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_MASTERenvironment 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:
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
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. pass def build_model(self): # Define and compile model graph. pass def build_training_data_loader(self): # Create the training data loader. This should return a keras.Sequence, # a tf.data.Dataset, or NumPy arrays. pass def build_validation_data_loader(self): # Create the validation data loader. This should return a keras.Sequence, # a tf.data.Dataset, or NumPy arrays. pass
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
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¶
build_model() method returns a
tf.keras.Model object. The Fashion MNIST model code uses
the Keras Sequential API and we can continue to use that API in our
build_model. The only minor difference is that the
model needs to be wrapped by calling
self.context.wrap_model() before it is
def build_model(self): model = keras.Sequential( [ keras.layers.Flatten(input_shape=(28, 28)), keras.layers.Dense(self.context.get_hparam("dense1"), activation="relu"), keras.layers.Dense(10), ] ) model = self.context.wrap_model(model) model.compile( optimizer=tf.keras.optimizers.Adam(name="Adam"), loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=[tf.keras.metrics.SparseCategoricalAccuracy(name="accuracy")], ) return model
Determined supports three ways of loading data into a
model: as a tf.keras.utils.Sequence,
a tf.data.Dataset, 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 hyperparameters: global_batch_size: 32 dense1: 128 records_per_epoch: 50000 searcher: name: single metric: val_accuracy max_length: 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
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
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
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.