For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit-google
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Pete Warden, Staff Research Engineer and TensorFlow Lite development lead at Google, presents the "Using TensorFlow Lite to Deploy Deep Learning on Cortex-M Microcontrollers" tutorial at the May 2019 Embedded Vision Summit.
Is it possible to deploy deep learning models on low-cost, low-power microcontrollers? While it may be surprising, the answer is a definite “yes”! In this talk, Warden explains how the new TensorFlow Lite framework enables creating very lightweight DNN implementations suitable for execution on microcontrollers. He illustrates how this works using an example of a 20 Kbyte DNN model that performs speech wake word detection, and discusses how this generalizes to image-based use cases. Warden introduces TensorFlow Lite, and explores the key steps in implementing lightweight DNNs, including model design, data gathering, hardware platform choice, software implementation and optimization.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit-google
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Pete Warden, Staff Research Engineer and TensorFlow Lite development lead at Google, presents the "Using TensorFlow Lite to Deploy Deep Learning on Cortex-M Microcontrollers" tutorial at the May 2019 Embedded Vision Summit.
Is it possible to deploy deep learning models on low-cost, low-power microcontrollers? While it may be surprising, the answer is a definite “yes”! In this talk, Warden explains how the new TensorFlow Lite framework enables creating very lightweight DNN implementations suitable for execution on microcontrollers. He illustrates how this works using an example of a 20 Kbyte DNN model that performs speech wake word detection, and discusses how this generalizes to image-based use cases. Warden introduces TensorFlow Lite, and explores the key steps in implementing lightweight DNNs, including model design, data gathering, hardware platform choice, software implementation and optimization.
TensorFlow is the most popular machine learning framework nowadays. TensorFlow Lite (TFLite), open sourced in late 2017, is TensorFlow’s runtime designed for mobile devices, esp. Android cell phones. TFLite is getting more and more mature. One the most interesting new components introduced recently are its GPU delegate and new NNAPI delegate. The GPU delegate uses Open GL ES compute shader on Android platforms and Metal shade on iOS devices. The original NNAPI delegate is an all-or-nothing design (if one of the ops in the compute graph is not supported by NNAPI, the whole graph is not delegated). The new one is a per-op design. When an op in a graph is not supported by NNAPI, the op is automatically fell back to the CPU runtime. I’ll have a quick review TFLite and its interpreter, then walk the audience through example usage of the two delegates and important source code of them.
Introduction to the new Tensorflow 2.x and the Coral AI Edge TPU hardware. The presentation introduces Tensorflow main features such as Sequential and Functional APIs, mobile support with Tensorflow Lite, web support with TensorflowJS and Google Cloud support with TFX.
In addition, the presentation introduces the new edge TPU architecture coming from Coral AI, including its main hardware features and description of the compiling flow.
Exploring Thermal Related Stuff in iDevices using Open-Source ToolKoan-Sin Tan
This is the era of so-called “dark silicon.” Thermal control is an important but seldom-talked topic. I could not find public information on how iOS does it. Recent checkm8 and follow-on checkra1n enable jailbreaking of iPhone 5s – iPhone X running iOS 12.3 and up. So that we can explore these devices with open-source tools
One bite and all your dreams will come true: Analyzing and Attacking Apple Ke...Priyanka Aash
"Though many security mechanisms are deployed in Apple's macOS and iOS systems, some old-fashioned or poor-quality kernel code still leaves the door widely open to attackers. Especially, as kernel's critical components, device drivers are frequently exploited to attack Apple systems. In fact, bug hunting in Apple kernel drivers is not easy since they are mostly closed-source and heavily relying on object-oriented programming. In this talk, we will share our experience of analyzing and attacking Apple kernel drivers. In specific, we will introduce a new tool called Ryuk. Ryuk employs static analysis techniques to discover bugs by itself or assist manual review.
In addition, we further combine static analysis with dynamic fuzzing for bug hunting in Apple drivers. In specific, we will introduce how we integrate Ryuk to the state-of-art Apple driver fuzzer, PassiveFuzzFrameworkOSX, for finding exploitable bugs.
