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Apache Deep Learning 201 - Philly Open Source


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Introduction talk for data engineers for deep learning on apache with apache mxnet, apache nifi, apache hive, apache hadoop, apache spark, python and other tools.

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Apache Deep Learning 201 - Philly Open Source

  1. 1. 1 @PaaSDev Apache Deep Learning 201 v0.04 (For Data Engineers) Timothy Spann #phillyopensource
  2. 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.
  3. 3. 3 @PaaSDev There are some who call him... DZone Zone Leader and Big Data MVB; Princeton and Charlotte Future of Data Meetup
  4. 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
  5. 5. 5 @PaaSDev Join me next week in Princeton, NJ: Machine Learning and Deep Learning Wednesday, November 14, 2018 6-8pm
  6. 6. 6 @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
  7. 7. 7 @PaaSDev
  8. 8. 8 @PaaSDev Apache Deep Learning Flow
  9. 9. 9 @PaaSDev Aggregate all the Data! Sensors, Drones, logs, Geo-location devices Photos, Images, Results from running predictions on Pre-trained models. Collect: Bring Together
  10. 10. 10 @PaaSDev 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
  11. 11. 11 @PaaSDev 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
  12. 12. 12 @PaaSDev 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
  13. 13. 13 @PaaSDev 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
  14. 14. 14 @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 your self) • Runs on Raspberry PI, NVidia Jetson TX1 and other constrained devices
  15. 15. 15 @PaaSDev • 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 pip3.6 install -U keras-mxnet pip3.6 install --pre --upgrade mxnet pip3.6 install gluonnlp
  16. 16. 16 @PaaSDev • 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
  17. 17. 17 @PaaSDev Apache MXNet GluonCV Zoo • 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
  18. 18. 18 @PaaSDev Object Detection: GluonCV YOLO v3 and Apache NiFi
  19. 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
  20. 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
  21. 21. 21 @PaaSDev Semantic Segmentation: DeepLabV3 with GluonCV model = gluoncv.model_zoo.get_model('deeplab_resnet101_ade', pretrained=True) GluonCV DeepLabV3 model on ADE20K dataset This one is a bit slower.
  22. 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
  23. 23. 23 @PaaSDev Apache MXNet Model Server from Apache NiFi pr.html
  24. 24. 24 @PaaSDev Apache MXNet Running on Edge Nodes (MiniFi)
  25. 25. 25 @PaaSDev 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
  26. 26. 26 @PaaSDev 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/ /opt/images/cat.jpg" - container_resources memory-mb=512,vcores=1 Uses: Python Any
  27. 27. 27 @PaaSDev Apache MXNet on Apache YARN 3.1 Native No Spark hdp.html
  28. 28. 28 @PaaSDev Apache MXNet on YARN 3.2 in Docker Using “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 ( Hadoop {Submarine} Project: Running deep learning workloads on YARN
  29. 29. 29 @PaaSDev Thanks!!!