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‎articles/machine-learning/concept-azure-machine-learning-v2.md

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@@ -10,7 +10,7 @@ ms.topic: conceptual
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ms.author: balapv
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author: balapv
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ms.reviewer: sgilley
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ms.date: 11/04/2022
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ms.date: 02/27/2024
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#Customer intent: As a data scientist, I want to understand the big picture about how Azure Machine Learning works.
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---
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This document provides a quick overview of these resources and assets.
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## Workspace
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## Prerequisites
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The workspace is the top-level resource for Azure Machine Learning, providing a centralized place to work with all the artifacts you create when you use Azure Machine Learning. The workspace keeps a history of all jobs, including logs, metrics, output, and a snapshot of your scripts. The workspace stores references to resources like datastores and compute. It also holds all assets like models, environments, components and data asset.
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### [Python SDK](#tab/sdk)
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### Create a workspace
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To use the Python SDK code examples in this article, create a connection to your Azure Machine Learning subscription. The examples all rely on `ml_client`.
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### [Azure CLI](#tab/cli)
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```python
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# import required libraries
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from azure.ai.ml import MLClient
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from azure.ai.ml.entities import Workspace
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from azure.identity import DefaultAzureCredential
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To create a workspace using CLI v2, use the following command:
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# Enter details of your subscription
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subscription_id = "<SUBSCRIPTION_ID>"
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resource_group = "<RESOURCE_GROUP>"
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[!INCLUDE [cli v2](includes/machine-learning-cli-v2.md)]
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# get a handle to the subscription (use this if you haven't created a workspace yet)
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ml_client = MLClient(DefaultAzureCredential(), subscription_id, resource_group)
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```bash
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az ml workspace create --file my_workspace.yml
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# for all examples after creating a workspace, also include the workspace name:
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workspace_name = "<WORKSPACE_NAME>"
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ml_client = ml_client = MLClient(DefaultAzureCredential(), subscription_id, resource_group, workspace_name)
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```
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For more information, see [workspace YAML schema](reference-yaml-workspace.md).
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### [Azure CLI](#tab/cli)
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To use the Azure CLI code examples in this article, you need to have the Azure CLI installed and configured. You can install the Azure CLI from the [Azure CLI documentation](https://docs.microsoft.com/cli/azure/install-azure-cli).
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### [Studio](#tab/azure-studio)
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Sign in to [Azure Machine Learning studio](https://ml.azure.com).
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---
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## Workspace
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The workspace is the top-level resource for Azure Machine Learning, providing a centralized place to work with all the artifacts you create when you use Azure Machine Learning. The workspace keeps a history of all jobs, including logs, metrics, output, and a snapshot of your scripts. The workspace stores references to resources like datastores and compute. It also holds all assets like models, environments, components and data asset.
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### Create a workspace
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### [Python SDK](#tab/sdk)
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[!INCLUDE [sdk v2](includes/machine-learning-sdk-v2.md)]
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```python
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ws_basic = Workspace(
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name="my-workspace",
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location="eastus", # Azure region (location) of workspace
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display_name="Basic workspace-example",
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description="This example shows how to create a basic workspace"
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# specify the workspace details
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ws = Workspace(
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name="my_workspace",
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location="eastus",
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display_name="My workspace",
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description="This example shows how to create a workspace",
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tags=dict(purpose="demo"),
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)
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ml_client.workspaces.begin_create(ws_basic) # use MLClient to connect to the subscription and resource group and create workspace
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ml_client.workspaces.begin_create(ws) # use MLClient to connect to the subscription and resource group and create workspace
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```
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This [Jupyter notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/resources/workspace/workspace.ipynb) shows more ways to create an Azure Machine Learning workspace using SDK v2.
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---
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## Compute
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### [Azure CLI](#tab/cli)
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A compute is a designated compute resource where you run your job or host your endpoint. Azure Machine Learning supports the following types of compute:
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To create a workspace using CLI v2, use the following command:
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* **Compute cluster** - a managed-compute infrastructure that allows you to easily create a cluster of CPU or GPU compute nodes in the cloud.
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[!INCLUDE [cli v2](includes/machine-learning-cli-v2.md)]
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[!INCLUDE [serverless compute](./includes/serverless-compute.md)]
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```bash
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az ml workspace create --file my_workspace.yml
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```
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* **Compute instance** - a fully configured and managed development environment in the cloud. You can use the instance as a training or inference compute for development and testing. It's similar to a virtual machine on the cloud.
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* **Inference cluster** - used to deploy trained machine learning models to Azure Kubernetes Service. You can create an Azure Kubernetes Service (AKS) cluster from your Azure Machine Learning workspace, or attach an existing AKS cluster.
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* **Attached compute** - You can attach your own compute resources to your workspace and use them for training and inference.
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For more information, see [workspace YAML schema](reference-yaml-workspace.md).
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### [Studio](#tab/azure-studio)
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### [Azure CLI](#tab/cli)
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Create a workspace in the studio welcome page by selecting **Create workspace**.
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To create a compute using CLI v2, use the following command:
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---
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[!INCLUDE [cli v2](includes/machine-learning-cli-v2.md)]
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## Compute
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```bash
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az ml compute --file my_compute.yml
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```
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A compute is a designated compute resource where you run your job or host your endpoint. Azure Machine Learning supports the following types of compute:
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For more information, see [compute YAML schema](reference-yaml-overview.md#compute).
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* **Compute instance** - a fully configured and managed development environment in the cloud. You can use the instance as a training or inference compute for development and testing. It's similar to a virtual machine on the cloud.
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* **Compute cluster** - a managed-compute infrastructure that allows you to easily create a cluster of CPU or GPU compute nodes in the cloud.
