vh
74dbfafdf1
On a train reaching succeeded, IN ADDITION to output/{name}.safetensors
(unchanged download source), COPY it into ComfyUI's loras search path at
/storetank/arbo/models/loras/trained/{train_id}/{name}.safetensors and
return published_lora_name (the ComfyUI-relative LoraLoader string) in the
terminal GET /train/{id} payload (arbo Phase 2 auto-registration, §4.1/§7).
- Copy not move; a publish failure NEVER fails the train (keeps succeeded,
omits published_lora_name, logs the reason to the tailable run log).
- INV-T7-safe: a copy to a fixed computed path, no new free-form args.
- train_id derived from the handoff layout (output_dir.parent.name).
- Provisions loras/trained/ (arbotrain 2775, group-write per the Phase-1
lesson; world-readable/traversable for ComfyUI) via the deploy playbook.
- ComfyUI verified to resolve nested loras subfolders (no flat fallback).
- Pure path helper unit-tested; 16 tests green.
114 lines
6.2 KiB
Python
114 lines
6.2 KiB
Python
"""Static configuration for the LoRA training worker.
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Everything load-bearing is an explicit module constant here (explicit-over-implicit): the
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paths the worker is allowed to touch, the fixed sd-scripts invocation surface, the tier
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table, and the SDXL-LoRA hyperparameters. Nothing about a training run is free-form — the
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worker only ever builds ONE command shape (INV-T7), and every knob that shapes it is
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visible in this file.
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The worker runs as `llmuser` on irv-ml1. It never imports torch / sd-scripts; it only
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*subprocesses* the fluxgym venv's `accelerate launch`. So this process stays tiny and the
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fluxgym venv stays pristine.
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"""
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from __future__ import annotations
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import os
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from pathlib import Path
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# ---- Network ---------------------------------------------------------------------------
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# 0.0.0.0 (not 127.0.0.1): arbo runs CONTAINERIZED on the traefik-net bridge and reaches
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# the host worker via host.docker.internal:host-gateway — host loopback is unreachable from
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# that bridge. irv-ml1 is WireGuard-only + ACL'd, so 0.0.0.0 exposure is bounded to the tunnel.
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HOST = os.environ.get("LORA_WORKER_HOST", "0.0.0.0")
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PORT = int(os.environ.get("LORA_WORKER_PORT", "8203"))
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# ---- Fluxgym / sd-scripts (the ONLY thing the worker executes) -------------------------
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FLUXGYM_ROOT = Path("/opt/fluxgym")
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FLUXGYM_VENV = FLUXGYM_ROOT / ".venv"
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ACCELERATE_BIN = FLUXGYM_VENV / "bin" / "accelerate"
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SD_SCRIPTS_DIR = FLUXGYM_ROOT / "sd-scripts"
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SDXL_TRAIN_SCRIPT = SD_SCRIPTS_DIR / "sdxl_train_network.py"
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# ---- Allowed path roots (INV-T7 path safety) -------------------------------------------
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# dataset_dir + output_dir MUST live under the shared handoff root (the group-shared,
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# setgid /worktank/arbo/train that both arbo-container-uid and llmuser can rw). base_model
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# must live under a known model root. Anything else → rejected before a process is spawned.
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# base_model must be an absolute HOST path (the worker runs on the host, not in arbo's
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# container — a container-internal path like /comfy/... or /basedir/... won't match).
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# /storetank/arbo/models — canonical model store (SDXL checkpoints; the 2026-06-13 move
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# to the 1.8TB /storetank volume; arbo mounts it → /basedir/models inside its container).
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# /opt/fluxgym/models — fluxgym base models (flux; Phase 4).
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# /worktank/arbo — handoff root (a base staged into the handoff, if ever).
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# (Dropped the pre-move /worktank/models [gone] + /worktank/comfyui [empty host path] roots.)
