pytorch resource_error ai_generated true

RuntimeError: DataLoader 工作进程（pid 12345）收到信号 11（段错误）。可能原因：共享内存耗尽或共享内存文件损坏。

RuntimeError: DataLoader worker (pid 12345) received signal 11 (Segmentation fault). Possible causes: shared memory exhausted or corrupted shared memory files.

ID: pytorch/dataloader-shared-memory-segfault

其他格式: JSON · Markdown 中文 · English
78%修复率
82%置信度
1证据数
2023-06-15首次发现

版本兼容性

版本状态引入弃用备注
torch>=1.10 active — — —
torch>=2.0 active — — —
Linux kernel 5.x+ active — — —
Docker containers active — — —

根因分析

DataLoader 工作进程使用多进程和共享内存（shm）时，/dev/shm 空间耗尽或共享内存文件损坏，导致段错误。

English

DataLoader workers using multiprocessing with shared memory (shm) run out of /dev/shm space or encounter corrupted shared memory files, causing segfault.

generic

官方文档

https://pytorch.org/docs/stable/data.html#multi-process-data-loading

解决方案

  1. Increase /dev/shm size: docker run --shm-size=8g ... or sudo mount -o remount,size=8G /dev/shm
  2. Set DataLoader with multiprocessing_context='spawn' and reduce num_workers: DataLoader(dataset, num_workers=2, multiprocessing_context='spawn')
  3. Disable shared memory by setting DataLoader with persistent_workers=False and pin_memory=False

无效尝试

常见但无效的做法:

  1. Increasing num_workers to speed up loading 70% 失败

    More workers consume more shared memory, exacerbating the exhaustion and making segfaults more frequent.

  2. Setting multiprocessing_context to 'fork' on Linux 60% 失败

    Fork inherits parent's memory space, but shared memory issues remain; 'spawn' is recommended for PyTorch.