Interoperating Deep Learning Models with ONNX.Jl
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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. -
Ways to Use Machine Learning Approaches for Software Development
Ways to use Machine Learning approaches for software development Nicklas Jonsson Nicklas Jonsson VT 2018 Examensarbete, 30 hp Supervisor: Eddie wadbro Extern Supervisor: C4 Contexture Examiner: Henrik Bjorklund¨ Master of Science Programme in Computing Science and Engineering, 300 hp Abstract With the rise of machine learning and in particular deep learning enter- ing all different types of fields, including software development. It could be a bit hard to know where to begin to search for the tools when some- one wants to use machine learning for a one’s problems. This thesis has looked at some available technologies of today for applying machine learning to one’s applications. This thesis has looked at some of the available cloud services, frame- works, and libraries for machine learning and it presents three different implementation structures that can be used with these technologies for the problem of image classification. Acknowledgements I want to thank C4 Contexture for giving me the thesis idea, support, and supplying me with a working station. I also want to thank Lantmannen¨ for supplying me with the image data that was used for this thesis. Finally, I want to thank Eddie Wadbro for his guidance during this thesis and of course a big thanks to my family and friends for their support during this period of my life. 1(45) Content 1 Introduction 3 1.1 The Client 3 1.1.1 C4 Contexture PIM software 4 1.2 The data 4 1.3 Goal 5 1.4 Limitation 5 2 Background 7 2.1 Artificial intelligence, machine learning and deep learning. -
Document Databases, JSON, Mongodb 17
MI-PDB, MIE-PDB: Advanced Database Systems http://www.ksi.mff.cuni.cz/~svoboda/courses/2015-2-MIE-PDB/ Lecture 13: Document Databases, JSON, MongoDB 17. 5. 2016 Lecturer: Martin Svoboda [email protected] Authors: Irena Holubová, Martin Svoboda Faculty of Mathematics and Physics, Charles University in Prague Course NDBI040: Big Data Management and NoSQL Databases Document Databases Basic Characteristics Documents are the main concept Stored and retrieved XML, JSON, … Documents are Self-describing Hierarchical tree data structures Can consist of maps, collections, scalar values, nested documents, … Documents in a collection are expected to be similar Their schema can differ Document databases store documents in the value part of the key-value store Key-value stores where the value is examinable Document Databases Suitable Use Cases Event Logging Many different applications want to log events Type of data being captured keeps changing Events can be sharded by the name of the application or type of event Content Management Systems, Blogging Platforms Managing user comments, user registrations, profiles, web-facing documents, … Web Analytics or Real-Time Analytics Parts of the document can be updated New metrics can be easily added without schema changes E-Commerce Applications Flexible schema for products and orders Evolving data models without expensive data migration Document Databases When Not to Use Complex Transactions Spanning Different Operations Atomic cross-document operations Some document databases do support (e.g., RavenDB) Queries against Varying Aggregate Structure Design of aggregate is constantly changing → we need to save the aggregates at the lowest level of granularity i.e., to normalize the data Document Databases Representatives Lotus Notes Storage Facility JSON JavaScript Object Notation Introduction • JSON = JavaScript Object Notation . -
Smart Grid Serialization Comparison
Downloaded from orbit.dtu.dk on: Sep 28, 2021 Smart Grid Serialization Comparison Petersen, Bo Søborg; Bindner, Henrik W.; You, Shi; Poulsen, Bjarne Published in: Computing Conference 2017 Link to article, DOI: 10.1109/SAI.2017.8252264 Publication date: 2017 Document Version Peer reviewed version Link back to DTU Orbit Citation (APA): Petersen, B. S., Bindner, H. W., You, S., & Poulsen, B. (2017). Smart Grid Serialization Comparison. In Computing Conference 2017 (pp. 1339-1346). IEEE. https://doi.org/10.1109/SAI.2017.8252264 General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. Users may download and print one copy of any publication from the public portal for the purpose of private study or research. You may not further distribute the material or use it for any profit-making activity or commercial gain You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Computing Conference 2017 18-20 July 2017 | London, UK Smart Grid Serialization Comparision Comparision of serialization for distributed control in the context of the Internet of Things Bo Petersen, Henrik Bindner, Shi You Bjarne Poulsen DTU Electrical Engineering DTU Compute Technical University of Denmark Technical University of Denmark Lyngby, Denmark Lyngby, Denmark [email protected], [email protected], [email protected] [email protected] Abstract—Communication between DERs and System to ensure that the control messages are received within a given Operators is required to provide Demand Response and solve timeframe, depending on the needs of the power grid. -
