我刚刚构建了一个深度学习装备（AMD 12 核 Threadripper；GeForce RTX 2080 ti；64Gb RAM）。我本来想在 Ubuntu 19.0 上安装 CUDnn 和 CUDA，但是安装太痛苦了，看了一些资料后，我决定切换到 Windows 10...
在 condas 内外安装了几次 tensorflow-gpu 后，我遇到了进一步的问题，我认为这些问题归结为 CUDnn-CUDA-tensorflow 兼容性，因此卸载了各种版本的 CUDA 和 tf.我的输出来自nvcc --version:
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2018 NVIDIA Corporation
Built on Sat_Aug_25_21:08:04_Central_Daylight_Time_2018
Cuda compilation tools, release 10.0, V10.0.130
还附上 nvidia-smi（显示 CUDA==11.0？！）
我也有：
if tf.test.gpu_device_name():
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
else:
print("Please install GPU version of TF")
print("keras version: {0} | Backend used: {1}".format(keras.__version__, backend.backend()))
print("tensorflow version: {0} | Backend used: {1}".format(tf.__version__, backend.backend()))
print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))
print("CUDA: {0} | CUDnn: {1}".format(tf_build_info.cuda_version_number, tf_build_info.cudnn_version_number))
与输出：
My device: [name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 12853915229880452239
, name: "/device:GPU:0"
device_type: "GPU"
memory_limit: 9104897474
lo
cality {
bus_id: 1
links {
}
}
incarnation: 7328135816345461398
physical_device_desc: "device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:42:00.0, compute capability: 7.5"
]
Default GPU Device: /device:GPU:0
keras version: 2.3.1 | Backend used: tensorflow
tensorflow version: 2.1.0 | Backend used: tensorflow
Num GPUs Available: 1
CUDA: 10.1 | CUDnn: 7
所以（我希望）我的安装至少部分有效，我只是仍然不知道 GPU 是否用于我的训练，或者它只是公认的与现有的一样，但是CPUis still正在使用。我该如何区分这一点？
我也用pycharm。建议安装 Visio Studio 和附加步骤here:
5. Include cudnn.lib in your Visual Studio project.
Open the Visual Studio project and right-click on the project name.
Click Linker > Input > Additional Dependencies.
Add cudnn.lib and click OK.
我没有做这一步。我还读到我需要在环境变量中设置以下内容，但我的目录是空的：
SET PATH=C:\tools\cuda\bin;%PATH%
有人能验证一下吗？
另外，我的 Kera 模型需要搜索超参数：
grid = GridSearchCV(estimator=model,
param_grid=param_grids,
n_jobs=-1, # -1 for all cores
cv=KFold(),
verbose=10)
grid_result = grid.fit(X_standardized, Y)
这在我的 MBP 上运行良好（当然假设 n_jobs=-1 占用所有 CPU 核心）。在我的 DL 设备上，我收到警告：
ERROR: The process with PID 5156 (child process of PID 1184) could not be terminated.
Reason: Access is denied.
ERROR: The process with PID 1184 (child process of PID 6920) could not be terminated.
Reason: There is no running instance of the task.
2020-03-28 20:29:48.598918: E tensorflow/stream_executor/cuda/cuda_blas.cc:238] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2020-03-28 20:29:48.599348: E tensorflow/stream_executor/cuda/cuda_blas.cc:238] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2020-03-28 20:29:48.599655: E tensorflow/stream_executor/cuda/cuda_blas.cc:238] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2020-03-28 20:29:48.603023: E tensorflow/stream_executor/cuda/cuda_blas.cc:238] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2020-03-28 20:29:48.603649: E tensorflow/stream_executor/cuda/cuda_blas.cc:238] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2020-03-28 20:29:48.604236: E tensorflow/stream_executor/cuda/cuda_blas.cc:238] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2020-03-28 20:29:48.604773: E tensorflow/stream_executor/cuda/cuda_blas.cc:238] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2020-03-28 20:29:48.605524: E tensorflow/stream_executor/cuda/cuda_blas.cc:238] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2020-03-28 20:29:48.608151: E tensorflow/stream_executor/cuda/cuda_blas.cc:238] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED
2020-03-28 20:29:48.608369: W tensorflow/stream_executor/stream.cc:2041] attempting to perform BLAS operation using StreamExecutor without BLAS support
2020-03-28 20:29:48.608559: W tensorflow/core/common_runtime/base_collective_executor.cc:217] BaseCollectiveExecutor::StartAbort Internal: Blas GEMM launch failed : a.shape=(10, 8), b.shape=(8, 4), m=10, n=4, k=8
[[{{node dense_1/MatMul}}]]
C:\Users\me\PycharmProjects\untitled\venv\lib\site-packages\sklearn\model_selection\_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
tensorflow.python.framework.errors_impl.InternalError: Blas GEMM launch failed : a.shape=(10, 8), b.shape=(8, 4), m=10, n=4, k=8
[[node dense_1/MatMul (defined at C:\Users\me\PycharmProjects\untitled\venv\lib\site-packages\keras\backend\tensorflow_backend.py:3009) ]] [Op:__inference_keras_scratch_graph_982]
我可以假设使用时GridSearchCV，这仅利用CPU，而不利用GPU？尽管如此，当在我的代码中运行和计时另一个方法时，我将 MBP 的时间（使用 2.8 GHz Intel Core i7 时约为 40 秒）与台式机的时间（使用 12 核 threadripper 时约为 43 秒）进行了比较。即使在比较 CPU 时，我预计时间也比 MBP 快得多。那么我的假设是错误的吗？
您可以看到以下详细信息here.
根据文档：
If a TensorFlow operation has both CPU and GPU implementations,
by default, the GPU devices will be given priority when the operation is assigned to a device.
For example, tf.matmul has both CPU and GPU kernels.
On a system with devices CPU:0 and GPU:0, the GPU:0 device will be selected to run tf.matmul unless you explicitly request running it on another device.
记录设备放置
tf.debugging.set_log_device_placement(True)
# Create some tensors
a = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
b = tf.constant([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
c = tf.matmul(a, b)
print(c)
Example Result
Executing op MatMul in device /job:localhost/replica:0/task:0/device:GPU:0
tf.Tensor(
[[22. 28.]
[49. 64.]], shape=(2, 2), dtype=float32)
对于手动设备放置
tf.debugging.set_log_device_placement(True)
# Place tensors on the CPU
with tf.device('/GPU:0'):
a = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
b = tf.constant([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
c = tf.matmul(a, b)
print(c)
Example Result:
Executing op MatMul in device /job:localhost/replica:0/task:0/device:GPU:0
tf.Tensor(
[[22. 28.]
[49. 64.]], shape=(2, 2), dtype=float32)