Memory leak with tensorflow-metal

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Created Jun ’21

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I am trying the new tensorflow-metal release. The good news is that I see a boost in speed when executing my models. The bad news is that whenever it I run it, it leaks memory. So if I run the prediction 1000x I end up with 50GB of memory usage and eventually run out. The same code works when using stock tensorflow, albeit slower.

I tried using pympler to find a leak, but the output didn't really look different between tensorflow-metal and normal tensorflow. I suspect the leak is in native code, so that wouldn't really help.

Known issue? Ideas on how to debug? Open to anything really.

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Answer 1

EugeneZhidkov OP

Jun ’21

Hi dandiep! Could you please provide the network or small repro case so we can investigate that? Thanks.

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Answer 2

Frameworks Engineer OP

Apple

Aug ’21

Hi @dandiep, Could you please try with the latest tensorflow-metal plugin (tensorflow-metal==0.1.2) and see if the issue is still seen ? We have fixed bunch of different memory leaks observed. If the issue still exists, please provide a reproducible case so that we can verify it at our end.

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