Agentic AI for Modern Deep Learning Experimentation

Deep learning experimentation often involves tedious, manual tasks like monitoring runs and tweaking hyperparameters, which consumes valuable research time. An agentic AI workflow can automate this "babysitting" by autonomously monitoring metrics, detecting failures, and applying predefined tuning strategies without human intervention. The system works by containerizing your existing training script and using a lightweight agent that you instruct through simple markdown and YAML files. This agent is equipped with tools to perform actions like visually analyzing TensorBoard charts, restarting failed jobs, and modifying configuration files on GitHub to streamline the research process.