ai-toolkit/manager/README.md

AI Toolkit Manager

Self-contained install/update manager for this checkout of AI Toolkit. Runs with any Python >= 3.8 and no dependencies, so it works before the training environment exists.

python3 -m manager install     # first-time setup: venv + torch + requirements
python3 -m manager check       # is an update available / are deps out of sync?
python3 -m manager update      # git pull, then sync deps + run migrations
python3 -m manager launch      # start the web UI (http://localhost:8675)
python3 -m manager doctor      # diagnose problems

Design

• The install logic lives in the repo it installs. Every commit knows how to install itself; external frontends (the desktop launcher, install.sh, install.ps1) just shell out to this CLI and stay dumb. Machine-readable output via check --json / detect --json.
• Hardware → spec mapping is in spec.py. One universal torch pin (2.12.0 / torchvision 0.27.0 / torchaudio 2.11.0) on every platform: cu130 wheels when the driver supports CUDA 13 (cu126 fallback for older drivers, refused outright on Blackwell GPUs which need cu130), same stack + Python 3.11 + dgx_requirements.txt on DGX/Grace, PyPI wheels on Mac, rocm7.1 (experimental) for AMD, --cpu to force a CPU install. Torch pins there must be updated together with the README install instructions, run_mac.zsh, and dgx_instructions.md.
• Accelerators everywhere wheels exist, via per-spec extra_packages (installed after requirements with --upgrade so they override pins) and optional_packages (installed one-by-one, warn-only on failure): torchcodec==0.15.0 on all platforms; flash-attn 2.8.3 prebuilt wheels (mjun0812) on Linux x86_64/aarch64 + Windows; NATTEN 0.21.7 wheels (whl.natten.org) on Linux both arches; triton bundled with torch on Linux and triton-windows 3.7.x on Windows. No flash-attn/NATTEN/triton on Mac, no NATTEN on Windows (no wheels exist).
• Nothing global is ever installed. FFmpeg (shared builds — the libs torchcodec dlopens) goes to .ffmpeg/ (ffmpeg.py), Node (when the system lacks >= 20) to .node/ (nodejs.py), the uv binary (when absent) to .uv/ (uvbin.py) with uv-managed Pythons kept in .uv/python/ via UV_PYTHON_INSTALL_DIR, and on Windows without git, portable MinGit to .mingit/ (gitwin.py) — all inside the repo and gitignored. The first clone on a git-less Windows box is handled by the bootstrap layer (install.ps1 / desktop launcher), which downloads MinGit itself and moves it into the checkout afterwards. manager launch puts them on PATH (and LD_LIBRARY_PATH on Linux) for the whole UI/training process tree, and a generated sitecustomize.py in the venv exposes ffmpeg to any direct use of the venv python (plus os.add_dll_directory on Windows).
• Hostile-environment hardening (learned from the community Windows installer): every python/pip subprocess runs with PYTHONPATH/PYTHONHOME/ CONDA/PYENV/PIP_* scrubbed from the env; git runs with GIT_LFS_SKIP_SMUDGE=1; git-pinned requirements (diffusers) are force-reinstalled when requirements change since pip skips unchanged version numbers; launch polls the UI port and opens the browser when ready (--no-browser to disable, auto-skipped on headless boxes).
• uv is used when present (fast installs, auto-downloads the right Python); plain venv + pip otherwise. The venv is created at .venv/ (an existing venv/ is also respected, matching ui/cron/pythonPath.ts).
• State (requirements hash, applied migrations) lives inside the venv (aitk_manager_state.json) — deleting the venv resets everything.
• Update flow: update pulls fast-forward only, then re-execs python -m manager sync so the freshly pulled manager code — not the stale in-memory copy — performs its own dependency sync and migrations. Local work is never overwritten: a dirty tree aborts the update by default (untracked files don't count), --auto (used by the run_* scripts) warns and skips the pull instead so launching still works, and there is no reset/clean anywhere — even --force relies on git itself refusing to clobber modified files.
• Migrations (migrations.py): one-time post-update steps, each applied at most once per environment.