Which CUDA, CUDnn, Nvidia versions to install for Deep Learning?
My NVIDIA GPU specs
These are my specs for NVIDIA GPU. I have tried installing CUDA 9.1, but it says "Your device is too old for CUDA version". I have tried installing lower CUDA version, then importing theano says "No CUDA device available".
drivers nvidia cuda gpu nvidia-geforce
asked Jan 24 '18 at 20:45
Umair HusainUmair Husain
141
add a comment |
My NVIDIA GPU specs
These are my specs for NVIDIA GPU. I have tried installing CUDA 9.1, but it says "Your device is too old for CUDA version". I have tried installing lower CUDA version, then importing theano says "No CUDA device available".
drivers nvidia cuda gpu nvidia-geforce
asked Jan 24 '18 at 20:45
Umair HusainUmair Husain
141
1
What graphics card do you have?
– rpmcruz
Mar 7 '18 at 22:04
idownvotedbecau.se/imageofcode
– user1251007
Mar 29 '18 at 13:12
And please accept the answer, if it is correct.
– user1251007
Mar 29 '18 at 13:13
add a comment |
My NVIDIA GPU specs
These are my specs for NVIDIA GPU. I have tried installing CUDA 9.1, but it says "Your device is too old for CUDA version". I have tried installing lower CUDA version, then importing theano says "No CUDA device available".
drivers nvidia cuda gpu nvidia-geforce
asked Jan 24 '18 at 20:45
Umair HusainUmair Husain
141
My NVIDIA GPU specs
These are my specs for NVIDIA GPU. I have tried installing CUDA 9.1, but it says "Your device is too old for CUDA version". I have tried installing lower CUDA version, then importing theano says "No CUDA device available".
drivers nvidia cuda gpu nvidia-geforce
drivers nvidia cuda gpu nvidia-geforce
asked Jan 24 '18 at 20:45
Umair HusainUmair Husain
141
asked Jan 24 '18 at 20:45
Umair HusainUmair Husain
141
asked Jan 24 '18 at 20:45
Umair HusainUmair Husain
141
asked Jan 24 '18 at 20:45
Umair HusainUmair Husain
141
asked Jan 24 '18 at 20:45
Umair HusainUmair Husain
141
141
1
What graphics card do you have?
– rpmcruz
Mar 7 '18 at 22:04
idownvotedbecau.se/imageofcode
– user1251007
Mar 29 '18 at 13:12
And please accept the answer, if it is correct.
– user1251007
Mar 29 '18 at 13:13
add a comment |
1
What graphics card do you have?
– rpmcruz
Mar 7 '18 at 22:04
idownvotedbecau.se/imageofcode
– user1251007
Mar 29 '18 at 13:12
And please accept the answer, if it is correct.
– user1251007
Mar 29 '18 at 13:13
1
1
What graphics card do you have?
– rpmcruz
Mar 7 '18 at 22:04
What graphics card do you have?
– rpmcruz
Mar 7 '18 at 22:04
idownvotedbecau.se/imageofcode
– user1251007
Mar 29 '18 at 13:12
idownvotedbecau.se/imageofcode
– user1251007
Mar 29 '18 at 13:12
And please accept the answer, if it is correct.
– user1251007
Mar 29 '18 at 13:13
And please accept the answer, if it is correct.
– user1251007
Mar 29 '18 at 13:13
add a comment |
2 Answers
2
active
oldest
votes
Your Geforce 820M GPU has a CUDA capability of 2.1 (see Intel geforce gpu list
This capability is too low for CUDA 9.0+, but does support CUDA 8.0. Try installing that CUDA version. The Nvidia cudnn has its own set of requirements: on link cuDNN installation Guide First 2.1 requirements bullet:
2.1 * A GPU of compute capability 3.0 or higher. To understand the compute capability of the GPU on your system, see: CUDA GPUs. Also see the cuDNN Support Matrix.
So your 820M GPU of capability 2.1 is not sufficient to run even the oldest cuDNN offered (See the cuDNN Support Matrix in the above link for details). That prevents anything depending upon cuDNN from running too (like TensorFlow or Therano?).
answered Mar 29 '18 at 15:37
ubfan1ubfan1
9,69641730
add a comment |
The GPU does not support CUDA.
There are two main variables involved here: the GPU architecture and the driver version. Looking at the error message, it could be the problem with the GPU architecture. Your GPU may have been manufactured using older architecture that does not support CUDA or does not have CUDA cores.
