Epoch Calculator
Calculate the number of training epochs needed given dataset size and step budget.
About Epoch Calculator
An epoch is one complete pass over the training dataset. The relationship between epochs, total training steps, dataset size, and batch size is: total_steps = epochs × dataset_size / batch_size, which rearranges to epochs = total_steps × batch_size / dataset_size. This calculator solves either direction: given a target step budget (e.g. from a compute budget), find how many epochs that represents, or given a target number of epochs, find the total number of optimizer steps that will run.
Formulas
Epochs from steps
epochs = (total_steps × batch_size) / dataset_size
- total_steps
- Total number of optimizer steps
- batch_size
- Samples per training step
- dataset_size
- Total number of samples in the dataset
Steps from epochs
total_steps = (epochs × dataset_size) / batch_size
- epochs
- Number of full passes over the dataset
- dataset_size
- Total number of samples in the dataset
- batch_size
- Samples per training step
Worked example
With the default values below, Epoch Calculator returns Number of Epochs of 3.200. Change any input above and every figure here is recalculated in your browser.
Inputs used
- Solve Forone of Number of Epochs, Total Training Steps
- Number of Epochs
- Dataset Sizeaccepts 1–1,000,000,000 samples
- 100,000 samples
- Batch Sizeaccepts 1–1,000,000 samples
- 32 samples
- Total Training Stepsaccepts 1–1,000,000,000
- 10,000
- Number of Epochsaccepts 0.01–100,000
- 10
Results
- Number of Epochs
- 3.200
- Total Training Steps
- 10,000
- Steps per Epoch
- 3,125.00
Step by step
- 1Steps per epoch: dataset_size ÷ batch_size= 3125.00
- 2Epochs: total_steps × batch_size ÷ dataset_size= 3.200
Assumptions and limits
- The result is derived only from the 5 inputs listed above. Anything not among them is not part of the calculation.
- In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
- Figures are rounded for display to 3 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.
How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.
Frequently Asked Questions
Why might epochs come out as a fraction?
A training run does not have to stop on an exact dataset boundary — a fractional epoch count (e.g. 3.4) simply means training stopped partway through the 4th pass over the data, which is common when step count is set by a compute or time budget rather than a whole number of passes.
Does this account for gradient accumulation?
No — if you use gradient accumulation, use the effective batch size (micro-batch × accumulation steps) as the batch size input, since that is the size of the update actually applied to the model per optimizer step.
How does dataset shuffling affect this calculation?
It doesn't — shuffling changes the order samples are seen in, not the count of samples per epoch or steps per epoch, so this formula holds regardless of shuffling strategy.
What if the last batch of an epoch is smaller than batch_size?
This calculator assumes an even division; in practice frameworks either drop the final partial batch or process it as a smaller batch, which introduces a small discrepancy of at most one batch per epoch.