自动化机器学习框架NexusCore1.0稳定版文档概述
NexusCore 1.0 稳定版文档
概述
NexusCore 是一个全面的自动化机器学习框架，专为构建、训练、部署和维护高性能机器学习模型而设计。1.0 稳定版提供了以下核心功能：
- 自动化模型训练：支持分布式训练、联邦学习和持续学习
- 智能模型压缩：自动量化、剪枝和知识蒸馏
- 自修复系统：自动检测异常并恢复训练
- 跨平台部署：支持云、边缘、移动和 Web 部署
- 安全与隐私：同态加密、安全多方计算和差分隐私
- 自动化监控：实时性能监控和模型健康评估
快速开始
安装
pip install nexus-core
基本使用
from nexus_core import NexusCore
# 初始化框架
nexus = NexusCore()
# 加载数据集
dataset = nexus.load_dataset("path/to/dataset")
# 创建模型
model = nexus.create_model("HierarchicalSSMEnhanced",
input_dim=784,
hidden_dims=[512, 256],
output_dim=10)
# 训练模型
trainer = nexus.get_trainer()
trainer.train(model, dataset, epochs=10)
# 部署模型
deployer = nexus.get_deployer()
deployer.deploy(model, "cloud")
# 监控模型
monitor = nexus.get_monitor()
monitor.start_monitoring(model)
核心功能
1. 自动化模型训练
分布式训练
dist_config = {
"strategy": "ParameterServer",
"nodes": 4,
"batch_size": 128
}
trainer = nexus.get_trainer(dist_config)
trainer.train(model, dataset)
联邦学习
fed_config = {
"clients": 10,
"rounds": 100,
"privacy": {"type": "DifferentialPrivacy", "epsilon": 0.5}
}
fed_trainer = nexus.get_federated_trainer(fed_config)
fed_trainer.train(model, clients_data)
2. 模型压缩
量化
compressor = nexus.get_compressor()
quantized_model = compressor.quantize(model, bits=8)
剪枝
pruned_model = compressor.prune(model, sparsity=0.5)
3. 模型部署
云部署
deployer.deploy(model, "cloud", config={"instance_type": "g4dn.xlarge"})
边缘部署
edge_config = {
"platform": "TensorRT",
"optimizations": ["fuse_ops", "fp16"]
}
deployer.deploy(model, "edge", config=edge_config)
4. 模型监控
monitor_config = {
"metrics": ["accuracy", "latency", "throughput"],
"alert_thresholds": {"accuracy": 0.85}
}
monitor = nexus.get_monitor(monitor_config)
monitor.start_monitoring(model)
高级功能
持续学习
cl_engine = nexus.get_continual_learning_engine()
cl_engine.set_task_sequence(tasks)
cl_engine.train(model)
自动异常处理
anomaly_detector = nexus.get_anomaly_detector()
recovery_system = nexus.get_recovery_system()
# 设置异常处理器
anomaly_detector.set_handler(recovery_system.handle_anomaly)
# 开始监控
anomaly_detector.start()
安全推理
he_config = {
"scheme": "CKKS",
"security_level": 128
}
secure_engine = nexus.get_secure_inference_engine(he_config)
encrypted_result = secure_engine.encrypted_inference(encrypted_input)
性能优化指南
1. 分布式训练优化
dist_config = {
"strategy": "HybridParallel",
"model_parallel_degree": 2,
"data_parallel_degree": 4,
"gradient_accumulation": 4,
"communication_backend": "NCCL"
}
2. 模型编译优化
compiler_config = {
"target": "TensorRT",
"optimizations": {
"precision": "FP16",
"kernel_fusion": True,
"memory_optimization": True
},
"profiling": True
}
compiled_model = compiler.compile(model, compiler_config)
3. 推理优化
inference_config = {
"batch_size": 64,
"precision": "INT8",
"execution_providers": ["CUDA", "CPU"],
"threads": 8
}
optimized_model = optimizer.optimize_for_inference(model, inference_config)
API 参考
核心类
"NexusCore"
"load_dataset(path: str) -> Dataset"
"create_model(model_type: str, **kwargs) -> Model"
"get_trainer(config: dict = None) -> Trainer"
"get_deployer() -> Deployer
"get_monitor(config: dict = None) -> Monitor"
"Model"
"train(dataset: Dataset, epochs: int)"
"evaluate(dataset: Dataset) -> dict"
"save(path: str)"
"load(path: str)"
"Trainer"
"train(model: Model, dataset: Dataset, epochs: int)"
"resume_training(checkpoint_path: str)"
"create_checkpoint()"
贡献指南
我们欢迎社区贡献！请遵循以下步骤：
代码规范
- 遵循 PEP 8 风格指南
- 所有公共 API 必须有文档字符串
- 新功能必须包含单元测试
- 保持代码简洁和模块化
测试要求
pytest tests/ --cov=nexus_core
故障排除
常见问题
1. 内存不足错误
- 减少批大小
- 使用梯度累积
- 启用混合精度训练
2. 训练不稳定
- 增加梯度裁剪
- 调整学习率
- 添加更多正则化
3. 部署失败
- 检查目标平台兼容性
- 验证模型输入/输出格式
- 确保依赖项正确安装
示例项目
图像分类
from nexus_core import NexusCore
nexus = NexusCore()
# 加载 CIFAR-10 数据集
dataset = nexus.load_dataset("cifar10")
# 创建模型
model = nexus.create_model("HierarchicalSSMEnhanced",
input_dim=(32, 32, 3),
hidden_dims=[256, 128],
output_dim=10)
# 训练配置
train_config = {
"optimizer": "Adam",
"learning_rate": 0.001,
"batch_size": 128,
"epochs": 50
}
# 训练模型
trainer = nexus.get_trainer(train_config)
trainer.train(model, dataset)
# 评估模型
metrics = model.evaluate(dataset.test_set)
print(f"Test accuracy: {metrics['accuracy']:.4f}")
# 部署到云
deployer = nexus.get_deployer()
deployer.deploy(model, "cloud")
时序预测
from nexus_core import NexusCore
nexus = NexusCore()
# 加载时序数据
dataset = nexus.load_dataset("timeseries.csv")
# 创建时序模型
model = nexus.create_model("TemporalSSM",
input_dim=10,
lookback=30,
forecast=7,
hidden_dims=[64, 32])
# 训练配置
train_config = {
"optimizer": "RMSprop",
"learning_rate": 0.0005,
"batch_size": 64,
"epochs": 100
}
# 训练模型
trainer = nexus.get_trainer(train_config)
trainer.train(model, dataset)
# 预测未来值
predictions = model.predict(dataset.test_set)
# 部署到边缘设备
edge_config = {
"platform": "TensorRT",
"optimizations": ["fp16", "quantization"]
}
deployer = nexus.get_deployer()
deployer.deploy(model, "edge", config=edge_config)
许可证
NexusCore 使用 Apache License 2.0 许可证。有关详细信息，请参阅 LICENSE 文件。