Batching, Shuffling & num_workers
Configure a DataLoader for speed.
Batching, Shuffling & num_workers 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.
Meet the DataLoader
A dataset hands over one sample at a time, but training wants groups. The DataLoader wraps your dataset and serves it in convenient batches. 📦
from torch.utils.data import DataLoader
loader = DataLoader(ds)Batching Saves Time
Set batch_size and the loader stacks that many samples into one tensor. Bigger batches use your hardware better and smooth out noisy updates.
loader = DataLoader(ds, batch_size=32)One Batch, Stacked Together
Each batch adds a new first dimension. Thirty-two samples of shape 784 become a single tensor shaped 32 by 784, ready for the model.
Loop Over Batches
You iterate the loader like any Python sequence. Each turn of the loop yields one batch of inputs and labels for your training step.
for xb, yb in loader:
pred = model(xb)Shuffle Every Epoch
Setting shuffle to True reorders samples each epoch. This breaks accidental ordering so the model cannot memorize the sequence of your data.
loader = DataLoader(ds, batch_size=32, shuffle=True)Shuffle Train, Not Test
Turn shuffling on for the training set but off for validation and test. Evaluation just measures performance, so a stable order is fine there.
num_workers Loads in Parallel
Reading and decoding data can stall the GPU. Setting num_workers above zero spawns helper processes that prepare the next batch while the model trains.
loader = DataLoader(ds, batch_size=32, num_workers=4)Pick a Sensible Worker Count
A common start for num_workers is the number of CPU cores you have. Too many can thrash memory, so measure rather than guess blindly.
pin_memory Speeds GPU Copies
When training on a GPU, set pin_memory to True. It places batches in page-locked memory so transfers to the device run noticeably faster.
loader = DataLoader(ds, batch_size=32, pin_memory=True)Handle the Last Batch
The final batch is often smaller than the rest. Use drop_last True to discard it when your model needs every batch the same size.
loader = DataLoader(ds, batch_size=32, drop_last=True)One Loader Per Split
In practice you build a separate loader for train, validation, and test. Each gets its own settings, like shuffle on only for training.
Quick Check
What does setting num_workers above zero actually do?
Recap
A DataLoader batches your dataset, shuffles training data, and uses num_workers to load batches in parallel. It keeps your model fed and fast. 🎉
Frequently asked questions
Is the “Batching, Shuffling & num_workers” lesson free?
Yes — the full text of “Batching, Shuffling & num_workers” 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 “Batching, Shuffling & num_workers”?
Configure a DataLoader for speed. 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 “Batching, Shuffling & num_workers” 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
- Write a Custom Dataset Class
- Batching, Shuffling & num_workers
- collate_fn for Variable-Length Inputs
- Normalize and Standardize Inputs