Clean Cache GPU with Pytorch

When using Pytorch for machine learning tasks, it is important to manage memory usage on the GPU efficiently. One way to do this is by cleaning the GPU cache regularly to free up memory and prevent out-of-memory errors.

Pytorch provides a simple way to clean the GPU cache using the following code snippet:


import torch
torch.cuda.empty_cache()


This function clears the memory cache on the GPU and releases any unused memory that was previously occupied by tensors.

It is recommended to call this function regularly, especially when working with large datasets or models that consume a lot of memory. By cleaning the GPU cache, you can avoid memory leaks and improve the overall performance of your machine learning models.

In addition to cleaning the GPU cache, you can also monitor memory usage on the GPU using tools like NVIDIA’s CUDA toolkit or the nvidia-smi command line tool. This will give you a better understanding of how your models are utilizing GPU memory and help you optimize memory usage.

Overall, cleaning the GPU cache with Pytorch is an important step in managing memory efficiently and preventing performance issues in machine learning applications. By incorporating this simple step into your workflow, you can ensure that your models are running smoothly and effectively on the GPU.