End-To-End Data-Science and Geospatial Analytics with Gpus, RAPIDS, and Apache Arrow
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
SOL: Effortless Device Support for AI Frameworks Without Source Code Changes
SOL: Effortless Device Support for AI Frameworks without Source Code Changes Nicolas Weber and Felipe Huici NEC Laboratories Europe Abstract—Modern high performance computing clusters heav- State of the Art Proposed with SOL ily rely on accelerators to overcome the limited compute power API (Python, C/C++, …) API (Python, C/C++, …) of CPUs. These supercomputers run various applications from different domains such as simulations, numerical applications or Framework Core Framework Core artificial intelligence (AI). As a result, vendors need to be able to Device Backends SOL efficiently run a wide variety of workloads on their hardware. In the AI domain this is in particular exacerbated by the Fig. 1: Abstraction layers within AI frameworks. existance of a number of popular frameworks (e.g, PyTorch, TensorFlow, etc.) that have no common code base, and can vary lines of code to their scripts in order to enable SOL and its in functionality. The code of these frameworks evolves quickly, hardware support. making it expensive to keep up with all changes and potentially We explore two strategies to integrate new devices into AI forcing developers to go through constant rounds of upstreaming. frameworks using SOL as a middleware, to keep the original In this paper we explore how to provide hardware support in AI frameworks without changing the framework’s source code in AI framework unchanged and still add support to new device order to minimize maintenance overhead. We introduce SOL, an types. The first strategy hides the entire offloading procedure AI acceleration middleware that provides a hardware abstraction from the framework, and the second only injects the necessary layer that allows us to transparently support heterogenous hard- functionality into the framework to enable the execution, but ware. -
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. -
1 Amazon Sagemaker
1 Amazon SageMaker 13:45~14:30 Amazon SageMaker 14:30~15:15 Amazon SageMaker re:Invent 15:15~15:45 Q&A | 15:45~17:00 Amazon SageMaker 20 SmartNews Data Scientist, Meng Lee Sagemaker SageMaker - FiNC FiNC Technologies SIGNATE Amazon SageMaker SIGNATE CTO 17:00~17:15© 2018, Amazon Web Services, Inc. or itsQ&A Affiliates. All rights reserved. Amazon Confidential and Trademark Amazon SageMaker Makoto Shimura, Solutions Architect 2019/01/15 © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark • • • • ⎼ Amazon Athena ⎼ AWS Glue ⎼ Amazon SageMaker © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark • • Amazon SageMaker • Amazon SageMasker • SageMaker SDK • [ | | ] • Amazon SageMaker • © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark 開発 学習 推論推論 学習に使うコードを記述 大量の GPU 大量のCPU や GPU 小規模データで動作確認 大規模データの処理 継続的なデプロイ 試行錯誤の繰り返し 様々なデバイスで動作 © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark 開発 学習 推論推論 エンジニアがプロダク データサイエンティストが開発環境で作業 ション環境に構築 開発と学習を同じ 1 台のインスタンスで実施 API サーバにデプロイ Deep Learning であれば GPU インスタンスを使用 エッジデバイスで動作 © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark & • 開発 学習 推論推論 • エンジニアがプロダク データサイエンティストが開発環境で作業 • ション環境に構築 開発と学習を同じ 1 台のインスタンスで実施 API サーバにデプロイ • Deep Learning であれば GPU インスタンスを使用 エッジデバイスで動作 • API • • © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark Amazon SageMaker © 2018, Amazon Web Services, Inc. -
Intel® Optimized AI Frameworks
