I'm starting to work with DeepLabCut and after a few promising preliminary results with an "old" GPU (Turing architecture), we decided to upgrade to the recent Ampere architecture. Since it is also backwards compatible with old CUDA versions, we thought that it would be fine. However, after trying many combinations of Tensorflow and CUDA, I cannot make it to work. Here are the combinations I have tried so far:
10 | 1.15.2 | 7.6.5 | Recognizes GPU and run some tf test, but takes to long and ended up failing
10 | 1.15.0 | 7.6.5 | Same as with tf 1.15.2
10 | 1.14.0 | 7.6.5 | Does not detect GPU
11 | 1.15.0 | 7.6.5 | Recognizes GPU and run some tf test, but takes to long and ended up failing
11 | 1.13.1 | 7.6.5 | Recognizes GPU and run some tf test, but takes to long and ended up failing
11 | 1.14.0 | 7.6.5 | Recognizes GPU and run some tf test, but takes to long and ended up failing
11 | 1.15.4 | 7.6.5 | Does not detect GPU
11 | 1.15.2 | 7.6.5 | Recognizes GPU and run some tf test, but takes to long and ended up failing
*Tensorflow 1.13.1 does not detect the GPU either.
Using the combinations mentioned above that recognizes the GPU, and can print "Hello, Tensorflow", I ended up stuck at this screen (see code below).
I know that in the documentation says that CUDA 10.+ is not supported, but with the old card we had, it was running fine with CUDA 11. I have very limited knowledge about this, so not sure why/how it worked.
Reading in CUDA documentation it says that Ampere architecture is compatible with CUDA 10.2 or earlier. Also, according to Tensorflow documentation, Tensorflow 1.15 should be compatible with ampere. The only caveat is that it takes too long to start (up to 30 min) but that can be fixed by increasing the cuda cache size.
So, to me, the only thing left that could be giving issues is Cudnn. According to Nvidia, support for Ampere only appeared in Cudnn 8. However, as far as I know, Anaconda only supports up to Cudnn 7.6.5 on Windows. Apparently it has reached Cudnn 8 on Linux.
Code output
[Selecting multi-animal trainer
Config:
{'all_joints': [[0],
[1],
[2],
[3],
[4],
[5],
[6],
[7],
[8],
[9],
[10],
[11],
[12]],
'all_joints_names': ['snout',
'cap',
'leftear',
'rightear',
'spine',
'lforepaw',
'rforepaw',
'lhindpaw',
'rhindpaw',
'tailbase',
'tailend',
'cornerofbox1',
'cornerofbox2'],
'batch_size': 8,
'crop_pad': 0,
'cropratio': 0.4,
'dataset': 'training-datasets\iteration-0\UnaugmentedDataSet_2CamTest9Oct4\2CamTest9_CF95shuffle3.pickle',
'dataset_type': 'multi-animal-imgaug',
'deterministic': False,
'display_iters': 500,
'fg_fraction': 0.25,
'global_scale': 0.8,
'init_weights': 'C:\Users\RyC\anaconda3\envs\dlc-gpu\lib\site-packages\deeplabcut\pose_estimation_tensorflow\models\pretrained\resnet_v1_50.ckpt',
'intermediate_supervision': False,
'intermediate_supervision_layer': 12,
'location_refinement': True,
'locref_huber_loss': True,
'locref_loss_weight': 0.05,
'locref_stdev': 7.2801,
'log_dir': 'log',
'max_input_size': 1500,
'mean_pixel': [123.68, 116.779, 103.939],
'metadataset': 'training-datasets\iteration-0\UnaugmentedDataSet_2CamTest9Oct4\Documentation_data-2CamTest9_95shuffle3.pickle',
'min_input_size': 64,
'mirror': False,
'multi_step': [[0.0001, 7500], [5e-05, 12000], [1e-05, 200000]],
'net_type': 'resnet_50',
'num_joints': 13,
'num_limbs': 55,
'optimizer': 'adam',
'pafwidth': 20,
'pairwise_huber_loss': False,
'pairwise_loss_weight': 0.1,
'pairwise_predict': False,
'partaffinityfield_graph': [[5, 9],
[4, 7],
[1, 3],
[6, 9],
[4, 8],
[5, 6],
[2, 8],
[0, 7],
[8, 9],
[1, 6],
[0, 10],
[3, 7],
[0, 3],
[2, 5],
[2, 4],
[5, 8],
[1, 2],
[4, 9],
[6, 7],
[2, 9],
[3, 10],
[6, 10],
[8, 10],
[1, 5],
[3, 6],
[0, 4],
[1, 10],
[7, 10],
[4, 10],
[2, 6],
[4, 5],
[1, 4],
[2, 10],
[9, 10],
[3, 9],
[0, 5],
[1, 9],
[2, 3],
[0, 8],
[3, 5],
[0, 1],
[2, 7],
[7, 9],
[7, 8],
[5, 10],
[4, 6],
[6, 8],
[5, 7],
[3, 8],
[0, 6],
[1, 8],
[1, 7],
[0, 9],
[3, 4],
[0, 2]],
'partaffinityfield_predict': True,
'pos_dist_thresh': 17,
'project_path': 'C:\Users\RyC\2CamTest9-CF-2020-10-04',
'regularize': False,
'rotation': 25,
'rotratio': 0.4,
'save_iters': 10000,
'scale_jitter_lo': 0.5,
'scale_jitter_up': 1.25,
'scoremap_dir': 'test',
'shuffle': True,
'snapshot_prefix': 'C:\Users\RyC\2CamTest9-CF-2020-10-04\dlc-models\iteration-0\2CamTest9Oct4-trainset95shuffle3\train\snapshot',
'stride': 8.0,
'weigh_negatives': False,
'weigh_only_present_joints': False,
'weigh_part_predictions': False,
'weight_decay': 0.0001}
Activating limb prediction...
