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41 lines
1.3 KiB
Markdown
41 lines
1.3 KiB
Markdown
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# HTCondor to ClusterCockpit Sync
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## HTCondor ClassAdLog Plugin
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### Building
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Requirements:
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A build environment reasonably similar to the submission nodes (might want to use the HTCondor nmi build docker containers).
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Use CMake to configure the project.
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```bash
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mkdir build ; cd build
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cmake .. -DCONDOR_SRC=<path/to/htcondor> -DCONDOR_BUILD=<path/to/htcondor/build> -DCMAKE_BUILD_TYPE=Release
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```
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### Configuration
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The target system will need the corresponding `curl` package installed.
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Adapt and add to `condor_config.local` or any other HTCondor config file:
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```
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SCHEDD.PLUGINS = $(SCHEDD.PLUGINS) /path/to/libhtcondor_cc_sync_plugin.so
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CCSYNC_URL=<ClusterCockpit-URL>
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CCSYNC_APIKEY=<API-Key>
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CCSYNC_CLUSTER_NAME=<ClusterCockpit's cluster name this submit node works for>
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CCSYNC_GPU_MAP=/path/to/gpu_map.json
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CCSYNC_SUBMIT_ID=<Unique submission node id, expected to be in 0..3 (see #globalJobIdToInt)>
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```
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`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:
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```
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{
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"hostname1": {
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"GPU-acb66c44": "0000:07:00.0",
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...
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},
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"hostname2": {
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"GPU-31f57da0": "0000:0A:00.0",
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...
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}
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}
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```
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