How to make TensorFlow models run faster on GPUs

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product: TensorFlow - General; re_ty: Publish;
  1. TensorFlow
  2. purpose: Educate
  3. pr_pr: TensorFlow
  4. series: Coding TensorFlow
  5. type: DevByte (deck cleanup 10-20min)
  6. GDS: Yes
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Комментарии
Автор

Thank you, It would be a great help if you can share the Jupyter notebook with the profiler configurations.

rtesr
Автор

is there any support for utilizing pre-compiled kernels for larger shapes through multiple invocations while compiling a new kernel in the background? Or any other kind of intelligence around dynamic recompilation?

retfordb
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Is that pseudocode of the Add function a real language? Or just an example?

bensouthall
Автор

I tried running my model with jit compile=True, I got constantly 100% GPU utilization and the terminal stated the graph is using XLA. However, training was faster (and around 80% GPU utilization) WITHOUT XLA. How is that possible? What did I do wrong?

mathieswedler
Автор

Я тоже по-английски умею говорить, но нахера ты это выложил?

WillFalcon