GPR Wait Time

The GPU Provisioning Requests (GPR) wait time is calculated based on the current GPU availability and the requested GPU resources. If the requested GPU resources are not immediately available, EGS estimates the wait time based on the current queue of GPU requests and their priorities.

Get Wait Time For a GPU Request

Use this API to get the estimated wait time for a GPU request.

Method and URL

POST /api/v1/gpr/waittime

Parameters

Parameter	Parameter Type	Description	Required
requestName	String	The unique name for the GPU provisioning request. The maximum length is 40 characters.	Mandatory
sliceName	String	The name of the workspace (slice) where the GPU resources are requested.	Mandatory
preferredClusters	Array of strings	The list of preferred clusters where the GPU resources can be provisioned. This parameter is required only when enableAutoClusterSelection is false. If enableAutoClusterSelection is true, this parameter must be empty.	Optional
enableAutoClusterSelection	Boolean	This is the parameter to enable or disable automatic cluster selection. If set to true, EGS automatically selects the best cluster based on resource availability and preferredClusters values must be empty. If set to false, EGS uses the preferredClusters list to select clusters.	Mandatory
enableAutoGpuSelection	Boolean	This is the parameter to enable or disable automatic GPU selection. If set to true, EGS automatically selects the best GPU shape based on resource availability. If set to false, EGS uses the instanceType parameter to select the GPU shape.	Mandatory
numberOfGPUs	Integer	Total number of GPUs required for the GPU request. The value must be greater than or equal to 1.	Mandatory
numberOfGPUNodes	Integer	Number of nodes required for the GPU request. The value must be greater than or equal to 1.	Mandatory
instanceType	String	The type of GPU instance requested. This parameter is required if the enableAutoGpuSelection is set to false.	Optional
exitDuration	String	The duration for which the GPU resources are required. The format is XdYhZm, where X is days, Y is hours, and Z is minutes. The value must be greater than zero. For example, the duration is 1d2h30m.	Mandatory
memoryPerGpu	Integer	The amount of memory (in GB) required per GPU. The value must be greater than or equal to 1.	Mandatory
priority	Integer	The priority of the request. You can change the priority of a GPR in the queue. You can select a GPR and increase the priority number (low: 1-100, medium: 101-200, high: 201-300) to move a GPR higher in the queue. The maximum priority is 300. The default priority is 1 (low).	Mandatory
enableEviction	Boolean	This is the parameter to enable or disable eviction of lower priority GPU requests to accommodate GPU request. If set totrue, lower priority requests may be evicted to free up resources. If set to false, the request will only be fulfilled if sufficient resources are available without evicting other requests.	Optional

Example Scenarios

Example 1: Get Wait Time for a GPR with Cluster and GPU Auto-Selection Enabled

When both the enableAutoClusterSelection and enableAutoGpuSelection parameters are set to true, EGS automatically selects the best cluster and GPU shape based on the resource availability. During GPR creation, you cannot select any options obtained from waittime API request. All cluster and GPU-related parameters are automatically handled by the GPR Manager.

Example Request

curl --location --globoff '{{host}}/api/v1/gpr/waittime' \
--data '{

    "requestName": "tesla-workload",
    "sliceName": "dashboard",
    "numberOfGPUs": 2,
    "numberOfGPUNodes": 1,
    "exitDuration": "00d06h00m",
    "memoryPerGpu": 15,
    "priority": 1,
    "enableEviction": true,
    "enableAutoClusterSelection": true,
    "enableAutoGpuSelection": true
}'

Example Responses

Response: Success

{
    "message": "Wait time fetched successfully",
    "status": "success",
    "data": {
        "items": [
            {
                "cluster": "worker-3",
                "instanceType": "n1-highcpu-2",
                "gpuShape": "Tesla-T4",
                "waitduration": "0d0h0m0s",
                "expectedStartTime": "2025-09-25T14:04:36Z",
                "evictableGPRs": []
            },
            {
                "cluster": "worker-4",
                "instanceType": "n1-highcpu-2",
                "gpuShape": "Tesla-T4",
                "waitduration": "0d0h0m0s",
                "expectedStartTime": "2025-09-25T14:04:36Z",
                "evictableGPRs": [
                    {
                        "requestName": "tesla-workload",
                        "requestId": "gpr-e318186c-15dc",
                        "clusterName": "worker-4",
                        "sliceName": "dashboard",
                        "gpuReq": {
                            "numberOfGPUs": 1,
                            "numberOfGPUNodes": 1,
                            "instanceType": "n1-highcpu-2"
                        },
                        "priority": 1,
                        "exitDuration": "6h0m0s",
                        "expectedStartTime": "",
                        "creationTime": "2025-09-25 14:01:24 +0000 UTC",
                        "autoEvictionEnabled": true
                    }
                ]
            },
            {
                "cluster": "worker-1",
                "instanceType": "VM.GPU.A10.2",
                "gpuShape": "NVIDIA-A10",
                "waitduration": "0d0h0m0s",
                "expectedStartTime": "2025-09-25T14:04:36Z",
                "evictableGPRs": []
            }
        ]
    }
}

