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2.2 KiB
2.2 KiB
API Reference - Edge Computing & ML Features
Edge GPU Endpoints
GET /v1/marketplace/edge-gpu/profiles
Get consumer GPU profiles with filtering options.
Query Parameters:
architecture(optional): Filter by GPU architecture (turing, ampere, ada_lovelace)edge_optimized(optional): Filter for edge-optimized GPUsmin_memory_gb(optional): Minimum memory requirement
Response:
{
"profiles": [
{
"id": "cgp_abc123",
"gpu_model": "RTX 3060",
"architecture": "ampere",
"consumer_grade": true,
"edge_optimized": true,
"memory_gb": 12,
"power_consumption_w": 170,
"edge_premium_multiplier": 1.0
}
],
"count": 1
}
POST /v1/marketplace/edge-gpu/scan/{miner_id}
Scan and register edge GPUs for a miner.
Response:
{
"miner_id": "miner_123",
"gpus_discovered": 2,
"gpus_registered": 2,
"edge_optimized": 1
}
GET /v1/marketplace/edge-gpu/metrics/{gpu_id}
Get real-time edge GPU performance metrics.
Query Parameters:
hours(optional): Time range in hours (default: 24)
POST /v1/marketplace/edge-gpu/optimize/inference/{gpu_id}
Optimize ML inference request for edge GPU.
ML ZK Proof Endpoints
POST /v1/ml-zk/prove/inference
Generate ZK proof for ML inference correctness.
Request:
{
"inputs": {
"model_id": "model_123",
"inference_id": "inference_456",
"expected_output": [2.5]
},
"private_inputs": {
"inputs": [1, 2, 3, 4],
"weights1": [0.1, 0.2, 0.3, 0.4],
"biases1": [0.1, 0.2]
}
}
POST /v1/ml-zk/verify/inference
Verify ZK proof for ML inference.
POST /v1/ml-zk/fhe/inference
Perform ML inference on encrypted data using FHE.
Request:
{
"scheme": "ckks",
"provider": "tenseal",
"input_data": [[1.0, 2.0, 3.0, 4.0]],
"model": {
"weights": [[0.1, 0.2, 0.3, 0.4]],
"biases": [0.5]
}
}
GET /v1/ml-zk/circuits
List available ML ZK circuits.
Error Codes
Edge GPU Errors
400: Invalid GPU parameters404: GPU not found500: GPU discovery failed
ML ZK Errors
400: Invalid proof parameters404: Circuit not found500: Proof generation/verification failed