When I execute a flyte workflow, do the nodes in that workflow run in separate Kubernetes pods? If so, How can I adjust the amount of cpu/memory I allocate to a particular kubernetes pod?

Yes they do

Here is an example. https://docs.flyte.org/projects/cookbook/en/latest/auto/deployment/customizing_resources.html#sphx-glr-auto-deployment-customizing-resources-py

hi @Ryan Delgado so these two unionml decorators take in all the

kwargs

of the flyte

@task

decorator: • Dataset.reader • Model.trainer So you can do

flytekit.Resources(cpu="4")

for the dataset reading step and another configuration for the training step. FYI, the

dataset.loader

,

parser

,

splitter

, are all invoked within the training task to reduce overhead of spinning up pods.