Response: Bad Request

{
  "statusCode": 422,
  "status": "Error",
  "message": "Bad Request",
  "data": null,
  "error": {
    "errorKey": "",
    "message": "Bad Request",
    "data": "{\"error\":{\"Message\":\"Error while fetching GPR wait time\",\"Status\":422,\"DetailedError\":{\"Errormessage\":\"Could not find any cluster which satisfies the GPR requirements. Errors: Number of GPUs requested (3) is exceeding the available GPUs (2) for cluster: worker-4, instance type: n1-highcpu-2; Number of GPUs requested (3) is exceeding the available GPUs (2) for cluster: worker-3, instance type: n1-highcpu-2\",\"Reason\":\"\",\"Details\":\"\"},\"StatusCode\":422,\"ReferenceDocumentation\":\"NA\"}}"
  }
}

Example 2: Get Wait Time for a GPR with Only Cluster Auto-Selection Enabled

When the enableAutoClusterSelection parameter is set to true, the preferredClusters parameter values must be empty. EGS automatically selects the optimal cluster based on available resources. During GPR creation, you cannot select any options obtained from the waittime API request. All cluster and GPU-related parameters are automatically handled by the GPR Manager.

Example Request

curl --location --globoff '{{host}}/api/v1/gpr/waittime' \
--data '{
    "requestName": "tesla-workload",
    "sliceName": "dashboard",
    "numberOfGPUs": 2,
    "numberOfGPUNodes": 1,
    "instanceType": "n1-highcpu-2",
    "exitDuration": "00d06h00m",
    "memoryPerGpu": 15,
    "priority": 101,
    "enableEviction": true,
    "enableAutoClusterSelection": true,
    "enableAutoGpuSelection": false
}'

Example Response

Response: Success

{
    "message": "Wait time fetched successfully",
    "status": "success",
    "data": {
        "items": [
            {
                "cluster": "worker-3",
                "instanceType": "n1-highcpu-2",
                "gpuShape": "Tesla-T4",
                "waitduration": "0d0h0m0s",
                "expectedStartTime": "2025-09-25T14:12:44Z",
                "evictableGPRs": []
            },
            {
                "cluster": "worker-4",
                "instanceType": "n1-highcpu-2",
                "gpuShape": "Tesla-T4",
                "waitduration": "0d0h0m0s",
                "expectedStartTime": "2025-09-25T14:12:44Z",
                "evictableGPRs": [
                    {
                        "requestName": "tesla-workload",
                        "requestId": "gpr-e318186c-15dc",
                        "clusterName": "worker-4",
                        "sliceName": "dashboard",
                        "gpuReq": {
                            "numberOfGPUs": 1,
                            "numberOfGPUNodes": 1,
                            "instanceType": "n1-highcpu-2"
                        },
                        "priority": 1,
                        "exitDuration": "6h0m0s",
                        "expectedStartTime": "",
                        "creationTime": "2025-09-25 14:01:24 +0000 UTC",
                        "autoEvictionEnabled": true
                    }
                ]
            }
        ]
    }
}

Response: Bad Request

{
  "statusCode": 422,
  "status": "Error",
  "message": "Bad Request",
  "data": null,
  "error": {
    "errorKey": "",
    "message": "Bad Request",
    "data": "{\"error\":{\"Message\":\"Error while fetching GPR wait time\",\"Status\":422,\"DetailedError\":{\"Errormessage\":\"Could not find any cluster which satisfies the GPR requirements. Errors: Number of GPUs requested (3) is exceeding the available GPUs (2) for cluster: worker-4, instance type: n1-highcpu-2; Number of GPUs requested (3) is exceeding the available GPUs (2) for cluster: worker-3, instance type: n1-highcpu-2\",\"Reason\":\"\",\"Details\":\"\"},\"StatusCode\":422,\"ReferenceDocumentation\":\"NA\"}}"
  }
}

Example 3: Get Wait Time for a GPR with Only GPU Auto-Selection Enabled

When you set the enableAutoGpuSelection parameter to true, skip providing the gpuShape and instanceType parameters values. EGS automatically selects the optimal GPU shape based on available resources. During GPR creation, you cannot select any options obtained from the waittime API request. All cluster and GPU-related parameters are automatically handled by the GPR Manager.