Most importantly, we will illustrate Ryuk's power with several new vulnerabilities that are recently discovered by Ryuk. In specific, we will show how we exploit these vulnerabilities for privilege escalation on macOS 10.13.3 and 10.13.2. We will not only explain why these bugs occur and how we find them, but also demonstrate how we exploit them with innovative kernel exploitation techniques."
The Libre-SOC Project aims to create an entirely Libre-Licensed, transparently-developed fully auditable Hybrid 3D CPU-GPU-VPU, using the Supercomputer-class OpenPOWER ISA as the foundation.
Our first test ASIC is a 180nm "Fixed-Point" Power ISA v3.0B processor, 5.1mm x 5.9mm, as a proof-of-concept for the team, whose primary expertise is in Software Engineering. Software Engineering training brings a radically different approach to Hardware development: extensive unit tests, source code revision control, automated development tools are normal. Libre Project Management brings even more: bug trackers, mailing lists, auditable IRC logs and a wiki are standard fare for Libre Projects that are simply not normal Industry-Standard practice.
This talk therefore goes through the workflow, from the original HDL through to the GDS-II layout, showing how we were able to keep track of the development that led to the IMEC 180nm tape-out in July 2021. In particular, by following a parallel development process involving "Real" and "Symbolic" Cell Libraries, developed by Chips4Makers, will be shown how our developers did not need to sign a Foundry NDA, but were still able to work side-by-side with a University that did. With this parallel development process, the University upheld their NDA obligations, and Libre-SOC were simultaneously able to honour its Transparency Objectives.
Rainbow Over the Windows: More Colors Than You Could ExpectPeter Hlavaty
As time goes on operating systems keep evolving, like Microsoft Windows do, it ships new designs, features and codes from time to time. However sometimes it also ships more than bit of codes for complex subsystems residing in its kernel ... and at some future point it starts implementing new designs to prevent unnecessary access to it. However is it safe enough?
As we can see from security bulletins, win32k subsystem attracts lots of attention. It looks that with efforts of many security researchers who has dug into this area, finding bugs here shall becomes pretty tough and almost fruitless. But unfortunately this is not true, as win32k is backed up by very complex logic and large amount of code by nature..
We will present our point of view to Windows graphic subsystem, as well as schema of our fuzzing strategies. We will introduce some unusual areas of win32k, its extensions and how it can breaks even locked environments.
Part of our talk will be dedicated to CVE-2016-0176, the bug we used for this year's Pwn2Own Edge sandbox bypass, from its discovery to its exploitation techniques, which could serves as an example for universal DirectX escape which is independent of graphics vendors.
Introduction to Python GUI development with Delphi for Python - Part 1: Del...Embarcadero Technologies
Learn how Embarcadero’s newly released free Python modules bring the power and flexibility of Delphi’s GUI frameworks to Python. VCL and FireMonkey (FMX) are mature GUI libraries. VCL is focused on native Windows development, while FireMonkey brings a powerful flexible GUI framework to Windows, Linux, macOS, and even Android. This webinar will introduce you to these new free Python modules and how you can use them to build graphical users interfaces with Python. Part 2 will show you how to target Android GUI applications with Python!
Combining the Strenghts of Python and Delphi
Links replay and more
https://blogs.embarcadero.com/combining-the-strengths-of-delphi-and-python/
Python4Delphi repository
https://github.com/pyscripter/python4delphi
Part 1
https://blogs.embarcadero.com/webinar-replay-python-for-delphi-developers-part-1-introduction/
Learn how Embarcadero's newly released free Python modules bring the power and flexibility of Delphi's GUI frameworks to Python. VCL and FireMonkey (FMX) are mature GUI libraries. VCL is focused on native Windows development, while FireMonkey brings a powerful flexible GUI framework to Windows, Linux, macOS, and even Android. This webinar will introduce you to these new free Python modules and how you can use them to build graphical users interfaces with Python. Part 2 will show you how to target Android GUI applications with Python!
TensorFlow is the most popular machine learning framework nowadays. TensorFlow Lite (TFLite), open sourced in late 2017, is TensorFlow’s runtime designed for mobile devices, esp. Android cell phones. TFLite is getting more and more mature. One the most interesting new components introduced recently are its GPU delegate and new NNAPI delegate. The GPU delegate uses Open GL ES compute shader on Android platforms and Metal shade on iOS devices. The original NNAPI delegate is an all-or-nothing design (if one of the ops in the compute graph is not supported by NNAPI, the whole graph is not delegated). The new one is a per-op design. When an op in a graph is not supported by NNAPI, the op is automatically fell back to the CPU runtime. I’ll have a quick review TFLite and its interpreter, then walk the audience through example usage of the two delegates and important source code of them.
