Introduction of Chainer, a framework for neural networks, v1.11. Slides used for the student seminar on July 20, 2016, at Sugiyama-Sato lab in the Univ. of Tokyo.
Published on 11 may, 2018
Chainer is a deep learning framework which is flexible, intuitive, and powerful.
This slide introduces some unique features of Chainer and its additional packages such as ChainerMN (distributed learning), ChainerCV (computer vision), ChainerRL (reinforcement learning), Chainer Chemistry (biology and chemistry), and ChainerUI (visualization).
Introduction of Chainer, a framework for neural networks, v1.11. Slides used for the student seminar on July 20, 2016, at Sugiyama-Sato lab in the Univ. of Tokyo.
Published on 11 may, 2018
Chainer is a deep learning framework which is flexible, intuitive, and powerful.
This slide introduces some unique features of Chainer and its additional packages such as ChainerMN (distributed learning), ChainerCV (computer vision), ChainerRL (reinforcement learning), Chainer Chemistry (biology and chemistry), and ChainerUI (visualization).
Notes from 2016 bay area deep learning school Niketan Pansare
Slide-deck for the lunch talk at IBM Almaden Research Center on Oct 11, 2016.
Abstract: In this lunch talk, I will give a high-level summary of bay area deep learning school which was held at Stanford on Sept 24 and 25. The videos and slides of the lectures are available online at http://www.bayareadlschool.org/. I will also give a very brief introduction of deep learning.
FCN-Based 6D Robotic Grasping for Arbitrary Placed ObjectsKusano Hitoshi
This is the slide used for IEEE International Conference on Robotics and Automation (ICRA) 2017, Workshop on Learning and Control for Autonomous Manipulation Systems on June 2nd, 2017.
Chainer is a deep learning framework which is flexible, intuitive, and powerful.
This slide introduces some unique features of Chainer and its additional packages such as ChainerMN (distributed learning), ChainerCV (computer vision), ChainerRL (reinforcement learning)
PyTorch crash course: Introduction to PyTorch deep learning framework and step by step guide to configuring PyCharm for using a remote server for implementing deep learning, plus a summary of Linux's most relevant commands.
The search for faster computing remains of great importance to the software community. Relatively inexpensive modern hardware, such as GPUs, allows users to run highly parallel code on thousands, or even millions of cores on distributed systems.
Building efficient GPU software is not a trivial task, often requiring a significant amount of engineering hours to attain the best performance. Similarly, distributed computing systems are inherently complex. In recent years, several libraries were developed to solve such problems. However, they often target a single aspect of computing, such as GPU computing with libraries like CuPy, or distributed computing with Dask.
Libraries like Dask and CuPy tend to provide great performance while abstracting away the complexity from non-experts, being great candidates for developers writing software for various different applications. Unfortunately, they are often difficult to be combined, at least efficiently.
With the recent introduction of NumPy community standards and protocols, it has become much easier to integrate any libraries that share the already well-known NumPy API. Such changes allow libraries like Dask, known for its easy-to-use parallelization and distributed computing capabilities, to defer some of that work to other libraries such as CuPy, providing users the benefits from both distributed and GPU computing with little to no change in their existing software built using the NumPy API.
Highlighted notes of:
Chapter 9: Atomics
Book:
CUDA by Example
An Introduction to General Purpose GPU Computing
Authors:
Jason Sanders
Edward Kandrot
“This book is required reading for anyone working with accelerator-based computing systems.”
–From the Foreword by Jack Dongarra, University of Tennessee and Oak Ridge National Laboratory
CUDA is a computing architecture designed to facilitate the development of parallel programs. In conjunction with a comprehensive software platform, the CUDA Architecture enables programmers to draw on the immense power of graphics processing units (GPUs) when building high-performance applications. GPUs, of course, have long been available for demanding graphics and game applications. CUDA now brings this valuable resource to programmers working on applications in other domains, including science, engineering, and finance. No knowledge of graphics programming is required–just the ability to program in a modestly extended version of C.
CUDA by Example, written by two senior members of the CUDA software platform team, shows programmers how to employ this new technology. The authors introduce each area of CUDA development through working examples. After a concise introduction to the CUDA platform and architecture, as well as a quick-start guide to CUDA C, the book details the techniques and trade-offs associated with each key CUDA feature. You’ll discover when to use each CUDA C extension and how to write CUDA software that delivers truly outstanding performance.
