Make Deep Learning Faster Across Frameworks — No Rewrites Needed
This tool lets people turn the math behind a neural network into fast code that runs on many systems, without rewriting whole projects.
It finds ways to speed up work on a GPU so models learn and run much quicker, saving time and money.
Engineers and researchers can test new ideas faster because they wont spend weeks building custom code.
The system looks at the steps a model needs and combines them smartly, which cuts extra work and makes better use of the hardware.
You get more speed and less coding, so teams focus on ideas, not plumbing.
It also tunes itself to find good settings automatically, a kind of built-in autotuner that hunts best results.
Works with different tools people already use, giving flexibility without forcing a big switch.
If you build or play with models, this approach helps your experiments run faster and be simpler to manage, so new tricks get tried sooner and your results show up quicker.
Read article comprehensive review in Paperium.net:
Tensor Comprehensions: Framework-Agnostic High-Performance Machine LearningAbstractions
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