%global _empty_manifest_terminate_build 0 Name: python-auto-tensorflow Version: 1.3.4 Release: 1 Summary: Build Low Code Automated Tensorflow, What-IF explainable models in just 3 lines of code. To make Deep Learning on Tensorflow absolutely easy for the masses with its low code framework and also increase trust on ML models through What-IF model explainability. License: Apache License 2.0 URL: https://github.com/rafiqhasan/auto-tensorflow Source0: https://mirrors.aliyun.com/pypi/web/packages/c0/7c/fff75e95d1ee18464a2fc375f7a19f357b45ceb4f62be91f23be1f31d0b8/auto_tensorflow-1.3.4.tar.gz BuildArch: noarch Requires: python3-keras-tuner Requires: python3-tensorflow-text Requires: python3-tfx Requires: python3-witwidget Requires: python3-tensorflow Requires: python3-tensorflow-hub Requires: python3-tensorflow-metadata Requires: python3-ipython Requires: python3-tensorflow-estimator Requires: python3-joblib Requires: python3-tensorboard-plugin-wit Requires: python3-tensorboard-data-server Requires: python3-google-api-core Requires: python3-google-cloud-aiplatform Requires: python3-google-cloud Requires: python3-apache-beam Requires: python3-protobuf Requires: python3-jupyterlab-widgets Requires: python3-PyYAML Requires: python3-pytz Requires: python3-tensorflow-model-analysis Requires: python3-tensorflow-data-validation Requires: python3-tensorboard Requires: python3-six Requires: python3-requests Requires: python3-widgetsnbextension %description [![Downloads](https://static.pepy.tech/personalized-badge/auto-tensorflow?period=total&units=none&left_color=grey&right_color=brightgreen&left_text=Downloads)](https://pepy.tech/project/auto-tensorflow) [![Generic badge](https://img.shields.io/pypi/v/auto-tensorflow.svg?logo=pypi&logoColor=white&color=orange)](https://pypi.org/project/auto-tensorflow/) ![Generic badge](https://img.shields.io/badge/python-v3.6%20%7C%203.7%20%7C%203.8-blue) ![example workflow](https://github.com/rafiqhasan/auto-tensorflow/actions/workflows/codeql-analysis.yml/badge.svg) ![Open issues](https://img.shields.io/github/issues-raw/rafiqhasan/auto-tensorflow?color=red) ### **Auto Tensorflow - Mission:** **Build Low Code Automated Tensorflow, What-IF explainable models in just 3 lines of code.** To make Deep Learning on Tensorflow absolutely easy for the masses with its low code framework and also increase trust on ML models through What-IF model explainability. ### **Under the hood:** Built on top of the powerful **Tensorflow** ecosystem tools like **TFX** , **TF APIs** and **What-IF Tool** , the library automatically does all the heavy lifting internally like EDA, schema discovery, feature engineering, HPT, model search etc. This empowers developers to focus only on building end user applications quickly without any knowledge of Tensorflow, ML or debugging. Built for handling large volume of data / BigData - using only TF scalable components. Moreover the models trained with auto-tensorflow can directly be deployed on any cloud like GCP / AWS / Azure. ### **Official Launch**: https://youtu.be/sil-RbuckG0 ### **Features:** 1. Build Classification / Regression models on CSV data 2. Automated Schema Inference 3. Automated Feature Engineering - Discretization - Scaling - Normalization - Text Embedding - Category encoding 5. Automated Model build for mixed data types( Continuous, Categorical and Free Text ) 6. Automated Hyper-parameter tuning 7. Automated GPU Distributed training 8. Automated UI based What-IF analysis( Fairness, Feature Partial dependencies, What-IF ) 9. Control over complexity of model 10. No dependency over Pandas / SKLearn 11. Can handle dataset of any size - including multiple CSV files ### **Tutorials**: 1. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/rafiqhasan/auto-tensorflow/blob/main/tutorials/TFAuto_%7C_Classification.ipynb) - Auto Classification on CSV data 2. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/rafiqhasan/auto-tensorflow/blob/main/tutorials/TFAuto_%7C_Regression.ipynb) - Auto Regression on CSV data ### **Setup:** 1. Install library - PIP(Recommended): ```pip install auto-tensorflow``` - Nightly: ```pip install git+https://github.com/rafiqhasan/auto-tensorflow.git``` 2. Works best on UNIX/Linux/Debian/Google Colab/MacOS ### **Usage:** 1. Initialize TFAuto Engine ``` from auto_tensorflow.tfa import TFAuto tfa = TFAuto(train_data_path='/content/train_data/', test_data_path='/content/test_data/', path_root='/content/tfauto') ``` 2. Step 1 - Automated EDA and Schema discovery ``` tfa.step_data_explore(viz=True) ##Viz=False for no visualization ``` 3. Step 2 - Automated ML model build and train ``` tfa.step_model_build(label_column = 'price', model_type='REGRESSION', model_complexity=1) ``` 4. Step 3 - Automated What-IF Tool launch ``` tfa.step_model_whatif() ``` ### **API Arguments:** - Method **TFAuto** - ```train_data_path```: Path where training data is stored - ```test_data_path```: Path where Test / Eval data is stored - ```path_root```: Directory for running TFAuto( Directory should NOT exist ) - Method **step_data_explore** - ```viz```: Is data visualization required ? - True or False( Default ) - Method **step_model_build** - `label_column`: The feature to be used as Label - `model_type`: Either of 'REGRESSION'( Default ), 'CLASSIFICATION' - `model_complexity`: - `0` : Model with default hyper-parameters - `1` (Default): Model with automated hyper-parameter tuning - `2` : Complexity 1 + Advanced fine-tuning of Text layers ### **Current limitations:** There are a few limitations in the initial release but we are working day and night to resolve these and **add them as future features**. 1. Doesn't support Image / Audio data ### **Future roadmap:** 1. Add support for Timeseries / Audio / Image data 2. Add feature to download full pipeline model Python code for advanced tweaking ### **Release History:** **1.3.3** - 09/12/2022 - [Release Notes](https://github.com/rafiqhasan/auto-tensorflow/releases/tag/1.3.3) **1.3.2** - 27/11/2021 - [Release Notes](https://github.com/rafiqhasan/auto-tensorflow/releases/tag/1.3.2) **1.3.1** - 18/11/2021 - [Release Notes](https://github.com/rafiqhasan/auto-tensorflow/releases/tag/1.3.1) **1.2.0** - 24/07/2021 - [Release Notes](https://github.com/rafiqhasan/auto-tensorflow/releases/tag/1.2.0) **1.1.1** - 14/07/2021 - [Release Notes](https://github.com/rafiqhasan/auto-tensorflow/releases/tag/1.1.1) **1.0.1** - 07/07/2021 - [Release Notes](https://github.com/rafiqhasan/auto-tensorflow/releases/tag/1.0.1) %package -n python3-auto-tensorflow Summary: Build Low Code Automated Tensorflow, What-IF explainable models in just 3 lines of code. To make Deep Learning on Tensorflow absolutely easy for the masses with its low code framework and also increase trust on ML models through What-IF model explainability. Provides: python-auto-tensorflow BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-auto-tensorflow [![Downloads](https://static.pepy.tech/personalized-badge/auto-tensorflow?period=total&units=none&left_color=grey&right_color=brightgreen&left_text=Downloads)](https://pepy.tech/project/auto-tensorflow) [![Generic badge](https://img.shields.io/pypi/v/auto-tensorflow.svg?logo=pypi&logoColor=white&color=orange)](https://pypi.org/project/auto-tensorflow/) ![Generic badge](https://img.shields.io/badge/python-v3.6%20%7C%203.7%20%7C%203.8-blue) ![example workflow](https://github.com/rafiqhasan/auto-tensorflow/actions/workflows/codeql-analysis.yml/badge.svg) ![Open issues](https://img.shields.io/github/issues-raw/rafiqhasan/auto-tensorflow?color=red) ### **Auto Tensorflow - Mission:** **Build Low Code Automated Tensorflow, What-IF explainable models in just 3 lines of code.** To make Deep Learning on Tensorflow absolutely easy for the masses with its low code framework and also increase trust on ML models through What-IF model explainability. ### **Under the hood:** Built on top of the powerful **Tensorflow** ecosystem tools like **TFX** , **TF APIs** and **What-IF Tool** , the library automatically does all the heavy lifting internally like EDA, schema discovery, feature engineering, HPT, model search etc. This empowers developers to focus only on building end user applications quickly without any knowledge of Tensorflow, ML or debugging. Built for handling large volume of data / BigData - using only TF scalable components. Moreover the models trained with auto-tensorflow can directly be deployed on any cloud like GCP / AWS / Azure. ### **Official Launch**: https://youtu.be/sil-RbuckG0 ### **Features:** 1. Build Classification / Regression models on CSV data 2. Automated Schema Inference 3. Automated Feature Engineering - Discretization - Scaling - Normalization - Text Embedding - Category encoding 5. Automated Model build for mixed data types( Continuous, Categorical and Free Text ) 6. Automated Hyper-parameter tuning 7. Automated GPU Distributed training 8. Automated UI based What-IF analysis( Fairness, Feature Partial dependencies, What-IF ) 9. Control over complexity of model 10. No dependency over Pandas / SKLearn 11. Can handle dataset of any size - including multiple CSV files ### **Tutorials**: 1. