parameter devicePlugin.deviceSplitCount does not work

i use helm to install k8s-vgpu-scheduler, set devicePlugin.deviceSplitCount = 5. after deployed successfully, i run 'kubectl describe node ', i can see the allocatable resources 'nvidia.com/gpu' count 40 (it has 8 A40 card in machine). Then i create 6 pod, every pod assign 1 'nvidia.com/gpu'， but when i create a pod which needs 3 'nvidia.com/gpu'，the k8s said the pod can't not be schedulerd.

the logs of vgpu-scheduler is showed below, it seems said only 2 gpu card can usable？
`I0313 00:58:35.594437 1 score.go:65] "devices status"
I0313 00:58:35.594467 1 score.go:67] "device status" device id="GPU-0707087e-8264-4ba4-bc45-30c70272ec4a" device detail={"Id":"GPU-0707087e-8264-4ba4-bc45-30c70272ec4a","Index":0,"Used":0,"Count":10,"Usedmem":0,"Totalmem":46068,"Totalcore":100,"Usedcores":0,"Numa":0,"Type":"NVIDIA-NVIDIA A40","Health":true}
I0313 00:58:35.594519 1 score.go:67] "device status" device id="GPU-b3e35ad4-81ee-0aee-9865-4787748b93ce" device detail={"Id":"GPU-b3e35ad4-81ee-0aee-9865-4787748b93ce","Index":1,"Used":0,"Count":10,"Usedmem":0,"Totalmem":46068,"Totalcore":100,"Usedcores":0,"Numa":0,"Type":"NVIDIA-NVIDIA A40","Health":true}
I0313 00:58:35.594542 1 score.go:67] "device status" device id="GPU-d38a391c-9f2f-395e-2f91-1785a648f6c4" device detail={"Id":"GPU-d38a391c-9f2f-395e-2f91-1785a648f6c4","Index":2,"Used":1,"Count":10,"Usedmem":46068,"Totalmem":46068,"Totalcore":100,"Usedcores":0,"Numa":0,"Type":"NVIDIA-NVIDIA A40","Health":true}
I0313 00:58:35.594568 1 score.go:67] "device status" device id="GPU-7099a282-5a75-55f8-0cd0-a4b48098ae1e" device detail={"Id":"GPU-7099a282-5a75-55f8-0cd0-a4b48098ae1e","Index":3,"Used":1,"Count":10,"Usedmem":46068,"Totalmem":46068,"Totalcore":100,"Usedcores":0,"Numa":0,"Type":"NVIDIA-NVIDIA A40","Health":true}
I0313 00:58:35.594600 1 score.go:67] "device status" device id="GPU-56967eb2-30b7-c808-367a-225b8bd8a12e" device detail={"Id":"GPU-56967eb2-30b7-c808-367a-225b8bd8a12e","Index":4,"Used":1,"Count":10,"Usedmem":46068,"Totalmem":46068,"Totalcore":100,"Usedcores":0,"Numa":0,"Type":"NVIDIA-NVIDIA A40","Health":true}
I0313 00:58:35.594639 1 score.go:67] "device status" device id="GPU-54191405-e5a9-2f7b-8ac4-f4e86c6669cb" device detail={"Id":"GPU-54191405-e5a9-2f7b-8ac4-f4e86c6669cb","Index":5,"Used":1,"Count":10,"Usedmem":46068,"Totalmem":46068,"Totalcore":100,"Usedcores":0,"Numa":0,"Type":"NVIDIA-NVIDIA A40","Health":true}
I0313 00:58:35.594671 1 score.go:67] "device status" device id="GPU-e731cd15-879f-6d00-485d-d1b468589de9" device detail={"Id":"GPU-e731cd15-879f-6d00-485d-d1b468589de9","Index":6,"Used":1,"Count":10,"Usedmem":46068,"Totalmem":46068,"Totalcore":100,"Usedcores":0,"Numa":0,"Type":"NVIDIA-NVIDIA A40","Health":true}