Introduction to the new Tensorflow 2.x and the Coral AI Edge TPU hardware. The presentation introduces Tensorflow main features such as Sequential and Functional APIs, mobile support with Tensorflow Lite, web support with TensorflowJS and Google Cloud support with TFX.
In addition, the presentation introduces the new edge TPU architecture coming from Coral AI, including its main hardware features and description of the compiling flow.
Exploring Thermal Related Stuff in iDevices using Open-Source ToolKoan-Sin Tan
This is the era of so-called “dark silicon.” Thermal control is an important but seldom-talked topic. I could not find public information on how iOS does it. Recent checkm8 and follow-on checkra1n enable jailbreaking of iPhone 5s – iPhone X running iOS 12.3 and up. So that we can explore these devices with open-source tools
One bite and all your dreams will come true: Analyzing and Attacking Apple Ke...Priyanka Aash
"Though many security mechanisms are deployed in Apple's macOS and iOS systems, some old-fashioned or poor-quality kernel code still leaves the door widely open to attackers. Especially, as kernel's critical components, device drivers are frequently exploited to attack Apple systems. In fact, bug hunting in Apple kernel drivers is not easy since they are mostly closed-source and heavily relying on object-oriented programming. In this talk, we will share our experience of analyzing and attacking Apple kernel drivers. In specific, we will introduce a new tool called Ryuk. Ryuk employs static analysis techniques to discover bugs by itself or assist manual review.
In addition, we further combine static analysis with dynamic fuzzing for bug hunting in Apple drivers. In specific, we will introduce how we integrate Ryuk to the state-of-art Apple driver fuzzer, PassiveFuzzFrameworkOSX, for finding exploitable bugs.
Most importantly, we will illustrate Ryuk's power with several new vulnerabilities that are recently discovered by Ryuk. In specific, we will show how we exploit these vulnerabilities for privilege escalation on macOS 10.13.3 and 10.13.2. We will not only explain why these bugs occur and how we find them, but also demonstrate how we exploit them with innovative kernel exploitation techniques."
The Libre-SOC Project aims to create an entirely Libre-Licensed, transparently-developed fully auditable Hybrid 3D CPU-GPU-VPU, using the Supercomputer-class OpenPOWER ISA as the foundation.
Our first test ASIC is a 180nm "Fixed-Point" Power ISA v3.0B processor, 5.1mm x 5.9mm, as a proof-of-concept for the team, whose primary expertise is in Software Engineering. Software Engineering training brings a radically different approach to Hardware development: extensive unit tests, source code revision control, automated development tools are normal. Libre Project Management brings even more: bug trackers, mailing lists, auditable IRC logs and a wiki are standard fare for Libre Projects that are simply not normal Industry-Standard practice.
This talk therefore goes through the workflow, from the original HDL through to the GDS-II layout, showing how we were able to keep track of the development that led to the IMEC 180nm tape-out in July 2021. In particular, by following a parallel development process involving "Real" and "Symbolic" Cell Libraries, developed by Chips4Makers, will be shown how our developers did not need to sign a Foundry NDA, but were still able to work side-by-side with a University that did. With this parallel development process, the University upheld their NDA obligations, and Libre-SOC were simultaneously able to honour its Transparency Objectives.
Rainbow Over the Windows: More Colors Than You Could ExpectPeter Hlavaty
As time goes on operating systems keep evolving, like Microsoft Windows do, it ships new designs, features and codes from time to time. However sometimes it also ships more than bit of codes for complex subsystems residing in its kernel ... and at some future point it starts implementing new designs to prevent unnecessary access to it. However is it safe enough?
As we can see from security bulletins, win32k subsystem attracts lots of attention. It looks that with efforts of many security researchers who has dug into this area, finding bugs here shall becomes pretty tough and almost fruitless. But unfortunately this is not true, as win32k is backed up by very complex logic and large amount of code by nature..
