The multi-threading issues on RandomForestClassifier #6023
Description
Hi,
I'm using RandomForestClassifier to train a model on Ubuntu14.04 with python2.7.11 thru anaconda package. Below is the core coding:
rf = RandomForestClassifier(n_jobs = -1, random_state = seed)
parameters = {'n_estimators': [2000],
'criterion': ['entropy'],
'max_depth': [10],
'min_samples_leaf': [3],
#'oob_score': [False, True],
'max_features': ['auto']}
print "Start paramter grid search..."
start = time()
clf = GridSearchCV(rf, parameters, n_jobs = 4, scoring = 'roc_auc',
cv = StratifiedKFold(y_train, n_folds = 4, shuffle = True, random_state = 128),
verbose=2, refit = True)
clf.fit(x_train, y_train)
I turned on the CPU monitor to watch the CPU status on a quad-core system. However, in the beginning, all CPUs are 99% used. However, after ~1hr, the CPU's usage drops to ~0.3%, which does not seem normal.
Below is the output on from the terminal:
Fitting 4 folds for each of 1 candidates, totalling 4 fits
[CV] max_features=auto, n_estimators=2000, criterion=entropy, max_
n_samples_leaf=3
[CV] max_features=auto, n_estimators=2000, criterion=entropy, max_
n_samples_leaf=3
[CV] max_features=auto, n_estimators=2000, criterion=entropy, max_
n_samples_leaf=3
[CV] max_features=auto, n_estimators=2000, criterion=entropy, max_
n_samples_leaf=3
[CV] max_features=auto, n_estimators=2000, criterion=entropy, max
in_samples_leaf=3 -68.4min
[Parallel(n_jobs=4)]: Done 1 jobs | elapsed: 68.4min
[CV] max_features=auto, n_estimators=2000, criterion=entropy, max
in_samples_leaf=3 -68.8min
Below is the status of CPU usage:
top - 01:40:46 up 4:40, 0 users, load average: 0.00, 0.00, 0.95
Tasks: 66 total, 1 running, 65 sleeping, 0 stopped, 0 zombie
%Cpu0 : 0.3 us, 0.0 sy, 0.0 ni, 99.7 id, 0.0 wa, 0.0 hi, 0.0 si, 0.0 st
%Cpu1 : 0.0 us, 0.3 sy, 0.0 ni, 99.7 id, 0.0 wa, 0.0 hi, 0.0 si, 0.0 st
%Cpu2 : 0.3 us, 0.3 sy, 0.0 ni, 99.3 id, 0.0 wa, 0.0 hi, 0.0 si, 0.0 st
%Cpu3 : 0.0 us, 0.0 sy, 0.0 ni,100.0 id, 0.0 wa, 0.0 hi, 0.0 si, 0.0 st
KiB Mem: 6553600 total, 4579708 used, 1973892 free, 0 buffers
KiB Swap: 6553600 total, 5511600 used, 1042000 free. 36652 cached Mem
PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND
994 root 20 0 23116 60 60 S 0.0 0.0 0:00.00 ptyserved
997 root 20 0 39372 0 0 S 0.0 0.0 0:00.01 nginx
1000 root 20 0 39876 924 536 S 0.0 0.0 0:01.75 nginx
1002 root 20 0 12736 0 0 S 0.0 0.0 0:00.00 getty
1004 root 20 0 12736 0 0 S 0.0 0.0 0:00.00 getty
1435 root 20 0 18144 24 24 S 0.0 0.0 0:00.00 bash
1455 root 20 0 59568 0 0 S 0.0 0.0 0:00.00 su
1456 root 20 0 18140 0 0 S 0.0 0.0 0:00.00 bash
1467 root 20 0 21916 800 504 R 0.0 0.0 0:12.72 top
1888 root 20 0 61316 4 4 S 0.0 0.0 0:00.00 sshd
1950 postfix 20 0 27408 272 184 S 0.0 0.0 0:00.05 qmgr
2062 root 20 0 59568 92 92 S 0.0 0.0 0:00.00 su
2063 root 20 0 18144 60 60 S 0.0 0.0 0:00.00 bash
2074 root 20 0 5861480 3260 888 S 0.0 0.0 1:26.11 python***
2087 root 20 0 8268200 2.020g 216 S 0.0 32.3 66:39.13 python***
2090 root 20 0 8268200 2.034g 1676 S 0.0 32.5 66:38.78 python***
2184 postfix 20 0 27356 524 240 S 0.0 0.0 0:00.00 pickup
2210 root 20 0 5861480 2836 104 S 0.0 0.0 0:00.00 python ****
2225 root 20 0 5861480 4040 820 S 0.0 0.1 0:00.00 python ****
Although the training data is about 207MB with 300 features, the CPU usage drop doesn't seem to usual.
Do you know what is going on?
Thank you very much!