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.
This is a presentation I presented at NVIDIA AI Conference in Korea. It's about building the largest GPU - DGX-2, the most powerful supercomputer in one node.
RoCEv2 is an extension of the original RoCE specification announced in 2010 that brought the benefits of Remote Direct Memory Access (RDMA) I/O architecture to Ethernet-based networks. RoCEv2 addresses the needs of today’s evolving enterprise data centers by enabling routing across Layer 3 networks. Extending RoCE to allow Layer 3 routing provides better traffic isolation and enables hyperscale data center deployments.
Watch the video presentation: http://insidehpc.com/2014/09/slidecast-ibta-releases-updated-specification-rocev2/
This is a presentation I presented at NVIDIA AI Conference in Korea. It's about building the largest GPU - DGX-2, the most powerful supercomputer in one node.
RoCEv2 is an extension of the original RoCE specification announced in 2010 that brought the benefits of Remote Direct Memory Access (RDMA) I/O architecture to Ethernet-based networks. RoCEv2 addresses the needs of today’s evolving enterprise data centers by enabling routing across Layer 3 networks. Extending RoCE to allow Layer 3 routing provides better traffic isolation and enables hyperscale data center deployments.
Watch the video presentation: http://insidehpc.com/2014/09/slidecast-ibta-releases-updated-specification-rocev2/
NVIDIA compute GPUs and software toolkits are key drivers behind major advancements in machine learning. Of particular interest is a technique called "deep learning", which utilizes what are known as Convolution Neural Networks (CNNs) having landslide success in computer vision and widespread adoption in a variety of fields such as autonomous vehicles, cyber security, and healthcare. In this talk is presented a high level introduction to deep learning where we discuss core concepts, success stories, and relevant use cases. Additionally, we will provide an overview of essential frameworks and workflows for deep learning. Finally, we explore emerging domains for GPU computing such as large-scale graph analytics, in-memory databases.
https://tech.rakuten.co.jp/
Shared Memory Centric Computing with CXL & OMIAllan Cantle
Discusses how CXL can be better utilized as a separate Fabric Cache domain to a processors own Local Cache Domain. This is done by leveraging a Shared Memory Centric architectures that utilize both the Open Memory Interface OMI, and Compute eXpress Link, CXL, for the memory ports.
High-Performance Networking Using eBPF, XDP, and io_uringScyllaDB
In the networking world there are a number of ways to increase performance over naive use of basic Berkeley sockets. These techniques have ranged from polling blocking sockets, non-blocking sockets controlled by Epoll, all the way through completely bypassing the Linux kernel for maximum network performance where you talk directly to the network interface card by using something like DPDK or Netmap. All these tools have their place, and generally occupy a space from convenience to performance. But in recent years, that landscape has changed massively.. The tools available to the average Linux systems developer have improved from the creation of io_uring, to the expansion of bpf from a simple filtering language to a full-on programming environment embedded directly in the kernel. Along with that came something called XDP (express datapath). This was Linux kernel's answer to kernel-bypass networking. AF_XDP is the new socket type created by this feature, and generally works very similarly to something like DPDK. History lessons out of the way, this talk will look into, and discuss the merits of this technology, it's place in the broader ecosystem and how it can be used to attain the highest level of performance possible. This talk will dive into crucial details, such as how AF_XDP works, how it can be integrated into a larger system and finally more advanced topics such as request sharding/load balancing. There will be detailed look at the design of AF_XDP, the eBpf code used, as well as the userspace code required to drive it all. It will also include performance numbers from this setup compared to regular kernel networking. And most importantly how to put all this together to handle as much data as possible on a single modern multi-core system.
AMD has been away from the HPC space for a while, but now they are coming back in a big way with an open software approach to GPU computing. The Radeon Open Compute Platform (ROCm) was born from the Boltzman Initiative announced last year at SC15. Now available on GitHub, the ROCm Platform bringing a rich foundation to advanced computing by better integrating the CPU and GPU to solve real-world problems.
"We are excited to present ROCm, the first open-source HPC/ultrascale-class platform for GPU computing that’s also programming-language independent. We are bringing the UNIX philosophy of choice, minimalism and modular software development to GPU computing. The new ROCm foundation lets you choose or even develop tools and a language run time for your application."
Watch the video presentation: http://wp.me/p3RLHQ-fJT
Learn more: https://radeonopencompute.github.io/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Facebook presented, "Chiplets in Data Centers," at the ODSA Workshop. The charter of the ODSA (Open Domain Specification Architecture) Workgroup is to define an open specification that enables building of Domain Specific Accelerator silicon using best-of-breed components from the industry made available as chiplet dies that can be integrated together as Lego blocks on an organic substrate packaging layer. The resulting multi-chip module (MCM) silicon can be produced at significantly lower development and manufacturing costs, and will deliver much needed performance per watt and performance per dollar efficiencies in networking, security, machine learning and other applications. The ODSA Workgroup also intends to deliver implementations of the specification as board-level prototypes, RTL code and libraries.
