install-pytorch.sh does not work.

I have a Jetson Nano with the OS installed from nv-jetson-nano-sd-card-image-r32.3.1.zip
As a newbie to Jetson and Python I started working through the Hello AI World pages and all was going well until I came to the "Transfer Learning with PyTorch". Running the "install-pytorch.sh" and selecting "PyTorch c1.1.0 for Pyhon 3.6" seemed to work OK. But the installed torchvision does not work.
$ python3
Python 3.6.9 (default, Nov  7 2019, 10:44:02)
[GCC 8.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> print(torch.__version__)
1.1.0
>>> print('CUDA available: ' + str(torch.cuda.is_available()))
CUDA available: True
>>> a = torch.cuda.FloatTensor(2).zero_()
>>> a = torch.cuda.FloatTensor(2).zero_()
>>> print('Tensor a = ' + str(a))
Tensor a = tensor([0., 0.], device='cuda:0')
>>> b = torch.randn(2).cuda()
>>> print('Tensor b = ' + str(b))
Tensor b = tensor([-0.0116, -0.2358], device='cuda:0')
>>> c = a + b
>>> print('Tensor c = ' + str(c))
Tensor c = tensor([-0.0116, -0.2358], device='cuda:0')
>>> import torchvision
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python3.6/dist-packages/torchvision-0.3.0-py3.6-linux-aarch64.egg/torchvision/__init__.py", line 2, in <module>
  File "/usr/local/lib/python3.6/dist-packages/torchvision-0.3.0-py3.6-linux-aarch64.egg/torchvision/datasets/__init__.py", line 9, in <module>
  File "/usr/local/lib/python3.6/dist-packages/torchvision-0.3.0-py3.6-linux-aarch64.egg/torchvision/datasets/fakedata.py", line 3, in <module>
  File "/usr/local/lib/python3.6/dist-packages/torchvision-0.3.0-py3.6-linux-aarch64.egg/torchvision/transforms/__init__.py", line 1, in <module>
  File "/usr/local/lib/python3.6/dist-packages/torchvision-0.3.0-py3.6-linux-aarch64.egg/torchvision/transforms/transforms.py", line 17, in <module>
  File "/usr/local/lib/python3.6/dist-packages/torchvision-0.3.0-py3.6-linux-aarch64.egg/torchvision/transforms/functional.py", line 5, in <module>
ImportError: cannot import name 'PILLOW_VERSION'
>>>

I subsequently tried to install PyTorch v1.4.0 from the announcement post on the forum here: https://devtalk.nvidia.com/default/topic/1049071/jetson-nano/pytorch-for-jetson-nano-version-1-4-0-now-available/1
That also failed, sadly I did not record the error.
So how does one install PyTorch on  Nano with the current OS such that one can complete the inference exercises?
Can the document be updated to describe whatever installation process does work?

Hi @ZiCog, can you try running pip3 install 'pillow<7' and then see if you can import torchvision ?
Here is the PyTorch bug about this:  https://github.com/pytorch/vision/issues/1712

Bingo! That did it.
$ pip3 install "pillow<7"
Defaulting to user installation because normal site-packages is not writeable
Collecting pillow<7
  Downloading Pillow-6.2.2.tar.gz (37.8 MB)
     |████████████████████████████████| 37.8 MB 6.4 kB/s
Building wheels for collected packages: pillow
  Building wheel for pillow (setup.py) ... done
  Created wheel for pillow: filename=Pillow-6.2.2-cp36-cp36m-linux_aarch64.whl size=966497 sha256=d06570fe4c6adf508f1c4a3766f545ff62d77f7628e164733235e37faaf5d87e
  Stored in directory: /home/michael/.cache/pip/wheels/19/4f/2e/b77ea60ebf837f5b06ed497d4b6798c3e4da683def2ad65ca2
Successfully built pillow
Installing collected packages: pillow
Successfully installed pillow-6.2.2
michael@jetson-nano:~/jetson-inference/build$ python3
Python 3.6.9 (default, Nov  7 2019, 10:44:02)
[GCC 8.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torchvision
>>> print(torchvision.__version__)
0.3.0
>>>

Thanks.

OK thanks, I just patched the install-pytorch.sh script in master for this - see commit 83f2ae
I also updated the manual install instructions for torchvision here.

Great.
As I said the instructions for installing PyTorch v1.4.0 on that forum page do not work on the Nano. But I guess that is another story.
Anyway the cat-dog training is now underway here.
Thanks again.

