Cut GPU Memory Usage
Checkpointing and smarter tensor handling.
Cut GPU Memory Usage is a free Deep Learning Academy lesson on CoddyKit — lesson 4 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 Dreaded OOM
Run out of GPU memory and training crashes with an out-of-memory error. The good news is several simple tactics free up space fast.
Where Memory Goes
Your GPU holds the model weights, the gradients, the optimizer state, and the activations saved for backward. Activations are often the biggest.
Shrink the Batch
The fastest fix is a smaller batch size. Fewer samples per step means fewer activations to store, and accumulation can recover the effective size.
No Grad for Inference
During evaluation you do not need gradients. Wrapping inference in torch.no_grad skips storing activations and saves a lot of memory.
with torch.no_grad():
preds = model(x)Mixed Precision Helps Here Too
Half-precision tensors are simply smaller. Turning on autocast cuts activation and weight memory roughly in half during training.
with torch.autocast(device_type='cuda'):
out = model(x)Gradient Checkpointing
Checkpointing trades compute for memory: it drops most activations and recomputes them during backward instead of keeping them all.
from torch.utils.checkpoint import checkpointApply Checkpointing
Wrap a heavy block in checkpoint so its activations are rebuilt on the backward pass. You save memory at the cost of extra recompute.
out = checkpoint(heavy_block, x)Detach What You Log
Keeping a loss tensor around holds its whole graph in memory. Call .item() to log just the number and let the graph be freed.
running_loss += loss.item()Use set_to_none
Pass set_to_none to zero_grad so gradient tensors are released instead of merely filled with zeros, freeing their memory between steps.
optimizer.zero_grad(set_to_none=True)Clear the Cache
PyTorch caches freed blocks for reuse. When you truly need space back, empty_cache returns it to the GPU, though it rarely fixes real leaks.
torch.cuda.empty_cache()Inspect Your Usage
Check how much you hold with memory_allocated. Watching this number while you tune confirms which change actually freed space.
print(torch.cuda.memory_allocated())Quick Check
Which technique saves memory by recomputing activations during backward?
Recap
Beat out-of-memory by shrinking batches, using no_grad for inference, autocast, gradient checkpointing, and set_to_none. Measure with memory_allocated. 🧹
Frequently asked questions
Is the “Cut GPU Memory Usage” lesson free?
Yes — the full text of “Cut GPU Memory Usage” 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 “Cut GPU Memory Usage”?
Checkpointing and smarter tensor handling. 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 4 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Cut GPU Memory Usage” 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.