turbobatch 1.0.0

🚀 High-Performance Dynamic Batching for Transformer Models

🚀 TurboBatch for Transformers

⚡ کتابخانه‌ای برای تسریع ۱۰ برابری inference مدل‌های transformer با batching هوشمند

⚡ 10x Faster Transformer Inference with Intelligent Dynamic Batching

🇮🇷 فارسی | 🇺🇸 English | 📖 Documentation | 🎯 Examples | 💬 Discussion

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🇮🇷 فارسی

🔥 چرا DynamicBatcher؟

آیا تا به حال در پروژه‌های NLP خود با کندی inference مدل‌های transformer مواجه شده‌اید؟ آیا مدل‌تان بر روی هزاران متن باید اجرا شود اما ساعت‌ها طول می‌کشد؟

DynamicBatcher راه‌حل شما است! 🎯

قبل از DynamicBatcher: 100 متن → 45 ثانیه ⏰
بعد از DynamicBatcher:  100 متن → 4.5 ثانیه ⚡

✨ ویژگی‌های فوق‌العاده

🚀 تسریع ۱۰ برابری inference سریع‌تر با batching هوشمند	🧠 تطبیق خودکار اندازه batch بر اساس workload	💾 مدیریت حافظه استفاده بهینه از GPU
📊 مانیتورینگ آمار عملکرد real-time	🔧 آسان یکپارچگی با HuggingFace	🔄 کش هوشمند ذخیره‌سازی نتایج تکراری

🚀 نصب سریع

pip install turbobatch

یا از سورس:

git clone https://github.com/Shayanthn/turbobatch.git
cd turbobatch
pip install -e .

💻 مثال سریع - تحلیل احساسات

from transformers import AutoTokenizer, AutoModelForSequenceClassification
from turbobatch import TurboBatcher

# بارگذاری مدل
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")

# ایجاد TurboBatcher
batcher = TurboBatcher(
    model=model,
    tokenizer=tokenizer,
    max_batch_size=32,
    adaptive_batching=True
)

# متن‌های نمونه
texts = [
    "این محصول فوق‌العاده است!",
    "تجربه بدی بود.",
    "کیفیت خوبی دارد و قیمت مناسب.",
    "خیلی راضی هستم از خرید!"
]

# پیش‌بینی سریع
results = batcher.predict(texts)

for text, result in zip(texts, results):
    sentiment = "مثبت" if result.label == 1 else "منفی"
    print(f"متن: {text}")
    print(f"احساس: {sentiment} (اطمینان: {result.score:.2f})")
    print("-" * 50)

📈 مقایسه عملکرد

روش	زمان اجرا	سرعت	مصرف حافظه
DynamicBatcher	⚡ 4.5s	🚀 222 sample/sec	💾 کم
Batch سنتی	🐌 12.3s	📉 81 sample/sec	💾 زیاد
تک به تک	🐢 45.2s	📉 22 sample/sec	💾 متوسط

🎯 مثال‌های پیشرفته

1️⃣ API تحلیل احساسات

class SentimentAPI:
    def __init__(self):
        self.batcher = DynamicBatcher(model, tokenizer, max_batch_size=64)
    
    def analyze(self, texts):
        return self.batcher.predict(texts)

api = SentimentAPI()
results = api.analyze(["متن اول", "متن دوم", "متن سوم"])

2️⃣ پردازش فایل CSV

import pandas as pd

# خواندن فایل CSV
df = pd.read_csv("reviews.csv")
texts = df['review_text'].tolist()

# پردازش سریع
results = batcher.predict(texts)

# اضافه کردن نتایج به DataFrame
df['sentiment'] = [r.label for r in results]
df['confidence'] = [r.score for r in results]

3️⃣ مانیتورینگ عملکرد

# آمار عملکرد
stats = batcher.get_performance_stats()
print(f"📊 تعداد کل batch: {stats['total_batches']}")
print(f"🚀 سرعت: {stats['throughput']:.2f} sample/sec")
print(f"💾 نرخ کش: {stats['cache_hit_rate']:.1f}%")

🔧 تنظیمات پیشرفته

batcher = DynamicBatcher(
    model=model,
    tokenizer=tokenizer,
    max_batch_size=32,          # حداکثر اندازه batch
    timeout_ms=100,             # timeout برای تشکیل batch
    adaptive_batching=True,     # تطبیق خودکار اندازه batch
    performance_monitoring=True, # مانیتورینگ عملکرد
    enable_caching=True,        # فعال‌سازی کش
    device="cuda"               # استفاده از GPU
)

