Automatic Full Compilation of Julia Programs and ML Models to Cloud
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Intro to Tensorflow 2.0 MBL, August 2019
Intro to TensorFlow 2.0 MBL, August 2019 Josh Gordon (@random_forests) 1 Agenda 1 of 2 Exercises ● Fashion MNIST with dense layers ● CIFAR-10 with convolutional layers Concepts (as many as we can intro in this short time) ● Gradient descent, dense layers, loss, softmax, convolution Games ● QuickDraw Agenda 2 of 2 Walkthroughs and new tutorials ● Deep Dream and Style Transfer ● Time series forecasting Games ● Sketch RNN Learning more ● Book recommendations Deep Learning is representation learning Image link Image link Latest tutorials and guides tensorflow.org/beta News and updates medium.com/tensorflow twitter.com/tensorflow Demo PoseNet and BodyPix bit.ly/pose-net bit.ly/body-pix TensorFlow for JavaScript, Swift, Android, and iOS tensorflow.org/js tensorflow.org/swift tensorflow.org/lite Minimal MNIST in TF 2.0 A linear model, neural network, and deep neural network - then a short exercise. bit.ly/mnist-seq ... ... ... Softmax model = Sequential() model.add(Dense(256, activation='relu',input_shape=(784,))) model.add(Dense(128, activation='relu')) model.add(Dense(10, activation='softmax')) Linear model Neural network Deep neural network ... ... Softmax activation After training, select all the weights connected to this output. model.layers[0].get_weights() # Your code here # Select the weights for a single output # ... img = weights.reshape(28,28) plt.imshow(img, cmap = plt.get_cmap('seismic')) ... ... Softmax activation After training, select all the weights connected to this output. Exercise 1 (option #1) Exercise: bit.ly/mnist-seq Reference: tensorflow.org/beta/tutorials/keras/basic_classification TODO: Add a validation set. Add code to plot loss vs epochs (next slide). Exercise 1 (option #2) bit.ly/ijcav_adv Answers: next slide. -
Keras2c: a Library for Converting Keras Neural Networks to Real-Time Compatible C
Keras2c: A library for converting Keras neural networks to real-time compatible C Rory Conlina,∗, Keith Ericksonb, Joeseph Abbatec, Egemen Kolemena,b,∗ aDepartment of Mechanical and Aerospace Engineering, Princeton University, Princeton NJ 08544, USA bPrinceton Plasma Physics Laboratory, Princeton NJ 08544, USA cDepartment of Astrophysical Sciences at Princeton University, Princeton NJ 08544, USA Abstract With the growth of machine learning models and neural networks in mea- surement and control systems comes the need to deploy these models in a way that is compatible with existing systems. Existing options for deploying neural networks either introduce very high latency, require expensive and time con- suming work to integrate into existing code bases, or only support a very lim- ited subset of model types. We have therefore developed a new method called Keras2c, which is a simple library for converting Keras/TensorFlow neural net- work models into real-time compatible C code. It supports a wide range of Keras layers and model types including multidimensional convolutions, recurrent lay- ers, multi-input/output models, and shared layers. Keras2c re-implements the core components of Keras/TensorFlow required for predictive forward passes through neural networks in pure C, relying only on standard library functions considered safe for real-time use. The core functionality consists of ∼ 1500 lines of code, making it lightweight and easy to integrate into existing codebases. Keras2c has been successfully tested in experiments and is currently in use on the plasma control system at the DIII-D National Fusion Facility at General Atomics in San Diego. 1. Motivation TensorFlow[1] is one of the most popular libraries for developing and training neural networks. -
Fault Tolerance and Re-Training Analysis on Neural Networks
