On the recordRead 11 Oct 202619 facts
Oríon
On the record
- Algorithms
- The installation guide lists Random Search, Hyperband, TPE, and ASHA as included algorithms, with more available through plugins.orion.readthedocs.io · read 4 Oct 2026
- Database backends
- The documented built-in backends are EphemeralDB, file-based PickledDB, and MongoDB; MongoDB is recommended for large-scale parallel optimization above 50 workers.orion.readthedocs.io · read 11 Oct 2026
- Experiment tracking
- Oríon stores trials in a configured database and offers experiment versioning to track trials and their parameters for reproducibility.orion.readthedocs.io · read 11 Oct 2026
- Installation
- The installation guide directs users to install Oríon from PyPI with pip install orion.orion.readthedocs.io · read 11 Oct 2026
- Integration
- A script can report its optimization objective through Oríon’s Python client API.orion.readthedocs.io · read 4 Oct 2026
- Intended users
- The documentation describes Oríon as useful for machine learning researchers and engineers, while saying it is not itself a machine learning framework, pipeline, or automated machine learning product.orion.readthedocs.io · read 11 Oct 2026
- License
- The project documentation says Oríon is licensed under the BSD license.orion.readthedocs.io · read 4 Oct 2026
- Optimization algorithms
- The documentation lists Random Search, Hyperband, TPE, and ASHA as included algorithms, and supports adding algorithms through plugins.orion.readthedocs.io · read 11 Oct 2026
- Parallel execution
- Oríon is natively asynchronous and the documentation says it can run in parallel from a laptop to a computing farm with thousands of processors.orion.readthedocs.io · read 11 Oct 2026
- Purpose
- Oríon is an asynchronous framework for black-box function optimization, including hyperparameter optimization for machine learning.orion.readthedocs.io · read 11 Oct 2026
- Scaling limit
- The documentation says PickledDB is generally not suited to parallel optimization with more than 50 workers, though it describes this as a rule of thumb.orion.readthedocs.io · read 11 Oct 2026
- Search spaces
- Oríon supports search spaces defined by priors, including almost all SciPy distributions, configured through the command line or a configuration file.orion.readthedocs.io · read 11 Oct 2026
- Storage
- The documented database backends are in-memory EphemeralDB, file-based PickledDB, and MongoDB.orion.readthedocs.io · read 4 Oct 2026
- Storage limit
- The documentation says PickledDB is generally not suited to parallel optimization with more than 50 workers; MongoDB is recommended above that scale.orion.readthedocs.io · read 4 Oct 2026
- Support
- The project directs users to report questions, issues, bugs, or feature suggestions through its GitHub issue tracker.orion.readthedocs.io · read 11 Oct 2026
- Visualization
- The documentation includes experiment visualizations such as regret curves, parallel coordinates, local parameter importance, and partial dependencies.orion.readthedocs.io · read 4 Oct 2026
- Workflow
- It can tune command-line scripts with minimal changes to a researcher’s existing workflow.orion.readthedocs.io · read 4 Oct 2026
- Workflow integration
- The documentation says users can integrate Oríon into an existing workflow by calling report_objective() at the end of their script, or use its Python API.orion.readthedocs.io · read 11 Oct 2026
