How Can I Limit TensorFlow's GPU Memory Allocation?

Limiting TensorFlow GPU Memory Allocation

TensorFlow's default behavior allocates the entirety of available GPU memory upon launch, presenting a challenge in shared computational environments. When running concurrent training on the same GPU with multiple users, it is imperative to prevent excessive memory consumption.

Solution: GPU Memory Fraction

To address this issue, TensorFlow provides the option to specify a fraction of GPU memory to allocate. By setting the per_process_gpu_memory_fraction field in a tf.GPUOptions object, you can limit memory consumption. Here's an example:

# Restrict memory allocation to 4GB on a 12GB GPU
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)

# Create a session with the GPU options
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))

This approach provides a hard upper bound on GPU memory usage for the current process on all GPUs on the same machine. However, note that this fraction is applied uniformly across all GPUs, and there is no option for per-GPU memory allocation.

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