text:
Compiling TF 2.6 in debug mode on Windows env.

System information

Have I written custom code (as opposed to using a stock example script provided in TensorFlow): No
OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Windows 10
Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device: No
TensorFlow installed from (source or binary): source
TensorFlow version (use command below): r2.6.0
Python version: 3.8
Bazel version (if compiling from source): 3.7.2
GCC/Compiler version (if compiling from source):
CUDA/cuDNN version: 11.3/8
GPU model and memory: GTX 1060

Describe the current behavior
I try to compile my example program that utilises TF:
// tensorflow/cc/example/example.cc

#include "tensorflow/cc/client/client_session.h"
#include "tensorflow/cc/ops/standard_ops.h"
#include "tensorflow/core/framework/tensor.h"

int main() {
  using namespace tensorflow;
  using namespace tensorflow::ops;
  Scope root = Scope::NewRootScope();
  // Matrix A = [3 2; -1 0]
  auto A = Const(root, { {3.f, 2.f}, {-1.f, 0.f} });
  // Vector b = [3 5]
  auto b = Const(root, { {3.f, 5.f} });
  // v = Ab^T
  auto v = MatMul(root.WithOpName("v"), A, b, MatMul::TransposeB(true));
  std::vector<Tensor> outputs;
  ClientSession session(root);
  // Run and fetch v
  TF_CHECK_OK(session.Run({v}, &outputs));
  // Expect outputs[0] == [19; -3]
  LOG(INFO) << outputs[0].matrix<float>();
  return 0;
}

bazel build --local_ram_resources=HOST_RAM*.7 --config=dbg --config=windows --copt=/FS --copt=-nvcc_options=disable-warnings --linkopt=/DEBUG:FULL --strip=never --define=no_tensorflow_py_deps=true -s --verbose_explanations --subcommands=pretty_print //tensorflow/cc/example:example

The compilation fails with exception:
LINK : warning LNK4286: symbol 'TF_DeleteShapeHandle' defined in 'ops.lo.lib(ops.obj)' is imported by 'merge_summary_op_lib.lo.lib(merge_summary.obj)'
LINK : warning LNK4286: symbol 'TF_DeleteShapeHandle' defined in 'ops.lo.lib(ops.obj)' is imported by 'summary_op_lib.lo.lib(summary.obj)'
LINK : warning LNK4217: symbol 'TF_DeleteDimensionHandle' defined in 'ops.lo.lib(ops.obj)' is imported by 'bitcast_op_lib.lo.lib(bitcast.obj)' in function '"void __cdecl ComputeNewShape(struct TF_ShapeInferenceContext *,struct TF_ShapeHandle *,enum TF_DataType,enum TF_DataType,struct TF_Status *)" (?ComputeNewShape@@YAXPEAUTF_ShapeInferenceContext@@PEAUTF_ShapeHandle@@W4TF_DataType@@2PEAUTF_Status@@@Z)'
LINK : warning LNK4217: symbol 'TF_ShapeInferenceContextScalar' defined in 'ops.lo.lib(ops.obj)' is imported by 'histogram_summary_op_lib.lo.lib(histogram_summary.obj)' in function '"void __cdecl histogram_summary_shape_inference_fn(struct TF_ShapeInferenceContext *,struct TF_Status *)" (?histogram_summary_shape_inference_fn@@YAXPEAUTF_ShapeInferenceContext@@PEAUTF_Status@@@Z)'
LINK : warning LNK4217: symbol 'TF_ShapeInferenceContextScalar' defined in 'ops.lo.lib(ops.obj)' is imported by 'merge_summary_op_lib.lo.lib(merge_summary.obj)' in function '"void __cdecl merge_summary_shape_inference_fn(struct TF_ShapeInferenceContext *,struct TF_Status *)" (?merge_summary_shape_inference_fn@@YAXPEAUTF_ShapeInferenceContext@@PEAUTF_Status@@@Z)'
LINK : warning LNK4217: symbol 'TF_ShapeInferenceContextScalar' defined in 'ops.lo.lib(ops.obj)' is imported by 'summary_op_lib.lo.lib(summary.obj)' in function '"void __cdecl scalar_summary_shape_inference_fn(struct TF_ShapeInferenceContext *,struct TF_Status *)" (?scalar_summary_shape_inference_fn@@YAXPEAUTF_ShapeInferenceContext@@PEAUTF_Status@@@Z)'
LINK : warning LNK4217: symbol 'TF_NumDims' defined in 'tf_tensor.lib(tf_tensor.obj)' is imported by 'tensor_shape_utils.lib(tensor_shape_utils.obj)' in function '"class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > __cdecl tensorflow::ShapeDebugString(struct TF_Tensor *)" (?ShapeDebugString@tensorflow@@YA?AV?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@PEAUTF_Tensor@@@Z)'
LINK : warning LNK4217: symbol 'TF_Dim' defined in 'tf_tensor.lib(tf_tensor.obj)' is imported by 'tensor_shape_utils.lib(tensor_shape_utils.obj)' in function '"class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > __cdecl tensorflow::ShapeDebugString(struct TF_Tensor *)" (?ShapeDebugString@tensorflow@@YA?AV?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@PEAUTF_Tensor@@@Z)'
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,int,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<int const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<int,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@H$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBH$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@H$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,signed char>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@C@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,unsigned char,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<unsigned char const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<unsigned char,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@E$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBE$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@E$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,unsigned char>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@E@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,class tensorflow::Variant,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<class tensorflow::Variant const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<class tensorflow::Variant,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@VVariant@tensorflow@@$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBVVariant@tensorflow@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@VVariant@tensorflow@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,class tensorflow::Variant>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@VVariant@tensorflow@@@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,class tensorflow::ResourceHandle,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<class tensorflow::ResourceHandle const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<class tensorflow::ResourceHandle,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@VResourceHandle@tensorflow@@$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBVResourceHandle@tensorflow@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@VResourceHandle@tensorflow@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,class tensorflow::ResourceHandle>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@VResourceHandle@tensorflow@@@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,class tensorflow::tstring,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<class tensorflow::tstring const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<class tensorflow::tstring,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@Vtstring@tensorflow@@$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBVtstring@tensorflow@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@Vtstring@tensorflow@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,class tensorflow::tstring>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@Vtstring@tensorflow@@@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,bool,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<bool const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<bool,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@_N$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CB_N$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@_N$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,bool>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@_N@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,class std::complex<double>,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<class std::complex<double> const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<class std::complex<double>,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@V?$complex@N@std@@$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBV?$complex@N@std@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@V?$complex@N@std@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,class std::complex<double> >::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@V?$complex@N@std@@@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,class std::complex<float>,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<class std::complex<float> const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<class std::complex<float>,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@V?$complex@M@std@@$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBV?$complex@M@std@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@V?$complex@M@std@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,class std::complex<float> >::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@V?$complex@M@std@@@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,double,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<double const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<double,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@N$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBN$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@N$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,double>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@N@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,float,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<float const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<float,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@M$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBM$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@M$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,float>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@M@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,struct Eigen::bfloat16,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<struct Eigen::bfloat16 const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<struct Eigen::bfloat16,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@Ubfloat16@2@$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBUbfloat16@Eigen@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@Ubfloat16@Eigen@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,struct Eigen::bfloat16>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@Ubfloat16@2@@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,struct Eigen::half,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<struct Eigen::half const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<struct Eigen::half,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@Uhalf@2@$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBUhalf@Eigen@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@Uhalf@Eigen@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,struct Eigen::half>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@Uhalf@2@@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,signed char,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<signed char const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<signed char,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@C$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBC$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@C$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,signed char>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@C@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,short,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<short const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<short,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@F$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBF$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@F$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,short>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@F@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,unsigned short,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<unsigned short const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<unsigned short,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@G$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBG$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@G$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,unsigned short>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@G@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,unsigned int,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<unsigned int const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<unsigned int,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@I$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBI$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@I$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,unsigned int>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@I@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,__int64,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<__int64 const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<__int64,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@_J$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CB_J$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@_J$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,__int64>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@_J@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(spacetodepth_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::SpaceToDepthOpFunctor<struct Eigen::ThreadPoolDevice,unsigned __int64,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<unsigned __int64 const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<unsigned __int64,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$SpaceToDepthOpFunctor@UThreadPoolDevice@Eigen@@_K$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CB_K$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@_K$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::SpaceToDepthOp<struct Eigen::ThreadPoolDevice,unsigned __int64>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$SpaceToDepthOp@UThreadPoolDevice@Eigen@@_K@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,int,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<int const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<int,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@H$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBH$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@H$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,signed char>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@C@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,class tensorflow::Variant,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<class tensorflow::Variant const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<class tensorflow::Variant,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@VVariant@tensorflow@@$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBVVariant@tensorflow@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@VVariant@tensorflow@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,class tensorflow::Variant>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@VVariant@tensorflow@@@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,class tensorflow::ResourceHandle,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<class tensorflow::ResourceHandle const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<class tensorflow::ResourceHandle,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@VResourceHandle@tensorflow@@$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBVResourceHandle@tensorflow@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@VResourceHandle@tensorflow@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,class tensorflow::ResourceHandle>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@VResourceHandle@tensorflow@@@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,class tensorflow::tstring,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<class tensorflow::tstring const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<class tensorflow::tstring,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@Vtstring@tensorflow@@$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBVtstring@tensorflow@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@Vtstring@tensorflow@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,class tensorflow::tstring>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@Vtstring@tensorflow@@@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,bool,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<bool const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<bool,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@_N$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CB_N$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@_N$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,bool>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@_N@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,class std::complex<double>,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<class std::complex<double> const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<class std::complex<double>,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@V?$complex@N@std@@$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBV?$complex@N@std@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@V?$complex@N@std@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,class std::complex<double> >::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@V?$complex@N@std@@@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,class std::complex<float>,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<class std::complex<float> const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<class std::complex<float>,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@V?$complex@M@std@@$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBV?$complex@M@std@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@V?$complex@M@std@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,class std::complex<float> >::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@V?$complex@M@std@@@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,double,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<double const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<double,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@N$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBN$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@N$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,double>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@N@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,float,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<float const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<float,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@M$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBM$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@M$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,float>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@M@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,struct Eigen::bfloat16,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<struct Eigen::bfloat16 const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<struct Eigen::bfloat16,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@Ubfloat16@2@$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBUbfloat16@Eigen@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@Ubfloat16@Eigen@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,struct Eigen::bfloat16>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@Ubfloat16@2@@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,struct Eigen::half,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<struct Eigen::half const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<struct Eigen::half,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@Uhalf@2@$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBUhalf@Eigen@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@Uhalf@Eigen@@$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,struct Eigen::half>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@Uhalf@2@@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,signed char,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<signed char const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<signed char,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@C$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBC$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@C$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,signed char>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@C@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,unsigned char,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<unsigned char const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<unsigned char,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@E$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBE$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@E$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,unsigned char>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@E@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,short,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<short const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<short,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@F$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBF$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@F$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,short>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@F@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,unsigned short,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<unsigned short const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<unsigned short,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@G$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBG$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@G$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,unsigned short>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@G@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,unsigned int,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<unsigned int const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<unsigned int,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@I$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CBI$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@I$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,unsigned int>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@I@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,__int64,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<__int64 const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<__int64,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@_J$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CB_J$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@_J$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,__int64>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@_J@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
depth_space_ops.lo.lib(depthtospace_op.obj) : error LNK2019: unresolved external symbol "public: void __cdecl tensorflow::functor::DepthToSpaceOpFunctor<struct Eigen::ThreadPoolDevice,unsigned __int64,1>::operator()(struct Eigen::ThreadPoolDevice const &,class Eigen::TensorMap<class Eigen::Tensor<unsigned __int64 const ,4,1,__int64>,16,struct Eigen::MakePointer>,int,class Eigen::TensorMap<class Eigen::Tensor<unsigned __int64,4,1,__int64>,16,struct Eigen::MakePointer>)" (??R?$DepthToSpaceOpFunctor@UThreadPoolDevice@Eigen@@_K$00@functor@tensorflow@@QEAAXAEBUThreadPoolDevice@Eigen@@V?$TensorMap@V?$Tensor@$$CB_K$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@HV?$TensorMap@V?$Tensor@_K$03$00_J@Eigen@@$0BA@UMakePointer@2@@4@@Z) referenced in function "public: virtual void __cdecl tensorflow::DepthToSpaceOp<struct Eigen::ThreadPoolDevice,unsigned __int64>::Compute(class tensorflow::OpKernelContext *)" (?Compute@?$DepthToSpaceOp@UThreadPoolDevice@Eigen@@_K@tensorflow@@UEAAXPEAVOpKernelContext@2@@Z)
bazel-out\x64_windows-dbg\bin\tensorflow\cc\example\example.exe : fatal error LNK1120: 36 unresolved externals
Target //tensorflow/cc/example:example failed to build
INFO: Elapsed time: 5521.801s, Critical Path: 3434.58s
INFO: 2724 processes: 112 internal, 2612 local.
FAILED: Build did NOT complete successfully

