text: import atexit
  import logging
  import os
  import shutil
  import stat
  import sys
  
  import numpy
  
  import theano
  from theano.compile import optdb
  from theano.gof.cmodule import get_lib_extension
  from theano.gof.compilelock import get_lock, release_lock
  from theano.configparser import config, AddConfigVar, StrParam
  import nvcc_compiler
  
  _logger_name = 'theano.sandbox.cuda'
  _logger = logging.getLogger(_logger_name)
  _logger.setLevel(logging.WARNING)
  
  AddConfigVar('cuda.root',
          """directory with bin/, lib/, include/ for cuda utilities.
          This directory is included via -L and -rpath when linking
          dynamically compiled modules.  If AUTO and nvcc is in the
          path, it will use one of nvcc parent directory.  Otherwise
          /usr/local/cuda will be used.  Leave empty to prevent extra
          linker directives.  Default: environment variable "CUDA_ROOT"
          or else "AUTO".
          """,
          StrParam(os.getenv('CUDA_ROOT', "AUTO")))
  
  if config.cuda.root == "AUTO":
      # set nvcc_path correctly and get the version
      nvcc_compiler.set_cuda_root()
  
  #is_nvcc_available called here to initialize global vars in
  #nvcc_compiler module
  nvcc_compiler.is_nvcc_available()
  
  # Compile cuda_ndarray.cu
  # This need that nvcc (part of cuda) is installed. If it is not, a warning is
  # printed and this module will not be working properly (we set `cuda_available`
  # to False).
  
  # This variable is True by default, and set to False if nvcc is not
  # available or their is no cuda card or something goes wrong when
  # trying to initialize cuda.
  cuda_available = True
  
  # Global variable to avoid displaying the same warning multiple times.
  cuda_warning_is_displayed = False
  
  #This variable is set to True when we enable cuda.(i.e. when use() is called)
  cuda_enabled = False
  
  
  # Code factorized within a function so that it may be called from multiple
  # places (which is not currently the case, but may be useful in the future).
  def set_cuda_disabled():
      """Function used to disable cuda.
  
      A warning is displayed, so that the user is aware that cuda-based code is
      not going to work.
      Note that there is no point calling this function from outside of
      `cuda.__init__`, since it has no effect once the module is loaded.
      """
      global cuda_available, cuda_warning_is_displayed
      cuda_available = False
  
  #cuda_ndarray compile and import
  cuda_path = os.path.abspath(os.path.split(__file__)[0])
  
  cuda_ndarray_loc = os.path.join(config.compiledir, 'cuda_ndarray')
  cuda_ndarray_so = os.path.join(cuda_ndarray_loc,
                                 'cuda_ndarray.' + get_lib_extension())
  libcuda_ndarray_so = os.path.join(cuda_ndarray_loc,
                                 'libcuda_ndarray.' + get_lib_extension())
  
  
  def try_import():
      """
      load the cuda_ndarray module if present and up to date
      return True if loaded correctly, otherwise return False
      """
      cuda_files = (
          'cuda_ndarray.cu',
          'cuda_ndarray.cuh',
          'conv_full_kernel.cu',
          'conv_kernel.cu')
      stat_times = [os.stat(os.path.join(cuda_path, cuda_file))[stat.ST_MTIME]
                    for cuda_file in cuda_files]
      date = max(stat_times)
      if os.path.exists(cuda_ndarray_so):
          if date >= os.stat(cuda_ndarray_so)[stat.ST_MTIME]:
              return False
      try:
          # If we load a previously-compiled version, config.compiledir should
          # be in sys.path.
          if config.compiledir not in sys.path:
              sys.path.append(config.compiledir)
          import cuda_ndarray.cuda_ndarray
      except ImportError:
          return False
      return True
  
