Custom resource allocation.

Author: Ahmad, Sufian

netapp_dataops_k8s/docs/inference_server_management.md

@@ -38,6 +38,7 @@ The following options/arguments are optional:
     -n, --namespace=            Kubernetes namespace to create new server instance in. If not specified, server will be created in namespace "default".
     -p, --cpu=                  Number of CPUs to reserve for Triton instance. Format: '0.5', '1', etc. If not specified, no CPUs will be reserved.
     -b, --load-balancer         Option to use a LoadBalancer instead of using NodePort service. If not specified, NodePort service will be utilized.
+    -r, --allocate-resource=    Option to specify custom resource allocations. If not specified, no custom resource will be allocated. 
 ```
 
 ##### Example Usage
@@ -162,6 +163,7 @@ def create_triton_server(
     request_cpu: str = None,                                     # Number of CPUs to reserve for Triton instance. Format: '0.5', '1', etc. If not specified, no CPUs will be reserved.
     request_memory: str = None,                                  # Amount of memory to reserve for Triton instance. Format: '1024Mi', '100Gi', '10Ti', etc. If not specified, no memory will be reserved.
     request_nvidia_gpu: str = None,                              # Number of NVIDIA GPUs to allocate to Triton instance. Format: '1', '4', etc. If not specified, no GPUs will be allocated.
+    allocate_resource: str = None,                               # Option to specify custom resource allocations. If not specified, no custom resource will be allocated. 
     print_output: bool = False                                   # Denotes whether or not to print messages to the console during execution.
 ) -> str :
 ```