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Workloads Overview


Run:ai schedules Workloads. Run:ai workloads are comprised of:

  • The Kubernetes object (Job, Deployment, etc) which is used to launch the container, inside which the data science code runs.
  • A set of additional resources that are required to run the Workload. Examples: a service entry point that allows access to the Job, a persistent volume claim to access data on the network, and more.

All of these components are created together and deleted together when the Workload ends.

Run:ai currently supports the following Workloads types:

Workload Type Kubernetes Name Description
Interactive InteractiveWorkload Submit an interactive workload
Training TrainingWorkload Submit a training workload
Distributed Training DistributedWorkload Submit a distributed training workload using TensorFlow, PyTorch or MPI
Inference InferenceWorkload Submit an inference workload


A Workload will typically have a list of values (sometimes called flags), such as name, image, and resources. A full list of values is available in the runai-submit Command-line reference.

How to Submit

A Workload can be submitted via various channels:

Workload Policies

As an administrator, you can set Policies on Workloads. Policies allow administrators to impose restrictions and set default values for Researcher Workloads. For more information see Workload Policies.

Last update: 2023-03-21
Created: 2022-05-01