The Edge to AI Deep Dive Barcelona Meetup March 2019

Edge to AI: Deep Dive
Future of Data: Barcelona Meetup
TIMOTHY SPANN, Senior Solutions Engineer, Cloudera
https://www.datainmotion.dev/

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There are some who call him...
DZone Zone Leader and Big Data MVB;
Princeton Future of Data Meetup
https://github.com/tspannhw
https://community.hortonworks.com/users/9304/tspann.html
https://dzone.com/users/297029/bunkertor.html
https://www.meetup.com/futureofdata-princeton/

Hadoop {Submarine} Project: Running deep learning workloads on YARN ,
Tim Spann (Cloudera)

IoT Edge Processing with Apache MiniFi and Multiple Deep Learning Libraries

AI
MACHINE
LEARNING
DATA SCIENCE
ANALYTICS
"BIG DATA"

MACHINE LEARNING PHASES
Where to Connect to Apache NiFi

INDUSTRIALIZED AI REQUIRES LARGER DATA PLATFORM
Streaming
Ingest
Batch Ingest
Machine
Learning Tools
BI Tools and
SQL Editors
Data Products
DATA, METADATA, SECURITY, GOVERNANCE, WORKLOAD MANAGEMENT
MACHINE
LEARNING
DATA
ENGINEERING
DATA
WAREHOUSE
OPERATIONAL
DATABASE

Speed of Data Model Training Model Scoring Use Case
Batch
Batch
Batch
Batch Reporting,
Analytics,
Applications
Online
DS Applications/
Interactive
Dashboards
Streaming
In-stream
Streaming
Applications
Incremental/Online In-stream
Streaming
Applications
Training, Scoring and Monitoring

TENSORFLOW IN GATEWAY

Using TensorFlow Lite on The Edge with Sensors and Google Coral
(MiNiFi)
https://github.com/tspannhw/nifi-minifi-coral
https://www.datainmotion.dev/2019/03/using-
raspberry-pi-3b-with-apache-nifi.html
{
"endtime": "1552164369.27",
"memory": "19.1",
"cputemp": "32",
"ipaddress": "192.168.1.183",
"diskusage": "50336.5",
"score_2": "0.14",
"score_1": "0.68", "runtime": "4.74",
"host": "mv2",
"starttime": "03/09/2019 15:46:04",
"label_1": "hard disc, hard disk, fixed disk",
"uuid": "20190309204609_05c9a240-d801-
4bac-b029-e5bf38c02d40",
"label_2": "buckle",
"systemtime": "03/09/2019 15:46:09"
}

Using TensorFlow Lite on The Edge with Sensors and Google Coral
(MiNiFi)

CLOUDERA DATA SCIENCE WORKBENCH
Accelerate machine learning from research to production
For data scientists
• Experiment faster
Use R, Python, or Scala with on-
demand compute and secure
CDH data access
• Work together
Share reproducible research
with your whole team
• Deploy with confidence
Get to production repeatably
and without recoding
For IT professionals
• Bring data science to the data
Give your data science team
more freedom while reducing
the risk and cost of silos
• Secure by default
Leverage common security and
governance across workloads
• Run anywhere
On-premises or in the cloud

ACCELERATED DEEP LEARNING WITH GPUS
Multi-tenant GPU support on-premises or cloud
• Extend CDSW to deep learning
• Schedule & share GPU resources
• Train on GPUs, deploy on CPUs
• Works on-premises or cloud
CDSW
GPUCPU
CDH
CPU
CDH
CPU
single-node
training
distributed
training, scoring
“Our data scientists want GPUs, but
we need multi-tenancy. If they go to
the cloud on their own, it’s
expensive and we lose governance.”
GPU On CDH coming in C6

INTRODUCING MODELS
Machine learning models as one-click microservices (REST APIs)
Model APIs made easy!
1. Choose Python/R file, e.g. score.py
2. Choose function, e.g. forecast
f = open('model.pk', 'rb')
model = pickle.load(f)
def forecast(data):
return model.predict(data)
3. Choose resources

Select a Project, Create a Session, Load Libraries and Data

Load a File and Run It

Install Python Libraries for Python 2 or Python 3

Test your function with an argument

Create a model from that file and function

CLOUDERA DATA SCIENCE WORKBENCHList All The Models

CLOUDERA DATA SCIENCE WORKBENCHCheckout The Build

CLOUDERA DATA SCIENCE WORKBENCHDeploy the Model

CLOUDERA DATA SCIENCE WORKBENCHTest the Model

CLOUDERA DATA SCIENCE WORKBENCHValidate the Model Results

WHAT’S NEW: CLOUDERA DATA SCIENCE WORKBENCH
Accelerate and simplify machine learning from research to production
ANALYZE DATA
• Explore data securely and
share insights with the
team
TRAIN MODELS
• Run, track, and compare
reproducible experiments
DEPLOY APIs
• Deploy and monitor models
as APIs to serve predictions
NEW! NEW!
MANAGE SHARED RESOURCES
• Provide a secure, collaborative, self-service platform for your data science teams

CLOUDERA DATA SCIENCE WORKBENCHMonitor The Running Models

MODEL MANAGEMENT
View, test, monitor, and update models by team or project

CLOUDERA DATA SCIENCE WORKBENCHInvoke the Model From Apache NiFi In Flow

CLOUDERA DATA SCIENCE WORKBENCHQuery Results of Classification in Flow
{ "class1": "cat", "cpu": 38.3, "end": "1549672761.1262221",
"host": "gluoncv-apache-mxnet-29-50-7fb5cfc5b9-sx6dg", "memory": 14.9,
"pct1": "98.15670800000001",
"shape": "(1, 3, 566, 512)", "systemtime": "02/09/2019 00:39:21",
"te": "3.380652666091919"
}
CLOUDERA DATA-IN-MOTION (APACHE NIFI)

● https://blog.cloudera.com/blog/2019/02/integrating-machine-learning-models-into-your-big-data-pipelines-in-
real-time-with-no-coding/
● https://community.hortonworks.com/articles/239961/using-cloudera-data-science-workbench-with-apache.html
● https://community.hortonworks.com/content/kbentry/239858/integrating-machine-learning-models-into-your-
big.html
● https://github.com/tspannhw/nifi-cdsw-gluoncv
● https://community.hortonworks.com/articles/227560/real-time-stock-processing-with-apache-nifi-and-ap.html
● https://www.datainmotion.dev/2019/03/iot-series-sensors-utilizing-breakout_1.html
● https://www.datainmotion.dev/2019/03/edge-to-ai-apache-spark-apache-nifi.html
REFERENCES

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