Hi
I’m using an EC2 Deep Learning Windows 10 g2.2xlarge instance. I have this problem when I try to run an implementation within jupyter notebook,
in the Terminal:
“cudaGetDevice () failed. Status: CUDA driver version is insufficient for CUDA runtime version”
This is my code:
from keras.applications.resnet50 import ResNet50
define ResNet50 model
ResNet50_model = ResNet50(weights=‘imagenet’)
output:
InternalError Traceback (most recent call last)
in ()
2
3 # define ResNet50 model
----> 4 ResNet50_model = ResNet50(weights=‘imagenet’)
C:\ProgramData\Anaconda3\envs\MXNet\lib\site-packages\keras_applications\resnet50.py in ResNet50(include_top, weights, input_tensor, input_shape, pooling, classes)
215 padding=‘valid’,
216 name=‘conv1’)(x)
→ 217 x = layers.BatchNormalization(axis=bn_axis, name=‘bn_conv1’)(x)
218 x = layers.Activation(‘relu’)(x)
219 x = layers.MaxPooling2D((3, 3), strides=(2, 2))(x)
C:\ProgramData\Anaconda3\envs\MXNet\lib\site-packages\keras\engine\base_layer.py in call(self, inputs, **kwargs)
458 # Actually call the layer,
459 # collecting output(s), mask(s), and shape(s).
→ 460 output = self.call(inputs, **kwargs)
461 output_mask = self.compute_mask(inputs, previous_mask)
462
C:\ProgramData\Anaconda3\envs\MXNet\lib\site-packages\keras\layers\normalization.py in call(self, inputs, training)
181 normed_training, mean, variance = K.normalize_batch_in_training(
182 inputs, self.gamma, self.beta, reduction_axes,
→ 183 epsilon=self.epsilon)
184
185 if K.backend() != ‘cntk’:
C:\ProgramData\Anaconda3\envs\MXNet\lib\site-packages\keras\backend\tensorflow_backend.py in normalize_batch_in_training(x, gamma, beta, reduction_axes, epsilon)
1833 “”"
1834 if ndim(x) == 4 and list(reduction_axes) in [[0, 1, 2], [0, 2, 3]]:
→ 1835 if not _has_nchw_support() and list(reduction_axes) == [0, 2, 3]:
1836 return _broadcast_normalize_batch_in_training(x, gamma, beta,
1837 reduction_axes,
C:\ProgramData\Anaconda3\envs\MXNet\lib\site-packages\keras\backend\tensorflow_backend.py in _has_nchw_support()
287 “”"
288 explicitly_on_cpu = _is_current_explicit_device(‘CPU’)
→ 289 gpus_available = len(_get_available_gpus()) > 0
290 return (not explicitly_on_cpu and gpus_available)
291
C:\ProgramData\Anaconda3\envs\MXNet\lib\site-packages\keras\backend\tensorflow_backend.py in _get_available_gpus()
273 global _LOCAL_DEVICES
274 if _LOCAL_DEVICES is None:
→ 275 _LOCAL_DEVICES = get_session().list_devices()
276 return [x.name for x in _LOCAL_DEVICES if x.device_type == ‘GPU’]
277
C:\ProgramData\Anaconda3\envs\MXNet\lib\site-packages\keras\backend\tensorflow_backend.py in get_session()
181 config = tf.ConfigProto(intra_op_parallelism_threads=num_thread,
182 allow_soft_placement=True)
→ 183 _SESSION = tf.Session(config=config)
184 session = _SESSION
185 if not _MANUAL_VAR_INIT:
C:\ProgramData\Anaconda3\envs\MXNet\lib\site-packages\tensorflow\python\client\session.py in init(self, target, graph, config)
1558
1559 “”"
→ 1560 super(Session, self).init(target, graph, config=config)
1561 # NOTE(mrry): Create these on first __enter__ to avoid a reference cycle.
1562 self._default_graph_context_manager = None
C:\ProgramData\Anaconda3\envs\MXNet\lib\site-packages\tensorflow\python\client\session.py in init(self, target, graph, config)
631 if self._created_with_new_api:
632 # pylint: disable=protected-access
→ 633 self._session = tf_session.TF_NewSession(self._graph._c_graph, opts)
634 # pylint: enable=protected-access
635 else:
InternalError: Failed to create session.
How can i solve this problem?