We will present our point of view to Windows graphic subsystem, as well as schema of our fuzzing strategies. We will introduce some unusual areas of win32k, its extensions and how it can breaks even locked environments.
Part of our talk will be dedicated to CVE-2016-0176, the bug we used for this year's Pwn2Own Edge sandbox bypass, from its discovery to its exploitation techniques, which could serves as an example for universal DirectX escape which is independent of graphics vendors.
Introduction to Python GUI development with Delphi for Python - Part 1: Del...Embarcadero Technologies
Learn how Embarcadero’s newly released free Python modules bring the power and flexibility of Delphi’s GUI frameworks to Python. VCL and FireMonkey (FMX) are mature GUI libraries. VCL is focused on native Windows development, while FireMonkey brings a powerful flexible GUI framework to Windows, Linux, macOS, and even Android. This webinar will introduce you to these new free Python modules and how you can use them to build graphical users interfaces with Python. Part 2 will show you how to target Android GUI applications with Python!
Combining the Strenghts of Python and Delphi
Links replay and more
https://blogs.embarcadero.com/combining-the-strengths-of-delphi-and-python/
Python4Delphi repository
https://github.com/pyscripter/python4delphi
Part 1
https://blogs.embarcadero.com/webinar-replay-python-for-delphi-developers-part-1-introduction/
Learn how Embarcadero's newly released free Python modules bring the power and flexibility of Delphi's GUI frameworks to Python. VCL and FireMonkey (FMX) are mature GUI libraries. VCL is focused on native Windows development, while FireMonkey brings a powerful flexible GUI framework to Windows, Linux, macOS, and even Android. This webinar will introduce you to these new free Python modules and how you can use them to build graphical users interfaces with Python. Part 2 will show you how to target Android GUI applications with Python!
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/mar-2018-alliance-vitf-facebook
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Fei Sun, software engineer at Facebook, delivers the presentation "The Caffe2 Framework for Mobile and Embedded Deep Learning" at the Embedded Vision Alliance's March 2018 Vision Industry and Technology Forum. Sun introduces Caffe2, a new open-source machine learning framework, and explains how Facebook is using it to enable computer vision in mobile and embedded devices.
SenchaCon 2016: Develop, Test & Deploy with Docker - Jonas Schwabe Sencha
Have you ever heard the phrase: "Everything works fine on my machine?" Docker is here to rescue you. Running your toolchain, Ext JS application, back-end server, and even your database - all in a standardized container format that can be transported and reused, throughout your process. In this session, you will learn how to automate a typical workflow, including developing, testing, and deploying, by using Docker containers and common continuous integration solutions.
From Zero to Hero - All you need to do serious deep learning stuff in R Kai Lichtenberg
Slides from my talk at the useR Group Münster 04/17/18 on how to start with GPU enabled deep learning in R. First I'm showing how to create a NVIDIA docker based image with RStudio, TensorFlow and Keras for R and then comes an introduction to deep learning (classic MNIST classification with MLP and CNN).
Trends in Systems and How to Get Efficient Performanceinside-BigData.com
In this video from Switzerland HPC Conference, Martin Hilgeman from Dell presents: HPC Workload Efficiency and the Challenges for System Builders.
"With all the advances in massively parallel and multi-core computing with CPUs and accelerators it is often overlooked whether the computational work is being done in an efficient manner. This efficiency is largely being determined at the application level and therefore puts the responsibility of sustaining a certain performance trajectory into the hands of the user. It is observed that the adoption rate of new hardware capabilities is decreasing and lead to a feeling of diminishing returns. This presentation shows the well-known laws of parallel performance from the perspective of a system builder. It also covers through the use of real case studies, examples of how to program for energy efficient parallel application performance."
Watch the video: http://wp.me/p3RLHQ-gIS
Learn more: http://dell.com
and
http://www.hpcadvisorycouncil.com/events/2017/swiss-workshop/agenda.php
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Topics of this presentation:
- Basics and best practices of developing single-page applications (SPA) and Web API Services on Microsoft .NET -
- Core with Docker and Linux.
- PowerShell Core automated builds.
- Markdown/PDF documentation.
- Documentation of public interfaces with Swagger/OAS/YAML.
