Cetana

An advanced machine learning library empowering developers to build intelligent applications with ease, written in Rust.

Cetana (चेतन) is a Sanskrit word meaning "consciousness" or "intelligence," reflecting the library's goal of bringing machine intelligence to your applications.

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Overview

Cetana is a Rust-based machine learning library designed to provide efficient and flexible machine learning operations across multiple compute platforms. It focuses on providing a clean, safe API while maintaining high performance.

Features

• Type-safe Tensor Operations
• Neural Network Building Blocks
• Automatic Differentiation
• Model Serialization
• Multiple Activation Functions (ReLU, Sigmoid, Tanh)
• Basic Optimizers and Loss Functions
• CPU Backend (with planned GPU support)

Table of Contents

• Example Usage
• Compute Backends
• Roadmap
  • Phase 1: Core Implementation (CPU)
  • Phase 2: GPU Acceleration
  • Phase 3: Advanced Features
  • Phase 4: High-Level APIs
• Contributing
• License

Example Usage

Compute Backends

• CPU (in progress)
• CUDA
• Metal Performance Shaders (MPS)
• Vulkan

Roadmap

Phase 1: Core Implementation (CPU)

• Basic tensor operations
  • Addition, subtraction
  • Matrix multiplication
  • Element-wise operations
  • Broadcasting support
• Neural Network Modules
  • Linear layers
  • Activation functions (ReLU, Sigmoid, Tanh)
  • Convolutional layers
  • Pooling layers
• Automatic Differentiation
  • Backpropagation
  • Gradient computation
  • Auto Grad
• Loss Functions
  • MSE (Mean Squared Error)
  • Cross Entropy
  • Binary Cross Entropy
• Training Utilities
  • Basic training loops
  • Advanced batch processing
    • Mini-batch handling
    • Batch normalization
    • Dropout layers
  • Data loaders
    • Dataset abstraction
    • Data augmentation
    • Custom dataset support
• Model Serialization
  • Save/Load models
  • Export/Import weights

Phase 2: GPU Acceleration

• CUDA Backend
  • Basic initialization
  • Memory management
  • Basic operations
  • Advanced operations
  • cuBLAS integration
• MPS Backend (Apple Silicon)
  • Basic operations
  • Performance optimizations
• Vulkan Backend
  • Device initialization
  • Basic compute pipeline
  • Memory management
  • Advanced operations
  • Performance optimizations

Phase 3: Advanced Features

• Distributed Training
  • Multi-GPU support
  • Data parallelism
  • Model parallelism
• Automatic Mixed Precision
• Model Quantization
• Performance Profiling
  • Operation timing
  • Memory usage tracking
  • Bottleneck analysis
• Advanced Optimizations
  • Kernel fusion
  • Memory pooling
  • Operation scheduling
  • Graph optimization

Phase 4: High-Level APIs

• Model Zoo
• Pre-trained Models
• Easy-to-use Training APIs
• Integration Examples
• Comprehensive Documentation

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

Licensed under the Apache License, Version 2.0. See LICENSE for details.