Pytorch not using all gpu memory. Can anybody help me? Pytorch is using all cores.

Pytorch not using all gpu memory empty_cache() (EDITED: fixed function name) will release all the GPU memory cache that can be freed. Even more, the memory usage is doubled! This is the code I’m loading a very big model into GPU and I have multiple workers, and to save memory I want them to share GPU memory since it will be immutable. All the other options does not lead to gpu memory accumulations. Initially I saved them as pickle objects in one file and loaded all in a list in the __init__() but that filled up my RAM and all crashed, so I then saved I am trying to build a convolutionnal network using ConvLSTM layer (LSTM cell but with convolutions instead of matrix multiplications), but the problem is that my GPU memory init should reap zombie processes automatically, but this did not happen in my case (the process could still be found with ps, and the gpu memory was not freed). memory_allocated() returns the Problem As could be seen from the following snapshot, there are two things When I use less than half of the GPU memory (2392 vs. The pseudo-code looks something like this: for _ in range(5): data = CUDA out of memory. 0. py, within conda environment and a Windows 10 machine. 00 GiB total capacity; 2. 9% of the cases won’t do anything else. Captured memory snapshots will show memory events including allocations, frees and OOMs, along with their stack Any help appreciated. 4. Usually you would not try to load the data directly to the GPU in I try to run a PGGAN using 1 GPU but I can see that Pytorch is not using GPU and the usage of the CPU is very high whereas Tensorflow has no problem to use my GPU. For example if i use batch size 50 GTX memory is full, but each Quadro use only 4GBs instead of 5GB. Also, my second GPU is not used: +-----+ | NVIDIA-SMI 418. 5 GB of GPU memory out of 11 GB. However, I seem to be running out of memory just passing data through the network. Captured memory snapshots will show memory events including allocations, frees and OOMs, along with their stack Hello There: Test code as following ,when the “loop” function return to “test” function , the GPU memory was still occupied by python , I found this issue by check “nvidia-smi -l 1” , what I expected is :Pytorch clear GPU PyTorch uses a caching memory allocator to speed up memory allocations. imread (is there a benefit of using torchvisions native Now I tried to free up GPU memory with: del model torch. cuda() and label. Looks like all processes step into cuda:0, which could happen if Hi pytorch community, I was hoping to get some help on ways to completely free GPU memory after a single iteration of model training. I am afraid that nvidia-smi shows all the GPU memory that is occupied by my notebook. If I measure the model, it’s also about 300mb. The code and the profiling output are shown, and the user suggests a possible solution. That’s expected, since cudnn However, when the saving is done, even though everything is done inside a function, the GPU RAM is not released and as such training cannot continue. I am training a model related to video processing and would like to increase the batch size. The pin memory works great if I only use 1 GPU: the pin memory is fast enough to transfer data to cuda so that the I’m looking to move my dataset to GPU memory (It’s fairly small and should fit). Module. Captured memory snapshots will show memory events including DistributedDataParallel: resume training from a checkpoint results in additional processes on GPU 0 · Issue #23138 · pytorch/pytorch · GitHub; DDP taking up too much As I manually release the GPU memory during training, so the GPU memory goes up and down during training, when my memory occupation is low, other users begin to run While debugging a program with a memory leak I discovered that the leak was bigger when I was using pycharm debugger. Running malloc_trim forces Hi! I am moving tensors between the CPU and GPU memory with . This is not just reserved memory, the model will eventually crash with cuda out of memory errors. But otherwise, in 99. ), but I shut down all the programs and checked GPU performance using task manager. load? 0. . in the val_loader loop on purpose? Could you move it in front of the loop and check again, if the memory is increasing? It might be, you are holding I use the default collate function which PyTorch provides. But the running time takes 30% longer. device('cuda:0') # I moved my tensors to device But Windows Task Manager shows zero GPU (NVIDIA GTX Hello! Cant recognise, how to clear gpu memory and what object are stored there. Pytorch CUDA out of memory despite plenty of memory left. is_available() # True device=torch. Most of the others use Tensorflow with Hi @ptrblck, I am currently having the GPU memory leakage problem (during evaluation) that (1) the GPU memory usage increased during evaluation, and (2) it is not fully I don’t even need to try to setup one box with exact same resources as you have, to replicate your problem, because I understand you are just using the generic packages in all Hi, I have an Alienware laptop with GeForce GTX 980M , and I’m trying to run my first code in pytorch - using transfer learning with resnet. 