This repository contains a catalogue of application examples that have been optimised to run on Graphcore IPUs for both training and inference. Access reproducible code for a wide range of popular models covering NLP, Computer Vision, Speech, Multimodal, GNNs, AI for Simulation, Recommender Systems, and more. This includes a selection of models that achieve state of the art performance on IPUs, as well as code examples for self-learning.

Run models out-the-box on IPUs integrated with popular ML frameworks and libraries:

To see what's new, check out our Model Garden 🌷, where you can easily filter applications by domain and framework.

For more detailed benchmark information, visit our Performance Results page.

The code presented here requires you to use Poplar SDK 3.2.x, and has been tested using Ubuntu 20.04 and Python 3.8

Please install and enable the Poplar SDK following the instructions in the Getting Started guide for your IPU system.

• Documentation: Explore our software documentation, user guides, and technical notes
• Tutorials: Hands-on code tutorials, simple application and feature examples
• How-to Videos: Watch practical how-to videos and demos by Graphcore engineers
• Research Papers: Read publications from Graphcore's Research team and IPU innovators

If you encounter a problem or want to suggest an improvement to our example applications please raise a GitHub issue, contact us at [email protected], or get in touch through the #help channel of the Graphcore Slack Community!

If you require POD128 or POD256 setup and configuration for our applications, please contact our engineering support.

1. Computer Vision
2. Natural Language Processing
3. Speech
4. Multimodal
5. Graph Neural Network
6. AI for Simulation
7. Recommender Systems
8. Reinforcement Learning
9. Sparsity
10. Probability
11. Miscellaneous
12. Archived

To easily run the examples with tested and optimised configurations and to reproduce the performance shown on our Performance Results page, you can use the examples-utils benchmarking module, which comes with every example when you install its requirements. To use this simple, shared interface for almost any of the examples provided here, locate and look through the example's benchmarks.yml file and run:

python3 -m examples_utils benchmark --spec <path to benchmarks.yml file> --benchmark <name of benchmark>

Refer to the examples-utils benchmarking module README for more information.

Visualise your code's inner workings with PopVision, a user-friendly, graphical interface to optimise your machine learning models.

Download the PopVision tools to analyse IPU performance and utilisation.

The utils/ folder contains utility libraries and scripts that are used across the code examples. This includes:

• utils/examples_tests - Common Python helper functions for the examples repository unit tests
• utils/benchmarks - Common Python helper functions for running benchmarks on the IPU in different frameworks

Unless otherwise specified by a LICENSE file in a subdirectory, the LICENSE referenced at the top level applies to the files in this repository.