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BabitMF
docker运行blur_gpu module报错
1.docker pull babitmf/bmf_runtime:latest；
2.nvidia-smi +-----------------------------------------------------------------------------+ | NVIDIA-SMI 460.84 Driver Version: 460.84 CUDA Version: 11.2 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |===============================+======================+======================| | 0 Tesla P40 Off | 00000000:88:00.0 Off | 0 | | N/A 35C P0 50W / 250W | 16435MiB / 22919MiB | 0% Default | | | | N/A | +-------------------------------+----------------------+----------------------+ | 1 Tesla P40 Off | 00000000:8D:00.0 Off | 0 | | N/A 41C P0 51W / 250W | 18213MiB / 22919MiB | 0% Default | | | | N/A | +-------------------------------+----------------------+----------------------+ | 2 Tesla P40 Off | 00000000:B3:00.0 Off | 0 | | N/A 32C P0 49W / 250W | 15643MiB / 22919MiB | 0% Default | | | | N/A | +-------------------------------+----------------------+----------------------+ | 3 Tesla P40 Off | 00000000:B6:00.0 Off | 0 | | N/A 34C P0 50W / 250W | 11013MiB / 22919MiB | 0% Default | | | | N/A | +-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=============================================================================| +-----------------------------------------------------------------------------+
3.nvcc --version nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2022 NVIDIA Corporation Built on Wed_Sep_21_10:33:58_PDT_2022 Cuda compilation tools, release 11.8, V11.8.89 Build cuda_11.8.r11.8/compiler.31833905_0
4.cvcuda.gaussian_into报错 Line 563: '' failed: no kernel image is available for execution on the device
请问docker环境还需要怎么配置吗？
这个问题是cvcuda的问题，不是bmf的问题
no kernel image is available表示当前CV-CUDA的binary不支持运行时使用的GPU。P40(sm_61)是Pascal架构，你需要检查下安装的CV-CUDA的二进制中是否包含sm_61的arch，如果没有需要手动添加sm_61后重新编译CV-CUDA
