GWAK 2.0 🥑🦾

This repo is dedicated to the updated version of the algorithm presented in the MLST.

The current projects include

• data - Scripts for generating training and testing data
• train - Pytorch (lightning) code for training neural-networks

The project uses poetry, Conda and Snakemake to run the code. Follow installation instructions below to prepare your environment.

Installation ⚙️

Optional step (only if you don't have Miniconda3)

If you do not have Miniconda installed on your machine, follow first those steps

• use the quickstart repo to setup Miniconda and install poetry

$ git clone git@github.com:ml4gw/quickstart.git
$ cd quickstart
$ make

If you see this error, it is already known in issue#7

Verifying checksum... Done.
Preparing to install helm into /you/path/miniconda3-tmp/bin/
helm installed into /you/path/miniconda3-tmp/bin//helm
helm not found. Is /you/path/miniconda3-tmp/bin/ on your $PATH?
Failed to install helm
    For support, go to https://github.com/helm/helm.
make: *** [Makefile:65: install-helm] Error 1

do the following commands:

$ source ~/.bashrc
$ make install-poetry install-kubectl install-s3cmd

If everything was installed successfully, continue to the steps below.

Main installation

If you do have Miniconda already installed on your machine, follow those steps

• checkout this repo and clone submodules (such as ml4gw)

$ git clone git@github.com:ML4GW/gwak2.git
$ cd gwak2
$ git submodule update --init --recursive

• create a new Conda environment

$ conda env create -n gwak --file environment.yaml
$ conda activate gwak

• install gwak project in the editing mode

$ pip install -e .

Now you are ready to gwak! As a first step, you can run the training by doing

$ cd gwak
$ snakemake -c1 train_all

• if you want to modify any of the submodules, first do the changes localy and then re-install gwak to pick up the changes:

$ pip install -e .