# 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](https://docs.neptune.ai/logging/what_you_can_log)
*Example dashboard with train-valid metrics and selected parameters*
## Resources
* [Documentation](https://docs.neptune.ai/integrations/lightgbm)
* [Code example on GitHub](https://github.com/neptune-ai/examples/blob/main/integrations-and-supported-tools/lightgbm/scripts/Neptune_LightGBM_train_summary.py)
* [Example of a run logged in the Neptune app](https://app.neptune.ai/o/common/org/lightgbm-integration/e/LGBM-86/dashboard/train-cls-summary-6c07f9e0-36ca-4432-9530-7fd3457220b6)
* [Run example in Google Colab](https://colab.research.google.com/github/neptune-ai/examples/blob/main/integrations-and-supported-tools/lightgbm/notebooks/Neptune_LightGBM.ipynb)
## Example
```
# On the command line:
pip install neptune-lightgbm
```
```python
# 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](https://docs.neptune.ai/getting-started/getting-help#frequently-asked-questions)
* 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
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"description": "# Neptune + LightGBM Integration\n\nExperiment tracking, model registry, data versioning, and live model monitoring for LightGBM trained models.\n\n## What will you get with this integration?\n\n* Log, display, organize, and compare ML experiments in a single place\n* Version, store, manage, and query trained models, and model building metadata\n* Record and monitor model training, evaluation, or production runs live\n\n## What will be logged to Neptune?\n\n* training and validation metrics,\n* parameters,\n* feature names, num_features, and num_rows for the train set,\n* hardware consumption (CPU, GPU, memory),\n* stdout and stderr logs,\n* training code and Git commit information,\n* [other metadata](https://docs.neptune.ai/logging/what_you_can_log)\n\n\n*Example dashboard with train-valid metrics and selected parameters*\n\n\n## Resources\n\n* [Documentation](https://docs.neptune.ai/integrations/lightgbm)\n* [Code example on GitHub](https://github.com/neptune-ai/examples/blob/main/integrations-and-supported-tools/lightgbm/scripts/Neptune_LightGBM_train_summary.py)\n* [Example of a run logged in the Neptune app](https://app.neptune.ai/o/common/org/lightgbm-integration/e/LGBM-86/dashboard/train-cls-summary-6c07f9e0-36ca-4432-9530-7fd3457220b6)\n* [Run example in Google Colab](https://colab.research.google.com/github/neptune-ai/examples/blob/main/integrations-and-supported-tools/lightgbm/notebooks/Neptune_LightGBM.ipynb)\n\n## Example\n\n```\n# On the command line:\npip install neptune-lightgbm\n```\n\n```python\n# In Python:\nimport lightgbm as lgb\nimport neptune\nfrom neptune.integrations.lightgbm import NeptuneCallback\n\n# Start a run\nrun = neptune.init_run(\n project=\"common/lightgbm-integration\",\n api_token=neptune.ANONYMOUS_API_TOKEN,\n)\n\n# Create a NeptuneCallback instance\nneptune_callback = NeptuneCallback(run=run)\n\n# Prepare datasets\n...\nlgb_train = lgb.Dataset(X_train, y_train)\n\n# Define model parameters\nparams = {\n \"boosting_type\": \"gbdt\",\n \"objective\": \"multiclass\",\n \"num_class\": 10,\n ...\n}\n\n# Train the model\ngbm = lgb.train(\n params,\n lgb_train,\n callbacks=[neptune_callback],\n)\n```\n\n## Support\n\nIf you got stuck or simply want to talk to us, here are your options:\n\n* Check our [FAQ page](https://docs.neptune.ai/getting-started/getting-help#frequently-asked-questions)\n* You can submit bug reports, feature requests, or contributions directly to the repository.\n* 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),\n* You can just shoot us an email at support@neptune.ai\n\n",
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