How Can I Control GPU Memory Allocation in TensorFlow?

Regulating GPU Memory Allocation in TensorFlow

In a shared computational environment, effective resource management is crucial. TensorFlow, a popular machine learning library, has a tendency to allocate the entire available GPU memory upon launch, even for smaller models. This can hinder simultaneous training by multiple users.

Restricting GPU Memory Allocation

To address this issue, TensorFlow provides the ability to limit the GPU memory allocated by a training process. By setting the per_process_gpu_memory_fraction property of tf.GPUOptions within the config argument of a tf.Session, you can specify a fraction of the total GPU memory to be used.

For example, to allocate approximately 4 GB of GPU memory from a 12 GB Titan X GPU, the following code can be used:

gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))

This setting acts as an upper bound, ensuring that the amount of GPU memory used does not exceed the specified fraction. However, it applies uniformly across all GPUs on the same machine and cannot be adjusted individually for each GPU.

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