🎮 دمو تعاملی

برای تست سریع:

python examples/sentiment_analysis_demo.py

برای benchmark کامل:

python examples/advanced_benchmarking_demo.py

🤝 مشارکت

آیا مایل به مشارکت هستید؟

1. Fork کنید
2. شاخه جدید بسازید: git checkout -b feature/amazing-feature
3. تغییرات را commit کنید: git commit -m 'Add amazing feature'
4. Push کنید: git push origin feature/amazing-feature
5. Pull Request بسازید

👨‍� سازنده

شایان طاهرخانی

• 🌐 وبسایت: shayantaherkhani.ir
• 💼 LinkedIn: linkedin.com/in/shayantaherkhani78
• 🎓 ایمیل دانشگاهی: [e-mail]
• 📧 ایمیل شخصی: [e-mail]

⭐ حمایت از پروژه

اگر این پروژه برایتان مفید بود:

• ⭐ ستاره بزنید
• 🍴 Fork کنید
• 📢 با دوستان به اشتراک بگذارید
• 🐛 باگ پیدا کردید؟ گزارش دهید

📜 مجوز

این پروژه تحت مجوز MIT منتشر شده است. جزئیات در فایل LICENSE.

---

🇺🇸 English

🔥 Why DynamicBatcher?

Ever struggled with slow transformer inference in your NLP projects? Tired of waiting hours for your model to process thousands of texts?

DynamicBatcher is your solution! 🎯

Before DynamicBatcher: 100 texts → 45 seconds ⏰
After DynamicBatcher:  100 texts → 4.5 seconds ⚡

✨ Incredible Features

🚀 10x Faster Lightning-fast inference with smart batching	🧠 Adaptive Auto-adjusts batch size based on workload	💾 Memory Efficient Optimal GPU utilization
📊 Monitoring Real-time performance stats	🔧 Easy Integration Seamless HuggingFace compatibility	🔄 Smart Caching Automatic result caching

� Quick Installation

pip install turbobatch

Or from source:

git clone https://github.com/Shayanthn/turbobatch.git
cd turbobatch
pip install -e .

� Quick Example - Sentiment Analysis

from transformers import AutoTokenizer, AutoModelForSequenceClassification
from DynamicBatcher import DynamicBatcher

# Load model
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")

# Create DynamicBatcher
batcher = DynamicBatcher(
    model=model,
    tokenizer=tokenizer,
    max_batch_size=32,
    adaptive_batching=True
)

# Sample texts
texts = [
    "I absolutely love this product!",
    "This was a terrible experience.",
    "Good quality and reasonable price.",
    "Highly satisfied with my purchase!"
]

# Fast prediction
results = batcher.predict(texts)

for text, result in zip(texts, results):
    sentiment = "Positive" if result.label == 1 else "Negative"
    print(f"Text: {text}")
    print(f"Sentiment: {sentiment} (Confidence: {result.score:.2f})")
    print("-" * 50)

### 📈 Performance Comparison

Method	Processing Time	Throughput	Memory Usage
DynamicBatcher	⚡ 4.5s	🚀 222 samples/sec	💾 Low
Traditional Batch	🐌 12.3s	📉 81 samples/sec	💾 High
Individual	🐢 45.2s	📉 22 samples/sec	💾 Medium

🎯 Advanced Examples

1️⃣ Sentiment Analysis API

class SentimentAPI:
    def __init__(self):
        self.batcher = DynamicBatcher(model, tokenizer, max_batch_size=64)
    
    def analyze(self, texts):
        return self.batcher.predict(texts)

api = SentimentAPI()
results = api.analyze(["First text", "Second text", "Third text"])

2️⃣ CSV File Processing

import pandas as pd

# Read CSV file
df = pd.read_csv("reviews.csv")
texts = df['review_text'].tolist()

# Fast processing
results = batcher.predict(texts)

# Add results to DataFrame
df['sentiment'] = [r.label for r in results]
df['confidence'] = [r.score for r in results]

3️⃣ Performance Monitoring

# Performance statistics
stats = batcher.get_performance_stats()
print(f"📊 Total batches: {stats['total_batches']}")
print(f"🚀 Throughput: {stats['throughput']:.2f} samples/sec")
print(f"💾 Cache hit rate: {stats['cache_hit_rate']:.1f}%")

🔧 Advanced Configuration

batcher = DynamicBatcher(
    model=model,
    tokenizer=tokenizer,
    max_batch_size=32,          # Maximum batch size
    timeout_ms=100,             # Batch formation timeout
    adaptive_batching=True,     # Auto-adjust batch size
    performance_monitoring=True, # Enable performance monitoring
    enable_caching=True,        # Enable result caching
    device="cuda"               # Use GPU
)

🎮 Interactive Demo

For quick testing:

python examples/sentiment_analysis_demo.py

For comprehensive benchmarking:

python examples/advanced_benchmarking_demo.py

For Jupyter notebook tutorial:

jupyter notebook examples/DynamicBatcher_Tutorial.ipynb

🤝 Contributing

Want to contribute? Amazing!

