Metadata-Version: 2.1
Name: dstack
Version: 0.0.2rc2
Summary: A Command Line Interface for training models with https://dstack.ai
Home-page: https://dstack.ai
Author: peterschmidt85
Author-email: team@dstack.ai
License: UNKNOWN
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: Other/Proprietary License
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
Requires-Dist: pyyaml
Requires-Dist: requests
Requires-Dist: gitpython
Requires-Dist: tabulate
Requires-Dist: boto3
Requires-Dist: colorama
Requires-Dist: tqdm
Requires-Dist: jsonschema
Requires-Dist: botocore
Requires-Dist: python-dateutil

![](https://github.com/dstackai/dstackai.github.io/raw/main/docs/images/splash.png?raw=true)

[Website](https://dstack.ai) | [Documentation](https://docs.dstack.ai)

Typical ML workflows include multiple steps, e.g. pre-processing data, training, fine-tuning, validation, etc.

With `dstack`, you can define ML workflows in a simple YAML format, and run them from CLI on either your own servers or
using spot instances in your own cloud.

### How dstack works

1. You define `.dstack/workflows.yaml` and `.dstack/variables.yaml` files inside your project (must be a Git repository)
   .

![](https://github.com/dstackai/dstackai.github.io/raw/main/docs/images/dstack_screenshots_workflows.png?raw=true)


2. You install the `dstack` CLI via `pip`.
3. You either install `dstack-runner` daemon on your servers, or use the `dstack aws configure` to authorize dstack to
   use your own cloud to create runners on-demand using spot instances.
4. You use the `dstack` CLI to run workflows, manage runs, jobs, logs, artifacts, runners.

![](https://github.com/dstackai/dstackai.github.io/raw/main/docs/images/dstack_screenshots_commands.png?raw=true)

5. When a workflow is submitted via the CLI (e.g. via `dstack run`) , the request is sent to the dstack server. The dstack
   server creates jobs for the submitted run, and assign them to available runners (either servers where you've
   installed dstack-runner or on-demand spot instances in your cloud that you allowed creating).
6. Runners execute assigned jobs, report their logs in real time, and upload artifacts once the job is finished.


For more information, please visit https://dstack.ai or https://docs.dstack.ai.

