This presentation discusses advances in CPLEX 12.5.1 with special attention to using the remote object facility for building advanced distributed memory optimization algorithms. Performance improvements in CPLEX 12.5.1 complete the presentation.
This presentation given at INFORMS in November 2013 highlights the new capabilities and performance improvements in CPLEX 12.6, the lastest version of the mathematical programming engine
In November 2013, at INFORMS, we introduced one of the new features in CPLEX 12.6: Distributed MIP. This gives you the ability to solve a single MIP problem on several computers.
This Virtual User Group session, held on 2014-01-22, presents some of the techniques and algorithms used to improve the CPLEX MIP solver in versions 12.5.1 and 12.6.
Modeling and Solving Scheduling Problems with CP OptimizerPhilippe Laborie
This presentation focuses on using CP Optimizer to address scheduling problems. We will initially cover modeling concepts related with scheduling in CP Optimizer. Using examples we will then provide details on tools, functionalities and tips for speeding-up the development of your scheduling models and improving their efficiency.
This presentation introduces CP Optimizer a model & run optimization engine for solving discrete combinatorial problems with a particular focus on scheduling problems.
Accelerating the Development of Efficient CP Optimizer ModelsPhilippe Laborie
The IBM Constraint Programming optimization system CP Optimizer was designed to provide automatic search and a simple modeling of discrete optimization problems, with a particular focus on scheduling applications. It is used in industry for solving operational planning and scheduling problems. We will give an overview of CP Optimizer and then describe in further detail a set of features such as input/output file format, warm-start or conflict refinement that help accelerate the development of efficient models.
This presentation given at INFORMS in November 2013 highlights the new capabilities and performance improvements in CPLEX 12.6, the lastest version of the mathematical programming engine
In November 2013, at INFORMS, we introduced one of the new features in CPLEX 12.6: Distributed MIP. This gives you the ability to solve a single MIP problem on several computers.
This Virtual User Group session, held on 2014-01-22, presents some of the techniques and algorithms used to improve the CPLEX MIP solver in versions 12.5.1 and 12.6.
Modeling and Solving Scheduling Problems with CP OptimizerPhilippe Laborie
This presentation focuses on using CP Optimizer to address scheduling problems. We will initially cover modeling concepts related with scheduling in CP Optimizer. Using examples we will then provide details on tools, functionalities and tips for speeding-up the development of your scheduling models and improving their efficiency.
This presentation introduces CP Optimizer a model & run optimization engine for solving discrete combinatorial problems with a particular focus on scheduling problems.
Accelerating the Development of Efficient CP Optimizer ModelsPhilippe Laborie
The IBM Constraint Programming optimization system CP Optimizer was designed to provide automatic search and a simple modeling of discrete optimization problems, with a particular focus on scheduling applications. It is used in industry for solving operational planning and scheduling problems. We will give an overview of CP Optimizer and then describe in further detail a set of features such as input/output file format, warm-start or conflict refinement that help accelerate the development of efficient models.
Presented for the first time at INFORMS in November 2013, this deck explains how CPLEX 12.5.1 exploits random performance variability through parallel root cut loops.
Conditional interval variables: A powerful concept for modeling and solving c...Philippe Laborie
Scheduling is not only about deciding when to schedule a predefined set of activities. Most of real-world scheduling problems also involve selecting a subset of activities (oversubscribed problems) and a particular way to execute them (resource or mode allocation, alternative recipes, preemptive activity splitting, etc.). We present the notion of conditional interval variable in the context of Constraint Programming and show how this concept can be leveraged to model and solve complex scheduling problems involving both temporal and non-temporal decisions.
This slide deck was presented at the 21st International Symposium on Mathematical Programming (ISMP 2012).
Philippe Laborie
Solving Large Scale Optimization Problems using CPLEX Optimization Studiooptimizatiodirectdirect
Recent advancements in Linear and Mixed Programing give us the capability to solve larger Optimization Problems. In this talk using CPLEX Optimization Studio we will discuss modeling practices, case studies and demonstrate good practices for solving Hard Optimization Problems. We will also discuss recent CPLEX performance improvements and recently added features.
