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.

Runs

Operations