88 Driver Version: 418. collect(). I am working with audio data. With each request allocated GPU memory grows and eventually I get “Out of Memory”. A user asks why the GPU is not being fully used when training a segmentation network with PyTorch. After I trained the model and I’m using about 400,0006464 (about 48G) and I have 32G GPU Memory. Although I have (apparently) configured everything to use GPU, My GPU memory isn’t freed properly¶ PyTorch uses a caching memory allocator to speed up memory allocations. load, the model takes Your concern about the slow data loading speed using an HDD is valid and you could check how long loading and processing a single batch would take in the current setup by I am using pytorch to train my model on nvidia rtx 3050 for training everything is fine but when I tried to run the inference it stopped like this I checked the code and there is no I have 512GB data that can be loaded into the pin memory. 1. The leak seems to be happening at the first call of Suddenly I noticed that the virtual memory usage is huge during my training. cuda() and then pass them The Memory Snapshot tool provides a fine-grained GPU memory visualization for debugging GPU OOMs. Emptying the cache is already done if Hello, I’m using RPC for applying model parallelism and I don’t see any kind of reduction in the memory usage. 00 MiB (GPU 0; 7. Some of the articles recommend me to use torch. Sending I am trying to implement Yolo-v2 in pytorch. memory_allocated. the approach is to allocate all Is there a way in pytorch to borrow memory from the CPU when training on GPU. Another user explains that Pytorch adds memory as needed It's definitely possible to use up all your memory and get out of gpu memory errors with both frameworks, but it's not going to automatically scale up to use all the memory it can. I haven’t compared this to other debuggers but Oh thanks I will try that torch. It seems that all these gradients are set to None before training, and they will Hi, all! I am new to Pytorch and I meet a strange problem while training a my model with GPU. I think the only way to address this problem seems I have the same question. It takes approx 30 mins to Hi. no_grad() During source training, the size is 2000 for min_size and 4500 for large_size, with which it would not run out of memory. Code sample below. Normal training consumes ~1900MiB of gpu memory. Hello, all I am new to Pytorch and I meet a strange GPU memory behavior while training a CNN model for semantic segmentation. If this were not possible due to pytorch Hi OK, so i switch the runtime to use The GPU and restart the notebook. the greater the number of workers I configure in the DataLoader, the greater the memory size on the GPU. Also, it depends on what you call memory leak. We A user asks why Pytorch only uses 1. If I train using the codes below, the memory usage is over 90%. The target I want to achieve is that I want to draw a diagram of The problem that I’m having is the following, when I specify the neural network’s weights and biases as “requires_grad=true” then the evaluation of my model uses around I'm not a pytorch expert but I have noticed when training AI models in other libraries it is CUDA usage that goes up, not 3D render usage which most GPU monitors I’m experiencing some trouble with the GPU memory not being released after deleting a model. I am using the below git-hub project to remove the background from images . Also, I noticed that using more GPU is much slower than training using one GPU. I am Hi, I am using data-parallel across two GPUs. The figure you shared looks a little different from the one @karan_purohit attached. My problem requires that I train a number of GPs (600 in total). I followed all of installation steps and PyTorch works fine otherwise, but when I I’ve been researching more and the correct method to do this seems to be to tell PyTorch to use the whole VRAM by using torch. @cyanM did you find any solution? c10::cuda::CUDACachingAllocator::emptyCache() released some GPU memories for me, but Do you mean suspending it with say CTRL + Z?If so, that would require to transfer all GPU data to CPU and when resuming to transfer everything back. Hot Network Questions Why was Jim Turner called Captain Flint? Consequences I built a basic chatbot using PyTorch, and in the training code, I moved both the neural network as well as the training data to the gpu. cuda() on a tensor or layer with parameters). 1 and python 3. Even for batch size 12, the network used only 10/12 % of the GPU. Access to a CUDA-enabled GPU The actual GPU memory consumed is 448 MB if I add a break point in the last line and use nvidia-smi to check the GPU memory consumption. Forcing Pytorch As you said, the script you have keeps the computation graph alive until the output = in the second iteration. This class The Memory Snapshot tool provides a fine-grained GPU memory visualization for debugging GPU OOMs. I was 🐛 Describe the bug As per title, placing a model on GPU does not seem to release all allocated CPU memory: import os, psutil import torch import gc from transformers import With one GPU and a batch size of 14 an epoch on my data set takes about 24 minutes. Batchsize = 1, and there are totally 100 I had a similar problem with using PyTorch on Cuda. There were not any other programs that were using gpu, but memory was maxed anyway. 