- Automated testing of SPA on Protractor and testing the Web API on Postman/Newman.
This presentation by Sergii Fradkov (Consultant, Engineering), Andrii Zarharov (Lead Software Engineer, Consultant), Igor Magdich (Lead Test Engineer, Consultant) was delivered at GlobalLogic Kharkiv .NET TechTalk #1 on May 24, 2019.
Kubernetes Forum Seoul 2019: Re-architecting Data Platform with KubernetesSeungYong Oh
Session Video: https://youtu.be/7MPH1mknIxE
In this talk, we share Devsisters' journey of migrating its internal data platform including Spark to Kubernetes, with its benefits and issues.
데브시스터즈에서 데이터플랫폼 컴포넌트를 쿠버네티스로 옮기면서 얻은 장점들과 이슈들에 대해 공유합니다.
Conference session page:
- English: https://sched.co/WIRK
- Korean: https://sched.co/WYRc
These slides provide an overview of .NET Core and also the changes to ASP.NET Core after the RC2 release. There is also some demos and source code.
This talk was given at the Let's Dev This Roadshow in London, ON on May 26, 2016.
Introduction to Civil Infrastructure PlatformSZ Lin
CIP is target to establish an open source base layer of industrial grade software to enable the use and implementation of software. This slide will introduce the current status and road map in CIP
Common Pitfalls of Functional Programming and How to Avoid Them: A Mobile Gam...gree_tech
This material is presented on CUFP 2013.
Functional programming is already an established technology is many areas. However, the lack of skilled developers has been a challenging hurdle in the adoption of such languages. It is easy for an inexperienced programmer to fall into the many traps of functional programming, resulting in a loss of productivity and bad software quality. Resource leaks caused by Haskell's lazy evaluation, for instance, are only the tip of the iceberg. Knowledge sharing and a mature tool-assisted development process are ways to avoid such pitfalls. At GREE, one of the largest mobile gaming companies, we use Haskell and Scala to develop major components of our platform, such as a distributed NoSQL solution, or an image storage infrastructure. However, only 11 programmers use functional programming on their daily task. In this talk, we will describe some unexpected functional programming issues we ran into, how we solved them and how we hope to avoid them in the future. We have developed a system testing framework to enhance regression testing, spent lots of time documenting pitfalls and introduced technical reviews. Recently, we even started holding lunchtime presentations about functional programming in order to attract beginners and prevent them from falling into the same traps.
Bring-your-ML-Project-into-Production-v2.pdfLiang Yan
Machine Learning (ML) is quite popular today in the academic/research world. However, it is quite difficult to put into a product, especially a product with a huge customer base. This session will give Kubeflow, the open source ML toolkit on top of Kubernetes, a deep look from the MLOps perspective. Furthermore, we will have a brief look at Distributed MLSys and how Kubeflow copes with scalability. Last, a demo of the Kubeflow stack setup and ML project pipeline deployment will be demonstrated in the cloud.
Getting Started with Apache Spark on KubernetesDatabricks
Community adoption of Kubernetes (instead of YARN) as a scheduler for Apache Spark has been accelerating since the major improvements from Spark 3.0 release. Companies choose to run Spark on Kubernetes to use a single cloud-agnostic technology across their entire stack, and to benefit from improved isolation and resource sharing for concurrent workloads. In this talk, the founders of Data Mechanics, a serverless Spark platform powered by Kubernetes, will show how to easily get started with Spark on Kubernetes.
Using Deep Learning Toolkits with Kubernetes clustersJoy Qiao
Slides for the talk at the O'Reilly AI Conference San Francisco 2017 - https://conferences.oreilly.com/artificial-intelligence/ai-ca/public/schedule/detail/59613
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
2. Quick Intro
• Caffe 2
• 2nd generation of Caffe, which was the most popular deep learning
framework (before TensorFlow) from Berkeley
• What's the difference? Caffe2 improves Caffe 1.0 in a series of directions:
• first-class support for large-scale distributed training
• mobile deployment
• new hardware support (in addition to CPU and CUDA)
• flexibility for future directions such as quantized computation
• stress tested by the vast scale of Facebook applications
https://caffe2.ai/docs/caffe-migration.html
3. Caffe2 on Android
• Official Android demo
• https://caffe2.ai/docs/AI-Camera-demo-android.html, https://github.com/caffe2/
AICamera
• SqueezeNet 1.1:
• 5.8/5.7 fps on Samsung S7 and Google Pixel
• not very impressive
• OpenGL backend
• https://www.facebook.com/Caffe2AI/videos/126340488008269/
• up to 6X speedup (24 FPS) compared to CPU on high-end Android devices (e.g.
