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What If What I Need Is Not in Powerai (Yet)? What You Need to Know to Build from Scratch?
IBM Systems What if what I need is not in PowerAI (yet)? What you need to know to build from scratch? Jean-Armand Broyelle June 2018 IBM Systems – Cognitive Era Things to consider when you have to rebuild a framework © 2017 International Business Machines Corporation 2 IBM Systems – Cognitive Era CUDA Downloads © 2017 International Business Machines Corporation 3 IBM Systems – Cognitive Era CUDA 8 – under Legacy Releases © 2017 International Business Machines Corporation 4 IBM Systems – Cognitive Era CUDA 8 Install Steps © 2017 International Business Machines Corporation 5 IBM Systems – Cognitive Era cuDNN and NVIDIA drivers © 2017 International Business Machines Corporation 6 IBM Systems – Cognitive Era cuDNN v6.0 for CUDA 8.0 © 2017 International Business Machines Corporation 7 IBM Systems – Cognitive Era cuDNN and NVIDIA drivers © 2017 International Business Machines Corporation 8 IBM Systems – Cognitive Era © 2017 International Business Machines Corporation 9 IBM Systems – Cognitive Era © 2017 International Business Machines Corporation 10 IBM Systems – Cognitive Era cuDNN and NVIDIA drivers © 2017 International Business Machines Corporation 11 IBM Systems – Cognitive Era Prepare your environment • When something goes wrong it’s better to Remove local anaconda installation $ cd ~; rm –rf anaconda2 .conda • Reinstall anaconda $ cd /tmp; wget https://repo.anaconda.com/archive/Anaconda2-5.1.0-Linux- ppc64le.sh $ bash /tmp/Anaconda2-5.1.0-Linux-ppc64le.sh • Activate PowerAI $ source /opt/DL/tensorflow/bin/tensorflow-activate • When you -
Open Source Copyrights
Kuri App - Open Source Copyrights: 001_talker_listener-master_2015-03-02 ===================================== Source Code can be found at: https://github.com/awesomebytes/python_profiling_tutorial_with_ros 001_talker_listener-master_2016-03-22 ===================================== Source Code can be found at: https://github.com/ashfaqfarooqui/ROSTutorials acl_2.2.52-1_amd64.deb ====================== Licensed under GPL 2.0 License terms can be found at: http://savannah.nongnu.org/projects/acl/ acl_2.2.52-1_i386.deb ===================== Licensed under LGPL 2.1 License terms can be found at: http://metadata.ftp- master.debian.org/changelogs/main/a/acl/acl_2.2.51-8_copyright actionlib-1.11.2 ================ Licensed under BSD Source Code can be found at: https://github.com/ros/actionlib License terms can be found at: http://wiki.ros.org/actionlib actionlib-common-1.5.4 ====================== Licensed under BSD Source Code can be found at: https://github.com/ros-windows/actionlib License terms can be found at: http://wiki.ros.org/actionlib adduser_3.113+nmu3ubuntu3_all.deb ================================= Licensed under GPL 2.0 License terms can be found at: http://mirrors.kernel.org/ubuntu/pool/main/a/adduser/adduser_3.113+nmu3ubuntu3_all. deb alsa-base_1.0.25+dfsg-0ubuntu4_all.deb ====================================== Licensed under GPL 2.0 License terms can be found at: http://mirrors.kernel.org/ubuntu/pool/main/a/alsa- driver/alsa-base_1.0.25+dfsg-0ubuntu4_all.deb alsa-utils_1.0.27.2-1ubuntu2_amd64.deb ====================================== -
Advanced Model Deployments with Tensorflow Serving Presentation.Pdf
Most models don’t get deployed. Hi, I’m Hannes. An inefficient model deployment import json from flask import Flask from keras.models import load_model from utils import preprocess model = load_model('model.h5') app = Flask(__name__) @app.route('/classify', methods=['POST']) def classify(): review = request.form["review"] preprocessed_review = preprocess(review) prediction = model.predict_classes([preprocessed_review])[0] return json.dumps({"score": int(prediction)}) Simple Deployments @app.route('/classify', methods=['POST']) Why Flask is insufficient def classify(): review = request.form["review"] ● No consistent APIs ● No consistent payloads preprocessed_review = preprocess(review) ● No model versioning prediction = model.predict_classes( ● No mini-batching support [preprocessed_review])[0] ● Inefficient for large models return json.dumps({"score": int(prediction)}) Image: Martijn Baudoin, Unsplash TensorFlow Serving TensorFlow Serving Production ready Model Serving ● Part of the TensorFlow Extended Ecosystem ● Used internally at Google ● Highly scalable model serving solution ● Works well for large models up to 2GB TensorFlow 2.0 ready! * * With small exceptions Deploy your models in 90s ... Export your Model import tensorflow as tf TensorFlow 2.0 Export tf.saved_model.save( ● Consistent model export model, ● Using Protobuf format export_dir="/tmp/saved_model", ● Export of graphs and signatures=None estimators possible ) $ tree saved_models/ Export your Model saved_models/ └── 1555875926 ● Exported model as Protobuf ├── assets (Saved_model.pb) -
Mochi-JCST-01-20.Pdf
Ross R, Amvrosiadis G, Carns P et al. Mochi: Composing data services for high-performance computing environments. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 35(1): 121–144 Jan. 2020. DOI 10.1007/s11390-020-9802-0 Mochi: Composing Data Services for High-Performance Computing Environments Robert B. Ross1, George Amvrosiadis2, Philip Carns1, Charles D. Cranor2, Matthieu Dorier1, Kevin Harms1 Greg Ganger2, Garth Gibson3, Samuel K. Gutierrez4, Robert Latham1, Bob Robey4, Dana Robinson5 Bradley Settlemyer4, Galen Shipman4, Shane Snyder1, Jerome Soumagne5, and Qing Zheng2 1Argonne National Laboratory, Lemont, IL 60439, U.S.A. 2Parallel Data Laboratory, Carnegie Mellon University, Pittsburgh, PA 15213, U.S.A. 3Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada 4Los Alamos National Laboratory, Los Alamos NM, U.S.A. 5The HDF Group, Champaign IL, U.S.A. E-mail: [email protected]; [email protected]; [email protected]; [email protected]; [email protected] E-mail: [email protected]; [email protected]; [email protected]; [email protected] E-mail: [email protected]; [email protected]; [email protected]; {bws, gshipman}@lanl.gov E-mail: [email protected]; [email protected]; [email protected] Received July 1, 2019; revised November 2, 2019. Abstract Technology enhancements and the growing breadth of application workflows running on high-performance computing (HPC) platforms drive the development of new data services that provide high performance on these new platforms, provide capable and productive interfaces and abstractions for a variety of applications, and are readily adapted when new technologies are deployed. The Mochi framework enables composition of specialized distributed data services from a collection of connectable modules and subservices. -
Selenium Python Bindings Release 2
Selenium Python Bindings Release 2 Baiju Muthukadan Sep 03, 2021 Contents 1 Installation 3 1.1 Introduction...............................................3 1.2 Installing Python bindings for Selenium.................................3 1.3 Instructions for Windows users.....................................3 1.4 Installing from Git sources........................................4 1.5 Drivers..................................................4 1.6 Downloading Selenium server......................................4 2 Getting Started 7 2.1 Simple Usage...............................................7 2.2 Example Explained............................................7 2.3 Using Selenium to write tests......................................8 2.4 Walkthrough of the example.......................................9 2.5 Using Selenium with remote WebDriver................................. 10 3 Navigating 13 3.1 Interacting with the page......................................... 13 3.2 Filling in forms.............................................. 14 3.3 Drag and drop.............................................. 15 3.4 Moving between windows and frames.................................. 15 3.5 Popup dialogs.............................................. 16 3.6 Navigation: history and location..................................... 16 3.7 Cookies.................................................. 16 4 Locating Elements 17 4.1 Locating by Id.............................................. 18 4.2 Locating by Name............................................ 18 4.3 -
