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import time
import threading
import logging
import importlib
import gc
import os
from typing import Dict, Any, Optional
import json
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class ModelManager:
"""Manages loading, unloading, and lifecycle of multiple models"""
def __init__(self, unload_timeout: int = 1800): # 30 minutes default
self.models: Dict[str, Any] = {}
self.model_modules: Dict[str, Any] = {}
self.last_used: Dict[str, float] = {}
self.unload_timeout = unload_timeout
self.lock = threading.RLock()
self._cleanup_thread = None
self._start_cleanup_thread()
def _start_cleanup_thread(self):
"""Start background thread for automatic model cleanup"""
if self._cleanup_thread is None or not self._cleanup_thread.is_alive():
self._cleanup_thread = threading.Thread(
target=self._cleanup_models, daemon=True
)
self._cleanup_thread.start()
def _cleanup_models(self):
"""Background thread to unload unused models"""
while True:
try:
time.sleep(300) # Check every 5 minutes
current_time = time.time()
models_to_unload = []
with self.lock:
for model_name, last_used_time in self.last_used.items():
if current_time - last_used_time > self.unload_timeout:
models_to_unload.append(model_name)
for model_name in models_to_unload:
self.unload_model(model_name)
logger.info(f"Auto-unloaded model: {model_name}")
except Exception as e:
logger.error(f"Error in cleanup thread: {e}")
def register_model_module(self, name: str, module: Any):
"""Register a model module"""
with self.lock:
if not hasattr(module, "load_model"):
raise ValueError(f"Model module {name} must have 'load_model' function")
if not hasattr(module, "predict"):
raise ValueError(f"Model module {name} must have 'predict' function")
self.model_modules[name] = module
logger.info(f"Registered model module: {name}")
def load_model(self, name: str) -> bool:
"""Load a specific model"""
with self.lock:
try:
if name in self.models:
self.last_used[name] = time.time()
return True
if name not in self.model_modules:
logger.error(f"Model module not found: {name}")
return False
logger.info(f"Loading model: {name}")
module = self.model_modules[name]
model = module.load_model()
self.models[name] = model
self.last_used[name] = time.time()
logger.info(f"Successfully loaded model: {name}")
return True
except Exception as e:
logger.error(f"Failed to load model {name}: {e}")
return False
def unload_model(self, name: str) -> bool:
"""Unload a specific model"""
with self.lock:
try:
if name in self.models:
if name in self.model_modules:
module = self.model_modules[name]
if hasattr(module, "unload_model"):
try:
module.unload_model(self.models[name])
except Exception as e:
logger.warning(
f"Error in custom unload for {name}: {e}"
)
del self.models[name]
if name in self.last_used:
del self.last_used[name]
gc.collect()
logger.info(f"Unloaded model: {name}")
return True
return False
except Exception as e:
logger.error(f"Failed to unload model {name}: {e}")
return False
def get_model(self, name: str) -> Optional[Any]:
"""Get a loaded model, loading it if necessary"""
with self.lock:
if name not in self.models:
if not self.load_model(name):
return None
self.last_used[name] = time.time()
return self.models[name]
def predict(self, model_name: str, **kwargs) -> Any:
"""Make prediction using specified model"""
try:
if model_name not in self.model_modules:
raise ValueError(f"Unknown model: {model_name}")
model = self.get_model(model_name)
if model is None:
raise RuntimeError(f"Failed to load model: {model_name}")
module = self.model_modules[model_name]
# Try to determine if predict function expects model parameter
import inspect
sig = inspect.signature(module.predict)
if len(sig.parameters) > 0:
first_param = list(sig.parameters.keys())[0]
if first_param in ["model", "m", "net", "network"]:
return module.predict(model, **kwargs)
return module.predict(**kwargs)
except Exception as e:
logger.error(f"Prediction failed for {model_name}: {e}")
raise
def discover_models_in_src():
"""Discover and register models from src directory"""
src_path = os.path.join(os.path.dirname(__file__), "src")
if not os.path.exists(src_path):
logger.warning("src directory not found")
return {}
discovered_models = {}
# Look for Python files in src/
for item in os.listdir(src_path):
if item.endswith(".py") and not item.startswith("__"):
model_name = item[:-3]
try:
module = importlib.import_module(f"src.{model_name}")
discovered_models[model_name] = module
logger.info(f"Discovered model: {model_name}")
except Exception as e:
logger.warning(f"Could not load model {model_name}: {e}")
# Look for directories
elif os.path.isdir(os.path.join(src_path, item)) and not item.startswith("__"):
model_dir = os.path.join(src_path, item)
possible_files = ["model.py", "main.py", "__init__.py", f"{item}.py"]
for filename in possible_files:
if os.path.exists(os.path.join(model_dir, filename)):
try:
if filename == "__init__.py":
module = importlib.import_module(f"src.{item}")
else:
module_name = filename[:-3]
module = importlib.import_module(
f"src.{item}.{module_name}"
)
discovered_models[item] = module
logger.info(f"Discovered model: {item} (from {filename})")
break
except Exception as e:
logger.warning(
f"Could not load model {item} from {filename}: {e}"
)
continue
return discovered_models
# Example model files for reference:
# src/resnet.py
"""
import torch
import torchvision.models as models
_resnet_model = None
def load_model():
global _resnet_model
if _resnet_model is None:
_resnet_model = models.resnet50(pretrained=True)
_resnet_model.eval()
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
_resnet_model.to(device)
return _resnet_model
def predict(model, input_tensor, **kwargs):
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
with torch.no_grad():
if isinstance(input_tensor, list):
input_tensor = torch.tensor(input_tensor)
input_tensor = input_tensor.to(device)
output = model(input_tensor)
return output.cpu().numpy().tolist()
def unload_model(model):
global _resnet_model
_resnet_model = None
if torch.cuda.is_available():
torch.cuda.empty_cache()
"""
# src/birefnet.py
"""
_birefnet_model = None
def load_model():
global _birefnet_model
if _birefnet_model is None:
# Your BiRefNet loading code here
_birefnet_model = "BiRefNet_Model_Placeholder"
return _birefnet_model
def predict(input_data, **kwargs):
if _birefnet_model is None:
raise RuntimeError("Model not loaded")
# Your prediction code here
return f"BiRefNet prediction for {input_data}"
def unload_model(model):
global _birefnet_model
_birefnet_model = None
"""
# requirements.txt
"""
torch>=1.9.0
torchvision>=0.10.0
transformers>=4.20.0
numpy>=1.21.0
Pillow>=8.3.0
"""
# Example usage in HF Inference Endpoints:
"""
# Input format 1 - Single model
{
"model": "resnet",
"inputs": {
"input_tensor": [[[[0.5, 0.3, ...]]]]
}
}
# Input format 2 - Multiple models
{
"models": [
{"name": "resnet", "inputs": {"input_tensor": [...]}},
{"name": "birefnet", "inputs": {"input_data": "image.jpg"}}
]
}
# Input format 3 - Direct inputs (uses first available model)
{
"input_tensor": [[[[0.5, 0.3, ...]]]]
}
"""
# Environment variables you can set in HF Inference Endpoints:
"""
PRELOAD_MODELS=true # Load all models at startup (default: true)
MODEL_UNLOAD_TIMEOUT=1800 # Unload models after 30 minutes (default: 1800)
LOG_LEVEL=INFO # Logging level
"""
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