The 0.3.0 FuseML release brings significant improvements to installer and workflow extensions/components. The range of extensions has been extended to integrate more 3rd party AI/ML tools, such as Seldon Core, Intel OpenVino and NVidia Triton. The existing workflow extensions have also been enhanced with new options and parameters.
This is a summary of extension additions and improvements available in this release:
- new converter workflow component: OpenVINO Model Optimizer
- new predictor workflow component: OpenVINO Model Server (OVMS) and Seldon Core
- new installer extensions: OpenVINO Model Server Operator and Seldon Core
- extended the KServe and Seldon Core workflow predictor components to support Keras trained models and the Triton inference protocol
- extended the MLflow builder workflow component to permit Intel CPU acceleration to be used with TensorFlow projects
- all workflow components have overall been extended with more input parameters, such as verbosity and kubernetes resource specifiers
- MLflow has been refactored and is now installed as a standalone extension
The documentation has been updated with new tutorials featuring newly added extensions and more complex MLOps workflows.
As usual, the built-in components, as well as the 3rd party tools that can be installed through the FuseML installer have been updated to their latest and greatest version:
- updated Gitea to 1.15.3
- updated Istio to 1.11.4
- updated Tekton operator to 0.51.2
- updated Trow to 0.3.3
- KFServing was updated to KServe v0.7.0
- Knative was updated to v1.0.0
- MLflow was updated to 1.20.2
Another important improvement that comes with v0.3.0 is ARM support for the installer, the FuseML CLI, the FuseML core container image and the container images built for all workflow components with the exception of the OpenVINO converter. This allows FuseML to be installed in Kubernetes clusters running on an ARM CPU architecture.
New sample ML projects that can be used with FuseML are also available in this release:
- a sample project that builds and saves a Scikit-learn linear regression model in ONNX format
- a CNN image classification Keras model trained on the CIFAR-10 dataset
- a CNN image classification Keras model trained on a medical X-ray dataset
Implemented enhancements:
- Triton inference protocol support for Seldon Core #286
- Remove limitation of having a single predictor (application) per codeset #266
- Allow setting workflow step resource requests and limits #261
- Don't (re)install an extension if it's already running #205
- URLs are not shown for services exposed by installer extensions #222
- Separate MLFlow helm chart from main installer #168
Fixed bugs:
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upgrade: new config map does not have right values #242
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kfserving extension fails to install with FuseML #218
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fuseml-installer (re-run to incrementally move on) #206
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Assigning the same codeset to different workflows results in app name conflict #183
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fuseml upgrades fail with volume mount errors #232
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can't list workflow runs in case of PVC failures #221
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fuseml-installer lacking arm64 support #207
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installer: rerunning the installer with extensions option fails #184
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First mlflow-sklearn-e2e workflow run stuck for several minutes in the build step #134
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When deleting a codeset, also removes its assignments to workflows #113
Closed issues:
- Installing/uninstalling extensions requires domain #292
- FuseML should detect 3rd party extensions and not try to reinstall them #273
- extensions should not be deleted if not owned #272
- fuseml uninstall fails when no extensions were installed #270
mlflow-builderimage not supported by glibc compiled libraries #262- Add support for Intel OVMS as an inference platform #257
- update wofklows Role based on extension description #247
- Add support for seldon-core: integration into FuseML #238
- CI:
mlflow-e2ejob taking too long to complete #174