Kaggle/docker-python

Pytorch CUDA P100 GPU Incompatibility

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#1,546 建立於 2026年3月20日

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bughelp wanted

描述

🐛 Bug

The model fails to train on GPU due to a CUDA capability mismatch. The installed version of PyTorch requires CUDA capability sm_70 or higher, but the available GPU (Tesla P100-PCIE-16GB) only supports sm_60. Here is traceback:

/usr/local/lib/python3.12/dist-packages/torch/cuda/__init__.py:435: UserWarning: 
    Found GPU0 Tesla P100-PCIE-16GB which is of cuda capability 6.0.
    Minimum and Maximum cuda capability supported by this version of PyTorch is
    (7.0) - (12.0)
    
  queued_call()
/usr/local/lib/python3.12/dist-packages/torch/cuda/__init__.py:435: UserWarning: 
    Please install PyTorch with a following CUDA
    configurations:  12.6 following instructions at
    https://pytorch.org/get-started/locally/
    
  queued_call()
/usr/local/lib/python3.12/dist-packages/torch/cuda/__init__.py:435: UserWarning: 
Tesla P100-PCIE-16GB with CUDA capability sm_60 is not compatible with the current PyTorch installation.
The current PyTorch install supports CUDA capabilities sm_70 sm_75 sm_80 sm_86 sm_90 sm_100 sm_120.
If you want to use the Tesla P100-PCIE-16GB GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/

  queued_call()

To Reproduce

Use P100 GPU on Kaggle, move Pytorch neural network model to GPU and start training loop.

Expected behavior

Successful forward training pass without errors.

Additional context

This was the error given:

AcceleratorError: CUDA error: no kernel image is available for execution on the device
Search for `cudaErrorNoKernelImageForDevice' in https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html for more information.

Code worked fine for me a few weeks ago, but I'm guessing a change in the Docker environment broke something.

貢獻者指南

技術棧
pythonpytorchdocker
領域
infrastructuredevops
議題類型
bug
難度面向新貢獻者的預計實作難度，1 表示很小改動，5 表示專家級工作。
3
預計時間有經驗貢獻者完成調查、實作、測試並準備 pull request 的粗略時間範圍。
1-3 hours
活動狀態議題目前的可參與程度：新鮮、活躍、陳舊、阻塞或等待維護者輸入。
fresh
清晰度議題是否清楚說明預期改動、驗收標準和下一步。
clear
前置要求
DockerPyTorchCUDA
新手友善度1-100 的估計分數，表示該議題對首次貢獻者的友善程度。
40
研究方向
The issue is caused by the PyTorch version in the Kaggle Docker image requiring CUDA capability sm 70+ but the P100 GPU only supports sm 60. The solution likely involves either downgrading PyTorch to a version compatible with sm 60 (e.g., PyTorch 1.x) or upgrading the GPU. The Dockerfile at https://github.com/Kaggle/docker python should be modified to install a compatible PyTorch version. The specific line installing PyTorch might need a version pin. Also, consider using a different base image or installing PyTorch from source with the correct CUDA architecture flags.