Epoch

As explained in Distributed Machine Learning Patterns

An epoch is one complete pass through every training example in a dataset during model training.

An epoch is one complete pass through every example in a training dataset, so each sample is seen once. Training often repeats this pass because optimization may need more information than the available examples provide. section 2.5

Multiple epochs improve access to limited training data but increase ingestion cost. If one Fashion-MNIST epoch takes three hours, two sequential epochs take six hours, making repeated reads and preprocessing operational concerns. section 2.5.1 section 7.2.1

A caching pattern reduces the cost of later epochs when resources permit:

• Read and train on batches during the first epoch while appending consumed batches to an in-memory cache. section 2.5.2
• Feed cached batches directly to the model during subsequent epochs instead of repeatedly reading from the original data source. section 2.5.2 section 7.2.2
• Cache preprocessed batches when transformations are expensive, avoiding repeated preprocessing before model training. section 2.5.2

Fashion-MNIST with cached batches

During the first epoch, process Fashion-MNIST batches and retain them in memory. During the second and later epochs, train from those cached batches rather than reading the dataset again. section 2.5 section 2.5.2

Figure 2.17 A diagram of model training for multiple epochs at time t0, t1, and so on, using a cache instead of reading from the data source again

Visualizes the transition from reading the data source during the first epoch to reusing cached batches in later epochs. section 2.5.2

How Distributed Machine Learning Patterns covers it

How deep it goes

Defines a complete dataset pass, quantifies sequential cost, and explains batch caching without descending into optimizer internals.

Examples

Uses Fashion-MNIST, 60,000 examples, three-hour epochs, sequential batches, and in-memory cached or preprocessed batches.

What it assumes

Assumes a training dataset and model-training process, while defining epochs and batch-based reuse from the beginning.

What you can do after

Apply it via a caching pattern to reduce repeated dataset reads and preprocessing across training epochs.

How it connects

Connects epochs to batch gradient descent, datasets, preprocessing, model training, and caching.

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Related concepts

• trainingpart of · Training is a process of developing or improving a person’s or model’s capabilities through instruction, experience, or adjustment using data.
• training datasetuses · A collection of examples or observations supplied to a learning procedure to fit or adapt a model or learn its patterns or parameters.
• fine-tuningconfigures · Fine-tuning is adapting a pretrained machine-learning model by training it further on targeted data or objectives for a task, domain, or behavior.
• training looppart of · A training loop is an iterative procedure that processes training data, evaluates a model objective, and updates model parameters to improve performance.
• time_pointused by · std::chrono::time_point is a C++ standard library class template representing a point in time relative to a clock's epoch, measured using a duration.
• hyperparameter tuningused by · Hyperparameter tuning is the process of selecting or adjusting configurable model or procedure settings and evaluating them against a chosen objective.

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