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
- Documentation
- Code example on GitHub
- Example of a run logged in the Neptune app
- Run example in Google Colab
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
Metadata
Release files for neptune-xgboost 1.1.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| neptune_xgboost-1.1.1.tar.gz | 10.5 kB | Without provenance | Details for neptune_xgboost-1.1.1.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| neptune_xgboost-1.1.1-py3-none-any.whl | Python 3 | none | any | Details for neptune_xgboost-1.1.1-py3-none-any.whl |
Total release size: 22.5 kB
Release files / neptune_xgboost-1.1.1.tar.gz
| Download URL | neptune_xgboost-1.1.1.tar.gz |
|---|---|
| Size | 10.5 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/4.0.2 CPython/3.9.16
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Release files / neptune_xgboost-1.1.1-py3-none-any.whl
| Download URL | neptune_xgboost-1.1.1-py3-none-any.whl |
|---|---|
| Size | 12.0 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.9.16
|
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