Increase the size of /dev/shm in Azure ML Studio

I’m trying to execute the following code in Azure ML Studio notebook:

from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score
from sklearn.cross_validation import KFold, cross_val_score

for C in np.linspace(0.01, 0.2, 30):
    cv = KFold(n=X_train.shape[0], n_folds=7, shuffle=True, random_state=12345)
    clf = LogisticRegression(C=C, random_state=12345)
    print C, sum(cross_val_score(clf, X_train_scaled, y_train, scoring='roc_auc', cv=cv, n_jobs=2)) / 7.0

and I’m getting this error:

Failed to save <type 'numpy.ndarray'> to .npy file:
Traceback (most recent call last):
  File "/home/nbcommon/env/lib/python2.7/site-packages/sklearn/externals/joblib/numpy_pickle.py", line 271, in save
    obj, filename = self._write_array(obj, filename)
  File "/home/nbcommon/env/lib/python2.7/site-packages/sklearn/externals/joblib/numpy_pickle.py", line 231, in _write_array
    self.np.save(filename, array)
  File "/home/nbcommon/env/lib/python2.7/site-packages/numpy/lib/npyio.py", line 491, in save
    pickle_kwargs=pickle_kwargs)
  File "/home/nbcommon/env/lib/python2.7/site-packages/numpy/lib/format.py", line 585, in write_array
    array.tofile(fp)
IOError: 19834920 requested and 8384502 written

---------------------------------------------------------------------------
IOError                                   Traceback (most recent call last)
<ipython-input-29-9740e9942629> in <module>()
      6     cv = KFold(n=X_train.shape[0], n_folds=7, shuffle=True, random_state=12345)
      7     clf = LogisticRegression(C=C, random_state=12345)
----> 8     print C, sum(cross_val_score(clf, X_train_scaled, y_train, scoring='roc_auc', cv=cv, n_jobs=2)) / 7.0

/home/nbcommon/env/lib/python2.7/site-packages/sklearn/cross_validation.pyc in cross_val_score(estimator, X, y, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch)
   1431                                               train, test, verbose, None,
   1432                                               fit_params)
-> 1433                       for train, test in cv)
   1434     return np.array(scores)[:, 0]
   1435 

/home/nbcommon/env/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in __call__(self, iterable)
    808                 # consumption.
    809                 self._iterating = False
--> 810             self.retrieve()
    811             # Make sure that we get a last message telling us we are done
    812             elapsed_time = time.time() - self._start_time

/home/nbcommon/env/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in retrieve(self)
    725                 job = self._jobs.pop(0)
    726             try:
--> 727                 self._output.extend(job.get())
    728             except tuple(self.exceptions) as exception:
    729                 # Stop dispatching any new job in the async callback thread

/home/nbcommon/env/lib/python2.7/multiprocessing/pool.pyc in get(self, timeout)
    565             return self._value
    566         else:
--> 567             raise self._value
    568 
    569     def _set(self, i, obj):

IOError: [Errno 28] No space left on device

With n_jobs=1 it works fine.

I think this is because joblib library tries to save my data to /dev/shm. The problem is that it has only 64M capacity:

Filesystem         Size  Used Avail Use% Mounted on
none               786G  111G  636G  15% /
tmpfs               56G     0   56G   0% /dev
shm                 64M     0   64M   0% /dev/shm
tmpfs               56G     0   56G   0% /sys/fs/cgroup
/dev/mapper/crypt  786G  111G  636G  15% /etc/hosts

I can’t change this folder by setting JOBLIB_TEMP_FOLDER environment variable (export doesn’t work).

In [35]: X_train_scaled.nbytes

Out[35]: 158679360

Thanks for any advice!

Answer

The /dev/shm is a virtual filesystem for passing data between programs that implementation of traditional shared memory on Linux.

So you could not increase it via set up some options on Application Layout.

But for example, you can remount /dev/shm with 8G size in Linux Shell with administrator permission like root as follows.

mount -o remount,size=8G /dev/shm

However, it seems that Azure ML studio not support remote access via SSH protocol, so the feasible plan is upgrade the standard tier if using free tier at present.