Task hardware environment

Task hardware environment#

Customizing task resources#

You can customize the hardware environment in which your task code executes.

Depending on your needs, there are two different of ways to define and register tasks with their own custom hardware requirements:

  • Configuration in the @task decorator

  • Defining a PodTemplate

Using the @task decorator#

You can specify requests and limits on:

  • CPU number

  • GPU number

  • Memory size

  • Ephemeral storage size

See Customizing task resources for details.

Using PodTemplate#

If your needs are more complex, you can use Kubernetes-level configuration to constrain a task to only run on a specific machine type.

This requires that you coordinate with Union to set up the required machine types and node groups with the appropriate node assignment configuration (node selector labels, node affinities, taints, tolerations, etc.)

In your task definition you then use a PodTemplate that that uses the matching node assignment configuration to make sure that the task will only be scheduled on the appropriate machine type.

pod_template and pod_template_name @task parameters#

The pod_template parameter can be used to supply a custom Kubernetes PodTemplate to the task. This can be used to define details about node selectors, affinity, tolerations, and other Kubernetes-specific settings.

The pod_template_name is a related parameter that can be used to specify the name of an already existing PodTemplate resource which will be used in this task.

For details see Configuring task pods with K8s PodTemplates⬀.

Accelerators#

If you specify GPUs, you can also specify the type of GPU to be used by setting the accelerator parameter. See Accelerators for more information.

Task-level monitoring#

You can also monitor the hardware resources used by a task. See Task-level monitoring for details.

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