compute targets to train an agent in this notebook below." ] @@ -153,15 +153,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Create compute targets\n", + "### Create compute target\n", "\n", - "In this example, we show how to set up separate compute targets for the Ray nodes.\n", + "In this example, we show how to set up a compute target for the Ray nodes.\n", "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", - "\n", - "#### Create head compute target\n", - "\n", - "First we define the head cluster with GPU for the Ray head node. One CPU of the head node will be used for the Ray head process and the rest of the CPUs will be used by the Ray worker processes." + "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist." ] }, { @@ -211,40 +207,6 @@ " print(compute_target.get_status().serialize())" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "gather": { - "logged": 1646093795069 - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - }, - "outputs": [], - "source": [ - "from azureml.core import Environment\n", - "import os\n", - "\n", - "ray_environment_name = 'pong-cpu'\n", - "ray_environment_dockerfile_path = os.path.join(os.getcwd(), 'docker', 'Dockerfile-cpu')\n", - "\n", - "# Build CPU image\n", - "ray_cpu_env = Environment. \\\n", - " from_dockerfile(name=ray_environment_name, dockerfile=ray_environment_dockerfile_path). \\\n", - " 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)" - ] - }, { "cell_type": "code", "execution_count": null, @@ -264,6 +226,7 @@ }, "outputs": [], "source": [ + "import os\n", "from azureml.core import Environment\n", "\n", "ray_environment_name = 'pong-gpu'\n", @@ -289,12 +252,11 @@ "compute target, number of nodes, and environment image to use.\n", "\n", "We specify `episode_reward_mean` to 18 as we want to stop the training as soon as the trained agent reaches an average win margin of at least 18 point over opponent over all episodes in the training epoch.\n", - "Number of Ray worker processes are defined by parameter `num_workers`. We set it to 13 as we have 13 CPUs available in our compute targets. Multiple Ray worker processes parallelizes agent training and helps in achieving our goal faster. \n", + "Number of Ray worker processes are defined by parameter `num_workers`. We set it to 13 as we have 11 CPUs available in our compute targets. Multiple Ray worker processes parallelizes agent training and helps in achieving our goal faster. \n", "\n", "```\n", - "Number of CPUs in head_compute_target = 6 CPUs in 1 node = 6\n", - "Number of CPUs in worker_compute_target = 2 CPUs in each of 4 nodes = 8\n", - "Number of CPUs available = (Number of CPUs in head_compute_target) + (Number of CPUs in worker_compute_target) - (1 CPU for head node) = 6 + 8 - 1 = 13\n", + "Number of CPUs in the compute cluster = 6 * 2 = 12 CPUs over 2 nodes\n", + "Number of CPUs available = (Number of CPUs in the compute cluster) - (1 CPU for head node) = 12 - 1 = 11\n", "```" ] }, @@ -308,8 +270,7 @@ }, "outputs": [], "source": [ - "from azureml.core import RunConfiguration, ScriptRunConfig, Experiment\n", - "from azureml.core.runconfig import DockerConfiguration, RunConfiguration\n", + "from azureml.core import RunConfiguration, ScriptRunConfig\n", "\n", "experiment_name = 'rllib-pong-multi-node'\n", "\n", @@ -318,7 +279,6 @@ "\n", "aml_run_config_ml = RunConfiguration(communicator='OpenMpi')\n", "aml_run_config_ml.target = compute_target\n", - "aml_run_config_ml.docker = DockerConfiguration(use_docker=True)\n", "aml_run_config_ml.node_count = 2\n", "aml_run_config_ml.environment = ray_environment\n", "\n", @@ -516,16 +476,13 @@ "how-to-use-azureml", "reinforcement-learning" ], - "interpreter": { - "hash": "13382f70c1d0595120591d2e358c8d446daf961bf951d1fba9a32631e205d5ab" - }, "kernel_info": { - "name": "python3-azureml" + "name": "python38-azureml" }, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { @@ -537,11 +494,16 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.0" + "version": "3.7.3" }, "notice": "Copyright (c) Microsoft Corporation. All rights reserved.