Learn about the algorithms and associated implementations that power SigOpt, a platform for efficiently conducting model development and hyperparameter optimization. Get started on your AI Developer Journey @ software.intel.com/ai.
Medical images (CT scans, X-Rays) must be segmented to identify the region of interest; then areas of interest must be classified for diagnosis and reporting Applied for Lung Disease diagnosis from Chest X-Rays/CT-Scans Segmentation/classification can be a tedious process. AI can help! Wipro used Deep Learning to develop a Medical Image Segmentation & Diagnosis Solution running on Intel’s AI platform.
Whether you are an AI, HPC, IoT, Graphics, Networking or Media developer, visit the Intel Developer Zone today to access the latest software products, resources, training, and support. Test-drive the latest Intel hardware and software products on DevCloud, our online development sandbox, and use DevMesh, our online collaboration portal, to meet and work with other innovators and product leaders. Get started by joining the Intel Developer Community @ software.intel.com.
Enterprise Video Hosting: Introducing the Intel Video PortalIT@Intel
Intel IT developed an enterprise video hosting solution in order to meet the needs of employees who wanted to create and share videos in an easy-to-use and secure manner.
Reducing Deep Learning Integration Costs and Maximizing Compute Efficiency| S...Intel® Software
oneDNN Graph API extends oneDNN with a graph interface which reduces deep learning integration costs and maximizes compute efficiency across a variety of AI hardware including AI accelerators. Get started on your AI Developer Journey @ software.intel.com/ai.
AWS & Intel Webinar Series - Accelerating AI ResearchIntel® Software
Scale your research workloads faster with Intel on AWS. Learn how the performance and productivity of Intel Hardware and Software help bridge the gap between ideation and results in Data Science. Get started on your AI Developer Journey @ software.intel.com/ai.
Medical images (CT scans, X-Rays) must be segmented to identify the region of interest; then areas of interest must be classified for diagnosis and reporting Applied for Lung Disease diagnosis from Chest X-Rays/CT-Scans Segmentation/classification can be a tedious process. AI can help! Wipro used Deep Learning to develop a Medical Image Segmentation & Diagnosis Solution running on Intel’s AI platform.
Whether you are an AI, HPC, IoT, Graphics, Networking or Media developer, visit the Intel Developer Zone today to access the latest software products, resources, training, and support. Test-drive the latest Intel hardware and software products on DevCloud, our online development sandbox, and use DevMesh, our online collaboration portal, to meet and work with other innovators and product leaders. Get started by joining the Intel Developer Community @ software.intel.com.
Enterprise Video Hosting: Introducing the Intel Video PortalIT@Intel
Intel IT developed an enterprise video hosting solution in order to meet the needs of employees who wanted to create and share videos in an easy-to-use and secure manner.
Reducing Deep Learning Integration Costs and Maximizing Compute Efficiency| S...Intel® Software
oneDNN Graph API extends oneDNN with a graph interface which reduces deep learning integration costs and maximizes compute efficiency across a variety of AI hardware including AI accelerators. Get started on your AI Developer Journey @ software.intel.com/ai.
AWS & Intel Webinar Series - Accelerating AI ResearchIntel® Software
Scale your research workloads faster with Intel on AWS. Learn how the performance and productivity of Intel Hardware and Software help bridge the gap between ideation and results in Data Science. Get started on your AI Developer Journey @ software.intel.com/ai.
Build a Deep Learning Video Analytics Framework | SIGGRAPH 2019 Technical Ses...Intel® Software
Explore how to build a unified framework based on FFmpeg and GStreamer to enable video analytics on all Intel® hardware, including CPUs, GPUs, VPUs, FPGAs, and in-circuit emulators.
AI for good: Scaling AI in science, healthcare, and more.Intel® Software
How do we scale AI to its full potential to enrich the lives of everyone on earth? Learn about AI hardware and software acceleration and how Intel AI technologies are being used to solve critical problems in high energy physics, cancer research, financial inclusion, and more. Get started on your AI Developer Journey @ software.intel.com/ai
AI for All: Biology is eating the world & AI is eating Biology Intel® Software
Advances in cell biology and creation of an immense amount of data are converging with advances in Machine learning to analyze this data. Biology is experiencing its AI moment and driving the massive computation involved in understanding biological mechanisms and driving interventions. Learn about how cutting edge technologies such as Software Guard Extensions (SGX) in the latest Intel Xeon Processors and Open Federated Learning (OpenFL), an open framework for federated learning developed by Intel, are helping advance AI in gene therapy, drug design, disease identification and more.
