H2O at Poznan R Meetup
•
2 recomendaciones•755 vistas
Introduction to H2O, IoT Use Cases and Deep Water
Recomendados
Recomendados
Más contenido relacionado
La actualidad más candente
La actualidad más candente (19)
Similar a H2O at Poznan R Meetup
Similar a H2O at Poznan R Meetup (20)
Más de Jo-fai Chow
Más de Jo-fai Chow (14)
Último
Último (20)
H2O at Poznan R Meetup
- 1. H2O at Poznan R Meetup Introduction to H2O, IoT Use Cases and Deep Water Jo-fai (Joe) Chow Data Scientist joe@h2o.ai @matlabulous Poznan R 20th April, 2017
- 2. About Me • Civil (Water) Engineer • 2010 – 2015 • Consultant (UK) • Utilities • Asset Management • Constrained Optimization • Industrial PhD (UK) • Infrastructure Design Optimization • Machine Learning + Water Engineering • Discovered H2O in 2014 • Data Scientist • 2015 • Virgin Media (UK) • Domino Data Lab (Silicon Valley) • 2016 – Present • H2O.ai (Silicon Valley) 2
- 3. About Me 3
- 4. Side Project #1 – Crime Data Visualization 4 https://github.com/woobe/rApps/tree/master/crimemap http://insidebigdata.com/2013/11/30/visualization-week-crimemap/
- 5. Side Project #2 – Data Visualization Contest 5 https://github.com/woobe/rugsmaps http://blog.revolutionanalytics.com/2014/08/winner-for-revolution-analytics-user-group-map-contest.html
- 6. Side Project #3 6 Developing R Packages for Fun rPlotter (2014)
- 7. Side Project #4 – Kaggle Blog Post 7 R + H2O + Domino for Kaggle Guest Blog Post for Domino & H2O (2014) • The Long Story • bit.ly/joe_kaggle_story
- 8. Agenda • Introduction • Company • Machine Learning Platform • IoT Use Cases • Predictive Maintenance • Outlier Detection • Break (25 mins) • Deep Water • Motivation / Benefits • Interfaces / Demo 8
- 10. Company Overview Founded 2011 Venture-backed, debuted in 2012 Products • H2O Open Source In-Memory AI Prediction Engine • Sparkling Water • Steam Mission Operationalize Data Science, and provide a platform for users to build beautiful data products Team 70 employees • Distributed Systems Engineers doing Machine Learning • World-class visualization designers Headquarters Mountain View, CA 10
- 11. 11 Our Team Joe
- 13. 13
- 14. 0 10000 20000 30000 40000 50000 60000 70000 1-Jan-15 1-Jul-15 1-Jan-16 1-Oct-16 # H2O Users H2O Community Growth Tremendous Momentum Globally 65,000+ users globally (Sept 2016) • 65,000+ users from ~8,000 companies in 140 countries. Top 5 from: Large User Circle * DATA FROM GOOGLE ANALYTICS EMBEDDED IN THE END USER PRODUCT 14 0 2000 4000 6000 8000 10000 1-Jan-15 1-Jul-15 1-Jan-16 1-Oct-16 # Companies Using H2O ~8,000+ companies (Sept 2016) +127% +60%
- 16. H2O for Kaggle Competitions 16
- 17. H2O for Academic Research 17 http://www.sciencedirect.com/science/article/pii/S0377221716308657 https://arxiv.org/abs/1509.01199
- 18. Users In Various Verticals Adore H2O Financial Insurance MarketingTelecom Healthcare 18
- 21. H2O Machine Learning Platform 21
- 22. 22
- 23. 23
- 24. HDFS S3 NFS Distributed In-Memory Load Data Loss-less Compression H2O Compute Engine Production Scoring Environment Exploratory & Descriptive Analysis Feature Engineering & Selection Supervised & Unsupervised Modeling Model Evaluation & Selection Predict Data & Model Storage Model Export: Plain Old Java Object Your Imagination Data Prep Export: Plain Old Java Object Local SQL High Level Architecture 24
- 25. HDFS S3 NFS Distributed In-Memory Load Data Loss-less Compression H2O Compute Engine Production Scoring Environment Exploratory & Descriptive Analysis Feature Engineering & Selection Supervised & Unsupervised Modeling Model Evaluation & Selection Predict Data & Model Storage Model Export: Plain Old Java Object Your Imagination Data Prep Export: Plain Old Java Object Local SQL High Level Architecture 25 Import Data from Multiple Sources
