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HTCondor to ClusterCockpit Sync
curlpp@592552a165 | ||
scripts | ||
src | ||
.gitignore | ||
.gitmodules | ||
CMakeLists.txt | ||
LICENSE | ||
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.