Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR using GPU on PyAgender












1















Sample code runs fine with CUDNN but using code installed with pip returns Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR.



I am using Tensorflow 1.12, CUDA 10.0 and CUDNN 7.4.1 compiled with GPU 2080 (x2)



This is Ubuntu 16.04 LTS. I also use venv. Everything is done in venv using Python 3.5.2.



I am trying to use Py-Agender from https://pypi.org/project/py-agender/



After compiling CUDA with GPU flags and successfully verifying that it works, I tried:
enter code herepip3 install py-agender.



Running it then causes an internal CUDNN error.



I have removed ~/.nv as suggested in other replies and it did not help



I have also confirmed that CuDNN does in fact see and can use GPUs:



print(device_lib.list_local_devices()) returns:



2019-01-03 09:31:30.353768: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:964] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero

2019-01-03 09:31:30.354169: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0 with properties:

name: GeForce RTX 2080 major: 7 minor: 5 memoryClockRate(GHz): 1.8

pciBusID: 0000:01:00.0


... so we at least something works!



b. mNIST sample runs and returns values as expected.



The part of code is as follows:



agender = PyAgender() 



def get_gender(value) :

    if value > 0.5:

        return 'female'

    else :

        return 'male'



input_dir = sys.argv[1]



directories = os.listdir(input_dir)



if len(directories):

    for video in directories:

        files = glob.glob(input_dir + video + '/*')



        if len(files):

            for file_path in files:



                filename, file_extension = os.path.splitext(os.path.basename(file_path))

                faces = agender.detect_genders_ages(cv2.imread(file_path))


The code worked fine with Tensorflow compiled with CPU only. With GPU, the full error is:



$ python age-gender.py ./frames/part1/



Using TensorFlow backend.

- Began to analyze the age and gender 

2019-01-03 09:44:24.229618: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:964] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero

2019-01-03 09:44:24.230019: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0 with properties:

name: GeForce RTX 2080 major: 7 minor: 5 memoryClockRate(GHz): 1.8

pciBusID: 0000:01:00.0 totalMemory: 7.76GiB freeMemory: 7.62GiB

2019-01-03 09:44:24.302145: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:964] su

ccessful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero

2019-01-03 09:44:24.302602: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found

 device 1 with properties:name: GeForce RTX 2080 major: 7 minor: 5 memoryClockRate(GHz): 1.8 pciBusID: 0000:02:00.0 totalMemory: 7.77GiB freeMemory: 7.62GiB

2019-01-03 09:44:24.302625: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Addin

g visible gpu devices: 0, 1

2019-01-03 09:44:24.665465: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device

 interconnect StreamExecutor with strength 1 edge matrix:

2019-01-03 09:44:24.665492: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988]      0 1

2019-01-03 09:44:24.665499: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0:   N Y

2019-01-03 09:44:24.665504: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 1:   Y N

2019-01-03 09:44:24.665749: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 7335 MB memory) ->

 physical GPU (device: 0, name: GeForce RTX 2080, pci bus id: 0000:01:00.0, compute capability: 7.5)

2019-01-03 09:44:24.666056: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 7338 MB memory) ->

 physical GPU (device: 1, name: GeForce RTX 2080, pci bus id: 0000:02:00.0, compute capability: 7.5)

2019-01-03 09:44:25S :: Filename: ./frames/part1/2017/658.jpg

2019-01-03 09:44:25S :: Filename:  ./frames/part1/2017/698.jpg

2019-01-03 09:44:25S :: Filename:  ./frames/part1/2017/746.jpg 

2019-01-03 09:44:25S :: Filename:  ./frames/part1/2017/371.jpg 

2019-01-03 09:44:25S :: Filename:  ./frames/part1/2017/218.jpg 

2019-01-03 09:44:25S :: Filename:  ./frames/part1/2017/860.jpg 

2019-01-03 09:44:26.702699: E tensorflow/stream_executor/cuda/cuda_dnn.cc:373] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR

2019-01-03 09:44:26.709821: E tensorflow/stream_executor/cuda/cuda_dnn.cc:373] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR

