dora-search
Easy grid searches for ML.
Decision gist · record as of 2026-08-14
Yes, but with caution. Dora is well-suited for researchers running grid searches on clusters and needing experiment deduplication and monitoring. However, the package is dormant (last release 2023-05-23, no recent commits), so expect no active maintenance or support for newer dependencies. Install only if your workflow matches its design and you can tolerate potential incompatibilities with recent library versions.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- High install friction due to source build requirement; requires Python >=3.7.0
- Installation requires building from source with high friction.
- The package is dormant—last release was 2023-05-23 with no commits since 2023-10-05—so expect limited maintenance and no active bug fixes or updates.
License · maintenance · safety
MIT (permissive) — MIT license is permissive, allowing commercial and private use with minimal restrictions, making it suitable for most projects without legal concern.
last release 2023-05-23 (1179 days) · last repo commit 2023-10-05 · 314 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 177,883 downloads/mo, #10,203 on PyPI
Alternatives
Verify before relying
pip install dora-search
from dora import argparse_main, get_xp
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--lr', type=float)
@argparse_main(dir='./outputs', parser=parser)
def main():
xp = get_xp()
print(f'Running with config: {xp.cfg}')
xp.link.push_metrics({'loss': 0.5})- Whether submitit dependency (required for remote job launching) is automatically installed or must be manually configured
- Current compatibility with modern Hydra versions beyond 1.1 mentioned in changelog
- Whether PyTorch Lightning integration still works with recent versions of that library
What it is and what it does
Dora is a command-line experiment manager designed for machine learning workflows. It lets you define grid searches in pure Python, automatically schedule and deduplicate experiments based on their argument signatures, and monitor progress from the terminal. The tool integrates with argparse, Hydra, and PyTorch Lightning, and can launch experiments locally or remotely via Slurm/Submitit.
You decorate your training script's main function with `@argparse_main` or `@hydra_main`, then create grid files that call a launcher repeatedly with different hyperparameter sets. Dora assigns each experiment a signature for deduplication, manages checkpointing and resumption, and provides a monitoring interface to track metrics and compare runs. It's built for researchers running many related experiments and needing to avoid redundant computation.
Use it for
- Running systematic hyperparameter sweeps across a cluster without manually scheduling each job
- Automatically resuming interrupted experiments from checkpoints using experiment signatures
- Monitoring multiple concurrent ML training runs and comparing metrics in a terminal table
- Deduplicating experiments so requesting the same hyperparameters twice merges into one run
- Integrating experiment tracking into existing PyTorch Lightning or Hydra-based projects
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, but with caution.
Dora is well-suited for researchers running grid searches on clusters and needing experiment deduplication and monitoring. However, the package is dormant (last release 2023-05-23, no recent commits), so expect no active maintenance or support for newer dependencies. Install only if your workflow matches its design and you can tolerate potential incompatibilities with recent library versions.
Install
dora-search on PyPI
Before you install
Installation requires building from source with high friction. The package is dormant—last release was 2023-05-23 with no commits since 2023-10-05—so expect limited maintenance and no active bug fixes or updates.
High install friction due to source build requirement; requires Python >=3.7.0
License in practice
MIT license is permissive, allowing commercial and private use with minimal restrictions, making it suitable for most projects without legal concern.
Quickstart
pip install dora-search
from dora import argparse_main, get_xp
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--lr', type=float)
@argparse_main(dir='./outputs', parser=parser)
def main():
xp = get_xp()
print(f'Running with config: {xp.cfg}')
xp.link.push_metrics({'loss': 0.5})Verify before relying
- Whether submitit dependency (required for remote job launching) is automatically installed or must be manually configured
- Current compatibility with modern Hydra versions beyond 1.1 mentioned in changelog
- Whether PyTorch Lightning integration still works with recent versions of that library
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.7.0 |
| Install friction | High. Source build required |
| Runtime dependencies | None |
| Maintenance | Dormant 1,179 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 177,883 / month, #10,203 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: MIT LicenseTopic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: dora_search-0.1.12.tar.gz
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