Prepare. Train. Finetune. Reproduce.

Define reproducible deep learning workflows. Run them via the CLI. Use any infrastructure. Browse logs and artifacts in real-time.

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Device frame

Workflows

For each workflow, specify a Docker image, commands, other workflows it depends on, and output artifacts.

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Variables

Each workflow may have variables and their default values. Any of the variables can be overridden when you run the workflow via the CLI.

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Self-hosted runners

Register your local machine or any remote servers as self-hosted runners. To do that, you only need to run one line of bash code.

After you set up self-hosted runners, submit any workflow to run there from your laptop using the CLI.

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Spot instances

Aulternatively to self-hosted runners, you can authorize dstack to set up spot instances in your own cloud account.

If any of the spot instances terminate before finishing the workflow, dstack will re-assign it to a new instance without losing the checkpoints.

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Runs

Run workflows via the CLI. Browse real-time logs and artifacts. Stop and resume runs.

Mark sucessful runs with tags for later reuse.

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F.A.Q.

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