Advanced Topics

Skipping Checkpoints

Ray Workflows provides strong fault tolerance and exactly-once execution semantics by checkpointing. However, checkpointing could be time consuming, especially when you have large inputs and outputs for workflow tasks. When exactly-once execution semantics is not required, you can skip some checkpoints to speed up your workflow.

Checkpoints can be skipped by specifying checkpoint=False:

import ray
from ray import workflow

@ray.remote
def read_data(num: int):
    return [i for i in range(num)]

data = read_data.options(**workflow.options(checkpoint=False)).bind(10)

This example skips checkpointing the output of read_data. During recovery, read_data would be executed again if recovery requires its output.

If the output of a task is another task (i.e., for dynamic workflows), we skip checkpointing the entire task.