* First version of nGraph Python API documentation pages.

* Remove use of (`) apostrophes since they generate Sphinx warnings.

* Fix submodule rendering in Sphinx PythonAPI docs

* Add updated index and conf py files

* Updated version of Python API doc.

* Better description for the autosummary

cleanup some enable flags in cmake. add flags to optionally disable building unit tests and tool (#917)

* simplifier for sum

* add comment, remove visualization passes

* Fix ngraph ONNX install directions

* Remove make j from list of install commands

* Added cudnn batch norm operation to GPU transformer.
Brought batchnorm tests out of cpu_tests and into
backend_tests. Need to add JIRA ticket for interpreter
SKIPS.

* CUDNN batchnorm is implemented. In the ForwardTraining branch
CUDNN seems to calculate the batch mean correctly but the batch variance incorrectly.
Currently the batchnorm output and mean are calculated correctly for tests:
* GPU.batchnorm_fprop_b2c2h3w3_mean_var
* GPU.batchnorm_fprop_b1c2h2w2
* GPU.batchnorm_fprop_b2c2h2w1
but the variance calculated for the batches in these tests is incorrectly calculated by CUDNN.

Also added an additional test and cleaned up some of the old tests.

* MKLDNN internally utilizes the biased estimate of the population variance
and the tests have been crafted to suit MKLDNN. According to the original
batchnorm publication (https://arxiv.org/pdf/1502.03167v3.pdf), population
(unbiased) statistics should be used for inference, and mini-batch (biased)
statistics should be used training (forward/backward). For the variance this
means utlitizing the following equations, respectively:

  (biased)   Var[X] = 1/m * Sum_i(x_i-mu)^2      :: used in training
  (unbiased) Var[X] = 1/(m-1) * Sum_i(x_i-mu)^2  :: used in inference

  s.t. x_i are elements of X and m = N*D*H*W.

For large batch sizes in inference this may not impact convergence as m >> 1,
but for small batch sizes it will. CUDNN internally utilizes the unbiased
variance.

Changes:
* Added Multiply op to Forward pass of batchnorm to convert
  the unbiased variance to a biased one. The op utilizes the
  blending scaling factors to apply the bias factor.
* Adds emission for the BatchNormBackprop kernel and cleans up
  the emitter implementation.

* Added hashing to cudnn::batchnorm op.

* Formatting.

* Changed hashing of epsilon in cudnn batchnorm.

* Remove implicit conversion and default case in switch for bn.

* Added skips for IE transformer on batchnorm.

* add cudnn include path to compiler.cpp

* seperate two path

* PR #892 and #825 which were recently merged both forgot skips for the GPU backend.
Adding them in as they are unimplemented ops.

* The allocation and deletion of primitives was occuring in seperate
translation units with raw c pointers. Because of this, it was not
clear that these were being freed appropriate, nor did it indicate
ownership of the pointers.

In this commit these raw pointers have been converted over to
std::unique_ptrs such that the construction/destruction is managed
automatically. Furthermore, GPUPrimitiveEmitter::insert now only
takes an r-value reference, requiring move-semantics to indicate
that when inserting a primitive, the GPUPrimitiveEmitter takes
ownership of the pointer.

All instances of primitive creation have been modified.

* CUDNN_SAFE_CALL

* Removed redundant comment and made variable names more verbose.

* Change from conditionals to case-switch in pooling to conform to
batchnorm per @fengleitian's suggestion.

* add cudnn include path to compiler.cpp

* seperate two path

* Skipping one_hot tests for CPU as
CI is failing. JIRA bug report: https://jira01.devtools.intel.com/browse/NGRAPH-1682.

* infra for algebraic simplification and simplifications for Add and Multiply (including broadcast consts)

* add tests, fix bugs

* negative tests, 0*0, 0*1, 0+0

* possible fix for 0*1

* remove stale test

* fix merge comp errors

* fix comp errors

* get all ops working

* enable autodiff tests for IE backend

* Add logical-and, logical-or ops

* Restore accidentally-deleted test

* add new ops to IE backend

* Enable users to request default/row-major layouts on result nodes

* copy default layout attribute when copying the result ops

* Result nodes cannot be replaced. use direct graph manipulation instead

* Add unit test to verify default layouts on result nodes when requested

* any -> skip

* run style check

* add CUDNN_SAFE_CALL and CUBLAS_SAFE_CALL

* using sstream

* passed all unit test

* format error msg

* fix ( ) bug

* fix convolution bug by retain primary context for device
* add release to destructor

* ie backend and manager with passing unit tests except for select/function

* fix function_call and select

* simplify implemenation by removing support for convert and select

* remove manager

This better supports non-ubuntu/debian systems.

* Support Concat with mkldnn (two inputs)

* Support concat with mkldnn (multiple inputs)

* Address feedback

* Remove unused variable

* Allow rank two tensor to mkldnn for concat & add a test case for 2D inputs

* Add mkldnn_any layout to concat

* Make API changes to get consistent with master

executables not shared libraries. This change will remove the following
warning.
ld: warning: -pie being ignored. It is only used when linking a main executable

Force test to fail if any env fails.

* Move runtime::Manager functionality into runtime::Backend

* Remove unused files

* remove obsolete function

* remove obsolete classes

* Remove usage of CMAKE_MAKE_PROGRAM as it slows down parallel build

* Make make properly propagate to child and add back targeted build.

* Revert "Make make properly propagate to child and add back targeted build."

This reverts commit b4b1d8db0f0d42850e53d4e0f773261c292ccaf6.

* * cuda_emitter::build_pad now utilizes pad_value.

* Added TypeInfo class for dispatching c-type information from the underlying ngraph element::Type.
  Adjusted test to use all_close when comparing floating point values (max_pool_2d_1channel_1image_overpadded).

* Refactored max_pool_1d into cuda_emitter so that numeric_limits<c_type>::lowest() could be used for initial max value.
Test max_pool_2d_1channel_1image_padded_negative_values now enabled and passes.

* Removed old function and switch to size_t to match ngraph.

* Added virtual dtor.

* Adding support for interior padding. All op::Pad functionality is now included.

* More info in runtime_error for checking of tensor dimensions. Removed commented code.

* CPU weight fusion initial version

* add tests for weight_fusion

* address @jbobba's feedback

* before cleaning up convolution_weight_optimization.cpp

* clean up, rename, fix perms, fix format

* reenable unit test

- Set default input axes order.

* remove tensor_call from backends

* remove obsolete methods

* CMake: Allow build target arch to be overridden

* Add DNGRAPH_TARGET_ARCH option to install docs

* get_input_op -> get_argument

* more replacing

* more replacing2

* Update default paths in setup.py

* Update defaults arguments in tox