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seff-array.py
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seff-array.py
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#!/gpfs/gibbs/pi/support/software/utilities/bin/python
import argparse
import subprocess
import sys
import numpy as np
import pandas as pd
from io import StringIO
import os
import termplotlib as tpl
__version__ = 0.4
debug = False
def time_to_float(time):
""" converts [dd-[hh:]]mm:ss time to seconds """
if isinstance(time, float):
return time
days, hours = 0, 0
if "-" in time:
days = int(time.split("-")[0]) * 86400
time = time.split("-")[1]
time = time.split(":")
if len(time) > 2:
hours = int(time[0]) * 3600
mins = int(time[-2]) * 60
secs = float(time[-1])
return days + hours + mins + secs
#@profile
def job_eff(job_id=0, cluster=os.getenv('SLURM_CLUSTER_NAME')):
if job_id==0:
df_short = pd.read_csv('seff_test_oneline.csv', sep='|')
df_long = pd.read_csv('seff_test.csv', sep='|')
else:
fmt = '--format=JobID,JobName,Elapsed,ReqMem,ReqCPUS,Timelimit,State,TotalCPU,NNodes,User,Group,Cluster'
if cluster != None:
q = f'sacct -X --units=G -P {fmt} -j {job_id} --cluster {cluster}'
else:
q = f'sacct -X --units=G -P {fmt} -j {job_id}'
res = subprocess.check_output([q], shell=True)
res = str(res, 'utf-8')
df_short = pd.read_csv(StringIO(res), sep='|')
fmt = '--format=JobID,JobName,Elapsed,ReqMem,ReqCPUS,Timelimit,State,TotalCPU,NNodes,User,Group,Cluster,MaxVMSize'
if cluster != None:
q = f'sacct --units=G -P {fmt} -j {job_id} --cluster {cluster}'
else:
q = f'sacct --units=G -P {fmt} -j {job_id}'
res = subprocess.check_output([q], shell=True)
res = str(res, 'utf-8')
df_long = pd.read_csv(StringIO(res), sep='|')
# filter out pending and running jobs
finished_state = ['COMPLETED', 'FAILED', 'OUT_OF_MEMORY', 'TIMEOUT', 'PREEMPTEED']
df_long_finished = df_long[df_long.State.isin(finished_state)]
if len(df_long_finished) == 0:
print(f"No jobs in {job_id} have completed.")
return -1
# cleaning
df_short = df_short.fillna(0.)
df_long = df_long.fillna(0.)
df_long['JobID'] = df_long.JobID.map(lambda x: x.split('.')[0])
df_long['MaxVMSize'] = df_long.MaxVMSize.str.replace('G', '').astype('float')
df_long['ReqMem'] = df_long.ReqMem.str.replace('G', '').astype('float')
df_long['TotalCPU'] = df_long.TotalCPU.map(lambda x: time_to_float(x))
df_long['Elapsed'] = df_long.Elapsed.map(lambda x: time_to_float(x))
df_long['Timelimit'] = df_long.Timelimit.map(lambda x: time_to_float(x))
# job info
if isinstance(df_short['JobID'][0], np.int64):
job_id = df_short['JobID'][0]
array_job = False
else:
job_id = df_short['JobID'][0].split('_')[0]
array_job = True
job_name = df_short['JobName'][0]
cluster = df_short['Cluster'][0]
user = df_short['User'][0]
group = df_short['Group'][0]
nodes = df_short['NNodes'][0]
cores = df_short['ReqCPUS'][0]
req_mem = df_short['ReqMem'][0]
req_time = df_short['Timelimit'][0]
print("--------------------------------------------------------")
print("Job Information")
print(f"ID: {job_id}")
print(f"Name: {job_name}")
print(f"Cluster: {cluster}")
print(f"User/Group: {user}/{group}")
print(f"Requested CPUs: {cores} cores on {nodes} node(s)")
print(f"Requested Memory: {req_mem}")
print(f"Requested Time: {req_time}")
print("--------------------------------------------------------")
print("Job Status")
states = np.unique(df_short['State'])
for s in states:
print(f"{s}: {len(df_short[df_short.State == s])}")
print("--------------------------------------------------------")
# filter out pending and running jobs
finished_state = ['COMPLETED', 'FAILED', 'OUT_OF_MEMORY', 'TIMEOUT', 'PREEMPTEED']
df_long_finished = df_long[df_long.State.isin(finished_state)]
if len(df_long_finished) == 0:
print(f"No jobs in {job_id} have completed.")
return -1
cpu_use = df_long_finished.TotalCPU.loc[df_long_finished.groupby('JobID')['TotalCPU'].idxmax()]
time_use = df_long_finished.Elapsed.loc[df_long_finished.groupby('JobID')['Elapsed'].idxmax()]
mem_use = df_long_finished.MaxVMSize.loc[df_long_finished.groupby('JobID')['MaxVMSize'].idxmax()]
cpu_eff = np.divide(np.divide(cpu_use.to_numpy(), time_use.to_numpy()),cores)
print("--------------------------------------------------------")
print("Finished Job Statistics")
print("(excludes pending, running, and cancelled jobs)")
print(f"Average CPU Efficiency {cpu_eff.mean()*100:.2f}%")
print(f"Average Memory Usage {mem_use.mean():.2f}G")
print(f"Average Run-time {time_use.mean():.2f}s")
print("---------------------")
if array_job:
print('\nCPU Efficiency (%)\n---------------------')
fig = tpl.figure()
h, bin_edges = np.histogram(cpu_eff*100, bins=np.linspace(0,100,num=11))
fig.hist(h, bin_edges, orientation='horizontal')
fig.show()
print('\nMemory Efficiency (%)\n---------------------')
fig = tpl.figure()
h, bin_edges = np.histogram(mem_use*100/float(req_mem[0:-1]), bins=np.linspace(0,100,num=11))
fig.hist(h, bin_edges, orientation='horizontal')
fig.show()
print('\nTime Efficiency (%)\n---------------------')
fig = tpl.figure()
h, bin_edges = np.histogram(time_use*100/time_to_float(req_time), bins=np.linspace(0,100,num=11))
fig.hist(h, bin_edges, orientation='horizontal')
fig.show()
print("--------------------------------------------------------")
if __name__ == "__main__":
desc = (
"""
seff-array v%s
https://github.com/ycrc/seff-array
---------------
An extension of the Slurm command 'seff' designed to handle job arrays and display information in a histogram.
To use seff-array on the job array with ID '12345678', simply run 'seff-array 12345678'.
Other things can go here in the future.
-----------------
"""
% __version__
)
parser = argparse.ArgumentParser(
formatter_class=argparse.RawDescriptionHelpFormatter,
description=desc,
)
parser.add_argument("jobid")
parser.add_argument("-c", "--cluster", action="store", dest="cluster")
parser.add_argument('--version', action='version', version='%(prog)s {version}'.format(version=__version__))
args = parser.parse_args()
job_eff(args.jobid, args.cluster)