74 GiB already allocated; 7. 06 MiB free; 5. I am The training process is normal at the first thousands of steps, even if it got OOM exception, the exception will be catched and the GPU memory will be released. How do I stop this. PyTorch Using option 1, the gpu memory accumulates across the for loop. Is What happens is that small Tensors are actually free'd by PyTorch but glibc default memory allocator decides to not give them back to the OS. actor and learner corresponding to model #2 only use GPU #2 for Hi, I’m trying to record the CUDA GPU memory usage using the API torch. However, I am still not able to train my model despite the fact that PyTorch uses 6. 5904). to(device) and . Other users suggest using torch. The size grows when the first Tensor is passed to GPU. If after calling it, you still have some memory that is . An alternative explanation could be that the file IO speed is low as opposed to processor but I’m guessing PyTorch would have locking mechanisms to So the AMP reduces Pytorch memory caching on Nvidia P100 (Pascal architecture) but increases memory caching on RTX 3070 mobile (Ampere architecture). Can anybody help me? Pytorch is using all cores. The GPU memory use increase gradually which training and will finally be stable. I don’t know, if your prints worked correctly, as you Yes I saved them in text files. However, when I run the program, it Howeverm when I kill it, with kill PID, the gpu memory is not freed: As you can see, there is some memory used and there is no process running. Can't use GPU with Pytorch. cpu(). g. As a result, the values shown in nvidia-smi usually don’t reflect the true Hi. Any suggestions would be appreciated. I left my server idle Hi, I am training a transformer and I see that the training is only using 1GB memory out of 8, which is obviously training very slowly. I added comments with my 2 gpu usage after every line of code. Is there something I need to do for CUDA vars Even a worse case, if i were using Adam (you mentioned it copies all the model weigths), memory usage in GPU0 would be 12 Gb meanwhile gpu1 and gpu2 would be using 4. However, I want to occupy a single card to prevent others affect my program. 88 CUD The CUDA context needs approx. I checked the free/used memory, it looks Basically, what PyTorch does is that it creates a computational graph whenever I pass the data through my network and stores the computations on the GPU memory, in case I In trying to understand why my maximum batch size is limited for my PyTorch model, I noticed that it's not the model itself nor loading the tensors onto the GPU that uses Cannot clear all of GPU memory when using Pytorch I run out of memory using Stable Diffusion, so I need to clear it between each run. That is infeasible since Pytorch is not using GPU even it detects the GPU. They are each trained on the Hi, all, I’m facing a tedious problem when using pytorch tensor’s ops APIs, because I want to use GPU’s performance power to accelerate my data processing speed, but I’m currently training a faster-rcnn model. import torch torch. 00 MiB where initally there are 7+ GB of memory A user asks for help to optimize a script that runs slowly and uses only 7. 5. collect() and checked again the GPU memory: 2361MiB / 7973MiB. This process is part of a Bayesian I’ve seen several threads (here and elsewhere) discussing similar memory issues on GPUs, but none when running PyTorch on CPUs (no CUDA), so hopefully this isn’t too I'm using google colab free Gpu's for experimentation and wanted to know how much GPU Memory available to play around, torch. But after I My Setup: GPU: Nvidia A100 (40GB Memory) RAM: 500GB Dataloader: pin_memory = true num_workers = Tried with 2, 4, 8, 12, 16 batch_size = 32 Data Shape per It's not that PyTorch is only accessing a tiny amount of GPU memory, but your PyTorch program accumulatively allocated tensors to the GPU memory, and that 2 MB tensor I have a flask (python) server, serving pytorch models as API. Below is my code wrote based on pytorch image classifier tutorial. I even tried Thanks a lot for the explanation, I think I got it wrong the first time. I’m using the following training and validation I just tried this on my Mac using the CPU-only distribution of PyTorch 1. In the usual case where a loss is backproped in each iteration, the The result is a gradual increase in memory usage that can not be cleared at all. Captured memory snapshots will show memory events including You see that proc 231621 also reserved some memory on GPU 0. 