pydeevo 0.0.1
PyDeevo is a comprehensive framework for neural network architecture search, hyperparameter optimization, and efficient training. It integrates modern deep learning tools into a cohesive, modular ecosystem:  PyTorch Lightning: For structured, scalable deep learning Optuna: For efficient hyperparameter optimization PyGAD: For evolutionary architecture search Polars & DuckDB: For high-performance data processing and analytics Advanced Utils: For distributed training, profiling, and deployment
Requires Python: >=3.13

# PyDeevo: Advanced Neural Architecture Evolution Framework

PyDeevo is a comprehensive framework for neural network architecture search, hyperparameter optimization, and efficient training. It integrates modern deep learning tools into a cohesive, modular ecosystem:

- **PyTorch Lightning**: For structured, scalable deep learning
- **Optuna**: For efficient hyperparameter optimization
- **PyGAD**: For evolutionary architecture search
- **Polars & DuckDB**: For high-performance data processing and analytics
- **Advanced Utils**: For distributed training, profiling, and deployment

## 🔥 Key Features

### 🧬 Architecture Search & Optimization
- **Evolutionary Architecture Search**: Discover optimal network architectures using genetic algorithms
- **Multi-level Optimization**: Search both architecture space and hyperparameter space efficiently
- **Flexible Encodings**: Support for MLP and CNN architecture evolution

### 🚀 Performance & Scaling
- **Distributed Training**: Seamless scaling from single GPU to multi-node with FSDP/DDP
- **Memory Optimization**: Activation checkpointing, mixed precision, and other memory-saving techniques
- **Batch Size Optimization**: Automatically find optimal batch sizes for your hardware

### 📊 Data Processing & Analytics
- **High-Performance DataFrames**: Fast data processing with Polars integration
- **SQL Analytics**: Powerful SQL-based analytics with DuckDB
- **Efficient Memory Management**: Stream data from disk to model with minimal memory footprint
- **ETL Pipelines**: Build data pipelines with SQL and Polars transformations
- **Analytical Workflows**: Create and execute complex analytical pipelines

### 🔍 Profiling & Benchmarking
- **Model Analysis**: Profile inference time, memory usage, and computational complexity (FLOPs)
- **Architecture Comparison**: Benchmark different architectures with comprehensive metrics
- **Training Insights**: Monitor performance during training with detailed profiling

### 📦 Deployment & Inference
- **Model Export**: Export to ONNX, TorchScript, and SafeTensors formats
- **Inference Optimization**: Quantization and other performance optimizations
- **Security**: Protection against deserialization vulnerabilities

## 🛠️ Installation

```bash
pip install pydeevo
```

Or install from source:

```bash
git clone https://github.com/example/pydeevo.git
cd pydeevo
pip install -e .
```

## 🚀 Quick Start

### Evolutionary Architecture Search

```python
from pydeevo import PyDeevo
from pydeevo.models.base import FlexibleModule

# Initialize PyDeevo
pydeevo = PyDeevo(base_dir="./output")

# Evolutionary search for optimal architecture
result = pydeevo.evolve_architecture(
    input_shape=input_shape,
    output_size=output_size,
    train_loader=train_loader,
    val_loader=val_loader,
    network_type="mlp",
    population_size=20,
    num_generations=10
)

# Get best architecture and train final model
best_architecture = result["best_architecture"]
best_hyperparams = result["best_hyperparameters"]
model = FlexibleModule(architecture=best_architecture, **best_hyperparams)
```

### Data Processing with Polars

```python
from pydeevo.utils.data import (
    PolarsDataProcessor, 
    DatasetBuilder, 
    normalize_features, 
    create_polynomial_features
)

# Create processor and builder
processor = PolarsDataProcessor(cache_dir="./data_cache")
builder = DatasetBuilder(processor)

# Define preprocessing steps
preprocessing_steps = [
    normalize_features(feature_cols, method='z-score'),
    create_polynomial_features(['feature1', 'feature2'], degree=2)
]

# Create PyTorch datasets
train_dataset, val_dataset, test_dataset = builder.from_file(
    file_path="data.csv",
    feature_cols=feature_cols,
    target_col="target",
    preprocessing_steps=preprocessing_steps,
    cache_key="processed_data"
)
```

