I have this problem I know quite a lot of people heard about. I upgraded from my laptop with GTX 1050 Ti to a PC with RTX 3060 Ti. I'm running everything in an Anaconda Virtual Environment. I've copied my env from the laptop to the PC. Now the TensorFlow GPU takes a lot of time to start up. Even if I write the 2 lines of code:
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())
It takes a lot of time (more than 30 minutes). The same thing works perfectly on my laptop with GTX 1050 Ti. I tried a lot of stuff:
After TensorFlow starts up, the RTX 3060 Ti is working properly, training very fast. I googled a lot, but I see that there are still a lot of people in my place right now, so I'm not expecting an answer pretty soon:).
Anyways, if someone manages to find an answer, please share it with me! Thanks in advance and have a great day!
P.S. If you need code or console logs, here you go. I've written a quick MNIST program:
from tensorflow.keras.datasets import mnist
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Flatten
from tensorflow.keras.optimizers import SGD
from numpy import mean
from numpy import std
from matplotlib import pyplot as plt
from sklearn.model_selection import KFold
# Load and prepare the train and test set
def load_dataset():
# Load the dataset
(trainX, trainY), (testX, testY) = mnist.load_data()
# Reshape the dataset to have a single channel
trainX = trainX.reshape((trainX.shape[0], 28, 28, 1))
testX = testX.reshape((testX.shape[0], 28, 28, 1))
# One hot encode target values
trainY = to_categorical(trainY)
testY = to_categorical(testY)
return trainX, trainY, testX, testY
# Scale pixels
def prep_pixels(train, test):
# Convert from integers to float
train_norm = train.astype('float32')
test_norm = test.astype('float32')
# Normalize to range 0-1
train_norm = train_norm / 255.0
test_norm = test_norm / 255.0
return train_norm, test_norm
# Define the CNN classifier
def define_classifier():
# Build the structure
classifier = Sequential()
classifier.add(Conv2D(32, (3, 3), activation = 'relu', input_shape = (28, 28, 1)))
classifier.add(MaxPooling2D(pool_size = (2, 2)))
classifier.add(Conv2D(64, (3, 3), activation = 'relu'))
classifier.add(Conv2D(64, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D((2, 2)))
classifier.add(Flatten())
classifier.add(Dense(100, activation = 'relu'))
classifier.add(Dense(10, activation = 'softmax'))
# Compile the model
classifier.compile(optimizer = SGD(lr = 0.01, momentum = 0.9), loss = 'categorical_crossentropy',
metrics = ['accuracy'])
return classifier
# Evaluate the classifier using the K-Fold Cross-Validation
def evaluate_classifier(dataX, dataY, n_folds = 5):
scores, histories = list(), list()
# Prepare Cross-Validation
kfold = KFold(n_folds, shuffle = True, random_state = 1)
# Enumerate splits
for trainX_i, testX_i in kfold.split(dataX):
# Define classifier
classifier = define_classifier()
# Select rows for train and test
trainX, trainY, testX, testY = dataX[trainX_i], dataY[trainX_i], dataX[testX_i], dataY[testX_i]
# Fit the classifier
history = classifier.fit(trainX, trainY, batch_size = 32, epochs = 10,
validation_data = (testX, testY), verbose = 1)
# Evaluate the classifier
_, acc = classifier.evaluate(testX, testY, verbose = 1)
print('> ACC: %.3f' % (acc * 100.0))
# Store history, accuracy
scores.append(acc)
histories.append(history)
return scores, histories
# Plot learning curves
def visualise_learning(histories):
for i in range(len(histories)):
plt.tight_layout()
# Plot LOSS
plt.subplot(2, 1, 1)
plt.title('Cross-Entropy Loss')
plt.plot(histories[i].history['loss'], color = 'blue', label = 'train')
plt.plot(histories[i].history['val_loss'], color = 'orange', label = 'test')
# Plot ACCURACY
plt.subplot(2, 1, 2)
plt.title('Classification Accuracy')
plt.plot(histories[i].history['accuracy'], color = 'blue', label = 'train')
plt.plot(histories[i].history['val_accuracy'], color = 'orange', label = 'test')
plt.show()
# Summarize classifier performance
def summarize_performance(scores):
print('Accuracy: mean=%.3f std=%.3f, n=%d' % (mean(scores) * 100, std(scores) * 100, len(scores)))
# Run all parts together
def run():
trainX, trainY, testX, testY = load_dataset()
trainX, testX = prep_pixels(trainX, testX)
scores, histories = evaluate_classifier(trainX, trainY)
visualise_learning(histories)
summarize_performance(scores)
def save_model():
trainX, trainY, testX, testY = load_dataset()
trainX, testX = prep_pixels(trainX, testX)
classifier = define_classifier()
classifier.fit(trainX, trainY, epochs = 25, batch_size = 32, verbose = 1)
classifier.save('final_classifier.h5')
##############################################################################################################
