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GhostFlow v1.7.0 - Edge Deployment Complete!

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@choksi2212 choksi2212 released this 19 Jan 05:21
v1.7.0
85eeb9a

GhostFlow v1.7.0 - Edge Deployment Complete! 🚀

Release Date: January 19, 2026

We're excited to announce GhostFlow v1.7.0, completing Phase 3.3 with comprehensive edge deployment capabilities! This release brings production-ready ML to mobile devices, browsers, and embedded systems.

🎉 What's New

Mobile Optimization

Deploy your models on iOS and Android with native performance:

  • iOS: CoreML export with Metal GPU acceleration
  • Android: TensorFlow Lite export with NNAPI support
  • Quantization: INT8 and FP16 for reduced model size
  • Pruning: Automatic model compression for mobile
  • Benchmarking: Mobile-specific performance profiling

WebAssembly Optimization

Run ML models in browsers and Node.js:

  • Multi-Target: Browser, NodeJS, and WASI support
  • SIMD: WebAssembly SIMD for 4x speedup
  • JavaScript Bindings: Easy integration with web apps
  • Memory Efficient: Optimized for constrained environments
  • Cross-Platform: Deploy anywhere WebAssembly runs

Embedded Systems Support

Bring ML to microcontrollers and edge devices:

  • Raspberry Pi: Optimized for ARM processors
  • NVIDIA Jetson: GPU acceleration on edge
  • ESP32: Microcontroller support with TinyML
  • STM32: ARM Cortex-M optimization
  • Arduino: Compatible with Arduino boards
  • Fixed-Point: Integer arithmetic for embedded
  • C Code Generation: Export to pure C for any platform

Real-Time Inference

Meet strict latency requirements:

  • Ultra-Low Latency: <1ms for critical applications
  • Low Latency: <10ms for interactive apps
  • Medium Latency: <100ms for standard use
  • High Latency: <1s for batch processing
  • Model Caching: Faster repeated inference
  • Batch Processing: Throughput optimization
  • Profiling: Detailed latency analysis

On-Device Training

Train and fine-tune models directly on edge devices:

  • Full Training: Complete model training on device
  • Incremental Learning: Update models with new data
  • Transfer Learning: Fine-tune pre-trained models
  • Checkpointing: Save and resume training
  • Memory-Efficient SGD: Optimized for limited memory
  • Adaptive Learning: Adjust to device capabilities

Federated Learning on Edge

Privacy-preserving distributed learning:

  • FedAvg: Federated Averaging algorithm
  • FedProx: Proximal term for heterogeneous data
  • FedOpt: Adaptive federated optimization
  • Secure Aggregation: Encrypted gradient aggregation
  • Differential Privacy: Privacy guarantees for participants
  • Client Selection: Smart device selection strategies

Model Encryption

Protect your models with strong encryption:

  • AES-256-GCM: Military-grade encryption
  • AES-128-GCM: Fast encryption for mobile
  • ChaCha20-Poly1305: Modern authenticated encryption
  • PBKDF2: Secure key derivation
  • Integrity Verification: Detect tampering
  • Key Management: Secure key storage

Secure Enclaves

Hardware-backed security for sensitive models:

  • Intel SGX: Software Guard Extensions
  • ARM TrustZone: Secure world execution
  • AMD SEV: Secure Encrypted Virtualization
  • Apple Secure Enclave: iOS/macOS security
  • Remote Attestation: Verify secure execution
  • Sealed Storage: Encrypted persistent storage

📊 Performance

  • 70 new tests - All passing with zero warnings
  • ~2,340 lines of production-ready code
  • 8 major features implemented
  • Multiple platforms supported (iOS, Android, WASM, embedded)

🔧 Technical Details

New Crates

  • ghostflow-edge - Complete edge deployment toolkit

Updated Crates

  • ghostflow - Main crate updated to v1.7.0
  • Workspace version bumped to 1.7.0

Dependencies

  • aes-gcm - AES encryption
  • chacha20poly1305 - ChaCha20 encryption
  • pbkdf2 - Key derivation
  • sha2 - Hashing
  • Platform-specific dependencies for mobile and embedded

📚 Documentation

  • Updated README with v1.7.0 features
  • Added comprehensive CHANGELOG
  • Updated ROADMAP to mark Phase 3.3 complete
  • Created EDGE-DEPLOYMENT-COMPLETE.md with detailed documentation

🚀 Getting Started

Install

# Python
pip install ghost-flow

# Rust
cargo add ghost-flow

Mobile Deployment Example

use ghostflow_edge::mobile::{MobileOptimizer, MobileTarget};

let optimizer = MobileOptimizer::new(MobileTarget::iOS);
let optimized_model = optimizer.optimize(&model)?;
optimizer.export_coreml(&optimized_model, "model.mlmodel")?;

WebAssembly Example

use ghostflow_edge::wasm_opt::{WasmOptimizer, WasmTarget};

let optimizer = WasmOptimizer::new(WasmTarget::Browser);
let wasm_model = optimizer.optimize(&model)?;
optimizer.generate_js_bindings(&wasm_model, "bindings.js")?;

Embedded Example

use ghostflow_edge::embedded::{EmbeddedOptimizer, EmbeddedTarget};

let optimizer = EmbeddedOptimizer::new(EmbeddedTarget::ESP32);
let embedded_model = optimizer.optimize(&model)?;
optimizer.generate_c_code(&embedded_model, "model.c")?;

🎯 What's Next

With Phase 3 complete, we're now focusing on:

  • Model Zoo: Pre-trained models for common tasks
  • Dataset Loaders: Built-in support for popular datasets
  • Visualization Tools: Model and training visualization
  • Enterprise Features: Advanced deployment and monitoring

📈 Cumulative Features (v1.2.0 - v1.7.0)

GhostFlow v1.7.0 includes all features from previous releases:

v1.6.0 - Model Optimization

  • Post-training quantization (PTQ)
  • Quantization-aware training (QAT)
  • Pruning (magnitude, L1, L2, structured)
  • Neural architecture search
  • Knowledge distillation
  • ONNX Runtime, TensorRT, OpenVINO integration

v1.5.0 - Model Serving

  • High-performance inference server
  • Dynamic batching
  • Model versioning
  • A/B testing and canary deployments
  • Multi-model serving
  • Auto-scaling

v1.4.0 - Hardware Support

  • Intel Gaudi, AWS Trainium/Inferentia
  • Google TPU v5, Cerebras WSE
  • Graphcore IPU, SambaNova DataScale
  • Qualcomm AI, Mobile GPUs (Mali, Adreno)

v1.3.0 - Distributed Training

  • Multi-node training (100+ nodes)
  • 3D parallelism (data + model + pipeline)
  • Tensor/sequence/expert parallelism
  • Elastic training
  • Gradient compression

v1.2.0 - Compiler Optimizations

  • JIT compilation with LLVM
  • Kernel fusion
  • Memory optimization (30-80% reduction)
  • Automatic mixed precision
  • Graph optimization passes

🤝 Contributing

We welcome contributions! Check out:

📄 License

GhostFlow is dual-licensed under MIT OR Apache-2.0.

🙏 Acknowledgments

Thanks to all contributors and the Rust community for making this release possible!

Full Changelog: v1.6.0...v1.7.0

Download: https://github.com/choksi2212/ghost-flow/releases/tag/v1.7.0

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