Table of Contents
Why CUDA? Why Now?
Getting Started
Introduction to CUDA C
Parallel Programming in CUDA C
Thread Cooperation
Constant Memory and Events
Texture Memory
Graphics Interoperability
Atomics
Streams
CUDA C on Multiple GPUs
The Final Countdown
All the CUDA software tools you’ll need are freely available for download from NVIDIA.
Jason Sanders is a senior software engineer in NVIDIA’s CUDA Platform Group, helped develop early releases of CUDA system software and contributed to the OpenCL 1.0 Specification, an industry standard for heterogeneous computing. He has held positions at ATI Technologies, Apple, and Novell.
Edward Kandrot is a senior software engineer on NVIDIA’s CUDA Algorithms team, has more than twenty years of industry experience optimizing code performance for firms including Adobe, Microsoft, Google, and Autodesk.
This presentation deals with how one can utilize multiple cores, while working with C/C++ applications using an API called OpenMP. It's a shared memory programming model, built on top of POSIX thread. Also the fork-join model, parallel design pattern are discussed using PetriNets.
An introduction to the OpenMP parallel programming model.
From the Scalable Computing Support Center at Duke University (http://wiki.duke.edu/display/scsc)
FaceBook のAIチームが研究の発表論文である "Memory networks"とその拡張である"Towards AI-complete question answering: A set of prerequisite toy tasks."を簡単に紹介します。
[1] Weston, J., Chopra, S., and Bordes, A. Memory networks. In International Conference on Learning Representations (ICLR), 2015a.
[2] Weston, J., Bordes, A., Chopra, S., and Mikolov, T. Towards AI-complete question answering: A set of prerequisite toy tasks. arXiv preprint: 1502.05698, 2015b.
Notes from 2016 bay area deep learning school Niketan Pansare
Slide-deck for the lunch talk at IBM Almaden Research Center on Oct 11, 2016.
Abstract: In this lunch talk, I will give a high-level summary of bay area deep learning school which was held at Stanford on Sept 24 and 25. The videos and slides of the lectures are available online at http://www.bayareadlschool.org/. I will also give a very brief introduction of deep learning.
FCN-Based 6D Robotic Grasping for Arbitrary Placed ObjectsKusano Hitoshi
This is the slide used for IEEE International Conference on Robotics and Automation (ICRA) 2017, Workshop on Learning and Control for Autonomous Manipulation Systems on June 2nd, 2017.
Chainer is a deep learning framework which is flexible, intuitive, and powerful.
This slide introduces some unique features of Chainer and its additional packages such as ChainerMN (distributed learning), ChainerCV (computer vision), ChainerRL (reinforcement learning)
PyTorch crash course: Introduction to PyTorch deep learning framework and step by step guide to configuring PyCharm for using a remote server for implementing deep learning, plus a summary of Linux's most relevant commands.
The search for faster computing remains of great importance to the software community. Relatively inexpensive modern hardware, such as GPUs, allows users to run highly parallel code on thousands, or even millions of cores on distributed systems.
Building efficient GPU software is not a trivial task, often requiring a significant amount of engineering hours to attain the best performance. Similarly, distributed computing systems are inherently complex. In recent years, several libraries were developed to solve such problems. However, they often target a single aspect of computing, such as GPU computing with libraries like CuPy, or distributed computing with Dask.
Libraries like Dask and CuPy tend to provide great performance while abstracting away the complexity from non-experts, being great candidates for developers writing software for various different applications. Unfortunately, they are often difficult to be combined, at least efficiently.
With the recent introduction of NumPy community standards and protocols, it has become much easier to integrate any libraries that share the already well-known NumPy API. Such changes allow libraries like Dask, known for its easy-to-use parallelization and distributed computing capabilities, to defer some of that work to other libraries such as CuPy, providing users the benefits from both distributed and GPU computing with little to no change in their existing software built using the NumPy API.
Highlighted notes of:
Chapter 9: Atomics
Book:
CUDA by Example
An Introduction to General Purpose GPU Computing
Authors:
Jason Sanders
Edward Kandrot
“This book is required reading for anyone working with accelerator-based computing systems.”
