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Neptune + LightGBM Integration

Experiment tracking, model registry, data versioning, and live model monitoring for LightGBM 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?

  • training and validation metrics,
  • parameters,
  • feature names, num_features, and num_rows for the train set,
  • hardware consumption (CPU, GPU, memory),
  • stdout and stderr logs,
  • training code and Git commit information,
  • other metadata

image Example dashboard with train-valid metrics and selected parameters

Resources

Example

# On the command line:
pip install neptune-lightgbm
# In Python:
import lightgbm as lgb
import neptune
from neptune.integrations.lightgbm import NeptuneCallback

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

# Create a NeptuneCallback instance
neptune_callback = NeptuneCallback(run=run)

# Prepare datasets
...
lgb_train = lgb.Dataset(X_train, y_train)

# Define model parameters
params = {
    "boosting_type": "gbdt",
    "objective": "multiclass",
    "num_class": 10,
    ...
}

# Train the model
gbm = lgb.train(
    params,
    lgb_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 [email protected]