Mlops Tensorboard 集成

# 框架集成指南 将 TensorBoard 与主流机器学习框架集成的完整指南。 ## 目录 - PyTorch - TensorFlow/Keras - PyTorch Lightning - HuggingFace Transformers - Fast.ai - JAX - scikit-learn ## PyTorch ### 基础集成 python import torch import torch.nn as nn from torch.utils.tensorboard import SummaryWriter # 创建 writer writer = SummaryWriter('runs/pytorch_experiment') # 模型和优化器 model = ResNet50() optimizer = torch.optim.Adam(model.parameters(), lr=0.001) criterion = nn.CrossEntropyLoss() # 记录模型图 dummy_input = torch.randn(1, 3, 224, 224) writer.add_graph(model, dummy_input) # 训练循环 for epoch in range(100): model.train() train_loss = 0.0 for batch_idx, (data, target) in enumerate(train_loader): optimizer.zero_grad() output = model(data) loss = criterion(output, target) loss.backward() optimizer.step() train_loss += loss.item() # 记录批次指标 if batch_idx % 100 == 0: global_step = epoch * len(train_loader) + batch_idx writer.add_scalar('Loss/train_batch', loss.item(), global_step) # Epoch 指标 train_loss /= len(train_loader) writer.add_scalar('Loss/train_epoch', train_loss, epoch) # 记录直方图 for name, param in model.named_parameters(): writer.add_histogram(name, param, epoch) writer.close() ### torchvision 集成 python from torchvision.utils import make_grid # 记录图像批次 for batch_idx, (images, labels) in enumerate(train_loader): if batch_idx == 0: # 第一批次 img_grid = make_grid(images[:64], nrow=8) writer.add_image('Training_batch', img_grid, epoch) break ### 分布式训练 python import torch.distributed as dist from torch.nn.parallel import DistributedDataParallel as DDP # 设置 dist.init_process_group(backend='nccl') rank = dist.get_rank() # 仅从 rank 0 记录 if rank == 0: writer = SummaryWriter('runs/distributed_experiment') model = DDP(model, device_ids=[rank]) for epoch in range(100): train_loss = train_epoch() # 仅从 rank 0 记录 if rank == 0: writer.add_scalar('Loss/train', train_loss, epoch) ## TensorFlow/Keras ### Keras 回调 python import tensorflow as tf # TensorBoard 回调 tensorboard_callback = tf.keras.callbacks.TensorBoard( log_dir='logs/keras_experiment', histogram_freq=1,