Inteligencia artificial, open source e IBM Call for Code
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The document outlines IBM's initiatives in open source and artificial intelligence, highlighting the contributions of Luciano Resende and the various projects like the Model Asset Exchange and the Fabric for Deep Learning (FFDL). It emphasizes the importance of open source in IBM's AI development, detailing how their platform supports deep learning across multiple frameworks and enhances enterprise applications through tools like the Jupyter Enterprise Gateway. The document also describes IBM's historical achievements in AI and the future direction of their open-source efforts.
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Inteligencia artificial, open source e IBM Call for Code
- 1. Open Source @ IBM Inteligência Artificial, Open Source e IBM Call for Code 2018 / © 2018 IBM Corporation 1 Luciano Resende Data Science Platform Architect
- 2. About me - Luciano Resende 2 Data Science Platform Architect – IBM – CODAIT • Have been contributing to open source at ASF for over 10 years • Currently contributing to : Jupyter Notebook ecosystem, Apache Bahir, Apache Toree, Apache Spark among other projects related to AI/ML platforms [email protected] https://www.linkedin.com/in/lresende @lresende1975 https://github.com/lresende
- 3. 3 Learn Open Source @ IBM Program touches 78,000 IBMers annually Consume Virtually all IBM products contain some open source • 40,363 pkgs Per Year Contribute • >62K OS Certs per year • ~10K IBM commits per month Connect > 1000 active IBM Contributors Working in key OS projects 2018 / © 2018 IBM Corporation Open Source participation and usage is simpler than ever
- 4. 4 Open Source is essential to Developer Advocacy IBM generated open source innovation • 137 Code Open (dWO) projects w/1000+ Github projects • 4 graduates: Node-Red, OpenWhisk, SystemML, Blockchain fabric to full open governance in the last year • developer.ibm.com/code/open/code/ Community • IBM focused on 18 strategic communities • Drive open governance in “Centers of Gravity” • IBM Leaders drive key technologies and assure freedom of action The IBM OS Way is now open sourced • Training, Recognition, Tooling • Organization, Consuming, Contributing 2018 / © 2018 IBM Corporation
- 5. 5 IBM’s history of strong AI leadership 1997: Deep Blue • Deep Blue became the first machine to beat a world chess champion in tournament play 2011: Jeopardy! • Watson beat two top Jeopardy! champions 1968, 2001: A Space Odyssey • IBM was a technical advisor • HAL is “the latest in machine intelligence” 2018: Open Tech, AI & emerging standards • New IBM centers of gravity for AI • OS projects increasing exponentially • Emerging global standards in AI 2018 / © 2018 IBM Corporation
- 6. AI Scenarios Today 82018 / © 2018 IBM Corporation
- 7. Home Automation & Security - Multiple connected or standalone devices - Controlled by Voice - Amazon Echo (Alexa) - Google Home - Apple HomePod (Siri) 9
- 8. TESLA connected cars CONNECTED VEHICLES. It’s not just about Google Maps in cars. When Tesla finds a software fault with their vehicle rather than issuing an expensive and damaging recall, they simply updated the car’s operating system over the air. [hcp://www.wired.com/2014/02/te slas- air-fix-best-example-yet- internet-things/] 10
- 9. AMAZON Go AMAZON GO – No lines, no checkout, just grab and go 11
- 10. Model Asset eXchange 122018 / © 2018 IBM Corporation Enabling domain experts to use deep learning in the enterprise
- 11. Q: What is deep learning? A: Machine learning using deep neural networks. 132018 / © 2018 IBM Corporation InceptionV3 Convolutional Neural Net (A “medium-sized” deep learning model) Image Source: https://github.com/tensorflow/models/blob/master/research/inception/g3doc /inception_v3_architecture.png
- 12. Characteristics of Deep Learning (1) 14 State-of-the-Art prediction quality in many domains – Image classification – Machine translation – Facial recognition – Time series prediction – Many more 2018 / © 2018 IBM Corporation
- 13. Characteristics of Deep Learning (2) 15 Large, complex models – Model size generally determined by “how big a model can you fit on your device?” 2018 / © 2018 IBM Corporation Each box ≈ between 32 and 768 linear regression models
- 14. Characteristics of Deep Learning (3) 16 Poorly understood today …even by experts – Why do the models converge? – Why do the models converge with low loss? – Why do the models generalize? 2018 / © 2018 IBM Corporation
- 15. Focus of this Talk 17 Incorporating well-understood deep learning models into enterprise applications. 2018 / © 2018 IBM Corporation
- 16. 182018 / © 2018 IBM Corporation Sounds easy!