IBM ILOG CP Optimizer for Detailed Scheduling Illustrated on Three ProblemsPhilippe Laborie
Since version 2.0, IBM ILOG CP Optimizer provides a new
scheduling language supported by a robust and efficient automatic search. This presentation illustrates both the expressivity of the modelling language and the robustness of the automatic search on three problems recently studied in the scheduling literature. We show that all three problems
can easily be modelled with CP Optimizer in only a few dozen lines (the complete models are provided) and that on average the automatic search outperforms existing problem specific approaches.
This slide deck was presented at CP-AI-OR 2009 conference. Complete reference:
Philippe Laborie. "IBM ILOG CP Optimizer for Detailed Scheduling Illustrated on Three Problems". Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (CP-AI-OR 2009). Lecture Notes in Computer Science. Volume 5547, 2009, pp 148-162.
Modeling and Solving Resource-Constrained Project Scheduling Problems with IB...Philippe Laborie
Since version 2.0, IBM ILOG CP Optimizer provides a new scheduling language supported by a robust and efficient automatic search. We show how the main features of resource-constrained project scheduling such as work-breakdown structures, optional tasks, different types of resources, multiple modes and skills, resource calendars and objective functions such as earliness/tardiness, unperformed tasks or resource costs can be modeled in CP Optimizer. The robustness of the automatic search will be illustrated on some classical resource-constrained project scheduling benchmarks.
This slide deck was presented at EURO 2009 conference (http://www.euro-2009.de/).
Philippe Laborie
A (Not So Short) Introduction to CP Optimizer for SchedulingPhilippe Laborie
CP Optimizer is a generic Constraint Programming (CP) based system to model and solve scheduling problems (among other combinatorial problems). It provides an algebraic language with simple mathematical concepts such as intervals or functions to capture the temporal dimension of scheduling problems in a combinatorial optimization framework. From the very beginning, CP Optimizer was designed with the goal to provide a similar experience as Mathematical Programming (MP) tools like CPLEX, with a strong focus on usability. In particular CP Optimizer implements a model & run paradigm that does not require the user to understand Constraint Programming or scheduling algorithms: declarative modeling is the only thing that matters. The automatic search provides good out of the box performance and is continuously improving from version to version. The convergence with MP goes even further, with a convergence of the tools and functionalities around the engine like an input/output format, modeling assistance with warnings and conflict refiner, interactive executable, etc. These tools accelerate the development and maintenance of models for complex industrial scheduling problems that will be efficiently solved by the automatic search. This tutorial, heavily illustrated with examples, gives an overview of CP Optimizer for scheduling. No prior knowledge of Constraint Programming is required.
This talk was given at INFORMS in November 2014. It presents some of the recent improvements made in CPLEX 12.6.1.
Topics include performance improvements, Local Implied Bound cuts, support for Python 3, Opportunistic Distributed MIP, and MIQP linearization.
Classical scheduling problems (like job-shop or RCPSP) are among the most difficult problems studied in combinatorial optimization. Still, they are far from accounting for all the complexity of industrial scheduling applications. Since more than 20 years, our team at ILOG (now IBM) develops and integrates a large panel of techniques from AI (constraint programming, temporal reasoning, learning, ...) and OR (mathematical programming, graph algorithms, local search, ...) to solve our customers most complex scheduling problems. These works have lead to the design of CP Optimizer, a generic solver based on a very expressive (but still, quite concise) mathematical modeling language to formulate complex scheduling problems. The models are solved with an automatic search algorithm that is exact, efficient, robust and continuously improving. This talk gives a short overview of CP Optimizer.
