Deep learning on HDP 2018 Prague

1 © Hortonworks Inc. 2011–2018. All rights reserved.
© Hortonworks, Inc. 2011-2018. All rights reserved. | Hortonworks confidential and proprietary information.
Deep Learning on HDP
Prague 2018
Timothy Spann, Solutions Engineer
Hortonworks @PaaSDev

Disclaimer
• This document may contain product features and technology directions that are under
development, may be under development in the future or may ultimately not be
developed.
• Technical feasibility, market demand, user feedback, and the Apache Software
Foundation community development process can all effect timing and final delivery.
• This document’s description of these features and technology directions does not
represent a contractual commitment, promise or obligation from Hortonworks to deliver
these features in any generally available product.
• Product features and technology directions are subject to change, and must not be
included in contracts, purchase orders, or sales agreements of any kind.
• Since this document contains an outline of general product development plans,
customers should not rely upon it when making a purchase decision.

Agenda
• Data Engineering With Deep Learning
• TensorFlow with Apache NiFi
• TensorFlow on YARN
• Apache MXNet Pre-Built Models
• Apache MXNet Model Server With Apache NiFi
• Apache MXNet in Apache Zeppelin Notebooks
• Apache MXNet On YARN
• Demos
• Questions

Deep Learning for Big Data Engineers
Multiple users, frameworks, languages, data sources & clusters
BIG DATA ENGINEER
• Experience in ETL
• Coding skills in Scala,
Python, Java
• Experience with Apache
Hadoop
• Knowledge of database
query languages such as
SQL
• Knowledge of Hadoop tools
such as Hive, or Pig
• Expert in ETL (Eating, Ties
and Laziness)
• Social Media Maven
• Deep SME in Buzzwords
• No Coding Skills
• Interest in Pig and Falcon
CAT AI
• Will Drive your Car
• Will Fix Your Code
• Will Beat You At Q-Bert
• Will Not Be Discussed
Today
• Will Not Finish This Talk For
Me, This Time
http://gluon.mxnet.io/chapter01_crashcourse/preface.html

Use Cases
So Why Am I Orchestrating These Complex Deep Learning Workflows?
Computer Vision
• Object Recognition
• Image Classification
• Object Detection
• Motion Estimation
• Annotation
• Visual Question and Answer
• Autonomous Driving
• Speech to Text
• Speech Recognition
• Chat Bot
• Voice UI
Speech Recognition Natural Language Processing
• Sentiment Analysis
• Text Classification
• Named Entity Recognition
https://github.com/zackchase/mxnet-the-straight-dope
Recommender Systems
• Content-based
Recommendations

Deep Learning Options
• TensorFlow (C++, Python, Java)
• TensorFlow on Spark (Yahoo)
• Caffe on Spark (Yahoo)
• Apache MXNet (Baidu, Amazon, Nvidia, MS, CMU, NYU, intel)
• Deep Learning 4 J (Skymind) JVM
• PyTorch
• H2o Deep Water
• Keras ontop of TensorFlow and DL4J
• Apache Singa
• Caffe2 (Facebook)

Recommendations
• Install CPU Version on CPU YARN Nodes
• Install GPU Version on Nvidia (CUDA)
• Do training on GPU YARN Nodes where possible
• Apply Model on All Nodes and Trigger with Apache NiFi
• What helps Hadoop and Spark will help TensorFlow. More RAM, More and
Faster Cores, More Nodes.
• Today, Run either pure TensorFlow with Keras <or> TensorFlow on Spark.
• Try YARN 3.0 Containerized TensorFlow later in the year.
• Consider Alluxio or Apache Ignite for in-memory optimization
• Download the model zoos
• Evaluate other Deep Learning Frameworks like MXNet and PyTorch

Deep Learning Options

TensorFlow on Hadoop
https://www.tensorflow.org/deploy/hadoop
HDFS files can be used as a distributed source for input producers for training, allowing one fast cluster to
Store these massive datasets and share them amongst your cluster.
This requires setting a few environment variables:
JAVA_HOME
HADOOP_HDFS_HOME
LD_LIBRARY_PATH
CLASSPATH

TensorFlow Serving on YARN 3.0 https://github.com/NVIDIA/nvidia-docker
We use NVIDIA Docker
containers on top of YARN

