![Amazon ConfidentialAmazon Confidential
Getting started
acbc32cf4de3:image-classification smola$ ssh ec2-user@54.210.246.140
Last login: Fri Nov 11 05:58:58 2016 from 72-21-196-69.amazon.com
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_| ( / Deep Learning AMI for Amazon Linux
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This is beta version of the Deep Learning AMI for Amazon Linux.
The README file for the AMI ➜➜➜➜➜➜➜➜➜➜➜➜➜➜➜➜➜➜➜➜ /home/ec2-user/src/README.md
Tests for deep learning frameworks ➜➜➜➜➜➜➜➜➜➜➜➜ /home/ec2-user/src/bin
=============================================================================
7 package(s) needed for security, out of 75 available
Run "sudo yum update" to apply all updates.
Amazon Linux version 2016.09 is available.
[ec2-user@ip-172-31-55-21 ~]$ cd src/
[ec2-user@ip-172-31-55-21 src]$ ls
anaconda2 bazel caffe cntk keras mxnet OpenBLAS README.md Theano
anaconda3 bin caffe3 demos logs Nvidia_Cloud_EULA.pdf opencv tensorflow torch](https://image.slidesharecdn.com/amazonaidaymxnetintrov2-170120023425/85/Amazon-Deep-Learning-35-320.jpg)
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Amazon Deep Learning
- 1. © 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential Joseph Spisak | Manager, Product Management | Deep Learning [email protected] Amazon Deep Learning Amazon AI Day | San Francisco
- 2. Amazon Confidential Agenda • AI and Deep Learning at Amazon • Brief Primer on Deep Learning & Applications • MXNet Overview and Investments • Deep Learning Tools and Usage • Application Example: Deploying MXNet in ECS/Docker • Application Example: MXNet in a ‘Server-less’ Lambda Environment • Next Steps and Call to Action
- 3. Amazon Confidential Artificial Intelligence At Amazon Thousands Of Employees Across The Company Focused on AI Discovery & Search Fulfilment & Logistics Enhance Existing Products Define New Categories Of Products Bring Machine Learning To All
- 4. Amazon Confidential AI on AWS Today • Zillow –Zestimate (using Apache Spark) • Howard Hughes Corp –Lead scoring for luxury real estate purchase predictions • FINRA –Anomaly detection, sequence matching, regression analysis, network/tribe analysis • Netflix –Recommendation engine • Pinterest –Image recognition search • Fraud.net –Detect online payment fraud • DataXu –Leverage automated & unattended ML at large scale (Amazon EMR + Spark) • Mapillary –Computer vision for crowd sourced maps • Hudl –Predictive analytics on sports plays • Upserve –Restaurant table mgmt & POS for forecasting customer traffic • TuSimple –Computer Vision for Autonomous Driving • Clarifai – Computer Vision APIs
- 5. Amazon Confidential The Advent Of Deep Learning Algorithms
- 6. Amazon Confidential The Advent Of Deep Learning Data Algorithms
- 7. Amazon Confidential The Advent Of Deep Learning Data GPUs & Acceleration Algorithms
- 8. Amazon Confidential The Advent Of Deep Learning Data GPUs & Acceleration Programming models Algorithms
- 9. Amazon Confidential Overview of Deep Learning
- 10. Amazon Confidential Deep Learning Significantly improve many applications on multiple domains “deep learning” trend in the past 10 years image understanding speech recognition natural language processing … autonomy
- 11. Amazon Confidential Image Classification Layer 1 Layer 2 Output multilevel feature extractions from raw pixels to semantic meanings explore spatial information with convolution layers
- 12. Amazon Confidential Image Classification • Hard to define the network • the definition of the inception network has >1k lines of codes in Caffe • A single image requires billions floating-point operations • Intel i7 ~500 GFLOPS • Nvidia Titan X: ~5 TFLOPS • Memory consumption is linear with number of layers State-of-the-art networks have tens to hundreds layers
- 13. Amazon Confidential Language Modeling • Variable length of input and output sequences • State-of-the-art networks have many layers • Billions of floating-point operations per sentence • Memory consumption is linear with both sequence length and number of layers <go> hello hello world input output state world ! recurrent neural networks:
- 14. Amazon Confidential TX1 on Flying Drone TX1 with customized board Drone Realtime detection and tracking on TX1 ~10 frame/sec with 640x480 resolution