Industrial project and machine scheduling with Constraint ProgrammingPhilippe Laborie
More often than not, project and machine scheduling problems are addressed either by generic mathematical programming techniques (like MILP) or by problem-specific exact or heuristic approaches. MILP formulations are commonly used to describe the problem in mathematical terms and to provide optimal solutions or bounds to small problem instances. As they usually do not scale well, one usually resorts to using heuristics for handling large and complex industrial problems.
Though constraint programming (CP) techniques represent the state of the art in several classical project and machine scheduling benchmarks and have been used for almost 30 years for solving industrial problems, they are still seldom considered as an alternative approach in the scheduling community. A possible reason is that, for years, in the absence of efficient and robust automatic search algorithms, CP techniques have been difficult to use for non-CP experts.
We will explain why we think this time is over and illustrate our arguments with CP Optimizer, a generic system, largely based on CP, for modeling and solving real-world scheduling problems.
CP Optimizer extends linear programming with an algebraic language using simple mathematical concepts (such as intervals, sequences and functions) to capture the temporal dimension of scheduling problems in a combinatorial optimization framework. CP Optimizer implements a model-and-run paradigm that vastly reduces the burden on the user to understand CP or scheduling algorithms: modeling is by far the most important. The automatic search combines a wide variety of techniques from Artificial Intelligence (constraint programming, temporal reasoning, learning etc.) and Operations Research (mathematical programming, graph algorithms, local search, etc.) in an exact algorithm that provides good performance out of the box and which is continuously improving.
An Update on the Comparison of MIP, CP and Hybrid Approaches for Mixed Resour...Philippe Laborie
We consider a well known resource allocation and scheduling problem for which different approaches like mixed-integer programming (MIP), constraint programming (CP), constraint integer programming (CIP), logic-based Benders decompositions (LBBD) and SAT-modulo theories (SMT) have been proposed and experimentally compared in the last decade. Thanks to the recent improvements in CP Optimizer, a commercial CP solver for solving generic scheduling problems, we show that a standalone tiny CP model can out-perform all previous approaches and close all the 335 instances of the benchmark. The article explains which components of the automatic search of CP Optimizer are responsible for this success. We finally propose an extension of the original benchmark with larger and more challenging instances.
This paper presents the concept of objective landscape in the context of Constraint Programming. An objective landscape is a light-weight structure providing some information on the relation between decision variables and objective values, that can be quickly computed once and for all at the beginning of the resolution and is used to guide the search. It is particularly useful on decision variables with large domains and with a continuous semantics, which is typically the case for time or resource quantity variables in scheduling problems. This concept was recently implemented in the automatic search of CP Optimizer and resulted in an average speed-up of about 50% on scheduling problems with up to almost 2 orders of magnitude for some applications.
Boosting your HTML Apps – Overview of OpenCL and Hello World of WebCLJanakiRam Raghumandala
WebCL enables you boost the performance of select HTML application where lots of computation is involved. For example, fluid simulation, image manipulation, video manipulation.
Since, OpenCL is the underlying platform, the same has been introduced in the beginning and then WebCL.
Contents:
Motivation
Introduction to OpenCL
Introduction to WebCL
Hello World Program of WebCL
These slides are from my Ph.D. defense at the University of California, Santa Barbara, discussing how we contribute research tools to forward how science is performed with cloud systems.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/08/khronos-group-standards-powering-the-future-of-embedded-vision-a-presentation-from-the-khronos-group/
Neil Trevett, Vice President of Developer Ecosystems at NVIDIA and President of the Khronos Group, presents the “Khronos Group Standards: Powering the Future of Embedded Vision” tutorial at the May 2021 Embedded Vision Summit.
Open standards play an important role in enabling interoperability for faster, easier deployment of vision-based systems. With advances in machine learning, the number of accelerators, processors, libraries and compilers in the market is rapidly increasing. Proprietary APIs and formats create a complex industry landscape that can hinder overall market growth.
