September 14, 2022 - When we run torch.is_available() it allocates 11GB for one GPU and 44.2GB when we use six GPUs. Then when we start the workers in the training loop that CPU allocation is copied to each worker, so we see this massive memory use.

July 14, 2023 - I’m quite new to trying to productionalize PyTorch and we currently have a setup where I don’t necessarily have access to a GPU at inference time, but I want to make sure the model will have enough resources to run. Based on the documentation I found, I have 2 main tools available, one is the profiler and the other is torch.cuda.max_memory_allocated(). The latter is quite straightforward, apparently my model is using around 1GB of CUDA memory at inference.

December 30, 2021 - cc @ptrblck I have a question regarding pytorch tensor memory usage, it seems that what should be functionally similar designs consumes drastically different amount of CPU memory, I have not tried GPU memory yet. Below are two implementations of replay buffer used in RL: Implementation 1, uses 4.094GiB memory, creates 20003 tensors in total from time import sleep from copy import deepcopy import gc import torch as t if __name__ == "__main__": buffer = [] state = t.randint(0, 255, [1...

November 13, 2018 - Hi, I have a question regarding allocation of RAM/virtual memory (Not GPU memory) when torch.cuda.init() is called If i use the code import torch torch.cuda.init() The virtual memory usage goes up to about 10GB, and 135M in RAM (from almost ...

A simple Pytorch memory usages profiler · Raw · mem_report.py · This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.

Element type Size Used MEM ------------------------------------------------------------------------------- Storage on cuda:0 0.weight (1024, 1024) 4.00M 0.weight.grad (1024, 1024) 4.00M 0.bias (1024,) 4.00K 0.bias.grad (1024,) 4.00K 1.bias (1024,) 4.00K 1.bias.grad (1024,) 4.00K Tensor0 (512, 1024) 2.00M Tensor1 (1,) 512.00B ------------------------------------------------------------------------------- Total Tensors: 2625537 Used Memory: 10.02M The allocated memory on cuda:0: 10.02M ------------------------------------------------------------------------------- You can better understand the m

July 31, 2023 - While installing pytorch models with the gpu option, I see about 4 GB usage in cpu ram. It already uses 2.5 GB from my gpu memory. I could never understand the reason. My codes and ram information are below.

Continue to ../../2.12/generated/torch.mps.current_allocated_memory.html

January 18, 2021 - Since X is a huge matrix, it occupies the whole RAM. My expectation is that in the second iteration X amount of memory is dropped, and the new fresh Y amount of memory is allocated. Unfortunately, this doesn't happen and raises out of memory issues.

December 30, 2021 - Let’s say that I have a PyTorch tensor that I’m loading onto CPU. I would now like to experiment with different shapes and how they affect the memory consumption, and I thought the best way to do this is creating a simple random tensor and then measuring the memory consumptions of different ...

January 28, 2025 - The setting, pin_memory=True can allocate the staging memory for the data on the CPU host directly and save the time of transferring data from pageable memory to staging memory (i.e., pinned memory a.k.a., page-locked memory).

November 10, 2021 - Well, I'm using a package that uses pytorch models to do their job (easyocr/JaiddedAI). The problem is that, when a new model is loaded, its resources are kept in my memory even though I deallocated manually (del model) not sure why that is a thing since I'm currently using a CPU, and the cache tensor way is a GPU thing.

May 15, 2023 - Hi, from my current browsing of the documentation, it seems that the only way to provide a custom CUDA memory allocator is by the CUDAPluggableAllocator class, correct? What I want to achieve is that given a simple linear model: in-> A->B->C->D->E-> out I want to be able to control where the GPU memory of these 5 nodes(A~E) will be allocated/stored.(in fact, it will be great if I can control the allocation of weights between these nodes too) It’s related to the gradient-checkpointing techniqu...

November 4, 2021 - Further, it can be seen that GPU memory usage increased to 6027 MiB after feed forwarding because PyTorch automatically allocated a memory space to additionally store intermediate results generated by performing feed forwarding.

June 28, 2018 - It appears to me that calling module.to(cuda_device) copies to GPU RAM, but doesn’t release memory of CPU RAM. Is there a way to reclaim some/most of CPU RAM that was originally allocated for loading/initialization after moving my modules to GPU? Some more info: Line 214, uses about 2GB to ...

May 30, 2021 - When I run my experiments on GPU, it occupies large amount of cpu memory (~2.3GB). However, when I run my exps on cpu, it occupies very small amount of cpu memory (<500MB). This memory overhead restricts me on training m…

February 7, 2025 - Learn how to use PyTorch to build, train, and test artificial neural networks in this course. ... .max_memory_allocated(): Returns the peak GPU memory usage since the start of the program or last reset.

December 14, 2023 - In this snapshot, there are 3 peaks showing the memory allocations over 3 training iterations (this is configerable). When looking at the peaks, it is easy to see the rise of memory in the forward pass and the fall during the backward pass as the gradients are computed. It is also possible to see that the program has the same pattern of memory use iteration to iteration. One thing that stands out is the many tiny spikes in memory, by mousing over them, we see that they are buffers used temporarily by convolution operators.