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dstack is a lightweight alternative to Kubernetes, designed specifically for managing the development, training, and deployment of AI models at any scale.

dstack is easy to use with any cloud provider (AWS, GCP, Azure, OCI, Lambda, TensorDock, Vast.ai, RunPod, etc.) or any on-prem clusters.

If you already use Kubernetes, dstack can be used with it.

dstack supports NVIDIA GPU and Google Cloud TPU out of the box.

• [2024/07] dstack 0.18.8: GCP volumes (Release)
• [2024/07] dstack 0.18.7: Fleets, RunPod volumes, dstack apply, and more (Release)
• [2024/05] dstack 0.18.4: Google Cloud TPU, and more (Release)
• [2024/05] dstack 0.18.3: OCI, and more (Release)
• [2024/05] dstack 0.18.2: On-prem clusters, private subnets, and more (Release)

Before using dstack through CLI or API, set up a dstack server.

If you want the dstack server to run containers or manage clusters in your cloud accounts (or use Kubernetes), create the ~/.dstack/server/config.yml file and configure backends.

Once the ~/.dstack/server/config.yml file is configured, proceed to start the server:

$ pip install "dstack[all]" -U
$ dstack server

Applying ~/.dstack/server/config.yml...

The admin token is "bbae0f28-d3dd-4820-bf61-8f4bb40815da"
The server is running at http://127.0.0.1:3000/

Note It's also possible to run the server via Docker.

The dstack server can run anywhere: on your laptop, a dedicated server, or in the cloud. Once it's up, you can use either the CLI or the API.

To point the CLI to the dstack server, configure it with the server address, user token, and project name:

$ pip install dstack
$ dstack config --url http://127.0.0.1:3000 \
    --project main \
    --token bbae0f28-d3dd-4820-bf61-8f4bb40815da
    
Configuration is updated at ~/.dstack/config.yml

If you want the dstack server to run containers on your on-prem servers, use fleets.

Before using dstack, install the server and configure backends.

dstack supports the following configurations:

• Dev environments — for interactive development using a desktop IDE
• Tasks — for scheduling jobs (incl. distributed jobs) or running web apps
• Services — for deployment of models and web apps (with auto-scaling and authorization)
• Fleets — for managing cloud and on-prem clusters
• Volumes — for managing persisted volumes
• Gateways — for configuring the ingress traffic and public endpoints

Configuration can be defined as YAML files within your repo.

Apply the configuration either via the dstack apply CLI command or through a programmatic API.

dstack automatically manages provisioning, job queuing, auto-scaling, networking, volumes, run failures, out-of-capacity errors, port-forwarding, and more — across clouds and on-prem clusters.

For additional information and examples, see the following links:

• Docs
• Examples
• Changelog
• Discord

You're very welcome to contribute to dstack. Learn more about how to contribute to the project at CONTRIBUTING.md.

Mozilla Public License 2.0