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HANDOFF_ROOT = Path(os.environ.get("LORA_WORKER_HANDOFF_ROOT", "/worktank/arbo/train"))
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ALLOWED_MODEL_ROOTS = tuple(
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Path(p)
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for p in os.environ.get(
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"LORA_WORKER_MODEL_ROOTS",
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"/storetank/arbo/models:/opt/fluxgym/models:/worktank/arbo",
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).split(":")
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if p
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)
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# ---- LoRA publish step (Phase 2 — auto-registration into ComfyUI Generate) -------------
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# On a train reaching `succeeded`, IN ADDITION to output/{name}.safetensors (the unchanged
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# download source), the worker COPIES the LoRA into ComfyUI's loras search path under
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# PUBLISH_SUBDIR/{train_id}/ and reports `published_lora_name` (the path RELATIVE to the
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# loras root — the exact string a ComfyUI LoraLoader.lora_name widget takes). ComfyUI
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# (verified 2026-07-07) resolves loras SUBfolders, so the nested scheme works. A publish
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# failure NEVER fails the train — it just omits published_lora_name (download still works).
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# Stays inside INV-T7 (a copy to a fixed computed path; no new free-form args).
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LORAS_PUBLISH_ROOT = Path(os.environ.get("LORA_WORKER_LORAS_ROOT", "/storetank/arbo/models/loras"))
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PUBLISH_SUBDIR = "trained" # loras/trained/{train_id}/{name}.safetensors
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# ---- Worker state + logs (survives a worker restart for boot reconciliation, §4.3) -----
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STATE_DIR = Path(os.environ.get("LORA_WORKER_STATE_DIR", "/opt/lora-training-worker/state"))
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LOG_DIR = Path(os.environ.get("LORA_WORKER_LOG_DIR", "/opt/lora-training-worker/logs"))
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JOBS_FILE = STATE_DIR / "jobs.json"
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MAX_RETAINED_JOBS = 50 # keep terminal jobs queryable for arbo's status proxy
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# ---- Tier table (§4.6) -----------------------------------------------------------------
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# tier -> (max_train_steps, network_dim, resolution). Quality (1024) is A6000-only; the
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# device-fit guard lives in invocation.build_command (quality on the 3090 → rejected).
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TIERS = {
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"fast": {"steps": 400, "dim": 16, "resolution": 768},
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"balanced": {"steps": 1500, "dim": 32, "resolution": 768},
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"quality": {"steps": 3000, "dim": 32, "resolution": 1024},
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}
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# ---- Device model (CUDA_DEVICE_ORDER=PCI_BUS_ID) ---------------------------------------
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# Under PCI_BUS_ID (which the recipe pins), index 0 = RTX 3090, index 1 = RTX A6000.
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# Quality (1024) will not fit the 3090's ~24GB LoRA envelope → allowed on the A6000 only.
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DEVICE_3090 = 0
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DEVICE_A6000 = 1
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QUALITY_ONLY_DEVICES = (DEVICE_A6000,)
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# ---- SDXL-LoRA hyperparameters (agent-discretion defaults; confirm vs Sindra) ----------
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# The contract (§4.6) pins the flag SET + tier->(steps,dim,res) but NOT lr/scheduler/batch.
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# These are standard, conservative SDXL-LoRA values, surfaced here so they're one-line
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# auditable + tunable. Flagged to comfy-dev to cross-check against the proven Sindra runs.
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SDXL_HPARAMS = {
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"learning_rate": "1e-4",
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"lr_scheduler": "cosine",
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"lr_warmup_steps": "0",
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"train_batch_size_lean": "1", # 3090
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"train_batch_size_full": "2", # A6000
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# alpha = dim * ratio. 0.5 (alpha=dim/2) matches the PROVEN Sindra v1/v2 runs (comfy-dev
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# cross-check 2026-07-06) — it's the scaling that produced the validated likeness. alpha=dim
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# (1.0) is a valid stronger default but wasn't what Sindra used; operator can override.
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"network_alpha_ratio": 0.5,
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"min_snr_gamma": "5",
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"noise_offset": "0.1",
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"save_precision": "bf16",
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"mixed_precision": "bf16",
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"max_data_loader_n_workers": "2",
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}
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# ---- TTS-liveness heuristic (GET /gpu-status → tts_on_3090) -----------------------------
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# arbo's scheduler steers a lean train off the 3090 when TTS is live there. We report the
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# raw per-device VRAM + a boolean derived from (a) a compute-app on the 3090 whose cmdline
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# matches a TTS marker, else (b) a used-MB floor fallback.
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TTS_CMDLINE_MARKERS = ("chatterbox", "omnivoice", "tts_server", "chatterbox_fast")
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TTS_3090_USED_MB_FLOOR = int(os.environ.get("LORA_WORKER_TTS_FLOOR_MB", "2000"))
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