Failed to import deform_conv_cuda

Hi, @U @haofeixu ,
I meet the following trouble when I run your model.
Traceback (most recent call last):
  File "inf_time.py", line 8, in <module>
    import nets
  File "/home/lthpc/PRO/aanet-master/nets/__init__.py", line 1, in <module>
    from .aanet import AANet
  File "/home/lthpc/PRO/aanet-master/nets/aanet.py", line 4, in <module>
    from nets.feature import (StereoNetFeature, PSMNetFeature, GANetFeature, GCNetFeature,
  File "/home/lthpc/PRO/aanet-master/nets/feature.py", line 5, in <module>
    from nets.deform import DeformConv2d
  File "/home/lthpc/PRO/aanet-master/nets/deform.py", line 3, in <module>
    from nets.deform_conv import DeformConv, ModulatedDeformConv
  File "/home/lthpc/PRO/aanet-master/nets/deform_conv/__init__.py", line 1, in <module>
    from .deform_conv import (DeformConv, DeformConvPack, ModulatedDeformConv,
  File "/home/lthpc/PRO/aanet-master/nets/deform_conv/deform_conv.py", line 9, in <module>
    from . import deform_conv_cuda
ImportError: /home/lthpc/PRO/aanet-master/nets/deform_conv/deform_conv_cuda.cpython-37m-x86_64-linux-gnu.so: undefined symbol: _ZN3c105ErrorC1ENS_14SourceLocationERKSs

The building of your deform conv is finished, during which I have the following output:
running build_ext
building 'deform_conv_cuda' extension
creating build
creating build/temp.linux-x86_64-3.7
creating build/temp.linux-x86_64-3.7/src
gcc -pthread -B /opt/anaconda3/envs/AutoML-new_master2/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include -I/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/torch/csrc/api/include -I/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/TH -I/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/THC -I/usr/local/cuda-10.0/include -I/opt/anaconda3/envs/AutoML-new_master2/include/python3.7m -c src/deform_conv_cuda.cpp -o build/temp.linux-x86_64-3.7/src/deform_conv_cuda.o -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=deform_conv_cuda -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++11
cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++ [enabled by default]
In file included from /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/core/Device.h:5:0,
                 from /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/core/Allocator.h:6,
                 from /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/ATen/ATen.h:3,
                 from /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
                 from /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
                 from /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
                 from /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
                 from /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
                 from /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
                 from /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
                 from /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/torch/extension.h:4,
                 from src/deform_conv_cuda.cpp:4:
src/deform_conv_cuda.cpp: In function ‘void shape_check(at::Tensor, at::Tensor, at::Tensor*, at::Tensor, int, int, int, int, int, int, int, int, int, int)’:
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:20: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                    ^
src/deform_conv_cuda.cpp:66:3: note: in expansion of macro ‘AT_CHECK’
   AT_CHECK(weight.ndimension() == 4,
   ^
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:40: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                                        ^
src/deform_conv_cuda.cpp:66:3: note: in expansion of macro ‘AT_CHECK’
   AT_CHECK(weight.ndimension() == 4,
   ^
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:20: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                    ^
src/deform_conv_cuda.cpp:71:3: note: in expansion of macro ‘AT_CHECK’
   AT_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous");
   ^
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:40: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                                        ^
src/deform_conv_cuda.cpp:71:3: note: in expansion of macro ‘AT_CHECK’
   AT_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous");
   ^
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:20: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                    ^
src/deform_conv_cuda.cpp:73:3: note: in expansion of macro ‘AT_CHECK’
   AT_CHECK(kW > 0 && kH > 0,
   ^
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:40: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                                        ^
src/deform_conv_cuda.cpp:73:3: note: in expansion of macro ‘AT_CHECK’
   AT_CHECK(kW > 0 && kH > 0,
   ^
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:20: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                    ^
src/deform_conv_cuda.cpp:77:3: note: in expansion of macro ‘AT_CHECK’
   AT_CHECK((weight.size(2) == kH && weight.size(3) == kW),
   ^
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:40: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                                        ^
src/deform_conv_cuda.cpp:77:3: note: in expansion of macro ‘AT_CHECK’
   AT_CHECK((weight.size(2) == kH && weight.size(3) == kW),
   ^
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:20: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                    ^
src/deform_conv_cuda.cpp:82:3: note: in expansion of macro ‘AT_CHECK’
   AT_CHECK(dW > 0 && dH > 0,
   ^
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:40: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                                        ^
src/deform_conv_cuda.cpp:82:3: note: in expansion of macro ‘AT_CHECK’
   AT_CHECK(dW > 0 && dH > 0,
   ^
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:20: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                    ^
src/deform_conv_cuda.cpp:85:3: note: in expansion of macro ‘AT_CHECK’
   AT_CHECK(
   ^
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:40: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                                        ^
src/deform_conv_cuda.cpp:85:3: note: in expansion of macro ‘AT_CHECK’
   AT_CHECK(
   ^
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:20: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                    ^
src/deform_conv_cuda.cpp:101:3: note: in expansion of macro ‘AT_CHECK’
   AT_CHECK(ndim == 3 || ndim == 4, "3D or 4D input tensor expected but got: %s",
   ^
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:40: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                                        ^
src/deform_conv_cuda.cpp:101:3: note: in expansion of macro ‘AT_CHECK’
   AT_CHECK(ndim == 3 || ndim == 4, "3D or 4D input tensor expected but got: %s",
   ^
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:20: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                    ^
src/deform_conv_cuda.cpp:113:3: note: in expansion of macro ‘AT_CHECK’
   AT_CHECK(nInputPlane % deformable_group == 0,
   ^
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:40: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                                        ^
src/deform_conv_cuda.cpp:113:3: note: in expansion of macro ‘AT_CHECK’
   AT_CHECK(nInputPlane % deformable_group == 0,
   ^
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:20: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                    ^
src/deform_conv_cuda.cpp:123:3: note: in expansion of macro ‘AT_CHECK’
   AT_CHECK(input.size(1) == nInputPlane,
   ^
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:40: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                                        ^
src/deform_conv_cuda.cpp:123:3: note: in expansion of macro ‘AT_CHECK’
   AT_CHECK(input.size(1) == nInputPlane,
   ^
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:20: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                    ^
src/deform_conv_cuda.cpp:127:3: note: in expansion of macro ‘AT_CHECK’
   AT_CHECK((inputHeight >= kH && inputWidth >= kW),
   ^
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:40: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                                        ^
src/deform_conv_cuda.cpp:127:3: note: in expansion of macro ‘AT_CHECK’
   AT_CHECK((inputHeight >= kH && inputWidth >= kW),
   ^
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:20: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                    ^
src/deform_conv_cuda.cpp:130:3: note: in expansion of macro ‘AT_CHECK’
   AT_CHECK((offset.size(2) == outputHeight && offset.size(3) == outputWidth),
   ^
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:40: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                                        ^
src/deform_conv_cuda.cpp:130:3: note: in expansion of macro ‘AT_CHECK’
   AT_CHECK((offset.size(2) == outputHeight && offset.size(3) == outputWidth),
   ^
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:20: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                    ^
src/deform_conv_cuda.cpp:135:3: note: in expansion of macro ‘AT_CHECK’
   AT_CHECK((offset.size(1) == deformable_group * 2 * kH * kW),
   ^
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:40: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                                        ^
src/deform_conv_cuda.cpp:135:3: note: in expansion of macro ‘AT_CHECK’
   AT_CHECK((offset.size(1) == deformable_group * 2 * kH * kW),
   ^
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:20: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                    ^
src/deform_conv_cuda.cpp:139:5: note: in expansion of macro ‘AT_CHECK’
     AT_CHECK(gradOutput->size(dimf) == nOutputPlane,
     ^
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:40: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                                        ^
src/deform_conv_cuda.cpp:139:5: note: in expansion of macro ‘AT_CHECK’
     AT_CHECK(gradOutput->size(dimf) == nOutputPlane,
     ^
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:20: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                    ^
src/deform_conv_cuda.cpp:143:5: note: in expansion of macro ‘AT_CHECK’
     AT_CHECK((gradOutput->size(dimh) == outputHeight &&
     ^
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:40: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                                        ^
src/deform_conv_cuda.cpp:143:5: note: in expansion of macro ‘AT_CHECK’
     AT_CHECK((gradOutput->size(dimh) == outputHeight &&
     ^
src/deform_conv_cuda.cpp: In function ‘int deform_conv_forward_cuda(at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, int, int, int, int, int, int, int, int, int, int, int)’:
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:20: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                    ^
src/deform_conv_cuda.cpp:194:3: note: in expansion of macro ‘AT_CHECK’
   AT_CHECK((offset.size(0) == batchSize), "invalid batch size of offset");
   ^
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:40: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                                        ^
src/deform_conv_cuda.cpp:194:3: note: in expansion of macro ‘AT_CHECK’
   AT_CHECK((offset.size(0) == batchSize), "invalid batch size of offset");
   ^
src/deform_conv_cuda.cpp: In function ‘int deform_conv_backward_input_cuda(at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, int, int, int, int, int, int, int, int, int, int, int)’:
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:20: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                    ^
src/deform_conv_cuda.cpp:301:3: note: in expansion of macro ‘AT_CHECK’
   AT_CHECK((offset.size(0) == batchSize), 3, "invalid batch size of offset");
   ^
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:40: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                                        ^
src/deform_conv_cuda.cpp:301:3: note: in expansion of macro ‘AT_CHECK’
   AT_CHECK((offset.size(0) == batchSize), 3, "invalid batch size of offset");
   ^
src/deform_conv_cuda.cpp: In function ‘int deform_conv_backward_parameters_cuda(at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, int, int, int, int, int, int, int, int, int, int, float, int)’:
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:20: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                    ^
src/deform_conv_cuda.cpp:417:3: note: in expansion of macro ‘AT_CHECK’
   AT_CHECK((offset.size(0) == batchSize), "invalid batch size of offset");
   ^
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:40: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                                        ^
src/deform_conv_cuda.cpp:417:3: note: in expansion of macro ‘AT_CHECK’
   AT_CHECK((offset.size(0) == batchSize), "invalid batch size of offset");
   ^
src/deform_conv_cuda.cpp: In function ‘void modulated_deform_conv_cuda_forward(at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, int, int, int, int, int, int, int, int, int, int, bool)’:
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:20: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                    ^
src/deform_conv_cuda.cpp:497:3: note: in expansion of macro ‘AT_CHECK’
   AT_CHECK(input.is_contiguous(), "input tensor has to be contiguous");
   ^
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:40: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                                        ^
src/deform_conv_cuda.cpp:497:3: note: in expansion of macro ‘AT_CHECK’
   AT_CHECK(input.is_contiguous(), "input tensor has to be contiguous");
   ^
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:20: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                    ^
src/deform_conv_cuda.cpp:498:3: note: in expansion of macro ‘AT_CHECK’
   AT_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous");
   ^
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:40: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                                        ^
src/deform_conv_cuda.cpp:498:3: note: in expansion of macro ‘AT_CHECK’
   AT_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous");
   ^
src/deform_conv_cuda.cpp: In function ‘void modulated_deform_conv_cuda_backward(at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, int, int, int, int, int, int, int, int, int, int, bool)’:
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:20: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                    ^
src/deform_conv_cuda.cpp:579:3: note: in expansion of macro ‘AT_CHECK’
   AT_CHECK(input.is_contiguous(), "input tensor has to be contiguous");
   ^
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:40: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                                        ^
src/deform_conv_cuda.cpp:579:3: note: in expansion of macro ‘AT_CHECK’
   AT_CHECK(input.is_contiguous(), "input tensor has to be contiguous");
   ^
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:20: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                    ^
src/deform_conv_cuda.cpp:580:3: note: in expansion of macro ‘AT_CHECK’
   AT_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous");
   ^
/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:339:40: warning: ‘void c10::detail::deprecated_AT_CHECK()’ is deprecated (declared at /opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/c10/util/Exception.h:314) [-Wdeprecated-declarations]
     ::c10::detail::deprecated_AT_CHECK();                 \
                                        ^
src/deform_conv_cuda.cpp:580:3: note: in expansion of macro ‘AT_CHECK’
   AT_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous");
   ^
/usr/local/cuda-10.0/bin/nvcc -I/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include -I/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/torch/csrc/api/include -I/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/TH -I/opt/anaconda3/envs/AutoML-new_master2/lib/python3.7/site-packages/torch/include/THC -I/usr/local/cuda-10.0/include -I/opt/anaconda3/envs/AutoML-new_master2/include/python3.7m -c src/deform_conv_cuda_kernel.cu -o build/temp.linux-x86_64-3.7/src/deform_conv_cuda_kernel.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=deform_conv_cuda -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++11
creating build/lib.linux-x86_64-3.7
g++ -pthread -shared -B /opt/anaconda3/envs/AutoML-new_master2/compiler_compat -L/opt/anaconda3/envs/AutoML-new_master2/lib -Wl,-rpath=/opt/anaconda3/envs/AutoML-new_master2/lib -Wl,--no-as-needed -Wl,--sysroot=/ build/temp.linux-x86_64-3.7/src/deform_conv_cuda.o build/temp.linux-x86_64-3.7/src/deform_conv_cuda_kernel.o -L/usr/local/cuda-10.0/lib64 -lcudart -o build/lib.linux-x86_64-3.7/deform_conv_cuda.cpython-37m-x86_64-linux-gnu.so
copying build/lib.linux-x86_64-3.7/deform_conv_cuda.cpython-37m-x86_64-linux-gnu.so ->

