Set up the environment
SkillDev toolsInstall, repair, or verify the ModernTSF Python environment and PyTorch backend. Use for first-time setup, dependency failures, CUDA detection problems, or hardware changes.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Set up the environment skill
What this skill tells your AI
The instructions your AI receives, as published by diaugeia/moderntsf in .agents/skills/setup-environment/SKILL.md and read by ahel’s review.
Run bash scripts/detect_hardware.sh, then:
UV_TORCH_BACKEND=auto uv sync --python 3.12
uv run tsf env audit --json
uv run tsf --help
uv run tsf repo audit
Use an explicit backend only when auto-detection is wrong or reproducibility requires it. Do not change dependency pins to mask a driver mismatch. Report Python and torch versions, selected backend, accelerator visibility, and lockfile changes.
For a specific experiment, use tsf env audit --config <run.toml> --json to
check execution readiness. Audit reports facts and failures; it never installs or
changes the environment. Optional trackers are installed only when requested.
Signals
- GitHub stars
- 65
- Forks
- 8
- Last commit
- Sep 2026
Advanced
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setup-environment- Source
- github.com/diaugeia/moderntsf