Traceback (most recent call last):

  File "age-gender.py", line 50, in <module>

    faces = agender.detect_genders_ages(cv2.imread(file_path))

  File "/home/sv/.local/lib/python3.5/site-packages/pyagender/pyagender.py", line 56, in detect_genders_ages

    height=face['height'])

  File "/home/sv/.local/lib/python3.5/site-packages/pyagender/pyagender.py", line 82, in gender_age

    result = self.resnet.predict(np.array([test_img]))

  File "/opt/inta/venv/lib/python3.5/site-packages/keras/engine/training.py", line 1169, in predict

    steps=steps)

  File "/opt/inta/venv/lib/python3.5/site-packages/keras/engine/training_arrays.py", line 294, in predict_loop

    batch_outs = f(ins_batch)

  File "/opt/inta/venv/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py", line 2715, in __call__

    return self._call(inputs)

  File "/opt/inta/venv/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py", line 2675, in _call

    fetched = self._callable_fn(*array_vals)

  File "/opt/inta/venv/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1439, in __call__

    run_metadata_ptr)

  File "/opt/inta/venv/lib/python3.5/site-packages/tensorflow/python/framework/errors_impl.py", line 528, in __exit__

    c_api.TF_GetCode(self.status.status))

tensorflow.python.framework.errors_impl.UnknownError: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.

         [[{{node conv2d_1/convolution}} = Conv2D[T=DT_FLOAT, _class=["loc:@batch_normalization_1/cond/FusedBatchNorm/Switch"], data_format="NCHW", dilations=[1, 1, 1, 1], padding="SAME", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:GPU:0"](conv2d_1/convolution-0-TransposeNHWCToNCHW-LayoutOptimizer, conv2d_1/kernel/read)]]





python tensorflow







share|improve this question

























  • This might be similar to the issue raised here: github.com/tensorflow/tensorflow/issues/24496

    – SMX
    Jan 15 at 5:14
















1















Sample code runs fine with CUDNN but using code installed with pip returns Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR.



I am using Tensorflow 1.12, CUDA 10.0 and CUDNN 7.4.1 compiled with GPU 2080 (x2)



This is Ubuntu 16.04 LTS. I also use venv. Everything is done in venv using Python 3.5.2.



I am trying to use Py-Agender from https://pypi.org/project/py-agender/



After compiling CUDA with GPU flags and successfully verifying that it works, I tried:
enter code herepip3 install py-agender.



Running it then causes an internal CUDNN error.



I have removed ~/.nv as suggested in other replies and it did not help



I have also confirmed that CuDNN does in fact see and can use GPUs:



print(device_lib.list_local_devices()) returns:



2019-01-03 09:31:30.353768: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:964] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero

2019-01-03 09:31:30.354169: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0 with properties:

name: GeForce RTX 2080 major: 7 minor: 5 memoryClockRate(GHz): 1.8

pciBusID: 0000:01:00.0


... so we at least something works!



b. mNIST sample runs and returns values as expected.



The part of code is as follows:



agender = PyAgender() 



def get_gender(value) :

    if value > 0.5:

        return 'female'

    else :

        return 'male'



input_dir = sys.argv[1]



directories = os.listdir(input_dir)



if len(directories):

    for video in directories:

        files = glob.glob(input_dir + video + '/*')



        if len(files):

            for file_path in files:



                filename, file_extension = os.path.splitext(os.path.basename(file_path))

                faces = agender.detect_genders_ages(cv2.imread(file_path))


The code worked fine with Tensorflow compiled with CPU only. With GPU, the full error is:



$ python age-gender.py ./frames/part1/



Using TensorFlow backend.

