1. Introduction

The purpose of this document is to introduce the TensorFlow framework from the perspective of developing and training models for the IPU. It assumes you have some knowledge of machine learning and TensorFlow.


This document is for TensorFlow 2. For information on TensorFlow 1 please refer to Targeting the IPU from TensorFlow 1.

For more information about the IPU architecture, abstract programming model and tools, as well as algorithmic techniques, refer to the IPU Programmer’s Guide.

The Graphcore implementation of TensorFlow 2 is included in the Poplar SDK. See the Getting Started guide for your system for how to install the Poplar SDK. Refer to the TensorFlow 2 Quick Start for installing the TensorFlow 2 for IPU wheel. The quick start guide also shows how to run a simple TensorFlow 2 application on the IPU.

TensorFlow is a powerful graph-modelling framework that can be used for the development, training and deployment of deep learning models. In the Graphcore software stack, TensorFlow sits at the highest level of abstraction. Poplar and PopLibs provide a software interface to operations running on the IPU. XLA (Accelerated Linear Algebra) is a domain-specific compiler for linear algebra that can accelerate TensorFlow models. TensorFlow graphs are compiled into Poplar executables using our XLA backend.

TensorFlow abstraction

Fig. 1.1 TensorFlow abstraction in relation to Poplar and the IPU

There are two main differences in the Graphcore implementation of TensorFlow:

  • Some machine-learning ops are optimised for the IPU hardware. For example, our custom dropout op is designed to use less memory by not storing the dropout mask between forward and backward passes.

  • It provides extra IPU-specific functions, such as those for selecting and configuring IPUs.

Implementing programs at the framework level is relatively independent of the underlying hardware. The process of defining a graph and its components (for example, how a convolutional layer is defined) is largely the same when targeting the IPU.

There are a small number of changes that you need to make to your TensorFlow code, when constructing and executing a model, in order to target the IPU efficiently. These include IPU-specific API configurations, model parallelism, error logging and report generation, as well as strategies for dealing with out-of-memory (OOM) issues.

These will be described in the appropriate sections of this document.


Many of the sections are shared with the TensorFlow 1 version of this document. The function ipu_compiler.compile() is used within an IPU device scope when using tf.compat.v1.Session. It is not required when using the IPUStrategy.

1.1. Document overview

  • The first section provides information about selecting specific IPU hardware and configuring the code to run on it.

  • The IPUStrategy class and its use in Keras is described in the section on support for TensorFlow 2

  • The next section describes executable caches and pre-compilation of executables.

  • The following sections provide information on various methods related to training a model on the IPU.

  • The next few sections provide information on IPU-specific features.

  • Finally, there are reference chapters describing the API and supported operators.

1.2. Other resources

You can find further information on porting a TensorFlow program to the IPU and parallelising it, in our TensorFlow technical notes.

Switching from GPUs to IPUs for Machine Learning Models provides a high-level overview of the programming changes required when switching from GPUs to IPUs and memory-performance-optimisation presents guidelines to help you develop high-performance machine learning models running on the IPU.

The Graphcore Examples GitHub repository contains TensorFlow 2 applications, feature examples, tutorials and simple applications. Further developer resources can be found on Graphcore’s developer portal.