Fast Insights to Optimized Vectorization and Memory Using Cache-aware Rooflin...Intel® Software
Integrated into Intel® Advisor, Cache-aware Roofline Modeling (CARM) provides insight into how an application behaves by helping to determine a) how optimally it works on a given hardware, b) the main factors that limit performance, c) if the workload is memory or compute-bound, and d) the right strategy to improve application performance.
Software AI Accelerators: The Next Frontier | Software for AI Optimization Su...Intel® Software
Software AI Accelerators deliver orders of magnitude performance gain for AI across deep learning, classical machine learning, and graph analytics and are key to enabling AI Everywhere. Get started on your AI Developer Journey @ software.intel.com/ai.
oneAPI: Industry Initiative & Intel ProductTyrone Systems
With the growth of AI, machine learning, and data-centric applications, the industry needs a programming model that allows developers to take advantage of rapid innovation in processor architectures. TensorFlow supports the oneAPI industry initiative and its standards-based open specification.
oneAPI complements TensorFlow’s modular design and provides increased choice of hardware vendor and processor architecture, and faster support of next-generation accelerators. TensorFlow uses oneAPI today on Xeon processors and we look forward to using oneAPI to run on future Intel architectures.
For the full video of this presentation, please visit:
https://www.edge-ai-vision.com/2020/11/getting-efficient-dnn-inference-performance-is-it-really-about-the-tops-a-presentation-from-intel/
For more information about edge AI and computer vision, please visit:
https://www.edge-ai-vision.com
Gary Brown, Director of AI Marketing at Intel, presents the “Getting Efficient DNN Inference Performance: Is It Really About the TOPS?” tutorial at the September 2020 Embedded Vision Summit.
This presentation looks at how performance is measured among deep learning inference platforms, starting with the simple peak TOPS metric, why it’s used and why it might be misleading. Brown looks at compute efficiency as measured by real benchmark workload performance and how it relates to peak TOPS, comparing performance across Intel’s inference platforms. He also discusses how developers can use Intel’s DevCloud for the Edge to quickly access Intel’s inference platforms.
Review state-of-the-art techniques that use neural networks to synthesize motion, such as mode-adaptive neural network and phase-functioned neural networks. See how next-generation CPUs with reinforcement learning can offer better performance.
Python Data Science and Machine Learning at Scale with Intel and AnacondaIntel® Software
Python is the number 1 language for data scientists, and Anaconda is the most popular python platform. Intel and Anaconda have partnered to bring scalability and near-native performance to Python with simple installations. Learn how data scientists can now access oneAPI-optimized Python packages such as NumPy, Scikit-Learn, Modin, Pandas, and XGBoost directly from the Anaconda repository through simple installation and minimal code changes.
The field of machine programming — the automation of the development of software — is making notable research advances. This is, in part, due to the emergence of a wide range of novel techniques in machine learning. In today’s technological landscape, software is integrated into almost everything we do, but maintaining software is a time-consuming and error-prone process. When fully realized, machine programming will enable everyone to express their creativity and develop their own software without writing a single line of code. Intel realizes the pioneering promise of machine programming, which is why it created the Machine Programming Research (MPR) team in Intel Labs. The MPR team’s goal is to create a society where everyone can create software, but machines will handle the “programming” part.
Advanced Single Instruction Multiple Data (SIMD) Programming with Intel® Impl...Intel® Software
Explore practical elements, such as performance profiling, debugging, and porting advice. Get an overview of advanced programming topics, like common design patterns, SIMD lane interoperability, data conversions, and more.
In this deck from ATPESC 2019, James Moawad and Greg Nash from Intel present: FPGAs and Machine Learning.
"Neural networks are inspired by biological systems, in particular the human brain. Through the combination of powerful computing resources and novel architectures for neurons, neural networks have achieved state-of-the-art results in many domains such as computer vision and machine translation. FPGAs are a natural choice for implementing neural networks as they can handle different algorithms in computing, logic, and memory resources in the same device. Faster performance comparing to competitive implementations as the user can hardcore operations into the hardware. Software developers can use the OpenCL device C level programming standard to target FPGAs as accelerators to standard CPUs without having to deal with hardware level design."