Hi @jvishnuvardhan ,Could you please look at this issue!

The error comes from 2 files:
tensorflow/core/kernels/depthtospace_op.cc and tensorflow/core/kernels/spacetodepth_op.cc
I think it's related to another bug report https://github.com/tensorflow/tensorflow/issues/41118

depthtospace_op.cc
I added #if GOOGLE_CUDA || TENSORFLOW_USE_ROCM
and #endif  // GOOGLE_CUDA || TENSORFLOW_USE_ROCM
between lines 115-130, because this code is related only to GPU

Same fix for tensorflow/core/kernels/spacetodepth_op.cc
for lines 129-149 & 155-156

The compilation passed.

Another reference: https://stackoverflow.com/a/63339256/1100913

By the way, the issue comes from the fact that I build tensorflow without GPU support.

@mihaimaruseac @jvishnuvardhan A general question: Are you a part of the TF dev team?

I left TF dev team 2 months ago

@mohantym I just said I left TF, please don't reassing me

Sorry @mihaimaruseac ! I accidentally assigned you while assigning myself.

Hi @AndreyOrb !
Could you provide a new example to replicate this issue. The path ''//tensorflow/cc/example:example " does not exist anymore in 2.9 version or later.
Thank you!

authors: AndreyOrb AndreyPlotkinOr mihaimaruseac mohantym


text:
TensorFlow 2.7 does not detect CUDA installed through conda

System information

Have I written custom code (as opposed to using a stock example script provided in TensorFlow): no
OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Ubuntu 18.04
Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device: N/A
TensorFlow installed from (source or binary): binary
TensorFlow version (use command below): 2.7.0
Python version: 3.8
Bazel version (if compiling from source): N/A
GCC/Compiler version (if compiling from source): N/A
CUDA/cuDNN version: 11.2/8.1
GPU model and memory: GTX 2080Ti

Describe the current behavior
After installing cuda/cudnn through conda (conda install cudatoolkit=11.2 cudnn=8.1), TensorFlow 2.7 reports that it cannot find the cuda libraries.
2021-11-08 14:49:16.412959: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2021-11-08 14:49:16.413006: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
2021-11-08 14:49:22.640508: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2021-11-08 14:49:22.640617: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublas.so.11'; dlerror: libcublas.so.11: cannot open shared object file: No such file or directory
2021-11-08 14:49:22.640698: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublasLt.so.11'; dlerror: libcublasLt.so.11: cannot open shared object file: No such file or directory
2021-11-08 14:49:22.640776: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcufft.so.10'; dlerror: libcufft.so.10: cannot open shared object file: No such file or directory
2021-11-08 14:49:22.640853: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcurand.so.10'; dlerror: libcurand.so.10: cannot open shared object file: No such file or directory
2021-11-08 14:49:22.640941: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcusolver.so.11'; dlerror: libcusolver.so.11: cannot open shared object file: No such file or directory
2021-11-08 14:49:22.641022: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcusparse.so.11'; dlerror: libcusparse.so.11: cannot open shared object file: No such file or directory
2021-11-08 14:49:22.641099: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudnn.so.8'; dlerror: libcudnn.so.8: cannot open shared object file: No such file or directory
2021-11-08 14:49:22.641120: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1850] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and se
tup the required libraries for your platform.

Installing TensorFlow 2.6 (or earlier) in the same environment, with the same cuda/cudnn installation, doesn't show any problem, it detects the libraries and GPU support works as expected.
The problem can be worked around by manually adding the conda lib directory to LD_LIBRARY_PATH (export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib). However, obviously this is not ideal, as it needs to be repeated/adjusted for every new conda environment. It would be better if TensorFlow just detected the conda installed libraries, as it did in TensorFlow < 2.7.
Describe the expected behavior
TensorFlow should detect cuda/cudnn libraries installed through conda, as it did in TensorFlow<2.7.
Contributing

Do you want to contribute a PR? (yes/no): no
Briefly describe your candidate solution(if contributing):

Standalone code to reproduce the issue
conda create -n tmp python=3.8
conda activate tmp
conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1
pip install "tensorflow==2.7.0"
python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"  # displays []
LD_LIBRARY_PATH=LD_LIBRARY_PATH:$CONDA_PREFIX/lib python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"  # displays [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
pip install "tensorflow<2.7.0"
python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"  # displays [[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]]

@drasmuss ,
We can see that you have installed tensorflow from conda environment.Installation issues within the Anaconda environment are tracked in the Anaconda repo.Please try to install in new virtual environment from this link and let us know if it is still an issue.Thanks!

I'm not installing tensorflow from conda, just cuda/cudnn. Tensorflow is being installed from pip like normal. And you can see in the reproduction steps I posted above that we're starting from a new virtual environment (repeated below for convenience).
conda create -n tmp python=3.8
conda activate tmp
conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1
pip install "tensorflow==2.7.0"
python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"  # displays []
LD_LIBRARY_PATH=LD_LIBRARY_PATH:$CONDA_PREFIX/lib python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"  # displays [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
pip install "tensorflow<2.7.0"
python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"  # displays [[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]]

Also note that nothing has changed on the conda side of things; we're still using the exact same environment with the same cuda/cudnn libraries, but it works in TF 2.6 and fails in TF 2.7. So I don't think the issue is on the conda side, something has changed in TensorFlow that has made this stop working.

Open the terminal and type
nano ~/.bashrc
at the end of the file add the following two lines
export PATH=$PATH:/usr/local/cuda-11.2/bin
`export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-11.2/lib64
ensure no spaces on both side of '=' sign.
if it still does not works, try adding for version 11.0
export PATH=$PATH:/usr/local/cuda-11.0/bin
`export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-11.0/lib64

As mentioned, CUDA is being installed through conda, so /usr/local/cuda- is not the correct path (the correct path is given in the original post: $CONDA_PREFIX/lib). However, hard coding that into .bashrc isn't a solution, because $CONDA_PREFIX changes depending on which conda environment you have active.

