RAPIDS: Deep Dive Into How the Platform Works Paul Mahler, 3/18/19 - - PowerPoint PPT Presentation

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RAPIDS: Deep Dive Into How the Platform Works Paul Mahler, 3/18/19 - - PowerPoint PPT Presentation

Sep 30, 2023 38 likes •209 views

RAPIDS: Deep Dive Into How the Platform Works Paul Mahler, 3/18/19 Introduction to RAPIDS 2 DATA SCIENCE WORKFLOW WITH RAPIDS Open Source, GPU-accelerated ML Built On CUDA cuDF cuML VISUALIZE DAT PREDICTIONS A Data ML model Dataset

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Paul Mahler, 3/18/19

RAPIDS: Deep Dive Into How the Platform Works

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Introduction to RAPIDS

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DATA SCIENCE WORKFLOW WITH RAPIDS

Open Source, GPU-accelerated ML Built On CUDA

Data preparation / wrangling

cuDF

ML model training

cuML VISUALIZE

Dataset exploration

DAT A PREDICTIONS

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WHAT IS RAPIDS?

rapids.ai Suit of open-source, end-to-end data science tools Built on CUDA Pandas-like API for data cleaning and transformation Scikit-learn-like API A unifying framework for GPU data science

The New GPU Data Science Pipeline

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“CLASSIC” MACHINE LEARNING

The daily work of most data scientists

Comprehensible to average data scientists and analysts Higher level of interpretability Solutions for unlabeled data Techniques such as regression and decision trees, clustering Scikit-learn

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Ecosystem Partners

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RAPIDS ROADMAP

cuML LIBRARY cuGRAPH LIBRARY

DATA ANALYTICS MACHINE LEARNING GRAPH ANALYSIS

IO OPERATORS REGRESSION DIMENSION REDUCTION CLASSIFICATION COMMUNITY DETECTION CENTRALITY cuDF LIBRARY

UP TO 5-15X SPEEDUP UP TO 10-20X SPEEDUP UP TO 100-500X SPEEDUP

PATH FINDING

DATA FORMATS

(CSV, ORC, PARQUET, JSON)

DATA SOURCES

(CLOUD, HDFS)

DATA TYPES

(INT64, FP64, STRINGS)

JOINS GROUPBYS WINDOWING GBDT LOGISTIC GBDT RIDGE PAGE RANK SINGLE SHORTEST PATH BREADTH-FIRST SEARCH DEPTH FIRST SEARCH SPECTRAL CLUSTERING LOUVAIN CLUSTERING SUBGRAPH EXTRACTION STRINGS UDFs

TIME SERIES PREPROCESSING CLUSTERING SIMILARITY

WEIGHTED JACCARD JACCARD SIMILARITY TRIANGLE COUNTING SVM LINEAR LASSO RANDOM FOREST UMAP PCA SVD T-SNE KNN K-MEANS DBSCAN KALMAN FILTERING HOLT WINTERS ARIMA

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RAPIDS PREREQUISITES

  • NVIDIA Pascal™ GPU architecture or better
  • CUDA 9.2 or 10.0 compatible NVIDIA driver
  • Ubuntu 16.04 or 18.04
  • Docker CE v18+
  • nvidia-docker v2+

See more at rapids.ai

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GETTING STARTED RESOURCES

Rapids.ai cuDF Documentation: https://rapidsai.github.io/projects/cudf/en/latest/ cuML Documentation: https://rapidsai.github.io/projects/cuml/en/latest/ Github: https://github.com/RAPIDSai Twitter: @rapidsai

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Architecture

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RMM Memory Pool Allocation

Use large cudaMalloc allocation as memory pool Custom memory management in pool Streams enable asynchronous malloc/free RMM currently uses CNMem as it’s Sub-allocator https://github.com/NVIDIA/cnmem RMM is standalone and free to use in your own projects!

https://github.com/rapidsai/rmm

GPU Memory cudaMalloc’d Memory Pool Previously Allocated Blocks bufferA bufferB

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cuML architecture

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Let’s Dive into the Tutorial!

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Getting GCP Set Up

Get GCP IP address ssh pydata@{IP} Password: gtc2019 conda activate rapids Get the data: wget -v -O black_friday.zip -L https://goo.gl/3EYV8r (if you don’t have wget, you can install it on mac via homebrew) Download Jupyter Notebook wget -v -O gtc_tutorial_student.ipynb -L https://bit.ly/2Ht8hLe jupyter-notebook --allow-root --ip=0.0.0.0 --port 8888 --no-browser --NotebookApp.token=''

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Paul Mahler @realpaulmahler