Metadata-Version: 2.1
Name: neptune-xgboost
Version: 0.10.0
Summary: Neptune.ai XGBoost integration library
Home-page: https://github.com/neptune-ai/neptune-xgboost
Author: neptune.ai
Author-email: contact@neptune.ai
License: Apache License 2.0
Project-URL: Tracker, https://github.com/neptune-ai/neptune-xgboost/issues
Project-URL: Source, https://github.com/neptune-ai/neptune-xgboost
Project-URL: Documentation, https://docs.neptune.ai/integrations-and-supported-tools/model-training/xgboost
Description: # 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, and
        * training code and git commit information
        * [other metadata](https://docs.neptune.ai/you-should-know/what-can-you-log-and-display)
        
        ![image](https://user-images.githubusercontent.com/97611089/160614588-5d839a11-e2f9-4eed-a3d1-39314ebdb1ea.png)
        *Example dashboard with train-valid metrics and selected parameters*
        
        
        ## Resources
        
        * [Documentation](https://docs.neptune.ai/integrations-and-supported-tools/model-training/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
        
        ```python
        # On the command line:
        pip install neptune-client xgboost>=1.3.0 neptune-xgboost
        ```
        ```python
        # In Python:
        import neptune.new as neptune
        import xgboost as xgb
        from neptune.new.integrations.xgboost import NeptuneCallback
        
        # Start a run
        run = neptune.init(
            project="common/xgboost-integration",
            api_token="ANONYMOUS",
        )
        
        # 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-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
        
Keywords: MLOps,ML Experiment Tracking,ML Model Registry,ML Model Store,ML Metadata Store
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Description-Content-Type: text/markdown
Provides-Extra: all
