reproducibility-challenge/REPRODUCIBILITY.md

Machine Learning Reproducibility Report

This document helps provide a concise representation of the project's reproducibility ranking.

1. Code

• Runnable code for the project exists in the following project: https://dagshub.com/{user}/{project_name}/
• The example input is located in this link: {link_to_input} (this must be a proper link, ideally tracked as part of the project). Note: using this input in a project run should achieve the stated results without errors.

2. Configuration

• The following files include all relevant configuration parameters for the project:
  • {params_1}.yml
  • {params_2}.yml
  • ...

Note: make sure that changing the parameters in these files will modify the project's actual run parameters.

3. Data (+ Artifacts)

• All project data and artifacts can be found here: {link_to_project_data}.
• The structure/schema of input data is the following: {Add data structure/schema here}.
• If running the project is resource intensive – the project consists of the following steps:
  1. Data preprocessing:
  • Code: {link to code for data processing step}
  • Outputs: {link to output 1}, {link to output 2}
  2. Model training:
  • Code: ...
  • Outputs: ...
  3. ...

4. Environment

• Software package documentation: {link to reuiqrements.txt or equivalent file}
• Example environment: {link to docker hub or other container registry with the environment docker image}
• If the project uses specific hardware – the hardware needed to run the project properly:
  • 2 V100 GPUs
  • At least 32GB RAM
  • ...

5. Evaluation

• To evaluate the model go to: {link to hosted Streamlit app or Google Colab notebook that enables users to perform predictions on arbitrary inputs}
• Evaluation app code:
  • {link to file with Streamlit app code}
  • In order to run this locally: {link to instructions on running the streamlit app}