Watch the video: https://wp.me/p3RLHQ-lnc
Learn more: https://extremecomputingtraining.anl.gov/archive/atpesc-2019/agenda-2019/
and
https://www.intel.com/content/www/us/en/products/programmable/fpga.html
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Build a Deep Learning Video Analytics Framework | SIGGRAPH 2019 Technical Ses...Intel® Software
Explore how to build a unified framework based on FFmpeg and GStreamer to enable video analytics on all Intel® hardware, including CPUs, GPUs, VPUs, FPGAs, and in-circuit emulators.
AI for good: Scaling AI in science, healthcare, and more.Intel® Software
How do we scale AI to its full potential to enrich the lives of everyone on earth? Learn about AI hardware and software acceleration and how Intel AI technologies are being used to solve critical problems in high energy physics, cancer research, financial inclusion, and more. Get started on your AI Developer Journey @ software.intel.com/ai
AI for All: Biology is eating the world & AI is eating Biology Intel® Software
Advances in cell biology and creation of an immense amount of data are converging with advances in Machine learning to analyze this data. Biology is experiencing its AI moment and driving the massive computation involved in understanding biological mechanisms and driving interventions. Learn about how cutting edge technologies such as Software Guard Extensions (SGX) in the latest Intel Xeon Processors and Open Federated Learning (OpenFL), an open framework for federated learning developed by Intel, are helping advance AI in gene therapy, drug design, disease identification and more.
Fast Insights to Optimized Vectorization and Memory Using Cache-aware Rooflin...Intel® Software
Integrated into Intel® Advisor, Cache-aware Roofline Modeling (CARM) provides insight into how an application behaves by helping to determine a) how optimally it works on a given hardware, b) the main factors that limit performance, c) if the workload is memory or compute-bound, and d) the right strategy to improve application performance.
Software AI Accelerators: The Next Frontier | Software for AI Optimization Su...Intel® Software
Software AI Accelerators deliver orders of magnitude performance gain for AI across deep learning, classical machine learning, and graph analytics and are key to enabling AI Everywhere. Get started on your AI Developer Journey @ software.intel.com/ai.
oneAPI: Industry Initiative & Intel ProductTyrone Systems
With the growth of AI, machine learning, and data-centric applications, the industry needs a programming model that allows developers to take advantage of rapid innovation in processor architectures. TensorFlow supports the oneAPI industry initiative and its standards-based open specification.
oneAPI complements TensorFlow’s modular design and provides increased choice of hardware vendor and processor architecture, and faster support of next-generation accelerators. TensorFlow uses oneAPI today on Xeon processors and we look forward to using oneAPI to run on future Intel architectures.
For the full video of this presentation, please visit:
https://www.edge-ai-vision.com/2020/11/getting-efficient-dnn-inference-performance-is-it-really-about-the-tops-a-presentation-from-intel/
For more information about edge AI and computer vision, please visit:
https://www.edge-ai-vision.com
Gary Brown, Director of AI Marketing at Intel, presents the “Getting Efficient DNN Inference Performance: Is It Really About the TOPS?” tutorial at the September 2020 Embedded Vision Summit.
This presentation looks at how performance is measured among deep learning inference platforms, starting with the simple peak TOPS metric, why it’s used and why it might be misleading. Brown looks at compute efficiency as measured by real benchmark workload performance and how it relates to peak TOPS, comparing performance across Intel’s inference platforms. He also discusses how developers can use Intel’s DevCloud for the Edge to quickly access Intel’s inference platforms.
Review state-of-the-art techniques that use neural networks to synthesize motion, such as mode-adaptive neural network and phase-functioned neural networks. See how next-generation CPUs with reinforcement learning can offer better performance.
Python Data Science and Machine Learning at Scale with Intel and AnacondaIntel® Software
Python is the number 1 language for data scientists, and Anaconda is the most popular python platform. Intel and Anaconda have partnered to bring scalability and near-native performance to Python with simple installations. Learn how data scientists can now access oneAPI-optimized Python packages such as NumPy, Scikit-Learn, Modin, Pandas, and XGBoost directly from the Anaconda repository through simple installation and minimal code changes.