\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00afLicensed under the MIT License.\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00af ", "nteract": { "version": "nteract-front-end@1.0.0" + }, + "vscode": { + "interpreter": { + "hash": "00c28698cbad9eaca051e9759b1181630e646922505b47b4c6352eb5aa72ddfc" + } } }, "nbformat": 4, 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 63734d11..d174dc11 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 @@ -316,7 +316,6 @@ "\n", "aml_run_config_ml = RunConfiguration(communicator='OpenMpi')\n", "aml_run_config_ml.target = compute_target\n", - "aml_run_config_ml.docker = DockerConfiguration(use_docker=True)\n", "aml_run_config_ml.node_count = 1\n", "aml_run_config_ml.environment = ray_environment\n", "\n", @@ -339,7 +338,7 @@ "This is the list of parameters we are passing into `tune.run()` via the `script_params` parameter:\n", "\n", "- `run_or_experiment`: name of the [built-in algorithm](https://ray.readthedocs.io/en/latest/rllib-algorithms.html#rllib-algorithms), 'PPO' in our example,\n", - "- `config`: Algorithm-specific configuration. This includes specifying the environment, `env`, which in our example is the gym **[CartPole-v0](https://gym.openai.com/envs/CartPole-v0/)** environment,\n", + "- `config`: Algorithm-specific configuration. This includes specifying the environment, `env`, which in our example is the gym **[CartPole-v0](https://www.gymlibrary.dev/environments/classic_control/cart_pole/)** environment,\n", "- `stop`: stopping conditions, which could be any of the metrics returned by the trainer. Here we use \"mean of episode reward\", and \"total training time in seconds\" as stop conditions, and\n", "- `checkpoint_freq` and `checkpoint_at_end`: Frequency of taking checkpoints (number of training iterations between checkpoints), and if a checkpoint should be taken at the end.\n", "\n", @@ -636,7 +635,6 @@ "\n", "aml_run_config_ml = RunConfiguration(communicator='OpenMpi')\n", "aml_run_config_ml.target = compute_target\n", - "aml_run_config_ml.docker = DockerConfiguration(use_docker=True)\n", "aml_run_config_ml.node_count = 1\n", "aml_run_config_ml.environment = ray_environment\n", "aml_run_config_ml.data\n", @@ -734,16 +732,13 @@ "how-to-use-azureml", "reinforcement-learning" ], - "interpreter": { - "hash": "13382f70c1d0595120591d2e358c8d446daf961bf951d1fba9a32631e205d5ab" - }, "kernel_info": { - "name": "python3-azureml" + "name": "python38-azureml" }, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { @@ -755,7 +750,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.9" + "version": "3.7.3" }, "microsoft": { "host": { @@ -767,6 +762,11 @@ "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License.", "nteract": { "version": "nteract-front-end@1.0.0" + }, + "vscode": { + "interpreter": { + "hash": "00c28698cbad9eaca051e9759b1181630e646922505b47b4c6352eb5aa72ddfc" + } } }, "nbformat": 4, 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 c1b5cd9a..6b936c6a 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 @@ -289,7 +289,6 @@ "ray_environment = Environment.get(ws, name=ray_environment_name)\n", "run_config = RunConfiguration(communicator='OpenMpi')\n", "run_config.target = compute_target\n", - "run_config.docker = DockerConfiguration(use_docker=True)\n", "run_config.node_count = 1\n", "run_config.environment = ray_environment\n", "command=[\"python\", script_name, *script_arguments]\n", @@ -298,12 +297,12 @@ " command = [\"xvfb-run -s '-screen 0 640x480x16 -ac +extension GLX +render' \"] + command\n", " run_config.environment_variables[\"SDL_VIDEODRIVER\"] = \"dummy\"\n", "\n", - "trainint_config = ScriptRunConfig(source_directory='./files',\n", + "training_config = ScriptRunConfig(source_directory='./files',\n", " command=command,\n", " run_config = run_config\n", " )\n", "\n", - "training_run = experiment.submit(trainint_config)" + "training_run = experiment.submit(training_config)" ] }, { @@ -317,7 +316,7 @@ "This is the list of parameters we are passing into `tune.run()` via the `script_params` parameter:\n", "\n", "- `run_or_experiment`: name of the [built-in algorithm](https://ray.readthedocs.io/en/latest/rllib-algorithms.html#rllib-algorithms), 'PPO' in our example,\n", - "- `config`: Algorithm-specific configuration. This includes specifying the environment, `env`, which in our example is the gym **[CartPole-v0](https://gym.openai.com/envs/CartPole-v0/)** environment,\n", + "- `config`: Algorithm-specific configuration. This includes specifying the environment, `env`, which in our example is the gym **[CartPole-v0](https://www.gymlibrary.dev/environments/classic_control/cart_pole/)** environment,\n", "- `stop`: stopping conditions, which could be any of the metrics returned by the trainer. Here we use \"mean of episode reward\", and \"total training time in seconds\" as stop conditions, and\n", "- `checkpoint_freq` and `checkpoint_at_end`: Frequency of taking checkpoints (number of training iterations between checkpoints), and if a checkpoint should be taken at the end.\n", "\n", @@ -859,16 +858,13 @@ "how-to-use-azureml", "reinforcement-learning" ], - "interpreter": { - "hash": "13382f70c1d0595120591d2e358c8d446daf961bf951d1fba9a32631e205d5ab" - }, "kernel_info": { "name": "python38-azureml" }, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { @@ -880,11 +876,16 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.9" + "version": "3.7.3" }, "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License.", "nteract": { "version": "nteract-front-end@1.0.0" + }, + "vscode": { + "interpreter": { + "hash": "00c28698cbad9eaca051e9759b1181630e646922505b47b4c6352eb5aa72ddfc" + } } }, "nbformat": 4, diff --git a/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/particle.ipynb b/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/particle.ipynb index 97ea9a75..6f3351dc 100644 --- a/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/particle.ipynb +++ b/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/particle.ipynb @@ -319,7 +319,6 @@ "\n", "aml_run_config_ml = RunConfiguration(communicator='OpenMpi')\n", "aml_run_config_ml.target = cpu_cluster\n", - "aml_run_config_ml.docker = DockerConfiguration(use_docker=True)\n", "aml_run_config_ml.node_count = 1\n", "aml_run_config_ml.environment = ray_environment\n", "\n", @@ -541,9 +540,9 @@ "name": "python38-azureml" }, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/responsible-ai/visualize-upload-loan-decision/rai-loan-decision.ipynb b/how-to-use-azureml/responsible-ai/visualize-upload-loan-decision/rai-loan-decision.ipynb index bc474171..62ae6c08 100644 --- a/how-to-use-azureml/responsible-ai/visualize-upload-loan-decision/rai-loan-decision.ipynb +++ b/how-to-use-azureml/responsible-ai/visualize-upload-loan-decision/rai-loan-decision.ipynb @@ -696,9 +696,9 @@ } ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/responsible-ai/visualize-upload-loan-decision/rai-loan-decision.yml b/how-to-use-azureml/responsible-ai/visualize-upload-loan-decision/rai-loan-decision.yml index 0f93f3a0..c7ac9cb1 100644 --- a/how-to-use-azureml/responsible-ai/visualize-upload-loan-decision/rai-loan-decision.yml +++ b/how-to-use-azureml/responsible-ai/visualize-upload-loan-decision/rai-loan-decision.yml @@ -8,7 +8,7 @@ dependencies: - matplotlib - azureml-dataset-runtime - ipywidgets - - raiwidgets~=0.21.0 + - raiwidgets~=0.22.0 - liac-arff - packaging>=20.9 - itsdangerous==2.0.1 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 e91efe4b..271569ea 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 @@ -101,7 +101,7 @@ "\n", "# Check core SDK version number\n", "\n", - "print(\"This notebook was created using SDK version 1.45.0, you are currently running version\", azureml.core.VERSION)" + "print(\"This notebook was created using SDK version 1.46.0, you are