CUDA memory
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Enhancing PyTorch Training with TraceML
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TraceML has been updated to enhance real-time observability during PyTorch training, particularly for long or remote runs. Key improvements include live monitoring of dataloader fetch times to identify input pipeline stalls, tracking GPU step time drift using non-blocking CUDA events, and monitoring CUDA memory to detect leaks before out-of-memory errors occur. Optional layer-wise timing and memory tracking are available for deeper debugging, and the tool is designed to complement existing profilers. Currently tested on single-GPU setups, with plans for multi-GPU support, TraceML aims to address common issues like step drift and memory creep across various training pipelines. Feedback is sought from users to refine signal detection. This matters because it helps optimize machine learning training processes by identifying and addressing runtime issues early.
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Real-time Visibility in PyTorch Training with TraceML
Read Full Article: Real-time Visibility in PyTorch Training with TraceML
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.