RuntimeError: cuDNN error: CUDNN_STATUS_INTERNAL_ERROR

[wuzuiyuzui]

Exception raised from findAlgorithms at /pytorch/aten/src/ATen/native/cudnn/Conv.cpp:550 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x42 (0x7f459cfc11e2 in /home/ljy/anaconda3/envs/yolo/lib/python3.8/site-packages/torch/lib/libc10.so)
frame #1: + 0xee1cd7 (0x7f452e269cd7 in /home/ljy/anaconda3/envs/yolo/lib/python3.8/site-packages/torch/lib/libtorch_cuda.so)
frame #2: + 0xeda95e (0x7f452e26295e in /home/ljy/anaconda3/envs/yolo/lib/python3.8/site-packages/torch/lib/libtorch_cuda.so)
frame #3: + 0xed59ee (0x7f452e25d9ee in /home/ljy/anaconda3/envs/yolo/lib/python3.8/site-packages/torch/lib/libtorch_cuda.so)
frame #4: + 0xed75db (0x7f452e25f5db in /home/ljy/anaconda3/envs/yolo/lib/python3.8/site-packages/torch/lib/libtorch_cuda.so)
frame #5: at::native::cudnn_convolution_backward_input(c10::ArrayRef, at::Tensor const&, at::Tensor const&, c10::ArrayRef, c10::ArrayRef, c10::ArrayRef, long, bool, bool) + 0xb2 (0x7f452e25fb32 in /home/ljy/anaconda3/envs/yolo/lib/python3.8/site-packages/torch/lib/libtorch_cuda.so)
frame #6: + 0xf3cd3b (0x7f452e2c4d3b in /home/ljy/anaconda3/envs/yolo/lib/python3.8/site-packages/torch/lib/libtorch_cuda.so)
frame #7: + 0xf6cb58 (0x7f452e2f4b58 in /home/ljy/anaconda3/envs/yolo/lib/python3.8/site-packages/torch/lib/libtorch_cuda.so)
frame #8: at::cudnn_convolution_backward_input(c10::ArrayRef, at::Tensor const&, at::Tensor const&, c10::ArrayRef, c10::ArrayRef, c10::ArrayRef, long, bool, bool) + 0x1ad (0x7f456516b88d in /home/ljy/anaconda3/envs/yolo/lib/python3.8/site-packages/torch/lib/libtorch_cpu.so)
frame #9: at::native::cudnn_convolution_backward(at::Tensor const&, at::Tensor const&, at::Tensor const&, c10::ArrayRef, c10::ArrayRef, c10::ArrayRef, long, bool, bool, std::array<bool, 2ul>) + 0x223 (0x7f452e25e203 in /home/ljy/anaconda3/envs/yolo/lib/python3.8/site-packages/torch/lib/libtorch_cuda.so)
frame #10: + 0xf3ce25 (0x7f452e2c4e25 in /home/ljy/anaconda3/envs/yolo/lib/python3.8/site-packages/torch/lib/libtorch_cuda.so)
frame #11: + 0xf6cbb4 (0x7f452e2f4bb4 in /home/ljy/anaconda3/envs/yolo/lib/python3.8/site-packages/torch/lib/libtorch_cuda.so)
frame #12: at::cudnn_convolution_backward(at::Tensor const&, at::Tensor const&, at::Tensor const&, c10::ArrayRef, c10::ArrayRef, c10::ArrayRef, long, bool, bool, std::array<bool, 2ul>) + 0x1e2 (0x7f456517a242 in /home/ljy/anaconda3/envs/yolo/lib/python3.8/site-packages/torch/lib/libtorch_cpu.so)
frame #13: + 0x2ec9c62 (0x7f4566e3dc62 in /home/ljy/anaconda3/envs/yolo/lib/python3.8/site-packages/torch/lib/libtorch_cpu.so)
frame #14: + 0x2ede224 (0x7f4566e52224 in /home/ljy/anaconda3/envs/yolo/lib/python3.8/site-packages/torch/lib/libtorch_cpu.so)
frame #15: at::cudnn_convolution_backward(at::Tensor const&, at::Tensor const&, at::Tensor const&, c10::ArrayRef, c10::ArrayRef, c10::ArrayRef, long, bool, bool, std::array<bool, 2ul>) + 0x1e2 (0x7f456517a242 in /home/ljy/anaconda3/envs/yolo/lib/python3.8/site-packages/torch/lib/libtorch_cpu.so)
frame #16: torch::autograd::generated::CudnnConvolutionBackward::apply(std::vector<at::Tensor, std::allocatorat::Tensor >&&) + 0x258 (0x7f4566cc4c38 in /home/ljy/anaconda3/envs/yolo/lib/python3.8/site-packages/torch/lib/libtorch_cpu.so)
frame #17: + 0x3375bb7 (0x7f45672e9bb7 in /home/ljy/anaconda3/envs/yolo/lib/python3.8/site-packages/torch/lib/libtorch_cpu.so)
frame #18: torch::autograd::Engine::evaluate_function(std::shared_ptrtorch::autograd::GraphTask&, torch::autograd::Node*, torch::autograd::InputBuffer&, std::shared_ptrtorch::autograd::ReadyQueue const&) + 0x1400 (0x7f45672e5400 in /home/ljy/anaconda3/envs/yolo/lib/python3.8/site-packages/torch/lib/libtorch_cpu.so)
frame #19: torch::autograd::Engine::thread_main(std::shared_ptrtorch::autograd::GraphTask const&) + 0x451 (0x7f45672e5fa1 in /home/ljy/anaconda3/envs/yolo/lib/python3.8/site-packages/torch/lib/libtorch_cpu.so)
frame #20: torch::autograd::Engine::thread_init(int, std::shared_ptrtorch::autograd::ReadyQueue const&, bool) + 0x89 (0x7f45672de119 in /home/ljy/anaconda3/envs/yolo/lib/python3.8/site-packages/torch/lib/libtorch_cpu.so)
frame #21: torch::autograd::python::PythonEngine::thread_init(int, std::shared_ptrtorch::autograd::ReadyQueue const&, bool) + 0x4a (0x7f45a3ed070a in /home/ljy/anaconda3/envs/yolo/lib/python3.8/site-packages/torch/lib/libtorch_python.so)
frame #22: + 0xc819d (0x7f45a586a19d in /home/ljy/anaconda3/envs/yolo/bin/../lib/libstdc++.so.6)
frame #23: + 0x9609 (0x7f45a9bb4609 in /lib/x86_64-linux-gnu/libpthread.so.0)
frame #24: clone + 0x43 (0x7f45a9adb293 in /lib/x86_64-linux-gnu/libc.so.6)

