Container component with existing pod spec for nccl test pipeline.

[thsmfe001]

I already tested nccl test with four pods then i tried to make component and pipeline with same configurations.
But i faced with some attribute issue and type issue during executing pipeline generated below python code.
I already checked kubeflow document and it said container component support only image, command and args.
If so how to modify below code to apply existing environment of pods?

from kfp import dsl
from kfp.dsl import ContainerSpec, OutputPath
from kubernetes import client as k8s_client

Define your component using dsl.container_component decorator

@dsl.container_component
def create_component(image: str, node: str, name: str): # Keep argument for clarity

return dsl.ContainerSpec(
    # Container name is automatically generated, remove the argument
    image=image,
    command=['/bin/sh', '-c'],
    arguments=['cd /workspace && tail -f /dev/null'],
    resources=k8s_client.V1ResourceRequirements(
        requests={
            'cpu': '1',
            'memory': '2Gi',
            'nvidia.com/gpu': '1',
            'intel.com/intel_sriov_netdevice': '1'
        },
        limits={
            'cpu': '2',
            'memory': '4Gi',
            'nvidia.com/gpu': '1',
            'intel.com/intel_sriov_netdevice': '1'
        }
    ),
    env=[
        k8s_client.V1EnvVar(name='NVIDIA_VISIBLE_DEVICES', value='all'),
        k8s_client.V1EnvVar(name='NVIDIA_DRIVER_CAPABILITIES', value='all'),
        k8s_client.V1EnvVar(name='LD_PRELOAD', value='/usr/lib/x86_64-linux_gnu/libtcmalloc_minimal.so.4')
    ],
    node_selector={
        'kubernetes.io/hostname': node
    },
    annotations={
        'k8s.v1.cni.cncf.io/networks': 'sriov-net5'
    },
    security_context=k8s_client.V1SecurityContext(privileged=True),
    image_pull_policy='Never',
    service_account_name='default-editor',
)

Define your pipeline

@dsl.pipeline(
name='nccl test Pipeline',
description='A pipeline that deploys a master node and multiple worker nodes for distributed training'
)
def nccl_test_pipeline():
work_node1 = create_component(
image='docker.io/library/pytorch_nccl_master:local',
node='atc01',
name='work-node1' # Argument for clarity, not used in KFP v2
)

work_node2 = create_component(
    image='docker.io/library/pytorch_nccl_work:local',
    node='atc01',
    name='work-node2'  # Argument for clarity, not used in KFP v2
)

work_node3 = create_component(
    image='docker.io/library/pytorch_nccl_work:local',
    node='atc02',
    name='work-node3'  # Argument for clarity, not used in KFP v2
)

work_node4 = create_component(
    image='docker.io/library/pytorch_nccl_work:local',
    node='atc02',
    name='work-node4'  # Argument for clarity, not used in KFP v2
)

Compile the pipeline

if name == 'main':
import kfp.compiler as compiler
compiler.Compiler().compile(
pipeline_func=nccl_test_pipeline,
package_path='nccl_test_new_local.yaml'
)