error in running the './test_yolo.py model_data/yolo.h5'

[yh284914425]

Using TensorFlow backend. I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcublas.so.8.0 loca lly I tensorflow/stream_executor/dso_loader.cc:126] Couldn't open CUDA library libcudnn.so.5. LD_LIBRARY_P ATH: I tensorflow/stream_executor/cuda/cuda_dnn.cc:3517] Unable to load cuDNN DSO I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcufft.so.8.0 local ly I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcuda.so.1 locally I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcurand.so.8.0 loca lly Creating output path images/out W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations. W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4 .1 instructions, but these are available on your machine and could speed up CPU computations. W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4 .2 instructions, but these are available on your machine and could speed up CPU computations. W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations. W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations. W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations. I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:910] successful NUMA node read from SysFS had n egative value (-1), but there must be at least one NUMA node, so returning NUMA node zero I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties: name: Tesla M60 major: 5 minor: 2 memoryClockRate (GHz) 1.1775 pciBusID 0000:00:15.0 Total memory: 7.43GiB Free memory: 7.35GiB I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0 I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0: Y I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device : 0, name: Tesla M60, pci bus id: 0000:00:15.0) /root/anaconda3/envs/yad2k/lib/python3.6/site-packages/keras/models.py:248: UserWarning: No training c onfiguration found in save file: the model was not compiled. Compile it manually. warnings.warn('No training configuration found in save file: ' model_data/yolo.h5 model, anchors, and classes loaded. F tensorflow/stream_executor/cuda/cuda_dnn.cc:222] Check failed: s.ok() could not find cudnnCreate in cudnn DSO; dlerror: /root/anaconda3/envs/yad2k/lib/python3.6/site-packages/tensorflow/python/_pywrap_t ensorflow.so: undefined symbol: cudnnCreate Aborted (core dumped)

[alecGraves]

It's been a couple days so not sure if you still have this issue, but this looks like this is an issue with your cuda installation. I wold try to reinstall cudnn.

[Flock1]

@shadySource and @yh284914425 can you tell me what version of tf-gpu have you installed? I was using it on conda environment as suggested. All I did was to install tf-gpu 1.4

[xugaoxiang]

@yh284914425 @Flock1, yes, upgrade tensorflow-gpu can fix this error.