XGBoost Profiling and Monitoring

See the Quick Start Guide on how to install and configure the profiler.

To profile XGBoost training, use a callback. Only some of the iterations will be profiled; the profiler decides which iterations to profile for optimal statistics and low overhead.

Profile training:

from graphsignal.profilers.xgboost import GraphsignalCallback

bst = xgb.train(..., callbacks=GraphsignalCallback()])

Examples

The XGBoost Iris example illustrates how to add the profiler callback.

Distributed workloads

Graphsignal provides built-in support for distributed training and inference. Depending on the platform, it may be necessary to provide a run ID to all workers. Refer to Distributed Workloads section for more information.

Dashboards

After starting the run, profiling and monitoring data will be available in the cloud dashboards.