dstack 0.0.2rc4

A Command Line Interface for training models with https://dstack.ai

Website | Documentation

Typical ML workflows include multiple steps, e.g. pre-processing data, training, fine-tuning, validation, etc.

With dstack, you can define ML workflows in a simple YAML format, and run them from CLI on either your own servers or using spot instances in your own cloud.

How dstack works

1. You define .dstack/workflows.yaml and .dstack/variables.yaml files inside your project (must be a Git repository) .

2. You install the dstack CLI via pip.
3. You either install dstack-runner daemon on your servers, or use the dstack aws configure to authorize dstack to use your own cloud to create runners on-demand using spot instances.
4. You use the dstack CLI to run workflows, manage runs, jobs, logs, artifacts, runners.

5. When a workflow is submitted via the CLI (e.g. via dstack run) , the request is sent to the dstack server. The dstack server creates jobs for the submitted run, and assign them to available runners (either servers where you've installed dstack-runner or on-demand spot instances in your cloud that you allowed creating).
6. Runners execute assigned jobs, report their logs in real time, and upload artifacts once the job is finished.

For more information, please visit https://dstack.ai or https://docs.dstack.ai.