Conda installs are not officially supported by Google

I installed Tensorflow 2.7 on Windows with CUDA 11.2 and cuDNN 8.1 (no conda involved). I received the same Could not load dynamic library errors. I switched to CUDA to 11.0 and it worked. I am guessing that the pip packages for Tensorflow 2.7 were accidentally built against CUDA 11.0 instead of 11.2.

Open the terminal and type
nano ~/.bashrc
at the end of the file add the following two lines
export PATH=$PATH:/usr/local/cuda-11.2/bin export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-11.2/lib64
ensure no spaces on both side of '=' sign.
if it still does not works, try adding for version 11.0
export PATH=$PATH:/usr/local/cuda-11.0/bin export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-11.0/lib64

Thank you, this also works with cuda-11.4. But how would you fix this issue in a notebook? For the pretty niche use case that you would need tf=2.7.0 features.

This seems to solve the issue:
cd $CONDA_PREFIX
mkdir -p ./etc/conda/activate.d
mkdir -p ./etc/conda/deactivate.d
touch ./etc/conda/activate.d/env_vars.sh
touch ./etc/conda/deactivate.d/env_vars.sh

Edit ./etc/conda/activate.d/env_vars.sh as follows:
#!/bin/sh

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib

Edit ./etc/conda/deactivate.d/env_vars.sh as follows:
#!/bin/sh

unset LD_LIBRARY_PATH

Source
https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#macos-and-linux

I don't want to be dismissive here, but there is a lack of understanding of the problem specifically introduced by TF 2.7:

A conda environment does install native libraries and does ensure they will be found by the os dynamic loader mechanism for the programs that want to find these libraries.
Until TF 2.7 this was the way it worked, like the gazillion other native apps (including cuda ones)
TF 2.7, not conda, specifically broke that by ignoring the os loading mechanism for an unknown/undocumented reason

This problem is not just a techie point, it does have deep implication for businesses that do real products.
This method of working is the only reliable one for teams that work on more than one TF project, require multiple TF/CUDA/Python combinations on the same workstation (without root access).
By the way, the CUDA stack from the official nvidia channel, like nvcc/ptxas perfectly work in conda and is recommended by Nvidia itself.
For my suffering peers, if you don't have access to root, you can use this small poorly-documented feature in your environment.yml:
name: base-tf-cuda-env
channel:

nvidia
conda-forge
defaults
dependencies:
python=3.8

Install cuda libs + ptxas compiler from nvidia channel
This will accelerate the compilation of kernels for your specific card

cudatoolkit=11
cudnn=8
cupti=11
cuda-nvcc
...
pip
pip:

tensorflow==2.7.*
variables:

In case you want to see your own logs and tame the TF loggorrhea
TF_CPP_MIN_LOG_LEVEL: 3
Adjust to point to your local env path:
LD_LIBRARY_PATH: /home/me/.conda/envs/thisenvname/lib
Upon conda activate, the env variables will be set for you, and unset on deactivation.
Better than nothing, but might interfere with some other configuration...
This bug will probably never bubble up to the right decision level but as a motivation
just to let you know the general sentiment at my company is:
Another Pythorch nail in the Tensorflow coffin.
(yes, Pytorch recommends conda for installation)

Upon conda activate, the env variables will be set for you, and unset on deactivation.
Better than nothing, but might interfere with some other configuration...

Really appreciate the file you provided!
There is a typo for channelS part, but that's awesome, thanks :+1:

Upon conda activate, the env variables will be set for you, and unset on deactivation. Better than nothing, but might interfere with some other configuration...

@holongate 's env is a good workaround and solves the problem for me.
I'm quite astonished by how little thought was given on the issue - which is clearly a problem with TF 2.7 itself, and not with conda - and by how much time you waste on commenting that conda installs are not supported by Google.

For anyone looking for a one-liner solution, you can do
conda env config vars set LD_LIBRARY_PATH=$CONDA_PREFIX/lib

(with the environment you want to modify activated). This has a similar effect as @jesusdpa1's solution here https://github.com/tensorflow/tensorflow/issues/52988#issuecomment-984025384, it'll set LD_LIBRARY_PATH when the environment is activated and unset it when it's deactivated.
You still need to repeat that for every new conda environment though. It would be better if TensorFlow just detected the conda installed libraries, as it did in TensorFlow<=2.6.

fwiw when modifying LD_LIBRARY_PATH to $CONDA_PREFIX/lib, you would risk conflicts in OpenSSL (thereby making it impossible to use git or ssh); this generally impacts fedora-like systems (CentOS and the like). Of course, if you have the cuda libraries elsewhere (e.g. /usr/local/cuda) that would not be an issue; and generally pointing LD_LIBRARY_PATH to these local libraries (non-conda) will work

Not defending this change, since it is obviously inconvenient and can cause serious issues for people on HPC (e.g. OpenSSL conflicts), but this LD_LIBRARY_PATH stuff seems to have been documented here: https://www.tensorflow.org/install/gpu#linux_setup (not sure when it was added)

Upon conda activate, the env variables will be set for you, and unset on deactivation. Better than nothing, but might interfere with some other configuration...

@holongate 's env is a good workaround and solves the problem for me.
I'm quite astonished by how little thought was given on the issue - which is clearly a problem with TF 2.7 itself, and not with conda - and by how much time you waste on commenting that conda installs are not supported by Google.

Right. Quite sad to see there is an army of TF guardians of the orthodoxy to censor my remarks about lack of interest for this kind of issue but none to engage in a conversation.
And writing anything about the P-devil competition is almost instantly torched deleted

I'm continuing to have issues related to this change. Using conda's lib folder as the LD_LIBRARY_PATH affects too much of the system to be a good recommended solution. For me, when it renders my terminal useless becuase I can't use less.
The comment that "Conda installs are not officially supported by Google" might have been true at one point, but the official Tensorflow installation instructions now tell you to blindly export LD_LIBRARY_PATH. The TensorFlow team should revert the change that was made in TensorFlow 2.7 and use the ld.so system correctly.

However, since the chances of that seem slim, here's a workaround that is, in my opinion, better than the LD_LIBRARY_PATH because it only loads the cuda/cudnn libraries and not everything in the $CONDA_PREFIX/lib directory. There may be some downsides to this method, but so far it is working for me.
Building on https://github.com/tensorflow/tensorflow/issues/52988#issuecomment-1024604306:
conda env config vars set LD_PRELOAD=$CONDA_PREFIX/lib/libcudart.so:$CONDA_PREFIX/lib/libcublas.so:$CONDA_PREFIX/lib/libcublasLt.so:$CONDA_PREFIX/lib/libcufft.so:$CONDA_PREFIX/lib/libcurand.so:$CONDA_PREFIX/lib/libcusolver.so:$CONDA_PREFIX/lib/libcusparse.so:$CONDA_PREFIX/lib/libcudnn.so

This will need to be updated when the list of libraries used by TensorFlow changes.

I also had to stop using the LD_LIBRARY_PATH approach because it had too many side-effects on other system packages. @tbekolay's fix is an improvement, but is obviously quite inconvenient (I have to google this issue every time I want to set up a new conda environment), and likely to break in future TensorFlow/CUDA releases.
Since TensorFlow's installation instructions now explicitly suggest using conda:
conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/
python3 -m pip install tensorflow

it really would be nice to have conda properly supported, as it was in TensorFlow < 2.7.

I haven't tested performance, but for anyone experiencing the issue in this thread, I can confirm that installing tensorflow from conda-forge, rather than pip, works without having to set LD_LIBRARY_PATH or LD_PRELOAD, so I would say it's the preferred method of installation for people experiencing this issue (which I believe is anyone using TF 2.7+ from pip).

I haven't tested performance, but for anyone experiencing the issue in this thread, I can confirm that installing tensorflow from conda-forge, rather than pip, works without having to set LD_LIBRARY_PATH or LD_PRELOAD, so I would say it's the preferred method of installation for people experiencing this issue (which I believe is anyone using TF 2.7+ from pip).

I can confirm this too.

Hi @drasmuss !
We are checking to see whether you still need help in this issue . Official documentation has been updated now to facilitate Cuda configuration through Conda now.
Could you test with 2.8/2.10  and let us know from your side.
Thank you!

The official documentation suggests manually doing export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/ every time you want to import tensorflow, which obviously isn't really a feasible solution.
This is discussed above, but I'll reiterate the main points here for anyone coming across this thread:

Currently the best solution is to use the community-maintained TensorFlow installation from conda-forge (e.g. conda install -c conda-forge tensorflow). Generally speaking that should just work.
If 1. isn't possible for some reason (e.g. because you need to use a very recent release of TensorFlow that isn't yet available on conda-forge), the easiest solution is here https://github.com/tensorflow/tensorflow/issues/52988#issuecomment-1024604306.
However, sometimes 2. can cause problems with other system packages, since you're modifying the global LD_LIBRARY_PATH (note that this is also a problem with the approach recommended in the official documentation). If you run into issues like that, you can try this approach https://github.com/tensorflow/tensorflow/issues/52988#issuecomment-1160849524, with the caveats mentioned there that this might break in future updates.

