How to control randomness？如何控制随机性？

[JasonYangCode]

Prerequisite

• I have searched the existing and past issues but cannot get the expected help.
• I have read the FAQ documentation but cannot get the expected help.
• The bug has not been fixed in the latest version.

🐞 Describe the bug

I used the official recommended code (https://mmyolo.readthedocs.io/zh-cn/dev/common_usage/set_random_seed.html), but I couldn't control the randomness of the training results. Is there any other way to control the randomness of training? What details do I need to pay attention to?
我使用了官方的建议代码（https://mmyolo.readthedocs.io/zh-cn/dev/common_usage/set_random_seed.html），无法控制训练结果的随机性，请问还有其他方法可以控制训练的随机性吗？我需要注意哪些细节？

python ./tools/train.py
${CONFIG} \ # 配置文件路径
--cfg-options randomness.seed=2023 \ # 设置随机种子为 2023
[randomness.diff_rank_seed=True] \ # 根据 rank 来设置不同的种子。
[randomness.deterministic=True] # 把 cuDNN 后端确定性选项设置为 True

[] 代表可选参数，实际输入命令行时，不用输入 []

Environment

sys.platform: win32
Python: 3.8.19 (default, Mar 20 2024, 19:55:45) [MSC v.1916 64 bit (AMD64)]
CUDA available: True
MUSA available: False
numpy_random_seed: 2147483648
GPU 0: NVIDIA GeForce RTX 3090
CUDA_HOME: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8
NVCC: Cuda compilation tools, release 11.8, V11.8.89
GCC: n/a
PyTorch: 2.0.1
PyTorch compiling details: PyTorch built with:

• C++ Version: 199711
• MSVC 193431937
• Intel(R) Math Kernel Library Version 2020.0.2 Product Build 20200624 for Intel(R) 64 architecture applications
• Intel(R) MKL-DNN v2.7.3 (Git Hash 6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e)
• OpenMP 2019
• LAPACK is enabled (usually provided by MKL)
• CPU capability usage: AVX2
• CUDA Runtime 11.8
• NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_
  70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90;-gencode;arch=compute_37,code=compute_37
• CuDNN 8.7
• Magma 2.5.4
• Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.8, CUDNN_VERSION=8.7.0, CXX_COMPILER=C:/cb/pytorch_1000000000000/work/tmp_bin/sccache-cl.exe, CXX_FLAGS=/DWIN32 /D_WINDOWS /
  GR /EHsc /w /bigobj /FS -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE, LAPACK_INFO=mkl, PERF_WIT
  H_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=OFF, TORCH_VERSION=2.0.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=OFF, USE_NNPACK=OFF, USE_OPENMP=ON, USE_ROCM=OFF,

TorchVision: 0.15.2
OpenCV: 4.10.0
MMEngine: 0.10.4
MMCV: 2.0.1
MMDetection: 3.3.0
MMYOLO: 0.6.0+8c4d9dc

Additional information

No response