# make a prediction for a new image.
from tensorflow.keras.preprocessing.image import load_img
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.models import load_model
# load and prepare the image
def load_image(filename):
# load the image
img = load_img(filename, grayscale=True, target_size=(28, 28))
# convert to array
img = img_to_array(img)
# reshape into a single sample with 1 channel
img = img.reshape(1, 28, 28, 1)
# prepare pixel data
img = img.astype('float32')
img = img / 255.0
return img
# load an image and predict the class
def run_example():
# load the image
img = load_image('image.png')
# load model
model = load_model('final_classifier.h5')
# predict the class
digit = model.predict_classes(img)
print(digit[0])
# entry point, run the example
#run_example()
run()
And here is the console log:
Python 3.7.9 (default, Aug 31 2020, 17:10:11) [MSC v.1916 64 bit (AMD64)]
Type "copyright", "credits" or "license" for more information.
IPython 7.19.0 -- An enhanced Interactive Python.
runcell(0, 'C:/Python/Projects/Handwritten Digit Recognition/digit_recognizer.py')
2021-01-14 13:47:28.396292: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
2021-01-14 13:47:28.396292: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
2021-01-14 13:47:31.018731: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll
2021-01-14 13:47:31.041720: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce RTX 3060 Ti computeCapability: 8.6
coreClock: 1.8GHz coreCount: 38 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 417.29GiB/s
2021-01-14 13:47:31.041751: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
2021-01-14 13:47:28.396292: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
2021-01-14 13:47:31.018731: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll
2021-01-14 13:47:31.041720: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce RTX 3060 Ti computeCapability: 8.6
coreClock: 1.8GHz coreCount: 38 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 417.29GiB/s
2021-01-14 13:47:31.041751: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
2021-01-14 13:47:31.395981: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll
2021-01-14 13:47:31.430370: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll
2021-01-14 13:47:31.452057: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll
2021-01-14 13:47:31.659034: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll
2021-01-14 13:47:31.837570: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll
2021-01-14 13:47:32.055598: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
2021-01-14 13:47:32.056116: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
2021-01-14 13:47:28.396292: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
2021-01-14 13:47:31.018731: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll
2021-01-14 13:47:31.041720: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce RTX 3060 Ti computeCapability: 8.6
coreClock: 1.8GHz coreCount: 38 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 417.29GiB/s
2021-01-14 13:47:31.041751: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
2021-01-14 13:47:31.395981: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll
2021-01-14 13:47:31.430370: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll
2021-01-14 13:47:31.452057: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll
2021-01-14 13:47:31.659034: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll
2021-01-14 13:47:31.837570: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll
2021-01-14 13:47:32.055598: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
2021-01-14 13:47:32.056116: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
2021-01-14 13:47:32.652696: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
2021-01-14 13:47:32.655023: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce RTX 3060 Ti computeCapability: 8.6
coreClock: 1.8GHz coreCount: 38 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 417.29GiB/s
2021-01-14 13:47:32.655039: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
2021-01-14 13:47:32.655046: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll
2021-01-14 13:47:32.655051: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll
2021-01-14 13:47:32.655057: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll
2021-01-14 13:47:32.655062: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll
2021-01-14 13:47:32.655067: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll
2021-01-14 13:47:32.655072: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
2021-01-14 13:47:32.655095: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
2021-01-14 13:47:28.396292: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
2021-01-14 13:47:31.018731: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll
2021-01-14 13:47:31.041720: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce RTX 3060 Ti computeCapability: 8.6
coreClock: 1.8GHz coreCount: 38 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 417.29GiB/s
2021-01-14 13:47:31.041751: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
2021-01-14 13:47:31.395981: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll
2021-01-14 13:47:31.430370: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll
2021-01-14 13:47:31.452057: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll
2021-01-14 13:47:31.659034: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll
2021-01-14 13:47:31.837570: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll
2021-01-14 13:47:32.055598: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
2021-01-14 13:47:32.056116: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
2021-01-14 13:47:32.652696: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
2021-01-14 13:47:32.655023: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce RTX 3060 Ti computeCapability: 8.6
coreClock: 1.8GHz coreCount: 38 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 417.29GiB/s
2021-01-14 13:47:32.655039: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
2021-01-14 13:47:32.655046: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll
2021-01-14 13:47:32.655051: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll
2021-01-14 13:47:32.655057: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll
2021-01-14 13:47:32.655062: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll
2021-01-14 13:47:32.655067: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll
2021-01-14 13:47:32.655072: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
2021-01-14 13:47:32.655095: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
2021-01-14 13:50:57.038023: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1096] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-01-14 13:50:57.038040: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] 0
2021-01-14 13:50:57.038045: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] 0: N
2021-01-14 13:50:57.039526: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1241] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6699 MB memory) -> physical GPU (device: 0, name: GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6)
Train on 48000 samples, validate on 12000 samples
Epoch 1/10
2021-01-14 13:47:28.396292: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
2021-01-14 13:47:31.018731: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll
2021-01-14 13:47:31.041720: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce RTX 3060 Ti computeCapability: 8.6
coreClock: 1.8GHz coreCount: 38 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 417.29GiB/s
2021-01-14 13:47:31.041751: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
2021-01-14 13:47:31.395981: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll
2021-01-14 13:47:31.430370: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll
2021-01-14 13:47:31.452057: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll
2021-01-14 13:47:31.659034: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll
2021-01-14 13:47:31.837570: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll
2021-01-14 13:47:32.055598: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
2021-01-14 13:47:32.056116: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
2021-01-14 13:47:32.652696: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
2021-01-14 13:47:32.655023: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce RTX 3060 Ti computeCapability: 8.6
coreClock: 1.8GHz coreCount: 38 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 417.29GiB/s
2021-01-14 13:47:32.655039: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
2021-01-14 13:47:32.655046: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll
2021-01-14 13:47:32.655051: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll
2021-01-14 13:47:32.655057: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll
2021-01-14 13:47:32.655062: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll
2021-01-14 13:47:32.655067: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll
2021-01-14 13:47:32.655072: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
2021-01-14 13:47:32.655095: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
2021-01-14 13:50:57.038023: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1096] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-01-14 13:50:57.038040: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] 0
2021-01-14 13:50:57.038045: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] 0: N
2021-01-14 13:50:57.039526: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1241] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6699 MB memory) -> physical GPU (device: 0, name: GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6)
2021-01-14 13:50:57.563527: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll
2021-01-14 13:47:28.396292: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
2021-01-14 13:47:31.018731: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll
2021-01-14 13:47:31.041720: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce RTX 3060 Ti computeCapability: 8.6
coreClock: 1.8GHz coreCount: 38 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 417.29GiB/s
2021-01-14 13:47:31.041751: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
2021-01-14 13:47:31.395981: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll
2021-01-14 13:47:31.430370: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll
2021-01-14 13:47:31.452057: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll
2021-01-14 13:47:31.659034: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll
2021-01-14 13:47:31.837570: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll
2021-01-14 13:47:32.055598: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
2021-01-14 13:47:32.056116: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