currently running version\", azureml.core.VERSION)" ] }, { @@ -597,9 +597,9 @@ ], "friendly_name": "Logging APIs", "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { 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 c80480e4..e3f38d5b 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 @@ -614,9 +614,9 @@ "friendly_name": "Managing your training runs", "index_order": 2, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/track-and-monitor-experiments/tensorboard/export-run-history-to-tensorboard/export-run-history-to-tensorboard.ipynb b/how-to-use-azureml/track-and-monitor-experiments/tensorboard/export-run-history-to-tensorboard/export-run-history-to-tensorboard.ipynb index 7dcf5a8c..ec615863 100644 --- a/how-to-use-azureml/track-and-monitor-experiments/tensorboard/export-run-history-to-tensorboard/export-run-history-to-tensorboard.ipynb +++ b/how-to-use-azureml/track-and-monitor-experiments/tensorboard/export-run-history-to-tensorboard/export-run-history-to-tensorboard.ipynb @@ -9,6 +9,13 @@ "Licensed under the MIT License." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/track-and-monitor-experiments/export-run-history-to-tensorboard/export-run-history-to-tensorboard.png)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -219,10 +226,26 @@ "name": "roastala" } ], + "category": "training", + "compute": [ + "None" + ], + "datasets": [ + "None" + ], + "deployment": [ + "None" + ], + "exclude_from_index": false, + "framework": [ + "TensorFlow" + ], + "friendly_name": "Using Tensorboard", + "index_order": 1, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { @@ -236,26 +259,10 @@ "pygments_lexer": "ipython3", "version": "3.6.5" }, - "friendly_name": "Using Tensorboard", - "exclude_from_index": false, - "index_order": 1, - "category": "training", - "task": "Export the run history as Tensorboard logs", - "datasets": [ - "None" - ], - "compute": [ - "None" - ], - "deployment": [ - "None" - ], - "framework": [ - "TensorFlow" - ], "tags": [ "None" - ] + ], + "task": "Export the run history as Tensorboard logs" }, "nbformat": 4, "nbformat_minor": 2 diff --git a/how-to-use-azureml/track-and-monitor-experiments/tensorboard/tensorboard/tensorboard.ipynb b/how-to-use-azureml/track-and-monitor-experiments/tensorboard/tensorboard/tensorboard.ipynb index ba03e005..9e33539b 100644 --- a/how-to-use-azureml/track-and-monitor-experiments/tensorboard/tensorboard/tensorboard.ipynb +++ b/how-to-use-azureml/track-and-monitor-experiments/tensorboard/tensorboard/tensorboard.ipynb @@ -565,9 +565,9 @@ "friendly_name": "Tensorboard integration with run history", "index_order": 3, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-local/train-local.ipynb b/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-local/train-local.ipynb index 06acf0a2..058d713f 100644 --- a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-local/train-local.ipynb +++ b/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-local/train-local.ipynb @@ -242,9 +242,9 @@ "friendly_name": "Use MLflow with AML for a local training run", "index_order": 7, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-local/train-projects-local.ipynb b/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-local/train-projects-local.ipynb index dbe4e312..1358d78c 100644 --- a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-local/train-projects-local.ipynb +++ b/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-local/train-projects-local.ipynb @@ -277,9 +277,9 @@ ], "friendly_name": "Use MLflow projects with Azure Machine Learning to train a model with local compute", "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-remote/train-projects-remote.ipynb b/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-remote/train-projects-remote.ipynb index 55debb55..4ed92831 100644 --- a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-remote/train-projects-remote.ipynb +++ b/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-remote/train-projects-remote.ipynb @@ -248,9 +248,9 @@ ], "friendly_name": "Use MLflow projects with Azure Machine Learning to train a model", "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-remote/train-remote.ipynb b/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-remote/train-remote.ipynb index d5b68249..d6eff6bf 100644 --- a/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-remote/train-remote.ipynb +++ b/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-remote/train-remote.ipynb @@ -313,9 +313,9 @@ "friendly_name": "Use MLflow with AML for a remote training run", "index_order": 8, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/training/train-in-spark/train-in-spark.ipynb b/how-to-use-azureml/training/train-in-spark/train-in-spark.ipynb index a6c4ae27..d0527bce 100644 --- a/how-to-use-azureml/training/train-in-spark/train-in-spark.ipynb +++ b/how-to-use-azureml/training/train-in-spark/train-in-spark.ipynb @@ -299,9 +299,9 @@ "friendly_name": "Training in Spark", "index_order": 1, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { 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 70d2d571..843eeddc 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 @@ -470,9 +470,9 @@ "friendly_name": "Train on Azure Machine Learning Compute", "index_order": 1, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/training/train-on-local/train-on-local.ipynb b/how-to-use-azureml/training/train-on-local/train-on-local.ipynb index 478f7857..6a9d730e 100644 --- a/how-to-use-azureml/training/train-on-local/train-on-local.ipynb +++ b/how-to-use-azureml/training/train-on-local/train-on-local.ipynb @@ -685,9 +685,9 @@ "friendly_name": "Train on local compute", "index_order": 1, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.ipynb b/how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.ipynb index ce42357c..54ed9f79 100644 --- a/how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.ipynb +++ b/how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.ipynb @@ -641,9 +641,9 @@ "friendly_name": "Train in a remote Linux virtual machine", "index_order": 1, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/training/using-environments/using-environments.ipynb b/how-to-use-azureml/training/using-environments/using-environments.ipynb index bdfe91e6..ad0899be 100644 --- a/how-to-use-azureml/training/using-environments/using-environments.ipynb +++ b/how-to-use-azureml/training/using-environments/using-environments.ipynb @@ -446,9 +446,9 @@ "friendly_name": "Using Azure ML environments", "index_order": 1, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/work-with-data/datadrift-tutorial/datadrift-tutorial.ipynb b/how-to-use-azureml/work-with-data/datadrift-tutorial/datadrift-tutorial.ipynb index b848d08d..50a54a87 100644 --- a/how-to-use-azureml/work-with-data/datadrift-tutorial/datadrift-tutorial.ipynb +++ b/how-to-use-azureml/work-with-data/datadrift-tutorial/datadrift-tutorial.ipynb @@ -435,9 +435,9 @@ "friendly_name": "Data drift quickdemo", "index_order": 1, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.ipynb b/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.ipynb index 14044ce4..f1698f31 100644 --- a/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.ipynb +++ b/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.ipynb @@ -388,7 +388,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Azure Machine Learning dataset makes it easy to trace how your data is used in ML. [Learn More](https://docs.microsoft.com/azure/machine-learning/service/how-to-version-track-datasets#track-datasets-in-experiments)
\n", + "Azure Machine Learning dataset makes it easy to trace how your data is used in ML. [Learn More](https://docs.microsoft.com/azure/machine-learning/v1/how-to-version-track-datasets)
