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Introduction to machine learning with GPUs

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Demystifying machine learning and deep learning

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Introduction to machine learning with GPUs

  1. 1. 2020 GPU-ACCELERATED MACHINE LEARNING INTRO
  2. 2. 2 •  PMM at NVIDIA •  Author of free Apache Spark 3.0 ebook: https://www.nvidia.com/en-us/deep- learning-ai/solutions/data-science/ apache-spark-3/ Carol McDonald
  3. 3. 3 Introduction to Machine Learning Supervised Machine Learning Unsupervised Machine Learning Deep Learning GPU Accelerated Machine Learning Agenda 3
  4. 4. 4 Agenda Introduction to Machine Learning Supervised Machine Learning Unsupervised Machine Learning Deep Learning
  5. 5. 5 What is AI?
  6. 6. 6 AI at NSA, MIT Late 80s
  7. 7. 7 Problems with hard coded Rules •  Rules are manual, uses a human expert –  difficult to maintain –  give a one size fits all decision! (2 times overdose same as 38 times) •  Machine learning uses data and statistics –  can give sorted probabilty, can precisely match/target individuals
  8. 8. 8 What is Machine Learning? Train Algorithm Build Model Features Predictions f(X)
  9. 9. 9 What has changed in the past 10 years? Big Data and Distributed computing Improved machine learning Algorithms
  10. 10. 10 Apache Spark Distributed Datasets Distributed Dataset Node Executor P4 Node Executor P1 P3 Node Executor P2 partitioned Partition 1 8213034705, 95, 2.927373, jake7870, 0…… Partition 2 8213034705, 115, 2.943484, Davidbresler2, 1…. Partition 3 8213034705, 100, 2.951285, gladimacowgirl, 58… Partition 4 8213034705, 117, 2.998947, daysrus, 95…. •  Data read into Memory Cache •  Partitioned across a cluster •  Operated on in parallel •  Cached in memory for iterations
  11. 11. 11 GPUs speed up Multi core servers for parallel processing Cluster of GPUs 1 million times faster than Cray-1
  12. 12. 12 Mythbusters explain Parallel GPU vs Sequential CPU •  Painting a smily face with a sequential paint gun
  13. 13. 13 Mythbusters explain Parallel graphics with GPU •  Painting a smiling face with one blast from a parallel paint gun !
  14. 14. 14 Agenda Introduction to Machine Learning Supervised Machine Learning Unsupervised Machine Learning Deep Learning
  15. 15. 15 Supervised and Unsupervised Machine Learning Machine Learning Unsupervised •  Clustering •  Collaborative Filtering •  Frequent Pattern Mining Supervised •  Classification •  Regression Label
  16. 16. 16 Supervised Algorithms use Labeled Data
  17. 17. 17 ML Discovery Model Building Update Monitor
  18. 18. 18 Supervised Machine Learning: Classification & Regression Classification Identifies category for item Spam / Not Spam
  19. 19. 19 Form of ML that: •  Identifies which category an item belongs to •  Uses supervised learning algorithms –  Data is labeled Classification: Definition Sentiment
  20. 20. 20 Credit Card Fraud Example •  What are we trying to predict? –  This is the Label or Target outcome: –  Fraud or Not Fraud •  What are the “if questions” or properties we can use to predict? –  These are the Features: –  Is the amount spent today > historical daily average? –  Are there transactions close in time at locations far apart ? –  Are the number of transactions today > historical average? –  Are there new state or foreign purchases? Credit Card Transaction Features Number of Transactions last 24 hours Total $ Amount last 24 hours Average Amount last 24 hours Average Amount last 24 hours compared to historical use Location and Time difference since Last Transaction Average transaction fraud risk of merchant type Merchant types for day compared to historical use Features derived From Transaction History
  21. 21. 21 Decision Tree For Classification •  Tree of decisions about features •  Estimates IF THEN ELSE questions •  Gives probability of a correct decision Is the amount spent in 24 hours > average Is the number of states used from > 2 Are there multiple Purchases today from risky merchants? YES NO NoYES Fraud 90% Not Fraud 50% Fraud 90% Not Fraud 30% YES No
