chore: remove configuration files and enhance blockchain explorer with advanced search, analytics, and export features
- Delete .aitbc.yaml.example CLI configuration template - Delete .lycheeignore link checker exclusion rules - Delete .nvmrc Node.js version specification - Add advanced search panel with filters for address, amount range, transaction type, time range, and validator - Add analytics dashboard with transaction volume, active addresses, and block time metrics - Add Chart.js integration
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oib
217
cli/tests/gpu/gpu_test.py
Executable file
217
cli/tests/gpu/gpu_test.py
Executable file
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#!/usr/bin/env python3
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"""
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GPU Access Test - Check if miner can access local GPU resources
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"""
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import argparse
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import subprocess
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import json
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import time
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import psutil
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def check_nvidia_gpu():
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"""Check NVIDIA GPU availability"""
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print("🔍 Checking NVIDIA GPU...")
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try:
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# Check nvidia-smi
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result = subprocess.run(
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["nvidia-smi", "--query-gpu=name,memory.total,memory.free,utilization.gpu",
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"--format=csv,noheader,nounits"],
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capture_output=True,
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text=True
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)
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if result.returncode == 0:
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lines = result.stdout.strip().split('\n')
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print(f"✅ NVIDIA GPU(s) Found: {len(lines)}")
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for i, line in enumerate(lines, 1):
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parts = line.split(', ')
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if len(parts) >= 4:
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name = parts[0]
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total_mem = parts[1]
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free_mem = parts[2]
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util = parts[3]
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print(f"\n GPU {i}:")
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print(f" 📦 Model: {name}")
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print(f" 💾 Memory: {free_mem}/{total_mem} MB free")
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print(f" ⚡ Utilization: {util}%")
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return True
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else:
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print("❌ nvidia-smi command failed")
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return False
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except FileNotFoundError:
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print("❌ nvidia-smi not found - NVIDIA drivers not installed")
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return False
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def check_cuda():
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"""Check CUDA availability"""
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print("\n🔍 Checking CUDA...")
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try:
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# Try to import pynvml
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import pynvml
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pynvml.nvmlInit()
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device_count = pynvml.nvmlDeviceGetCount()
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print(f"✅ CUDA Available - {device_count} device(s)")
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for i in range(device_count):
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handle = pynvml.nvmlDeviceGetHandleByIndex(i)
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name = pynvml.nvmlDeviceGetName(handle).decode('utf-8')
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memory_info = pynvml.nvmlDeviceGetMemoryInfo(handle)
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print(f"\n CUDA Device {i}:")
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print(f" 📦 Name: {name}")
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print(f" 💾 Memory: {memory_info.free // 1024**2}/{memory_info.total // 1024**2} MB free")
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return True
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except ImportError:
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print("⚠️ pynvml not installed - install with: pip install pynvml")
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return False
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except Exception as e:
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print(f"❌ CUDA error: {e}")
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return False
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def check_pytorch():
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"""Check PyTorch CUDA support"""
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print("\n🔍 Checking PyTorch CUDA...")
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try:
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import torch
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print(f"✅ PyTorch Installed: {torch.__version__}")
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print(f" CUDA Available: {torch.cuda.is_available()}")
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if torch.cuda.is_available():
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print(f" CUDA Version: {torch.version.cuda}")
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print(f" GPU Count: {torch.cuda.device_count()}")
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for i in range(torch.cuda.device_count()):
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props = torch.cuda.get_device_properties(i)
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print(f"\n PyTorch GPU {i}:")
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print(f" 📦 Name: {props.name}")
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print(f" 💾 Memory: {props.total_memory // 1024**2} MB")
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print(f" Compute: {props.major}.{props.minor}")
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return torch.cuda.is_available()
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except ImportError:
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print("❌ PyTorch not installed - install with: pip install torch")
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return False
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def run_gpu_stress_test(duration=10):
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"""Run a quick GPU stress test"""
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print(f"\n🔥 Running GPU Stress Test ({duration}s)...")
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try:
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import torch
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if not torch.cuda.is_available():
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print("❌ CUDA not available for stress test")
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return False
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device = torch.device('cuda')
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# Create tensors and perform matrix multiplication
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print(" ⚡ Performing matrix multiplications...")
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start_time = time.time()
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while time.time() - start_time < duration:
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# Create large matrices
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a = torch.randn(1000, 1000, device=device)
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b = torch.randn(1000, 1000, device=device)
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# Multiply them
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c = torch.mm(a, b)
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# Sync to ensure computation completes
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torch.cuda.synchronize()
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print("✅ Stress test completed successfully")
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return True
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except Exception as e:
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print(f"❌ Stress test failed: {e}")
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return False
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def check_system_resources():
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"""Check system resources"""
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print("\n💻 System Resources:")
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# CPU
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cpu_percent = psutil.cpu_percent(interval=1)
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print(f" 🖥️ CPU Usage: {cpu_percent}%")
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print(f" 🧠 CPU Cores: {psutil.cpu_count()} logical, {psutil.cpu_count(logical=False)} physical")
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# Memory
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memory = psutil.virtual_memory()
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print(f" 💾 RAM: {memory.used // 1024**2}/{memory.total // 1024**2} MB used ({memory.percent}%)")
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# Disk
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disk = psutil.disk_usage('/')
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print(f" 💿 Disk: {disk.used // 1024**3}/{disk.total // 1024**3} GB used")
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def main():
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parser = argparse.ArgumentParser(description="GPU Access Test for AITBC Miner")
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parser.add_argument("--stress", type=int, default=0, help="Run stress test for N seconds")
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parser.add_argument("--all", action="store_true", help="Run all tests including stress")
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args = parser.parse_args()
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print("🚀 AITBC GPU Access Test")
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print("=" * 60)
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# Check system resources
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check_system_resources()
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# Check GPU availability
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has_nvidia = check_nvidia_gpu()
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has_cuda = check_cuda()
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has_pytorch = check_pytorch()
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# Summary
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print("\n📊 SUMMARY")
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print("=" * 60)
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if has_nvidia or has_cuda or has_pytorch:
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print("✅ GPU is available for mining!")
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if args.stress > 0 or args.all:
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run_gpu_stress_test(args.stress if args.stress > 0 else 10)
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print("\n💡 Miner can run GPU-intensive tasks:")
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print(" • Model inference (LLaMA, Stable Diffusion)")
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print(" • Training jobs")
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print(" • Batch processing")
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else:
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print("❌ No GPU available - miner will run in CPU-only mode")
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print("\n💡 To enable GPU mining:")
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print(" 1. Install NVIDIA drivers")
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print(" 2. Install CUDA toolkit")
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print(" 3. Install PyTorch with CUDA: pip install torch")
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# Check if miner service is running
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print("\n🔍 Checking miner service...")
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try:
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result = subprocess.run(
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["systemctl", "is-active", "aitbc-gpu-miner"],
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capture_output=True,
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text=True
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)
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if result.stdout.strip() == "active":
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print("✅ Miner service is running")
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else:
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print("⚠️ Miner service is not running")
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print(" Start with: sudo systemctl start aitbc-gpu-miner")
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except:
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print("⚠️ Could not check miner service status")
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if __name__ == "__main__":
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main()
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