My environment is set as listed in README.md

How did you solve this problem

@MJITG 同学，跑这个代码遇到点问题，能不能加个联系方式请教下，谢谢了。我微信c937688576

How did you solve this problem

@Hahaemmm Sorry for late response. I just switched to another machine and all worked fine.

@MJITG 同学，跑这个代码遇到点问题，能不能加个联系方式请教下，谢谢了。我微信c937688576

关于上面这个问题，我也只是换了一台机器就解决了。。。很玄学

好的，我已解决，谢谢！
---Original---
From<EMAIL_ADDRESS>Date: Fri, Dec 18, 2020 19:51 PM
To<EMAIL_ADDRESS>Cc: "Comment"<comment<EMAIL_ADDRESS>Subject: Re: [haofeixu/aanet] Failed to import deform_conv_cuda (#32)
@MJITG 同学，跑这个代码遇到点问题，能不能加个联系方式请教下，谢谢了。我微信c937688576
关于上面这个问题，我也只是换了一台机器就解决了。。。很玄学
—
You are receiving this because you commented.
Reply to this email directly, view it on GitHub, or unsubscribe.

好的，我已解决，谢谢！
…
---Original--- From<EMAIL_ADDRESS>Date: Fri, Dec 18, 2020 19:51 PM To<EMAIL_ADDRESS>Cc: "Comment"<comment<EMAIL_ADDRESS>Subject: Re: [haofeixu/aanet] Failed to import deform_conv_cuda (#32) @MJITG 同学，跑这个代码遇到点问题，能不能加个联系方式请教下，谢谢了。我微信c937688576 关于上面这个问题，我也只是换了一台机器就解决了。。。很玄学 — You are receiving this because you commented. Reply to this email directly, view it on GitHub, or unsubscribe.

你好，你是怎么解决的呢

hi. I still have this error while running <interface.py> or <predict.py> and I've tried on different machines but didn't run without this error. Can you please help me?
Traceback (most recent call last):   File "inference.py", line 11, in <module>     import nets   File "D:\aanet\nets\__init__.py", line 1, in <module>     from .aanet import AANet   File "D:\aanet\nets\aanet.py", line 4, in <module>     from nets.feature import (StereoNetFeature, PSMNetFeature, GANetFeature, GCNetFeature,   File "D:\aanet\nets\feature.py", line 5, in <module>     from nets.deform import DeformConv2d   File "D:\aanet\nets\deform.py", line 3, in <module>     from nets.deform_conv import DeformConv, ModulatedDeformConv   File "D:\aanet\nets\deform_conv\__init__.py", line 1, in <module>     from .deform_conv import (DeformConv, DeformConvPack, ModulatedDeformConv,   File "D:\aanet\nets\deform_conv\deform_conv.py", line 9, in <module>     from . import deform_conv_cuda ImportError: cannot import name 'deform_conv_cuda' from 'nets.deform_conv' (D:\aanet\nets\deform_conv\_ _init__.py)

@foruhari 你遇到的这个错误解决了吗

@foruhari 你遇到的这个错误解决了吗

hello, unfortunately not yet, could you please help me?

不好意思，我也没有解决
------------------ 原始邮件 ------------------
发件人: @.>;
发送时间: 2021年8月15日(星期天) 中午11:49
收件人: @.>;
抄送: @.>; @.>;
主题: Re: [haofeixu/aanet] Failed to import deform_conv_cuda (#32)
@foruhari 你遇到的这个错误解决了吗
hello, unfortunately not yet, could you please help me?
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Perhaps you forgot to run:
cd nets/deform_conv && bash build.sh

Thank you! I'll go back and check it. I haven't done AANet for a long time. I'm doing GANs recently.
------------------ 原始邮件 ------------------
发件人:                                                                                "haofeixu/aanet"                                                                                @.>;
发送时间: 2021年9月8日(星期三) 晚上8:21
@.>;
<EMAIL_ADDRESS>主题: Re: [haofeixu/aanet] Failed to import deform_conv_cuda (#32)
Perhaps you forgot to run:
cd nets/deform_conv && bash build.sh
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请问有人遇到过这个问题么？我用colab跑的....
error in modulated_deformable_im2col_cuda: no kernel image is available for execution on the device

这个预训练模型我怎么下载不了，有什么推荐的解决办法吗？谢谢

Hi all, sorry for the late response.
If this issue is still relavant to you, I would suggest to try our new GMStereo model: https://haofeixu.github.io/unimatch/ & https://github.com/autonomousvision/unimatch. No CUDA op is required. A Colab demo is also provided to try our model in your browser. Hope it helps, thanks.

Thank you for your attention to this issue, I will try your team's new model, may your team get better and better
---Original---
From: "Haofei @.>
Date: Sun, Nov 13, 2022 12:08 PM
To: @.>;
Cc<EMAIL_ADDRESS>Subject: Re: [haofeixu/aanet] Failed to import deform_conv_cuda (#32)
Hi all, sorry for the late response.
If this issue is still relavant to you, I would suggest to try our new GMStereo model: https://haofeixu.github.io/unimatch/ & https://github.com/autonomousvision/unimatch. No CUDA op is required. A Colab demo is also provided to try our model in your browser. Hope it helps, thanks.
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You are receiving this because you commented.Message ID: @.***>


[Question] Increase logging frequency

Looks awesome so far!
Only issue for me is that you can't really see the "current" state of the database. So for example if you have lots of new connections you might only see these after 10 minutes in the dashboard.
You think there are any issues with changing the execution time to something lower (like a minute or even every 10 seconds)?
Of course it would consume more storage, but besides that?