  22. 22. 22 •  Random Forest model consists of multiple decision trees from subsets of data •  Final Prediction is combined average output of all trees •  Improves accuracy •  Gradient Boosted Decision Trees •  Iteratively uses residual error to improve multiple trees. Final prediciton weighted sum. •  XGBoost distributed faster version of GBDT Random Forest, Gradient Boosting Decision Trees, XGBoost
  23. 23. 23 Examples •  Retail Example: –  Predict price, sales •  Telecom: –  Predict customer will churn •  Healthcare Example: –  Probability of readmission •  Marketing –  Predict probability customer will click on add
  24. 24. 24 Supervised Learning House Price Prediction Example •  What are we trying to predict? –  This is the Label or Target outcome: –  The house price $ •  What are the “if questions” or properties we can use to predict? –  These are the Features: –  The size of the house (square meters)
  25. 25. 25 Label: House Price Y X Feature: house size (square meters) Data point: price, size House Price = intercept + coefficient * house size y = a + bx House Price Regression Example
  26. 26. 26 Decision Tree for House Price
  27. 27. 27 Regression Predicts a Numeric Value •  Regression predicts a numeric value (eg price) •  Retail Example: –  Sales based on an event •  Healthcare Example: –  Days of hospital stay
  28. 28. 28 Agenda Introduction to Machine Learning Supervised Machine Learning Unsupervised Machine Learning Deep Learning
  29. 29. 29 Supervised and Unsupervised Machine Learning Machine Learning Unsupervised •  Clustering •  Collaborative Filtering •  Frequent Pattern Mining Supervised •  Classification •  Regression Label
  30. 30. 30 Unsupervised Algorithms use Unlabeled data Customer GroupsBuild ModelTrain Algorithm Finds patterns New Customer Purchase Data Use Model Similar Customer Group Contains patterns Recognizes patterns Customer purchase data
  31. 31. 31 Unsupervised Machine Learning: Clustering Clustering group news articles into different categories
  32. 32. 32 Clustering: Definition Groups objects into clusters with high feature similarity
  33. 33. 33 Uses of Clustering Clustering Examples include: •  Grouping: –  Similar search results , text –  Similar customers –  Patient similarity –  Similar Products •  Anomaly detection: finding outliers, what’s outside of the groups not similar
  34. 34. 34 Clustering: Example Group similar objects
  35. 35. 35 Clustering: Example Group similar objects Use MLlib K-means algorithm 1.  Initialize coordinates to K cluster centers
  36. 36. 36 Clustering: Example Group similar objects Use MLlib K-means algorithm 1.  Initialize coordinates to K clusters centers (centroid) 2.  Assign all points to nearest cluster center (centroid)
  37. 37. 37 Clustering: Example Group similar objects Use MLlib K-means algorithm 1.  Initialize coordinates to center of clusters (centroid) 2.  Assign all points to nearest centroid 3.  Update centroids to center of assigned points
  38. 38. 38 Clustering: Example Group similar objects Use MLlib K-means algorithm 1.  Initialize coordinates to center of clusters (centroid) 2.  Assign all points to nearest centroid 3.  Update centroids to center of points 4.  Repeat until conditions met
  39. 39. 39 Association, Co-Occurrence, Market Basket Recommendations •  Retail –  Products which are purchased together •  Take action: –  Store layouts –  Which products to put on specials, promote, coupons… •  Healthcare –  Patients like mine cohorts
  40. 40. 40 Agenda Introduction to Machine Learning Supervised Machine Learning Unsupervised Machine Learning Deep Learning
  41. 41. 41 Deep Learning Multilayered neural networks
  42. 42. 42 The Network is trained with images
  43. 43. 43 Neural network neuron or node Each node takes input data and a weight and outputs a confidence score to the next layer
  44. 44. 44 Each node outputs a confidence score to the next layer
  45. 45. 45 Errors are calculted at the output layer
  46. 46. 46 Errors are sent back through the network
  47. 47. 47 This process is repeated, adjusting weights, until correct
  48. 48. 48 This process is repeated with lots of images
