dstack is a streamlined alternative to Kubernetes, specifically designed for AI. It simplifies container orchestration
for AI workloads both in the cloud and on-prem, speeding up the development, training, and deployment of AI models.
dstack is easy to use with any cloud provider as well as on-prem servers.
dstack supports NVIDIA GPU, AMD GPU, and Google Cloud TPU out of the box.
- [2024/10] dstack 0.18.17: on-prem AMD GPUs, AWS EFA, and more
- [2024/08] dstack 0.18.11: AMD, encryption, and more
- [2024/08] dstack 0.18.10: Control plane UI
- [2024/07] dstack 0.18.7: Fleets, RunPod volumes, dstack apply, and more
- [2024/05] dstack 0.18.4: Google Cloud TPU, and more
- [2024/05] dstack 0.18.2: On-prem clusters, private subnets, and more
Before using
dstackthrough CLI or API, set up adstackserver. If you already have a runningdstackserver, you only need to set up the CLI.
To use dstack with your own cloud accounts, create the ~/.dstack/server/config.yml file and
configure backends. Alternatively, you can configure backends via the control plane UI after you start the server.
You can skip backends configuration if you intend to run containers only on your on-prem servers. Use SSH fleets for that.
Once the backends are 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/For more details on server configuration options, see the server deployment guide.
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.ymldstack 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:
You're very welcome to contribute to dstack.
Learn more about how to contribute to the project at CONTRIBUTING.md.