sudo pacman -SyuSkip if you’ve installed properitary nvidia driver
sudo pacman -S nvidia nvidia-utils
# or nvidia-dkms and linux-header for custom kernelsudo pacman -S cuda cudnnIf you havn’t set the path for dynamic/shared library, it’s time to set it. Otherwise errors like # Unimplemented: DNN library is not found might present when running CNN
Add the following line to either ~/.bashrc or ~/.bash_profile or .profile (last two are preferred)
export LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/usr/lib"Verify the installation
cd /opt/cuda/extras/demo_suite
./deviceQuery CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: "NVIDIA GeForce RTX 3090"
CUDA Driver Version / Runtime Version 11.8 / 11.8
CUDA Capability Major/Minor version number: 8.6
Total amount of global memory: 24265 MBytes (25443893248 bytes)
(82) Multiprocessors, (128) CUDA Cores/MP: 10496 CUDA Cores
GPU Max Clock rate: 1695 MHz (1.70 GHz)
Memory Clock rate: 9751 Mhz
Memory Bus Width: 384-bit
L2 Cache Size: 6291456 bytes
Maximum Texture Dimension Size (x,y,z) 1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
Maximum Layered 1D Texture Size, (num) layers 1D=(32768), 2048 layers
Maximum Layered 2D Texture Size, (num) layers 2D=(32768, 32768), 2048 layers
Total amount of constant memory: 65536 bytes
Total amount of shared memory per block: 49152 bytes
Total number of registers available per block: 65536
Warp size: 32
Maximum number of threads per multiprocessor: 1536
Maximum number of threads per block: 1024
Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535)
Maximum memory pitch: 2147483647 bytes
Texture alignment: 512 bytes
Concurrent copy and kernel execution: Yes with 2 copy engine(s)
Run time limit on kernels: Yes
Integrated GPU sharing Host Memory: No
Support host page-locked memory mapping: Yes
Alignment requirement for Surfaces: Yes
Device has ECC support: Disabled
Device supports Unified Addressing (UVA): Yes
Device supports Compute Preemption: Yes
Supports Cooperative Kernel Launch: Yes
Supports MultiDevice Co-op Kernel Launch: Yes
Device PCI Domain ID / Bus ID / location ID: 0 / 9 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 11.8, CUDA Runtime Version = 11.8, NumDevs = 1, Device0 = NVIDIA GeForce RTX 3090
Result = PASSpython-tensorflow-opt-cuda vs python-tensorflow-cuda
sudo pacman -S python-tensorflow-cuda kerasfrom keras import layers
from keras import models
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))
print(model.summary())
from keras.datasets import mnist
from keras.utils import to_categorical
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
train_images = train_images.reshape((60000, 28, 28, 1))
train_images = train_images.astype('float32') / 255
test_images = test_images.reshape((10000, 28, 28, 1))
test_images = test_images.astype('float32') / 255
train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=5, batch_size=64)
test_loss, test_acc = model.evaluate(test_images, test_labels)
print('test_acc:', test_acc)2022-11-19 02:48:54.199427: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE3 SSE4.1 SSE4.2 AVX AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-11-19 02:48:54.280223: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2022-11-19 02:48:55.103112: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-11-19 02:48:55.112502: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-11-19 02:48:55.112652: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-11-19 02:48:55.112918: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE3 SSE4.1 SSE4.2 AVX AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-11-19 02:48:55.114464: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-11-19 02:48:55.114573: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-11-19 02:48:55.114662: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-11-19 02:48:55.310342: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-11-19 02:48:55.310471: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-11-19 02:48:55.310579: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-11-19 02:48:55.310662: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 21342 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:09:00.0, compute capability: 8.6
2022-11-19 02:48:55.310982: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 26, 26, 32) 320
max_pooling2d (MaxPooling2D (None, 13, 13, 32) 0
)
conv2d_1 (Conv2D) (None, 11, 11, 64) 18496
max_pooling2d_1 (MaxPooling (None, 5, 5, 64) 0
2D)
conv2d_2 (Conv2D) (None, 3, 3, 64) 36928
flatten (Flatten) (None, 576) 0
dense (Dense) (None, 64) 36928
dense_1 (Dense) (None, 10) 650
=================================================================
Total params: 93,322
Trainable params: 93,322
Non-trainable params: 0
_________________________________________________________________
None
Epoch 1/5
2022-11-19 02:48:56.553880: I tensorflow/stream_executor/cuda/cuda_dnn.cc:384] Loaded cuDNN version 8600
2022-11-19 02:48:56.923119: I tensorflow/stream_executor/cuda/cuda_blas.cc:1614] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.
938/938 [==============================] - 3s 2ms/step - loss: 0.1702 - accuracy: 0.9469
Epoch 2/5
938/938 [==============================] - 2s 2ms/step - loss: 0.0460 - accuracy: 0.9853
Epoch 3/5
938/938 [==============================] - 2s 2ms/step - loss: 0.0321 - accuracy: 0.9902
Epoch 4/5
938/938 [==============================] - 2s 2ms/step - loss: 0.0254 - accuracy: 0.9919
Epoch 5/5
938/938 [==============================] - 2s 2ms/step - loss: 0.0204 - accuracy: 0.9940
313/313 [==============================] - 0s 970us/step - loss: 0.0403 - accuracy: 0.9880
test_acc: 0.9879999756813049