Global Config

Device Configs: ConfigMap

note

All the configurations listed below are managed within the hami-scheduler-device ConfigMap.

You can update these configurations using one of the following methods:

1. Directly edit the ConfigMap: If HAMi has already been successfully installed, you can manually update the hami-scheduler-device ConfigMap using the kubectl edit command to manually update the hami-scheduler-device ConfigMap.

   kubectl edit configmap hami-scheduler-device -n <namespace>

   After making changes, restart the related HAMi components to apply the updated configurations.

2. Modify Helm Chart: Update the corresponding values in the ConfigMap, then reapply the Helm Chart to regenerate the ConfigMap.

   Argument	Type	Description	Default
   nvidia.deviceMemoryScaling	Float	The ratio for NVIDIA device memory scaling, can be greater than 1 (enables virtual device memory, experimental feature). For an NVIDIA GPU with M memory, if set to S, vGPUs split from this GPU will get S * M memory in Kubernetes.	1
   nvidia.deviceSplitCount	Integer	Maximum jobs assigned to a single GPU device.	10
   nvidia.migstrategy	String	"none" for ignoring MIG features, "mixed" for allocating MIG devices by separate resources.	"none"
   nvidia.disablecorelimit	String	"true" to disable core limit, "false" to enable core limit.	"false"
   nvidia.defaultMem	Integer	The default device memory of the current job, in MB. '0' means using 100% of the device memory.	0
   nvidia.defaultCores	Integer	Percentage of GPU cores reserved for the current job. 0 allows any GPU with enough memory; 100 reserves the entire GPU exclusively.	0
   nvidia.defaultGPUNum	Integer	Default number of GPUs. If set to 0, it will be filtered out. If nvidia.com/gpu is not set in the pod resource, the webhook checks nvidia.com/gpumem, resource-mem-percentage, and nvidia.com/gpucores, adding nvidia.com/gpu with this default value if any of them are set.	1
   nvidia.resourceCountName	String	vGPU number resource name.	"nvidia.com/gpu"
   nvidia.resourceMemoryName	String	vGPU memory size resource name.	"nvidia.com/gpumem"
   nvidia.resourceMemoryPercentageName	String	vGPU memory fraction resource name.	"nvidia.com/gpumem-percentage"
   nvidia.resourceCoreName	String	vGPU core resource name.	"nvidia.com/cores"
   nvidia.resourcePriorityName	String	vGPU job priority name.	"nvidia.com/priority"

Chart Configs: arguments

You can customize your vGPU support by setting the following arguments using -set, for example

helm install hami hami-charts/hami --set devicePlugin.deviceMemoryScaling=5 ...

Argument	Type	Description	Default
devicePlugin.service.schedulerPort	Integer	Scheduler webhook service nodePort.	31998
scheduler.defaultSchedulerPolicy.nodeSchedulerPolicy	String	GPU node scheduling policy: "binpack" allocates jobs to the same GPU node as much as possible. "spread" allocates jobs to different GPU nodes as much as possible.	"binpack"
scheduler.defaultSchedulerPolicy.gpuSchedulerPolicy	String	GPU scheduling policy: "binpack" allocates jobs to the same GPU as much as possible. "spread" allocates jobs to different GPUs as much as possible.	"spread"

Pod configs: annotations

Argument	Type	Description	Example
nvidia.com/use-gpuuuid	String	If set, devices allocated by this pod must be one of the UUIDs defined in this string.	"GPU-AAA,GPU-BBB"
nvidia.com/nouse-gpuuuid	String	If set, devices allocated by this pod will NOT be in the UUIDs defined in this string.	"GPU-AAA,GPU-BBB"
nvidia.com/nouse-gputype	String	If set, devices allocated by this pod will NOT be in the types defined in this string.	"Tesla V100-PCIE-32GB, NVIDIA A10"
nvidia.com/use-gputype	String	If set, devices allocated by this pod MUST be one of the types defined in this string.	"Tesla V100-PCIE-32GB, NVIDIA A10"
hami.io/node-scheduler-policy	String	GPU node scheduling policy: "binpack" allocates the pod to used GPU nodes for execution. "spread" allocates the pod to different GPU nodes for execution.	"binpack" or "spread"
hami.io/gpu-scheduler-policy	String	GPU scheduling policy: "binpack" allocates the pod to the same GPU card for execution. "spread" allocates the pod to different GPU cards for execution.	"binpack" or "spread"
nvidia.com/vgpu-mode	String	The type of vGPU instance this pod wishes to use.	"hami-core" or "mig"

Container configs: env

Argument	Type	Description	Default
GPU_CORE_UTILIZATION_POLICY	String	Defines GPU core utilization policy: "default": Default utilization policy. "force": Limits core utilization below "nvidia.com/gpucores". "disable": Ignores the utilization limitation set by "nvidia.com/gpucores" during job execution.	"default"
CUDA_DISABLE_CONTROL	Boolean	If "true", HAMi-core will not be used inside the container, leading to no resource isolation and limitation (for debugging purposes).	false