Jetson AGX Xavier and the New Era of Autonomous MachinesDustin Franklin
Deep-dive on NVIDIA Jetson AGX Xavier, designed to help you deploy advanced AI onboard robots, drones, and other autonomous machines. View the webinar here: https://bit.ly/2BWVWv1
Tracing Summit 2014, Düsseldorf. What can Linux learn from DTrace: what went well, and what didn't go well, on its path to success? This talk will discuss not just the DTrace software, but lessons from the marketing and adoption of a system tracer, and an inside look at how DTrace was really deployed and used in production environments. It will also cover ongoing problems with DTrace, and how Linux may surpass them and continue to advance the field of system tracing. A world expert and core contributor to DTrace, Brendan now works at Netflix on Linux performance with the various Linux tracers (ftrace, perf_events, eBPF, SystemTap, ktap, sysdig, LTTng, and the DTrace Linux ports), and will summarize his experiences and suggestions for improvements. He has also been contributing to various tracers: recently promoting ftrace and perf_events adoption through articles and front-end scripts, and testing eBPF.
Predictive Maintenance Using Recurrent Neural NetworksJustin Brandenburg
My presentation from AnacondaCON 2018 where I discussed using Recurrent Neural Networks, Python, Tensorflow and the MapR Platform to develop deploy a predictive maintenance model for an IoT device in the manufacturing industry.
Small introduction to FPGA acceleration and the impact of the new High Level Synthesis toolchains to their programmability
Video here: https://www.linkedin.com/posts/marcobarbone_can-my-application-benefit-from-fpga-acceleration-activity-6848674747375460352-0fua
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/renesas/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Yoshio Sato, Senior Product Marketing Manager in the Industrial Business Unit at Renesas, presents the "Dynamically Reconfigurable Processor Technology for Vision Processing" tutorial at the May 2019 Embedded Vision Summit.
The Dynamically Reconfigurable Processing (DRP) block in the Arm Cortex-A9 based RZ/A2M MPU accelerates image processing algorithms with spatially pipelined, time-multiplexed, reconfigurable- hardware compute resources. This hybrid ARM/DRP architecture combines the economy, flexibility and ease-of-use of microprocessors with the high throughput and low latency of performance- optimized hardware.
DRP technology achieves silicon area efficiency by dividing large data paths into sub- blocks that can be swapped into the DRP hardware on each clock cycle to accelerate multiple complex algorithms while avoiding the cost and power penalties associated with large FPGAs. Pre-built libraries and a C-language programming environment deliver these benefits without the need for hardware design expertise. Designs can be iteratively enhanced through pre-production and even after mass-market deployment.
In this presentation, Sato examines the DRP block’s architecture and operation, presents benchmarks demonstrating performance up to 20x greater than traditional CPUs and introduces resources for developing DRP-based embedded vision systems with the RZ/A2M MPU.
Nvidia (History, GPU Architecture and New Pascal Architecture)Saksham Tanwar
This presentation focuses on Nvidia GPUs and explores the topics of what a GPU is, its basic architecture, how it is different from a CPU, its basic working, and what new Nvidia has to offer in consumer as well as server market
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2022/06/intel-video-ai-box-converging-ai-media-and-computing-in-a-compact-and-open-platform-a-presentation-from-intel/
Richard Chuang, Principal AI Engineer at Intel, presents the “Intel Video AI Box—Converging AI, Media and Computing in a Compact and Open Platform” tutorial at the May 2022 Embedded Vision Summit.
As a system integrator, solution provider or AI developer, you need to run your AI applications efficiently at the edge with sufficient throughput. Does your edge device run either generic computing or deep learning inferencing, but not both? Intel Video AI Box with Core CPU and integrated Xe LP graphics offers a compact solution to run video AI analytics at the edge with the support to orchestrate AI applications and workloads in cloud-to-edge deployments.
In this presentation, you’ll learn about Intel’s new platform, comprising an Intel CPU with integrated graphics and the Edge AI Box for Video Analytics software package, and how it enables developing cutting-edge video solutions faster. Chuang also explores EFLOW enablement on the platform, which allows Windows-based business applications to run rich Linux AI workload containers with Azure cloud connections for scalable deployments.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2022/06/seamless-deployment-of-multimedia-and-machine-learning-applications-at-the-edge-a-presentation-from-qualcomm/
Megha Daga, Senior Director of Product Management for AIoT at Qualcomm, presents the “Seamless Deployment of Multimedia and Machine Learning Applications at the Edge” tutorial at the May 2022 Embedded Vision Summit.
