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dstack is an open-source orchestration engine for running AI workloads on any cloud or on-premises.
dstack
is an open-source alternative to Kubernetes and Slurm, designed to simplify GPU allocation and AI workload
orchestration for ML teams across top clouds and on-prem clusters.
dstack
supports NVIDIA
, AMD
, Google TPU
, Intel Gaudi
, and Tenstorrent
accelerators out of the box.
Before using
dstack
through CLI or API, set up adstack
server. If you already have a runningdstack
server, you only need to set up the CLI.
To use dstack
with cloud providers, configure backends
via the ~/.dstack/server/config.yml
file.
For more details on how to configure backends, check Backends.
For using
dstack
with on-prem servers, create SSH fleets once the server is up.
You can install the server on Linux, macOS, and Windows (via WSL 2). It requires Git and OpenSSH.
$ uv tool install "dstack[all]" -U
$ pip install "dstack[all]" -U
Once it's installed, go ahead and start the server.
$ 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
$ pip 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
dstack
supports the following configurations:
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, see the following links:
You're very welcome to contribute to dstack
.
Learn more about how to contribute to the project at CONTRIBUTING.md.
FAQs
dstack is an open-source orchestration engine for running AI workloads on any cloud or on-premises.
We found that dstack demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
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