update samples from Release-200 as a part of 1.54.0 SDK stable release

README.md

@@ -1,8 +1,6 @@
 # Azure Machine Learning Python SDK notebooks
 
-
-** **With the introduction of AzureML SDK v2, this samples repository for the v1 SDK is now deprecated and will not be monitored or updated. Users are encouraged to visit the [v2 SDK samples repository](https://github.com/Azure/azureml-examples) instead for up-to-date and enhanced examples of how to build, train, and deploy machine learning models with AzureML's newest features.** **
-
+### **With the introduction of AzureML SDK v2, this samples repository for the v1 SDK is now deprecated and will not be monitored or updated. Users are encouraged to visit the [v2 SDK samples repository](https://github.com/Azure/azureml-examples) instead for up-to-date and enhanced examples of how to build, train, and deploy machine learning models with AzureML's newest features.**
 
 Welcome to the Azure Machine Learning Python SDK notebooks repository!
 

configuration.ipynb

@@ -103,7 +103,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.53.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.54.0 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

@@ -7,21 +7,22 @@ dependencies:
   # The python interpreter version.
   # Azure ML only supports 3.7.0 and later.
 - pip==22.3.1
-- python>=3.8,<3.9
-- holidays==0.10.3
-- pandas==1.3.5
+- python>=3.9,<3.10
+- holidays==0.29
 - scipy==1.10.1
-- Cython==0.29.14
 - tqdm==4.66.1
+  # TODO: Remove this requirement when azureml-responsibleai will
+  # upgrade responsibleai to version to >=0.30.0
 - scikit-learn<1.1
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.53.0
-  - azureml-defaults~=1.53.0
-  - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.53.2/validated_win32_requirements.txt [--no-deps]
+  - azureml-widgets~=1.54.0
+  - azureml-defaults~=1.54.0
+  - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.54.0/validated_win32_requirements.txt [--no-deps]
   - matplotlib==3.7.1
   - xgboost==1.3.3
   - prophet==1.1.4
+  - pandas==1.3.5
   - cmdstanpy==1.1.0
   - setuptools-git==1.2

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

@@ -7,27 +7,26 @@ dependencies:
   # The python interpreter version.
   # Azure ML only supports 3.7 and later.
 - pip==22.3.1
-- python>=3.8,<3.9
+- python>=3.9,<3.10
 - matplotlib==3.7.1
-- numpy==1.22.3
-- cython==0.29.14
+- numpy>=1.21.6,<=1.23.5
 - urllib3==1.26.7
 - scipy==1.10.1
-- scikit-learn==1.1.3
+  # TODO: Upgrade this requirement to 1.1.3 when azureml-responsibleai will
+  # upgrade responsibleai to version to >=0.30.0
+- scikit-learn<1.1
 - py-xgboost<=1.3.3
-- holidays==0.10.3
-- conda-forge::prophet==1.1.4
+- holidays==0.29
 - pytorch::pytorch=1.11.0
 - cudatoolkit=10.1.243
 - notebook
-- scikit-learn<1.1
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.53.0
-  - azureml-defaults~=1.53.0
+  - azureml-widgets~=1.54.0
+  - azureml-defaults~=1.54.0
   - pytorch-transformers==1.0.0
   - spacy==2.2.4
-  - pystan==2.19.1.1
+  - prophet==1.1.4
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-  - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.53.2/validated_linux_requirements.txt [--no-deps]
+  - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.54.0/validated_linux_requirements.txt [--no-deps]

