See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF这个问题该怎么解决

Memory management in PyTorch refers to the allocation, deallocation, and management of memory resources during the training or inference of a deep learning model. PyTorch provides several tools and techniques to manage memory in an efficient way, such as automatic memory management, manual memory management, and memory profiling.

Regarding the PYTORCH_CUDA_ALLOC_CONF issue, this is an environment variable that can be used to optimize the allocation of memory on CUDA devices. The variable can be set to a comma-separated list of key-value pairs in the format <allocation_type>:<limit>. For example, PYTORCH_CUDA_ALLOC_CONF="0:4096,1:4096" sets a limit of 4096 MB for both the default and the pinned memory allocations.

To set the PYTORCH_CUDA_ALLOC_CONF variable, you can use the following commands:

Linux/Mac:

export PYTORCH_CUDA_ALLOC_CONF=<configuration>

Windows:

set PYTORCH_CUDA_ALLOC_CONF=<configuration>

You can also set the variable programmatically in Python using the following code:

import os
os.environ['PYTORCH_CUDA_ALLOC_CONF'] = '<configuration>'

Keep in mind that the optimal configuration of PYTORCH_CUDA_ALLOC_CONF may vary depending on your specific hardware and workload. Therefore, it is recommended to experiment with different configurations and measure the performance to find the best one for your use case.