feat: implement v0.2.0 release features - agent-first evolution
✅ v0.2 Release Preparation: - Update version to 0.2.0 in pyproject.toml - Create release build script for CLI binaries - Generate comprehensive release notes ✅ OpenClaw DAO Governance: - Implement complete on-chain voting system - Create DAO smart contract with Governor framework - Add comprehensive CLI commands for DAO operations - Support for multiple proposal types and voting mechanisms ✅ GPU Acceleration CI: - Complete GPU benchmark CI workflow - Comprehensive performance testing suite - Automated benchmark reports and comparison - GPU optimization monitoring and alerts ✅ Agent SDK Documentation: - Complete SDK documentation with examples - Computing agent and oracle agent examples - Comprehensive API reference and guides - Security best practices and deployment guides ✅ Production Security Audit: - Comprehensive security audit framework - Detailed security assessment (72.5/100 score) - Critical issues identification and remediation - Security roadmap and improvement plan ✅ Mobile Wallet & One-Click Miner: - Complete mobile wallet architecture design - One-click miner implementation plan - Cross-platform integration strategy - Security and user experience considerations ✅ Documentation Updates: - Add roadmap badge to README - Update project status and achievements - Comprehensive feature documentation - Production readiness indicators 🚀 Ready for v0.2.0 release with agent-first architecture
This commit is contained in:
AITBC System
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dev/gpu/test_gpu_performance.py
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275
dev/gpu/test_gpu_performance.py
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#!/usr/bin/env python3
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"""
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GPU Performance Benchmarking Suite
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Tests GPU acceleration capabilities for AITBC mining and computation
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"""
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import pytest
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import torch
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import cupy as cp
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import numpy as np
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import time
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import json
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from typing import Dict, List, Tuple
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import pynvml
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# Initialize NVML for GPU monitoring
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try:
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pynvml.nvmlInit()
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NVML_AVAILABLE = True
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except:
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NVML_AVAILABLE = False
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class GPUBenchmarkSuite:
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"""Comprehensive GPU benchmarking suite"""
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def __init__(self):
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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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self.results = {}
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def get_gpu_info(self) -> Dict:
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"""Get GPU information"""
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info = {
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"pytorch_available": torch.cuda.is_available(),
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"pytorch_version": torch.__version__,
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"cuda_version": torch.version.cuda if torch.cuda.is_available() else None,
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"gpu_count": torch.cuda.device_count() if torch.cuda.is_available() else 0,
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}
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if torch.cuda.is_available():
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info.update({
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"gpu_name": torch.cuda.get_device_name(0),
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"gpu_memory": torch.cuda.get_device_properties(0).total_memory / 1e9,
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"gpu_compute_capability": torch.cuda.get_device_capability(0),
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})
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if NVML_AVAILABLE:
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try:
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handle = pynvml.nvmlDeviceGetHandleByIndex(0)
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info.update({
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"gpu_driver_version": pynvml.nvmlSystemGetDriverVersion().decode(),
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"gpu_temperature": pynvml.nvmlDeviceGetTemperature(handle, pynvml.NVML_TEMPERATURE_GPU),
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"gpu_power_usage": pynvml.nvmlDeviceGetPowerUsage(handle) / 1000, # Watts
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"gpu_clock": pynvml.nvmlDeviceGetClockInfo(handle, pynvml.NVML_CLOCK_GRAPHICS),
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})
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except:
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pass
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return info
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@pytest.mark.benchmark(group="matrix_operations")
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def test_matrix_multiplication_pytorch(self, benchmark):
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"""Benchmark PyTorch matrix multiplication"""
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if not torch.cuda.is_available():
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pytest.skip("CUDA not available")
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def matmul_op():
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size = 2048
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a = torch.randn(size, size, device=self.device)
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b = torch.randn(size, size, device=self.device)
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c = torch.matmul(a, b)
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return c
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result = benchmark(matmul_op)
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self.results['pytorch_matmul'] = {
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'ops_per_sec': 1 / benchmark.stats['mean'],
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'mean': benchmark.stats['mean'],
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'std': benchmark.stats['stddev']
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}
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return result
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@pytest.mark.benchmark(group="matrix_operations")
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def test_matrix_multiplication_cupy(self, benchmark):
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"""Benchmark CuPy matrix multiplication"""
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try:
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def matmul_op():
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size = 2048
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a = cp.random.randn(size, size, dtype=cp.float32)
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b = cp.random.randn(size, size, dtype=cp.float32)
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c = cp.dot(a, b)
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return c
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result = benchmark(matmul_op)
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self.results['cupy_matmul'] = {
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'ops_per_sec': 1 / benchmark.stats['mean'],
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'mean': benchmark.stats['mean'],
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'std': benchmark.stats['stddev']
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}
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return result
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except:
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pytest.skip("CuPy not available")
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@pytest.mark.benchmark(group="mining_operations")
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def test_hash_computation_gpu(self, benchmark):
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"""Benchmark GPU hash computation (simulated mining)"""
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if not torch.cuda.is_available():
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pytest.skip("CUDA not available")
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def hash_op():
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# Simulate hash computation workload
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batch_size = 10000
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data = torch.randn(batch_size, 32, device=self.device)
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# Simple hash simulation
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hash_result = torch.sum(data, dim=1)
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hash_result = torch.abs(hash_result)
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# Additional processing
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processed = torch.sigmoid(hash_result)
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return processed
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result = benchmark(hash_op)
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self.results['gpu_hash_computation'] = {
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'ops_per_sec': 1 / benchmark.stats['mean'],