This script is for collecting some useful statistics,
for real time monitoring I prefer tools like Workbench and many others
(eg. telegraf+InfluxDB+Grafana).
The main problem is that You can increase the frequence
but You increase the storage used and the database load too.
Sometimes I use a 1 minute interval (eg. during application test)
but I prefer 10 minute interval (like sar tool for instance).

This script is for collecting some useful statistics,
for real time monitoring I prefer tools like Workbench and many others
(eg. telegraf+InfluxDB+Grafana).
The main problem is that You can increase the frequence
but You increase the storage used and the database load too.
Sometimes I use a 1 minute interval (eg. during application test)
but I prefer 10 minute interval (like sar tool for instance).

I agreed with this. In developer mode, if I want to change the time to 1 minute for proving something.
Where the point to change?
Thanks.

@narongwate-scm Its specified in the event that is scheduled you can edit it directly in the db.

I do confirm!


Invalid Function getNativeBalance when trying to use the newly added Web3API feature

I'm submitting a ...
[X] bug report
[ ] feature request
[ ] question about the decisions made in the repository
[ ] question about how to use this project

Summary
Greetings, I'm working on a side a project involving Moralis and React-Moralis i'm using latest versions
here's my package.json details
"moralis": "^0.0.37",
"react-moralis": "^0.2.1"
I'm using yarn for package management and I made sure that i have upgraded properly from 0.2.0 to 0.2.1 same goes for the moralis package.
Just to be sure i even removed them and re-added them using the appropriate yarn commands still didn't resolve my issue.
Here's an example component which I created to test the new functionality which was nicely provided, much appreceated.
Both getNativeBalanceQuery.fetch() and Moralis.Web3API.account.getNativeBalance() fail with the same error

{"code":141,"error":"Invalid function: "getNativeBalance""}
Any help will be highly appreciated, thanks in advance.
import React from 'react'
import { useMoralis,useMoralisWeb3Api, useMoralisWeb3ApiCall } from "react-moralis"
const Charts = () => {
const { native: { getBlock }, account: { getNativeBalance } } = useMoralisWeb3Api();
const getNativeBalanceQuery = useMoralisWeb3ApiCall(getNativeBalance, {
address:"0xaA44AAEf36D866BA4fe9Def5d11426B9A87115E1",
});
const {authenticate, logout, isAuthenticated,authError, hasAuthError,user, enableWeb3,isAuthenticating, isWeb3Enabled, Moralis, web3} = useMoralis()
return (

{isAuthenticated && isWeb3Enabled ?
<>
<button onClick={ async ()=>{
console.log(Moralis.Web3API);
//Neither this worked
console.log(await Moralis.Web3API.account.getNativeBalance({chain:"0x61",address:"0xaA44AAEf36D866BA4fe9Def5d11426B9A87115E1"}))
//Or this worked both result in same error.
getNativeBalanceQuery.fetch()
}}>Test func
</>
:    null}

)
}
export default Charts

Moralis server wasn't updated. Issue closed thanks!


Port Codebase to foundry

Sirs, when foundry?
https://github.com/foundry-rs/foundry

Yes, totally agree. Guess it was clear for us but maybe not for everyone. ✌


Cannot navigate top-level menu with keyboard

VSCode Version: Code 1.17.0 (be377c0faf7574a59f84940f593a6849f12e4de7, 2017-10-04T23:45:46.804Z)
OS Version: Windows_NT x64 6.1.7601
Extensions:

Extension
Author (truncated)
Version

python
don
0.7.0

gitlens
eam
5.6.0

vscode-auto-open-markdown-preview
hnw
0.0.4

cpptools
ms-
0.13.1

csharp
ms-
1.12.1

PowerShell
ms-
1.4.3

java
red
0.11.0

vscode-icons
rob
7.15.0

vscode-header-source
ryz
1.1.0

vscode-spotify
shy
2.2.1

perforce
sle
2.2.1

Steps to Reproduce:

Run Code on Windows, press Alt+F to show the File menu
Press the right arrow key. The Edit menu should now open but it does not.

Reproduces without extensions: Yes/No

This is not markdown-related. I had a .md file opened when I added this. I see no way to remove a tag.

This is a limitation of Electron, closing as an upstream issue.


chore: Update the Poetry dependency and project config

Sure that's the e-mail address of us?

Yes, just tested today.


Add Markov future price model

This PR adds a Markov Future price model. The model is based on the multi-factor HJM model used for rates.

Codecov Report

Merging #13 (33988fd) into main (c841680) will decrease coverage by 0.18%.
The diff coverage is 96.15%.

@@            Coverage Diff             @@
##             main      #13      +/-   ##
==========================================
- Coverage   98.26%   98.09%   -0.18%
==========================================
  Files          17       18       +1
  Lines         578      630      +52
==========================================
+ Hits          568      618      +50
- Misses         10       12       +2

Files Changed
Coverage Δ

src/DiffFusion.jl
100.00% <ø> (ø)

src/models/futures/MarkovFutureModels.jl
96.00% <96.00%> (ø)

src/models/hybrid/CompositeModel.jl
100.00% <100.00%> (ø)

:mega: We’re building smart automated test selection to slash your CI/CD build times. Learn more


How to run the examples/android in Windows Environment?

System information
Windows 8 cpu

TensorFlow installed from (source or binary):
TensorFlow version:1.12.0
Python version:3.6
Installed using virtualenv? pip? conda?:conda
Bazel version (if compiling from source):no

Describe the problem
I have searched a huge amounts of information about how to use this example but the problem is that most of people are using it on the linux OS. I don't know how to run it on my Windows OS with android studio. Need I install .jar & .so file?And how to deploy the android studio?
Provide the exact sequence of commands / steps that you executed before running into the problem
Any other info / logs
Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached.

This is not Build/Installation or Bug/Performance issue. Please post this kind of support questions at Stackoverflow. There is a big community to support and learn from your questions. GitHub is mainly for addressing bugs in installation and performance. Thanks!

@Ekinkit Hi! I have mentioned some links below. Can you please take a look at these.
TFLite code Repository : https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite
TFLite Website Guide : https://www.tensorflow.org/lite/overview
TFLite Github Guide : https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/guide/android.md
As stated by @jvishnuvardhan , try posting this question at Stackoverflow and close this issue. Thank you 😄

@Ayush517 Thank you for your reply~


Fix 529: Make all AMM transaction IssuedCurrencyAmount fields CurrencyAmounts

Fixes #529 by retyping all IssuedCurrencyAmounts in AMM related transactions to CurrencyAmounts.
This does not constitute a breaking change, because these transactions were marked @Beta.

Any idea when this PR will be fixed & a release cut?

@exp0nge We're planning on merging this and releasing a fix version some time this week or early next (this week is Apex)

@nkramer44 Any updates on releasing a patched version with this fix?

Hi @abhaar-gemini and @exp0nge -- see https://github.com/XRPLF/xrpl4j/releases/tag/v3.3.1 and let us know if you run into any issues.


Replace optimist as library is deprecated

While looking at what is needed to update our electron version I noticed that we require optimist at the start of main.js. Optimist's npm page reveals that is has since become deprecated.
The recommended replacements by the package author are minimist, yargs, or nomnom.
If there is little interest in replacing optimist then we should at least update to the final release.

If this hasn't yet been done, I would like to do it!


Migrate sample server from v1alpha1 to v1beta1 - schema

Partial migration from v1alpha1 to v1beta1 -
The only difference between versions is that v1beta does not have the
operation notation so i completely removed it from the server
implementation.
Remaining work:

Implement the missing storage functions: BatchCreateNotes,
BatchCreateOccurrences and GetVulnerabilityOccurrencesSummary
Delete unused storage function (operation related)
Move impl.go from folder v1alpha1 to folder v1beta1

I can do the reaming work in this commit, or do it in a separate commit

This PR looks fine, but I want to hold off on merging it. As mentioned in the issue thread, let's discuss at the next Grafeas sync.
Specifically we are planning to remove the samples folder and use the go folder instead as we have opensourced our internal implementation of the v1beta1 API layer. So the v1alpha1 API layer you've updated in this PR is going to be removed.
The storage code is going to be updated to work with the new v1beta1 interfaces.
But let's discuss more at the sync. Thanks for your contributions so far!


Add macos support #184

The commit allow to compile with SwiftPM and if necessary I can make a PR just for this one
The second one add models dumped from apple web site
There is some double when using "identifier" so I put the PR as WIP
I do not know what to do. Merge into one. Find a way to make some alias with template generations
My very dirty dump script is available here  https://github.com/phimage/MacModelDump

manually remove the double and old mac devices
wip: removed

Next to the little thing a very nice addition!

Thanks for the review. I updated manually the iMacPro name

I make a markdown renderer by default now in my swift executable
https://github.com/phimage/MacModelDump
To output for DeviceKit the DeviceRenderer must be set to DeviceKitRenderer() https://github.com/phimage/MacModelDump/blob/master/Sources/AppleDump/Dump.swift#L16

New macbook pro 16 inch 2019
https://support.apple.com/kb/SP809 https://www.apple.com/macbook-pro-16/
https://www.youtube.com/watch?v=ysRigNyavF4
but no model identifier yet! https://support.apple.com/en-us/HT201300

@phimage Sorry for late reply and inactivity. I found some formatting inconsistencies. Please fix those.
Also, the device identifier is available now: MacBookPro16,1

    case macPro2019

Mac Pro (2019)

With also some missing macPro that apple add to the its support page

Hey, any updates on this? Looks pretty useful.

