A unified orchestration layer for heterogeneous AI compute. It standardizes how to manage compute and run training and inference on GPU clouds, Kubernetes, VMs, or bare-metal clusters.
See the code
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.
dstack supports NVIDIA, AMD, Google TPU, and Tenstorrent accelerators out of the box.
Before using
dstackthrough CLI or API, set up adstackserver. If you already have a runningdstackserver, you only need to install the CLI.
To orchestrate compute across GPU clouds or Kubernetes clusters, you need to configure backends.
When using
dstackwith on-prem servers, backend configuration isn’t required. Simply create 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.
$ 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 guide.
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 tool install dstack -U
To point the CLI to the dstack server, configure it
with the server address, user token, and project name:
$ 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
Install dstack skills to help AI agents use the CLI and edit configuration files.
$ npx skills add dstackai/dstack
AI agents like Claude, Codex, and Cursor can now create and manage fleets and submit workloads on your behalf.
dstack supports the following configurations:
Configuration can be defined as YAML files within your repo.
Apply the configuration via the dstack apply CLI command, a programmatic API, or through 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.
For additional information, see the following links:
You're very welcome to contribute to dstack.
Learn more about how to contribute to the project at CONTRIBUTING.md.
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119 followers · starred Mar 2023
3,910 followers · starred Jul 2024
7,300 followers · starred Aug 2023
1,833 followers · starred May 2022
A unified orchestration layer for heterogeneous AI compute. It standardizes how to manage compute and run training and inference on GPU clouds, Kubernetes, VMs, or bare-metal clusters.
See the code
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.
dstack supports NVIDIA, AMD, Google TPU, and Tenstorrent accelerators out of the box.
Before using
dstackthrough CLI or API, set up adstackserver. If you already have a runningdstackserver, you only need to install the CLI.
To orchestrate compute across GPU clouds or Kubernetes clusters, you need to configure backends.
When using
dstackwith on-prem servers, backend configuration isn’t required. Simply create 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.
$ 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 guide.
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 tool install dstack -U
To point the CLI to the dstack server, configure it
with the server address, user token, and project name:
$ 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
Install dstack skills to help AI agents use the CLI and edit configuration files.
$ npx skills add dstackai/dstack
AI agents like Claude, Codex, and Cursor can now create and manage fleets and submit workloads on your behalf.
dstack supports the following configurations:
Configuration can be defined as YAML files within your repo.
Apply the configuration via the dstack apply CLI command, a programmatic API, or through 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.
For additional information, see the following links:
You're very welcome to contribute to dstack.
Learn more about how to contribute to the project at CONTRIBUTING.md.
(top 24 of 49)
119 followers · starred Mar 2023
3,910 followers · starred Jul 2024
7,300 followers · starred Aug 2023
1,833 followers · starred May 2022