Analytical and Other Software on the Secure Research Environment
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Deep Learning Frameworks | NVIDIA Developer
4/10/2017 Deep Learning Frameworks | NVIDIA Developer Deep Learning Frameworks The NVIDIA Deep Learning SDK accelerates widelyused deep learning frameworks such as Caffe, CNTK, TensorFlow, Theano and Torch as well as many other deep learning applications. Choose a deep learning framework from the list below, download the supported version of cuDNN and follow the instructions on the framework page to get started. Caffe is a deep learning framework made with expression, speed, and modularity in mind. Caffe is developed by the Berkeley Vision and Learning Center (BVLC), as well as community contributors and is popular for computer vision. Caffe supports cuDNN v5 for GPU acceleration. Supported interfaces: C, C++, Python, MATLAB, Command line interface Learning Resources Deep learning course: Getting Started with the Caffe Framework Blog: Deep Learning for Computer Vision with Caffe and cuDNN Download Caffe Download cuDNN The Microsoft Cognitive Toolkit —previously known as CNTK— is a unified deeplearning toolkit from Microsoft Research that makes it easy to train and combine popular model types across multiple GPUs and servers. Microsoft Cognitive Toolkit implements highly efficient CNN and RNN training for speech, image and text data. Microsoft Cognitive Toolkit supports cuDNN v5.1 for GPU acceleration. Supported interfaces: Python, C++, C# and Command line interface Download CNTK Download cuDNN TensorFlow is a software library for numerical computation using data flow graphs, developed by Google’s Machine Intelligence research organization. TensorFlow supports cuDNN v5.1 for GPU acceleration. Supported interfaces: C++, Python Download TensorFlow Download cuDNN https://developer.nvidia.com/deeplearningframeworks 1/3 4/10/2017 Deep Learning Frameworks | NVIDIA Developer Theano is a math expression compiler that efficiently defines, optimizes, and evaluates mathematical expressions involving multidimensional arrays. -
Music Instrument Localization in Virtual Reality Environments Using Audio-Visual Cues
Music Instrument Localization in Virtual Reality Environments using audio-visual cues Siddhartha Bhattacharyya B.Tech A Dissertation Presented to the University of Dublin, Trinity College in partial fulfilment of the requirements for the degree of Master of Science in Computer Science (Data Science) Supervisor: Aljosa Smolic and Cagri Ozcinar September 2020 Declaration I, the undersigned, declare that this work has not previously been submitted as an exercise for a degree at this, or any other University, and that unless otherwise stated, is my own work. Siddhartha Bhattacharyya September 6, 2020 Permission to Lend and/or Copy I, the undersigned, agree that Trinity College Library may lend or copy this thesis upon request. Siddhartha Bhattacharyya September 6, 2020 Acknowledgments I would like to thank my supervisors Professors Aljosa Smolic and Cagri Ozcinar for their dedicated support, understanding and leadership. My heartfelt gratitude goes out to my peers and the batch of 2019-2020 for their support and friendship. I would also like to thank the Trinity VR community for their help. I would like to extend my sincerest gratitude to the admins at SCSS labs who gave endless support in ensuring the availability of remote servers. During this time of crisis and remote work, this dissertation would not have been possible without their support. Last but not the least, I would like to thank my family for their trust and belief in me. Siddhartha Bhattacharyya University of Dublin, Trinity College September 2020 iii Music Instrument Localization in Virtual Reality Environments using audio-visual cues Siddhartha Bhattacharyya, Master of Science in Computer Science University of Dublin, Trinity College, 2020 Supervisor: Aljosa Smolic and Cagri Ozcinar This research work aims to develop and assess the capabilities of convolution neu- ral networks to identify and localize musical instruments in 360 videos. -
Lightweight Django USING REST, WEBSOCKETS & BACKBONE
Lightweight Django USING REST, WEBSOCKETS & BACKBONE Julia Elman & Mark Lavin Lightweight Django LightweightDjango How can you take advantage of the Django framework to integrate complex “A great resource for client-side interactions and real-time features into your web applications? going beyond traditional Through a series of rapid application development projects, this hands-on book shows experienced Django developers how to include REST APIs, apps and learning how WebSockets, and client-side MVC frameworks such as Backbone.js into Django can power the new or existing projects. backend of single-page Learn how to make the most of Django’s decoupled design by choosing web applications.” the components you need to build the lightweight applications you want. —Aymeric Augustin Once you finish this book, you’ll know how to build single-page applications Django core developer, CTO, oscaro.com that respond to interactions in real time. If you’re familiar with Python and JavaScript, you’re good to go. “Such a good idea—I think this will lower the barrier ■ Learn a lightweight approach for starting a new Django project of entry for developers ■ Break reusable applications into smaller services that even more… the more communicate with one another I read, the more excited ■ Create a static, rapid prototyping site as a scaffold for websites and applications I am!” —Barbara Shaurette ■ Build a REST API with django-rest-framework Python Developer, Cox Media Group ■ Learn how to use Django with the Backbone.js MVC framework ■ Create a single-page web application on top of your REST API Lightweight ■ Integrate real-time features with WebSockets and the Tornado networking library ■ Use the book’s code-driven examples in your own projects Julia Elman, a frontend developer and tech education advocate, started learning Django in 2008 while working at World Online. -