There has been an explosion of opportunities for edge compute solutions across the internet of things. This growth in opportunities and the diversity of applications is leading to fragmentation in the IoT space both in hardware and software, which creates challenges for developers. In addition, customers and developers are facing challenges in efficient data management and optimized application deployment on embedded edge platforms.
In this session, Daga introduces the Qualcomm Intelligent Multimedia SDK, which empowers developers to tackle these challenges and deploy edge compute applications in a scalable, flexible and optimized way. The Qualcomm Intelligent Multimedia SDK easily decodes and organizes sensor data and executes applications efficiently on edge platforms.
NVIDIA compute GPUs and software toolkits are key drivers behind major advancements in machine learning. Of particular interest is a technique called "deep learning", which utilizes what are known as Convolution Neural Networks (CNNs) having landslide success in computer vision and widespread adoption in a variety of fields such as autonomous vehicles, cyber security, and healthcare. In this talk is presented a high level introduction to deep learning where we discuss core concepts, success stories, and relevant use cases. Additionally, we will provide an overview of essential frameworks and workflows for deep learning. Finally, we explore emerging domains for GPU computing such as large-scale graph analytics, in-memory databases.
https://tech.rakuten.co.jp/
Shared Memory Centric Computing with CXL & OMIAllan Cantle
Discusses how CXL can be better utilized as a separate Fabric Cache domain to a processors own Local Cache Domain. This is done by leveraging a Shared Memory Centric architectures that utilize both the Open Memory Interface OMI, and Compute eXpress Link, CXL, for the memory ports.
High-Performance Networking Using eBPF, XDP, and io_uringScyllaDB
In the networking world there are a number of ways to increase performance over naive use of basic Berkeley sockets. These techniques have ranged from polling blocking sockets, non-blocking sockets controlled by Epoll, all the way through completely bypassing the Linux kernel for maximum network performance where you talk directly to the network interface card by using something like DPDK or Netmap. All these tools have their place, and generally occupy a space from convenience to performance. But in recent years, that landscape has changed massively.. The tools available to the average Linux systems developer have improved from the creation of io_uring, to the expansion of bpf from a simple filtering language to a full-on programming environment embedded directly in the kernel. Along with that came something called XDP (express datapath). This was Linux kernel's answer to kernel-bypass networking. AF_XDP is the new socket type created by this feature, and generally works very similarly to something like DPDK. History lessons out of the way, this talk will look into, and discuss the merits of this technology, it's place in the broader ecosystem and how it can be used to attain the highest level of performance possible. This talk will dive into crucial details, such as how AF_XDP works, how it can be integrated into a larger system and finally more advanced topics such as request sharding/load balancing. There will be detailed look at the design of AF_XDP, the eBpf code used, as well as the userspace code required to drive it all. It will also include performance numbers from this setup compared to regular kernel networking. And most importantly how to put all this together to handle as much data as possible on a single modern multi-core system.
AMD has been away from the HPC space for a while, but now they are coming back in a big way with an open software approach to GPU computing. The Radeon Open Compute Platform (ROCm) was born from the Boltzman Initiative announced last year at SC15. Now available on GitHub, the ROCm Platform bringing a rich foundation to advanced computing by better integrating the CPU and GPU to solve real-world problems.
"We are excited to present ROCm, the first open-source HPC/ultrascale-class platform for GPU computing that’s also programming-language independent. We are bringing the UNIX philosophy of choice, minimalism and modular software development to GPU computing. The new ROCm foundation lets you choose or even develop tools and a language run time for your application."
Watch the video presentation: http://wp.me/p3RLHQ-fJT
Learn more: https://radeonopencompute.github.io/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Facebook presented, "Chiplets in Data Centers," at the ODSA Workshop. The charter of the ODSA (Open Domain Specification Architecture) Workgroup is to define an open specification that enables building of Domain Specific Accelerator silicon using best-of-breed components from the industry made available as chiplet dies that can be integrated together as Lego blocks on an organic substrate packaging layer. The resulting multi-chip module (MCM) silicon can be produced at significantly lower development and manufacturing costs, and will deliver much needed performance per watt and performance per dollar efficiencies in networking, security, machine learning and other applications. The ODSA Workgroup also intends to deliver implementations of the specification as board-level prototypes, RTL code and libraries.
