File size: 2,881 Bytes
b177b83 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | """Kaggle notebook body: distil the flow head on the seven mined voices.
Order matters. A smoke pass (5 texts, 5 optimizer steps) validates the
trajectory and training code paths before spending an hour generating 840
trajectories; the earlier failures in this project each cost a full session.
smoke traj x5 on one GPU, train x5 steps ~4 min
traj 7 voices x 120 texts, sharded over 2 GPUs ~70 min
train 4-step student on all shards ~25 min
export ONNX fp32 + int4 ~5 min
eval 40 sentences x 2 voices x {teacher, stu} ~25 min
"""
import os
import subprocess
import sys
import time
from notebook_eval import BUNDLE, MODEL, ROOT, WEIGHTS, pull, stage_deps, stage_repo
BUNDLE_REPO = "idefinity/webfm-eval"
SHARDS = [["a01", "a03", "a04"], ["a05", "a06", "a07", "a08"]]
EVAL_VOICES = "a08,a01"
def run(tag: str, env_extra: dict, gpu: int = 0) -> int:
env = dict(os.environ, WORK_ROOT=str(ROOT), **env_extra)
if gpu is not None:
env["CUDA_VISIBLE_DEVICES"] = str(gpu)
log = open(ROOT / f"train_{tag}.log", "w")
p = subprocess.Popen([sys.executable, str(BUNDLE / "kaggle/train_voices.py")],
env=env, stdout=log, stderr=subprocess.STDOUT, cwd=str(ROOT))
return p.wait()
def main() -> None:
stage_repo()
stage_deps()
pull(WEIGHTS, MODEL)
pull(BUNDLE_REPO, BUNDLE, repo_type="dataset")
print("== smoke: traj x5, train x5 ==", flush=True)
rc = run("smoke_traj", {"STAGE": "traj", "VOICES": "a08", "SMOKE": "1", "N_TEXTS": "5"})
print("smoke traj rc", rc)
rc = run("smoke_train", {"STAGE": "train", "TRAIN_STEPS": "5", "BATCH": "4"})
print("smoke train rc", rc)
if rc != 0:
print((ROOT / "train_smoke_train.log").read_text()[-3000:])
raise SystemExit("training path failed in smoke")
print("== full trajectories ==", flush=True)
procs = []
for gpu, shard in enumerate(SHARDS):
env = dict(os.environ, WORK_ROOT=str(ROOT), STAGE="traj",
VOICES=",".join(shard), CUDA_VISIBLE_DEVICES=str(gpu))
log = open(ROOT / f"traj_gpu{gpu}.log", "w")
procs.append(subprocess.Popen([sys.executable, str(BUNDLE / "kaggle/train_voices.py")],
env=env, stdout=log, stderr=subprocess.STDOUT, cwd=str(ROOT)))
while any(p.poll() is None for p in procs):
time.sleep(60)
n = len(list((ROOT / "train/traj").glob("*/*.pt")))
print(f"[{time.strftime('%H:%M')}] trajectories={n}", flush=True)
print("traj exits:", [p.returncode for p in procs])
print("== train + export + eval ==", flush=True)
rc = run("final", {"STAGE": "all", "TRAIN_STEPS": "1500", "BATCH": "16",
"EVAL_VOICES": EVAL_VOICES}, gpu=0)
print("final rc", rc)
if __name__ == "__main__":
main()
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