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Activating Citizen Data Scientists: Executive Tutorial from Pienso & Graphcore

In this Executive Tutorial from Pienso and Graphcore, you will find out how non-technical teams can apply AI and Machine Learning to their business strategies with no-code/low-code high-performance machine intelligence.

What you will learn in this Executive Tutorial:
  • What’s a Citizen Data Scientist?
  • Where to start a DIY AI/ML project?
  • What’s Structured vs Unstructured data?

How to run Baidu's PaddlePaddle on the IPU

What you'll learn:

  • Baidu PaddlePaddle deep learning framework overview, technical designs and developer community
  • PaddlePaddle and Graphcore IPU's integration design
  • Training & inference demonstration with MNIST model


Key takeaways:

  • Findings in the price prediction research paper from the Oxford-Man Institute of Quantitative Finance
  • Accelerating multi-horizon forecasting on IPUs
  • Introduction to IPU hardware and its architectural advantages
  • Use cases of IPU technology in finance and future applications 

Hands-on: Running PyTorch models on the IPU

What you'll learn:

  • Introduction to PopTorch and the Graphcore software stack
  • From PyTorch to PopTorch: Overview of PopTorch functionality and a quick guide to porting your application
  • Leveraging multiple IPU devices: how to easily accelerate training using advanced execution strategies
  • Implementing efficient training pipelines in distributed environments

Using the Graphcore IPU at the Convergence of AI and HPC

Key takeaways:

  • Convergence of HPC and AI technologies: common patterns in scientific computing and machine learning
  • Insights into the IPU’s unique architecture and its flexibility to support different workloads
  • Identifying which scientific applications would fit well with IPU acceleration and potential benefits of enabling sparsity 
  • The latest research in harnessing IPUs for data-dependent graph computations



Programming on the IPU 101

What you'll learn:

  • Introduction to the IPU architecture and an understanding of the programming model structure
  • Overview of the Poplar Software Stack including its latest features, tools and libraries
  • How developers can port and run models built with popular ML frameworks such as PyTorch and TensorFlow to the IPU
  • Different considerations compared to GPU and using IPU techniques to leverage the advantages of the IPU such as parallel execution strategies

Introducing the Graphcore Academic Programme

What you’ll learn:

  • Introduction to the Graphcore Academic Programme: benefits of the programme and how you can participate
  • Discover research papers and case studies from some of the world's leading academics and institutions using IPU hardware
  • Latest performance benchmarks on Graphcore’s Mk2 IPU processor
  • How to test IPU hardware and access the latest Graphcore software tools, including Poplar and PopART

Transform your Data Centre with Graphcore IPU-POD Scale-out Technology

What you'll learn:

  • Introduction to IPU-Fabric: enabling direct connection between large clusters of IPUs for better resiliency and flexibility
  • Compatibility with existing infrastructure & industry standard tools and why it matters
  • How IPU-PODs are built for virtualisation and how to manage flexible and dynamic resource allocation with Virtual-IPU

Introduction to Poplar Software

What you'll learn:

  • Introduction to the Poplar Software Stack including its latest features and capabilities
  • Why Poplar was co-designed with the IPU and how it enables even the most demanding AI workloads to scale seamlessly across IPU systems
  • Technical deep dive into Graphcore’s second generation IPU platforms designed for AI infrastructure at scale
  • How Poplar makes it possible to execute current and next generation machine intelligence applications efficiently on IPU hardware

IPU-M2000 and IPU-POD: New Breakthroughs in AI at Scale

Key Takeaways:

  • Why compute, data efficiency and communications are integral to enabling innovation in machine intelligence

  • Insights into Graphcore's new generation of scale-out products: IPU-Machine: M2000 & IPU-POD64

  • How Graphcore's MK2 IPU architecture facilitates the research and deployment of new models

  • How IPU systems enhance model deployability and efficiency


Enabling Machine Learning Innovation with IPU Technology

Key Takeaways:

  • How machine intelligence is evolving and what this means for AI processors

  • Insights into IPU technology & Poplar software delivered via IPU-Server

  • Poplar software supports multi-IPU constructs to enable a world of growing model sizes and complexity

  • Overview of next generation image classification models using ResNeXt as an example, showing IPU benchmarks & use-case implementation