- Began to analyze the age and gender 

2019-01-03 09:44:24.229618: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:964] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero

2019-01-03 09:44:24.230019: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0 with properties:

name: GeForce RTX 2080 major: 7 minor: 5 memoryClockRate(GHz): 1.8

pciBusID: 0000:01:00.0 totalMemory: 7.76GiB freeMemory: 7.62GiB

2019-01-03 09:44:24.302145: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:964] su

ccessful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero

2019-01-03 09:44:24.302602: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found

 device 1 with properties:name: GeForce RTX 2080 major: 7 minor: 5 memoryClockRate(GHz): 1.8 pciBusID: 0000:02:00.0 totalMemory: 7.77GiB freeMemory: 7.62GiB

2019-01-03 09:44:24.302625: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Addin

g visible gpu devices: 0, 1

2019-01-03 09:44:24.665465: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device

 interconnect StreamExecutor with strength 1 edge matrix:

2019-01-03 09:44:24.665492: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988]      0 1

2019-01-03 09:44:24.665499: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0:   N Y

2019-01-03 09:44:24.665504: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 1:   Y N

2019-01-03 09:44:24.665749: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 7335 MB memory) ->

 physical GPU (device: 0, name: GeForce RTX 2080, pci bus id: 0000:01:00.0, compute capability: 7.5)

2019-01-03 09:44:24.666056: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 7338 MB memory) ->

 physical GPU (device: 1, name: GeForce RTX 2080, pci bus id: 0000:02:00.0, compute capability: 7.5)

2019-01-03 09:44:25S :: Filename: ./frames/part1/2017/658.jpg

2019-01-03 09:44:25S :: Filename:  ./frames/part1/2017/698.jpg

2019-01-03 09:44:25S :: Filename:  ./frames/part1/2017/746.jpg 

2019-01-03 09:44:25S :: Filename:  ./frames/part1/2017/371.jpg 

2019-01-03 09:44:25S :: Filename:  ./frames/part1/2017/218.jpg 

2019-01-03 09:44:25S :: Filename:  ./frames/part1/2017/860.jpg 

2019-01-03 09:44:26.702699: E tensorflow/stream_executor/cuda/cuda_dnn.cc:373] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR

2019-01-03 09:44:26.709821: E tensorflow/stream_executor/cuda/cuda_dnn.cc:373] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR

Traceback (most recent call last):

  File "age-gender.py", line 50, in <module>

    faces = agender.detect_genders_ages(cv2.imread(file_path))

  File "/home/sv/.local/lib/python3.5/site-packages/pyagender/pyagender.py", line 56, in detect_genders_ages

    height=face['height'])

  File "/home/sv/.local/lib/python3.5/site-packages/pyagender/pyagender.py", line 82, in gender_age

    result = self.resnet.predict(np.array([test_img]))

  File "/opt/inta/venv/lib/python3.5/site-packages/keras/engine/training.py", line 1169, in predict

    steps=steps)

  File "/opt/inta/venv/lib/python3.5/site-packages/keras/engine/training_arrays.py", line 294, in predict_loop

    batch_outs = f(ins_batch)

  File "/opt/inta/venv/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py", line 2715, in __call__

    return self._call(inputs)

  File "/opt/inta/venv/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py", line 2675, in _call

    fetched = self._callable_fn(*array_vals)

  File "/opt/inta/venv/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1439, in __call__

    run_metadata_ptr)

  File "/opt/inta/venv/lib/python3.5/site-packages/tensorflow/python/framework/errors_impl.py", line 528, in __exit__

    c_api.TF_GetCode(self.status.status))

tensorflow.python.framework.errors_impl.UnknownError: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.

         [[{{node conv2d_1/convolution}} = Conv2D[T=DT_FLOAT, _class=["loc:@batch_normalization_1/cond/FusedBatchNorm/Switch"], data_format="NCHW", dilations=[1, 1, 1, 1], padding="SAME", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:GPU:0"](conv2d_1/convolution-0-TransposeNHWCToNCHW-LayoutOptimizer, conv2d_1/kernel/read)]]





python tensorflow







share|improve this question

























  • This might be similar to the issue raised here: github.com/tensorflow/tensorflow/issues/24496

    – SMX
    Jan 15 at 5:14














1












1








1








Sample code runs fine with CUDNN but using code installed with pip returns Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR.



