Metadata-Version: 2.1
Name: BuilT
Version: 0.0.4
Summary: Easily build your trainer for DNNs.
Home-page: https://github.com/UoA-CARES/BuilT
Author: JongYoon Lim
Author-email: jy.lim@auckland.ac.nz
License: UNKNOWN
Project-URL: Bug Tracker, https://github.com/UoA-CARES/BuilT/issues
Description: [![Build Status](https://travis-ci.com/UoA-CARES/BuilT.svg?branch=master)](https://travis-ci.com/UoA-CARES/BuilT)
        [![codecov](https://codecov.io/gh/UoA-CARES/BuilT/branch/master/graph/badge.svg)](https://codecov.io/gh/UoA-CARES/BuilT)
        
        # BuilT(Build a Trainer)
        Easily build a trainer for your Depp Neural Network model and experiment as many as you want to find optimal combination of components(model, optimizer, scheduler) and hyper-parameters in a well-organized manner. 
        - No more boilerplate code to train and evaluate your DNN model. just focus on your model. 
        - Simply swap your dataset, model, optimizer and scheduler in the configuration file to find optimal combination. Your code doesn't need to be changed!!!. 
        - Support Cross Validation, OOF(Out of Fold) Prediction 
        - Support WandB(https://wandb.ai/) or tensorboard logging.
        - Support checkpoint management(Save and load a model. Resume the previous training)
        - BuilT easily integrates with Kaggle(https://www.kaggle.com/) notebook. (todo: add notebook link)
        
        ## Installation
        Please follow the instruction below to install BuilT. 
        
        ### Installation of BuilT package from the source code
        ```
        git clone https://github.com/UoA-CARES/BuilT.git
        cd BuilT
        python setup.py install
        ```
        
        ### Installation of BuilT package using pip
        BuilT can be installed using pip(https://pypi.org/project/BuilT/). 
        ```
        pip install built
        ```
        
        ## Usage
        
        ### Configuration
        ### Builder
        
        ### Trainer
        ### Dataset
        
        ### Model
        
        ### Loss
        ### Optimizer
        
        ### Scheduler
        
        ### Logger
        
        ### Metric
        
        ### Inference
        
        ### Ensemble
        
        
        
        ## Examples
        ### MNIST hand-written image classification
        (todo)
        
        ### Sentiment Classification
        (todo)
        
        
        ## Developer Guide
        (todo)
        ```
        conda create -n conda_BuilT python=3.7
        conda activate conda_BuilT
        pip install -r requirements.txt
        ```
        
        # Reference
        https://packaging.python.org/tutorials/packaging-projects/
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
