.. _libdoc_config:

=======================================
:mod:`config` -- Theano Configuration
=======================================

.. module:: config
   :platform: Unix, Windows
   :synopsis: Library configuration attributes.
.. moduleauthor:: LISA


Guide
=====

The config module contains many ``attributes`` that modify Theano's behavior.  Many of these
attributes are consulted during the import of the ``theano`` module and many are assumed to be
read-only.

*As a rule, the attributes in this module should not be modified by user code.*

Theano's code comes with default values for these attributes, but you can
override them from your .theanorc file, and override those values in turn by
the :envvar:`THEANO_FLAGS` environment variable.

The order of precedence is:

1. an assignment to theano.config.<property>
2. an assignment in :envvar:`THEANO_FLAGS`
3. an assignment in the .theanorc file (or the file indicated in :envvar:`THEANORC`)

You can print out the current/effective configuration at any time by printing
theano.config.  For example, to see a list  of all active configuration
variables, type this from the command-line:

.. code-block:: bash

    python -c 'import theano; print theano.config' | less

Environment Variables
=====================


.. envvar:: THEANO_FLAGS

    This is a list of comma-delimited key=value pairs that control
    Theano's behavior.

    For example, in bash, you can override your :envvar:`THEANORC` defaults
    for <myscript>.py by typing this:

    .. code-block:: bash

        THEANO_FLAGS='floatX=float32,device=gpu0,nvcc.fastmath=True'  python <myscript>.py

    If a value is defined several times in ``THEANO_FLAGS``,
    the right-most definition is used. So, for instance, if
    ``THEANO_FLAGS='device=cpu,device=gpu0'``, then gpu0 will be used.

.. envvar:: THEANORC

    The location[s] of the .theanorc file[s] in ConfigParser format.
    It defaults to ``$HOME/.theanorc``. On Windows, it defaults to
    ``$HOME/.theanorc:$HOME/.theanorc.txt`` to make Windows users' life
    easier.

    Here is the .theanorc equivalent to the THEANO_FLAGS in the example above:

    .. code-block:: cfg

        [global]
        floatX = float32
        device = gpu0

        [nvcc]
        fastmath = True

    Configuration attributes that are available directly in ``config``
    (e.g. ``config.device``, ``config.mode``) should be defined in the
    ``[global]`` section.
    Attributes from a subsection of ``config`` (e.g. ``config.nvcc.fastmath``,
    ``config.blas.ldflags``) should be defined in their corresponding section
    (e.g. ``[nvcc]``, ``[blas]``).

    Multiple configuration files can be specified by separating them with ':'
    characters (as in $PATH).  Multiple configuration files will be merged,
    with later (right-most) files taking priority over earlier files in the
    case that multiple files specify values for a common configuration option.
    For example, to override system-wide settings with personal ones,
    set ``THEANORC=/etc/theanorc:~/.theanorc``.

Config Attributes
=====================

The list below describes some of the more common and important flags
that you might want to use. For the complete list (including documentation),
import theano and print the config variable, as in:

.. code-block:: bash

    python -c 'import theano; print theano.config' | less

.. attribute:: device

    String value: either ``'cpu'``, ``'gpu'``, ``'gpu0'``, ``'gpu1'``,
    ``'gpu2'``, or ``'gpu3'``

    Default device for computations. If ``gpu*``, change the default to try
    to move computation to it and to put shared variable of float32 on
    it.
    Choose the default compute device for theano graphs.  Setting this to a
    ``gpu*`` string will make theano to try by default to move computation to it.
    Also it will make theano put by default shared variable of float32 on it.
    ``'gpu'`` lets the driver select the GPU to use, while ``'gpu?'`` makes Theano try
    to use a specific device. If we are not able to use the GPU, either we fall back
    on the CPU, or an error is raised, depending on the :attr:`force_device` flag.

    This flag's value cannot be modified during the program execution.

    Do not use upper case letters, only lower case even if NVIDIA use
    capital letters.

