Synaptic-Delta-Sync (SDS) 🚀
Synaptic-Delta-Sync (SDS) is a high-performance, production-grade infrastructure library designed for atomic, secure, and zero-downtime neural network weight updates.
In modern AI environments, updating models often requires a full restart, leading to service disruption. SDS bridges this gap by enabling hot-swapping of weights directly into live inference engines without stopping the process.
🏗️ Architecture Overview
The SDS pipeline is designed for modularity, speed, and integrity. The architecture separates weight extraction, cryptographic validation, and device-specific injection into distinct, highly optimized layers.
graph TD
subgraph "Phase 1: Preparation"
A[Live Model] -->|Extract| B[Delta Engine]
B -->|Sparse Diff| C[Serialized .sds Patch]
end
subgraph "Phase 2: Injection"
C -->|Verify| D[Patch Validator]
D -->|Hash Check| E[Atomic Injector]
end
subgraph "Phase 3: Execution"
E -->|Route| F{Device Selection}
F -->|CPU| G[Atomic Injection]
F -->|GPU| H[Async CUDA Stream]
G --> I[Updated Model]
H --> I
end
💎 Core Technical Pillars
- Atomic Injection & Zero-Downtime: SDS modifies model weights at the memory-pointer level using
torch.no_grad(), ensuring zero-drop inference. - Sparse Delta Engine: Reduces patch sizes from MBs to KBs using mathematical delta computation.
- Cryptographic Security: SHA-256 integrity handshake verifies every patch before application.
- Dual-Engine Performance: Optimized paths for both CPU (direct memory) and GPU (Async CUDA Streams).
📦 Quick Start
Installation
Clone the repository to your local workspace:
git clone [https://github.com/ANSH-GIT77/synaptic-delta-sync.git](https://github.com/ANSH-GIT77/synaptic-delta-sync.git)
cd synaptic-delta-sync
Basic Usage
The AtomicInjector class handles all the heavy lifting automatically:
from sds.core.injection import AtomicInjector
from sds.protocol.serializer import load_patch
# 1. Initialize engine
injector = AtomicInjector(model)
# 2. Load secure patch
delta, metadata, patch_hash = load_patch("updates.sds")
# 3. Apply patch
injector.apply_patch(delta, patch_hash=patch_hash)
📁 Project Structure
synaptic-delta-sync/
├── sds/
│ ├── core/ # Engine Logic
│ ├── hot_swap/ # Validation
│ └── protocol/ # Serialization
├── examples/ # Tests
└── pyproject.toml # Config
📈 Roadmap
- v0.1.0: Core Engine & Security Validator.
- Next: Distributed Patching (RPC) & Multi-GPU sharding.
🤝 Author
Ansh Nimbalkar | AI Infrastructure Engineer
Metadata
Release files for synaptic-delta-sync 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| synaptic_delta_sync-0.1.0.tar.gz | 7.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| synaptic_delta_sync-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 15.2 kB
Release files / synaptic_delta_sync-0.1.0.tar.gz
| Download URL | synaptic_delta_sync-0.1.0.tar.gz |
|---|---|
| Size | 7.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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twine/6.2.0 CPython/3.14.3
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Release files / synaptic_delta_sync-0.1.0-py3-none-any.whl
| Download URL | synaptic_delta_sync-0.1.0-py3-none-any.whl |
|---|---|
| Size | 8.0 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/6.2.0 CPython/3.14.3
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