add available memory check to accelerators #4508
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mrwyattii
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Oct 12, 2023
Co-authored-by: Michael Wyatt <[email protected]>
|
@delock, FYI |
tjruwase
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Oct 12, 2023
mrwyattii
approved these changes
Oct 13, 2023
Thanks for reminding. I think the CPU part is good. We will add to XPU backend as well. |
baodii
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Nov 7, 2023
* add available memory check to accelerator * catch case where nvmlInit fails * add pynvml to reqs * fix for cpu systems * Update accelerator/cuda_accelerator.py Co-authored-by: Michael Wyatt <[email protected]> * simplify --------- Co-authored-by: Michael Wyatt <[email protected]>
mauryaavinash95
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Feb 17, 2024
* add available memory check to accelerator * catch case where nvmlInit fails * add pynvml to reqs * fix for cpu systems * Update accelerator/cuda_accelerator.py Co-authored-by: Michael Wyatt <[email protected]> * simplify --------- Co-authored-by: Michael Wyatt <[email protected]>
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There are many scenarios where we need to get an accurate estimate of the available memory on a device. Currently we rely on the torch memory allocator stats to give us this information, however there are several cases where memory may be allocated outside the view of torch. This means that
torch.cuda.get_device_properties(device_index).total_memory - torch.cuda.memory_allocated(device_index)is not accurate. This is usually less of a problem on data center GPUs but quite common on consumer grade GPUs that are often shared between torch and the operating system.This PR introduces
available_memoryto the abstract accelerator interface. On CUDA devices we can rely onpynvmlto get the ground truth w.r.t. available memory.This also introduces a hard dependency on
pynvml. I have tested on non-GPU systems and this package seems to install successfully but fails at runtime at thenvmlInit()call. We fall back to using torch stats for memory in cases where pynvml is not functional.