2021-01-14 13:47:32.652696: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
2021-01-14 13:47:32.655023: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce RTX 3060 Ti computeCapability: 8.6
coreClock: 1.8GHz coreCount: 38 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 417.29GiB/s
2021-01-14 13:47:32.655039: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
2021-01-14 13:47:32.655046: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll
2021-01-14 13:47:32.655051: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll
2021-01-14 13:47:32.655057: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll
2021-01-14 13:47:32.655062: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll
2021-01-14 13:47:32.655067: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll
2021-01-14 13:47:32.655072: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
2021-01-14 13:47:32.655095: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
2021-01-14 13:50:57.038023: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1096] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-01-14 13:50:57.038040: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] 0
2021-01-14 13:50:57.038045: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] 0: N
2021-01-14 13:50:57.039526: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1241] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6699 MB memory) -> physical GPU (device: 0, name: GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6)
2021-01-14 13:50:57.563527: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll
2021-01-14 13:52:17.763274: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
7232/48000 [===>..........................] - ETA: 1:21:26 - loss: 2.3010 - accuracy: 0.1114
2021-01-14 13:47:28.396292: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
2021-01-14 13:47:31.018731: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll
2021-01-14 13:47:31.041720: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce RTX 3060 Ti computeCapability: 8.6
coreClock: 1.8GHz coreCount: 38 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 417.29GiB/s
2021-01-14 13:47:31.041751: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
2021-01-14 13:47:31.395981: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll
2021-01-14 13:47:31.430370: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll
2021-01-14 13:47:31.452057: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll
2021-01-14 13:47:31.659034: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll
2021-01-14 13:47:31.837570: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll
2021-01-14 13:47:32.055598: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
2021-01-14 13:47:32.056116: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
2021-01-14 13:47:32.652696: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
2021-01-14 13:47:32.655023: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce RTX 3060 Ti computeCapability: 8.6
coreClock: 1.8GHz coreCount: 38 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 417.29GiB/s
2021-01-14 13:47:32.655039: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
2021-01-14 13:47:32.655046: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll
2021-01-14 13:47:32.655051: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll
2021-01-14 13:47:32.655057: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll
2021-01-14 13:47:32.655062: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll
2021-01-14 13:47:32.655067: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll
2021-01-14 13:47:32.655072: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
2021-01-14 13:47:32.655095: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
2021-01-14 13:50:57.038023: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1096] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-01-14 13:50:57.038040: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] 0
2021-01-14 13:50:57.038045: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] 0: N
2021-01-14 13:50:57.039526: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1241] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6699 MB memory) -> physical GPU (device: 0, name: GeForce RTX 3060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6)
2021-01-14 13:50:57.563527: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll
2021-01-14 13:52:17.763274: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
2021-01-14 14:05:23.645822: W tensorflow/stream_executor/gpu/redzone_allocator.cc:312] Internal: Invoking GPU asm compilation is supported on Cuda non-Windows platforms only
Relying on driver to perform ptx compilation. This message will be only logged once.
48000/48000 [==============================] - 869s 18ms/sample - loss: 2.3019 - accuracy: 0.1101 - val_loss: 2.3014 - val_accuracy: 0.1144
It took a lot of time after the lines "Adding visible gpu devices: 0" and openning the dynamic libraries after that.
GTX 1050 Ti cards are based on Pascal architecture for which compatible CUDA version start with 8.x, where as RTX 3060 Ti cards are based on the Ampere architecture for which compatible CUDA version start with 11.x.
So compatible tensorflow version for your gpu card is 2.4.0 and cuDNN is 8.0.
Thank you njuffa for the CUDNN support matrix and insights. You can check tensorflow tested build configurations for windows and Linux.