metadata and md updates (#2150)
This commit is contained in:
Sheri Gilley
GitHub
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31b4358caa
Коммит
14ab3c78a8
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@ -40,9 +40,9 @@ Test Status is for branch - **_main_**
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|azureml-in-a-day|[azureml-in-a-day](azureml-in-a-day/azureml-in-a-day.ipynb)|Learn how a data scientist uses Azure Machine Learning (Azure ML) to train a model, then use the model for prediction. This tutorial will help you become familiar with the core concepts of Azure ML and their most common usage.|[](https://github.com/Azure/azureml-examples/actions/workflows/tutorials-azureml-in-a-day-azureml-in-a-day.yml)|
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|e2e-distributed-pytorch-image|[e2e-object-classification-distributed-pytorch](e2e-distributed-pytorch-image/e2e-object-classification-distributed-pytorch.ipynb)|Prepare data, test and run a multi-node multi-gpu pytorch job. Use mlflow to analyze your metrics|[](https://github.com/Azure/azureml-examples/actions/workflows/tutorials-e2e-distributed-pytorch-image-e2e-object-classification-distributed-pytorch.yml)|
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|e2e-ds-experience|[e2e-ml-workflow](e2e-ds-experience/e2e-ml-workflow.ipynb)|Create production ML pipelines with Python SDK v2 in a Jupyter notebook|[](https://github.com/Azure/azureml-examples/actions/workflows/tutorials-e2e-ds-experience-e2e-ml-workflow.yml)|
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|get-started-notebooks|[cloud-workstation](get-started-notebooks/cloud-workstation.ipynb)|*no description*|[](https://github.com/Azure/azureml-examples/actions/workflows/tutorials-get-started-notebooks-cloud-workstation.yml)|
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|get-started-notebooks|[deploy-model](get-started-notebooks/deploy-model.ipynb)|*no description*|[](https://github.com/Azure/azureml-examples/actions/workflows/tutorials-get-started-notebooks-deploy-model.yml)|
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|get-started-notebooks|[explore-data](get-started-notebooks/explore-data.ipynb)|*no description*|[](https://github.com/Azure/azureml-examples/actions/workflows/tutorials-get-started-notebooks-explore-data.yml)|
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|get-started-notebooks|[cloud-workstation](get-started-notebooks/cloud-workstation.ipynb)|Notebook cells that accompany the Develop on cloud tutorial.|[](https://github.com/Azure/azureml-examples/actions/workflows/tutorials-get-started-notebooks-cloud-workstation.yml)|
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|get-started-notebooks|[deploy-model](get-started-notebooks/deploy-model.ipynb)|Learn to deploy a model to an online endpoint, using Azure Machine Learning Python SDK v2.|[](https://github.com/Azure/azureml-examples/actions/workflows/tutorials-get-started-notebooks-deploy-model.yml)|
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|get-started-notebooks|[explore-data](get-started-notebooks/explore-data.ipynb)|Upload data to cloud storage, create a data asset, create new versions for data assets, use the data for interactive development.|[](https://github.com/Azure/azureml-examples/actions/workflows/tutorials-get-started-notebooks-explore-data.yml)|
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|get-started-notebooks|[pipeline](get-started-notebooks/pipeline.ipynb)|Create production ML pipelines with Python SDK v2 in a Jupyter notebook|[](https://github.com/Azure/azureml-examples/actions/workflows/tutorials-get-started-notebooks-pipeline.yml)|
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|get-started-notebooks|[quickstart](get-started-notebooks/quickstart.ipynb)|*no description*|[](https://github.com/Azure/azureml-examples/actions/workflows/tutorials-get-started-notebooks-quickstart.yml)|
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|get-started-notebooks|[train-model](get-started-notebooks/train-model.ipynb)|*no description*|[](https://github.com/Azure/azureml-examples/actions/workflows/tutorials-get-started-notebooks-train-model.yml)|
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@ -414,7 +414,7 @@
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"description": "A quickstart tutorial to train and deploy an image classification model on Azure Machine Learning studio"
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},
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"kernelspec": {
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"display_name": "Python 3.10 - SDK V2",
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"display_name": "Python 3.10 - SDK v2",
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"language": "python",
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"name": "python310-sdkv2"
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},
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@ -802,9 +802,9 @@
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"description": "Learn how a data scientist uses Azure Machine Learning (Azure ML) to train a model, then use the model for prediction. This tutorial will help you become familiar with the core concepts of Azure ML and their most common usage."
