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Merge pull request #1976 from Azure/release_update_stablev2/Release-247

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update samples from Release-247 as a part of 1.59.0 SDK stable release
This commit is contained in:
jeff-shepherd
2024-12-13 08:50:52 -08:00
committed by GitHub
parent eaa7c56590 bf4cb1e86c
commit 5b019e28de
12 changed files with 17 additions and 20 deletions
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2
configuration.ipynb
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@@ -103,7 +103,7 @@
"source": [ "source": [
"import azureml.core\n", "import azureml.core\n",
"\n", "\n",
"print(\"This notebook was created using version 1.58.0 of the Azure ML SDK\")\n", "print(\"This notebook was created using version 1.59.0 of the Azure ML SDK\")\n",
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
] ]
}, },

7
how-to-use-azureml/automated-machine-learning/automl_env.yml
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@@ -14,13 +14,12 @@ dependencies:
- pip: - pip:
# Required packages for AzureML execution, history, and data preparation. # Required packages for AzureML execution, history, and data preparation.
- azureml-widgets~=1.58.0 - azureml-widgets~=1.59.0
- azureml-defaults~=1.58.0 - azureml-defaults~=1.59.0
- -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.58.0/validated_win32_requirements.txt [--no-deps] - -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.59.0/validated_win32_requirements.txt [--no-deps]
- matplotlib==3.7.1 - matplotlib==3.7.1
- xgboost==1.5.2 - xgboost==1.5.2
- prophet==1.1.4 - prophet==1.1.4
- pandas==1.3.5
- setuptools-git==1.2 - setuptools-git==1.2
- spacy==3.7.4 - spacy==3.7.4
- https://aka.ms/automl-resources/packages/en_core_web_sm-3.7.1.tar.gz - https://aka.ms/automl-resources/packages/en_core_web_sm-3.7.1.tar.gz

6
how-to-use-azureml/automated-machine-learning/automl_env_linux.yml
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@@ -20,11 +20,11 @@ dependencies:
- pip: - pip:
# Required packages for AzureML execution, history, and data preparation. # Required packages for AzureML execution, history, and data preparation.
- azureml-widgets~=1.58.0 - azureml-widgets~=1.59.0
- azureml-defaults~=1.58.0 - azureml-defaults~=1.59.0
- pytorch-transformers==1.0.0 - pytorch-transformers==1.0.0
- spacy==3.7.4 - spacy==3.7.4
- xgboost==1.5.2 - xgboost==1.5.2
- prophet==1.1.4 - prophet==1.1.4
- https://aka.ms/automl-resources/packages/en_core_web_sm-3.7.1.tar.gz - https://aka.ms/automl-resources/packages/en_core_web_sm-3.7.1.tar.gz
- -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.58.0/validated_linux_requirements.txt [--no-deps] - -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.59.0/validated_linux_requirements.txt [--no-deps]

6
how-to-use-azureml/automated-machine-learning/automl_env_mac.yml
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@@ -15,12 +15,12 @@ dependencies:
- pip: - pip:
# Required packages for AzureML execution, history, and data preparation. # Required packages for AzureML execution, history, and data preparation.
- azureml-widgets~=1.58.0 - azureml-widgets~=1.59.0
- azureml-defaults~=1.58.0 - azureml-defaults~=1.59.0
- pytorch-transformers==1.0.0 - pytorch-transformers==1.0.0
- prophet==1.1.4 - prophet==1.1.4
- xgboost==1.5.2 - xgboost==1.5.2
- spacy==3.7.4 - spacy==3.7.4
- matplotlib==3.7.1 - matplotlib==3.7.1
- https://aka.ms/automl-resources/packages/en_core_web_sm-3.7.1.tar.gz - https://aka.ms/automl-resources/packages/en_core_web_sm-3.7.1.tar.gz
- -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.58.0/validated_darwin_requirements.txt [--no-deps] - -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.59.0/validated_darwin_requirements.txt [--no-deps]

2
how-to-use-azureml/automated-machine-learning/experimental/autofeaturization-codegen/codegen-for-autofeaturization.ipynb
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@@ -97,7 +97,7 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"print(\"This notebook was created using version 1.58.0 of the Azure ML SDK\")\n", "print(\"This notebook was created using version 1.59.0 of the Azure ML SDK\")\n",
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
] ]
}, },

