April 4, 2020 - train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_worker = BIG NUMBER) Here’s a good thread that might help you in deciding the suitable number for your case. Note: leakage might decrease when shuffle=False as well.

May 9, 2019 - I have the following situation, I’m trying to train a Unet Learner using fastai’s Library. My data is stored as float16 tensor saved by using torch.save and loaded via a custom load function. In fastai, you create a Lear…

October 29, 2018 - CPU memory will leak if the DataLoader num_workers > 0.

April 8, 2024 - Hello, i am trying to use pytorchs Dataset and DataLoader to load a large dataset of several 100GB. This is of course too large to be stored in RAM, so parallel, lazy loading is needed. I am trying to load one large HDF…

RuntimeError: DataLoader worker (pid 10666) is killed by signal: Bus error. It is possible that dataloader's workers are out of shared memory. Please try to raise your shared memory limit. ERROR: Unexpected bus error encountered in worker.

December 24, 2022 - The essence of the solution is to let all processes share memory through a single torch.Tensor object, which needs to be moved to Linux shared memory by PyTorch's custom pickling routine.

July 4, 2019 - I’m trying to train a CNN using a custom dataset and I’ve noticed that CPU memory usage balloons (in spite of the fact that the data and model are on a GPU). The code has been stripped away to the point that the only things left are a loop that gets samples from the dataloader and for loop for the epochs.

August 17, 2023 - I’ve made custom dataset in pytorch for image segmentation my image sizes are 640x640 and my masks are 320x320 when i’m trying to loop in dataloader over time it increase using ram memory and my code will crash import torch from torch.utils.data import Dataset, DataLoader from ...

March 23, 2023 - train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=8, sampler=train_sampler, num_workers=16, pin_memory=True, prefetch_factor=16, persistent_workers=True) But while training I found the host memory cost too much(nearly 90% of total 768GB RAM), from our node's monitor.

July 2, 2024 - When I tried to use the dataloader provided in “https://github.com/PatrickTUM/UnCRtainTS/blob/main/data/dataLoader.py,” I noticed a memory leak issue. The dataloader gradually consumes all my RAM, eventually causing my desktop to freeze. some experiments I did to fix it (1) I tried to transfer ...

October 29, 2025 - The leak occurs specifically when the DataLoader's iterator is kept alive for a large number of steps (e.g., in an "infinite" sampling loop for step-based training). ... System RAM continuously increases until the program crashes or the system ...

July 22, 2021 - - How you installed PyTorch: PyTorch is pre-installed in container - Python version: 3.7.10 (provided by docker image) - CUDA/cuDNN version: 10.2 (provided by docker image) - GPU models and configuration: Nvidia V100 (but given the code snippet has no GPU-related code, it shouldn't matter) ... module: dataloaderRelated to torch.utils.data.DataLoader and SamplerRelated to torch.utils.data.DataLoader and Samplermodule: memory usagePyTorch is using more memory than it should, or it is leaking memoryPyTorch is using more memory than it should, or it is leaking memorytriagedThis issue has been looked at a team member, and triaged and prioritized into an appropriate moduleThis issue has been looked at a team member, and triaged and prioritized into an appropriate module

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November 28, 2019 - I’m only read data, and not train model. when read dara, every batch after, gpu memory increase , add torch.cuda. empty_cache () not use code class CustomIterableDataset(IterableDataset): def __init__(self, task_def, task_id, batch_size=32, gpu=True, is_train=True, epochs=10, maxlen=128, dropout_w=0.005): super(CustomIterableDataset).__init__() self.task_def = task_def self.task_id = task_id self.batch_size = batch_size ...

August 8, 2021 - I have a distributed code base I am trying to work with, but, with every epoch I see that my CPU memory increases almost linearly eventually running into OOM on a large 128GB machine :(( Without distributed, the code runs fine with no such issues. The isue is exactly describe here: CPU memory gradually leaks when num_workers > 0 in the DataLoader · Issue #13246 · pytorch/pytorch · GitHub I do use num_workers=16 but the solution posted there, using pyarrow, does not solve my issue - I still ha...

March 15, 2018 - Furthermore, if I change the batch_size of DataLoader to 1000, then on epoch 1, before on_end_epoch, you could see the memory use is just only 715MB, it's strange:

October 29, 2019 - Hi, I’ve got a problem with memory leak during training. I suspect the main cause of that problem is Dataset created by using torchvision.datasets.ImageFolder, (when I used torchvision.datasets.CIFAR10 instead of my dat…

September 1, 2021 - Today I'm going to talk about a famous issue in PyTorch, DataLoader with num_workers > 0 causes memory leak (https://github.com/pytorch/pytorch/issues/13246). This bug is a good opportunity to talk ab...