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HTCondor to ClusterCockpit Sync
21 Commits 1 Branch 0 Tags 207 KiB
C++ 98.7%
Python 1.1%
CMake 0.2%
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Joachim Meyer e38e6fa5b9 Don't start on JobStatus chng & stop on R -> IDLE. 
This is handling edgecases, where a startd died and after a timeout of 1hr the job is requeued.
The IDLE -> RUNNING state then already has all other attributes (like RemoteHost)  set, from the last execution.
We should wait for `CpusProvisioned`, though, so that all required values are already up-to-date.

Additionally, since timed-out jobs are not held, but instead just requeued, stop jobs as well when the state changes from `RUNNING` to `IDLE`.
2022-12-19 11:24:24 +01:00
contrib Add systemd service and timer to start this script every minute 2022-09-06 15:02:28 +02:00
curlpp@592552a165 Add Schedd plugin to synch with CC. 2022-12-15 16:13:45 +01:00
scripts Fix AssignedGPUs parsing. 2022-12-16 13:42:04 +01:00
src Don't start on JobStatus chng & stop on R -> IDLE. 2022-12-19 11:24:24 +01:00
.gitignore Add Schedd plugin to synch with CC. 2022-12-15 16:13:45 +01:00
.gitmodules Add Schedd plugin to synch with CC. 2022-12-15 16:13:45 +01:00
CMakeLists.txt Limit verbosity a bit (SCHEDD_DEBUG=D_FULLDEBUG). 2022-12-16 09:35:10 +01:00
ReadMe.md Limit verbosity a bit (SCHEDD_DEBUG=D_FULLDEBUG). 2022-12-16 09:35:10 +01:00

ReadMe.md

HTCondor to ClusterCockpit Sync

HTCondor ClassAdLog Plugin

Building

Requirements: A build environment reasonably similar to the submission nodes (might want to use the HTCondor nmi build docker containers).

Use CMake to configure the project.

mkdir build ; cd build
cmake .. -DCONDOR_SRC=<path/to/htcondor> -DCONDOR_BUILD=<path/to/htcondor/build> -DCMAKE_BUILD_TYPE=Release

Configuration

The target system will need the corresponding curl package installed.

Adapt and add to condor_config.local or any other HTCondor config file:

SCHEDD.PLUGINS = $(SCHEDD.PLUGINS) /path/to/libhtcondor_cc_sync_plugin.so

CCSYNC_URL=<ClusterCockpit-URL>
CCSYNC_APIKEY=<API-Key>
CCSYNC_CLUSTER_NAME=<ClusterCockpit's cluster name this submit node works for>
CCSYNC_GPU_MAP=/path/to/gpu_map.json
CCSYNC_SUBMIT_ID=<Unique submission node id, expected to be in 0..3 (see #globalJobIdToInt)>

gpu_map.json is expected in the format and can be generated with condor_status_to_gpu_map.py <path/to/condor_status.json>, where condor_status.json is generated by calling condor_status -json > condor_status.json on the cluster:

{
    "hostname1": {
        "GPU-acb66c44": "0000:07:00.0",
        ...
    },
    "hostname2": {
        "GPU-31f57da0": "0000:0A:00.0",
        ...
    }
}

For getting a debug dump of the class ads at the end of the endTransaction, build with -DVERBOSE (automatically set for Debug or RelWithDebInfo builds) and set SCHEDD_DEBUG=D_FULLDEBUG in the condor config.