I'll reiterate again, that all of these solutions are a downgrade in the user experience from TensorFlow < 2.7, when TensorFlow just correctly detected the conda-installed CUDA libraries without any fiddling required from the user.

A kind-of semi-automated snippet for solving cudatoolkit PATH problem in conda environment that I am using:
`
conda activate tf_env
conda install –c conda-forge cudatoolkit cudnn
mkdir -p $CONDA_PREFIX/etc/conda/activate.d
mkdir -p $CONDA_PREFIX/etc/conda/deactivate.d
printf '#!/bin/sh\nexport OLD_LD_LIBRARY_PATH=$LD_LIBRARY_PATH\nexport LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/\n' > $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
printf '#!/bin/sh\nexport LD_LIBRARY_PATH=$OLD_LD_LIBRARY_PATH\nunset OLD_LD_LIBRARY_PATH\n' > $CONDA_PREFIX/etc/conda/deactivate.d/env_vars.sh
`
This snippet automatically set and unset neccessary environment variables when you activate or deactivate conda environment. It could be useful not only for TF users, but for some other library where it needs CUDA dependencies to be built manually from source.

Hi @drasmuss ,
Could you please refer this documentation source.

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/

For your convenience it is recommended that you automate it with the following commands. The system paths will be automatically configured when you activate this conda environment.
mkdir -p $CONDA_PREFIX/etc/conda/activate.d
echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/'$CONDA_PREFIX/etc/conda/activate.d/env_vars.sh

With the above two lines of code it is not required to use the command export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/ every time you want to use tensorflow.Its one time setup and after that you can use the environment any no of times.
I hope this shall address the issue.Please confirm if still missing anything here. Thanks!

Hi @SuryanarayanaY,
See https://github.com/tensorflow/tensorflow/issues/52988#issuecomment-1323825349 for a summary of the discussion in this thread. The short answer is that no, that solution doesn't address the issue.
Longer answer: The solution you describe from the docs is basically a worse version of idea 2 from that summary above. Worse in that it's less parsimonious, and it won't unset LD_LIBRARY_PATH when the environment is deactivated. But as mentioned above, idea 2 is not really a viable solution because LD_LIBRARY_PATH is a global environment variable, and modifying it has negative side effects on lots of other packages besides TensorFlow.
And, to reiterate again, all of these "solutions" are downgrades from the behaviour prior to TensorFlow 2.7, where TensorFlow just correctly detected the CUDA libraries without requiring any manual intervention from users.

@drasmuss , I'm just curious if you have observed the same behavior in 2.11 version.
Also, since 2.12 release is around the corner, you can wait for few days and check it, since we are bumping the CUDA supported version to 11.8. Thanks!

Yes, the behaviour is the same in 2.11 and 2.12.0rc1 (I wouldn't expect it to change between rc1 and the full 2.12 release).

did you guys solve this problem?

Hi,
Thank you for opening this issue. Since this issue has been open for a long time, the code/debug information for this issue may not be relevant with the current state of the code base.
The Tensorflow team is constantly improving the framework by fixing bugs and adding new features. We suggest you try the latest TensorFlow version with the latest compatible hardware configuration which could potentially resolve the issue. If you are still facing the issue, please create a new GitHub issue with your latest findings, with all the debugging information which could help us investigate.
Please follow the release notes to stay up to date with the latest developments which are happening in the Tensorflow space.

source: gharchive/issue

authors: ChainYo SuryanarayanaY TuanBC Venkat6871 ddaspit drasmuss filippocastelli githubskiy holongate janniksinz jesusdpa1 madarax64 mihaimaruseac mohantym ngam pradyyadav sachinprasadhs tbekolay tilakrayal

repo: tensorflow/tensorflow

license_type: permissive

license_source: github-api


text:
Docker images for Tensorflow 2.6.2 are missing

TensorFlow 2.6.2 was released, but I cannot find docker images for TF 2.6.2 in docker hub.
https://hub.docker.com/r/tensorflow/tensorflow/tags?page=1&name=2.6.2

Since it is similar to Tensorflow version 2.6.1, the docker Images for version 2.6.2 are not included in docker hub.
You can use the equivalent Images for 2.6.2 with 2.6.1.

@sachinprasadhs Thank you for the response!

authors: jeongukjae sachinprasadhs


text:
App attribute missing

Click to expand!
Issue Type
Build/Install
Source
source
Tensorflow Version
2.9.1
Custom Code
Yes
OS Platform and Distribution
Linux Ubuntu 20.04
Mobile device
No response
Python version
3.7.6
Bazel version
No response
GCC/Compiler version
No response
CUDA/cuDNN version
No response
GPU model and memory
No response
Current Behaviour?
File "resnet_main.py", line 18, in <module>
    FLAGS = tf.app.flags.FLAGSAttributeError: module 'tensorflow' has no attribute 'app'

Standalone code to reproduce the issue
File "resnet_main.py", line 18, in <module>
    FLAGS = tf.app.flags.FLAGSAttributeError: module 'tensorflow' has no attribute 'app'

Relevant log output
No response

Hi @ShubhAgarwal-dev !
You can use Flags using compat.v1 mode using below command.
tf.compat.v1.flags.FLAGS
Reference
Thank you!

authors: ShubhAgarwal-dev mohantym


text:
Failure to compile on PPC due to Eigen incompatibility

Issue Type
Build/Install
Have you reproduced the bug with TF nightly?
Yes
Source
source
Tensorflow Version
2.9.1
Current Behaviour?
Compiling TensorFlow from source yields:
external/eigen_archive/unsupported/Eigen/CXX11/../../../Eigen/src/Core/arch/AltiVec/MatrixVectorProduct.h: In instantiation of 'void EigenForTFLite::internal::gemv_row(Index, Index, const LhsMapper&, const RhsMapper&, ResScalar*, Index, ResScalar) [with Index = long int; LhsScalar = float; LhsMapper = EigenForTFLite::internal::TensorContractionInputMapper<float, long int, 1, EigenForTFLite::TensorEvaluator<const EigenForTFLite::TensorReshapingOp<const EigenForTFLite::DSizes<long int, 2>, const EigenForTFLite::TensorMap<EigenForTFLite::Tensor<const float, 4, 1, long int>, 16> >, EigenForTFLite::ThreadPoolDevice>, std::array<long int, 1>, std::array<long int, 1>, 4, true, false, 16, EigenForTFLite::MakePointer>; RhsScalar = float; RhsMapper = EigenForTFLite::internal::TensorContractionInputMapper<float, long int, 0, EigenForTFLite::TensorEvaluator<const EigenForTFLite::TensorReshapingOp<const EigenForTFLite::DSizes<long int, 2>, const EigenForTFLite::TensorImagePatchOp<-1, -1, const EigenForTFLite::TensorMap<EigenForTFLite::Tensor<const float, 4, 1, long int>, 16> > >, EigenForTFLite::ThreadPoolDevice>, std::array<long int, 1>, std::array<long int, 1>, 4, true, false, 0, EigenForTFLite::MakePointer>; ResScalar = float]':
external/eigen_archive/unsupported/Eigen/CXX11/../../../Eigen/src/Core/arch/AltiVec/MatrixVectorProduct.h:2019:1:   required from 'static void EigenForTFLite::internal::general_matrix_vector_product<Index, float, LhsMapper, 0, ConjugateLhs, float, RhsMapper, ConjugateRhs, Version>::run(Index, Index, const LhsMapper&, const RhsMapper&, EigenForTFLite::internal::general_matrix_vector_product<Index, float, LhsMapper, 0, ConjugateLhs, float, RhsMapper, ConjugateRhs, Version>::ResScalar*, Index, EigenForTFLite::internal::general_matrix_vector_product<Index, float, LhsMapper, 0, ConjugateLhs, float, RhsMapper, ConjugateRhs, Version>::ResScalar) [with Index = long int; LhsMapper = EigenForTFLite::internal::TensorContractionInputMapper<float, long int, 1, EigenForTFLite::TensorEvaluator<const EigenForTFLite::TensorReshapingOp<const EigenForTFLite::DSizes<long int, 2>, const EigenForTFLite::TensorMap<EigenForTFLite::Tensor<const float, 4, 1, long int>, 16> >, EigenForTFLite::ThreadPoolDevice>, std::array<long int, 1>, std::array<long int, 1>, 4, true, false, 16, EigenForTFLite::MakePointer>; bool ConjugateLhs = false; RhsMapper = EigenForTFLite::internal::TensorContractionInputMapper<float, long int, 0, EigenForTFLite::TensorEvaluator<const EigenForTFLite::TensorReshapingOp<const EigenForTFLite::DSizes<long int, 2>, const EigenForTFLite::TensorImagePatchOp<-1, -1, const EigenForTFLite::TensorMap<EigenForTFLite::Tensor<const float, 4, 1, long int>, 16> > >, EigenForTFLite::ThreadPoolDevice>, std::array<long int, 1>, std::array<long int, 1>, 4, true, false, 0, EigenForTFLite::MakePointer>; bool ConjugateRhs = false; int Version = 0; EigenForTFLite::internal::general_matrix_vector_product<Index, float, LhsMapper, 0, ConjugateLhs, float, RhsMapper, ConjugateRhs, Version>::ResScalar = float]'

