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Cloud Computing Was Built for Web Developers—What Does v2 Look Like for Deep Learning with Daniel Kobran

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What we call the public cloud was developed primarily to manage and deploy web servers. The target audience for these products is Dev Ops. While this is a massive and exciting market, the world of Data Science and Deep Learning is very different — and possibly even bigger. Unfortunately, the tools available today are not designed for this new audience and the cloud needs to evolve. This talk would cover what the next 10 years of cloud computing will look like.

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Cloud Computing Was Built for Web Developers—What Does v2 Look Like for Deep Learning with Daniel Kobran

  1. 1. 1 / 22 Paperspace DO NOT DISTRIBUTE Paperspace www.paperspace.com Serverless AI for the future of intelligence.
  2. 2. 2 / 22 Deep Learning platform built for developers. Infrastructure automation and software layer to build intelligent applications. Introduction
  3. 3. 3 / 22 A new generation of AI developers require a rethinking of tooling and workflows.
  4. 4. 4 / 22 Developers spend 75% of their time managing infrastructure. Why this matters
  5. 5. 5 / 22 The cloud was built for a different use- case (web servers) and a different audience (DevOps). So what’s the underlying problem?
  6. 6. 6 / 22 Traditional Web Services Deep Learning The DevOps ecosystem is rich storage, CDN, deploy, monitor, VPC, load balance, IPsec, CI/CD, DNS ... data, notebooks, train, visualize, collaborate, version, hyperparameters ... ? + 100s more ? ? ? ? ? ??
  7. 7. 7 / 22 The key to solving this problem is finding the right layer of abstraction.
  8. 8. 8 / 22 • BI • Prediction • Optimization • Recommender systems • Any heuristic • GPUs • Datastore • Algorithms (CNNs, RNNs, ...) • Frameworks (Pytorch, TensorFlow, etc) Put Uber/Facebook-grade AI platform in the hands of every developer InfrastructureBusiness Objective There is a huge disconnect between modern business objectives and the DL tools that can fulfill them. Closing the Gap
  9. 9. 9 / 22 Paperspace abstracts powerful infrastructure behind a simple software layer making cloud ML as easy as modern web services.
  10. 10. 10 / 22 A complete platform for modern deep learning Ingest → Train → Analyze → Deploy + Manage, collaborate, share • Fully-managed GPU infrastructure • Unified dev experience • 1 click Jupyter Notebooks/Lab • API & language integrations • ACL/team controls
  11. 11. 11 / 22 GRAI° Model building AI orchestration fabric • Job queuing / management • Cloud agnostic • Accelerator architecture native (GPU, FPGA, ASIC, TPU, etc) • Unified compute • Extensible • Built on best practices (containers, kubernetes, and data policies) Bare-metal Frameworks Tooling TensorFlow VPS Private cloud (VPC) Public Cloud GRAI° AI orchestration fabric PytorchKeras PythonJupyter Data Quilt Job queuing Unified compute Container deployment Data management Encryption / key managment
  12. 12. 12 / 22 > import paperspace as ps # Run job on GPU cluster > ps({‘Type’: ‘TPU’, ‘container’: ‘TensorFlow’ ... }) Connecting modern ML and the cloud by converting infrastructure into code. Raw compute is not sufficient. Gradient Toolstack GRAI Framework Cloud Orchestration & Automation Cloud Infra Network . Storage . Compute Cloudscale with a single line of code
  13. 13. 13 / 22 The Pipeline
  14. 14. 14 / 22 Trends: 1. Chip renaissance 2. Evolution of ML/AI in practice 3. Consolidation around best practices Remarks from the trenches
  15. 15. 15 / 22 Chip renaissance • Graphcore • Cerebras • Nervana • Wave • Google TPU ... The big question today is whether accelerator architectures will follow commodity CPU x86 or lead to a golden era for high-end, use-specific hardware.
  16. 16. 16 / 22 Consumable API → Refit the Model → Model as core IP The evolution of ML/AI in practice 2016 2018 • Clarifai • AWS Rekognition • Google Cloud Vision • MS cognitive services • Paperspace • Algorithmia • FloydHub • ClusterOne
  17. 17. 17 / 22 • Containerization • Jupyter • Job runner architecture • Pipeline • etc. Consolidation around best practices
  18. 18. Thank you. Paperspace 20 Jay St. Suite 312 Brooklyn, NY 11201 hello@paperspace.com www.paperspace.com (718) 619 4325

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