%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)
*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)
*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)
*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
* Tue May 30 2023 Python_Bot <Python_Bot@openeuler.org> - 1.1.1-1
- Package Spec generated