FAULT TOLERANCE AND RE-TRAINING ANALYSIS ON NEURAL NETWORKS by ABHINAV KURIAN GEORGE B.Tech Electronics and Communication Engineering Amrita Vishwa Vidhyapeetham, Kerala, 2012 A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science, Computer Engineering, College of Engineering and Applied Science, University of Cincinnati, Ohio 2019 Thesis Committee: Chair: Wen-Ben Jone, Ph.D. Member: Carla Purdy, Ph.D. Member: Ranganadha Vemuri, Ph.D. ABSTRACT In the current age of big data, artificial intelligence and machine learning technologies have gained much popularity. Due to the increasing demand for such applications, neural networks are being targeted toward hardware solutions. Owing to the shrinking feature size, number of physical defects are on the rise. These growing number of defects are preventing designers from realizing the full potential of the on-chip design. The challenge now is not only to find solutions that balance high-performance and energy-efficiency but also, to achieve fault-tolerance of a computational model. Neural computing, due to its inherent fault tolerant capabilities, can provide promising solutions to this issue. The primary focus of this thesis is to gain deeper understanding of fault tolerance in neural network hardware. As a part of this work, we present a comprehensive analysis of fault tolerance by exploring effects of faults on popular neural models: multi-layer perceptron model and convolution neural network. We built the models based on conventional 64-bit floating point representation. In addition to this, we also explore the recent 8-bit integer quantized representation. A fault injector model is designed to inject stuck-at faults at random locations in the network. -
Tensorflow, Theano, Keras, Torch, Caffe Vicky Kalogeiton, Stéphane Lathuilière, Pauline Luc, Thomas Lucas, Konstantin Shmelkov Introduction
TensorFlow, Theano, Keras, Torch, Caffe Vicky Kalogeiton, Stéphane Lathuilière, Pauline Luc, Thomas Lucas, Konstantin Shmelkov Introduction TensorFlow Google Brain, 2015 (rewritten DistBelief) Theano University of Montréal, 2009 Keras François Chollet, 2015 (now at Google) Torch Facebook AI Research, Twitter, Google DeepMind Caffe Berkeley Vision and Learning Center (BVLC), 2013 Outline 1. Introduction of each framework a. TensorFlow b. Theano c. Keras d. Torch e. Caffe 2. Further comparison a. Code + models b. Community and documentation c. Performance d. Model deployment e. Extra features 3. Which framework to choose when ..? Introduction of each framework TensorFlow architecture 1) Low-level core (C++/CUDA) 2) Simple Python API to define the computational graph 3) High-level API (TF-Learn, TF-Slim, soon Keras…) TensorFlow computational graph - auto-differentiation! - easy multi-GPU/multi-node - native C++ multithreading - device-efficient implementation for most ops - whole pipeline in the graph: data loading, preprocessing, prefetching... TensorBoard TensorFlow development + bleeding edge (GitHub yay!) + division in core and contrib => very quick merging of new hotness + a lot of new related API: CRF, BayesFlow, SparseTensor, audio IO, CTC, seq2seq + so it can easily handle images, videos, audio, text... + if you really need a new native op, you can load a dynamic lib - sometimes contrib stuff disappears or moves - recently introduced bells and whistles are barely documented Presentation of Theano: - Maintained by Montréal University group. - Pioneered the use of a computational graph. - General machine learning tool -> Use of Lasagne and Keras. - Very popular in the research community, but not elsewhere. Falling behind. What is it like to start using Theano? - Read tutorials until you no longer can, then keep going. -
In-Datacenter Performance Analysis of a Tensor Processing Unit
In-Datacenter Performance Analysis of a Tensor Processing Unit Presented by Josh Fried Background: Machine Learning Neural Networks: ● Multi Layer Perceptrons ● Recurrent Neural Networks (mostly LSTMs) ● Convolutional Neural Networks Synapse - each edge, has a weight Neuron - each node, sums weights and uses non-linear activation function over sum Propagating inputs through a layer of the NN is a matrix multiplication followed by an activation Background: Machine Learning Two phases: ● Training (offline) ○ relaxed deadlines ○ large batches to amortize costs of loading weights from DRAM ○ well suited to GPUs ○ Usually uses floating points ● Inference (online) ○ strict deadlines: 7-10ms at Google for some workloads ■ limited possibility for batching because of deadlines ○ Facebook uses CPUs for inference (last class) ○ Can use lower precision integers (faster/smaller/more efficient) ML Workloads @ Google 90% of ML workload time at Google spent on MLPs and LSTMs, despite broader focus