With regards to the GPU architecture, in one part of the online documentation (ref: https://github.com/NVIDIA/nvidia-docker/wiki/Installation-(version-2.0)#prerequisites ), NVIDIA specify that they support the GPUs with architecture newer than Fermi. While this may not immediately translate into minimum version for CUDA, this may hint that the minimum GPU versions supported will be those with Kepler architecture.
The list of NVIDIA graphics card models built with Kepler architecture or newer that should -in theory- support CUDA in this article: http://tech.amikelive.com/node-685/list-of-nvidia-desktop-graphics-card-models-for-building-deep-learning-ai-system/
Looking at the GPU information provided, the graphics card model is GeForce 820M. The GPU code name for this model is GF117. This model is built with Fermi architecture. So, it can be expected that the GPU does not support CUDA.
edited Mar 29 '18 at 13:58
user1251007
681828
answered Mar 29 '18 at 12:24
MikeMike
8111
I'd like to add some more explanation to prevent confusion on your side. If you look at CUDA capability only, it will be incorrect to say that your GPU does not support CUDA. However, as I understood, the focus is to evaluate the GPU against deep learning use. Like other deep learning frameworks, Theano harnesses cuDNN library. The library will only run if the CUDA capability is bigger than 2.1 (Kepler architecture or newer). It is more practical to notify user that there is no CUDA device other than saying there is a CUDA device but it is too primitive to perform deep learning tasks.
– Mike
Mar 30 '18 at 2:06
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
Your Geforce 820M GPU has a CUDA capability of 2.1 (see Intel geforce gpu list
This capability is too low for CUDA 9.0+, but does support CUDA 8.0. Try installing that CUDA version. The Nvidia cudnn has its own set of requirements: on link cuDNN installation Guide First 2.1 requirements bullet:
2.1 * A GPU of compute capability 3.0 or higher. To understand the compute capability of the GPU on your system, see: CUDA GPUs. Also see the cuDNN Support Matrix.
So your 820M GPU of capability 2.1 is not sufficient to run even the oldest cuDNN offered (See the cuDNN Support Matrix in the above link for details). That prevents anything depending upon cuDNN from running too (like TensorFlow or Therano?).
answered Mar 29 '18 at 15:37
ubfan1ubfan1
9,69641730
add a comment |
Your Geforce 820M GPU has a CUDA capability of 2.1 (see Intel geforce gpu list
This capability is too low for CUDA 9.0+, but does support CUDA 8.0. Try installing that CUDA version. The Nvidia cudnn has its own set of requirements: on link cuDNN installation Guide First 2.1 requirements bullet:
2.1 * A GPU of compute capability 3.0 or higher. To understand the compute capability of the GPU on your system, see: CUDA GPUs. Also see the cuDNN Support Matrix.
So your 820M GPU of capability 2.1 is not sufficient to run even the oldest cuDNN offered (See the cuDNN Support Matrix in the above link for details). That prevents anything depending upon cuDNN from running too (like TensorFlow or Therano?).
answered Mar 29 '18 at 15:37
ubfan1ubfan1
9,69641730
add a comment |
Your Geforce 820M GPU has a CUDA capability of 2.1 (see Intel geforce gpu list
This capability is too low for CUDA 9.0+, but does support CUDA 8.0. Try installing that CUDA version. The Nvidia cudnn has its own set of requirements: on link cuDNN installation Guide First 2.1 requirements bullet:
2.1 * A GPU of compute capability 3.0 or higher. To understand the compute capability of the GPU on your system, see: CUDA GPUs. Also see the cuDNN Support Matrix.
So your 820M GPU of capability 2.1 is not sufficient to run even the oldest cuDNN offered (See the cuDNN Support Matrix in the above link for details). That prevents anything depending upon cuDNN from running too (like TensorFlow or Therano?).
answered Mar 29 '18 at 15:37
ubfan1ubfan1
9,69641730
Your Geforce 820M GPU has a CUDA capability of 2.1 (see Intel geforce gpu list
This capability is too low for CUDA 9.0+, but does support CUDA 8.0. Try installing that CUDA version. The Nvidia cudnn has its own set of requirements: on link cuDNN installation Guide First 2.1 requirements bullet:
2.1 * A GPU of compute capability 3.0 or higher. To understand the compute capability of the GPU on your system, see: CUDA GPUs. Also see the cuDNN Support Matrix.
