在windows 10中使用gpu Quadro M1200的Tensorflow

我能够为我的小型Quadro M1200安装NVIDIA驱动程序，并运行TensorFlow 2.3.0

使用GPU工程测试TensorFlow：

[2]中的

：tf.config.list\u物理设备（'GPU'）

Tt返回[2]：[PhysicalDevice（名称='/physical\U设备：GPU:0'，设备\U类型='GPU'）]以及一些详细信息，如：

• 已成功打开动态库nvcuda.dll
• 找到属性为：pciBusID:0000:01:00.0的设备0名称：Quadro M1200计算能力：5.0
• 已成功打开动态库cudart64_101.dll
• 已成功打开动态库cublas64_10.dll
• 已成功打开动态库cufft64_10.dll
• 已成功打开动态库curand64_10.dll
• 已成功打开动态库cusolver64_10.dll
• 已成功打开动态库cusparse64_10.dll
• 已成功打开动态库cudnn64_7.dll
• 正在添加可见gpu设备：0

然而，当我创建一个模型时，TensorFlow崩溃了。代码如下：

In [4]: model = keras.Sequential([^M
   ...:     keras.layers.Flatten(input_shape=(28, 28)),^M
   ...:     keras.layers.Dense(128, activation='relu'),^M
   ...:     keras.layers.Dense(10)^M
   ...: ])
2020-09-26 14:48:36.858359: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations:  AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2020-09-26 14:48:36.936376: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x25353e19ea0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-09-26 14:48:36.945965: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2020-09-26 14:48:36.956140: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: Quadro M1200 computeCapability: 5.0
coreClock: 1.148GHz coreCount: 5 deviceMemorySize: 4.00GiB deviceMemoryBandwidth: 74.65GiB/s
2020-09-26 14:48:36.971039: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll
2020-09-26 14:48:36.977439: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cublas64_10.dll
2020-09-26 14:48:36.984968: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cufft64_10.dll
2020-09-26 14:48:36.991871: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library curand64_10.dll
2020-09-26 14:48:37.000114: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cusolver64_10.dll
2020-09-26 14:48:37.007128: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cusparse64_10.dll
2020-09-26 14:48:37.018878: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudnn64_7.dll
2020-09-26 14:48:37.025486: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
2020-09-26 14:48:37.602596: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-09-26 14:48:37.610835: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263]      0
2020-09-26 14:48:37.617839: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0:   N
2020-09-26 14:48:37.625390: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3119 MB memory) -> physical GPU (device: 0, name: Quadro M1200, pci bus id: 0000:01:00.0, compute capability: 5.0)
2020-09-26 14:48:37.649822: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2535430b520 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2020-09-26 14:48:37.661985: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Quadro M1200, Compute Capability 5.0
---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-4-0eeba0e16040> in <module>
----> 1 model = keras.Sequential([
      2     keras.layers.Flatten(input_shape=(28, 28)),
      3     keras.layers.Dense(128, activation='relu'),
      4     keras.layers.Dense(10)
      5 ])

~\Miniconda3\lib\site-packages\tensorflow\python\training\tracking\base.py in _method_wrapper(self, *args, **kwargs)
    455     self._self_setattr_tracking = False  # pylint: disable=protected-access
    456     try:
--> 457       result = method(self, *args, **kwargs)
    458     finally:
    459       self._self_setattr_tracking = previous_value  # pylint: disable=protected-access

~\Miniconda3\lib\site-packages\tensorflow\python\keras\engine\sequential.py in __init__(self, layers, name)
    114     """
    115     # Skip the init in FunctionalModel since model doesn't have input/output yet
--> 116     super(functional.Functional, self).__init__(  # pylint: disable=bad-super-call
    117         name=name, autocast=False)
    118     self.supports_masking = True

~\Miniconda3\lib\site-packages\tensorflow\python\training\tracking\base.py in _method_wrapper(self, *args, **kwargs)
    455     self._self_setattr_tracking = False  # pylint: disable=protected-access
    456     try:
--> 457       result = method(self, *args, **kwargs)
    458     finally:
    459       self._self_setattr_tracking = previous_value  # pylint: disable=protected-access

~\Miniconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in __init__(self, *args, **kwargs)
    306     self._steps_per_execution = None
    307
--> 308     self._init_batch_counters()
    309     self._base_model_initialized = True
    310     _keras_api_gauge.get_cell('model').set(True)

~\Miniconda3\lib\site-packages\tensorflow\python\training\tracking\base.py in _method_wrapper(self, *args, **kwargs)
    455     self._self_setattr_tracking = False  # pylint: disable=protected-access
    456     try:
--> 457       result = method(self, *args, **kwargs)
    458     finally:
    459       self._self_setattr_tracking = previous_value  # pylint: disable=protected-access

~\Miniconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in _init_batch_counters(self)
    315     # `evaluate`, and `predict`.
    316     agg = variables.VariableAggregationV2.ONLY_FIRST_REPLICA
--> 317     self._train_counter = variables.Variable(0, dtype='int64', aggregation=agg)
    318     self._test_counter = variables.Variable(0, dtype='int64', aggregation=agg)
    319     self._predict_counter = variables.Variable(

~\Miniconda3\lib\site-packages\tensorflow\python\ops\variables.py in __call__(cls, *args, **kwargs)
    260       return cls._variable_v1_call(*args, **kwargs)
    261     elif cls is Variable:
--> 262       return cls._variable_v2_call(*args, **kwargs)
    263     else:
    264       return super(VariableMetaclass, cls).__call__(*args, **kwargs)