Run TensorFlow on YARN 3.0
https://community.hortonworks.com/articles/83872/data-lake-30-containerization-erasure-coding-gpu-p.html

Apache Deep Learning Flow
Ingestion
Simple Event Processing
Engine
Stream Processing
Destination
Data Bus
Build
Predictive Model
From Historical Data
Deploy
Predictive Model
For Real-time Insights
Perishable Insights
Historical Insights

© Hortonworks Inc. 2011
Streaming Apache Deep Learning
Page 13
Data Acquisition
Edge Processing
Deep Learning
Real Time Stream Analytics
Rapid Application Development
IoT
ANALYTICS
CLOUD
Acquire Move
Routing
&
Filtering
Deliver Parse Analysis
Aggregation
Modeling

Apache Deep Learning Components
Streaming Analytics
Manager
Machine Learning
Distributed queue
Buffering
Process decoupling
Streaming and SQL
Orchestration
Queueing
Simple Event Processing
REST API
Secure Spark Execution

Streaming Analytics
Manager
Run everywhere
Detect metadata and data
Extract metadata and data
Content Analysis
Deep Learning Framework
Entity Resolution
Natural Language Processing
Apache Deep Learning Components

http://mxnet.incubator.apache.org/
• Cloud ready
• Experienced team (XGBoost)
• AWS, Microsoft, NVIDIA, Baidu, Intel backing
• Apache Incubator Project
• Run distributed on YARN
• In my early tests, faster than TensorFlow.
• Runs on Raspberry PI, Nvidia Jetson TX1
and other constrained devices
• Great documentation
• Gluon
• Great Python Interaction
• Model Server Available
• ONNX Support
• Now in Version 1.1!
• Great Model Zoo
https://mxnet.incubator.apache.org/how_to/cloud.html
https://github.com/apache/incubator-mxnet/tree/1.1.0/example

Deep Learning Architecture
HDP Node X
Node
Manager
Datanode
HBase
Region
HDP Node Y
Node
Manager
Datanode
HBase
Region
HDF Node
Apache NiFi
Zookeeper
Apache Spark
MLib
Apache Spark
MLib
GPU Node
Neural Network
Apache Spark
MLib
Apache Spark
MLib
Pipeline
GPU Node
Neural Network
Pipeline
MiNiFi Java
Agent
MiNiFi C++
Agent
HDF Node
Apache NiFi
Zookeeper
Apache Livy

What do we want to do?
• MiniFi ingests camera images and
sensor data
• MiniFi executes Apache MXNet at the
edge
• Run Apache MXNet Inception to
recognize objects in image
• Apache NiFi stores images, metadata
and enriched data in Hadoop
• Apache NiFi ingests social data and
REST feeds
• Apache OpenNLP and Apache Tika for
textual data

Aggregate all data from sensors, drones, logs, geo-location devices,
machines and social feeds
Collect: Bring Together
Mediate point-to-point and bi-directional data flows, delivering data
reliably to Apache HBase, Apache Hive, HDFS, Slack and Email.
Conduct: Mediate the Data Flow
Parse, filter, join, transform, fork, query, sort, dissect; enrich with weather,
location, sentiment analysis, image analysis, object detection, image
recognition, voice recognition with Apache Tika, Apache OpenNLP and
Apache MXNet.
Curate: Gain Insights

Why Apache NiFi?
• Guaranteed delivery
• Data buffering
- Backpressure
- Pressure release
• Prioritized queuing
• Flow specific QoS
- Latency vs. throughput
- Loss tolerance
• Data provenance
• Supports push and pull
models
• Hundreds of processors
• Visual command and
control
• Over a fifty sources
• Flow templates
• Pluggable/multi-role
security
• Designed for extension
• Clustering
• Version Control

• Apache MXNet via Execute Process (Python)
• Apache MXNet Running on Edge Nodes (MiniFi) S2S
• Apache MXNet Model Server Integration (REST API)
Not Covered Today
• *Dockerized Apache MXNet on Hadoop YARN 3 with NVidia GPU
• *Apache MXNet on Spark
Apache NiFi Integration with Apache MXNet Options