<snip>

external/eigen_archive/unsupported/Eigen/CXX11/../../../Eigen/src/Core/arch/AltiVec/MatrixVectorProduct.h:1976:5: error: 'class EigenForTFLite::internal::TensorContractionInputMapper<float, long int, 0, EigenForTFLite::TensorEvaluator<const EigenForTFLite::TensorReshapingOp<const EigenForTFLite::DSizes<long int, 2>, const EigenForTFLite::TensorImagePatchOp<-1, -1, const EigenForTFLite::TensorMap<EigenForTFLite::Tensor<const float, 4, 1, long int>, 16> > >, EigenForTFLite::ThreadPoolDevice>, std::array<long int, 1>, std::array<long int, 1>, 4, true, false, 0, EigenForTFLite::MakePointer>' has no member named 'load'

See my issue in Eigen: https://gitlab.com/libeigen/eigen/-/merge_requests/764#note_1231907378
Standalone code to reproduce the issue
Compile TensorFlow on PPC/POWER9
The issue is that the specialized TensorContractionInputMapper in eigen_spatial_convolutions-inl.h (at master at https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tsl/framework/convolution/eigen_spatial_convolutions-inl.h) is missing the new load functions as required by Eigen

Seems to be fixed in 2.10.1 either by a change in TensorFlow or the updated Eigen


text:
Keeping last layer names in Stacked Model

Issue type
Feature Request
Have you reproduced the bug with TensorFlow Nightly?
No
Source
source
TensorFlow version
2.13.0
Custom code
No
OS platform and distribution
macOS 13.4
Mobile device
No response
Python version
3.9
Bazel version
No response
GCC/compiler version
No response
CUDA/cuDNN version
No response
GPU model and memory
No response
Current behavior?
When using Model Stacking, I would like to access the last layer in the last stacked model to pass individual loss functions to them
# ------------------ Encoder ------------------
encoder_model = Model(inputs=all_inputs, outputs=latent_space, name="encoder")
# ... irrelevant code

# ------------------ Decoder ------------------
# Output Layers
numeric_output = Dense(
    self.numeric_dim, activation="linear", name="numeric_output")(decoder2)
binary_output = Dense(
    self.binary_dim, activation="sigmoid", name="binary_output")(decoder2)

decoder_output = [numeric_output] + [binary_output]
decoder_model = Model(inputs=latent_input, outputs=decoder_output, name="decoder")

# ------------------ Autoencoder ------------------
autoencoder_output = decoder_model(encoder_model(all_inputs))
autoencoder = Model(inputs=all_inputs, outputs=pass_through_layers, name="autoencoder")

# This will not work:
losses = {
            "numeric_output": "mse",
            "binary_output": "binary_crossentropy"
}
autoencoder.compile(optimizer=Adam(learning_rate=lr), loss=losses)

The last layer will be renamed to decoder1, decoder2, etc.
It would be much nicer if there was some passthrough argument in a model that would allow referencing the last layer in the stacked model directly
Standalone code to reproduce the issue
This will fail

import numpy as np
from tensorflow.keras.layers import Input, Dense
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam

# Input dimensions
input_dim = 10
latent_dim = 5
numeric_dim = 3
binary_dim = 2

# Learning rate
lr = 0.001

# Number of samples
n_samples = 1000

# Generate random data
X = np.random.rand(n_samples, input_dim)
y_numeric = np.random.rand(n_samples, numeric_dim)
y_binary = np.random.randint(0, 2, size=(n_samples, binary_dim))

# ------------------ Encoder ------------------
all_inputs = Input(shape=(input_dim,), name="all_inputs")
latent_space = Dense(latent_dim, activation="relu", name="latent_space")(all_inputs)
encoder_model = Model(inputs=all_inputs, outputs=latent_space, name="encoder")

# ------------------ Decoder ------------------
latent_input = Input(shape=(latent_dim,), name="latent_input")
decoder2 = Dense(10, activation="relu", name="decoder2")(latent_input)

numeric_output = Dense(numeric_dim, activation="linear", name="numeric_output")(decoder2)
binary_output = Dense(binary_dim, activation="sigmoid", name="binary_output")(decoder2)

decoder_output = [numeric_output, binary_output]
decoder_model = Model(inputs=latent_input, outputs=decoder_output, name="decoder")

# ------------------ Autoencoder ------------------
autoencoder_output = decoder_model(encoder_model(all_inputs))

# This will not work:
losses = {
    "numeric_output": "mse",
    "binary_output": "binary_crossentropy"
}
autoencoder = Model(inputs=all_inputs, outputs=autoencoder_output, name="autoencoder")
autoencoder.compile(optimizer=Adam(learning_rate=lr), loss=losses)

# Fit the model
autoencoder.fit(
    x=X,
    y=[y_numeric, y_binary],
    epochs=10,
    batch_size=32
)

Relevant log output
No response

@TkinterinShanghai , Thanks for reporting the issue.
Since this feature request is specific to Keras, could you please close this and open a new issue in Keras repo https://github.com/keras-team/keras/issues

authors: TkinterinShanghai sachinprasadhs


text:
updated TensorFlow official example: cifar10

I found a problem in the official example: cifar10
It not working in the newest TensorFlow because of API change.
But it's an excellent tutorials for newcomer.
So i updated it and hope more people could see it.
Except API changes,I didn't do any other change.
I hope this tutorials can add to official example again.

Can one of the admins verify this patch?

I signed it!

Please check this~

The files are all in a cfair10/ directory, instead of cifar10/, can you check?

Sorry~
now moved

Can you update it in the models repo instead?
https://github.com/tensorflow/models/tree/master/tutorials/image/cifar10

source: gharchive/pull-request

authors: jhseu tensorflow-jenkins vincentvanhoucke wqj97


text:
Return output_graph_def in tools/freeze_graph.py

It makes sense that def freeze_graph works with files for both the input graph and frozen output graph.
It does not make sense that def freeze_graph_with_def_protos only uses a tf.GraphDef for the input, but doesn't actually return the frozen graph (and instead only writes it to a file).
This pull request changes freeze_graph_with_def_protos to optionally write the output to file (for backwards-compatibility) when output_graph is set as usual, but also returns the GraphDef protobuf to make the API more symmetric.
Otherwise you have to write and read from a tempfile just to use freeze_graph, which is a hassle.

Can one of the admins verify this patch?

Jenkins, test this please.

authors: carlthome drpngx tensorflow-jenkins


text:
Update bazel toolchains dependency.

PiperOrigin-RevId: 186650360

The failure looks legitimate, have we seen this before?

@gunan I cannot tell since tests didn't even build prior to this. Can I still merge and debug that test after?

I was thinking when we were doing the release.
But sure, let's first merge and then debug. I think this test was passing when we did 1.6, so I would like to avoid disabling the test for 1.6 release.


text:
fix doc bug in extract patches

changed ksizes -> sizes in tf.image.extract_patches and updated the docs to reflect the current API.

@andrewnc Thank you for your contribution. Can you please sign CLA? Thanks!

Closing since change is already in master https://github.com/tensorflow/tensorflow/blob/4f6e48e9fd6fd8d536a96d78a6e6006e4ac0074c/tensorflow/python/ops/array_ops.py#L5389-L5393

authors: andrewnc gbaned mihaimaruseac


text:
Fix flexible members crash in clang 7.1

Fix flexible members crash in clang 7.1 (Fixes #42468)
Defining data as data[] causes compiler crash in clang 7.1 when defining
const TfLiteIntArray kZeroLengthIntArray = {0, {}};
in micro_allocator.cc

Note for @gbaned:
Per this comment, let's wait until @yair-ehrenwald has had a chance to make a github issue and link to it from this PR before we merge it.

this is the relevant github issue: https://github.com/tensorflow/tensorflow/issues/42468

@yair-ehrenwald -- thanks. I have updated the PR description comment to annotate that this PR should fix the issue that you created.
I think we are ready to merge.

authors: advaitjain yair-ehrenwald


text:
(+18,,.!)NEW~VIRAL@S.E.X@VIDEOS]*TikToke Imsha Rehman Video Original Video Link Pakistani TikToker Imsha Rehman Video Viral On Social Media X Trending Now #269 #1878 #2846