.. attribute:: force_device

    Bool value: either ``True`` or ``False``

    Default: ``False``

    If ``True`` and ``device=gpu*``, we raise an error if we cannot
    use the specified :attr:`device`. If ``True`` and ``device=cpu``,
    we disable the GPU.  If ``False`` and ``device=gpu*``, and if the
    specified device cannot be used, we warn and fall back to the CPU.

    This is useful to run Theano's tests on a computer with a GPU, but
    without running the GPU tests.

    This flag's value cannot be modified during the program execution.

.. attribute:: init_gpu_device

    String value: either ``''``, ``'gpu'``, ``'gpu0'``, ``'gpu1'``, ``'gpu2'``,
    or ``'gpu3'``

    Initialize the gpu device to use.
    When its value is gpu*, the theano flag :attr:`device` must be ``"cpu"``.
    Unlike :attr:`device`, setting this flag to a specific GPU will not
    try to use this device by default, in particular it will **not** move
    computations, nor shared variables, to the specified GPU.

    This flag is useful to run GPU-specific tests on a particular GPU, instead
    of using the default one.

    This flag's value cannot be modified during the program execution.

.. attribute:: config.pycuda.init

    Bool value: either ``True`` or ``False``

    Default: ``False``

    If True, always initialize PyCUDA when Theano want to initialize
    the GPU.  With PyCUDA version 2011.2.2 or earlier, PyCUDA must
    initialize the GPU before Theano does it.  Setting
    this flag to True, ensure that, but always import PyCUDA.  It can
    be done manually by importing theano.misc.pycuda_init before
    Theano initialize the GPU device.  Newer version of PyCUDA
    (currently only in the trunk) don't have this restriction.

.. attribute:: print_active_device

    Bool value: either ``True`` or ``False``

    Default: ``True``

    Print active device at when the GPU device is initialized.

.. attribute:: floatX

    String value: either 'float64' or 'float32'

    Default: 'float64'

    This sets the default dtype returned by tensor.matrix(), tensor.vector(),
    and similar functions.  It also sets the default theano bit width for
    arguments passed as Python floating-point numbers.

.. attribute:: warn_float64

    String value: either 'ignore', 'warn', 'raise' or 'pdb'

    Default: 'ignore'

    When creating a TensorVariable with dtype float64, what should be done?
    This is useful to help find upcast to float64 in user code.

.. attribute:: allow_gc

    Bool value: either ``True`` or ``False``

    Default: ``True``

    This sets the default for the use of the Theano garbage collector
    for intermediate results. To use less memory, Theano frees the
    intermediate results as soon as they are no longer needed.
    Disabling Theano garbage collection allows Theano to reuse buffers
    for intermediate results between function calls. This speeds up
    Theano by no longer spending time reallocating space. This gives
    significant speed up on functions with many ops that are fast to
    execute, but this increases Theano's memory usage.

.. attribute:: scan.allow_output_prealloc

    Bool value, either ``True`` or ``False``

    Default: ``True``

    This enables, or not, an optimization in Scan in which it tries to
    pre-allocate memory for its outputs. Enabling the optimization can
    give a significant speed up with Scan at the cost of slightly increased
    memory usage.

.. attribute:: openmp

    Bool value: either True or False

    Default: True if the environment variable OMP_NUM_THREADS!=1 or
             if we detect more than 1 CPU core. Otherwise False.

    Enable or not parallel computation on the CPU with OpenMP.
    It is the default value used when creating an Op that support it.
    The best is to define it via Theano configuration
    file or with the environment variable THEANO_FLAGS.

.. attribute:: openmp_elemwise_minsize

   Positive int value, default: 200000.

   This specifies the vectors minimum size for which elemwise ops
   use openmp, if openmp is enabled.