• https://github.com/apache/incubator-mxnet/tree/master/tools/coreml
• https://github.com/Leliana/WhatsThis
• https://github.com/apache/incubator-mxnet/tree/master/amalgamation/jni
• https://hub.docker.com/r/mxnet/
• https://github.com/apache/incubator-mxnet/tree/master/scala-
package/spark
Other Options

Apache MXNet Pre-Built Models
• CaffeNet
• SqueezeNet v1.1
• Inception v3
• Single Shot Detection (SSD)
• VGG19
• ResidualNet 152
• LSTM
http://mxnet.incubator.apache.org/model_zoo/index.html
https://github.com/dmlc/mxnet-model-gallery

python3 -W ignore analyze.py
{"uuid": "mxnet_uuid_img_20180208204131", "top1pct": "30.0999999046", "top1":
"n02871525 bookshop, bookstore, bookstall", "top2pct": "23.7000003457", "top2":
"n04200800 shoe shop, shoe-shop, shoe store", "top3pct": "4.80000004172", "top3":
"n03141823 crutch", "top4pct": "2.89999991655", "top4": "n04370456 sweatshirt",
"top5pct": "2.80000008643", "top5": "n02834397 bib", "imagefilename":
"images/tx1_image_img_20180208204131.jpg", "runtime": "2"}
Apache MXNet via Python (OSX Local with WebCam)

Apache MXNet Running on with Apache NiFi Node

Apache MXNet Running on Edge Nodes (MiniFi)
https://community.hortonworks.com/articles/83100/deep-learning-iot-workflows-with-raspberry-pi-mqtt.html
https://github.com/tspannhw/mxnet_rpi
https://community.hortonworks.com/articles/146704/edge-analytics-with-nvidia-jetson-tx1-
running-apac.html

Apache MXNet Model Server with Apache NiFi
https://community.hortonworks.com/articles/155435/using-the-new-mxnet-model-server.html
sudo pip3 install mxnet-model-server --upgrade

Apache MXNet Running in Apache Zeppelin

Apache MXNet on Apache YARN
https://github.com/tspannhw/nifi-mxnet-yarn
https://community.hortonworks.com/articles/174399/apache-deep-learning-101-using-apache-
mxnet-on-apa.html
dmlc-submit --cluster yarn --num-workers 1 --server-cores 2
--server-memory 1G --log-level DEBUG --log-file mxnet.log analyzeyarn.py

Apache OpenNLP for Entity Resolution
Processor
https://github.com/tspannhw/nifi-nlp-
processor
Requires installation of NAR and Apache
OpenNLP Models
(http://opennlp.sourceforge.net/models-1.5/).
This is a non-supported processor that I wrote
and put into the community. You can write
one too!
Apache OpenNLP with Apache NiFi
https://community.hortonworks.com/articles/80418/open-nlp-example-apache-nifi-processor.html

Why TensorFlow? Also Apache MXNet,
PyTorch and DL4J.
• Google
• Multiple platform
support
• Hadoop integration
• Spark integration
• Keras
• Large Community
• Python and Java APIs
• GPU Support
• Mobile Support
• Inception v3
• Clustering
• Fully functional demos
• Open Source
• Apache Licensed
• Large Model Library
• Buzz
• Extensive Documentation
• Raspberry Pi Support

Streaming Analytics
Manager
Part of MiniFi C++ Agent
Detect metadata and data
Extract metadata and data
Content Analysis
Deep Learning Framework
Complex Event Processing
Joining DataSets for Streaming Analytics
Open Source Image Analytical Components
Enabling Record Processing
Schema Management

python classify_image.py --image_file /opt/demo/dronedata/Bebop2_20160920083655-0400.jpg
solar dish, solar collector, solar furnace (score = 0.98316)
window screen (score = 0.00196)
manhole cover (score = 0.00070)
radiator (score = 0.00041)
doormat, welcome mat (score = 0.00041)
bazel-bin/tensorflow/examples/label_image/label_image --
image=/opt/demo/dronedata/Bebop2_20160920083655-0400.jpg
tensorflow/examples/label_image/main.cc:204] solar dish (577): 0.983162I
tensorflow/examples/label_image/main.cc:204] window screen (912): 0.00196204I
tensorflow/examples/label_image/main.cc:204] manhole cover (763): 0.000704005I
tensorflow/examples/label_image/main.cc:204] radiator (571): 0.000408321I
tensorflow/examples/label_image/main.cc:204] doormat (972): 0.000406186
TensorFlow via Python or C++ Binary