The Khronos Group’s open standards for accelerating parallel programming play a major role in deploying inferencing and embedded vision applications and include SYCL, OpenVX, NNEF, Vulkan, SPIR, and OpenCL. Trevett provides an up-to-the-minute overview and update on the Khronos embedded vision ecosystem, highlighting the capabilities and benefits of each API, giving viewers insight into which standards may be relevant to their own embedded vision projects, and discussing the future directions of these key industry initiatives.
[HKOSCon x COSCUP 2020][20200801][Ansible: From VM to Kubernetes]Wong Hoi Sing Edison
By using Ansible for DevOps, we could manage both VM, Docker image provision, Kubernetes and CephFS provision, or even Kubernetes Pod runtime management.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/intel/embedded-vision-training/videos/pages/may-2017-embedded-vision-summit-pisarevsky
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Vadim Pisarevsky, Software Engineering Manager at Intel, presents the "Making OpenCV Code Run Fast" tutorial at the May 2017 Embedded Vision Summit.
OpenCV is the de facto standard framework for computer vision developers, with a 16+ year history, approximately one million lines of code, thousands of algorithms and tens of thousands of unit tests. While OpenCV delivers decent performance out-of-the-box for some classical algorithms on desktop PCs, it lacks sufficient performance when using some modern algorithms, such as deep neural networks, and when running on embedded platforms. Pisarevsky examines current and forthcoming approaches to performance optimization of OpenCV, including the existing OpenCL-based transparent API, newly added support for OpenVX, and early experimental results using Halide.
He demonstrates the use of the OpenCL-based transparent API on a popular CV problem: pedestrian detection. Because OpenCL does not provide good performance-portability, he explores additional approaches. He discusses how OpenVX support in OpenCV accelerates image processing pipelines and deep neural network execution. He also presents early experimental results using Halide, which provides a higher level of abstraction and ease of use, and is being actively considered for future support in OpenCV.
3 years ago, Meetic chose to rebuild it's backend architecture using microservices and an event driven strategy. As we where moving along our old legacy application, testing features became gradually a pain, especially when those features rely on multiple changes across multiple components. Whatever the number of application you manage, unit testing is easy, as well as functional testing on a microservice. A good gherkin framework and a set of docker container can do the job. The real challenge is set in end-to-end testing even more when a feature can involve up to 60 different components.
To solve that issue, Meetic is building a Kubernetes strategy around testing. To do such a thing we need to :
- Be able to generate a docker container for each pull-request on any component of the stack
- Be able to create a full testing environment in the simplest way
- Be able to launch automated test on this newly created environment
- Have a clean-up process to destroy testing environment after tests To separate the various testing environment, we chose to use Kubernetes Namespaces each containing a variant of the Meetic stack. But when it comes to Kubernetes, managing multiple namespaces can be hard. Yaml configuration files need to be shared in a way that each people / automated job can access to them and modify them without impacting others.
This is typically why Meetic chose to develop it's own tool to manage namespace through a cli tool, or a REST API on which we can plug a friendly UI.
In this talk we will tell you the story of our CI/CD evolution to satisfy the need to create a docker container for each new pull request. And we will show you how to make end-to-end testing easier using Blackbeard, the tool we developed to handle the need to manage namespaces inspired by Helm.
Groovy founder Guillaume Laforge built on top of the Java standard proposal for type safe Units of Measurements, JSR-275 with his case study of a Domain-Specific Language for unit manipulations some while ago.
Based on Unit-API the successor to JSR-275, and its leading Open Source implementation Eclipse UOMo together with Xtext/TS we'll see, how a similar DSL for unit manipulations could be created with Xtext. As well as other languages including Groovy or Scala.
Presented for the first time at INFORMS in November 2013, this deck explains how CPLEX 12.5.1 exploits random performance variability through parallel root cut loops.
Conditional interval variables: A powerful concept for modeling and solving c...Philippe Laborie
Scheduling is not only about deciding when to schedule a predefined set of activities. Most of real-world scheduling problems also involve selecting a subset of activities (oversubscribed problems) and a particular way to execute them (resource or mode allocation, alternative recipes, preemptive activity splitting, etc.). We present the notion of conditional interval variable in the context of Constraint Programming and show how this concept can be leveraged to model and solve complex scheduling problems involving both temporal and non-temporal decisions.
