Commit graph

3 commits

Author SHA1 Message Date
Gulshan Yadav
a7a4a7effc test: add comprehensive test suite (1,357 tests total)
Phase 1 - Test Writing (8 parallel agents):
- synord: Added 133 unit tests for node, config, services
- synor-rpc: Expanded to 207 tests for API endpoints
- synor-bridge: Added 144 tests for transfer lifecycle
- synor-zk: Added 279 tests for circuits, proofs, state
- synor-mining: Added 147 tests for kHeavyHash, miner
- synor-types: Added 146 tests for hash, amount, address
- cross-crate: Created 75 integration tests
- byzantine: Created 40+ fault scenario tests

Key additions:
- tests/cross_crate_integration.rs (new)
- apps/synord/tests/byzantine_fault_tests.rs (new)
- crates/synor-storage/src/cf.rs (new)
- src/lib.rs for workspace integration tests

Fixes during testing:
- synor-compute: Added Default impl for GpuVariant
- synor-bridge: Fixed replay protection in process_lock_event
- synor-storage: Added cf module and database exports

All 1,357 tests pass with 0 failures.
2026-01-20 06:35:28 +05:30
Gulshan Yadav
89fc542da4 feat(compute): add model registry and training APIs
Adds comprehensive model management and training capabilities:

synor-compute (Rust):
- ModelRegistry with pre-registered popular models
  - LLMs: Llama 3/3.1, Mistral, Mixtral, Qwen, DeepSeek, Phi, CodeLlama
  - Embedding: BGE, E5
  - Image: Stable Diffusion XL, FLUX.1
  - Speech: Whisper
  - Multi-modal: LLaVA
- ModelInfo with parameters, format, precision, context length
- Custom model upload and registration
- Model search by name/category

Flutter SDK:
- Model registry APIs: listModels, getModel, searchModels
- Custom model upload with multipart upload
- Training APIs: train(), fineTune(), trainStream()
- TrainingOptions: framework, epochs, batch_size, learning_rate
- TrainingProgress for real-time updates
- ModelUploadOptions and ModelUploadResult

Example code for:
- Listing available models by category
- Fine-tuning pre-trained models
- Uploading custom Python/ONNX models
- Streaming training progress

This enables users to:
1. Use pre-registered models like 'llama-3-70b'
2. Upload their own custom models
3. Fine-tune models on custom datasets
4. Track training progress in real-time
2026-01-11 15:22:26 +05:30
Gulshan Yadav
4c36ddbdc2 feat(compute): add Phase 11 Synor Compute L2 heterogeneous compute layer
- Add synor-compute crate for heterogeneous compute orchestration
- Implement processor abstraction for CPU/GPU/TPU/NPU/LPU/FPGA/DSP
- Add device registry with cross-vendor capability tracking
- Implement task scheduler with work stealing and load balancing
- Add energy-aware and latency-aware balancing strategies
- Create spot market for compute resources with order matching
- Add memory manager with tensor handles and cross-device transfers
- Support processor capability profiles (H100, TPU v5p, Groq LPU, etc.)
- Implement priority work queues with task decomposition

Processor types supported:
- CPU (x86-64 AVX512, ARM64 SVE, RISC-V Vector)
- GPU (NVIDIA CUDA, AMD ROCm, Intel OneAPI, Apple Metal)
- TPU (v2-v5p, Edge TPU)
- NPU (Apple Neural Engine, Qualcomm Hexagon, Intel VPU)
- LPU (Groq Language Processing Unit)
- FPGA (Xilinx, Intel Altera)
- DSP (TI, Analog Devices)
- WebGPU and WASM runtimes
2026-01-11 13:53:57 +05:30