Intel® optimized AI frameworks Dr. Fabio Baruffa & Shailen Sobhee Technical Consulting Engineers, Intel IAGS Visit: www.intel.ai/technology Speed up development using open AI software Machine learning Deep learning TOOLKITS App Open source platform for building E2E Analytics & Deep learning inference deployment Open source, scalable, and developers AI applications on Apache Spark* with distributed on CPU/GPU/FPGA/VPU for Caffe*, extensible distributed deep learning TensorFlow*, Keras*, BigDL TensorFlow*, MXNet*, ONNX*, Kaldi* platform built on Kubernetes (BETA) Python R Distributed Intel-optimized Frameworks libraries * • Scikit- • Cart • MlLib (on Spark) * * And more framework Data learn • Random • Mahout optimizations underway • Pandas Forest including PaddlePaddle*, scientists * * • NumPy • e1071 Chainer*, CNTK* & others Intel® Intel® Data Analytics Intel® Math Kernel Library Kernels Distribution Acceleration Library Library for Deep Neural Networks for Python* (Intel® DAAL) (Intel® MKL-DNN) developers Intel distribution High performance machine Open source compiler for deep learning optimized for learning & data analytics Open source DNN functions for model computations optimized for multiple machine learning library CPU / integrated graphics devices (CPU, GPU, NNP) from multiple frameworks (TF, MXNet, ONNX) 2 Visit: www.intel.ai/technology Speed up development using open AI software Machine learning Deep learning TOOLKITS App Open source platform for building E2E Analytics & Deep learning inference deployment Open source, scalable, -
Mastering Machine Learning with Scikit-Learn
www.it-ebooks.info Mastering Machine Learning with scikit-learn Apply effective learning algorithms to real-world problems using scikit-learn Gavin Hackeling BIRMINGHAM - MUMBAI www.it-ebooks.info Mastering Machine Learning with scikit-learn Copyright © 2014 Packt Publishing All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews. Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book. Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information. First published: October 2014 Production reference: 1221014 Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK. ISBN 978-1-78398-836-5 www.packtpub.com Cover image by Amy-Lee Winfield [email protected]( ) www.it-ebooks.info Credits Author Project Coordinator Gavin Hackeling Danuta Jones Reviewers Proofreaders Fahad Arshad Simran Bhogal Sarah -
Python on Gpus (Work in Progress!)
Python on GPUs (work in progress!) Laurie Stephey GPUs for Science Day, July 3, 2019 Rollin Thomas, NERSC Lawrence Berkeley National Laboratory Python is friendly and popular Screenshots from: https://www.tiobe.com/tiobe-index/ So you want to run Python on a GPU? You have some Python code you like. Can you just run it on a GPU? import numpy as np from scipy import special import gpu ? Unfortunately no. What are your options? Right now, there is no “right” answer ● CuPy ● Numba ● pyCUDA (https://mathema.tician.de/software/pycuda/) ● pyOpenCL (https://mathema.tician.de/software/pyopencl/) ● Rewrite kernels in C, Fortran, CUDA... DESI: Our case study Now Perlmutter 2020 Goal: High quality output spectra Spectral Extraction CuPy (https://cupy.chainer.org/) ● Developed by Chainer, supported in RAPIDS ● Meant to be a drop-in replacement for NumPy ● Some, but not all, NumPy coverage import numpy as np import cupy as cp cpu_ans = np.abs(data) #same thing on gpu gpu_data = cp.asarray(data) gpu_temp = cp.abs(gpu_data) gpu_ans = cp.asnumpy(gpu_temp) Screenshot from: https://docs-cupy.chainer.org/en/stable/reference/comparison.html eigh in CuPy ● Important function for DESI ● Compared CuPy eigh on Cori Volta GPU to Cori Haswell and Cori KNL ● Tried “divide-and-conquer” approach on both CPU and GPU (1, 2, 5, 10 divisions) ● Volta wins only at very large matrix sizes ● Major pro: eigh really easy to use in CuPy! legval in CuPy ● Easy to convert from NumPy arrays to CuPy arrays ● This function is ~150x slower than the cpu version! ● This implies there -
Theano: a Python Framework for Fast Computation of Mathematical Expressions (The Theano Development Team)∗