Starting with multi-animal imaug + adam pose-dataset loader.
Batch Size is 8
Getting specs multi-animal-imgaug 55 13
Initializing ResNet
Loading ImageNet-pretrained resnet_50
2020-10-05 10:40:16.943131: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2020-10-05 10:40:16.946595: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Found device 0 with properties:
name: GeForce RTX 3090 major: 8 minor: 6 memoryClockRate(GHz): 1.695
pciBusID: 0000:08:00.0
2020-10-05 10:40:16.946675: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_100.dll
2020-10-05 10:40:16.948226: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_100.dll
2020-10-05 10:40:16.948570: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_100.dll
2020-10-05 10:40:16.948928: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_100.dll
2020-10-05 10:40:16.949263: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_100.dll
2020-10-05 10:40:16.949302: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_100.dll
2020-10-05 10:40:16.949559: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
2020-10-05 10:40:16.949840: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1767] Adding visible gpu devices: 0
2020-10-05 10:40:17.963045: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1180] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-10-05 10:40:17.963140: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1186] 0
2020-10-05 10:40:17.964083: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1199] 0: N
2020-10-05 10:40:17.964440: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1325] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 22071 MB memory) -> physical GPU (device: 0, name: GeForce RTX 3090, pci bus id: 0000:08:00.0, compute capability: 8.6)
Max_iters overwritten as 3000
Display_iters overwritten as 10
Save_iters overwritten as 50
Training parameters:
{'stride': 8.0, 'weigh_part_predictions': False, 'weigh_negatives': False, 'fg_fraction': 0.25, 'mean_pixel': [123.68, 116.779, 103.939], 'shuffle': True, 'snapshot_prefix': 'C:\Users\RyC\2CamTest9-CF-2020-10-04\dlc-models\iteration-0\2CamTest9Oct4-trainset95shuffle3\train\snapshot', 'log_dir': 'log', 'global_scale': 0.8, 'location_refinement': True, 'locref_stdev': 7.2801, 'locref_loss_weight': 0.05, 'locref_huber_loss': True, 'optimizer': 'adam', 'intermediate_supervision': False, 'intermediate_supervision_layer': 12, 'regularize': False, 'weight_decay': 0.0001, 'crop_pad': 0, 'scoremap_dir': 'test', 'batch_size': 8, 'dataset_type': 'multi-animal-imgaug', 'deterministic': False, 'mirror': False, 'pairwise_huber_loss': False, 'weigh_only_present_joints': False, 'partaffinityfield_predict': True, 'pairwise_predict': True, 'all_joints': [[0], [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12]], 'all_joints_names': ['snout', 'cap', 'leftear', 'rightear', 'spine', 'lforepaw', 'rforepaw', 'lhindpaw', 'rhindpaw', 'tailbase', 'tailend', 'cornerofbox1', 'cornerofbox2'], 'cropratio': 0.4, 'dataset': 'training-datasets\iteration-0\UnaugmentedDataSet_2CamTest9Oct4\2CamTest9_CF95shuffle3.pickle', 'display_iters': 500, 'init_weights': 'C:\Users\RyC\anaconda3\envs\dlc-gpu\lib\site-packages\deeplabcut\pose_estimation_tensorflow\models\pretrained\resnet_v1_50.ckpt', 'max_input_size': 1500, 'metadataset': 'training-datasets\iteration-0\UnaugmentedDataSet_2CamTest9Oct4\Documentation_data-2CamTest9_95shuffle3.pickle', 'min_input_size': 64, 'multi_step': [[0.0001, 7500], [5e-05, 12000], [1e-05, 200000]], 'net_type': 'resnet_50', 