The Complete Guide to Building with PyTorch 2.0 and torch.compile in 2026

PyTorch 2.0's torch.compile became the standard way to train deep learning models in 2025-2026, delivering 30-200% speedups through graph compilation and kernel fusion. The transition from eager mode to compiled mode is now the default for serious training workloads.

Here's the practical guide.

torch.compile Basics

import torch

# Old way (eager mode)
model = MyModel().cuda()
optimizer = torch.optim.Adam(model.parameters())

for batch in dataloader:
    inputs, targets = batch.cuda()

    outputs = model(inputs)           # Each forward pass interpreted
    loss = criterion(outputs, targets)

    optimizer.zero_grad()
    loss.backward()                  # Each backward pass interpreted
    optimizer.step()

# New way (compiled)
model = torch.compile(MyModel().cuda())  # Compile the model
optimizer = torch.optim.Adam(model.parameters())

for batch in dataloader:
    inputs, targets = batch.cuda()

    outputs = model(inputs)          # Now runs on compiled graph
    loss = criterion(outputs, targets)

    optimizer.zero_grad()
    loss.backward()                  # Compiled backward pass
    optimizer.step()

Simple CNN

import torch
import torch.nn as nn

class CNN(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
        self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
        self.pool = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(64 * 8 * 8, 256)
        self.fc2 = nn.Linear(256, 10)
        self.relu = nn.ReLU()

    def forward(self, x):
        x = self.pool(self.relu(self.conv1(x)))
        x = self.pool(self.relu(self.conv2(x)))
        x = x.view(x.size(0), -1)
        x = self.relu(self.fc1(x))
        x = self.fc2(x)
        return x

model = torch.compile(CNN().cuda(), mode="reduce-overhead")

Data Loading

from torch.utils.data import DataLoader
from torchvision import datasets, transforms

transform = transforms.Compose([
    transforms.Resize(32),
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])

train_dataset = datasets.CIFAR10(
    root="./data",
    train=True,
    download=True,
    transform=transform
)

train_loader = DataLoader(
    train_dataset,
    batch_size=64,
    shuffle=True,
    num_workers=4,
    pin_memory=True  # Faster GPU transfer
)

for batch_idx, (inputs, targets) in enumerate(train_loader):
    inputs, targets = inputs.cuda(), targets.cuda()
    # Training loop

Training Loop with Mixed Precision

from torch.cuda.amp import autocast, GradScaler

scaler = GradScaler()
model = torch.compile(MyModel().cuda())

for batch in train_loader:
    inputs, targets = batch

    with autocast():  # Mixed precision (fp16)
        outputs = model(inputs)
        loss = criterion(outputs, targets)

    scaler.scale(loss).backward()
    scaler.step(optimizer)
    scaler.update()
    optimizer.zero_grad()

Saving and Loading

# Save compiled model (just the state_dict)
torch.save(model.state_dict(), "model.pt")

# Load
model = torch.compile(MyModel())
model.load_state_dict(torch.load("model.pt"))
model.eval()  # Or .train()

# For full model save (with optimizer)
torch.save({
    "model": model.state_dict(),
    "optimizer": optimizer.state_dict(),
    "epoch": epoch,
}, "checkpoint.pt")

Inference Optimization

# Quantization for faster inference
model = torch.compile(MyModel())
model.eval()

# Dynamic quantization (smallest, fastest)
quantized = torch.quantization.quantize_dynamic(
    model, {nn.Linear}, dtype=torch.qint8
)

# TorchScript for deployment
model = torch.compile(MyModel())
model.eval()
scripted = torch.jit.script(model)
scripted.save("model_scripted.pt")