{"user_id": user_obj["id"],
"user": user_obj,
"success": False, "failure": False, "timeout": False, "stopped": False
}
],
"recipients": {},
"attachments": [],
"include_logs": True,
"report_attachments": [],
"success_recipients": [],
"failure_recipients": [],
"timeout_recipients": [],
"stopped_recipients": []
}
new_job = cml.create_job(create_jobs_params)
new_job_id = new_job["id"]
print("Created new job with jobid", new_job_id)
##
# Start the Training Job
job_env_params = {}
start_job_params = {"environment": job_env_params}
job_id = new_job_id
job_status = cml.start_job(job_id, start_job_params)
print("Training Job started")
# Stop a job
#job_dict = cml.start_job(job_id, start_job_params)
#cml.stop_job(job_id, start_job_params)
"failure": False,
"timeout": False,
"stopped": False
}],
"recipients": {},
"attachments": [],
"include_logs":
True,
"report_attachments": [],
"success_recipients": [],
"failure_recipients": [],
"timeout_recipients": [],
"stopped_recipients": []
}
new_job = cml.create_job(create_jobs_params)
new_job_id = new_job["id"]
print("Created new job with jobid", new_job_id)
##
# Start a job
job_env_params = {}
start_job_params = {"environment": job_env_params}
job_status = cml.start_job(new_job_id, start_job_params)
print("Job started")
# Create model build script
cdsw_script = """#!/bin/bash
pip3 install -r requirements.txt"""
with open("cdsw-build.sh", 'w+') as f:
{"user_id": user_obj["id"],
"user": user_obj,
"success": False, "failure": False, "timeout": False, "stopped": False
}
],
"recipients": {},
"attachments": [],
"include_logs": True,
"report_attachments": [],
"success_recipients": [],
"failure_recipients": [],
"timeout_recipients": [],
"stopped_recipients": []
}
new_job = cml.create_job(create_jobs_params)
new_job_id = new_job["id"]
print("Created new job with jobid", new_job_id)
### Step 5: Create and Schedule two CML Jobs for
# 02_PredictPipeline.py, 03_A_RetrainPipeline_PySpark.py and 03_B_RetrainPipeline_Sklearn.py
# Create Job
create_jobs_params = {"name": "Customer Scoring Job",
"type": "cron",
"script": "02_PredictPipeline.py",
"timezone": "America/Los_Angeles",
"environment": {},
"kernel": "python3",
"cpu": 2,
"memory": 4,