'num_joints': 13, 'num_limbs': 55, 'pafwidth': 20, 'pairwise_loss_weight': 0.1, 'partaffinityfield_graph': [[5, 9], [4, 7], [1, 3], [6, 9], [4, 8], [5, 6], [2, 8], [0, 7], [8, 9], [1, 6], [0, 10], [3, 7], [0, 3], [2, 5], [2, 4], [5, 8], [1, 2], [4, 9], [6, 7], [2, 9], [3, 10], [6, 10], [8, 10], [1, 5], [3, 6], [0, 4], [1, 10], [7, 10], [4, 10], [2, 6], [4, 5], [1, 4], [2, 10], [9, 10], [3, 9], [0, 5], [1, 9], [2, 3], [0, 8], [3, 5], [0, 1], [2, 7], [7, 9], [7, 8], [5, 10], [4, 6], [6, 8], [5, 7], [3, 8], [0, 6], [1, 8], [1, 7], [0, 9], [3, 4], [0, 2]], 'pos_dist_thresh': 17, 'project_path': 'C:\Users\RyC\2CamTest9-CF-2020-10-04', 'rotation': 25, 'rotratio': 0.4, 'save_iters': 10000, 'scale_jitter_lo': 0.5, 'scale_jitter_up': 1.25}
Starting multi-animal training....
2020-10-05 10:40:27.731872: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll]
Upon reading in some forums, some people have been succesful using Symlink in other applications, so I tried that with Cudnn64_7.dll and hardlinked to Cudnn64_8.dll inside DLC-GPU enviroment, but I have not been able to make it work. It shows an error saying that compute capabilities does not match.
Many thanks in advance.
OS: Win 10
DeepLabCut Version: 2.2b8
Anaconda env used: DLC-GPU (cloned from Alex's github)
WxPython version: 4.0.7.post2
Tensorflow version: many, installed with pip (see below)
Cuda version: 10 and 11
Hi everyone,
First of all, I wanted to thank all the authors for this amazing software!
I'm starting to work with DeepLabCut and after a few promising preliminary results with an "old" GPU (Turing architecture), we decided to upgrade to the recent Ampere architecture. Since it is also backwards compatible with old CUDA versions, we thought that it would be fine. However, after trying many combinations of Tensorflow and CUDA, I cannot make it to work. Here are the combinations I have tried so far:
Cuda | Tensorflow | Cudnn | Works?
10 | 1.15.2 | 7.6.5 | Recognizes GPU and run some tf test, but takes to long and ended up failing
10 | 1.15.0 | 7.6.5 | Same as with tf 1.15.2
10 | 1.14.0 | 7.6.5 | Does not detect GPU
11 | 1.15.0 | 7.6.5 | Recognizes GPU and run some tf test, but takes to long and ended up failing
11 | 1.13.1 | 7.6.5 | Recognizes GPU and run some tf test, but takes to long and ended up failing
11 | 1.14.0 | 7.6.5 | Recognizes GPU and run some tf test, but takes to long and ended up failing
11 | 1.15.4 | 7.6.5 | Does not detect GPU
11 | 1.15.2 | 7.6.5 | Recognizes GPU and run some tf test, but takes to long and ended up failing
*Tensorflow 1.13.1 does not detect the GPU either.
Using the combinations mentioned above that recognizes the GPU, and can print "Hello, Tensorflow", I ended up stuck at this screen (see code below).
I know that in the documentation says that CUDA 10.+ is not supported, but with the old card we had, it was running fine with CUDA 11. I have very limited knowledge about this, so not sure why/how it worked.
Reading in CUDA documentation it says that Ampere architecture is compatible with CUDA 10.2 or earlier. Also, according to Tensorflow documentation, Tensorflow 1.15 should be compatible with ampere. The only caveat is that it takes too long to start (up to 30 min) but that can be fixed by increasing the cuda cache size.
So, to me, the only thing left that could be giving issues is Cudnn. According to Nvidia, support for Ampere only appeared in Cudnn 8. However, as far as I know, Anaconda only supports up to Cudnn 7.6.5 on Windows. Apparently it has reached Cudnn 8 on Linux.