2
how-to-use-azureml/automated-machine-learning/experimental/autofeaturization-custom-model-training/custom-model-training-from-autofeaturization-run.ipynb
View File

@@ -97,7 +97,7 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"print(\"This notebook was created using version 1.58.0 of the Azure ML SDK\")\n", "print(\"This notebook was created using version 1.59.0 of the Azure ML SDK\")\n",
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
] ]
}, },

2
how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.ipynb
View File

@@ -91,7 +91,7 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"print(\"This notebook was created using version 1.58.0 of the Azure ML SDK\")\n", "print(\"This notebook was created using version 1.59.0 of the Azure ML SDK\")\n",
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
] ]
}, },

2
how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-parameter-tuning-with-hyperdrive.ipynb
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@@ -292,7 +292,7 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"tf_env = Environment.get(ws, name='AzureML-tensorflow-2.16-cuda11')" "tf_env = Environment.get(ws, name='AzureML-tensorflow-2.16-cuda12')"
] ]
}, },
{ {

2
how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb
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@@ -101,7 +101,7 @@
"\n", "\n",
"# Check core SDK version number\n", "# Check core SDK version number\n",
"\n", "\n",
"print(\"This notebook was created using SDK version 1.58.0, you are currently running version\", azureml.core.VERSION)" "print(\"This notebook was created using SDK version 1.59.0, you are currently running version\", azureml.core.VERSION)"
] ]
}, },
{ {

2
how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-remote/train-remote.ipynb
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@@ -186,7 +186,7 @@
"\n", "\n",
"# Specify conda dependencies with scikit-learn and temporary pointers to mlflow extensions\n", "# Specify conda dependencies with scikit-learn and temporary pointers to mlflow extensions\n",
"cd = CondaDependencies.create(\n", "cd = CondaDependencies.create(\n",
" pip_packages=[\"azureml-mlflow\", \"scikit-learn\", \"matplotlib\", \"pandas\", \"numpy\"]\n", " pip_packages=[\"azureml-mlflow\", \"scikit-learn\", \"matplotlib\", \"pandas\", \"numpy\", \"protobuf==5.28.3\"]\n",
" )\n", " )\n",
"\n", "\n",
"env.python.conda_dependencies = cd" "env.python.conda_dependencies = cd"

