跑maskrcnn demo时遇到如下问题

Question

An error ocurred while starting the kernel
2021󈚧󈚩 10:24:24.686025: I T:\src\github\tensorflow\tensorflow\core\platform\cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2021󈚧󈚩 10:24:24.925516: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1344] Found device 0 with properties: 
name: GeForce GTX 1660 Ti major: 7 minor: 5 memoryClockRate(GHz): 1.59
pciBusID: 0000:01:00.0
totalMemory: 6.00GiB freeMemory: 4.92GiB
2021󈚧󈚩 10:24:24.928663: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1423] Adding visible gpu devices: 0
2021󈚧󈚩 10:24:25.641280: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:911] Device interconnect StreamExecutor with strength 1 edge matrix:
2021󈚧󈚩 10:24:25.641783: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:917] 0 
2021󈚧󈚩 10:24:25.642116: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:930] 0: N 
2021󈚧󈚩 10:24:25.642549: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1041] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4677 MB memory) ‑> physical GPU (device: 0, name: GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5)
2021󈚧󈚩 10:24:30.266109: E T:\src\github\tensorflow\tensorflow\stream_executor\cuda\cuda_dnn.cc:396] Loaded runtime CuDNN library: 7605 (compatibility version 7600) but source was compiled with 7003 (compatibility version 7000). If using a binary install, upgrade your CuDNN library to match. If building from sources, make sure the library loaded at runtime matches a compatible version specified during compile configuration.
2021󈚧󈚩 10:24:30.268110: F T:\src\github\tensorflow\tensorflow\core\kernels\conv_ops.cc:712] Check failed: stream‑>parent()‑>GetConvolveAlgorithms( conv_parameters.ShouldIncludeWinogradNonfusedAlgo(), &algorithms) 

Answer 1

cudnn版本和tensorflow版本不对应。你先看下你的cuda和cudnn版本，然后看看tensorflow的版本支不支持这个版本的cuda和cudnn，要么换tensorflow的版本，要么重新装cuda和cudnn。