–From the Foreword by Jack Dongarra, University of Tennessee and Oak Ridge National Laboratory
CUDA is a computing architecture designed to facilitate the development of parallel programs. In conjunction with a comprehensive software platform, the CUDA Architecture enables programmers to draw on the immense power of graphics processing units (GPUs) when building high-performance applications. GPUs, of course, have long been available for demanding graphics and game applications. CUDA now brings this valuable resource to programmers working on applications in other domains, including science, engineering, and finance. No knowledge of graphics programming is required–just the ability to program in a modestly extended version of C.
CUDA by Example, written by two senior members of the CUDA software platform team, shows programmers how to employ this new technology. The authors introduce each area of CUDA development through working examples. After a concise introduction to the CUDA platform and architecture, as well as a quick-start guide to CUDA C, the book details the techniques and trade-offs associated with each key CUDA feature. You’ll discover when to use each CUDA C extension and how to write CUDA software that delivers truly outstanding performance.
Table of Contents
Why CUDA? Why Now?
Getting Started
Introduction to CUDA C
Parallel Programming in CUDA C
Thread Cooperation
Constant Memory and Events
Texture Memory
Graphics Interoperability
Atomics
Streams
CUDA C on Multiple GPUs
The Final Countdown
All the CUDA software tools you’ll need are freely available for download from NVIDIA.
Jason Sanders is a senior software engineer in NVIDIA’s CUDA Platform Group, helped develop early releases of CUDA system software and contributed to the OpenCL 1.0 Specification, an industry standard for heterogeneous computing. He has held positions at ATI Technologies, Apple, and Novell.
Edward Kandrot is a senior software engineer on NVIDIA’s CUDA Algorithms team, has more than twenty years of industry experience optimizing code performance for firms including Adobe, Microsoft, Google, and Autodesk.
This presentation deals with how one can utilize multiple cores, while working with C/C++ applications using an API called OpenMP. It's a shared memory programming model, built on top of POSIX thread. Also the fork-join model, parallel design pattern are discussed using PetriNets.
An introduction to the OpenMP parallel programming model.
From the Scalable Computing Support Center at Duke University (http://wiki.duke.edu/display/scsc)
FaceBook のAIチームが研究の発表論文である "Memory networks"とその拡張である"Towards AI-complete question answering: A set of prerequisite toy tasks."を簡単に紹介します。
[1] Weston, J., Chopra, S., and Bordes, A. Memory networks. In International Conference on Learning Representations (ICLR), 2015a.
[2] Weston, J., Bordes, A., Chopra, S., and Mikolov, T. Towards AI-complete question answering: A set of prerequisite toy tasks. arXiv preprint: 1502.05698, 2015b.
Chainer is a deep learning framework which is flexible, intuitive, and powerful. This slide introduces some unique features of Chainer and its additional packages such as ChainerMN (distributed learning), ChainerCV (computer vision), ChainerRL (reinforcement learning)
Democratizing machine learning on kubernetesDocker, Inc.
One of the largest challenges facing the machine learning community today is understanding how to build a platform to run common open-source machine learning libraries such as Tensorflow. Both Joy and Lachie are both passionate about making machine learning accessible to the masses using Kubernetes. In this session they'll share how to deploy a distributed Tensorflow training cluster complete with GPU scheduling on Kubernetes. We'll also share how distributed Tensorflow training works, various options for distributed training, and when to choose what option. We'll also share some best practices on using distributed Tensorflow on top of Kubernetes, based on our latest performance tests performed on public cloud providers. All work presented in this session will be accessible via a public Github repository.
Divide and stress: the journey to component load test talk was given at ExpoQA 2017 under the track of Quality Assurance and Performance.
Describes what are the most common pains that big companies suffer on load testing processes with expesive cost of 1:1s replicas of production environment for performance testing.
In order to reduce those expesive costs, The Workshop designed a new methodology that aims to reduce operational costs, human errors and enables the performance testing in Continuous Delivery pipelines, that it also can be adopted by Continuous Deployment scenarios.
Component Based Load Testing (CBT) is a methodology designed in The Workshop (http://theworkshop.com) that rethinks what the future of performance testing should do.
