Graphcore Application Examples
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
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.
Developer resources
- 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
Support
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.
Repository contents
- Computer Vision
- Natural Language Processing
- Speech
- Multimodal
- Graph Neural Network
- AI for Simulation
- Recommender Systems
- Reinforcement Learning
- Sparsity
- Probability
- Miscellaneous
- Archived
Computer Vision
| Model | Domain | Type | Source | Run on Gradient |
|---|---|---|---|---|
| ResNet | Image classification | Training, Inference | TensorFlow 2, PyTorch, PyTorch Lightning | - |
| EfficientNet | Image classification | Training, Inference | PyTorch, PyTorch Lightning | - |
| MobileNetv3 | Image classification | Training, Inference | PyTorch | - |
| ViT (Vision Transformer) | Image classification | Training | PyTorch, Hugging Face Optimum | PyTorch, Hugging Face Optimum |
| DINO | Image classification | Training | PyTorch | - |
| Swin | Image classification | Training | PyTorch | - |
| MAE (Masked AutoEncoder) | Image classification | Training | PyTorch | - |
| Yolov4-P5 | Object detection | Inference | PyTorch | PyTorch |
| EfficientDet | Object detection | Inference | TensorFlow 2 | - |
| UNet (Medical) | Image segmentation | Training, Inference | TensorFlow 2 | - |
| Neural Image Fields | Neural radiance fields | Training | TensorFlow 2 | - |
Natural Language Processing
| Model | Domain | Type | Source | Run on Gradient |
|---|---|---|---|---|
| BERT | NLP | Training, Inference | PyTorch , TensorFlow 2, PopXL, PaddlePaddle, Hugging Face Optimum | - |
| Packed BERT | NLP | Training | PyTorch | - |
| BERT-large | NLP | Fine-tuning | Hugging Face Optimum | Hugging Face Optimum |
| GPT2 | NLP | Training | PyTorch, Hugging Face Optimum | - |
| GPTJ | NLP | Training | PopXL | - |
| GPT3-2.7B | NLP | Training | PopXL | - |
| GPT3-175B | NLP | Training | PopXL | - |
| RoBERTa | NLP | Training | Hugging Face Optimum | - |
| DeBERTa | NLP | Training | Hugging Face Optimum | - |
| HuBERT | NLP | Training | Hugging Face Optimum | - |
| BART Base | NLP | Training | Hugging Face Optimum | Hugging Face Optimum |
| T5-small | NLP | Training | Hugging Face Optimum | Hugging Face Optimum |
| Bloom | NLP | Inference | PopXL | - |
| Dolly | NLP | Inference | PopXL | PopXL |
| MT5-small | NLP | Fine-tuning | Hugging Face Optimum | Hugging Face Optimum |
| MT5-large | NLP | Fine-tuning | Hugging Face Optimum | Hugging Face Optimum |
Speech
| Model | Domain | Type | Source | Run on Gradient |
|---|---|---|---|---|
| Fastpitch | TTS (TextToSpeech) | Training | PyTorch | - |
| Conformer | STT (SpeechToText) | Training, Inference | PyTorch | - |
| wav2vec2-base | STT (SpeechToText) | Fine-tuning, Inference | Hugging Face Optimum | Hugging Face Optimum (Fine-tuning), Hugging Face Optimum (Inference) |
| wav2vec2-large | STT (SpeechToText) | Fine-tuning, Inference | Hugging Face Optimum | - |
| Whisper-tiny | STT (SpeechToText) | Inference | Hugging Face Optimum | Hugging Face Optimum |
Multimodal
| Model | Domain | Type | Source | Run on Gradient |
|---|---|---|---|---|
| miniDALL-E | Multimodal | Training | PyTorch | - |
| CLIP | Multimodal | Training | PyTorch | - |
| LXMERT | Multimodal | Training | Hugging Face Optimum | - |
| Frozen in time | Multimodal | Training, Inference | PyTorch | - |
Graph Neural Network
| Model | Domain | Type | Source | Run on Gradient |
|---|---|---|---|---|
| MPNN (Message Passing Neural Networks) | GNN | Training, Inference | PyTorch Geometric , TensorFlow 2 | - |
| Spektral GNN library with QM9 | GNN | Training | TensorFlow 2 | - |
| Cluster GCN | GNN | Training, Inference | PyTorch Geometric | PyTorch Geometric |
| TGN (Temporal Graph Networks) | GNN | Training | PyTorch | - |
| NBFNet | GNN | Training, Inference | PyTorch Geometric | PyTorch Geometric (Training) |
| SchNet | GNN | Training, Inference | PyTorch Geometric | PyTorch Geometric |
| GPS++ - OGB-LSC PCQM4Mv2 competition submission | GNN | Training, Inference | TensorFlow 2 | TensorFlow 2 (Training), TensorFlow 2 (Inference) |
AI for Simulation
| Model | Domain | Type | Source | Run on Gradient |
|---|---|---|---|---|
| Approximate Bayesian Computation (ABC) COVID-19 | Medical | Inference | TensorFlow 2 | - |
Benchmarking tools
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.
