TensorFlow, a popular open-source machine learning library, is designed to automatically utilize the available GPU resources on a device. By default, TensorFlow will use all available GPU resources when training or running a model.
Tensorflow Interview Questions and Answersimport tensorflow as tf
physical_devices = tf.config.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(physical_devices[0], True)
import os
os.environ["OMP_NUM_THREADS"] = "4"
It’s worth noting that even if TensorFlow is using all available GPU resources, the performance of your model may still be limited by other factors such as the amount of data, the complexity of the model, and the number of training iterations.
It’s also important to mention that to ensure the best performance it’s always best to measure and test your model with different settings and configurations, depending on the specific use-case and dataset.
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