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main.py
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main.py
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#!/usr/bin/python3
#
# Get various Slurm metrics['partition'] and feed them into an InfluxDB time-series database
# Xand Meaden, King's College London
import datetime
import grp
import influxdb
import json
import ldap
import pyslurm
import pwd
import re
import socket
import struct
import sys
import time
import yaml
def tres_to_dict(tres_csv):
resources = {}
for resource in tres_csv.split(','):
[k, v] = resource.split('=')
resources[k] = v
return resources
try:
with open('config.yaml') as fh:
config = yaml.load(fh, Loader=yaml.SafeLoader)
except:
sys.stderr.write('Failed to load configuration\n')
sys.exit(1)
try:
client = influxdb.InfluxDBClient(host=config['influxdb_host'], port=config['influxdb_port'], username=config['influxdb_username'], password=config['influxdb_password'], ssl=config['influxdb_ssl'], verify_ssl=config['influxdb_verify_ssl'])
except:
sys.stderr.write('Failed to connect to InfluxDB\n')
sys.exit(2)
try:
pyslurmnode = pyslurm.node()
except:
sys.stderr.write('Failed to get Slurm data\n')
sys.exit(3)
if config['user_lookup']:
try:
ldap_c = ldap.initialize('ldaps://%s:636' % config['ldap_hostname'])
ldap_c.simple_bind_s(config['ldap_username'], config['ldap_password'])
except:
sys.stderr.write('Failed to bind to LDAP\n')
sys.exit(4)
groups = config['groups']
partitions = pyslurm.partition().get()
node_partitions = {}
metrics = {}
metrics['partition'] = {}
metrics['partition']['cpu_total'] = {}
metrics['partition']['cpu_usage'] = {}
metrics['partition']['cpu_usage_pc'] = {}
metrics['partition']['gpu_total'] = {}
metrics['partition']['gpu_usage'] = {}
metrics['partition']['gpu_usage_pc'] = {}
metrics['partition']['mem_total'] = {}
metrics['partition']['mem_usage'] = {}
metrics['partition']['mem_usage_pc'] = {}
metrics['partition']['jobs_running'] = {}
metrics['partition']['jobs_pending'] = {}
metrics['partition']['queue_time'] = {}
metrics['partition']['queue_jobs'] = {}
metrics['user'] = {}
metrics['user']['cpu_usage'] = {}
metrics['user']['gpu_usage'] = {}
metrics['user']['mem_usage'] = {}
metrics['user']['jobs_running'] = {}
metrics['user']['jobs_pending'] = {}
metrics['user']['queue_time'] = {}
metrics['user']['queue_jobs'] = {}
metrics['group'] = {}
metrics['group']['cpu_usage'] = {}
metrics['group']['gpu_usage'] = {}
metrics['group']['mem_usage'] = {}
metrics['group']['jobs_running'] = {}
metrics['group']['jobs_pending'] = {}
metrics['group']['queue_time'] = {}
metrics['group']['queue_jobs'] = {}
if config['user_lookup']:
metrics['ldap_attrib'] = {}
metrics['ldap_attrib']['cpu_usage'] = {}
metrics['ldap_attrib']['gpu_usage'] = {}
metrics['ldap_attrib']['mem_usage'] = {}
metrics['ldap_attrib']['jobs_running'] = {}
metrics['ldap_attrib']['jobs_pending'] = {}
metrics['ldap_attrib']['queue_time'] = {}
metrics['ldap_attrib']['queue_jobs'] = {}
user_ids = {}
user_groups = {}
user_ldap = {}
now = datetime.datetime.utcnow().strftime('%Y-%m-%dT%H:%M:%SZ')
# Setup data structures, with stats set to 0
for part in list(partitions.keys()) + ['ALL']:
if part != 'ALL':
hl = pyslurm.hostlist()
hl.create(partitions[part]['nodes'])
for node in hl.get_list():
if node not in node_partitions:
node_partitions[node] = []
node_partitions[node].append(part)
metrics['partition']['cpu_total'][part] = 0
metrics['partition']['cpu_usage'][part] = 0
metrics['partition']['cpu_usage_pc'][part] = 0
metrics['partition']['gpu_total'][part] = 0
metrics['partition']['gpu_usage'][part] = 0
metrics['partition']['gpu_usage_pc'][part] = 0
metrics['partition']['mem_total'][part] = 0
metrics['partition']['mem_usage'][part] = 0
metrics['partition']['mem_usage_pc'][part] = 0
metrics['partition']['jobs_running'][part] = 0
metrics['partition']['jobs_pending'][part] = 0
metrics['partition']['queue_time'][part] = 0