\n", "For each Machine Learning experiment, you can easily trace the datasets used as the input through `Run` object." ] }, @@ -468,9 +468,9 @@ "friendly_name": "Datasets with ML Pipeline", "index_order": 1, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/work-with-data/datasets-tutorial/scriptrun-with-data-input-output/how-to-use-scriptrun.ipynb b/how-to-use-azureml/work-with-data/datasets-tutorial/scriptrun-with-data-input-output/how-to-use-scriptrun.ipynb index c32bc978..d5f3dcc4 100644 --- a/how-to-use-azureml/work-with-data/datasets-tutorial/scriptrun-with-data-input-output/how-to-use-scriptrun.ipynb +++ b/how-to-use-azureml/work-with-data/datasets-tutorial/scriptrun-with-data-input-output/how-to-use-scriptrun.ipynb @@ -287,9 +287,9 @@ ], "friendly_name": "How to use ScriptRun with data input and output", "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/work-with-data/datasets-tutorial/timeseries-datasets/tabular-timeseries-dataset-filtering.ipynb b/how-to-use-azureml/work-with-data/datasets-tutorial/timeseries-datasets/tabular-timeseries-dataset-filtering.ipynb index 946b2d9c..5aa9ee49 100644 --- a/how-to-use-azureml/work-with-data/datasets-tutorial/timeseries-datasets/tabular-timeseries-dataset-filtering.ipynb +++ b/how-to-use-azureml/work-with-data/datasets-tutorial/timeseries-datasets/tabular-timeseries-dataset-filtering.ipynb @@ -551,9 +551,9 @@ "friendly_name": "Filtering data using Tabular Timeseiries Dataset related API", "index_order": 1, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.ipynb b/how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.ipynb index cd5c5b0c..65097a80 100644 --- a/how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.ipynb +++ b/how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.ipynb @@ -665,9 +665,9 @@ "friendly_name": "Train with Datasets (Tabular and File)", "index_order": 1, "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/setup-environment/configuration.ipynb b/setup-environment/configuration.ipynb index 785d0958..bef5a8dd 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 1.45.0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.46.0 of the Azure ML SDK\")\n", "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" ] }, @@ -269,9 +269,9 @@ } ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/tutorials/compute-instance-quickstarts/quickstart-azureml-automl/quickstart-azureml-automl.ipynb b/tutorials/compute-instance-quickstarts/quickstart-azureml-automl/quickstart-azureml-automl.ipynb index 6069c73a..d4ed6932 100644 --- a/tutorials/compute-instance-quickstarts/quickstart-azureml-automl/quickstart-azureml-automl.ipynb +++ b/tutorials/compute-instance-quickstarts/quickstart-azureml-automl/quickstart-azureml-automl.ipynb @@ -472,9 +472,9 @@ } ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/tutorials/compute-instance-quickstarts/quickstart-azureml-in-10mins/quickstart-azureml-in-10mins.ipynb b/tutorials/compute-instance-quickstarts/quickstart-azureml-in-10mins/quickstart-azureml-in-10mins.ipynb index 4087d70d..54c1554f 100644 --- a/tutorials/compute-instance-quickstarts/quickstart-azureml-in-10mins/quickstart-azureml-in-10mins.ipynb +++ b/tutorials/compute-instance-quickstarts/quickstart-azureml-in-10mins/quickstart-azureml-in-10mins.ipynb @@ -476,9 +476,9 @@ } ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/tutorials/compute-instance-quickstarts/quickstart-azureml-python-sdk/quickstart-azureml-python-sdk.ipynb b/tutorials/compute-instance-quickstarts/quickstart-azureml-python-sdk/quickstart-azureml-python-sdk.ipynb index eafa8d84..c0909c28 100644 --- a/tutorials/compute-instance-quickstarts/quickstart-azureml-python-sdk/quickstart-azureml-python-sdk.ipynb +++ b/tutorials/compute-instance-quickstarts/quickstart-azureml-python-sdk/quickstart-azureml-python-sdk.ipynb @@ -328,9 +328,9 @@ } ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/tutorials/create-first-ml-experiment/tutorial-1st-experiment-sdk-train.ipynb b/tutorials/create-first-ml-experiment/tutorial-1st-experiment-sdk-train.ipynb index 54d5d233..eb5acc78 100644 --- a/tutorials/create-first-ml-experiment/tutorial-1st-experiment-sdk-train.ipynb +++ b/tutorials/create-first-ml-experiment/tutorial-1st-experiment-sdk-train.ipynb @@ -371,9 +371,9 @@ } ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/tutorials/image-classification-mnist-data/img-classification-part1-training.ipynb b/tutorials/image-classification-mnist-data/img-classification-part1-training.ipynb index 62e0ec1a..7fb7eb9e 100644 --- a/tutorials/image-classification-mnist-data/img-classification-part1-training.ipynb +++ b/tutorials/image-classification-mnist-data/img-classification-part1-training.ipynb @@ -671,9 +671,9 @@ } ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/tutorials/image-classification-mnist-data/img-classification-part2-deploy.ipynb b/tutorials/image-classification-mnist-data/img-classification-part2-deploy.ipynb index 2eae3a40..2c88cfb9 100644 --- a/tutorials/image-classification-mnist-data/img-classification-part2-deploy.ipynb +++ b/tutorials/image-classification-mnist-data/img-classification-part2-deploy.ipynb @@ -27,7 +27,7 @@ "> * Deploy the model to ACI\n", "> * Test the deployed model\n", "\n", - "ACI is a great solution for testing and understanding the workflow. For scalable production deployments, consider using Azure Kubernetes Service. For more information, see [how to deploy and where](https://docs.microsoft.com/azure/machine-learning/service/how-to-deploy-and-where).\n", + "ACI is a great solution for testing and understanding the workflow. For scalable production deployments, consider using Azure Kubernetes Service. For more information, see [how to deploy and where](https://docs.microsoft.com/azure/machine-learning/v1/how-to-deploy-and-where).\n", "\n", "\n", "## Prerequisites\n", @@ -551,9 +551,9 @@ } ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/tutorials/image-classification-mnist-data/img-classification-part3-deploy-encrypted.ipynb b/tutorials/image-classification-mnist-data/img-classification-part3-deploy-encrypted.ipynb index 18c6c209..ce646ac8 100644 --- a/tutorials/image-classification-mnist-data/img-classification-part3-deploy-encrypted.ipynb +++ b/tutorials/image-classification-mnist-data/img-classification-part3-deploy-encrypted.ipynb @@ -27,7 +27,7 @@ "> * Deploy the model to ACI\n", "> * Test the deployed model\n", "\n", - "ACI is a great solution for testing and understanding the workflow. For scalable production deployments, consider using Azure Kubernetes Service. For more information, see [how to deploy and where](https://docs.microsoft.com/azure/machine-learning/service/how-to-deploy-and-where).\n", + "ACI is a great solution for testing and understanding the workflow. For scalable production deployments, consider using Azure Kubernetes Service. For more information, see [how to deploy and where](https://docs.microsoft.com/azure/machine-learning/v1/how-to-deploy-and-where).\n", "\n", "\n", "## Prerequisites\n", @@ -594,9 +594,9 @@ ], "celltoolbar": "Edit Metadata", "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { diff --git a/tutorials/machine-learning-pipelines-advanced/tutorial-pipeline-batch-scoring-classification.ipynb b/tutorials/machine-learning-pipelines-advanced/tutorial-pipeline-batch-scoring-classification.ipynb index 43989ae1..390d0609 100644 --- a/tutorials/machine-learning-pipelines-advanced/tutorial-pipeline-batch-scoring-classification.ipynb +++ b/tutorials/machine-learning-pipelines-advanced/tutorial-pipeline-batch-scoring-classification.ipynb @@ -657,9 +657,9 @@ } ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": { 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 a8a316e4..0b6b16b8 100644 --- a/tutorials/regression-automl-nyc-taxi-data/regression-automated-ml.ipynb +++ b/tutorials/regression-automl-nyc-taxi-data/regression-automated-ml.ipynb @@ -591,9 +591,9 @@ } ], "kernelspec": { - "display_name": "Python 3.6", + "display_name": "Python 3.8 - AzureML", "language": "python", - "name": "python36" + "name": "python38-azureml" }, "language_info": { "codemirror_mode": {