2
index.md
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@@ -18,7 +18,6 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an
| [Forecasting BikeShare Demand](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb) | Forecasting | BikeShare | Remote | None | Azure ML AutoML | Forecasting | | [Forecasting BikeShare Demand](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb) | Forecasting | BikeShare | Remote | None | Azure ML AutoML | Forecasting |
| [Forecasting orange juice sales with deployment](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb) | Forecasting | Orange Juice Sales | Remote | Azure Container Instance | Azure ML AutoML | None | | [Forecasting orange juice sales with deployment](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb) | Forecasting | Orange Juice Sales | Remote | Azure Container Instance | Azure ML AutoML | None |
| [Forecasting orange juice sales with deployment](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-pipelines/auto-ml-forecasting-pipelines.ipynb) | Forecasting | Orange Juice Sales | Remote | Azure Container Instance | Azure ML AutoML | None | | [Forecasting orange juice sales with deployment](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-pipelines/auto-ml-forecasting-pipelines.ipynb) | Forecasting | Orange Juice Sales | Remote | Azure Container Instance | Azure ML AutoML | None |
| :star:[Data drift quickdemo](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datadrift-tutorial/datadrift-tutorial.ipynb) | Filtering | NOAA | Remote | None | Azure ML | Dataset, Timeseries, Drift |
| :star:[Datasets with ML Pipeline](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.ipynb) | Train | Fashion MNIST | Remote | None | Azure ML | Dataset, Pipeline, Estimator, ScriptRun | | :star:[Datasets with ML Pipeline](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.ipynb) | Train | Fashion MNIST | Remote | None | Azure ML | Dataset, Pipeline, Estimator, ScriptRun |
| :star:[Filtering data using Tabular Timeseiries Dataset related API](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datasets-tutorial/timeseries-datasets/tabular-timeseries-dataset-filtering.ipynb) | Filtering | NOAA | Local | None | Azure ML | Dataset, Tabular Timeseries | | :star:[Filtering data using Tabular Timeseiries Dataset related API](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datasets-tutorial/timeseries-datasets/tabular-timeseries-dataset-filtering.ipynb) | Filtering | NOAA | Local | None | Azure ML | Dataset, Tabular Timeseries |
| :star:[Train with Datasets (Tabular and File)](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.ipynb) | Train | Iris, Diabetes | Remote | None | Azure ML | Dataset, Estimator, ScriptRun | | :star:[Train with Datasets (Tabular and File)](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.ipynb) | Train | Iris, Diabetes | Remote | None | Azure ML | Dataset, Estimator, ScriptRun |
@@ -62,7 +61,6 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an
| [Training with hyperparameter tuning using PyTorch](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb) | Train an image classification model using transfer learning with the PyTorch estimator | ImageNet | AML Compute | Azure Container Instance | PyTorch | None | | [Training with hyperparameter tuning using PyTorch](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb) | Train an image classification model using transfer learning with the PyTorch estimator | ImageNet | AML Compute | Azure Container Instance | PyTorch | None |
| [Training and hyperparameter tuning with Scikit-learn](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/scikit-learn/train-hyperparameter-tune-deploy-with-sklearn/train-hyperparameter-tune-deploy-with-sklearn.ipynb) | Train a support vector machine (SVM) to perform classification | Iris | AML Compute | None | Scikit-learn | None | | [Training and hyperparameter tuning with Scikit-learn](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/scikit-learn/train-hyperparameter-tune-deploy-with-sklearn/train-hyperparameter-tune-deploy-with-sklearn.ipynb) | Train a support vector machine (SVM) to perform classification | Iris | AML Compute | None | Scikit-learn | None |
| [Hyperparameter tuning and warm start using the TensorFlow estimator](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow.ipynb) | Train a deep neural network | MNIST | AML Compute | Azure Container Instance | TensorFlow | None | | [Hyperparameter tuning and warm start using the TensorFlow estimator](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow.ipynb) | Train a deep neural network | MNIST | AML Compute | Azure Container Instance | TensorFlow | None |
| [Training and hyperparameter tuning using the TensorFlow estimator](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb) | Train a deep neural network | MNIST | AML Compute | Azure Container Instance | TensorFlow | None |
| [Resuming a model](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/tensorflow/train-tensorflow-resume-training/train-tensorflow-resume-training.ipynb) | Resume a model in TensorFlow from a previously submitted run | MNIST | AML Compute | None | TensorFlow | None | | [Resuming a model](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/ml-frameworks/tensorflow/train-tensorflow-resume-training/train-tensorflow-resume-training.ipynb) | Resume a model in TensorFlow from a previously submitted run | MNIST | AML Compute | None | TensorFlow | None |
| [Using Tensorboard](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/tensorboard/export-run-history-to-tensorboard/export-run-history-to-tensorboard.ipynb) | Export the run history as Tensorboard logs | None | None | None | TensorFlow | None | | [Using Tensorboard](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/tensorboard/export-run-history-to-tensorboard/export-run-history-to-tensorboard.ipynb) | Export the run history as Tensorboard logs | None | None | None | TensorFlow | None |
| [Training in Spark](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/train-in-spark/train-in-spark.ipynb) | Submiting a run on a spark cluster | None | HDI cluster | None | PySpark | None | | [Training in Spark](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/train-in-spark/train-in-spark.ipynb) | Submiting a run on a spark cluster | None | HDI cluster | None | PySpark | None |

2
setup-environment/configuration.ipynb
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@@ -102,7 +102,7 @@
"source": [ "source": [
"import azureml.core\n", "import azureml.core\n",
"\n", "\n",
"print(\"This notebook was created using version 1.58.0 of the Azure ML SDK\")\n", "print(\"This notebook was created using version 1.59.0 of the Azure ML SDK\")\n",
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
] ]
}, },