Python TensorFlow：Blas GEMM 启动失败
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/43990046/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
TensorFlow: Blas GEMM launch failed
提问by Nicolas
When I'm trying to use TensorFlow with Keras using the gpu, I'm getting this error message:
当我尝试使用 GPU 将 TensorFlow 与 Keras 结合使用时，我收到以下错误消息：
C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\ipykernel\__main__.py:2: UserWarning: Update your `fit_generator` call to the Keras 2 API: `fit_generator(<keras.pre..., 37800, epochs=2, validation_data=<keras.pre..., validation_steps=4200)`
from ipykernel import kernelapp as app
Epoch 1/2
InternalError Traceback (most recent call last)
C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py in _do_call(self, fn, *args)
1038 try:
-> 1039 return fn(*args)
1040 except errors.OpError as e:
C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py in _run_fn(session, feed_dict, fetch_list, target_list, options, run_metadata)
1020 feed_dict, fetch_list, target_list,
-> 1021 status, run_metadata)
1022
C:\Users\nicol\Anaconda3\envs\tensorflow\lib\contextlib.py in __exit__(self, type, value, traceback)
65 try:
---> 66 next(self.gen)
67 except StopIteration:
C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\framework\errors_impl.py in raise_exception_on_not_ok_status()
465 compat.as_text(pywrap_tensorflow.TF_Message(status)),
--> 466 pywrap_tensorflow.TF_GetCode(status))
467 finally:
InternalError: Blas GEMM launch failed : a.shape=(64, 784), b.shape=(784, 10), m=64, n=10, k=784
[[Node: dense_1/MatMul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device="/job:localhost/replica:0/task:0/gpu:0"](flatten_1/Reshape, dense_1/kernel/read)]]
During handling of the above exception, another exception occurred:
InternalError Traceback (most recent call last)
<ipython-input-13-2a52d1079a66> in <module>()
1 history=model.fit_generator(batches, batches.n, nb_epoch=2,
----> 2 validation_data=val_batches, nb_val_samples=val_batches.n)
C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\keras\legacy\interfaces.py in wrapper(*args, **kwargs)
86 warnings.warn('Update your `' + object_name +
87 '` call to the Keras 2 API: ' + signature, stacklevel=2)
---> 88 return func(*args, **kwargs)
89 wrapper._legacy_support_signature = inspect.getargspec(func)
90 return wrapper
C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\keras\models.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_q_size, workers, pickle_safe, initial_epoch)
1108 workers=workers,
1109 pickle_safe=pickle_safe,
-> 1110 initial_epoch=initial_epoch)
1111
1112 @interfaces.legacy_generator_methods_support
C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\keras\legacy\interfaces.py in wrapper(*args, **kwargs)
86 warnings.warn('Update your `' + object_name +
87 '` call to the Keras 2 API: ' + signature, stacklevel=2)
---> 88 return func(*args, **kwargs)
89 wrapper._legacy_support_signature = inspect.getargspec(func)
90 return wrapper
C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\keras\engine\training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_q_size, workers, pickle_safe, initial_epoch)
1888 outs = self.train_on_batch(x, y,
1889 sample_weight=sample_weight,
-> 1890 class_weight=class_weight)
1891
1892 if not isinstance(outs, list):
C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\keras\engine\training.py in train_on_batch(self, x, y, sample_weight, class_weight)
1631 ins = x + y + sample_weights
1632 self._make_train_function()
-> 1633 outputs = self.train_function(ins)
1634 if len(outputs) == 1:
1635 return outputs[0]
C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\keras\backend\tensorflow_backend.py in __call__(self, inputs)
2227 session = get_session()
2228 updated = session.run(self.outputs + [self.updates_op],
-> 2229 feed_dict=feed_dict)
2230 return updated[:len(self.outputs)]
2231
C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py in run(self, fetches, feed_dict, options, run_metadata)
776 try:
777 result = self._run(None, fetches, feed_dict, options_ptr,
--> 778 run_metadata_ptr)
779 if run_metadata:
780 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
980 if final_fetches or final_targets:
981 results = self._do_run(handle, final_targets, final_fetches,
--> 982 feed_dict_string, options, run_metadata)
983 else:
984 results = []
C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
1030 if handle is None:
1031 return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
-> 1032 target_list, options, run_metadata)
1033 else:
1034 return self._do_call(_prun_fn, self._session, handle, feed_dict,
C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py in _do_call(self, fn, *args)
1050 except KeyError:
1051 pass
-> 1052 raise type(e)(node_def, op, message)
1053
1054 def _extend_graph(self):
InternalError: Blas GEMM launch failed : a.shape=(64, 784), b.shape=(784, 10), m=64, n=10, k=784
