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How can I oblige tensorflow to use all gpu power?
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.
However, there are a few things you can do to ensure that TensorFlow is using all of the GPU resources available:
- Set the GPU memory growth option: TensorFlow allows you to set a flag to control the GPU memory growth. You can set the flag by using the following command:
import tensorflow as tf
physical_devices = tf.config.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(physical_devices[0], True)- Limit the number of CPU threads: By default, TensorFlow will use all available CPU threads, which can limit the amount of GPU resources available. You can set the number of CPU threads that TensorFlow should use by using the following command:
import os
os.environ["OMP_NUM_THREADS"] = "4"
- Ensure that you have the latest Tensorflow version and GPU drivers: Newer Tensorflow versions includes more optimized GPU utilization, the same goes for the GPU driver, making sure that you have the latest version of both of them could help boost your GPU performance.
- Manage GPU resources with CUDA: if you’re using CUDA with Tensorflow you can use CUDA streams to synchronize and manage multiple GPU resources.
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.