Discover our Financial Solutions

Key Takeaways:

  • How the IPU is able to achieve faster financial model accelerations than other hardware available on the market

  • How to use IPUs for financial modelling training and inference

  • Insights into advanced models, use cases and IPU benchmarks


IPU-M2000 and IPU-POD: New Breakthroughs in AI at Scale


  • 计算、数据和通信为什么是实现机器智能创新的关键;

  • 深入了解Graphcore的新一代横向大规模扩展产品:IPU-M2000和IPU-POD64;

  • Graphcore的MK2 IPU架构如何促进新模型的研究和部署;

  • IPU系统如何增强模型的可部署性和效率


利用Graphcore IPU驱动机器学习创新


  • 机器智能如何演变以及这对AI处理器意味着什么

  • 首款IPU服务器——戴尔EMC DSS8440

  • Poplar软件支持多种IPU构造,以实现模型尺寸和复杂性不断增长的世界

  • 微软借助IPU加速ResNeXt-50医学成像推理的案例


利用G‌‍R‍A‍P‌HCOR‍E IP‍U‌加速AI金融模型


  • 如何利用金融算法模型预知黑天鹅事件,规避风险

  • IPU如何能够实现比其他现有硬件更快的金融模型加速

  • 如何使用IPU进行模型的训练和推理

  • 先进算法模型洞察,案例以及IPU benchmark


POPLAR SDK로 확장 가능한 머신 인텔리전스 시스템 지원

주요 내용:

  • Poplar 소프트웨어 스택 및 최신 기능과 성능 소개
  • Poplar를 IPU와 공동 설계한 이유 및 가장 까다로운 AI 워크로드를 충족하여 여러 IPU 시스템에서 원활하게 확장 가능한 Poplar의 특성
  • 대규모 AI 인프라용으로 설계된 그래프코어 2세대 IPU 플랫폼의 기술적 특성에 대한 심층 탐구
  • Poplar가 IPU 하드웨어에서 기존 및 차세대 머신 인텔리전스 애플리케이션의 효율적인 실행을 지원하는 방법

IPU-M2000 및 IPU-POD: 확장성 뛰어난 AI 분야의 새로운 혁신

해당 웨비나에서는 다음과 같은 내용을 다룹니다:

  • 머신 인텔리전스 분야 혁신을 구현하기 위해 필수적인 컴퓨팅, 데이터 효율성 및 통신

  • 그래프코어의 2세대 스케일아웃 신제품 소개 - IPU-머신: M2000 & IPI-POD64

  • 그래프코어의 MK2 IPU 아키텍처가 연구 및 신규 모델 구축을 활성화하는 방법

  • IPU 시스템이 모델 구축 및 효율성을 강화하는 방법


최대 26배 빠른 금융 모델을 운영하십시오

해당 웨비나는 다음과 같은 내용을 다룰 예정입니다:

  • 기존 하드웨어 대비 빠르게 금융 모델 가속화를 달성하는 IPU

  • 금융 모델 구축 학습 및 추론을 위한 IPU 활용법

  • 어드밴스드 모델, 활용 사례, IPU 벤치마크에 대한 심층 정보


ウェビナー: IP‍Uの力を最大限に引き出すA‌I学習最適化テクニック


  • Vision Transformer 概要とその利点
  • 機械学習フレームワークPyTorchを用いたVision Transformer 移植方法
  • Vision Transformer の性能改善方法
  • IPU-PODアーキテクチャの利点とプロファイリングツールPopVision



  • IPUプロセッサのアーキテクチャとIPU-M2000システムおよびIPU-PODシステムの紹介 
  • 最新の機能やツール、ライブラリなど、Poplarソフトウェアスタックの概要 
  • PyTorchTensorFlow、またはKerasで構築したモデルをIPUに移植して実行する方法 
  • GPUとは異なる検討事項と、IPU-PODシステムから最大限のパフォーマンスを引き出す方法 



  • 世界で最もAIの為に最適設計された複雑なプロセッサIPU、およびIPUシステムの概要。
  • HPC Systemsの会社紹介。HPC/AIシステムの製造および開発の経験、日本でのAI技術の導入事例等。
  • スケールアウト効率をサポートする新しいGraphcore技術への洞察。
  • 次世代のAI大躍進に備えるために革新者ができること。