How do TensorFlow and PyTorch optimize model performance?

TensorFlow and PyTorch optimize model performance by using techniques like mixed precision training, efficient data loading, and graph-mode execution to speed up computation. Both leverage hardware accelerators (GPUs/TPUs) and optimize memory usage to enhance training and inference efficiency.

They improve performance by enabling mixed precision to speed up calculations, optimizing data pipelines for faster loading, and compiling computation graphs for efficient execution on GPUs or TPUs. This leads to faster training and lower memory consumption without sacrificing accuracy.

Code

from torch.cuda.amp import autocast, GradScaler

scaler = GradScaler()
for data, label in train_loader:
    optimizer.zero_grad()
    with autocast():
        output = model(data)
        loss = criterion(output, label)
    scaler.scale(loss).backward()
    scaler.step(optimizer)
    scaler.update()