The document outlines a presentation on Apache Deep Learning, specifically aimed at data engineers, detailing the integration and use of Apache software and its features. It highlights various Apache tools and frameworks, such as Apache NiFi and MXNet, their capabilities in data processing, and contributions to machine learning and deep learning. Additionally, it emphasizes the importance of community feedback and the evolving nature of product features, underscoring that this document reflects the author's ideas rather than representing a formal commitment from any company.

1. 1 @PaaSDev
Apache Deep Learning 201 v0.04
(For Data Engineers)
Timothy Spann
https://github.com/tspannhw/ApacheDeepLearning201/
#phillyopensource

2. 2 @PaaSDev
Disclaimer
• This is my personal integration and use of Apache software, no companies vision.
• 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. This is Tim’s ideas only.
• 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.

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There are some who call him...
DZone Zone Leader and Big Data MVB;
Princeton and Charlotte Future of Data Meetup
https://github.com/tspannhw

4. 4 @PaaSDev
Thank you!
Thanks to Comcast for supporting Apache and the open source!
And for hosting this conference!
Thanks for Sponsoring ApacheCon 2018

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Join me next week in Princeton, NJ:
Machine Learning and Deep Learning
https://www.meetup.com/futureofdata-princeton/events/254821251/
Wednesday, November 14, 2018 6-8pm

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Deep Learning for Big Data Engineers
Multiple users, frameworks, languages, devices, 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

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8. 8 @PaaSDev
Apache Deep Learning Flow

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Aggregate all the Data!
Sensors, Drones, logs,
Geo-location devices
Photos, Images,
Results from running predictions on
Pre-trained models.
Collect: Bring Together

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Mediate point-to-point and
Bi-directional data flows
Delivering data reliably to and from
Apache HBase, Druid, Apache Phoenix,
Apache Hive, HDFS, Slack and Email.
Conduct: Mediate the Data Flow

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Orchestrate, parse, merge, aggregate,
filter, join, transform, fork, query, sort,
dissect, store, enrich with weather,
location, sentiment analysis, image
analysis, object detection, image
recognition, …
Curate: Gain Insights

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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 sixty sources
• Flow templates
• Pluggable/multi-role
security
• Designed for extension
• Clustering
• Version Control

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Edge Intelligence with Apache NiFi Subproject - MiNiFi
à Guaranteed delivery
à Data buffering
‒ Backpressure
‒ Pressure release
à Prioritized queuing
à Flow specific QoS
‒ Latency vs. throughput
‒ Loss tolerance
à Data provenance
à Recovery / recording a rolling log
of fine-grained history
à Designed for extension
Different from Apache NiFi
à Design and Deploy
à Warm re-deploys
Key Features

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• Cloud ready
• Python, C++, Scala, R, Julia, Matlab, MXNet.js and Perl Support
• Experienced team (XGBoost)
• AWS, Microsoft, NVIDIA, Baidu, Intel
• Apache Incubator Project
• Run distributed on YARN and Spark
• In my early tests, faster than TensorFlow. (Try this your self)
• Runs on Raspberry PI, NVidia Jetson TX1 and other constrained devices
https://mxnet.incubator.apache.org/how_to/cloud.html
https://github.com/apache/incubator-mxnet/tree/1.3.0/example
https://gluon-cv.mxnet.io/api/model_zoo.html

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• Great documentation
• Crash Course
• Gluon (Open API), GluonCV, GluonNLP
• Keras (One API Many Runtime Options)
• Great Python Interaction
• Open Source Model Server Available
• ONNX (Open Neural Network Exchange Format) Support for AI Models
• Now in Version 1.3
• Rich Model Zoo!
• TensorBoard compatible
http://mxnet.incubator.apache.org/ http://gluon.mxnet.io/https://onnx.ai/
pip3.6 install -U keras-mxnet
https://gluon-nlp.mxnet.io/
pip3.6 install --pre --upgrade mxnet pip3.6 install gluonnlp

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• Apache MXNet Running in Apache Zeppelin Notebooks
• Apache MXNet Running on YARN 3.1 In Hadoop 3.1 In Dockerized Containers
• Apache MXNet Running on YARN
Apache NiFi Integration with Apache Hadoop Options
https://community.hortonworks.com/articles/176789/apache-deep-learning-101-using-apache-mxnet-in-apa.html
https://community.hortonworks.com/articles/174399/apache-deep-learning-101-using-apache-mxnet-on-apa.html
https://www.slideshare.net/Hadoop_Summit/deep-learning-on-yarn-running-distributed-tensorflow-etc-on-hadoop-cluster-v3