So your 820M GPU of capability 2.1 is not sufficient to run even the oldest cuDNN offered (See the cuDNN Support Matrix in the above link for details). That prevents anything depending upon cuDNN from running too (like TensorFlow or Therano?).
answered Mar 29 '18 at 15:37
ubfan1ubfan1
9,69641730
edited Feb 9 at 16:48
answered Mar 29 '18 at 15:37
ubfan1ubfan1
9,69641730
answered Mar 29 '18 at 15:37
ubfan1ubfan1
9,69641730
answered Mar 29 '18 at 15:37
ubfan1ubfan1
9,69641730
9,69641730
add a comment |
add a comment |
The GPU does not support CUDA.
There are two main variables involved here: the GPU architecture and the driver version. Looking at the error message, it could be the problem with the GPU architecture. Your GPU may have been manufactured using older architecture that does not support CUDA or does not have CUDA cores.
With regards to the GPU architecture, in one part of the online documentation (ref: https://github.com/NVIDIA/nvidia-docker/wiki/Installation-(version-2.0)#prerequisites ), NVIDIA specify that they support the GPUs with architecture newer than Fermi. While this may not immediately translate into minimum version for CUDA, this may hint that the minimum GPU versions supported will be those with Kepler architecture.
The list of NVIDIA graphics card models built with Kepler architecture or newer that should -in theory- support CUDA in this article: http://tech.amikelive.com/node-685/list-of-nvidia-desktop-graphics-card-models-for-building-deep-learning-ai-system/
Looking at the GPU information provided, the graphics card model is GeForce 820M. The GPU code name for this model is GF117. This model is built with Fermi architecture. So, it can be expected that the GPU does not support CUDA.
edited Mar 29 '18 at 13:58
user1251007
681828
answered Mar 29 '18 at 12:24
MikeMike
8111
I'd like to add some more explanation to prevent confusion on your side. If you look at CUDA capability only, it will be incorrect to say that your GPU does not support CUDA. However, as I understood, the focus is to evaluate the GPU against deep learning use. Like other deep learning frameworks, Theano harnesses cuDNN library. The library will only run if the CUDA capability is bigger than 2.1 (Kepler architecture or newer). It is more practical to notify user that there is no CUDA device other than saying there is a CUDA device but it is too primitive to perform deep learning tasks.
– Mike
Mar 30 '18 at 2:06
add a comment |
The GPU does not support CUDA.
There are two main variables involved here: the GPU architecture and the driver version. Looking at the error message, it could be the problem with the GPU architecture. Your GPU may have been manufactured using older architecture that does not support CUDA or does not have CUDA cores.
With regards to the GPU architecture, in one part of the online documentation (ref: https://github.com/NVIDIA/nvidia-docker/wiki/Installation-(version-2.0)#prerequisites ), NVIDIA specify that they support the GPUs with architecture newer than Fermi. While this may not immediately translate into minimum version for CUDA, this may hint that the minimum GPU versions supported will be those with Kepler architecture.
The list of NVIDIA graphics card models built with Kepler architecture or newer that should -in theory- support CUDA in this article: http://tech.amikelive.com/node-685/list-of-nvidia-desktop-graphics-card-models-for-building-deep-learning-ai-system/
Looking at the GPU information provided, the graphics card model is GeForce 820M. The GPU code name for this model is GF117. This model is built with Fermi architecture. So, it can be expected that the GPU does not support CUDA.
edited Mar 29 '18 at 13:58
user1251007
681828
answered Mar 29 '18 at 12:24
MikeMike
8111
I'd like to add some more explanation to prevent confusion on your side. If you look at CUDA capability only, it will be incorrect to say that your GPU does not support CUDA. However, as I understood, the focus is to evaluate the GPU against deep learning use. Like other deep learning frameworks, Theano harnesses cuDNN library. The library will only run if the CUDA capability is bigger than 2.1 (Kepler architecture or newer). It is more practical to notify user that there is no CUDA device other than saying there is a CUDA device but it is too primitive to perform deep learning tasks.
– Mike
Mar 30 '18 at 2:06
add a comment |
The GPU does not support CUDA.
There are two main variables involved here: the GPU architecture and the driver version. Looking at the error message, it could be the problem with the GPU architecture. Your GPU may have been manufactured using older architecture that does not support CUDA or does not have CUDA cores.
With regards to the GPU architecture, in one part of the online documentation (ref: https://github.com/NVIDIA/nvidia-docker/wiki/Installation-(version-2.0)#prerequisites ), NVIDIA specify that they support the GPUs with architecture newer than Fermi. While this may not immediately translate into minimum version for CUDA, this may hint that the minimum GPU versions supported will be those with Kepler architecture.