I0313 00:58:35.594693 1 score.go:67] "device status" device id="GPU-865edbf8-5d63-8e57-5e14-36682179eaf6" device detail={"Id":"GPU-865edbf8-5d63-8e57-5e14-36682179eaf6","Index":7,"Used":1,"Count":10,"Usedmem":46068,"Totalmem":46068,"Totalcore":100,"Usedcores":0,"Numa":0,"Type":"NVIDIA-NVIDIA A40","Health":true}
I0313 00:58:35.594725 1 score.go:90] "Allocating device for container request" pod="default/gpu-pod-2" card request={"Nums":5,"Type":"NVIDIA","Memreq":0,"MemPercentagereq":100,"Coresreq":0}
I0313 00:58:35.594757 1 score.go:93] "scoring pod" pod="default/gpu-pod-2" Memreq=0 MemPercentagereq=100 Coresreq=0 Nums=5 device index=7 device="GPU-b3e35ad4-81ee-0aee-9865-4787748b93ce"
I0313 00:58:35.594800 1 score.go:140] "first fitted" pod="default/gpu-pod-2" device="GPU-b3e35ad4-81ee-0aee-9865-4787748b93ce"
I0313 00:58:35.594829 1 score.go:93] "scoring pod" pod="default/gpu-pod-2" Memreq=0 MemPercentagereq=100 Coresreq=0 Nums=4 device index=6 device="GPU-0707087e-8264-4ba4-bc45-30c70272ec4a"
I0313 00:58:35.594850 1 score.go:140] "first fitted" pod="default/gpu-pod-2" device="GPU-0707087e-8264-4ba4-bc45-30c70272ec4a"
I0313 00:58:35.594869 1 score.go:93] "scoring pod" pod="default/gpu-pod-2" Memreq=0 MemPercentagereq=100 Coresreq=0 Nums=3 device index=5 device="GPU-865edbf8-5d63-8e57-5e14-36682179eaf6"
I0313 00:58:35.594889 1 score.go:93] "scoring pod" pod="default/gpu-pod-2" Memreq=0 MemPercentagereq=100 Coresreq=0 Nums=3 device index=4 device="GPU-e731cd15-879f-6d00-485d-d1b468589de9"
I0313 00:58:35.594911 1 score.go:93] "scoring pod" pod="default/gpu-pod-2" Memreq=0 MemPercentagereq=100 Coresreq=0 Nums=3 device index=3 device="GPU-54191405-e5a9-2f7b-8ac4-f4e86c6669cb"
I0313 00:58:35.594929 1 score.go:93] "scoring pod" pod="default/gpu-pod-2" Memreq=0 MemPercentagereq=100 Coresreq=0 Nums=3 device index=2 device="GPU-56967eb2-30b7-c808-367a-225b8bd8a12e"
I0313 00:58:35.594948 1 score.go:93] "scoring pod" pod="default/gpu-pod-2" Memreq=0 MemPercentagereq=100 Coresreq=0 Nums=3 device index=1 device="GPU-7099a282-5a75-55f8-0cd0-a4b48098ae1e"
I0313 00:58:35.594966 1 score.go:93] "scoring pod" pod="default/gpu-pod-2" Memreq=0 MemPercentagereq=100 Coresreq=0 Nums=3 device index=0 device="GPU-d38a391c-9f2f-395e-2f91-1785a648f6c4"
I0313 00:58:35.594989 1 score.go:211] "calcScore:node not fit pod" pod="default/gpu-pod-2" node="gpu-230"`

the kubectl describe node gpu-230 said:

the nvidia-smi said:

so somebody can solve this issue? thanks

Issue này liên quan đến việc tham số Helm devicePlugin.deviceSplitCount không hoạt động như mong đợi. Trước tiên, hãy kiểm tra các values của Helm chart và logic cấu hình của device plugin. Xem logic scoring của scheduler trong score.go (được tham chiếu trong logs) để hiểu cách số lượng device được tính. Kiểm tra cách các tài nguyên có thể cấp phát được báo cáo so với cách scheduler diễn giải chúng. Bản sửa lỗi có thể sẽ liên quan đến việc căn chỉnh cấu hình device split với logic đếm và cấp phát device của scheduler.

Do mô hình lập chỉ mục viết ra từ nội dung của issue.