Building distributed deep learning engine
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The document discusses building a distributed deep learning engine. It describes deep learning and its applications in areas like speech recognition, image processing, and natural language processing. It then discusses the challenges of deep learning like needing large amounts of data and having large models. The rest of the document details the distributed deep learning platform being built, including a model-parallel engine to partition models across a cluster, distributed parameter servers for coordination, and supporting various deep learning algorithms and use cases.
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Building distributed deep learning engine
- 1. Building Distributed Deep Learning Engine Guangdeng Liao, Zhan Zhang and Murtaza Zafer SRA-SV | Cloud Research Lab Slide 1
- 2. What is Deep Learning Deep learning is a set of algorithms that attempt to model high-level abstractions in data by using architectures composed of multiple non-linear transformations Learn hidden features Learning state emission prob. Learning word vectors SRA-SV | Cloud Research Lab Slide 2
- 3. Thanks Big Data, Deep Learning is not only research Usage Scenario: Speech Recognition, Image processing and NLP SRA-SV | Cloud Research Lab Slide 3
- 4. Why Samsung needs Deep Learning? To make our devices smarter and more intelligent by recognizing voice, image and even language SRA-SV | Cloud Research Lab Slide 4
- 5. How does Deep Learning look like? Many more examples (millions to billions parameters ) in Speech Recognition, Image Processing and NLP Krizhevsky, A., Sutskever, I. and Hinton, G. E. ImageNet Classification with Deep Convolutional Neural Networks SRA-SV | Cloud Research Lab Slide 5
- 6. Deep Learning is challenging.. BIG DATA + BIG MODEL Building a distributed deep learning platform for Quite new, no mature platform yet Samsung R&D Hard to design and develop DL algorithms SRA-SV | Cloud Research Lab Slide 6
- 7. Distributed Deep Learning Platform we are building Object recognition App. Speech recognition …. RBM FF DA CNN …. Model-parallel engine Algorithms Infrastructure I/O …. Parameter server Execution engine Math SRA-SV | Cloud Research Lab Slide 7 Our focus
- 8. Now, Let’s dive deeper and more technically… SRA-SV | Cloud Research Lab Slide 8
- 9. Model-Parallel Engine (MPE) Parallelize a big ML model over Hadoop YARN cluster Auto-deployment of topology (in-memory) SRA-SV | Cloud Research Lab Slide 9 User defined model Auto-generation of model topology Auto-partition of topology over cluster c1 c2 c3 Neuron-like programming Message-based communication Message-driven computation -Define nodes -Define groups -Define connections
- 10. MPE’s Architecture Container Node Manager Data Communication: • node-level • group-level Container Node manager Control comm. based on Thrift Data comm. based on Netty Application Master Controller Partition and deploy topology Container Node manager SRA-SV | Cloud Research Lab Slide 10
- 11. How to partition big models Vertical Partition Horizontal Partition SRA-SV | Cloud Research Lab Slide 11
- 12. Execution Engine (Layer-by-Layer Training) Can stack different layers and training algorithms HDFS/LFS HDFS/LFS SRA-SV | Cloud Research Lab Slide 12
- 13. Model-parallel itself is not scalable enough SRA-SV | Cloud Research Lab Slide 13
- 14. Deep Learning Infra.: Hybrid of Data-parallelism and Model-parallelism …….. Data Chunk Data Chunk …….. Model-parallel Model-parallel Parameter Server 1 Parameters coordination Parameter Server n …….. Data-parallelism Lots of model instances Parameter servers help models learn each other SRA-SV | Cloud Research Lab Slide 14
- 15. Distributed Parameter Servers Currently we support asynchronous stochastic gradient descent with AdaGrad Client Client Client Server 1 Server 2 Server 3 In-memory cache/storage HBase/HDFS In-memory cache/storage Asyn. communication In-memory cache/storage SRA-SV | Cloud Research Lab Slide 15 Pull/Push
- 16. Deep Learning Algorithms Deep Learning Algorithms Feed-forward Neural Network Restricted Boltzmann Machine Denoise Auto-encoder Deep Belief Network More importantly, we can stack them layer by layer SRA-SV | Cloud Research Lab Slide 16
- 17. More Challenging Algorithm: Convolutional Neural Network Different convolutional, normalization and pooling layers Weight shared and non-shared feature maps Feature map is minimum partition unit SRA-SV | Cloud Research Lab Slide 17 第17 页 Layer: Multi-dimensional feature map neurons Output: Dense layer feed-forward neurons Input: e.g. image, spectral map of voice data Layer: Multi-dimensional feature map neurons
- 18. Sharing some early experiences/lessons Infrastructure Computation abstraction might be too low level (a lot of pros and cons) A generic deep learning platform is very challenging (like recurrent NN) Communication is important Methods of partitioning models are important High performance mathematical library is useful SRA-SV | Cloud Research Lab Slide 18
- 19. Sharing some early experiences Algorithm/Models Models for ASR are relatively small Models for image are much larger Models for NLP are typical small DA seems more efficient than RBM for image Accelerated SGD or Hessian free optimizations need to be explored SRA-SV | Cloud Research Lab Slide 19
- 20. Usage cases of Deep Learning SRA-SV | Cloud Research Lab Slide 20
- 21. Image Recognition Object models Object parts SRA-SV | Cloud Research Lab Slide 21 Edges Pixels Image pixels Hand-designed Feature Extraction (SIFT, HOG etc.) Trainable Classifier Object Category Featured Learner (Convolutional NN is popular) Learned high level features Data augmentation Central and corner crops Original Image
- 22. Speech Recognition • DNN is used to replace GMM to learn state output probability in HMM. • FF and DBN have been used for ASR • CNN starts being used to further improve WER • Rectified Linear Activation seems better than SRA-SV | Cloud Research Lab Slide 22 Sigmoid • Models are relatively small (e.g. 5 layers, 2560 neurons/hidden layer) Li Deng, A Tutorial Survey of Architectures, Algorithms, and Applications for Deep Learning
- 23. NLP Deep Learning in NLP is quite new Learn word vector Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean, Efficient Estimation of Word Representations in Vector Space SRA-SV | Cloud Research Lab Slide 23
- 24. NLP Based on word vector, map sentences to vector space now Sentiment Analysis Richard Socher Jeffrey Pennington Eric H. Huang Andrew Y. Ng Christopher D. Manning, Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions SRA-SV | Cloud Research Lab Slide 24
- 25. Q&A
Editor's Notes
- #17: Plan to validate Stanford’s alg. For ASR??