Dakota 08e14cc745 feat: add minimum batch allocation support for environments ... - Add min_batch_allocation parameter to ensure environments contribute minimum proportion to each batch - Implement grab_batch_with_minimum_allocations function with proper scaling when allocations exceed 100% - Add mixed-size group buffering to handle variable-sized data submissions - Update server to use minimum allocation logic when any env has min_batch_allocation set - Add comprehensive tests for minimum allocation scenarios - Update documentation in API README and CONFIG.md - Update example environments to demonstrate the feature This feature allows critical environments to guarantee they contribute at least a specified proportion (0.0-1.0) to each training batch, ensuring important data sources are always represented during training. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>		2025-07-07 08:50:28 -05:00
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api	feat: add minimum batch allocation support for environments	2025-07-07 08:50:28 -05:00
cli	add tasks_per_step arg to multiply by group_size for bs calculation	2025-06-10 01:54:52 -07:00
envs	feat: add minimum batch allocation support for environments	2025-07-07 08:50:28 -05:00
frontend	--slurm and --testing in outer namespace	2025-05-02 03:46:34 -07:00
tests	feat: add minimum batch allocation support for environments	2025-07-07 08:50:28 -05:00
utils	Remove unused config_handler.py and its import	2025-06-04 10:21:46 -05:00
__init__.py	first commit	2025-04-29 12:10:10 -07:00
FAQ.md	linting	2025-05-16 20:40:15 -07:00
type_definitions.py	Fix import ordering with isort	2025-06-04 10:40:41 -05:00