on CNNs RankBrain (search) Inception (image classification), Google Translate AlphaGo (and others) Background: Hardware Trends End of Moore’s Law & Dennard Scaling ● Moore - transistor density is doubling every two years ● Dennard - power stays proportional to chip area as transistors shrink Machine Learning causing a huge growth in demand for compute ● 2006: Excess CPU capacity in datacenters is enough ● 2013: Projected 3 minutes per-day per-user of speech recognition ○ will require doubling datacenter compute capacity! Google’s Answer: Custom ASIC Goal: Build a chip that improves cost-performance for NN inference What are the main costs? Capital Costs Operational Costs (power bill!) TPU (V1) Design Goals Short design-deployment cycle: ~15 months! Plugs in to PCIe slot on existing servers Accelerates matrix multiplication operations Uses 8-bit integer operations instead of floating point How does the TPU work? CISC instructions, issued by host. -
Abstractions for Programming Graphics Processors in High-Level Programming Languages
Abstracties voor het programmeren van grafische processoren in hoogniveau-programmeertalen Abstractions for Programming Graphics Processors in High-Level Programming Languages Tim Besard Promotor: prof. dr. ir. B. De Sutter Proefschrift ingediend tot het behalen van de graad van Doctor in de ingenieurswetenschappen: computerwetenschappen Vakgroep Elektronica en Informatiesystemen Voorzitter: prof. dr. ir. K. De Bosschere Faculteit Ingenieurswetenschappen en Architectuur Academiejaar 2018 - 2019 ISBN 978-94-6355-244-8 NUR 980 Wettelijk depot: D/2019/10.500/52 Examination Committee Prof. Filip De Turck, chair Department of Information Technology Faculty of Engineering and Architecture Ghent University Prof. Koen De Bosschere, secretary Department of Electronics and Information Systems Faculty of Engineering and Architecture Ghent University Prof. Bjorn De Sutter, supervisor Department of Electronics and Information Systems Faculty of Engineering and Architecture Ghent University Prof. Jutho Haegeman Department of Physics and Astronomy Faculty of Sciences Ghent University Prof. Jan Lemeire Department of Electronics and Informatics Faculty of Engineering Vrije Universiteit Brussel Prof. Christophe Dubach School of Informatics College of Science & Engineering The University of Edinburgh Prof. Alan Edelman Computer Science & Artificial Intelligence Laboratory Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology ii Dankwoord Ik wist eigenlijk niet waar ik aan begon, toen ik in 2012 in de cata- comben van het Technicum op gesprek ging over een doctoraat. Of ik al eens met LLVM gewerkt had. Ondertussen zijn we vele jaren verder, werk ik op een bureau waar er wel daglicht is, en is het eindpunt van deze studie zowaar in zicht. Dat mag natuurlijk wel, zo vertelt men mij, na 7 jaar. -
P1360R0: Towards Machine Learning for C++: Study Group 19
P1360R0: Towards Machine Learning for C++: Study Group 19 Date: 2018-11-26(Post-SAN mailing): 10 AM ET Project: ISO JTC1/SC22/WG21: Programming Language C++ Audience SG19, WG21 Authors : Michael Wong (Codeplay), Vincent Reverdy (University of Illinois at Urbana-Champaign, Paris Observatory), Robert Douglas (Epsilon), Emad Barsoum (Microsoft), Sarthak Pati (University of Pennsylvania) Peter Goldsborough (Facebook) Franke Seide (MS) Contributors Emails: [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] Reply to: [email protected] Introduction 2 Motivation 2 Scope 5 Meeting frequency and means 6 Outreach to ML/AI/Data Science community 7 Liaison with other groups 7 Future meetings 7 Conclusion 8 Acknowledgements 8 References 8 Introduction This paper proposes a WG21 SG for Machine Learning with the goal of: ● Making Machine Learning a first-class citizen in ISO C++ It is the collaboration of a number of key industry, academic, and research groups, through several connections in CPPCON BoF[reference], LLVM 2018 discussions, and C++ San Diego meeting. The intention is to support such an SG, and describe the scope of such an SG. This is in terms of potential work resulting in papers submitted for future C++ Standards, or collaboration with other SGs. We will also propose ongoing teleconferences, meeting frequency and locations, as well as outreach to ML data scientists, conferences, and liaison with other Machine Learning groups such as at Khronos, and ISO. As of the SAN meeting, this group has been officially created as SG19, and we will begin teleconferences immediately, after the US thanksgiving, and after NIPS. -