@phimage @Zandor300 anything that blocks merge? 😄

Hello, it is a really useful PR do you plan to merge it soon? Thank you!

Would also love to see this merged.

Why wasn't this merged...? No more discussion on it, and this is just stagnant.. very important feature for DeviceKit imo.

@Zandor300 would love to see this getting merged as well! 🙏

Hi, my Pull Request is very old, a lot of change happens on DeviceKit (see conflicts and new features)
and I do not use it anymore (so I no work on it)
If someone want macOS I think, first is to make a new Pull Request

I do want to take a look at this still at some point, just need to find the time...

Codecov Report

Merging #217 (ecdfbb1) into master (f19f9f8) will not change coverage.
The diff coverage is n/a.

@@      Coverage Diff      @@
##   master   #217   +/-   ##
=============================
=============================

:mega: We’re building smart automated test selection to slash your CI/CD build times. Learn more

Bumping this. It would be good to get support in for iOS apps running on Apple Silicon. I was able to get some basic functionality up using the ProcessInfo.processInfo.isiOSAppOnMac property and checking the UIDevice.current.userInterfaceIdiom, since the string identifier comes back as an iPad for an iOS app running on Apple Silicon.

Bumping this. It would be good to get support in for iOS apps running on Apple Silicon. I was able to get some basic functionality up using the ProcessInfo.processInfo.isiOSAppOnMac property and checking the UIDevice.current.userInterfaceIdiom, since the string identifier comes back as an iPad for an iOS app running on Apple Silicon.

FYI, this spinoff project is designed to work on all Apple platforms including macOS and we'd love to have your contributions and suggestions: https://www.github.com/kudit/Device


homebridge-enphase-envoy v4.6.15-beta.18

[3/2/2021, 9:20:42 AM] [enphaseEnvoy] Device: <IP_ADDRESS> Envoy-S, requesting devices info eror: TypeError: Cannot read property 'state' of undefined
    at envoyDevice.getDeviceInfo (/homebridge/node_modules/homebridge-enphase-envoy/index.js:1469:49), state: Offline trying to reconnect.

I can see in the log it connects somewaht to the Envoy-S but not completely and plugin is not working in EVE app
Here the complete log:
[3/2/2021, 9:25:57 AM] [enphaseEnvoy] Device <IP_ADDRESS> Envoy-S, get devices data inventory: [ [Object], [Object], [Object] ] info: <?xml version='1.0' encoding='UTF-8'?>
<envoy_info>
  <time>1614673546</time>
  <device>
    <sn>121921106049</sn>
    <pn>800-00553-r02</pn>
    <software>R4.10.35</software>
    <euaid>4c8675</euaid>
    <seqnum>0</seqnum>
    <apiver>1</apiver>
    <imeter>false</imeter>
  </device>
  <package name='rootfs'>
    <pn>500-00001-r01</pn>
    <version>02.00.00</version>
    <build>945</build>
  </package>
  <package name='kernel'>
    <pn>500-00011-r01</pn>
    <version>04.00.00</version>
    <build>5bb754</build>
  </package>
  <package name='boot'>
    <pn>590-00018-r01</pn>
    <version>02.00.01</version>
    <build>426697</build>
  </package>
  <package name='app'>
    <pn>500-00002-r01</pn>
    <version>04.10.35</version>
    <build>6ed292</build>
  </package>
  <package name='devimg'>
    <pn>500-00005-r01</pn>
    <version>01.02.186</version>
    <build>d0d70f</build>
  </package>
  <package name='geo'>
    <pn>500-00008-r01</pn>
    <version>02.01.22</version>
    <build>06e201</build>
  </package>
  <package name='backbone'>
    <pn>500-00010-r01</pn>
    <version>04.10.25</version>
    <build>7b7de5</build>
  </package>
  <package name='meter'>
    <pn>500-00013-r01</pn>
    <version>03.02.07</version>
    <build>4c9d48</build>
  </package>
  <package name='agf'>
    <pn>500-00012-r01</pn>
    <version>02.02.00</version>
    <build>c00a8f</build>
  </package>
  <package name='security'>
    <pn>500-00016-r01</pn>
    <version>02.00.00</version>
    <build>54a6dc</build>
  </package>
  <build_info>
    <build_time_gmt>1542157882</build_time_gmt>
    <build_id>release-4.10.x-103-Nov-12-18-18:25:06</build_id>
  </build_info>
</envoy_info>
 meters: []
[3/2/2021, 9:25:57 AM] [enphaseEnvoy] Device: <IP_ADDRESS> Envoy-S, parse info.xml successful: {
  "envoy_info": {
    "time": [
      "1614673546"
    ],
    "device": [
      {
        "sn": [
          "121921106049"
        ],
        "pn": [
          "800-00553-r02"
        ],
        "software": [
          "R4.10.35"
        ],
        "euaid": [
          "4c8675"
        ],
        "seqnum": [
          "0"
        ],
        "apiver": [
          "1"
        ],
        "imeter": [
          "false"
        ]
      }
    ],
    "package": [
      {
        "$": {
          "name": "rootfs"
        },
        "pn": [
          "500-00001-r01"
        ],
        "version": [
          "02.00.00"
        ],
        "build": [
          "945"
        ]
      },
      {
        "$": {
          "name": "kernel"
        },
        "pn": [
          "500-00011-r01"
        ],
        "version": [
          "04.00.00"
        ],
        "build": [
          "5bb754"
        ]
      },
      {
        "$": {
          "name": "boot"
        },
        "pn": [
          "590-00018-r01"
        ],
        "version": [
          "02.00.01"
        ],
        "build": [
          "426697"
        ]
      },
      {
        "$": {
          "name": "app"
        },
        "pn": [
          "500-00002-r01"
        ],
        "version": [
          "04.10.35"
        ],
        "build": [
          "6ed292"
        ]
      },
      {
        "$": {
          "name": "devimg"
        },
        "pn": [
          "500-00005-r01"
        ],
        "version": [
          "01.02.186"
        ],
        "build": [
          "d0d70f"
        ]
      },
      {
        "$": {
          "name": "geo"
        },
        "pn": [
          "500-00008-r01"
        ],
        "version": [
          "02.01.22"
        ],
        "build": [
          "06e201"
        ]
      },
      {
        "$": {
          "name": "backbone"
        },
        "pn": [
          "500-00010-r01"
        ],
        "version": [
          "04.10.25"
        ],
        "build": [
          "7b7de5"
        ]
      },
      {
        "$": {
          "name": "meter"
        },
        "pn": [
          "500-00013-r01"
        ],
        "version": [
          "03.02.07"
        ],
        "build": [
          "4c9d48"
        ]
      },
      {
        "$": {
          "name": "agf"
        },
        "pn": [
          "500-00012-r01"
        ],
        "version": [
          "02.02.00"
        ],
        "build": [
          "c00a8f"
        ]
      },
      {
        "$": {
          "name": "security"
        },
        "pn": [
          "500-00016-r01"
        ],
        "version": [
          "02.00.00"
        ],
        "build": [
          "54a6dc"
        ]
      }
    ],
    "build_info": [
      {
        "build_time_gmt": [
          "1542157882"
        ],
        "build_id": [
          "release-4.10.x-103-Nov-12-18-18:25:06"
        ]
      }
    ]
  }
}
[3/2/2021, 9:25:57 AM] [enphaseEnvoy] Device: <IP_ADDRESS> Envoy-S, saved Device Info successful.
[3/2/2021, 9:25:57 AM] [enphaseEnvoy] Device: <IP_ADDRESS> Envoy-S, requesting devices info eror: TypeError: Cannot read property 'state' of undefined
    at envoyDevice.getDeviceInfo (/homebridge/node_modules/homebridge-enphase-envoy/index.js:1469:49), state: Offline trying to reconnect.