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/rafiqhasan/auto-tensorflow/blob/main/tutorials/TFAuto_%7C_Classification.ipynb) - Auto Classification on CSV data 2. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/rafiqhasan/auto-tensorflow/blob/main/tutorials/TFAuto_%7C_Regression.ipynb) - Auto Regression on CSV data ### **Setup:** 1. Install library - PIP(Recommended): ```pip install auto-tensorflow``` - Nightly: ```pip install git+https://github.com/rafiqhasan/auto-tensorflow.git``` 2. Works best on UNIX/Linux/Debian/Google Colab/MacOS ### **Usage:** 1. Initialize TFAuto Engine ``` from auto_tensorflow.tfa import TFAuto tfa = TFAuto(train_data_path='/content/train_data/', test_data_path='/content/test_data/', path_root='/content/tfauto') ``` 2. Step 1 - Automated EDA and Schema discovery ``` tfa.step_data_explore(viz=True) ##Viz=False for no visualization ``` 3. Step 2 - Automated ML model build and train ``` tfa.step_model_build(label_column = 'price', model_type='REGRESSION', model_complexity=1) ``` 4. Step 3 - Automated What-IF Tool launch ``` tfa.step_model_whatif() ``` ### **API Arguments:** - Method **TFAuto** - ```train_data_path```: Path where training data is stored - ```test_data_path```: Path where Test / Eval data is stored - ```path_root```: Directory for running TFAuto( Directory should NOT exist ) - Method **step_data_explore** - ```viz```: Is data visualization required ? - True or False( Default ) - Method **step_model_build** - `label_column`: The feature to be used as Label - `model_type`: Either of 'REGRESSION'( Default ), 'CLASSIFICATION' - `model_complexity`: - `0` : Model with default hyper-parameters - `1` (Default): Model with automated hyper-parameter tuning - `2` : Complexity 1 + Advanced fine-tuning of Text layers ### **Current limitations:** There are a few limitations in the initial release but we are working day and night to resolve these and **add them as future features**. 1. Doesn't support Image / Audio data ### **Future roadmap:** 1. Add support for Timeseries / Audio / Image data 2. Add feature to download full pipeline model Python code for advanced tweaking ### **Release History:** **1.3.3** - 09/12/2022 - [Release Notes](https://github.com/rafiqhasan/auto-tensorflow/releases/tag/1.3.3) **1.3.2** - 27/11/2021 - [Release Notes](https://github.com/rafiqhasan/auto-tensorflow/releases/tag/1.3.2) **1.3.1** - 18/11/2021 - [Release Notes](https://github.com/rafiqhasan/auto-tensorflow/releases/tag/1.3.1) **1.2.0** - 24/07/2021 - [Release Notes](https://github.com/rafiqhasan/auto-tensorflow/releases/tag/1.2.0) **1.1.1** - 14/07/2021 - [Release Notes](https://github.com/rafiqhasan/auto-tensorflow/releases/tag/1.1.1) **1.0.1** - 07/07/2021 - [Release Notes](https://github.com/rafiqhasan/auto-tensorflow/releases/tag/1.0.1) %package help Summary: Development documents and examples for auto-tensorflow Provides: python3-auto-tensorflow-doc %description help [![Downloads](https://static.pepy.tech/personalized-badge/auto-tensorflow?period=total&units=none&left_color=grey&right_color=brightgreen&left_text=Downloads)](https://pepy.tech/project/auto-tensorflow) [![Generic badge](https://img.shields.io/pypi/v/auto-tensorflow.svg?logo=pypi&logoColor=white&color=orange)](https://pypi.org/project/auto-tensorflow/) ![Generic badge](https://img.shields.io/badge/python-v3.6%20%7C%203.7%20%7C%203.8-blue) ![example workflow](https://github.com/rafiqhasan/auto-tensorflow/actions/workflows/codeql-analysis.yml/badge.svg) ![Open issues](https://img.shields.io/github/issues-raw/rafiqhasan/auto-tensorflow?color=red) ### **Auto Tensorflow - Mission:** **Build Low Code Automated Tensorflow, What-IF explainable models in just 3 lines of code.