What is torch not compiled with cuda enabled?

When a torch is not compiled with CUDA enabled, it means that it cannot use the power of GPU for faster computations of machine learning models. Instead, it uses the CPU, which is less efficient in handling large datasets and complex computations. This can result in slower training and inference times, making the entire process take a lot longer than it would with a GPU.

Therefore, it is recommended to ensure CUDA compatibility when working with deep learning frameworks like PyTorch. By enabling CUDA, you can speed up training times significantly, allowing you to iterate and optimize models faster and more efficiently. However, it is also worth noting that not all hardware configurations support CUDA, so it's essential to check your system requirements before proceeding.

In summary, enabling CUDA provides significant advantages when working with machine learning models, particularly when training or running complex operations. Without it, the efficiency and speed of the process can be significantly hindered, requiring more time and resources to complete the same tasks.