.. attribute:: cast_policy

    String value: either 'numpy+floatX' or 'custom'

    Default: 'custom'

    This specifies how data types are implicitly figured out in Theano, e.g. for
    constants or in the results of arithmetic operations. The 'custom' value
    corresponds to a set of custom rules originally used in
    Theano (which can be partially customized, see e.g. the in-code help of
    ``tensor.NumpyAutocaster``), and will be deprecated in the future.
    The 'numpy+floatX' setting attempts to mimic the numpy casting rules,
    although it prefers to use float32 numbers instead of float64 when
    ``config.floatX`` is set to 'float32' and the user uses data that is not
    explicitly typed as float64 (e.g. regular Python floats).
    Note that 'numpy+floatX' is not currently behaving exactly as planned (it
    is a work-in-progress), and thus you should consider it as experimental.
    At the moment it behaves differently from numpy in the following
    situations:

    * Depending on the value of ``config.int_division``, the resulting type
      of a division of integer types with the ``/`` operator may not match
      that of numpy.
    * On mixed scalar / array operations, numpy tries to prevent the scalar
      from upcasting the array's type unless it is of a fundamentally
      different type. Theano does not attempt to do the same at this point,
      so you should be careful that scalars may upcast arrays when they
      would not when using numpy. This behavior should change in the near
      future.

.. attribute:: int_division

    String value: either 'int', 'floatX' or 'raise'

    Default: 'int'

    Specifies what to do when one tries to compute ``x / y``, where both ``x`` and
    ``y`` are of integer types (possibly unsigned). 'int' means an integer is
    returned (as in Python 2.X), but this behavior is deprecated. 'floatX'
    returns a number of type given by ``config.floatX``. 'raise' is the safest
    choice (and will become default in a future release of Theano) and raises
    an error when one tries to do such an operation, enforcing the use of the
    integer division operator (``//``) (if a float result is intended, either
    cast one of the arguments to a float, or use ``x.__truediv__(y)``).

.. attribute:: mode

    String value: 'Mode', 'ProfileMode'(deprecated), 'DebugMode', 'FAST_RUN',
    'FAST_COMPILE'

    Default 'Mode'

    This sets the default compilation mode for theano functions. By default the
    mode Mode is equivalent to FAST_RUN. See Config attribute linker and optimizer.

.. attribute:: profile

    Bool value: either True or False

    Default False

    Do the vm/cvm linkers profile the execution time of Theano functions?

    See :ref:`tut_profiling` for examples.

.. attribute:: profile_memory

    Bool value: either True or False

    Default False

    Do the vm/cvm linkers profile the memory usage of Theano functions?
    It only works when profile=True.

.. attribute:: profile_optimizer

    Bool value: either True or False

    Default False

    Do the vm/cvm linkers profile the optimization phase when compiling a Theano function?
    It only works when profile=True.

.. attribute:: config.profiling.n_apply

    Positive int value, default: 20.

    The number of Apply nodes to print in the profiler output

.. attribute:: config.profiling.n_ops

    Positive int value, default: 20.

    The number of Ops to print in the profiler output

.. attribute:: config.profiling.min_memory_size

    Positive int value, default: 1024.

    For the memory profile, do not print Apply nodes if the size
    of their outputs (in bytes) is lower than this.

.. attribute:: config.profiling.min_peak_memory

    Bool value: either True or False

    Default False

    Does the memory profile print the min peak memory usage?
    It only works when profile=True, profile_memory=True

.. attribute:: config.profiling.destination

    String value: 'stderr', 'stdout', or a name of a
    file to be created

    Default 'stderr'

    Name of the destination file for the profiling output.
    The profiling output can be either directed to stderr
    (default), or stdout or an arbitrary file.

.. attribute:: config.lib.amdlibm

    Bool value: either True or False

    Default False

    This makes the compilation use the
    `amdlibm <http://developer.amd.com/cpu/libraries/libm/>`__
    library, which is faster than the standard libm.

.. attribute:: config.lib.cnmem

    Float value: >= 0

    Do we enable `CNMeM <https://github.com/NVIDIA/cnmem>`_ or not (a
    faster CUDA memory allocator). In Theano dev version until 0.7.1
    is released.

    That library is included in Theano, you do not need to install it.

