Metadata-Version: 2.1
Name: chip-wallet-dstack
Version: 1.4
Summary: dstack is an open-source orchestration engine for running AI workloads on any cloud or on-premises.
Home-page: https://dstack.ai
Author: Andrey Cheptsov
Author-email: andrey@dstack.ai
Project-URL: Source, https://github.com/dstackai/dstack
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
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Description-Content-Type: text/markdown
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<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 AI workloads in any cloud
</h3>

<p align="center">
<a href="https://dstack.ai/docs" target="_blank"><b>Docs</b></a> •
<a href="https://github.com/dstackai/dstack/tree/master/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 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.

## Latest news ✨

- [2024/05] [dstack 0.18.2: On-prem clusters, private subnets, and more](https://github.com/dstackai/dstack/releases/tag/0.18.2) (Release)
- [2024/04] [dstack 0.18.1: On-prem servers, and new example](https://github.com/dstackai/dstack/releases/tag/0.18.1) (Release)
- [2024/04] [dstack 0.18.0: RunPod, mult-node tasks, and more](https://github.com/dstackai/dstack/releases/tag/0.18.0) (Release)
- [2024/03] [dstack 0.17.0: Auto-scaling, and other improvements](https://github.com/dstackai/dstack/releases/tag/0.17.0) (Release)
- [2024/02] [dstack 0.16.0: Pools](https://github.com/dstackai/dstack/releases/tag/0.16.0) (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`:

```shell
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`.

See the [server/config.yml reference](https://dstack.ai/docs/reference/server/config.yml.md#examples)
for details on how to configure backends for all supported cloud providers.

### Start the server

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

<div class="termy">

```shell
$ 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/
```

</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

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.

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

### Tasks

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.

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

### Services

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.

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

### Pools

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.

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

## Examples

Here are some featured examples:

- [Llama 3](examples/llms/llama3)
- [Alignment Handbook](examples/fine-tuning/alignment-handbook)
- [vLLM](examples/deployment/vllm)
- [Axolotl](examples/fine-tuning/axolotl)
- [TGI](examples/deployment/tgi)
- [Ollama](examples/deployment/ollama)
- [LoRaX](examples/deployment/lorax)

Browse [examples](examples) for more examples.

## More information

For additional information and examples, see the following links:

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

## Licence

[Mozilla Public License 2.0](LICENSE.md)
