Use for limited testing and troubleshooting. Use this table to choose an appropriate compute target. The compute target you use to host your model will affect the cost and availability of your deployed endpoint. This container is then used in a compute target. When performing inference, Azure Machine Learning creates a Docker container that hosts the model and associated resources needed to use it. If you run out of disk space, use the terminal to clear at least 1-2 GB before you stop or restart the compute instance. Azure Databricks can be used as a training resource for local runs and machine learning pipelines, but not as a remote target for other training. Not all resources can be used for automated machine learning, machine learning pipelines, or designer. You can use any of the following resources for a training compute target for most jobs. For machine learning pipelines, use the appropriate pipeline step for each compute target. For example, after you attach a remote VM to your workspace, you can reuse it for multiple jobs. You can also attach your own compute resource, although support for different scenarios might vary.Ĭompute targets can be reused from one training job to the next. 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. At this stage, use a local environment like your local computer or a cloud-based VM. A typical model development lifecycle starts with development or experimentation on a small amount of data. Training compute targetsĪzure Machine Learning has varying support across different compute targets. Compute resources other than the local machine are shared by users of the workspace. The compute resources you use for your compute targets are attached to a workspace. ![]() After your model is ready, deploy it to a web hosting environment with one of these deployment compute targets.Scale up to larger data, or do distributed training by using one of these training compute targets.At this stage, use your local environment, such as a local computer or cloud-based virtual machine (VM), as your compute target. Start by developing and experimenting on a small amount of data.In a typical model development lifecycle, you might: Using compute targets makes it easy for you to later change your compute environment without having to change your code. This location might be your local machine or a cloud-based compute resource. A compute target is a designated compute resource or environment where you run your training script or host your service deployment.
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