Submiting job to kubernetes

job_submit.py

import os
import re

import kubernetes
import logging

import math
from kubernetes.client import V1Container
from kubernetes.client import V1EnvVar
from kubernetes.client import V1Job
from kubernetes.client import V1JobSpec
from kubernetes.client import V1ObjectMeta
from kubernetes.client import V1PodSpec
from kubernetes.client import V1PodTemplateSpec
from kubernetes.client import V1ResourceRequirements

_KUBERNETES_NAMESPACE = "echoserver"
_DEADLINE_SECONDS = 60 * 60 * 12  # 12h
_BYTES_PER_TILE = 500000  # 500 KB
_BASE_MEM_BYTES = 3000 * 1024 * 1024  # 3000 MiB


def _plan_to_job_name(plan_id):
    clean_plan = plan_id.replace("_", "-").lower()
    clean_plan = re.sub("[^-a-z0-9]+", "-", clean_plan)
    return "worker-" + clean_plan


def _tiles_to_memory_limit(tiles):
    bytes_req = _BASE_MEM_BYTES + _BYTES_PER_TILE * len(tiles)
    memory_req = str(int(math.ceil(bytes_req / (1024 * 1024)))) + "Mi"
    logging.info(
        "Memory requirement",
        extra={
            "tiles_len": len(tiles),
            "memory_req_bytes": bytes_req,
            "memory_req": memory_req
        })
    return memory_req


class KubernetesTfApi(object):
    def __init__(self):
        logging.info("Initializing kubernetes api")
        kubernetes.config.load_incluster_config()
        self._batch_api = kubernetes.client.apis.batch_v1_api.BatchV1Api()
        self._core_api = kubernetes.client.CoreV1Api()

    def clean_old_job(self, plan_id):
        label_selector = "plan=={}".format(plan_id)
        self._batch_api.delete_collection_namespaced_job(
            namespace=_KUBERNETES_NAMESPACE,
            label_selector=label_selector,
        )
        self._core_api.delete_collection_namespaced_pod(
            namespace=_KUBERNETES_NAMESPACE,
            label_selector=label_selector
        )

    def request_plan_processing(self, plan_id, tiles):
        job_name = _plan_to_job_name(plan_id)
        self.clean_old_job(plan_id)
        object_meta = V1ObjectMeta(name=job_name, labels={"plan": plan_id})

        memory_usage = {"memory": _tiles_to_memory_limit(tiles)}
        pod_spec = V1PodSpec(
            containers=[
                V1Container(
                    args=["python2", "/workspace/worker/main.py", plan_id],
                    env=[V1EnvVar(name="SERVER_STAGE",
                                  value=os.environ["SERVER_STAGE"]),
                         V1EnvVar(name="OPENBLAS_NUM_THREADS",
                                  value="2")],
                    image_pull_policy="IfNotPresent",
                    image="gcr.io/tensorflight/worker:27.0.0",
                    name=job_name,
                    resources=V1ResourceRequirements(
                        limits=memory_usage,
                        requests=memory_usage
                    )
                )
            ],
            restart_policy="OnFailure",
            active_deadline_seconds=_DEADLINE_SECONDS
        )
        job_spec = V1JobSpec(
            active_deadline_seconds=_DEADLINE_SECONDS,
            completions=1,
            parallelism=1,
            template=V1PodTemplateSpec(
                spec=pod_spec,
                metadata=object_meta
            ),
        )
        self._batch_api.create_namespaced_job(namespace=_KUBERNETES_NAMESPACE,
                                              body=V1Job(spec=job_spec,
                                                         metadata=object_meta))

Hi. I have a python script bh1.py. i am going to run it on cluster. but I don't know how to run it with your code. my python script has been copied into /home/sassan/ directory in the cluster.