回顾：用 PyTorch Lightning 构建可扩展 AI 模型

使用 PyTorch Lightning 构建可扩展 AI 模型

Sergiy Tkachuk

Director, GenAI Productivity

用 PyTorch Lightning 构建可扩展模型

神经网络结构化
高效训练逻辑
实现 LightningModule

import lightning.pytorch as pl
import torch.nn as nn

class ClassificationModel(pl.LightningModule):
    def __init__(self, input_dim,
                 hidden_dim, num_class):
          # Initialize parent class
        super().__init__()
        # First layer
        self.layer1 = nn.Linear(input_dim,
                                hidden_dim)
        # Activation function
        self.relu = nn.ReLU()
        # Output layer
        self.layer2 = nn.Linear(hidden_dim,
                                num_class)

PyTorch Lightning 进阶技术

数据管理与预处理
验证与测试方法
优化 Lightning 回调

from lightning.pytorch import Trainer
from lightning.pytorch.callbacks import EarlyStopping, ModelCheckpoint

checkpoint = ModelCheckpoint(
    monitor='val_accuracy',
    save_top_k=2,
    mode='max')
early_stopping = EarlyStopping(
    monitor='val_accuracy',
    patience=5,
    mode='max')

为可扩展性优化模型

模型量化策略
模型剪枝以提效
用 TorchScript 部署

import torch
import torch.nn as nn

class SimpleModel(nn.Module):
    def forward(self, x):
        return x * 2

scripted_model = torch.jit.script(SimpleModel())

torch.jit.save(scripted_mod,"model.pt") # Save the model
loaded_model=torch.jit.load("model.pt") # Load the model

恭喜！

使用 PyTorch Lightning 构建可扩展 AI 模型