Ignoring visible gpu device with compute capability 3.0. The minimum required Cuda capability is 3.5
I just spent a day trying to build this thing from source and what worked for me finally is quite surprising: the pre-built wheel for TF 1.5.0 does not complain about this anymore, while pre-built wheel for TF 1.14.0 does complain. It seems you have used the same version, so it's quite interesting, but I thought I would share, so if anyone struggles with this, there seems to be an easy way out.
Configs:

Visual Studio version: 2017
Cuda compute capabilitz: 3.0
GPU: two Geforce GPU 755M
OS: Windows 10
Python: 3.6.8
Cuda Toolkit: 9.0
CuDNN: 7.0 (the earliest available is needed from, but it will complain anyway)
I am running Tensorflow 1.5.0 on a docker container because i need to use a version that doesn't use the AVX bytecodes because the hardware i am running on is too old to support it.
I finally got tensorflow-gpu to import correctly (after downgrading the docker image to tf 1.5.0) but now when i run any code to detect the GPU it says the GPU is not there.
I looked at the docker log and Jupyter is spitting out this message
Ignoring visible gpu device (device: 0, name: GeForce GTX 760, 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.
The tensorflow website says that GPUs with compute capability of 3.0 is supported so why does it says it needs compute capability 3.5?
Is there any way to get a docker image for tensorflow and jupyter that uses tf 1.5.0 but supports GPUs with compute capability?