I believe you mean that AWS, GCP, and Azure have their own tools to provision infrastructure for ML workflows. Yes, they do.

dstack offers something that none of the cloud vendors offer – a light-weight and developer-friendly CLI that is integrated with Git and can be used from the IDE.

Basically, dstack is a light-weight and developer-friendly alternative to the end-to-end MLOps platform.

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.

/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.

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/

Can you share the link? Thanks!