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dstack 0.7: Hub Preview

A preview of the new tool for teams to manage their ML workflows.

Last October, we open-sourced the dstack CLI for defining ML workflows as code and running them easily on any cloud or locally. The tool abstracts ML engineers from vendor APIs and infrastructure, making it convenient to run scripts, development environments, and applications.

Today, we are excited to announce a preview of Hub, a new way to use dstack for teams to manage their model development workflows effectively on any cloud platform.

How does it work?

Previously, the dstack CLI configured a cloud account as a remote to use local cloud credentials for direct requests to the cloud. Now, the CLI allows configuration of Hub as a remote, enabling requests to the cloud using user credentials stored in Hub.

  participant CLI
  participant Hub
  participant Cloud
  %  Note right of Cloud: AWS, GCP, etc
  CLI->>Hub: Run a workflow
  activate Hub
      Hub-->>Hub: User authentication
      loop Workflow provider
        Hub-->>Cloud: Submit workflow jobs
  Hub-->>CLI: Return the workflow status
  deactivate Hub
  loop Workflow scheduler
    Hub-->>Cloud: Re-submit workflow jobs

The Hub not only provides basic features such as authentication and credential storage, but it also has built-in workflow scheduling capabilities. For instance, it can monitor the availability of spot instances and automatically resubmit jobs.

Why does it matter?

As you start developing models more regularly, you'll encounter the challenge of automating your ML workflows to reduce time spent on infrastructure and manual work.

While many cloud vendors offer tools to automate ML workflows, they do so through opinionated UIs and APIs, leading to a suboptimal developer experience and vendor lock-in.

In contrast, dstack aims to provide a non-opinionated and developer-friendly interface that can work across any vendor.

Try the preview

Here's a quick guide to get started with Hub:

  1. Start the Hub application
  2. Visit the URL provided in the output to log in as an administrator
  3. Create a project and configure its backend (AWS or GCP)
  4. Configure the CLI to use the project as a remote

For more details, visit the Hub documentation.

What's next?

Currently, the only way to run or manage workflows is through the dstack CLI. There are scenarios when you'd prefer to run workflows other ways, e.g. from Python code or programmatically via API. To support these scenarios, we plan to release soon Python SDK and REST API.

The built-in scheduler currently monitors spot instance availability and automatically resubmits jobs. Our plan is to enhance this feature and include additional capabilities. Users will be able to track cloud compute usage, and manage quotes per team via the user interface.

Lastly, and of utmost importance, we plan to extend support to other cloud platforms, not limiting ourselves to AWS, GCP, and Azure.


You are encouraged to report any bugs, suggest new features, and provide feedback to improve Hub through GitHub issues.

If you wish to have dstack support additional workflow providers or cloud backends, and are willing to contribute to the cause, please get in touch with us through Slack or Twitter.