Rolled back to 4.6.14 and no errors and EVE is working
CMD: npm install --save<EMAIL_ADDRESS>[3/2/2021, 9:28:34 AM] [enphaseEnvoy] Device <IP_ADDRESS> Envoy-S, get devices data inventory: [ [Object], [Object], [Object] ] info: <?xml version='1.0' encoding='UTF-8'?>
<envoy_info>
  <time>1614673703</time>
  <device>
    <sn>121921106049</sn>
    <pn>800-00553-r02</pn>
    <software>R4.10.35</software>
    <euaid>4c8675</euaid>
    <seqnum>0</seqnum>
    <apiver>1</apiver>
    <imeter>false</imeter>
  </device>
  <package name='rootfs'>
    <pn>500-00001-r01</pn>
    <version>02.00.00</version>
    <build>945</build>
  </package>
  <package name='kernel'>
    <pn>500-00011-r01</pn>
    <version>04.00.00</version>
    <build>5bb754</build>
  </package>
  <package name='boot'>
    <pn>590-00018-r01</pn>
    <version>02.00.01</version>
    <build>426697</build>
  </package>
  <package name='app'>
    <pn>500-00002-r01</pn>
    <version>04.10.35</version>
    <build>6ed292</build>
  </package>
  <package name='devimg'>
    <pn>500-00005-r01</pn>
    <version>01.02.186</version>
    <build>d0d70f</build>
  </package>
  <package name='geo'>
    <pn>500-00008-r01</pn>
    <version>02.01.22</version>
    <build>06e201</build>
  </package>
  <package name='backbone'>
    <pn>500-00010-r01</pn>
    <version>04.10.25</version>
    <build>7b7de5</build>
  </package>
  <package name='meter'>
    <pn>500-00013-r01</pn>
    <version>03.02.07</version>
    <build>4c9d48</build>
  </package>
  <package name='agf'>
    <pn>500-00012-r01</pn>
    <version>02.02.00</version>
    <build>c00a8f</build>
  </package>
  <package name='security'>
    <pn>500-00016-r01</pn>
    <version>02.00.00</version>
    <build>54a6dc</build>
  </package>
  <build_info>
    <build_time_gmt>1542157882</build_time_gmt>
    <build_id>release-4.10.x-103-Nov-12-18-18:25:06</build_id>
  </build_info>
</envoy_info>
 meters: []
[3/2/2021, 9:28:34 AM] [enphaseEnvoy] Device: <IP_ADDRESS> Envoy-S, parse info.xml successful: {
  "envoy_info": {
    "time": [
      "1614673703"
    ],
    "device": [
      {
        "sn": [
          "121921106049"
        ],
        "pn": [
          "800-00553-r02"
        ],
        "software": [
          "R4.10.35"
        ],
        "euaid": [
          "4c8675"
        ],
        "seqnum": [
          "0"
        ],
        "apiver": [
          "1"
        ],
        "imeter": [
          "false"
        ]
      }
    ],
    "package": [
      {
        "$": {
          "name": "rootfs"
        },
        "pn": [
          "500-00001-r01"
        ],
        "version": [
          "02.00.00"
        ],
        "build": [
          "945"
        ]
      },
      {
        "$": {
          "name": "kernel"
        },
        "pn": [
          "500-00011-r01"
        ],
        "version": [
          "04.00.00"
        ],
        "build": [
          "5bb754"
        ]
      },
      {
        "$": {
          "name": "boot"
        },
        "pn": [
          "590-00018-r01"
        ],
        "version": [
          "02.00.01"
        ],
        "build": [
          "426697"
        ]
      },
      {
        "$": {
          "name": "app"
        },
        "pn": [
          "500-00002-r01"
        ],
        "version": [
          "04.10.35"
        ],
        "build": [
          "6ed292"
        ]
      },
      {
        "$": {
          "name": "devimg"
        },
        "pn": [
          "500-00005-r01"
        ],
        "version": [
          "01.02.186"
        ],
        "build": [
          "d0d70f"
        ]
      },
      {
        "$": {
          "name": "geo"
        },
        "pn": [
          "500-00008-r01"
        ],
        "version": [
          "02.01.22"
        ],
        "build": [
          "06e201"
        ]
      },
      {
        "$": {
          "name": "backbone"
        },
        "pn": [
          "500-00010-r01"
        ],
        "version": [
          "04.10.25"
        ],
        "build": [
          "7b7de5"
        ]
      },
      {
        "$": {
          "name": "meter"
        },
        "pn": [
          "500-00013-r01"
        ],
        "version": [
          "03.02.07"
        ],
        "build": [
          "4c9d48"
        ]
      },
      {
        "$": {
          "name": "agf"
        },
        "pn": [
          "500-00012-r01"
        ],
        "version": [
          "02.02.00"
        ],
        "build": [
          "c00a8f"
        ]
      },
      {
        "$": {
          "name": "security"
        },
        "pn": [
          "500-00016-r01"
        ],
        "version": [
          "02.00.00"
        ],
        "build": [
          "54a6dc"
        ]
      }
    ],
    "build_info": [
      {
        "build_time_gmt": [
          "1542157882"
        ],
        "build_id": [
          "release-4.10.x-103-Nov-12-18-18:25:06"
        ]
      }
    ]
  }
}
[3/2/2021, 9:28:34 AM] [enphaseEnvoy] Device: <IP_ADDRESS> Envoy-S, saved Device Info successful.
[3/2/2021, 9:28:34 AM] [enphaseEnvoy] -------- Envoy-S --------
[3/2/2021, 9:28:34 AM] [enphaseEnvoy] Manufacturer: Enphase
[3/2/2021, 9:28:34 AM] [enphaseEnvoy] Model: Envoy
[3/2/2021, 9:28:34 AM] [enphaseEnvoy] Meters: 0
[3/2/2021, 9:28:34 AM] [enphaseEnvoy] Q-Relays: 1
[3/2/2021, 9:28:34 AM] [enphaseEnvoy] Encharges: 0
[3/2/2021, 9:28:34 AM] [enphaseEnvoy] Inverters: 14
[3/2/2021, 9:28:34 AM] [enphaseEnvoy] Firmware: R4.10.35
[3/2/2021, 9:28:34 AM] [enphaseEnvoy] SerialNr:<PHONE_NUMBER>49
[3/2/2021, 9:28:34 AM] [enphaseEnvoy] Time: 3/2/2021, 9:28:23 AM
[3/2/2021, 9:28:34 AM] [enphaseEnvoy] ----------------------------------
[3/2/2021, 9:28:40 AM] [enphaseEnvoy] Debug home: {
  software_build_epoch:<PHONE_NUMBER>,
  is_nonvoy: false,
  db_size: '39 MB',
  db_percent_full: '10',
  timezone: 'Europe/Brussels',
  current_date: '03/02/2021',
  current_time: '09:28',
  network: [Object],
  tariff: 'none',
  comm: [Object],
  alerts: [],
  update_status: 'satisfied'
}, inventory: [ [Object], [Object], [Object] ], meters: [], production: {
  wattHoursToday: 191,
  wattHoursSevenDays: 55876,
  wattHoursLifetime: 5035270,
  wattsNow: 187
} productionCT: { production: [Array], storage: [Array] }
[3/2/2021, 9:28:42 AM] [enphaseEnvoy] Debug pcuCommCheck: {
  '121932043894': 3,
  '121932016547': 3,
  '121932034199': 3,
  '121932033746': 4,
  '121932016549': 3,
  '121932039512': 3,
  '121932038960': 4,
  '121932043380': 4,
  '121932039459': 4,
  '121910052727': 3,
  '121932039006': 4,
  '121931158966': 3,
  '121932035604': 3,
  '121931157713': 4,
  '121932039565': 4
}
[3/2/2021, 9:28:42 AM] [enphaseEnvoy] Debug production inverters: [
  [Object], [Object],
  [Object], [Object],
  [Object], [Object],
  [Object], [Object],
  [Object], [Object],
  [Object], [Object],
  [Object], [Object]
]
[3/2/2021, 9:28:42 AM] [enphaseEnvoy] Device: <IP_ADDRESS> Envoy-S, savedProductionPowerMax: 3.445 kW
[3/2/2021, 9:28:42 AM] [enphaseEnvoy] Device: <IP_ADDRESS> Envoy-S, productionPowerMax: 3.445 kW
[3/2/2021, 9:28:42 AM] [enphaseEnvoy] prepareAccessory
[3/2/2021, 9:28:42 AM] [enphaseEnvoy] prepareInformationService
[3/2/2021, 9:28:42 AM] [enphaseEnvoy] prepareEnphaseServices
[3/2/2021, 9:28:43 AM] [enphaseEnvoy] Device: <IP_ADDRESS>, publishExternalAccessories: Envoy-S.

Try beta 19

Try beta 19

Thanks! Fixed
[3/2/2021, 11:56:50 AM] [enphaseEnvoy] Device: <IP_ADDRESS> Envoy-S, saved Device Info successful.
[3/2/2021, 11:56:50 AM] [enphaseEnvoy] -------- Envoy-S --------
[3/2/2021, 11:56:50 AM] [enphaseEnvoy] Manufacturer: Enphase
[3/2/2021, 11:56:50 AM] [enphaseEnvoy] Model: Envoy
[3/2/2021, 11:56:50 AM] [enphaseEnvoy] Api ver: 1
[3/2/2021, 11:56:50 AM] [enphaseEnvoy] Firmware: R4.10.35
[3/2/2021, 11:56:50 AM] [enphaseEnvoy] SerialNr:<PHONE_NUMBER>49
[3/2/2021, 11:56:50 AM] [enphaseEnvoy] Time: 3/2/2021, 11:56:39 AM
[3/2/2021, 11:56:50 AM] [enphaseEnvoy] ------------------------------
[3/2/2021, 11:56:50 AM] [enphaseEnvoy] Meters: No
[3/2/2021, 11:56:50 AM] [enphaseEnvoy] Q-Relays: 1
[3/2/2021, 11:56:50 AM] [enphaseEnvoy] Encharges: 0
[3/2/2021, 11:56:50 AM] [enphaseEnvoy] Inverters: 14
[3/2/2021, 11:56:50 AM] [enphaseEnvoy] ------------------------------

Beta 20 will identify your envoy correct

Just as info, if U have Mac device then the Controller app is now available also for Mac.

Beta 20 will identify your envoy correct

Is this correct?
[3/2/2021, 12:06:56 PM] [enphaseEnvoy] -------- Envoy-S --------
[3/2/2021, 12:06:56 PM] [enphaseEnvoy] Manufacturer: Enphase
[3/2/2021, 12:06:56 PM] [enphaseEnvoy] Model: ENV-S-WB-230
[3/2/2021, 12:06:56 PM] [enphaseEnvoy] Api ver: 1
[3/2/2021, 12:06:56 PM] [enphaseEnvoy] Firmware: R4.10.35
[3/2/2021, 12:06:56 PM] [enphaseEnvoy] SerialNr:<PHONE_NUMBER>49
[3/2/2021, 12:06:56 PM] [enphaseEnvoy] Time: 3/2/2021, 12:06:45 PM
[3/2/2021, 12:06:56 PM] [enphaseEnvoy] ------------------------------
[3/2/2021, 12:06:56 PM] [enphaseEnvoy] Meters: No
[3/2/2021, 12:06:56 PM] [enphaseEnvoy] Q-Relays: 1
[3/2/2021, 12:06:56 PM] [enphaseEnvoy] Encharges: 0
[3/2/2021, 12:06:56 PM] [enphaseEnvoy] Inverters: 14
[3/2/2021, 12:06:56 PM] [enphaseEnvoy] ------------------------------

Just as info, if U have Mac device then the Controller app is now available also for Mac.

