.build_release/test/test_all.testbin 0 --gtest_shuffle Cuda number of devices: 3 Setting to use device 0 Current device id: 0 Current device name: GeForce GTX 1080 Ti Note: Randomizing tests' orders with a seed of 48866 . [===] Running 2361 tests from 309 test cases. [---] Glob...
.build_release/test/test_all.testbin 0 --gtest_shuffle Cuda number of devices: 3 Setting to use device 0 Current device id: 0 Current device name: GeForce GTX 1080 Ti Note: Randomizing tests' orders with a seed of 48866 . [===] Running 2361 tests from 309 test cases. [---] Glob...
该问题的原因:train_lenet.sh脚本文件中的代码: ./build/tools/caffetrain的路径为caffe当前目录下的路径,以至于你不是在caffe目录下执行shtrain_lenet.sh命令时会出现错误。 解决方法:回退到caffe的当前目录下,执行该命令: ./examples/mnist/train_lenet.sh即可。
I am using cuda_12.2, torch 2.1.0a0+29c30b1, bitsandbytes 0.43.3, python 3.10 Driver Version: 535.113.01 NVIDIA GeForce RTX 2080 Ti Traceback (most recent call last): File "/root/share/Latte/infer.py", line 43, in <module> prompt_embeds,...
所有的CUDA API返回值都是CUDA中定义的一个错误代码，这种返回值的方式也是我们在写程序中经常用到的。
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奇怪的是,同样的代码,之前也使用过,没有出现这样的问题,在网上搜索发现这篇博客: https://xmfbit.github.io/2018/02/08/bug-pycaffe-using-cublas/ 原因是第0号显卡内存已满,导致出现这种情况,通过在运行脚本前指定可见显卡,可以解决该问题: CUDA_VISIBLE_DEVICES=1 bash create_lmdb.sh...