TensorFlow-GPU-Fighting compatible versions for Udacity Deep learning tutorial
0
I'm having a compile issue.
The code I'm executing is from Udacity's deep learning tutorial assignment #4.
This leads me to believe that the problem does not lie within the code but within the software tools that I'm using. I didn't have any issues with the previous three assignments, but now I'm using TensorFlow conv2d member. My system details and error output are listed below. Any help would be greatly appreciated.
If you need the code, let me know and I'll post it.
System Details:
- System: Windows 10 home 64-bit, x64-based processor
- Cuda: v 9.0.176
- CUDNN: v 9.0 win10x64 7.3.1.2
- tf-gpu: v 1.5.0 via PIP
- NVIDIA: GTX 1060 6 GiB
- NVIDIA DRIVER VERSION: 417.35
- python v: 3.6.7
Output:
~DocumentsUdacityDeep LearningAssignment 4 (CNN's)> python main.py
Training set (200000, 28, 28) (200000,)
Validation set (10000, 28, 28) (10000,)
Test set (10000, 28, 28) (10000,)
Training set (200000, 28, 28, 1) (200000, 10)
Validation set (10000, 28, 28, 1) (10000, 10)
Test set (10000, 28, 28, 1) (10000, 10)
2019-01-04 15:40:09.714793: I C:tf_jenkinsworkspacerel-winMwindows-gpuPY36tensorflowcoreplatformcpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
2019-01-04 15:40:10.003545: I C:tf_jenkinsworkspacerel-winMwindows-gpuPY36tensorflowcorecommon_runtimegpugpu_device.cc:1105] Found device 0 with properties:
name: GeForce GTX 1060 major: 6 minor: 1 memoryClockRate(GHz): 1.6705
pciBusID: 0000:01:00.0
totalMemory: 6.00GiB freeMemory: 4.97GiB
2019-01-04 15:40:10.013346: I C:tf_jenkinsworkspacerel-winMwindows-gpuPY36tensorflowcorecommon_runtimegpugpu_device.cc:1195] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: GeForce GTX 1060, pci bus
id: 0000:01:00.0, compute capability: 6.1)
Initialized
2019-01-04 15:40:12.584016: E C:tf_jenkinsworkspacerel-winMwindows-gpuPY36tensorflowstream_executorcudacuda_dnn.cc:378] Loaded runtime CuDNN library: 7301 (compatibility version 7300) but source was compiled with 7003 (compatibility version 7000). If using a binary install, upgrade your CuDNN library to match. If building from sources, make sure the library loaded at runtime matches a compatible version specified during compile configuration.
2019-01-04 15:40:12.601433: F C:tf_jenkinsworkspacerel-winMwindows-gpuPY36tensorflowcorekernelsconv_ops.cc:717] Check failed: stream->parent()->GetConvolveAlgorithms( conv_parameters.ShouldIncludeWinogradNonfusedAlgo(), &algorithms)
python gpu
edited 2 days ago
Scott
15.6k113889
asked 2 days ago
Brad RydalchBrad Rydalch
1
New contributor
Brad Rydalch is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
0
I'm having a compile issue.
The code I'm executing is from Udacity's deep learning tutorial assignment #4.
This leads me to believe that the problem does not lie within the code but within the software tools that I'm using. I didn't have any issues with the previous three assignments, but now I'm using TensorFlow conv2d member. My system details and error output are listed below. Any help would be greatly appreciated.
If you need the code, let me know and I'll post it.