49 seconds ago
L𝚎aked Video Imsha Rehman Original Video Viral Video L𝚎aked on X Twitter Telegram
𝖫𝖾𝖺𝚔𝖾𝖽 𝗏𝗂𝗋𝖺𝗅 𝗅𝗂𝗇𝗄 2024 𝖫𝖾𝖺𝚔𝖾𝖽 𝖵𝗂𝖽𝖾𝗈 𝖵𝗂𝗋𝖺𝗅 𝖮𝗇 𝖲𝗈𝖼𝗂𝖺𝗅 𝖬𝖾𝖽𝗂𝖺 2024.𝖫𝖾𝖺𝚔𝖾𝖽 𝗏𝗂𝗋𝖺𝗅 𝗅𝗂𝗇𝗄 2024 𝖫𝖾𝖺𝚔𝖾𝖽 𝖵𝗂𝖽𝖾𝗈 𝖵𝗂𝗋𝖺𝗅 𝖮𝗇 𝖲𝗈𝖼𝗂𝖺𝗅 𝖬𝖾𝖽𝗂𝖺 2024. 𝖳𝗁𝖾 𝗋𝖾𝖼𝗎𝗋𝗋𝖾𝗇𝗍 𝗍𝗁𝖾𝗆𝖾 𝗈𝖿 𝖫𝖾𝖺𝚔𝖾𝖽 𝗍𝖺𝗉𝖾𝗌 𝖺𝗇𝖽 𝗍𝗁𝖾 𝗌𝗎𝖻𝗌𝖾𝗊𝗎𝖾𝗇𝗍 𝖿𝖺𝗅𝗅𝗈𝗎𝗍 𝗌𝖾𝗋𝗏𝖾𝗌 𝖺𝗌 𝖺 𝗋𝖾𝗆𝗂𝗇𝖽𝖾𝗋 𝗈𝖿 𝗍𝗁𝖾 𝖿𝗋𝖺𝗀𝗂𝗅𝗂𝗍𝗒 𝗈𝖿 𝗋𝖾𝗉𝗎𝗍𝖺𝗍𝗂𝗈𝗇 𝗂𝗇 𝗍𝗁𝖾 𝖽𝗂𝗀𝗂𝗍𝖺𝗅 𝖾𝗋𝖺. 𝖠𝗌 𝗍𝗁𝖾 𝗅𝗂𝗇𝖾𝗌 𝖻𝖾𝗍𝗐𝖾𝖾𝗇 𝗉𝗋𝗂𝗏𝖺𝗍𝖾 𝖺𝗇𝖽 𝗉𝗎𝖻𝗅𝗂𝖼 𝗅𝗂𝖿𝖾 𝖼𝗈𝗇𝗍𝗂𝗇𝗎𝖾 𝗍𝗈 𝖻𝗅𝗎𝗋, 𝖼𝖾𝗅𝖾𝖻𝗋𝗂𝗍𝗂𝖾𝗌 𝗅𝗂𝗄𝖾 𝖯𝗋𝗂𝗌𝗈𝗇 𝖮𝖿𝖿𝗂𝖼𝖾𝗋𝖿𝗂𝗇𝖽 𝗍𝗁𝖾𝗆𝗌𝖾𝗅𝗏𝖾𝗌 𝖺𝗍 𝗍𝗁𝖾 𝗆𝖾𝗋𝖼𝗒 𝗈𝖿 𝗂𝗇𝗍𝖾𝗋𝗇𝖾𝗍 𝖼𝗁𝖺𝗍𝗍𝖾𝗋, 𝗐𝗁𝖾𝗋𝖾 𝖺 𝗋𝗎𝗆𝗈𝗋 𝖼𝖺𝗇 𝗂𝗀𝗇𝗂𝗍𝖾 𝖺 𝖿𝗂𝗋𝖾𝗌𝗍𝗈𝗋𝗆 𝗈𝖿 𝗌𝗉𝖾𝖼𝗎𝗅𝖺𝗍𝗂𝗈𝗇 𝖺𝗇𝖽 𝗃𝗎𝖽𝗀𝗆𝖾𝗇𝗍.
𝖨𝗇 𝗍𝗁𝖾 𝖾𝗏𝖾𝗋 𝖾𝗏𝗈𝗅𝗏𝗂𝗇𝗀 𝗅𝖺𝗇𝖽𝗌𝖼𝖺𝗉𝖾 𝗈𝖿 𝖼𝖾𝗅𝖾𝖻𝗋𝗂𝗍𝗒 𝖼𝗎𝗅𝗍𝗎𝗋𝖾, 𝗍𝗁𝖾 𝖨𝗌𝗁𝗈𝗐𝗌𝗉𝖾𝖾𝖽𝗌𝖼𝖺𝗇𝖽𝖺𝗅 𝗎𝗇𝖽𝖾𝗋𝗌𝖼𝗈𝗋𝖾𝗌 𝗍𝗁𝖾 𝗋𝖾𝗅𝖾𝗇𝗍𝗅𝖾𝗌𝗌 𝗉𝗎𝗋𝗌𝗎𝗂𝗍 𝗈𝖿 𝗌𝖾𝗇𝗌𝖺𝗍𝗂𝗈𝗇𝖺𝗅𝗂𝗌𝗆, 𝖺 𝗉𝗎𝗋𝗌𝗎𝗂𝗍 𝗍𝗁𝖺𝗍 𝗈𝖿𝗍𝖾𝗇 𝖼𝗈𝗆𝖾𝗌 𝖺𝗍 𝗍𝗁𝖾 𝖾𝗑𝗉𝖾𝗇𝗌𝖾 𝗈𝖿 𝗍𝗋𝗎𝗍𝗁 𝖺𝗇𝖽 𝖽𝗂𝗀𝗇𝗂𝗍𝗒. 𝖠𝗌 𝗐𝖾 𝗇𝖺𝗏𝗂𝗀𝖺𝗍𝖾 𝗍𝗁𝖾 𝖼𝗈𝗆𝗉𝗅𝖾𝗑𝗂𝗍𝗂𝖾𝗌 𝗈𝖿 𝗍𝗁𝖾 𝖽𝗂𝗀𝗂𝗍𝖺𝗅 𝖺𝗀𝖾, 𝗍𝗁𝖾 𝗅𝗂𝗇𝖾 𝖻𝖾𝗍𝗐𝖾𝖾𝗇 𝖾𝗇𝗍𝖾𝗋𝗍𝖺𝗂𝗇𝗆𝖾𝗇𝗍 𝖺𝗇𝖽 𝖾𝗑𝗉𝗅𝗈𝗂𝗍𝖺𝗍𝗂𝗈𝗇 𝗋𝖾𝗆𝖺𝗂𝗇𝗌 𝗉𝖾𝗋𝗂𝗅𝗈𝗎𝗌𝗅𝗒 𝗍𝗁𝗂𝗇.
𝖠𝗌 𝗍𝗁𝖾 𝗌𝗂𝗍𝗎𝖺𝗍𝗂𝗈𝗇 𝗎𝗇𝖿𝗈𝗅𝖽𝗌, 𝗍𝗁𝖾 𝗍𝗋𝗎𝗍𝗁 𝗋𝖾𝗆𝖺𝗂𝗇𝗌 𝗌𝗁𝗋𝗈𝗎𝖽𝖾𝖽 𝗂𝗇 𝗆𝗒𝗌𝗍𝖾𝗋𝗒, 𝗅𝖾𝖺𝗏𝗂𝗇𝗀 𝗍𝗁𝖾 𝗉𝗎𝖻𝗅𝗂𝖼 𝗍𝗈 𝗉𝗈𝗇𝖽𝖾𝗋 𝗍𝗁𝖾 𝖺𝗎𝗍𝗁𝖾𝗇𝗍𝗂𝖼𝗂𝗍𝗒 𝗈𝖿 𝗍𝗁𝖾 𝗋𝗎𝗆𝗈𝗋𝗌. 𝖨𝗇 𝖺 𝗐𝗈𝗋𝗅𝖽 𝗐𝗁𝖾𝗋𝖾 𝖿𝖺𝗆𝖾 𝖺𝗇𝖽 𝗂𝗇𝖿𝖺𝗆𝗒 𝖺𝗋𝖾 𝗍𝗐𝗈 𝗌𝗂𝖽𝖾𝗌 𝗈𝖿 𝗍𝗁𝖾 𝗌𝖺𝗆𝖾 𝖼𝗈𝗂𝗇, 𝗍𝗁𝖾 𝗌𝖺𝗀𝖺 𝗈𝖿 𝖨𝗌𝗁𝗈𝗐𝗌𝗉𝖾𝖾𝖽𝗂𝗌 𝖺 𝗍𝖾𝗌𝗍𝖺𝗆𝖾𝗇𝗍 𝗍𝗈 𝗍𝗁𝖾 𝗉𝗈𝗐𝖾𝗋 𝗈𝖿 𝗌𝗈𝖼𝗂𝖺𝗅 𝗆𝖾𝖽𝗂𝖺 𝗍𝗈 𝗌𝗁𝖺𝗉𝖾 𝗇𝖺𝗋𝗋𝖺𝗍𝗂𝗏𝖾𝗌 𝖺𝗇𝖽 𝖼𝗁𝖺𝗅𝗅𝖾𝗇𝗀𝖾 𝗍𝗁𝖾 𝖻𝗈𝗎𝗇𝖽𝖺𝗋𝗂𝖾𝗌 𝗈𝖿 𝗉𝗋𝗂𝗏𝖺𝖼𝗒 𝖺𝗇𝖽 𝖼𝗈𝗇𝗌𝖾𝗇𝗍.
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cxvxcv

repo: tensorflow/tflite-micro

license_source: bigquery


text:
TFLite Micro framework changes to support log refactor

This PR-

Changes the TFLite Micro Framework classes to eliminate the usage of MicroErrorReporter and ErrorReporter classes.
Keeps the version of the APIs, that expect ErrorReporter for backwards compatibility.

This compatibility is limited to the bare minimum set of API currently being used.
The minimum support to the version of API expecting ErrorReporter will be removed as a follow-up PR, expected to be merged next week.
Any new applications or examples using TFLM should no longer be creating and passing-in an ErrorReporter.

BUG=http://b/245802069

@advaitjain addressed the review comments. Please take a look when you get a chance.


text:
#4445: Split act reads into two RISCs for first convs (ie. K=16 convs)

Regular reader does first half of output block h reads
Additional reader loop in other RISC after weights are done reading
Update b20 layer 1 conv unit test to have same block scheme as metalResnet50
Minor host side clean up for optimized_conv_sharded

Closing this PR, since there's no perf improvements. Looking into it...