I have a mac. Are you pointing to the bitbar solution with homebrew?

Y do not need home brew just look in the Mac App Store for Controller app

Beta 20 will identify your envoy correct

Is this correct?
[3/2/2021, 12:06:56 PM] [enphaseEnvoy] -------- Envoy-S --------
[3/2/2021, 12:06:56 PM] [enphaseEnvoy] Manufacturer: Enphase
[3/2/2021, 12:06:56 PM] [enphaseEnvoy] Model: ENV-S-WB-230
[3/2/2021, 12:06:56 PM] [enphaseEnvoy] Api ver: 1
[3/2/2021, 12:06:56 PM] [enphaseEnvoy] Firmware: R4.10.35
[3/2/2021, 12:06:56 PM] [enphaseEnvoy] SerialNr:<PHONE_NUMBER>49
[3/2/2021, 12:06:56 PM] [enphaseEnvoy] Time: 3/2/2021, 12:06:45 PM
[3/2/2021, 12:06:56 PM] [enphaseEnvoy] ------------------------------
[3/2/2021, 12:06:56 PM] [enphaseEnvoy] Meters: No
[3/2/2021, 12:06:56 PM] [enphaseEnvoy] Q-Relays: 1
[3/2/2021, 12:06:56 PM] [enphaseEnvoy] Encharges: 0
[3/2/2021, 12:06:56 PM] [enphaseEnvoy] Inverters: 14
[3/2/2021, 12:06:56 PM] [enphaseEnvoy] ------------------------------

Yes it is correct Envoy-S Standard EU

Just as info, if U have Mac device then the Controller app is now available also for Mac.

I have a mac. Are you pointing to the bitbar solution with homebrew?

Regarding homebrew just try the command from readme in Mac console.

You do not need homebrew just look in the Mac App Store for Controller app. Since today is also available for mac

I can't find it, do you have a link?

https://apps.apple.com/us/app/controller-for-homekit/id1198176727


[7.x] Add comment to Query Builder

Hi!
I saw this on laravel/ideas #1754 and thought it would have been helpful when debugging slow queries.
I've implemented it with only one comment in mind. If you add more than comment, only the last one will stay.
Any feedback will be welcome :)

Thanks for your pull request to Laravel!
Unfortunately, I'm going to delay merging this code for now. To preserve our ability to adequately maintain the framework, we need to be very careful regarding the amount of code we include.
If possible, please consider releasing your code as a package so that the community can still take advantage of your contributions!
If you feel absolutely certain that this code corrects a bug in the framework, please "@" mention me in a follow-up comment with further explanation so that GitHub will send me a notification of your response.


Newline breaks CJK chars

the invalid char appears around the line break --- it seems that the newline char was inserted in the middle of code point.
�
��

How can I reproduce this behavior?
Here is how webtail processes lines:

line := <-tw.tf.Lines // tw.tf is a https://pkg.go.dev/github.com/nxadm/tail#Tail
msg := &TailerMessage{Channel: tw.channel, Data: line.Text}
data, _ := json.Marshal(TailMessage{Type: "log", Data: msg.Data})
client.send <- data // see https://github.com/gorilla/websocket/blob/master/examples/chat/client.go#L48

Do you have an idea what step that newline char might be inserted in?

Check this out with the default Docker image:
test.log
You can see the broken chars. And if you remove the heading 1or2, it looks normal.

@hyorigo, thank you for this sample.
The cause of such behaviour lies in read buffer limit which is 180 bytes by default.
Here this default value is set
Here nxadm/tail translates it to bufio
I think there are 2 solutions:
1, Add rune support to nxadm/tail
2. Run webtail with --skip 0 or some value which is big enough

== no feedback for 3+ months. Closing ==


fix(kill_all_flag): kill main process after killing childs

I really don't sure about PR as I am not profound linux user. But this issue is https://github.com/kimmobrunfeldt/concurrently/issues/4 a real blocker as I believe concurrently used most of the time with npm run. So with this PR I try to solve this by killing main process after trying to kill childs. At least this kills everything so seems like valid solution.

Checked again and looks like it does not solve the problem at all. Closing


GEODE-7374: Fix class cast exception

Authored-by: Joris Melchior<EMAIL_ADDRESS>Added test to replicate the issue and fixed code so that ResultModel is wrapped in CommandResult to fix the issue
For all changes:

[x] Is there a JIRA ticket associated with this PR? Is it referenced in the commit message?

[x] Has your PR been rebased against the latest commit within the target branch (typically develop)?

[x] Is your initial contribution a single, squashed commit?

[x] Does gradlew build run cleanly?

[x] Have you written or updated unit tests to verify your changes?

[ ] If adding new dependencies to the code, are these dependencies licensed in a way that is compatible for inclusion under ASF 2.0?

Note:
Please ensure that once the PR is submitted, check Concourse for build issues and
submit an update to your PR as soon as possible. If you need help, please send an
email to<EMAIL_ADDRESS>

@jinmeiliao @onichols-pivotal @dschneider-pivotal


Binary Ninja - Update subsystem: Windows - Cannot Update; cannot locate Python

Binary Ninja Version: 1.1.1259 Personal, f51163e8
Platform: Windows 10 (10.0)
When: Brand new install @ 20180830.
What:

Errors identified in the log:
-- Unable to initialize Python API
-- Error fetching channel list: Download provider: DefaultDownloadProvider not available!

Warning identified in the log:
-- Falling back to bundled Python; system failed to load ''

Prior to this issue, I also experienced the DownloadProvider issue with the 1.1.30... release-personal-dev update channel.
Impact from error:

Python not available within Binary Ninja
Binary Ninja unable to locate correct settings to populate the Update Channel and Select Version fields in the Updates section of the Preferences dialog.

Note: Have not attempted to recreate in Linux.
[Edit @ 20180830 :: 0212 EST] Additional reproduction steps:

Reverted to installer for 1.1.922 Personal, Build ID 4d40ebb7 (clean install)
Restarted application, the auto-upgrade feature downloaded successfully.
Installation update verifier runs.
Result: update to: 1.1.1259 Personal, Build ID f5116e8
-- Python API still won't initialize
-- DefaultDownloadProvider no longer available (original reported error)

The warning about falling back to buit in isn't a problem, it was fixed in recent dev builds. That said, the error and lack of updates is a problem.
Can you verify the hash of the installer via https://binary.ninja/js/hashes.js to make sure it wasn't corrupted?
Do you have anything in your settings.json? (See: https://docs.binary.ninja/getting-started/index.html).

As a quick additional check: do you have a version of Python installed system-wide, and if so, is PYTHONPATH set in your environment?
If so, I'd try both starting Binary Ninja with no Python environment variables set, seeing if that lets our bundled Python initialize successfully, or alternatively manually pointing the Python interpreter in settings.json to the python27.dll from your system/user installation.

Sorry for the wall of text (and delay getting you answers):
Quick question: what's the settings.json config string to point to a specific python interepreter? I didn't see that in the getting-started table of settings, and the preferences modal doesn't allow you to specify there.

C:\Users\usera>echo %PYTHONPATH%
C:\Python27\Lib;C:\Python27\DLLs
Installers:
BinaryNinja-personal-windows.exe                            |
$ md5sum BinaryNinja-personal-windows_1.1.922_4d40ebb7.exe
2f8ccf9b4aaaabd3352d8f62dc8a7e07 *BinaryNinja-personal-windows.exe
usera@lalala /cygdrive/f/tools/BinaryNinja
$ sha256sum BinaryNinja-personal-windows_1.1.922_4d40ebb7.exe
82582985bdaf5e03bc95e86c9fc7240143e8c5c6d95c4972c4ab4e95ba946f05 *BinaryNinja-personal-windows.exe
BinaryNinja-personal-windows_20180830_1.1.1259_f51163e8.exe |
$ md5sum BinaryNinja-personal-windows_20180830_1.1.1259_f51163e8.exe
2bef8e453450b2b7bb7f19c6e83cf9d6 *BinaryNinja-personal-windows_20180830_1.1.1259_f51163e8.exe
usera@lalala /cygdrive/f/tools/BinaryNinja
$ sha256sum BinaryNinja-personal-windows_20180830_1.1.1259_f51163e8.exe
fa517214a0f7464a5579cd17bc00a628c6a6febbf38e390fe72afe53e16217bc *BinaryNinja-personal-windows_20180830_1.1.1259_f51163e8.exe

[Fresh Install]
1.1.922 Personal, Build ID 4d40ebb7
C:\Users\usera\AppData\Roaming\Binary Ninja\settings.json
pre-install:    N/A
post-install:   N/A
running:
{
"ui" :
{
"colorblind" : false
}
}
update/ba42f444c143ece14f00d93076b91b5a9fcee30b17ef9cec2f7bf5bdf60acc50/manifest
{"auto":true,"channel":"release-personal","last_check":0,"pending":false}
Auto-update ran automatically; successfully
Restart:
1.1.1259 Personal, Build ID f51163e8
Result: Auto-update cannot find DefaultDownload Provider
[Interrupt auto-update by specifying settings in the manifest file]