System Details:
- System: Windows 10 home 64-bit, x64-based processor
- Cuda: v 9.0.176
- CUDNN: v 9.0 win10x64 7.3.1.2
- tf-gpu: v 1.5.0 via PIP
- NVIDIA: GTX 1060 6 GiB
- NVIDIA DRIVER VERSION: 417.35
- python v: 3.6.7
Output:
~DocumentsUdacityDeep LearningAssignment 4 (CNN's)> python main.py
Training set (200000, 28, 28) (200000,)
Validation set (10000, 28, 28) (10000,)
Test set (10000, 28, 28) (10000,)
Training set (200000, 28, 28, 1) (200000, 10)
Validation set (10000, 28, 28, 1) (10000, 10)
Test set (10000, 28, 28, 1) (10000, 10)
2019-01-04 15:40:09.714793: I C:tf_jenkinsworkspacerel-winMwindows-gpuPY36tensorflowcoreplatformcpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
2019-01-04 15:40:10.003545: I C:tf_jenkinsworkspacerel-winMwindows-gpuPY36tensorflowcorecommon_runtimegpugpu_device.cc:1105] Found device 0 with properties:
name: GeForce GTX 1060 major: 6 minor: 1 memoryClockRate(GHz): 1.6705
pciBusID: 0000:01:00.0
totalMemory: 6.00GiB freeMemory: 4.97GiB
2019-01-04 15:40:10.013346: I C:tf_jenkinsworkspacerel-winMwindows-gpuPY36tensorflowcorecommon_runtimegpugpu_device.cc:1195] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: GeForce GTX 1060, pci bus
id: 0000:01:00.0, compute capability: 6.1)
Initialized
2019-01-04 15:40:12.584016: E C:tf_jenkinsworkspacerel-winMwindows-gpuPY36tensorflowstream_executorcudacuda_dnn.cc:378] Loaded runtime CuDNN library: 7301 (compatibility version 7300) but source was compiled with 7003 (compatibility version 7000). If using a binary install, upgrade your CuDNN library to match. If building from sources, make sure the library loaded at runtime matches a compatible version specified during compile configuration.
2019-01-04 15:40:12.601433: F C:tf_jenkinsworkspacerel-winMwindows-gpuPY36tensorflowcorekernelsconv_ops.cc:717] Check failed: stream->parent()->GetConvolveAlgorithms( conv_parameters.ShouldIncludeWinogradNonfusedAlgo(), &algorithms)
python gpu
edited 2 days ago
Scott
15.6k113889
asked 2 days ago
Brad RydalchBrad Rydalch
1
New contributor
Brad Rydalch is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
0
0
0
I'm having a compile issue.
The code I'm executing is from Udacity's deep learning tutorial assignment #4.
This leads me to believe that the problem does not lie within the code but within the software tools that I'm using. I didn't have any issues with the previous three assignments, but now I'm using TensorFlow conv2d member. My system details and error output are listed below. Any help would be greatly appreciated.
If you need the code, let me know and I'll post it.
System Details:
- System: Windows 10 home 64-bit, x64-based processor
- Cuda: v 9.0.176
- CUDNN: v 9.0 win10x64 7.3.1.2
- tf-gpu: v 1.5.0 via PIP
- NVIDIA: GTX 1060 6 GiB
- NVIDIA DRIVER VERSION: 417.35
- python v: 3.6.7
Output:
~DocumentsUdacityDeep LearningAssignment 4 (CNN's)> python main.py
Training set (200000, 28, 28) (200000,)
Validation set (10000, 28, 28) (10000,)
Test set (10000, 28, 28) (10000,)
Training set (200000, 28, 28, 1) (200000, 10)
Validation set (10000, 28, 28, 1) (10000, 10)
Test set (10000, 28, 28, 1) (10000, 10)
2019-01-04 15:40:09.714793: I C:tf_jenkinsworkspacerel-winMwindows-gpuPY36tensorflowcoreplatformcpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
2019-01-04 15:40:10.003545: I C:tf_jenkinsworkspacerel-winMwindows-gpuPY36tensorflowcorecommon_runtimegpugpu_device.cc:1105] Found device 0 with properties:
name: GeForce GTX 1060 major: 6 minor: 1 memoryClockRate(GHz): 1.6705
pciBusID: 0000:01:00.0
totalMemory: 6.00GiB freeMemory: 4.97GiB
2019-01-04 15:40:10.013346: I C:tf_jenkinsworkspacerel-winMwindows-gpuPY36tensorflowcorecommon_runtimegpugpu_device.cc:1195] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: GeForce GTX 1060, pci bus
id: 0000:01:00.0, compute capability: 6.1)
Initialized
2019-01-04 15:40:12.584016: E C:tf_jenkinsworkspacerel-winMwindows-gpuPY36tensorflowstream_executorcudacuda_dnn.cc:378] Loaded runtime CuDNN library: 7301 (compatibility version 7300) but source was compiled with 7003 (compatibility version 7000). If using a binary install, upgrade your CuDNN library to match. If building from sources, make sure the library loaded at runtime matches a compatible version specified during compile configuration.
