Neptune + XGBoost integration

Experiment tracking, model registry, data versioning, and live model monitoring for XGBoost trained models.

What will you get with this integration?

Log, display, organize, and compare ML experiments in a single place
Version, store, manage, and query trained models, and model building metadata
Record and monitor model training, evaluation, or production runs live

What will be logged to Neptune?

metrics,
parameters,
learning rate,
pickled model,
visualizations (feature importance chart and tree visualizations),
hardware consumption (CPU, GPU, Memory),
stdout and stderr logs,
training code and Git commit information,
other metadata

Example dashboard with train-valid metrics and selected parameters

Resources

Example

On the command line:

pip install xgboost>=1.3.0 neptune-xgboost

In Python:

import neptune
import xgboost as xgb
from neptune.integrations.xgboost import NeptuneCallback

# Start a run
run = neptune.init_run(
    project="common/xgboost-integration",
    api_token=neptune.ANONYMOUS_API_TOKEN,
)

# Create a NeptuneCallback instance
neptune_callback = NeptuneCallback(run=run, log_tree=[0, 1, 2, 3])

# Prepare datasets
...
data_train = xgb.DMatrix(X_train, label=y_train)

# Define model parameters
model_params = {
    "eta": 0.7,
    "gamma": 0.001,
    "max_depth": 9,
    ...
}

# Train the model and log metadata to the run in Neptune
xgb.train(
    params=model_params,
    dtrain=data_train,
    callbacks=[neptune_callback],
)

Support

If you got stuck or simply want to talk to us, here are your options:

Check our FAQ page
You can submit bug reports, feature requests, or contributions directly to the repository.
Chat! When in the Neptune application click on the blue message icon in the bottom-right corner and send a message. A real person will talk to you ASAP (typically very ASAP),
You can just shoot us an email at support@neptune.ai