** To make Deep Learning on Tensorflow absolutely easy for the masses with its low code framework and also increase trust on ML models through What-IF model explainability. ### **Under the hood:** Built on top of the powerful **Tensorflow** ecosystem tools like **TFX** , **TF APIs** and **What-IF Tool** , the library automatically does all the heavy lifting internally like EDA, schema discovery, feature engineering, HPT, model search etc. This empowers developers to focus only on building end user applications quickly without any knowledge of Tensorflow, ML or debugging. Built for handling large volume of data / BigData - using only TF scalable components. Moreover the models trained with auto-tensorflow can directly be deployed on any cloud like GCP / AWS / Azure. ### **Official Launch**: https://youtu.be/sil-RbuckG0 ### **Features:** 1. Build Classification / Regression models on CSV data 2. Automated Schema Inference 3. Automated Feature Engineering - Discretization - Scaling - Normalization - Text Embedding - Category encoding 5. Automated Model build for mixed data types( Continuous, Categorical and Free Text ) 6. Automated Hyper-parameter tuning 7. Automated GPU Distributed training 8. Automated UI based What-IF analysis( Fairness, Feature Partial dependencies, What-IF ) 9. Control over complexity of model 10. No dependency over Pandas / SKLearn 11. Can handle dataset of any size - including multiple CSV files ### **Tutorials**: 1. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/rafiqhasan/auto-tensorflow/blob/main/tutorials/TFAuto_%7C_Classification.ipynb) - Auto Classification on CSV data 2. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/rafiqhasan/auto-tensorflow/blob/main/tutorials/TFAuto_%7C_Regression.ipynb) - Auto Regression on CSV data ### **Setup:** 1. Install library - PIP(Recommended): ```pip install auto-tensorflow``` - Nightly: ```pip install git+https://github.com/rafiqhasan/auto-tensorflow.git``` 2. Works best on UNIX/Linux/Debian/Google Colab/MacOS ### **Usage:** 1. Initialize TFAuto Engine ``` from auto_tensorflow.tfa import TFAuto tfa = TFAuto(train_data_path='/content/train_data/', test_data_path='/content/test_data/', path_root='/content/tfauto') ``` 2. Step 1 - Automated EDA and Schema discovery ``` tfa.step_data_explore(viz=True) ##Viz=False for no visualization ``` 3. Step 2 - Automated ML model build and train ``` tfa.step_model_build(label_column = 'price', model_type='REGRESSION', model_complexity=1) ``` 4. Step 3 - Automated What-IF Tool launch ``` tfa.step_model_whatif() ``` ### **API Arguments:** - Method **TFAuto** - ```train_data_path```: Path where training data is stored - ```test_data_path```: Path where Test / Eval data is stored - ```path_root```: Directory for running TFAuto( Directory should NOT exist ) - Method **step_data_explore** - ```viz```: Is data visualization required ? - True or False( Default ) - Method **step_model_build** - `label_column`: The feature to be used as Label - `model_type`: Either of 'REGRESSION'( Default ), 'CLASSIFICATION' - `model_complexity`: - `0` : Model with default hyper-parameters - `1` (Default): Model with automated hyper-parameter tuning - `2` : Complexity 1 + Advanced fine-tuning of Text layers ### **Current limitations:** There are a few limitations in the initial release but we are working day and night to resolve these and **add them as future features**. 1. Doesn't support Image / Audio data ### **Future roadmap:** 1. Add support for Timeseries / Audio / Image data 2. Add feature to download full pipeline model Python code for advanced tweaking ### **Release History:** **1.3.3** - 09/12/2022 - [Release Notes](https://github.com/rafiqhasan/auto-tensorflow/releases/tag/1.3.3) **1.3.2** - 27/11/2021 - [Release Notes](https://github.com/rafiqhasan/auto-tensorflow/releases/tag/1.3.2) **1.3.1** - 18/11/2021 - [Release Notes](https://github.com/rafiqhasan/auto-tensorflow/releases/tag/1.3.1) **1.2.0** - 24/07/2021 - [Release Notes](https://github.com/rafiqhasan/auto-tensorflow/releases/tag/1.2.0) **1.1.1** - 14/07/2021 - [Release Notes](https://github.com/rafiqhasan/auto-tensorflow/releases/tag/1.1.1) **1.0.1** - 07/07/2021 - [Release Notes](https://github.com/rafiqhasan/auto-tensorflow/releases/tag/1.0.1) %prep %autosetup -n auto_tensorflow-1.3.4 %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-auto-tensorflow -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue Jun 20 2023 Python_Bot - 1.3.4-1 - Package Spec generated