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

El issue trata de que el parámetro de Helm devicePlugin.deviceSplitCount no funciona como se esperaba. Empieza examinando los valores del chart de Helm y la lógica de configuración del device plugin. Revisa la lógica de scoring del scheduler en score.go (referenciada en los logs) para entender cómo se calculan las cantidades de dispositivos. Comprueba cómo se notifican los recursos asignables frente a cómo los interpreta el scheduler. Es probable que la corrección consista en alinear la configuración de división de dispositivos con la lógica de conteo y asignación de dispositivos del scheduler.

Escrito por el modelo de indexación a partir del texto del issue.