无法创建cudnn句柄: CUDNN_STATUS_INTERNAL_ERROR
我在装有GeForce GT 750M的Macbook Pro上安装了tensorflow 1.0.1GPU版本。还安装了CUDA8.0.71和cuDNN 5.1。我正在运行一个tf代码，它在非CPU tensorflow上工作得很好，但在GPU版本上，我得到了这个错误(有时它也可以工作)：
name: GeForce GT 750M
major: 3 minor: 0 memoryClockRate (GHz) 0.9255
pciBusID 0000:01:00.0
Total memory: 2.00GiB
Free memory: 67.48MiB
I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0
I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0: Y
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GT 750M, pci bus id: 0000:01:00.0)
E tensorflow/stream_executor/cuda/cuda_driver.cc:1002] failed to allocate 67.48M (70754304 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
Training...
E tensorflow/stream_executor/cuda/cuda_dnn.cc:397] could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR
E tensorflow/stream_executor/cuda/cuda_dnn.cc:364] could not destroy cudnn handle: CUDNN_STATUS_BAD_PARAM
F tensorflow/core/kernels/conv_ops.cc:605] Check failed: stream->parent()->GetConvolveAlgorithms(&algorithms)
Abort trap: 6这里发生了什么？这是tensorflow中的错误吗？请帮帮忙。
下面是我运行python代码时的GPU内存空间：
Device 0 [PCIe 0:1:0.0]: GeForce GT 750M (CC 3.0): 83.477 of 2047.6 MB (i.e. 4.08%) Free
MacBook-Pro:cuda-smi-master xxxxxx$ ./cuda-smi
Device 0 [PCIe 0:1:0.0]: GeForce GT 750M (CC 3.0): 83.477 of 2047.6 MB (i.e. 4.08%) Free
MacBook-Pro:cuda-smi-master xxxxxx$ ./cuda-smi
Device 0 [PCIe 0:1:0.0]: GeForce GT 750M (CC 3.0): 83.477 of 2047.6 MB (i.e. 4.08%) Free
MacBook-Pro:cuda-smi-master xxxxxx$ ./cuda-smi
Device 0 [PCIe 0:1:0.0]: GeForce GT 750M (CC 3.0): 1.1016 of 2047.6 MB (i.e. 0.0538%) Free
MacBook-Pro:cuda-smi-master xxxxxx$ ./cuda-smi
Device 0 [PCIe 0:1:0.0]: GeForce GT 750M (CC 3.0): 1.1016 of 2047.6 MB (i.e. 0.0538%) Free
MacBook-Pro:cuda-smi-master xxxxxx$ ./cuda-smi
Device 0 [PCIe 0:1:0.0]: GeForce GT 750M (CC 3.0): 1.1016 of 2047.6 MB (i.e. 0.0538%) Free
MacBook-Pro:cuda-smi-master xxxxxx$ ./cuda-smi
Device 0 [PCIe 0:1:0.0]: GeForce GT 750M (CC 3.0): 1.1016 of 2047.6 MB (i.e. 0.0538%) Free
MacBook-Pro:cuda-smi-master xxxxxx$ ./cuda-smi
Device 0 [PCIe 0:1:0.0]: GeForce GT 750M (CC 3.0): 91.477 of 2047.6 MB (i.e. 4.47%) Free
MacBook-Pro:cuda-smi-master xxxxxx$ ./cuda-smi
Device 0 [PCIe 0:1:0.0]: GeForce GT 750M (CC 3.0): 22.852 of 2047.6 MB (i.e. 1.12%) Free
MacBook-Pro:cuda-smi-master xxxxxx$ ./cuda-smi
Device 0 [PCIe 0:1:0.0]: GeForce GT 750M (CC 3.0): 22.852 of 2047.6 MB (i.e. 1.12%) Free
MacBook-Pro:cuda-smi-master xxxxxx$ ./cuda-smi
Device 0 [PCIe 0:1:0.0]: GeForce GT 750M (CC 3.0): 36.121 of 2047.6 MB (i.e. 1.76%) Free
MacBook-Pro:cuda-smi-master xxxxxx$ ./cuda-smi
Device 0 [PCIe 0:1:0.0]: GeForce GT 750M (CC 3.0): 71.477 of 2047.6 MB (i.e. 3.49%) Free
MacBook-Pro:cuda-smi-master xxxxxx$ ./cuda-smi
Device 0 [PCIe 0:1:0.0]: GeForce GT 750M (CC 3.0): 67.477 of 2047.6 MB (i.e. 3.3%) Free
MacBook-Pro:cuda-smi-master xxxxxx$ ./cuda-smi
Device 0 [PCIe 0:1:0.0]: GeForce GT 750M (CC 3.0): 67.477 of 2047.6 MB (i.e. 3.3%) Free
MacBook-Pro:cuda-smi-master xxxxxx$ ./cuda-smi
Device 0 [PCIe 0:1:0.0]: GeForce GT 750M (CC 3.0): 67.477 of 2047.6 MB (i.e. 3.3%) Free回答 19
Stack Overflow用户
发布于 2019-11-04 04:40:12
在Tensorflow 2.0中，我的问题通过设置内存增长得到了解决。在TF2.0中不推荐使用ConfigProto，我使用的是tf.config.experimental。我的电脑规格是：
操作系统: Ubuntu 18.04
图形处理器: GeForce腾讯通2070
Nvidia驱动程序: 430.26
Tensorflow: 2.0
Cudnn: 7.6.2
Cuda: 10.0
我使用的代码是：
physical_devices = tf.config.experimental.list_physical_devices('GPU')
assert len(physical_devices) > 0, "Not enough GPU hardware devices available"
config = tf.config.experimental.set_memory_growth(physical_devices[0], True)Stack Overflow用户
发布于 2018-02-05 19:14:29
通过删除主文件夹中的.nv文件夹，我设法让它正常工作：
sudo rm -rf ~/.nv/Stack Overflow用户
发布于 2019-07-16 20:19:50
在我的例子中，在检查cuDNN和CUDA版本后，我发现我的GPU内存不足..。使用watch -n 0.1 nvidia-smi在另一个bash终端中，2019-07-16 19:54:05.122224: E tensorflow/stream_executor/cuda/cuda_dnn.cc:334] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR开始是GPU内存接近满的那一刻。
截图
所以我设置了tnsorflow使用我的gpu的限制。因为我使用
模块中，我将以下代码添加到程序的开头：
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.9
tf.keras.backend.set_session(tf.Session(config=config));那么，问题就解决了！
您可以更改您的batch_size或者使用更智能的方式输入训练数据(例如tf.data.Dataset以及使用高速缓存)。我希望我的答案能对其他人有所帮助。
https://stackoverflow.com/questions/43147983
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