Default Update channel: "latest Binary Ninja Personal release"
Switch to "Binary Ninja Personal development build 1.1.1313-dev Personal" via manifest (or modal)

manually updating manifest: {"auto":false,"channel":"dev-personal","last_check":0,"pending":false}

Help - Check for Updates - <Downloading...> - 975 files
Update path: C:\Users\usera\AppData\Roaming\Binary Ninja\update\ba42f444c143ece14f00d93076b91b5a9fcee30b17ef9cec2f7bf5bdf60acc50
manifest file: {"auto":false,"channel":"dev-personal","last_check":0,"pending":false}
application restart: version reflected: 1.1.1313-dev Personal, Build ID 3f8fa19e
mainfest file updates: shows the hash of all files
update directory updated, 964 / 975 files deleted
BinaryNinja main path: C:\Program Files\Vector35\BinaryNinja
-- uninstall.exe does not exist after update
-- python path: C:\Program Files\Vector35\BinaryNinja\python\binaryninja | python scripts exist

[Fresh Install]
1.1.1259 Personal, Build ID f51163e8
C:\Users\usera\AppData\Roaming\Binary Ninja\settings.json
pre-install:    N/A
post-install:   N/A
running:
{
"ui" :
{
"colorblind" : false
}
}
manually updating manifest: {"auto":false,"channel":"release-personal","last_check":0,"pending":false}
Result: Auto-update cannot find DefaultDownload Provider
Specifying the manifest directly: {"auto":false,"channel":"release-personal","last_check":0,"pending":false}
Select version dropdown box is empty

Yup, definitely looks like the problem is the existing python install conflicting. You have two options: either remove the pythonpath so that our existing python doesn't try to load libraries from a different install, or point BN to be using that python directly instead (with something like: https://gist.github.com/psifertex/a1cd72f2a4ecafb309fe64ce0c5e6612)

Haven't heard back yet, but we're pretty confident the above solution will work so I'm going to close this issue out. If you still have problems after following the above troubleshooting, feel free to re-open.


Allow input csv and output csv in experiment run

For #102

Codecov Report
Attention: Patch coverage is 54.54545% with 25 lines in your changes missing coverage. Please review.

Project coverage is 63.41%. Comparing base (b6257fe) to head (680becf).

Files with missing lines
Patch %
Lines

src/prompto/experiment.py
50.00%
23 Missing :warning:

src/prompto/scripts/run_experiment.py
71.42%
2 Missing :warning:

Additional details and impacted files
@@            Coverage Diff             @@
##             main     #103      +/-   ##
==========================================
- Coverage   63.86%   63.41%   -0.45%
==========================================
  Files          40       40
  Lines        2142     2184      +42
==========================================
+ Hits         1368     1385      +17
- Misses        774      799      +25

:umbrella: View full report in Codecov by Sentry.
:loudspeaker: Have feedback on the report? Share it here.


Blending Animations together in AnimationPlayer (Editor)

I see no way of blending two animations together inside the AnimationPlayer (Editor).
Why is is not possible?

You can use an AnimationTree node to blend animations:
https://docs.godotengine.org/en/stable/tutorials/animation/animation_tree.html

Please don't ask support questions here, as this issue tracker is meant to be used for bug reports only. Use one of the other community channels instead.


Why using up-pooling instead of 2 stride convtranspose when UP

Hey professor. I remember that the 2 stride conv is more stable than up-pooling (according to DCGAN). However, in your model, the downsampling part is 2 stride conv, while up-pooling in upsampling part.
Could you give us a reason for this?
Looking forward to your reply

this has been discussed in cyclegan
https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/190#issuecomment-358505314


Show categories

You only list the tags of the Hugo posts. But Hugo also supports categories as taxonomies. Could you please also show them when viewing the post?
Example of the markdown page section with categories:
categories:
  - Beginner
  - Server
tags:
  - cicd
  - docker
  - free

Yes - curious on how this can be done. When a post is added, the tags show at the bottom - how can the category also show?

Figured it out. This can be further modified to customize the look.

Added a new partial under layouts/partials called "categories.html" with the below code:
`
{{ range .GetTerms "categories" }}
Categories:
    {{- .LinkTitle -}}

{{ end }}
`

Then in layouts/_default, modified single.html by adding the categories above the {{ Content }} like this:
{{- partial "categories.html" . -}}

Now the categories show up on the posts.


Cucumber expressions: Remove single transformer

I didn't like the choices between Transformer and Function, so I removed Function (and SingleTransformer).
Merging this will cause cucumber/cucumber#1248 to break, but since it's never been released I'm not sure we need to bump the minor release.
I will have to make some minor consistency changes to Ruby and JavaScript, but I don't think it will affect behaviour externally.

Ideally, when using a lambda expression, you won't notice those.  By defining the lambda as  (String[] matches) -> .... or String match -> .... you can decide if you want to use all parameter or only the first one. I expect that most parameter types will use the whole matching making this a nice bit of sugar.
The reason we have a function class is because I had to back port this to 1.7 which doesn't support lambdas yet (but this won't prevent clients at 1.8 from using them).
In javascript you can get the same behaviour when using transformFn.call(null, matches).  If the transformFn is defined as  function(arg1, arg2, ... argN) the matches will be bound to the arguments.

Ok @mpkorstanje I added some more generics, bringing back support for single argument transforms while keeping a single interface.
I couldn't figure out a way to avoid the static factory methods ParameterType.single and ParameterType.multi, but perhaps that's not so bad?
WDYT?

You've found the only two ways to do this. Java needs the type information which requires that you spell out exactly what each method looks like. And as you've also noticed generics are erased which means you also need to use static factory methods to differentiate when using different generics.
So either the information about the number of arguments is contained in the lambda signature (String[] args) -> ... vs (String arg) -> ... or it is contained in the name of the static factory method ParameterType.single vs ParameterType.multi.
Given that choice, the type information has to be conveyed one way or the other, I would prefer to use the lambda signature as it conveys more clearly what the type of args will be and does so very near the location that args will be used. The static factory method conveys it from further away and does so implicitly.
It is also important to keep in mind is that the DataTableType in datatable follows the same pattern. If we change to static constructors here we should do so too for DataTableType. And looking at the difference between DataTableType and ParameterType I can see that that the former spells out the different transformers TableTransformer, TableEntryTransformer, TableRowTransformer and TableCellTransformer. In the latter we have Transformer and Function. So I think we just need to come up with a better name for those two.

Ok. How about Transformer (using String) and MultiTransformer (using String[])?

I added MultiTransformer and got rid of the static ctors.
The only thing that annoys me now is that method references only work if I cast:
(Transformer<Currency>) Currency::getInstance

Should we try to find a way to avoid that?

Should we try to find a way to avoid that?

You can't really avoid that. Both ParameterType and Currency::getInstance are overloaded. This combination prevents resolution. It will work fine with non overloaded method references.

I've removed Function all together.  We only expose Transformer<T> and MultiTransformer<T> so ParameterType.transformer will always be either one or the other. We could try to capture this somehow but this provides us no benefit.
So I've put the adapter pattern back in again. But it is no longer exposed to the world.

@mpkorstanje I tried it out with Java8, and this doesn't seem to work with method references. Did you try it?

Yes! They work for me provided you're using one that is not overloaded. This means Currency::getInstance just won't won't work in this setup. I don't think this is such a bad thing though.
I do think switching parameter order is worse. It makes the API inconsistent.
If you think this is a must we could make the Transform an instance with no public methods and static  constructors for both use cases:
<T> Transformer.transform(Function<String,T>)
<T> Transformer.transformCaptureGroups(Function<String[], T>)

I think it would be a shame not to be able to use method references. User code would be very verbose without them. Not sure how you'd implement it though - want to give it a go?

@aslakhellesoy  check this:
https://github.com/cucumber/cucumber/blob/d7f11dc1335f6db67ce2e776493b0d7cc1faaf9c/cucumber-expressions/java/src/main/java/io/cucumber/cucumberexpressions/Examples.java#L50
I faked out a few parts to just get the point across.
Broadly considered we have to make a trade off between 3 options.

We only support a single transform  (String[] args) -> ..... This means that people will often have to do args[0] in their lambda's and can only use static constructors that accept an string array. Otherwise it's very concise.

We support  (String[] args) -> .... and  (String arg) -> ..... This means that people can use the more common static constructors that accept a string. They won't be able to  use overloaded method references but is otherwise quite concise. The verbosity of a lambda is not much higher then a method reference.

We support transformCaptureGroups((String[] args) -> ....) and transform(String arg) -> ....). This means people can use overloaded method references but adds a constant cost to all cases and doesn't improve the verbosity much when dealing with over loaded method references.

I reckon option 1 is right out. Normally I'd data mine the choice between one and 2 and 3, but from experience I'd say 2 is the best one.

I've renamed the MultiTransformer to CaptureGroupTransformer. It doesn't transform capture groups, but it does transform the parts of the string matching those specific capture group. So... close enough.
 I've fixed that too.
I'm happy for now. Lets release this!


[SPARK-23014][SS] Fully remove V1 memory sink.

What changes were proposed in this pull request?
There is a MemorySink v2 already so v1 can be removed. In this PR I've removed it completely.
What this PR contains:

V2 memory sink removal
V2 memory sink renamed to become the only implementation
Since DSv2 sends exceptions in a chained format (linking them with cause field) I've made python side compliant
Adapted all the tests

How was this patch tested?
Existing unit tests.

Can one of the admins verify this patch?

Can one of the admins verify this patch?

