How to Use Only One Gpu For Tensorflow Session?

To use only one GPU for a TensorFlow session, you can start by specifying the GPU device when creating the TensorFlow session. This can be done by setting the "CUDA_VISIBLE_DEVICES" environment variable to the index of the GPU you want to use. For example, if you want to use GPU 0, you can set CUDA_VISIBLE_DEVICES=0 before running your TensorFlow code.

Another way to specify the GPU device is to use the "tf.device()" context manager in your code. You can wrap the operations that you want to run on a specific GPU inside the tf.device() context manager with the device name of the GPU you want to use. For example, you can use tf.device('/device:GPU:0') to run operations on GPU 0.

Additionally, you can set the "gpu_options" parameter when creating a TensorFlow session to specify which GPU to use. You can pass the "gpu_options" parameter with the "visible_device_list" attribute set to the index of the GPU you want to use.

By following these methods, you can ensure that your TensorFlow session is running on only a single GPU.

How can I set TensorFlow to run on a specific GPU?

To set TensorFlow to run on a specific GPU, you can use the following code snippet:

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import tensorflow as tf

# Determine which GPU to use (i.e. '/gpu:0', '/gpu:1', etc.)
gpu_number = 0  # Change this to the GPU number you want to use
device = f'/gpu:{gpu_number}'

# Create a TensorFlow session and set the GPU device to use
with tf.device(device):
    # Your TensorFlow code here

In this code snippet, you can specify the GPU you want to use by changing the gpu_number variable to the desired GPU number. Then, when creating a TensorFlow session, you can specify the GPU device to use by passing the device variable to the tf.device() function.

This way, you can control which GPU TensorFlow runs on and distribute computational tasks across multiple GPUs if needed.

How to prevent TensorFlow from using multiple GPUs simultaneously?

To prevent TensorFlow from using multiple GPUs simultaneously, you can specify the GPU device that TensorFlow should use by setting the CUDA_VISIBLE_DEVICES environment variable before running your code.

For example, if you want TensorFlow to only use GPU 0, you can set the CUDA_VISIBLE_DEVICES environment variable as follows:

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export CUDA_VISIBLE_DEVICES=0

Alternatively, you can use the tf.config.experimental.set_visible_devices function in your TensorFlow code to specify the list of visible devices that TensorFlow should use.

Here's an example of how you can set TensorFlow to only use GPU 0 within your Python code:

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import tensorflow as tf

gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
    # Restrict TensorFlow to only use the first GPU
    tf.config.experimental.set_visible_devices(gpus[0], 'GPU')
    logical_gpus = tf.config.experimental.list_logical_devices('GPU')
    print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPU")

# Your TensorFlow code here

This will prevent TensorFlow from using multiple GPUs simultaneously and restrict it to using only the specified GPU.

What is the option for controlling TensorFlow to use only one GPU?

To control TensorFlow to use only one GPU, you can set the environment variable CUDA_VISIBLE_DEVICES to the index of the GPU you want to use. For example, to only use GPU 0, you can set the environment variable using the following command before running your TensorFlow code:

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export CUDA_VISIBLE_DEVICES=0

Alternatively, you can set the gpu_options.allow_growth parameter in the TensorFlow session configuration to limit TensorFlow to only use one GPU. Here is an example of how to set this parameter:

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import tensorflow as tf

config = tf.ConfigProto()
config.gpu_options.allow_growth = True

sess = tf.Session(config=config)

By setting config.gpu_options.allow_growth to True, TensorFlow will dynamically allocate memory on the GPU and limit the GPU memory usage to only what is needed for the operations being performed. This can help prevent memory issues and ensure that TensorFlow only uses one GPU for processing.