Jobstats is a job monitoring platform composed of data exporters, Prometheus, Grafana and the Slurm database whereas jobstats is a command that operates on the Jobstats platform. If you are looking to setup the Jobstats platform then see below and this manuscript.
See our PEARC 2023 presentation: "Jobstats: A Slurm-Compatible Job Monitoring Platform for CPU and GPU Clusters" (PDF).
The jobstats command provides users with a Slurm job efficiency report for a given jobid:
$ jobstats 39798795
================================================================================
Slurm Job Statistics
================================================================================
Job ID: 39798795
NetID/Account: aturing/math
Job Name: sys_logic_ordinals
State: COMPLETED
Nodes: 2
CPU Cores: 48
CPU Memory: 256GB (5.3GB per CPU-core)
GPUs: 4
QOS/Partition: della-gpu/gpu
Cluster: della
Start Time: Fri Mar 4, 2022 at 1:56 AM
Run Time: 18:41:56
Time Limit: 4-00:00:00
Overall Utilization
================================================================================
CPU utilization [||||| 10%]
CPU memory usage [||| 6%]
GPU utilization [|||||||||||||||||||||||||||||||||| 68%]
GPU memory usage [||||||||||||||||||||||||||||||||| 66%]
Detailed Utilization
================================================================================
CPU utilization per node (CPU time used/run time)
della-i14g2: 1-21:41:20/18-16:46:24 (efficiency=10.2%)
della-i14g3: 1-18:48:55/18-16:46:24 (efficiency=9.5%)
Total used/runtime: 3-16:30:16/37-09:32:48, efficiency=9.9%
CPU memory usage per node - used/allocated
della-i14g2: 7.9GB/128.0GB (335.5MB/5.3GB per core of 24)
della-i14g3: 7.8GB/128.0GB (334.6MB/5.3GB per core of 24)
Total used/allocated: 15.7GB/256.0GB (335.1MB/5.3GB per core of 48)
GPU utilization per node
della-i14g2 (GPU 0): 65.7%
della-i14g2 (GPU 1): 64.5%
della-i14g3 (GPU 0): 72.9%
della-i14g3 (GPU 1): 67.5%
GPU memory usage per node - maximum used/total
della-i14g2 (GPU 0): 26.5GB/40.0GB (66.2%)
della-i14g2 (GPU 1): 26.5GB/40.0GB (66.2%)
della-i14g3 (GPU 0): 26.5GB/40.0GB (66.2%)
della-i14g3 (GPU 1): 26.5GB/40.0GB (66.2%)
Notes
================================================================================
* This job only used 6% of the 256GB of total allocated CPU memory. For
future jobs, please allocate less memory by using a Slurm directive such
as --mem-per-cpu=1G or --mem=10G. This will reduce your queue times and
make the resources available to other users. For more info:
https://researchcomputing.princeton.edu/support/knowledge-base/memory
* This job only needed 19% of the requested time which was 4-00:00:00. For
future jobs, please request less time by modifying the --time Slurm
directive. This will lower your queue times and allow the Slurm job
scheduler to work more effectively for all users. For more info:
https://researchcomputing.princeton.edu/support/knowledge-base/slurm
* For additional job metrics including metrics plotted against time:
https://mydella.princeton.edu/pun/sys/jobstats (VPN required off-campus)
For completed jobs, the data is taken from a call to sacct with several fields including AdminComment. For running jobs, the Prometheus database must be queried.
Importantly, the jobstats command is also used to replace smail, which is the Slurm executable used for sending email reports that are based on seff. This means that users receive emails that are the exact output of jobstats including the notes.
The installation requirements for jobstats are Python 3.6+ and version 1.17+ of the Python blessed package which is used for coloring and styling text.
