GraphCanon updated today · GitHub synced today
Decision brief
Vendor-agnostic AI workload orchestration tool supports GPU providers like NVIDIA and AMD across cloud, Kubernetes, and bare metal.
Good fit when
- If your project requires support for multiple hardware vendors such as NVIDIA, AMD, TPU, or Tenstorrent
- For managing both training and inference workloads in a unified manner
Avoid when
- When sticking to single-vendor solutions where tightly integrated proprietary tools are preferred
- If the project strictly avoids open-source components with Mozilla Public License (MPL-2.0)
Observed Jul 15, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Very active (0d since push)
- As of today
- Provenance
- Not a fork · Organization account
- As of today
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install dstackSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Provides tools for managing training, inference, and agentic tasks on various hardware vendors like NVIDIA, AMD, TPU, and Tenstorrent in cloud, Kubernetes, and bare metal environments.
Capability facts
- CLI
- CLI entrypoint
Source: pyproject.toml:[project.scripts] · Aug 24, 2026
- Languages
- python
Source: github.language+pyproject.toml · Aug 24, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 24, 2026)
$ npx skills add dstackai/dstackSource link
Source: README excerpt (regex_v1, Aug 24, 2026)
AI agents like Claude, Codex, and Cursor can now create and manage fleets and submit workloads on your behalf.Source link
Tags
README
Install the CLI
If the CLI is not installed with the server
Once the server is up, you can access it via the dstack CLI.
The CLI can be installed on Linux, macOS, and Windows. It requires Git and OpenSSH.
$ uv tool install dstack -U
To point the CLI to the dstack server, configure it
with the server address, user token, and project name:
$ dstack project add \
--name main \
--url http://127.0.0.1:3000 \
--token bbae0f28-d3dd-4820-bf61-8f4bb40815da
Configuration is updated at ~/.dstack/config.yml
Install agent skills
Install dstack skills to help AI agents use the CLI and edit configuration files.
$ npx skills add dstackai/dstack
AI agents like Claude, Codex, and Cursor can now create and manage fleets and submit workloads on your behalf.
License
Mozilla Public License 2.0
For agents
This page has a .md twin and JSON over the API.