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Time SDM Random Forests runs out of memory #6
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The problem is that memory is shared between worker cores. Each core gets a copy of the full forest and all training data, which gets real big real fast. Going to try setting ulimit to = 90% of total. |
ulimit: core file size (blocks, -c) 0 data seg size (kbytes, -d) unlimited scheduling priority (-e) 0 file size (blocks, -f) unlimited pending signals (-i) 28716 max locked memory (kbytes, -l) 64 max memory size (kbytes, -m) unlimited open files (-n) 65536 pipe size (512 bytes, -p) 8 POSIX message queues (bytes, -q) 819200 real-time priority (-r) 0 stack size (kbytes, -s) 8192 cpu time (seconds, -t) unlimited max user processes (-u) 28716 virtual memory (kbytes, -v) unlimited file locks (-x) unlimited RInfo: R version 3.3.1 (2016-06-21) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Debian GNU/Linux 8 (jessie) System Memory Usage: |
Changes:
model <- foreach(ntree=rep(rfTrees, ncores), .combine=combine, .multicombine=TRUE, .packages='randomForest') %do% { randomForest(x, y, ntree=ntree)} New code: treesPerCore <- round(rfTrees / ncores) model <- foreach(ntree=rep(treesPerCore, ncores), .combine=combine, .multicombine=TRUE, .packages='randomForest') %do% { randomForest(x, y, ntree=ntree, nodesize=15)} Before I was growing |
It looks like |
Log Dump:
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