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dstack is an open-source engine for running GPU workloads across any cloud provider.

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dstack

Orchestrate GPU workloads effortlessly on any cloud

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dstack is an open-source engine for running GPU workloads on any cloud. It works with a wide range of cloud GPU providers (AWS, GCP, Azure, Lambda, TensorDock, Vast.ai, etc.) as well as on-premises servers.

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Installation

Before using dstack through CLI or API, set up a dstack server.

Install the server

The easiest way to install the server, is via pip:

pip install "dstack[all]" -U

Configure backends

If you have default AWS, GCP, or Azure credentials on your machine, the dstack server will pick them up automatically.

Otherwise, you need to manually specify the cloud credentials in ~/.dstack/server/config.yml.

For further details on setting up the server, refer to installation.

Start the server

To start the server, use the dstack server command:

$ 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/

Note It's also possible to run the server via Docker.

CLI & API

Once the server is up, you can use either dstack's CLI or API to run workloads. Below is a live demo of how it works with the CLI.

Dev environments

Dev environments allow you to quickly provision a machine with a pre-configured environment, resources, IDE, code, etc.

Tasks

Tasks are perfect for scheduling all kinds of jobs (e.g., training, fine-tuning, processing data, batch inference, etc.) as well as running web applications.

Services

Services make it very easy to deploy any model or web application as a public endpoint.

Examples

Here are some featured examples:

Browse dstack.ai/examples for more examples.

More information

For additional information and examples, see the following links:

Licence

Mozilla Public License 2.0

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