%global _empty_manifest_terminate_build 0 Name: python-neptune-lightgbm Version: 2.0.0 Release: 1 Summary: Neptune.ai LightGBM integration library License: Apache-2.0 URL: https://neptune.ai/ Source0: https://mirrors.aliyun.com/pypi/web/packages/24/20/8e12db5d9b599986450a2155581dfd42241ecfd29d353adf6099968560ca/neptune_lightgbm-2.0.0.tar.gz BuildArch: noarch Requires: python3-graphviz Requires: python3-importlib-metadata Requires: python3-lightgbm Requires: python3-matplotlib Requires: python3-neptune Requires: python3-pre-commit Requires: python3-pytest Requires: python3-pytest-cov Requires: python3-scikit-plot %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](https://docs.neptune.ai/logging/what_you_can_log) ![image](https://user-images.githubusercontent.com/97611089/160637021-6d324be7-00f0-4b89-bffd-ae937f6802b4.png) *Example dashboard with train-valid metrics and selected parameters* ## Resources * [Documentation](https://docs.neptune.ai/integrations/lightgbm) * [Code example on GitHub](https://github.com/neptune-ai/examples/blob/main/integrations-and-supported-tools/lightgbm/scripts/Neptune_LightGBM_train_summary.py) * [Example of a run logged in the Neptune app](https://app.neptune.ai/o/common/org/lightgbm-integration/e/LGBM-86/dashboard/train-cls-summary-6c07f9e0-36ca-4432-9530-7fd3457220b6) * [Run example in Google Colab](https://colab.research.google.com/github/neptune-ai/examples/blob/main/integrations-and-supported-tools/lightgbm/notebooks/Neptune_LightGBM.ipynb) ## Example ``` # On the command line: pip install neptune-lightgbm ``` ```python # In Python: import lightgbm as lgb import neptune from neptune.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](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 %package -n python3-neptune-lightgbm Summary: Neptune.ai LightGBM integration library Provides: python-neptune-lightgbm BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-neptune-lightgbm # 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](https://docs.neptune.ai/logging/what_you_can_log) ![image](https://user-images.githubusercontent.com/97611089/160637021-6d324be7-00f0-4b89-bffd-ae937f6802b4.png) *Example dashboard with train-valid metrics and selected parameters* ## Resources * [Documentation](https://docs.neptune.ai/integrations/lightgbm) * [Code example on GitHub](https://github.com/neptune-ai/examples/blob/main/integrations-and-supported-tools/lightgbm/scripts/Neptune_LightGBM_train_summary.py) * [Example of a run logged in the Neptune app](https://app.neptune.ai/o/common/org/lightgbm-integration/e/LGBM-86/dashboard/train-cls-summary-6c07f9e0-36ca-4432-9530-7fd3457220b6) * [Run example in Google Colab](https://colab.research.google.com/github/neptune-ai/examples/blob/main/integrations-and-supported-tools/lightgbm/notebooks/Neptune_LightGBM.ipynb) ## Example ``` # On the command line: pip install neptune-lightgbm ``` ```python # In Python: import lightgbm as lgb import neptune from neptune.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](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 %package help Summary: Development documents and examples for neptune-lightgbm Provides: python3-neptune-lightgbm-doc %description help # 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](https://docs.neptune.ai/logging/what_you_can_log) ![image](https://user-images.githubusercontent.com/97611089/160637021-6d324be7-00f0-4b89-bffd-ae937f6802b4.png) *Example dashboard with train-valid metrics and selected parameters* ## Resources * [Documentation](https://docs.neptune.ai/integrations/lightgbm) * [Code example on GitHub](https://github.com/neptune-ai/examples/blob/main/integrations-and-supported-tools/lightgbm/scripts/Neptune_LightGBM_train_summary.py) * [Example of a run logged in the Neptune app](https://app.neptune.ai/o/common/org/lightgbm-integration/e/LGBM-86/dashboard/train-cls-summary-6c07f9e0-36ca-4432-9530-7fd3457220b6) * [Run example in Google Colab](https://colab.research.google.com/github/neptune-ai/examples/blob/main/integrations-and-supported-tools/lightgbm/notebooks/Neptune_LightGBM.ipynb) ## Example ``` # On the command line: pip install neptune-lightgbm ``` ```python # In Python: import lightgbm as lgb import neptune from neptune.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](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 %prep %autosetup -n neptune_lightgbm-2.0.0 %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-lightgbm -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Thu Jun 08 2023 Python_Bot - 2.0.0-1 - Package Spec generated