### SQL Analytics with DuckDB

```python
from pydeevo.utils.database import (
    AnalyticalDataManager,
    sql_to_pytorch_dataset
)

# Set up analytics manager
analytics = AnalyticalDataManager("analytics.duckdb")

# Load data
analytics.load_data("customers.csv", "customers")
analytics.load_data("transactions.csv", "transactions")

# Run analytical query
results = analytics.execute_sql("""
    SELECT c.segment, AVG(t.amount) as avg_spend
    FROM customers c
    JOIN transactions t ON c.customer_id = t.customer_id
    GROUP BY c.segment
""", cache_key="segment_analysis")

# Create PyTorch dataset directly from SQL
dataset = sql_to_pytorch_dataset(
    db_path="analytics.duckdb",
    query="SELECT * FROM prepared_features",
    feature_cols=["feature1", "feature2", "feature3"],
    target_col="target"
)
```

### Distributed Training

```python
from pydeevo.utils.distributed import DistributedTrainingHelper, MemoryOptimization
import torch.nn as nn

# Set up distributed training
dist_helper = DistributedTrainingHelper(
    precision='16-mixed',
    devices='auto',
    strategy='auto'
)

# Apply memory optimizations
model = MemoryOptimization.optimize_memory_usage(
    model, 
    use_channels_last=True,
    use_compile=True
)

# Set up Fabric
fabric = dist_helper.setup_fabric(transformer_modules=[nn.Linear, nn.Conv2d])
model, optimizer = dist_helper.setup_model_and_optimizer(model, optimizer_fn)
```

### Model Profiling and Export

```python
from pydeevo.utils.profiling import ModelProfiler, FlopsCalculator
from pydeevo.utils.export import ModelExporter

# Profile model performance
profiler = ModelProfiler(model, input_shape=(batch_size, *input_dim))
memory_stats = profiler.profile_memory_usage()
inference_stats = profiler.profile_inference_time()
flops = FlopsCalculator(model, input_shape).calculate_flops()

# Export for deployment
exporter = ModelExporter(model, input_shape=(1, *input_dim))
export_paths = exporter.export_all_formats()  # ONNX, TorchScript, SafeTensors
```

## 🧩 Framework Components

PyDeevo is built with modularity in mind. Major components include:

### `models`
- `FlexibleModule`: Adaptable PyTorch Lightning module for MLPs
- `CNNModule`: Adaptable module for convolutional networks

### `evolution`
- Architecture encodings for genetic algorithms
- Multi-level optimization strategies
- Evolutionary search implementations

### `optimization`
- Hyperparameter optimization with Optuna
- Search space definitions and strategies

### `training`
- Extended Lightning training utilities
- Custom callbacks for visualization and monitoring

### `utils`
- **data**: High-performance data processing with Polars
- **database**: SQL analytics with DuckDB integration
- **distributed**: Utilities for distributed training and memory optimization
- **profiling**: Performance analysis and benchmarking
- **export**: Model export and deployment optimization

## 🔬 Advanced Use Cases

PyDeevo supports advanced deep learning workflows:

1. **Large Model Training**: Train models too large for a single GPU using memory optimization techniques and model sharding
2. **Complex Feature Engineering**: Create sophisticated features with Polars before training
3. **SQL-Based Data Pipelines**: Use DuckDB for efficient data preparation and feature engineering
4. **Production Deployment**: Export optimized models with quantization for inference
5. **Performance Optimization**: Profile and optimize models for specific hardware targets
6. **End-to-End ML Workflows**: From data ingestion to model training and deployment in a single framework

## 📚 Example Gallery

See the `examples` directory for full implementations:

- `mnist_example.py`: Basic architecture search for MNIST
- `cifar10_example.py`: CNN architecture search for CIFAR-10
- `hyperopt_example.py`: Hyperparameter optimization with fixed architecture
- `distributed_example.py`: Distributed training with Lightning Fabric
- `polars_example.py`: Data processing with Polars integration
- `duckdb_example.py`: SQL analytics with DuckDB integration

## 📋 Requirements

- Python 3.8+
- PyTorch 1.9+
- PyTorch Lightning 2.0+
- Optuna 3.0+
- PyGAD 2.18+
- Polars 0.19+
- DuckDB 0.8+
- NumPy 1.20+
- Matplotlib 3.5+

## 📄 License

This project is licensed under the MIT License - see the LICENSE file for details.