An Evaluation of Tensorflow As a Programming Framework for HPC Applications
DEGREE PROJECT IN COMPUTER SCIENCE AND ENGINEERING, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2018 An Evaluation of TensorFlow as a Programming Framework for HPC Applications WEI DER CHIEN KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE An Evaluation of TensorFlow as a Programming Framework for HPC Applications WEI DER CHIEN Master in Computer Science Date: August 28, 2018 Supervisor: Stefano Markidis Examiner: Erwin Laure Swedish title: En undersökning av TensorFlow som ett utvecklingsramverk för högpresterande datorsystem School of Electrical Engineering and Computer Science iii Abstract In recent years, deep-learning, a branch of machine learning gained increasing popularity due to their extensive applications and perfor- mance. At the core of these application is dense matrix-matrix multipli- cation. Graphics Processing Units (GPUs) are commonly used in the training process due to their massively parallel computation capabili- ties. In addition, specialized low-precision accelerators have emerged to specifically address Tensor operations. Software frameworks, such as TensorFlow have also emerged to increase the expressiveness of neural network model development. In TensorFlow computation problems are expressed as Computation Graphs where nodes of a graph denote operation and edges denote data movement between operations. With increasing number of heterogeneous accelerators which might co-exist on the same cluster system, it became increasingly difficult for users to program efficient and scalable applications. TensorFlow provides a high level of abstraction and it is possible to place operations of a computation graph on a device easily through a high level API. In this work, the usability of TensorFlow as a programming framework for HPC application is reviewed. -
N $NUM GPUS Python Train.Py
Neural Network concurrency Tal Ben-Nun and Torsten Hoefler, Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis, 2018, Data Parallelism vs Model Parallelism Hardware and Libraries ● It is not only a matter of computational power: ○ CPU (MKL-DNN) ○ GPU (cuDNN) ○ FGPA ○ TPU ● Input/Output matter ○ SSD ○ Parallel file system (if you run parallel algorithm) ● Communication and interconnection too, if you are running in distributed mode ○ MPI ○ gRPC +verbs (RDMA) ○ NCCL Install TensorFlow from Source [~]$ wget https://github.com/.../bazel-0.15.2-installer-linux-x86_64.sh [~]$ ./bazel-0.15.2-installer-linux-x86_64.sh --prefix=... [~]$ wget https://github.com/tensorflow/tensorflow/archive/v1.10.0.tar.gz ... [~]$ python3 -m venv $TF_INSTALL_DIR [~]$ source $TF_INSTALL_DIR/bin/activate [~]$ pip3 install numpy wheel [~]$ ./configure ... [~]$ bazel build --config=mkl/cuda \ //tensorflow/tools/pip_package:build_pip_package [~]$ bazel-bin/tensorflow/tools/pip_package/build_pip_package $WHEELREPO [~]$ pip3 install $WHEELREPO/$WHL --ignore-installed [~]$ pip3 install keras horovod ... Input pipeline If using accelerators like GPU, pipeline tha data load exploiting the CPU with the computation on GPU The tf.data API helps to build flexible and efficient input pipelines Optimizing for CPU ● Built from source with all of the instructions supported by the target CPU and the MKL-DNN option for Intel® CPU. ● Adjust thread pools ○ intra_op_parallelism_threads: Nodes that can use multiple threads to parallelize their execution -
I Have Often Mused About How the Architecture of the Cpus We Use Influ
