CUDA Profiler for Production Inference

What it does

Graphsignal is a production-scale inference profiling platform that helps engineers optimize AI performance across models, engines, GPUs, and other accelerators. It provides essential visibility across the inference stack, including: UV_TOOL_BIN_DIR=/usr/local/bin uv tool install ' graphsignal[cu12] ' # CUDA 12.x # or UV_TOOL_BIN_DIR=/usr/local/bin uv tool install ' graphsignal[cu13] ' # CUDA 13.x Alternative: install into your workload environment If you prefer a single environment, or you use the graphsignal.watch() Python API (which requires graphsignal importable by your application), install it directly into your workload's environment instead: pip install ' graphsignal[cu12] ' # CUDA…from github.com

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  Hi HN, I'm the founder of Graphsignal (https://graphsignal.com). Graphsignal is a machine learning profiler. We've created it to make ML profiling simple and usable. It provides performance summaries, ML operation and kernel level statistics as well as detailed resource usage information necessary for making training and inference faster and more efficient. Profilers help fix performance issues, improve user experience and reduce computation costs. Such improvements benefit machine learning profoundly; model training jobs that run for hours or days could be made much shorter and…

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  Graphsignal – ML profiler to speed up training and inference2022 · github.com · ▲16

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