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Tutorial 10: How to use FastEstimator Command Line Interface (CLI)

Overview

FastEstimator comes with a set of CLI commands that can help users train and test their models quickly. In this tutorial, we will go through the CLI usage and the arguments these CLI commands take. This tutorial is divided into the following sections:

How Does the CLI Work

Given a python file, the FastEstimator CLI looks for a get_estimator function to get the estimator definition. It then calls either the fit() or test() functions on the returned estimator instance to train or test the model.

CLI Usage

In this section we will show the actual commands that we can use to train and test our models. We will use mnist_tf.py for illustration.

To call estimator.fit() and start the training on terminal:

$ fastestimator train mnist_tf.py

To call estimator.test() and start testing on terminal:

$ fastestimator test mnist_tf.py

Sending Input Args to get_estimator

We can also pass arguments to the get_estimator function call from the CLI. The following code snippet shows the get_estimator method for our MNIST example: 

def get_estimator(epochs=2, batch_size=32, ...):
    ...

Next, we try to change these arguments in two ways:

Using --arg

To pass the arguments directly from the CLI we can use the --arg format. The following shows an example of how we can set the number of epochs to 3 and batch_size to 64:

$ fastestimator train mnist_tf.py --epochs 3 --batch_size 64

Using a JSON file

The other way we can send arguments is by using the --hyperparameters argument and passing it a json file containing all the training hyperparameters like epochs, batch_size, optimizer, etc. This option is really useful when you want to repeat the training job more than once and/or the list of the hyperparameter is getting really long. The following shows an example JSON file and how it could be used for our MNIST example: 

JSON:
{
    "epochs": 1,
    "batch_size": 64
}

$ fastestimator train mnist_tf.py --hyperparameters hp.json

System argument

There are some default system arguments in the CLI, here are a list of them: * warmup: controls whether to perform warmup checking before the actual training starts. Default is True. Users can disable warmup before training by --warmup False. * summary: this is the same argument used in estimator.fit() or estimator.test(), it allows users to specify experiment name when generating reports. For example, Users can set experiment name by --summary exp_name.