Example Request

curl --location --globoff '{{host}}/api/v1/gpr/waittime' \
--data '{
    "requestName": "tesla-workload",
    "sliceName": "dashboard",
    "preferredClusters": [
        "worker-4",
        "worker-3"
    ],
    "numberOfGPUs": 2,
    "numberOfGPUNodes": 1,
    "exitDuration": "00d06h00m",
    "memoryPerGpu": 15,
    "priority": 1,
    "enableEviction": true,
    "enableAutoClusterSelection": false,
    "enableAutoGpuSelection": true
}'

Example Responses

Response: Success

{
    "statusCode": 200,
    "status": "OK",
    "message": "Success",
    "data": {
        "items": [
            {
                "cluster": "worker-4",
                "instanceType": "n1-highcpu-2",
                "gpuShape": "Tesla-T4",
                "waitduration": "0d0h0m0s",
                "expectedStartTime": "2025-09-25T14:25:44Z",
                "evictableGPRs": []
            },
            {
                "cluster": "worker-1",
                "instanceType": "VM.GPU.A10.2",
                "gpuShape": "NVIDIA-A10",
                "waitduration": "0d0h0m0s",
                "expectedStartTime": "2025-09-25T14:25:43Z",
                "evictableGPRs": []
            }
        ]
    }
}

Response: Bad Request

{
  "statusCode": 422,
  "status": "Error",
  "message": "Bad Request",
  "data": null,
  "error": {
    "errorKey": "",
    "message": "Bad Request",
    "data": "{\"error\":{\"Message\":\"Error while fetching GPR wait time\",\"Status\":422,\"DetailedError\":{\"Errormessage\":\"Could not find any cluster which satisfies the GPR requirements. Errors: Number of GPUs requested (3) is exceeding the available GPUs (2) for cluster: worker-4, instance type: n1-highcpu-2; Number of GPUs requested (3) is exceeding the available GPUs (2) for cluster: worker-3, instance type: n1-highcpu-2\",\"Reason\":\"\",\"Details\":\"\"},\"StatusCode\":422,\"ReferenceDocumentation\":\"NA\"}}"
  }
}

Example 4: Get Wait Time for a GPR with No Auto-Selection Enabled

If the enableAutoClusterSelection and enableAutoGpuSelection parameters are set to false. The preferredClusters, gpuShape and instanceType parameters are required. During GPR creation, you have to select one of the options obtained from the waittime API request.

Example Request

{
    "requestName": "tesla-workload",
    "sliceName": "dashboard",
    "preferredClusters": [
        "worker-4",
        "worker-3"
    ],
    "numberOfGPUs": 2,
    "numberOfGPUNodes": 1,
    "instanceType": "n1-highcpu-2",
    "exitDuration": "00d02h00m",
    "memoryPerGpu": 15,
    "priority": 1,
    "enableEviction": true,
    "enableAutoClusterSelection": false,
    "enableAutoGpuSelection": false
}

Example Responses

Response: Success

{
    "message": "Wait time fetched successfully",
    "status": "success",
    "data": {
        "items": [
            {
                "cluster": "worker-4",
                "instanceType": "n1-highcpu-2",
                "gpuShape": "Tesla-T4",
                "waitduration": "0d5h26m52s",
                "expectedStartTime": "2025-09-25T20:01:39Z",
                "evictableGPRs": []
            },
            {
                "cluster": "worker-3",
                "instanceType": "n1-highcpu-2",
                "gpuShape": "Tesla-T4",
                "waitduration": "0d0h0m0s",
                "expectedStartTime": "2025-09-25T14:34:46Z",
                "evictableGPRs": []
            }
        ]
    }
}

Response: Bad Request

{
  "statusCode": 422,
  "status": "Error",
  "message": "Bad Request",
  "data": null,
  "error": {
    "errorKey": "",
    "message": "Bad Request",
    "data": "{\"error\":{\"Message\":\"Error while fetching GPR wait time\",\"Status\":422,\"DetailedError\":{\"Errormessage\":\"Could not find any cluster which satisfies the GPR requirements. Errors: Number of GPUs requested (3) is exceeding the available GPUs (2) for cluster: worker-4, instance type: n1-highcpu-2; Number of GPUs requested (3) is exceeding the available GPUs (2) for cluster: worker-3, instance type: n1-highcpu-2\",\"Reason\":\"\",\"Details\":\"\"},\"StatusCode\":422,\"ReferenceDocumentation\":\"NA\"}}"
  }
}