Setting up Your Environment for the TF Developer Certificate Exam
Set up your environment to take the TensorFlow Developer Ceicate Exam Questions? Email [email protected]. Last Updated: June 23, 2021 This document describes how to get your environment ready to take the TensorFlow Developer Ceicate exam. It does not discuss what the exam is or how to take it. For more information about the TensorFlow Developer Ceicate program, please go to tensolow.org/ceicate. Contents Before you begin Refund policy Install Python 3.8 Install PyCharm Get your environment ready for the exam What libraries will the exam infrastructure install? Make sure that PyCharm isn't subject to le-loading controls Windows and Linux Users: Check your GPU drivers Mac Users: Ensure you have Python 3.8 All Users: Create a Test Viual Environment that uses TensorFlow in PyCharm Create a new PyCharm project Install TensorFlow and related packages Check the suppoed version of Python in PyCharm Practice training TensorFlow models Setup your environment for the TensorFlow Developer Certificate exam 1 FAQs How do I sta the exam? What version of TensorFlow is used in the exam? Is there a minimum hardware requirement? Where is the candidate handbook? Before you begin The TensorFlow ceicate exam runs inside PyCharm. The exam uses TensorFlow 2.5.0. You must use Python 3.8 to ensure accurate grading of your models. The exam has been tested with Python 3.8.0 and TensorFlow 2.5.0. Before you sta the exam, make sure your environment is ready: ❏ Make sure you have Python 3.8 installed on your computer. ❏ Check that your system meets the installation requirements for PyCharm here. -
Tensorflow and Keras Installation Steps for Deep Learning Applications in Rstudio Ricardo Dalagnol1 and Fabien Wagner 1. Install
Version 1.0 – 03 July 2020 Tensorflow and Keras installation steps for Deep Learning applications in Rstudio Ricardo Dalagnol1 and Fabien Wagner 1. Install OSGEO (https://trac.osgeo.org/osgeo4w/) to have GDAL utilities and QGIS. Do not modify the installation path. 2. Install ImageMagick (https://imagemagick.org/index.php) for image visualization. 3. Install the latest Miniconda (https://docs.conda.io/en/latest/miniconda.html) 4. Install Visual Studio a. Install the 2019 or 2017 free version. The free version is called ‘Community’. b. During installation, select the box to include C++ development 5. Install the latest Nvidia driver for your GPU card from the official Nvidia site 6. Install CUDA NVIDIA a. I installed CUDA Toolkit 10.1 update2, to check if the installed or available NVIDIA driver is compatible with the CUDA version (check this in the cuDNN page https://docs.nvidia.com/deeplearning/sdk/cudnn-support- matrix/index.html) b. Downloaded from NVIDIA website, searched for CUDA NVIDIA download in google: https://developer.nvidia.com/cuda-10.1-download-archive-update2 (list of all archives here https://developer.nvidia.com/cuda-toolkit-archive) 7. Install cuDNN (deep neural network library) – I have installed cudnn-10.1- windows10-x64-v7.6.5.32 for CUDA 10.1, the last version https://docs.nvidia.com/deeplearning/sdk/cudnn-install/#install-windows a. To download you have to create/login an account with NVIDIA Developer b. Before installing, check the NVIDIA driver version if it is compatible c. To install on windows, follow the instructions at section 3.3 https://docs.nvidia.com/deeplearning/sdk/cudnn-install/#install-windows d. -
Julia: a Modern Language for Modern ML