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. -
Spindle Documentation Release 2.0.0
spindle Documentation Release 2.0.0 Jorge Ortiz, Jason Liszka June 08, 2016 Contents 1 Thrift 3 1.1 Data model................................................3 1.2 Interface definition language (IDL)...................................4 1.3 Serialization formats...........................................4 2 Records 5 2.1 Creating a record.............................................5 2.2 Reading/writing records.........................................6 2.3 Record interface methods........................................6 2.4 Other methods..............................................7 2.5 Mutable trait...............................................7 2.6 Raw class.................................................7 2.7 Priming..................................................7 2.8 Proxies..................................................8 2.9 Reflection.................................................8 2.10 Field descriptors.............................................8 3 Custom types 9 3.1 Enhanced types..............................................9 3.2 Bitfields..................................................9 3.3 Type-safe IDs............................................... 10 4 Enums 13 4.1 Enum value methods........................................... 13 4.2 Companion object methods....................................... 13 4.3 Matching and unknown values...................................... 14 4.4 Serializing to string............................................ 14 4.5 Examples................................................. 14 5 Working -
Automated Elastic Pipelining for Distributed Training of Transformers
PipeTransformer: Automated Elastic Pipelining for Distributed Training of Transformers Chaoyang He 1 Shen Li 2 Mahdi Soltanolkotabi 1 Salman Avestimehr 1 Abstract the-art convolutional networks ResNet-152 (He et al., 2016) and EfficientNet (Tan & Le, 2019). To tackle the growth in The size of Transformer models is growing at an model sizes, researchers have proposed various distributed unprecedented rate. It has taken less than one training techniques, including parameter servers (Li et al., year to reach trillion-level parameters since the 2014; Jiang et al., 2020; Kim et al., 2019), pipeline paral- release of GPT-3 (175B). Training such models lel (Huang et al., 2019; Park et al., 2020; Narayanan et al., requires both substantial engineering efforts and 2019), intra-layer parallel (Lepikhin et al., 2020; Shazeer enormous computing resources, which are luxu- et al., 2018; Shoeybi et al., 2019), and zero redundancy data ries most research teams cannot afford. In this parallel (Rajbhandari et al., 2019). paper, we propose PipeTransformer, which leverages automated elastic pipelining for effi- T0 (0% trained) T1 (35% trained) T2 (75% trained) T3 (100% trained) cient distributed training of Transformer models. In PipeTransformer, we design an adaptive on the fly freeze algorithm that can identify and freeze some layers gradually during training, and an elastic pipelining system that can dynamically Layer (end of training) Layer (end of training) Layer (end of training) Layer (end of training) Similarity score allocate resources to train the remaining active layers. More specifically, PipeTransformer automatically excludes frozen layers from the Figure 1. Interpretable Freeze Training: DNNs converge bottom pipeline, packs active layers into fewer GPUs, up (Results on CIFAR10 using ResNet). -
Rdf Repository Replacing Relational Database
RDF REPOSITORY REPLACING RELATIONAL DATABASE 1B.Srinivasa Rao, 2Dr.G.Appa Rao 1,2Department of CSE, GITAM University Email:[email protected],[email protected] Abstract-- This study is to propose a flexible enable it. One such technology is RDF (Resource information storage mechanism based on the Description Framework)[2]. RDF is a directed, principles of Semantic Web that enables labelled graph for representing information in the information to be searched rather than queried. Web. This can be perceived as a repository In this study, a prototype is developed where without any predefined structure the focus is on the information rather than the The information stored in the traditional structure. Here information is stored in a RDBMS’s requires structure to be defined structure that is constructed on the fly. Entities upfront. On the contrary, information could be in the system are connected and form a graph, very complex to structure upfront despite the similar to the web of data in the Internet. This tremendous potential offered by the existing data is persisted in a peculiar way to optimize database systems. In the ever changing world, querying on this graph of data. All information another important characteristic of information relating to a subject is persisted closely so that in a system that impacts its structure is the reqeusting any information of a subject could be modification/enhancement to the system. This is handled in one call. Also, the information is a big concern with many software systems that maintained in triples so that the entire exist today and there is no tidy approach to deal relationship from subject to object via the with the problem. -
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.