Practical overview
When it fits
Use it when you need repository-based code for machine-learning benchmark experiments, especially the documented domain-adaptation validator workflow, and can configure the required Python packages, data paths and compute environment.
Who it is for
- Researchers comparing validation methods in unsupervised domain-adaptation experiments.
- Machine-learning practitioners organizing model-training hyperparameter searches.
- Teams running experiments through SLURM and collecting progress or logs.
Documented uses
- Run and organize model-training hyperparameter searches.
- Evaluate validation methods using the repository's validator-test code.
- Submit experiment-related commands through SLURM and check job progress.
- Create LaTeX tables from experiment data.
- Optionally upload SLURM logs and experiment progress to a configured Google Drive folder.
Requirements & limits
- The domain-adaptation branch contains code for unsupervised domain-adaptation validators and benchmark ranking; the metric-learning branch contains code associated with metric-learning research.
- The README identifies the domain-adaptation branch as the one for which the maintainer can provide technical support.
- Setup requires cloning the repository, installing requirements.txt packages and configuring paths in constants.yaml.
- SLURM execution depends on suitable configuration files; Google Drive log uploads require an optional destination-folder setting.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Set up the repository and experiment paths
Install the project requirements and configure where datasets, experiment folders and SLURM logs are stored.
Before you start
- Git and Python with pip
- A local checkout of the repository
Steps
Clone the repository and change into its directory.
Install the packages listed in requirements.txt.
Set exp_folder, dataset_folder and slurm_folder in constants.yaml.
Expected outcome
The repository dependencies are installed and the documented storage paths are configured.
Guide limitations
- This setup does not run a benchmark or validator job.
- SLURM and Google Drive settings are separate options in constants.yaml.
Submit a validator-results collection job with SLURM
Use the documented simple_slurm.py pattern to run validator_tests/collect_dfs.py for experiment groups with a selected prefix.
Before you start
- The repository and dependencies are set up.
- A SLURM environment and a suitable configuration under validator_tests are available.
Steps
Choose the SLURM configuration name and experiment-group prefix.
Run simple_slurm.py with the command python validator_tests/collect_dfs.py, slurm_config_folder set to validator_tests, and the selected configuration and prefix.
Expected outcome
The documented command submits a separate SLURM job for each matching experiment group.
Guide limitations
- The README's example uses the a100 configuration and office31 prefix; other clusters may require configuration changes.
- This recipe submits the collection command; it does not describe training a model or evaluating a validator from scratch.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 18 October 2026
- Original website title
- KevinMusgrave/powerful-benchmarker · Official homepage title · retained in its source language