Installation issues: I adapted the TSN Docker container – feel free to use it

[yjxiong]

Installation issues: I adapted the TSN Docker container – feel free to use it

[hgminh95]

Thanks, that is really helpful (:

[yagmurshn]

Hi, i had the same trouble cas of dense_flow package which caused me not to run cuhk project on locally. There's no opencv version that it works compatible with dense_flow. Btw cuhk project does not work with python 3 version. My question is after i pull your docker image to my locally and enter inside to container, when i try to run following command prompt in order to see the classification results for plastering.avi, i'm getting the error below, so i cann't run the classification.

python examples/classify_video.py --use_flow data/plastering.avi

root@488aab6d3886:/app/anet2016-cuhk# python examples/classify_video.py --use_flow data/plastering.avi 
WARNING: Logging before InitGoogleLogging() is written to STDERR
E0805 18:02:37.780357    16 common.cpp:138] Cannot create Cublas handle. Cublas won't be available.
E0805 18:02:37.780797    16 common.cpp:145] Cannot create Curand generator. Curand won't be available.
Setting device 0
F0805 18:02:37.781041    16 common.cpp:190] Check failed: error == cudaSuccess (35 vs. 0)  CUDA driver version is insufficient for CUDA runtime version
*** Check failure stack trace: ***
Aborted (core dumped)

Could the cuda driver version inside the docker image be outdated or incompatible?

[yagmurshn]

The error i still keep getting, when i run following script.
python examples/classify_video.py data/plastering.avi

F0807 20:06:21.927471  1948 cudnn_conv_layer.cu:34] Check failed: status == CUDNN_STATUS_SUCCESS (8 vs. 0)  CUDNN_STATUS_EXECUTION_FAILED
*** Check failure stack trace: ***
Aborted (core dumped)

[wssxjtu]

Have you solved this problem yet? I'm having the same problem. @yagmurshn

[wssxjtu]

The error i still keep getting, when i run following script. python examples/classify_video.py data/plastering.avi

F0807 20:06:21.927471  1948 cudnn_conv_layer.cu:34] Check failed: status == CUDNN_STATUS_SUCCESS (8 vs. 0)  CUDNN_STATUS_EXECUTION_FAILED
*** Check failure stack trace: ***
Aborted (core dumped)

I'm having the same problem. @yagmurshn

[yagmurshn]

Regrettably, I've exhausted all local solutions, including using nvidia-docker with the --gpus all parameter, but I continue to encounter this error.

[yagmurshn]

It's quite disappointing that the CUHK paper received such widespread recognition in various articles and even secured the winner rank, yet its code failed to work at all, the code did not function as expected.

[wssxjtu]

It's quite disappointing that the CUHK paper received such widespread recognition in various articles and even secured the winner rank, yet its code failed to work at all, the code did not function as expected.

Regrettably, I've exhausted all local solutions, including using nvidia-docker with the --gpus all parameter, but I continue to encounter this error.

Thank you very much for your reply!