Musings RIK FARROWEDITORIAL Rik is the editor of ;login:. have often mused about how the architecture of the CPUs we use influ- [email protected] ences the way our operating systems and applications are designed. A I book I reviewed in this issue on the history of computing managed to cement those ideas in my head. Basically, we’ve been reprising time-sharing systems since the mid-60s, whereas most systems serve very different pur- poses today. Along the way, I also encountered a couple of interesting data points, via pointers from friends who have been influencing me. One was Halvar Flake’s CYCON 2018 talk [1], and another was about a new OS project at Google. The opinion piece by Jan Mühlberg and Jo Van Bulck that appears in this issue influenced me as well, as it describes a CPU feature that, among other things, could block the use of gadgets in return-oriented programming (ROP). Flake explained that much of our current problems with security have to do with cheap com- plexity. Even though a device, like a microwave oven or intravenous drip-rate controller, only requires a PIC (Programmable Interrupt Controller), it may instead have a full-blown CPU running Windows or Linux inside it. CPUs are, by design, flexible enough to model any set of states, making them much more complex than what is needed inside a fairly simple device. Designers instead choose to use a full-blown CPU, usually with an OS not designed to be embedded, to model the states required. Vendors do this because many more people under- stand Windows or Linux programming than know how to program a PIC. -
Enforcing Abstract Immutability
Enforcing Abstract Immutability by Jonathan Eyolfson A thesis presented to the University of Waterloo in fulfillment of the thesis requirement for the degree of Doctor of Philosophy in Electrical and Computer Engineering Waterloo, Ontario, Canada, 2018 © Jonathan Eyolfson 2018 Examining Committee Membership The following served on the Examining Committee for this thesis. The decision of the Examining Committee is by majority vote. External Examiner Ana Milanova Associate Professor Rensselaer Polytechnic Institute Supervisor Patrick Lam Associate Professor University of Waterloo Internal Member Lin Tan Associate Professor University of Waterloo Internal Member Werner Dietl Assistant Professor University of Waterloo Internal-external Member Gregor Richards Assistant Professor University of Waterloo ii I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. iii Abstract Researchers have recently proposed a number of systems for expressing, verifying, and inferring immutability declarations. These systems are often rigid, and do not support “abstract immutability”. An abstractly immutable object is an object o which is immutable from the point of view of any external methods. The C++ programming language is not rigid—it allows developers to express intent by adding immutability declarations to methods. Abstract immutability allows for performance improvements such as caching, even in the presence of writes to object fields. This dissertation presents a system to enforce abstract immutability. First, we explore abstract immutability in real-world systems. We found that developers often incorrectly use abstract immutability, perhaps because no programming language helps developers correctly implement abstract immutability. -
Blau Mavi Blue
4 / 2014 Quartierzeitung für das Untere Kleinbasel Mahalle Gazetesi Aşağ Küçükbasel için www.mozaikzeitung.ch Novine za cˇetvrt donji Mali Bazel Blau Mavi Blue Bilder, Stimmungen, Töned i t Resimler, Farkli sessler, d i Tonlart visions, moods, sounds e ˇ ivotu. Bazelu pricˇa21 o svom Z Foto: Jum Soon Kim Spezial:Jedna Familija u malom HOLZKOMPETENZ BACK INT NACH MASS BAL NCNNCECE Ihre Wunschvorstellung. Unser ALEXANDER-TECHNIK Handwerk. Resultat: Möbel Christina Stahlberger und Holzkonstruktionen, die dipl. Lehrerin für Alexander-Technik SVLAT M_000278 Matthäusstrasse 7, 4057 Basel Sie ein Leben lang begleiten. +41 (0)77 411 99 89 Unsere Spezialgebiete sind [email protected] I www.back-into-balance.com Haus- und Zimmertüren, Schränke, Küchen und Bade- Ed. Borer AG · Schreinerei · Wiesenstrasse 10 · 4057 Basel zimmermöbel sowie Repara- T 061 631 11 15 · F 061 631 11 26 · [email protected] turen und Restaurationen. M_000236 M_000196 Stadtteilsekretariat Kleinbasel M_000028 Darf ich hier Für Fragen, Anliegen und Probleme betreffend: • Wohnlichkeit und Zusammenleben grillieren? • Mitwirkung der Quartierbevölkerung Öffnungszeiten: Mo, Di und Do, 15 – 18.30 h Klybeckstrasse 61, 4057 Basel Tel: 061 681 84 44, Email: [email protected] www.stadtteilsekretariatebasel.ch M_000024 Wir danken unserer Kundschaft Offenburgerstrasse 41, CH-4057 Basel 061 5 54 23 33 Täglich bis 22.00 Uhr geöffnet! Täglich frische Produkte und Bio-Produkte! 365 Tage im Jahr, auch zwischen Weihnacht und Neujahr offen! Lebensmittel- und Getränkemarkt Wir bieten stets beste Qualität und freuen uns auf Ihren Besuch. Gratis-Lieferdienst für ältere Menschen M_000280 Öffnungszeiten: Montag 11.00–22.00 Uhr Dienstag–Sonntag und Feiertage: 8.00–22.00 Uhr Feldbergstrasse 32, 4057 Basel, Telefon 061 693 00 55 M_000006 M_000049 LACHENMEIER.CH SCHREINEREI konstruiert. -