- 17. “cat” The Components of a Deep Learning Model 192018 / © 2018 IBM Corporation Dense (3×8) Dense (8×6) Input (3) Output (2)Dense (6×4) Dense (4×2) Neural Network Graph Weights (not to scale) Driver Program
- 18. Example: Get an Image Classifier 20 Step 1: Find a suitable neural network graph. – Need to read some papers 2018 / © 2018 IBM Corporation
- 19. Example: Get an Image Classifier 21 Step 2: Find code to generate the neural network graph 2018 / © 2018 IBM Corporation TensorFlow code to build ResNet50 neural network graph
- 20. Example: Get an Image Classifier 22 Step 3: Find some pre-trained weights for your graph 2018 / © 2018 IBM Corporation Caffe2 ResNet50 model weights
- 21. Example: Get an Image Classifier 23 Step 4: Find example code that performs model inference 2018 / © 2018 IBM Corporation TensorFlow code for training and batch inference on ResNet50
- 22. Example: Get an Image Classifier 24 Step 5: Write your own code to perform model inference on one image at a time Step 6: Package your inference code, graph creation code, and pre- trained weights together Step 7: Deploy your package 2018 / © 2018 IBM Corporation
- 23. Model Marketplaces 25 Collections of well-understood deep learning models Provide a central place to find known-good implementations of these models 2018 / © 2018 IBM Corporation
- 24. IBM Model Asset eXchange MAX is a one-stop shop open source ecosystem for data scientists and AI developers to share and consume models that use machine learning engines, such as TensorFlow, PyTorch and Caffe2. It also provides a standard approach to classify, annotate, and deploy these models for prediction and inferencing. MAX https://developer.ibm.com/cod e/exchanges/models/ 2018 / © 2018 IBM Corporation 26
- 25. 272018 / © 2018 IBM Corporation Demo! https://developer.ibm.com/code/exchanges/models/ https://developer.ibm.com/code/patterns/create-web-app-interact-machine-learning-generated-image-captions/
- 26. Summary 28 Free, open-source models. Wide variety of domains. Multiple deep learning frameworks. Vetted and tested code and IP. Build and deploy a web service in 30 seconds. Start training on Watson Studio in minutes. 2018 / © 2018 IBM Corporation
- 27. MAX: Future Plans 29 Many more models – Train with Watson Studio/DLaaS – Run inference on IBM infrastructure Revamped website Integration with Watson Catalog IBMer-uploaded models More IBM Code code patterns showing usage 2018 / © 2018 IBM Corporation https://developer.ibm.com/code/exchanges/models/
- 28. Click to edit Master title style FfDL Fabric for Deep Learning 2018 / © 2018 IBM Corporation 30 FfDL provides a scalable, resilient, and fault tolerant deep-learning framework
- 29. Fabric for Deep Learning https://github.com/IBM/FfDL 2018 / © 2018 IBM Corporation FfDL provides a scalable, resilient, and fault tolerant deep-learning framework FfDL Github Page https://github.com/IBM/FfDL FfDL dwOpen Page https://developer.ibm.com/code/open/projects/fabri c-for-deep-learning-ffdl/ FfDL Announcement Blog http://developer.ibm.com/code/2018/03/20/fabric- for-deep-learning FfDL Technical Architecture Blog http://developer.ibm.com/code/2018/03/20/democr atize-ai-with-fabric-for-deep-learning Deep Learning as a Service within Watson Studio https://www.ibm.com/cloud/deep-learning Research paper: “Scalable Multi-Framework Management of Deep Learning Training Jobs” http://learningsys.org/nips17/assets/papers/paper_ 29.pdf • Fabric for Deep Learning or FfDL (pronounced as ‘fiddle’) is an open source project which aims at making Deep Learning easily accessible to the people it matters the most i.e. Data Scientists, and AI developers. • FfDL Provides a consistent way to deploy, train and visualize Deep Learning jobs across multiple frameworks like TensorFlow, Caffe, PyTorch, Keras etc. • FfDL is being developed in close collaboration with IBM Research and IBM Watson. It forms the core of Watson`s Deep Learning service in open source. FfDL 31
- 30. Fabric for Deep Learning https://github.com/IBM/FfDL FfDL is built using Microservices architecture on Kubernetes • FfDL platform uses a microservices architecture to offer resilience, scalability, multi-tenancy, and security without modifying the deep learning frameworks, and with no or minimal changes to model code. • FfDL control plane microservices are deployed as pods on Kubernetes to manage this cluster of GPU- and CPU-enabled machines effectively • Tested Platforms: Minikube, IBM Cloud Public, IBM Cloud Private, GPUs using both Kubernetes feature gate Accelerators and NVidia device plugins 322018 / © 2018 IBM Corporation
