remove the automatic importing mpi4py

GPy/inference/latent_function_inference/var_dtc_parallel.py

@@ -9,10 +9,6 @@ import numpy as np
 from . import LatentFunctionInference
 log_2_pi = np.log(2*np.pi)
 
-try:
-    from mpi4py import MPI
-except:
-    pass
 
 class VarDTC_minibatch(LatentFunctionInference):
     """
@@ -123,6 +119,7 @@ class VarDTC_minibatch(LatentFunctionInference):
             YRY_full = trYYT*beta
 
         if self.mpi_comm != None:
+            from mpi4py import MPI
             psi0_all = np.array(psi0_full)
             psi1Y_all = psi1Y_full.copy()
             psi2_all = psi2_full.copy()
@@ -146,6 +143,7 @@ class VarDTC_minibatch(LatentFunctionInference):
         num_data, output_dim = Y.shape
         input_dim = Z.shape[0]
         if self.mpi_comm != None:
+            from mpi4py import MPI
             num_data_all = np.array(num_data,dtype=np.int32)
             self.mpi_comm.Allreduce([np.int32(num_data), MPI.INT], [num_data_all, MPI.INT])
             num_data = num_data_all
@@ -387,6 +385,7 @@ def update_gradients(model, mpi_comm=None):
 
     # Gather the gradients from multiple MPI nodes
     if mpi_comm != None:
+        from mpi4py import MPI
         if het_noise:
             raise "het_noise not implemented!"
         kern_grad_all = kern_grad.copy()
@@ -409,6 +408,7 @@ def update_gradients(model, mpi_comm=None):
     model.variational_prior.update_gradients_KL(X)
 
     if mpi_comm != None:
+        from mpi4py import MPI
         KL_div_all = np.array(KL_div)
         mpi_comm.Allreduce([np.float64(KL_div), MPI.DOUBLE], [KL_div_all, MPI.DOUBLE])
         KL_div = KL_div_all
@@ -468,6 +468,7 @@ def update_gradients_sparsegp(model, mpi_comm=None):
     
     # Gather the gradients from multiple MPI nodes
     if mpi_comm != None:
+        from mpi4py import MPI
         if het_noise:
             raise "het_noise not implemented!"
         kern_grad_all = kern_grad.copy()

GPy/plotting/matplot_dep/base_plots.py

@@ -148,6 +148,10 @@ def x_frame1D(X,plot_limits=None,resolution=None):
     """
     assert X.shape[1] ==1, "x_frame1D is defined for one-dimensional inputs"
     if plot_limits is None:
+        from ...core.parameterization.variational import VariationalPosterior
+        if isinstance(X, VariationalPosterior):
+            xmin,xmax = X.mean.min(0),X.mean.max(0)
+        else:
             xmin,xmax = X.min(0),X.max(0)
         xmin, xmax = xmin-0.2*(xmax-xmin), xmax+0.2*(xmax-xmin)
     elif len(plot_limits)==2:

GPy/util/parallel.py

@@ -2,26 +2,15 @@
 The module of tools for parallelization (MPI)
 """
 import numpy as np
-try:
+
+def get_id_within_node(comm=None):
     from mpi4py import MPI
-    def get_id_within_node(comm=MPI.COMM_WORLD):
+    if comm is None: comm = MPI.COMM_WORLD
     rank = comm.rank
     nodename =  MPI.Get_processor_name()
     nodelist = comm.allgather(nodename)
     return len([i for i in nodelist[:rank] if i==nodename])
 
-    numpy_to_MPI_typemap = {
-        np.dtype(np.float64) : MPI.DOUBLE,
-        np.dtype(np.float32) : MPI.FLOAT,
-        np.dtype(np.int)     : MPI.INT,
-        np.dtype(np.int8)    : MPI.CHAR,
-        np.dtype(np.uint8)   : MPI.UNSIGNED_CHAR,
-        np.dtype(np.int32)   : MPI.INT,
-        np.dtype(np.uint32)  : MPI.UNSIGNED_INT,
-    }
-except:
-    pass
-
 def divide_data(datanum, rank, size):
     assert rank<size and datanum>0
     

setup.py

@@ -88,7 +88,7 @@ setup(name = 'GPy',
       test_suite = 'GPy.testing',
       long_description=read_to_rst('README.md'),
       install_requires=['numpy>=1.7', 'scipy>=0.16', 'six'],
-      extras_require = {'docs':['matplotlib >=1.3','Sphinx','IPython']},
+      extras_require = {'docs':['matplotlib >=1.3','Sphinx','IPython'],'optional':['mpi4py']},
       classifiers=['License :: OSI Approved :: BSD License',
                    'Natural Language :: English',
                    'Operating System :: MacOS :: MacOS X',