Is my GPU being utilized in training? Keras

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Hi I am trying to use my 3060 to increase training speed, I have install cudnn and nvidia drivers, (following YouTube tutorial) I get the message in the terminal.) I haven’t specified anything in my code as I have read keras will automatically use GPU if available. However my training speed doesn’t seem to have changed by any noteable amount.

This is the message within the terminal once I run the code

2022-04-20 18:34:04.900724: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-04-20 18:34:05.477447: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 9621 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3060, pci bus id: 0000:09:00.0, compute capability: 8.6
Epoch 1/500

2022-04-20 18:34:04.900724: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-04-20 18:34:05.477447: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 9621 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3060, pci bus id: 0000:09:00.0, compute capability: 8.6
2022-04-20 18:35:00.079826: I tensorflow/stream_executor/cuda/cuda_dnn.cc:368] Loaded cuDNN version 8400
 1/41 [..............................] - ETA: 11:49 - loss: 0.3319
2022-04-20 18:34:04.900724: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-04-20 18:34:05.477447: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 9621 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3060, pci bus id: 0000:09:00.0, compute capability: 8.6
2022-04-20 18:35:00.079826: I tensorflow/stream_executor/cuda/cuda_dnn.cc:368] Loaded cuDNN version 8400
2022-04-20 18:35:07.220634: I tensorflow/stream_executor/cuda/cuda_blas.cc:1786] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.

Any help or insight would be greatly appreciated

keras python
1 Answers

You can check the availability of GPUs by the following code:

import tensorflow as tf
tf.test.gpu_device_name()

if any GPUs are found to the environment, the name of the GPU will be shown: /device:GPU:0

Note: this is the advice mentioned in the book "The TENSORFLOW Workshop" by PacktPub.

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