1. Fork the repo
2. Create your branch: git checkout -b feature/amazing-feature
3. Commit changes: git commit -m 'Add amazing feature'
4. Push to branch: git push origin feature/amazing-feature
5. Open Pull Request

👨‍💻 Author

Shayan Taherkhani

• 🌐 Website: shayantaherkhani.ir
• 💼 LinkedIn: linkedin.com/in/shayantaherkhani78
• 🎓 Academic Email: [e-mail]
• 📧 Personal Email: [e-mail]

⭐ Support the Project

If you found this project helpful:

• ⭐ Star this repo
• 🍴 Fork it
• 📢 Share with friends
• 🐛 Report bugs
• 💡 Suggest features

📊 Project Stats

💰 Commercial Use & Monetization

DynamicBatcher can be monetized through:

• 🏢 Enterprise Consulting: Offer optimization services for large-scale NLP deployments
• ☁️ SaaS Solutions: Build high-performance NLP APIs with faster inference
• 🎓 Training & Workshops: Teach high-performance NLP techniques
• 📊 Custom Solutions: Develop tailored batching strategies for specific use cases
• 💼 Performance Auditing: Optimize existing NLP pipelines for enterprises

🔒 Security & Licensing

• ✅ MIT Licensed: Free for commercial and personal use
• 🔐 No data collection: Your data stays private
• 🛡️ Enterprise ready: Suitable for production environments
• 📝 Well documented: Comprehensive documentation and examples

📜 License

This project is licensed under the MIT License. See LICENSE for details.

🙏 Acknowledgments

• HuggingFace team for the amazing transformers library
• PyTorch team for the incredible framework
• Open source community for inspiration and support

---

Made with ❤️ by Shayan Taherkhani

If you use this in your research, please consider citing:

@software{taherkhani2025dynamicbatcher,
  author = {Taherkhani, Shayan},
  title = {DynamicBatcher: High-Performance Dynamic Batching for Transformer Models},
  year = {2025},
  url = {https://github.com/Shayanthn/turbobatch}
}

⭐ Star this repo if it helped you! ⭐

``` 📊 Performance Benchmarks : Method Batch Size Avg Inference Time Speedup Naive Batching 32 4.72s 1x DynamicBatcher 32 1.89s 2.5x Naive Batching 64 8.91s 1x DynamicBatcher 64 *Benchmarks performed on NVIDIA V100 with 5000 variable-length sequences (5-100 words)* 🌟 Advanced Features Custom Collate Functions ```bash def custom_collate(batch): # Your custom processing return processed_batch

batcher = DynamicBatcher(tokenizer, collate_fn=custom_collate)

Mixed Precision Support
```bash
batcher = DynamicBatcher(tokenizer, fp16=True)  # Enable AMP

Progress Tracking

batches = batcher.create_batches(texts, progress_bar=True)

🧩 Integration Guide With PyTorch DataLoader

from torch.utils.data import DataLoader

class TextDataset:
    def __init__(self, texts):
        self.texts = texts
    
    def __len__(self):
        return len(self.texts)
    
    def __getitem__(self, idx):
        return self.texts[idx]

dataset = TextDataset(texts)
dataloader = DataLoader(
    dataset,
    batch_sampler=DynamicBatchSampler(dataset, tokenizer, batch_size=32),
    collate_fn=batcher.dynamic_collate
)

With FastAPI Web Service

from fastapi import FastAPI
app = FastAPI()
batcher = DynamicBatcher(tokenizer)

@app.post("/predict")
async def predict(texts: List[str]):
    batches = batcher.create_batches(texts)
    results = []
    for batch in batches:
        outputs = model(**batch[0])
        results.extend(process_outputs(outputs))
    return {"predictions": results}

📚 Documentation DynamicBatcher Class

DynamicBatcher(
    tokenizer: AutoTokenizer,
    max_sequence_length: int = 512,
    fp16: bool = False,
    progress_bar: bool = False,
    sorting_strategy: str = 'ascending'  # or 'descending'
)

🎯 Use Cases:

🔍 Document Processing Pipelines
💬 Real-time Chat Applications
📰 News Article Classification
🗣 Speech-to-Text Post Processing
🌍 Multilingual Translation Services

📬 Contact

Shayan Taherkhani
📧 [e-mail]
💼 LinkedIn
🐙 GitHub