Short tutorial about H2O.ai Rpubs for demo code: http://rpubs.com/bwv988/h2odemo GitHub repo: https://github.com/bwv988/h2o-tutorial

1. H2O – Tutorial and Demo
A brief intro to H2O
Ralph Schlosser
https://github.com/bwv988
July 2017
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2. Overview
Agenda
H2O at a glance
H2O projects
Architecture
Data input and output
ML algorithms
Demo
Links
Git repo: https://github.com/bwv988/h2o-tutorial
Demo: http://rpubs.com/bwv988/h2odemo
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3. H2O at a glance
“H2O is an open source, in-memory, distributed, fast, and scalable
machine learning and predictive analytics platform that allows you to
build machine learning models on big data and provides easy
productionalization of those models in an enterprise environment.”
— H2O.ai documentation
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4. H2O at a glance
Open source software, backed by commercial company.
Implemented from scratch in Java.
Multiple language bindings, or APIs: Java, Scala, R, Python.
Big data: Interfaces to Spark, Hadoop.
Deep Learning: Has abstraction layer to call TensorFlow, MXNet, Caffe
back-ends.
H2O company advisers include Rob Tibshirani and Trevor Hastie of
ESL fame. ;)
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5. H2O projects
H2O: Core project.
Sparkling Water: Execute H2O workloads on a Spark cluster.
Deep Water (preview): Call TensorFlow, MXNet, Caffee from within
H2O.
Flow: Integral part of H2O; web-based, notebook-style, interactive,
“point-and-click” UI for H2O.
Steam: Cluster management, collaborate, manage models, data,
teams.
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6. Architecture: Highlights
The H2O cloud consists of one or more nodes.
Each node runs as a separate JVM process.
Employs three-layered architecture: Language, Algorithms,
Infrastructure.
Language:
Native language support for Java, Scala.
REST API for other languages.
Algorithms
Has own implementation of many common ML algorithms.
Separate slide for this.
Infrastructure
Manage distributed data sets.
Manage distributed (parallel) computations: MapReduce.
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7. Architecture: Overview
H2O components
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8. Architecture: How R (or Python) interface with H2O
No actual H2O computations, or data operations are done in R.
# Example: Load data from HDFS into H2O.
h2o_df = h2o.importFile("hdfs://give/me/data.csv")
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9. Data input and output
Many formats and sources are supported. Automatic data schema
discovery for most scenarios.
Formats
CSV
ORC – Optimized Row
Columnar, new in Hadoop &
Hive
SVMLight – Sparse data format
ARFF – Attribute Relation File
Format, from Weka
XLS, XLSX
Avro
Parquet
Sources
Local files
Remote files
HDFS
S3
Alluxio
JDBC
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10. ML algorithms
H2O provides highly optimized from-scratch implementations of
classical, as well as modern techniques on top of its distributed,
in-memory processing engine.
Deep Learning: Native implementation of a multi-layer, feed-forward
ANN. 1
Distributed Random Forest
GLM
GBM
k-Means clustering
PCA
. . . and many more: http://docs.h2o.ai/h2o/latest-stable/
h2o-docs/data-science.html
1
CNN and RNN only through 3rd party integrations, e.g. TensorFlow.
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11. Demo
Supervised learning example in R.
Flow demo.
Steam demo.
http: // rpubs. com/ bwv988/ h2odemo
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12. References
H2O website: https://www.h2o.ai/
Some pictures “stolen” from H2O’s documentation: http:
//docs.h2o.ai/h2o/latest-stable/h2o-docs/welcome.html
Erin Ledell’s excellent presentation: https://www.stat.berkeley.
edu/~ledell/docs/h2o_hpccon_oct2015.pdf
Darren Cook: “Practical Machine Learning with H2O” –
https://www.amazon.com/
Practical-Machine-Learning-H2O-Techniques/dp/149196460X
Example code is available in GitHub:
https://github.com/DarrenCook/h2o
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