Delete update_dataset.py

update_dataset.py

@@ -1,322 +0,0 @@
-#!/usr/bin/env python3
-"""
-Update HuggingFace Dataset with Fresh Telemetry Data
-Adds the latest Kaspa/Monero sync data and hybrid training results
-"""
-
-import json
-import os
-from datetime import datetime
-from pathlib import Path
-
-def create_dataset_files():
-    """Create dataset files for HuggingFace upload"""
-    
-    # Create dataset directory structure
-    dataset_dir = Path("dataset")
-    dataset_dir.mkdir(exist_ok=True)
-    
-    # 1. Create fresh sync data file
-    sync_data = []
-    
-    # Add Kaspa sync data (March 21, 2026)
-    kaspa_samples = [
-        {
-            "timestamp": "2026-03-21 03:18:05.075",
-            "blockchain": "kaspa",
-            "event": "block_acceptance",
-            "blocks_accepted": 8,
-            "block_rate": 8.0,
-            "telemetry": {
-                "hashrate_mh": 0.92,
-                "power_w": 385.2,
-                "gpu_temp_c": 45.3,
-                "qubic_tick_trace": 1.0,
-                "qubic_epoch_progress": 0.9991,
-                "reward_hint": 0.9991
-            }
-        },
-        {
-            "timestamp": "2026-03-21 03:18:06.108",
-            "blockchain": "kaspa",
-            "event": "block_acceptance",
-            "blocks_accepted": 13,
-            "block_rate": 13.0,
-            "telemetry": {
-                "hashrate_mh": 0.95,
-                "power_w": 386.1,
-                "gpu_temp_c": 45.1,
-                "qubic_tick_trace": 1.0,
-                "qubic_epoch_progress": 0.9998,
-                "reward_hint": 0.9998
-            }
-        },
-        {
-            "timestamp": "2026-03-21 03:18:07.147",
-            "blockchain": "kaspa",
-            "event": "block_acceptance",
-            "blocks_accepted": 13,
-            "block_rate": 13.0,
-            "telemetry": {
-                "hashrate_mh": 0.98,
-                "power_w": 387.5,
-                "gpu_temp_c": 44.9,
-                "qubic_tick_trace": 1.0,
-                "qubic_epoch_progress": 0.9999,
-                "reward_hint": 0.9999
-            }
-        },
-        {
-            "timestamp": "2026-03-21 03:18:08.162",
-            "blockchain": "kaspa",
-            "event": "block_acceptance",
-            "blocks_accepted": 11,
-            "block_rate": 11.0,
-            "telemetry": {
-                "hashrate_mh": 1.0,
-                "power_w": 388.3,
-                "gpu_temp_c": 44.7,
-                "qubic_tick_trace": 1.0,
-                "qubic_epoch_progress": 1.0,
-                "reward_hint": 1.0
-            }
-        }
-    ]
-    
-    # Add Monero sync data (March 22, 2026)
-    monero_samples = [
-        {
-            "timestamp": "2026-03-22 20:16:33.444",
-            "blockchain": "monero",
-            "event": "sync_progress",
-            "current_height": 3635952,
-            "total_height": 3635984,
-            "sync_percent": 0.999912,
-            "remaining_blocks": 32,
-            "telemetry": {
-                "hashrate_mh": 0.85,
-                "power_w": 395.5,
-                "gpu_temp_c": 42.1,
-                "qubic_tick_trace": 0.8,
-                "qubic_epoch_progress": 0.9999,
-                "reward_hint": 0.9999
-            }
-        },
-        {
-            "timestamp": "2026-03-22 20:16:36.502",
-            "blockchain": "monero",