metrics['partition']['queue_jobs'][part] = 0
for group in groups:
metrics['group']['cpu_usage'][group] = 0
metrics['group']['gpu_usage'][group] = 0
metrics['group']['mem_usage'][group] = 0
metrics['group']['jobs_running'][group] = 0
metrics['group']['jobs_pending'][group] = 0
metrics['group']['queue_time'][group] = 0
metrics['group']['queue_jobs'][group] = 0
members = grp.getgrnam(group)[3]
for user in members:
if user not in user_groups:
user_groups[user] = []
user_groups[user].append(group)
# Go through all the nodes and get their cpu/gpu/memory usage and store for each partition they belong to
nodes = pyslurmnode.get()
for node in nodes:
node_data = nodes.get(node)
metrics['partition']['cpu_total']['ALL'] += node_data['cpus']
metrics['partition']['cpu_usage']['ALL'] += node_data['alloc_cpus']
metrics['partition']['cpu_usage_pc']['ALL'] = 100 * (float(metrics['partition']['cpu_usage']['ALL']) / float(metrics['partition']['cpu_total']['ALL']))
metrics['partition']['mem_total']['ALL'] += node_data['real_memory'] * 1048576
metrics['partition']['mem_usage']['ALL'] += node_data['alloc_mem'] * 1048576
metrics['partition']['mem_usage_pc']['ALL'] = 100 * (float(metrics['partition']['mem_usage']['ALL']) / float(metrics['partition']['mem_total']['ALL']))
gpu_total = 0
gpu_usage = 0
if node_data['gres']:
gres_total = pyslurm.node().parse_gres(node_data['gres'][0])
gres_usage = pyslurm.node().parse_gres(node_data['gres_used'][0])
for g in gres_total:
is_gpu = re.match(r'^gpu:([0-9]+)\(?', g)
if is_gpu:
gpu_total = int(is_gpu.group(1))
if gpu_total > 0:
for g in gres_usage:
is_gpu = re.match(r'^gpu:(?:[^:]*:?)([0-9]+)\(?', g)
if is_gpu:
gpu_usage = int(is_gpu.group(1))
metrics['partition']['gpu_total']['ALL'] += gpu_total
metrics['partition']['gpu_usage']['ALL'] += gpu_usage
if metrics['partition']['gpu_total']['ALL'] > 0:
metrics['partition']['gpu_usage_pc']['ALL'] = 100 * (float(metrics['partition']['gpu_usage']['ALL']) / metrics['partition']['gpu_total']['ALL'])
if node in node_partitions:
for part in node_partitions[node]:
metrics['partition']['cpu_total'][part] += node_data['cpus']
metrics['partition']['cpu_usage'][part] += node_data['alloc_cpus']
metrics['partition']['cpu_usage_pc'][part] = 100 * (float(metrics['partition']['cpu_usage'][part]) / metrics['partition']['cpu_total'][part])
metrics['partition']['mem_total'][part] += node_data['real_memory'] * 1048576
metrics['partition']['mem_usage'][part] += node_data['alloc_mem'] * 1048576
metrics['partition']['mem_usage_pc'][part] = 100 * (float(metrics['partition']['mem_usage'][part]) / metrics['partition']['mem_total'][part])
metrics['partition']['gpu_total'][part] += gpu_total
metrics['partition']['gpu_usage'][part] += gpu_usage
if metrics['partition']['gpu_total'][part] > 0:
metrics['partition']['gpu_usage_pc'][part] = 100 * (float(metrics['partition']['gpu_usage'][part]) / metrics['partition']['gpu_total'][part])
# Now go through the jobs list to see user-specific stuff
jobs = pyslurm.job().get()
for job in jobs:
job = jobs.get(job)
if job['user_id'] not in user_ids:
user = pwd.getpwuid(job['user_id'])[0]
user_ids[job['user_id']] = user
metrics['user']['cpu_usage'][user] = 0
metrics['user']['gpu_usage'][user] = 0
metrics['user']['mem_usage'][user] = 0
metrics['user']['jobs_running'][user] = 0
metrics['user']['jobs_pending'][user] = 0
metrics['user']['queue_time'][user] = 0
metrics['user']['queue_jobs'][user] = 0
else:
user = user_ids[job['user_id']]
if config['user_lookup']:
if user not in user_ldap:
result_id = ldap_c.search(config['ldap_userbase'], ldap.SCOPE_SUBTREE, '(%s=%s)' % (config['ldap_username_attrib'], user), [config['ldap_grouping_attrib']])
result_type, result_data = ldap_c.result(result_id, 0)
if result_data == []:
user_ldap[user] = 'unknown'
else:
user_ldap[user] = result_data[0][1][config['ldap_grouping_attrib']][0]
if user_ldap[user] not in metrics['ldap_attrib']['jobs_running']:
