def calculate_cpu_request_for_clusterloader2()

in modules/python/clusterloader2/autoscale/autoscale.py [0:0]

• 23 lines of code
• 5 McCabe index (conditional complexity)

def calculate_cpu_request_for_clusterloader2(node_label_selector, node_count, pod_count, warmup_deployment, cl2_config_dir):
    client = KubernetesClient(os.path.expanduser("~/.kube/config"))
    timeout = 10  # 10 minutes
    nodes = []

    try:
        nodes = client.wait_for_nodes_ready(1, timeout, label_selector=node_label_selector)
        if len(nodes) == 0:
            raise Exception(f"No nodes found with label selector: {node_label_selector}")
    except Exception as e:
        raise Exception(f"Error while getting nodes: {e}") from e

    node = nodes[0]
    allocatable_cpu = node.status.allocatable["cpu"]
    logger.info(f"Node {node.metadata.name} has allocatable cpu of {allocatable_cpu}")

    cpu_value = int(allocatable_cpu.replace("m", ""))
    allocated_cpu, _ = client.get_daemonsets_pods_allocated_resources("kube-system", node.metadata.name)
    logger.info(f"Node {node.metadata.name} has allocated cpu of {allocated_cpu}")

    cpu_value -= allocated_cpu
    # Remove warmup deployment cpu request from the total cpu value
    if warmup_deployment in ["true", "True"]:
        cpu_value -= 100
        cleanup_warmup_deployment_for_karpeneter(cl2_config_dir)

    # Calculate the cpu request for each pod
    pods_per_node = pod_count // node_count
    cpu_request = cpu_value // pods_per_node
    return cpu_request