本文介绍了错误:分配具有形状的张量时出现OOM的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
在使用Apache JMeter进行性能测试期间，我的初始模型遇到了问题.
解决方案
OOM表示内存不足.这意味着您的GPU空间不足，可能是因为您分配了其他过大的张量.您可以通过缩小模型或减小批次大小来解决此问题.从外观上看，您正在输入一张大图像(800x1280)，您可能需要考虑下采样.
i am facing issue with my inception model during the performance testing with Apache JMeter.
解决方案
OOM stands for Out Of Memory. That means that your GPU has run out of space, presumably because you've allocated other tensors which are too large. You can fix this by making your model smaller or reducing your batch size. By the looks of it, you're feeding in a large image (800x1280) you may want to consider downsampling.
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