Please help ! 
@NaToh5 You're encountering compatibility issues with TensorFlow 1.13.1, your RTX 3060 GPU, and the CUDA 10.0 version. The primary recommendation is to upgrade TensorFlow to a more recent version that is better suited for your RTX 3060 GPU and CUDA 10.0. TensorFlow 1.13.1 predates this hardware and might not work optimally. Keep in mind that upgrading TensorFlow might require some code adjustments due to changes in the API and the way TensorFlow's updated version handles certain operations. @AyushChauhan-ui  Thank you for your reply. Can I use tensorflow-gpu 1.15.0 with my RTX 3060 and cuda version 10 or 11? I cannot use more advance tensorflow version on that Octopus model because that model is quite old and only working on tensorflow-gpu old version. 

Or how can I use tensorflow-gpu 1.13.1 ? Should I change my GPU and cuda? Hi @NaToh5 ,

For tensorflow-gpu version 1.13.1 and above, the CUDA version has to be 10.
Please refer the built configurations [here](https://www.tensorflow.org/install/source#gpu).

Thank you!! @Varsha-anjanappa  so the issue is coming from my RTX 3060 GPU card or cuda ? I already install cuda 10.0 and still not working.  @NaToh5 CUDA 10 may not be compatible with RTX3060. Tensorflow 1.15.0 might work. You can try a newer version of CUDA. @AyushChauhan-ui thank you for your reply. The model cannot change to tensorflow upgraded version. Look like only working on tensorflow -gpu 1.13.1 . Do you think it might work when I upgrade to cuda 11 version with tensorflow 1.13.1 ? @NaToh5 Well Tensorflow 1.13.1 supports only upto CUDA 10. This combination will work but your RTX3060 will not be compatible with them. @AyushChauhan-ui It's working well on my old laptop with cuda 10 (tensorflow 1.13.1). Do you think 1.15.0 will work on cuda 11 ?  @NaToh5 Tensorflow 1.13.1 works well with CUDA 10 but they are not compatible with RTX3060. @NaToh5 Sorry I mean tensorflow-gpu 1.15.0 will compatible with cuda 11.5?  @NaToh5 Check the compatibility combinations here (https://www.tensorflow.org/install/source#gpu) @AyushChauhan-ui  Thank you!! Are you satisfied with the resolution of your issue?
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61669">Yes</a>
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61669">No</a>

### Issue type

Bug

### Have you reproduced the bug with TensorFlow Nightly?

No

### Source

source

### TensorFlow version

1.13.1

### Custom code

Yes

### OS platform and distribution

Linux Ubuntu 20.0

### Mobile device

_No response_

### Python version

3.6

### Bazel version

_No response_

### GCC/compiler version

10.5.0

### CUDA/cuDNN version

10

### GPU model and memory

RTX 3060 12G

### Current behavior?

I  am running the Octopus repo (https://github.com/thmoa/octopus) which use tensorflow-gpu version 1.13.1 . When I run that model with python, I got some errors from tensorflow. Please help me. 

### Standalone code to reproduce the issue

```shell
import os
import argparse
import tensorflow as tf
import keras.backend as K




from glob import glob

from lib.io import openpose_from_file, read_segmentation, write_mesh
from model.octopus import Octopus



def main(weights, name, segm_dir, pose_dir, out_dir, opt_pose_steps, opt_shape_steps):
    segm_files = sorted(glob(os.path.join(segm_dir, '*.png')))
    pose_files = sorted(glob(os.path.join(pose_dir, '*.json')))

    if len(segm_files) != len(pose_files) or len(segm_files) == len(pose_files) == 0:
        exit('Inconsistent input.')

    K.set_session(tf.Session(config=tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True))))

    model = Octopus(num=len(segm_files))
    model.load(weights)

    segmentations = [read_segmentation(f) for f in segm_files]

    joints_2d, face_2d = [], []
    for f in pose_files:
        j, f = openpose_from_file(f)

        assert(len(j) == 25)
        assert(len(f) == 70)

        joints_2d.append(j)
        face_2d.append(f)

    if opt_pose_steps:
        print('Optimizing for pose...')
        model.opt_pose(segmentations, joints_2d, opt_steps=opt_pose_steps)

    if opt_shape_steps:
        print('Optimizing for shape...')
        model.opt_shape(segmentations, joints_2d, face_2d, opt_steps=opt_shape_steps)

    print('Estimating shape...')
    pred = model.predict(segmentations, joints_2d)

    write_mesh('{}/{}.obj'.format(out_dir, name), pred['vertices'][0], pred['faces'])

    print('Done.')


if __name__ == '__main__':
    parser = argparse.ArgumentParser()

    parser.add_argument(
        'name',
        type=str,
        help="Sample name")

    parser.add_argument(
        'segm_dir',
        type=str,
        help="Segmentation images directory")

    parser.add_argument(
        'pose_dir',
        type=str,
        help="2D pose keypoints directory")

    parser.add_argument(
        '--opt_steps_pose', '-p', default=5, type=int,
        help="Optimization steps pose")

    parser.add_argument(
        '--opt_steps_shape', '-s', default=15, type=int,
        help="Optimization steps")

    parser.add_argument(
        '--out_dir', '-od',
        default='out',
        help='Output directory')

    parser.add_argument(
        '--weights', '-w',
        default='weights/octopus_weights.hdf5',
        help='Model weights file (*.hdf5)')

    args = parser.parse_args()
    main(args.weights, args.name, args.segm_dir, args.pose_dir, args.out_dir, args.opt_steps_pose, args.opt_steps_shape)

```


### Relevant log output

```shell
Processing sample...
> Optimizing for pose...
  0%|                                                                                                                                                                                                                 | 0/10 [00:00<?, ?it/s]2023-08-22 16:24:18.296359: I tensorflow/stream_executor/dso_loader.cc:152] successfully opened CUDA library libcublas.so.10.0 locally
2023-08-22 16:25:50.156420: I tensorflow/core/kernels/cuda_solvers.cc:159] Creating CudaSolver handles for stream 0x55fa094fdcf0
2023-08-22 16:26:08.284736: E tensorflow/stream_executor/cuda/cuda_blas.cc:698] failed to run cuBLAS routine cublasGemmBatchedEx: CUBLAS_STATUS_EXECUTION_FAILED
2023-08-22 16:26:08.284773: E tensorflow/stream_executor/cuda/cuda_blas.cc:2620] Internal: failed BLAS call, see log for details
2023-08-22 16:26:08.326578: I tensorflow/stream_executor/stream.cc:5014] [stream=0x55fa0950bb90,impl=0x55fa093dbf20] did not memcpy device-to-host; source: 0x813bc6700
2023-08-22 16:26:08.326623: F tensorflow/core/framework/op_kernel.cc:1408] Check failed: nullptr == ctx->op_kernel().AsAsync() (nullptr vs. 0x55fa38108400)Use OP_REQUIRES_ASYNC in AsyncOpKernel implementations.
Aborted
```
