How to

The “How to” articles in this section explain how to do specific tasks with nGraph components. The recipes are all framework agnostic; in other words, if an entity (framework or user) wishes to make use of target-based computational resources, it can either:

  • Do the tasks programatically through a framework, or
  • Provide a serialized model that can be imported to run on one of the nGraph backends.

Note

This section is aimed at intermediate-level developers. It assumes an understanding of the concepts in the previous sections. It does not assume knowledge of any particular frontend framework.

Since our primary audience is developers who are pushing the boundaries of deep learning systems, we go beyond the use of deep learning primitives, and include APIs and documentation for developers who want the ability to write programs that use custom backends. For example, we know that GPU resources can be useful backends for some kinds of algorithmic operations while they impose inherent limitations or slow down others.

One of our goals with the nGraph library is to enable developers with tools to quickly build programs that access and process data from a breadth of edge and networked devices. This might mean bringing compute resources closer to edge devices, or it might mean programatically adjusting a model or the compute resources it requires, at an unknown or arbitrary time after it has been deemed to be trained well enough.

To get started, we’ve provided a basic example for how to Execute a computation a computation that can run on an nGraph backend; this is analogous to a framework bridge. We also provide a larger example for training and evaluating a simple MNIST MLP model.

For data scientists or algorithm developers who are trying to extract specifics about the state of a model at a certain node, or who want to optimize a model at a more granular level, we provide an example for how to Import a model and run inference after it has been exported from a DL framework.

This section is under development; we’ll continually populate it with more articles geared toward data scientists, algorithm designers, framework developers, backend engineers, and others. We welcome ideas and contributions from the community.