I am very troubled by this problem. Do you have any solutions？？？

[github-actions]

Hello @wuzuiyuzui, thank you for your interest in 🚀 YOLOv5! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

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Requirements

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$ pip install -r requirements.txt

Environments

YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

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[glenn-jocher]

@wuzuiyuzui thank you for your interest in our work! This issue seems to lack the minimum requirements for a proper response, or is insufficiently detailed for us to help you. Please note that most technical problems are due to:

• Your modified or out-of-date code. If your issue is not reproducible in a new git clone version of this repo we can not debug it. Before going further run this code and verify your issue persists:

$ git clone https://github.com/ultralytics/yolov5 yolov5_new  # clone latest
$ cd yolov5_new
$ python detect.py  # verify detection

# CODE TO REPRODUCE YOUR ISSUE HERE

• Your custom data. If your issue is not reproducible in one of our 3 common datasets (COCO, COCO128, or VOC) we can not debug it. Visit our Custom Training Tutorial for guidelines on training your custom data. Examine train_batch0.jpg and test_batch0.jpg for a sanity check of your labels and images.

• Your environment. If your issue is not reproducible in one of the verified environments below we can not debug it. If you are running YOLOv5 locally, verify your environment meets all of the requirements.txt dependencies specified below. If in doubt, download Python 3.8.0 from https://www.python.org/, create a new venv, and then install requirements.

If none of these apply to you, we suggest you close this issue and raise a new one using the Bug Report template, providing screenshots and minimum viable code to reproduce your issue. Thank you!

Requirements

Python 3.8 or later with all requirements.txt dependencies installed, including torch>=1.6. To install run:

$ pip install -r requirements.txt

Environments

YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Google Colab Notebook with free GPU:
• Kaggle Notebook with free GPU: https://www.kaggle.com/ultralytics/yolov5
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Docker Image https://hub.docker.com/r/ultralytics/yolov5. See Docker Quickstart Guide

Status

If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are passing. These tests evaluate proper operation of basic YOLOv5 functionality, including training (train.py), testing (test.py), inference (detect.py) and export (export.py) on MacOS, Windows, and Ubuntu.

[louis-she]

utils/datasets.py
find the line: pin_memory=True
change it to: pin_memory=False

[glenn-jocher]

@louis-she should this be a permanent change to the code, i.e. a PR?

[github-actions]

This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.