* Adding more datatypes support in datavec-python
* Using numpy C API for creating numpy arrays
* Adding parameterized tests
* Adding support for BFLOAT16 (by converting it to FLOAT)
* Cleanup
* Using casting instead of creating an array
* Giving out a warning while casting array from BFLOAT16 to FLOAT
* Add syncToPrimary and syncToSpecial methods to BaseDataBuffer
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* Python exec: sync to host before passing pointers
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* Added copyright header
* use np api (#267)
* python exec / numpy - check object type before cast (#268)
* use np api
* verify object before cast
* fix cong
* cuda fix
* inplace test + tiny fix
* more test
* fix double alloc
* rem tags
* fix cuda check
* Fix implicit CUDA dependency in datavec-python tests; remove new method, add test
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Co-authored-by: Alex Black <blacka101@gmail.com>
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* Revive and start updating DL4J integration tests
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* Add SameDiff support - first pass
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* SameDiff test case generation
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* SameDiff integration tests polishing
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* More SameDiff integration test fixes
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* Final polish
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* Small test tweak
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* initial set of include changes
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* one more tweak
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* few more rearrangements
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* few more rearrangements
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* few more rearrangements
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* cuda includes rearrangements
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* java update
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* = namespace changed to sd
- few CMake variables renamed with SD_ prefix
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* java update
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* LoopKind minor fix
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* few more changes
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* few more changes
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* few more changes
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* sanitizer is optional now
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* dev tests updated
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* few more changes
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* last update
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* java update
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* - profiling of concat op (both cuda and cpu)
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* better comparison for large concat
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* - further improving of concat op
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* some loggin
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* - add possibility to verify presence of trailing unities in shape and set strides/ews correspondingly
- restrict second simple case in concat op to c order only
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* - move concat op to specials_single.cpp file
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* - get rid of second concat op declaration in transforms.cpp file
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* special workaround methods for DataBuffer.write
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* one test removed
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* more of unsynced
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* missing asLong for BaseCudaDataBuffer
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* initial commit
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* - one more test for OneHot with dtype
- one more signature in Nd4j
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* ones_as/zeros_as now accept dtype
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* one more test
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* - more updates for configurable data types
- ones_as/zeros_as java side + tests
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* few c++ tests fixed
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* few more changes around DArgs
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* missing alloc validation in RandomGenerator for CUDA
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* set error message if rng alloc failed
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* check for error code during RNG creation in java
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* nd4j-aeron profiles
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* nd4j-aeron profiles
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* skip one long test
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* skip one long test
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* kryo profile
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* few more profiles
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* few more profiles
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* few more profiles
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* Add maven profile + base tests methods for integration tests
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* Switch from system property to environment variable; seems more reliable in intellij
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* Add nd4j-common-tests module, and common base test; cleanup
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* Ensure all ND4J tests extend BaseND4JTest
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* Test spam reduction, import fix
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* Add test logging to nd4j-aeron
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* Fix unintended change
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* Reduce sprint test log spam
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* More test spam cleanup
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* Significantly speed up TSNE tests
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* W2V iterator test unit/integration split
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* More NLP test speedups
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* Avoid debug/verbose mode leaking between tests
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* test tweak
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* Arbiter extends base DL4J test
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* Arbiter test speedup
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* nlp-uima test speedup
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* More test speedups
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* Fix ND4J base test
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* Few small ND4J test speed improvements
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* DL4J tests speedup
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* More tweaks
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* Even more test speedups
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* More tweaks
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* Various test fixes
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* More test fixes
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* Add ability to specify number of threads for C++ ops in BaseDL4JTest and BaseND4JTest
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* nd4j-aeron test profile fix for CUDA
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* initial commit
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* one file
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* few more includes
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* m?
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* const
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* cudnn linkage in tests
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* culibos
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* static reminder
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* platform engine tag
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* HAVE_CUDNN moved to config.h.in
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* include
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* include
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* skip cudnn handle creation if there's not cudnn
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* meh
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* target device in context
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* platform engines
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* platform engines
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* allow multiple -h args
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* allow multiple -h args
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* move mkldnn out of CPU block
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* link to mkldnn on cuda
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* less prints
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* minor tweaks
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* next step
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* conv2d NCHW draft
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* conv2d biasAdd
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* test for MKL/CUDNN combined use
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* - provide additional code for conv2d ff based on cudnn api, not tested yet
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* - further work on conv2d helper based on using cudnn api
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* - fixing several cuda bugs which appeared after cudnn lib had been started to use
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* - implementation of conv2d backprop op based on cudnn api
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* - implementaion of conv3d and conv3d_bp ops based on cudnn api
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* - bugs fixing in conv3d/conv3d_bp ops (cudnn in use)
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* - implementation of depthwiseConv2d (ff/bp) op based on cudnn api
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* - implementation of batchnorm ff op based on cudnn api
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* - disable cudnn batchnorm temporary
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* - add minor change in cmake
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* engine for depthwise mkldnn
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* couple of includes
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* - provide permutation to cudnn batchnorm ff when format is NHWC
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* lgamma fix
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* - eliminate memory leak in two tests
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* Allow scalar op result array auto allocation
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* Don't swallow underlying exception for calculateOutputShape execution failures
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* Ignore for known keras failure
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* Refactored resize images ops to use TF-like bool args as input.
