What's the ideal batch size for my model?

There's no universal answer. Larger batches train faster but require more memory and may need larger learning rates. Common starting points: 16-64 for transformers, 64-256 for CNNs, 32-128 for general use. Start with 32 and adjust based on GPU memory and training stability.

How does batch size affect learning rate?

Larger batch sizes typically need larger learning rates. A common rule: when doubling batch size, increase learning rate by √2 (linear scaling). Some research suggests linear scaling works too. Always validate with experiments.

What is gradient accumulation?

Gradient accumulation simulates larger batch sizes without increasing memory. Instead of one update per batch, you accumulate gradients over N smaller batches before updating weights. Effective batch size = actual batch size × accumulation steps.

Should I use drop_last=True in training?

It depends. For training: usually yes, as uneven batches can affect batch normalization and loss averaging. For validation/testing: usually no, to ensure all samples are evaluated. Some frameworks handle this automatically.

How do I calculate steps for distributed training?

With N GPUs using data parallelism: Global batch size = Local batch size × N. Steps per epoch = Dataset size / Global batch size. Each GPU processes 1/Nth of each batch, so local steps equal global steps.

Technology

Epoch & Batch Size Calculator

Calculate training iterations, steps per epoch, and optimize batch sizes for machine learning models. Essential for understanding ML training dynamics and memory optimization.

Dataset Size

samples

Batch Size

samples/batch

Number of Epochs

epochs

Time per Step (optional)

seconds

Quick Batch Size Selection

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Master ML Training Iterations

Understanding epochs, batches, and steps is fundamental to machine learning training. This calculator helps you plan your training loop, optimize batch sizes for memory efficiency, and estimate training duration. Whether you're fine-tuning a pre-trained model or training from scratch, knowing your iteration counts is essential.

Understanding Epochs, Batches, and Steps

In machine learning, an epoch is one complete pass through the entire training dataset. A batch is a subset of samples processed together in one forward/backward pass. Steps (or iterations) are the number of batches processed. The relationship is: Steps per Epoch = Dataset Size / Batch Size. These concepts determine how often weights are updated and how memory is utilized.

Core Formula

Steps per Epoch = ⌈Dataset Size / Batch Size⌉

Why Calculate Epoch & Batch Size?

Memory Optimization

Batch size directly affects GPU memory usage. Find the largest batch that fits in memory for optimal training throughput.

Training Stability

Batch size impacts gradient noise and learning dynamics. Too small batches can be unstable, too large may miss local optima.

Learning Rate Scheduling

Many schedulers depend on total steps or steps per epoch. Accurate counts are essential for proper warmup and decay.

Checkpoint Planning

Know when to save checkpoints based on step counts for recovery and evaluation.

Time Estimation

Predict training duration by multiplying steps by time per iteration.

How to Calculate Training Steps

Use Cases for Epoch & Batch Calculations

DataLoader Configuration

Set up PyTorch/TensorFlow DataLoaders with optimal batch sizes and decide whether to drop incomplete last batches.

Learning Rate Warmup

Calculate exact steps for linear or cosine warmup schedules based on epochs or total steps.

Gradient Accumulation

When batch size exceeds memory, calculate accumulation steps to achieve target effective batch size.

Progress Tracking

Set up progress bars and logging with accurate total step counts.

Multi-GPU Training

Calculate effective global batch size and steps when using distributed training.

Experiment Planning

Estimate total iterations across hyperparameter sweeps with varying batch sizes.

Frequently Asked Questions

The last batch will be smaller. For example, 1000 samples with batch size 64 gives 15 full batches (960 samples) and 1 partial batch (40 samples). You can use drop_last=True in DataLoader to skip the incomplete batch, ensuring consistent batch sizes.

Master ML Training Iterations

Understanding Epochs, Batches, and Steps

Core Formula

Steps per Epoch = ⌈Dataset Size / Batch Size⌉