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- Add SQLCipher encryption for ait-mainnet database with configurable flag - Add db_encryption_enabled and db_encryption_key_path config settings - Implement encryption key loading and PRAGMA key setup via connection events - Add shutdown_db function for proper database cleanup - Export middleware classes in aitbc/__init__.py - Fix import path in sync.py for settings - Remove duplicate agent documentation from docs
108 lines
2.2 KiB
Markdown
108 lines
2.2 KiB
Markdown
# API Reference - Edge Computing & ML Features
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## Edge GPU Endpoints
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### GET /v1/marketplace/edge-gpu/profiles
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Get consumer GPU profiles with filtering options.
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**Query Parameters:**
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- `architecture` (optional): Filter by GPU architecture (turing, ampere, ada_lovelace)
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- `edge_optimized` (optional): Filter for edge-optimized GPUs
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- `min_memory_gb` (optional): Minimum memory requirement
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**Response:**
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```json
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{
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"profiles": [
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{
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"id": "cgp_abc123",
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"gpu_model": "RTX 3060",
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"architecture": "ampere",
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"consumer_grade": true,
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"edge_optimized": true,
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"memory_gb": 12,
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"power_consumption_w": 170,
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"edge_premium_multiplier": 1.0
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}
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],
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"count": 1
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}
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```
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### POST /v1/marketplace/edge-gpu/scan/{miner_id}
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Scan and register edge GPUs for a miner.
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**Response:**
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```json
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{
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"miner_id": "miner_123",
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"gpus_discovered": 2,
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"gpus_registered": 2,
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"edge_optimized": 1
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}
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```
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### GET /v1/marketplace/edge-gpu/metrics/{gpu_id}
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Get real-time edge GPU performance metrics.
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**Query Parameters:**
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- `hours` (optional): Time range in hours (default: 24)
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### POST /v1/marketplace/edge-gpu/optimize/inference/{gpu_id}
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Optimize ML inference request for edge GPU.
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## ML ZK Proof Endpoints
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### POST /v1/ml-zk/prove/inference
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Generate ZK proof for ML inference correctness.
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**Request:**
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```json
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{
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"inputs": {
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"model_id": "model_123",
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"inference_id": "inference_456",
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"expected_output": [2.5]
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},
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"private_inputs": {
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"inputs": [1, 2, 3, 4],
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"weights1": [0.1, 0.2, 0.3, 0.4],
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"biases1": [0.1, 0.2]
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}
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}
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```
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### POST /v1/ml-zk/verify/inference
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Verify ZK proof for ML inference.
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### POST /v1/ml-zk/fhe/inference
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Perform ML inference on encrypted data using FHE.
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**Request:**
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```json
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{
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"scheme": "ckks",
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"provider": "tenseal",
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"input_data": [[1.0, 2.0, 3.0, 4.0]],
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"model": {
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"weights": [[0.1, 0.2, 0.3, 0.4]],
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"biases": [0.5]
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}
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}
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```
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### GET /v1/ml-zk/circuits
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List available ML ZK circuits.
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## Error Codes
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### Edge GPU Errors
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- `400`: Invalid GPU parameters
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- `404`: GPU not found
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- `500`: GPU discovery failed
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### ML ZK Errors
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- `400`: Invalid proof parameters
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- `404`: Circuit not found
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- `500`: Proof generation/verification failed
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