- 26. HDFS S3 NFS Distributed In-Memory Load Data Loss-less Compression H2O Compute Engine Production Scoring Environment Exploratory & Descriptive Analysis Feature Engineering & Selection Supervised & Unsupervised Modeling Model Evaluation & Selection Predict Data & Model Storage Model Export: Plain Old Java Object Your Imagination Data Prep Export: Plain Old Java Object Local SQL High Level Architecture 26 Fast, Scalable & Distributed Compute Engine Written in Java
- 27. HDFS S3 NFS Distributed In-Memory Load Data Loss-less Compression H2O Compute Engine Production Scoring Environment Exploratory & Descriptive Analysis Feature Engineering & Selection Supervised & Unsupervised Modeling Model Evaluation & Selection Predict Data & Model Storage Model Export: Plain Old Java Object Your Imagination Data Prep Export: Plain Old Java Object Local SQL High Level Architecture 27 Fast, Scalable & Distributed Compute Engine Written in Java
- 28. Supervised Learning • Generalized Linear Models: Binomial, Gaussian, Gamma, Poisson and Tweedie • Naïve Bayes Statistical Analysis Ensembles • Distributed Random Forest: Classification or regression models • Gradient Boosting Machine: Produces an ensemble of decision trees with increasing refined approximations Deep Neural Networks • Deep learning: Create multi-layer feed forward neural networks starting with an input layer followed by multiple layers of nonlinear transformations Algorithms Overview Unsupervised Learning • K-means: Partitions observations into k clusters/groups of the same spatial size. Automatically detect optimal k Clustering Dimensionality Reduction • Principal Component Analysis: Linearly transforms correlated variables to independent components • Generalized Low Rank Models: extend the idea of PCA to handle arbitrary data consisting of numerical, Boolean, categorical, and missing data Anomaly Detection • Autoencoders: Find outliers using a nonlinear dimensionality reduction using deep learning 28
- 29. H2O Deep Learning in Action 29
- 30. HDFS S3 NFS Distributed In-Memory Load Data Loss-less Compression H2O Compute Engine Production Scoring Environment Exploratory & Descriptive Analysis Feature Engineering & Selection Supervised & Unsupervised Modeling Model Evaluation & Selection Predict Data & Model Storage Model Export: Plain Old Java Object Your Imagination Data Prep Export: Plain Old Java Object Local SQL High Level Architecture 30 Multiple Interfaces
- 31. H2O + R 31 Package ‘h2o’ from CRAN or H2O’s website Start a local H2O (Java Virtual Machine) cluster Simple ‘iris’ example
- 32. H2O + R 32
- 33. H2O + Python 33
- 34. 34 H2O Flow (Web) Interface
- 35. HDFS S3 NFS Distributed In-Memory Load Data Loss-less Compression H2O Compute Engine Production Scoring Environment Exploratory & Descriptive Analysis Feature Engineering & Selection Supervised & Unsupervised Modeling Model Evaluation & Selection Predict Data & Model Storage Model Export: Plain Old Java Object Your Imagination Data Prep Export: Plain Old Java Object Local SQL High Level Architecture 35 Export Standalone Models for Production
- 36. H2O Overview 36
- 37. 37 docs.h2o.ai
- 38. H2O Tutorials • Introduction to Machine Learning with H2O and Python • Basic Extract, Transform and Load (ETL) • Supervised Learning • Parameters Tuning • Stacking • GitHub Repository • bit.ly/joe_h2o_tutorials • R Code Examples (available soon) 38
- 39. H2O IoT Use Cases Predictive Maintenance and Outlier Detection 39
- 40. Predictive Maintenance – SECOM Dataset 40
- 41. Predictive Maintenance – SECOM Dataset 41 We want to predict fails in the future.
