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 - 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 ...

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…

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.

August 18, 2022 - This halves the memory used in the forward pass. These memory savings are not reflected in the current PyTorch implementation of mixed precision (torch.cuda.amp), but are available in Nvidia’s Apex library with `opt_level=02` and are on the roadmap for the main PyTorch code base.

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 ...

Currently the CUDA context of pytorch requires about 1 GB CUDA memory, which means even all Tensors are on CPU, 1GB of CUDA memory is wasted, :-(. However it's still under investigation if I can fully destroy the context and then re-init.

May 31, 2023 - I recently updated the pytorch v1.6 to v1.9.0+cu111. After the upgrade i see there is increase in RAM utilization of ~3 GB when i load the model. I’ve noticed this behavior in my power edge: OS: Ubuntu 20.04.4 ...

October 15, 2019 - $ python3 pytorch_high_mem.py --n 500 torch 1.3.0+cpu heights: mean=1004, p50=278 p95=5100 max=7680 n=100 memory growth (kb): 2,624,296 n=200 memory growth (kb): 4,480,012 n=300 memory growth (kb): 5,579,568 n=400 memory growth (kb): 5,600,888 time: mean=0.676 s, p50=0.196 s, p95=3.825 s memory (kb): 187,840 initial, 6,200,664 growth · Expected behavior is low memory usage as in pytorch 1.1.

March 13, 2020 - I did some experiment with below code: import torch cpu, gpu = torch.device('cpu'), torch.device('cuda:0') size = (1, 1, 1) t_gpu1 = torch.zeros(size, device=gpu) With command pmap -x pid, I see that there is a large anon mapping memory(about 1GB), which is non file-backend memory and most of it is in RSS.

April 10, 2021 - I’m a developer of a commercial application that requires initializing CUDA between dozens of independent processes. These processes are containerized for versioning - I can’t share much more, but using a single runtime …

August 15, 2024 - After moving to GPU: The memory usage on the CPU doesn't drop much. It seems like some data or buffers might still be retained in CPU memory. I've tried deleting references to the model on CPU using 'del' and forced garbage collection using 'gc.collect()' but this doesn't seem to affect the memory. So is that because PyTorch inherently keep some CPU memory for caching or other purposes?

March 30, 2024 - I have a training pipeline which offloads various components (model, model ema, optimizer) to CPU at various training step stages, and does so asynchronously (e.g. for each data buffer, calling buffer.to('cpu', non_blocking=True). Making these transfers non-blocking results in significant speed increases (almost 2x).

February 21, 2023 - Hi guys, I am new to PyTorch, and I encountered a problem during training of a language model using PyTorch with CPU. I monitor the memory usage of the training program using memory-profiler and cat /proc/xxx/status | grep Vm. It seems that the RAM isn’t freed after each epoch ends.

March 25, 2021 - Hi All, I was wondering if there ... (as calculated via psutil.Process(os.getpid()).memory_info()[0]/(2.**30) ) increases by about 0.2GB on average....

November 4, 2021 - In addition, PyTorch can run Python code on the CPU while computing on the GPU via the CUDA Stream mechanism. Thus, PyTorch can increase the utilization of the GPU even when using Python, which has a large execution overhead. PyTorch is implemented to dynamically allocate and reuse memory according to the characteristics of deep learning, so users can use memory efficiently.

June 6, 2022 - Hi, I am noticing a ~3Gb increase in CPU RAM occupancy after the first .cuda() call. The CPU RAM occupancy increase is partially independent from the moved object original CPU size, whether it is a single tensor or a nn.Module subclass. I’ve noticed this behavior in my workstation: OS: Ubuntu ...