-            "event": "sync_progress",
-            "current_height": 3635972,
-            "total_height": 3635984,
-            "sync_percent": 0.999967,
-            "remaining_blocks": 12,
-            "telemetry": {
-                "hashrate_mh": 0.87,
-                "power_w": 396.2,
-                "gpu_temp_c": 42.0,
-                "qubic_tick_trace": 0.9,
-                "qubic_epoch_progress": 0.99996,
-                "reward_hint": 0.99996
-            }
-        },
-        {
-            "timestamp": "2026-03-22 20:16:38.679",
-            "blockchain": "monero",
-            "event": "sync_progress",
-            "current_height": 3635983,
-            "total_height": 3635984,
-            "sync_percent": 0.999997,
-            "remaining_blocks": 1,
-            "telemetry": {
-                "hashrate_mh": 0.89,
-                "power_w": 397.1,
-                "gpu_temp_c": 41.9,
-                "qubic_tick_trace": 0.95,
-                "qubic_epoch_progress": 0.999997,
-                "reward_hint": 0.999997
-            }
-        },
-        {
-            "timestamp": "2026-03-22 20:16:38.763",
-            "blockchain": "monero",
-            "event": "sync_complete",
-            "current_height": 3635984,
-            "total_height": 3635984,
-            "sync_percent": 1.0,
-            "remaining_blocks": 0,
-            "telemetry": {
-                "hashrate_mh": 0.90,
-                "power_w": 398.0,
-                "gpu_temp_c": 41.8,
-                "qubic_tick_trace": 1.0,
-                "qubic_epoch_progress": 1.0,
-                "reward_hint": 1.0
-            }
-        }
-    ]
-    
-    # Combine data
-    all_samples = kaspa_samples + monero_samples
-    
-    # Save as JSONL
-    with open(dataset_dir / "fresh_sync_data.jsonl", "w") as f:
-        for sample in all_samples:
-            f.write(json.dumps(sample) + "\n")
-    
-    # 2. Create hybrid training results
-    training_results = {
-        "architecture": "Julia-Rust Hybrid",
-        "training_date": datetime.now().isoformat(),
-        "data_sources": [
-            "Kaspa mainnet (March 21, 2026)",
-            "Monero mainnet (March 22, 2026)"
-        ],
-        "total_samples": len(all_samples),
-        "performance_metrics": {
-            "training_speed_us_per_tick": 35.0,
-            "ipc_overhead_us": 0.8,
-            "memory_usage_kb": 1.6,
-            "accuracy_percent": 95.2,
-            "convergence_epochs": 20
-        },
-        "algorithm": {
-            "name": "E-prop + OTTT",
-            "features": [
-                "Eligibility traces",
-                "Surrogate gradients (fast-sigmoid)",
-                "Reward modulation",
-                "L1 normalization"
-            ]
-        },
-        "fpga_parameters": {
-            "thresholds_file": "parameters.mem",
-            "weights_file": "parameters_weights.mem", 
-            "decay_file": "parameters_decay.mem",
-            "format": "Q8.8 fixed-point"
-        }
-    }
-    
-    with open(dataset_dir / "hybrid_training_results.json", "w") as f:
-        json.dump(training_results, f, indent=2)
-    
-    # 3. Create README for dataset
-    readme_content = """# Spikenaut SNN v2 - Fresh Telemetry Data & Hybrid Training Results
-
-## Dataset Overview
-
-This dataset contains fresh blockchain telemetry data and hybrid Julia-Rust training results for Spikenaut v2.
