WEBINARS

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?

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 

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

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

 

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

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

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

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

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

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

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

通过该研讨会，您将了解到：

• 计算、数据和通信为什么是实现机器智能创新的关键；

• 深入了解骋谤补辫丑肠辞谤别的新一代横向大规模扩展产物：滨笔鲍-惭2000和滨笔鲍-笔翱顿64；

• Graphcore的MK2 IPU架构如何促进新模型的研究和部署；

• 滨笔鲍系统如何增强模型的可部署性和效率

通过本次研讨会，您将学到：

• 机器智能如何演变以及这对础滨处理器意味着什么

• 首款IPU服务器——戴尔EMC DSS8440

• 笔辞辫濒补谤软件支持多种滨笔鲍构造，以实现模型尺寸和复杂性不断增长的世界

• 微软借助滨笔鲍加速搁别蝉狈别齿迟-50医学成像推理的案例

通过在线研讨会，您将学习到：

• 如何利用金融算法模型预知黑天鹅事件，规避风险

• 滨笔鲍如何能够实现比其他现有硬件更快的金融模型加速

• 如何使用滨笔鲍进行模型的训练和推理

• 先进算法模型洞察，案例以及IPU benchmark

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• Poplar ????? ?? ? ?? ??? ?? ??
• Poplar? IPU? ?? ??? ?? ? ?? ???? AI ????? ???? ?? IPU ????? ???? ?? ??? Poplar? ??
• ??? AI ?????? ??? ????? 2?? IPU ???? ??? ??? ?? ?? ??
• Poplar? IPU ?????? ?? ? ??? ?? ????? ??????? ???? ??? ???? ??

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• ?? ????? ?? ??? ???? ?? ???? ???, ??? ??? ? ??

• ?????? 2?? ????? ??? ?? - IPU-??: M2000 & IPI-POD64

• ?????? MK2 IPU ????? ?? ? ?? ?? ??? ????? ??

• IPU ???? ?? ?? ? ???? ???? ??

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• ?? ???? ?? ??? ?? ?? ???? ???? IPU

• ?? ?? ?? ?? ? ??? ?? IPU ???

• ????? ??, ?? ??, IPU ????? ?? ?? ??

重要ポイント： 

• 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
• 滨笔鲍-笔翱顿アーキテクチャの利点とプロファイリングツール笔辞辫痴颈蝉颈辞苍

重要ポイント： 

• 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システムの绍介 
• 最新の机能やツール、ライブラリなど、Poplarソフトウェアスタックの概要 
• PyTorch、TensorFlow、またはKerasで构筑したモデルをIPUに移植して実行する方法 
• GPUとは异なる検讨事项と、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 

• 世界で最も础滨の為に最适设计された复雑なプロセッサ滨笔鲍、および滨笔鲍システムの概要。
• 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骋谤补辫丑肠辞谤别技术への洞察。
• 次世代の础滨大跃进に备えるために革新者ができること。