Introducing Powerful Benchmarker
The Powerful Benchmarker is an intriguing project hosted on GitHub, aimed at providing a comprehensive framework for benchmarking in machine learning. This project, accessible through this link, comes equipped with a sleek logo, thanks to the creative efforts of Jeff Musgrave.
Project Branches
The project is meticulously organized into different branches, each tailored to specific research areas:
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Domain-Adaptation Branch: This branch caters to those interested in unsupervised domain adaptation. It includes intriguing research outlined in a paper titled "Three New Validators and a Large-Scale Benchmark Ranking for Unsupervised Domain Adaptation." The feature is enriched with new validators and comprehensive benchmarks to enhance the framework's utility.
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Metric-Learning Branch: Focused on the field of metric learning, this branch is grounded in the findings presented in the paper "A Metric Learning Reality Check." It offers a thoughtful examination of metric learning methodologies.
Currently, the project offers technical support primarily for the domain-adaptation branch, ensuring users can seek assistance with code and bug-related issues.
Installation Guide
To get started with the Powerful Benchmarker, users are encouraged to clone the repository using:
git clone https://github.com/KevinMusgrave/powerful-benchmarker.git
After cloning, navigate into the project directory and install the required dependencies:
cd powerful-benchmarker
pip install -r requirements.txt
Setting Up Paths
Configuration is crucial for seamless project operation. Users are required to modify constants.yaml to define several paths:
exp_folder: The directory for all saved experiments.dataset_folder: The location for downloading datasets, such as the MNIST dataset.conda_env: An optional field for specifying the conda environment used in Slurm jobs.slurm_folder: Stipulates where Slurm job logs are stored.gdrive_folder: An optional setting to designate a Google Drive folder for log uploads.
Folder Structure
The project is organized into several folders, each serving a unique function:
latex: Houses code for generating LaTeX tables from experimental data.notebooks: Contains various Jupyter notebooks.powerful_benchmarker: Includes code for hyperparameter optimization in training models.scripts: Offers a collection of bash scripts, including those for uploading logs to Google Drive.unit_tests: Dedicated to tests that identify bugs within the project.validator_tests: Contains code for assessing the effectiveness of different validation methods.
Helpful Top-Level Scripts
The project includes several scripts to streamline processes:
delete_slurm_logs.py: Deletes unnecessary Slurm logs either entirely or selectively by experiment group prefix.kill_all.py: Terminates ongoing model training or validator test jobs.print_progress.py: Reports the progress of hyperparameter trials, with an option to save the output to a file.simple_slurm.py: Provides an effortless method to execute programs via Slurm, with or without specifying experiment groups.upload_logs.py: Automates the uploading of logs and experimental progress to a designated Google Drive folder at specified intervals.
Citing the Work
The project plays a significant role in the academic community. Those who use this project in their research are encouraged to cite the associated paper:
@article{Musgrave2022ThreeNew,
title={Three New Validators and a Large-Scale Benchmark Ranking for Unsupervised Domain Adaptation},
author={Kevin Musgrave and Serge J. Belongie and Ser Nam Lim},
journal={ArXiv},
year={2022},
volume={abs/2208.07360}
}
In summary, the Powerful Benchmarker project serves as a valuable tool for researchers and developers aiming to benchmark machine learning models with precision and ease. Its structured approach and supportive community make it an excellent resource for pushing the boundaries in domain adaptation and metric learning.