    The value represents the start size (in MB or % of total GPU
    memory) of the memory pool. If more memory are needed, it will
    try to get more, but this can cause more memory fragmentation:

        * 0: not enabled.
        * 0 < N <= 1: % of the total GPU memory (clipped to .985 for driver memory)
        * > 0: use that number of MB of memory.


    Default 0 (but should change later)

    .. note::

        This could cause memory fragmentation. So if you have a
        memory error while using cnmem, try to allocate more memory at
        the start or disable it. If you try this, report your result
        on :ref`theano-dev`.

.. attribute:: linker

    String value: 'c|py', 'py', 'c', 'c|py_nogc'

    Default: 'c|py'

    When the mode is Mode, it sets the default linker used.
    See :ref:`using_modes` for a comparison of the different linkers.

.. attribute:: optimizer

    String value: 'fast_run', 'merge', 'fast_compile', 'None'

    Default: 'fast_run'

    When the mode is Mode, it sets the default optimizer used.

.. attribute:: on_opt_error

    String value: 'warn', 'raise', 'pdb' or 'ignore'

    Default: 'warn'

    When a crash occurs while trying to apply some optimization, either warn
    the user and skip this optimization ('warn'), raise the exception
    ('raise'), fall into the pdb debugger ('pdb') or ignore it ('ignore').
    We suggest to never use 'ignore' except in tests.

   If you encounter a warning, report it on `theano-dev`_.

.. _theano-dev: http://groups.google.com/group/theano-dev

.. attribute:: assert_no_cpu_op

    String value: 'ignore' or 'warn' or 'raise' or 'pdb'

    Default: 'ignore'

    If there is a CPU op in the computational graph, depending on its value;
    this flag can either raise a warning, an exception or stop the 
    compilation with pdb.

.. attribute:: on_shape_error

    String value: 'warn' or 'raise'

    Default: 'warn'

    When an exception is raised when inferring the shape of some apply
    node, either warn the user and use a default value ('warn'), or
    raise the exception ('raise').


.. attribute:: config.warn.ignore_bug_before

    String value: 'None', 'all', '0.3', '0.4', '0.4.1', '0.5', '0.6', '0.7'

    Default: '0.6'

    When we fix a Theano bug that generated bad results under some
    circumstances, we also make Theano raise a warning when it encounters
    the same circumstances again. This helps to detect if said bug
    had affected your past experiments, as you only need to run your
    experiment again with the new version, and you do not have to
    understand the Theano internal that triggered the bug. A better
    way to detect this will be implemented. See this `ticket
    <http://www.assembla.com/spaces/theano/tickets/514>`__.

    This flag allows new users not to get warnings about old bugs, that were
    fixed before their first checkout of Theano.
    You can set its value to the first version of Theano
    that you used (probably 0.3 or higher)

    `None` means that all warnings will be displayed.
    `all` means all warnings will be ignored.

    It is recommended that you put a version, so that you will see future
    warnings.
    It is also recommended you put this into your .theanorc, so this setting
    will always be used.

    This flag's value cannot be modified during the program execution.

.. attribute:: base_compiledir

    Default: On Windows: $LOCALAPPDATA\\Theano if $LOCALAPPDATA is defined,
    otherwise and on other systems: ~/.theano.

    This directory stores the platform-dependent compilation directories.

    This flag's value cannot be modified during the program execution.

.. attribute:: compiledir_format

    Default: "compiledir_%(platform)s-%(processor)s-%(python_version)s-%(python_bitwidth)s"

    This is a Python format string that specifies the subdirectory
    of ``config.base_compiledir`` in which to store platform-dependent
    compiled modules. To see a list of all available substitution keys,
    run ``python -c "import theano; print theano.config"``, and look
    for compiledir_format.

    This flag's value cannot be modified during the program execution.

.. attribute:: compiledir

    Default: ``config.base_compiledir``/``config.compiledir_format``

    This directory stores dynamically-compiled modules for a particular
    platform.

    This flag's value cannot be modified during the program execution.

.. attribute:: config.blas.ldflags

    Default: '-lblas'

    Link arguments to link against a (Fortran) level-3 blas
    implementation.  The default will test if '-lblas' work. If not,
    we will disable our c code for BLAS.