Jetson AGX Xavier and the New Era of Autonomous MachinesDustin Franklin
Deep-dive on NVIDIA Jetson AGX Xavier, designed to help you deploy advanced AI onboard robots, drones, and other autonomous machines. View the webinar here: https://bit.ly/2BWVWv1
Tracing Summit 2014, Düsseldorf. What can Linux learn from DTrace: what went well, and what didn't go well, on its path to success? This talk will discuss not just the DTrace software, but lessons from the marketing and adoption of a system tracer, and an inside look at how DTrace was really deployed and used in production environments. It will also cover ongoing problems with DTrace, and how Linux may surpass them and continue to advance the field of system tracing. A world expert and core contributor to DTrace, Brendan now works at Netflix on Linux performance with the various Linux tracers (ftrace, perf_events, eBPF, SystemTap, ktap, sysdig, LTTng, and the DTrace Linux ports), and will summarize his experiences and suggestions for improvements. He has also been contributing to various tracers: recently promoting ftrace and perf_events adoption through articles and front-end scripts, and testing eBPF.
Predictive Maintenance Using Recurrent Neural NetworksJustin Brandenburg
My presentation from AnacondaCON 2018 where I discussed using Recurrent Neural Networks, Python, Tensorflow and the MapR Platform to develop deploy a predictive maintenance model for an IoT device in the manufacturing industry.
Small introduction to FPGA acceleration and the impact of the new High Level Synthesis toolchains to their programmability
Video here: https://www.linkedin.com/posts/marcobarbone_can-my-application-benefit-from-fpga-acceleration-activity-6848674747375460352-0fua
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/renesas/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Yoshio Sato, Senior Product Marketing Manager in the Industrial Business Unit at Renesas, presents the "Dynamically Reconfigurable Processor Technology for Vision Processing" tutorial at the May 2019 Embedded Vision Summit.
The Dynamically Reconfigurable Processing (DRP) block in the Arm Cortex-A9 based RZ/A2M MPU accelerates image processing algorithms with spatially pipelined, time-multiplexed, reconfigurable- hardware compute resources. This hybrid ARM/DRP architecture combines the economy, flexibility and ease-of-use of microprocessors with the high throughput and low latency of performance- optimized hardware.
DRP technology achieves silicon area efficiency by dividing large data paths into sub- blocks that can be swapped into the DRP hardware on each clock cycle to accelerate multiple complex algorithms while avoiding the cost and power penalties associated with large FPGAs. Pre-built libraries and a C-language programming environment deliver these benefits without the need for hardware design expertise. Designs can be iteratively enhanced through pre-production and even after mass-market deployment.
In this presentation, Sato examines the DRP block’s architecture and operation, presents benchmarks demonstrating performance up to 20x greater than traditional CPUs and introduces resources for developing DRP-based embedded vision systems with the RZ/A2M MPU.
Nvidia (History, GPU Architecture and New Pascal Architecture)Saksham Tanwar
This presentation focuses on Nvidia GPUs and explores the topics of what a GPU is, its basic architecture, how it is different from a CPU, its basic working, and what new Nvidia has to offer in consumer as well as server market
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2022/06/intel-video-ai-box-converging-ai-media-and-computing-in-a-compact-and-open-platform-a-presentation-from-intel/
Richard Chuang, Principal AI Engineer at Intel, presents the “Intel Video AI Box—Converging AI, Media and Computing in a Compact and Open Platform” tutorial at the May 2022 Embedded Vision Summit.
As a system integrator, solution provider or AI developer, you need to run your AI applications efficiently at the edge with sufficient throughput. Does your edge device run either generic computing or deep learning inferencing, but not both? Intel Video AI Box with Core CPU and integrated Xe LP graphics offers a compact solution to run video AI analytics at the edge with the support to orchestrate AI applications and workloads in cloud-to-edge deployments.
In this presentation, you’ll learn about Intel’s new platform, comprising an Intel CPU with integrated graphics and the Edge AI Box for Video Analytics software package, and how it enables developing cutting-edge video solutions faster. Chuang also explores EFLOW enablement on the platform, which allows Windows-based business applications to run rich Linux AI workload containers with Azure cloud connections for scalable deployments.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2022/06/seamless-deployment-of-multimedia-and-machine-learning-applications-at-the-edge-a-presentation-from-qualcomm/
Megha Daga, Senior Director of Product Management for AIoT at Qualcomm, presents the “Seamless Deployment of Multimedia and Machine Learning Applications at the Edge” tutorial at the May 2022 Embedded Vision Summit.
There has been an explosion of opportunities for edge compute solutions across the internet of things. This growth in opportunities and the diversity of applications is leading to fragmentation in the IoT space both in hardware and software, which creates challenges for developers. In addition, customers and developers are facing challenges in efficient data management and optimized application deployment on embedded edge platforms.
In this session, Daga introduces the Qualcomm Intelligent Multimedia SDK, which empowers developers to tackle these challenges and deploy edge compute applications in a scalable, flexible and optimized way. The Qualcomm Intelligent Multimedia SDK easily decodes and organizes sensor data and executes applications efficiently on edge platforms.
