%global _empty_manifest_terminate_build 0 Name: python-neptune-xgboost Version: 1.1.1 Release: 1 Summary: Neptune.ai XGBoost integration library License: Apache-2.0 URL: https://neptune.ai/ Source0: https://mirrors.nju.edu.cn/pypi/web/packages/3c/1e/20ae6cd96fdece210b058f884cfc5f935d177b50c6d17376df985252f74e/neptune_xgboost-1.1.1.tar.gz BuildArch: noarch Requires: python3-graphviz Requires: python3-importlib-metadata Requires: python3-matplotlib Requires: python3-neptune Requires: python3-pre-commit Requires: python3-pytest Requires: python3-pytest-cov Requires: python3-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, * training code and Git commit information, * [other metadata](https://docs.neptune.ai/logging/what_you_can_log) ![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/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 support@neptune.ai %package -n python3-neptune-xgboost Summary: Neptune.ai XGBoost integration library Provides: python-neptune-xgboost BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-neptune-xgboost # 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](https://docs.neptune.ai/logging/what_you_can_log) ![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/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 support@neptune.ai %package help Summary: Development documents and examples for neptune-xgboost Provides: python3-neptune-xgboost-doc %description help # 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](https://docs.neptune.ai/logging/what_you_can_log) ![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/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 support@neptune.ai %prep %autosetup -n neptune-xgboost-1.1.1 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-neptune-xgboost -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Mon May 15 2023 Python_Bot - 1.1.1-1 - Package Spec generated