System Architecture

from kyne.sdk import KyneClient

# Initialize client
client = KyneClient(
    sui_endpoint="https://sui-rpc.example.com",
    walrus_endpoint="https://walrus.example.com"
)

# Upload dataset and create marketplace listing
dataset_id = await client.upload_dataset(
    path="./my_dataset",
    metadata={
        "name": "Training Data v1",
        "price": 1000, # in SUI tokens
        "reward_share": 0.1 # 10% to contributors
    }
)

# Deploy AI model
model_id = await client.deploy_model(
    model_path="./my_model",
    input_dataset=dataset_id,
    compute_config={
        "gpu": "t4",
        "batch_size": 32
    }
)

# Store data with Walrus
blob_id = await client.store_blob(
    data=my_data,
    config={
        "min_nodes": 7,     # 2f+1 for Byzantine fault tolerance
        "epochs": 12,       # Storage duration
        "redundancy": 2     # Red Stuff encoding level
    }
)

# Verify storage proof on SUI
proof = await client.verify_storage(blob_id)