TensorFlow Keras Fashion MNIST Tutorial

This tutorial describes how to port an existing tf.keras model to Determined. We will port a simple image classification model for the Fashion MNIST dataset. This tutorial is based on the official TensorFlow Basic Image Classification Tutorial.


  • Access to a Determined cluster. If you have not yet installed Determined, refer to the installation instructions.

  • The Determined CLI should be installed on your local machine. For installation instructions, see here. After installing the CLI, configure it to connect to your Determined cluster by setting the DET_MASTER environment variable to the hostname or IP address where Determined is running.


To use a TensorFlow model in Determined, you need to port the model to Determined’s API. For most models, this porting process is straightforward, and once the model has been ported, all of the features of Determined will then be available: for example, you can do distributed training or hyperparameter search without changing your model code, and Determined will store and visualize your model metrics automatically.

When training a tf.keras model, Determined provides a built-in training loop that feeds batches of data into your model, performs backpropagation, and computes training metrics. Determined also handles evaluating your model on the validation set, as well as other details like checkpointing, log management, and device initialization. To plug your model code into the Determined training loop, you define methods to perform the following tasks:

  • initialization

  • build the model graph

  • load the training dataset

  • load the validation dataset

The Determined training loop will then invoke these functions automatically. These methods should be organized into a trial class, which is a user-defined Python class that inherits from determined.keras.TFKerasTrial. The following sections walk through how to write your first trial class and then how to run a training job with Determined.

The complete code for this tutorial can be downloaded here: fashion_mnist_tf_keras.tgz. After downloading this file, open a terminal window, extract the file, and cd into the fashion_mnist_tf_keras directory:

tar xzvf fashion_mnist_tf_keras.tgz
cd fashion_mnist_tf_keras

We suggest you follow along with the code as you read through this tutorial.

Building a Trial Class

Here is what the skeleton of our trial class looks like:

import keras
from determined.keras import TFKerasTrial, TFKerasTrialContext

class FashionMNISTTrial(TFKerasTrial):
    def __init__(self, context: TFKerasTrialContext):
        # Initialize the trial class.

    def build_model(self):
        # Define and compile model graph.

    def build_training_data_loader(self):
        # Create the training data loader. This should return a keras.Sequence,
        # a, or NumPy arrays.

    def build_validation_data_loader(self):
        # Create the validation data loader. This should return a keras.Sequence,
        # a, or NumPy arrays.

We now discuss how to implement each of these methods in more detail.


As with any Python class, the __init__ method is invoked to construct our trial class. Determined passes this method a single parameter, TrialContext. The trial context contains information about the trial, such as the values of the hyperparameters to use for training. For the time being, we don’t need to access any properties from the trial context object, but we assign it to an instance variable so that we can use it later:

def __init__(self, context: TFKerasTrialContext):
    # Store trial context for later use.
    self.context = context

Building the Model

The build_model() method returns a compiled tf.keras.Model object. The Fashion MNIST model code uses the Keras Sequential API and we can continue to use that API in our implementation of build_model. The only minor differences is that the model needs to be wrapped by calling self.context.wrap_model() before it is compiled and the optimizer needs to be wrapped by calling self.context.wrap_optimizer().

def build_model(self):
    model = keras.Sequential(
            keras.layers.Flatten(input_shape=(28, 28)),
            keras.layers.Dense(self.context.get_hparam("dense1"), activation="relu"),

    # Wrap the model.
    model = self.context.wrap_model(model)

    # Create and wrap optimizer.
    optimizer = tf.keras.optimizers.Adam()
    optimizer = self.context.wrap_optimizer(optimizer)

    return model

Loading Data

The last two methods we need to define are build_training_data_loader() and build_validation_data_loader(). Determined uses these methods to load the training and validation datasets, respectively.

Determined supports three ways of loading data into a tf.keras model: as a tf.keras.utils.Sequence, a, or as a pair of NumPy arrays. Because the dataset is small, the Fashion MNIST model represents the data using NumPy arrays.

def build_training_data_loader(self):
    train_images, train_labels = data.load_training_data()
    train_images = train_images / 255.0

    return train_images, train_labels

The implementation of build_validation_data_loader is similar:

def build_validation_data_loader(self):
    test_images, test_labels = data.load_validation_data()
    test_images = test_images / 255.0

    return test_images, test_labels

For more information on loading data in Determined, refer to the tutorial on Accessing Data.

Training the Model

Now that we have ported our model code to the trial API, we can use Determined to train a single instance of the model or to do a hyperparameter search. In Determined, a trial is a training task that consists of a dataset, a deep learning model, and values for all of the model’s hyperparameters. An experiment is a collection of one or more trials: an experiment can either train a single model (with a single trial), or it can perform a search over a user-defined hyperparameter space.

To create an experiment, we start by writing a configuration file which defines the kind of experiment we want to run. In this case, we want to train a single model for five epochs, using fixed values for the model’s hyperparameters:

description: fashion_mnist_keras_const
    global_batch_size: 32
    dense1: 128
records_per_epoch: 50000
    name: single
    metric: val_accuracy
      epochs: 5
entrypoint: model_def:FashionMNISTTrial

For this model, we have chosen two hyperparameters: the size of the Dense layer and the batch size. Training the model for five epochs should reach about 85% accuracy on the validation set, which matches the original tf.keras implementation.

The entrypoint specifies the name of the trial class to use. This is useful if our model code contains more than one trial class. In this case, we use an entrypoint of model_def:FashionMNISTTrial because our trial class is named FashionMNISTTrial and it is defined in a Python file named

For more information on experiment configuration, see the experiment configuration reference.

Running an Experiment

The Determined CLI can be used to create a new experiment, which will immediately start running on the cluster. To do this, we run:

det experiment create const.yaml .

Here, the first argument (const.yaml) is the name of the experiment configuration file and the second argument (.) is the location of the directory that contains our model definition files. You may need to configure the CLI with the network address where the Determined master is running, via the -m flag or the DET_MASTER environment variable. For more information, see the CLI reference page.

Once the experiment is started, you will see a notification:

Preparing files (../fashion_mnist_tf_keras) to send to master... 2.5KB and 4 files
Created experiment xxx

Evaluating the Model

Model evaluation is done automatically for you by Determined. To access information on both training and validation performance, simply go to the WebUI by entering the address of the Determined master in your web browser.

Once you are on the Determined landing page, you can find your experiment either via the experiment ID (xxx) or via its description.