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Autotune seems to be "forgetting" after a short delay within the same session. #355
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I'm trying to process 128x128 pixel images real-time on a low-powered PC. I've been profiling my code by programmatically recording millisecond timings. We've created a CAE.pb file and it seems to run properly, and the time that the session.run() call is made in rapid succession (using the same session each time), the time it takes decreases (up to a certain point). Specifically, when I process the first image takes 1400ms, second image 1150ms, third image 900ms... at the 7th our 8th image it levels off at 350ms per image processing. The problem is that this speed increase only increases if I push images to TensorFlow in rapid succession. If I pause for more than approx 350ms, then the time it takes to process the next image (with the same session being reused the whole time), jumps back up to 1400ms. I'm assuming that the decreasing time is due to autotune running. However, autotune seems to "forget" it's session values if I don't keep it running and fed with new data constantly. Am I doing something wrong or is this a bug? Is there a RunOption or SessionOption that I'm unaware of that controls how long until autotune (or what I'm presuming is autotune) "forgets"? :P
Note: Unlike in Issue #330 and #345, I'm being very careful to reuse my session. And strangely, when my co-worker runs the same CAE.pb in python using TensorFlow directly, the file seems to process as expected (ie. first time slow, subsequent times super fast). What's going on?
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