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dstack
Vendor-agnostic orchestration for training, inference and agentic workloads across NVIDIA, AMD, TPU, and Tenstorrent on clouds, Kubernetes, and bare metal.
2.2kstars
232forks
Python
agent-skillsagentic-orchestrationamdcloudcontainersdockerfine-tuninggpuinferencek8skubernetesllms
Quality signals
StaleNo testsNo CIThin docsNo licensePopular
AI summary
Auto-generated overview of what this skill does and when to reach for it.
Unified orchestration tool for running GPU workloads across clouds, Kubernetes, and bare metal with any hardware or framework.
Use this when
- Need to run ML training across multiple cloud providers
- Want to deploy inference services with auto-scaling
- Running agentic workloads on heterogeneous GPU hardware
- Managing on-prem GPU clusters without vendor lock-in
- Provisioning dev environments with GPU acceleration
Don't use when
- Running non-GPU workloads
- Need a simple local-only scheduler without networking
- Prefer cloud-specific managed services
Capabilities
- Supports NVIDIA, AMD, TPU, and Tenstorrent accelerators
- Orchestrates across clouds, Kubernetes, and bare metal
- Auto-scales GPU fleets based on workload demand
- Manages persisted volumes for training data
- Handles port-forwarding and networking automatically
- Provides SSH access to provisioned GPU instances
InstallSKILLInstall as a Claude Code skill
Universal installANY TOOL
$ npx skillcity@latest dstackai/dstackAuto-detects the skill or MCP server in this repo and installs it for Claude Code, Codex, Cursor, Cline, OpenCode, Gemini CLI, Anti-Gravity, OpenClaw and Hermes.
Requires Node.js 18+ (ships with npx).
Or install manually
# Install into ~/.claude/skills/ mkdir -p ~/.claude/skills/dstack curl -fsSL https://raw.githubusercontent.com/dstackai/dstack/main/SKILL.md -o ~/.claude/skills/dstack/SKILL.md
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dstack — sandboxAI sandbox
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