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

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 support@neptune.ai