  
  # Add the theano cache directory's cuda_ndarray subdirectory to the
  # list of places that are hard-coded into compiled modules' runtime
  # library search list.  This works in conjunction with
  # nvcc_compiler.NVCC_compiler.compile_str which adds this folder during
  # compilation with -L and also adds -lcuda_ndarray when compiling
  # modules.
  nvcc_compiler.add_standard_rpath(cuda_ndarray_loc)
  
  compile_cuda_ndarray = True
  
  if not compile_cuda_ndarray:
      compile_cuda_ndarray = not try_import()
  
  if compile_cuda_ndarray:
      get_lock()
      try:
          # Retry to load again in case someone else compiled it
          # while we waited for the lock
          if not try_import():
              try:
                  if not nvcc_compiler.is_nvcc_available():
                      set_cuda_disabled()
  
                  if cuda_available:
                      code = open(os.path.join(cuda_path,
                                               "cuda_ndarray.cu")).read()
  
                      if not os.path.exists(cuda_ndarray_loc):
                          os.makedirs(cuda_ndarray_loc)
  
                      # If $TMPDIR is defined, nvopencc wants it to exist
                      if 'TMPDIR' in os.environ:
                          tmpdir = os.environ['TMPDIR']
                          if not os.path.exists(tmpdir):
                              os.makedirs(tmpdir)
  
                      compiler = nvcc_compiler.NVCC_compiler()
                      compiler.compile_str(
                              'cuda_ndarray',
                              code,
                              location=cuda_ndarray_loc,
                              include_dirs=[cuda_path], libs=['cublas'],
                              preargs=compiler.compile_args())
                      from cuda_ndarray.cuda_ndarray import *
              except Exception, e:
                  _logger.error("Failed to compile cuda_ndarray.cu: %s", str(e))
                  set_cuda_disabled()
      finally:
          release_lock()
  del compile_cuda_ndarray
  
  if cuda_available:
      # The module should be compiled.
      from cuda_ndarray.cuda_ndarray import *
  
      # If necessary,
      # create a symlink called libcuda_ndarray.so
      # which nvcc_compiler.NVCC_compiler uses when linking
      # any module except "cuda_ndarray" itself.
      def ok():
          """
          Check if an existing library exists and can be read.
          """
          try:
              open(libcuda_ndarray_so).close()
              return True
          except IOError:
              return False
      if not ok():
          if sys.platform == "win32":
              # The Python `os` module does not support symlinks on win32.
              shutil.copyfile(cuda_ndarray_so, libcuda_ndarray_so)
          else:
              try:
                  os.symlink(cuda_ndarray_so, libcuda_ndarray_so)
              except OSError, e:
                  # This may happen for instance when running multiple
                  # concurrent jobs, if two of them try to create the
                  # symlink simultaneously.
                  # If that happens, we verify that the existing symlink is
                  # indeed working.
                  if getattr(e, 'errno', None) != errno.EEXIST or not ok():
                      raise
      try:
          # This only test if the cuda driver is available and if there
          # is at least one GPU that support cuda. This do not select a
          # device.
          gpu_init()
          cuda_available = True
          cuda_initialization_error_message = ""
  # actively closing our gpu session presents segfault-on-exit on some systems
          atexit.register(gpu_shutdown)
      except EnvironmentError, e:
          cuda_available = False
          cuda_initialization_error_message = e.message
  
  
  class GpuOp(theano.gof.Op):
  
      """
      Parent class for all GPU Ops.
  
      This class ensures we verify the GPU is working properly when a GPU Op is
      used for the first time.
  
