v0.3.0

Version 0.3.0

We are excited to present the AutoGluon-Cloud 0.3.0 release. This version brings significant enhancements and features, including support for leaderboard API, batch inference improvements, and compatibility with AutoGluon 1.0.0. As always, we encourage you to try this new release and share your feedback!

AutoGluon-Cloud now aligns with AutoGluon version 1.0.0 with Python 3.11 support, ensuring a seamless and efficient experience for users.

Explore more in our updated tutorial.

This release includes 17 commits from 4 contributors.

Special

See the full commit change-log here: 0.2.0...0.3.0

Full Contributor List (ordered by # of commits):

• @yinweisu, @tonyhoo, @YiruMu, @Jwely

This release continues to support Python versions 3.8, 3.9, 3.10 and 3.11.

NEW: leaderboard API

• Support for Leaderboard API allowing more insights into model performance. @YiruMu (#94)

Updates and Improvements:

• Updated dependency versions to match AutoGluon 1.0.0 requirements. @tonyhoo (#97)
• Enhanced batch inference capabilities, including support for no-header scenarios. @yinweisu (#91)
• Support for extra arguments for real-time prediction, enhancing flexibility. @yinweisu (#78)
• Addition of predictor_path to to_local_predictor for better model management. @yinweisu (#88)

Tutorial Enhancements:

• Distributed training tutorial improvements, providing comprehensive guidance. @yinweisu (#87)
• General tutorial updates for better user guidance. @yinweisu (#133848f)

Infrastructure and Permissions:

• Improved permissions handling for enhanced security. @yinweisu (#86)
• Optional specification of cloud output paths for more control over data storage. @yinweisu (#85)
• Deployment options now include volume deployment for additional flexibility. @yinweisu (#84)

Continuous Integration and Model Persistence:

• Fixes and updates to continuous integration processes. @yinweisu (#81)
• Enabling model persistence for long-term utility. @yinweisu (#76)
• Support for pickle to facilitate model serialization. @yinweisu (#75)

Cluster Management and Distributed Training:

• Using latest AMI for cluster management, ensuring up-to-date infrastructure. @yinweisu (#73)
• Introduction of Tabular Distributed Training, paving the way for scalable model training. @yinweisu (#72)

Miscellaneous:

• Nightly release process improvements. @yinweisu (#74)
• Minor version update to 0.2.1 as part of ongoing maintenance. @yinweisu (#66)

As always, we thank our community for their ongoing support and contributions. We look forward to your feedback on AutoGluon-Cloud 0.3.0!