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v1.15.0 Feature

This release adds 3 notable features for engineering teams evaluating rollout.

Published 1mo Model Serving & MLOps
✓ No known CVEs patched
Read the diff → Tool health → What is this tool? →

✓ No known CVEs patched in this version

Topics

ai code datasets devops devops-tools gguf
+13 more
kubernetes kubernetes-deployment machine-learning mlops mlops-tools model-interpretability model-serving models opensource platform-engineering pytorch sklearn tensorflow

Summary

AI summary

Updates New Features, Bug Fixes, and https://github.com/kitops-ml/kitops/pull/1202 across a mixed release.

Full changelog

Welcome to the v1.15.0 release of Kit! This release improves support for the CNCF ModelPack format, adds support for more options when packing ModelKit data, and adds support for MCP bundles.

New Features

Improved CNCF ModelPack support

Kit is now better at handling CNCF ModelPack artifacts. When working with a ModelPack that was not generated by Kit, Kit can interpret the fields normally found within a Kitfile from the ModelPack's configuration and annotations.

To use Kit to package artifacts in the ModelPack format instead of ModelKit, you can use the flag --use-model-pack for the pack command. Note that not all KitOps features are supported by ModelPacks currently.

Ultimately, you should be able to use ModelPacks with KitOps, regardless of how they were created. For example, the Docker CLI recently added support for creating ModelPacks, and those are now compatible with KitOps.

For more details, see PR #1202

Additional format and compression options for artifact layers

By default, Kit packages each layer (model weights, datasets, code, etc.) as an uncompressed tarball, which provides a good tradeoff in terms of usability, size, and speed. With KitOps v1.15.0, the following extra options are now available while packing ModelKits (or ModelPacks)

  • Compression options: in addition to the existing --compression=gzip option, KitOps now supports using Zstandard compression via the flag --compression=zstd.

  • Layer format options: instead of packaging files and/or directories as tarballs, Kit can now package single files as-is -- i.e. with no tar wrapper. The primary benefit of the raw format is that the layer DiffID (and digest, for the no compression case) match the actual SHA256 sum of the file on disk, making it easier to track which files are stored in which ModelKits.

    To enable raw layers, use the flag --layer-format=raw on the pack command, though note that this flag currently applies to all layers in the Kitfile.

These changes were added as part of improving ModelPack support; for more information, see PR #1202

MCP support in ModelKits

With Kit v1.15.0, the Kitfile now has a mcpServers section, which can be used to package MCP bundles (.mcpb) files in a new layer format. These bundles are a common way to distribute MCP servers, and adding them to the Kitfile allows for building automation and management around MCP servers specifically.

For more details on how MCP bundles are handled, see PR #1214

Bug Fixes

  • Improve auth header handling for push operations by @amisevsk in https://github.com/kitops-ml/kitops/pull/1194
  • Respect KITOPS_HOME in library config path by @puneetdixit200 in https://github.com/kitops-ml/kitops/pull/1189
  • Add image manifest mediatype to manifests created by Kit by @amisevsk in https://github.com/kitops-ml/kitops/pull/1218
  • fix: tolerate missing content in local list and remove by @gorkem in https://github.com/kitops-ml/kitops/pull/1217

New Contributors

  • @puneetdixit200 made their first contribution in https://github.com/kitops-ml/kitops/pull/1189

Full Changelog: https://github.com/kitops-ml/kitops/compare/v1.14.0...v1.15.0

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About kitops

An open source DevOps tool from the CNCF for packaging and versioning AI/ML models, datasets, code, and configuration into an OCI Artifact.

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