How to Check if TensorFlow is Using GPU 8 Methods for Python Users

TensorFlow is a popular open-source machine learning framework developed by Google. It provides support for running computations on both CPUs and GPUs, allowing users to take advantage of the high parallel processing power of GPUs to speed up their machine learning tasks. In this answer, I will provide eight methods for using GPUs with TensorFlow in Python.

1. Check if TensorFlow is using GPU

You can check if TensorFlow is using GPU by running the following code:

import tensorflow as tf
print(tf.test.gpu_device_name())

If TensorFlow is using GPU, this code will print the name of the GPU device being used.

2. Check available GPUs

You can check the available GPUs by running the following code:

import tensorflow as tf
print(tf.config.list_physical_devices('GPU'))

This will print a list of available GPU devices.

3. Set a specific GPU to use

If you have multiple GPUs, you can specify which one to use by running the following code:

import tensorflow as tf
gpus = tf.config.list_physical_devices('GPU')
if gpus:
  # Set the GPU to use
  try:
    tf.config.set_visible_devices(gpus[1], 'GPU')
    logical_gpus = tf.config.list_logical_devices('GPU')
    print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPU")
  except RuntimeError as e:
    # Visible devices must be set before GPUs have been initialized
    print(e)

In this example, the second GPU is being set to use.

4. Using a GPU-enabled TensorFlow distribution

If you’re using a pre-built TensorFlow distribution, make sure that it’s GPU-enabled. You can download the GPU-enabled version of TensorFlow using pip:

pip install tensorflow-gpu

This will install the GPU-enabled version of TensorFlow, which includes support for running computations on GPUs.

5. Enable GPU usage in TensorFlow

To enable GPU usage in TensorFlow, you can add the following code at the beginning of your script:

import tensorflow as tf
physical_devices = tf.config.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(physical_devices[0], True)

This will enable memory growth for the first available GPU device.

6. Train a model on GPU

To train a model on GPU, you can use the following code:

import tensorflow as tf

# Set up GPU usage
physical_devices = tf.config.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(physical_devices[0], True)

# Build and compile the model
model = tf.keras.models.Sequential([
  # Add layers here
])
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Train the model on GPU
model.fit(x_train, y_train, epochs=10, batch_size=32)

In this example, the fit() method is used to train the model on GPU.

7. Use a GPU-accelerated backend

You can use a GPU-accelerated backend like CuDNN to speed up computations on GPU. To use CuDNN with TensorFlow, you need to install the CuDNN library and set the environment variables LD_LIBRARY_PATH and CUDA_HOME. Once this is done, TensorFlow will automatically use CuDNN for GPU-accelerated computations.

8. Monitor GPU usage

You can monitor GPU usage during training by running the following code:

import tensorflow as tf
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())

This will print a list of all available devices, including GPUs, and their memory usage.