- 31. source code training definition Auto-allocation means infrastructure is used only when needed Kubernetes container training artifacts compute cluster NVIDIA Tesla K80, P100, V100 Cloud Object Storage Training assets are managed and tracked. Access to elastic compute leveraging Kubernetes 332018 / © 2018 IBM Corporation
- 32. NVIDIA GPUs Kubernetes container orchestration training runs containers server cluster dataset Cloud Object Storage Model training distributed across containers 342018 / © 2018 IBM Corporation
- 33. 35 FfDL: Architecture 2018 / © 2018 IBM Corporation
- 34. 36 https://arxiv.org/abs/1709.05871 FfDL: Research Papers 2018 / © 2018 IBM Corporation
- 35. Click to edit Master title style Jupyter Enterprise Gateway 2018 / © 2018 IBM Corporation 37 Provides multi-tenant, scalable and secure remote Jupyter Notebook kernels
- 36. Jupyter Notebooks Overview 38© 2018 IBM Corporation
- 37. Jupyter Notebooks © 2018 IBM Corporation 39 Notebooks are interactive computational environments, in which you can combine code execution, rich text, mathematics, plots and rich media.
- 38. Jupyter Notebooks © 2018 IBM Corporation 40 • Notebook UI runs on the browser • The Notebook Server serves the ’Notebooks’ • Kernels interpret/execute cell contents – Are responsible for code execution – Abstracts different languages
- 39. Building a Data Science Analytical Platform 41© 2018 IBM Corporation
- 40. Building an Data Science Platform © 2018 IBM Corporation Large pool of shared computing resources • Enterprise Cloud, Public Cloud or Hybrid • Data in the cloud (Data Lakes/Object Storage) Distributed Consumers • Notebooks running local (users laptop) or as a service (e.g. Jupyter Hub) Different Resource Utilization Patterns • High number of idle resources
- 41. Vanilla Jupyter Notebooks © 2018 IBM Corporation 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 43 8 8 8 8 0 10 20 30 40 50 60 70 80 4 Nodes 8 Nodes 12 Nodes 16 NodesMaxKernels(4GBHeap) Cluster Size (32GB Nodes) MAXIMUM NUMBER OF SIMULTANEOUS KERNELS Kernel Kernel Kernel Kernel Limitations of Jupyter Notebook Stack • Security limitations • Single user sharing the same privileges • Users can see and control each other process using Jupyter administrative utilities • Scalability limitations • Jupyter Kernels running as local process • Resources are limited by what is available on the one single node that runs all Kernels and associated Spark drivers Kernel
- 42. Jupyter Enterprise Gateway © 2018 IBM Corporation Jupyter Enterprise Gateway at IBM Code https://developer.ibm.com/code/openprojects/jupyter-enterprise-gateway/ Jupyter Enterprise Gateway source code at GitHub https://github.com/jupyter-incubator/enterprise_gateway Jupyter Enterprise Gateway Documentation http://jupyter-enterprise-gateway.readthedocs.io/en/latest/ Supported Kernels Supported Platforms 45 A lightweight, multi-tenant, scalable and secure gateway that enables Jupyter Notebooks to share resources across an Apache Spark or Kubernetes cluster for Enterprise/Cloud use cases Spectrum Conductor +
- 43. Jupyter Enterprise Gateway © 2018 IBM Corporation 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 46 16 32 48 64 0 10 20 30 40 50 60 70 80 4 Nodes 8 Nodes 12 Nodes 16 NodesMaxKernels(4GBHeap) Cluster Size (32GB Nodes) MAXIMUM NUMBER OF SIMULTANEOUS KERNELS Kernel Kernel KernelKernel Optimized Resource Allocation – Utilize resources on all cluster nodes by running kernels as Spark applications in YARN Cluster Mode. – Pluggable architecture to enable support for additional Resource Managers Enhanced Security – End-to-End secure communications • Secure socket communications • Encrypted HTTP communication using SSL Multiuser support with user impersonation – Enhance security and sandboxing by enabling user impersonation when running kernels (using Kerberos). – Individual HDFS home folder for each notebook user. – Use the same user ID for notebook and batch jobs. KernelKernel Kernel
