复现deep speaker遇到的问题，记录一下

最新推荐文章于 2025-10-28 09:45:46 发布

原创最新推荐文章于 2025-10-28 09:45:46 发布 · 531 阅读

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本文详细解析了在使用TensorFlow进行深度学习模型训练时遇到的GPU内存溢出错误，具体表现为在分配形状为[64]的浮点型张量时，GPU设备上发生资源耗尽错误。文章提供了添加report_tensor_allocations_upon_oom选项至RunOptions以获取内存溢出时的张量分配信息的方法，帮助读者理解和解决类似问题。

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tensorflow.python.framework.errors_impl.ResourceExhaustedError: 2 root error(s) found.
(0) Resource exhausted: OOM when allocating tensor with shape[64] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[{{node res1_0_branch_2a_bn/FusedBatchNormV3}}]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

[[ln/l2_normalize/_1259]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

(1) Resource exhausted: OOM when allocating tensor with shape[64] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[{{node res1_0_branch_2a_bn/FusedBatchNormV3}}]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

0 successful operations.
0 derived errors ignored.

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TensorFlow 是由Google Brain 团队开发的开源机器学习框架,广泛应用于深度学习研究和生产环境。它提供了一个灵活的平台,用于构建和训练各种机器学习模型