Installing R
Installing R Russell Almond August 29, 2020 Objectives When you finish this lesson, you will be able to 1) Start and Stop R and R Studio 2) Download, install and run the tidyverse package. 3) Get help on R functions. What you need to Download R is a programming language for statistics. Generally, the way that you will work with R code is you will write scripts—small programs—that do the analysis you want to do. You will also need a development environment which will allow you to edit and run the scripts. I recommend RStudio, which is pretty easy to learn. In general, you will need three things for an analysis job: • R itself. R can be downloaded from https://cloud.r-project.org. If you use a package manager on your computer, R is likely available there. The most common package managers are homebrew on Mac OS, apt-get on Debian Linux, yum on Red hat Linux, or chocolatey on Windows. You may need to search for ‘cran’ to find the name of the right package. For Debian Linux, it is called r-base. • R Studio development environment. R Studio https://rstudio.com/products/rstudio/download/. The free version is fine for what we are doing. 1 There are other choices for development environments. I use Emacs and ESS, Emacs Speaks Statistics, but that is mostly because I’ve been using Emacs for 20 years. • A number of R packages for specific analyses. These can be downloaded from the Comprehensive R Archive Network, or CRAN. Go to https://cloud.r-project.org and click on the ‘Packages’ tab. -
RZ/G Verified Linux Package for 64Bit Kernel V1.0.5-RT Release Note For
Release Note RZ/G Verified Linux Package for 64bit kernel Version 1.0.5-RT R01TU0311EJ0102 Rev. 1.02 Release Note for HTML5 Sep 7, 2020 Introduction This release note describes the contents, building procedures for HTML5 (Gecko) and important points of the RZ/G Verified Linux Package for 64bit kernel (hereinafter referred to as “VLP64”). In this release, Linux packages for HTML5 is preliminary and provided AS IS with no warranty. If you need information to build Linux BSPs without a GUI Framework of HTML5, please refer to “RZ/G Verified Linux Package for 64bit kernel Version 1.0.5-RT Release Note”. Contents 1. Release Items ................................................................................................................. 2 2. Build environment .......................................................................................................... 4 3. Building Instructions ...................................................................................................... 6 3.1 Setup the Linux Host PC to build images ................................................................................. 6 3.2 Building images to run on the board ........................................................................................ 8 3.3 Building SDK ............................................................................................................................. 12 4. Components ................................................................................................................. 13 5. Restrictions -
Evaluating Usage of Images for App Classification
Evaluating Usage of Images for App Classification Kushal Singla, Niloy Mukherjee, Hari Manassery Koduvely, Joy Bose Samsung R&D Institute Bangalore, India [email protected] Abstract— App classification is useful in a number of In this paper, we seek to evaluate different methods in applications such as adding apps to an app store or building a which app images can be used to improve the accuracy of the user model based on the installed apps. Presently there are a app classification. One such method involves extracting text number of existing methods to classify apps based on a given from the app images using optical character recognition taxonomy on the basis of their text metadata. However, text (OCR) and using the extracted text to classify the app. based methods for app classification may not work in all cases, Another method involves generating text descriptions of the such as when the text descriptions are in a different language, app images by summarizing the images using a tool, and or missing, or inadequate to classify the app. One solution in using the resulting text descriptions for the app classification. such cases is to utilize the app images to supplement the text Yet another method involves identifying the objects in the description. In this paper, we evaluate a number of approaches in which app images can be used to classify the apps. In one app images and using the identified objects to classify the approach, we use Optical character recognition (OCR) to app. An ensemble of such different models can also be used, extract text from images, which is then used to supplement the perhaps along with text based classification of apps. -
Cherrypy Documentation Release 8.5.1.Dev0+Ng3a7e7f2.D20170208
