Compute capability of GTX 1660 Ti

[Gotlaik]

Hi all

 

I want to install Tensorflow gpu on my PC (Windows 10, Ryzen 1700, GTX 1660 Ti) and for this I need to know what the compute capability is of my GPU. This should be listed here, but it seems like they forgot to list the GTX 1660 Ti. Does anyone know what its compute capability might be?

 

Also I tried this a couple years ago (on a different GPU) and I think that by installing CuDNN, I broke my graphics drivers and I could not game anymore until I deleted CuDNN again. Does anyone have experience with this or can anyone confirm if it's possible to install CuDNN while still being able to game on the same PC?

 

Thanks a lot!!

[Gegger]

i believe it's 7.5, not sure though

[herpiko]

I can confirm that 1660 Ti works. The training is significantly faster than using the CPU.
 

➜  imagerec git:(updated-deps) ✗ nvidia-smi
Tue May  5 15:00:40 2020       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 440.33.01    Driver Version: 440.33.01    CUDA Version: 10.2     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  GeForce GTX 166...  On   | 00000000:01:00.0  On |                  N/A |
| 32%   35C    P8    12W / 130W |    293MiB /  5941MiB |      2%      Default |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|    0      1549      G   /usr/lib/xorg/Xorg                            18MiB |
|    0      1953      G   /usr/bin/gnome-shell                          48MiB |
|    0      2592      G   /usr/lib/xorg/Xorg                           108MiB |
|    0      2725      G   /usr/bin/gnome-shell                         114MiB |
+-----------------------------------------------------------------------------+
➜  imagerec git:(updated-deps) ✗ python
Python 3.7.7 (default, May  5 2020, 04:20:38) 
[GCC 7.5.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from tensorflow.python.client import device_lib
2020-05-05 15:01:16.900600: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libnvinfer.so.6
2020-05-05 15:01:16.901543: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libnvinfer_plugin.so.6
>>> device_lib.list_local_devices()
2020-05-05 15:01:21.071110: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2020-05-05 15:01:21.100259: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3692985000 Hz
2020-05-05 15:01:21.100906: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55d1bb829330 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-05-05 15:01:21.100968: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2020-05-05 15:01:21.105053: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2020-05-05 15:01:21.217053: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-05 15:01:21.217492: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55d1bb8b7590 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2020-05-05 15:01:21.217525: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): GeForce GTX 1660 Ti, Compute Capability 7.5
2020-05-05 15:01:21.217760: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-05 15:01:21.218479: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties: 
pciBusID: 0000:01:00.0 name: GeForce GTX 1660 Ti computeCapability: 7.5
coreClock: 1.86GHz coreCount: 24 deviceMemorySize: 5.80GiB deviceMemoryBandwidth: 268.26GiB/s
2020-05-05 15:01:21.218590: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-05-05 15:01:21.218654: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-05-05 15:01:21.220646: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-05-05 15:01:21.221035: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-05-05 15:01:21.223176: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-05-05 15:01:21.224561: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-05-05 15:01:21.224651: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-05-05 15:01:21.224857: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-05 15:01:21.225935: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-05 15:01:21.226839: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
2020-05-05 15:01:21.226907: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-05-05 15:01:21.443386: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1096] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-05-05 15:01:21.443435: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102]      0 
2020-05-05 15:01:21.443446: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] 0:   N 
2020-05-05 15:01:21.443650: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-05 15:01:21.444083: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-05 15:01:21.444563: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1241] Created TensorFlow device (/device:GPU:0 with 5176 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5)
[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 7827239615455337760
, name: "/device:XLA_CPU:0"
device_type: "XLA_CPU"
memory_limit: 17179869184
locality {
}
incarnation: 9475332420332566901
physical_device_desc: "device: XLA_CPU device"
, name: "/device:XLA_GPU:0"
device_type: "XLA_GPU"
memory_limit: 17179869184
locality {
}
incarnation: 11551463581523616297
physical_device_desc: "device: XLA_GPU device"
, name: "/device:GPU:0"
device_type: "GPU"
memory_limit: 5427691520
locality {
  bus_id: 1
  links {
  }
}
incarnation: 634448236828243524
physical_device_desc: "device: 0, name: GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5"
]
>>>