实际上,cuda_launch_blocking=1是通过环境变量设置的,如上所述。 编译时使用torch_use_cuda_dsa: 如果您是从源代码编译PyTorch,并且您的CUDA版本支持Device-Side Assertions(DSA),您可以尝试在编译时启用它。这可以通过在编译命令中添加-DTORCH_USE_CUDA_DSA=1来实现。 请注意,这通常不是解决运行时错误的常规...
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...
Here are a few suggestions that we hope will be helpful to you: 1.Check for out-of-bounds data: This error often occurs when there is an out-of-bounds value or illegal operation on tensors. For example, in classification problems, if the labels exceed the number of classes, this could...
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
在A8002卡(每张卡80G显存)机器上启动xinference,启动了两个模型,第一个是deepseek模型,启动正常。第二个是qwen-14B模型 当在卡1上启动qwen-14B模型会报下面的错误 allel_size': 1, 'block_size': 16, 'swap_space': 4, 'gpu_memory_utilization': 0.9, 'max_num_seqs':
怀疑和访问量增大有关 Expected behavior / 期待表现 RuntimeError: CUDA error: device-side assert triggered 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 withTORCH_...