Introduction to Software Defined Visualization (SDVis)Intel® Software
Software defined visualization (SDVis) is an open-source initiative from Intel and industry collaborators. Improve the visual fidelity, performance, and efficiency of prominent visualization solutions, while supporting the rapidly growing big data use on workstations through high-performance computing (HPC) on supercomputing clusters without memory limitations and cost of GPU-based solutions.
Simplifying and accelerating converged media with Open Visual CloudLiz Warner
Challenges exist with media transformation into Visual Cloud services and the flexibility to migrate those services to new HW platforms. Learn how Intel and partners are solving these challenges with highly optimized cloud native media processing, media analytics, and graphics/rendering components to quickly and easily deliver end-to-end visual cloud services with scalable open source software. Two visual cloud services around media delivery and media analytics will be demonstrated to showcase how to enable faster time to market for innovative “new media” services.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2022/08/open-standards-powering-the-future-of-embedded-vision-a-presentation-from-the-khronos-group/
Neil Trevett, President of the Khronos Group and Vice President of Developer Ecosystems at NVIDIA, presents the “Open Standards: Powering the Future of Embedded Vision” tutorial at the May 2022 Embedded Vision Summit.
Open standards play an important role in enabling interoperability for efficient deployment of vision-based systems. In this session, Trevett shares an update on the family of Khronos Group standards for programming and deploying accelerated inferencing and embedded vision, including OpenCL, Vulkan Safety Critical, OpenVX, SYCL and NNEF.
Trevett discusses the evolving roadmap for these standards and provides insights to help you understand which standards are relevant to your projects. In addition, he introduces the new Khronos Embedded Camera API initiative. Trevett outlines the technical direction of the Embedded Camera API working group to create an open standard to streamline the integration and control of sophisticated embedded camera systems, and highlights how attendees can participate in this important industry initiative.
DCC Labs provides DVB compliant middleware and other embedded software for Set-Top Boxes and digital TV devices. We specialize in small footprint, optimised performance applications running under Linux, OS20, OS21 and similar operating systems.
Debugging Effectively in the Cloud - Felipe Fidelix - Presentation at eZ Con...eZ Systems
Felipe Fidelix, Business Development Manager (UK) at Platform.sh spoke at eZ Conference 2017 on Debugging Effectively in the Cloud. Debugging PHP can be quite fun, if you just know how to do it. But what happens when you need to go beyond that? In his presentation, Felipe explains in depth how to debug PHP and related services using not-often-explored techniques like filesystem monitoring, mysql proxy interception, system call tracing, debugging remotely and a lot more.
Webinar: NVIDIA JETSON – A Inteligência Artificial na palma de sua mãoEmbarcados
Objetivo do Webinar: Venha saber como a plataforma NVIDIA Jetson e suas ferramentas habilitam você a desenvolver e implantar robôs, drones, aplicativos de IVA e outras máquinas autônomas com tecnologia AI que pensam por conta própria.
Apoio: Arrow e NVIDIA.
Convidado: Marcel Saraiva
Gerente de Contas Enterprise da NVIDIA, executivo com 20 anos de expereincia no mercado de TI, teve na sua carreia passagens pela SGI (Silicon Graphics), Intel e Scansource. Engenheiro eletrico formado pela FEI, com pós-graduação em Marketing pela FAAP e MBA em Gestão Empresarial pela FGV.
Link para o Webinar: https://www.embarcados.com.br/webinars/nvidia-jetson-a-inteligencia-artificial-na-palma-de-sua-mao/
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit-trevett
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Neil Trevett, President of the Khronos Group and Vice President at NVIDIA, presents the "APIs for Accelerating Vision and Inferencing: An Industry Overview of Options and Trade-offs" tutorial at the May 2019 Embedded Vision Summit.
The landscape of SDKs, APIs and file formats for accelerating inferencing and vision applications continues to evolve rapidly. Low-level compute APIs, such as OpenCL, Vulkan and CUDA are being used to accelerate inferencing engines such as OpenVX, CoreML, NNAPI and TensorRT, being fed by neural network file formats such as NNEF and ONNX.
Some of these APIs, like OpenCV, are vision-specific, while others, like OpenCL, are general-purpose. Some engines, like CoreML and TensorRT, are supplier-specific, while others such as OpenVX, are open standards that any supplier can adopt. Which ones should you use for your project? Trevett answers these and other questions in this presentation.
NFV and SDN: 4G LTE and 5G Wireless Networks on Intel(r) ArchitectureMichelle Holley
The Presentation will outline the KPIs and key optimizations at the platform, NFVi and Stack level in implementing wireless base station stack and Telco Edge cloud on Intel Architecture. The presentation will use the FlexRAN LTE Reference PHY and NEV SDK for MEC to outline the NFV and 5G use cases like network slicing.
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.
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.
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.
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
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.
Advanced Techniques to Accelerate Model Tuning | Software for AI Optimization...Intel® Software
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.
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.
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.
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.
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.
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.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
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.
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
-------------------------------------------
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
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
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
2. Charlie Wang, Peng Tu, Mikhail Nikolsky, Jerry Dong
Building a Deep Learning Video Analytics
Framework for Intel AI Platforms
SIGGRAPH 2019 | LOS ANGLES | 28 JULY - 1 AUGUST
3. Speakers
• Charlie Wang
• Principal Engineer, VTT
• Peng Tu
• Principal Engineer, CPDP
• Mikhail Nikolsky
• Sr. Staff Engineer, CPDP
3
Intel Architecture Graphics Software (IAGS)
4. Agenda
▪ Video Analytics Usages
▪ Build a Video Analytics Framework for all Intel HWs
▪ FFMPEG Filter Implementation
▪ GStreamer Plugin Implementation
▪ Video Analytics as REST Service
▪ Demo
▪ Resources
5. Intelligence on Video Data
Retailanalytics Industrialinspection Content filtering Parking management
Super Resolution Autonomous driving Action recognitionEncode Quality Control
6. Intel Video Analytics HW
6
Intel® CPU
Client and server
Intel® Vision
AcceleratorDesign
with Intel® Movidius™
Vision Processing
Units (VPU)
Intel® GPU
Integrated and
discrete
Intel® Vision
AcceleratorDesign
with an Intel® Arria 10
FPGA (preview)
Scalar Vector Matrix Spatial
8. Map to Intel Hardwares
Decode
Scale
/csc
Inference
Object
tracking
Post processing
+ Encode
Crop
scale
Inference
Inference
CPU
Media FF
CPU
GPU
VPU
FPGA
CPU
GPU
Programmable
CPU
Media FF
GPU
CPU
Media FF
GPU
CPU
GPU
VPU
FPGA
Video analyticsrequire heavy video and compute interaction
9. Agenda
▪ Video Analytics Usages
▪ Build a Video Analytics Framework for all Intel HWs
▪ FFMPEG Filter Implementation
▪ GStreamer Plugin Implementation
▪ Video Analytics as REST Service
▪ Demo
▪ Resources
10. Popular video Processing software
frameworks
Video and audio demux, decode, processing, encoding, rendering, and muxing, also
allow customizedplugin/filter
11. Intel Video software Offering
11
CPU GPU Media FF FPGA
VAAPI/DXVA
Next Gen Media Library
SW codec
App
FFMPEG GStreamer
App App
Customized
Framework
FPGA driver
Intel GPU media FF support video decode/encode and video processing
12. Intel Computer VISION Software Offering
Deep Learning for Computer Vision
Accelerate and deploy convolutional neuralnetworks
(CNN) on Intel® platforms with the Deep Learning
DeploymentToolkit includedOpenVINO
Traditional Computer Vision
Develop and optimize classic computer vision applications
built with the OpenCV library or OpenVX API.
13. OpenVINO for Inference
13
CPU GPU VPU
MKLDNN
GPU PluginCPU Plugin
DL Inference Engine API
FPGA
MVNC
VPU Plugin
DLA
FPGA Plugin
Heterogeneous Execution Engine
CLDNN
Inference App
Single interface supports all platforms, no SW change.
Library designed for CNN inference accelerationon Intel HW
14. Inference Engine
This execution engine uses a common API to deliver inference
solutionson the platform of your choice: CPU, GPU, VPU, or
FPGA.
Model Optimizer
This Python*-based command line tool imports trained
models from popular deep learning frameworks such as
Caffe*, TensorFlow*, and Apache MXNet*, and Open
Neural Network Exchange (ONNX).
Intel Deep Learning Deployment Toolkit
15. Now let’s build the video
analytics framework
Write-once, Deploy on All HWs
16. Video Analytics Framework
Video Analytics Application
CPU GPU VPU FPGA
VAAPI
Media Driver
MKLDNN
ComputeDriver
Next Gen Media Library OpenVINO
CLDNN DLA
CPU codec
FFMPEG Plugin
Video Analytics Application
Gstreamer Plugin
Video Analytics Application
Your own framework
NV12
The framework supports load balanceamong devices
17. Media & Compute interoperability
• Media and Compute/Inferenceuse differentcolor format
Tiled NV12
Surface
Decode
Linear RGBP
SurfaceScaling/csc Meta dataInference
A copy is required if we don’t
handlebuffer sharing
correctly
Common media format – YUV
with padding, pitch, etc.