I am using Tensorflow 1.12, CUDA 10.0 and CUDNN 7.4.1 compiled with GPU 2080 (x2)



This is Ubuntu 16.04 LTS. I also use venv. Everything is done in venv using Python 3.5.2.



I am trying to use Py-Agender from https://pypi.org/project/py-agender/



After compiling CUDA with GPU flags and successfully verifying that it works, I tried:
enter code herepip3 install py-agender.



Running it then causes an internal CUDNN error.



I have removed ~/.nv as suggested in other replies and it did not help



I have also confirmed that CuDNN does in fact see and can use GPUs:



print(device_lib.list_local_devices()) returns:



2019-01-03 09:31:30.353768: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:964] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero

2019-01-03 09:31:30.354169: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0 with properties:

name: GeForce RTX 2080 major: 7 minor: 5 memoryClockRate(GHz): 1.8

pciBusID: 0000:01:00.0


... so we at least something works!



b. mNIST sample runs and returns values as expected.



The part of code is as follows:



agender = PyAgender() 



def get_gender(value) :

    if value > 0.5:

        return 'female'

    else :

        return 'male'



input_dir = sys.argv[1]



directories = os.listdir(input_dir)



if len(directories):

    for video in directories:

        files = glob.glob(input_dir + video + '/*')



        if len(files):

            for file_path in files:



                filename, file_extension = os.path.splitext(os.path.basename(file_path))

                faces = agender.detect_genders_ages(cv2.imread(file_path))


The code worked fine with Tensorflow compiled with CPU only. With GPU, the full error is:



$ python age-gender.py ./frames/part1/



Using TensorFlow backend.

- Began to analyze the age and gender 

2019-01-03 09:44:24.229618: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:964] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero

2019-01-03 09:44:24.230019: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0 with properties:

name: GeForce RTX 2080 major: 7 minor: 5 memoryClockRate(GHz): 1.8

pciBusID: 0000:01:00.0 totalMemory: 7.76GiB freeMemory: 7.62GiB

2019-01-03 09:44:24.302145: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:964] su

ccessful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero

2019-01-03 09:44:24.302602: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found

 device 1 with properties:name: GeForce RTX 2080 major: 7 minor: 5 memoryClockRate(GHz): 1.8 pciBusID: 0000:02:00.0 totalMemory: 7.77GiB freeMemory: 7.62GiB

2019-01-03 09:44:24.302625: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Addin

g visible gpu devices: 0, 1

2019-01-03 09:44:24.665465: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device

 interconnect StreamExecutor with strength 1 edge matrix:

2019-01-03 09:44:24.665492: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988]      0 1

2019-01-03 09:44:24.665499: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0:   N Y

2019-01-03 09:44:24.665504: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 1:   Y N

2019-01-03 09:44:24.665749: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 7335 MB memory) ->

 physical GPU (device: 0, name: GeForce RTX 2080, pci bus id: 0000:01:00.0, compute capability: 7.5)

2019-01-03 09:44:24.666056: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 7338 MB memory) ->

 physical GPU (device: 1, name: GeForce RTX 2080, pci bus id: 0000:02:00.0, compute capability: 7.5)

2019-01-03 09:44:25S :: Filename: ./frames/part1/2017/658.jpg

2019-01-03 09:44:25S :: Filename:  ./frames/part1/2017/698.jpg

2019-01-03 09:44:25S :: Filename:  ./frames/part1/2017/746.jpg 

2019-01-03 09:44:25S :: Filename:  ./frames/part1/2017/371.jpg 

2019-01-03 09:44:25S :: Filename:  ./frames/part1/2017/218.jpg 

2019-01-03 09:44:25S :: Filename:  ./frames/part1/2017/860.jpg 

2019-01-03 09:44:26.702699: E tensorflow/stream_executor/cuda/cuda_dnn.cc:373] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR

2019-01-03 09:44:26.709821: E tensorflow/stream_executor/cuda/cuda_dnn.cc:373] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR

Traceback (most recent call last):

  File "age-gender.py", line 50, in <module>

    faces = agender.detect_genders_ages(cv2.imread(file_path))