- 15. Amazon Confidential Deploy Everywhere Fit the core library with all dependencies into a single C++ source file Easy to compile on … Beyond BlindTool by Joseph Paul Cohen, demo on Nexus 4 Amalgamation Runs in browser with Javascript The first image for search “dog” at images.google.com Outputs “beagle” with prob = 73% within 1 sec
- 16. Amazon Confidential Deep RL | Playing Flappy Birds • Reinforcement learning: Observe environment Take Action Achieve Reward Repeat. Goal is to maximize rewards over time. • There are three interfaces: • getInitState() for initialization • getAction() • setPerception(nextObservation,action,reward,termin al) • Resources: • http://ww1.sinaimg.cn/mw690/8708cad7jw1f8naomr mweg209n0fo7wj.gif • https://github.com/li-haoran/DRL-FlappyBird
- 17. Amazon Confidential Deep Learning Toolkits MXNET, CNTK, Tensorflow, Caffe, Keras, Theano, Torch
- 19. Amazon Confidential CNTK • Languages: Python, C++, BrainScript • Supports multiple GPUs and multiple machines • Code for many existing models available / CUDA / MKL integration
- 20. Amazon Confidential Tensorflow • Multi-machine multi-GPU • Broad language support • Automatic parallelization (see compute graph) • Mobile device support
- 21. Amazon Confidential Caffe - Deep Learning Framework by the BVLC Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors. Yangqing Jia created the project. • Expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard- coding. Switch between CPU and GPU by setting a single flag. • Supports multiple GPUs but not multiple machines. • Caffe on Spark and Caffe con Troll are some attempts to scale it. • Community. Caffe powers academic research projects, startup prototypes, and large-scale industrial applications in vision, speech, and multimedia.
- 22. Amazon Confidential Torch Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation. • powerful N-dimensional array • lots of routines for indexing, slicing, transposing, ... • amazing interface to C, via LuaJIT • linear algebra routines • neural network, and energy-based models • numeric optimization routines • Fast and efficient GPU support • Embeddable, with ports to iOS, Android and FPGA backends
- 23. Amazon Confidential Theano Multi-GPU support (experimental). Multi-machine is very limited.
- 25. Amazon Confidential MXNet Overview • Founded by: U.Washington, Carnegie Mellon U. (~1.5yrs old) • State of the Art Model Support: Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) • Ultra-scalable: Near-linear scaling equals fastest time to model • Multi-language: Support for Scala, Python, R, etc.. for legacy code leverage and easy integration with Spark • Ecosystem: Vibrant community from Academia and Industry Open Source Project on Github | Apache-2 Licensed
- 26. Amazon Confidential Collaborations and Community 4th DL Framework in Popularity (Outpacing Torch, CNTK and Theano) 0 27.5 55 82.5 110 137.5 TensorFlow Caffe Keras MXNet Theano Deeplearning4j CNTK Torch7 Popularity Diverse Community (Spans Industry and Academia) 0 15000 30000 45000 60000 Bing Xu (Apple) Tianqi Chen (UW) Mu Li (CMU/AWS) Eric Xie (UW/AWS) Yizhi Liu (Mediav) Chiyuan Zhang (MIT) Tianjun Xiao (Micrsoft) Yutian Li (Face++) Guo Jian (Tusimple) Guosheng Dong (sogou) Yu Zhang (MIT) Depeng Liang (?) Qiang Kou (Indiana U) Xingjian Shi (HKUST) Naiyan Wang (Tusimple) Top Contributors
- 27. Amazon Confidential Scalability on Multi-GPUs • AWS EC2 P2.16xlarge • 8 Nvidia Tesla K80 (16 GPUs) • Synchronous SGD (Stochastic Gradient Descent) 1 4.75 8.5 12.25 16 1 4.75 8.5 12.25 16 Speedup(x) # GPUs Resnet 152 Inceptin V3 Alexnet Ideal *AWS internally generated data
- 28. Amazon Confidential Scalability on Multi-GPU Instances • 8x AWS EC2 P2.16xlarge • 8 Nvidia Tesla K80 (16 GPUs) per P2.16xlarge • Synchronous SGD (Stochastic Gradient Descent) *AWS internally generated data
- 29. Amazon Confidential Roadmap / Areas of Investment • NNVM Migration (complete) • Apache project (proposal submitted) • Usability • Documentation (installation, native documents, etc.) • Tutorials, examples • Platform support (Linux, Windows, OS X, mobile …) • Language bindings (Python, C++, R, Scala, Julia, JavaScript …) • Sparse datatypes and LSTM performance improvements
- 30. Amazon Confidential AWS Deep Learning Tools
- 31. Amazon Confidential One-Click GPU or CPU Deep Learning AWS Deep Learning AMI Up to~40k CUDA cores MXNet TensorFlow Theano Caffe Torch Pre-configured CUDA drivers Anaconda, Python3 + CloudFormation template + Container Image