[[Node: dense_1/MatMul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device="/job:localhost/replica:0/task:0/gpu:0"](flatten_1/Reshape, dense_1/kernel/read)]]
Caused by op 'dense_1/MatMul', defined at:
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\ipykernel\__main__.py", line 3, in <module>
app.launch_new_instance()
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\traitlets\config\application.py", line 658, in launch_instance
app.start()
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\ipykernel\kernelapp.py", line 477, in start
ioloop.IOLoop.instance().start()
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\zmq\eventloop\ioloop.py", line 177, in start
super(ZMQIOLoop, self).start()
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\tornado\ioloop.py", line 888, in start
handler_func(fd_obj, events)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\tornado\stack_context.py", line 277, in null_wrapper
return fn(*args, **kwargs)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\zmq\eventloop\zmqstream.py", line 440, in _handle_events
self._handle_recv()
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\zmq\eventloop\zmqstream.py", line 472, in _handle_recv
self._run_callback(callback, msg)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\zmq\eventloop\zmqstream.py", line 414, in _run_callback
callback(*args, **kwargs)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\tornado\stack_context.py", line 277, in null_wrapper
return fn(*args, **kwargs)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\ipykernel\kernelbase.py", line 283, in dispatcher
return self.dispatch_shell(stream, msg)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\ipykernel\kernelbase.py", line 235, in dispatch_shell
handler(stream, idents, msg)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\ipykernel\kernelbase.py", line 399, in execute_request
user_expressions, allow_stdin)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\ipykernel\ipkernel.py", line 196, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\ipykernel\zmqshell.py", line 533, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\IPython\core\interactiveshell.py", line 2683, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\IPython\core\interactiveshell.py", line 2787, in run_ast_nodes
if self.run_code(code, result):
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\IPython\core\interactiveshell.py", line 2847, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-10-1e7a3b259f23>", line 4, in <module>
model.add(Dense(10, activation='softmax'))
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\keras\models.py", line 466, in add
output_tensor = layer(self.outputs[0])
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\keras\engine\topology.py", line 585, in __call__
output = self.call(inputs, **kwargs)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\keras\layers\core.py", line 840, in call
output = K.dot(inputs, self.kernel)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\keras\backend\tensorflow_backend.py", line 936, in dot
out = tf.matmul(x, y)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\ops\math_ops.py", line 1801, in matmul
a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\ops\gen_math_ops.py", line 1263, in _mat_mul
transpose_b=transpose_b, name=name)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 768, in apply_op
op_def=op_def)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\framework\ops.py", line 2336, in create_op
original_op=self._default_original_op, op_def=op_def)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\framework\ops.py", line 1228, in __init__
self._traceback = _extract_stack()
InternalError (see above for traceback): Blas GEMM launch failed : a.shape=(64, 784), b.shape=(784, 10), m=64, n=10, k=784
[[Node: dense_1/MatMul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device="/job:localhost/replica:0/task:0/gpu:0"](flatten_1/Reshape, dense_1/kernel/read)]]
When I'm trying to use TensorFlow with Keras using the cpu, I'm getting this error message:
当我尝试使用 cpu 将 TensorFlow 与 Keras 结合使用时，我收到此错误消息：
C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\ipykernel\__main__.py:5: UserWarning: Update your `fit_generator` call to the Keras 2 API: `fit_generator(<keras.pre..., 37800, validation_steps=4200, validation_data=<keras.pre..., epochs=2)`
Epoch 1/2
---------------------------------------------------------------------------
InternalError Traceback (most recent call last)
C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py in _do_call(self, fn, *args)
1038 try:
-> 1039 return fn(*args)
1040 except errors.OpError as e:
C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py in _run_fn(session, feed_dict, fetch_list, target_list, options, run_metadata)
1020 feed_dict, fetch_list, target_list,
-> 1021 status, run_metadata)
1022
C:\Users\nicol\Anaconda3\envs\tensorflow\lib\contextlib.py in __exit__(self, type, value, traceback)
65 try:
---> 66 next(self.gen)
