Gradient Accumulation for Big Batches
Simulate large batches on small GPUs.
Gradient Accumulation for Big Batches is a free Deep Learning Academy lesson on CoddyKit — lesson 2 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Deep Learning Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
The Big-Batch Problem
Large batches often train more smoothly, but they also need lots of GPU memory. A small card simply cannot hold a giant batch at once.
The Core Trick
Gradient accumulation splits one big batch into small chunks. You add up their gradients and update once, as if the whole batch ran together.
Gradients Already Accumulate
PyTorch adds each backward pass into .grad rather than replacing it. This default behavior is exactly what accumulation relies on.
Pick an Accumulation Count
Choose how many mini-batches make one update. With accum_steps of four, four small batches behave like one batch four times larger.
accum_steps = 4Do Not Zero Every Step
The key change is timing: zero_grad only at the start of an accumulation cycle, not after every single mini-batch.
if step % accum_steps == 0:
optimizer.zero_grad()Scale the Loss
Divide each mini-batch loss by accum_steps before backward. This keeps the average gradient identical to running the full batch at once.
loss = loss_fn(model(x), y) / accum_steps
loss.backward()Step Only When Full
After enough mini-batches pile up, call optimizer.step. The accumulated gradients now reflect the whole large batch.
if (step + 1) % accum_steps == 0:
optimizer.step()The Full Pattern
Put it together: scale the loss, backward every step, but only step and zero once per cycle. The loop stays simple.
for step, (x, y) in enumerate(loader):
loss = loss_fn(model(x), y) / accum_steps
loss.backward()
if (step + 1) % accum_steps == 0:
optimizer.step()
optimizer.zero_grad()Memory Stays Small
You only ever hold one mini-batch in memory at a time. That is why a modest GPU can mimic a batch many times its real capacity.
The Trade-Off
Accumulation trades time for memory: more forward and backward passes per update mean each effective batch takes a little longer to finish.
Mind Batch Norm
Batch norm still sees only the small mini-batch, so its statistics are noisier than a true large batch would produce. Keep that in mind.
Quick Check
You accumulate over 4 mini-batches. When should you call optimizer.step()?
Recap
Split a big batch into chunks, divide the loss by accum_steps, backward every chunk, and step only once per cycle to fake a large batch on small memory. 🧮
Frequently asked questions
Is the “Gradient Accumulation for Big Batches” lesson free?
Yes — the full text of “Gradient Accumulation for Big Batches” is free to read here on the web, and the Deep Learning Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Deep Learning Academy course, upgrade to CoddyKit PRO.
What will I learn in “Gradient Accumulation for Big Batches”?
Simulate large batches on small GPUs. You practise Deep Learning Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start Deep Learning Academy?
No prior experience is required. Deep Learning Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 2 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Gradient Accumulation for Big Batches” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this Deep Learning Academy lesson?
Yes. Every Deep Learning Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Mixed Precision with autocast & GradScaler
- Gradient Accumulation for Big Batches
- Profile the Bottleneck
- Cut GPU Memory Usage