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

@@ -7,23 +7,22 @@ dependencies:
   # The python interpreter version.
   # Currently Azure ML only supports 3.7 and later.
 - pip==22.3.1
-- python>=3.8,<3.9
-- numpy==1.22.3
-- cython==0.29.14
+- python>=3.9,<3.10
+- numpy>=1.21.6,<=1.23.5
 - scipy==1.10.1
-- scikit-learn==1.1.3
-- holidays==0.10.3
+  # TODO: Upgrade this requirement to 1.1.3 when azureml-responsibleai will
+  # upgrade responsibleai to version to >=0.30.0
 - scikit-learn<1.1
+- holidays==0.29
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.53.0
-  - azureml-defaults~=1.53.0
+  - azureml-widgets~=1.54.0
+  - azureml-defaults~=1.54.0
   - pytorch-transformers==1.0.0
   - spacy==2.2.4
-  - pystan==2.19.1.1
   - prophet==1.1.4
   - xgboost==1.3.3
   - matplotlib==3.7.1
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-  - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.53.2/validated_darwin_requirements.txt [--no-deps]
+  - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.54.0/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 1.53.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.54.0 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 1.53.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.54.0 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/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 1.53.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.54.0 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 1.53.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.54.0 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/forecasting-pipelines/auto-ml-forecasting-pipelines.ipynb

@@ -1,5 +1,21 @@
 {
   "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/forecasting-pipelines/auto-ml-forecasting-pipelines.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -55,7 +71,6 @@
         "import logging\n",
         "import os\n",
         "\n",
-        "from matplotlib import pyplot as plt\n",
         "import pandas as pd\n",
         "\n",
         "import azureml.core\n",
@@ -441,8 +456,6 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.pipeline.core import PipelineData\n",
-        "\n",
         "# The model name with which to register the trained model in the workspace.\n",
         "model_name_str = \"ojmodel\"\n",
         "model_name = PipelineParameter(\"model_name\", default_value=model_name_str)\n",

how-to-use-azureml/automated-machine-learning/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.ipynb

@@ -1,5 +1,21 @@
 {
   "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},

how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb

@@ -1,5 +1,21 @@
 {
   "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},

how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb

@@ -260,7 +260,7 @@
       "source": [
         "from azureml.core.conda_dependencies import CondaDependencies \n",
         "\n",
-        "myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime\", \"azureml-core\", \"azureml-defaults\"])\n",
+        "myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime==1.15.1\", \"azureml-core\", \"azureml-defaults\"])\n",
         "\n",
         "with open(\"myenv.yml\",\"w\") as f:\n",
         "    f.write(myenv.serialize_to_string())"

how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb

@@ -334,7 +334,7 @@
         "from azureml.core.conda_dependencies import CondaDependencies \n",
         "\n",
         "\n",
-        "myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime\", \"azureml-core\", \"azureml-defaults\"])\n",
+        "myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime==1.15.1\", \"azureml-core\", \"azureml-defaults\"])\n",
         "\n",
         "with open(\"myenv.yml\",\"w\") as f:\n",
         "    f.write(myenv.serialize_to_string())"

how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb

@@ -319,7 +319,7 @@
       "source": [
         "from azureml.core.conda_dependencies import CondaDependencies \n",
         "\n",
-        "myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime\", \"azureml-core\", \"azureml-defaults\"])\n",
+        "myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime==1.15.1\", \"azureml-core\", \"azureml-defaults\"])\n",
         "\n",
         "with open(\"myenv.yml\",\"w\") as f:\n",
         "    f.write(myenv.serialize_to_string())"

how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb

@@ -242,7 +242,7 @@
         "from azureml.core.conda_dependencies import CondaDependencies \n",
         "\n",
         "\n",
-        "myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime\", \"azureml-core\", \"azureml-defaults\"])\n",
+        "myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime==1.15.1\", \"azureml-core\", \"azureml-defaults\"])\n",
         "\n",
         "with open(\"myenv.yml\",\"w\") as f:\n",
         "    f.write(myenv.serialize_to_string())"

how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb

@@ -416,7 +416,7 @@
       "source": [
         "from azureml.core.conda_dependencies import CondaDependencies \n",
         "\n",
-        "myenv = CondaDependencies.create(pip_packages=[\"numpy\",\"onnxruntime\",\"azureml-core\", \"azureml-defaults\"])\n",
+        "myenv = CondaDependencies.create(pip_packages=[\"numpy\",\"onnxruntime==1.15.1\",\"azureml-core\", \"azureml-defaults\"])\n",
         "\n",
         "with open(\"myenv.yml\",\"w\") as f:\n",
         "    f.write(myenv.serialize_to_string())"

how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-distributeddataparallel/distributed-pytorch-with-distributeddataparallel.ipynb

