Order of CUDA devices [duplicate] - tensorflow

Question

This question already has answers here:
How does CUDA assign device IDs to GPUs?
(4 answers)
Closed 4 years ago.
I saw this solution, but it doesn't quite answer my question; it's also quite old so I'm not sure how relevant it is.
I keep getting conflicting outputs for the order of GPU units. There are two of them: Tesla K40 and NVS315 (legacy device that is never used). When I run deviceQuery, I get
Device 0: "Tesla K40m"
...
Device PCI Domain ID / Bus ID / location ID: 0 / 4 / 0
Device 1: "NVS 315"
...
Device PCI Domain ID / Bus ID / location ID: 0 / 3 / 0
On the other hand, nvidia-smi produces a different order:
0 NVS 315
1 Tesla K40m
Which I find very confusing. The solution I found for Tensorflow (and a similar one for Pytorch) is to use
import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"]="0"
PCI Bus ID is 4 for Tesla and 3 for NVS, so it should set it to 3 (NVS), is that right?
In pytorch I set
os.environ['CUDA_VISIBLE_DEVICES']='0'
...
device = torch.cuda.device(0)
print torch.cuda.get_device_name(0)
to get Tesla K40m
when I set instead
os.environ['CUDA_VISIBLE_DEVICES']='1'
device = torch.cuda.device(1)
print torch.cuda.get_device_name(0)
to get
UserWarning:
Found GPU0 NVS 315 which is of cuda capability 2.1.
PyTorch no longer supports this GPU because it is too old.
warnings.warn(old_gpu_warn % (d, name, major, capability[1]))
NVS 315
So I'm quite confused: what's the true order of GPU devices that tf and pytorch use?

Answer 1

By default, CUDA orders the GPUs by computing power. GPU:0 will be the fastest GPU on your host, in your case the K40m.
If you set CUDA_DEVICE_ORDER='PCI_BUS_ID' then CUDA orders your GPU depending on how you set up your machine meaning that GPU:0 will be the GPU on your first PCI-E lane.
Both Tensorflow and PyTorch use the CUDA GPU order. That is consistent with what you showed:
os.environ['CUDA_VISIBLE_DEVICES']='0'
...
device = torch.cuda.device(0)
print torch.cuda.get_device_name(0)
Default order so GPU:0 is the K40m since it is the most powerful card on your host.
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ['CUDA_VISIBLE_DEVICES']='0'
...
device = torch.cuda.device(0)
print torch.cuda.get_device_name(0)
PCI-E lane order, so GPU:0 is the card with the lowest bus-id in your case the NVS.