The field of machine programming — the automation of the development of software — is making notable research advances. This is, in part, due to the emergence of a wide range of novel techniques in machine learning. In today’s technological landscape, software is integrated into almost everything we do, but maintaining software is a time-consuming and error-prone process. When fully realized, machine programming will enable everyone to express their creativity and develop their own software without writing a single line of code. Intel realizes the pioneering promise of machine programming, which is why it created the Machine Programming Research (MPR) team in Intel Labs. The MPR team’s goal is to create a society where everyone can create software, but machines will handle the “programming” part.
Advanced Single Instruction Multiple Data (SIMD) Programming with Intel® Impl...Intel® Software
Explore practical elements, such as performance profiling, debugging, and porting advice. Get an overview of advanced programming topics, like common design patterns, SIMD lane interoperability, data conversions, and more.
In this deck from ATPESC 2019, James Moawad and Greg Nash from Intel present: FPGAs and Machine Learning.
"Neural networks are inspired by biological systems, in particular the human brain. Through the combination of powerful computing resources and novel architectures for neurons, neural networks have achieved state-of-the-art results in many domains such as computer vision and machine translation. FPGAs are a natural choice for implementing neural networks as they can handle different algorithms in computing, logic, and memory resources in the same device. Faster performance comparing to competitive implementations as the user can hardcore operations into the hardware. Software developers can use the OpenCL device C level programming standard to target FPGAs as accelerators to standard CPUs without having to deal with hardware level design."
Watch the video: https://wp.me/p3RLHQ-lnc
Learn more: https://extremecomputingtraining.anl.gov/archive/atpesc-2019/agenda-2019/
and
https://www.intel.com/content/www/us/en/products/programmable/fpga.html
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/qualcomm/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit-baum
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Felix Baum, Director of Product Management for AI Software at Qualcomm, presents the "Efficient Deployment of Quantized ML Models at the Edge Using Snapdragon SoCs" tutorial at the May 2019 Embedded Vision Summit.
Increasingly, machine learning models are being deployed at the edge, and these models are getting bigger. As a result, we are hitting the constraints of edge devices: bandwidth, performance and power. One way to reduce ML computation demands and increase power efficiency is quantization—a set of techniques that reduce the number of bits needed, and hence reduce bandwidth, computation and storage requirements.
Qualcomm Snapdragon SoCs provide a robust hardware solution for deploying ML applications in embedded and mobile devices. Many Snapdragon SoCs incorporate the Qualcomm Artificial Intelligence Engine, comprised of hardware and software components to accelerate on-device ML.
In this talk, Baum explores the performance and accuracy offered by the accelerator cores within the AI Engine. He also highlights the tools and techniques Qualcomm offers for developers targeting these cores, utilizing intelligent quantization to deliver optimal performance with low power consumption while maintaining algorithm accuracy.
Smart Camera for Non-Intrusive Heart Detectionitaistam
At the recent years there is a rise in vision based applications from autonomous driving to smart cameras that perfect the picture based on the scene. Those application also drove the development of AI accelerators, that can effectively provide the needed computation for the mobile devices. Nevertheless, compared to the future devices, this is just a small glimpse. In this talk we will discuss some of the capabilities of future smart cameras, which today can automatically choose interesting scene or distinct between known people and strangers. However, those cameras can also be used to detect physical health parameters, like heart rate, for reliable and nonintrusive monitoring babies sleep. While some of the capabilities were available via cloud based computation and now this can be done in the node level (when privacy is the main, but not only, benefit of this advancement).
In this deck I’m going to show you how SigOpt can help you amplify your trading models by optimally tuning them using our black-box optimization platform.
Intelligent data summit: Self-Service Big Data and AI/ML: Reality or Myth?SnapLogic
Companies collect more data but struggle with how to glean the best insights. Use of Machine Learning also needs power data integration.
In this presentation, Janet Jaiswal, SnapLogic's VP of product marketing, reviews key strategies and technologies to deliver intelligent data via self-service ML models.
