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title titleSuffix description services ms.service ms.subservice ms.topic ms.author author ms.reviewer ms.date ms.custom monikerRange
Understand compute targets
Azure Machine Learning
Learn how to designate a compute resource or environment to train or deploy your model with Azure Machine Learning.
machine-learning
machine-learning
compute
conceptual
sgilley
sdgilley
vijetaj
01/23/2024
cliv2
build-2023
ignite-2023
azureml-api-2 || azureml-api-1

What are compute targets in Azure Machine Learning?

A compute target is a designated compute resource or environment where you run your training script or host your service deployment. This location might be your local machine or a cloud-based compute resource. Using compute targets makes it easy for you to later change your compute environment without having to change your code.

Azure Machine Learning has varying support across different compute targets. In a typical model development lifecycle, you might:

  1. Start by developing and experimenting on a small amount of data. At this stage, use your local environment, such as a local computer or cloud-based virtual machine (VM), as your compute target.
  2. Scale up to larger data, or do distributed training by using one of these training compute targets.
  3. After your model is ready, deploy it to a web hosting environment with one of these deployment compute targets.

The compute resources you use for your compute targets are attached to a workspace. Compute resources other than the local machine are shared by users of the workspace.

Training compute targets

As you scale up your training on larger datasets or perform distributed training, use Azure Machine Learning compute to create a single- or multi-node cluster that autoscales each time you submit a job. You can also attach your own compute resource, although support for different scenarios might vary.

[!INCLUDE aml-compute-target-train]

Compute targets for inference

When performing inference, Azure Machine Learning creates a Docker container that hosts the model and associated resources needed to use it. This container is then used in a compute target.

The compute target you use to host your model affects the cost and availability of your deployed endpoint. Use this table to choose an appropriate compute target.

:::moniker range="azureml-api-2"

Compute target Used for GPU support Description
Azure Machine Learning endpoints Real-time inference

Batch inference		 Yes Fully managed computes for real-time (managed online endpoints) and batch scoring (batch endpoints) on serverless compute.
Azure Machine Learning Kubernetes Real-time inference

Batch inference		 Yes Run inference workloads on on-premises, cloud, and edge Kubernetes clusters.
:::moniker-end
:::moniker range="azureml-api-1"
Compute target Used for GPU support Description
----- ----- ----- -----
Local web service Testing/debugging   Use for limited testing and troubleshooting. Hardware acceleration depends on use of libraries in the local system.
Azure Machine Learning Kubernetes Real-time inference Yes Run inference workloads in the cloud.
Azure Container Instances Real-time inference

Recommended for dev/test purposes only.		   Use for low-scale CPU-based workloads that require less than 48 GB of RAM. Doesn't require you to manage a cluster.

Only suitable for models less than 1 GB in size.

Supported in the designer.
:::moniker-end

Note

When choosing a cluster SKU, first scale up and then scale out. Start with a machine that has 150% of the RAM your model requires, profile the result and find a machine that has the performance you need. Once you've learned that, increase the number of machines to fit your need for concurrent inference.

:::moniker range="azureml-api-2" Deploy and score a machine learning model by using an online endpoint. :::moniker-end :::moniker range="azureml-api-1" Deploy machine learning models to Azure. :::moniker-end

Azure Machine Learning compute (managed)

Azure Machine Learning creates and manages the managed compute resources. This type of compute is optimized for machine learning workloads. Azure Machine Learning compute clusters, serverless compute, and compute instances are the only managed computes.

There's no need to create serverless compute. You can create Azure Machine Learning compute instances or compute clusters from:

[!INCLUDE serverless compute]

When created, these compute resources are automatically part of your workspace, unlike other kinds of compute targets.

Capability Compute cluster Compute instance
Single- or multi-node cluster ✓ Single node cluster
Autoscales each time you submit a job ✓
Automatic cluster management and job scheduling ✓ ✓
Support for both CPU and GPU resources ✓ ✓

Note

To avoid charges when the compute is idle:

Supported VM series and sizes

[!INCLUDE retiring vms]

When you select a node size for a managed compute resource in Azure Machine Learning, you can choose from among select VM sizes available in Azure. Azure offers a range of sizes for Linux and Windows for different workloads. To learn more, see VM types and sizes.

