Initial model adaptation strategy

Dockerfile

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+FROM python:3.10-slim
+
+WORKDIR /workspace
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    git git-lfs \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY main.py .
+
+ENV PYTHONUNBUFFERED=1
+
+ENTRYPOINT ["python", "main.py"]

main.py

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+"""
+Model Adaptation Strategy for ModelHub XC.
+
+Runs inside a ModelHub container. Receives model info via environment variables.
+Performs model download, validation, and produces adaptation results.
+"""
+
+import json
+import os
+import sys
+import subprocess
+import traceback
+from datetime import datetime
+
+
+def log(msg: str) -> None:
+    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    print(f"[{timestamp}] {msg}", flush=True)
+
+
+def run_cmd(cmd: str, cwd: str = "/workspace") -> tuple[int, str]:
+    log(f"Running: {cmd}")
+    result = subprocess.run(
+        cmd, shell=True, cwd=cwd,
+        capture_output=True, text=True, timeout=3600,
+    )
+    if result.stdout:
+        print(result.stdout)
+    if result.stderr:
+        print(result.stderr, file=sys.stderr)
+    return result.returncode, result.stdout
+
+
+def download_model(model_address: str, output_dir: str) -> bool:
+    log(f"Downloading model from: {model_address}")
+
+    if "huggingface.co" in model_address or "hf.co" in model_address:
+        from huggingface_hub import snapshot_download
+        model_id = model_address.split("huggingface.co/")[-1]
+        snapshot_download(repo_id=model_id, local_dir=output_dir)
+    elif "modelscope.cn" in model_address:
+        from modelscope import snapshot_download
+        model_id = model_address.split("modelscope.cn/models/")[-1]
+        snapshot_download(model_id, cache_dir=output_dir)
+    else:
+        log(f"Unknown source, trying git clone: {model_address}")
+        run_cmd(f"git clone {model_address} {output_dir}")
+
+    if os.path.isdir(output_dir) and os.listdir(output_dir):
+        log(f"Model downloaded to {output_dir}")
+        return True
+    log("Model download failed - empty output directory")
+    return False
+
+
+def main():
+    log("=" * 60)
+    log("ModelHub Adaptation Strategy Starting")
+    log("=" * 60)
+
+    model_address = os.environ.get("MODEL_ADDRESS", "")
+    config_params = os.environ.get("CONFIG_PARAMS", "{}")
+
+    try:
+        config = json.loads(config_params)
+    except json.JSONDecodeError:
+        config = {}
+
+    log(f"Model: {model_address}")
+    log(f"Config: {json.dumps(config, indent=2, ensure_ascii=False)}")
+
+    if not model_address:
+        log("ERROR: MODEL_ADDRESS not set")
+        sys.exit(1)
+
+    os.makedirs("/workspace/output", exist_ok=True)
+    os.makedirs("/workspace/model", exist_ok=True)
+
+    result = {
+        "status": "success",
+        "model_address": model_address,
+        "started_at": datetime.now().isoformat(),
+        "steps": [],
+    }
+
+    try:
+        ok = download_model(model_address, "/workspace/model")
+        result["steps"].append({"step": "download", "status": "success" if ok else "failed"})
+        if not ok:
+            raise RuntimeError("Model download failed")
+
+        log("Validating model files...")
+        model_files = []
+        for root, dirs, files in os.walk("/workspace/model"):
+            for f in files:
+                model_files.append(os.path.join(root, f))
+        size_mb = sum(os.path.getsize(f) for f in model_files) / (1024 * 1024)
+        log(f"Files: {len(model_files)}, Size: {size_mb:.1f} MB")
+
+        result["model_files"] = len(model_files)
+        result["model_size_mb"] = round(size_mb, 2)
+        result["steps"].append({"step": "validate", "status": "success"})
+
+    except Exception as e:
+        log(f"ERROR: {e}")
+        traceback.print_exc()
+        result["status"] = "failed"
+        result["error"] = str(e)
+
+    result["finished_at"] = datetime.now().isoformat()
+    with open("/workspace/output/result.json", "w", encoding="utf-8") as f:
+        json.dump(result, f, indent=2, ensure_ascii=False)
+
+    log(f"Result: {json.dumps(result, indent=2, ensure_ascii=False)}")
+    return 0 if result["status"] == "success" else 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())

requirements.txt

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+torch>=2.0.0
+transformers>=4.30.0
+huggingface_hub>=0.20.0
+modelscope>=1.10.0