-
Notifications
You must be signed in to change notification settings - Fork 3
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
[SARC-328] Implémenter les alertes : Nombre de jobs CPU/GPU (actives …
…ou inactives) sur un cluster sur une période X (#128) * [SARC-328] Implémenter les alertes : Nombre de jobs CPU/GPU (actives ou inactives) sur un cluster sur une période X * Rebase and update comments. * Fix a column name * Select sub-dataframe with given cluster names to compute stats, then use full dataframe to check warnings Add supplementary tests * - Rename files - Remove `exclude` parameter and use only `cluster_names` for both adding and excluding clusters from checking. * Compute statistics for each cluster separately. * Use file_regression for tests.
- Loading branch information
1 parent
6b6c0f2
commit 0e6ba5c
Showing
7 changed files
with
333 additions
and
1 deletion.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,129 @@ | ||
| import logging | ||
| import sys | ||
| from datetime import datetime, timedelta | ||
| from typing import List, Optional | ||
|
|
||
| import pandas | ||
|
|
||
| from sarc.config import MTL | ||
| from sarc.jobs.series import compute_time_frames, load_job_series | ||
|
|
||
| logger = logging.getLogger(__name__) | ||
|
|
||
|
|
||
| def check_nb_jobs_per_cluster_per_time( | ||
| time_interval: Optional[timedelta] = timedelta(days=7), | ||
| time_unit=timedelta(days=1), | ||
| cluster_names: Optional[List[str]] = None, | ||
| nb_stddev=2, | ||
| verbose=False, | ||
| ): | ||
| """ | ||
| Check if we have scraped enough jobs per time unit per cluster on given time interval. | ||
| Log a warning for each cluster where number of jobs per time unit is lower than a limit | ||
| computed using mean and standard deviation statistics from this cluster. | ||
| Parameters | ||
| ---------- | ||
| time_interval: timedelta | ||
| If given, only jobs which ran in [now - time_interval, time_interval] will be used for checking. | ||
| Default is last 7 days. | ||
| If None, all jobs are used. | ||
| time_unit: timedelta | ||
| Time unit in which we must check cluster usage through time_interval. Default is 1 day. | ||
| cluster_names: list | ||
| Optional list of clusters to check. | ||
| If empty (or not specified), use all clusters available among jobs retrieved with time_interval. | ||
| nb_stddev: int | ||
| Amount of standard deviation to remove from average statistics to compute checking threshold. | ||
| For each cluster, threshold is computed as: | ||
| max(0, average - nb_stddev * stddev) | ||
| verbose: bool | ||
| If True, print supplementary info about clusters statistics. | ||
| """ | ||
|
|
||
| # Parse time_interval | ||
| start, end, clip_time = None, None, False | ||
| if time_interval is not None: | ||
| end = datetime.now(tz=MTL) | ||
| start = end - time_interval | ||
| clip_time = True | ||
|
|
||
| # Get data frame | ||
| df = load_job_series(start=start, end=end, clip_time=clip_time) | ||
|
|
||
| # Split data frame into time frames using `time_unit` | ||
| tf = compute_time_frames(df, frame_size=time_unit) | ||
|
|
||
| # List all available timestamps. | ||
| # We will check each timestamp for each cluster. | ||
| timestamps = sorted(tf["timestamp"].unique()) | ||
|
|
||
| # List clusters | ||
| if cluster_names: | ||
| cluster_names = sorted(cluster_names) | ||
| else: | ||
| cluster_names = sorted(df["cluster_name"].unique()) | ||
|
|
||
| # Iter for each cluster. | ||
| for cluster_name in cluster_names: | ||
| # Select only jobs for current cluster, | ||
| # group jobs by timestamp, and count jobs for each timestamp. | ||
| f_stats = ( | ||
| tf[tf["cluster_name"] == cluster_name] | ||
| .groupby(["timestamp"])[["job_id"]] | ||
| .count() | ||
| ) | ||
|
|
||
| # Create a dataframe with all available timestamps | ||
| # and associate each timestamp to 0 jobs by default. | ||
| c = ( | ||
| pandas.DataFrame({"timestamp": timestamps, "count": [0] * len(timestamps)}) | ||
| .groupby(["timestamp"])[["count"]] | ||
| .sum() | ||
| ) | ||
| # Set each timestamp valid for this cluster with real number of jobs scraped in this timestamp. | ||
| c.loc[f_stats.index, "count"] = f_stats["job_id"] | ||
|
|
||
| # We now have number of jobs for each timestamp for this cluster, | ||
| # with count 0 for timestamps where no jobs run on cluster, | ||
|
|
||
