Stonesjtu/pytorch_memlab

Health v2 · maintenance only

Adoption not part of the score

1077 stars · 39 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A Python library providing line-level CUDA memory profiling and tensor inspection tools for PyTorch. It helps debug out-of-memory errors by showing per-line memory usage and reporting which tensors occupy GPU memory.

Use cases

• profile cuda memory usage per line of pytorch code
• debug pytorch out-of-memory errors
• inspect which tensors are occupying gpu memory
• find memory leaks in pytorch training loop
• temporarily move cuda tensors to cpu to free gpu memory
• profile memory usage in jupyter notebook

When to choose

• debugging CUDA OOM errors in PyTorch models
• you need line-by-line GPU memory attribution like line_profiler
• working in Jupyter/IPython and want %mlrun magic profiling

When to avoid

• profiling CPU memory or non-PyTorch frameworks
• you need full GPU utilization or speed profiling rather than memory
• working with frameworks other than PyTorch such as TensorFlow or JAX

Facets

library · maturity active

monitoring benchmarking developer-tools machine-learning deep-learning developer-tools performance python cross-platform pytorch cuda memory-profiler profiling debugging oom jupyter ipython-magic gpu

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markdown · JSON · MCP: product_card(name="Stonesjtu/pytorch_memlab")