How Can I Control GPU Memory Allocation in TensorFlow for Shared Environments?

Manage GPU Memory Allocation in TensorFlow for Shared Environments

When working with shared computational resources, it becomes essential to optimize GPU memory utilization for multiple concurrent training tasks. By default, TensorFlow often allocates the entirety of the available GPU memory, potentially limiting the flexibility and efficiency of resource sharing. To address this, TensorFlow provides a configurable option to customize GPU memory allocation.

Limiting GPU Memory Usage

To prevent TensorFlow from allocating all GPU memory, the tf.GPUOptions configuration can be utilized. By setting the per_process_gpu_memory_fraction parameter within tf.GPUOptions, users can specify a fractional limit on the amount of GPU memory to be allocated.

# Allocation of approximately 4GB out of 12GB of GPU memory
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)

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

This configuration ensures that the process will not use more than the specified fraction of the GPU memory, allowing multiple users to simultaneously train models within the allocated limit.

Important Notes:

• The specified memory fraction is applied uniformly across all GPUs on the machine.
• By limiting GPU memory allocation, it is possible to improve scalability and enable concurrent training tasks within shared environments without sacrificing individual training speeds.

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