67 except StopIteration:
C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\framework\errors_impl.py in raise_exception_on_not_ok_status()
465 compat.as_text(pywrap_tensorflow.TF_Message(status)),
--> 466 pywrap_tensorflow.TF_GetCode(status))
467 finally:
InternalError: Blas GEMM launch failed : a.shape=(64, 784), b.shape=(784, 10), m=64, n=10, k=784
[[Node: dense_1/MatMul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device="/job:localhost/replica:0/task:0/gpu:0"](flatten_1/Reshape, dense_1/kernel/read)]]
[[Node: Assign_3/_84 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_374_Assign_3", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
During handling of the above exception, another exception occurred:
InternalError Traceback (most recent call last)
<ipython-input-14-f66b4d3d5b88> in <module>()
3 with tf.device('/cpu:0'):
4 history=model.fit_generator(batches, batches.n, nb_epoch=2,
----> 5 validation_data=val_batches, nb_val_samples=val_batches.n)
C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\keras\legacy\interfaces.py in wrapper(*args, **kwargs)
86 warnings.warn('Update your `' + object_name +
87 '` call to the Keras 2 API: ' + signature, stacklevel=2)
---> 88 return func(*args, **kwargs)
89 wrapper._legacy_support_signature = inspect.getargspec(func)
90 return wrapper
C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\keras\models.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_q_size, workers, pickle_safe, initial_epoch)
1108 workers=workers,
1109 pickle_safe=pickle_safe,
-> 1110 initial_epoch=initial_epoch)
1111
1112 @interfaces.legacy_generator_methods_support
C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\keras\legacy\interfaces.py in wrapper(*args, **kwargs)
86 warnings.warn('Update your `' + object_name +
87 '` call to the Keras 2 API: ' + signature, stacklevel=2)
---> 88 return func(*args, **kwargs)
89 wrapper._legacy_support_signature = inspect.getargspec(func)
90 return wrapper
C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\keras\engine\training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_q_size, workers, pickle_safe, initial_epoch)
1888 outs = self.train_on_batch(x, y,
1889 sample_weight=sample_weight,
-> 1890 class_weight=class_weight)
1891
1892 if not isinstance(outs, list):
C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\keras\engine\training.py in train_on_batch(self, x, y, sample_weight, class_weight)
1631 ins = x + y + sample_weights
1632 self._make_train_function()
-> 1633 outputs = self.train_function(ins)
1634 if len(outputs) == 1:
1635 return outputs[0]
C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\keras\backend\tensorflow_backend.py in __call__(self, inputs)
2227 session = get_session()
2228 updated = session.run(self.outputs + [self.updates_op],
-> 2229 feed_dict=feed_dict)
2230 return updated[:len(self.outputs)]
2231
C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py in run(self, fetches, feed_dict, options, run_metadata)
776 try:
777 result = self._run(None, fetches, feed_dict, options_ptr,
--> 778 run_metadata_ptr)
779 if run_metadata:
780 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
980 if final_fetches or final_targets:
981 results = self._do_run(handle, final_targets, final_fetches,
--> 982 feed_dict_string, options, run_metadata)
983 else:
984 results = []
C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
1030 if handle is None:
1031 return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
-> 1032 target_list, options, run_metadata)
1033 else:
1034 return self._do_call(_prun_fn, self._session, handle, feed_dict,
C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py in _do_call(self, fn, *args)
1050 except KeyError:
1051 pass
-> 1052 raise type(e)(node_def, op, message)
1053
1054 def _extend_graph(self):
InternalError: Blas GEMM launch failed : a.shape=(64, 784), b.shape=(784, 10), m=64, n=10, k=784
[[Node: dense_1/MatMul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device="/job:localhost/replica:0/task:0/gpu:0"](flatten_1/Reshape, dense_1/kernel/read)]]
[[Node: Assign_3/_84 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_374_Assign_3", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
Caused by op 'dense_1/MatMul', defined at:
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\ipykernel\__main__.py", line 3, in <module>
app.launch_new_instance()
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\traitlets\config\application.py", line 658, in launch_instance
app.start()
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\ipykernel\kernelapp.py", line 477, in start
ioloop.IOLoop.instance().start()
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\zmq\eventloop\ioloop.py", line 177, in start
super(ZMQIOLoop, self).start()
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\tornado\ioloop.py", line 888, in start
handler_func(fd_obj, events)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\tornado\stack_context.py", line 277, in null_wrapper