Installing and Running Tensorflow
Installing and Running Tensorflow DOWNLOAD AND INSTALLATION INSTRUCTIONS TensorFlow is now distributed under an Apache v2 open source license on GitHub. STEP 1. INSTALL NVIDIA CUDA To use TensorFlow with NVIDIA GPUs, the first step is to install the CUDA Toolkit. STEP 2. INSTALL NVIDIA CUDNN Once the CUDA Toolkit is installed, download cuDNN v5.1 Library for Linux (note that you will need to register for the Accelerated Computing Developer Program). Once downloaded, uncompress the files and copy them into the CUDA Toolkit directory (assumed here to be in /usr/local/cuda/): $ sudo tar -xvf cudnn-8.0-linux-x64-v5.1-rc.tgz -C /usr/local STEP 3. INSTALL AND UPGRADE PIP TensorFlow itself can be installed using the pip package manager. First, make sure that your system has pip installed and updated: $ sudo apt-get install python-pip python-dev $ pip install --upgrade pip STEP 4. INSTALL BAZEL To build TensorFlow from source, the Bazel build system must first be installed as follows. Full details are available here. $ sudo apt-get install software-properties-common swig $ sudo add-apt-repository ppa:webupd8team/java $ sudo apt-get update $ sudo apt-get install oracle-java8-installer $ echo "deb http://storage.googleapis.com/bazel-apt stable jdk1.8" | sudo tee /etc/apt/sources.list.d/bazel.list $ curl https://storage.googleapis.com/bazel-apt/doc/apt-key.pub.gpg | sudo apt-key add - $ sudo apt-get update $ sudo apt-get install bazel STEP 5. INSTALL TENSORFLOW To obtain the best performance with TensorFlow we recommend building it from source. First, clone the TensorFlow source code repository: $ git clone https://github.com/tensorflow/tensorflow $ cd tensorflow $ git reset --hard 70de76e Then run the configure script as follows: $ ./configure Please specify the location of python. -
From XML to Flat Buffers: Markup in the Twenty-Teens Warning! the Contenders
Elliotte Rusty Harold [email protected] August 2018 From XML to Flat Buffers: Markup in the Twenty-teens Warning! The Contenders ● XML ● JSON ● YAML ● EXI ● Protobufs ● Flat Protobufs XML JSON YAML EXI Protobuf Flat Buffers App Engine X X Standard Java App Engine X Flex What Uses What Kubernetes X X From technology, tools, and systems Eclipse X I use frequently. There are many others. Maven X Ant X Google X X X X X “APIs” Publishing X XML XML ● Very well defined standard ● By far the most general format: ○ Mixed content ○ Attributes and elements ● By far the best tool support. Nothing else is close: ○ XSLT ○ XPath ○ Many schema languages: ■ W3C XSD ■ RELAX NG More Reasons to Choose XML ● Most composable for mixing and matching markup; e.g. MathML+SVG in HTML ● Does not require a schema. ● Streaming support: very large documents ● Better for interchange amongst unrelated parties ● The deeper your needs the more likely you’ll end up here. Why Not XML? ● Relatively complex for simple tasks ● Limited to no support for non-string programming types: ○ Numbers, booleans, dates, money, etc. ○ Lists, maps, sets ○ You can encode all these but APIs don’t necessarily recognize or support them. ● Lots of sharp edges to surprise the non-expert: ○ 9/10 are namespace related ○ Attribute value normalization ○ White space ● Some security issues if you’re not careful (Billion laughs) JSON ● Simple for object serialization and program data. If your data is a few basic types (int, string, boolean, float) and data structures (list, map) this works well. ● More or less standard (7-8 of them in fact) ● Consumption libraries for essentially all significant languages Why Not JSON? ● It is surprising how fast needs grow past a few basic types and data structures.