hackasaurus-rex

Zenodo URL: https://zenodo.org/record/8063997

Prediction

To run the prediction:

1. Adjust the config file configs/detr_prot_predict.yml and update the data_root to point to your data directory.
2. Once the config is updated, check to make sure that the data location is included in the TOMOUNT list of paths in scripts/launch_job.sbatch. The TOMOUNT variable is composed of paths which WHICH ARE SEPERATED BY COMMAS!
3. Run scripts/predict.sh to initialize the container and run the testing. The logs should end up in /hkfs/work/workspace/scratch/ih5525-E2/slurm_logs.

Installation

In order to set up the necessary environment:

1. install in a container

   enroot create -n pyxis_torch PATH
   

2. To run code, use scripts/launch_job.sbatch This will launch a new sbatch job with the specified python job.

Running a Testing job

1. Make sure that the script in scripts/launch_job.sbatch is the target python script

2. Ensure that the config selected in scripts/launch_job.sbatch is what you want to run (if you want to run on a different dataset, change the target parameter). If you want to train on the entire dataset you are giving it, make sure to set split_data in the config to False

3. launch the job with sbatch scripts/launch_job.sbatch

Project Organization

├── AUTHORS.md              <- List of developers and maintainers.
├── CHANGELOG.md            <- Changelog to keep track of new features and fixes.
├── CONTRIBUTING.md         <- Guidelines for contributing to this project.
├── Dockerfile              <- Build a docker container with `docker build .`.
├── LICENSE.txt             <- License as chosen on the command-line.
├── README.md               <- The top-level README for developers.
├── configs                 <- Directory for configurations of model & application.
├── data
│   ├── external            <- Data from third party sources.
│   ├── interim             <- Intermediate data that has been transformed.
│   ├── processed           <- The final, canonical data sets for modeling.
│   └── raw                 <- The original, immutable data dump.
├── docs                    <- Directory for Sphinx documentation in rst or md.
├── environment.yml         <- The conda environment file for reproducibility.
├── models                  <- Trained and serialized models, model predictions,
│                              or model summaries.
├── notebooks               <- Jupyter notebooks. Naming convention is a number (for
│                              ordering), the creator's initials and a description,
│                              e.g. `1.0-fw-initial-data-exploration`.
├── pyproject.toml          <- Build configuration. Don't change! Use `pip install -e .`
│                              to install for development or to build `tox -e build`.
├── references              <- Data dictionaries, manuals, and all other materials.
├── reports                 <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures             <- Generated plots and figures for reports.
├── scripts                 <- Analysis and production scripts which import the
│                              actual PYTHON_PKG, e.g. train_model.
├── setup.cfg               <- Declarative configuration of your project.
├── setup.py                <- [DEPRECATED] Use `python setup.py develop` to install for
│                              development or `python setup.py bdist_wheel` to build.
├── src
│   └── hackasaurus_rex     <- Actual Python package where the main functionality goes.
├── tests                   <- Unit tests which can be run with `pytest`.
├── .coveragerc             <- Configuration for coverage reports of unit tests.
├── .isort.cfg              <- Configuration for git hook that sorts imports.
└── .pre-commit-config.yaml <- Configuration of pre-commit git hooks.

Note

This project has been set up using PyScaffold 4.4.1 and the dsproject extension 0.7.2.