UnimplementedError DNN library is not found when running a Conv1D layer

[bluetail14]

Windows 10
cudnn 8.1.1.33, CUDA 11.2.2_461.33
Nvidia driver 11.2.109
tensorflow 2.8.0
Python 3.9.12
Anaconda environment
Nvidia GeForece GPU RTX 3060, 16GB RAM, 512GB SSD

UnimplementedError Node: 'sequential/conv1d/Conv1D'
DNN library is not found.

# Parameters
embedding_dim = 16
filters = 128
kernel_size = 5
dense_dim = 6

# Model Definition with Conv1D
model_conv = tf.keras.Sequential([
    tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),
    tf.keras.layers.Conv1D(filters, kernel_size, activation='relu'),
    tf.keras.layers.GlobalMaxPooling1D(),
    tf.keras.layers.Dense(dense_dim, activation='relu'),
    tf.keras.layers.Dense(1, activation='sigmoid')
])

# Set the training parameters
model_conv.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])

# Print the model summary
model_conv.summary()

Model: "sequential"

_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 embedding (Embedding)       (None, 120, 16)           160000    
                                                                 
 conv1d (Conv1D)             (None, 116, 128)          10368     
                                                                 
 global_max_pooling1d (Globa  (None, 128)              0         
 lMaxPooling1D)                                                  
                                                                 
 dense (Dense)               (None, 6)                 774       
                                                                 
 dense_1 (Dense)             (None, 1)                 7         
                                                                 
=================================================================

Total params: 171,149
Trainable params: 171,149
Non-trainable params: 0

NUM_EPOCHS = 10

# Train the model
history_conv = model_conv.fit(training_padded, training_labels, epochs=NUM_EPOCHS, validation_data=(testing_padded, testing_labels))

Epoch 1/10

UnimplementedError Traceback (most recent call last)
Input In [6], in <cell line: 4>()
1 NUM_EPOCHS = 10
3 # Train the model
----> 4 history_conv = model_conv.fit(training_padded, training_labels, epochs=NUM_EPOCHS, validation_data=(testing_padded, testing_labels))

File ~.conda\envs\tf-gpu\lib\site-packages\keras\utils\traceback_utils.py:67, in filter_traceback..error_handler(*args, **kwargs)
65 except Exception as e: # pylint: disable=broad-except
66 filtered_tb = _process_traceback_frames(e.traceback)
---> 67 raise e.with_traceback(filtered_tb) from None
68 finally:
69 del filtered_tb

File ~.conda\envs\tf-gpu\lib\site-packages\tensorflow\python\eager\execute.py:54, in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
52 try:
53 ctx.ensure_initialized()
---> 54 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
55 inputs, attrs, num_outputs)
56 except core._NotOkStatusException as e:
57 if name is not None:

UnimplementedError: Graph execution error:

