Transformers, PlaidML

What is a backend?

Backends are responsible for function execution and value allocation. They can be used to carry out a programmed computation from a framework by using a CPU or GPU; or they can be used with an Interpreter mode, which is primarily intended for testing, to analyze a program, or for a framework developer to develop customizations. Experimental APIs to support current and future nGraph Backends are also available; see, for example, the section on PlaidML Backend.

Hybrid Transformer

Coming soon

CPU Backend

Coming soon

GPU Backend

Coming soon

PlaidML Backend

The nGraph ecosystem has recently added initial (experimental) support for PlaidML, which is an advanced Machine Learning library that can further accelerate training models built on GPUs. When you select the PlaidML option as a backend, it behaves as an advanced tensor compiler that can further speed up training with large data sets.