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Releases: mlrun/ce

mlrun-ce-0.5.2-rc3

15 Jan 11:42
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mlrun-ce-0.5.2-rc2

13 Jan 21:18
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mlrun-ce-0.5.2-rc1

12 Jan 14:55
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mlrun-ce-0.5.1

08 Jan 10:23
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mlrun-ce-0.5.1

MLRUn Open Source Stack 0.5.1

What's Changed

  • [Feature Store] Change feature store default targets to parquet by @Tankilevitch in #35
  • [Jupyter] Fix MLRun UI URL generated by jupyter to include /mlrun by @quaark in #37
  • Consolidate CE to one Full flavor of deployment by @quaark in #36
  • Bump nuclio and fix requirements.lock by @quaark in #38
  • Artifact path and Feature-store targets default-path should use s3 schema and the standard url template by @assaf758 in #39
  • Bump MLRun Version + 0.5.1-rc2 by @quaark in #40
  • Fix Broken Chart by @quaark in #41
  • CI - Add lint by @liranbg in #43
  • Doc - working with ecr by @liranbg in #42
  • Bump Nuclio to v1.11.6 by @TomerShor in #44
  • Fix Spark Webhook and Service Account Name by @quaark in #45
  • Fix Requirements lock by @quaark in #46
  • MLRun Configs For Displaying Values in MLRun UI by @quaark in #48
  • Fix Chart Serialization Error by @quaark in #49
  • Release - 0.5.1 by @liranbg in #50

New Contributors

Full Changelog: mlrun-ce-0.5.0...mlrun-ce-0.5.1

Contributors

assaf758, liranbg, and 3 other contributors
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mlrun-ce-0.5.1-rc6

28 Dec 13:21
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mlrun-ce-0.5.1-rc5

27 Dec 15:12
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mlrun-ce-0.5.1-rc4

20 Dec 13:08
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mlrun-ce-0.5.1-rc2

15 Dec 08:28
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mlrun-ce-0.5.1-rc1

14 Dec 12:10
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mlrun-ce-0.5.0

10 Nov 15:00
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mlrun-ce-0.5.0

🎉 🎉 We’re happy to announce GA of MLRun Community-Edition first version 🎉 🎉

MLRUn Open Source Stack

The community edition marks a milestone. This is the first ever open-source MLOps release that, out-of-the-box, includes not just the components required to execute the models and pipelines in production, but also integration with services. In addition, it includes a fully functional feature store as part of the MLOps stack where the online service is implemented on top of Redis.

It also includes a CloudFormation stack that deploys the community edition to AWS, saving users significant time set-up and provision resources.

Lite

  • MLRun
  • Nuclio
  • Real-time serving pipelines
  • MPI / Horovod for distributed training
  • Jupyter with out-of-the-box configuration and demos
  • Minio for artifact storage
  • Redis integration (optional, the Redis service needs to be installed independently)

Full:

  • Spark operator
  • Kubeflow pipelines
  • Prometheus
  • Grafana
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