Hugging Face BERT-Medium inference fine-tuned on SQUADv2
Graphcore AI Engineer Kate Hodesdon demonstrates how to develop PyTorch models for the IPU using a Hugging Face BERT-Medium model example.This demo also introduces PopTorch – a lightweight set of extensions to PyTorch that allows developers to run PyTorch directly on the IPU. PopTorch makes IPU directly accessible from within PyTorch code – meaning that developers can take advantage of IPU hardware acceleration without learning a whole new framework.
An introduction to the PopVision™ Graph Analyser
Graphcore Customer Engineer Marie-Anne Le Menn introduces the PopVision™ Graph Analyser, including code walkthrough.PopVision™ is used to analyse the programs built for and executed on Graphcore’s IPU systems. It can be used for analysing and optimising the memory use and performance of programs.
Poplar demo using a basic addition example
Field Applications Engineer Alex Titterton provides a quick introduction to Graphcore's Poplar® software.
This walkthrough covers the essentials of IPU architecture from a software perspective and finishing with a demonstration of a simple addition example running on the IPU using the Poplar C++ framework.
Graphcore's machine learning framework
Graphcore AI Engineer Kate Hodesdon introduces PopART™. The Poplar Advanced Run Time (PopART) is part of the Poplar SDK for implementing and running algorithms on networks of Graphcore IPU processors.
It enables you to import models using the Open Neural Network Exchange (ONNX) and run them using the Poplar tools. ONNX is a serialisation format for neural network systems that can be created and read by several frameworks including Caffe2, PyTorch and MXNet.
ResNeXt-101 inference demo
Graphcore AI Engineer Kate Hodesdon demonstrates how to run a PyTorch model on Graphcore's IPU, using Graphcore's Poplar software and Open Neural Network Exchange (ONNX).
Using the example of a ResNeXt-101 computer vision model, Kate demonstrates how simple it is to port a pre-existing model to the IPU. The demonstration references work done by European search engine Qwant, whose use of ResNeXt-101 on the IPU yielded dramatic improvements in throughput and latency, compared to a GPU.
Markov Chain Monte Carlo (MCMC) model demo
Graphcore Senior AI Engineer Alex Tsyplikhin demonstrates the ease of moving a TensorFlow model, originally designed to run on a GPU, onto Graphcore's IPU processor.
In this walkthrough, Alex uses the Markov Chain Monte Carlo (MCMC) method, common in the financial service industry where it is used for tasks such as risk estimation and option pricing.
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