UnknownError: Failed to get convolution algorithm

Question

Complete Error :

UnknownError: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above. [Op:Conv2D]

command used for package installation :

conda install -c anaconda keras-gpu

It installed :

• tensorboard 2.0.0 pyhb38c66f_1
• tensorflow 2.0.0 gpu_py37h57d29ca_0
• tensorflow-base 2.0.0 gpu_py37h390e234_0
• tensorflow-estimator 2.0.0 pyh2649769_0
• tensorflow-gpu 2.0.0 h0d30ee6_0 anaconda
• cudatoolkit 10.0.130 0
• cudnn 7.6.5 cuda10.0_0
• keras-applications 1.0.8 py_0
• keras-base 2.2.4 py37_0
• keras-gpu 2.2.4 0 anaconda
• keras-preprocessing 1.1.0 py_1

I have tried installing cuda-toolkit from nvidia website it did not resolved the issue so do suggest related to conda commands.

Some blogs suggesting installing visual studio but what is the need if I have spyder IDE ?

Code :

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Convolution2D
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Dense

classifier = Sequential()

classifier.add(Convolution2D(32, 3, 3, input_shape = (64, 64, 3), activation = 'relu'))

classifier.add(MaxPooling2D(pool_size = (2, 2)))

classifier.add(Convolution2D(32, 3, 3, activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))

classifier.add(Flatten())

classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dense(units = 1, activation = 'sigmoid'))

classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])

from tensorflow.keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(rescale = 1./255,
                                   shear_range = 0.2,
                                   zoom_range = 0.2,
                                   horizontal_flip = True)

test_datagen = ImageDataGenerator(rescale = 1./255)

training_set = train_datagen.flow_from_directory('dataset/training_set',
                                                 target_size = (64, 64),
                                                 batch_size = 4,
                                                 class_mode = 'binary')

test_set = test_datagen.flow_from_directory('dataset/test_set',
                                            target_size = (64, 64),
                                            batch_size = 4,
                                            class_mode = 'binary')

classifier.fit_generator(training_set,
                         steps_per_epoch = 8000,
                         epochs = 25,
                         validation_data = test_set,
                         validation_steps = 2000)

After executing code below I am getting error :

classifier.fit_generator(training_set,
                             steps_per_epoch = 8000,
                             epochs = 25,
                             validation_data = test_set,
                             validation_steps = 2000)

edit 1 : Traceback

Traceback (most recent call last):

  File "D:\Machine Learning\Machine Learning A-Z Template Folder\Part 8 - Deep Learning\Section 40 - Convolutional Neural Networks (CNN)\cnn.py", line 70, in <module>
    validation_steps = 2000)

  File "C:\Anaconda\envs\ML\lib\site-packages\tensorflow_core\python\keras\engine\training.py", line 1297, in fit_generator
    steps_name='steps_per_epoch')

  File "C:\Anaconda\envs\ML\lib\site-packages\tensorflow_core\python\keras\engine\training_generator.py", line 265, in model_iteration
    batch_outs = batch_function(*batch_data)

  File "C:\Anaconda\envs\ML\lib\site-packages\tensorflow_core\python\keras\engine\training.py", line 973, in train_on_batch
    class_weight=class_weight, reset_metrics=reset_metrics)

  File "C:\Anaconda\envs\ML\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py", line 264, in train_on_batch
    output_loss_metrics=model._output_loss_metrics)

  File "C:\Anaconda\envs\ML\lib\site-packages\tensorflow_core\python\keras\engine\training_eager.py", line 311, in train_on_batch
    output_loss_metrics=output_loss_metrics))

  File "C:\Anaconda\envs\ML\lib\site-packages\tensorflow_core\python\keras\engine\training_eager.py", line 252, in _process_single_batch
    training=training))

  File "C:\Anaconda\envs\ML\lib\site-packages\tensorflow_core\python\keras\engine\training_eager.py", line 127, in _model_loss
    outs = model(inputs, **kwargs)

  File "C:\Anaconda\envs\ML\lib\site-packages\tensorflow_core\python\keras\engine\base_layer.py", line 891, in __call__
    outputs = self.call(cast_inputs, *args, **kwargs)

  File "C:\Anaconda\envs\ML\lib\site-packages\tensorflow_core\python\keras\engine\sequential.py", line 256, in call
    return super(Sequential, self).call(inputs, training=training, mask=mask)

  File "C:\Anaconda\envs\ML\lib\site-packages\tensorflow_core\python\keras\engine\network.py", line 708, in call
    convert_kwargs_to_constants=base_layer_utils.call_context().saving)

  File "C:\Anaconda\envs\ML\lib\site-packages\tensorflow_core\python\keras\engine\network.py", line 860, in _run_internal_graph
    output_tensors = layer(computed_tensors, **kwargs)

  File "C:\Anaconda\envs\ML\lib\site-packages\tensorflow_core\python\keras\engine\base_layer.py", line 891, in __call__
    outputs = self.call(cast_inputs, *args, **kwargs)

  File "C:\Anaconda\envs\ML\lib\site-packages\tensorflow_core\python\keras\layers\convolutional.py", line 197, in call
    outputs = self._convolution_op(inputs, self.kernel)

  File "C:\Anaconda\envs\ML\lib\site-packages\tensorflow_core\python\ops\nn_ops.py", line 1134, in __call__
    return self.conv_op(inp, filter)

  File "C:\Anaconda\envs\ML\lib\site-packages\tensorflow_core\python\ops\nn_ops.py", line 639, in __call__
    return self.call(inp, filter)

  File "C:\Anaconda\envs\ML\lib\site-packages\tensorflow_core\python\ops\nn_ops.py", line 238, in __call__
    name=self.name)

  File "C:\Anaconda\envs\ML\lib\site-packages\tensorflow_core\python\ops\nn_ops.py", line 2010, in conv2d
    name=name)

  File "C:\Anaconda\envs\ML\lib\site-packages\tensorflow_core\python\ops\gen_nn_ops.py", line 1031, in conv2d
    data_format=data_format, dilations=dilations, name=name, ctx=_ctx)

  File "C:\Anaconda\envs\ML\lib\site-packages\tensorflow_core\python\ops\gen_nn_ops.py", line 1130, in conv2d_eager_fallback
    ctx=_ctx, name=name)

  File "C:\Anaconda\envs\ML\lib\site-packages\tensorflow_core\python\eager\execute.py", line 67, in quick_execute
    six.raise_from(core._status_to_exception(e.code, message), None)

  File "<string>", line 3, in raise_from

UnknownError: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above. [Op:Conv2D]

Answer 1

Below code solved the issue :

import tensorflow as tf
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
    try:
        for gpu in gpus:
            tf.config.experimental.set_memory_growth(gpu, True)

    except RuntimeError as e:
        print(e)

Answer 2

The error comes from the fact that there is an incompatibility between the:

• CUDA version
• CuDNN version
• TensorFlow version

In the answer below I have provided working combinations of tensorflow, cuda and cudnn. Please have a look at the question which is similar to yours: Tensorflow 2.0 can't use GPU, something wrong in cuDNN? :Failed to get convolution algorithm. This is probably because cuDNN failed to initialize

Eg. Cuda 10.0 + CuDNN 7.6.3 + / TensorFlow 1.13/1.14 / TensorFlow 2.0.

Eg2 Cuda 9 + CuDNN 7.0.5 + TensorFlow 1.10 works