@@ -282,7 +282,7 @@
       "source": [
         "### Create an environment\n",
         "\n",
-        "In this tutorial, we will use one of Azure ML's curated PyTorch environments for training. [Curated environments](https://docs.microsoft.com/azure/machine-learning/how-to-use-environments#use-a-curated-environment) are available in your workspace by default. Specifically, we will use the PyTorch 1.6 GPU curated environment."
+        "In this tutorial, we will use one of Azure ML's curated PyTorch environments for training. [Curated environments](https://docs.microsoft.com/azure/machine-learning/how-to-use-environments#use-a-curated-environment) are available in your workspace by default. Specifically, we will use the PyTorch 2.0 GPU curated environment."
       ]
     },
     {
@@ -293,7 +293,7 @@
       "source": [
         "from azureml.core import Environment\n",
         "\n",
-        "pytorch_env = Environment.get(ws, name='azureml-acpt-pytorch-1.11-cuda11.3')"
+        "pytorch_env = Environment.get(ws, name='azureml-acpt-pytorch-1.13-cuda11.7')"
       ]
     },
     {

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 1.53.0, you are currently running version\", azureml.core.VERSION)"
+        "print(\"This notebook was created using SDK version 1.54.0, you are currently running version\", azureml.core.VERSION)"
       ]
     },
     {

how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/keras-mnist-fashion/prepare.py

@@ -28,9 +28,9 @@ def convert(imgf, labelf, outf, n):
 mounted_output_path = sys.argv[2]
 os.makedirs(mounted_output_path, exist_ok=True)
 
-convert(os.path.join(mounted_input_path, 'mnist-fashion/train-images-idx3-ubyte'),
-        os.path.join(mounted_input_path, 'mnist-fashion/train-labels-idx1-ubyte'),
+convert(os.path.join(mounted_input_path, 'train-images-idx3-ubyte'),
+        os.path.join(mounted_input_path, 'train-labels-idx1-ubyte'),
         os.path.join(mounted_output_path, 'mnist_train.csv'), 60000)
-convert(os.path.join(mounted_input_path, 'mnist-fashion/t10k-images-idx3-ubyte'),
-        os.path.join(mounted_input_path, 'mnist-fashion/t10k-labels-idx1-ubyte'),
+convert(os.path.join(mounted_input_path, 't10k-images-idx3-ubyte'),
+        os.path.join(mounted_input_path, 't10k-labels-idx1-ubyte'),
         os.path.join(mounted_output_path, 'mnist_test.csv'), 10000)

index.md

@@ -86,7 +86,6 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an
 |:----|:-----|:-------:|:----------------:|:-----------------:|:------------:|:------------:|
 | [Deploy MNIST digit recognition with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb) | Image Classification | MNIST | Local | Azure Container Instance | ONNX | ONNX Model Zoo |
 | [Deploy Facial Expression Recognition (FER+) with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb) | Facial Expression Recognition | Emotion FER | Local | Azure Container Instance | ONNX | ONNX Model Zoo |
-| :star:[Register model and deploy as webservice](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.ipynb) | Deploy a model with Azure Machine Learning | Diabetes | None | Azure Container Instance | Scikit-learn | None |
 | [Train MNIST in PyTorch, convert, and deploy with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb) | Image Classification | MNIST | AML Compute | Azure Container Instance | ONNX | ONNX Converter |
 | [Deploy ResNet50 with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb) | Image Classification | ImageNet | Local | Azure Container Instance | ONNX | ONNX Model Zoo |
 | :star:[Convert and deploy TinyYolo with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb) | Object Detection | PASCAL VOC | local | Azure Container Instance | ONNX | ONNX Converter |

setup-environment/configuration.ipynb

@@ -102,7 +102,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.53.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.54.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },