How to install Digits 5.1 on Ubuntu 14
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This document provides instructions for installing deep learning software on Ubuntu 14. It discusses: 1. Requirements for Nvidia GPU support such as CUDA and cuDNN versions 2. Steps for installing Ubuntu 14, the Nvidia driver, CUDA, cuDNN, and DIGITS for deep learning 3. Instructions for updating an existing DIGITS installation to version 5.1 It also briefly mentions two large datasets that are useful for computer vision and deep learning research: the Open Images dataset for static images and the YouTube-8M dataset for videos.
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Highlighted notes of:
Chapter 2: Getting Started
Book:
CUDA by Example
An Introduction to General Purpose GPU Computing
Authors:
Jason Sanders
Edward Kandrot
“This book is required reading for anyone working with accelerator-based computing systems.”
–From the Foreword by Jack Dongarra, University of Tennessee and Oak Ridge National Laboratory
CUDA is a computing architecture designed to facilitate the development of parallel programs. In conjunction with a comprehensive software platform, the CUDA Architecture enables programmers to draw on the immense power of graphics processing units (GPUs) when building high-performance applications. GPUs, of course, have long been available for demanding graphics and game applications. CUDA now brings this valuable resource to programmers working on applications in other domains, including science, engineering, and finance. No knowledge of graphics programming is required–just the ability to program in a modestly extended version of C.
CUDA by Example, written by two senior members of the CUDA software platform team, shows programmers how to employ this new technology. The authors introduce each area of CUDA development through working examples. After a concise introduction to the CUDA platform and architecture, as well as a quick-start guide to CUDA C, the book details the techniques and trade-offs associated with each key CUDA feature. You’ll discover when to use each CUDA C extension and how to write CUDA software that delivers truly outstanding performance.
Table of Contents
Why CUDA? Why Now?
Getting Started
Introduction to CUDA C
Parallel Programming in CUDA C
Thread Cooperation
Constant Memory and Events
Texture Memory
Graphics Interoperability
Atomics
Streams
CUDA C on Multiple GPUs
The Final Countdown
All the CUDA software tools you’ll need are freely available for download from NVIDIA.
Jason Sanders is a senior software engineer in NVIDIA’s CUDA Platform Group, helped develop early releases of CUDA system software and contributed to the OpenCL 1.0 Specification, an industry standard for heterogeneous computing. He has held positions at ATI Technologies, Apple, and Novell.
Edward Kandrot is a senior software engineer on NVIDIA’s CUDA Algorithms team, has more than twenty years of industry experience optimizing code performance for firms including Adobe, Microsoft, Google, and Autodesk.
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Introduction to Project atomic (CentOS Dojo Bangalore)
The talk was given in CentOS Dojo Bangalore on 29th April 2015
http://wiki.centos.org/Events/Dojo/Bangalore2015
This slides contains introduction to Project Atomic and CentOS Atomic SIG.
Archlinux dev environment
This document provides instructions for setting up a minimum development environment on Arch Linux in one day. It includes steps to install Arch Linux using the ISO/USB image, configure storage and basic OS settings during installation. Additionally, it outlines how to set up the package manager Pacman, select common software packages for development like the GCC toolchain, and configure the system for development tasks. Finally, it discusses setting up an X environment and configuring the locale for internationalization.
Docker 101 - from 0 to Docker in 30 minutes
This Dockerfile defines an image for running MongoDB. It installs MongoDB from the official MongoDB repository, configures it to listen on all interfaces, exposes port 27017, and sets mongod as the default command to run a MongoDB instance when containers are created from this image. Building this image would result in a balboa/mongo image that can be run to start a MongoDB server in a container.
OpenNebula Conf 2014: CentOS, QA an OpenNebula - Christoph Galuschka
CentOS, the Community Enterprise OS, uses Opennebula as virtualization plattform for its automated QA-process. The opennebula setup consists of 3 nodes, all running CentOS-6, who handle the following tasks:
– sunstone as cloud controller
– local mirror/DNS-Server/http-Server for the VMs to pull in packages
– one VM to run a jenkins instance to launch the various tests (ci.de.centos.org)
– nginx on the cloud controller to forward http traffic to the jenkins VM
A public git repository (http://www.gitorious.org/testautomation) is used to allow whoever wants to contribute to pull the current test suite – t_functional, a series of bash scripts used to do funtional tests of various applications, binaries, configuration files and Trademark issues. As new tests are added to the repo via personal clones and merge requests, those tests first need to complete a test run via jenkins. Each test run currently consists of 4 VMs (one for each arch for C5 and C6 – C7 to come), which run the complete test suite. All VMs used for theses tests are instantiated and torn down on demand, whenever the call to testrun a personal clone is issued (via IRC).
Once completed successfully, the request is merged into the main repo. The jenkins node monitors this repository and which automatically triggers another complete test run.
Besides these triggered test runs, the test suite is automatically triggered daily to run. This is used to verify functionality of published updates – a handfull of failty updates have allready been discovered this way.
Besides t_functional, the Linux Test Project Suite of tests is also run on a daily basis, also to verify functionality of the OS and all updates.
The third setup is used to test the available and functional integrity of published docker images for CentOS.
All these tests are later – during the QA-phase of a point release – used to verify functionality of new packages inside the CentOS QA-Setup.
Embedded Recipes 2018 - swupdate: update your embedded device - Charles-Anto...
Nowadays a lot of embedded system are connected to Internet. And every years, more devices are available in the market but without maintenance. Due to this situation, a lot of security issues raised which could compromised the lifetime of the product and the privacy of their users. To fix these bugs, these security issues or to add new features, updating remotely these systems on regular basis is very important. We have to think about update process for each new product, to be easy, reliable, efficient and not too costly for the required bandwidth or hardware performances.
Several update designs are available to fit your requirements. Due to these constraints, you have to make choice and find the right balance.
Hopefully one free software allows us to perform this task in a easy and flexible way: swupdate. This solution is very well integrated with U-boot, buildroot and Yocto. You can describe exactly how the update should be done.
This talk is to explain the main designs to update an embedded system with pro and cons of all of them and then explain how to implement them with swupdate for your embedded system.
Ansible Workshop for Pythonistas
Get started with Ansible - an introduction for Python developers
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Qt5 on ti processors
This document discusses differences between Qt5 and Qt4 when using it on TI processors. It provides information on prerequisites for building Qt5, available platform plugins, and steps for configuring and building Qt5 using the eglfs and xcb platforms. It also describes how to execute demos on the target platform after installing Qt5. Key points include that Qt5 uses OpenGL ES2 for rendering, platform plugins like eglfs and xcb are available, and demos can be run by setting environment variables and using tools like qmlscene.
Velero search & practice 20210609
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[KubeConUS2019 Docker, Inc. Booth] Distributed Builds on Kubernetes with Bui...
1. Kubernetes can be used to distribute Docker builds across multiple nodes for improved performance and scalability using BuildKit and the new BuildX toolkit.
2. BuildX introduces a Kubernetes driver that allows builds to run on Kubernetes pods with no complex YAML or TLS configuration required, providing the same user experience as local Docker builds.
3. Builds can be executed without root privileges on Kubernetes for improved security, and the Kubernetes driver supports caching of build artifacts both locally on pods and remotely to a registry for faster rebuilds.
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[DockerCon 2019] Hardening Docker daemon with Rootless mode
[DockerCon 2019] Hardening Docker daemon with Rootless mode
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Podman, Buildah, and Quarkus - The Latest in Linux Containers Technologies
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[DockerCon 2020] Hardening Docker daemon with Rootless Mode
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Embedded Recipes 2018 - swupdate: update your embedded device - Charles-Anto...
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This document provides instructions for setting up various deep learning frameworks including TensorFlow, CUDA, cuDNN, Nvidia Caffe, and DIGITS on Ubuntu 16. It includes steps for installing prerequisites like CUDA and cuDNN and then installing each framework via pip, cloning from GitHub, and building from source. The frameworks are TensorFlow 1.3, Nvidia Caffe 0.15, and DIGITS 6.0. Additional tips are provided for resolving errors.
CUDA by Example : Getting Started : Notes
Highlighted notes of:
Chapter 2: Getting Started
Book:
CUDA by Example
An Introduction to General Purpose GPU Computing
Authors:
Jason Sanders
Edward Kandrot
“This book is required reading for anyone working with accelerator-based computing systems.”
–From the Foreword by Jack Dongarra, University of Tennessee and Oak Ridge National Laboratory
CUDA is a computing architecture designed to facilitate the development of parallel programs. In conjunction with a comprehensive software platform, the CUDA Architecture enables programmers to draw on the immense power of graphics processing units (GPUs) when building high-performance applications. GPUs, of course, have long been available for demanding graphics and game applications. CUDA now brings this valuable resource to programmers working on applications in other domains, including science, engineering, and finance. No knowledge of graphics programming is required–just the ability to program in a modestly extended version of C.
CUDA by Example, written by two senior members of the CUDA software platform team, shows programmers how to employ this new technology. The authors introduce each area of CUDA development through working examples. After a concise introduction to the CUDA platform and architecture, as well as a quick-start guide to CUDA C, the book details the techniques and trade-offs associated with each key CUDA feature. You’ll discover when to use each CUDA C extension and how to write CUDA software that delivers truly outstanding performance.
Table of Contents
Why CUDA? Why Now?
Getting Started
Introduction to CUDA C
Parallel Programming in CUDA C
Thread Cooperation
Constant Memory and Events
Texture Memory
Graphics Interoperability
Atomics
Streams
CUDA C on Multiple GPUs
The Final Countdown
All the CUDA software tools you’ll need are freely available for download from NVIDIA.
Jason Sanders is a senior software engineer in NVIDIA’s CUDA Platform Group, helped develop early releases of CUDA system software and contributed to the OpenCL 1.0 Specification, an industry standard for heterogeneous computing. He has held positions at ATI Technologies, Apple, and Novell.
Edward Kandrot is a senior software engineer on NVIDIA’s CUDA Algorithms team, has more than twenty years of industry experience optimizing code performance for firms including Adobe, Microsoft, Google, and Autodesk.
ASP.NET 5 auf Raspberry PI & docker
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2. It provides steps for installing .NET Core, ASP.NET 5, and other prerequisites on the Raspberry Pi, and includes examples of building and running a simple ASP.NET app.
3. It also covers using Docker to build an image for an ASP.NET app, including defining a Dockerfile and building/running the image to host the app in a container.
State of Big Data on ARM64 / AArch64 - Apache Bigtop
This document discusses Apache Bigtop, an open source project that provides tools for building, deploying, and testing big data software stacks across multiple platforms. It summarizes Bigtop's components and goals, contributions from ARM to support AArch64, challenges faced in the porting process, and the roadmap for further improving Bigtop including adding Kubernetes support and predefined sample stacks. It also demonstrates Bigtop's sandbox feature for building and running pseudo clusters using Docker.
CentOS Stream at Facebook
We'll talk about how Facebook is leveraging CentOS Stream to manage our production fleet at scale. We'll cover the latest updates on our fleet migration from CentOS 7, talk about the tooling and processes we've developed and how they've evolved, and how we're working with the CentOS and Fedora communities. This talk is a followup to "Upgrading CentOS on the Facebook fleet" (https://www.youtube.com/watch?v=EajAjFCZz4Q&t=3s) from DevConf.cz 2020.
DCSF 19 Accelerating Docker Containers with NVIDIA GPUs
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instruction of install Caffe on ubuntu
instruction of install Caffe step by step on ubuntu 14.04
برای دریافت دی وی دی های این کارگاه (شامل فیلم کارگاه و فایل های مورد نیاز) به آدرس زیر ایمیل بزنید:
pouya.ahmadvand@gmail.com
Best practices for optimizing Red Hat platforms for large scale datacenter de...
This presentation is from NVIDIA GTC DC on Oct 23, 2018:
https://youtu.be/z5gEUL6dJRI
Corresponding Press Release: https://www.redhat.com/en/about/press-releases/red-hat-nvidia-align-open-source-solutions-fuel-emerging-workloads
Blog: https://www.redhat.com/en/blog/red-hat-and-nvidia-positioning-red-hat-enterprise-linux-and-openshift-primary-platforms-artificial-intelligence-and-other-gpu-accelerated-workloads
Demo Video:
https://www.youtube.com/watch?v=9iVYjA_WJgU
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Leveraging docker for hadoop build automation and big data stack provisioning
https://dataworkssummit.com/san-jose-2017/sessions/leveraging-docker-for-hadoop-build-automation-and-big-data-stack-provisioning/
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Overview About Deep Belief Image recognition SDK presented in TuniPiJam.
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Docker to the Rescue of an Ops Team
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Docker to the Rescue of an Ops Team
Slides from my DockerCon EU 2017 Talk.
Find the abstract below:
"In this talk, we'll discover how Docker comes to the rescue of the Ops Team, while rebuilding from scratch our monitoring infrastructure. We'll start by quickly describing the challenges, to focus on why and how using docker saved the project. From fixing dependencies and isolation issues, implementing rolling upgrades and new features hot addition, to building a completely modular, scalable and resilient infrastructure, we'll talk about why CI/CD workflows, docker tooling and Docker Swarm were the key to success."
GPU and Deep learning best practices
Despite the increase of deep learning practitioners and researchers, many of them do not use GPUs, this may lead to long training/evaluation cycles and non-practical research.
In his talk, Lior shares how to get started with GPUs and some of the best practices that helped him during research and work. The talk is for everyone who works with machine learning (deep learning experience is NOT mandatory!), It covers the very basics of how GPU works, CUDA drivers, IDE configuration, training, inference, and multi-GPU training.
Make Accelerator Pluggable for Container Engine
This document proposes making hardware accelerators like GPUs and FPGAs pluggable for container engines. It discusses identifying accelerator requirements, preparing runtimes, and managing resources. A pluggable accelerator interface is suggested to integrate different plugins for devices like GPUs, DPDK, and FPGAs in a standardized way and manage their lifecycles together with containers. This would allow containers to easily leverage various hardware accelerators.
Deploying software at Scale
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5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
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Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
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What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
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Overview
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Paper Link: https://eprint.iacr.org/2024/257
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How to install Digits 5.1 on Ubuntu 14
- 2. GPU Nvidia support • Find the your Nvidia GPU “Compute Capability” or architecture from https://developer.nvidia.com/cuda-gpus • CUDA SDK 6.5: Last Version with support for Tesla with Compute Capability 1.x • CUDA SDK 7.5 support for Compute Capability 2.0 – 5.x (Fermi, Kepler, Maxwell) • CUDA SDK 8.0 support for Compute Capability 2.0 – 6.x (Fermi, Kepler, Maxwell, Pascal)
- 3. Installation deep learning for DIGITS • Ubuntu 14 • Nvidia driver (375) • CUDA 8 (do not install driver) • cuDNN 5.1 • DIGITS • If you want Update DIGITS 5.1
- 4. Install Ubuntu 14 • In windows you can create bootable usb flash by “rufus-2.11.exe” • You can install Ubuntu alongside windows • You should used Ubuntu as a training then when the model is trained you can use it in another platform • If you have one partition (“C”) you need to shrink and make around 100GB free space then install Ubuntu on the free space in your hard disk. If your windows not show in the booting menu used below command • Go to terminal by “Alt+ ctrl+F1”, put user and password • Sudo update-grub • Restart.
- 5. Nvidia driver • Alt+ctrl+F1 (if you have two GPU used none nvidia for monitor) • sudo stop lightdm • sudo add-apt-repository ppa:graphics-drivers/ppa • sudo apt-get update • sudo apt-get –y install nvidia-375
- 6. Install CUDA 8 • https://developer.nvidia.com/cuda-downloads • Download the latest version of CUDA • The NVIDIA CUDA Toolkit: A development environment for building GPU-accelerated applications. This toolkit includes a compiler specifically designed for NVIDIA GPUs and associated math libraries + optimization routines.
- 7. cuDNN • Register free account in nvidia website • Download cuDNN install and configure to Ubuntu library • The cuDNN library: A GPU-accelerated library of primitives for deep neural networks. Using the cuDNN package, you can increase training speeds by upwards of 44%, with over 6x speedups in Torch and Caffe.
- 8. Install DIGITS • Search “nvidia digits github” • Fallowing the instructions Deb packages Ubuntu 14.04 14.04 repo docs/Ubunt uInstall.md
- 9. Update DIGITS 5.1 • https://github.com/NVIDIA/DIGITS/blob/master/docs/BuildDigits.md • sudo apt-get install --no-install-recommends git graphviz python-dev python-flask python-flaskext.wtf python-gevent python-h5py python- numpy python-pil python-pip python-protobuf python-scipy • DIGITS_ROOT_Farshid=~/digits_Farshid • git clone https://github.com/NVIDIA/DIGITS.git $ DIGITS_ROOT_Farshid • sudo pip install -r $ DIGITS_ROOT_Farshid/requirements.txt • sudo pip install -e $DIGITS_ROOT_Farshid • cd digits_Farshid • ./digits-devserver –p 7000
- 10. Datasets for Computer vision + Deep Learning Google Research: Computer vision + Deep Learning 1. Images: Open Images Dataset 2. Videos: YouTube-8M: A Large and Diverse Labeled Video Dataset for Video Understanding Research
- 11. Datasets Images • Deep learning needs large amount of inputs for training. detecting and classifying objects in static images • Open Images Dataset automatically caption images natural language replies in response to shared photos ~9 million URLs to images 6000 categories each image has about 8 labels assigned Inception v3 model
- 12. Datasets Video I 1. video is much more time-consuming to annotate manually than images • video annotation system, which identifies relevant Knowledge Graph topics • video metadata and content analysis • only public videos with more than 1000 views • frequency analysis, automated filtering, verification by human raters • 24 top-level verticals 2. video is very computationally expensive to process and store • extracted frame-level features • Inception-V3 image annotation model
- 13. Datasets Video II • YouTube-8M: A Large and Diverse Labeled Video Dataset for Video Understanding Research • 8 million YouTube video URLs (representing over 500,000 hours of video) • 4800 Knowledge Graph entities (classes)
- 14. For research • Reduce the size of model • Deep compression • Pruning deep learning • Hash table neural network