CherryPy Documentation Release 8.5.1.dev0+ng3a7e7f2.d20170208 CherryPy Team February 08, 2017 Contents 1 Foreword 1 1.1 Why CherryPy?.............................................1 1.2 Success Stories..............................................2 2 Installation 5 2.1 Requirements...............................................5 2.2 Supported python version........................................5 2.3 Installing.................................................5 2.4 Run it...................................................6 3 Tutorials 9 3.1 Tutorial 1: A basic web application...................................9 3.2 Tutorial 2: Different URLs lead to different functions.......................... 10 3.3 Tutorial 3: My URLs have parameters.................................. 11 3.4 Tutorial 4: Submit this form....................................... 12 3.5 Tutorial 5: Track my end-user’s activity................................. 12 3.6 Tutorial 6: What about my javascripts, CSS and images?........................ 13 3.7 Tutorial 7: Give us a REST....................................... 15 3.8 Tutorial 8: Make it smoother with Ajax................................. 17 3.9 Tutorial 9: Data is all my life...................................... 19 3.10 Tutorial 10: Make it a modern single-page application with React.js.................. 22 3.11 Tutorial 11: Organize my code...................................... 25 4 Basics 27 4.1 The one-minute application example.................................. 28 4.2 Hosting one or more applications................................... -
A Tensor Compiler for Unified Machine Learning Prediction Serving
A Tensor Compiler for Unified Machine Learning Prediction Serving Supun Nakandala, UC San Diego; Karla Saur, Microsoft; Gyeong-In Yu, Seoul National University; Konstantinos Karanasos, Carlo Curino, Markus Weimer, and Matteo Interlandi, Microsoft https://www.usenix.org/conference/osdi20/presentation/nakandala This paper is included in the Proceedings of the 14th USENIX Symposium on Operating Systems Design and Implementation November 4–6, 2020 978-1-939133-19-9 Open access to the Proceedings of the 14th USENIX Symposium on Operating Systems Design and Implementation is sponsored by USENIX A Tensor Compiler for Unified Machine Learning Prediction Serving Supun Nakandalac,∗, Karla Saurm, Gyeong-In Yus,∗, Konstantinos Karanasosm, Carlo Curinom, Markus Weimerm, Matteo Interlandim mMicrosoft, cUC San Diego, sSeoul National University {<name>.<surname>}@microsoft.com,[email protected], [email protected] Abstract TensorFlow [13] combined, and growing faster than both. Machine Learning (ML) adoption in the enterprise requires Acknowledging this trend, traditional ML capabilities have simpler and more efficient software infrastructure—the be- been recently added to DNN frameworks, such as the ONNX- spoke solutions typical in large web companies are simply ML [4] flavor in ONNX [25] and TensorFlow’s TFX [39]. untenable. Model scoring, the process of obtaining predic- When it comes to owning and operating ML solutions, en- tions from a trained model over new data, is a primary con- terprises differ from early adopters in their focus on long-term tributor to infrastructure complexity and cost as models are costs of ownership and amortized return on investments [68]. trained once but used many times. In this paper we propose As such, enterprises are highly sensitive to: (1) complexity, HUMMINGBIRD, a novel approach to model scoring, which (2) performance, and (3) overall operational efficiency of their compiles featurization operators and traditional ML models software infrastructure [14]. -
Naiad: a Timely Dataflow System
Naiad: A Timely Dataflow System Derek G. Murray Frank McSherry Rebecca Isaacs Michael Isard Paul Barham Mart´ın Abadi Microsoft Research Silicon Valley {derekmur,mcsherry,risaacs,misard,pbar,abadi}@microsoft.com Abstract User queries Low-latency query are received responses are delivered Naiad is a distributed system for executing data parallel, cyclic dataflow programs. It offers the high throughput Queries are of batch processors, the low latency of stream proces- joined with sors, and the ability to perform iterative and incremental processed data computations. Although existing systems offer some of Complex processing these features, applications that require all three have re- incrementally re- lied on multiple platforms, at the expense of efficiency, Updates to executes to reflect maintainability, and simplicity. Naiad resolves the com- data arrive changed data plexities of combining these features in one framework. A new computational model, timely dataflow, under- Figure 1: A Naiad application that supports real- lies Naiad and captures opportunities for parallelism time queries on continually updated data. The across a wide class of algorithms. This model enriches dashed rectangle represents iterative processing that dataflow computation with timestamps that represent incrementally updates as new data arrive. logical points in the computation and provide the basis for an efficient, lightweight coordination mechanism. requirements: the application performs iterative process- We show that many powerful high-level programming ing on a real-time data stream, and supports interac- models can be built on Naiad’s low-level primitives, en- tive queries on a fresh, consistent view of the results. abling such diverse tasks as streaming data analysis, it- However, no existing system satisfies all three require- erative machine learning, and interactive graph mining. -
