DVCLive allows you to easily add experiment tracking capabilities to your LightGBM projects.
LightGBM is a gradient boosting framework that uses tree based learning algorithms.
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
to the callbacks list passed to the
+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.
model_file- The name of the file where the model will be saved at the end of each
lightgbm.train( param, train_data, valid_sets=[validation_data], num_round=5, callbacks=[DvcLiveCallback(model_file="lgbm_model.txt")])