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AI and Spark - IBM Community AI Day
- 2. About @MLnick on Twitter & Github Principal Engineer, IBM CODAIT - Center for Open-Source Data & AI Technologies Machine Learning & AI Apache Spark committer & PMC Author of Machine Learning with Spark Various conferences & meetups
- 3. Center for Open Source Data and AI Technologies CODAIT codait.org DBG / Oct 4, 2018 / © 2018 IBM Corporation CODAIT aims to make AI solutions dramatically easier to create, deploy, and manage in the enterprise Relaunch of the Spark Technology Center (STC) to reflect expanded mission Improving Enterprise AI Lifecycle in Open Source Gather Data Analyze Data Machine Learning Deep Learning Deploy Model Maintain Model Python Data Science Stack Fabric for Deep Learning (FfDL) Mleap + PFA Scikit-LearnPandas Apache Spark Apache Spark Jupyter Model Asset eXchange Keras + Tensorflow
- 5. Applying Machine Learning: Perception
- 6. In reality the workflow spans teams …
- 7. … and tools
- 8. Spark provides a unified platform
- 10. What is a “model”?
- 11. Pipelines in Spark ML
- 12. Example – Text Classifier Pipeline
- 13. Example – Text Classifier PipelineModel
- 14. Spark ML Components 14 Source: http://spark.apache.org/docs/latest
- 15. Deep Learning DBG / June 6, 2018 / © 2018 IBM Corporation
- 16. Deep Learning Overview • Original theory from 1940s; computer models originated around 1960s; fell out of favor in 1980s/90s • Recent resurgence due to • Bigger (and better) data; standard datasets (e.g. ImageNet) • Better hardware (GPUs) • Improvements to algorithms, architectures and optimization • Leading to new state-of-the-art results in computer vision (images and video); speech/text; language translation and more Source: Wikipedia
- 17. Modern Neural Networks • Deep (multi-layer) networks • Computer vision • Convolution neural networks (CNNs) • Image classification, object detection, segmentation • Sequences and time-series • Recurrent neural networks (RNNs) • Machine translation, text generation • Embeddings • Text, categorical features • Deep learning frameworks • Flexibility, computation graphs, auto-differentiation, GPUs Source: Stanford CS231n
- 18. Deep Learning Frameworks * Logos trademarks of their respective projects
- 19. Computation Graphs Source: Google AI Blog *MnasNet Network *Inception V3
- 20. DL Frameworks on Spark
- 21. Major Frameworks 21 • Deeplearning4J • BigDL • Deep Learning Pipelines • TensorFlowOnSpark • Microsoft Machine Learning on Spark (MMLSpark)
- 22. Deeplearning4J 22 • Distributed GPU support for all major deep learning architectures • CPU / Distributed CPU / Single GPU options exist • Supports Convolutional Nets, LSTMs / RNNs, Feedforward Nets, Word2Vec, custom layers • Supported by startup Skymind.io • Backed by its own linear algebra library – ND4J • APIs in Scala, Java, Python • Newer Scala API, Keras-like • Keras import / export for Python API • Production serving is through proprietary layer • DataVec for ETL
- 23. BigDL 23 • Distributed CPU with Intel MKL • No GPU support • Most DL models – CNN, RNN • Backed by Intel • Natively integrated with Spark • Scala, Python API • Support for Spark ML pipelines • Uses private internal Spark components for distributed training • Load Keras, Caffe, Torch models • New Keras-style API
- 24. Deep Learning Pipelines 24 • Created by Databricks • Focus on scoring models (TensorFlow / Keras) and basic transfer learning • No support for training the DL model • Focus on image data & use cases • Natively integrated with Spark • Scala, Python API • Support for Spark ML pipelines • Support for scoring models as a SQL UDF • Largely dormant currently
- 25. TensorFlowOnSpark 25 • Created by Yahoo • Scale out TF on Spark clusters • Use Spark executors to launch TF processes • Supports distributed training through TF parameter servers • RDMA / Infiniband improvement to TF to speed up distributed training • Good support for TensorBoard • Good integration with Spark • But only Python API • Some support for Spark ML pipelines • Relatively inactive recently
- 26. MMLSpark 26 • Created by Microsoft • Supports training using CNTK including distributed • Image, text data • Good integration with Spark • Scala, Python, R API • Support for Spark ML pipelines • Varied deployment options • Relatively active, seems quite well supported
- 27. Other Frameworks 27 • H20 AI / DeepWater • Apache MXNet Spark integration • TensorFrames • CaffeOnSpark • scalable-deep-learning on Github • MLlib – MLPClassifier only • Sparknet (abandoned)
- 28. Integration Challenges 28 • Moving data from Spark to DL framework (and back) • Serialization overhead – especially Python • Managing DL computation graphs from Spark executors means fault tolerance is difficult to achieve • GPU awareness • Optimize and standardize data exchange - SPARK-24579 • Apache Arrow • Barrier Execution Mode - SPARK-24374 • Accelerator-aware scheduling - SPARK- 24615
- 29. 29 Thank you! codait.org twitter.com/MLnick github.com/MLnick developer.ibm.com FfDL Sign up for IBM Cloud and try Watson Studio! https://ibm.biz/BdYhXz https://datascience.ibm.com/ MAX
- 30. Brought to you by community.ibm.com/icpfordata Catch the replay at ibmaicommunity.bemyapp.com