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* **Serverless compute** - a compute cluster you access on the fly. When you use serverless compute, you don't need to create your own cluster. All compute lifecycle management is offloaded to Azure Machine Learning.
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* **Inference cluster** - used to deploy trained machine learning models to Azure Kubernetes Service. You can create an Azure Kubernetes Service (AKS) cluster from your Azure Machine Learning workspace, or attach an existing AKS cluster.
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* **Attached compute** - You can attach your own compute resources to your workspace and use them for training and inference.
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### Create a compute
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### [Python SDK](#tab/sdk)
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To create a compute using Python SDK v2, you can use the following code:
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To create a compute cluster using Python SDK v2, you can use the following code:
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[!INCLUDE [sdk v2](includes/machine-learning-sdk-v2.md)]
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This [Jupyter notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/resources/compute/compute.ipynb) shows more ways to create compute using SDK v2.
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### [Azure CLI](#tab/cli)
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To create a compute using CLI v2, use the following command:
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[!INCLUDE [cli v2](includes/machine-learning-cli-v2.md)]
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```bash
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az ml compute --file my_compute.yml
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```
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For more information, see [compute YAML schema](reference-yaml-overview.md#compute).
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### [Studio](#tab/azure-studio)
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1. Select a workspace if you are not already in one.
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1. From the left-hand menu, select **Compute**.
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1. On the top, select a tab to specify the type of compute you want to create.
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1. Select **New** to create the new compute.
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1.
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---
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## Datastore
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* Azure Data Lake
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* Azure Data Lake Gen2
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### [Azure CLI](#tab/cli)
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To create a datastore using CLI v2, use the following command:
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[!INCLUDE [cli v2](includes/machine-learning-cli-v2.md)]
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```bash
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az ml datastore create --file my_datastore.yml
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```
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For more information, see [datastore YAML schema](reference-yaml-overview.md#datastore).
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### Create a datastore
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### [Python SDK](#tab/sdk)
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[!INCLUDE [sdk v2](includes/machine-learning-sdk-v2.md)]
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```python
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import AzureBlobDatastore
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blob_datastore1 = AzureBlobDatastore(
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name="blob-example",
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name="blob_example",
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description="Datastore pointing to a blob container.",
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account_name="mytestblobstore",
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container_name="data-container",
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This [Jupyter notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/resources/datastores/datastore.ipynb) shows more ways to create datastores using SDK v2.
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---
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## Model
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Azure machine learning models consist of the binary file(s) that represent a machine learning model and any corresponding metadata. Models can be created from a local or remote file or directory. For remote locations `https`, `wasbs` and `azureml` locations are supported. The created model will be tracked in the workspace under the specified name and version. Azure Machine Learning supports three types of storage format for models:
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* `custom_model`
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* `mlflow_model`
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* `triton_model`
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### Creating a model
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### [Azure CLI](#tab/cli)
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To create a model using CLI v2, use the following command:
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To create a datastore using CLI v2, use the following command:
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[!INCLUDE [cli v2](includes/machine-learning-cli-v2.md)]
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az ml datastore create --file my_datastore.yml
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```
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For more information, see [model YAML schema](reference-yaml-model.md).
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For more information, see [datastore YAML schema](reference-yaml-overview.md#datastore).
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### [Studio](#tab/azure-studio)
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### [Python SDK](#tab/sdk)
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1. Select a workspace if you are not already in one.
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1. From the left-hand menu, select **Data**.
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1. On the top, select **Datastores**.
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1. Select **Create** to create a new datastore.
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To create a model using Python SDK v2, you can use the following code:
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---
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[!INCLUDE [sdk v2](includes/machine-learning-sdk-v2.md)]
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## Model
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```python
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my_model = Model(
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path="model.pkl", # the path to where my model file is located
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type="custom_model", # can be custom_model, mlflow_model or triton_model
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name="my-model",
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description="Model created from local file.",
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)
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Azure machine learning models consist of the binary file(s) that represent a machine learning model and any corresponding metadata. Models can be created from a local or remote file or directory. For remote locations `https`, `wasbs` and `azureml` locations are supported. The created model will be tracked in the workspace under the specified name and version. Azure Machine Learning supports three types of storage format for models:
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ml_client.models.create_or_update(my_model) # use the MLClient to connect to workspace and create/register the model
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```
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* `custom_model`
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* `triton_model`
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---
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### Create a model in the model registry
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Model registration allows you to store and version your models in the Azure cloud, in your workspace. The model registry helps you organize and keep track of your trained models.
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For more information on how to create models in the registry, see [Work with models in Azure Machine Learning](how-to-manage-models).
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## Environment
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### Create an Azure Machine Learning custom environment
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```
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### [Python SDK](#tab/sdk)
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To create an environment using Python SDK v2, see [Create an envirnment](how-to-manage-environments-v2&tabs=python#create-an-environment).
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### [Python SDK](#tab/sdk)
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This [Jupyter notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/assets/environment/environment.ipynb) shows more ways to create custom environments using SDK v2.
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To create an environment using Python SDK v2, you can use the following code:
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### [Azure CLI](#tab/cli)
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[!INCLUDE [sdk v2](includes/machine-learning-sdk-v2.md)]
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To create an environment using CLI v2, see [Create an envirnment](how-to-manage-environments-v2&tabs=cli#create-an-environment).
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)
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For more information, see [environment YAML schema](reference-yaml-environment.md).
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```
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### [Studio](#tab/azure-studio)
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1. Select a workspace if you are not already in one.
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1. From the left-hand menu, select **Environments**.
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1. On the top, select **Custom environments**.
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1. Select **Create** to create a new custom environment.
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