This slide deck was presented at the 21st International Symposium on Mathematical Programming (ISMP 2012).
Philippe Laborie
Solving Large Scale Optimization Problems using CPLEX Optimization Studiooptimizatiodirectdirect
Recent advancements in Linear and Mixed Programing give us the capability to solve larger Optimization Problems. In this talk using CPLEX Optimization Studio we will discuss modeling practices, case studies and demonstrate good practices for solving Hard Optimization Problems. We will also discuss recent CPLEX performance improvements and recently added features.
IBM ILOG CP Optimizer for Detailed Scheduling Illustrated on Three ProblemsPhilippe Laborie
Since version 2.0, IBM ILOG CP Optimizer provides a new
scheduling language supported by a robust and efficient automatic search. This presentation illustrates both the expressivity of the modelling language and the robustness of the automatic search on three problems recently studied in the scheduling literature. We show that all three problems
can easily be modelled with CP Optimizer in only a few dozen lines (the complete models are provided) and that on average the automatic search outperforms existing problem specific approaches.
This slide deck was presented at CP-AI-OR 2009 conference. Complete reference:
Philippe Laborie. "IBM ILOG CP Optimizer for Detailed Scheduling Illustrated on Three Problems". Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (CP-AI-OR 2009). Lecture Notes in Computer Science. Volume 5547, 2009, pp 148-162.
Modeling and Solving Resource-Constrained Project Scheduling Problems with IB...Philippe Laborie
Since version 2.0, IBM ILOG CP Optimizer provides a new scheduling language supported by a robust and efficient automatic search. We show how the main features of resource-constrained project scheduling such as work-breakdown structures, optional tasks, different types of resources, multiple modes and skills, resource calendars and objective functions such as earliness/tardiness, unperformed tasks or resource costs can be modeled in CP Optimizer. The robustness of the automatic search will be illustrated on some classical resource-constrained project scheduling benchmarks.
This slide deck was presented at EURO 2009 conference (http://www.euro-2009.de/).
Philippe Laborie
A (Not So Short) Introduction to CP Optimizer for SchedulingPhilippe Laborie
CP Optimizer is a generic Constraint Programming (CP) based system to model and solve scheduling problems (among other combinatorial problems). It provides an algebraic language with simple mathematical concepts such as intervals or functions to capture the temporal dimension of scheduling problems in a combinatorial optimization framework. From the very beginning, CP Optimizer was designed with the goal to provide a similar experience as Mathematical Programming (MP) tools like CPLEX, with a strong focus on usability. In particular CP Optimizer implements a model & run paradigm that does not require the user to understand Constraint Programming or scheduling algorithms: declarative modeling is the only thing that matters. The automatic search provides good out of the box performance and is continuously improving from version to version. The convergence with MP goes even further, with a convergence of the tools and functionalities around the engine like an input/output format, modeling assistance with warnings and conflict refiner, interactive executable, etc. These tools accelerate the development and maintenance of models for complex industrial scheduling problems that will be efficiently solved by the automatic search. This tutorial, heavily illustrated with examples, gives an overview of CP Optimizer for scheduling. No prior knowledge of Constraint Programming is required.
This talk was given at INFORMS in November 2014. It presents some of the recent improvements made in CPLEX 12.6.1.
Topics include performance improvements, Local Implied Bound cuts, support for Python 3, Opportunistic Distributed MIP, and MIQP linearization.
