2023-06-09 14:39:18.552419: E tensorflow/core/common_runtime/gpu/gpu_cudamallocasync_allocator.cc:288] gpu_async_0 cuMemAllocAsync failed to allocate 805306368 bytes: CUDA error: out of memory (CUDA_ERROR_OUT_OF_MEMORY) Reported by CUDA: Free memory/Total memory: 0/12884377600

这个错误是 TensorFlow 运行时发出的，表示在分配显存时出现了内存不足的情况。具体来说，是使用 CUDA API 分配显存失败，导致 TensorFlow 报告了这个错误。可能是因为模型太大或者数据量太大，导致显存不够。

解决方法可以尝试减小模型规模或者训练数据集的大小，或者使用更大的显存。另外，也可以尝试设置环境变量 `TF_GPU_ALLOCATOR=cuda_malloc_async` 来提高显存使用效率。如果这个错误频繁出现，建议检查一下显卡驱动是否最新、显存是否正常等硬件问题。

相关问题

2023-06-09 14:36:39.705522: W tensorflow/core/common_runtime/bfc_allocator.cc:479] Allocator (GPU_0_bfc) ran out of memory trying to allocate 768.00MiB (rounded to 805306368)requested by op AddV2 If the cause is memory fragmentation maybe the environment variable 'TF_GPU_ALLOCATOR=cuda_malloc_async' will improve the situation.

这个警告是 TensorFlow 运行时发出的，表示在分配显存时出现了内存不足的情况。可能是因为模型太大或者数据量太大，导致显存不够。警告中提到可以尝试设置环境变量 `TF_GPU_ALLOCATOR=cuda_malloc_async` 来解决内存碎片化的问题，进而提高显存使用效率。如果这个警告频繁出现，可以考虑减小模型规模或者使用更大的显存。

Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.34GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2023-05-30 22:40:50.059248: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.28GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2023-05-30 22:40:50.089391: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.21GiB with freed_by_count=0. The caller indicates that this is not

这是由于GPU内存不足导致的错误。有两种解决方法：

1. 减少模型的内存占用。可以尝试减少模型的层数或者每层的神经元数量等。

2. 增加GPU内存。可以安装更大的GPU或者使用分布式训练等方法来增加内存。

另外，也可以尝试使用TensorFlow的自动增长GPU内存的功能来解决这个问题。可以在代码中添加以下代码：

import tensorflow as tf
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.Session(config=config)

这将允许TensorFlow根据需要动态增加GPU内存。