Train models at your convenience

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

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Train models remotely from your laptop

Train models conveniently on a pool of VMs. Use your own servers or on-demand spot instances from your cloud set up automatically by dstack.

Dstack hero material

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 material
  • 2

    Set up runners

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

    You can either use your own servers by installing `dstack-runner` there via a bash command or allow dstack to set up spot instances in your own cloud automatically when they are needed - based on the demand.

    Dstack runner material
  • 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.

    If you use on-demand runners, dstack will set up a runner to run the workflow and will tear the runner down when it's not needed anymore.

    Workflow logs and artifacts are stored in dstack's storage.

    Dstack run material

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.

Supported plans

For more flexible plans or additional features, write to hello@dstack.ai

Free

For individuals
$
0
/month
$
/year

Enterprise

For teams
Contact us
Bring your own runners
✓
✓
On-demand runners in your cloud
✓
✓
Storage
10GB
Custom
Team quotes
✗
✓
Hosting
dstack.ai cloud
Your cloud or on-prem

FAQ

Get early access to dstack

Sign up to join our waitlist

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