Code output
[Selecting multi-animal trainer
Config:
{'all_joints': [[0],
[1],
[2],
[3],
[4],
[5],
[6],
[7],
[8],
[9],
[10],
[11],
[12]],
'all_joints_names': ['snout',
'cap',
'leftear',
'rightear',
'spine',
'lforepaw',
'rforepaw',
'lhindpaw',
'rhindpaw',
'tailbase',
'tailend',
'cornerofbox1',
'cornerofbox2'],
'batch_size': 8,
'crop_pad': 0,
'cropratio': 0.4,
'dataset': 'training-datasets\iteration-0\UnaugmentedDataSet_2CamTest9Oct4\2CamTest9_CF95shuffle3.pickle',
'dataset_type': 'multi-animal-imgaug',
'deterministic': False,
'display_iters': 500,
'fg_fraction': 0.25,
'global_scale': 0.8,
'init_weights': 'C:\Users\RyC\anaconda3\envs\dlc-gpu\lib\site-packages\deeplabcut\pose_estimation_tensorflow\models\pretrained\resnet_v1_50.ckpt',
'intermediate_supervision': False,
'intermediate_supervision_layer': 12,
'location_refinement': True,
'locref_huber_loss': True,
'locref_loss_weight': 0.05,
'locref_stdev': 7.2801,
'log_dir': 'log',
'max_input_size': 1500,
'mean_pixel': [123.68, 116.779, 103.939],
'metadataset': 'training-datasets\iteration-0\UnaugmentedDataSet_2CamTest9Oct4\Documentation_data-2CamTest9_95shuffle3.pickle',
'min_input_size': 64,
'mirror': False,
'multi_step': [[0.0001, 7500], [5e-05, 12000], [1e-05, 200000]],
'net_type': 'resnet_50',
'num_joints': 13,
'num_limbs': 55,
'optimizer': 'adam',
'pafwidth': 20,
'pairwise_huber_loss': False,
'pairwise_loss_weight': 0.1,
'pairwise_predict': False,
'partaffinityfield_graph': [[5, 9],
[4, 7],
[1, 3],
[6, 9],
[4, 8],
[5, 6],
[2, 8],
[0, 7],
[8, 9],
[1, 6],
[0, 10],
[3, 7],
[0, 3],
[2, 5],
[2, 4],
[5, 8],
[1, 2],
[4, 9],
[6, 7],
[2, 9],
[3, 10],
[6, 10],
[8, 10],
[1, 5],
[3, 6],
[0, 4],
[1, 10],
[7, 10],
[4, 10],
[2, 6],
[4, 5],
[1, 4],
[2, 10],
[9, 10],
[3, 9],
[0, 5],
[1, 9],
[2, 3],
[0, 8],
[3, 5],
[0, 1],
[2, 7],
[7, 9],
[7, 8],
[5, 10],
[4, 6],
[6, 8],
[5, 7],
[3, 8],
[0, 6],
[1, 8],
[1, 7],
[0, 9],
[3, 4],
[0, 2]],
'partaffinityfield_predict': True,
'pos_dist_thresh': 17,
'project_path': 'C:\Users\RyC\2CamTest9-CF-2020-10-04',
'regularize': False,
'rotation': 25,
'rotratio': 0.4,
'save_iters': 10000,
'scale_jitter_lo': 0.5,
'scale_jitter_up': 1.25,
'scoremap_dir': 'test',
'shuffle': True,
'snapshot_prefix': 'C:\Users\RyC\2CamTest9-CF-2020-10-04\dlc-models\iteration-0\2CamTest9Oct4-trainset95shuffle3\train\snapshot',
'stride': 8.0,
'weigh_negatives': False,
'weigh_only_present_joints': False,
'weigh_part_predictions': False,
'weight_decay': 0.0001}
Activating limb prediction...
Starting with multi-animal imaug + adam pose-dataset loader.
