Upload kaggle/notebook_director_lora.py with huggingface_hub

kaggle/notebook_director_lora.py

@@ -5,8 +5,8 @@ of snapshot_download: this avoids importing huggingface_hub before the
 transformers 5.x upgrade (a stale already-imported hub breaks transformers'
 `from huggingface_hub.utils import httpx`).
 
-Output lands in /kaggle/working/director_lora_out: adapter/, model_fp16/,
-sample.txt, train_meta.json. The ONNX export is a separate step.
+Output lands in /kaggle/working/director_lora_out: adapter/, adapter.zip,
+model_fp16/, sample.txt, train_meta.json. The ONNX export is a separate step.
 """
 import os
 import shutil
@@ -18,58 +18,85 @@ from pathlib import Path
 
 ROOT = Path("/kaggle/working")
 REPO = os.environ.get("BUNDLE_REPO", "idefinity/webfm-eval")
-BASE = f"https://huggingface.co/datasets/{REPO}/resolve/main"
+BASES = [f"https://huggingface.co/datasets/{REPO}/resolve/main",
+         f"https://hf-mirror.com/datasets/{REPO}/resolve/main"]
 SFT = ROOT / "director_lora" / "sft.jsonl"          # new protocol, not the old radio set
 TRAIN = ROOT / "kaggle" / "director_lora_train.py"
 OUT = ROOT / "director_lora_out"
 
 FILES = {"director_lora/sft.jsonl": SFT, "kaggle/director_lora_train.py": TRAIN}
 
+# Pinned: 5.22.2 is the version whose Qwen3_5 auto mapping and module names
+# were checked. hub 1.x is what transformers 5.22.2 requires.
+PIP = ("pip install -q --upgrade "
+       "'transformers==5.22.2' 'huggingface_hub>=1.31,<2' "
+       "'peft==0.21.2' 'accelerate>=1.1'")
+CHECK = (sys.executable + " -c \""
+         "import torch, transformers, peft, huggingface_hub; "
+         "print('torch', torch.__version__, 'cuda', torch.version.cuda, "
+         "'gpus', torch.cuda.device_count()); "
+         "print('transformers', transformers.__version__, "
+         "'peft', peft.__version__, 'hub', huggingface_hub.__version__)\"")
+
 
 def log(msg: str) -> None:
     print(f"[{time.strftime('%H:%M:%S')}] {msg}", flush=True)
 
 
-def fetch(url: str, dst: Path) -> None:
+def fetch(rel: str, dst: Path) -> None:
     dst.parent.mkdir(parents=True, exist_ok=True)
-    for attempt in range(3):
-        try:
-            with urllib.request.urlopen(url, timeout=180) as r, open(dst, "wb") as f:
-                shutil.copyfileobj(r, f)
-            size = dst.stat().st_size
-            if size < 1000:
-                raise RuntimeError(f"only {size} bytes")
-            log(f"fetched {url} -> {dst} ({size/1e6:.2f} MB)")
-            return
-        except Exception as exc:
-            log(f"fetch attempt {attempt + 1} failed: {exc}")
-            time.sleep(4)
-    raise SystemExit(f"cannot fetch {url}")
+    for attempt in range(6):
+        for base in BASES:
+            url = f"{base}/{rel}"
+            try:
+                with urllib.request.urlopen(url, timeout=180) as r, open(dst, "wb") as f:
+                    shutil.copyfileobj(r, f)
+                size = dst.stat().st_size
+                if size < 1000:
+                    raise RuntimeError(f"only {size} bytes")
+                log(f"fetched {url} -> {dst} ({size/1e6:.2f} MB)")
+                return
+            except Exception as exc:
+                log(f"fetch attempt {attempt + 1} via {base} failed: {exc}")
+        time.sleep(4)
+    raise SystemExit(f"cannot fetch {rel}")
+
+
+def run(cmd: str, env=None) -> int:
+    log(f"$ {cmd[:220]}")
+    return subprocess.run(cmd, shell=True, env=env).returncode
 
 
 def main() -> None:
+    log(f"python {sys.version.split()[0]}")
     for rel, dst in FILES.items():
-        fetch(f"{BASE}/{rel}", dst)
+        fetch(rel, dst)
 
-    cmd = ("pip install -q --upgrade 'transformers>=5' "
-           "'huggingface_hub>=1.31,<2' peft accelerate")
-    log(f"$ {cmd}")
-    if subprocess.run(cmd, shell=True).returncode != 0:
+    if run(PIP) != 0:
         raise SystemExit("dependency install failed")
+    if run(CHECK) != 0:
+        raise SystemExit("environment check failed (torch/hub mismatch?)")
 
     env = dict(os.environ, WORK_ROOT=str(ROOT), DATA=str(SFT), LORA_OUT=str(OUT),
+               CUDA_VISIBLE_DEVICES="0", HF_HUB_DISABLE_XET="1",
                EPOCHS=os.environ.get("EPOCHS", "2"),
                LR=os.environ.get("LR", "1e-4"),
                R=os.environ.get("R", "16"),
                ALPHA=os.environ.get("ALPHA", "32"),
                MAX_LEN=os.environ.get("MAX_LEN", "2048"),
                ACCUM=os.environ.get("ACCUM", "4"),
+               DTYPE=os.environ.get("DTYPE", "auto"),
                GRAD_CKPT=os.environ.get("GRAD_CKPT", "0"))
     log(f"training on {SFT}")
     rc = subprocess.run([sys.executable, str(TRAIN)], env=env).returncode
     log(f"train rc={rc}; outputs in {OUT}")
+    if OUT.exists():
+        for p in sorted(OUT.rglob("*")):
+            if p.is_file():
+                log(f"artifact {p.relative_to(OUT)} {p.stat().st_size/1e6:.2f} MB")
     if rc != 0:
         raise SystemExit("director LoRA training failed")
+    log("DONE")
 
 
 if __name__ == "__main__":