There are a few exceptions and limitations to choosing a VM size:

  • Some VM series aren't supported in Azure Machine Learning.
  • Some VM series, such as GPUs and other special SKUs, might not initially appear in your list of available VMs. But you can still use them, once you request a quota change. For more information about requesting quotas, see Request quota and limit increases. See the following table to learn more about supported series.

| Supported VM series | Category | Supported by | |------------|------------|------------|------------| | DDSv4 | General purpose | Compute clusters and instance | | Dv2 | General purpose | Compute clusters and instance | | Dv3 | General purpose | Compute clusters and instance | | DSv2 | General purpose | Compute clusters and instance | | DSv3 | General purpose | Compute clusters and instance | | EAv4 | Memory optimized | Compute clusters and instance | | Ev3 | Memory optimized | Compute clusters and instance | | ESv3 | Memory optimized | Compute clusters and instance | | FSv2 | Compute optimized | Compute clusters and instance | | FX | Compute optimized | Compute clusters | | H | High performance compute | Compute clusters and instance | | HB | High performance compute | Compute clusters and instance | | HBv2 | High performance compute | Compute clusters and instance | | HBv3 | High performance compute | Compute clusters and instance | | HC | High performance compute | Compute clusters and instance | | LSv2 | Storage optimized | Compute clusters and instance | | M | Memory optimized | Compute clusters and instance | | NC | GPU | Compute clusters and instance | | NC Promo | GPU | Compute clusters and instance | | NCv2 | GPU | Compute clusters and instance | | NCv3 | GPU | Compute clusters and instance | | ND | GPU | Compute clusters and instance | | NDv2 | GPU | Compute clusters and instance | | NV | GPU | Compute clusters and instance | | NVv3 | GPU | Compute clusters and instance | | NCasT4_v3 | GPU | Compute clusters and instance | | NDasrA100_v4 | GPU | Compute clusters and instance |

While Azure Machine Learning supports these VM series, they might not be available in all Azure regions. To check whether VM series are available, see Products available by region.

:::moniker range="azureml-api-1"

Note

Azure Machine Learning doesn't support all VM sizes that Azure Compute supports. To list the available VM sizes, use the following method:

If you use the GPU-enabled compute targets, it's important to ensure that the correct CUDA drivers are installed in the training environment. Use the following table to determine the correct CUDA version to use:

GPU Architecture Azure VM series Supported CUDA versions
Ampere NDA100_v4 11.0+
Turing NCT4_v3 10.0+
Volta NCv3, NDv2 9.0+
Pascal NCv2, ND 9.0+
Maxwell NV, NVv3 9.0+
Kepler NC, NC Promo 9.0+

In addition to ensuring the CUDA version and hardware are compatible, also ensure that the CUDA version is compatible with the version of the machine learning framework you're using:

Compute isolation

Azure Machine Learning compute offers VM sizes that are isolated to a specific hardware type and dedicated to a single customer. Isolated VM sizes are best suited for workloads that require a high degree of isolation from other customers' workloads for reasons that include meeting compliance and regulatory requirements. Utilizing an isolated size guarantees that your VM is the only one running on that specific server instance.

The current isolated VM offerings include:

  • Standard_M128ms
  • Standard_F72s_v2
  • Standard_NC24s_v3
  • Standard_NC24rs_v3 (RDMA capable)

To learn more about isolation, see Isolation in the Azure public cloud.

Unmanaged compute

Azure Machine Learning doesn't manage an unmanaged compute target. You create this type of compute target outside Azure Machine Learning and then attach it to your workspace. Unmanaged compute resources can require extra steps for you to maintain or to improve performance for machine learning workloads.

Azure Machine Learning supports the following unmanaged compute types:

For more information, see Manage compute resources.

Next step

:::moniker range="azureml-api-2"