Batch Size is 8
Getting specs multi-animal-imgaug 55 13
Initializing ResNet
Loading ImageNet-pretrained resnet_50
2020-10-05 10:40:16.943131: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2020-10-05 10:40:16.946595: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Found device 0 with properties:
name: GeForce RTX 3090 major: 8 minor: 6 memoryClockRate(GHz): 1.695
pciBusID: 0000:08:00.0
2020-10-05 10:40:16.946675: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_100.dll
2020-10-05 10:40:16.948226: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_100.dll
2020-10-05 10:40:16.948570: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_100.dll
2020-10-05 10:40:16.948928: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_100.dll
2020-10-05 10:40:16.949263: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_100.dll
2020-10-05 10:40:16.949302: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_100.dll
2020-10-05 10:40:16.949559: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
2020-10-05 10:40:16.949840: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1767] Adding visible gpu devices: 0
2020-10-05 10:40:17.963045: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1180] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-10-05 10:40:17.963140: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1186] 0
2020-10-05 10:40:17.964083: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1199] 0: N
2020-10-05 10:40:17.964440: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1325] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 22071 MB memory) -> physical GPU (device: 0, name: GeForce RTX 3090, pci bus id: 0000:08:00.0, compute capability: 8.6)
Max_iters overwritten as 3000
Display_iters overwritten as 10
Save_iters overwritten as 50
Training parameters:
{'stride': 8.0, 'weigh_part_predictions': False, 'weigh_negatives': False, 'fg_fraction': 0.25, 'mean_pixel': [123.68, 116.779, 103.939], 'shuffle': True, 'snapshot_prefix': 'C:\Users\RyC\2CamTest9-CF-2020-10-04\dlc-models\iteration-0\2CamTest9Oct4-trainset95shuffle3\train\snapshot', 'log_dir': 'log', 'global_scale': 0.8, 'location_refinement': True, 'locref_stdev': 7.2801, 'locref_loss_weight': 0.05, 'locref_huber_loss': True, 'optimizer': 'adam', 'intermediate_supervision': False, 'intermediate_supervision_layer': 12, 'regularize': False, 'weight_decay': 0.0001, 'crop_pad': 0, 'scoremap_dir': 'test', 'batch_size': 8, 'dataset_type': 'multi-animal-imgaug', 'deterministic': False, 'mirror': False, 'pairwise_huber_loss': False, 'weigh_only_present_joints': False, 'partaffinityfield_predict': True, 'pairwise_predict': True, 'all_joints': [[0], [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12]], 'all_joints_names': ['snout', 'cap', 'leftear', 'rightear', 'spine', 'lforepaw', 'rforepaw', 'lhindpaw', 'rhindpaw', 'tailbase', 'tailend', 'cornerofbox1', 'cornerofbox2'], 'cropratio': 0.4, 'dataset': 'training-datasets\iteration-0\UnaugmentedDataSet_2CamTest9Oct4\2CamTest9_CF95shuffle3.pickle', 'display_iters': 500, 'init_weights': 'C:\Users\RyC\anaconda3\envs\dlc-gpu\lib\site-packages\deeplabcut\pose_estimation_tensorflow\models\pretrained\resnet_v1_50.ckpt', 'max_input_size': 1500, 'metadataset': 'training-datasets\iteration-0\UnaugmentedDataSet_2CamTest9Oct4\Documentation_data-2CamTest9_95shuffle3.pickle', 'min_input_size': 64, 'multi_step': [[0.0001, 7500], [5e-05, 12000], [1e-05, 200000]], 'net_type': 'resnet_50', 'num_joints': 13, 'num_limbs': 55, 'pafwidth': 20, 'pairwise_loss_weight': 0.1, 'partaffinityfield_graph': [[5, 9], [4, 7], [1, 3], [6, 9], [4, 8], [5, 6], [2, 8], [0, 7], [8, 9], [1, 6], [0, 10], [3, 7], [0, 3], [2, 5], [2, 4], [5, 8], [1, 2], [4, 9], [6, 7], [2, 9], [3, 10], [6, 10], [8, 10], [1, 5], [3, 6], [0, 4], [1, 10], [7, 10], [4, 10], [2, 6], [4, 5], [1, 4], [2, 10], [9, 10], [3, 9], [0, 5], [1, 9], [2, 3], [0, 8], [3, 5], [0, 1], [2, 7], [7, 9], [7, 8], [5, 10], [4, 6], [6, 8], [5, 7], [3, 8], [0, 6], [1, 8], [1, 7], [0, 9], [3, 4], [0, 2]], 'pos_dist_thresh': 17, 'project_path': 'C:\Users\RyC\2CamTest9-CF-2020-10-04', 'rotation': 25, 'rotratio': 0.4, 'save_iters': 10000, 'scale_jitter_lo': 0.5, 'scale_jitter_up': 1.25}
Starting multi-animal training....
2020-10-05 10:40:27.731872: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll]
Upon reading in some forums, some people have been succesful using Symlink in other applications, so I tried that with Cudnn64_7.dll and hardlinked to Cudnn64_8.dll inside DLC-GPU enviroment, but I have not been able to make it work. It shows an error saying that compute capabilities does not match.
Do you have any suggestion that I might try?
Many thanks in advance.