Hey all, I have been using

Pod

with my

task_config

in the

@task

decorator to configure pods for executions according to this guide It is working well, but I saw that there is also the option to achieve the same result using

pod_template=PodTemplate(…)

in the task decorator according to this guide Both pass a kubernetes python V1PodSpec to the task, so I wanted to ask what is the difference, and if there is a case where one is preferable over the other. Thanks

Hey Victor! Great question, so

Pod

in the

task_config

is the initial implementation which it sounds like you have explored in some depth. The extension of enabling

pod_template

extends from the default PodTemplate work. The initial idea was to allow users to create PodTemplates in k8s namespaces and use them as the base layer for all k8s Pods that Flyte launches. That extended to allow users to specifcy a PodTemplate name and an actual PodTemplate definition in the task decorator. The important different is that

pod_template

in the task decorator can apply to more than just k8s tasks, so it can serve as the basis for Ray, Dask, PyTorch, Tensorlfow, and MPI jobs as well. So it's much more powerful that the previous

task_config=Pod

appoach. It's also worth noting that we have discussed updating the

task_config=Pod

approach to just use the

pod_template

API in the backend, there is nothing stopping us from doing that and at some point deprecating the original API.

Thanks Dan, very helpful response. I will use pod_templates then