utility to launch your program, see Third-party backends.This blog post was originally published at NVIDIA’s website. If you use DistributedDataParallel, you could use By using multiprocessing,Įach GPU has its dedicated process, this avoids the performance overhead caused Uses multiprocessing where a process is created for each GPU, whileĭataParallel uses multithreading. The difference between DistributedDataParallel and Instead of DataParallel to do multi-GPU training, even if It is recommended to use DistributedDataParallel, Requirements exactly, it is likely that your program will have incorrect or Multiprocessing unless care is taken to meet the data handling There are significant caveats to using CUDA models with Using DistributedDataParallel to utilize more Most use cases involving batched inputs and multiple GPUs should default to Use nn.parallel.DistributedDataParallel instead of multiprocessing or nn.DataParallel ¶ The capacity of the cache for device 1, one can write Object or a device index, and access one of the above attributes. To control and query plan caches of a non-default device, you can index the _plan_cache.size gives the number of plans Setting this value directly modifies the capacity. _plan_cache.max_size gives the capacity of theĬache (default is 4096 on CUDA 10 and newer, and 1023 on older CUDA versions). You may control and query the properties of the cache of current device with Because some cuFFT plans may allocate GPU memory, Running FFT methods (e.g., ()) on CUDA tensors of same geometry PYTORCH_NO_CUDA_MEMORY_CACHING=1 in your environment to disable caching.įor each CUDA device, an LRU cache of cuFFT plans is used to speed up repeatedly To debug memory errors using cuda-memcheck, set Use of a caching allocator can interfere with memory checking tools such asĬuda-memcheck. Underlying allocation patterns produced by your code. Memory_snapshot(), which can help you understand the We also offer the capability to capture aĬomplete snapshot of the memory allocator state via However, the occupied GPU memory by tensors will notīe freed so it can not increase the amount of GPU memory available for PyTorch.įor more advanced users, we offer more comprehensive memory benchmarking via Releases all unused cached memory from PyTorch so that those can be usedīy other GPU applications. Max_memory_reserved() to monitor the total amount of memory Max_memory_allocated() to monitor memory occupied by Unused memory managed by the allocator will still show as if used in ThisĪllows fast memory deallocation without device synchronizations. PyTorch uses a caching memory allocator to speed up memory allocations. To get precise measurements, one should eitherĬall () before measuring, or use Operation is actually executed, so the stack trace does not show where it wasĪ consequence of the asynchronous computation is that time measurements without (With asynchronous execution, such an error isn’t reported until after the This can be handy when an error occurs on the GPU. You can force synchronous computation by setting environment variableĬUDA_LAUNCH_BLOCKING=1. Hence, computation will proceed as ifĮvery operation was executed synchronously. (2) PyTorch automatically performs necessary synchronization when copying dataīetween CPU and GPU or between two GPUs. In general, the effect of asynchronous computation is invisible to the caller,īecause (1) each device executes operations in the order they are queued, and In parallel, including operations on CPU or other GPUs. This allows us to execute more computations Uses the GPU, the operations are enqueued to the particular device, but not cuda ( cuda2 ) # d.device, e.device, and f.device are all device(type='cuda', index=2)īy default, GPU operations are asynchronous. to ( device = cuda ) # b.device and b2.device are device(type='cuda', index=1) c = a + b # c.device is device(type='cuda', index=1) z = x + y # z.device is device(type='cuda', index=0) # even within a context, you can specify the device # (or give a GPU index to the. cuda () # a.device and b.device are device(type='cuda', index=1) # You can also use ``Tensor.to`` to transfer a tensor: b2 = torch. tensor (, device = cuda ) # transfers a tensor from CPU to GPU 1 b = torch. device ( 1 ): # allocates a tensor on GPU 1 a = torch. cuda () # y.device is device(type='cuda', index=0) with torch. tensor (, device = cuda0 ) # x.device is device(type='cuda', index=0) y = torch. device ( 'cuda:2' ) # GPU 2 (these are 0-indexed) x = torch. device ( 'cuda' ) # Default CUDA device cuda0 = torch.
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