Comparative Study of Deep Learning Software Frameworks
Comparative Study of Deep Learning Software Frameworks Soheil Bahrampour, Naveen Ramakrishnan, Lukas Schott, Mohak Shah Research and Technology Center, Robert Bosch LLC {Soheil.Bahrampour, Naveen.Ramakrishnan, fixed-term.Lukas.Schott, Mohak.Shah}@us.bosch.com ABSTRACT such as dropout and weight decay [2]. As the popular- Deep learning methods have resulted in significant perfor- ity of the deep learning methods have increased over the mance improvements in several application domains and as last few years, several deep learning software frameworks such several software frameworks have been developed to have appeared to enable efficient development and imple- facilitate their implementation. This paper presents a com- mentation of these methods. The list of available frame- parative study of five deep learning frameworks, namely works includes, but is not limited to, Caffe, DeepLearning4J, Caffe, Neon, TensorFlow, Theano, and Torch, on three as- deepmat, Eblearn, Neon, PyLearn, TensorFlow, Theano, pects: extensibility, hardware utilization, and speed. The Torch, etc. Different frameworks try to optimize different as- study is performed on several types of deep learning ar- pects of training or deployment of a deep learning algorithm. chitectures and we evaluate the performance of the above For instance, Caffe emphasises ease of use where standard frameworks when employed on a single machine for both layers can be easily configured without hard-coding while (multi-threaded) CPU and GPU (Nvidia Titan X) settings. Theano provides automatic differentiation capabilities which The speed performance metrics used here include the gradi- facilitates flexibility to modify architecture for research and ent computation time, which is important during the train- development. Several of these frameworks have received ing phase of deep networks, and the forward time, which wide attention from the research community and are well- is important from the deployment perspective of trained developed allowing efficient training of deep networks with networks. -
Comparative Study of Caffe, Neon, Theano, and Torch
Workshop track - ICLR 2016 COMPARATIVE STUDY OF CAFFE,NEON,THEANO, AND TORCH FOR DEEP LEARNING Soheil Bahrampour, Naveen Ramakrishnan, Lukas Schott, Mohak Shah Bosch Research and Technology Center fSoheil.Bahrampour,Naveen.Ramakrishnan, fixed-term.Lukas.Schott,[email protected] ABSTRACT Deep learning methods have resulted in significant performance improvements in several application domains and as such several software frameworks have been developed to facilitate their implementation. This paper presents a comparative study of four deep learning frameworks, namely Caffe, Neon, Theano, and Torch, on three aspects: extensibility, hardware utilization, and speed. The study is per- formed on several types of deep learning architectures and we evaluate the per- formance of the above frameworks when employed on a single machine for both (multi-threaded) CPU and GPU (Nvidia Titan X) settings. The speed performance metrics used here include the gradient computation time, which is important dur- ing the training phase of deep networks, and the forward time, which is important from the deployment perspective of trained networks. For convolutional networks, we also report how each of these frameworks support various convolutional algo- rithms and their corresponding performance. From our experiments, we observe that Theano and Torch are the most easily extensible frameworks. We observe that Torch is best suited for any deep architecture on CPU, followed by Theano. It also achieves the best performance on the GPU for large convolutional and fully connected networks, followed closely by Neon. Theano achieves the best perfor- mance on GPU for training and deployment of LSTM networks. Finally Caffe is the easiest for evaluating the performance of standard deep architectures. -
Fpm Documentation Release 1.7.0
fpm Documentation Release 1.7.0 Jordan Sissel Sep 08, 2017 Contents 1 Backstory 3 2 The Solution - FPM 5 3 Things that should work 7 4 Table of Contents 9 4.1 What is FPM?..............................................9 4.2 Installation................................................ 10 4.3 Use Cases................................................. 11 4.4 Packages................................................. 13 4.5 Want to contribute? Or need help?.................................... 21 4.6 Release Notes and Change Log..................................... 22 i ii fpm Documentation, Release 1.7.0 Note: The documentation here is a work-in-progress. If you want to contribute new docs or report problems, I invite you to do so on the project issue tracker. The goal of fpm is to make it easy and quick to build packages such as rpms, debs, OSX packages, etc. fpm, as a project, exists with the following principles in mind: • If fpm is not helping you make packages easily, then there is a bug in fpm. • If you are having a bad time with fpm, then there is a bug in fpm. • If the documentation is confusing, then this is a bug in fpm. If there is a bug in fpm, then we can work together to fix it. If you wish to report a bug/problem/whatever, I welcome you to do on the project issue tracker. You can find out how to use fpm in the documentation. Contents 1 fpm Documentation, Release 1.7.0 2 Contents CHAPTER 1 Backstory Sometimes packaging is done wrong (because you can’t do it right for all situations), but small tweaks can fix it.