To learn more, visit https://www.snaplogic.com
Datarobot, 자동화된 분석 적용 시 분석 절차의 변화 및 효용 - 홍운표 데이터 사이언티스트, DataRobot :: AWS Sum...Amazon Web Services Korea
스폰서 발표 세션 | Datarobot, 자동화된 분석 적용 시 분석 절차의 변화 및 효용
홍운표 데이터 사이언티스트, DataRobot
데이터로봇은 기존 분석 소프트웨어와 달리 자동화된 분석 플랫폼입니다. 현업 담당자는 데이터 정의만 완료되면 자신의 업무에 AI를 적용하여 업무 효율을 얻을 수 있고, 데이터 과학자도 기존 분석업무 대비 수십배의 효율성을 얻을 수 있습니다. 데이터로봇은 이렇게 기업 업무에 AI를 쉽게 적용하여, 비지니스 가치를 실현하도록 도와드릴 수 있습니다. 본 세션에서는 데이터로봇이 제공하는 자동화된 분석의 세부 기능을 살펴보고 제품 데모를 통해 자동화된 분석이 어떻게 분석 결과물의 품질을 높이고, 기존 분석 작업보다 훨씬 효율적인 업무를 수행할 수 있게 도와드리는지 확인하실 수 있습니다.
In this video I’m going to show you how SigOpt can help you amplify your machine learning and AI models by optimally tuning them using our black-box optimization platform.
Video: https://youtu.be/EjGrRxXWg8o
The SigOpt platform provides an ensemble of state-of-the-art Bayesian and Global optimization algorithms via a simple Software-as-a-Service API.
For the full video of this presentation, please visit:
https://www.edge-ai-vision.com/2020/11/smarter-manufacturing-with-intels-deep-learning-based-machine-vision-a-presentation-from-intel/
For more information about edge AI and computer vision, please visit:
https://www.edge-ai-vision.com
Tara K. Thimmanaik, Solutions Architect at Intel, presents the “Smarter Manufacturing with Intel’s Deep Learning-Based Machine Vision” tutorial at the September 2020 Embedded Vision Summit.
As demand for smarter and more efficient manufacturing is growing, IoT technologies—including sensors, edge devices, gateways, servers and the cloud—are being used throughout the factory to compute deep learning analytics workloads at the appropriate location. Efficient data-driven manufacturing can help to reduce labor costs, increase quality and maximize profit. The biggest hindrance to achieving these outcomes is the difficulty in extracting data from vendor-locked and proprietary systems for analytics downstream.
In this presentation, Thimmanaik covers Intel’s approach to developing open, flexible and scalable solutions, including:
• Intel’s technologies such as OpenVINO, Movidius Vision Processor Units, Edge Insights Software (EIS) and deep learning algorithms
• How Intel’s offerings come together in the industrial marketplace with partnerships forged to address the constraints of manufacturing infrastructure
• Real-world examples highlighting defect detection in textile printing (where 90% accuracy at 50 fps was achieved) and smartphone screen production (where false negatives were only 0.6%)
Accelerate AI/ML Adoption with Intel Processors and C3IoT on AWS (AIM386-S) -...Amazon Web Services
Today, organizations deploy more AI/ML workloads on AWS than on any other cloud platform. The cloud has removed many of the challenges associated with scalability, and it’s never been easier or more cost effective to build custom and intelligent data models. In this session, learn how the C3 Platform leverages the full power of Intel Xeon Scalable processors on AWS to rapidly train, deploy, and operationalize AI/ML and big data applications like C3 Inventory Optimization and C3 Predictive Maintenance. In addition, a customer shares how these solutions helped achieve demonstrable value. This session is brought to you by AWS partner, Intel.
BigDL: A Distributed Deep Learning Library on Spark: Spark Summit East talk b...Spark Summit
BigDL is a distributed deep Learning framework built for Big Data platform using Apache Spark. It combines the benefits of “high performance computing” and “Big Data” architecture, providing native support for deep learning functionalities in Spark, orders of magnitude speedup than out-of-box open source DL frameworks (e.g., Caffe/Torch) wrt single node performance (by leveraging Intel MKL), and the scale-out of deep learning workloads based on the Spark architecture. We’ll also share how our users adopt BigDL for their deep learning applications (such as image recognition, object detection, NLP, etc.), which allows them to use their Big Data (e.g., Apache Hadoop and Spark) platform as the unified data analytics platform for data storage, data processing and mining, feature engineering, traditional (non-deep) machine learning, and deep learning workloads.
SigOpt's Fay Kallel, Head of Product, and Jim Blomo, Head of Engineering, describe the latest updates to SigOpt, a suite of features that help you manage your modeling process.