- 42. Predictive Maintenance – SECOM Dataset 42
- 43. Predictive Maintenance – SECOM Dataset • Dataset Summary • Inputs: • 591 numerical features • Binary Outcome: • Pass (-1) • Fail (1) • Size: • 1567 samples 43
- 44. Basic H2O Usage – GBM with Default Settings • Link to Jupyter Notebook • https://github.com/woobe/h2o_tutorials/blob/master/use_cases/predictive _maintenance/step_01_basics.ipynb 44
- 45. 45 H2O’s R package Start a local H2O Cluster (JVM) “nthreads = -1” means using all available virtual cores Information of the H2O Cluster
- 46. 46 Import data into H2O cluster (instead of R’s memory)
- 47. 47 Convert numerical to categorical values
- 48. 48
- 49. 49 Split data into training / test in order to evaluate out-of-bag performance later
- 50. 50 H2O automatically ignore columns with constant values
- 51. Classification Performance – Confusion Matrix 51
- 53. 53 Looking at metrics based on training (in-sample) data only It doesn’t represent out-of- bag performance
- 54. 54 Users could reduce number of features based on these findings
- 55. 55 Metrics based on test (out-of- bag) samples
- 56. 56 H2O returns predicted class as well as probabilities of each class
- 57. Advanced H2O Usage – Random Grid Search • Link to Jupyter Notebook • https://github.com/woobe/h2o_tutorials/blob/master/use_cases/predictive_ maintenance/step_02_random_grid_search.ipynb • Using Random Grid Search to fine-tune hyper-parameters 57
- 58. 58 API for performing a Random Grid Search
- 59. 59 Grid search results sorted by specific metric. Best model on top.
- 60. 60 Extract the best model
- 61. 61 Performance metrics based on 5-fold cross-validation
- 62. Comparison – Default vs. Tuned 62GBM with Default Settings GBM with Random Grid Search Still not perfect but better than default settings
- 63. H2O Tutorials • Introduction to Machine Learning with H2O and Python • Basic Extract, Transform and Load (ETL) • Supervised Learning • Parameters Tuning • Stacking • GitHub Repository • bit.ly/joe_h2o_tutorials • R Code Examples (available soon) 63
- 64. Outlier Detection 64 Photo credit: www.dbta.com
- 65. Outlier Detection – MNIST Dataset 65 Photo credit: http://www.opendeep.org/v0.0.5/docs/tutorial-classifying-handwritten-mnist-images
- 66. Outlier Detection – MNIST Dataset • 784 Inputs • 28 x 28 = 784 pixels • 1 Output • 0, 1, 2, 3, 4, 5, 6, 7, 8 or 9 • File (from Kaggle) • Train (42k Records) • kaggle_mnist_train.csv.gz • Source • https://www.kaggle.com/c/digit- recognizer/data 66 Photo credit: https://ml4a.github.io/ml4a/neural_networks/
- 67. Advanced H2O Usage – Random Grid Search • Link to Data and Code • https://github.com/woobe/h2o_tutorials/tree/master/use_cases/outlier_det ection 67
- 68. 68
- 69. 69 Import data (directly from a gz) Remove the label (not needed for unsupervised learning)
- 70. 70 Input = 784 pixels Output = 784 pixels Nodes = 50
- 71. 71 Quantify the average reconstruction error per sample - ) / no. of pixels = Avg. Error(
- 72. 72 Helper functions for plotting graphs only (not related to H2O algorithms)
- 73. 73
- 77. 77
- 78. End of First Talk Let’s have a break ☺ 78
- 79. H2O Deep Water H2O’s Integration with TensorFlow, mxnet and Caffe 79
- 80. TensorFlow • Open source machine learning framework by Google • Python / C++ API • TensorBoard • Data Flow Graph Visualization • Multi CPU / GPU • v0.8+ distributed machines support • Multi devices support • desktop, server and Android devices • Image, audio and NLP applications • HUGE Community • Support for Spark, Windows … 80 https://github.com/tensorflow/tensorflow
- 82. Caffe • Convolution Architecture For Feature Extraction (CAFFE) • Pure C++ / CUDA architecture for deep learning • Command line, Python and MATLAB interface • Model Zoo • Open collection of models 82 https://docs.google.com/presentation/d/1UeKXVgRvvxg9OUdh_UiC5G71UMscNPlvArsWER41PsU/
- 84. Both TensorFlow and H2O are widely used 84