Inference format – tensor array
18. Media format as a compute/Inference
Format
Decode
NV12
SurfaceScaling Meta dataInferenceC
Y channel
Layer1 (csc+convolution)
weight1
weight2
weight3
bias
csc
R
G
B
UV
channel
Inference time reduced by 3% to 20% depends on resolution
Model Resolution Time reduction
GoogleNetV1 224x224 3.6%
YoloTinyV1 448x448 9.3%
Mtcnn 1280x720 20.9%
19. Video Analytics e2e flow
Trained
Model
OfflineModel
Opt
Model
IR
DLDT
Inference Engine API
Application
Inference Engine
DLDT
MSDK Library
GSTVA
DEC/ENC/VPP API
MSDK
DEC/ENC/VPP/INF API
FFMPEG-VA/GST-VA
VA Pipeline DesignVideo
sources
VA Pipeline Implementation
20. Agenda
▪ Video Analytics Usages
▪ Build a Video Analytics Framework for all Intel HWs
▪ FFMPEG Filter Implementation
▪ GStreamer Plugin Implementation
▪ Video Analytics as REST Service
▪ Demo
▪ Resources
21. 21
FFMPEG componentsCommands
(console)
ffmpeg ffplay ffprobe ffserver
Libraries
libavdevice
libavformat
libavcodec
libavfilter
libavutil
libpostproc
libavresample
libswresamplemux / demux
Libraryfora/vfilters which to implement
all kind ofeffects,such as scale, crop, frc,
etc
Commandtoolto do
transcoding
Simple playerwith SDL
usingffmpeg
demux/decoder
Tool to extract the
informationofmulti-
media stream
Real-time stream
server to broadcast
multi-mediastream
A Library to implement mostofA/Vcodec,
and used bymost ofpopular codectools
Common tool library
We are adding inference as
filters
22. 22
Tensorflow
ffmpeg or ffplay
CPU GPU FPGAVPU
DNN INTERFACEDNNModel DNNModel
Tensorflow
Backend
Inference Engine
Backend
InferenceEngine(OpenVINO)
MKLD
NN
clDNN Movidius DLA
SR Filter Classify FilterDetect Filter
APP
LIBAVFILTER
3RD
LIBRARIESHW
Kafka
produce
r
Meta
data
muxer
LIBAVFORMAT
Librdkafka
FFmpeg Filter VA Architecture new
FFmpeg
hardware
3rd party
…
23. 23
• Face detection + emotion&age_gender recognition:
ffmpeg -i clip.mp4 -vf “
detect=model=$DETECT_MODEL1:model_proc=$MODEL1-JSON:device=$CPU,
classify=model=$EMOTION_MODEL2:model_proc=&MODEL2-JSON:device=$CPU,
classify=model=$AGE_GENDER_MODEL3:model_proc=$MODEL3-JSON:device=$CPU”
-an -y -f iemetadata metadata.json
The pipelines look like ...
23
input decoder face emotion age-gender convert send to
detection recognition recognition to JSON kafka server
stream detect classify classify iemetadata Kafkadecode
24. Agenda
▪ Video Analytics Usages
▪ Build a Video Analytics Framework for all Intel HWs
▪ FFMPEG Filter Implementation
▪ GStreamer Plugin Implementation
▪ Video Analytics as REST Service
▪ Demo
▪ Resources
25. WHY AddING VA to Gstreamer
• Multiplatform:Linux, Windows, Mac OS X, Android, …
• Comprehensivecore:graph-based multi-threadedpipeline, lightweightdata
passing
• Broad coverageof media technologies: file and streamingpacket i/o, codecs,
metadata,video and audio
• Extensivedevelopmenttools: gst-launch, Python, C++ API
• Easy to extend and reusethrough plugins and metadata
25
26. GStreamer Plugins Architecture
26
GStreamer API
Application
MetadataPer-plugin params/API
pipeline control pipeline events inference plugins configuration inference res ults
zero-copy,multi-channel inference
OpenVINO Inference Engine
MKLDNN
plugin
clDNN
plugin
MDK
plugin
iGPU/
dGPU
CPU
KMB/
HDDL
VAAPI
libav/
ffmpeg
VAAPI SVT
GPU
i/d/VSI
CPU
GPU
i/d/VSI
CPU
RTSP,
WebRTC,
Render,
File IO,
…
V4L2
Media HW acceleration
ImageInference API
Kafka
MQTT
Video
sources
gvainference
Video Analytics plugins (GVA*)Media plugins
HW
decode
SW
decode
HW
encode
SW
encode
Other plugins
200+
plugins
gvadetect gvaclassify gvaidentify
Meta
convert
publish
decodebin
27. GVA Inference Plugins Architecture
27
Inference Shared Instance
OpenVINO IEOpenVINO IE
GvaDetect
Thread
sink
padDMABuf
or RGBx
(any
resolution)
Attach GstMeta
to GstBuffer
source
pad
Output Layer
Post-Processing
GstBuffer + GstMeta’s
VASurface or RGBx
VAAPI
original GstBuffer
queueInput Layer
Pre-Processing
Inference queue
per device
DownScale
NV12→RGBP
28. GStreamer pipeline example
input HW/SW face age/gender emotion landmark re-identify face convert overlay
decode detection classification recognition points inference recognition to JSON result
28
filesrc decodebin gvaclassify gvaclassify gvaclassifygvadetect gvaidentify gvametaconvert gvawatermark
Video Analytics pipeline in Ad Insertion demo – facedetection plus age, gender, person recognition
gvaclassify
gst-launch-1.0 filesrc location=${FILE} !
