Train models at your convenience

Run multiple machine learning experiments interactively from your laptop. Use any infrastructure. Keep any experiment reproducible.

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Train models remotely from your laptop

Train models on a pool of your own VMs from your laptop. All dependencies and artifacts are tracked.

Dstack hero pc

How dstack works

  • 1

    Define workflows

    Machine learning workflows consist of steps. Every step may have its own config, may depend on other steps, and may produce artifacts.

    Define your workflows in the form of YAML files within your project.

    No changes to your code are required.

    Dstack workflow pc
  • 2

    Set up runners

    Workflows run on a pool of machines that are called runners.

    You can register any AWS' EC2 instance or GCP's or Azure's VM, or your own server as a runner by running a simple bash command.

    Dstack install runner pc
  • 3

    Run workflows interactively

    Run any workflow via the CLI on your laptop. Override config values. Run a parameter sweep if you want to use a combination of multiple parameters.

    The workflow will run on one of the available runners.

    Dstack run workflow from cli pc

Because dstack is aware of your workflows, their exact steps, and input parameters, any run can be back-tracked and reproduced end-to-end on any infrastructure.

FAQ

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