65% CPU & 15% Memory with Tensorflow on Google Cloud ML -


i'm running google cloud ml job using "scaletier": "basic_gpu", , following chart outlines details on utilisation:

job details in google cloud ml engine

i'm executing experiment using learn_runner.run(...) on custom estimator , feed input using pipeline based approach using file name queue read data.

is using pipeline based approach main reason low memory utilisation , there should consider optimize training utilisation?


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