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Commit 8b371261 authored by SIMONIN Matthieu's avatar SIMONIN Matthieu
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Update metric endpoint

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......@@ -594,47 +594,6 @@ In [2]: # gk is your entry point
#+END_SRC
** Metrics API
*** Get the timeseries corresponding to a job
Credits to ~lturpin~.
#+BEGIN_SRC python :exports code :tangle examples/job_timeseries.py
import logging
import os
from grid5000 import Grid5000
logging.basicConfig(level=logging.DEBUG)
def get_job_consumption(job_id, gk, site):
metrics = gk.sites[site].metrics
job = gk.sites[site].jobs[job_id]
# nodes as list : "cluster-number.site.grid5000.fr"
nodes_dom = job.assigned_nodes
# nodes as list : "cluster-number"
nodes = map(lambda node_dom: node_dom.split('.')[0], nodes_dom)
# nodes as string : "cluster-number,cluster-number,..."
nodes_str = ','.join(nodes)
start = job.started_at
end = job.stopped_at
kwargs = {
"only": nodes_str,
"resolution": 1,
"from": start,
"to": end
}
timeseries = metrics["power"].timeseries.list(**kwargs)
return timeseries
conf_file = os.path.join(os.environ.get("HOME"), ".python-grid5000.yaml")
gk = Grid5000.from_yaml(conf_file)
timeseries = get_job_consumption("1092446", gk, "lyon")
print(timeseries)
#+END_SRC
*** Get some timeseries (and plot them)
For this example you'll need ~matplotlib~, ~seaborn~ and ~pandas~.
......@@ -649,47 +608,43 @@ print(timeseries)
import seaborn as sns
import time
logging.basicConfig(level=logging.DEBUG)
logging.basicConfig(level=logging.INFO)
conf_file = os.path.join(os.environ.get("HOME"), ".python-grid5000.yaml")
gk = Grid5000.from_yaml(conf_file)
metrics = gk.sites["lyon"].metrics
metrics = gk.sites["lyon"].clusters["nova"].metrics
print("--- available metrics")
print(metrics.list())
print("---- power metric")
print(metrics["power"])
print(metrics)
print("----- a timeserie")
now = time.time()
# NOTE that you can pass a job_id here
kwargs = {
"only": "nova-1,nova-2,nova-3",
"resolution": 1,
"from": int(now - 600),
"to": int(now)
"nodes": "nova-1,nova-2,nova-3",
"metrics": "wattmetre_power_watt",
"start_time": int(now - 600),
}
timeseries = metrics["power"].timeseries.list(**kwargs)
metrics = gk.sites["lyon"].metrics.list(**kwargs)
# let's visualize this
df = pd.DataFrame()
for timeserie in timeseries:
print(timeserie)
timestamp = timeserie.timestamps
value = timeserie.values
measurement = timeserie.uid
for metric in metrics:
timestamp = metric.timestamp
value = metric.value
device_id = metric.device_id
df = pd.concat([df, pd.DataFrame({
"timestamp": timestamp,
"value": value,
"measurement": [measurement]*len(timestamp)
"timestamp": [timestamp],
"value": [value],
"device_id": [device_id]
})])
sns.relplot(data=df,
x="timestamp",
y="value",
hue="measurement",
hue="device_id",
kind="line")
plt.show()
#+END_SRC
......
examples/job_timeseries.py deleted 100644 → 0
+ 0
− 36
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import logging
import os
from grid5000 import Grid5000
logging.basicConfig(level=logging.DEBUG)
def get_job_consumption(job_id, gk, site):
metrics = gk.sites[site].metrics
job = gk.sites[site].jobs[job_id]
# nodes as list : "cluster-number.site.grid5000.fr"
nodes_dom = job.assigned_nodes
# nodes as list : "cluster-number"
nodes = map(lambda node_dom: node_dom.split('.')[0], nodes_dom)
# nodes as string : "cluster-number,cluster-number,..."
nodes_str = ','.join(nodes)
start = job.started_at
end = job.stopped_at
kwargs = {
"only": nodes_str,
"resolution": 1,
"from": start,
"to": end
}
timeseries = metrics["power"].timeseries.list(**kwargs)
return timeseries
conf_file = os.path.join(os.environ.get("HOME"), ".python-grid5000.yaml")
gk = Grid5000.from_yaml(conf_file)
timeseries = get_job_consumption("1092446", gk, "lyon")
print(timeseries)
examples/timeseries.py
+ 16
− 21
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......@@ -8,44 +8,39 @@ import matplotlib.pyplot as plt
import seaborn as sns
import time
logging.basicConfig(level=logging.DEBUG)
logging.basicConfig(level=logging.INFO)
conf_file = os.path.join(os.environ.get("HOME"), ".python-grid5000.yaml")
gk = Grid5000.from_yaml(conf_file)
metrics = gk.sites["lyon"].metrics
metrics = gk.sites["lyon"].clusters["nova"].metrics
print("--- available metrics")
print(metrics.list())
print("---- power metric")
print(metrics["power"])
print(metrics)
print("----- a timeserie")
now = time.time()
# NOTE that you can pass a job_id here
kwargs = {
"only": "nova-1,nova-2,nova-3",
"resolution": 1,
"from": int(now - 600),
"to": int(now)
"nodes": "nova-1,nova-2,nova-3",
"metrics": "wattmetre_power_watt",
"start_time": int(now - 600),
}
timeseries = metrics["power"].timeseries.list(**kwargs)
metrics = gk.sites["lyon"].metrics.list(**kwargs)
# let's visualize this
df = pd.DataFrame()
for timeserie in timeseries:
print(timeserie)
timestamp = timeserie.timestamps
value = timeserie.values
measurement = timeserie.uid
for metric in metrics:
timestamp = metric.timestamp
value = metric.value
device_id = metric.device_id
df = pd.concat([df, pd.DataFrame({
"timestamp": timestamp,
"value": value,
"measurement": [measurement]*len(timestamp)
"timestamp": [timestamp],
"value": [value],
"device_id": [device_id]
})])
sns.relplot(data=df,
x="timestamp",
y="value",
hue="measurement",
hue="device_id",
kind="line")
plt.show()
grid5000/base.py
+ 4
− 0
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import copy
import importlib
import json
......@@ -164,3 +165,6 @@ class RESTObject(object):
def __eq__(self, value):
return self.get_id() == value.get_id()
def to_dict(self):
return copy.deepcopy(self._attrs)
\ No newline at end of file
grid5000/objects.py
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− 0
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......@@ -16,6 +16,15 @@ class Timeserie(RESTObject):
class Metric(RESTObject):
_managers = (("timeseries", "TimeserieManager"),)
_id_attr = None
def __repr__(self):
keys = ["timestamp", "value", "metric_id", "device_id"]
try:
_repr = ["%s:%s" % (k, getattr(self, k)) for k in keys]
return "<%s %s>" % (self.__class__.__name__, " ".join(_repr))
except Exception:
return super().__repr__()
class Server(RESTObject):
......
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