  49. 49. 49 Deep Learning During this process layers learn the optimal features for the model
  50. 50. 50 Deep Learning Features •  Advantage: –  Features do not have to be predetermined •  Disadvantage: –  Decisions are a black box Feature Decisions ?
  51. 51. 51 Deep Neural Networks •  Classification and •  Forecasting
  52. 52. 52 Convolutional Neural Networks for Images •  Image crunchers to identify objects •  Today’s eyes for identifying cancer, driving cars, oil exploration
  53. 53. 53 Recurrent Neural Networks for Sequenced data •  Sequence of events and language patterns •  Amazon’s Alexa, Apple’s Siri, Google’s autocomplete, fraud, stock predictions and algorithmic trading
  54. 54. 54 Agenda Introduction to Machine Learning Supervised Machine Learning Unsupervised Machine Learning Deep Learning GPUs
  55. 55. 55 GPUs have been responsible for the advancement of deep learning in the past several years https://developer.nvidia.com/deep-learning-software
  56. 56. 56 cuDF cuIO Analytics Data Preparation VisualizationModel Training cuML Machine Learning cuGraph Graph Analytics PyTorch, TensorFlow, MxNet Deep Learning cuxfilter, pyViz, plotly Visualization Dask GPU Memory RAPIDS End-to-End Accelerated GPU Data Science https://developer.nvidia.com/blog/building-an-accelerated-data-science-ecosystem-rapids-hits-two-years/
  57. 57. 57 � All systems utilize the same memory format � No overhead for cross-system communication � Projects can share functionality (eg, Parquet-to- Arrow reader) Source: From Apache Arrow Home Page - https://arrow.apache.org/ GPU DataFrame and Apache Arrow
  58. 58. 58 Data processing challenges with CPU-powered spark Hadoop brought scale-out processing to data analytics ETL and traditional machine-learning workloads continued to be written with tools like Scikit-Learn or multi-CPU distributed solutions like Spark. Limited Horizontal Scalability 2030202020102000 Data Processing Requirements CPU Gap in Data Processing Data to Analyze CPU Processing Hadoop Era Spark Era
  59. 59. 59 DATA PROCESSING SOLUTION FOR TODAY AND TOMORROW Spark 3.0 on GPUs Spark 3.0 clusters can now be accelerated using RAPIDS software and NVIDIA GPUs and exceed data processing requirements
  60. 60. 60 BENEFITS OF GPU ACCELERATED SPARK 3.0 Accelerate data science pipelines without code changes One pipeline, from ingest to data prep to training Data preparation and model training are both GPU-accelerated Infrastructure is consolidated and simplified
  61. 61. 61 NVIDIA innovations in Spark 3.0 •  RAPIDS Accelerator for Spark 3.0 GPU Acceleration of: •  Spark Data Frames •  Spark SQL •  Shuffle Accelerating Spark pipelines end-to-end
  62. 62. 62 To dive deeper into Apache Spark 3.0, download the free Apache Spark 3.0 ebook: https://www.nvidia.com/en-us/deep- learning-ai/solutions/data-science/ apache-spark-3/ TO LEARN MORE
  63. 63. 63 •  Developers https://developer.nvidia.com/ •  RAPIDS https://developer.nvidia.com/rapids •  Deep Learning https://developer.nvidia.com/deep-learning •  https://news.developer.nvidia.com/oak-ridge-national-laboratory-coronavirus- research/ •  https://developer.nvidia.com/blog/jumpstarting-ai-with-covid-19-ct-inference- pipeline-and-clara-deploy-quickstart-vm/ •  https://news.developer.nvidia.com/ •  https://news.developer.nvidia.com/new-resource-for-nvidia-developers-access- technical-content-through-nvidia-on-demand/ •  https://blazingsql.com/ TO LEARN MORE
  64. 64. 64 Integrations, feedback, documentation support, pull requests, new issues, or code donations welcomed! APACHE ARROW GPU OPEN ANALYTICS INITIATIVE https://arrow.apache.org/ @ApacheArrow http://gpuopenanalytics.com/ @GPUOAI RAPIDS https://rapids.ai @RAPIDSAI DASK https://dask.org @Dask_dev Join the Movement Everyone Can Help!
  65. 65. 65 GITHUB DOCKER https://github.com/rapidsai ANACONDA NGC https://anaconda.org/rapidsai/ https://ngc.nvidia.com/registry/ nvidia-rapidsai-rapidsai https://hub.docker.com/r/ rapidsai/rapidsai/ RAPIDS How Do I Get the Software?
  • ecassamc

    Mar. 28, 2021

Demystifying machine learning and deep learning

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