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+%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.nju.edu.cn/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
+* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 2.0.0-1
+- Package Spec generated