我正在尝试在计算机上运行NVidia's face generating demo。我正在使用Windows10。我已经下载了源代码，并试图按照页面下方的步骤进行操作。我已经为我的GTX1060安装了最新的NVidia驱动程序，该驱动程序应该是支持cuda功能的设备。我已经安装了Cuda Toolkit和TensorFlow所需的cuDNN SDK。
但是，在运行import_example.py脚本时，出现以下错误。谁能告诉我我在做什么错？
2019-03-19 20:16:26.112574: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
WARNING:tensorflow:From C:\Users\me\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\framework\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
Traceback (most recent call last):
File "C:\Users\me\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\client\session.py", line 1334, in _do_call
return fn(*args)
File "C:\Users\me\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\client\session.py", line 1319, in _run_fn
options, feed_dict, fetch_list, target_list, run_metadata)
File "C:\Users\me\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\client\session.py", line 1407, in _call_tf_sessionrun
run_metadata)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Cannot assign a device for operation G_paper_1/Run/G_paper_1/latents_in: {{node G_paper_1/Run/G_paper_1/latents_in}}was explicitly assigned to /device:GPU:0 but available devices are [ /job:localhost/replica:0/task:0/device:CPU:0 ]. Make sure the device specification refers to a valid device. The requested device appears to be a GPU, but CUDA is not enabled.
[[{{node G_paper_1/Run/G_paper_1/latents_in}}]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File ".\import_example.py", line 21, in <module>
images = Gs.run(latents, labels)
File "C:\Users\me\Desktop\progressive_growing_of_gans-master\tfutil.py", line 668, in run
mb_out = tf.get_default_session().run(out_expr, dict(zip(self.input_templates, mb_in)))
File "C:\Users\me\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\client\session.py", line 929, in run
run_metadata_ptr)
File "C:\Users\me\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\client\session.py", line 1152, in _run
feed_dict_tensor, options, run_metadata)
File "C:\Users\me\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\client\session.py", line 1328, in _do_run
run_metadata)
File "C:\Users\me\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\client\session.py", line 1348, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Cannot assign a device for operation G_paper_1/Run/G_paper_1/latents_in: node G_paper_1/Run/G_paper_1/latents_in (defined at C:\Users\me\Desktop\progressive_growing_of_gans-master\tfutil.py:508) was explicitly assigned to /device:GPU:0 but available devices are [ /job:localhost/replica:0/task:0/device:CPU:0 ]. Make sure the device specification refers to a valid device. The requested device appears to be a GPU, but CUDA is not enabled.
[[node G_paper_1/Run/G_paper_1/latents_in (defined at C:\Users\me\Desktop\progressive_growing_of_gans-master\tfutil.py:508) ]]
Caused by op 'G_paper_1/Run/G_paper_1/latents_in', defined at:
File ".\import_example.py", line 21, in <module>
images = Gs.run(latents, labels)
File "C:\Users\me\Desktop\progressive_growing_of_gans-master\tfutil.py", line 645, in run
out_expr = self.get_output_for(*in_split[gpu], return_as_list=True, **dynamic_kwargs)
File "C:\Users\me\Desktop\progressive_growing_of_gans-master\tfutil.py", line 508, in get_output_for
named_inputs = [tf.identity(expr, name=name) for expr, name in zip(in_expr, self.input_names)]
File "C:\Users\me\Desktop\progressive_growing_of_gans-master\tfutil.py", line 508, in <listcomp>
named_inputs = [tf.identity(expr, name=name) for expr, name in zip(in_expr, self.input_names)]
File "C:\Users\me\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\util\dispatch.py", line 180, in wrapper
return target(*args, **kwargs)
File "C:\Users\me\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\ops\array_ops.py", line 81, in identity
ret = gen_array_ops.identity(input, name=name)
File "C:\Users\me\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\ops\gen_array_ops.py", line 4537, in identity
"Identity", input=input, name=name)
File "C:\Users\me\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 788, in _apply_op_helper
op_def=op_def)
File "C:\Users\me\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\util\deprecation.py", line 507, in new_func
return func(*args, **kwargs)
File "C:\Users\me\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\framework\ops.py", line 3300, in create_op
op_def=op_def)
File "C:\Users\me\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\framework\ops.py", line 1801, in __init__
self._traceback = tf_stack.extract_stack()
InvalidArgumentError (see above for traceback): Cannot assign a device for operation G_paper_1/Run/G_paper_1/latents_in: node G_paper_1/Run/G_paper_1/latents_in (defined at C:\Users\me\Desktop\progressive_growing_of_gans-master\tfutil.py:508) was explicitly assigned to /device:GPU:0 but available devices are [ /job:localhost/replica:0/task:0/device:CPU:0 ]. Make sure the device specification refers to a valid device. The requested device appears to be a GPU, but CUDA is not enabled.
[[node G_paper_1/Run/G_paper_1/latents_in (defined at C:\Users\me\Desktop\progressive_growing_of_gans-master\tfutil.py:508) ]]
答案 0 :(得分：2)
无法分配设备进行操作 G_paper_1 / Run / G_paper_1 / latents_in：{{节点 G_paper_1 / Run / G_paper_1 / latents_in}}已明确分配给 / device：GPU：0，但可用设备为[ / job：localhost /副本：0 /任务：0 /设备：CPU：0]
您是否安装了tensorflow或tensorflow-gpu？如果要使用GPU，则是后者。
这也可能是版本兼容性问题。
首先，检查您的nvidia驱动程序是否安装有：nvidia-smi，您应该得到类似以下的内容：
Mon Apr 1 12:30:02 2019
+------------------------------------------------------+
| NVIDIA-SMI 3.295.41 Driver Version: 295.41 |
|-------------------------------+----------------------+----------------------+
| Nb. Name | Bus Id Disp. | Volatile ECC SB / DB |
| Fan Temp Power Usage /Cap | Memory Usage | GPU Util. Compute M. |
|===============================+======================+======================|
| 0. GeForce GTX 580 | 0000:25:00.0 N/A | N/A N/A |
| 54% 70 C N/A N/A / N/A | 25% 383MB / 1535MB | N/A Default |
|-------------------------------+----------------------+----------------------|
| Compute processes: GPU Memory |
| GPU PID Process name Usage |
|=============================================================================|
| 0. Not Supported |
+-----------------------------------------------------------------------------+
然后，使用nvcc --version检查您拥有的cuda版本。示例：
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2016 NVIDIA Corporation
Built on Mon_Apr__1_12:34:01_CDT_2016
Cuda compilation tools, release 8.0, V8.0.44
最后，检查是否已安装python / tensorflow / cuda的兼容版本。因此，对于大多数人来说，使用table作为参考似乎是可行的。
安装驱动程序后，别忘了重新启动！