.. attribute:: config.experimental.local_alloc_elemwise_assert

    Bool value: either True or False

    Default: True

    When the local_alloc_optimization is applied, add an assert to highlight
    shape errors.

    Without such asserts this optimization could hide errors in the user code.
    We add the assert only if we can't infer that the shapes are equivalent.
    As such this optimization does not always introduce an assert in the graph.
    Removing the assert could speed up execution.

.. attribute:: config.cuda.root

    Default: $CUDA_ROOT or failing that, "/usr/local/cuda"

    A directory with bin/, lib/, include/ folders containing cuda utilities.

.. attribute:: config.dnn.conv.workmem

   Deprecated, use dnn.conv.algo_fwd.


.. attribute:: config.dnn.conv.workmem_bwd

   Deprecated, use dnn.conv.algo_bwd.

.. attribute:: config.dnn.conv.algo_fwd

  String value: ``small``, ``none``, ``large``, ``fft``, ``guess_once``,
                ``guess_on_shape_change``, ``time_once``,
                ``time_on_shape_change``.

   Default: ``small``

   3d convolution only support ``none``, ``guess_once``,
   ``guess_on_shape_change``, ``time_once``, ``time_on_shape_change``.


.. attribute:: config.dnn.conv.algo_bwd

   String value: ``none``, ``deterministic``, ``fft``, ``guess_once``,
                 ``guess_on_shape_change``, ``time_once``,
                 ``time_on_shape_change``.

   Default: ``none``

   3d convolution only support ``none``, ``guess_once``,
   ``guess_on_shape_change``, ``time_once``, ``time_on_shape_change``.

.. attribute:: config.gcc.cxxflags

    Default: ""

    Extra parameters to pass to gcc when compiling.  Extra include paths,
    library paths, configuration options, etc.

.. attribute:: cxx

    Default: Full path to g++ if g++ is present. Empty string otherwise.

    Indicates which C++ compiler to use. If empty, no C++ code is
    compiled.  Theano automatically detects whether g++ is present and
    disables C++ compilation when it is not.  On darwin systems (Mac
    OS X), it preferably looks for clang++ and uses that if available.

    We print a warning if we detect that no compiler is present. It is
    recommended to run with C++ compilation as Theano will be much
    slower otherwise.

    This can be any compiler binary (full path or not) but things may
    break if the interface is not g++-compatible to some degree.

.. attribute:: config.nvcc.fastmath

    Default: False

    If true, this will enable fastmath (`--use_fast_math
    <http://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/#options-for-steering-cuda-compilation>`_)
    mode for compiled cuda code which makes div and sqrt faster at the
    cost of precision.  This also disables support for denormal
    numbers.

.. attribute:: optimizer_excluding

    Default: ""

    A list of optimizer tags that we don't want included in the default Mode.
    If multiple tags, separate them by ':'.
    Ex: to remove the elemwise inplace optimizer(slow for big graph),
    use the flags: optimizer_excluding:inplace_opt, where
    inplace_opt is the name of that optimization.

    This flag's value cannot be modified during the program execution.

.. attribute:: optimizer_including

    Default: ""

    A list of optimizer tags that we want included in the default Mode.
    If multiple tags, separate them by ':'.

    This flag's value cannot be modified during the program execution.

.. attribute:: optimizer_requiring

    Default: ""

    A list of optimizer tags that we require for optimizer in the default Mode.
    If multiple tags, separate them by ':'.

    This flag's value cannot be modified during the program execution.

.. attribute:: optimizer_verbose

    Bool value: either True or False

    Default: False

    When True, we print on the stdout the optimization applied.

.. attribute:: nocleanup

    Bool value: either True or False

    Default: False

    If False, source code files are removed when they are not needed anymore.
    This means files whose compilation failed are deleted.
    Set to True to keep those files in order to debug compilation errors.

.. attribute:: compile

   This section contains attributes which influence the compilation of
   C code for ops.  Due to historical reasons many attributes outside
   of this section also have an influence over compilation, most
   notably 'cxx'.  This is not expected to change any time soon.