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},
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"kernelspec": {
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"display_name": "Python 3.10 - SDK V2",
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"display_name": "Python 3",
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"language": "python",
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"name": "python310-sdkv2"
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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@ -818,12 +818,7 @@
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"pygments_lexer": "ipython3",
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"version": "3.10.6"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "71a65d72c50a26f05c9d6876d6234594cb07dd1b273faf1eea7eaa26341f62bb"
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}
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}
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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@ -680,7 +680,7 @@
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"description": "Prepare data, test and run a multi-node multi-gpu pytorch job. Use mlflow to analyze your metrics"
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},
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"kernelspec": {
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"display_name": "Python 3.10 - SDK V2",
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"display_name": "Python 3.10 - SDK v2",
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"language": "python",
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"name": "python310-sdkv2"
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},
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@ -1326,7 +1326,7 @@
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"description": "Create production ML pipelines with Python SDK v2 in a Jupyter notebook"
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},
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"kernelspec": {
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"display_name": "Python 3.10 - SDK V2",
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"display_name": "Python 3.10 - SDK v2",
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"language": "python",
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"name": "python310-sdkv2"
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},
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@ -210,6 +210,9 @@
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}
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],
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"metadata": {
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"description": {
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"description": "Notebook cells that accompany the Develop on cloud tutorial."
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},
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"kernel_info": {
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"name": "python310-sdkv2"
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},
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@ -51,8 +51,6 @@
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" \n",
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" * If you're seeing this notebook elsewhere, complete [Create resources you need to get started](https://docs.microsoft.com/azure/machine-learning/quickstart-create-resources) to create an Azure Machine Learning workspace and a compute instance.\n",
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" \n",
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"1. If you already completed the earlier training tutorial, [Train a model](https://learn.microsoft.com/en-us/azure/machine-learning/tutorial-train-model), you can skip to the next prerequisite.\n",
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"\n",
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"1. View your VM quota and ensure you have enough quota available to create online deployments. In this tutorial, you will need at least 8 cores of `STANDARD_DS3_v2` and 12 cores of `STANDARD_F4s_v2`. To view your VM quota usage and request quota increases, see [Manage resource quotas](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-quotas#view-your-usage-and-quotas-in-the-azure-portal).\n",
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"\n",
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"## Set your kernel\n",
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@ -113,11 +111,12 @@
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"> [!NOTE]\n",
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"> Creating `MLClient` will not connect to the workspace. The client initialization is lazy and will wait for the first time it needs to make a call (in this notebook, that will happen during compute creation).\n"
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"> Creating `MLClient` will not connect to the workspace. The client initialization is lazy and will wait for the first time it needs to make a call (this will happen in the next code cell).\n"
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]
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},
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{
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@ -782,7 +781,7 @@
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"source": [
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"ml_client.online_deployments.begin_delete(\n",
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" name=\"blue\", endpoint_name=online_endpoint_name\n",
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").wait()"
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").result()"
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]
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},
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{
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@ -803,11 +802,14 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"ml_client.online_endpoints.begin_delete(name=online_endpoint_name)"
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"ml_client.online_endpoints.begin_delete(name=online_endpoint_name).result()"
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]
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}
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],
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"metadata": {
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"description": {
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"description": "Learn to deploy a model to an online endpoint, using Azure Machine Learning Python SDK v2."
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},
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"kernel_info": {
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"name": "python310-sdkv2"
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},
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@ -134,7 +134,7 @@
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"metadata": {},
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"source": [
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"> [!NOTE]\n",
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"> Creating MLClient will not connect to the workspace. The client initialization is lazy, it will wait for the first time it needs to make a call (in the notebook below, that will happen during compute creation)."
