https://github.com/neptune-ai/neptune-xgboost

Experiment tracking for XGBoost. 🧩 Log, organize, visualize and compare machine learning model metrics, parameters, dataset versions, and more.
https://github.com/neptune-ai/neptune-xgboost

comparison dashboard logger management metadata ml mlops tracker training versioning visualization

Last synced: about 2 months ago
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Experiment tracking for XGBoost. 🧩 Log, organize, visualize and compare machine learning model metrics, parameters, dataset versions, and more.

• Host: GitHub
• URL: https://github.com/neptune-ai/neptune-xgboost
• Owner: neptune-ai
• License: apache-2.0
• Created: 2021-04-29T07:37:10.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2023-09-14T08:11:38.000Z (over 1 year ago)
• Last Synced: 2024-10-28T17:21:53.865Z (2 months ago)
• Topics: comparison, dashboard, logger, management, metadata, ml, mlops, tracker, training, versioning, visualization
• Language: Python
• Homepage: https://docs.neptune.ai/integrations/xgboost/
• Size: 96.7 KB
• Stars: 6
• Watchers: 8
• Forks: 1
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: LICENSE

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README

        
# Neptune + XGBoost integration

Experiment tracking for XGBoost-trained models.

## What will you get with this integration?

* Log, organize, visualize, and compare ML experiments in a single place

* Monitor model training live

* Version and query production-ready models and associated metadata (e.g., datasets)

* Collaborate with the team and across the organization

## 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](https://docs.neptune.ai/logging/what_you_can_log)

![image](https://docs.neptune.ai/img/app/integrations/xgboost.png)

## Resources

* [Documentation](https://docs.neptune.ai/integrations/xgboost)

* [Code example on GitHub](https://github.com/neptune-ai/examples/blob/main/integrations-and-supported-tools/xgboost/scripts/Neptune_XGBoost_train.py)

* [Example of a run logged in the Neptune app](https://app.neptune.ai/o/common/org/xgboost-integration/e/XGBOOST-84/dashboard/train-e395296a-4f3d-4a58-ab88-6ef06bbac657)

* [Run example in Google Colab](https://colab.research.google.com/github/neptune-ai/examples/blob/main/integrations-and-supported-tools/xgboost/notebooks/Neptune_XGBoost.ipynb)

## Example

On the command line:

```

pip install xgboost>=1.3.0 neptune-xgboost

```

In Python:

```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](https://docs.neptune.ai/getting_help)

* 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]