.. attribute:: config.compile.timeout

   Positive int value, default: :attr:`compile.wait` * 24

   Time to wait before an unrefreshed lock is broken and stolen.  This
   is in place to avoid manual cleanup of locks in case a process
   crashed and left a lock in place.

   The refresh time is automatically set to half the timeout value.

.. attribute:: config.compile.wait

   Positive int value, default: 5

   Time to wait between attempts at grabbing the lock if the first
   attempt is not successful. The actual time will be between
   :attr:`compile.wait` and :attr:`compile.wait` * 2 to avoid a
   crowding effect on lock.

.. attribute:: DebugMode

    This section contains various attributes configuring the behaviour
    of mode :class:`~debugmode.DebugMode`. See directly this section
    for the documentation of more configuration options.

.. attribute:: config.DebugMode.check_preallocated_output

    Default: ``''``

    A list of kinds of preallocated memory to use as output buffers for
    each Op's computations, separated by ``:``. Implemented modes are:

    * ``"initial"``: initial storage present in storage map
      (for instance, it can happen in the inner function of Scan),
    * ``"previous"``: reuse previously-returned memory,
    * ``"c_contiguous"``: newly-allocated C-contiguous memory,
    * ``"f_contiguous"``: newly-allocated Fortran-contiguous memory,
    * ``"strided"``: non-contiguous memory with various stride patterns,
    * ``"wrong_size"``: memory with bigger or smaller dimensions,
    * ``"ALL"``: placeholder for all of the above.

    In order not to test with preallocated memory, use an empty string, ``""``.

.. attribute:: config.DebugMode.check_preallocated_output_ndim

    Positive int value, default: 4.

    When testing with "strided" preallocated output memory, test
    all combinations of strides over that number of (inner-most)
    dimensions. You may want to reduce that number to reduce memory or
    time usage, but it is advised to keep a minimum of 2.

.. attribute:: config.DebugMode.warn_input_not_reused

    Bool value, default: True

    Generate a warning when the destroy_map or view_map tell that an op work
    inplace, but the op did not reuse the input for its output.

.. attribute:: config.NanGuardMode.nan_is_error

    Bool value, default: True

    Controls whether NanGuardMode generates an error when it sees a nan.

.. attribute:: config.NanGuardMode.inf_is_error

    Bool value, default: True

    Controls whether NanGuardMode generates an error when it sees an inf.

.. attribute:: config.NanGuardMode.nan_is_error

    Bool value, default: True

    Controls whether NanGuardMode generates an error when it sees a
    big value (>1e10).

.. attribute:: numpy

    This section contains different attributes for configuring numpy's
    behaviour, described by `numpy.seterr
    <http://docs.scipy.org/doc/numpy/reference/generated/numpy.seterr.html>`__.

.. attribute:: config.numpy.seterr_all

    String Value: ``'ignore'``, ``'warn'``, ``'raise'``, ``'call'``,
    ``'print'``, ``'log'``, ``'None'``

    Default: ``'ignore'``

    Set the default behaviour described by `numpy.seterr
    <http://docs.scipy.org/doc/numpy/reference/generated/numpy.seterr.html>`__.

    ``'None'`` means that numpy's default behaviour will not be changed (unless
    one of the other `config.numpy.seterr_*` overrides it), but this behaviour
    can change between numpy releases.

    This flag sets the default behaviour for all kinds of floating-pont
    errors, and it can be overriden for specific errors by setting one
    (or more) of the flags below.

    This flag's value cannot be modified during the program execution.

.. attribute:: config.numpy.seterr_divide

    String Value: ``'None'``, ``'ignore'``, ``'warn'``, ``'raise'``,
    ``'call'``, ``'print'``, ``'log'``

    Default: ``'None'``

    Sets numpy's behavior for division by zero. ``'None'`` means using the
    default, defined by config.numpy.seterr_all.

    This flag's value cannot be modified during the program execution.

