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FastEstimator Application Hub

Welcome to the FastEstimator Application Hub! Here we showcase different end-to-end AI examples implemented in FastEstimator. We will keep implementing new AI ideas and making state-of-the-art solutions accessible to everyone.

Purpose of Application Hub

  • Provide a place to learn implementation details of state-of-the-art solutions
  • Showcase FastEstimator functionalities in an end-to-end fashion
  • Offer ready-made AI solutions for people to use in their own projects/products

Why not just learn from official implementations?

If you have ever spent time reading AI research papers, you will often find yourself asking: did I just spent 3 hours reading a paper where the underlying idea can be expressed in 3 minutes?

Similarly, people may use 5000 lines of code to implement an idea which could have been expressed in 500 lines using a different AI framework. In FastEstimator, we strive to make things simpler and more intuitive while preserving flexibility. As a result, many state-of-the-art AI implementations can be simplified greatly such that the code directly reflects the key ideas. As an example, the official implementation of PGGAN includes 5000+ lines of code whereas our implementation requires less than 500.

To summarize, we spent time learning from the official implementation, so you can save time by learning from us!

What's included in each example?

Each example contains three files:

  1. A TensorFlow python file (.py): The FastEstimator source code needed to run the example with TensorFlow.
  2. A PyTorch python file (.py): The FastEstimator source code needed to run the example with PyTorch.
  3. A jupyter notebook (.ipynb): A notebook that provides step-by-step instructions and explanations about the implementation.

How do I run each example

One can simply execute the python file of any example: 

$ python mnist_tf.py

Or use our Command-Line Interface (CLI):

$ fastestimator train mnist_torch.py

One benefit of the CLI is that it allows users to configure the input args of get_estimator:

$ fastestimator train lenet_mnist.py --batch_size 64 --epochs 4