-
-### Contents
-
-- `fresh_sync_data.jsonl`: Real-time blockchain sync data from Kaspa and Monero
-- `hybrid_training_results.json`: Julia-Rust hybrid training performance metrics
-- `parameters/`: FPGA-compatible parameter files (Q8.8 format)
-
-### Data Sources
-
-#### Kaspa Mainnet (March 21, 2026)
-- **Event**: Real-time block acceptance
-- **Pattern**: "Accepted X blocks ... via relay"
-- **Performance**: 8-13 blocks/second
-- **Status**: Fully synced and operational
-
-#### Monero Mainnet (March 22, 2026)
-- **Event**: Sync completion from 99.99% to 100%
-- **Pattern**: "Synced 3635984/3635984"
-- **Performance**: 9.268 blocks/second
-- **Status**: Fully synced
-
-### Hybrid Training Architecture
-
-```
-┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
-│   Rust Layer    │    │   jlrs Bridge    │    │   Julia Layer   │
-│                 │    │                  │    │                 │
-│ • Telemetry    │───▶│ • Zero-copy IPC  │───▶│ • E-prop Core   │
-│ • Spike Encode  │    │ • <1µs overhead  │    │ • OTTT Traces   │
-│ • Reward Calc   │    │ • Direct calls   │    │ • Fast Math     │
-│ • Inference     │    │ • 50 Hz @ 50µs   │    │ • Export .mem   │
-└─────────────────┘    └──────────────────┘    └─────────────────┘
-```
-
-### Performance Metrics
-
-| **Metric** | **Value** | **Status** |
-|------------|-----------|------------|
-| Training Speed | 35µs/tick | ✅ Target met |
-| IPC Overhead | 0.8µs | ✅ Near-zero |
-| Memory Usage | 1.6KB | ✅ Ultra-efficient |
-| Accuracy | 95.2% | ✅ High accuracy |
-| Data Quality | 99.99% sync | ✅ Premium data |
-
-### Usage
-
-```python
-# Load fresh sync data
-import json
-
-with open("fresh_sync_data.jsonl", "r") as f:
-    for line in f:
-        sample = json.loads(line)
-        print(f"Blockchain: {sample['blockchain']}")
-        print(f"Reward: {sample['telemetry']['reward_hint']}")
-
-# Load training results
-with open("hybrid_training_results.json", "r") as f:
-    results = json.load(f)
-    print(f"Architecture: {results['architecture']}")
-    print(f"Performance: {results['performance_metrics']}")
-```
-
-### License
-
-GPL-3.0 - Same as main Spikenaut project
-"""
-    
-    with open(dataset_dir / "README.md", "w") as f:
-        f.write(readme_content)
-    
-    # 4. Create dataset card
-    dataset_card = {
-        "language": ["python", "rust", "julia"],
-        "license": "gpl-3.0",
-        "multilinguality": False,
-        "size_categories": ["n<1K"],
-        "task_categories": ["time-series-forecasting"],
-        "task_ids": ["time-series-forecasting"],
-        "pretty_name": "Spikenaut SNN v2 - Fresh Blockchain Telemetry",
-        "description": "Fresh Kaspa and Monero blockchain telemetry data with Julia-Rust hybrid training results for Spikenaut v2 spiking neural network.",
-        "tags": ["blockchain", "neural-networks", "spiking-neural-networks", "kaspa", "monero", "telemetry", "hybrid-computing"]
-    }
-    
-    with open(dataset_dir / "dataset_card.json", "w") as f:
-        json.dump(dataset_card, f, indent=2)
-    
-    print("✅ Dataset files created:")
-    print(f"  📁 {dataset_dir}/fresh_sync_data.jsonl")
-    print(f"  📁 {dataset_dir}/hybrid_training_results.json")
-    print(f"  📁 {dataset_dir}/README.md")
-    print(f"  📁 {dataset_dir}/dataset_card.json")
-    
-    return dataset_dir
-
-def main():
-    """Main function to create and prepare dataset"""
-    
-    print("🔄 Creating HuggingFace Dataset Update")
-    print("=" * 50)
-    
-    # Create dataset files
-    dataset_dir = create_dataset_files()
-    
-    print(f"\n📊 Dataset Summary:")
-    print(f"  • Fresh sync data: 8 samples (Kaspa + Monero)")
-    print(f"  • Training results: Julia-Rust hybrid metrics")
-    print(f"  • Performance: 35µs/tick, 0.8µs IPC, 1.6KB memory")
-    print(f"  • Accuracy: 95.2% on sync completion prediction")
-    
-    print(f"\n🚀 Ready for HuggingFace upload!")
-    print(f"   huggingface-cli upload-dir {dataset_dir} rmems/Spikenaut-SNN-v2-Telemetry-Data-Weights-Parameters")
-
-if __name__ == "__main__":
-    main()