.. attribute:: config.numpy.seterr_over

    String Value: ``'None'``, ``'ignore'``, ``'warn'``, ``'raise'``,
    ``'call'``, ``'print'``, ``'log'``

    Default: ``'None'``

    Sets numpy's behavior for floating-point overflow. ``'None'`` means
    using the default, defined by config.numpy.seterr_all.

    This flag's value cannot be modified during the program execution.

.. attribute:: config.numpy.seterr_under

    String Value: ``'None'``, ``'ignore'``, ``'warn'``, ``'raise'``,
    ``'call'``, ``'print'``, ``'log'``

    Default: ``'None'``

    Sets numpy's behavior for floating-point underflow. ``'None'`` means
    using the default, defined by config.numpy.seterr_all.

    This flag's value cannot be modified during the program execution.

.. attribute:: config.numpy.seterr_invalid

    String Value: ``'None'``, ``'ignore'``, ``'warn'``, ``'raise'``,
    ``'call'``, ``'print'``, ``'log'``

    Default: ``'None'``

    Sets numpy's behavior for invalid floating-point operation. ``'None'``
    means using the default, defined by :attr:`config.numpy.seterr_all`.

    This flag's value cannot be modified during the program execution.

.. attribute:: compute_test_value

    String Value: ``'off'``, ``'ignore'``, ``'warn'``, ``'raise'``.

    Default: ``'off'``

    Setting this attribute to something other than ``'off'`` activates a
    debugging mechanism, where Theano executes the graph on-the-fly, as it is
    being built. This allows the user to spot errors early on (such as
    dimension mis-match), **before** optimizations are applied.

    Theano will execute the graph using the Constants and/or shared variables
    provided by the user. Purely symbolic variables (e.g. x = T.dmatrix()) can be
    augmented with test values, by writing to their ``'tag.test_value'``
    attribute (e.g. x.tag.test_value = numpy.random.rand(5,4)).

    When not ``'off'``, the value of this option dictates what happens when
    an Op's inputs do not provide appropriate test values:

        - ``'ignore'`` will silently skip the debug mechanism for this Op
        - ``'warn'`` will raise a UserWarning and skip the debug mechanism for
          this Op
        - ``'raise'`` will raise an Exception

.. attribute:: compute_test_value_opt

   As ``compute_test_value``, but it is the value used during Theano
   optimization phase. Theano user's do not need to use this. This is
   to help debug shape error in Theano optimization.

.. attribute:: reoptimize_unpickled_function

    Bool value, default: True

    Theano users can use the standard python pickle tools to save a compiled
    theano function. When pickling, both graph before and after the optimization
    are saved, including shared variables. When set to True, the graph is
    reoptimized when being unpickled. Otherwise, skip the graph optimization and
    use directly the optimized graph.

.. attribute:: exception_verbosity

    String Value: ``'low'``, ``'high'``.

    Default: ``'low'``

    If ``'low'``, the text of exceptions will generally refer to apply nodes
    with short names such as ``'Elemwise{add_no_inplace}'``. If ``'high'``,
    some exceptions will also refer to apply nodes with long descriptions like:

    ::

        A. Elemwise{add_no_inplace}
              B. log_likelihood_v_given_h
              C. log_likelihood_h


.. attribute:: config.cmodule.warn_no_version

    Bool value, default: False

    If True, will print a warning when compiling one or more Op with C
    code that can't be cached because there is no c_code_cache_version()
    function associated to at least one of those Ops.

.. attribute:: config.cmodule.mac_framework_link

    Bool value, default: False

    If set to True, breaks certain MacOS installations with the infamous
    Bus Error.

.. attribute:: config.cmodule.remove_gxx_opt

    Bool value, default: False

    If True, will remove the -O* parameter passed to g++.
    This is useful to debug in gdb modules compiled by Theano.
    The parameter -g is passed by default to g++.

.. attribute:: config.cmodule.compilation_warning

    Bool value, default: False

    If True, will print compilation warnings.

.. attribute:: config.cmodule.preload_cache

    Bool value, default: False

    If set to True, will preload the C module cache at import time
