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

2025-12-19 09:07:04 -05:00 · 2023-11-21 17:51:12 +00:00
parent e7dd826f34
commit ff22ac01cc
22 changed files with 93 additions and 52 deletions
--- a/README.md
+++ b/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!

--- a/configuration.ipynb
+++ b/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\")"
      ]
    },
--- a/how-to-use-azureml/automated-machine-learning/automl_env.yml
+++ b/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
--- a/how-to-use-azureml/automated-machine-learning/automl_env_linux.yml
+++ b/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]
--- a/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml
+++ b/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml
@@ -7,23 +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]
--- 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 1.53.0 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\")"
      ]
    },
--- 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 1.53.0 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\")"
      ]
    },
--- 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 1.53.0 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\")"
      ]
    },
--- 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 1.53.0 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\")"
      ]
    },
--- 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
@@ -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",
--- 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
@@ -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": {},
--- 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
@@ -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": {},
--- 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
@@ -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())"
--- 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
@@ -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())"
--- a/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb
+++ b/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())"
--- 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
@@ -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())"
--- 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
@@ -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())"
--- 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
@@ -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')"
      ]
    },
    {
--- 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 1.53.0, 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)"
      ]
    },
    {
--- a/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/keras-mnist-fashion/prepare.py
+++ b/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/keras-mnist-fashion/prepare.py
@@ -28,9 +28,9 @@ mounted_input_path = sys.argv[1]
 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)
--- a/index.md
+++ b/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 |
--- 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 1.53.0 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\")"
      ]
    },