cheptsov t1_is1g63l wrote on October 12, 2022 at 4:39 PM

Reply to comment by mietminderung in [Project] On simplifying MLOps stack by Kaudinya

Hey, the creator of dstack here.
Love DVC and other tools by Iterative.ai. Actually, was inspired originally by DVC and CML when I only started working on dstack.
As u/Kaudinya mentioned, dstack focuses on provisioning infrastructure and environment in the cloud.
On the other hand, dstack also helps manage data but doesn't use Git for that.
See https://docs.dstack.ai/examples/artifacts/ and https://docs.dstack.ai/examples/deps/

mietminderung t1_is1hvel wrote on October 12, 2022 at 4:50 PM

> dstack focuses on provisioning infrastructure and environment in the cloud.

> See https://docs.dstack.ai/examples/artifacts/ and https://docs.dstack.ai/examples/deps/

DVC also does "provision infra and environment in the cloud" based on your examples. Again, a comparison to specific similarites and differences would be best.

See https://dvc.org/doc/user-guide/pipelines/defining-pipelines

cheptsov t1_is1lezk wrote on October 12, 2022 at 5:14 PM

/u/mietminderung I don't really want to argue but DVC doesn't provision infrastructure ;) When you run things via DVC, they run locally. When you run things via dstack, they run in the configured cloud account.

mietminderung t1_is1oprk wrote on October 12, 2022 at 5:35 PM

I don’t want to argue either. Probably, you need to be extremely specific about “provision of infrastructure” means ;)

cheptsov t1_is1p9np wrote on October 12, 2022 at 5:39 PM

Yup. Basically, dstack allows you to run ML workflows in the cloud as if you did it locally. For example, you can specify how many GPUs you need or how much RAM and dstack will automatically create a cloud instance that satisfies the requirements to run the workflow.