Below is an outline of the steps that need to be taken to setup the Jobstats platform for a Slurm cluster:
- Switch to cgroup based job accounting from Linux process accounting
- Setup the exporters: cgroup, node, GPU (on the nodes) and, optionally, GPFS (centrally)
- Setup the prolog.d and epilog.d scripts on the GPU nodes
- Setup the Prometheus server and configure it to scrape data from the compute nodes and all configured exporters
- Setup the slurmctldepilog.sh script for long-term job summary retention
- Lastly, configure Grafana and Open OnDemand
We use these four exporters:
- node exporter: https://github.com/prometheus/node_exporter
- cgroup exporter: https://github.com/plazonic/cgroup_exporter
- nvidia gpu exporter: https://github.com/plazonic/nvidia_gpu_prometheus_exporter
- gpfs exporter: https://github.com/plazonic/gpfs-exporter
What follows is an example of production configuration used for the Tiger cluster that has both regular and GPU nodes.
---
global:
scrape_interval: 15s
evaluation_interval: 15s
external_labels:
monitor: master
- job_name: Tiger Nodes
scrape_interval: 30s
scrape_timeout: 30s
file_sd_configs:
- files:
- "/etc/prometheus/local_files_sd_config.d/tigernodes.json"
metric_relabel_configs:
- source_labels:
- __name__
regex: "^go_.*"
action: drop
- job_name: TigerGPU Nodes
scrape_interval: 30s
scrape_timeout: 30s
file_sd_configs:
- files:
- "/etc/prometheus/local_files_sd_config.d/tigergpus.json"
metric_relabel_configs:
- source_labels:
- __name__
regex: "^go_.*"
action: drop
tigernode.json looks like:
[
{
"labels": {
"cluster": "tiger"
},
"targets": [
"tiger-h19c1n10:9100",
"tiger-h19c1n10:9306",
...
]
}
]
where both node_exporter (port 9100) and cgroup_exporter (port 9306) are listed, for all of tiger's nodes. tigergpus.json looks very similar except that it collects data from nvidia_gpu_prometheus_exporter on port 9445.
Note the additional label cluster.
In order to correctly track which GPU is assigned to which jobid we use slurm prolog and epilog scripts to create files in /run/gpustat directory named either after GPU ordinal number (0, 1, ..) or, in the case of MIG cards, MIG-UUID. These files contain space separated jobid and uid number of the user. E.g.
# cat /run/gpustat/MIG-265a219d-a49f-578a-825d-222c72699c16
45916256 262563
These two scripts can be found in the slurm directory. For example, slurm/epilog.d/gpustats_helper.sh could be installed as /etc/slurm/epilog.d/gpustats_helper.sh and slurm/prolog.d/gpustats_helper.sh as /etc/slurm/prolog.d/gpustats_helper.sh with these slurm.conf config statements:
Prolog=/etc/slurm/prolog.d/*.sh
Epilog=/etc/slurm/epilog.d/*.sh
Grafana dashboard json that uses all of the exporters is included in the grafana subdirectory. It expects one parameter, jobid. As it may not be easy to find the time range we also use an ondemand job stats helper that generates the correct time range given a jobid, documented in the next section.
The following image illustrates what the dashboard looks like in use:
ood-jobstats-helper subdirectory contains an Open OnDemand app that, given a job id, uses sacct to generate a full Grafana URL with job's jobid, start and end times.
Job summaries, as described above, are generated and stored in the Slurm database at the end of each job by using slurmctld epilog script, e.g.:
EpilogSlurmctld=/usr/local/sbin/slurmctldepilog.sh
The script can be found in the slurm subdirectory, named "slurmctldepilog.sh".
For processing old jobs where slurmctld epilog script did not run or for jobs where it failed there is a per cluster ingest jobstats service. This is a python based script running on the slurmdbd host, as a systemd timer and service, querying and modifying slurm database directly. The script (ingest_jobstats.py) and systemd timer and service scripts are in the slurm directory.
We made heavy use of this script to generate job summaries for older jobs but with the current version of the Epilog script it should not be needed anymore.
We use slurm/jobstats_mail.sh as the slurm's Mail program. E.g. from slurm.conf:
MailProg=/usr/local/bin/jobstats_mail.sh
This will include jobstats information for jobs that have requested email notifications on completion.
