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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Executing a python file
Step1:  Executing a python function
Step2:  Executing a complete notebook
Step3:  Executing it with large #CPUs and huge Memory
You Kubernetes cluster should have a node pool that can satisfy these resource requests. For example, to schedule a job with 90 cpus and 600GB memory you need a nodepool created using n1-hihmem-624 in GCP.
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Python Code:
%%writefile train.py
print("hello world!")
job = TrainJob("train.py", backend=KubeflowGKEBackend())
job.submit()
Explanation: Executing a python file
End of explanation
def train():
    print("simple train job!")
job = TrainJob(train, backend=KubeflowGKEBackend())
job.submit()
Explanation: Executing a python function
End of explanation
%%writefile requirements.txt
papermill
jupyter
job = TrainJob("train.ipynb", backend=KubeflowGKEBackend(), input_files=["requirements.txt"])
job.submit()
Explanation: Executing a complete notebook
End of explanation
import multiprocessing
import os
def train():
    print("CPU count: {}".format(multiprocessing.cpu_count()))
    print("Memory: {}", os.sysconf('SC_PAGE_SIZE') * os.sysconf('SC_PHYS_PAGES')/(1024.**3))
train()
job = TrainJob(train, base_docker_image=None, docker_registry=None,  backend=KubeflowGKEBackend(), 
              pod_spec_mutators=[get_resource_mutator(cpu=90, memory=600)])
job.submit()
Explanation: Executing it with large #CPUs and huge Memory
You Kubernetes cluster should have a node pool that can satisfy these resource requests. For example, to schedule a job with 90 cpus and 600GB memory you need a nodepool created using n1-hihmem-624 in GCP.
End of explanation
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