PopVisionยฎ Tools
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.
Utilities
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 theexamplesrepository unit testsutils/benchmarks- Common Python helper functions for running benchmarks on the IPU in different frameworks
License
Unless otherwise specified by a LICENSE file in a subdirectory, the LICENSE referenced at the top level applies to the files in this repository.
Changelog
March 2023
- Added the following models:
- GNN: NBFNet (PyTorch Geometric), SchNet (PyTorch Geometric), Cluster-GCN (PyTorch Geometric), GIN (PyTorch Geometric), GPS++ - OGB-LSC PCQM4Mv2 competition submission (TensorFlow 2)
- NLP : GPT3_175B (PopXL), Bloom (PopXL)
- Removed all PopART applications, as well as the following:
- Miscellaneous: Monte-Carlo ray tracing
- AI for simulation: DeepDriveMD
- (Preview) Multimodel: ruDalle
- Speech: FastSpeech2
- Vision: ResNeXt inference
- Moved the contents of the
Graphcore/tutorialsrepository into this repository (PopART tutorials have also been removed)
Dec 2022
- Added the following models:
- GNN: TGN (PyTorch)
- Deprecating all PopART applications. Support will be removed in the next release.
- Removed all TensorFlow 1 applications.
- Ubuntu 18.04 no longer supported.
Sep 2022
- Added the following models:
- Vision : MAE (PyTorch), G16 EfficientNet (PyTorch)
- NLP : GPTJ (PopXL), GPT3-2.7B (PopXL)
- Multimodal : Frozen in time (PyTorch), ruDalle (Preview) (PopXL)
- Deprecating all TensorFlow 1 applications. Support will be removed in the next release.
Aug 2022
- Changed the folder name of the following models:
- NLP: from
gpttogpt2 - Speech: from
wenet-conformertoconformer
- NLP: from
July 2022
- Major reorganisation of all the apps so that they are arranged as: problem domain / model / framework.
- Problem domains: Vision, NLP, Speech, GNN, Sparsity, AI for Simulation, Recommender systems, Reinforcement learning, Probability, Multimodal, and Miscellaneous.
- Added the following models:
- Vision: Swin (PyTorch) , ViT (Hugging Face Optimum)
- NLP: GPT2 Small/Medium/Large (PyTorch), BERT-Base/Large (PopXL), BERT-Base (PaddlePaddle), BERT-Base/Large (Hugging Face Optimum), GPT2 Small/Medium (Hugging Face Optimum), RoBERTa Base/Large (Hugging Face Optimum), DeBERTa (Hugging Face Optimum), HuBERT (Hugging Face Optimum), BART (Hugging Face Optimum), T5 small (Hugging Face Optimum)
- Speech: Fastpitch (PyTorch), WeNet-Conformer-Medium (PyTorch) ,Wav2Vec2 (Hugging Face Optimum)
- Multimodal: CLIP (PyTorch), LXMERT (Hugging Face Optimum)
- AI for Simulation: et0 (TensorFlow 1)
- Removed Conformer-small/large (PyTorch)
- Archived Minigo (TensorFlow 1)
May 2022
- Added the following models:
- Vision : ViT-pretraining (PyTorch), DINO (PyTorch), EfficientDet-inference (TensorFlow 2), Neural Image Fields (TensorFlow 2)
- NLP : PackedBERT (PyTorch, PopART), BERT-Large (TensorFlow 2)
- Speech : FastSpeech2-inference (TensorFlow 2), Conformer-Large (PyTorch)
- GNN : Cluster GCN (TensorFlow 2)
- AI for Simulation : DeepDriveMD (TensorFlow 2)
December 2021
- Added the following models:
- Vision: miniDALL-E (PyTorch), Faster RCNN (PopART), UNet (TensorFlow 2), ResNet50 (TensorFlow 2)
- NLP: BERT (TensorFlow 2)
- Speech: FastSpeech2 (TensorFlow 2), Transformer Transducer (PopART), Conformer-Small (PyTorch)
- GNN: TGN (TensorFlow 1), MPNN (TensorFlow 2)
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