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Running on Zero
Running on Zero
File size: 2,758 Bytes
bc275c2 | 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 73 74 75 76 77 78 79 80 81 82 83 84 85 | from __future__ import annotations
import os
from pathlib import Path
from typing import Dict, Iterable, List
APP_TMP = Path("/tmp/bila-space-demo")
def _writable_data_dir() -> Path:
data = Path("/data")
if data.exists() and os.access(data, os.W_OK):
return data / "bila-space-demo"
return APP_TMP
def configure_runtime_cache() -> Path:
base = _writable_data_dir()
hf_home = Path(os.environ.get("HF_HOME", str(base / "hf-home")))
torch_home = Path(os.environ.get("TORCH_HOME", str(base / "torch-home")))
gradio_tmp = Path(os.environ.get("GRADIO_TEMP_DIR", str(base / "gradio-tmp")))
os.environ.setdefault("HF_HOME", str(hf_home))
os.environ.setdefault("TORCH_HOME", str(torch_home))
os.environ.setdefault("GRADIO_TEMP_DIR", str(gradio_tmp))
for path in (hf_home, torch_home, gradio_tmp):
path.mkdir(parents=True, exist_ok=True)
return base
def _allow_patterns_for_model(model_cfg: Dict) -> List[str]:
patterns = []
for rel_path in model_cfg["weights"].values():
if rel_path.endswith((".pth", ".bin", ".safetensors", ".json")):
patterns.append(rel_path)
else:
patterns.append(rel_path.rstrip("/") + "/**")
metric_file = model_cfg.get("evidence", {}).get("metric_file")
if metric_file:
patterns.append(metric_file)
return patterns
def resolve_model_root(model_key: str, model_cfg: Dict) -> Path:
local_root = os.environ.get("BILA_MODEL_ROOT")
if local_root:
return Path(local_root).expanduser().resolve()
repo_id = os.environ.get("BILA_WEIGHTS_REPO")
if not repo_id:
raise RuntimeError(
"Set BILA_WEIGHTS_REPO to the Hugging Face model repo containing demo weights, "
"or set BILA_MODEL_ROOT to a local directory with the same layout."
)
from huggingface_hub import snapshot_download
cache_dir = Path(os.environ.get("BILA_MODEL_CACHE", str(_writable_data_dir() / "hf-cache")))
cache_dir.mkdir(parents=True, exist_ok=True)
return Path(
snapshot_download(
repo_id=repo_id,
repo_type=os.environ.get("BILA_WEIGHTS_REPO_TYPE", "model"),
cache_dir=str(cache_dir),
allow_patterns=_allow_patterns_for_model(model_cfg),
token=os.environ.get("HF_TOKEN"),
)
)
def require_paths(root: Path, rel_paths: Iterable[str]) -> Dict[str, Path]:
resolved = {}
missing = []
for rel_path in rel_paths:
path = root / rel_path
resolved[rel_path] = path
if not path.exists():
missing.append(str(path))
if missing:
raise FileNotFoundError("Missing required weight paths:\n" + "\n".join(missing))
return resolved
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