Neural Network Python Error "Failed to get convolution algorithm"

Question

I am trying to run some neural network code in Python. I had it working okay on a Google Colab. I then moved the code to a Jupyter Notebook on a remote machine GPU.

It runs okay until I try to fit the model using:

history = model.fit_generator(generator=training_generator, validation_data=validation_generator, use_multiprocessing=True, workers=1, epochs=100, shuffle=True, verbose=1)

The full error message follows. I just don't know where to begin understanding what it means, so I'm looking for help. Thanks in advance:

UnknownError                              Traceback (most recent call last)
<ipython-input-15-d3d33225fec8> in <module>
      1 # Train model on dataset
----> 2 history = model.fit_generator(generator=training_generator, validation_data=validation_generator, use_multiprocessing=True, workers=1, epochs=100, shuffle=True, verbose=1)

~/miniconda3/lib/python3.7/site-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
     89                 warnings.warn('Update your `' + object_name + '` call to the ' +
     90                               'Keras 2 API: ' + signature, stacklevel=2)
---> 91             return func(*args, **kwargs)
     92         wrapper._original_function = func
     93         return wrapper

~/miniconda3/lib/python3.7/site-packages/keras/engine/training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
   1416             use_multiprocessing=use_multiprocessing,
   1417             shuffle=shuffle,
-> 1418             initial_epoch=initial_epoch)
   1419 
   1420     @interfaces.legacy_generator_methods_support

~/miniconda3/lib/python3.7/site-packages/keras/engine/training_generator.py in fit_generator(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
    215                 outs = model.train_on_batch(x, y,
    216                                             sample_weight=sample_weight,
--> 217                                             class_weight=class_weight)
    218 
    219                 outs = to_list(outs)

~/miniconda3/lib/python3.7/site-packages/keras/engine/training.py in train_on_batch(self, x, y, sample_weight, class_weight)
   1215             ins = x + y + sample_weights
   1216         self._make_train_function()
-> 1217         outputs = self.train_function(ins)
   1218         return unpack_singleton(outputs)
   1219 

~/miniconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py in __call__(self, inputs)
   2713                 return self._legacy_call(inputs)
   2714 
-> 2715             return self._call(inputs)
   2716         else:
   2717             if py_any(is_tensor(x) for x in inputs):

~/miniconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py in _call(self, inputs)
   2673             fetched = self._callable_fn(*array_vals, run_metadata=self.run_metadata)
   2674         else:
-> 2675             fetched = self._callable_fn(*array_vals)
   2676         return fetched[:len(self.outputs)]
   2677 

~/miniconda3/lib/python3.7/site-packages/tensorflow/python/client/session.py in __call__(self, *args, **kwargs)
   1437           ret = tf_session.TF_SessionRunCallable(
   1438               self._session._session, self._handle, args, status,
-> 1439               run_metadata_ptr)
   1440         if run_metadata:
   1441           proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

~/miniconda3/lib/python3.7/site-packages/tensorflow/python/framework/errors_impl.py in __exit__(self, type_arg, value_arg, traceback_arg)
    526             None, None,
    527             compat.as_text(c_api.TF_Message(self.status.status)),
--> 528             c_api.TF_GetCode(self.status.status))
    529     # Delete the underlying status object from memory otherwise it stays alive
    530     # as there is a reference to status from this from the traceback due to

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.
     [[{{node conv2d_1/convolution}}]]
     [[{{node metrics/acc/Mean}}]]

I have seen this error whenever CUDNN is not compatible with the CUDA version. Have you checked if your CUDNN, CUDA, Driver are compatible? — thushv89
– thushv89, Commented Dec 5, 2019 at 22:26
I'm not sure how to - but thanks for giving me a place to start :) — user1551817
– user1551817, Commented Dec 5, 2019 at 22:47
This page might be useful tensorflow.org/install/gpu . But might involve some trial and error — thushv89
– thushv89, Commented Dec 5, 2019 at 22:52

y.selivonchyk · Accepted Answer · 2019-12-06 06:19:04Z

1

As @thushv89 says, this is an issue with compatibility of TF binary and installed CUDNN version.

You can check your tensorflow version using:

python -c 'import tensorflow as tf; print(tf.__version__);'

Than check required CUDA/CUDNN version here: https://www.tensorflow.org/install/source#tested_build_configurations

Note: indicated CUDA/CUDNN versions are only relevant for official distribution of TF. For conda there should be a better way to deal with it.

Then you can check you CUDA version:

nvcc --version

Then check your CUDNN version using one of the following:

cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2
cat /usr/include/cudnn.h | grep CUDNN_MAJOR -A 2

answered Dec 6, 2019 at 6:19

y.selivonchyk

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