https://github.com/dstackai/dstack
dstack is a lightweight, open-source alternative to Kubernetes & Slurm, simplifying AI container orchestration with multi-cloud & on-prem support. It natively supports NVIDIA, AMD, TPU, and Intel accelerators.
https://github.com/dstackai/dstack
amd aws azure cloud docker fine-tuning gcp gpu inference k8s kubernetes llms machine-learning nvidia orchestration python slurm training
Last synced: 4 days ago
JSON representation
dstack is a lightweight, open-source alternative to Kubernetes & Slurm, simplifying AI container orchestration with multi-cloud & on-prem support. It natively supports NVIDIA, AMD, TPU, and Intel accelerators.
- Host: GitHub
- URL: https://github.com/dstackai/dstack
- Owner: dstackai
- License: mpl-2.0
- Created: 2022-01-04T10:29:46.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2025-04-04T17:44:01.000Z (10 days ago)
- Last Synced: 2025-04-05T22:09:44.878Z (9 days ago)
- Topics: amd, aws, azure, cloud, docker, fine-tuning, gcp, gpu, inference, k8s, kubernetes, llms, machine-learning, nvidia, orchestration, python, slurm, training
- Language: Python
- Homepage: https://dstack.ai/docs
- Size: 114 MB
- Stars: 1,746
- Watchers: 12
- Forks: 171
- Open Issues: 101
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.md
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
- awesome-llmops - Dstack - effective LLM development in any cloud (AWS, GCP, Azure, Lambda, etc). |  | (LLMOps / Observability)
- awesome-llm-eval - Dstack
README
[](https://github.com/dstackai/dstack/commits/)
[](https://github.com/dstackai/dstack/blob/master/LICENSE.md)
[](https://discord.gg/u8SmfwPpMd)
`dstack` is a streamlined alternative to Kubernetes and Slurm, 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.
#### Accelerators
`dstack` supports `NVIDIA`, `AMD`, `Google TPU`, and `Intel Gaudi` accelerators out of the box.
## Major news ✨
- [2025/02] [dstack 0.18.41: GPU blocks, Proxy jump, inactivity duration, and more](https://github.com/dstackai/dstack/releases/tag/0.18.41)
- [2025/01] [dstack 0.18.38: Intel Gaudi](https://github.com/dstackai/dstack/releases/tag/0.18.38)
- [2025/01] [dstack 0.18.35: Vultr](https://github.com/dstackai/dstack/releases/tag/0.18.35)
- [2024/12] [dstack 0.18.30: AWS Capacity Reservations and Capacity Blocks](https://github.com/dstackai/dstack/releases/tag/0.18.30)
- [2024/10] [dstack 0.18.21: Instance volumes](https://github.com/dstackai/dstack/releases/tag/0.18.21)
- [2024/10] [dstack 0.18.18: Hardware metrics monitoring](https://github.com/dstackai/dstack/releases/tag/0.18.18)
## Installation
> Before using `dstack` through CLI or API, set up a `dstack` server. If you already have a running `dstack` server, you only need to [set up the CLI](#set-up-the-cli).
### (Optional) Configure backends
To use `dstack` with cloud providers, configure [backends](https://dstack.ai/docs/concepts/backends).
For using `dstack` with on-prem servers, create [SSH fleets](https://dstack.ai/docs/concepts/fleets#ssh) instead.
### Start the server
Once the backends are configured, proceed to start the server:
```shell
$ 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](https://dstack.ai/docs/guides/server-deployment).
### Set up the CLI
To point the CLI to the `dstack` server, configure it
with the server address, user token, and project name:
```shell
$ 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
```
## How does it work?
### 1. Define configurations
`dstack` supports the following configurations:
* [Dev environments](https://dstack.ai/docs/dev-environments) — for interactive development using a desktop IDE
* [Tasks](https://dstack.ai/docs/tasks) — for scheduling jobs (incl. distributed jobs) or running web apps
* [Services](https://dstack.ai/docs/services) — for deployment of models and web apps (with auto-scaling and authorization)
* [Fleets](https://dstack.ai/docs/fleets) — for managing cloud and on-prem clusters
* [Volumes](https://dstack.ai/docs/concepts/volumes) — for managing persisted volumes
* [Gateways](https://dstack.ai/docs/concepts/gateways) — for configuring the ingress traffic and public endpoints
Configuration can be defined as YAML files within your repo.
### 2. Apply configurations
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.
## More information
For additional information and examples, see the following links:
* [Docs](https://dstack.ai/docs)
* [Examples](https://dstack.ai/examples)
* [Providers](https://dstack.ai/providers)
* [Discord](https://discord.gg/u8SmfwPpMd)
## Contributing
You're very welcome to contribute to `dstack`.
Learn more about how to contribute to the project at [CONTRIBUTING.md](CONTRIBUTING.md).
## License
[Mozilla Public License 2.0](LICENSE.md)