Metadata-Version: 2.1
Name: dstack
Version: 0.15.0
Summary: dstack is an open-source toolkit for running GPU workloads on any cloud.
Home-page: https://dstack.ai
Author: Andrey Cheptsov
Author-email: andrey@dstack.ai
License: UNKNOWN
Project-URL: Source, https://github.com/dstackai/dstack
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: Mozilla Public License 2.0 (MPL 2.0)
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Provides-Extra: all
Provides-Extra: aws
Provides-Extra: azure
Provides-Extra: datacrunch
Provides-Extra: gcp
Provides-Extra: kubernetes
Provides-Extra: lambda
License-File: LICENSE.md

<div align="center">
<h1 align="center">
  <a target="_blank" href="https://dstack.ai">
    <img alt="dstack" src="https://raw.githubusercontent.com/dstackai/dstack/master/docs/assets/images/dstack-logo.svg" width="350px"/>
  </a>
</h1>

<h3 align="center">
Orchestrate GPU workloads effortlessly on any cloud
</h3>

<p align="center">
<a href="https://dstack.ai/docs" target="_blank"><b>Docs</b></a> •
<a href="https://dstack.ai/examples" target="_blank"><b>Examples</b></a> •
<a href="https://discord.gg/u8SmfwPpMd" target="_blank"><b>Discord</b></a>
</p>

[![Last commit](https://img.shields.io/github/last-commit/dstackai/dstack?style=flat-square)](https://github.com/dstackai/dstack/commits/)
[![PyPI - License](https://img.shields.io/pypi/l/dstack?style=flat-square&color=blue)](https://github.com/dstackai/dstack/blob/master/LICENSE.md)
</div>

`dstack` is an open-source toolkit and orchestration engine for running GPU workloads. 
It's designed for development, training, and deployment of gen AI models on any cloud.

Supported providers: AWS, GCP, Azure, Lambda, TensorDock, Vast.ai, and DataCrunch.

## Latest news ✨

- [2024/01] [dstack 0.14.0: OpenAI-compatible endpoints preview](https://dstack.ai/blog/2024/01/19/openai-endpoints-preview/) (Release)
- [2023/12] [dstack 0.13.0: Disk size, CUDA 12.1, Mixtral, and more](https://dstack.ai/blog/2023/12/22/disk-size-cuda-12-1-mixtral-and-more/) (Release)
- [2023/11] [dstack 0.12.3: Vast.ai integration](https://dstack.ai/blog/2023/11/21/vastai/) (Release)
- [2023/10] [dstack 0.12.2: TensorDock integration](https://dstack.ai/blog/2023/10/31/tensordock/) (Release)
- [2023/09] [RAG with Llama Index and Weaviate](https://dstack.ai/examples/llama-index/) (Example)
- [2023/08] [Fine-tuning with QLoRA](https://dstack.ai/examples/qlora/) (Example)

## 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`:

```shell
pip install "dstack[all]" -U
```

### Configure credentials

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](https://dstack.ai/docs/installation/).

### Start the server

To start the server, use the `dstack server` command:

<div class="termy">

```shell
$ dstack server

Applying configuration from ~/.dstack/server/config.yml...

The server is running at http://127.0.0.1:3000/
The admin token is "bbae0f28-d3dd-4820-bf61-8f4bb40815da"
```

</div>

> **Note**
> It's also possible to run the server via [Docker](https://hub.docker.com/r/dstackai/dstack).

### 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.

<img src="https://raw.githubusercontent.com/dstackai/static-assets/main/static-assets/images/dstack-dev-environment.gif" width="650"/>

### Tasks

Tasks make it very easy to run any scripts, be it for training, data processing, or web apps. They allow you to pre-configure the environment, resources, code, etc.

<img src="https://raw.githubusercontent.com/dstackai/static-assets/main/static-assets/images/dstack-task.gif" width="650"/>

### Services

Services make it easy to deploy models and apps cost-effectively as public endpoints, allowing you to use any frameworks.

<img src="https://raw.githubusercontent.com/dstackai/static-assets/main/static-assets/images/dstack-service.gif" width="650"/>

## More information

For additional information and examples, see the following links:

- [Docs](https://dstack.ai/docs)
- [Examples](https://dstack.ai/examples)
- [Discord](https://discord.gg/u8SmfwPpMd)

## Licence

[Mozilla Public License 2.0](LICENSE.md)


