What is the difference between model.eval(), torch.no_grad(), and torch.inference_mode()? When do you use each?

These three mechanisms serve different but complementary purposes that are often confused. Understanding the distinction prevents subtle bugs in training, validation, and inference code.

import torch
import torch.nn as nn

model = nn.Sequential(
    nn.Linear(10, 64), nn.ReLU(),
    nn.BatchNorm1d(64), nn.Dropout(0.5),
    nn.Linear(64, 1)
)

# ─── Training ───────────────────────────────────────────────────────
model.train()   # Dropout ACTIVE, BatchNorm uses batch stats
x = torch.randn(32, 10)
out1 = model(x)
out2 = model(x)  # DIFFERENT — Dropout randomly drops each call

# ─── Validation (compute val loss, need backward later? No) ─────────
model.eval()
with torch.no_grad():
    # Dropout OFF, BatchNorm uses running stats, no computation graph
    val_out = model(x)
    val_loss = nn.MSELoss()(val_out, torch.zeros(32, 1))

# ─── Inference / deployment ─────────────────────────────────────────
model.eval()
with torch.inference_mode():  # fastest; cannot go back to autograd
    pred = model(torch.randn(1, 10))

# COMMON BUG: forgetting model.eval() at inference
# model.eval() and torch.no_grad() are INDEPENDENT — you need BOTH:
# - model.eval() alone: still builds graph (memory waste)
# - torch.no_grad() alone: Dropout still active (wrong predictions)