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Tags / Machine learning /

Experiment tracking

A tool that records training runs, parameters, metrics, and model versions for comparison.

Repositories

12

Net stars gained, last 7 days

+133

Top-level Tag

Machine learning

Net stars gained each day, last 90 days

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  1. mlflow/mlflow29K

    readme: **Experiment Tracking** — Track models, parameters, metrics, and evaluation results across experiments

  2. treeverse/dvc16K

    description: 🦉 Data Versioning and ML Experiments

  3. wandb/wandb11K

    readme: In your Python script or notebook, initialize a W&B run with `wandb.init()`. Specify hyperparameters and log metrics and other information to W&B.

  4. Netflix/metaflow10K

    readme: Rapid local prototyping, support for notebooks, and built-in support for experiment tracking, versioning and visualization.

  5. fossasia/visdom10K

    description: Tool for real-time visualization, monitoring and collaborative analysis of AI/ML experiments and live data.

  6. lanpa/tensorboardX8K

    readme: Write TensorBoard events with simple function call.

  7. tensorflow/tensorboard7.2K

    readme: TensorBoard's Scalar Dashboard visualizes scalar statistics that vary over time; for example, you might want to track the model's loss or learning rate.

  8. aimhubio/aim6.3K

    readme: Aim logs your training runs and any AI Metadata, enables a beautiful UI to compare, observe them and an API to query them programmatically.

  9. zenml-io/zenml5.6K

    readme: Tracking individual runs with metrics, logs, and metadata.

  10. IDSIA/sacred4.4K

    description: Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.

All 12 in the Repository Index

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