Theano: A Python framework for fast computation of mathematical expressions (The Theano Development Team)∗ Rami Al-Rfou,6 Guillaume Alain,1 Amjad Almahairi,1 Christof Angermueller,7, 8 Dzmitry Bahdanau,1 Nicolas Ballas,1 Fred´ eric´ Bastien,1 Justin Bayer, Anatoly Belikov,9 Alexander Belopolsky,10 Yoshua Bengio,1, 3 Arnaud Bergeron,1 James Bergstra,1 Valentin Bisson,1 Josh Bleecher Snyder, Nicolas Bouchard,1 Nicolas Boulanger-Lewandowski,1 Xavier Bouthillier,1 Alexandre de Brebisson,´ 1 Olivier Breuleux,1 Pierre-Luc Carrier,1 Kyunghyun Cho,1, 11 Jan Chorowski,1, 12 Paul Christiano,13 Tim Cooijmans,1, 14 Marc-Alexandre Cotˆ e,´ 15 Myriam Cotˆ e,´ 1 Aaron Courville,1, 4 Yann N. Dauphin,1, 16 Olivier Delalleau,1 Julien Demouth,17 Guillaume Desjardins,1, 18 Sander Dieleman,19 Laurent Dinh,1 Melanie´ Ducoffe,1, 20 Vincent Dumoulin,1 Samira Ebrahimi Kahou,1, 2 Dumitru Erhan,1, 21 Ziye Fan,22 Orhan Firat,1, 23 Mathieu Germain,1 Xavier Glorot,1, 18 Ian Goodfellow,1, 24 Matt Graham,25 Caglar Gulcehre,1 Philippe Hamel,1 Iban Harlouchet,1 Jean-Philippe Heng,1, 26 Balazs´ Hidasi,27 Sina Honari,1 Arjun Jain,28 Sebastien´ Jean,1, 11 Kai Jia,29 Mikhail Korobov,30 Vivek Kulkarni,6 Alex Lamb,1 Pascal Lamblin,1 Eric Larsen,1, 31 Cesar´ Laurent,1 Sean Lee,17 Simon Lefrancois,1 Simon Lemieux,1 Nicholas Leonard,´ 1 Zhouhan Lin,1 Jesse A. Livezey,32 Cory Lorenz,33 Jeremiah Lowin, Qianli Ma,34 Pierre-Antoine Manzagol,1 Olivier Mastropietro,1 Robert T. McGibbon,35 Roland Memisevic,1, 4 Bart van Merrienboer,¨ 1 Vincent Michalski,1 Mehdi Mirza,1 Alberto Orlandi, Christopher Pal,1, 2 Razvan Pascanu,1, 18 Mohammad Pezeshki,1 Colin Raffel,36 Daniel Renshaw,25 Matthew Rocklin, Adriana Romero,1 Markus Roth, Peter Sadowski,37 John Salvatier,38 Franc¸ois Savard,1 Jan Schluter,¨ 39 John Schulman,24 Gabriel Schwartz,40 Iulian Vlad Serban,1 Dmitriy Serdyuk,1 Samira Shabanian,1 Etienne´ Simon,1, 41 Sigurd Spieckermann, S. -
Introduction Shrinkage Factor Reference
Comparison study for implementation efficiency of CUDA GPU parallel computation with the fast iterative shrinkage-thresholding algorithm Younsang Cho, Donghyeon Yu Department of Statistics, Inha university 4. TensorFlow functions in Python (TF-F) Introduction There are some functions executed on GPU in TensorFlow. So, we implemented our algorithm • Parallel computation using graphics processing units (GPUs) gets much attention and is just using that functions. efficient for single-instruction multiple-data (SIMD) processing. 5. Neural network with TensorFlow in Python (TF-NN) • Theoretical computation capacity of the GPU device has been growing fast and is much higher Neural network model is flexible, and the LASSO problem can be represented as a simple than that of the CPU nowadays (Figure 1). neural network with an ℓ1-regularized loss function • There are several platforms for conducting parallel computation on GPUs using compute 6. Using dynamic link library in Python (P-DLL) unified device architecture (CUDA) developed by NVIDIA. (Python, PyCUDA, Tensorflow, etc. ) As mentioned before, we can load DLL files, which are written in CUDA C, using "ctypes.CDLL" • However, it is unclear what platform is the most efficient for CUDA. that is a built-in function in Python. 7. Using dynamic link library in R (R-DLL) We can also load DLL files, which are written in CUDA C, using "dyn.load" in R. FISTA (Fast Iterative Shrinkage-Thresholding Algorithm) We consider FISTA (Beck and Teboulle, 2009) with backtracking as the following: " Step 0. Take �! > 0, some � > 1, and �! ∈ ℝ . Set �# = �!, �# = 1. %! Step k. � ≥ 1 Find the smallest nonnegative integers �$ such that with �g = � �$&# � �(' �$ ≤ �(' �(' �$ , �$ . -
Installing Python Ø Basics of Programming • Data Types • Functions • List Ø Numpy Library Ø Pandas Library What Is Python?
