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LightGBM

DVCLive allows you to easily add experiment tracking capabilities to your LightGBM projects.

About LightGBM

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

Usage

To start using DVCLive you just need to add a few lines to your training code in any LightGBM project.

You just need to add the DvcLiveCallback to the callbacks list passed to the lightgbm.train call:

+from dvclive.lgbm import DvcLiveCallback

. . .

lightgbm.train(
  param,
  train_data,
  valid_sets=[validation_data],
-   num_round=5)
+   num_round=5,
+   callbacks=[DvcLiveCallback()])

This will generate the metrics logs and summaries as described in the Get Started.

💡Without requiring additional modifications to your training code, you can use DVCLive alongside DVC. See DVCLive with DVC for more info.

Parameters

  • model_file - (None by default) - The name of the file where the model will be saved at the end of each step.

  • **kwargs - Any additional arguments will be passed to Live.

Examples

  • Using model_file.
lightgbm.train(
    param,
    train_data,
    valid_sets=[validation_data],
    num_round=5,
    callbacks=[DvcLiveCallback(model_file="lgbm_model.txt")])
  • Using **kwargs to customize Live.
lightgbm.train(
    param,
    train_data,
    valid_sets=[validation_data],
    num_round=5,
    callbacks=[DvcLiveCallback(
      path="custom_path",
      summary=False)])
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