~\Miniconda3\lib\site-packages\tensorflow\python\ops\variables.py in _variable_v2_call(cls, initial_value, trainable, validate_shape, caching_device, name, variable_def, dtype, import_scope, constraint, synchronization, aggregation, shape)
    242     if aggregation is None:
    243       aggregation = VariableAggregation.NONE
--> 244     return previous_getter(
    245         initial_value=initial_value,
    246         trainable=trainable,

~\Miniconda3\lib\site-packages\tensorflow\python\ops\variables.py in <lambda>(**kws)
    235                         shape=None):
    236     """Call on Variable class. Useful to force the signature."""
--> 237     previous_getter = lambda **kws: default_variable_creator_v2(None, **kws)
    238     for _, getter in ops.get_default_graph()._variable_creator_stack:  # pylint: disable=protected-access
    239       previous_getter = _make_getter(getter, previous_getter)

~\Miniconda3\lib\site-packages\tensorflow\python\ops\variable_scope.py in default_variable_creator_v2(next_creator, **kwargs)
   2631   shape = kwargs.get("shape", None)
   2632
-> 2633   return resource_variable_ops.ResourceVariable(
   2634       initial_value=initial_value,
   2635       trainable=trainable,

~\Miniconda3\lib\site-packages\tensorflow\python\ops\variables.py in __call__(cls, *args, **kwargs)
    262       return cls._variable_v2_call(*args, **kwargs)
    263     else:
--> 264       return super(VariableMetaclass, cls).__call__(*args, **kwargs)
    265
    266

~\Miniconda3\lib\site-packages\tensorflow\python\ops\resource_variable_ops.py in __init__(self, initial_value, trainable, collections, validate_shape, caching_device, name, dtype, variable_def, import_scope, constraint, distribute_strategy, synchronization, aggregation, shape)
   1505       self._init_from_proto(variable_def, import_scope=import_scope)
   1506     else:
-> 1507       self._init_from_args(
   1508           initial_value=initial_value,
   1509           trainable=trainable,

~\Miniconda3\lib\site-packages\tensorflow\python\ops\resource_variable_ops.py in _init_from_args(self, initial_value, trainable, collections, caching_device, name, dtype, constraint, synchronization, aggregation, distribute_strategy, shape)
   1659           else:
   1660             shape = initial_value.shape
-> 1661           handle = eager_safe_variable_handle(
   1662               initial_value=initial_value,
   1663               shape=shape,

~\Miniconda3\lib\site-packages\tensorflow\python\ops\resource_variable_ops.py in eager_safe_variable_handle(initial_value, shape, shared_name, name, graph_mode)
    240   """
    241   dtype = initial_value.dtype.base_dtype
--> 242   return _variable_handle_from_shape_and_dtype(
    243       shape, dtype, shared_name, name, graph_mode, initial_value)
    244

~\Miniconda3\lib\site-packages\tensorflow\python\ops\resource_variable_ops.py in _variable_handle_from_shape_and_dtype(shape, dtype, shared_name, name, graph_mode, initial_value)
    172     # compatible with ASYNC execution mode. Further, since not all devices
    173     # support string tensors, we encode the assertion string in the Op name
--> 174     gen_logging_ops._assert(  # pylint: disable=protected-access
    175         math_ops.logical_not(exists), [exists], name="EagerVariableNameReuse")
    176

~\Miniconda3\lib\site-packages\tensorflow\python\ops\gen_logging_ops.py in _assert(condition, data, summarize, name)
     47       return _result
     48     except _core._NotOkStatusException as e:
---> 49       _ops.raise_from_not_ok_status(e, name)
     50     except _core._FallbackException:
     51       pass

~\Miniconda3\lib\site-packages\tensorflow\python\framework\ops.py in raise_from_not_ok_status(e, name)
   6841   message = e.message + (" name: " + name if name is not None else "")
   6842   # pylint: disable=protected-access
-> 6843   six.raise_from(core._status_to_exception(e.code, message), None)
   6844   # pylint: enable=protected-access
   6845

~\Miniconda3\lib\site-packages\six.py in raise_from(value, from_value)

InvalidArgumentError: assertion failed: [0] [Op:Assert] name: EagerVariableNameReuse


Unhandled exception in event loop:
  File "C:\Users\AMartinez\Miniconda3\lib\asyncio\proactor_events.py", line 768, in _loop_self_reading
    f.result()  # may raise
  File "C:\Users\AMartinez\Miniconda3\lib\asyncio\windows_events.py", line 808, in _poll
    value = callback(transferred, key, ov)
  File "C:\Users\AMartinez\Miniconda3\lib\asyncio\windows_events.py", line 457, in finish_recv
    raise ConnectionResetError(*exc.args)

Exception [WinError 995] The I/O operation has been aborted because of either a thread exit or an application request
Press ENTER to continue...
In [5]:

---

你知道如何解决这个问题吗？

错误出在CUDA驱动程序和TF版本2.3的不兼容性上。我把TF升级到了2.4，nvidia驱动，它可以工作。我还设法使其与以前的TF2版本兼容。

错误在于CUDA驱动程序与TF 2.3版本不兼容。我把TF升级到了2.4，nvidia驱动，它可以工作。我还设法使它与以前的TF2版本兼容。

它看起来

GPU

安装对我来说很好。请分享您试图执行的完整代码，这有助于为您的问题提供正确的解决方案。谢谢它看起来

GPU

安装对我来说很好。请分享您试图执行的完整代码，这有助于为您的问题提供正确的解决方案。谢谢

cudnn-10.1-windows10-x64-v7.6.1.34.zip
cudnn-10.1-windows10-x64-v7.6.2.24.zip
cudnn-10.1-windows10-x64-v7.6.3.30.zip