Classical scheduling problems (like job-shop or RCPSP) are among the most difficult problems studied in combinatorial optimization. Still, they are far from accounting for all the complexity of industrial scheduling applications. Since more than 20 years, our team at ILOG (now IBM) develops and integrates a large panel of techniques from AI (constraint programming, temporal reasoning, learning, ...) and OR (mathematical programming, graph algorithms, local search, ...) to solve our customers most complex scheduling problems. These works have lead to the design of CP Optimizer, a generic solver based on a very expressive (but still, quite concise) mathematical modeling language to formulate complex scheduling problems. The models are solved with an automatic search algorithm that is exact, efficient, robust and continuously improving. This talk gives a short overview of CP Optimizer.
Industrial project and machine scheduling with Constraint ProgrammingPhilippe Laborie
More often than not, project and machine scheduling problems are addressed either by generic mathematical programming techniques (like MILP) or by problem-specific exact or heuristic approaches. MILP formulations are commonly used to describe the problem in mathematical terms and to provide optimal solutions or bounds to small problem instances. As they usually do not scale well, one usually resorts to using heuristics for handling large and complex industrial problems.
Though constraint programming (CP) techniques represent the state of the art in several classical project and machine scheduling benchmarks and have been used for almost 30 years for solving industrial problems, they are still seldom considered as an alternative approach in the scheduling community. A possible reason is that, for years, in the absence of efficient and robust automatic search algorithms, CP techniques have been difficult to use for non-CP experts.
We will explain why we think this time is over and illustrate our arguments with CP Optimizer, a generic system, largely based on CP, for modeling and solving real-world scheduling problems.
CP Optimizer extends linear programming with an algebraic language using simple mathematical concepts (such as intervals, sequences and functions) to capture the temporal dimension of scheduling problems in a combinatorial optimization framework. CP Optimizer implements a model-and-run paradigm that vastly reduces the burden on the user to understand CP or scheduling algorithms: modeling is by far the most important. The automatic search combines a wide variety of techniques from Artificial Intelligence (constraint programming, temporal reasoning, learning etc.) and Operations Research (mathematical programming, graph algorithms, local search, etc.) in an exact algorithm that provides good performance out of the box and which is continuously improving.
An Update on the Comparison of MIP, CP and Hybrid Approaches for Mixed Resour...Philippe Laborie
We consider a well known resource allocation and scheduling problem for which different approaches like mixed-integer programming (MIP), constraint programming (CP), constraint integer programming (CIP), logic-based Benders decompositions (LBBD) and SAT-modulo theories (SMT) have been proposed and experimentally compared in the last decade. Thanks to the recent improvements in CP Optimizer, a commercial CP solver for solving generic scheduling problems, we show that a standalone tiny CP model can out-perform all previous approaches and close all the 335 instances of the benchmark. The article explains which components of the automatic search of CP Optimizer are responsible for this success. We finally propose an extension of the original benchmark with larger and more challenging instances.
This paper presents the concept of objective landscape in the context of Constraint Programming. An objective landscape is a light-weight structure providing some information on the relation between decision variables and objective values, that can be quickly computed once and for all at the beginning of the resolution and is used to guide the search. It is particularly useful on decision variables with large domains and with a continuous semantics, which is typically the case for time or resource quantity variables in scheduling problems. This concept was recently implemented in the automatic search of CP Optimizer and resulted in an average speed-up of about 50% on scheduling problems with up to almost 2 orders of magnitude for some applications.
Boosting your HTML Apps – Overview of OpenCL and Hello World of WebCLJanakiRam Raghumandala
WebCL enables you boost the performance of select HTML application where lots of computation is involved. For example, fluid simulation, image manipulation, video manipulation.
Since, OpenCL is the underlying platform, the same has been introduced in the beginning and then WebCL.
Contents:
Motivation
Introduction to OpenCL
Introduction to WebCL
Hello World Program of WebCL
These slides are from my Ph.D. defense at the University of California, Santa Barbara, discussing how we contribute research tools to forward how science is performed with cloud systems.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/08/khronos-group-standards-powering-the-future-of-embedded-vision-a-presentation-from-the-khronos-group/
Neil Trevett, Vice President of Developer Ecosystems at NVIDIA and President of the Khronos Group, presents the “Khronos Group Standards: Powering the Future of Embedded Vision” tutorial at the May 2021 Embedded Vision Summit.
