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Update app.py
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app.py
CHANGED
@@ -1,24 +1,20 @@
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import gc
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import psutil
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from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
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class MultiModelSystem:
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"""
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سیستم چندمدلی
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"""
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def __init__(self, memory_limit_gb=15):
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"""
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مقداردهی اولیه سیستم و تنظیم محدودیت حافظه.
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:param memory_limit_gb: حداکثر میزان استفاده از حافظه.
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"""
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self.models = {}
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self.memory_limit_gb = memory_limit_gb
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def check_memory_usage(self):
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"""
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بررسی میزان استفاده از حافظه.
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"""
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mem = psutil.virtual_memory()
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used_gb = mem.used / (1024 ** 3)
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print(f"Memory usage: {mem.percent}% ({used_gb:.2f} GB used)")
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@@ -27,32 +23,27 @@ class MultiModelSystem:
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def load_model(self, task, model_id):
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"""
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بارگذاری مدل
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:param task: نوع وظیفه (مثلاً ترجمه).
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:param model_id: شناسه مدل.
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"""
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if task not in self.models:
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self.check_memory_usage()
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print(f"Loading model for task '{task}'
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if
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model = AutoModelForSeq2SeqLM.from_pretrained(
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model_id,
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torch_dtype="auto", # بهینهسازی حافظه با FP16
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low_cpu_mem_usage=True
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)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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self.models[task] = pipeline("translation", model=model, tokenizer=tokenizer)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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self.models[task] = pipeline("question-answering", model=model, tokenizer=tokenizer)
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else:
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self.models[task] = pipeline(task, model=model_id)
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def unload_model(self, task):
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"""
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:param task: نوع وظیفه.
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"""
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if task in self.models:
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print(f"Unloading model for task '{task}'...")
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@@ -61,45 +52,33 @@ class MultiModelSystem:
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def process_task(self, task, model_id, **kwargs):
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"""
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پردازش وظیفه با
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:param task: نوع وظیفه.
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:param model_id: شناسه مدل.
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:return: نتیجه پردازش.
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"""
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self.load_model(task, model_id)
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model = self.models[task]
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if task == "translation":
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text = kwargs.get("text", "")
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if not text:
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raise ValueError("No input text provided for translation task.")
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return model(text)
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elif task == "qa":
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question = kwargs.get("question", "")
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context = kwargs.get("context", "")
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if not question or not context:
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raise ValueError("Both 'question' and 'context' must be provided for QA task.")
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return model(question=question, context=context)
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else:
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raise ValueError(f"Unsupported task: {task}")
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if __name__ == "__main__":
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# تنظیمات مدلها
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MODEL_CONFIG = {
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"translation": "
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"qa": "
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}
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# تعریف وظایف
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tasks = [
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{"task": "translation", "model_id": MODEL_CONFIG["translation"], "kwargs": {"text": "سلام دنیا!"}},
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{"task": "qa", "model_id": MODEL_CONFIG["qa"], "kwargs": {"question": "
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]
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# نمونهسازی سیستم
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system = MultiModelSystem(memory_limit_gb=15)
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# پردازش وظایف
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for task_info in tasks:
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try:
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system.check_memory_usage()
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@@ -108,4 +87,4 @@ if __name__ == "__main__":
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except Exception as e:
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print(f"Error during task '{task_info['task']}':", str(e))
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finally:
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system.unload_model(task_info["task"])
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import os
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import gc
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import psutil
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from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
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class MultiModelSystem:
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"""
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سیستم چندمدلی با مدیریت حافظه و ذخیرهسازی موقت مدلها در دیسک.
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"""
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def __init__(self, memory_limit_gb=15):
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self.models = {}
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self.memory_limit_gb = memory_limit_gb
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self.model_cache_dir = "model_cache"
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os.makedirs(self.model_cache_dir, exist_ok=True)
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def check_memory_usage(self):
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mem = psutil.virtual_memory()
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used_gb = mem.used / (1024 ** 3)
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print(f"Memory usage: {mem.percent}% ({used_gb:.2f} GB used)")
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def load_model(self, task, model_id):
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"""
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بارگذاری مدل از کش یا ذخیرهسازی.
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"""
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cache_path = os.path.join(self.model_cache_dir, f"{task}.bin")
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if task not in self.models:
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self.check_memory_usage()
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print(f"Loading model for task '{task}'...")
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if os.path.exists(cache_path):
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print(f"Loading model from cache: {cache_path}")
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self.models[task] = joblib.load(cache_path)
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else:
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model = AutoModelForSeq2SeqLM.from_pretrained(
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model_id, torch_dtype="auto", low_cpu_mem_usage=True
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)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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self.models[task] = pipeline("translation", model=model, tokenizer=tokenizer)
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joblib.dump(self.models[task], cache_path)
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print(f"Model cached at {cache_path}")
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def unload_model(self, task):
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"""
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تخلیه مدل از حافظه.
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"""
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if task in self.models:
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print(f"Unloading model for task '{task}'...")
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def process_task(self, task, model_id, **kwargs):
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"""
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پردازش وظیفه با بارگذاری موقت مدل.
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"""
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self.load_model(task, model_id)
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model = self.models[task]
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if task == "translation":
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text = kwargs.get("text", "")
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return model(text)
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elif task == "qa":
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question = kwargs.get("question", "")
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context = kwargs.get("context", "")
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return model(question=question, context=context)
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else:
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raise ValueError(f"Unsupported task: {task}")
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if __name__ == "__main__":
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MODEL_CONFIG = {
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"translation": "Helsinki-NLP/opus-mt-en-ro", # مدل سبکتر
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"qa": "distilbert-base-uncased-distilled-squad", # مدل فشرده
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}
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tasks = [
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{"task": "translation", "model_id": MODEL_CONFIG["translation"], "kwargs": {"text": "سلام دنیا!"}},
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{"task": "qa", "model_id": MODEL_CONFIG["qa"], "kwargs": {"question": "What is AI?", "context": "AI is artificial intelligence."}}
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]
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system = MultiModelSystem(memory_limit_gb=15)
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for task_info in tasks:
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try:
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system.check_memory_usage()
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except Exception as e:
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print(f"Error during task '{task_info['task']}':", str(e))
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finally:
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system.unload_model(task_info["task"])
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