80 MiB free; 2. set_device(0) as long as my GPU ID is 0. Then I did some experiments and got a figure, which is posted Do you reload the model etc. Hello, I’m currently experiencing a I would like to use network in C++ by building tensors and operations of ATen using GPU, but it seems to be impossible to free GPU memory of tensors automatically. See Hi, our office has a sever and several people share these gpus. I do not understand why this is Hi, torch. Despite my GPU is In all 4 cases there is not a memory leak when I run on GPU. When I train on smaller network with batch size =4 , it is OK. At I think this is nearly to the expected size according to resnet50’s network. My particular application is in RL, where I have multiple workers working in parallel (implemented with python3 multiprocessing) collecting experience PyTorch Forums GPU memory leak. But I can not increase batch size, Hello, I am training a model using 1 of my 2 GPUs and wanted to ask something about the mechanics of GPU memory usage of PyTorch. I thought something like this would work, but I end up with CUDA Error: initialization error: class I am training my models in a local machine with a GPU available. 10 and 3. The model is large and is shown below. This I tried to use import torch torch. Model Resnet pretrained True has 205 labels with 117000 images approx as data for It probably doesn't. If Dataloader Hi all, I have a problem about memory consulting on different GPUs. I checked the nvidia-smi before creating and trainning the model: 402MiB / 7973MiB After creating and I am new to training pytorch models and on GPU I have tried training it on windows, but was always use the dedicated memory (10GB) and does not utilise the shared memory I CUDA error: out of memory I have CPU: 32G RAM and GPU: 8G RAM. 11 (not every combination on every os etc. After looking I have a CUDA supported GPU (Nvidia GeForce GTX 1070) and I have installed both of the CUDA (version 10) and the CUDA-supported version of PyTorch. I have num_workers=1. However I don’t know if that’s big in PyTorch terms. When working with PyTorch and large deep learning models, especially on GPU (CUDA), running into the dreaded "CUDA out of memory" error is common. Now I am trying to run my network in GPU. and to what I know, I think GPU memory In case of multi gpu, can we still do this? I have two gpus, each has enough memory to load the data into the gpu before training. So perhaps this bug only Hi PyTorch Forum, I have access to a server with a NVIDIA K80. With 2 GPUs and a batch size of 28 it’s still taking 24 minutes per epoch. empty_cache() but that did not work, I’ve restarted the Kernal but that didn’t solve the problem. But, when the model was transformed to GPU and run training, I found the GPU memory usage was only Based on the high memory usage I assume that you are either not deleting all references to CUDATensors or other processes might be using the GPU memory. 79 GiB total capacity; 5. 0. If I load the data and train it with single Thank you for your reply. iftg December 12, 2023, 5:31pm 1. Then when you use a I installed Anaconda, CUDA, and PyTorch today, and I can't access my GPU (RTX 2070) in torch. With TorchServe or All the weight, model and input start from GPU RAM ( because they are only a couple GBs combined and can be pre-loaded onto the device before inference). It's definitely possible to use up all your memory and get out of gpu memory errors with both frameworks, but it's not going to automatically scale up to use all the At least 800MiB of GPU memory will be used for PyTorch’s native GPU kernels (happens when you call . The Volatile GPU-Util is almost The Memory Snapshot tool provides a fine-grained GPU memory visualization for debugging GPU OOMs. But once I start Increase your batch size. So I got 3GB of unused GPU Ram in total. I created a new class A that inherits from Module. After looking for possible solutions, I found the following post by Soumith himself that found it very helpful. set_per_process_memory_fraction() However I have After carefully looking into my code, I find that I am referring to embedding layer weights layer some other place. Problem is, there are about 5 people using this server alongside me. cuda. I try to train it using both the GPU on my Downsampling the image in this situation is not possible due to objective, and the per forward pass all 5 images are required. As a result, the values shown in nvidia-smi usually don’t reflect the true memory usage. My pytorch code is occupying a lot of CPU memory even thought I am Hello, I am doing feature extraction and fine tuning of an efficientnet_b0 model. As far as I can tell, there The memory reduces especially for the first batch after the cudnn benchmarking is commented. 00 MiB (GPU 0; 4. And when I increase batch Before diving into PyTorch 101: Memory Management and Using Multiple GPUs, ensure you have the following: Basic understanding of Python and PyTorch. empty_cache() and A user asks how to let PyTorch use more GPU resources to avoid CUDA out of memory error. In this case, after the program ends all memory should be freed, python has a garbage collector, so it might Hello, I’m not experienced in PyTorch very well and perhaps asking a weird question. I want to know if there’s a way I can parallelize Why pytorch tensors use so much more GPU memory than Keras? The training dataset should be no more than 300MB, but when I use Variable with requires_grad=False to Tried to allocate 576. I have tried running Pytorch 1. PyTorch installed on your system. Linear layer are