Similar to Advanced Techniques to Accelerate Model Tuning | Software for AI Optimization Summit 2021 Technical Session (20)
Streamline End-to-End AI Pipelines with Intel, Databricks, and OmniSciIntel® Software
Preprocess, visualize, and Build AI Faster at-Scale on Intel Architecture. Develop end-to-end AI pipelines for inferencing including data ingestion, preprocessing, and model inferencing with tabular, NLP, RecSys, video and image using Intel oneAPI AI Analytics Toolkit and other optimized libraries. Build at-scale performant pipelines with Databricks and end-to-end Xeon optimizations. Learn how to visualize with the OmniSci Immerse Platform and experience a live demonstration of the Intel Distribution of Modin and OmniSci.
RenderMan*: The Role of Open Shading Language (OSL) with Intel® Advanced Vect...Intel® Software
This talk focuses on the newest release in RenderMan* 22.5 and its adoption at Pixar Animation Studios* for rendering future movies. With native support for Intel® Advanced Vector Extensions, Intel® Advanced Vector Extensions 2, and Intel® Advanced Vector Extensions 512, it includes enhanced library features, debugging support, and an extensive test framework.
ANYFACE*: Create Film Industry-Quality Facial Rendering & Animation Using Mai...Intel® Software
ANYFACE* brings film industry-quality facial rendering and animation to mainstream PC platforms using novel approaches to create face details and control microsurfaces. The solution enables users to create high-fidelity game character facial models using photogrammetry.
Ray Tracing with Intel® Embree and Intel® OSPRay: Use Cases and Updates | SIG...Intel® Software
Explore practical examples of Intel® Embree and Intel® OSPRay in production rendering and the best practices of using the kernels in typical rendering pipelines.
Use Variable Rate Shading (VRS) to Improve the User Experience in Real-Time G...Intel® Software
Variable-rate shading (VRS) is a new feature of Microsoft DirectX* 12 and is supported on the 11th generation of Intel® graphics hardware. Get an overview and learn best practices, recommendations, and how to modify traditional 3D effects to take advantage of VRS.
Bring the Future of Entertainment to Your Living Room: MPEG-I Immersive Video...Intel® Software
Explore the proposed Metadata for Immersive Video (MIV) standard specification. MIV enables real-world content captured by cameras to be viewed by users with Six Degrees of Freedom (6DoF) movement, similar to a VR experience with synthetic content.
In this presentation, we describe a heuristic for modifying the structure of sparse deep convolutional networks during training. The heuristic allows us to train sparse networks directly to reach accuracies on par with accuracies obtained through compressing/pruning of big dense models. We show that exploring the network structure during training is essential to reach best accuracies, even when the optimal network structure is known a-priori.
Intel® AI: Non-Parametric Priors for Generative Adversarial Networks Intel® Software
This presentation proposes a novel prior which is derived using basic theorems from probability theory and off-the-shelf optimizers, to improve fidelity of image generation using GANs by interpolating along any Euclidean straight line without any additional training and architecture modifications
Pmemkv is an open source, key-value store for persistent memory based on the Persistent Memory Development Kit (PMDK). Written in C and C++, it provides optimized bindings for Java*, Javascript*, and Ruby on Rails*), and includes multiple storage engines for different use cases.
Big Data Uses with Distributed Asynchronous Object StorageIntel® Software
Learn about the architecture and features of Distributed Asynchronous Object Storage (DAOS). This open source object store is based on the Persistent Memory Development Kit (PMDK) for massively distributed non-volatile memory applications.
Debugging Tools & Techniques for Persistent Memory ProgrammingIntel® Software
Learn about pmempool, a Persistent Memory Development Kit tool that helps you prevent, diagnose, and recover from data corruption. The session also covers other debugging tools for persistent memory programming.
Persistent Memory Development Kit (PMDK): State of the ProjectIntel® Software
Get an introduction to a PMDK based on the Non-Volatile Memory (NVM) Programming Model from SNIA*. Review the goals, successes, and challenges that still remain.
How Does XfilesPro Ensure Security While Sharing Documents in Salesforce?XfilesPro
Worried about document security while sharing them in Salesforce? Fret no more! Here are the top-notch security standards XfilesPro upholds to ensure strong security for your Salesforce documents while sharing with internal or external people.
