Pin onnx on Windows

update samples from Release-247 as a part of 1.59.0 SDK stable release

configuration.ipynb

@@ -103,7 +103,7 @@
       "source": [
         "import azureml.core\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\")"
       ]
     },

how-to-use-azureml/automated-machine-learning/automl_env.yml

@@ -14,13 +14,13 @@ dependencies:
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.58.0
-  - azureml-defaults~=1.58.0
-  - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.58.0/validated_win32_requirements.txt [--no-deps]
+  - azureml-widgets~=1.59.0
+  - azureml-defaults~=1.59.0
+  - -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.59.0/validated_win32_requirements.txt [--no-deps]
   - matplotlib==3.7.1
   - xgboost==1.5.2
   - prophet==1.1.4
-  - pandas==1.3.5
+  - onnx==1.16.1
   - setuptools-git==1.2
   - spacy==3.7.4
   - https://aka.ms/automl-resources/packages/en_core_web_sm-3.7.1.tar.gz

how-to-use-azureml/automated-machine-learning/automl_env_linux.yml

@@ -20,11 +20,11 @@ dependencies:
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.58.0
-  - azureml-defaults~=1.58.0
+  - azureml-widgets~=1.59.0
+  - azureml-defaults~=1.59.0
   - pytorch-transformers==1.0.0
   - spacy==3.7.4
   - xgboost==1.5.2
   - prophet==1.1.4
   - 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]

how-to-use-azureml/automated-machine-learning/automl_env_mac.yml

@@ -15,12 +15,12 @@ dependencies:
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.58.0
-  - azureml-defaults~=1.58.0
+  - azureml-widgets~=1.59.0
+  - azureml-defaults~=1.59.0
   - pytorch-transformers==1.0.0
   - prophet==1.1.4
   - xgboost==1.5.2
   - spacy==3.7.4
   - matplotlib==3.7.1
   - 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]

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 1.58.0 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\")"
       ]
     },

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 1.58.0 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\")"
       ]
     },

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 1.58.0 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\")"
       ]
     },

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-2.16-cuda11')"
+        "tf_env = Environment.get(ws, name='AzureML-tensorflow-2.16-cuda12')"
       ]
     },
     {

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 1.58.0, 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)"
       ]
     },
     {

how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-remote/train-remote.ipynb

@@ -186,7 +186,7 @@
         "\n",
         "# Specify conda dependencies with scikit-learn and temporary pointers to mlflow extensions\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",
         "env.python.conda_dependencies = cd"

index.md

@@ -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 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 |
-| :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:[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 |
@@ -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 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 |
-| [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 |
 | [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 |

setup-environment/configuration.ipynb

@@ -102,7 +102,7 @@
       "source": [
         "import azureml.core\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\")"
       ]
     },