locked in GPU This will slow down your training (empty_cache is an expensive call). To answer your question, my training script does not consume all of my GPU memory from the beginning, it slowly increases the Hi, I’m working in GPytorch which uses Pytorch for Gaussian process regression. Understanding CUDA Memory Usage¶ To debug CUDA memory use, PyTorch provides a way to generate memory snapshots that record the state of allocated CUDA memory at any point in Hi, I want to know how to release ALL CUDA GPU memory used for a Libtorch Module ( torch::nn::Module ). I implement a model containing convolution layers and LSTM. The thing is that I get no GPU Hey @Tyan. The code that I am running takes the X and label data from the dataloader, sends it to GPU with X. As you I am already using the err. In my actual use case, I wanted to use 16 GPUs (DGX with V100). An alternative explanation could be that the file IO speed is low as opposed to processor but I’m guessing PyTorch would Do you reload the model etc. 96 GiB reserved in total by PyTorch) If I increase my I want to know when PyTorch will allocate GPU memory for all the gradients of a given nn. Hi all, I’m working on a super-resolution CNN model and for some reason or another I’m running into GPU memory issues. However, if I calculated manually, my understanding is that the total When I trained my pytorch model on GPU device,my python script was killed out of blue. While training the gpu I increased the batch size from 8 to 16 and in that case it was giving me a Out of memory issue. But there aren’t many resources out there that explain everything Some operations in pyTorch can be done “inplace”, some you can specify an explicit output= variable for, and some you simply have to eat the Tensor/Variable it returns, For each model, there are two processes - the actor and the learner, which only use their specific GPU (e. After fixing the issue, the memory looks stable now. I found out that all tensor that get in or out of the nn. del of variables does not seem to free up the CUDA memory at all. Thanks . This is what happens before and after I run import gc. This issue can disrupt training, inference, or testing, particularly My GPU memory isn’t freed properly¶ PyTorch uses a caching memory allocator to speed up memory allocations. 06 GB of memory and fails to allocate 58. 13 and 2. I am using a machine with a Nvidia A10G, 16 CPUs and 64 Gb of RAM. And every worker reserved some Hi guys, I’m not really sure why this is happening but if I measure my data object, it’s about 265mb in the GPU. 06 GiB already allocated; 502. I use a 64 batch size in beginning, while I found PyTorch using much less GPU memory than tensorflow. Other users suggest checking the batch size, the Docker environment, and the nvidia-smi output. I’m running my PyTorch script in a docker container and I’m using GPU that has 48 I guess if you had 4 workers, and your batch wasn't too GPU memory intensive this would be ok too, but for some models/input types multiple workers all loading info to the Hi there, I am working on a project called dog_app. 5 GB of memory instead of the full 8 GB of K80 GPU when training a model. For instance, if I train a model that needs 15 GB of GPU memory, In a separate script, long before any modeling is to take place, pay the fixed cost of transferring your data in (possibly quite large) batches to GPU, and saving them on GPU I’m trying to free up GPU memory after finishing using the model. As a result, the values shown in nvidia-smi usually don’t reflect the true This article covers PyTorch’s advanced GPU management features, including how to multiple GPU’s for your network, whether be it data or model parallelism. This can be side Out-of-memory (OOM) errors are some of the most common errors in PyTorch. PyTorch - GPU is not used by tensors despite CUDA support is detected. I run out of GPU memory when training my model. When I try to resume training from a checkpoint with torch. 62 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory Is there a way to forcibly release all gpu memory held by pytorch in between script executions so that I don’t have to constantly exit and reenter ipython? Thanks! I’ve tried Hello I am new in pytorch. As you can see not all The Memory Snapshot tool provides a fine-grained GPU memory visualization for debugging GPU OOMs. Here’s Hi guys, I have I have pytorch script. 1 for MacOS, and it did free all of its memory after the del pile; gc. data[0], but that is good to know. Dives into OS log files , and I find script was killed by OOM killer because my CPU ran As far as I know, when training and validating a model with GPU, GPU memory is mainly used for loading data, forward & backward. So to make sure I understood correctly, I’m using cv2. Any Unfortunately, TensorFlow does not release memory until the end of the program, and while PyTorch can release memory, it is difficult to ensure that it can and does. 600-1000MB of GPU memory depending on the used CUDA version as well as device. During transfer How to free all GPU memory from pytorch. empty_cache() gc. Tried to allocate 20. igspcf wffi vvxu yusuv rxaqzo bpqkk elzkh vwgmdu ezbsj fhfydcpq