How Can I Limit TensorFlow's GPU Memory Allocation in Shared Computing Environments?

Limiting GPU Memory Allocation for TensorFlow in Shared Environments

When working with multiple users sharing computational resources, efficient GPU memory allocation is crucial. TensorFlow, by default, allocates all available GPU memory, even for small models. This can hinder concurrent training for multiple users.

To address this, TensorFlow provides a mechanism to specify the fraction of GPU memory to be allocated. This can be achieved by setting the per_process_gpu_memory_fraction parameter in the GPUOptions object.

import tensorflow as tf

# Allocate 4GB of GPU memory
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)

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

The per_process_gpu_memory_fraction parameter acts as a hard limit on GPU memory usage. It applies uniformly to all GPUs on the machine. By specifying the appropriate fraction, users can ensure that concurrent training does not exhaust GPU memory. This flexibility improves resource utilization and allows for more efficient training in shared environments.

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