- 32. Amazon Confidential Amazon Machine Image for Deep Learning http://bit.ly/deepami
- 33. Amazon ConfidentialAmazon Confidential Getting started with Deep Learning • Tool for data scientists and developers • Setting up a DL system takes (install) time & skill • Keep packages up to date and compile • Install all dependencies • NVIDIA Drivers and CuDNN for G2 and P2 servers • Intel MKL Drivers for all other instances (C4, M4, …) http://bit.ly/deepami
- 34. Amazon ConfidentialAmazon Confidential Getting started with Deep Learning • Drivers CUDA / CUDNN / CUFFT / CUSPARSE / MKL • Development tools Python 2 and 3, Anaconda, Jupyter notebooks, Graphviz • Deep Learning Platforms (compiled & tested) • MXNet, Tensorflow, CNTK multi-GPU, multi-machine (MXNet recommended) • Caffe, Theano, Torch • Keras • Up and running in just a few minutes training a Neural Network Always up to date (less than 1 month), optimized & tested on AWS
- 35. Amazon ConfidentialAmazon Confidential Getting started acbc32cf4de3:image-classification smola$ ssh [email protected] Last login: Fri Nov 11 05:58:58 2016 from 72-21-196-69.amazon.com ============================================================================= __| __|_ ) _| ( / Deep Learning AMI for Amazon Linux ___|___|___| This is beta version of the Deep Learning AMI for Amazon Linux. The README file for the AMI ➜➜➜➜➜➜➜➜➜➜➜➜➜➜➜➜➜➜➜➜ /home/ec2-user/src/README.md Tests for deep learning frameworks ➜➜➜➜➜➜➜➜➜➜➜➜ /home/ec2-user/src/bin ============================================================================= 7 package(s) needed for security, out of 75 available Run "sudo yum update" to apply all updates. Amazon Linux version 2016.09 is available. [ec2-user@ip-172-31-55-21 ~]$ cd src/ [ec2-user@ip-172-31-55-21 src]$ ls anaconda2 bazel caffe cntk keras mxnet OpenBLAS README.md Theano anaconda3 bin caffe3 demos logs Nvidia_Cloud_EULA.pdf opencv tensorflow torch
- 36. Amazon Confidential AWS CloudFormation Template for Deep Learning http://bit.ly/deepcft
- 37. Amazon ConfidentialAmazon Confidential AWS CloudFormation Templates • Hardware as Code • Define compute resources • Define network • Define OS installation and configuration • Deep Learning • Many toolkits barely deal with multiple GPUs (DGX-1) • BigData needs massive compute resources • Hardware availability and software support
- 39. Amazon ConfidentialAmazon Confidential AWS CloudFormation Components • VPC in the customer account. • The requested number of worker instances in an Auto Scaling group within the VPC. Workers are launched in a private subnet. • Master instance in a separate Auto Scaling group that acts as a proxy to enable connectivity to the cluster via SSH. • Two security groups that open ports on the private subnet for communication between the master and workers. • IAM role that allows users to access and query Auto Scaling groups and the private IP addresses of the EC2 instances. • NAT gateway used by instances within the VPC to talk to the outside.
- 44. Amazon Confidential your master instance
- 45. Amazon ConfidentialAmazon Confidential Launching an MXNet job MNIST digit classification demo ../../tools/launch.py -n $DEEPLEARNING_WORKERS_COUNT -H $DEEPLEARNING_WORKERS_PATH python train_mnist.py --gpus $(seq -s , 0 1 $ (($DEEPLEARNING_WORKER_GPU_COUNT - 1))) --network lenet --kv-store dist_sync parameter server network choice update policy
- 46. Amazon ConfidentialAmazon Confidential Application Examples | Python notebooks https://github.com/dmlc/mxnet-notebooks Basic concepts • NDArray - multi-dimensional array computation • Symbol - symbolic expression for neural networks • Module - neural network training and inference Applications • MNIST: recognize handwritten digits • Check out the distributed training results • Predict with pre-trained models • LSTMs for sequence learning • Recommender systems • Train a state of the art Computer Vision model (CNN) • Lots more..
- 47. Call to Action MXNet Resources: • MXNet Blog Post | AWS Endorsement • Read up on MXNet and Learn More: mxnet.io • MXNet Github Repo • MXNet Talk by Mu Li Developer Resources: • Jeff Barr Blog on P2 | New P2 Instance Type for Amazon EC2 – Up to 16 GPUs • Deep Learning AMI • P2 Instance Information • CloudFormation Template Instructions • Deep Learning Benchmark
- 48. MXNet | EC2 Docker usage
- 49. MXNet | ‘Server-less’ using Lambda