CBT introduces the load test executon as part of CD pipelines, ensuring the quality of our products through defined exit criteria for the main metrics, determining if the changes of a new release is ready or not to progress to next environments (stage, prod, etc).
CBT tries to use a pool resources efficiently, making them avaiable for any load test execution requested by any of our products. CBT main mission is trying to reduce all operative costs of maintain 1:1 replicas of production environments, by having a reduce pool of resources where the performance tests are executed using dockers by a reduced time under really volatile environments.
Kubernetes 1.16 and rancher 2.3 enhancementsSaiyam Pathak
This presentation talks about the recent kubernetes 1.16 enhancements and Rancher 2.3 new features. It also has the references section that was used as a motivation for this presentation.
As the leading full-stack application framework for Java SE and EE, the Spring Framework continues to deliver significant benefits to Java developers, increasing development productivity and runtime performance while improving test coverage and application quality.
In this talk, core Spring Framework committer Sam Brannen will provide attendees an overview of the new features in Spring 3.2 as well as a sneak peak at the roadmap for 4.0.
Spring Framework 3.2 builds on several themes delivered in 3.1 with a continued focus on asynchronous MVC processing with Servlet 3.0, support for using @Autowired and @Value as meta-annotations, support for custom @Bean definition annotations, and early support for JCache 0.5. Regarding the internals, CGLIB 3.0 and ASM 4.0 have been inlined, and the framework is now built with Gradle and hosted on GitHub. For those interested in testing their Spring-based web applications, Spring 3.2 offers new support for loading WebApplicationContexts in the TestContext framework, and the formerly standalone Spring MVC Test project is now included in the spring-test module, allowing for first-class testing of Spring MVC applications.
Ember.js - introduction
I have searched for Ember ppt in the internet. Got many things but not like structured... So i have just combined and made a new one..
I am just learning and not an expert. Please share your comments, so i can keep up myself..
In this session, Kiran gives a talk about the rich ecosystem of tools (cmk, CAPC, Terraform, Ansible, Packer, csbench, mbx), that support Cloudstack.
Find out how the various tools work and how easy it is to integrate with Apache CloudStack.
This session provides a great way to speed up CloudStack adoption and improve performance by saving valuable time.
-----------------------------------------
The CloudStack India User Group 2024 took place in Hyderabad on 23rd February. The conference, arranged by a group of volunteers from the Apache CloudStack Community, saw multiple sessions held about the cloud orchestration platform and its latest advancements.
Azure DevOps added the Multi-Stage Pipelines as a part of the Pipeline offering which enables version controlled Ci/CD expressed as YAML.
These slides were based on the information available in Aug-2019 on how a pipeline can be constructed.
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/
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.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
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
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.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
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.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
6. How to use Chainer v2.0.0a1
pip install chainer –-pre
pip install cupy
Documentation: http://docs.chainer.org/en/v2.0.0a1/
CuPy Documentation: http://cupy.docs.chainer.org/
7. Features of v2.0.0a1
• CuPy is separated into an independent package
• Unified configuration / training mode
• Removed deprecated/obsolete APIs
• Interface improvements
8. CuPy is separated
• CuPy is now a separate project!
• https://github.com/cupy/cupy
• At the moment, the development of CuPy is still taking place
at Chainer v1
• Changes are merged to cupy/cupy after each minor release
• In the future, any changes that break compatibilities will be made in
the cupy repository
9. Unified configuration
Thread-local-like object to configure Chainer
• chainer.config: thread-local configuration
• chainer.global_config: process-wide configuration
• Users usually only have to touch chainer.config.
11. Training mode
• All functions/classes that have training/test mode
distinctions now use chainer.config.train flag
• Evaluator automatically switches the flag
→ No need to pass train/test flags manually anymore!
13. Removed/modified APIs
• Array-creation functions in chainer.cuda module
• FunctionSet
• wscale option of links and scale option of init_weight
(specify weight initializers instead)
• force_tuple option of F.split_axis is now set to True by
default
• Some minor updates
14. Major features planned for beta and final releases
• Optimizer with UpdateRule
Can specify hyperparameters for each parameter (e.g. learning rate,
hook functions)
• Uninitialized variable
Used to implement the parameter-shape placeholder
• Remove volatile flag
Use chainer.config.enable_backprop flag instead
• PyCharm-friendly Link/Chain APIs