First conv weights: 4x4x16  x 64 = ~16KB (in bfp8)
Ideal read from DRAM / m-cast is 512 cycles  + 512 cycles, even if 5x inefficient, it's 5K cycles. Just 2.5% of current 200K.
For each input block reader0 fills top half, reader1 fills bottom half of a the same CB slot (CB can still be double buffered).
We can create 2 CBs mapping to the same memory region. But each reader fills half of each block.
Each reader can do a push as if it filled the whole CB.
Compute needs to have 2 waits / 2 pushes for each input block, and they can refer to the whole slot of  CB (compute doesn't need to be aware of half-half split).

Pipelines:

Post commit: https://github.com/tenstorrent-metal/tt-metal/actions/runs/7466337012
Fast dispatch: https://github.com/tenstorrent-metal/tt-metal/actions/runs/7466339302
Slow dispatch: https://github.com/tenstorrent-metal/tt-metal/actions/runs/7466340880

Previously passing wormhole tests:

Fast dispatch wormhole: https://github.com/tenstorrent-metal/tt-metal/actions/runs/7454126441/job/20280853641
Slow dispatch wormhole: https://github.com/tenstorrent-metal/tt-metal/actions/runs/7454127628/job/20280857865

authors: TT-BrianLiu davorchap

repo: tenstorrent-metal/tt-metal


text:
Upload test logs to artifacts

Output test log to a file and upload log file to artifacts.

@vmilosevic once this one is ready for review, just add me as reviewer to do a double-check and signed it off :))

Yes, it is ready for review.
You can download the logs from here
https://github.com/tenstorrent/tt-forge-fe/actions/runs/11054703555/artifacts/1983115381
Since our projects use different loggers on every level it was complicated to control log level of what gets written to the console. This change usees tee to duplicate logs to a file and upload it to artifacts

authors: nvukobratTT vmilosevic

repo: tenstorrent/tt-forge-fe


text:
get_pcie_base_addr_from_device per chip

This attribute is tied to a chip. Although it could be argued that currently we only support a cluster of chips of same architecture, therefore this would be the same for all chips in a single cluster.
Still, this makes sense and further unblocks supporting multiple architectures in a single cluster/driver. It will also allow it to be lowered to TTDevice class which will is arch specific (currently architecture_implementation).
Related to #157 , contributes to #154 .

Breaks existing usages of get_pcie_base_addr_from_device
tt_metal corresponding PR: https://github.com/tenstorrent/tt-metal/pull/13949
tt_debuda change: Not used

Related https://github.com/tenstorrent/tt-metal/pull/13949 got approved, so now merging this PR.

repo: tenstorrent/tt-umd


text:
vagrant reload did not always acquire LAN IP

this could be related to my network, however, we need to find a way to make sure that LAN IP should always acquired after each vagrant reload.
I got 10.0.2.15 many times instead of 192.168.1.x
I tried:
diff --git a/Vagrantfile b/Vagrantfile
index 2f2dee3..6f74a95 100644
--- a/Vagrantfile
+++ b/Vagrantfile
@@ -424,6 +424,15 @@ Vagrant.configure("2") do |config|
     "inline" => fix_hosts_command
   })

+  fix_dhclient_command = "sudo dhclient eth0"
+
+  provisioners.unshift({
+    "type" => "shell",
+    "name" => "fix-dhclient",
+    "inline" => fix_dhclient_command,
+    "run" => "always"
+  })
+
   # append ip shell as the last item to always display the ip address
   provisioners << {
     "type" => "shell",

However, it did NOT work.

my current ifconfig:
vagrant@iorad:~$ ifconfig
docker0   Link encap:Ethernet  HWaddr 02:42:d0:d4:87:88
          inet addr:172.17.0.1  Bcast:172.17.255.255  Mask:255.255.0.0
          inet6 addr: fe80::42:d0ff:fed4:8788/64 Scope:Link
          UP BROADCAST RUNNING MULTICAST  MTU:1500  Metric:1
          RX packets:5262 errors:0 dropped:0 overruns:0 frame:0
          TX packets:5922 errors:0 dropped:0 overruns:0 carrier:0
          collisions:0 txqueuelen:0
          RX bytes:1729672 (1.7 MB)  TX bytes:2222162 (2.2 MB)

eth0      Link encap:Ethernet  HWaddr 08:00:27:3f:0a:28
          inet addr:192.168.1.241  Bcast:192.168.1.255  Mask:255.255.255.0
          inet6 addr: fe80::a00:27ff:fe3f:a28/64 Scope:Link
          inet6 addr: 2405:4800:294e:b4fe:a00:27ff:fe3f:a28/64 Scope:Global
          UP BROADCAST RUNNING MULTICAST  MTU:1500  Metric:1
          RX packets:1337 errors:1 dropped:40 overruns:0 frame:0
          TX packets:672 errors:0 dropped:0 overruns:0 carrier:0
          collisions:0 txqueuelen:1000
          RX bytes:1053723 (1.0 MB)  TX bytes:50801 (50.8 KB)
          Interrupt:16 Base address:0xd240

eth1      Link encap:Ethernet  HWaddr 08:00:27:10:47:e3
          inet addr:10.0.2.15  Bcast:10.0.2.255  Mask:255.255.255.0
          inet6 addr: fe80::a00:27ff:fe10:47e3/64 Scope:Link
          UP BROADCAST RUNNING MULTICAST  MTU:1500  Metric:1
          RX packets:5295 errors:0 dropped:0 overruns:0 frame:0
          TX packets:3352 errors:0 dropped:0 overruns:0 carrier:0
          collisions:0 txqueuelen:1000
          RX bytes:923653 (923.6 KB)  TX bytes:477807 (477.8 KB)

lo        Link encap:Local Loopback
          inet addr:127.0.0.1  Mask:255.0.0.0
          inet6 addr: ::1/128 Scope:Host
          UP LOOPBACK RUNNING  MTU:65536  Metric:1
          RX packets:55647 errors:0 dropped:0 overruns:0 frame:0
          TX packets:55647 errors:0 dropped:0 overruns:0 carrier:0
          collisions:0 txqueuelen:1
          RX bytes:25455563 (25.4 MB)  TX bytes:25455563 (25.4 MB)

veth150e103 Link encap:Ethernet  HWaddr d2:9f:82:f7:2e:b8
          inet6 addr: fe80::d09f:82ff:fef7:2eb8/64 Scope:Link
          UP BROADCAST RUNNING MULTICAST  MTU:1500  Metric:1
          RX packets:398 errors:0 dropped:0 overruns:0 frame:0
          TX packets:469 errors:0 dropped:0 overruns:0 carrier:0
          collisions:0 txqueuelen:0
          RX bytes:34812 (34.8 KB)  TX bytes:41708 (41.7 KB)

veth250e5b7 Link encap:Ethernet  HWaddr 9a:2a:ea:b4:8b:ef
          inet6 addr: fe80::982a:eaff:feb4:8bef/64 Scope:Link
          UP BROADCAST RUNNING MULTICAST  MTU:1500  Metric:1
          RX packets:222 errors:0 dropped:0 overruns:0 frame:0
          TX packets:265 errors:0 dropped:0 overruns:0 carrier:0
          collisions:0 txqueuelen:0
          RX bytes:45722 (45.7 KB)  TX bytes:31837 (31.8 KB)

veth410386b Link encap:Ethernet  HWaddr a6:af:01:6b:3c:b7
          inet6 addr: fe80::a4af:1ff:fe6b:3cb7/64 Scope:Link
          UP BROADCAST RUNNING MULTICAST  MTU:1500  Metric:1
          RX packets:0 errors:0 dropped:0 overruns:0 frame:0
          TX packets:18 errors:0 dropped:0 overruns:0 carrier:0
          collisions:0 txqueuelen:0
          RX bytes:0 (0.0 B)  TX bytes:1124 (1.1 KB)

veth608154f Link encap:Ethernet  HWaddr 36:2a:29:4b:65:97
          inet6 addr: fe80::342a:29ff:fe4b:6597/64 Scope:Link
          UP BROADCAST RUNNING MULTICAST  MTU:1500  Metric:1
          RX packets:188 errors:0 dropped:0 overruns:0 frame:0
          TX packets:219 errors:0 dropped:0 overruns:0 carrier:0
          collisions:0 txqueuelen:0
          RX bytes:18039 (18.0 KB)  TX bytes:19348 (19.3 KB)

veth7acddca Link encap:Ethernet  HWaddr da:ac:64:38:be:fd
          inet6 addr: fe80::d8ac:64ff:fe38:befd/64 Scope:Link
          UP BROADCAST RUNNING MULTICAST  MTU:1500  Metric:1
          RX packets:416 errors:0 dropped:0 overruns:0 frame:0
          TX packets:635 errors:0 dropped:0 overruns:0 carrier:0
          collisions:0 txqueuelen:0
          RX bytes:52124 (52.1 KB)  TX bytes:79844 (79.8 KB)

vethaf50540 Link encap:Ethernet  HWaddr 92:83:f5:e7:ca:11
          inet6 addr: fe80::9083:f5ff:fee7:ca11/64 Scope:Link
          UP BROADCAST RUNNING MULTICAST  MTU:1500  Metric:1
          RX packets:2362 errors:0 dropped:0 overruns:0 frame:0
          TX packets:2616 errors:0 dropped:0 overruns:0 carrier:0
          collisions:0 txqueuelen:0
          RX bytes:1342010 (1.3 MB)  TX bytes:1104819 (1.1 MB)

vethb062fcc Link encap:Ethernet  HWaddr 56:c3:86:d1:4c:3d
          inet6 addr: fe80::54c3:86ff:fed1:4c3d/64 Scope:Link
          UP BROADCAST RUNNING MULTICAST  MTU:1500  Metric:1
          RX packets:1316 errors:0 dropped:0 overruns:0 frame:0
          TX packets:1371 errors:0 dropped:0 overruns:0 carrier:0
          collisions:0 txqueuelen:0
          RX bytes:297699 (297.6 KB)  TX bytes:316345 (316.3 KB)

vethc542cd6 Link encap:Ethernet  HWaddr ae:6f:1e:b4:36:b5
          inet6 addr: fe80::ac6f:1eff:feb4:36b5/64 Scope:Link
          UP BROADCAST RUNNING MULTICAST  MTU:1500  Metric:1
          RX packets:1416 errors:0 dropped:0 overruns:0 frame:0
          TX packets:1516 errors:0 dropped:0 overruns:0 carrier:0
          collisions:0 txqueuelen:0
          RX bytes:146539 (146.5 KB)  TX bytes:769228 (769.2 KB)

