SKIL Documentation

Skymind Intelligence Layer

The community edition of the Skymind Intelligence Layer (SKIL) is free. It takes data science projects from prototype to production quickly and easily. SKIL bridges the gap between the Python ecosystem and the JVM with a cross-team platform for Data Scientists, Data Engineers, and DevOps/IT. It is an automation tool for machine-learning workflows that enables easy training on Spark-GPU clusters, experiment tracking, one-click deployment of trained models, model performance monitoring and more.

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Release Notes

SKIL 1.0.3

1.0.3 is a bug fix release that addresses the following issues.

  • Load balancer would not update the model server URLs in Multi-node deployments
  • MNIST dataset is no longer available at (dataset will be embedded into the RPM)
  • Model Server load balancer performance improvements.

Previous Release Notes

New Features and Changes in SKIL v1.0.2

  • Multi-node SKIL installations for inference are now supported
  • Completely offline installable RPMs
  • Added display names for processes
  • Ability to customize the configuration of the default zeppelin server
  • Configurable Logging
  • Many small UI and usability improvements

Known Issues in SKIL v1.0.2

  • Stopping a deployment can cause temporary errors in workspaces. Simply trying the action again should get rid of the error.
  • Currently not possible to delete a model with attached Evaluation Results from an Experiment.
  • The embedded Zookeeper in SKIL stores data in-memory and restarting the SKIL server will cause errors in Workspaces and deployments. Use of an external Zookeeper is recommended.

Release Notes