Baklava is the build and packaging system for ML models. Baklava leverages the python standard "setuptools" packaging system, and extends it to build docker containers that run Machine learning models. These containers are compatible with SageMaker, and in future, they will be compatible with Kubeflow.
License: Apache License 2.0
A clear and concise description of what the feature is.
Please outline the motivation for the proposal.
Similar to .git, .terraform, this change make the build process hidden and can also be merged with mlctl temp files when creating a new job.
Please provide an example for how this feature would be used.
Integrate with Github Actions and enable a continuous build and deployment of the baklava libraries to pypi.
When Baklava builds a serving container using the predict function, make the serving container compatible with the KFServing V2 API specification
This capability will enable containers produced by Baklava to be used directly on any KFServing compatible server, such as the Triton Inference Server
Add support in Baklava to produce containers that run on GCP for both training and inference
This capability will enable Baklava to be compatible with multiple clouds (currently supports only AWS SageMaker)
python setup.py train --target=gcp
A clear and concise description of what the feature is.
Please outline the motivation for the proposal.
Please provide an example for how this feature would be used.
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An Open Source Machine Learning Framework for Everyone
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