metrics['ldap_attrib']['jobs_running'][user_ldap[user]] = 0
metrics['ldap_attrib']['jobs_pending'][user_ldap[user]] = 0
metrics['ldap_attrib']['cpu_usage'][user_ldap[user]] = 0
metrics['ldap_attrib']['gpu_usage'][user_ldap[user]] = 0
metrics['ldap_attrib']['mem_usage'][user_ldap[user]] = 0
metrics['ldap_attrib']['queue_jobs'][user_ldap[user]] = 0
metrics['ldap_attrib']['queue_time'][user_ldap[user]] = 0
if job['job_state'] == 'RUNNING':
metrics['partition']['jobs_running']['ALL'] += 1
metrics['partition']['jobs_running'][job['partition']] += 1
tres_alloc = tres_to_dict(job['tres_alloc_str'])
cpu = int(tres_alloc['cpu'])
mem = 0
if 'mem' in tres_alloc:
m = re.match('^[0-9]+[MGT]$', tres_alloc['mem'])
if m:
mem = float(m.group(1))
if tres_alloc.group(2) == 'G':
mem *= 1024
elif tres_alloc.group(2) == 'T':
mem *= 1048576
mem *= 1048576
mem = int(mem)
gpu = 0
if 'tres_per_node' in job and job['tres_per_node']:
tres_per_node = re.match(r'gpu:([0-9]+)', job['tres_per_node'])
if tres_per_node:
gpu = int(tres_per_node.group(1)) * job['num_nodes']
metrics['user']['jobs_running'][user] += 1
metrics['user']['cpu_usage'][user] += cpu
metrics['user']['gpu_usage'][user] += gpu
metrics['user']['mem_usage'][user] += mem
queue_time = job['start_time'] - job['submit_time']
metrics['user']['queue_jobs'][user] += 1
metrics['user']['queue_time'][user] = (float(metrics['user']['queue_time'][user] + queue_time)) / metrics['user']['queue_jobs'][user]
metrics['partition']['queue_jobs']['ALL'] += 1
metrics['partition']['queue_time']['ALL'] = (float(metrics['partition']['queue_time']['ALL'] + queue_time)) / metrics['partition']['queue_jobs']['ALL']
metrics['partition']['queue_jobs'][job['partition']] += 1
metrics['partition']['queue_time'][job['partition']] = (float(metrics['partition']['queue_time'][job['partition']] + queue_time)) / metrics['partition']['queue_jobs'][job['partition']]
if user in user_groups:
for group in user_groups[user]:
metrics['group']['jobs_running'][group] += 1
metrics['group']['cpu_usage'][group] += cpu
metrics['group']['gpu_usage'][group] += gpu
metrics['group']['mem_usage'][group] += mem
metrics['group']['queue_jobs'][group] += 1
metrics['group']['queue_time'][group] = (float(metrics['group']['queue_time'][group] + queue_time)) / metrics['group']['queue_jobs'][group]
if config['user_lookup']:
metrics['ldap_attrib']['jobs_running'][user_ldap[user]] += 1
metrics['ldap_attrib']['cpu_usage'][user_ldap[user]] += cpu
metrics['ldap_attrib']['gpu_usage'][user_ldap[user]] += gpu
metrics['ldap_attrib']['mem_usage'][user_ldap[user]] += mem
metrics['ldap_attrib']['queue_jobs'][user_ldap[user]] += 1
metrics['ldap_attrib']['queue_time'][user_ldap[user]] = (float(metrics['ldap_attrib']['queue_time'][user_ldap[user]] + queue_time)) / metrics['ldap_attrib']['queue_jobs'][user_ldap[user]]
elif job['job_state'] == 'PENDING':
metrics['partition']['jobs_pending']['ALL'] += 1
for partition in job['partition'].split(','):
if partition in metrics['partition']['jobs_pending']:
metrics['partition']['jobs_pending'][partition] += 1
metrics['user']['jobs_pending'][user] += 1
if user in user_groups:
for group in user_groups[user]:
metrics['group']['jobs_pending'][group] += 1
if config['user_lookup']:
metrics['ldap_attrib']['jobs_pending'][user_ldap[user]] += 1
payload = []
for grouping in ['partition', 'user', 'group', 'ldap_attrib']:
for reading in ['cpu_total', 'cpu_usage', 'cpu_usage_pc', 'gpu_total', 'gpu_usage', 'gpu_usage_pc', 'mem_total', 'mem_usage', 'mem_usage_pc', 'jobs_running', 'jobs_pending', 'queue_time']:
if reading in metrics[grouping] and len(metrics[grouping][reading]) > 0:
for key in metrics[grouping][reading].keys():
payload.append({'measurement': '%s_%s' % (grouping, reading), 'time': now, 'fields': {reading: float(metrics[grouping][reading][key])}, 'tags': {grouping: key}})
client.write_points(payload, database=config['influxdb_database'])