Deep Learning with Python & TensorFlow

by Ian Lewis

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Deep Learning with Python & TensorFlow PyCon JP 2016 #pyconjp #pyconjp_201

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Confidential & Proprietary Google Cloud Platform 2 Ian Lewis Developer Advocate - Google Cloud Platform Tokyo, Japan +Ian Lewis @IanMLewis

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Confidential & Proprietary Google Cloud Platform 3

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Confidential & Proprietary Google Cloud Platform 4

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Confidential & Proprietary Google Cloud Platform 5 Deep Learning 101

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Confidential & Proprietary Google Cloud Platform 6 Neural Networks can find a way to solve the problem How do you classify these data points?

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Google Cloud Platform 7 ["cat"] Input Hidden Output(label) pixels( )

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Confidential & Proprietary Google Cloud Platform 8

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Google Cloud Platform 9 (x,y,z,?,?,?,?,...)

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Google Cloud Platform 10 v[x] => vector

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Google Cloud Platform 11 m[x][y][z] => matrix

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Google Cloud Platform 12 t[x][y][z][?][?]... => tensor

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Google Cloud Platform 13

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Confidential & Proprietary Google Cloud Platform 14

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Confidential & Proprietary Google Cloud Platform 15 Breakthroughs

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Google Cloud Platform 16 The Inception model (GoogLeNet, 2015)

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From: Andrew Ng

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DNN = a large matrix ops a few GPUs >> CPU (but it still takes hours/days to train) a supercomputer >> a few GPUs (but you don't have a supercomputer) You need Distributed Training

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Confidential & Proprietary Google Cloud Platform 21 TensorFlow

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22 Google's open source library for machine intelligence tensorflow.org launched in Nov 2015 The second generation Used by many production ML projects What is Tensorflow?

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23 Operates over tensors: n-dimensional arrays Using a flow graph: data flow computation framework TensorFlow ● Flexible, intuitive construction ● automatic differentiation ● Support for threads, queues, and asynchronous computation; distributed runtime ● Train on CPUs, GPUs ● Run wherever you like

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Google Cloud Platform 24 Core TensorFlow data structures and concepts... - Graph: A TensorFlow computation, represented as a dataflow graph. - collection of ops that may be executed together as a group - Operation: a graph node that performs computation on tensors - Tensor: a handle to one of the outputs of an Operation - provides a means of computing the value in a TensorFlow Session.

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Google Cloud Platform 25 Core TensorFlow data structures and concepts - Constants - Placeholders: must be fed with data on execution - Variables: a modifiable tensor that lives in TensorFlow's graph of interacting operations. - Session: encapsulates the environment in which Operation objects are executed, and Tensor objects are evaluated.

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Google Cloud Platform 26 Category Element-wise math ops Array ops Matrix ops Stateful ops NN building blocks Checkpointing ops Queue & synch ops Control flow ops Operations Examples Add, Sub, Mul, Div, Exp, Log, Greater, Less… Concat, Slice, Split, Constant, Rank, Shape… MatMul, MatrixInverse, MatrixDeterminant… Variable, Assign, AssignAdd... SoftMax, Sigmoid, ReLU, Convolution2D… Save, Restore Enqueue, Dequeue, MutexAcquire… Merge, Switch, Enter, Leave...

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Google Cloud Platform 27

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Distributed Training with TensorFlow

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● CPU/GPU scheduling ● Communications ○ Local, RPC, RDMA ○ 32/16/8 bit quantization ● Cost-based optimization ● Fault tolerance Distributed Training with TensorFlow

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Distributed Training Model Parallelism Sub-Graph ● Allows fine grained application of parallelism to slow graph components ● Larger more complex graph Full Graph ● Code is more similar to single process models ● Not necessarily as performant (large models) Data Parallelism Synchronous ● Prevents workers from “Falling behind” ● Workers progress at the speed of the slowest worker Asynchronous ● Workers advance as fast as they can ● Can result in runs that aren’t reproducible or difficult to debug behavior (large models)

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Fully managed, distributed training and prediction for custom TensorFlow graph Supports Regression and Classification initially Integrated with Cloud Dataflow and Cloud Datalab Limited Preview - cloud.google.com/ml Cloud Machine Learning (Cloud ML)

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Jeff Dean's keynote: YouTube video Define a custom TensorFlow graph Training at local: 8.3 hours w/ 1 node Training at cloud: 32 min w/ 20 nodes (15x faster) Prediction at cloud at 300 reqs / sec Cloud ML

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Tensor Processing Unit ASIC for TensorFlow Designed by Google 10x better perf / watt 8 bit quantization

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Confidential & Proprietary Google Cloud Platform 34 Thank You https://www.tensorflow.org/ https://cloud.google.com/ml/ http://bit.ly/tensorflow-workshop Ian Lewis @IanMLewis