To learn more, read the blog: https://www.xfilespro.com/how-does-xfilespro-make-document-sharing-secure-and-seamless-in-salesforce/
Globus Connect Server Deep Dive - GlobusWorld 2024Globus
We explore the Globus Connect Server (GCS) architecture and experiment with advanced configuration options and use cases. This content is targeted at system administrators who are familiar with GCS and currently operate—or are planning to operate—broader deployments at their institution.
First Steps with Globus Compute Multi-User EndpointsGlobus
In this presentation we will share our experiences around getting started with the Globus Compute multi-user endpoint. Working with the Pharmacology group at the University of Auckland, we have previously written an application using Globus Compute that can offload computationally expensive steps in the researcher's workflows, which they wish to manage from their familiar Windows environments, onto the NeSI (New Zealand eScience Infrastructure) cluster. Some of the challenges we have encountered were that each researcher had to set up and manage their own single-user globus compute endpoint and that the workloads had varying resource requirements (CPUs, memory and wall time) between different runs. We hope that the multi-user endpoint will help to address these challenges and share an update on our progress here.
How to Position Your Globus Data Portal for Success Ten Good PracticesGlobus
Science gateways allow science and engineering communities to access shared data, software, computing services, and instruments. Science gateways have gained a lot of traction in the last twenty years, as evidenced by projects such as the Science Gateways Community Institute (SGCI) and the Center of Excellence on Science Gateways (SGX3) in the US, The Australian Research Data Commons (ARDC) and its platforms in Australia, and the projects around Virtual Research Environments in Europe. A few mature frameworks have evolved with their different strengths and foci and have been taken up by a larger community such as the Globus Data Portal, Hubzero, Tapis, and Galaxy. However, even when gateways are built on successful frameworks, they continue to face the challenges of ongoing maintenance costs and how to meet the ever-expanding needs of the community they serve with enhanced features. It is not uncommon that gateways with compelling use cases are nonetheless unable to get past the prototype phase and become a full production service, or if they do, they don't survive more than a couple of years. While there is no guaranteed pathway to success, it seems likely that for any gateway there is a need for a strong community and/or solid funding streams to create and sustain its success. With over twenty years of examples to draw from, this presentation goes into detail for ten factors common to successful and enduring gateways that effectively serve as best practices for any new or developing gateway.
Strategies for Successful Data Migration Tools.pptxvarshanayak241
Data migration is a complex but essential task for organizations aiming to modernize their IT infrastructure and leverage new technologies. By understanding common challenges and implementing these strategies, businesses can achieve a successful migration with minimal disruption. Data Migration Tool like Ask On Data play a pivotal role in this journey, offering features that streamline the process, ensure data integrity, and maintain security. With the right approach and tools, organizations can turn the challenge of data migration into an opportunity for growth and innovation.
Developing Distributed High-performance Computing Capabilities of an Open Sci...Globus
COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among public health practitioners, mathematical modelers, and scientific computing specialists, while revealing critical gaps in exploiting advanced computing systems to support urgent decision making. Informed by our team’s work in applying high-performance computing in support of public health decision makers during the COVID-19 pandemic, we present how Globus technologies are enabling the development of an open science platform for robust epidemic analysis, with the goal of collaborative, secure, distributed, on-demand, and fast time-to-solution analyses to support public health.
Field Employee Tracking System| MiTrack App| Best Employee Tracking Solution|...informapgpstrackings
Keep tabs on your field staff effortlessly with Informap Technology Centre LLC. Real-time tracking, task assignment, and smart features for efficient management. Request a live demo today!
For more details, visit us : https://informapuae.com/field-staff-tracking/
OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoamtakuyayamamoto1800
In this slide, we show the simulation example and the way to compile this solver.
In this solver, the Helmholtz equation can be solved by helmholtzFoam. Also, the Helmholtz equation with uniformly dispersed bubbles can be simulated by helmholtzBubbleFoam.
Advanced Flow Concepts Every Developer Should KnowPeter Caitens
Tim Combridge from Sensible Giraffe and Salesforce Ben presents some important tips that all developers should know when dealing with Flows in Salesforce.