DATA_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
TensorFlow Python Example – Classify Image
https://github.com/tspannhw/OpenSourceComputerVision/blob/master/classify_image.py
currenttime= strftime("%Y-%m-%d %H:%M:%S",gmtime())
host = os.uname()[1]

TensorFlow Python Classifier Launcher
#!/bin/bash
DATE=$(date +"%Y-%m-%d_%H%M")
fswebcam -q -r 1280x720 --no-banner /opt/demo/images/$DATE.jpg
python2 -W ignore /opt/demo/classify_image.py /opt/demo/images/$DATE.jpg 2>/dev/null

TensorFlow Python Example – Classify Image
row = []
for node_id in top_k:
human_string = node_lookup.id_to_string(node_id)
score = predictions[node_id]
row.append( { 'node_id': node_id, 'image': image, 'host': host, 'ts': currenttime, 'human_string’:
str(human_string), 'score': str(score)} )
json_string = json.dumps(row)
print( json_string )

• TensorFlow (C++, Python, Java)
via ExecuteStreamCommand
• TensorFlow NiFi Java Custom Processor
• TensorFlow Running on Edge Nodes (MiniFi)
Apache NiFi Integration with TensorFlow Options

TensorFlow Java Processor in NiFi
https://community.hortonworks.com/content/kbentry/116803/building-a-custom-processor-in-
apache-nifi-12-for.html
https://github.com/tspannhw/nifi-tensorflow-processor
https://community.hortonworks.com/articles/178498/integrating-tensorflow-
16-image-labelling-with-hdf.html

Installation On A Single Node of Apache NiFi 1.5+
Download NAR here: https://github.com/tspannhw/nifi-tensorflow-
processor/releases/tag/1.6
Install NAR file to /usr/hdf/current/nifi/lib/
Create a model directory (/opt/demo/models)
wget https://raw.githubusercontent.com/tspannhw/nifi-tensorflow-processor/master/nifi-
tensorflow-processors/src/test/resources/models/imagenet_comp_graph_label_strings.txt
wget https://github.com/tspannhw/nifi-tensorflow-processor/blob/master/nifi-tensorflow-
processors/src/test/resources/models/tensorflow_inception_graph.pb?raw=true
Restart Apache NiFi via Ambari

TensorFlow Running on Edge Nodes (MiniFi)
CREATE EXTERNAL TABLE IF NOT EXISTS tfimage (image
STRING, ts STRING, host STRING, score STRING,
human_string STRING, node_id FLOAT) STORED AS ORC
LOCATION '/tfimage'

TensorFlow Installation (Edge)
apt-get install curl wget –y
wget https://github.com/bazelbuild/bazel/releases/download/0.11.1/bazel-0.11.1-installer-linux-x86_64.sh
./bazel-0.11.1-installer-linux-x86_64.sh
apt-get install libblas-dev liblapack-dev python-dev libatlas-base-dev gfortran python-setuptools python-h5py –y
pip3 install six numpy wheel
pip3 install --user numpy scipy matplotlib pandas sympy nose
pip3 install --upgrade tensorflow
git clone --recurse-submodules https://github.com/tensorflow/tensorflow
wget http://mirror.jax.hugeserver.com/apache/nifi/minifi/0.4.0/minifi-0.4.0-bin.zip
wget https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip
wget http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz

Questions?

Contact
https://github.com/tspannhw/ApacheDeepLearning101
https://community.hortonworks.com/users/9304/tspann.html
https://dzone.com/users/297029/bunkertor.html
https://www.meetup.com/futureofdata-princeton/
https://twitter.com/PaaSDev
https://community.hortonworks.com/articles/155435/using-the-new-mxnet-model-server.html
https://github.com/dmlc/dmlc-core/tree/master/tracker/yarn
https://news.developer.nvidia.com/nvidias-2017-open-source-deep-learning-frameworks-
contributions

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Deep learning on HDP 2018 Prague

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