[显存被占满，程序无法运行问题]ResourceExhaustedError (see above for traceback): OOM when allocating tensor
最近在实验室的服务器上跑tensorflow程序，一直都没有报错，但是今天却突然报错，而且出错提示显示的内容从未见到过，错误提示如下： 错误提示资源耗尽，无法分配tensor出错。通过在网上查找原因以后才明白，是因为后台存在其他进程占用GPU资源。问题出在两方面，一个是有其他进程正在GPU上运行占用GPU资源，另外一个是由于所写的模型逻辑出了问题，比如分配大小等等。第二种情况需要自己重新检查模型数
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相关文章
- 1. ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[682112,156]
- 2. ResourceExhaustedError: OOM when allocating tensor with shape[10,256,400,528]
- 3. ''tensorflow.python.framework.errors_impl.ResourceExhaustedError: OOM when allocating tensor with sh
- 4. Error：OOM when allocating tensor with shape[......]
- 5. 【tf.keras】Resource exhausted: OOM when allocating tensor with shape [9216,4096] and type float on /j
- 6. OOM when allocating tensor with shape[300,14,14,256] and type float on /job:localhost/replica:0/tas
- 7. InternalError (see above for traceback): Blas GEMM launch failed已解决
- 8. TensorFlow 报错：InvalidArgumentError (see above for traceback): tags and values not the same shape: [
- 9. Tensorflow：NotFoundError (see above for traceback): Key style_weight/Variable not found in checkpoin
- 10. InvalidArgumentError (see above for traceback): Assign requires shapes of both tensors to match. lhs