return fn(*args, **kwargs)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\zmq\eventloop\zmqstream.py", line 440, in _handle_events
self._handle_recv()
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\zmq\eventloop\zmqstream.py", line 472, in _handle_recv
self._run_callback(callback, msg)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\zmq\eventloop\zmqstream.py", line 414, in _run_callback
callback(*args, **kwargs)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\tornado\stack_context.py", line 277, in null_wrapper
return fn(*args, **kwargs)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\ipykernel\kernelbase.py", line 283, in dispatcher
return self.dispatch_shell(stream, msg)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\ipykernel\kernelbase.py", line 235, in dispatch_shell
handler(stream, idents, msg)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\ipykernel\kernelbase.py", line 399, in execute_request
user_expressions, allow_stdin)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\ipykernel\ipkernel.py", line 196, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\ipykernel\zmqshell.py", line 533, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\IPython\core\interactiveshell.py", line 2683, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\IPython\core\interactiveshell.py", line 2787, in run_ast_nodes
if self.run_code(code, result):
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\IPython\core\interactiveshell.py", line 2847, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-12-1e7a3b259f23>", line 4, in <module>
model.add(Dense(10, activation='softmax'))
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\keras\models.py", line 466, in add
output_tensor = layer(self.outputs[0])
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\keras\engine\topology.py", line 585, in __call__
output = self.call(inputs, **kwargs)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\keras\layers\core.py", line 840, in call
output = K.dot(inputs, self.kernel)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\keras\backend\tensorflow_backend.py", line 936, in dot
out = tf.matmul(x, y)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\ops\math_ops.py", line 1801, in matmul
a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\ops\gen_math_ops.py", line 1263, in _mat_mul
transpose_b=transpose_b, name=name)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 768, in apply_op
op_def=op_def)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\framework\ops.py", line 2336, in create_op
original_op=self._default_original_op, op_def=op_def)
File "C:\Users\nicol\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\framework\ops.py", line 1228, in __init__
self._traceback = _extract_stack()
InternalError (see above for traceback): Blas GEMM launch failed : a.shape=(64, 784), b.shape=(784, 10), m=64, n=10, k=784
[[Node: dense_1/MatMul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device="/job:localhost/replica:0/task:0/gpu:0"](flatten_1/Reshape, dense_1/kernel/read)]]
[[Node: Assign_3/_84 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_374_Assign_3", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
In both cases, the error is with InternalError (see above for traceback): Blas GEMM launch failed Can you tell me how to get Blas GEMM to launch? I installed tensorflow and keras in a 3.5 python anaconda environment where I also installed all needed module (numpy, pandas, scipy, scikit-learn). I have a Windows 10 with a NVIDIA gpu that can use CUDA. I downloaded CUDA and cuDNN. I'm using the Jupyter notebook on Chrome.
在这两种情况下，错误都与 InternalError 有关（回溯见上文）： Blas GEMM 启动失败 你能告诉我如何让 Blas GEMM 启动吗？我在 3.5 python anaconda 环境中安装了 tensorflow 和 keras，在那里我还安装了所有需要的模块（numpy、pandas、scipy、scikit-learn）。我有一个带有 NVIDIA GPU 的 Windows 10，可以使用 CUDA。我下载了 CUDA 和 cuDNN。我在 Chrome 上使用 Jupyter 笔记本。
Sometimes when I run my code, rather than having this error, I get that it starts running and then it crashes. After the crash, I can't do anything on my jupyter notebook and after some time a pop-up asks me if I want to kill the page. This is an image of what I got after the crash. !(http://www.hostingpics.net/viewer.php?id=647186tensorflowError.png)
有时当我运行我的代码时，而不是出现这个错误，我知道它开始运行然后崩溃。崩溃后，我无法在我的 jupyter 笔记本上做任何事情，一段时间后，一个弹出窗口询问我是否要终止该页面。这是我在崩溃后得到的图像。!( http://www.hostingpics.net/viewer.php?id=647186tensorflowError.png)
P.S. I know my problem is similar as in this question: Tensorflow Basic Example Error: CUBLAS_STATUS_NOT_INITIALIZEDbut it has not been solved there and I'm not sure this question is clear enough or is exactly the same problem as I have so I'm posting it with my own error message. This problem is different of: TensorFlow: InternalError: Blas SGEMM launch failedSince I have a problem with GEMM rather than SGEMM and that my problem is both with gpu and cpu and it is not solved by the answer of this question.
