Pytorch Find Memory Leak. I guess that somehow a copy of the graph remain in the memory

I guess that somehow a copy of the graph remain in the memory but Great, I think the GPU memory leak issue is in adding new nodes in the graph. Learn diagnostics, root causes, and memory optimization strategies for large-scale ML training. In this article, we will explore how to diagnose and fix memory leaks in Python. 03 GiB free; 29. is there some known workaround to this problem? Apparently detaching the tensors before saving removed In this part, we will use the Memory Snapshot to visualize a GPU memory leak caused by reference cycles, and then locate and remove them in Identify and fix GPU memory leaks in PyTorch with expert debugging techniques and best practices. 14 GiB already allocated; 14. forward (), then no leaks. The leak seems to be happening at the first call of loss. 1, cpu memory leaks; when I execute the following code in PyTorch Lightning is a lightweight PyTorch wrapper that simplifies the process of building, training, and evaluating deep learning models. 0 and cuda 11. If you don't detach such tensors from the computation graph, references are maintained to it which will persist even after that iteration ends Learn how GDB 2030 helps developers identify and fix memory leaks in brain-computer interface firmware with advanced neural pathway analysis and real-time debugging. detach() while you add it to train_loss. 0 torchvision>=0. 32 GiB total capacity; 15. py in Deep Learning with PyTorch: A 60 minutes Blitz, I find memory leak in loss. This is currently causing my hello, thank you for pytorch I am studying beginner tutorials when I run cifar10_tutorial. memory_allocated () and torch. My problem is that after the first Hi, I try to do the predicting part with multithreading. This can result in the application using more and more Hi All, I have a small model (2M params), and I’m using batch = 1. datasets. Can you share how to add placeholder in PyTorch? Hi, I am running into a memory leak when I try to run model inference in parallel using pythons multiprocessing library. Curiously, if I don’t consume the result of model. It turns out this is caused by the transformations I am doing to the images, using 🐛 Describe the bug In pytorch v2. Tried to allocate 14. 1, I encountered an memory leak when trying to input tensors in different shapes to the model. max_memory_allocated () to print a percent of used memory at the top of the training Try loss. Here are a few options: The It seems to be a long-standing problem, because my pytorch version is 2. d5a4198 majing921201 added a commit that references this issue on Mar 3, 2025 [Intel GPU] fix memory leak in deconv backward (pytorch#144385) Hi, I’ve got a problem with memory leak during training. The size of every batch varies, but in average, I’m using 5 gb per iteration for the first epoch. cuda. 15. However, like any complex software library, The memory builds up slowly (the left part), and when I kill it, there’s still some memory occupying the GPU memory (the right part). save caused by circular references in PyTorchPickler nfergu/pytorch#6 How to find a memory leak? As a lot of people on this thread it would seem, I regularly meet the infamous RuntimeError: CUDA out of memory after a few epochs, that drives me crazy. backward (). 6. 04 . If you don't detach such tensors from the computation graph, references are maintained to it which will persist even after that iteration ends (scope of variable ends) and thus GPU memory won't be released. In short this I am applying a gaussian to many images and then a regression with Memory Leakage with PyTorch If you’re reading this post, then most probably you’re facing this problem. 3. For example, in the first 1000 iterations, it uses GPU Mem 6G, and at a random If anyone is concerned about bi-directional LSTM and memory leak issues in pytorch - maybe a good place to start coding a new project is from the already working tutorial? Hello! I’m currently having some CUDA “out of memory” errors during training after a certain number of iterations, and only when back-propagation is being used and precisely when Hi All, I am very new to PyTorch and I’m seeing something weird when my code runs that I can’t figure out. the most useful way I found to debug is to use torch. 40 GiB (GPU 4; 44. I am using a recurrent model of which I got the code a few years Hello, I have been trying to debug an issue where, when working with a dataset, my RAM is filling up quickly. 3 training failures, memory leaks, and performance issues from debugging 50+ production models (Advanced) CUDA out of memory. In this article, we will analyze the causes of PyTorch memory leaks and GPU OOM errors, explore debugging techniques, and provide best practices to optimize deep learning models for I implemented a model in PyTorch 0. They are all in one When I trained my pytorch model on GPU device,my python script was killed out of blue. 72 GiB reserved in total by PyTorch) If reserved memory is I have a few lines of code that are contributing to a growing memory leak (torch 1. 04, CUDA Memory leaks in Python can occur when objects that are no longer being used are not correctly deallocated by the garbage collector. 0). 1. save memory leak #165204 Copilot mentioned this on Oct 22, 2025 Fix memory leak in torch. Everytime, Real solutions for PyTorch v2. RAM is full, in the very beginning of the training, your data is not huge, and maybe 🐛 Describe the bug When I execute the following code in torch>=2. Dives into OS log files , and I find script was killed by OOM Fixing PyTorch Lightning issues: resolving GPU memory leaks, gradient accumulation problems, and training performance bottlenecks for efficient deep learning. Here are codes to reproduce: Quick fix of torch. To identify memory leaks, you can take memory snapshots at regular intervals during the training or inference process and look for a steady increase in memory usage. This is what I run out of GPU memory when training my model. Memory snapshots Troubleshoot PyTorch GPU memory leaks and CUDA OOM errors. There are several tools that can be used to diagnose memory leaks in Python. 4. I suspect the main cause of that problem is Dataset created by using torchvision. Try loss. I don’t know how to fix them and I would appreciate some feedback. 0. But I find that the memory keeps increasing Is there anything wrong with my code? My development environment is: Ubuntu 14. Am I doing something wrong? I’m on Ubuntu 18. backward () To run using Obviously, this runs out of CUDA memory very quickly. ImageFolder, (when I used 🐛 Describe the bug The inductor backend for torch. 0, but find that GPU memory increases at some iterations randomly. compile leaks memory on every call to a compiled mmseg model.

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