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'mean': benchmark.stats['mean'],
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'std': benchmark.stats['stddev']
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}
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return result
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@pytest.mark.benchmark(group="mining_operations")
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def test_proof_of_work_simulation(self, benchmark):
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"""Benchmark proof-of-work simulation"""
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if not torch.cuda.is_available():
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pytest.skip("CUDA not available")
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def pow_op():
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# Simulate PoW computation
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nonce = torch.randint(0, 2**32, (1000,), device=self.device)
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data = torch.randn(1000, 64, device=self.device)
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# Hash simulation
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combined = torch.cat([nonce.float().unsqueeze(1), data], dim=1)
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hash_result = torch.sum(combined, dim=1)
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# Difficulty check
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difficulty = torch.tensor(0.001, device=self.device)
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valid = hash_result < difficulty
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return torch.sum(valid.float()).item()
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result = benchmark(pow_op)
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self.results['pow_simulation'] = {
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'ops_per_sec': 1 / benchmark.stats['mean'],
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'mean': benchmark.stats['mean'],
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'std': benchmark.stats['stddev']
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}
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return result
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@pytest.mark.benchmark(group="neural_operations")
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def test_neural_network_forward(self, benchmark):
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"""Benchmark neural network forward pass"""
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if not torch.cuda.is_available():
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pytest.skip("CUDA not available")
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# Simple neural network
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model = torch.nn.Sequential(
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torch.nn.Linear(784, 256),
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torch.nn.ReLU(),
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torch.nn.Linear(256, 128),
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torch.nn.ReLU(),
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torch.nn.Linear(128, 10)
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).to(self.device)
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def forward_op():
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batch_size = 64
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x = torch.randn(batch_size, 784, device=self.device)
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output = model(x)
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return output
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result = benchmark(forward_op)
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self.results['neural_forward'] = {
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'ops_per_sec': 1 / benchmark.stats['mean'],
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'mean': benchmark.stats['mean'],
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'std': benchmark.stats['stddev']
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}
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return result
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@pytest.mark.benchmark(group="memory_operations")
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def test_gpu_memory_bandwidth(self, benchmark):
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"""Benchmark GPU memory bandwidth"""
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if not torch.cuda.is_available():
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pytest.skip("CUDA not available")
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def memory_op():
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size = 100_000_000 # 100M elements
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# Allocate and copy data
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a = torch.randn(size, device=self.device)
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b = torch.randn(size, device=self.device)
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# Memory operations
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c = a + b
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d = c * 2.0
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return d
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result = benchmark(memory_op)
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self.results['memory_bandwidth'] = {
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'ops_per_sec': 1 / benchmark.stats['mean'],
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'mean': benchmark.stats['mean'],
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'std': benchmark.stats['stddev']
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}
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return result
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@pytest.mark.benchmark(group="crypto_operations")
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def test_encryption_operations(self, benchmark):
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"""Benchmark GPU encryption operations"""
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if not torch.cuda.is_available():
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pytest.skip("CUDA not available")
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def encrypt_op():
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# Simulate encryption workload
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batch_size = 1000
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key_size = 256
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data_size = 1024
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# Generate keys and data
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keys = torch.randn(batch_size, key_size, device=self.device)
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data = torch.randn(batch_size, data_size, device=self.device)
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# Simple encryption simulation
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encrypted = torch.matmul(data, keys.T) / 1000.0
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decrypted = torch.matmul(encrypted, keys) / 1000.0
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return torch.mean(torch.abs(data - decrypted))
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result = benchmark(encrypt_op)
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self.results['encryption_ops'] = {
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'ops_per_sec': 1 / benchmark.stats['mean'],
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'mean': benchmark.stats['mean'],
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'std': benchmark.stats['stddev']
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}
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return result
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def save_results(self, filename: str):
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"""Save benchmark results to file"""
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gpu_info = self.get_gpu_info()
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results_data = {
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"timestamp": time.time(),
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"gpu_info": gpu_info,
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"benchmarks": self.results
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}
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with open(filename, 'w') as f:
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json.dump(results_data, f, indent=2)
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# Test instance
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benchmark_suite = GPUBenchmarkSuite()
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# Pytest fixture for setup
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@pytest.fixture(scope="session")
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def gpu_benchmark():
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return benchmark_suite
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# Save results after all tests
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def pytest_sessionfinish(session, exitstatus):
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"""Save benchmark results after test completion"""
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try:
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benchmark_suite.save_results('gpu_benchmark_results.json')
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except Exception as e:
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print(f"Failed to save benchmark results: {e}")
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if __name__ == "__main__":
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# Run benchmarks directly
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import sys
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sys.exit(pytest.main([__file__, "-v", "--benchmark-only"]))
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