README.md
Rama master · réplica de lectura del repositorio original.
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`dstack` is a unified control plane for GPU provisioning and orchestration that works with any GPU cloud, Kubernetes, or on-prem clusters.
It streamlines development, training, and inference, and is compatible with any hardware, open-source tools, and frameworks.
#### Accelerators
`dstack` supports `NVIDIA`, `AMD`, `Google TPU`, and `Tenstorrent` accelerators out of the box.
## Latest news ✨
- [2026/08] [dstack 0.21.0: Pydantic v2, Gateway replicas](https://github.com/dstackai/dstack/releases/tag/0.21.0)
- [2026/07] [dstack 0.20.29: Presets — agent-driven inference optimization](https://dstack.ai/docs/concepts/presets/)
- [2026/07] [dstack 0.20.27: Slurm backend](https://github.com/dstackai/dstack/releases/tag/0.20.27)
- [2026/05] [dstack 0.20.21: Kubernetes multiple clusters](https://github.com/dstackai/dstack/releases/tag/0.20.21)
- [2026/05] [dstack 0.20.20: NVIDIA Dynamo integration](https://github.com/dstackai/dstack/releases/tag/0.20.20)
- [2026/04] [dstack 0.20.17: Kubernetes volumes](https://github.com/dstackai/dstack/releases/tag/0.20.17)
- [2026/02] [dstack 0.20.10: PD disaggregation support](https://github.com/dstackai/dstack/releases/tag/0.20.10)
- [2026/01] [dstack 0.20.7: Replica groups](https://github.com/dstackai/dstack/releases/tag/0.20.7)
## How does it work?
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### Launch the server
> Before using `dstack` through CLI or API, set up a `dstack` server. If you already have a running `dstack` server, you only need to [install the CLI](#install-the-cli).
To orchestrate compute across GPU clouds or Kubernetes clusters, you need to [configure backends](https://dstack.ai/docs/concepts/backends).
> When using `dstack` with on-prem servers, backend configuration isn’t required. Simply create [SSH fleets](https://dstack.ai/docs/concepts/fleets#ssh-fleets) once the server is up.
The server can be installed on Linux, macOS, and Windows (via WSL 2). It requires Git and
OpenSSH.
```shell
$ uv tool 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](https://dstack.ai/docs/guides/server-deployment) guide.
### Install the CLI
<details><summary>If the CLI is not installed with the server</summary>
Once the server is up, you can access it via the `dstack` CLI.
The CLI can be installed on Linux, macOS, and Windows. It requires Git and OpenSSH.
```shell
$ uv tool install dstack -U
```
To point the CLI to the `dstack` server, configure it
with the server address, user token, and project name:
```shell
$ dstack project add \
--name main \
--url http://127.0.0.1:3000 \
--token bbae0f28-d3dd-4820-bf61-8f4bb40815da
Configuration is updated at ~/.dstack/config.yml
```
</details>
### Install agent skills
Install [`dstack` skills](https://skills.sh/dstackai/dstack/dstack) to help AI agents use the CLI and edit configuration files.
```shell
$ npx skills add dstackai/dstack
```
AI agents like Claude, Codex, and Cursor can now create and manage fleets and submit workloads on your behalf.
### Define configurations
`dstack` supports the following configurations:
* [Fleets](https://dstack.ai/docs/concepts/fleets) — Provision and manage clusters across clouds, Kubernetes, and on-prem
* [Dev environments](https://dstack.ai/docs/concepts/dev-environments) — Launch dev environments to be accessed by agents or from your IDE
* [Tasks](https://dstack.ai/docs/concepts/tasks) — Run training, batch or other jobs across a single node or clusters
* [Services](https://dstack.ai/docs/concepts/services) — Deploy model inference as secure and scalable endpoints
* [Presets](https://dstack.ai/docs/concepts/presets) — Agent-driven inference optimization
* [Volumes](https://dstack.ai/docs/concepts/volumes) — Managing instance and network volumes for persisting data
Configuration can be defined as YAML files within your repo.
### Apply configurations
Apply the configuration via the `dstack apply` CLI command, a programmatic API, or through [AI agent skills](#install-ai-agent-skills).
`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.
## Useful links
For additional information, see the following links:
* [Docs](https://dstack.ai/docs)
* [Examples](https://dstack.ai/examples)
* [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)