How Can I Control TensorFlow's GPU Memory Allocation for Better Multi-User Performance?

How to Manage TensorFlow Memory Allocation for Enhanced GPU Utilization

TensorFlow, a powerful machine learning library, often poses challenges with its default allocation of all available GPU memory. This hinders efficient resource sharing in multi-user environments, where smaller models could benefit from concurrent training on a single GPU.

To address this issue, TensorFlow provides a solution to limit memory allocation per process. When constructing a tf.Session, you can specify a tf.GPUOptions object within the optional config argument:

# Assuming 12GB of GPU memory, allocate approximately 4GB:
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))

The per_process_gpu_memory_fraction parameter acts as an upper bound on the GPU memory usage. By setting a fraction below 1, you effectively limit the memory allocated to the TensorFlow process, allowing multiple users to simultaneously train on the same GPU.

It's important to note that this fraction applies uniformly to all GPUs on the machine, so you cannot specify different memory allocations for individual GPUs. However, this solution provides flexibility and efficient memory management for collaborative GPU environments.

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