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Bayesian Deep Learning for HER2 tissue image classification

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YastAI

Yet Another Session for Training Artificial Intelligence

This is a framework for easy development of new AI Research Projects.

Features

  • Anaconda Envirment.
  • AI project structure.
  • Tensorflow, Keras and Scikit-learn.
  • HPC jobs manager.
  • Pip Package.
  • Weight and Bias easy setup.
  • Notebooks.
  • Nose unit tests.
  • Singularity container.
  • Easy experiment reproducibility.

How to start a new project/research

This is a guide to understand the zen of this repository.

This repository structure

There are 5 folder for develop you new project:

AI/

AI folder is your AI project package, for more details check AI/README.md

If you want to rename the IA folder with your project name, you must also edit theses files:

  • setup.py: replace ia_name variable in line 7.
  • __version__.py inside AI folder.
  • Maybe you want to edit the conda envirment file yastai.yml.
  • Replace from AI import * in every script in train/.
  • Replace from AI import * in every nootebook in notebooks/.
  • Replace from AI import * in every nootebook in tests/.
  • Replace AI from files in docs/.
  • HPC train scripts maybe
  • conda environment maybe

train/

Collections of scripts for setup your data, config preprocessing pipes, and train/evaluate your models.

In these folder you can find also:

  • experiments/: Folder with experiments configurations and results. More details and name conventions in train/experiments/README.md.
  • debug.py: Utils functions for check GPU support, profiling keras/tensorflow models and plots models.
  • hpc/: Script and instructions for run experiments in HPC environments.

More details for train/ folder in train/README.md.

notebooks/

Contains notebook version of the scripts in \train. More details in notebooks/README.md.

Handy for Google Colab.

test/

Collections of user defined unit tests with nose.

Here a tutorial for nose oython testing.

More details in tests/README.md.

api/

Different options for consume a trained model.

More details in api/README.md.

environments

TODO

Load environment

Check anaconda documentation here.

  • environment.yml
conda env create -f environment.yml --prefix ./.env

Crete/Update environments

TODO Manually add packages to enviroment.yml and Pipfile.

Documentation

TODO

Publish in PyPi

TODO

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