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Upload all .pth checkpoints to a Hugging Face Model repo.
The Model repo holds the weights so the Space stays under its 1 GB cap.
The Space's app.py downloads from this repo via hf_hub_download.
Usage:
huggingface-cli login # one-time, paste your write token
python tools/upload_weights.py phiniqs/seg-models-weights
Folder layout in the Model repo:
baseline/{model}_{share}_{kind}.pth from clean_data_scaling_study
grid/{cfg_id}_best.pth from finetune_grid_search
"""
import argparse
import sys
from pathlib import Path
ROOT = Path(__file__).resolve().parents[1]
BASELINE_DIR = ROOT / "experiments" / "clean_data_scaling_study" / "checkpoints"
GRID_DIR = ROOT / "experiments" / "finetune_grid_search" / "checkpoints"
def main():
p = argparse.ArgumentParser()
p.add_argument("repo_id", help="HF model repo id, e.g. phiniqs/seg-models-weights")
p.add_argument("--private", action="store_true", help="create as private repo")
p.add_argument("--dry-run", action="store_true", help="show plan, don't upload")
args = p.parse_args()
try:
from huggingface_hub import HfApi, create_repo
except ImportError:
print("ERROR: install huggingface_hub first:")
print(" pip install --user huggingface_hub")
sys.exit(1)
baseline_files = sorted(BASELINE_DIR.glob("*.pth"))
grid_files = sorted(GRID_DIR.glob("*.pth"))
total_bytes = sum(f.stat().st_size for f in baseline_files + grid_files)
print(f"baseline checkpoints : {len(baseline_files):>3} "
f"({sum(f.stat().st_size for f in baseline_files) / 1e6:.1f} MB)")
print(f"grid checkpoints : {len(grid_files):>3} "
f"({sum(f.stat().st_size for f in grid_files) / 1e6:.1f} MB)")
print(f"total : {len(baseline_files) + len(grid_files)} files, "
f"{total_bytes / 1e9:.2f} GB")
print()
if args.dry_run:
print("[dry-run] would create Model repo:", args.repo_id)
print(f"[dry-run] would upload to baseline/ ({len(baseline_files)} files)")
for f in baseline_files[:3]:
print(f" {f.name}")
if len(baseline_files) > 3:
print(f" ... +{len(baseline_files) - 3} more")
print(f"[dry-run] would upload to grid/ ({len(grid_files)} files)")
for f in grid_files[:3]:
print(f" {f.name}")
if len(grid_files) > 3:
print(f" ... +{len(grid_files) - 3} more")
return
api = HfApi()
print(f"creating model repo: {args.repo_id} (private={args.private})")
create_repo(
repo_id=args.repo_id,
repo_type="model",
private=args.private,
exist_ok=True,
)
print(f"uploading baseline/ ({len(baseline_files)} files, "
f"{sum(f.stat().st_size for f in baseline_files) / 1e6:.1f} MB)…")
api.upload_folder(
folder_path=str(BASELINE_DIR),
path_in_repo="baseline",
repo_id=args.repo_id,
repo_type="model",
commit_message="Add baseline checkpoints (4 architectures × 3 shares)",
allow_patterns=["*.pth"],
)
print(f"uploading grid/ ({len(grid_files)} files, "
f"{sum(f.stat().st_size for f in grid_files) / 1e6:.1f} MB)…")
api.upload_folder(
folder_path=str(GRID_DIR),
path_in_repo="grid",
repo_id=args.repo_id,
repo_type="model",
commit_message="Add fine-tune grid checkpoints (54 configs)",
allow_patterns=["*.pth"],
)
print()
print(f"done. weights live at: https://huggingface.co/{args.repo_id}")
print(f"the Space's app.py reads from this repo via HF_WEIGHTS_REPO env var "
f"(default: phiniqs/seg-models-weights).")
if __name__ == "__main__":
main()
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