feat(autoresearch): add checkpoint, config, and queue management system#153
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BenBreaksIn wants to merge 2 commits intokarpathy:masterfrom
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feat(autoresearch): add checkpoint, config, and queue management system#153BenBreaksIn wants to merge 2 commits intokarpathy:masterfrom
BenBreaksIn wants to merge 2 commits intokarpathy:masterfrom
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BenBreaksIn
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Mar 10, 2026
BenBreaksIn
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- Add checkpoint.py for model save/load with resume capability
- Add config.py for centralized configuration with environment variable overrides
- Add gpu_queue.py for file-based GPU job queue and time-sharing across agents
- Add knowledge.py for agent knowledge base and context management
- Add launch.py for experiment orchestration and agent coordination
- Add run_experiment.py for end-to-end experiment execution
- Add programs/ directory with agent role definitions (analyst, director, explorer, optimizer, reviewer)
- Update .gitignore to exclude checkpoints/ and *.log files
- Update analysis.ipynb to support expanded results.tsv schema with additional metrics
- Update prepare.py and train.py for integration with new checkpoint and config systems
- Update program.md with comprehensive system documentation
- Enables multi-agent autoresearch with GPU sharing, checkpointing, and structured experimentation
- Add checkpoint.py for model save/load with resume capability - Add config.py for centralized configuration with environment variable overrides - Add gpu_queue.py for file-based GPU job queue and time-sharing across agents - Add knowledge.py for agent knowledge base and context management - Add launch.py for experiment orchestration and agent coordination - Add run_experiment.py for end-to-end experiment execution - Add programs/ directory with agent role definitions (analyst, director, explorer, optimizer, reviewer) - Update .gitignore to exclude checkpoints/ and *.log files - Update analysis.ipynb to support expanded results.tsv schema with additional metrics - Update prepare.py and train.py for integration with new checkpoint and config systems - Update program.md with comprehensive system documentation - Enables multi-agent autoresearch with GPU sharing, checkpointing, and structured experimentation
- Document variable time scales (probe, quick, standard, long, deep) for experiment budgets - Describe research memory system with experiments.jsonl, lessons.jsonl, and journal.md - Outline model checkpoint save/load functionality for scaling ladder - Define five specialized agent roles (Explorer, Optimizer, Analyst, Reviewer, Director) - Explain GPU queue priority system for multi-agent GPU sharing - Provide file structure overview for new infrastructure and agent programs - Include quick start examples for solo, duo, full org, and custom agent configurations - Document standalone tools for briefing, manual experiments, lessons, and knowledge base sync - List requirements (GPU, Python 3.10+, uv, Claude Code CLI)
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