Agen & Multi-Agen

dstack

dstackai/dstack

A vendor-agnostic control plane for provisioning and orchestrating training, inference, development, and agentic workloads across GPU clouds, Kubernetes, and on-premises infrastructure.

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Ringkasan

dstack provides a unified interface for managing compute across cloud backends, Kubernetes clusters, and bare-metal or on-premises servers. Repository-based YAML configurations describe fleets, development environments, tasks, services, and persistent volumes. Configurations can be applied through the CLI, a programmatic API, or supported AI agent skills. The repository metadata identifies Python as the primary language and shows that the project is active rather than archived.

Fitur utama

  • Vendor-agnostic orchestration across GPU clouds, Kubernetes, and on-premises clusters
  • Support for NVIDIA, AMD, Google TPU, and Tenstorrent accelerators
  • YAML configurations for fleets, development environments, tasks, services, and volumes
  • CLI, programmatic API, and AI agent skill workflows
  • Distributed jobs, model deployment, web applications, autoscaling, and authorization
  • Automated provisioning, job queuing, networking, volume handling, port forwarding, and failure handling
  • SSH fleets for on-premises servers without backend configuration

Persyaratan, instalasi, dan mulai cepat

Install and start the server with `uv tool install "dstack[all]" -U` followed by `dstack server`. If only the CLI is needed, install it with `uv tool install dstack -U`. Connect the CLI to a server with `dstack project add --name main --url http://127.0.0.1:3000 --token <user-token>`. Optional agent skills can be installed with `npx skills add dstackai/dstack`.

Penggunaan

Start the server, configure cloud or Kubernetes backends as needed, or create SSH fleets for on-premises machines. Add the server address, user token, and project name to the CLI. Define fleets, tasks, services, development environments, or volumes as YAML files in a repository, then submit them with `dstack apply`, the programmatic API, or installed agent skills. The README says skills can help Claude, Codex, and Cursor create and manage fleets, edit configuration files, and submit workloads.

Kompatibilitas model dan kasus penggunaan

The repository describes compatibility with open-source tools and frameworks but does not list specific model families. It supports inference and model deployment on NVIDIA, AMD, Google TPU, and Tenstorrent hardware. Claude, Codex, and Cursor are mentioned as agents that can use dstack skills; they are not identified as hosted inference models. MCP and RAG support are not stated in the repository metadata.

Catatan lisensi dan risiko

The repository metadata specifies the Mozilla Public License 2.0 (MPL-2.0). Review the repository license before redistribution or modification: https://api.github.com/licenses/mpl-2.0

Rilis dan pemeliharaan

The README lists releases including 0.20.17 for PD disaggregation and Kubernetes volumes, 0.20.16 for performance and an SSH proxy, 0.20.13 for exports and templates, 0.20.12 for Crusoe, 0.20.8 for skills, and 0.20.0 for a fleet-first UX and events. Full details are available from the repository releases linked in the README.

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