Run Commands
Read the PPS series >

Datum Timeout PPS

Set the maximum execution time allowed for each datum.

April 4, 2024

Spec #

This is a top-level attribute of the pipeline spec.

    "pipeline": {...},
    "transform": {...},
    "datum_timeout": string,

Behavior #

The datum_timeout attribute in a HPE ML Data Management pipeline is used to specify the maximum amount of time that a worker is allowed to process a single datum in the pipeline.

When a worker begins processing a datum, HPE ML Data Management starts a timer that tracks the elapsed time since the datum was first assigned to the worker. If the worker has not finished processing the datum before the datum_timeout period has elapsed, HPE ML Data Management will automatically mark the datum as failed and reassign it to another worker to retry. This helps to ensure that slow or problematic datums do not hold up the processing of the entire pipeline.

Other considerations:

  • Not set by default, allowing a datum to process for as long as needed.
  • Takes precedence over the parallelism or number of datums; no single datum is allowed to exceed this value.
  • The value must be a string that represents a time value, such as 1s, 5m, or 15h.

When to Use #

You should consider using the datum_timeout attribute in your HPE ML Data Management pipeline when you are processing large or complex datums that may take a long time to process, and you want to avoid having individual datums hold up the processing of the entire pipeline.

For example, if you are processing images or videos that are particularly large, or if your pipeline is doing complex machine learning or deep learning operations that can take a long time to run on individual datums, setting a reasonable datum_timeout can help ensure that your pipeline continues to make progress even if some datums are slow or problematic.