Just for the sake of understanding why python adaptation needed.
With V1 memory sink the following exception arrived:
Job aborted due to stage failure: Task 0 in stage 1.0 failed 1 times, most recent failure: Lost task 0.0 in stage 1.0 (TID 1, localhost, executor driver): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/Users/gaborsomogyi/spark/python/lib/pyspark.zip/pyspark/worker.py", line 428, in main
    process()
  File "/Users/gaborsomogyi/spark/python/lib/pyspark.zip/pyspark/worker.py", line 423, in process
    serializer.dump_stream(func(split_index, iterator), outfile)
  File "/Users/gaborsomogyi/spark/python/pyspark/serializers.py", line 457, in dump_stream
    self.serializer.dump_stream(self._batched(iterator), stream)
  File "/Users/gaborsomogyi/spark/python/pyspark/serializers.py", line 141, in dump_stream
    for obj in iterator:
  File "/Users/gaborsomogyi/spark/python/pyspark/serializers.py", line 446, in _batched
    for item in iterator:
  File "<string>", line 1, in <lambda>
  File "/Users/gaborsomogyi/spark/python/lib/pyspark.zip/pyspark/worker.py", line 86, in <lambda>
    return lambda *a: f(*a)
  File "/Users/gaborsomogyi/spark/python/pyspark/util.py", line 99, in wrapper
    return f(*args, **kwargs)
  File "/Users/gaborsomogyi/spark/python/pyspark/sql/tests/test_streaming.py", line 217, in <lambda>
    bad_udf = udf(lambda x: 1 / 0)
ZeroDivisionError: integer division or modulo by zero

    at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:453)
    at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$2.read(PythonUDFRunner.scala:81)
    at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$2.read(PythonUDFRunner.scala:64)
    at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:406)
    at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:489)
    at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:458)
    at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:458)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.processNext(generated.java:25)
    at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
    at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:748)
    at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:255)
    at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:852)
    at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:852)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:327)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:291)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
    at org.apache.spark.scheduler.Task.run(Task.scala:121)
    at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:425)
    at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1349)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:428)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    at java.lang.Thread.run(Thread.java:748)

Driver stacktrace:
=== Streaming Query ===
Identifier: this_query [id = fcda74c8-05e0-4c74-ac45-3444f3e50c63, runId = 7534a374-5d15-4986-bae1-8fa0664c647b]
Current Committed Offsets: {}
Current Available Offsets: {FileStreamSource[file:/Users/gaborsomogyi/spark/python/test_support/sql/streaming]: {"logOffset":0}}

Current State: ACTIVE
Thread State: RUNNABLE

Logical Plan:
Project [<lambda>(value#4) AS <lambda>(value)#17]
+- StreamingExecutionRelation FileStreamSource[file:/Users/gaborsomogyi/spark/python/test_support/sql/streaming], [value#4]

With V2 memory sink the following exception arrived (here additional exceptions are linked to the top level one, for instance ZeroDivisionError):
Writing job aborted.
=== Streaming Query ===
Identifier: this_query [id = d37ca2ea-edb7-4670-b4cc-75b0d704496e, runId = 587d62da-8355-4f66-a9d8-708c154ef953]
Current Committed Offsets: {}
Current Available Offsets: {FileStreamSource[file:/Users/gaborsomogyi/spark/python/test_support/sql/streaming]: {"logOffset":0}}

Current State: ACTIVE
Thread State: RUNNABLE

Logical Plan:
WriteToMicroBatchDataSource org.apache.spark.sql.execution.streaming.sources.MemoryStreamingWrite@1a3691ef
+- Project [<lambda>(value#18) AS <lambda>(value)#32]
   +- StreamingExecutionRelation FileStreamSource[file:/Users/gaborsomogyi/spark/python/test_support/sql/streaming], [value#18]

Test build #104701 has started for PR 24403 at commit 7b7d651.

Test build #104701 has finished for PR 24403 at commit 7b7d651.

This patch fails Scala style tests.
This patch merges cleanly.
This patch adds no public classes.

Merged build finished. Test FAILed.

Test FAILed.
Refer to this link for build results (access rights to CI server needed):
https://amplab.cs.berkeley.edu/jenkins//job/SparkPullRequestBuilder/104701/
Test FAILed.

Test build #104702 has started for PR 24403 at commit 247caad.

Test build #104702 has finished for PR 24403 at commit 247caad.

This patch fails Spark unit tests.
This patch merges cleanly.
This patch adds no public classes.

Merged build finished. Test FAILed.

Test FAILed.
Refer to this link for build results (access rights to CI server needed):
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Test FAILed.

Test build #104705 has started for PR 24403 at commit d65997d.

Test build #104705 has finished for PR 24403 at commit d65997d.

This patch fails Spark unit tests.
This patch merges cleanly.
This patch adds no public classes.

Merged build finished. Test FAILed.

Test FAILed.
Refer to this link for build results (access rights to CI server needed):
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Test FAILed.

Test build #104713 has started for PR 24403 at commit f693e2b.

Test build #104713 has finished for PR 24403 at commit f693e2b.

This patch fails Scala style tests.
This patch merges cleanly.
This patch adds no public classes.

Merged build finished. Test FAILed.

Test FAILed.
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Test FAILed.

Test build #104714 has started for PR 24403 at commit 4bd49c6.

Test build #104714 has finished for PR 24403 at commit 4bd49c6.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

Merged build finished. Test PASSed.

Test PASSed.
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Test PASSed.

cc @jose-torres since you've started this originally and @HyukjinKwon because of the python change.

Not much movement cc @vanzin

Test build #104992 has started for PR 24403 at commit 728fa06.

Test build #104992 has finished for PR 24403 at commit 728fa06.

This patch fails Scala style tests.
This patch merges cleanly.
This patch adds no public classes.

Merged build finished. Test FAILed.

Test FAILed.
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Test FAILed.

Test build #104995 has started for PR 24403 at commit 91d35a0.

Test build #104999 has started for PR 24403 at commit 41ffa5d.

Test build #104999 has finished for PR 24403 at commit 41ffa5d.

This patch fails Python style tests.
This patch merges cleanly.
This patch adds no public classes.

Merged build finished. Test FAILed.

Test FAILed.
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Test FAILed.

Test build #105000 has started for PR 24403 at commit 8f432f1.

Test build #105000 has finished for PR 24403 at commit 8f432f1.

This patch fails Python style tests.
This patch merges cleanly.
This patch adds no public classes.

Merged build finished. Test FAILed.

Test FAILed.
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Test FAILed.

Test build #105001 has started for PR 24403 at commit 2daaae3.

Test build #104995 has finished for PR 24403 at commit 91d35a0.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

Merged build finished. Test PASSed.

Test PASSed.
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Test PASSed.

Test build #105001 has finished for PR 24403 at commit 2daaae3.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

Merged build finished. Test PASSed.

Test PASSed.
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Test PASSed.

Merging to master.


Mapping source ArrayList<> to target class derived from ArrayList<>

Hi,
we ran into the following problem recently: Use case:
A source root object ("class-a") contains a property that is of type ArrayList<>.
The target root object ("class-b") contains a property that is derived from ArrayList<>. (These target classes are generated by a 3rd party code-generator, so we cannot change this behaviour).
Observation 1): On classes derived from ArrayList<>, Dozer does not "see" any properties.
I made and an attached an example project that demonstrates this behaviour.
The target class that is derived from ArrayList<> is called:
rms.bean_b.ListWrapper.
Note that this class does not make any use of the derived ArrayList<>, it's job is just to show that properties of a class are not seen by Dozer, if that class is derived by ArrayList<> (or any other java.util.* Collection class).
rms.bean_b.ListWrapper has these methods: getContainedList & setContainedList.
Error message at runtime:
Error: org.dozer.MappingException: No read or write method found for field (listwrapper.containedlist) in class (class rms.bean_b.RootNode)
org.dozer.MappingException: No read or write method found for field (listwrapper.containedlist) in class (class rms.bean_b.RootNode)
But if you simply remove "extends ArrayList<>" from the class definition, the properties are seen by Dozer and the respective setters and getters are called.
Observation 2): Dozer's automatic collection mapping functions are not triggered when mapping an ArrayList<> to a class derived from ArrayList<>. In that case the error message is:
Error: org.dozer.MappingException: Illegal object type for the method 'setListWrapper'.
Expected types:
rms.bean_b.ListWrapper
Actual types:
java.util.ArrayList
What we really want to achieve: Copy the source ArrayList to the target object which is derived by ArrayList, using the usual Dozer collection mapping features.
The only way we were able to map an ArrayList to a class derived from ArrayList is by using a configurable custom converter.
At least the behaviour described by Observation 1) I would regard as a bug.
DozerList2ObjMappingTest.tar.gz

@scrolls330 ; instead of attaching a tar, can you create a github repo which i can clone from please. Makes testing / explaining the problem a lot easier.

Sorry, here it is:
https://github.com/scrolls330/DozerList2ObjMappingTest.git
mvn package
mvn exec:java


add use SystemdCgroup e2e test

What is the problem you're trying to solve
current e2e missing use SystemdCgroup situation
Describe the solution you'd like
add use SystemdCgroup situation to e2e
Additional context
No response

/assign

I can try the issues?

I having provide a pr https://github.com/containerd/containerd/pull/9470, but have another issue, you can try https://github.com/containerd/containerd/issues/9720

ok thank you


Prevent journal overriding if existing ID is used when creating new journal

Currently if you use same ID when creating new journal - old one will be overriden.
Create Journal modal should block using existing ID.

Released in 1.3.0