The jobstats command analyzes each job and produces custom notes at the bottom of the output. Below are several examples:
* This job ran on the mig partition where each job is limited to 1 MIG
GPU, 1 CPU-core, 10 GB of GPU memory and 32 GB of CPU memory. A MIG GPU
is about 1/7th as powerful as an A100 GPU. Please continue using the mig
partition when possible. For more info:
https://researchcomputing.princeton.edu/systems/della#gpus
* This job completed while only needing 19% of the requested time which
was 2-00:00:00. For future jobs, please decrease the value of the --time
Slurm directive. This will lower your queue times and allow the Slurm
job scheduler to work more effectively for all users. For more info:
https://researchcomputing.princeton.edu/support/knowledge-base/slurm
* This job did not use the GPU. Please resolve this before running
additional jobs. Wasting resources prevents other users from getting
their work done and it causes your subsequent jobs to have a lower
priority. Is the code GPU-enabled? Please consult the documentation for
the code. For more info:
https://researchcomputing.princeton.edu/support/knowledge-base/gpu-computing
* This job only used 15% of the 100GB of total allocated CPU memory.
Please consider allocating less memory by using the Slurm directive
--mem-per-cpu=3G or --mem=18G. This will reduce your queue times and
make the resources available to other users. For more info:
https://researchcomputing.princeton.edu/support/knowledge-base/memory
* This job ran on a large-memory (datascience) node but it only used 117
GB of CPU memory. The large-memory nodes should only be used for jobs
that require more than 190 GB. Please allocate less memory by using the
Slurm directive --mem-per-cpu=9G or --mem=150G. For more info:
https://researchcomputing.princeton.edu/support/knowledge-base/memory
* The CPU utilization of this job (24%) is approximately equal to 1
divided by the number of allocated CPU-cores (1/4=25%). This suggests
that you may be running a code that can only use 1 CPU-core. If this is
true then allocating more than 1 CPU-core is wasteful. Please consult
the documentation for the software to see if it is parallelized. For
more info:
https://researchcomputing.princeton.edu/support/knowledge-base/parallel-code
* This job did not use the CPU. This suggests that something went wrong at
the very beginning of the job. Check your Slurm script for errors and
look for useful information in the file slurm-46987157.out if it exists.
* The Tiger cluster is intended for jobs that require multiple nodes. This
job ran in the serial partition where jobs are assigned the lowest
priority. On Tiger, a job will run in the serial partition if it only
requires 1 node. Consider carrying out this work elsewhere.
* For additional job metrics including metrics plotted against time:
https://mystellar.princeton.edu/pun/sys/jobstats (VPN required off-campus)
* For additional job metrics including metrics plotted against time:
https://stats.rc.princeton.edu (VPN required off-campus)
One can also output the raw JSON:
$ jobstats -j 39798795 | jq
{
"gpus": 4,
"nodes": {
"della-i14g2": {
"cpus": 24,
"gpu_total_memory": {
"0": 42949672960,
"1": 42949672960
},
"gpu_used_memory": {
"0": 28453568512,
"1": 28453568512
},
"gpu_utilization": {
"0": 65.7,
"1": 64.5
},
"total_memory": 137438953472,
"total_time": 164480.1,
"used_memory": 8444272640
},
"della-i14g3": {
"cpus": 24,
"gpu_total_memory": {
"0": 42949672960,
"1": 42949672960
},
"gpu_used_memory": {
"0": 28453634048,
"1": 28453634048
},
"gpu_utilization": {
"0": 72.9,
"1": 67.5
},
"total_memory": 137438953472,
"total_time": 154135.9,
"used_memory": 8419606528
}
},
"total_time": 67316
}
In addition to jobstats, the following software tools build on the Jobstats platform:
- gpudash - A command for generating a text-based GPU utilization dashboard
- job defense shield - A tool for sending automated email alerts to users
- reportseff - A command for displaying Slurm efficiency reports for several jobs at once
- utilization reports - A tool for sending detailed usage reports to users by email
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