TraceML is an innovative live observability tool designed for PyTorch training, providing real-time insights into various aspects of model training. It monitors dataloader fetch times to identify input pipeline stalls, GPU step times using non-blocking CUDA events to avoid synchronization overhead, and GPU CUDA memory to detect leaks before running out of memory. The tool offers two modes: a lightweight essential mode with minimal overhead and a deeper diagnostic mode for detailed layerwise analysis. Compatible with any PyTorch model, it has been tested on LLM fine-tuning and currently supports single GPU setups, with plans for multi-GPU support in the future. This matters because it enhances the efficiency and reliability of machine learning model training by offering immediate feedback and diagnostics.
