If you already have K8s setup, and are already using ClearML.
In your kubeflow Yaml:
trains-agent execute --id <task_id> --full-monitoringThis will install everything your Task needs inside the docker. Just make sure that you pass the env variable setting the ClearML , see here:
am.. it depends, it can be a small function but can also be a task. We are using
kale so it easier to group/create task and move data from one task to another. Each step is independent but the next is being creating from the data of the pervious one.
The first pipeline
step is calling init
GiddyPeacock64 Is this enough to track all the steps?
I guess my main question is every step in the pipeline an actual Task/Job or is it a single small function?
Kubeflow is great for simple DAGs but when you need to build more complex logic it is usually a bit limited
(for example the visibility into what's going on inside each step is missing so you cannot make a decision based on that).