The list of NVIDIA graphics card models built with Kepler architecture or newer that should -in theory- support CUDA in this article: http://tech.amikelive.com/node-685/list-of-nvidia-desktop-graphics-card-models-for-building-deep-learning-ai-system/
Looking at the GPU information provided, the graphics card model is GeForce 820M. The GPU code name for this model is GF117. This model is built with Fermi architecture. So, it can be expected that the GPU does not support CUDA.
edited Mar 29 '18 at 13:58
user1251007
681828
answered Mar 29 '18 at 12:24
MikeMike
8111
The GPU does not support CUDA.
There are two main variables involved here: the GPU architecture and the driver version. Looking at the error message, it could be the problem with the GPU architecture. Your GPU may have been manufactured using older architecture that does not support CUDA or does not have CUDA cores.
With regards to the GPU architecture, in one part of the online documentation (ref: https://github.com/NVIDIA/nvidia-docker/wiki/Installation-(version-2.0)#prerequisites ), NVIDIA specify that they support the GPUs with architecture newer than Fermi. While this may not immediately translate into minimum version for CUDA, this may hint that the minimum GPU versions supported will be those with Kepler architecture.
The list of NVIDIA graphics card models built with Kepler architecture or newer that should -in theory- support CUDA in this article: http://tech.amikelive.com/node-685/list-of-nvidia-desktop-graphics-card-models-for-building-deep-learning-ai-system/
Looking at the GPU information provided, the graphics card model is GeForce 820M. The GPU code name for this model is GF117. This model is built with Fermi architecture. So, it can be expected that the GPU does not support CUDA.
edited Mar 29 '18 at 13:58
user1251007
681828
answered Mar 29 '18 at 12:24
MikeMike
8111
edited Mar 29 '18 at 13:58
user1251007
681828
edited Mar 29 '18 at 13:58
user1251007
681828
edited Mar 29 '18 at 13:58
user1251007
681828
681828
answered Mar 29 '18 at 12:24
MikeMike
8111
answered Mar 29 '18 at 12:24
MikeMike
8111
answered Mar 29 '18 at 12:24
MikeMike
8111
8111
I'd like to add some more explanation to prevent confusion on your side. If you look at CUDA capability only, it will be incorrect to say that your GPU does not support CUDA. However, as I understood, the focus is to evaluate the GPU against deep learning use. Like other deep learning frameworks, Theano harnesses cuDNN library. The library will only run if the CUDA capability is bigger than 2.1 (Kepler architecture or newer). It is more practical to notify user that there is no CUDA device other than saying there is a CUDA device but it is too primitive to perform deep learning tasks.
– Mike
Mar 30 '18 at 2:06
add a comment |
I'd like to add some more explanation to prevent confusion on your side. If you look at CUDA capability only, it will be incorrect to say that your GPU does not support CUDA. However, as I understood, the focus is to evaluate the GPU against deep learning use. Like other deep learning frameworks, Theano harnesses cuDNN library. The library will only run if the CUDA capability is bigger than 2.1 (Kepler architecture or newer). It is more practical to notify user that there is no CUDA device other than saying there is a CUDA device but it is too primitive to perform deep learning tasks.
– Mike
Mar 30 '18 at 2:06
I'd like to add some more explanation to prevent confusion on your side. If you look at CUDA capability only, it will be incorrect to say that your GPU does not support CUDA. However, as I understood, the focus is to evaluate the GPU against deep learning use. Like other deep learning frameworks, Theano harnesses cuDNN library. The library will only run if the CUDA capability is bigger than 2.1 (Kepler architecture or newer). It is more practical to notify user that there is no CUDA device other than saying there is a CUDA device but it is too primitive to perform deep learning tasks.
– Mike
Mar 30 '18 at 2:06
I'd like to add some more explanation to prevent confusion on your side. If you look at CUDA capability only, it will be incorrect to say that your GPU does not support CUDA. However, as I understood, the focus is to evaluate the GPU against deep learning use. Like other deep learning frameworks, Theano harnesses cuDNN library. The library will only run if the CUDA capability is bigger than 2.1 (Kepler architecture or newer). It is more practical to notify user that there is no CUDA device other than saying there is a CUDA device but it is too primitive to perform deep learning tasks.
– Mike
Mar 30 '18 at 2:06
add a comment |
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1
What graphics card do you have?
– rpmcruz
Mar 7 '18 at 22:04
idownvotedbecau.se/imageofcode
– user1251007
Mar 29 '18 at 13:12
And please accept the answer, if it is correct.
– user1251007
Mar 29 '18 at 13:13