Galaxy S8) for style transfer models
5. • Tensorflow Lite is also looking for the possibility of
OpenGL ES backend
• https://github.com/tensorflow/tensorflow/issues/16189
6. What can we use on
Android now
https://github.com/caffe2/caffe2/tree/master/caffe2/mobile/contrib
7. Caffe2 backends for
Android I know
• ARM CPU:
• NNPACK, Eigen: quite mature
• OpenGL ES:
• OpenGL: not actively maintained (?)
• ARM Compute Library (GL ES part): newly added, still growing
• NEON, and OpenCL
• NNAPI: not fully integrated yet.
8. How to build
• > scripts/build_android.sh
• With that, no test command line binary test
• Caffe 2 has some tests and a simple command line benchmark tool
called speed_benchmark
> scripts/build_android.sh -DBUILD_TEST -DBUILD_BINARY
• then we can get build_android/bin/speed_benchmark and
other test binaries
• Pytorch has a good tutorial on using it, http://pytorch.org/tutorials/
advanced/super_resolution_with_caffe2.html
9. Some results
• > ./speed_benchmark --input_file input.blobproto --input
data --init_net init_net.pb --net predict_net.pb --
caffe2_log_level=0
01-06 23:15:42.073 32623 32623 I native : [I net_simple.cc:101] Starting benchmark.
01-06 23:15:42.074 32623 32623 I native : [I net_simple.cc:102] Running warmup runs.
01-06 23:15:42.074 32623 32623 I native : [I net_simple.cc:112] Main runs.
01-06 23:15:43.805 32623 32623 I native : [I net_simple.cc:123] Main run finished. Milliseconds per iter:
173.15. Iters per second: 5.77535
10. Some results
• ARM Compute Library backend: Caffe2 addend a Compute Libarry backend on in the end of Februrary 2018. With some tweaks, it's
possible to run SqueezeNet 1.1 faster than CPU (NNPAC) with OpenGL
01-04 03:41:38.297 25523 25523 I native : [I gl_model_test.h:52] [C2DEBUG] Benchmarking OpenGL Net
01-04 03:41:38.297 25523 25523 I native : [I net_gl.cc:104] Starting benchmark.
01-04 03:41:38.297 25523 25523 I native : [I net_gl.cc:105] Running warmup runs.
01-04 03:41:38.796 25523 25523 I native : [I net_gl.cc:121] Main runs.
01-04 03:41:43.107 25523 25523 I native : [I net_gl.cc:134] [C2DEBUG] Main run finished. Milliseconds per iter: 43.1077. Iters per
second: 23.1977
01-04 03:41:43.110 25523 25523 I native : [I gl_model_test.h:66] [C2DEBUG] Benchmarking CPU Net
01-04 03:41:43.110 25523 25523 I native : [I net_simple.cc:101] Starting benchmark.
01-04 03:41:43.110 25523 25523 I native : [I net_simple.cc:102] Running warmup runs.
01-04 03:41:43.768 25523 25523 I native : [I net_simple.cc:112] Main runs.
01-04 03:41:50.229 25523 25523 I native : [I net_simple.cc:123] Main run finished. Milliseconds per iter: 64.6136. Iters per
second: 15.4766
11. Comparing with TF Lite
• cmake is easier than bazel :-)
• Relatively large, or say comprehensive. If you want to enable something like on-device learning. It's
easier to start with TFLite.
• binary could be large
• Code looks cleaning
• Review process, or say, software engineering not as rigid as TensorFlow
• TF has a larger team (?)
• See, https://www.oreilly.com/ideas/how-the-tensorflow-team-handles-open-source-support
• Some interesting code,
• The Observer design pattern could be used to measure performance, https://en.wikipedia.org/wiki/
Observer_pattern
• https://github.com/caffe2/caffe2/tree/master/caffe2/observers