Julia: A modern language for modern ML Dr. Viral Shah and Dr. Simon Byrne www.juliacomputing.com What we do: Modernize Technical Computing Today’s technical computing landscape: • Develop new learning algorithms • Run them in parallel on large datasets • Leverage accelerators like GPUs, Xeon Phis • Embed into intelligent products “Business as usual” will simply not do! General Micro-benchmarks: Julia performs almost as fast as C • 10X faster than Python • 100X faster than R & MATLAB Performance benchmark relative to C. A value of 1 means as fast as C. Lower values are better. A real application: Gillespie simulations in systems biology 745x faster than R • Gillespie simulations are used in the field of drug discovery. • Also used for simulations of epidemiological models to study disease propagation • Julia package (Gillespie.jl) is the state of the art in Gillespie simulations • https://github.com/openjournals/joss- papers/blob/master/joss.00042/10.21105.joss.00042.pdf Implementation Time per simulation (ms) R (GillespieSSA) 894.25 R (handcoded) 1087.94 Rcpp (handcoded) 1.31 Julia (Gillespie.jl) 3.99 Julia (Gillespie.jl, passing object) 1.78 Julia (handcoded) 1.2 Those who convert ideas to products fastest will win Computer Quants develop Scientists prepare algorithms The last 25 years for production (Python, R, SAS, DEPLOY (C++, C#, Java) Matlab) Quants and Computer Compress the Scientists DEPLOY innovation cycle collaborate on one platform - JULIA with Julia Julia offers competitive advantages to its users Julia is poised to become one of the Thank you for Julia. Yo u ' v e k i n d l ed leading tools deployed by developers serious excitement. -
TRAINING NEURAL NETWORKS with TENSOR CORES Dusan Stosic, NVIDIA Agenda
TRAINING NEURAL NETWORKS WITH TENSOR CORES Dusan Stosic, NVIDIA Agenda A100 Tensor Cores and Tensor Float 32 (TF32) Mixed Precision Tensor Cores : Recap and New Advances Accuracy and Performance Considerations 2 MOTIVATION – COST OF DL TRAINING GPT-3 Vision tasks: ImageNet classification • 2012: AlexNet trained on 2 GPUs for 5-6 days • 2017: ResNeXt-101 trained on 8 GPUs for over 10 days T5 • 2019: NoisyStudent trained with ~1k TPUs for 7 days Language tasks: LM modeling RoBERTa • 2018: BERT trained on 64 GPUs for 4 days • Early-2020: T5 trained on 256 GPUs • Mid-2020: GPT-3 BERT What’s being done to reduce costs • Hardware accelerators like GPU Tensor Cores • Lower computational complexity w/ reduced precision or network compression (aka sparsity) 3 BASICS OF FLOATING-POINT PRECISION Standard way to represent real numbers on a computer • Double precision (FP64), single precision (FP32), half precision (FP16/BF16) Cannot store numbers with infinite precision, trade-off between range and precision • Represent values at widely different magnitudes (range) o Different tensors (weights, activation, and gradients) when training a network • Provide same relative accuracy at all magnitudes (precision) o Network weight magnitudes are typically O(1) o Activations can have orders of magnitude larger values How floating-point numbers work • exponent: determines the range of values o scientific notation in binary (base of 2) • fraction (or mantissa): determines the relative precision between values mantissa o (2^mantissa) samples between powers of -
Introduction to Keras Tensorflow
Introduction to Keras TensorFlow Marco Rorro [email protected] CINECA – SCAI SuperComputing Applications and Innovation Department 1/33 Table of Contents Introduction Keras Distributed Deep Learning 2/33 Introduction Keras Distributed Deep Learning 3/33 I computations are expressed as stateful data-flow graphs I automatic differentiation capabilities I optimization algorithms: gradient and proximal gradient based I code portability (CPUs, GPUs, on desktop, server, or mobile computing platforms) I Python interface is the preferred one (Java, C and Go also exist) I installation through: pip, Docker, Anaconda, from sources I Apache 2.0 open-source license TensorFlow I Google Brain’s second generation machine learning system 4/33 I automatic differentiation capabilities I optimization algorithms: gradient and proximal gradient based I code portability (CPUs, GPUs, on desktop, server, or mobile computing platforms) I Python interface is the preferred one (Java, C and Go also exist) I installation through: pip, Docker, Anaconda, from sources I Apache 2.0 open-source license TensorFlow I Google Brain’s second generation machine learning system I computations are expressed as stateful data-flow graphs 4/33 I optimization algorithms: gradient and proximal gradient based I code portability (CPUs, GPUs, on desktop, server, or mobile computing platforms) I Python interface is the preferred one (Java, C and Go also exist) I installation through: pip, Docker, Anaconda, from sources I Apache 2.0 open-source license TensorFlow I Google Brain’s second generation