n,p = 2194, 12277
X = np.random.normal(0, 1, (n, p))
y = X[:,1] + X[:,2] + np.random.normal(0,1, n)


l1 = LassoLarsCV(n_jobs=1).fit(X,y)
print(l1.coef_)
l = LassoLarsCV(n_jobs=-1).fit(X,y)
print(l.coef_)

OUTPUT

>>> 
>>> 
[ 0.          0.91122441  0.88051206 ...,  0.          0.          0.        ]
multiprocessing.pool.RemoteTraceback: 
"""
Traceback (most recent call last):
  File "/usr/local/lib/python3.4/dist-packages/sklearn/externals/joblib/parallel.py", line 94, in __call__
    return self.func(*args, **kwargs)
  File "/usr/local/lib/python3.4/dist-packages/sklearn/linear_model/least_angle.py", line 853, in _lars_path_residues
    X_train -= X_mean
ValueError: output array is read-only

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/lib/python3.4/multiprocessing/pool.py", line 119, in worker
    result = (True, func(*args, **kwds))
  File "/usr/local/lib/python3.4/dist-packages/sklearn/externals/joblib/parallel.py", line 104, in __call__
    raise TransportableException(text, e_type)
sklearn.externals.joblib.my_exceptions.TransportableException: TransportableException
___________________________________________________________________________
ValueError                                         Wed Apr 15 14:14:46 2015
PID: 4123                                    Python 3.4.0: /usr/bin/python3
...........................................................................
/usr/local/lib/python3.4/dist-packages/sklearn/linear_model/least_angle.py in _lars_path_residues(X_train=memmap([[ 1.00577915, -1.05126903,  1.30304231, ... -0.35100295,
        -0.9267396 ,  0.63928884]]), y_train=array([ 1.3232542 ,  0.84262663, -2.62905025, ...,  1.62472897,
       -1.45902494, -5.00504709]), X_test=array([[-0.31256648, -0.8413869 ,  0.46347277, ....  1.21977537,
        -1.12781956, -1.12695671]]), y_test=array([ -1.91413301e-02,  -7.90822410e-01,  -2.1...2352138e-01,  -2.06750561e+00,  -3.79778359e-01]), Gram='auto', copy=False, method='lasso', verbose=0, fit_intercept=True, normalize=True, max_iter=500, eps=2.2204460492503131e-16)
    848         X_test = X_test.copy()
    849         y_test = y_test.copy()
    850 
    851     if fit_intercept:
    852         X_mean = X_train.mean(axis=0)
--> 853         X_train -= X_mean
        X_train = memmap([[ 1.00577915, -1.05126903,  1.30304231, ... -0.35100295,
        -0.9267396 ,  0.63928884]])
        X_mean = memmap([-0.01531417,  0.02889734, -0.01592266, ...,  0.00315228,
       -0.00060194,  0.01170645])
    854         X_test -= X_mean
    855         y_mean = y_train.mean(axis=0)
    856         y_train = as_float_array(y_train, copy=False)
    857         y_train -= y_mean

ValueError: output array is read-only
___________________________________________________________________________
"""

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.4/dist-packages/sklearn/externals/joblib/parallel.py", line 518, in retrieve
    self._output.append(job.get())
  File "/usr/lib/python3.4/multiprocessing/pool.py", line 599, in get
    raise self._value
sklearn.externals.joblib.my_exceptions.TransportableException: TransportableException
___________________________________________________________________________
ValueError                                         Wed Apr 15 14:14:46 2015
PID: 4123                                    Python 3.4.0: /usr/bin/python3
...........................................................................
/usr/local/lib/python3.4/dist-packages/sklearn/linear_model/least_angle.py in _lars_path_residues(X_train=memmap([[ 1.00577915, -1.05126903,  1.30304231, ... -0.35100295,
        -0.9267396 ,  0.63928884]]), y_train=array([ 1.3232542 ,  0.84262663, -2.62905025, ...,  1.62472897,
       -1.45902494, -5.00504709]), X_test=array([[-0.31256648, -0.8413869 ,  0.46347277, ....  1.21977537,
        -1.12781956, -1.12695671]]), y_test=array([ -1.91413301e-02,  -7.90822410e-01,  -2.1...2352138e-01,  -2.06750561e+00,  -3.79778359e-01]), Gram='auto', copy=False, method='lasso', verbose=0, fit_intercept=True, normalize=True, max_iter=500, eps=2.2204460492503131e-16)
    848         X_test = X_test.copy()
    849         y_test = y_test.copy()
    850 
    851     if fit_intercept:
    852         X_mean = X_train.mean(axis=0)
--> 853         X_train -= X_mean
        X_train = memmap([[ 1.00577915, -1.05126903,  1.30304231, ... -0.35100295,
        -0.9267396 ,  0.63928884]])
        X_mean = memmap([-0.01531417,  0.02889734, -0.01592266, ...,  0.00315228,
       -0.00060194,  0.01170645])
    854         X_test -= X_mean
    855         y_mean = y_train.mean(axis=0)
    856         y_train = as_float_array(y_train, copy=False)
    857         y_train -= y_mean

ValueError: output array is read-only
___________________________________________________________________________

