OptunaAutoML
@OptunaAutoML
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Optuna is an open source hyperparameter optimization framework to automate hyperparameter search
Joined February 2020
Optuna v5 will be built with the power of the community! We'd love your input and participation — check out the feedback form on the roadmap:
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Optuna v4.4 is now available as of June 16! This release introduces the Optuna MCP Server, our first LLM-based toolchain. We're already hard at work on Optuna v5, following our roadmap. For full details, check out our blog post.
Optuna v4.4 will be released this month, and the roadmap for their next exciting major release- Optuna v5- has just been published! Read more on their blog here:
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Optuna v4.4 has been released with various new features, bug fixes, and enhancements. 🚀Optuna MCP server, which is our first LLM intensive toolchain.✅Gaussian process-based algorithm now supports multi-objective optimization.🌀A lot of new features in OptunaHub
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We've published the development roadmap for Optuna v5, the next major release! Scheduled for release next summer, v5 will focus on making Optuna even more powerful and user-friendly — especially at the intersection of generative AI and black-box optimization.
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Our OptunaHub Benchmarks article is featured on AutoML Space:) Sampler benchmarking becomes easier with OptunaHub Benchmarks!.
OptunaHub v0.2.0 has added a new feature, OptunaHub Benchmarks, which makes it easy to use various benchmark problems in the fields of black-box optimization and AutoML! Developer Yoshihiko Ozaki (@y0zaki) introduces how to use this feature.
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Optuna v4.3.0 has been released! This is a maintenance release with various minor bug fixes and improvements to the documentation and more. 👇See what��s new.
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In Optuna v4.2, the gRPC storage proxy has been added for large-scale distributed optimization. Check out our latest blog post for details!
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Excited to be a part of AutoML Space organized by world-leading researchers, a community for those interested in AutoML. Our first post covers a SOTA sampler integration by Difan Deng from @AutoML_org into OptunaHub.
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Kaito Baba (@kAIto47802) implemented constrained optimization for GPSampler, a Gaussian process-based Bayesian optimization! This allows handling constraints such as the maximum possible neural network size. Check out our article!.
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🚀 In Optuna v4.1, major optimizations were made to speed up RDBStorage up to 63%. Check out our latest blog post for details! . 🔗
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Difan Deng at @AutoML_org, the world-leading AutoML institute, integrated SMAC3, a SOTA Bayesian optimization algorithm, into OptunaHub! . Check out the article to learn how to use it from OptunaHub:.
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Optuna 4.2.1 patch just released. This release includes a bug fix addressing an issue where Optuna was unable to import if an older version of the grpcio package was installed.
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OptunaHub v0.2.0 has added a new feature, OptunaHub Benchmarks, which makes it easy to use various benchmark problems in the fields of black-box optimization and AutoML! Developer Yoshihiko Ozaki (@y0zaki) introduces how to use this feature.
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We introduce the latest works in blog posts. Stay tuned!. 🚀gRPC Storage Proxy for Large-Scale Distributed Optimization.🤖 SMAC3: Support for New State-of-the-Art Optimization Algorithm by @AutoML_org.📁 OptunaHub Supports Benchmarks.🧑💻 c-TPE and Constrained GPSampler.
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Optuna 4.2.0 and OptunaHub 0.2.0 are out! Try the newest Optuna and OptunaHub!. 👇See what’s new.
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