$ hostname -I
192.168.1.241 10.0.2.15 172.17.0.1 2405:4800:294e:b4fe:a00:27ff:fe3f:a28

Expected:
$ hostname -I | cut -d' ' -f2
192.168.1.241

Actual:
$ hostname -I | cut -d' ' -f2
10.0.2.15

After $ vagrant hostmanger,
Expected:
$ ping iorad.local
PING iorad.local (192.168.1.241): 56 data bytes

Actual:
$ ping iorad.local
PING iorad.local (10.0.2.15): 56 data bytes
Request timeout for icmp_seq 0
Request timeout for icmp_seq 1

related and could be the root cause: https://github.com/devopsgroup-io/vagrant-hostmanager/issues/121#issuecomment-69050265

so the root cause is that our workaround for printing ip address and ip resolver for vagrant hostmanager is broken and buggy, we need to fix this.

related code lines:
https://github.com/teracyhq/dev/blob/develop/Vagrantfile#L455
https://github.com/teracyhq/dev/blob/develop/Vagrantfile#L329

repo: teracyhq/dev


text:
fix: Use amazon alias for worker ami owner instead of owner id

PR o'clock
Description
Use amazon alias for for worker ami owner instead of setting the owner id. Those owner ids are different by region, but the alias remain the same.
Resolves https://github.com/terraform-aws-modules/terraform-aws-eks/issues/1037
Resolves https://github.com/terraform-aws-modules/terraform-aws-eks/issues/480
https://github.com/terraform-aws-modules/terraform-aws-eks/issues/824
https://github.com/terraform-aws-modules/terraform-aws-eks/issues/480
Checklist

[ ] CI tests are passing
[ ] README.md has been updated after any changes to variables and outputs. See https://github.com/terraform-aws-modules/terraform-aws-eks/#doc-generation

@terraform-aws-modules/triage-supporters Someone can help me review this ? I'm not a ninja of AMI filtering, so I would like have your feedback on this change. I don't know why we're using the AWS owner ID instead of alias since the beginning. I probably missed something.

I usually debug this is by running TF_LOG=debug terraform plan and see what kind of API call terraform sends to AWS. Example code should be very small to actually understand the verbose response.

The Amazon EKS node images from the container team are not in the same account as standard images from the EC2 amazon linux team. Although it appears that they both have the amazon ImageOwnerAlias. Maybe in the before times when the module was written this was not true so the account ID had to be specified?
Interesting that account aliases are not a one-to-one mapping to account IDs.
For example grab the, at time of writing, latest image for Amazon Linux 2 AMI (HVM) SSD 64-bit x86 (in us-west-1 it's ami-0bb3fad3c0286ebd5), and query for the OwnerId and ImageOwnerAlias:
aws ec2 describe-images --image-ids ami-0bb3fad3c0286ebd5 --query Images[].[Name,OwnerId,ImageOwnerAlias]
[
    [
        "amzn2-ami-hvm-2.0.20200917.0-x86_64-gp2",
        "137112412989",
        "amazon"
    ]
]

The current EKS 1.17 AMZN2 image in us-west-1 is ami-0c557e2a5ede1bae3:
aws ec2 describe-images --image-ids ami-0c557e2a5ede1bae3 --query Images[].[Name,OwnerId,ImageOwnerAlias]
[
    [
        "amazon-eks-node-1.17-v20201002",
        "602401143452",
        "amazon"
    ]
]

Existing module query:
$ aws ec2 describe-images --owner 602401143452 --filter 'Name=name,Values=amazon-eks-node-1.17-v*' --query 'sort_by(Images, &CreationDate)[].[Name,ImageId]' --output text | tail -n1
amazon-eks-node-1.17-v20201002	ami-0c557e2a5ede1bae3

$ aws ec2 describe-images --owner 801119661308 --filter 'Name=name,Values=Windows_Server-2019-English-Core-EKS_Optimized-1.17-**' --query 'sort_by(Images, &CreationDate)[].[Name,ImageId]' --output text | tail -n1
Windows_Server-2019-English-Core-EKS_Optimized-1.17-2020.09.09	ami-059dd50aa82b04356

New query:
$ aws ec2 describe-images --owner amazon --filter 'Name=name,Values=amazon-eks-node-1.17-v*' --query 'sort_by(Images, &CreationDate)[].[Name,ImageId]' --output text | tail -n1
amazon-eks-node-1.17-v20201002	ami-0c557e2a5ede1bae3

$ aws ec2 describe-images --owner amazon --filter 'Name=name,Values=Windows_Server-2019-English-Core-EKS_Optimized-1.17-**' --query 'sort_by(Images, &CreationDate)[].[Name,ImageId]' --output text | tail -n1
Windows_Server-2019-English-Core-EKS_Optimized-1.17-2020.09.09	ami-059dd50aa82b04356

Looks good to me.

Thanks @dpiddock.

This doesn't actually resolve #480, because for some reason, the account that owns the EKS AMIs in ap-east-1 has no alias whatsoever:
$ aws ec2 describe-images --region ap-east-1 --image-ids ami-0824aac4c54c763d3 --query 'Images[].Name,OwnerId,ImageOwnerAlias]' --output text
amazon-eks-node-1.18-v20201007  800184023465    None

$ aws ec2 describe-images --region ap-east-1 --owner 800184023465 --filter 'Name=name,Values=amazon-eks-node-1.18-v*' --query 'Images[].[Name,ImageId]' --output text
amazon-eks-node-1.18-v20201007  ami-0824aac4c54c763d3

$ aws ec2 describe-images --region ap-east-1 --owner amazon --filter 'Name=name,Values=amazon-eks-node-1.18-v*' --query 'Images[].[Name,ImageId]' --output text

$

That being the case, I don't think there's any way to really fix this other than either hard-coding a default list of multiple owner IDs, hard-coding a default mapping between regions and owner ids, or just expecting the module user to figure it out on their own. Or, I guess, bugging Amazon to add an account alias to 800184023465.

Hummm... Maybe the right way to fix this is to use the SSM datasource to get the AMI ID. But I don't know how to do that without breaking all the AMI filtering logic in this module.

authors: antonbabenko barryib dpiddock philomory

repo: terraform-aws-modules/terraform-aws-eks


text:
What is the purpose?

What is the purpose of using a module that creates only one resource?
Why would I want to use:
module "users_unencrypted" {
  source = "https://github.com/terraform-aws-modules/terraform-aws-sqs/"

  name = "users-unencrypted"
}

When I could just declare the resource directly:
resource "aws_sqs_queue" "users_unencrypted" {
  name = "users-unencrypted"
}

There are several reasons:

Module can be versioned as a whole, but resource can't.
Terragrunt uses modules natively

authors: antonbabenko lpiob

repo: terraform-aws-modules/terraform-aws-sqs


text:
chore(deps): update module github.com/terraform-ibm-modules/ibmcloud-terratest-wrapper to v1.41.4

This PR contains the following updates:

Package
Type
Update
Change

github.com/terraform-ibm-modules/ibmcloud-terratest-wrapper
require
patch
v1.41.3 -> v1.41.4

Release Notes

terraform-ibm-modules/ibmcloud-terratest-wrapper (github.com/terraform-ibm-modules/ibmcloud-terratest-wrapper)
v1.41.4
Compare Source
Bug Fixes

deps: update gomod (#893) (97dbfdb)

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text:
[Do Not Merge]test: temporary changes

Description
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This is NOT to be merged, PR created only to verify some changes.
Release required?

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text:
chore(deps): update terraform github.com/terraform-ibm-modules/terraform-ibm-key-protect-all-inclusive to v4.8.3

This PR contains the following updates:

Package
Type
Update
Change

github.com/terraform-ibm-modules/terraform-ibm-key-protect-all-inclusive
module
patch
v4.8.2 -> v4.8.3

Release Notes

terraform-ibm-modules/terraform-ibm-key-protect-all-inclusive (github.com/terraform-ibm-modules/terraform-ibm-key-protect-all-inclusive)
v4.8.3
Compare Source
Bug Fixes

deps: update terraform-module (#422) (bcef67b)

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text:
chore(deps): update ci dependencies

This PR contains the following updates:

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digest
eb1c66a -> 2a961d3

go (source)
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1.22.2 -> 1.22.3

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text:
chore(deps): update common-dev-assets digest to 93841a2

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