dstack is an open-source container orchestration engine designed for AI workloads across any cloud or data center.

The supported cloud providers include AWS, GCP, Azure, Lambda, TensorDock, Vast.ai, CUDO, and RunPod. You can also use dstack ro run workloads on on-prem servers.

• [2024/04] dstack 0.18.0: RunPod integration, multi-node tasks, and more (Release)
• [2024/03] dstack 0.17.0: Service auto-scaling, and other improvements (Release)
• [2024/02] dstack 0.16.0: Pools (Release)
• [2024/02] dstack 0.15.1: Kubernetes integration (Release)
• [2024/01] dstack 0.15.0: Resources, authorization, and more (Release)

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

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

pip install "dstack[all]" -U

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.

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.

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.

You specify the required environment and resources, then run it. dstack provisions the dev environment in the cloud and enables access via your desktop IDE.

Tasks allow for convenient scheduling of any kind of batch jobs, such as training, fine-tuning, or data processing, as well as running web applications.

Specify the environment and resources, then run it. dstack executes the task in the cloud, enabling port forwarding to your local machine for convenient access.

Services make it very easy to deploy any kind of model or web application as public endpoints.

Use any serving frameworks and specify required resources. dstack deploys it in the configured backend, handles authorization, and provides an OpenAI-compatible interface if needed.

Pools simplify managing the lifecycle of cloud instances and enable their efficient reuse across runs.

You can have instances provisioned in the cloud automatically, or add them manually, configuring the required resources, idle duration, etc.

Here are some featured examples:

• Llama 3
• Alignment Handbook
• vLLM
• Axolotl
• TGI
• Ollama

Browse examples for more examples.

For additional information and examples, see the following links:

• Docs
• Discord

Mozilla Public License 2.0