[顯存被佔滿，程序無法運行問題]ResourceExhaustedError (see above for traceback): OOM when allocating tensor
最近在實驗室的服務器上跑tensorflow程序，一直都沒有報錯，但是今天卻突然報錯，而且出錯提示顯示的內容從未見到過，錯誤提示如下： 錯誤提示資源耗盡，無法分配tensor出錯。通過在網上查找原因以後才明白，是因爲後臺存在其他進程佔用GPU資源。問題出在兩方面，一個是有其他進程正在GPU上運行佔用GPU資源，另外一個是由於所寫的模型邏輯出了問題，比如分配大小等等。第二種情況需要自己重新檢查模型數
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相關文章
- 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已解決
- 8. TensorFlow 報錯：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