- 44. Jupyter Enterprise Gateway – YARN © 2018 IBM Corporation 47 YARN Cluster YARN Workers Gateway Node Jupyter Enterprise Gateway • Multitenancy • Remote kernel lifecycle management via process proxies Spark Executors Spark Executors Spark Executors Yarn Container Jupyter Kernel Spark Driver Impersonation: Alice’s kernel runs under Alice’s user ID. Spark Executors Spark Executors Spark Executors Yarn Container Jupyter Kernel Spark Driver SecurityLayer nb2kg nb2kg Spark Executors Spark Executors Spark Executors Yarn Container Jupyter Kernel Spark Driver Bob Alice
- 45. Enterprise Gateway & Kubernetes © 2018 IBM Corporation Supported Platforms Kernel Kernel Kernel Kernel Before Jupyter Enterprise Gateway … • Scalability limitations • Resources are limited and the amount required to all kernels needs to be allocated during Notebook Server pod creation. • Resources are limited by what is available on the one single node that runs all Kernels and associated Spark drivers Kernel KernelKernel
- 46. Jupyter Enterprise Gateway - Kubernetes © 2018 IBM Corporation 49 Container images defined in kernelspec Community image Kernel Spark on K8 Kernel Distributed File System Vanilla Kernels Spark based kernels Gateway nb2kg nb2kg
- 47. Summary 54© 2018 IBM Corporation
- 48. Summary © 2018 IBM Corporation 55 • Model Asset Exchange • Curated set of models ready to use or embedded in your application or solution • Fabric for Deep Learning • Provides a consistent way for AI developers and Data Scientists to train their models • Jupyter Enterprise Gateway • Enables your Jupyter Notebook stack to scale in order to build Machine Learning and AI Models more resource effectively MAX https://developer.ibm.com/cod e/exchanges/models/
- 49. 56© 2018 IBM Corporation
- 50. 57May 17, 2018 / © 2018 IBM Corporation
- 51. 58© 2018 IBM Corporation
Editor's Notes
- #6: IBM has a long history around AI leadership. From “2001: A Space Odyssey”, to winning the world chess champion Garry Kasparov, and recently in 2011 winning Jeopardy against legendary champions Brad Rutter and Ken Jennings. And this brings us to CODAIT, where IBM is concentrating some of the efforts around Open AI leadership. Deep Blue versus Garry Kasparov was a pair of six-game chess matches between world chess champion Garry Kasparov and an IBM supercomputer called Deep Blue. The first match was played in Philadelphia in 1996 and won by Kasparov. The second was played in New York City in 1997 and won by Deep Blue. The 1997 match was the first defeat of a reigning world chess champion by a computer under tournament conditions. Watson is a question-answering computer system capable of answering questions posed in natural language,[2] developed in IBM's DeepQA project by a research team led by principal investigator David Ferrucci.[3] Watson was named after IBM's first CEO, industrialist Thomas J. Watson.[4][5] The computer system was initially developed to answer questions on the quiz show Jeopardy![6] and, in 2011, the Watson computer system competed on Jeopardy!against legendary champions Brad Rutter and Ken Jennings[4][7] winning the first place prize of $1 million.[8]
- #7: Lower the barrier of entrance
- #32: Training Deep Neural Networks is a highly complex and computer intensive task and in addition you need to have the right environment with the right combination of frameworks and resources. We have seen how hard is to actually build a deep learning model, and adding this additional requirement to the Ai engineer or Data Scientist will be a burden that they will not even try. From the operator perspective, each framework requires a different set of environment, dependencies, etc For the data scientist, FfDL brings a consistent way to train their models independent of what framework is being used (e.g. tensorflow, PyTorch, Keras, etc) As the operator, FfDL provides a consistent way to manage these environments that is used for training the deep neural network models i, also, as FfDL runs on top of kubernetes, and leverages various capabilities that kubernetes provide such as scalability, fault tolerance, resilience, Bases for Watson Studio Deep Learning as a Service - Start developing on premise, and move to the cloud when you need scalability - Go directly to cloud, etc We have
- #40: Jupyter Notebooks are an evolution from the good old way of doing interactive development with the Python console.
- #58: O call for code é uma iiciativa para reunir desenvolvedores e inspira-los a resolver uma das questões sociais mais urgentes no nosso tempo: previnir, responder e recuperar desastres naturais.