Python in Data Science Programming Ha Khanh Nguyen Agenda Ø What is Python? Ø The Python Ecosystem • Installing Python Ø Basics of Programming • Data Types • Functions • List Ø NumPy Library Ø Pandas Library What is Python? • “Python is an interpreted high-level general-purpose programming language.” – Wikipedia • It supports multiple programming paradigms, including: • Structured/procedural • Object-oriented • Functional The Python Ecosystem Source: Fabien Maussion’s “Getting started with Python” Workshop Installing Python • Install Python through Anaconda/Miniconda. • This allows you to create and work in Python environments. • You can create multiple environments as needed. • Highly recommended: install Python through Miniconda. • Are we ready to “play” with Python yet? • Almost! • Most data scientists use Python through Jupyter Notebook, a web application that allows you to create a virtual notebook containing both text and code! • Python Installation tutorial: [Mac OS X] [Windows] For today’s seminar • Due to the time limitation, we will be using Google Colab instead. • Google Colab is a free Jupyter notebook environment that runs entirely in the cloud, so you can run Python code, write report in Jupyter Notebook even without installing the Python ecosystem. • It is NOT a complete alternative to installing Python on your local computer. • But it is a quick and easy way to get started/try out Python. • You will need to log in using your university account (possible for some schools) or your personal Google account. Basics of Programming Indentations – Not Braces • Other languages (R, C++, Java, etc.) use braces to structure code. # R code (not Python) a = 10 if (a < 5) { result = TRUE b = 0 } else { result = FALSE b = 100 } a = 10 if (a < 5) { result = TRUE; b = 0} else {result = FALSE; b = 100} Basics of Programming Indentations – Not Braces • Python uses whitespaces (tabs or spaces) to structure code instead. -
Quick Install for AWS EMR
Quick Install for AWS EMR Version: 6.8 Doc Build Date: 01/21/2020 Copyright © Trifacta Inc. 2020 - All Rights Reserved. CONFIDENTIAL These materials (the “Documentation”) are the confidential and proprietary information of Trifacta Inc. and may not be reproduced, modified, or distributed without the prior written permission of Trifacta Inc. EXCEPT AS OTHERWISE PROVIDED IN AN EXPRESS WRITTEN AGREEMENT, TRIFACTA INC. PROVIDES THIS DOCUMENTATION AS-IS AND WITHOUT WARRANTY AND TRIFACTA INC. DISCLAIMS ALL EXPRESS AND IMPLIED WARRANTIES TO THE EXTENT PERMITTED, INCLUDING WITHOUT LIMITATION THE IMPLIED WARRANTIES OF MERCHANTABILITY, NON-INFRINGEMENT AND FITNESS FOR A PARTICULAR PURPOSE AND UNDER NO CIRCUMSTANCES WILL TRIFACTA INC. BE LIABLE FOR ANY AMOUNT GREATER THAN ONE HUNDRED DOLLARS ($100) BASED ON ANY USE OF THE DOCUMENTATION. For third-party license information, please select About Trifacta from the Help menu. 1. Release Notes . 4 1.1 Changes to System Behavior . 4 1.1.1 Changes to the Language . 4 1.1.2 Changes to the APIs . 18 1.1.3 Changes to Configuration 23 1.1.4 Changes to the Object Model . 26 1.2 Release Notes 6.8 . 30 1.3 Release Notes 6.4 . 36 1.4 Release Notes 6.0 . 42 1.5 Release Notes 5.1 . 49 2. Quick Start 55 2.1 Install from AWS Marketplace with EMR . 55 2.2 Upgrade for AWS Marketplace with EMR . 62 3. Configure 62 3.1 Configure for AWS . 62 3.1.1 Configure for EC2 Role-Based Authentication . 68 3.1.2 Enable S3 Access . 70 3.1.2.1 Create Redshift Connections 81 3.1.3 Configure for EMR . -
