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Introducing Gluon — An Easy-to-Use Programming Interface for Flexible Deep Learning

Deep learning just got simpler and faster with the new Gluon API.

  • The first result of this collaboration is the new Gluon interface, an open source library in Apache MXNet that allows developers of all skill levels to prototype, build, and train deep learning models.
  • It brings together the training algorithm and neural network model, thus providing flexibility in the development process without sacrificing performance.
  • Then, when speed becomes more important than flexibility (e.g., when you’re ready to feed in all of your training data), the Gluon interface enables you to easily cache the neural network model to achieve high performance and a reduced memory footprint.
  • For each iteration, there are four steps: (1) pass in a batch of data; (2) calculate the difference between the output generated by the neural network model and the actual truth (i.e., the loss); (3) use to calculate the derivatives of the model’s parameters with respect to their impact on…
  • To learn more about the Gluon interface and deep learning, you can reference this comprehensive set of tutorials, which covers everything from an introduction to deep learning to how to implement cutting-edge neural network models.

Today, AWS and Microsoft announced a new specification that focuses on improving the speed, flexibility, and accessibility of machine learning technology for all developers, regardless of their deep learning framework of choice. The first result of this collaboration is the new Gluon interface, an open source library in Apache MXNet that allows developers of all skill levels to prototype, build, and train deep learning models. This interface greatly simplifies the process of creating deep learning models without sacrificing training speed.

Today, AWS and Microsoft announced a new specification that focuses on improving the speed, flexibility, and accessibility of machine learning technology for all developers, regardless of their deep learning framework of choice. The first result of this collaboration is the new Gluon interface, an open source library in Apache MXNet that allows developers of all skill levels to prototype, build, and train deep learning models. This interface greatly simplifies the process of creating deep learning models without sacrificing training speed.

Here are Gluon’s four major advantages and code samples that demonstrate them:

(1) Simple, easy-to-understand code

In Gluon, you can define neural networks using simple, clear, and concise code. You get a full set of plug-and-play neural network building blocks, including predefined layers, optimizers, and initializers. These abstract away many of the complicated underlying implementation details. The following example shows how you can define a simple neural network with just a few lines of code:

The following diagram shows you the structure of the neural network:

For more information, go to this tutorial to learn how to build a simple neural network called a multilayer perceptron (MLP) with the Gluon neural network building blocks. It’s also easy to write parts of the neural network from scratch for more advanced use cases. Gluon allows you to mix and match predefined and custom components in your neural network.

Training neural network models is computationally intensive and, in some cases, can take days or even…

Introducing Gluon — An Easy-to-Use Programming Interface for Flexible Deep Learning