- 85. TensorFlow , MXNet, Caffe and H2O DL democratize the power of deep learning. H2O platform democratizes artificial intelligence & big data science. There are other open source deep learning libraries like Theano and Torch too. Let’s have a party, this will be fun! 85
- 86. 86
- 87. Deep Water Next-Gen Distributed Deep Learning with H2O H2O integrates with existing GPU backends for significant performance gains One Interface - GPU Enabled - Significant Performance Gains Inherits All H2O Properties in Scalability, Ease of Use and Deployment Recurrent Neural Networks enabling natural language processing, sequences, time series, and more Convolutional Neural Networks enabling Image, video, speech recognition Hybrid Neural Network Architectures enabling speech to text translation, image captioning, scene parsing and more Deep Water 87
- 88. Deep Water Architecture Node 1 Node N Scala Spark H2O Java Execution Engine TensorFlow/mxnet/Caffe C++ GPU CPU TensorFlow/mxnet/Caffe C++ GPU CPU RPC R/Py/Flow/Scala client REST API Web server H2O Java Execution Engine grpc/MPI/RDMA Scala Spark 88
- 89. Available Networks in Deep Water • LeNet • AlexNet • VGGNet • Inception (GoogLeNet) • ResNet (Deep Residual Learning) • Build Your Own 89 ResNet
- 90. 90 Example: Deep Water + H2O Flow Choosing different network structures
- 91. 91 Choosing different backends (TensorFlow, MXNet, Caffe)
- 92. Unified Interface (Deep Water + R) 92 Choosing different network structures
- 93. Unified Interface (Deep Water + Python) 93 Change backend to “mxnet”, “caffe” or “auto” Choosing different network structures
- 94. Easy Stacking with other H2O Models 94 Ensemble of Deep Water, Gradient Boosting Machine & Random Forest models
- 95. 95 docs.h2o.ai
- 98. Deep Water R Demo • H2O + MXNet + TensorFlow • Dataset – Cat/Dog/Mouse • MXNet & TF as GPU backend • Train LeNet (CNN) models • R Demo • Code and Data • github.com/h2oai/deepwater 98
- 99. Data – Cat/Dog/Mouse Images 99
- 100. Data – CSV 100
- 101. Deep Water – Basic Usage Live Demo if Possible 101
- 102. Start and Connect to H2O Deep Water Cluster 102 • Download Latest Nightly Build • https://s3.amazonaws.com/h2o-deepwater/public/nightly/latest/h2o.jar • In Terminal • cd to the folder containing h2o.jar • java –jar h2o.jar (this is the default command) • java –jar –Xmx16g h2o.jar (this is the command to allocate 16GB of memory) • In R • library(h2o) (latest stable release from h2o.ai website or CRAN) • h2o.connect(ip = “xxx.xxx.xxx.xxx”, strict_version_check = FALSE)
- 103. Import CSV 103
- 104. Train a CNN (LeNet) Model on GPU 104
- 105. Train a CNN (LeNet) Model on GPU 105 Using GPU for training
- 106. Model 106
- 107. Deep Water – Custom Network 107
- 108. Xxx 108 Saving the custom network structure as a file Configure custom network structure (MXNet syntax)
- 109. Train a Custom Network 109 Point it to the custom network structure file
- 110. Model 110
- 111. Conclusions 111
- 112. Project “Deep Water” • H2O + TF + MXNet + Caffe • A powerful combination of widely used open source machine learning libraries. • All Goodies from H2O • Inherits all H2O properties in scalability, ease of use and deployment. • Unified Interface • Allows users to build, stack and deploy deep learning models from different libraries efficiently. 112 • Latest Nightly Build • https://s3.amazonaws.com/h2o- deepwater/public/nightly/latest/h 2o.jar • 100% Open Source • The party will get bigger!
- 113. Other H2O Developments • H2O + xgboost [Link] • Stacked Ensembles [Link] • Automatic Machine Learning [Link] • Time Series [Link] • High Availability Mode in Sparkling Water [Link] • Model Interpretation [Link] • word2vec [Link] 113 • Previous Talks • https://github.com/h2oai/h2o- meetups/blob/master/2017_04_0 6_Amsterdam/2017_04_06_Latest _H2O_Developments.pdf
- 114. • Organizers & Sponsors • Poznan R Users Group (PAZUR) • H2O.ai 114 Thanks! • Code, Slides & Documents • bit.ly/h2o_meetups • docs.h2o.ai • Contact • joe@h2o.ai • @matlabulous • github.com/woobe • Please search/ask questions on Stack Overflow • Use the tag `h2o` (not H2 zero)