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Apache MXNet GluonCV Zoo
https://gluon-cv.mxnet.io/model_zoo/classification.html
• ResNet152_v2
• MobileNetV2_0.25
• VGG19_bn
• SqueezeNet1.1
• DenseNet201
• Darknet53
• InceptionV3
• CIFAR_ResNeXt29_16x64
• yolo3_darknet53_voc
• ssd_512_mobilenet1.0_coco
• faster_rcnn_resnet101_v1d_coco
• yolo3_darknet53_coco
• FCN model on PASCAL VOC

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Object Detection: GluonCV YOLO v3 and Apache NiFi
https://community.hortonworks.com/articles/222367/using-apache-nifi-with-apache-mxnet-gluoncv-for-yo.html

19. 19 @PaaSDev
Object Detection: Faster RCNN with GluonCV
net = gcv.model_zoo.get_model(faster_rcnn_resnet50_v1b_voc, pretrained=True)
Faster RCNN model trained on Pascal VOC dataset with
ResNet-50 backbone
https://gluon-cv.mxnet.io/api/model_zoo.html

20. 20 @PaaSDev
Instance Segmentation: Mask RCNN with GluonCV
net = model_zoo.get_model('mask_rcnn_resnet50_v1b_coco', pretrained=True)
Mask RCNN model trained on COCO dataset with ResNet-50 backbone
https://gluon-cv.mxnet.io/build/examples_instance/demo_mask_rcnn.html
https://arxiv.org/abs/1703.06870
https://github.com/matterport/Mask_RCNN

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Semantic Segmentation: DeepLabV3 with GluonCV
model = gluoncv.model_zoo.get_model('deeplab_resnet101_ade', pretrained=True)
GluonCV DeepLabV3 model on ADE20K dataset
https://gluon-cv.mxnet.io/build/examples_segmentation/demo_deeplab.html
run1.sh demo_deeplab_webcam.py
http://groups.csail.mit.edu/vision/datasets/ADE20K/ https://arxiv.org/abs/1706.05587
https://www.cityscapes-dataset.com/
This one is a bit slower.

22. 22 @PaaSDev
Semantic Segmentation: Fully Convolutional Networks
model = gluoncv.model_zoo.get_model(‘fcn_resnet101_voc ', pretrained=True)
GluonCV FCN model on PASCAL VOC dataset
https://gluon-cv.mxnet.io/build/examples_segmentation/demo_fcn.html
run1.sh demo_fcn_webcam.py
https://people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn.pdf

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Apache MXNet Model Server from Apache NiFi
https://community.hortonworks.com/articles/223916/posting-images-with-apache-nifi-17-and-a-custom-
pr.html

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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/OpenSourceComputerVision
https://github.com/tspannhw/ApacheDeepLearning101
https://github.com/tspannhw/mxnet-for-iot

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Storage Platform: HDFS in Apache Hadoop 3.1
Compute & GPU Platform: YARN in
Apache Hadoop 3.1HBase2.0
Security & Governance: Atlas 1.0, Ranger 1.0, Knox 1.0
Hive 3.0 Spark 2.3Phoenix
0.8
Operations: Ambari 2.7
Open Source Hadoop 3

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Apache MXNet on Apache YARN 3.1 Native No Spark
yarn jar /usr/hdp/current/hadoop-yarn-client/hadoop-yarn-applications-
distributedshell.jar -jar /usr/hdp/current/hadoop-yarn-client/hadoop-
yarn-applications-distributedshell.jar -shell_command python3.6 -
shell_args "/opt/demo/analyzex.py /opt/images/cat.jpg" -
container_resources memory-mb=512,vcores=1
Uses: Python Any

27. 27 @PaaSDev
Apache MXNet on Apache YARN 3.1 Native No Spark
https://community.hortonworks.com/content/kbentry/222242/running-apache-mxnet-deep-learning-on-yarn-31-
hdp.html
https://github.com/tspannhw/ApacheDeepLearning101/blob/master/analyzehdfs.py

28. 28 @PaaSDev
Apache MXNet on YARN 3.2 in Docker Using “Submarine”
https://github.com/apache/hadoop/tree/trunk/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-applications/hadoop-yarn-submarine
yarn jar hadoop-yarn-applications-submarine-<version>.jar job run
--name xyz-job-001 --docker_image <your docker image>
--input_path hdfs://default/dataset/cifar-10-data
--checkpoint_path hdfs://default/tmp/cifar-10-jobdir
--num_workers 1
--worker_resources memory=8G,vcores=2,gpu=2
--worker_launch_cmd "shell for Apache MXNet"
Wangda Tan (wangda@apache.org)
Hadoop {Submarine} Project: Running deep learning workloads on YARN
https://issues.apache.org/jira/browse/YARN-8135

29. 29 @PaaSDev
Thanks!!!
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://unsplash.com/ https://pixabay.com/
http://gluon-crash-course.mxnet.io/
https://github.com/tspannhw/ApacheDeepLearning201
https://www.meetup.com/futureofdata-philadelphia