dstack 0.20.11
dstack is an open-source orchestration engine for running AI workloads on any cloud or on-premises.
Requires Python: >=3.9

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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`, `Intel Gaudi`, and `Tenstorrent` accelerators out of the box.

## Latest news ✨
- [2025/12] [dstack 0.20.0: Fleet-first UX, Events, and more](https://github.com/dstackai/dstack/releases/tag/0.20.0)
- [2025/11] [dstack 0.19.38: Routers, SGLang Model Gateway integration](https://github.com/dstackai/dstack/releases/tag/0.19.38)
- [2025/10] [dstack 0.19.31: Kubernetes, GCP A4 spot](https://github.com/dstackai/dstack/releases/tag/0.19.31)
- [2025/08] [dstack 0.19.26: Repos](https://github.com/dstackai/dstack/releases/tag/0.19.26)
- [2025/08] [dstack 0.19.22: Service probes, GPU health-checks, Tenstorrent Galaxy](https://github.com/dstackai/dstack/releases/tag/0.19.22)
- [2025/07] [dstack 0.19.21: Scheduled tasks](https://github.com/dstackai/dstack/releases/tag/0.19.21)
- [2025/07] [dstack 0.19.17: Secrets, Files, Rolling deployment](https://github.com/dstackai/dstack/releases/tag/0.19.17)

## How does it work?

<img src="https://dstack.ai/static-assets/static-assets/images/dstack-architecture-diagram-v11.svg" width="750" />

### 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).

#### Set up the server

##### Configure backends

To orchestrate compute across GPU clouds or Kubernetes clusters, you need to configure backends.

Backends can be set up in `~/.dstack/server/config.yml` or through the [project settings page](https://dstack.ai/docs/concepts/projects#backends) in the UI.

For more details, see [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.

##### Start the server

You can install the server on Linux, macOS, and Windows (via WSL 2). It requires Git and
OpenSSH.

##### uv

```shell
$ uv tool install "dstack[all]" -U
```

##### pip

```shell
$ pip install "dstack[all]" -U
```

Once it's installed, go ahead and start the server.

```shell
$ 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.


<details><summary>Set up the CLI</summary>

#### Set up the CLI

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.

##### uv

```shell
$ uv tool install dstack -U
```

##### pip

```shell
$ pip 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>

### Define configurations

`dstack` supports the following configurations:
   
* [Fleets](https://dstack.ai/docs/concepts/fleets) &mdash; for managing cloud and on-prem clusters
* [Dev environments](https://dstack.ai/docs/concepts/dev-environments) &mdash; for interactive development using a desktop IDE
* [Tasks](https://dstack.ai/docs/concepts/tasks) &mdash; for scheduling jobs (incl. distributed jobs) or running web apps
* [Services](https://dstack.ai/docs/concepts/services) &mdash; for deployment of models and web apps (with auto-scaling and authorization)
* [Volumes](https://dstack.ai/docs/concepts/volumes) &mdash; for managing persisted volumes

Configuration can be defined as YAML files within your repo.

### 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 &mdash; 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)