      It is defined in __init__.py so that it exists even when `cuda_available`
      is False (this is necessary to avoid breaking the test suite).
      """
  
      def make_thunk(self, node, storage_map, compute_map, no_recycling):
          if theano.sandbox.cuda.use.device_number is None:
              theano.sandbox.cuda.use("gpu",
                                      force=True,
                                      default_to_move_computation_to_gpu=False,
                                      move_shared_float32_to_gpu=False,
                                      enable_cuda=False)
          return super(GpuOp, self).make_thunk(node, storage_map,
                                               compute_map, no_recycling)
  
  theano.compile.debugmode.default_make_thunk.append(GpuOp.make_thunk.im_func)
  
  # We must do those import to be able to create the full doc when
  # nvcc is not available
  from theano.sandbox.cuda.var import (CudaNdarrayVariable,
                                       CudaNdarrayConstant,
                                       CudaNdarraySharedVariable,
                                       float32_shared_constructor)
  from theano.sandbox.cuda.type import CudaNdarrayType
  
  
  if cuda_available:
      # check if their is an old cuda_ndarray that was loading instead of the one
      # we compiled!
      import cuda_ndarray.cuda_ndarray
      if cuda_ndarray_so != cuda_ndarray.cuda_ndarray.__file__:
          _logger.warning("cuda_ndarray was loaded from %s, but Theano expected "
                  "to load it from %s. This is not expected as theano should "
                  "compile it automatically for you. Do you have a directory "
                  "called cuda_ndarray in your LD_LIBRARY_PATH environment "
                  "variable? If so, please remove it as it is outdated.",
                  cuda_ndarray.cuda_ndarray.__file__,
                  cuda_ndarray_so)
  
      shared_constructor = float32_shared_constructor
  
      import basic_ops
      from basic_ops import (GpuFromHost, HostFromGpu, GpuElemwise,
                             GpuDimShuffle, GpuSum, GpuReshape, GpuContiguous,
                             GpuSubtensor, GpuIncSubtensor,
                             GpuAdvancedSubtensor1, GpuAdvancedIncSubtensor1,
                             GpuFlatten, GpuShape, GpuAlloc,
                             GpuJoin, fscalar, fvector, fmatrix, frow, fcol,
                             ftensor3, ftensor4,
                             scalar, vector, matrix, row, col,
                             tensor3, tensor4)
      from basic_ops import host_from_gpu, gpu_from_host, as_cuda_array
      import opt
      import cuda_ndarray
      from rng_curand import CURAND_RandomStreams
  
  
  def use(device,
          force=False,
          default_to_move_computation_to_gpu=True,
          move_shared_float32_to_gpu=True,
          enable_cuda=True,
          test_driver=True):
      """
      Error and warning about CUDA should be displayed only when this
      function is called.  We need to be able to load this module only
      to check if it is available!
  
      :param device: string "cpu", "gpu", "gpuN" (N is the device number to use)
      :param force: Will always raise an exception if we can't use the gpu.
      :param default_to_move_computation_to_gpu: If gpu init succeeded, enable by
                                                 default optimizations to move
                                                 computations to the gpu
      :param move_shared_float32_to_gpu: If gpu init succeeded, put new shared
                                         variables in float32 on the gpu.
      :param enable_cuda: If the gpu is correctly enabled,
                          set the variable cuda_enabled to True.
      """
      global cuda_enabled, cuda_initialization_error_message
      if force and not cuda_available and device.startswith('gpu'):
          if not nvcc_compiler.is_nvcc_available():
              raise EnvironmentError("You forced the use of gpu device '%s', but"
                                     " nvcc was not found. Set it in your PATH "
                                     "environment variable or set the Theano "
                                     "flags 'cuda.root' to its directory"
                                     "" % device)
          else:
              raise EnvironmentError("You forced the use of gpu device %s, "
                                     "but CUDA initialization failed "
                                     "with error:\n%s" % (
                  device, cuda_initialization_error_message))
      elif not nvcc_compiler.is_nvcc_available():
          _logger.error('nvcc compiler not found on $PATH.'
                ' Check your nvcc installation and try again.')
          return
      elif not cuda_available:
          error_addendum = ""
          try:
              if cuda_initialization_error_message:
                  error_addendum = (" (error: %s)" %
                                    cuda_initialization_error_message)
          except NameError:
  # cuda_initialization_error_message is not available b/c compilation failed
              pass
          _logger.warning('CUDA is installed, but device %s is not available %s',
                  device, error_addendum)
          return
  