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/donbeo/Desktop/prova.py", line 31, in <module>
    l = LassoLarsCV(n_jobs=-1).fit(X,y)
  File "/usr/local/lib/python3.4/dist-packages/sklearn/linear_model/least_angle.py", line 999, in fit
    for train, test in cv)
  File "/usr/local/lib/python3.4/dist-packages/sklearn/externals/joblib/parallel.py", line 666, in __call__
    self.retrieve()
  File "/usr/local/lib/python3.4/dist-packages/sklearn/externals/joblib/parallel.py", line 549, in retrieve
    raise exception_type(report)
sklearn.externals.joblib.my_exceptions.JoblibValueError: JoblibValueError
___________________________________________________________________________
Multiprocessing exception:
    ...........................................................................
/home/donbeo/Desktop/<stdin> in <module>()
----> 1 
      2 
      3 
      4 
      5 
      6 
      7 
      8 
      9 
     10 

...........................................................................
/home/donbeo/Desktop/prova.py in <module>()
     26 y = X[:,1] + X[:,2] + np.random.normal(0,1, n)
     27 
     28 
     29 l1 = LassoLarsCV(n_jobs=1).fit(X,y)
     30 print(l1.coef_)
---> 31 l = LassoLarsCV(n_jobs=-1).fit(X,y)
     32 print(l.coef_)
     33 
     34 
     35 

...........................................................................
/usr/local/lib/python3.4/dist-packages/sklearn/linear_model/least_angle.py in fit(self=LassoLarsCV(copy_X=True, cv=None, eps=2.22044604...normalize=True, precompute='auto', verbose=False), X=array([[-0.31256648, -0.8413869 ,  0.46347277, .... -0.35100295,
        -0.9267396 ,  0.63928884]]), y=array([-0.01914133, -0.79082241, -2.15777265, ...,  1.62472897,
       -1.45902494, -5.00504709]))
    994             delayed(_lars_path_residues)(
    995                 X[train], y[train], X[test], y[test], Gram=Gram, copy=False,
    996                 method=self.method, verbose=max(0, self.verbose - 1),
    997                 normalize=self.normalize, fit_intercept=self.fit_intercept,
    998                 max_iter=self.max_iter, eps=self.eps)
--> 999             for train, test in cv)
        cv = sklearn.cross_validation.KFold(n=2194, n_folds=3, shuffle=False, random_state=None)
   1000         all_alphas = np.concatenate(list(zip(*cv_paths))[0])
   1001         # Unique also sorts
   1002         all_alphas = np.unique(all_alphas)
   1003         # Take at most max_n_alphas values

...........................................................................
/usr/local/lib/python3.4/dist-packages/sklearn/externals/joblib/parallel.py in __call__(self=Parallel(n_jobs=-1), iterable=<generator object <genexpr>>)
    661             if pre_dispatch == "all" or n_jobs == 1:
    662                 # The iterable was consumed all at once by the above for loop.
    663                 # No need to wait for async callbacks to trigger to
    664                 # consumption.
    665                 self._iterating = False
--> 666             self.retrieve()
        self.retrieve = <bound method Parallel.retrieve of Parallel(n_jobs=-1)>
    667             # Make sure that we get a last message telling us we are done
    668             elapsed_time = time.time() - self._start_time
    669             self._print('Done %3i out of %3i | elapsed: %s finished',
    670                         (len(self._output),

    ---------------------------------------------------------------------------
    Sub-process traceback:
    ---------------------------------------------------------------------------
    ValueError                                         Wed Apr 15 14:14:46 2015
PID: 4123                                    Python 3.4.0: /usr/bin/python3
...........................................................................
/usr/local/lib/python3.4/dist-packages/sklearn/linear_model/least_angle.py in _lars_path_residues(X_train=memmap([[ 1.00577915, -1.05126903,  1.30304231, ... -0.35100295,
        -0.9267396 ,  0.63928884]]), y_train=array([ 1.3232542 ,  0.84262663, -2.62905025, ...,  1.62472897,
       -1.45902494, -5.00504709]), X_test=array([[-0.31256648, -0.8413869 ,  0.46347277, ....  1.21977537,
        -1.12781956, -1.12695671]]), y_test=array([ -1.91413301e-02,  -7.90822410e-01,  -2.1...2352138e-01,  -2.06750561e+00,  -3.79778359e-01]), Gram='auto', copy=False, method='lasso', verbose=0, fit_intercept=True, normalize=True, max_iter=500, eps=2.2204460492503131e-16)
    848         X_test = X_test.copy()
    849         y_test = y_test.copy()
    850 
    851     if fit_intercept:
    852         X_mean = X_train.mean(axis=0)
--> 853         X_train -= X_mean
        X_train = memmap([[ 1.00577915, -1.05126903,  1.30304231, ... -0.35100295,
        -0.9267396 ,  0.63928884]])
        X_mean = memmap([-0.01531417,  0.02889734, -0.01592266, ...,  0.00315228,
       -0.00060194,  0.01170645])
    854         X_test -= X_mean
    855         y_mean = y_train.mean(axis=0)
    856         y_train = as_float_array(y_train, copy=False)
    857         y_train -= y_mean

ValueError: output array is read-only
___________________________________________________________________________
>>> >>>