Detected at node 'sequential/conv1d/Conv1D' defined at (most recent call last):
File "C:\Users\me.conda\envs\tf-gpu\lib\runpy.py", line 197, in _run_module_as_main
return _run_code(code, main_globals, None,
File "C:\Users\me.conda\envs\tf-gpu\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "C:\Users\me.conda\envs\tf-gpu\lib\site-packages\ipykernel_launcher.py", line 16, in
app.launch_new_instance()
File "C:\Users\me.conda\envs\tf-gpu\lib\site-packages\traitlets\config\application.py", line 846, in launch_instance
app.start()
File "C:\Users\me.conda\envs\tf-gpu\lib\site-packages\ipykernel\kernelapp.py", line 677, in start
self.io_loop.start()
File "C:\Users\me.conda\envs\tf-gpu\lib\site-packages\tornado\platform\asyncio.py", line 199, in start
self.asyncio_loop.run_forever()
File "C:\Users\me.conda\envs\tf-gpu\lib\asyncio\base_events.py", line 601, in run_forever
self._run_once()
File "C:\Users\me.conda\envs\tf-gpu\lib\asyncio\base_events.py", line 1905, in _run_once
handle._run()
File "C:\Users\me.conda\envs\tf-gpu\lib\asyncio\events.py", line 80, in _run
self._context.run(self._callback, *self._args)
File "C:\Users\me.conda\envs\tf-gpu\lib\site-packages\ipykernel\kernelbase.py", line 473, in dispatch_queue
await self.process_one()
File "C:\Users\me.conda\envs\tf-gpu\lib\site-packages\ipykernel\kernelbase.py", line 462, in process_one
await dispatch(*args)
File "C:\Users\me.conda\envs\tf-gpu\lib\site-packages\ipykernel\kernelbase.py", line 369, in dispatch_shell
await result
File "C:\Users\me.conda\envs\tf-gpu\lib\site-packages\ipykernel\kernelbase.py", line 664, in execute_request
reply_content = await reply_content
File "C:\Users\me.conda\envs\tf-gpu\lib\site-packages\ipykernel\ipkernel.py", line 355, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "C:\Users\me.conda\envs\tf-gpu\lib\site-packages\ipykernel\zmqshell.py", line 532, in run_cell
return super().run_cell(*args, **kwargs)
File "C:\Users\me.conda\envs\tf-gpu\lib\site-packages\IPython\core\interactiveshell.py", line 2863, in run_cell
result = self._run_cell(
File "C:\Users\me.conda\envs\tf-gpu\lib\site-packages\IPython\core\interactiveshell.py", line 2909, in _run_cell
return runner(coro)
File "C:\Users\me.conda\envs\tf-gpu\lib\site-packages\IPython\core\async_helpers.py", line 129, in pseudo_sync_runner
coro.send(None)
File "C:\Users\me.conda\envs\tf-gpu\lib\site-packages\IPython\core\interactiveshell.py", line 3106, in run_cell_async
has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
File "C:\Users\me.conda\envs\tf-gpu\lib\site-packages\IPython\core\interactiveshell.py", line 3309, in run_ast_nodes
if await self.run_code(code, result, async=asy):
File "C:\Users\me.conda\envs\tf-gpu\lib\site-packages\IPython\core\interactiveshell.py", line 3369, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "C:\Users\me\AppData\Local\Temp\ipykernel_9748\331718196.py", line 4, in <cell line: 4>
history_conv = model_conv.fit(training_padded, training_labels, epochs=NUM_EPOCHS, validation_data=(testing_padded, testing_labels))
File "C:\Users\me.conda\envs\tf-gpu\lib\site-packages\keras\utils\traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "C:\Users\me.conda\envs\tf-gpu\lib\site-packages\keras\engine\training.py", line 1384, in fit
tmp_logs = self.train_function(iterator)
File "C:\Users\me.conda\envs\tf-gpu\lib\site-packages\keras\engine\training.py", line 1021, in train_function
return step_function(self, iterator)
File "C:\Users\me.conda\envs\tf-gpu\lib\site-packages\keras\engine\training.py", line 1010, in step_function
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "C:\Users\me.conda\envs\tf-gpu\lib\site-packages\keras\engine\training.py", line 1000, in run_step
outputs = model.train_step(data)
File "C:\Users\me.conda\envs\tf-gpu\lib\site-packages\keras\engine\training.py", line 859, in train_step
y_pred = self(x, training=True)
File "C:\Users\me.conda\envs\tf-gpu\lib\site-packages\keras\utils\traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "C:\Users\me.conda\envs\tf-gpu\lib\site-packages\keras\engine\base_layer.py", line 1096, in call
outputs = call_fn(inputs, *args, **kwargs)
File "C:\Users\me.conda\envs\tf-gpu\lib\site-packages\keras\utils\traceback_utils.py", line 92, in error_handler
return fn(*args, **kwargs)
File "C:\Users\me.conda\envs\tf-gpu\lib\site-packages\keras\engine\sequential.py", line 374, in call
return super(Sequential, self).call(inputs, training=training, mask=mask)
File "C:\Users\me.conda\envs\tf-gpu\lib\site-packages\keras\engine\functional.py", line 451, in call
return self._run_internal_graph(
File "C:\Users\me.conda\envs\tf-gpu\lib\site-packages\keras\engine\functional.py", line 589, in _run_internal_graph
outputs = node.layer(*args, **kwargs)
File "C:\Users\me.conda\envs\tf-gpu\lib\site-packages\keras\utils\traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "C:\Users\me.conda\envs\tf-gpu\lib\site-packages\keras\engine\base_layer.py", line 1096, in call
outputs = call_fn(inputs, *args, **kwargs)
File "C:\Users\me.conda\envs\tf-gpu\lib\site-packages\keras\utils\traceback_utils.py", line 92, in error_handler
return fn(*args, **kwargs)
File "C:\Users\me.conda\envs\tf-gpu\lib\site-packages\keras\layers\convolutional.py", line 248, in call
outputs = self.convolution_op(inputs, self.kernel)
File "C:\Users\me.conda\envs\tf-gpu\lib\site-packages\keras\layers\convolutional.py", line 233, in convolution_op
return tf.nn.convolution(
Node: 'sequential/conv1d/Conv1D'
DNN library is not found.
[[{{node sequential/conv1d/Conv1D}}]] [Op:__inference_train_function_842]

[tilakrayal]

@bluetail14 ,
Every TensorFlow release is compatible with a certain version, for more information I request to take a look at the tested build configurations.
Also we see that you are trying to install using anaconda.I request you to try installing the tensorflow using pip/docker from this doc link.Thanks!

[google-ml-butler]

This issue has been automatically marked as stale because it has no recent activity. It will be closed if no further activity occurs. Thank you.

[google-ml-butler]

Closing as stale. Please reopen if you'd like to work on this further.

[google-ml-butler]

Are you satisfied with the resolution of your issue?
Yes
No

[rayeeed]

Add this line of code at the beginning. Worked for me.
!apt install --allow-change-held-packages libcudnn8=8.4.1.50-1+cuda11.6