accessed via `self.config`.

step_settings = cast(MyOrchestratorSettings, self.get_settings(step)

# If your orchestrator supports setting resources like CPUs, GPUs or

# memory for the pipeline or specific steps, you can find out whether

# specific resources were specified for this step:

if self.requires_resources_in_orchestration_environment(step):

resources = step.config.resource_settings

To see a full end-to-end worked example of a custom orchestrator, see here.

Enabling CUDA for GPU-backed hardware

Note that if you wish to use your custom orchestrator to run steps on a GPU, you will need to follow the instructions on this page to ensure that it works. It requires adding some extra settings customization and is essential to enable CUDA for the GPU to give its full acceleration.

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Last updated 19 days ago