Package: auto-tensorflow
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 powerfulTensorflowecosystem tools likeTFX,TF APIsandWhat-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-RbuckG0Features:Build Classification / Regression models on CSV dataAutomated Schema InferenceAutomated Feature EngineeringDiscretizationScalingNormalizationText EmbeddingCategory encodingAutomated Model build for mixed data types( Continuous, Categorical and Free Text )Automated Hyper-parameter tuningAutomated GPU Distributed trainingAutomated UI based What-IF analysis( Fairness, Feature Partial dependencies, What-IF )Control over complexity of modelNo dependency over Pandas / SKLearnCan handle dataset of any size - including multiple CSV filesTutorials:- Auto Classification on CSV data- Auto Regression on CSV dataSetup:Install libraryPIP(Recommended):pip install auto-tensorflowNightly:pip install git+https://github.com/rafiqhasan/auto-tensorflow.gitWorks best on UNIX/Linux/Debian/Google Colab/MacOSUsage:Initialize TFAuto Enginefrom auto_tensorflow.tfa import TFAuto
tfa = TFAuto(train_data_path='/content/train_data/', test_data_path='/content/test_data/', path_root='/content/tfauto')Step 1 - Automated EDA and Schema discoverytfa.step_data_explore(viz=True) ##Viz=False for no visualizationStep 2 - Automated ML model build and traintfa.step_model_build(label_column = 'price', model_type='REGRESSION', model_complexity=1)Step 3 - Automated What-IF Tool launchtfa.step_model_whatif()API Arguments:MethodTFAutotrain_data_path: Path where training data is storedtest_data_path: Path where Test / Eval data is storedpath_root: Directory for running TFAuto( Directory should NOT exist )Methodstep_data_exploreviz: Is data visualization required ? - True or False( Default )Methodstep_model_buildlabel_column: The feature to be used as Labelmodel_type: Either of 'REGRESSION'( Default ), 'CLASSIFICATION'model_complexity:0: Model with default hyper-parameters1(Default): Model with automated hyper-parameter tuning2: Complexity 1 + Advanced fine-tuning of Text layersCurrent limitations:There are a few limitations in the initial release but we are working day and night to resolve these andadd them as future features.Doesn't support Image / Audio dataFuture roadmap:Add support for Timeseries / Audio / Image dataAdd feature to download full pipeline model Python code for advanced tweakingRelease History:1.3.3- 09/12/2022 -Release Notes1.3.2- 27/11/2021 -Release Notes1.3.1- 18/11/2021 -Release Notes1.2.0- 24/07/2021 -Release Notes1.1.1- 14/07/2021 -Release Notes1.0.1- 07/07/2021 -Release Notes