Exploring Innovations in Data Repository Solutions - Insights from the U.S. G...Globus
The U.S. Geological Survey (USGS) has made substantial investments in meeting evolving scientific, technical, and policy driven demands on storing, managing, and delivering data. As these demands continue to grow in complexity and scale, the USGS must continue to explore innovative solutions to improve its management, curation, sharing, delivering, and preservation approaches for large-scale research data. Supporting these needs, the USGS has partnered with the University of Chicago-Globus to research and develop advanced repository components and workflows leveraging its current investment in Globus. The primary outcome of this partnership includes the development of a prototype enterprise repository, driven by USGS Data Release requirements, through exploration and implementation of the entire suite of the Globus platform offerings, including Globus Flow, Globus Auth, Globus Transfer, and Globus Search. This presentation will provide insights into this research partnership, introduce the unique requirements and challenges being addressed and provide relevant project progress.
Check out the webinar slides to learn more about how XfilesPro transforms Salesforce document management by leveraging its world-class applications. For more details, please connect with sales@xfilespro.com
If you want to watch the on-demand webinar, please click here: https://www.xfilespro.com/webinars/salesforce-document-management-2-0-smarter-faster-better/
Into the Box Keynote Day 2: Unveiling amazing updates and announcements for modern CFML developers! Get ready for exciting releases and updates on Ortus tools and products. Stay tuned for cutting-edge innovations designed to boost your productivity.
Large Language Models and the End of ProgrammingMatt Welsh
Talk by Matt Welsh at Craft Conference 2024 on the impact that Large Language Models will have on the future of software development. In this talk, I discuss the ways in which LLMs will impact the software industry, from replacing human software developers with AI, to replacing conventional software with models that perform reasoning, computation, and problem-solving.
Gamify Your Mind; The Secret Sauce to Delivering Success, Continuously Improv...Shahin Sheidaei
Games are powerful teaching tools, fostering hands-on engagement and fun. But they require careful consideration to succeed. Join me to explore factors in running and selecting games, ensuring they serve as effective teaching tools. Learn to maintain focus on learning objectives while playing, and how to measure the ROI of gaming in education. Discover strategies for pitching gaming to leadership. This session offers insights, tips, and examples for coaches, team leads, and enterprise leaders seeking to teach from simple to complex concepts.
Unleash Unlimited Potential with One-Time Purchase
BoxLang is more than just a language; it's a community. By choosing a Visionary License, you're not just investing in your success, you're actively contributing to the ongoing development and support of BoxLang.
Providing Globus Services to Users of JASMIN for Environmental Data AnalysisGlobus
JASMIN is the UK’s high-performance data analysis platform for environmental science, operated by STFC on behalf of the UK Natural Environment Research Council (NERC). In addition to its role in hosting the CEDA Archive (NERC’s long-term repository for climate, atmospheric science & Earth observation data in the UK), JASMIN provides a collaborative platform to a community of around 2,000 scientists in the UK and beyond, providing nearly 400 environmental science projects with working space, compute resources and tools to facilitate their work. High-performance data transfer into and out of JASMIN has always been a key feature, with many scientists bringing model outputs from supercomputers elsewhere in the UK, to analyse against observational or other model data in the CEDA Archive. A growing number of JASMIN users are now realising the benefits of using the Globus service to provide reliable and efficient data movement and other tasks in this and other contexts. Further use cases involve long-distance (intercontinental) transfers to and from JASMIN, and collecting results from a mobile atmospheric radar system, pushing data to JASMIN via a lightweight Globus deployment. We provide details of how Globus fits into our current infrastructure, our experience of the recent migration to GCSv5.4, and of our interest in developing use of the wider ecosystem of Globus services for the benefit of our user community.
Cyaniclab : Software Development Agency Portfolio.pdfCyanic lab
CyanicLab, an offshore custom software development company based in Sweden,India, Finland, is your go-to partner for startup development and innovative web design solutions. Our expert team specializes in crafting cutting-edge software tailored to meet the unique needs of startups and established enterprises alike. From conceptualization to execution, we offer comprehensive services including web and mobile app development, UI/UX design, and ongoing software maintenance. Ready to elevate your business? Contact CyanicLab today and let us propel your vision to success with our top-notch IT solutions.
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Advanced Techniques to Accelerate Model Tuning | Software for AI Optimization Summit 2021 Technical Session
1. SigOpt. Confidential.
Advanced Techniques to Accelerate Model Tuning
Michael McCourt
Head of Engineering, SigOpt, an Intel company
June 8, 2021
Software for AI Optimization Summit