This document discusses benchmarks for GraphLab Create, a machine learning library. It summarizes benchmarking GraphLab Create on large datasets by running PageRank on a graph with 3.5 billion nodes and 128 billion links, and gradient boosted trees on a dataset with 4.3 billion rows and 39 features. The document also provides instructions for instantiating an Amazon EC2 instance with 32 cores and 244GB RAM to run the benchmarks, and includes a link to download GraphLab Create and access the benchmark notebooks on GitHub.

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GraphLab Create Benchmarks
April 21, 2016
Guy Rapaport, Data Scientist, Dato EMEA
guy@dato.com

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Dato: We Intelligent Applications

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Some of our Customers
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Business
must be intelligent
Machine learning
applications
• Recommenders
• Fraud detection
• Ad targeting
• Financial models
• Personalized medicine
• Churn prediction
• Smart UX
(video & text)
• Personal assistants
• IoT
• Socials networks
• Log analysis
Last decade:
Data management
Now:
Intelligent apps
?
Last 5 years:
Traditional analytics

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Example Intelligent Applications
- images
- text
- graphs
- tabular data
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Creating a model pipeline
exploration
data
modeling

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Creating a model pipeline
Ingest Transform Model Deploy
Unstructured Data

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GraphLab Create in a Line
“A general-purpose machine learning Python library that
scales on large datasets.”
 General purpose: classification, graph analytics…
 Python API on top, C++ open-source engine below.
 Scales vertically: more CPUs, RAM and faster disks.
 Large datasets: disk bound, not RAM bound.
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What will we cover today?
1. Instantiating a machine in the Amazon EC2 cloud
• r3.8xlarge instance
• 32 cores, 244GBs of RAM, 2 SSDs of 320GBs each
2. Run PageRank on a large graph
• CommonCrawl 2012 dataset – the internet as a graph
• 3.5 billion nodes, 128 billion links
3. Run Gradient Boosted Trees on a large dataset
• Criteo 1TB Click Logs Dataset
• 4.3 billion rows, 39 features (13 numerical, 26 categorical)
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What will you be able to do afterwards?
Instantiate an EC2 instance, grab our benchmark
notebooks, and try it yourself!
Everything is publicly available on github:
https://github.com/guy4261/glc_pagerank_benchmark
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Screen Primer
Command Action
sudo apt-get install –y screen Install screen
screen –S my_session Start a session named my_session
PS1=‘u@h(${STY}:${WINDOW}):w$’ Change your screen prompt (helpful)
# CTRL+A, then D Key combination to detach
screen -ls List all open screens
screen –r my_session Reattach to your screen
exit Exit the session and terminate the screen

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Confidential – Dato internal use only. ©2015 Dato, Inc.
Questions?
“For the purpose of learning the Answer to the
Ultimate Question of Life, The Universe, and Everything,
the supercomputer Deep Thought was specially built.
It takes Deep Thought 7½ million years to compute and check the
answer, which turns out to be 42. Deep Thought points out that
the answer seems meaningless because
the beings who instructed it
never actually knew what the Question was.”
- Douglas Adams, “The Hitchhiker’s Guide to the Galaxy”

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Our Machine Learning Specialization
in Coursera
https://www.coursera.org/learn/ml-foundations

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Confidential – Dato internal use only. ©2015 Dato, Inc.
Thanks!
Install using pip: $ pip install -U graphlab-create
Dato Launcher Download:
https://dato.com/download/
The benchmarks on GitHub:
https://github.com/guy4261/glc_pagerank_benchmark
Coursera Course:
https://www.coursera.org/learn/ml-foundations
Reach out: guy@dato.com