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"> Creating MLClient will not connect to the workspace. The client initialization is lazy, it will wait for the first time it needs to make a call (this will happen in the next code cell)."
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]
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},
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{
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@ -165,7 +165,7 @@
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"\n",
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"The next notebook cell creates the data asset. The code sample uploads the raw data file to the designated cloud storage resource. \n",
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"\n",
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"Each time you create a data asset, you need a unique version for it. If the version already exists, you'll get an error. In this code, we're using time to generate a unique version, which will mostly work. But if you happen to run this same cell on a different day at the exact same time, you'll get an error. If this occurs, chances are good that it will be successful if you re-run the cell.\n",
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"Each time you create a data asset, you need a unique version for it. If the version already exists, you'll get an error. In this code, we're using time to generate a unique version each time the cell is run.\n",
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"\n",
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"You can also omit the **version** parameter, and a version number is generated for you, starting with 1 and then incrementing from there. In this tutorial, we want to refer to specific version numbers, so we create a version number instead."
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]
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@ -197,7 +197,9 @@
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"# local filesystem\n",
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"\n",
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"my_path = \"./data/default_of_credit_card_clients.csv\"\n",
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"v1 = str(time.strftime(\"%H.%M.%S\", time.gmtime()))\n",
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"# set the version number of the data asset to the current UTC time\n",
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"v1 = time.strftime(\"%Y.%m.%d.%H%M%S\", time.gmtime())\n",
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"\n",
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"\n",
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"my_data = Data(\n",
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" name=\"credit-card\",\n",
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@ -525,6 +527,9 @@
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}
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],
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"metadata": {
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"description": {
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"description": "Upload data to cloud storage, create a data asset, create new versions for data assets, use the data for interactive development."
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},
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"kernel_info": {
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"name": "python310-sdkv2"
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},
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@ -112,11 +112,12 @@
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"> [!NOTE]\n",
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"> Creating MLClient will not connect to the workspace. The client initialization is lazy, it will wait for the first time it needs to make a call (in the notebook below, that will happen during compute creation).\n",
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"> Creating MLClient will not connect to the workspace. The client initialization is lazy, it will wait for the first time it needs to make a call (this will happen when creating the `credit_data` data asset, two code cells from here).\n",
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"\n",
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"## Register data from an external url\n",
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"\n",
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@ -105,7 +105,7 @@
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"metadata": {},
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"source": [
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"> [!NOTE]\n",
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"> Creating MLClient will not connect to the workspace. The client initialization is lazy, it will wait for the first time it needs to make a call (in the notebook below, that will happen during compute creation)."
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"> Creating MLClient will not connect to the workspace. The client initialization is lazy, it will wait for the first time it needs to make a call (in this notebook, that will happen in the cell that creates the compute cluster)."
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]
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},
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{
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@ -704,7 +704,7 @@
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}
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],
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"metadata": {
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"description": "Learn how a data scientist uses Azure Machine Learning (Azure ML) to train a model, then use the model for prediction. This tutorial will help you become familiar with the core concepts of Azure ML and their most common usage.",
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"description": "Learn how a data scientist uses Azure Machine Learning to train a model, then use the model for prediction. This tutorial will help you become familiar with the core concepts of Azure ML and their most common usage.",
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"kernel_info": {
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"name": "python310-sdkv2"
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},
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@ -723,7 +723,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.9"
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"version": "3.10.6"
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},
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"microsoft": {
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"ms_spell_check": {
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@ -122,7 +122,7 @@
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"metadata": {},
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"source": [
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"> [!NOTE]\n",
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"> Creating MLClient will not connect to the workspace. The client initialization is lazy, it will wait for the first time it needs to make a call (in the notebook below, that will happen during compute creation)."
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"> Creating MLClient will not connect to the workspace. The client initialization is lazy, it will wait for the first time it needs to make a call (this will happen in the next code cell)."
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]
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},
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{
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