Tensorflow can't find the GPU - tensorflow

Question

pip install tensorflow==1.14.0
pip install tensorflow-gpu==1.14
import tensorflow as tf
tf.test.is_gpu_available(
cuda_only=False,
min_cuda_compute_capability=None
)
False
GPUs = GPU.getGPUs()
gpu = GPUs[0]
def printm():
process = psutil.Process(os.getpid())
print("Gen RAM Free: " + humanize.naturalsize( psutil.virtual_memory().available ), " | Proc size: " + humanize.naturalsize( process.memory_info().rss))
print("# of CPU: {0}".format(psutil.cpu_count()))
print("CPU type: {0}".format(platform.uname()))
print("GPU Type: {0}".format(gpu.name))
print("GPU RAM Free: {0:.0f}MB | Used: {1:.0f}MB | Util {2:3.0f}% | Total {3:.0f}MB".format(gpu.memoryFree, gpu.memoryUsed, gpu.memoryUtil*100, gpu.memoryTotal))
printm()
Gen RAM Free: 65.8 GB | Proc size: 294.4 MB
# of CPU: 8
CPU type: uname_result(system='Linux', node='lian-2', release='5.3.0-53-generic', version='#47~18.04.1-Ubuntu SMP Thu May 7 13:10:50 UTC 2020', machine='x86_64', processor='x86_64')
GPU Type: GeForce RTX 2080 Ti
GPU RAM Free: 10992MB | Used: 26MB | Util 0% | Total 11018MB
I reinstalled cuda 10
Mon Jun 8 15:48:44 2020
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 440.82 Driver Version: 440.82 CUDA Version: 10.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce RTX 208... Off | 00000000:01:00.0 Off | N/A |
| 24% 28C P8 2W / 260W | 26MiB / 11018MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 1805 G /usr/lib/xorg/Xorg 9MiB |
| 0 2192 G /usr/bin/gnome-shell 14MiB |
+-----------------------------------------------------------------------------+
define CUDNN_MAJOR 7
#define CUDNN_MINOR 4
#define CUDNN_PATCHLEVEL 2
--
#define CUDNN_VERSION (CUDNN_MAJOR * 1000 + CUDNN_MINOR * 100 + CUDNN_PATCHLEVEL)
I'm trying to train this from a jupyter notebook (ubuntu 18 ssh):
model = Sequential()
model.add(layers.Conv1D(16, 9, activation='relu', input_shape=(800, X_train.shape[-1])))
model.add(layers.MaxPooling1D(2))
model.add(layers.Bidirectional(layers.CuDNNGRU(8, return_sequences=True)))
model.add(layers.Bidirectional(layers.CuDNNGRU(8)))
model.add(layers.Dense(8, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))
model.compile(optimizer= 'Adam', loss='binary_crossentropy', metrics=['acc'])
model.summary()
train_to_epoch = 150
start_epoch = 3
t1 = datetime.datetime.now()
print('Training start time = %s' % t1)
history = model.fit(X_train, y_train,
batch_size=128, epochs=train_to_epoch, verbose=0,
validation_data=(X_val, y_val))
print('\nTraining Duration = %s' % (datetime.datetime.now()-t1))
InvalidArgumentError: No OpKernel was registered to support Op 'CudnnRNN' used by {{node bidirectional_1/CudnnRNN}}with these attrs: [input_mode="linear_input", T=DT_FLOAT, direction="unidirectional", rnn_mode="gru", seed2=0, is_training=true, seed=87654321, dropout=0]
Registered devices: [CPU, XLA_CPU]
Registered kernels:
[[bidirectional_1/CudnnRNN]]
Maybe is another version of CUDA needed?

Answer 1

Yes, it is the CUDA 10.1 version.