Skip to main content

Neptune.ai LightGBM integration library

Project description

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-client lightgbm neptune-lightgbm
# In Python:
import lightgbm as lgb
import neptune.new as neptune
from neptune.new.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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

neptune_lightgbm-1.0.0.tar.gz (12.5 kB view details)

Uploaded Source

Built Distribution

neptune_lightgbm-1.0.0-py3-none-any.whl (13.3 kB view details)

Uploaded Python 3

File details

Details for the file neptune_lightgbm-1.0.0.tar.gz.

File metadata

  • Download URL: neptune_lightgbm-1.0.0.tar.gz
  • Upload date:
  • Size: 12.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.16

File hashes

Hashes for neptune_lightgbm-1.0.0.tar.gz
Algorithm Hash digest
SHA256 8f88e353e4f49470e9d18f1b49bbfb43740c8b6891f18a47418582426456e692
MD5 8eed9575efdb57c1d1e1c14ef22e65ab
BLAKE2b-256 75c401357b20d9594ab004b8d83149d6b5ca6b8543f972f1005efa23088bdf30

See more details on using hashes here.

File details

Details for the file neptune_lightgbm-1.0.0-py3-none-any.whl.

File metadata

File hashes

Hashes for neptune_lightgbm-1.0.0-py3-none-any.whl
Algorithm Hash digest
SHA256 c1d068056fc6bef6ecdfa9774d9d0a78e350c4762231e598bfbf827de42f8a2a
MD5 edaba9c50f50ecdab69116ed6c7b8300
BLAKE2b-256 cd1ce239a7c3589a57a48ca954fd9a81d1c385eeae678a2ba3989edaabee0381

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page