⏱️ Training Time Estimator
Enter your dataset size, epochs, batch size, and how many samples per second your hardware handles to project how long training will run — in batches and in hours and minutes.
🧮 Project Your Training Run
What is a Training Time Estimator?
Waiting for a model to train can take minutes or days, so it pays to know roughly how long before you start. This estimator turns your dataset size, epoch count, batch size, and hardware throughput into a clear wall-clock figure, along with the batch counts underneath it.
It's a practical way to see how each choice affects the clock: double the epochs and the time doubles, speed up your hardware and it shrinks. Experiment with the inputs to plan realistic experiments and avoid surprise overnight runs.
❓ Frequently Asked Questions
How is training time estimated?
The total number of samples processed is your dataset size times the number of epochs, because each epoch is one full pass over the data. Divide that by your hardware's throughput in samples per second and you get the wall-clock seconds, which the tool also breaks into hours and minutes. It also reports batches per epoch — the samples divided by batch size, rounded up — and the total batches across the whole run.
What is an epoch, and how many should I use?
An epoch is one complete pass through your entire training set. Models usually need several to many epochs to learn, but too many leads to overfitting, where the model memorises the training data and generalises poorly. There's no universal number — you watch validation performance and stop when it stops improving (a technique called early stopping). More epochs multiply training time directly, as this estimator makes clear.
How do I find my samples-per-second throughput?
Run training for a short while and watch how many samples the framework reports processing per second, or time one epoch and divide the sample count by the seconds it took. Throughput depends on your GPU or CPU, the model size, the batch size, and data-loading speed. Plug a realistic figure in here and you can project a full run without waiting for it to finish.
Why does the real run take longer than the estimate?
This is a lower-bound estimate based purely on raw compute throughput. Actual training also spends time loading and preprocessing data, running validation between epochs, saving checkpoints, and occasionally waiting on the CPU or disk. Those overheads can add anywhere from a little to a lot, so treat the number as a floor and budget extra headroom for a real project.