defaults: - storage: aws - optional region: ${storage.region} Hydra was notorious for adding 200–400ms to script startup time because it parsed every @dataclass and OmegaConf structure recursively. For long-running training jobs, this didn't matter. For serverless functions or CLIs? It hurt.
This change allows for better type checking and allows you to run Hydra inside Jupyter Notebooks (finally!) without weird hacks. Yes, but carefully. If you are starting a new project today, use Hydra 1.2 . The new composition rules and Jupyter support are worth it. hydra 1.2
Last week, the team released , and it is not just a minor patch—it changes how we think about configuration composition. defaults: - storage: aws - optional region: ${storage
# Old (Hydra 1.1) @hydra.main(config_path="conf", config_name="config") def main(cfg): ... def main(): cfg = hydra.initialize_and_run(config_path="conf", config_name="config", task_function=my_task) It hurt
pip install hydra-core --upgrade Happy composing! Let us know in the comments if you have found the 1.2 resolver syntax tricky—I will be writing a deep dive on that next week.
Navigating the Labyrinth: What’s New in Hydra 1.2
Version 1.2 introduces for certain resolver functions. Early benchmarks show a 40% reduction in instantiation time for large config suites. 5. Deprecation of hydra.main This is the breaking change you need to watch for. The decorator @hydra.main() has been a staple since day one. It now throws a DeprecationWarning . In Hydra 2.0 (planned for Q3 2026), it will be removed.