Prototyping and Developing GPU-Accelerated Solutions with Python and CUDA Luciano Martins and Robert Sohigian, 2018-11-22 Introduction to Python
Prototyping and Developing GPU-Accelerated Solutions with Python and CUDA Luciano Martins and Robert Sohigian, 2018-11-22 Introduction to Python GPU-Accelerated Computing NVIDIA® CUDA® technology Why Use Python with GPUs? Agenda Methods: PyCUDA, Numba, CuPy, and scikit-cuda Summary Q&A 2 Introduction to Python Released by Guido van Rossum in 1991 The Zen of Python: Beautiful is better than ugly. Explicit is better than implicit. Simple is better than complex. Complex is better than complicated. Flat is better than nested. Interpreted language (CPython, Jython, ...) Dynamically typed; based on objects 3 Introduction to Python Small core structure: ~30 keywords ~ 80 built-in functions Indentation is a pretty serious thing Dynamically typed; based on objects Binds to many different languages Supports GPU acceleration via modules 4 Introduction to Python 5 Introduction to Python 6 Introduction to Python 7 GPU-Accelerated Computing “[T]the use of a graphics processing unit (GPU) together with a CPU to accelerate deep learning, analytics, and engineering applications” (NVIDIA) Most common GPU-accelerated operations: Large vector/matrix operations (Basic Linear Algebra Subprograms - BLAS) Speech recognition Computer vision 8 GPU-Accelerated Computing Important concepts for GPU-accelerated computing: Host ― the machine running the workload (CPU) Device ― the GPUs inside of a host Kernel ― the code part that runs on the GPU SIMT ― Single Instruction Multiple Threads 9 GPU-Accelerated Computing 10 GPU-Accelerated Computing 11 CUDA Parallel computing -
Delft University of Technology Arrowsam In-Memory Genomics
Delft University of Technology ArrowSAM In-Memory Genomics Data Processing Using Apache Arrow Ahmad, Tanveer; Ahmed, Nauman; Peltenburg, Johan; Al-Ars, Zaid DOI 10.1109/ICCAIS48893.2020.9096725 Publication date 2020 Document Version Accepted author manuscript Published in 2020 3rd International Conference on Computer Applications & Information Security (ICCAIS) Citation (APA) Ahmad, T., Ahmed, N., Peltenburg, J., & Al-Ars, Z. (2020). ArrowSAM: In-Memory Genomics Data Processing Using Apache Arrow. In 2020 3rd International Conference on Computer Applications & Information Security (ICCAIS): Proceedings (pp. 1-6). [9096725] IEEE . https://doi.org/10.1109/ICCAIS48893.2020.9096725 Important note To cite this publication, please use the final published version (if applicable). Please check the document version above. Copyright Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons. Takedown policy Please contact us and provide details if you believe this document breaches copyrights. We will remove access to the work immediately and investigate your claim. This work is downloaded from Delft University of Technology. For technical reasons the number of authors shown on this cover page is limited to a maximum of 10. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.