| # Compute average number of jobs per timestamp for this cluster | ||
| avg = c["count"].mean() | ||
| # Compute standard deviation of job count per timestamp for this cluster | ||
| stddev = c["count"].std() | ||
| # Compute threshold to use for warnings: <average> - nb_stddev * <standard deviation> | ||
| threshold = max(0, avg - nb_stddev * stddev) | ||
|
|
||
| if verbose: | ||
| print(f"[{cluster_name}]", file=sys.stderr) | ||
| print(c, file=sys.stderr) | ||
| print(f"avg {avg}, stddev {stddev}, threshold {threshold}", file=sys.stderr) | ||
| print(file=sys.stderr) | ||
|
|
||
| if threshold == 0: | ||
| # If threshold is zero, no check can be done, as jobs count will be always >= 0. | ||
| # Instead, we log a general warning. | ||
| msg = f"[{cluster_name}] threshold 0 ({avg} - {nb_stddev} * {stddev})." | ||
| if len(timestamps) == 1: | ||
| msg += ( | ||
| f" Only 1 timestamp found. Either time_interval ({time_interval}) is too short, " | ||
| f"or this cluster should not be currently checked" | ||
| ) | ||
| else: | ||
| msg += ( | ||
| f" Either nb_stddev is too high, time_interval ({time_interval}) is too short, " | ||
| f"or this cluster should not be currently checked" | ||
| ) | ||
| logger.warning(msg) | ||
| else: | ||
| # With a non-null threshold, we can check each timestamp. | ||
| for timestamp in timestamps: | ||
| nb_jobs = c.loc[timestamp]["count"] | ||
| if nb_jobs < threshold: | ||
| logger.warning( | ||
| f"[{cluster_name}][{timestamp}] " | ||
| f"insufficient cluster scraping: {nb_jobs} jobs / cluster / time unit; " | ||
| f"minimum required for this cluster: {threshold} ({avg} - {nb_stddev} * {stddev}); " | ||
| f"time unit: {time_unit}" | ||
| ) |
58 changes: 58 additions & 0 deletions
58
tests/functional/usage_alerts/test_alert_cluster_scraping.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,58 @@ | ||
| import functools | ||
| import re | ||
|
|
||
| import pytest | ||
|
|
||
| from sarc.alerts.usage_alerts.cluster_scraping import check_nb_jobs_per_cluster_per_time | ||
|
|
||
| from ..jobs.test_func_load_job_series import MOCK_TIME | ||
| from .common import _get_warnings | ||
|
|
||
| get_warnings = functools.partial( | ||
| _get_warnings, | ||
| module="sarc.alerts.usage_alerts.cluster_scraping:cluster_scraping.py", | ||
| ) | ||
|
|
||
|
|
||
| @pytest.mark.freeze_time(MOCK_TIME) | ||
| @pytest.mark.usefixtures("read_only_db", "tzlocal_is_mtl") | ||
| @pytest.mark.parametrize( | ||
| "params", | ||
| [ | ||
| # Check with default params. In last 7 days from now (mock time: 2023-11-22), | ||
| # there is only 2 jobs from 1 cluster in 1 timestamp. So, threshold will be 0. | ||
| dict(verbose=True), | ||
| # Check with no time interval (i.e. all jobs). | ||
| dict(time_interval=None, verbose=True), | ||
| # Check with a supplementary cluster `another_cluster` which is not in data frame. | ||
| dict( | ||
| time_interval=None, | ||
| cluster_names=[ | ||
| "fromage", | ||
| "mila", | ||
| "patate", | ||
| "raisin", | ||
| "another_cluster", | ||
| ], | ||
| verbose=True, | ||
| ), | ||
| # Check above case with 2 clusters ignored. | ||
| dict( | ||
| time_interval=None, | ||
| cluster_names=[ | ||
| "mila", | ||
| "raisin", | ||
| "another_cluster", | ||
| ], | ||
| ), | ||
| ], | ||
| ) | ||
| def test_check_nb_jobs_per_cluster_per_time(params, capsys, caplog, file_regression): | ||
| check_nb_jobs_per_cluster_per_time(**params) | ||
| file_regression.check( | ||
| re.sub( | ||
| r"WARNING +sarc\.alerts\.usage_alerts\.cluster_scraping:cluster_scraping.py:[0-9]+ +", | ||
| "", | ||
| f"{capsys.readouterr().err}\n{caplog.text}", | ||
| ) | ||
| ) |
9 changes: 9 additions & 0 deletions
9
...e_alerts/test_alert_cluster_scraping/test_check_nb_jobs_per_cluster_per_time_params0_.txt
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
58 changes: 58 additions & 0 deletions
58
...e_alerts/test_alert_cluster_scraping/test_check_nb_jobs_per_cluster_per_time_params1_.txt
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
72 changes: 72 additions & 0 deletions
72
...e_alerts/test_alert_cluster_scraping/test_check_nb_jobs_per_cluster_per_time_params2_.txt
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
5 changes: 5 additions & 0 deletions
5
...e_alerts/test_alert_cluster_scraping/test_check_nb_jobs_per_cluster_per_time_params3_.txt
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters