# dstackai/dstack

Vendor-agnostic orchestration for training, inference and agentic workloads across NVIDIA, AMD, TPU, and Tenstorrent on clouds, Kubernetes, and bare metal.

Repository: https://github.com/dstackai/dstack
Canonical: https://ross.abutalabs.com/products/dstack
Homepage: https://dstack.ai/docs
Language: Python
License: MPL-2.0
License Family: copyleft
Topics: machine-learning, python, gpu, llms, cloud, orchestration, fine-tuning, training, kubernetes, k8s, amd, docker, inference, nvidia, slurm, containers, agent-skills, agentic-orchestration
Last push: 2026-08-26T15:01:27+00:00

## Health v2 (maintenance only)
Score: 95/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 99, release rhythm 86, longevity 100
- inputs: {"age_days": 1702, "days_push": 7, "days_rel": 14, "gap_med": 6, "n_releases_24m": 106}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2221, forks 251 (observed 2026-08-28T04:06:27.447561+00:00)

## What it is
dstack is an open-source, vendor-agnostic control plane for GPU provisioning and orchestration that works across GPU clouds, Kubernetes, and on-prem clusters via SSH. It supports NVIDIA, AMD, Google TPU, and Tenstorrent accelerators and streamlines dev environments, training tasks, and inference services through declarative YAML configurations applied via CLI or API.

## Use cases
- provision GPU instances across multiple cloud providers from one control plane
- run distributed LLM fine-tuning jobs on H100 or MI300X clusters
- deploy model inference endpoints with auto-scaling and ingress
- spin up cloud dev environments accessible from VS Code or Cursor via SSH
- manage on-prem GPU servers as SSH fleets without Kubernetes
- orchestrate agentic workloads and batch jobs on spot GPU instances
- benchmark and optimize LLM inference configurations with agent-driven presets

## When to choose
- you need multi-cloud or hybrid GPU orchestration without vendor lock-in
- you want declarative YAML-based provisioning for training and inference workloads
- you mix cloud providers, Kubernetes, and bare-metal SSH servers
- you need support for diverse accelerators including AMD, TPU, and Tenstorrent

## When to avoid
- you only run CPU-only workloads with no GPU needs
- you need a fully managed service and prefer not to self-host a control plane server
- your orchestration needs are simple single-machine Docker deployments

## Facets
- artifact type: framework
- maturity: active
- function: container-orchestration, deployment, scheduling, llm-inference, machine-learning, gpu-computing, cli, api-framework
- domain: machine-learning, deep-learning, large-language-models, cloud-computing, infrastructure-as-code, gpu-computing
- platform: windows, python, cli, self-hosted, cloud
- tags: gpu-orchestration, gpu-cloud, training, fine-tuning, inference-endpoints, fleets, ssh-fleets, slurm, tpu, tenstorrent, amd, nvidia, control-plane, dev-environments, agent-skills, containers, devops, ai-agents, linux, macos, docker, kubernetes

## Member repositories
- dstackai/dstack (main) score 95

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:27.447561+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:45:47.245993+00:00, confidence not recorded.
  - readme: https://github.com/dstackai/dstack (fetched 2026-08-28T04:06:27.447561+00:00, sha 1b084921e2fe)
  - homepage: https://dstack.ai/docs (fetched 2026-08-29T10:26:11.597769+00:00, sha d86d4817acca)
  - site_page: https://dstack.ai/docs/installation (fetched 2026-08-29T10:26:11.607438+00:00, sha bbfb12f9d0cf)
  - site_page: https://dstack.ai/docs/quickstart (fetched 2026-08-29T10:26:11.610028+00:00, sha 553e804b02ac)
  - site_page: https://dstack.ai/docs/concepts/backends (fetched 2026-08-29T10:26:11.611979+00:00, sha 508cd16ff90d)
  - site_page: https://dstack.ai/docs/concepts/fleets (fetched 2026-08-29T10:26:11.614777+00:00, sha a0caf52cb735)
  - site_page: https://dstack.ai/docs/concepts/dev-environments (fetched 2026-08-29T10:26:11.616812+00:00, sha 0a611185ec22)
  - site_page: https://dstack.ai/docs/concepts/tasks (fetched 2026-08-29T10:26:11.618911+00:00, sha 54bd284e7ea6)
  - site_page: https://dstack.ai/docs/concepts/services (fetched 2026-08-29T10:26:11.621385+00:00, sha 14ae9fb6ff89)
  - site_page: https://dstack.ai/docs/concepts/presets (fetched 2026-08-29T10:26:11.624062+00:00, sha f6d7a28a2526)
- Data as of 2026-08-30T08:39:29.467469+00:00.