Keras API#

In this guide, you’ll learn how to use the Keras API.

Visit the API reference

det.keras API Reference

This document guides you through training a Keras model in Determined. You need to implement a trial class that inherits TFKerasTrial and specify it as the entrypoint in the experiment configuration.

To learn about this API, you can start by reading the trial definitions from the following examples:

Load Data#


Before loading data, visit Prepare Data to understand how to work with different sources of data.

Loading data is done by defining build_training_data_loader() and build_validation_data_loader() methods. Each should return one of the following data types:

  1. A tuple (x, y) of NumPy arrays. x must be a NumPy array (or array-like), a list of arrays (in case the model has multiple inputs), or a dict mapping input names to the corresponding array, if the model has named inputs. y should be a numpy array.

  2. A tuple (x, y, sample_weights) of NumPy arrays.

  3. A returning a tuple of either (inputs, targets) or (inputs, targets, sample_weights).

  4. A keras.utils.Sequence returning a tuple of either (inputs, targets) or (inputs, targets, sample weights).

If using, users are required to wrap both their training and validation dataset using self.context.wrap_dataset. This wrapper is used to shard the dataset for distributed training. For optimal performance, users should wrap a dataset immediately after creating it.


To learn more about distributed training with Determined, visit the conceptual overview or the intro to implementing distributed training.

Define the Model#

Users are required wrap their model prior to compiling it using self.context.wrap_model. This is typically done inside build_model().

Customize Calling Model Fitting Function#

The TFKerasTrial interface allows the user to configure how is called by calling self.context.configure_fit().


A checkpoint includes the model definition (Python source code), experiment configuration file, network architecture, and the values of the model’s parameters (i.e., weights) and hyperparameters. When using a stateful optimizer during training, checkpoints will also include the state of the optimizer (i.e., learning rate). You can also embed arbitrary metadata in checkpoints via a Python SDK.

TensorFlow Keras trials are checkpointed to a file named determined-keras-model.h5 using tf.keras.models.save_model. You can learn more from the TF Keras docs.


To execute arbitrary Python code during the lifecycle of a TFKerasTrial, implement the determined.keras.callbacks.Callback interface (an extension of the tf.keras.callbacks.Callbacks interface) and supply them to the TFKerasTrial by implementing keras_callbacks().