Individual skill
dstack-presets
Create and manage dstack presets: a toolkit that streamlines model inference optimization with agents, and a portable preset format. Use together with the dstack skill, and only when the user explicitly asks to create a preset or manage existing presets, not for deploying or serving a model.
skills/dstack-presets/SKILL.md
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- machine-learning Repository topic
- python Repository topic
- gpu Repository topic
- llms Repository topic
- cloud Repository topic
- orchestration Repository topic
- fine-tuning Repository topic
- training Repository topic
- kubernetes Repository topic
- k8s Repository topic
- engineering Derived category
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# dstack Presets
Use `/dstack` for CLI commands, YAML fields, apply behavior, fleets, and other
dstack syntax. This skill covers creating and managing presets.
## Overview
Presets offer two things: a toolkit that streamlines model inference optimization using agents, and a portable format that deploys the final preset to any cloud, Kubernetes cluster, or bare-metal fleet. A preset holds the serving configuration that produced the result, the benchmark it reached, and the exact hardware it was verified on.
Presets are used for three kinds of work: finding an optimized baseline, optimizing through patching source code, and supporting new hardware.
**When to use this skill:**
- The user explicitly asks to create a preset, or to optimize model inference via a preset
- Managing already created presets: watching sessions, listing, exporting, and deleting them via `dstack preset` commands