DVCLive allows you to easily add experiment tracking capabilities to your XGBoost projects.
XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework.
To start using DVCLive you just need to add a few lines to your training code in any XGBoost project.
You just need to add the
to the callbacks list passed to the
+from dvclive.xgboost import DvcLiveCallback . . . xgboost.train( param, dtrain, - num_round=5) + num_round=5, + callbacks=[DvcLiveCallback("eval_data")], + evals=[(dval, "eval_data")])
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 - (
None by default) - The name of the file where the model will
be saved at the end of each
**kwargs - Any additional arguments will be passed to
xgboost.train( param, dtrain, num_round=5, callbacks=[ DvcLiveCallback( "eval_data", path="custom_path", summary=False)], evals=[(dval, "eval_data")])