      if device == 'gpu':
          pass
      elif device.startswith('gpu'):
          device = int(device[3:])
      elif device == 'cpu':
          device = -1
      else:
          raise ValueError("Invalid device identifier", device)
      if use.device_number is None:
          # No successful call to use() has been made yet
          if device != 'gpu' and device < 0:
              return
          if device in [None, ""]:
              device = 0
          try:
              if device != 'gpu':
                  assert isinstance(device, int)
                  gpu_init(device)
                  use.device_number = device
              else:
                  # This mean the driver should select the GPU.  As we
                  # need to get the device number now, we force the
                  # selection of the GPU by the driver now and then we
                  # query the active GPU. If we check the active GPU before
                  # the device is initialized we will always receive 0
                  # event if another device is selected later.
                  cuda_ndarray.cuda_ndarray.CudaNdarray.zeros((2, 3))
                  use.device_number = active_device_number()
  
              if test_driver:
                  import theano.sandbox.cuda.tests.test_driver
                  theano.sandbox.cuda.tests.test_driver.test_nvidia_driver1()
  
              if move_shared_float32_to_gpu:
                  handle_shared_float32(True)
  
              if enable_cuda:
                  cuda_enabled = True
              print >> sys.stderr, "Using gpu device %d: %s" % (
                  active_device_number(), active_device_name())
          except (EnvironmentError, ValueError, RuntimeError), e:
              _logger.error(("ERROR: Not using GPU."
                             " Initialisation of device %s failed:\n%s"),
                            str(device), e)
              cuda_enabled = False
              if force:
                  e.args += (("You asked to force this device and it failed."
                              " No fallback to the cpu or other gpu device."),)
                  raise
  
      elif use.device_number != device:
          _logger.warning(("Ignoring call to use(%s), GPU number %i "
              "is already in use."),
              str(device), use.device_number)
  
      if default_to_move_computation_to_gpu:
          optdb.add_tags('gpu_opt',
                         'fast_run',
                         'inplace')
          optdb.add_tags('gpu_after_fusion',
                         'fast_run',
                         'inplace')
  
      if force:
          try:
              #in case the device if just gpu,
              # we check that the driver init it correctly.
              cuda_ndarray.cuda_ndarray.CudaNdarray.zeros((5, 5))
          except (Exception, NameError), e:
              # NameError when no gpu present as cuda_ndarray is not loaded.
              e.args += ("ERROR: GPU forced but failed. ",)
              raise
  use.device_number = None
  
  
  def handle_shared_float32(tf):
      """Set the default shared type for float32 tensor to CudaNdarrayType
  
      This function is intended to be called from use(gpu_index), not directly.
      """
      if tf:
          import theano.compile
          theano.compile.shared_constructor(float32_shared_constructor)
  
      else:
          theano.compile.shared_constructor(float32_shared_constructor, True)
          assert (float32_shared_constructor not in
                  theano.compile.shared.constructors)
  
  # We can't test the driver during import here as this cause circular
  # import dependency. So we also test it in the file theano/__init__.py
  if config.device.startswith('gpu'):
      use(device=config.device, force=config.force_device, test_driver=False)
  elif config.init_gpu_device:
      assert config.device == "cpu", (
          "We can use the Theano flag init_gpu_device"
          " only when the Theano flag device=='cpu'")
      _logger.warning(("GPU device %s will be initialized, and used if a GPU is "
            "needed. "
            "However, no computation, nor shared variables, will be implicitly "
            "moved to that device. If you want that behavior, use the 'device' "
            "flag instead."),
            config.init_gpu_device)
      use(device=config.init_gpu_device,
          force=config.force_device,
          default_to_move_computation_to_gpu=False,
          move_shared_float32_to_gpu=False,
          enable_cuda=False, test_driver=False)
  