Minimum Cuda capability is 3.5? But, 3.0 stated on site

[tharindu-mathew]

I'm able to run the hello world examples, but the following warning (or error) is printed. So, I can't make use of my gpu? While this maybe a simple correction on the web page, is there anyway I can get a version that allows me to run with a Cuda 3.0 card?

OS: Ubuntu 16.04
GPU: K2000M

On the linux installation page, the minimum capability is written as 3.0. But, when I try to run hello world on a cuda 3.0 card, the following is printed:

name: Quadro K2000M major: 3 minor: 0 memoryClockRate(GHz): 0.745
pciBusID: 0000:01:00.0
totalMemory: 1.95GiB freeMemory: 977.81MiB
2018-03-05 13:43:54.533246: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1283] Ignoring visible gpu device (device: 0, name: Quadro K2000M, pci bus id: 0000:01:00.0, compute capability: 3.0) with Cuda compute capability 3.0. The minimum required Cuda capability is 3.5.

[yongtang]

I think minimal capability is 3.5 for binary install. It might still be possible to support 3.0 when building from source.

Create a PR #17448 for the doc fix.

[tharindu-mathew]

Ok, is this a regression? As per this thread this should be available?#25

[rohan100jain]

I think in the thread its mentioned that you might have to change some lines in common_runtime/gpu/gpu_device.cc for this to work. By default minimum is 3.5

[tharindu-mathew]

It can be compiled from source with CUDA capability 3.0. It is working for me this way.

[JoshuaC3]

@MackieM did you have to make the changes suggested by @rohan100jain before building from source?

[tharindu-mathew]

No. I believe basel configured it for me without having to touch the source.

…

On Thu, May 10, 2018, 7:13 PM JoshuaC3 ***@***.***> wrote: @MackieM <https://github.com/mackiem> did you have to make the changes suggested by @rohan100jain <https://github.com/rohan100jain> before building from source? — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <#17445 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/ABsR4TTovkhJvWb2wHx-bYL8xrKj5KVIks5txMmngaJpZM4Sc1Yr> .

[pabx06]

Hello i have a CUDA Compute Capability of 2.1 is there a chance i am able to change the source code to support my GPU lower capability? Or the tensorflow's algo need a specific capability ?

Cause CPU take way too long: been training the model for more than 48h so far on CPU
Likely i will be dying from old age before i can evaluate the inception model for my needs

• 19%-30% validation accuracy
• 1.5 m training step
• model : inceptions-v3 transfert learning & retraining of classification layer
• dataset 6GB
• 130 classes, ...
• GPU:Intel(R) Xeon(R) CPU E3-1220 V2 @ 3.10GHz

[tharindu-mathew]

Quite sure, 2.1 is too old for a lot of functionality and acceleration. Sorry.