PS 我知道我的问题与这个问题类似： Tensorflow Basic Example Error: CUBLAS_STATUS_NOT_INITIALIZED但它没有在那里解决，我不确定这个问题是否足够清楚或者与我遇到的问题完全一样，所以我发布它带有我自己的错误消息。此问题与以下内容不同： TensorFlow：InternalError：Blas SGEMM 启动失败因为我遇到了 GEMM 而不是 SGEMM 的问题，而且我的问题同时与 gpu 和 cpu 有关，并且无法通过此问题的答案解决。
回答by Linda MacPhee-Cobb
It's a simple fix, but it was a nightmare to figure it all out
这是一个简单的修复，但要弄清楚这一切是一场噩梦
On Windows I found the Keras install in Anaconda3\Lib\site-packages\keras
在 Windows 上，我在 Anaconda3\Lib\site-packages\keras 中找到了 Keras 安装
sources:
来源：
https://www.tensorflow.org/guide/using_gpu
https://www.tensorflow.org/guide/using_gpu
https://github.com/keras-team/keras/blob/master/keras/backend/tensorflow_backend.py
https://github.com/keras-team/keras/blob/master/keras/backend/tensorflow_backend.py
Find the following in your keras/tensorflow_backend.py file you'll add config.gpu_options.allow_growth= True in both places
在您的 keras/tensorflow_backend.py 文件中找到以下内容，您将在两个地方添加 config.gpu_options.allow_growth= True
if _SESSION is None:
if not os.environ.get('OMP_NUM_THREADS'):
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth=True
else:
num_thread = int(os.environ.get('OMP_NUM_THREADS'))
config = tf.ConfigProto(intra_op_parallelism_threads=num_thread,
allow_soft_placement=True)
config.gpu_options.allow_growth=True
_SESSION = tf.Session(config=config)
session = _SESSION
回答by socasanta
This worked for me on TensorFlow 2.1.0 (per: https://www.tensorflow.org/api_docs/python/tf/config/experimental/set_memory_growth)
这在 TensorFlow 2.1.0 上对我有用（每个：https: //www.tensorflow.org/api_docs/python/tf/config/experimental/set_memory_growth）
import tensorflow as tf
physical_devices = tf.config.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(physical_devices[0], True)
回答by fotis j
Had the same error. Maybe it is related to the problem that tensorflow is allocating all gpu memory. But the fix recommended there didn't work for me and it is not possible yet to limit tensorflow's gpu memory use via keras.json or commandline. Switching keras' backend to Theano resolved the issue for me (howto can be found here).
有同样的错误。可能与tensorflow正在分配所有gpu内存的问题有关。但是那里推荐的修复程序对我不起作用，并且还不可能通过 keras.json 或命令行限制 tensorflow 的 gpu 内存使用。将 keras 的后端切换到 Theano 为我解决了这个问题（方法可以在这里找到）。
回答by Michael Yadidya
This Answer is much related to Tensorflow:
这个答案与Tensorflow有很大关系：
Sometimes Tensorflow fails at creation in Windows.
有时 Tensorflow 在 Windows 中创建时会失败。
Restarting the notebook using gpu solves it in most cases
大多数情况下使用gpu重启笔记本即可解决
If it doesnt then try restarting the notebook after adding these options in your code.
如果没有，请在代码中添加这些选项后尝试重新启动笔记本。
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.9)
tf.Session(config=tf.ConfigProto(gpu_options=gpu_options,allow_soft_placement=True)
I never had such error while using KerasBut try restarting your notebook
我在使用Keras 时从未出现过这样的错误，但请尝试重新启动您的笔记本
回答by liwy
Make sure you have no other processes using the GPU running. Run nvidia-smi to check this.
确保没有其他进程使用 GPU 运行。运行 nvidia-smi 来检查这一点。
SOURCE:An issue brought up by @reedwm.
来源：@reedwm 提出的问题。
回答by Edgar H
I ran into this problem when trying to run several servers that use a model to serve predictions. As I wasn't training a model but simply using it, the difference between using GPU or CPU was minor. For this specific case, the issue can be avoided by forcing Tensorflow to use the CPU by "hiding" the GPU.
我在尝试运行多个使用模型进行预测的服务器时遇到了这个问题。由于我不是在训练模型而只是使用它，因此使用 GPU 或 CPU 之间的区别很小。对于这种特定情况，可以通过“隐藏”GPU 强制 Tensorflow 使用 CPU 来避免该问题。
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "-1" # Force TF to use only the CPU
回答by envi z
I was stuck with this for days and I was able to get rid of this error eventually.
我被这个问题困扰了好几天，最终我摆脱了这个错误。
I had the wrong versions of tensorflow and cuda installed in my PC. Just make sure you have the right versions of tf,cuda and cudnn installed.
我的 PC 中安装了错误版本的 tensorflow 和 cuda。只要确保您安装了正确版本的 tf、cuda 和 cudnn。
https://i.stack.imgur.com/Laiii.png
https://i.stack.imgur.com/Laiii.png
Use this link for reference.
使用此链接作为参考。
回答by xiaxia wang
I have got the same error,lucky,I have got it fixed.
my error is: the last time,I open the tensorflow sess = tf.Session(),but I forgot close the session.
我遇到了同样的错误，幸运的是，我已经解决了。我的错误是：最后一次，我打开了 tensorflow sess = tf.Session()，但我忘了关闭会话。
so I open the terminal, type command:
所以我打开终端，输入命令：
ps -aux | grep program_name
find the PID,and type command kill the PID:
找到PID，然后输入命令kill PID：
kill -9 PID
Ok,the GPU is realase.
好的，GPU 是 realase。
回答by Tairone
I was getting exactly the same error message. I realized that there was an error with my CUDA installation, specifically with the cuBLASlibrary.
我收到完全相同的错误消息。我意识到我的 CUDA 安装有错误，特别是cuBLAS库。
You can check if yours has the same problem by running the sample program simpleCUBLAS(it comes with the CUDA installation, you will probably find it in the CUDA home folder:$CUDA_HOME\samples\7_CUDALibraries\simpleCUBLAS)
您可以检查是否有你通过运行示例程序同样的问题simpleCUBLAS（它带有CUDA安装，你可能会发现它在CUDA主文件夹：$CUDA_HOME\samples\7_CUDALibraries\simpleCUBLAS）
Try running this program. If the test fails, you have a problem with your CUDA installation. You should try to reinstall it. That's how I solved the same problem here.
试试运行这个程序。如果测试失败，则您的 CUDA 安装存在问题。您应该尝试重新安装它。这就是我在这里解决同样问题的方法。
Renaming cublas64_10.dll to cublas64_100.dll may be a solution.
将 cublas64_10.dll 重命名为 cublas64_100.dll 可能是一个解决方案。
回答by Tairone
Try running the sample program simpleCUBLAS (it comes with CUDA) to test your CUBLAS installation and see if it works.
尝试运行示例程序 simpleCUBLAS（它随 CUDA 一起提供）来测试您的 CUBLAS 安装并查看它是否有效。
In my case (I am using Ubuntu) I had to reinstall CUDA to solve this issue. After I did that, simpleCUBLAS passed the test.
就我而言（我使用的是 Ubuntu），我不得不重新安装 CUDA 来解决这个问题。在我这样做之后，simpleCUBLAS 通过了测试。
For some reason I started running into the same issue after a while, and I found that cleaning the directory .nv (inside my home folder) resolved the issue, and simpleCUBLAS test passed again.
出于某种原因，一段时间后我开始遇到同样的问题，我发现清理 .nv 目录（在我的主文件夹内）解决了这个问题，并且 simpleCUBLAS 测试再次通过。
![带有异步定义的 Python [无效语法]](/res/img/loading.gif)