Neptune lets you monitor and debug model internals. Without tradeoffs. Whether your model has 5B or 150T parameters, you generate tens of thousands of per-layer metrics —losses, gradients, and activations. With Neptune, you can not only record but also search through and visualize all of them. No slowdowns, 100% accurate chart rendering.

neptune就是一个比较优秀的实验管理工具，它可以实现多人合作管理实验，追踪团队中每个人的实验，同时也可以对实验结果进行标记、过滤、分组、排序和比较等功能。

2025年2月10日 · Neptune is an experiment tracker. It enables researchers to monitor their model training, visualize and compare model metadata, and collaborate on AI/ML projects within a team. Get an overview. What is Neptune? What can you do with it? How does it work? Introduction. Try it out. See Neptune in action with our 5-minute "Hello Neptune" example.

We trained more than 120.000 models in total, for more than 7000 subproblems identified by various combinations of features. Due to Neptune, we were able to filter experiments for given subproblems and compare them to find the best one. Also, we stored a lot of metadata, visualizations of hyperparameters’ tuning, predictions, pickled models, etc.

2024年8月28日 · neptune.ai是一个专为团队设计的实验追踪平台，旨在帮助用户高效地管理和分析大量的实验数据。无论是日志百万次运行，还是实时监控和可视化长时间模型训练，neptune.ai都能提供无缝的体验。

A Neptune project is a collection of experiments. It typically represents one machine learning task. A Neptune workspace can contain projects and members. You can have project-level access control within a workspace. Learn more: Workspaces and projects →. I work with sensitive data. What should I know?

🚀 Optuna visualization dashboard that lets you log and monitor hyperparameter sweep live. 📌 Track & manage metadata, visualize & compare Kedro pipelines in a nice UI. 💡 Experiment tracking for TensorFlow/Keras. Log, organize, and compare model metrics, learning curves, hyperparameters, dataset versions, and more. Loading…

Neptune consists of: neptune – Python client library (API) for logging and querying model-building metadata. app.neptune.ai – web app for visualization, comparison, monitoring, and collaboration. You can have a workspace for each team or organization that you're working with. Within a workspace, you can create a project for each ML task you ...

Here’s an overview of Neptune’s comparison functionality. With all your experiment metadata in one place, you can identify which training strategies perform best, and why. Iterate through different hypotheses with more confidence, in less time.

An experiment is represented by one Neptune run at a time. To try multiple variants of an experiment, you can fork or restart it from an existing run. When exploring metrics in the web app, you can toggle the experiment history on or off. This way, you can focus on the most recent run of an experiment, or if needed, trace the history all the ...