CUDA error: initialization error CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1 Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions. 原因是 pytorch torchData...
For debugging consider passing CUDA_LAUNCH_BLOCKING=1. Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions. Traceback (most recent call last): File "/home/ma-user/work/pretrain/peft-baichuan2-13b-1/train.py", line 285, in <module> main() File "/home/ma-user/work/pre...
192.168.37.6: For debugging consider passing CUDA_LAUNCH_BLOCKING=1. 192.168.37.6: Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions. export TORCH_USE_CUDA_DSA=1 以上train在V100-32GB*16,大概率显存不足。 发布于 2024-01-14 13:51・广东...
export CUDA_LAUNCH_BLOCKING=1```然后再运行你的程序。2. 编译PyTorch时,使用`TORCH_USE_CUDA_DSA`选项,这会启用设备端断言(device-side assertions),有助于定位CUDA内核中的错误。重新编译PyTorch时,可以这样设置:```bashTORCH_USE_CUDA_DSA=1 python setup.py install```通过这两个方法,你可以更准确地定位...
For debugging consider passing CUDA_LAUNCH_BLOCKING=1. Compile withTORCH_USE_CUDA_DSAto enable device-side assertions. 2024-03-29 18:28:51,875 xinference.api.restful_api 8 ERROR [address=0.0.0.0:43266, pid=897] CUDA error: invalid argument ...
For debugging consider passing CUDA_LAUNCH_BLOCKING=1. Compile withTORCH_USE_CUDA_DSAto enable device-side assertions. 0%| | 0/98 [00:00<?, ?it/s] Assignees No one assigned Labels None yet Projects None yet Milestone No milestone
内核启动与执行(可以通过将CUDA_LAUNCH_BLOCKING设为1,来disable内核执行并行,debug使用) 设备端内部传输数据 64KB及以下的 host-to-device数据传输 使用流(带有Async前缀的内存传输函数)或内存映射传输数据(不再受64KB的限制) 设备端memset函数(cudaMemset()) 其中第3、4条说明,在使用cudaMemcpy()时,如果数据小于等...
For debugging consider passing CUDA_LAUNCH_BLOCKING=1. Compile with TORCH_USE_CUDA_DSA to enable device-side assertions. 我们翻译一下: RuntimeError: CUDA error: device-side assert triggered 运行时错误:CUDA设备端断言被触发。 这表明在GPU设备上执行的代码中有断言失败。断言是一种错误检查,如果代码中...
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在PyTorch 官网上有如下安装对照表,同时也有历史版本安装对照表 从零开始配置python深度学习环境大概有如下配置步骤: 方案一: 电脑安装显卡驱动,然后安装CUDA、cuDNN,安装miniconda3。前面都是在电脑基础环境配置,后面的操作都是在conda环境中,安装torch、cuda