dstack is an open-source toolkit for running GPU workloads on any cloud.
Project description
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.
Latest news ✨
- [2024/01] dstack 0.15.1: Kubernetes integration preview (Release)
- [2024/01] dstack 0.15.0: Resources, authentication, and more (Release)
- [2024/01] dstack 0.14.0: OpenAI-compatible endpoints preview (Release)
- [2023/12] dstack 0.13.0: Disk size, CUDA 12.1, Mixtral, and more (Release)
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.
More information
For additional information and examples, see the following links:
Licence
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