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Deep Learning to Production with MLflow & RedisAI

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Taking deep learning models to production and doing so reliably is one of the next frontiers of MLOps. With the advent of Redis modules and the availability of C APIs for the major deep learning frameworks, it is now possible to turn Redis into a reliable runtime for deep learning workloads, providing a simple solution for a model serving microservice. RedisAI is shipped with several cool features such as support for multiple frameworks, CPU and GPU backend, auto batching, DAGing, and soon will be with automatic monitoring abilities. In this talk, we'll explore some of these features of RedisAI and see how easy it is to integrate MLflow and RedisAI to build an efficient productionization pipeline.

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Deep Learning to Production with MLflow & RedisAI

  1. 1. TakingDeep Learningto Production MLflow Bay Area Meetup AnMLflow-RedisAItalk
  2. 2. Who am I? SeniorDev@TensorWerk. Engineer.Author. Educator.Farmer
  3. 3. Hangar • Branch, Merge & Time-travel • Full traceability • Clone, Fetch & Push • Scalable; Store locally or on Cloud • Partial cloning • Dataloaders for main ML/DL frameworks
  4. 4. Stockroom • Git for software 2.0 • Built on top of Hangar • Optimized to store Data, Experiment & Model • Auto-fetch popular DL datasets • Stockroom Universe
  5. 5. h t t p s : / / m l f l o w. o r g / d o c s / l a t e s t / m o d e l s . h t m l
  6. 6. ProductionStrategies • Python code behind a server. Eg. Flask • Execution service from Cloud Provider • Runtime • Tensorflow Serving • Torch Serving • Clipper • Nvidia TensorRT inference Server • ... • Bespoke Solutions (C++ ..) • Kubernetes-zie everything above
  7. 7. ProductionRequirement • Must Fit the tech stack • Run anywhere, any size • Composable building blocks • Must try to limit the amount of moving parts • And the moving data • Must make best use of resources
  8. 8. RedisAI Tensors as a datatype and Deep Learning Model Execution on CPU & GPU A Redis module providing It turns Redis into a full-fledged deep learning runtime while still being Redis
  9. 9. RedisAI
  10. 10. Where to get it? docker run -p 6379:6379 -it --rm redisai/redisai
  11. 11. Replication • Master-Replica • Cluster
  12. 12. • redisai-py • JRedisAI • redisai-go • redisai-js PS: Normal Redis Client in all the languages can still work with RedisAI Client Libraries
  13. 13. Features • Keep the data local • Run on multi-backend • Supports multiple devicee • Traditional ML on GPU • SCRIPTing • Free auto-batching • Ease of deployment • HA with master-replica & clustering • Run everywhere Redis runs • keep the stack short • Language independent • Ecosystem • DAG and parallel execution
  14. 14. mlflow deployments create -t redisai -m runs:/id/model --name resnet
  15. 15. I'mabouttoshowyouhowit'sdone