| """One GPU worker. Runs a list of ORIGINAL-trainer jobs through asanaai_train2's own wrappers, then uploads each |
| finished run to the private HF model repo IMMEDIATELY (a cancelled session must never again lose finished models).""" |
| import argparse, json, os, sys, time |
| ap = argparse.ArgumentParser() |
| ap.add_argument("--gpu", required=True) |
| ap.add_argument("--jobs", required=True) |
| a = ap.parse_args() |
| os.environ["CUDA_VISIBLE_DEVICES"] = a.gpu |
| |
| |
| os.environ["PIP_NO_INDEX"] = "1" |
| ROOT = "/kaggle/working" |
| sys.path.insert(0, f"{ROOT}/code/shim"); sys.path.insert(0, f"{ROOT}/code/modal") |
| import asanaai_train2 as T |
| T.VOL = f"{ROOT}/vol" |
| T.HOME = f"/home/w{a.gpu}" |
| |
| T.DATA = T.HOME |
| os.makedirs(T.DATA, exist_ok=True) |
| from huggingface_hub import HfApi |
| api = HfApi(token=os.environ["HF_REAL"]) |
| for job in json.loads(a.jobs): |
| t0 = time.time() |
| print(f"[gpu{a.gpu}] START {job}", flush=True) |
| if job["kind"] == "stgcn": |
| res = T.train_stgcn2(job["feats_tag"], job["tag"]) |
| else: |
| res = T.train_mlp2(job["csv_rel"], job["tag"]) |
| print(f"[gpu{a.gpu}] DONE {job['tag']} in {(time.time()-t0)/60:.1f} min -> {res}", flush=True) |
| d = f"{T.VOL}/runs/{job['tag']}" |
| if os.path.isdir(d) and os.listdir(d): |
| api.upload_folder(folder_path=d, path_in_repo=f"runs/{job['tag']}", repo_id="Arko007/asanaai-conference-runs", |
| repo_type="model", commit_message=f"{job['tag']}") |
| print(f"[gpu{a.gpu}] uploaded {job['tag']} to HF", flush=True) |
| else: |
| print(f"[gpu{a.gpu}] ABORT: nothing saved for {job['tag']} -> stopping this worker to protect GPU quota", flush=True) |
| sys.exit(3) |
|
|
