Excited to share with you the release of aimlflow, an integration that helps to seamlessly run a powerful experiment tracking UI on MLflow logs! 🎉
While MLflow provides a great foundation for managing machine learning projects, it can be challenging to effectively explore and understand the results of tracked experiments. Aim is a tool that addresses this challenge by providing a variety of features for deeply exploring and learning tracked experiments insights and understanding results via UI.
With aimlflow, MLflow users can now seamlessly view and explore their MLflow experiments using Aim’s powerful features, leading to deeper understanding and more effective decision-making.
To be able to explore MLflow logs with Aim, you need to convert MLflow experiments to Aim format. All the metrics, tags, config, artifacts, and experiment descriptions will be stored and live-synced in a
.aimrepo located on the file system.
This means that you can run your training script, and without modifying a single line of code, live-time view the logs on the beautiful UI of Aim. Isn’t it amazing? 🤩
Read the guide demonstrating how MLflow experiments can be explored with Aim on Medium: https://bit.ly/3YQdNuy
Would love to hear your feedback :))
If you have any questions join Aim community, share your feedback, open issues for new features and bugs. 🙌
Show some love by dropping a ⭐️ on GitHub, if you think Aim is useful.
Top comments (0)