2019-01-04 15:40:12.601433: F C:tf_jenkinsworkspacerel-winMwindows-gpuPY36tensorflowcorekernelsconv_ops.cc:717] Check failed: stream->parent()->GetConvolveAlgorithms( conv_parameters.ShouldIncludeWinogradNonfusedAlgo(), &algorithms)
python gpu
edited 2 days ago
Scott
15.6k113889
asked 2 days ago
Brad RydalchBrad Rydalch
1
New contributor
Brad Rydalch is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
I'm having a compile issue.
The code I'm executing is from Udacity's deep learning tutorial assignment #4.
This leads me to believe that the problem does not lie within the code but within the software tools that I'm using. I didn't have any issues with the previous three assignments, but now I'm using TensorFlow conv2d member. My system details and error output are listed below. Any help would be greatly appreciated.
If you need the code, let me know and I'll post it.
System Details:
- System: Windows 10 home 64-bit, x64-based processor
- Cuda: v 9.0.176
- CUDNN: v 9.0 win10x64 7.3.1.2
- tf-gpu: v 1.5.0 via PIP
- NVIDIA: GTX 1060 6 GiB
- NVIDIA DRIVER VERSION: 417.35
- python v: 3.6.7
Output:
~DocumentsUdacityDeep LearningAssignment 4 (CNN's)> python main.py
Training set (200000, 28, 28) (200000,)
Validation set (10000, 28, 28) (10000,)
Test set (10000, 28, 28) (10000,)
Training set (200000, 28, 28, 1) (200000, 10)
Validation set (10000, 28, 28, 1) (10000, 10)
Test set (10000, 28, 28, 1) (10000, 10)
2019-01-04 15:40:09.714793: I C:tf_jenkinsworkspacerel-winMwindows-gpuPY36tensorflowcoreplatformcpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
2019-01-04 15:40:10.003545: I C:tf_jenkinsworkspacerel-winMwindows-gpuPY36tensorflowcorecommon_runtimegpugpu_device.cc:1105] Found device 0 with properties:
name: GeForce GTX 1060 major: 6 minor: 1 memoryClockRate(GHz): 1.6705
pciBusID: 0000:01:00.0
totalMemory: 6.00GiB freeMemory: 4.97GiB
2019-01-04 15:40:10.013346: I C:tf_jenkinsworkspacerel-winMwindows-gpuPY36tensorflowcorecommon_runtimegpugpu_device.cc:1195] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: GeForce GTX 1060, pci bus
id: 0000:01:00.0, compute capability: 6.1)
Initialized
2019-01-04 15:40:12.584016: E C:tf_jenkinsworkspacerel-winMwindows-gpuPY36tensorflowstream_executorcudacuda_dnn.cc:378] Loaded runtime CuDNN library: 7301 (compatibility version 7300) but source was compiled with 7003 (compatibility version 7000). If using a binary install, upgrade your CuDNN library to match. If building from sources, make sure the library loaded at runtime matches a compatible version specified during compile configuration.
2019-01-04 15:40:12.601433: F C:tf_jenkinsworkspacerel-winMwindows-gpuPY36tensorflowcorekernelsconv_ops.cc:717] Check failed: stream->parent()->GetConvolveAlgorithms( conv_parameters.ShouldIncludeWinogradNonfusedAlgo(), &algorithms)
python gpu
python gpu
edited 2 days ago
Scott
15.6k113889
asked 2 days ago
Brad RydalchBrad Rydalch
1
New contributor
Brad Rydalch is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
edited 2 days ago
Scott
15.6k113889
asked 2 days ago
Brad RydalchBrad Rydalch
1
New contributor
Brad Rydalch is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
edited 2 days ago
Scott
15.6k113889
edited 2 days ago
Scott
15.6k113889
edited 2 days ago
Scott
15.6k113889
15.6k113889
asked 2 days ago
Brad RydalchBrad Rydalch
1
New contributor
Brad Rydalch is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
Brad RydalchBrad Rydalch
1
asked 2 days ago
Brad RydalchBrad Rydalch
1
1
New contributor
Brad Rydalch is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Brad Rydalch is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Brad Rydalch is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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