| """Kaggle CPU kernel: Modal volume -> private HF dataset (and models -> private HF model repo). Needs 3 Kaggle secrets: |
| MODAL_TOKEN_ID, MODAL_TOKEN_SECRET, HF_TOKEN. Idempotent: a prefix already present on HF is skipped. Uploads per path.""" |
| import json, os, shutil, subprocess, sys, time |
| from kaggle_secrets import UserSecretsClient |
| sec = UserSecretsClient() |
| os.environ["MODAL_TOKEN_ID"] = sec.get_secret("MODAL_TOKEN_ID") |
| os.environ["MODAL_TOKEN_SECRET"] = sec.get_secret("MODAL_TOKEN_SECRET") |
| HF = sec.get_secret("HF_TOKEN") |
| subprocess.run([sys.executable, "-m", "pip", "install", "-q", "modal"], check=True) |
| from huggingface_hub import HfApi |
| api = HfApi(token=HF) |
| assert api.whoami()["name"] == "Arko007", "wrong HF account" |
| DS, MD = "Arko007/Yoga-1M", "Arko007/asanaai-conference-runs" |
| |
| PATHS = [("cue", DS, "dataset", "vol/cue"), ("transcripts", DS, "dataset", "vol/transcripts"), |
| ("labelsrc", DS, "dataset", "vol/labelsrc"), ("eval", DS, "dataset", "vol/eval"), ("folds", DS, "dataset", "vol/folds"), |
| ("csv/cue", DS, "dataset", "vol/csv/cue"), ("photos", DS, "dataset", "vol/photos"), |
| ("runs", MD, "model", "runs"), |
| ("csv/cue2", DS, "dataset", "vol/csv/cue2"), ("landmarks", DS, "dataset", "vol/landmarks"), |
| ("landmarks_new", DS, "dataset", "vol/landmarks_new"), |
| ("stgcn/cueT2_f0", DS, "dataset", "vol/stgcn/cueT2_f0"), ("stgcn/cueT2_f1", DS, "dataset", "vol/stgcn/cueT2_f1"), |
| ("stgcn/cueT2_f2", DS, "dataset", "vol/stgcn/cueT2_f2"), ("stgcn/cueT2_all", DS, "dataset", "vol/stgcn/cueT2_all")] |
| report = [] |
| for vp, repo, rt, dst in PATHS: |
| t0 = time.time() |
| try: |
| have = [x for x in api.list_repo_tree(repo, path_in_repo=dst, repo_type=rt)] if True else [] |
| except Exception: |
| have = [] |
| if have: |
| report.append({"path": vp, "status": "already_on_hf"}); print("skip (already on HF):", vp, flush=True); continue |
| tmp = f"/kaggle/working/stage/{vp.replace('/', '__')}" |
| shutil.rmtree(tmp, ignore_errors=True); os.makedirs(tmp) |
| r = subprocess.run(["modal", "volume", "get", "asanaai-data", vp, tmp, "--force"], capture_output=True, text=True) |
| if r.returncode != 0: |
| report.append({"path": vp, "status": "modal_get_failed", "err": (r.stderr or r.stdout)[-300:]}); print("FAILED get", vp, r.stderr[-200:], flush=True); continue |
| base = os.path.join(tmp, os.path.basename(vp)) |
| src = base if os.path.exists(base) else tmp |
| size = sum(os.path.getsize(os.path.join(d, f)) for d, _, fs in os.walk(src) for f in fs) if os.path.isdir(src) else os.path.getsize(src) |
| if os.path.isdir(src): |
| api.upload_folder(folder_path=src, path_in_repo=dst, repo_id=repo, repo_type=rt, commit_message=f"transfer {vp}") |
| else: |
| api.upload_file(path_or_fileobj=src, path_in_repo=dst, repo_id=repo, repo_type=rt, commit_message=f"transfer {vp}") |
| shutil.rmtree(tmp, ignore_errors=True) |
| report.append({"path": vp, "status": "ok", "mb": round(size / 1e6, 1), "secs": round(time.time() - t0)}) |
| print("OK", vp, f"{size/1e6:.0f} MB", f"{time.time()-t0:.0f}s", flush=True) |
| json.dump(report, open("/kaggle/working/transfer_report.json", "w"), indent=1) |
| api.upload_file(path_or_fileobj="/kaggle/working/transfer_report.json", path_in_repo="transfer_report.json", repo_id=DS, repo_type="dataset") |
| print(json.dumps(report, indent=1)) |
|
|
