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Resources » How to Track ML Model Training: LightGBM + Neptune Integration

How to Track ML Model Training: LightGBM + Neptune Integration

What will you get with this integration?

LightGBM is a gradient boosting framework that uses tree-based learning algorithms.

Neptune + LightGBM integration, lets you:

  • Automatically log many types of metadata during training
    • Training and validation metrics

    • Parameters

    • Feature names, num_features and num_rows for the train set

    • Hardware consumption (CPU, GPU, memory)

    • Stdout and stderr logs

    • Training code and git commit information

  • Log model summary after training
    • Pickled model

    • Feature importance chart (gain and split)

    • Visualized trees

    • Trees saved as DataFrame

    • Confusion matrix (only for classification problems)

Read the documentation

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