Plan failed with a cudnnException

[Andd54]

Search before asking

• I have searched the YOLOv8 issues and discussions and found no similar questions.

Question

encountered non-fatal error python3.8/site-packages/torch/nn/modules/conv.py:456: UserWarning: Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:919.) while training, what could possibly be the reason?

Additional

No response

[github-actions]

👋 Hello @Andd54, thank you for your interest in Ultralytics YOLOv8 🚀! We recommend a visit to the Docs for new users where you can find many Python and CLI usage examples and where many of the most common questions may already be answered.

If this is a 🐛 Bug Report, please provide a minimum reproducible example to help us debug it.

If this is a custom training ❓ Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our Tips for Best Training Results.

Join the vibrant Ultralytics Discord 🎧 community for real-time conversations and collaborations. This platform offers a perfect space to inquire, showcase your work, and connect with fellow Ultralytics users.

Install

Pip install the ultralytics package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.

pip install ultralytics

Environments

YOLOv8 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

Status

If this badge is green, all Ultralytics CI tests are currently passing. CI tests verify correct operation of all YOLOv8 Modes and Tasks on macOS, Windows, and Ubuntu every 24 hours and on every commit.

[glenn-jocher]

It looks like you're encountering a cudnnException related to the CuDNN backend while training with PyTorch. This issue might be due to several reasons, such as an incompatible combination of CUDA, CuDNN, and PyTorch versions, or specific features not supported by your GPU.

Here are a few steps you can try to resolve this issue:

1. Update CUDA/CuDNN: Ensure that your CUDA and CuDNN installations are up to date and compatible with your PyTorch version.
2. Check GPU Compatibility: Verify that your GPU supports the features required by the model, especially if you're using advanced layers or operations.
3. Reduce Batch Size: Sometimes, reducing the batch size can help avoid memory issues that might lead to this error.

If the problem persists, consider running your training with torch.backends.cudnn.enabled = False to bypass CuDNN and use a more generic backend, although this might slow down your training:

import torch
torch.backends.cudnn.enabled = False
# Your training code here

Let us know if any of these suggestions help!