%global _empty_manifest_terminate_build 0 Name: python-neptune-tensorflow-keras Version: 2.1.1 Release: 1 Summary: Neptune.ai tensorflow-keras integration library License: Apache-2.0 URL: https://neptune.ai/ Source0: https://mirrors.nju.edu.cn/pypi/web/packages/77/03/eef0f99d51843695e6821d588ea12fcfa0597ab7e0a10f583bcc93523c6f/neptune_tensorflow_keras-2.1.1.tar.gz BuildArch: noarch Requires: python3-importlib-metadata Requires: python3-neptune Requires: python3-pre-commit Requires: python3-pydot Requires: python3-pytest Requires: python3-pytest-cov Requires: python3-tensorflow %description # Neptune + Keras integration Experiment tracking, model registry, data versioning, and live model monitoring for Keras 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 * Collaborate with a team ## What will be logged to Neptune? * hyperparameters for every run, * learning curves for losses and metrics during training, * hardware consumption and stdout/stderr output during training, * TensorFlow tensors as images to see model predictions live, * training code and Git commit information, * model weights, * [other metadata](https://docs.neptune.ai/logging/what_you_can_log) ![image](https://user-images.githubusercontent.com/97611089/160638338-8a276866-6ce8-4d0a-93f5-bd564d00afdf.png) *Example charts in the Neptune UI with logged accuracy and loss* ## Resources * [Documentation](https://docs.neptune.ai/integrations/keras) * [Code example on GitHub](https://github.com/neptune-ai/examples/blob/main/integrations-and-supported-tools/tensorflow-keras/scripts) * [Runs logged in the Neptune app](https://app.neptune.ai/o/common/org/tf-keras-integration/e/TFK-18/all) * [Run example in Google Colab](https://colab.research.google.com/github/neptune-ai/examples/blob/master/integrations-and-supported-tools/tensorflow-keras/notebooks/Neptune_TensorFlow_Keras.ipynb) ## Example On the command line: ``` pip install neptune-tensorflow-keras ``` In Python: ```python import neptune from neptune.integrations.tensorflow_keras import NeptuneCallback from neptune import ANONYMOUS_API_TOKEN # Start a run run = neptune.init_run( project="common/tf-keras-integration", api_token=ANONYMOUS_API_TOKEN, ) # Create a NeptuneCallback instance neptune_cbk = NeptuneCallback(run=run, base_namespace="metrics") # Pass the callback to model.fit() model.fit( x_train, y_train, epochs=5, batch_size=64, callbacks=[neptune_cbk], ) # Stop the run run.stop() ``` ## 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-tensorflow-keras Summary: Neptune.ai tensorflow-keras integration library Provides: python-neptune-tensorflow-keras BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-neptune-tensorflow-keras # Neptune + Keras integration Experiment tracking, model registry, data versioning, and live model monitoring for Keras 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 * Collaborate with a team ## What will be logged to Neptune? * hyperparameters for every run, * learning curves for losses and metrics during training, * hardware consumption and stdout/stderr output during training, * TensorFlow tensors as images to see model predictions live, * training code and Git commit information, * model weights, * [other metadata](https://docs.neptune.ai/logging/what_you_can_log) ![image](https://user-images.githubusercontent.com/97611089/160638338-8a276866-6ce8-4d0a-93f5-bd564d00afdf.png) *Example charts in the Neptune UI with logged accuracy and loss* ## Resources * [Documentation](https://docs.neptune.ai/integrations/keras) * [Code example on GitHub](https://github.com/neptune-ai/examples/blob/main/integrations-and-supported-tools/tensorflow-keras/scripts) * [Runs logged in the Neptune app](https://app.neptune.ai/o/common/org/tf-keras-integration/e/TFK-18/all) * [Run example in Google Colab](https://colab.research.google.com/github/neptune-ai/examples/blob/master/integrations-and-supported-tools/tensorflow-keras/notebooks/Neptune_TensorFlow_Keras.ipynb) ## Example On the command line: ``` pip install neptune-tensorflow-keras ``` In Python: ```python import neptune from neptune.integrations.tensorflow_keras import NeptuneCallback from neptune import ANONYMOUS_API_TOKEN # Start a run run = neptune.init_run( project="common/tf-keras-integration", api_token=ANONYMOUS_API_TOKEN, ) # Create a NeptuneCallback instance neptune_cbk = NeptuneCallback(run=run, base_namespace="metrics") # Pass the callback to model.fit() model.fit( x_train, y_train, epochs=5, batch_size=64, callbacks=[neptune_cbk], ) # Stop the run run.stop() ``` ## 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-tensorflow-keras Provides: python3-neptune-tensorflow-keras-doc %description help # Neptune + Keras integration Experiment tracking, model registry, data versioning, and live model monitoring for Keras 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 * Collaborate with a team ## What will be logged to Neptune? * hyperparameters for every run, * learning curves for losses and metrics during training, * hardware consumption and stdout/stderr output during training, * TensorFlow tensors as images to see model predictions live, * training code and Git commit information, * model weights, * [other metadata](https://docs.neptune.ai/logging/what_you_can_log) ![image](https://user-images.githubusercontent.com/97611089/160638338-8a276866-6ce8-4d0a-93f5-bd564d00afdf.png) *Example charts in the Neptune UI with logged accuracy and loss* ## Resources * [Documentation](https://docs.neptune.ai/integrations/keras) * [Code example on GitHub](https://github.com/neptune-ai/examples/blob/main/integrations-and-supported-tools/tensorflow-keras/scripts) * [Runs logged in the Neptune app](https://app.neptune.ai/o/common/org/tf-keras-integration/e/TFK-18/all) * [Run example in Google Colab](https://colab.research.google.com/github/neptune-ai/examples/blob/master/integrations-and-supported-tools/tensorflow-keras/notebooks/Neptune_TensorFlow_Keras.ipynb) ## Example On the command line: ``` pip install neptune-tensorflow-keras ``` In Python: ```python import neptune from neptune.integrations.tensorflow_keras import NeptuneCallback from neptune import ANONYMOUS_API_TOKEN # Start a run run = neptune.init_run( project="common/tf-keras-integration", api_token=ANONYMOUS_API_TOKEN, ) # Create a NeptuneCallback instance neptune_cbk = NeptuneCallback(run=run, base_namespace="metrics") # Pass the callback to model.fit() model.fit( x_train, y_train, epochs=5, batch_size=64, callbacks=[neptune_cbk], ) # Stop the run run.stop() ``` ## 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_tensorflow_keras-2.1.1 %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-tensorflow-keras -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Thu Jun 08 2023 Python_Bot - 2.1.1-1 - Package Spec generated