| PHASE = "stgcn" |
| """Kaggle 2xT4 kernel: ORIGINAL trainers (unmodified) via asanaai_train2's own wrappers, two GPUs in parallel, |
| each finished run uploaded to HF at once. Then the same held-out evaluations used all along.""" |
| import json, os, shutil, subprocess, sys, threading |
| from kaggle_secrets import UserSecretsClient |
| HF = UserSecretsClient().get_secret("HF_TOKEN") |
| from huggingface_hub import HfApi, snapshot_download |
| api = HfApi(token=HF) |
| assert api.whoami()["name"] == "Arko007", "wrong HF account" |
| ROOT = "/kaggle/working" |
| DS, MD = "Arko007/Yoga-1M", "Arko007/asanaai-conference-runs" |
| need = {"stgcn": ["code/**", "vol/stgcn/cueT2_*/**", "vol/eval/**", "vol/folds/**"], |
| "mlp": ["code/**", "vol/csv/cue2/vH/**", "vol/eval/**", "vol/folds/**", "vol/photos/public_corpus.npz"]}[PHASE] |
| snapshot_download(DS, repo_type="dataset", local_dir=ROOT, allow_patterns=need, token=HF) |
| snapshot_download(MD, repo_type="model", local_dir=f"{ROOT}/vol", allow_patterns=["runs/cueT_mlp_*/**"], token=HF) |
| os.makedirs("/opt/orig", exist_ok=True); os.makedirs("/models_src", exist_ok=True) |
| for f in os.listdir(f"{ROOT}/code/originals"): |
| shutil.copy(f"{ROOT}/code/originals/{f}", f"/opt/orig/{f}") |
| for f in os.listdir(f"{ROOT}/code/models_src"): |
| shutil.copy(f"{ROOT}/code/models_src/{f}", f"/models_src/{f}") |
| print("data ready:", subprocess.run(["du", "-sh", f"{ROOT}/vol"], capture_output=True, text=True).stdout.strip(), flush=True) |
|
|
| jobs = { |
| "stgcn": ([{"kind": "stgcn", "feats_tag": "cueT2_f0", "tag": "cueT2_stgcn_f0"}, {"kind": "stgcn", "feats_tag": "cueT2_f2", "tag": "cueT2_stgcn_f2"}], |
| [{"kind": "stgcn", "feats_tag": "cueT2_f1", "tag": "cueT2_stgcn_f1"}, {"kind": "stgcn", "feats_tag": "cueT2_all", "tag": "cueT2_stgcn_all"}]), |
| "mlp": ([{"kind": "mlp", "csv_rel": f"csv/cue2/vH/mlp_{n}.csv", "tag": f"cueH_mlp_{n}"} for n in ("fold0", "fold2", "fold4")], |
| [{"kind": "mlp", "csv_rel": f"csv/cue2/vH/mlp_{n}.csv", "tag": f"cueH_mlp_{n}"} for n in ("fold1", "fold3", "all")]), |
| }[PHASE] |
| env = {**os.environ, "HF_REAL": HF, "PYTHONUNBUFFERED": "1"} |
| procs = [] |
| for gpu, js in zip(("0", "1"), jobs): |
| p = subprocess.Popen([sys.executable, f"{ROOT}/code/kaggle_worker.py", "--gpu", gpu, "--jobs", json.dumps(js)], |
| env=env, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True) |
| procs.append(p) |
| def pump(p=p, g=gpu): |
| for line in p.stdout: |
| if line.startswith(("Epoch", "[gpu", "Using", "Unique", "exit", "saved", "Train size")) or "Error" in line or "Traceback" in line: |
| print(f"[w{g}] {line.rstrip()[:230]}", flush=True) |
| threading.Thread(target=pump, daemon=True).start() |
| for p in procs: |
| p.wait() |
| print("workers finished:", [p.returncode for p in procs], flush=True) |
|
|
| |
| os.environ["CUDA_VISIBLE_DEVICES"] = "0" |
| 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" |
| if PHASE == "stgcn": |
| T.eval_stgcn_oof(prefix="cueT2_stgcn_f", k=3, name="stgcn_target_heldout_video", test_prefix="cueT2_f") |
| else: |
| import numpy as np |
| T.eval_oof(prefix="cueH_mlp_fold", k=5, name="mlpH_commons_oof") |
| T.eval_public("cueH_mlp_all", name="mlpH_public") |
| cls = sorted({c.replace("imperfect_", "") for c in np.load(f"{T.VOL}/runs/cueH_mlp_all/mlp_3head_pose_encoder_v2.npy", allow_pickle=True)} - {"transition/unknown"}) |
| T.calibrate("cueH_mlp_all", "cueH_mlp_fold", ",".join(cls)) |
| ev = f"{T.VOL}/runs/_oof" |
| if os.path.isdir(ev): |
| api.upload_folder(folder_path=ev, path_in_repo=f"evals_{PHASE}", repo_id=MD, repo_type="model", commit_message=f"evals {PHASE}") |
| print("evals uploaded", flush=True) |
|
|
