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.