  File "/home/sv/.local/lib/python3.5/site-packages/pyagender/pyagender.py", line 56, in detect_genders_ages

    height=face['height'])

  File "/home/sv/.local/lib/python3.5/site-packages/pyagender/pyagender.py", line 82, in gender_age

    result = self.resnet.predict(np.array([test_img]))

  File "/opt/inta/venv/lib/python3.5/site-packages/keras/engine/training.py", line 1169, in predict

    steps=steps)

  File "/opt/inta/venv/lib/python3.5/site-packages/keras/engine/training_arrays.py", line 294, in predict_loop

    batch_outs = f(ins_batch)

  File "/opt/inta/venv/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py", line 2715, in __call__

    return self._call(inputs)

  File "/opt/inta/venv/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py", line 2675, in _call

    fetched = self._callable_fn(*array_vals)

  File "/opt/inta/venv/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1439, in __call__

    run_metadata_ptr)

  File "/opt/inta/venv/lib/python3.5/site-packages/tensorflow/python/framework/errors_impl.py", line 528, in __exit__

    c_api.TF_GetCode(self.status.status))

tensorflow.python.framework.errors_impl.UnknownError: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.

         [[{{node conv2d_1/convolution}} = Conv2D[T=DT_FLOAT, _class=["loc:@batch_normalization_1/cond/FusedBatchNorm/Switch"], data_format="NCHW", dilations=[1, 1, 1, 1], padding="SAME", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:GPU:0"](conv2d_1/convolution-0-TransposeNHWCToNCHW-LayoutOptimizer, conv2d_1/kernel/read)]]





python tensorflow







share|improve this question
















Sample code runs fine with CUDNN but using code installed with pip returns Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR.



I am using Tensorflow 1.12, CUDA 10.0 and CUDNN 7.4.1 compiled with GPU 2080 (x2)



This is Ubuntu 16.04 LTS. I also use venv. Everything is done in venv using Python 3.5.2.



I am trying to use Py-Agender from https://pypi.org/project/py-agender/



After compiling CUDA with GPU flags and successfully verifying that it works, I tried:
enter code herepip3 install py-agender.



Running it then causes an internal CUDNN error.



I have removed ~/.nv as suggested in other replies and it did not help



I have also confirmed that CuDNN does in fact see and can use GPUs:



print(device_lib.list_local_devices()) returns:



2019-01-03 09:31:30.353768: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:964] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero

2019-01-03 09:31:30.354169: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0 with properties:

name: GeForce RTX 2080 major: 7 minor: 5 memoryClockRate(GHz): 1.8

pciBusID: 0000:01:00.0


... so we at least something works!



b. mNIST sample runs and returns values as expected.



The part of code is as follows:



agender = PyAgender() 



def get_gender(value) :

    if value > 0.5:

        return 'female'

    else :

        return 'male'



input_dir = sys.argv[1]



directories = os.listdir(input_dir)



if len(directories):

    for video in directories:

        files = glob.glob(input_dir + video + '/*')



        if len(files):

            for file_path in files:



                filename, file_extension = os.path.splitext(os.path.basename(file_path))

                faces = agender.detect_genders_ages(cv2.imread(file_path))


The code worked fine with Tensorflow compiled with CPU only. With GPU, the full error is:



$ python age-gender.py ./frames/part1/



Using TensorFlow backend.

- Began to analyze the age and gender 

2019-01-03 09:44:24.229618: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:964] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero

2019-01-03 09:44:24.230019: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0 with properties:

name: GeForce RTX 2080 major: 7 minor: 5 memoryClockRate(GHz): 1.8

pciBusID: 0000:01:00.0 totalMemory: 7.76GiB freeMemory: 7.62GiB

2019-01-03 09:44:24.302145: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:964] su

ccessful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero

2019-01-03 09:44:24.302602: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found

 device 1 with properties:name: GeForce RTX 2080 major: 7 minor: 5 memoryClockRate(GHz): 1.8 pciBusID: 0000:02:00.0 totalMemory: 7.77GiB freeMemory: 7.62GiB