decodebin !
gvadetect model=face-detection-adas-0001.xml model-proc=face-detection-adas-0001.json ! queue !
gvaclassify device=CPU model=age-gender-recognition.xml model-proc=age-gender-recognition.json ! queue !
gvaclassify device=GPU model=emotions-recognition.xml model-proc=emotions-recognition.json ! queue !
gvaclassify device=CPU,GPU model=landmarks-regression.xml model-proc=landmarks-regression.json ! queue !
gvaclassify model=face-reidentification.xml model-proc=face-reidentification.json ! queue !
gvaidentify gallery=face_gallery.json ! queue !
gvawatermark ! videoconvert ! fpsdisplaysink sync=false
Platform specific tuning: gvaclassify device={CPU|CPU,GPU} cpu-streams=15 nireq=16 …
31. Agenda
▪ Video Analytics Usages
▪ Build a Video Analytics Framework for all Intel HWs
▪ FFMPEG Filter Implementation
▪ GStreamer Plugin Implementation
▪ Video Analytics as REST Service
▪ Demo
▪ Resources
32. Video Analytics API levels
• Low-level (per frame processing)
▪ VAAPI, OpenVINO, OpenCL, OpenCV
• Pipeline level
▪ GStreamer, FFMPEG, MediaFoundation, …
• Video Analytics as Service
▪ REST/gRPC multi-node service
32
33. Video Analytics REST Service
33
• Provide RESTfulinterfacesfor executing
and monitoringpipeline status
• Interface agnostic to underlying
implementation(GStreamer,FFMPEG,
Custom backend)
• Supportscaling through container
deploymentand orchestration frameworks
• Load balancing
Video Analytics Pipeline Service
GStreamer / FFMPEG
REST / gRPC / Message Bus
VAAPI / MSDKHW
Acceleration
Pipeline
Support
Video Analytics
Normalization
Edge / Cloud
Integration
Open VINO
PipelineA PipelineB …
Pipeline Manager Model Manager
Model A Model B…
CPU GPU VPU
34. Video Analytics Service Developer
Workflow
34
HW / OS
Optimized
Docker Files
Pipelines
Configuration
json files
Docker
Build
Model
Configuration
json files
HW/ OS
Optimized
Libraries
VA Pipeline Developer creates
pipeline templates with
customizable parameters and
models
VA Pipeline Developer builds
Docker image
Developer / System Integrator
deploys containers to Cloud or Edge
and integrates into application
HTTP request
1 2 3
Application 2
Scheduler
Application 1
Worker
HTTP request
Worker
…
…
36. Video Analytics Service is part of Open
Visual Cloud
36
https://01.org/openvisualcloud
The Open Visual Cloud is an open source project that offers a set of pre-defined reference
pipelines for various target visual cloud use cases.
37. Agenda
▪ Video Analytics Usages
▪ Build a Video Analytics Framework for all Intel HWs
▪ FFMPEG Filter Implementation
▪ Gstreamer Plugin Implementation
▪ Video Analytics as REST Service
▪ Demo
▪ Resources
39. Summary
• We presented Intel video analytics e2e pipeline in GStreamer
and FFMPEG
• It supports multiple Intel HWs with same SW pipeline/API
• It provides optimized flow between media and DL inference, zero
copy, inference on NV12 image
• It provides scalable deployment cross edge/cloud
• Call to Action
• Try it out and let us know your feedback
40. Resources
▪ OpenVINO - https://github.com/opencv/dldt
▪ MediaSDK - https://github.com/Intel-Media-SDK
▪ GStreamer Plugin - https://github.com/opencv/gst-video-analytics
▪ Open Visual Cloud
▪ Smart City sample
https://github.com/OpenVisualCloud/Smart-City-Sample
▪ Ad Insertion sample
https://github.com/OpenVisualCloud/Ad-Insertion-Sample
▪ Docker files including FFMPEG Video Analytics Filters
https://github.com/OpenVisualCloud/Dockerfiles
40