Open standards play an important role in enabling interoperability for faster, easier deployment of vision-based systems. With advances in machine learning, the number of accelerators, processors, libraries and compilers in the market is rapidly increasing. Proprietary APIs and formats create a complex industry landscape that can hinder overall market growth.
The Khronos Group’s open standards for accelerating parallel programming play a major role in deploying inferencing and embedded vision applications and include SYCL, OpenVX, NNEF, Vulkan, SPIR, and OpenCL. Trevett provides an up-to-the-minute overview and update on the Khronos embedded vision ecosystem, highlighting the capabilities and benefits of each API, giving viewers insight into which standards may be relevant to their own embedded vision projects, and discussing the future directions of these key industry initiatives.
[HKOSCon x COSCUP 2020][20200801][Ansible: From VM to Kubernetes]Wong Hoi Sing Edison
By using Ansible for DevOps, we could manage both VM, Docker image provision, Kubernetes and CephFS provision, or even Kubernetes Pod runtime management.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/intel/embedded-vision-training/videos/pages/may-2017-embedded-vision-summit-pisarevsky
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Vadim Pisarevsky, Software Engineering Manager at Intel, presents the "Making OpenCV Code Run Fast" tutorial at the May 2017 Embedded Vision Summit.
OpenCV is the de facto standard framework for computer vision developers, with a 16+ year history, approximately one million lines of code, thousands of algorithms and tens of thousands of unit tests. While OpenCV delivers decent performance out-of-the-box for some classical algorithms on desktop PCs, it lacks sufficient performance when using some modern algorithms, such as deep neural networks, and when running on embedded platforms. Pisarevsky examines current and forthcoming approaches to performance optimization of OpenCV, including the existing OpenCL-based transparent API, newly added support for OpenVX, and early experimental results using Halide.
He demonstrates the use of the OpenCL-based transparent API on a popular CV problem: pedestrian detection. Because OpenCL does not provide good performance-portability, he explores additional approaches. He discusses how OpenVX support in OpenCV accelerates image processing pipelines and deep neural network execution. He also presents early experimental results using Halide, which provides a higher level of abstraction and ease of use, and is being actively considered for future support in OpenCV.
3 years ago, Meetic chose to rebuild it's backend architecture using microservices and an event driven strategy. As we where moving along our old legacy application, testing features became gradually a pain, especially when those features rely on multiple changes across multiple components. Whatever the number of application you manage, unit testing is easy, as well as functional testing on a microservice. A good gherkin framework and a set of docker container can do the job. The real challenge is set in end-to-end testing even more when a feature can involve up to 60 different components.
To solve that issue, Meetic is building a Kubernetes strategy around testing. To do such a thing we need to :
- Be able to generate a docker container for each pull-request on any component of the stack
- Be able to create a full testing environment in the simplest way
- Be able to launch automated test on this newly created environment
- Have a clean-up process to destroy testing environment after tests To separate the various testing environment, we chose to use Kubernetes Namespaces each containing a variant of the Meetic stack. But when it comes to Kubernetes, managing multiple namespaces can be hard. Yaml configuration files need to be shared in a way that each people / automated job can access to them and modify them without impacting others.
This is typically why Meetic chose to develop it's own tool to manage namespace through a cli tool, or a REST API on which we can plug a friendly UI.
In this talk we will tell you the story of our CI/CD evolution to satisfy the need to create a docker container for each new pull request. And we will show you how to make end-to-end testing easier using Blackbeard, the tool we developed to handle the need to manage namespaces inspired by Helm.
Groovy founder Guillaume Laforge built on top of the Java standard proposal for type safe Units of Measurements, JSR-275 with his case study of a Domain-Specific Language for unit manipulations some while ago.
Based on Unit-API the successor to JSR-275, and its leading Open Source implementation Eclipse UOMo together with Xtext/TS we'll see, how a similar DSL for unit manipulations could be created with Xtext. As well as other languages including Groovy or Scala.