2019-01-03 09:44:24.302625: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Addin

g visible gpu devices: 0, 1

2019-01-03 09:44:24.665465: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device

 interconnect StreamExecutor with strength 1 edge matrix:

2019-01-03 09:44:24.665492: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988]      0 1

2019-01-03 09:44:24.665499: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0:   N Y

2019-01-03 09:44:24.665504: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 1:   Y N

2019-01-03 09:44:24.665749: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 7335 MB memory) ->

 physical GPU (device: 0, name: GeForce RTX 2080, pci bus id: 0000:01:00.0, compute capability: 7.5)

2019-01-03 09:44:24.666056: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 7338 MB memory) ->

 physical GPU (device: 1, name: GeForce RTX 2080, pci bus id: 0000:02:00.0, compute capability: 7.5)

2019-01-03 09:44:25S :: Filename: ./frames/part1/2017/658.jpg

2019-01-03 09:44:25S :: Filename:  ./frames/part1/2017/698.jpg

2019-01-03 09:44:25S :: Filename:  ./frames/part1/2017/746.jpg 

2019-01-03 09:44:25S :: Filename:  ./frames/part1/2017/371.jpg 

2019-01-03 09:44:25S :: Filename:  ./frames/part1/2017/218.jpg 

2019-01-03 09:44:25S :: Filename:  ./frames/part1/2017/860.jpg 

2019-01-03 09:44:26.702699: E tensorflow/stream_executor/cuda/cuda_dnn.cc:373] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR

2019-01-03 09:44:26.709821: E tensorflow/stream_executor/cuda/cuda_dnn.cc:373] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR

Traceback (most recent call last):

  File "age-gender.py", line 50, in <module>

    faces = agender.detect_genders_ages(cv2.imread(file_path))

  File "/home/sv/.local/lib/python3.5/site-packages/pyagender/pyagender.py", line 56, in detect_genders_ages

    height=face['height'])

  File "/home/sv/.local/lib/python3.5/site-packages/pyagender/pyagender.py", line 82, in gender_age

    result = self.resnet.predict(np.array([test_img]))

  File "/opt/inta/venv/lib/python3.5/site-packages/keras/engine/training.py", line 1169, in predict

    steps=steps)

  File "/opt/inta/venv/lib/python3.5/site-packages/keras/engine/training_arrays.py", line 294, in predict_loop

    batch_outs = f(ins_batch)

  File "/opt/inta/venv/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py", line 2715, in __call__

    return self._call(inputs)

  File "/opt/inta/venv/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py", line 2675, in _call

    fetched = self._callable_fn(*array_vals)

  File "/opt/inta/venv/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1439, in __call__

    run_metadata_ptr)

  File "/opt/inta/venv/lib/python3.5/site-packages/tensorflow/python/framework/errors_impl.py", line 528, in __exit__

    c_api.TF_GetCode(self.status.status))

tensorflow.python.framework.errors_impl.UnknownError: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.

         [[{{node conv2d_1/convolution}} = Conv2D[T=DT_FLOAT, _class=["loc:@batch_normalization_1/cond/FusedBatchNorm/Switch"], data_format="NCHW", dilations=[1, 1, 1, 1], padding="SAME", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:GPU:0"](conv2d_1/convolution-0-TransposeNHWCToNCHW-LayoutOptimizer, conv2d_1/kernel/read)]]





python tensorflow




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edited Jan 3 at 2:17









talonmies

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asked Jan 3 at 0:54









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  • This might be similar to the issue raised here: github.com/tensorflow/tensorflow/issues/24496

    – SMX
    Jan 15 at 5:14



















  • This might be similar to the issue raised here: github.com/tensorflow/tensorflow/issues/24496

    – SMX
    Jan 15 at 5:14

















This might be similar to the issue raised here: github.com/tensorflow/tensorflow/issues/24496

– SMX
Jan 15 at 5:14





This might be similar to the issue raised here: github.com/tensorflow/tensorflow/issues/24496

– SMX
Jan 15 at 5:14












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