（对不起，如果这个问题太新手，但我不太明白，想要仔细检查我是否以正确的方式并行使用两个gpus，我问你以下问题。）
两个gpus（使用相同型号）安装在我正在使用的电脑中。在pycharm项目中，我运行tensorflow代码设置
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
然后运行初始运行日志
Using TensorFlow backend.
2018-09-15 03:36:36.727152: I T:\src\github\tensorflow\tensorflow\core\platform\cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2018-09-15 03:36:37.080157: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1405] Found device 0 with properties:
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.6705
pciBusID: 0000:17:00.0
totalMemory: 11.00GiB freeMemory: 9.08GiB
2018-09-15 03:36:37.080671: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1484] Adding visible gpu devices: 0
2018-09-15 03:36:37.796088: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:965] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-09-15 03:36:37.796320: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:971] 0
2018-09-15 03:36:37.796469: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:984] 0: N
2018-09-15 03:36:37.796723: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1097] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 8783 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:17:00.0, compute capability: 6.1)
然后在另一个pycharm项目中，我运行一个tensorflow代码设置
os.environ["CUDA_VISIBLE_DEVICES"] = '1'
然后在运行日志中显示
Using TensorFlow backend.
2018-09-15 03:37:00.119630: I T:\src\github\tensorflow\tensorflow\core\platform\cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2018-09-15 03:37:00.468546: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1405] Found device 0 with properties:
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.6705
pciBusID: 0000:65:00.0
totalMemory: 11.00GiB freeMemory: 9.08GiB
2018-09-15 03:37:00.468930: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1484] Adding visible gpu devices: 0
2018-09-15 03:37:01.199726: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:965] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-09-15 03:37:01.199950: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:971] 0
2018-09-15 03:37:01.200096: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:984] 0: N
2018-09-15 03:37:01.200349: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1097] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 8783 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:65:00.0, compute capability: 6.1)
令我担心的是他们都有设备0.但他们的pciBusID是不同的。
所以我的简单问题是我是否以正确的方式并行使用两个gpus？
当我使用Windows 10时，我使用设备管理器监视gpu使用情况，这对我来说似乎是正确的。但我只想听听专家的意见。
如果您可以回答，那么什么是pci总线ID？为什么他们都显示设备0？