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@IronPan IronPan released this Dec 5, 2018

You can install ML Pipeline services by running:
kubectl create -f https://storage.googleapis.com/ml-pipeline/release/0.1.3/bootstrapper.yaml

Install python SDK (python 3.5 above) by running:
pip3 install https://storage.googleapis.com/ml-pipeline/release/0.1.3/kfp.tar.gz --upgrade

Changelog since v0.1.2

SDK

  • Support setting GPU limit and node selector to specify GPU type (#346)
  • Support Kubernetes Volume, VolumeMount and Env APIs for Container Operator(#300)
  • Add option to Container Operator to mount default GCP service account credential(#430)
  • SDK/Components/Python - Removed python_op in favor of python_component (#85)
  • SDK/DSL - Improved compilation of dsl.Conditional (steps->dag) (#177)
  • SDK/Components - Renamed dockerContainer spec to container (#323)
  • SDK/Components - Renamed container.arguments to container.args (#437)
  • SDK/DSL - Added support for conditions: !=, <, <=, >=, > (#309)
  • SDK/DSL - Pipeline function takes direct default values rather than dsp.PipelineParam. (#110)
  • SDK/Components/Python - add support for dependencies in the component image building (#219)
  • Notebook - Display highlighted logs only when there are errors. (#292)

First party components:

  • Reorganized files: kubeflow (#232), dataflow (#338), “local” (#357)

UI

  • View pipelines for runs created from notebooks (#447).
  • Support cloning runs created from notebooks (#465).
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