* Refactored helpers for cpu implementation of resize_bilinear and resize_nearest_neighbor ops.
* Refactored cuda implementation for image.resize_bilinear and image.resize_nearest_neighbor ops helpers.
* Refactored nearest_neighbor resize op.
* Added a pair of tests for special case of resize_bilinear algorithm.
* Fixed issue with resize_bilinear op.
* Refactored cpu implementation for helpers with resize_nearest_neighbor op.
* Final fixed for resize ops to conform TF v.1.5
* Refactored cuda helpers for resize_neares_neighbor op.
* Fixed resize_bilinear to accept proper data.
* Fixed issue with non-float input for resize_bilinear op.
* Refactored cuda helper for resize_bilinear to proper process non-float inputs.
* Added tests for resize_bilinear to int inputs.
* Fixed ResizeBilinear wrapper
* Tests fixed
* Fixed float and bool constant to avoid overflow for some kind of compilers.
* Corrected float constants with float data type.
* Added f suffix for float constants.
* Corrected float constant to avoid overflow with initializing lists.
* Corrected float initializing list with float input.
* Corrected bool constant with initalizing list.
* Corrected float and bool values with initializing lists.
* Fixed wrong constant.
* Fixed issue with 1x1 input picture for resize.
* ResizeBilinear default values on import fix
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* Corrected input checking and tests for bitcast op.
* Fixed an issue with non_max_suppression form generation and processing with score threshold given.
* Fixed bilinear resize kernel and tests.
* push for Serhii
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* Added test for nearest_neighbor resize with int input.
* Added data type check for input/output match.
* Eliminate error in macros.
* Improved output message for type checking.
* Fixed input/output types for op.
* Eliminated waste logging.
* Refactored resize_bilinear helper for multithreading for cpu platform.
* Cosmetic changes only.
* Fixed error for string substitution.
* Skip test for cbow_batch with cuda.
* fix for resizeNearestNeighbor output dtype
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* Refactored non_max_suppression helper.
* Refactored shape generation and input handling.
* Added additional test.
* ND4J: Fix OpenBLAS loading for nd4j-native and remove bundling of OpenMP
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* Bundle back libgomp.so.1 for Linux
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* Readd preload directories for ARM
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* Add back preloads for GCC on Windows
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* Add explicit preloadpaths for ARM and POWER to bundle correct library
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* - create op
- skip exec for empty inputs for non_max_suppression
- EmptyHandling idea
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* Create op and mapping for it
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* - get rid of some copy procedures in mmulHelper ops
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* - further work on embedding cuda api for batched gemm (cublasGemmBatchedEx) in our mmulHelper class
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* - further work on cuda batched gamm api
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* - write own cuda kernel performing batched gemm
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* missing include in MmulHelper
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* - forgot to keep in code previous correct kernels for mmulNxN, since it may happen that new onw will fail for some reason in future
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* disable old tensordot
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* - rewrite cuda kernels for usualGemm and usualGemv
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* - profiling mmul helpers
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* - prints to check shapes were added
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* - correct type of output array Cin mmulNxN
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* - take into account possible nans in C array
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* slightly change numThreads message
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* - make corrections in accordance to given notes in pr review
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* one range test
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* few Context convenience singatures
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* one more range test
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* "range" "fix"
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* adjuct_contrast_v2 now allows scale factor to be provided via input_variable
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* adjust_contrast now allows scale factor as variable too
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* bitcast shape tests
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* BitCast import dtype added
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* few more BitCast signatures
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* - platform helpers can be disabled on per-op basis now via Context::allowHelpers
- java has access to it as well
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* global platform-helpers trigger
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* few signatures renamed
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* - few new env variables to follow
- maxThreads/masterThreads differentiation
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* Javadoc update
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Shams Ul Azeem
raver119
Yurii Shyrma
Alex Black
shugeo
Fariz Rahman
Samuel Audet
Alexander Stoyakin
Robert Altena