#DWS19 Apache Deep Learning - Apache MXNet, Apache NiFi A quick integration for Big Data Engineers on how to use Apache MXNet with Apache NiFi in streams, at the edge, in a processor and on Linux and OSX.

1. @PaaSDev
Apache Deep Learning 202 v1.00
(For Data Engineers)
Timothy Spann
https://github.com/tspannhw/ApacheDeepLearning202/

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.

3. @PaaSDev
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/

4. @PaaSDev
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

5. @PaaSDev
• 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 yourself)
• Runs on Raspberry PI, NVidia Jetson Nano and other constrained devices
https://mxnet.incubator.apache.org/how_to/cloud.html
https://github.com/apache/incubator-mxnet/tree/1.3.1/example
https://gluon-cv.mxnet.io/api/model_zoo.html
https://elinux.org/Jetson_Nano

6. @PaaSDev
• Great documentation
• Crash Course
• Gluon (Open API), GluonCV, GluonNLP
• Keras (One API Many Runtime Options)
• Great Python Interaction. Java and Scala APIs!
• Open Source Model Server Available
• ONNX (Open Neural Network Exchange Format) Support for AI Models
• Now in Version 1.4.0!
• Rich Model Zoo!
• Math Kernel Library and NVidia CUDA Optimizations
• 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 --upgrade mxnet
pip3.6 install gluonnlp pip3.6 install gluoncv
pip3.6 install mxnet-mkl>=1.3.0 --upgrade

7. @PaaSDev
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

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

9. @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

10. @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

11. @PaaSDev
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.

12. @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

13. @PaaSDev
Simple Pose Estimation
https://gluon-cv.mxnet.io/build/examples_pose/cam_demo.html
pip3.6 install gluoncv --pre --upgrade
https://github.com/dmlc/gluon-cv/tree/master/scripts/pose/simple_pose
yolo3_mobilenet1.0_coco + simple_pose_resnet18_v1b

14. @PaaSDev
Apache MXNet Native Processor for Apache NiFi
This is a beta, community release by me using the new beta Java API for Apache MXNet.
https://github.com/tspannhw/nifi-mxnetinference-processor
https://community.hortonworks.com/articles/229215/apache-nifi-processor-for-apache-mxnet-ssd-single.html
https://www.youtube.com/watch?v=Q4dSGPvqXSA

15. @PaaSDev
Using Apache MXNet on The Edge with Sensors and Google Coral (MiNiFi)
https://www.datainmotion.dev/2019/03/using-raspberry-pi-3b-with-apache-nifi.html