RTP NPUG: Ansible Intro and Integration with ACIJoel W. King
Ansible is one of the newer and more exciting automation toolsets for networking. Ansible (unlike Puppet and Chef) is agentless, which makes it significantly easier to automate existing devices that may not have an agent installed – such as many networking devices.
Networks are evolving from hundreds or thousands of individual devices to the Software-Defined Network paradigm of a single fabric under a central controller. The GUI on top of an SDN controller isn’t sufficient and will still need automation.
This presentation describes how Ansible can add value to configuration management of a Cisco Application Centric Infrastructure (ACI) infrastructure.
Ever since the “CloudNative revolution” took over our development environment (devenv), we have never been more challenged (or more excited). With Kubernetes, Docker (Containerd) & many other microservice-related technologies, we have a handful of technologies to master before we write the first line of code.
Developer Experience Cloud Native - From Code Gen to Git Commit without a CI/...Michael Hofmann
Developing cloud native applications bring in a lot of complexities for developers. Without using tools to compensate these complexities, you will not become very efficient. Additional, cloud developers often suffer a rising frustration, by fighting these problems.
Before I push my code into Git, I want to test different things in my cloud environment. Therefore it is essential to have a fast and easy round trip. A classic round trip starts by writing or generating code, create a Docker image, deploy it into Kubernetes and test or remote debug the application in Docker or in Kubernetes. Without some elementary tools, this round trip will not be very fast or simple and therefore error prone.
This Lab will show you some open source tools, making your live as a developer more easy. Short demos will demonstrate the simple handling of these tools. Starting point is the generation of a MicroProfile and a SpringBoot application. By using the different tools (e.g. Helm, Shell completion, kubectl cp, Ksync, Stern, Kubefwd, Telepresence, …) on these applications, the complete round trip will be shown. Most of these tools can also be used with other programming languages. Every tool works on its own which makes it easy to switch between these tools.
Finally you will get an evaluation of these tools and I will show you an outlook on tools which are more focused on larger developer teams.
.NET Core, ASP.NET Core Course, Session 3aminmesbahi
Session 3,
Introducing to Compiler
What is the LLVM?
LLILC
RyuJIT
AOT Compilation
Preprocessors and Conditional Compilation
An Overview on Dependency Injection
Despite all of the recent interest, concurrency in standard C++ is still barely in its infancy. This talk uses the primitives supplied by C++14 to build a simple, reference, implementation of a task system. The goal is to learn to write software that doesn’t wait.
Klepsydra Streaming Distribution Optimiser (SDO):
• • • •
•
Runs on a separate computer
Executes several dry runs on the OBC
Collect statistics
Runs a genetic algorithm to find the optimal solution for latency, power or throughput
The main variable to optimise is the distribution of layers are the two dimension of the threading model.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/altera/embedded-vision-training/videos/pages/may-2015-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Deshanand Singh, Director of Software Engineering at Altera, presents the "Efficient Implementation of Convolutional Neural Networks using OpenCL on FPGAs" tutorial at the May 2015 Embedded Vision Summit.
Convolutional neural networks (CNN) are becoming increasingly popular in embedded applications such as vision processing and automotive driver assistance systems. The structure of CNN systems is characterized by cascades of FIR filters and transcendental functions. FPGA technology offers a very efficient way of implementing these structures by allowing designers to build custom hardware datapaths that implement the CNN structure. One challenge of using FPGAs revolves around the design flow that has been traditionally centered around tedious hardware description languages.
In this talk, Deshanand gives a detailed explanation of how CNN algorithms can be expressed in OpenCL and compiled directly to FPGA hardware. He gives detail on code optimizations and provides comparisons with the efficiency of hand-coded implementations.
Similar to CPLEX 12.5.1 remote object - June 2013 (20)
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.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
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
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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
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
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.
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.
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.
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.
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.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