Dusty-nv,
Thanks for the update. I'm running Jetpack 4.3 on my TX2. I ran the wheel files to get pytorch installed on python 2.7 and then python 3.6.9. I seem to be able to import torch and torchvision fine under python 2.7 but I'm only able to import torch on python 3.6.9, no torchvision.
Jetpack 4.3
Ubuntu 18.04
Torch 1.4.0 (+libopenblas-base)

import torchvision
Traceback (most recent call last):
File "", line 1, in
ModuleNotFoundError: No Module named 'torchvision'

When trying to install torchvision on the string above my installation exits with;
error: command 'aarch64-linux-gnu-gcc' failed with exit status 1
Anything jump out here? I'm so close. Hate to have to rebuild this again. Best,

When trying to install torchvision per the link in your message above my installation exits with;
error: command 'aarch64-linux-gnu-gcc' failed with exit status 1

Hmm were there any other errors listed while building torchvision?   Typically you would see a fuller error log above a message like this.
If you are building torchvision for Python 2 and then Python 3, you may need a sudo python setup.py clean in between.  And then for Python 3, remember to use sudo python3 setup.py install (instead of python2)

Wow...thanks so much for quick response dusty-nv- I've been stepping through setup.py all morning. I suspect something in my branch.
I used your setup string;
$ sudo apt-get install libjpeg-dev zlib1g-dev
$ git clone --branch build/v0.5.0 https://github.com/pytorch/vision torchvision
$ cd torchvision
$ sudo python3 setup.py install
Unfortuneately, the log is several 1000 lines long so I appeviated it a bit to show the recycling error;
tx2:/jetson-inference/torchvision$ sudo python setup.py clean
Building wheel torchvision-0.5.0
running clean
tx2:/jetson-inference/torchvision$ sudo python3 setup.py install
Building wheel torchvision-0.5.0
running install
running bdist_egg
running egg_info
creating torchvision.egg-info
writing torchvision.egg-info/PKG-INFO
writing dependency_links to torchvision.egg-info/dependency_links.txt
writing requirements to torchvision.egg-info/requires.txt
writing top-level names to torchvision.egg-info/top_level.txt
writing manifest file 'torchvision.egg-info/SOURCES.txt'
reading manifest file 'torchvision.egg-info/SOURCES.txt'
reading manifest template 'MANIFEST.in'
warning: no previously-included files matching 'pycache' found under directory ''
warning: no previously-included files matching '.py[co]' found under directory ''
writing manifest file 'torchvision.egg-info/SOURCES.txt'
installing library code to build/bdist.linux-aarch64/egg
running install_lib
running build_py
creating build
creating build/lib.linux-aarch64-3.6
creating build/lib.linux-aarch64-3.6/torchvision
copying torchvision/extension.py -> build/lib.linux-aarch64-3.6/torchvision
copying torchvision/init.py -> build/lib.linux-aarch64-3.6/torchvision
copying torchvision/utils.py -> build/lib.linux-aarch64-3.6/torchvision
copying torchvision/version.py -> build/lib.linux-aarch64-3.6/torchvision
creating build/lib.linux-aarch64-3.6/torchvision/models
copying torchvision/models/alexnet.py -> build/lib.linux-aarch64-3.6/torchvision/models
copying torchvision/models/googlenet.py -> build/lib.linux-aarch64-3.6/torchvision/models
copying torchvision/models/densenet.py -> build/lib.linux-aarch64-3.6/torchvision/models
copying torchvision/models/shufflenetv2.py -> build/lib.linux-aarch64-3.6/torchvision/models
copying torchvision/models/init.py -> build/lib.linux-aarch64-3.6/torchvision/models
copying torchvision/models/utils.py -> build/lib.linux-aarch64-3.6/torchvision/models
copying torchvision/models/resnet.py -> build/lib.linux-aarch64-3.6/torchvision/models
copying torchvision/models/inception.py -> build/lib.linux-aarch64-3.6/torchvision/models
copying torchvision/models/mnasnet.py -> build/lib.linux-aarch64-3.6/torchvision/models
copying torchvision/models/_utils.py -> build/lib.linux-aarch64-3.6/torchvision/models
copying torchvision/models/mobilenet.py -> build/lib.linux-aarch64-3.6/torchvision/models
copying torchvision/models/squeezenet.py -> build/lib.linux-aarch64-3.6/torchvision/models
copying torchvision/models/vgg.py -> build/lib.linux-aarch64-3.6/torchvision/models
creating build/lib.linux-aarch64-3.6/torchvision/ops
copying torchvision/ops/boxes.py -> build/lib.linux-aarch64-3.6/torchvision/ops
copying torchvision/ops/deform_conv.py -> build/lib.linux-aarch64-3.6/torchvision/ops
copying torchvision/ops/_register_onnx_ops.py -> build/lib.linux-aarch64-3.6/torchvision/ops
copying torchvision/ops/ps_roi_align.py -> build/lib.linux-aarch64-3.6/torchvision/ops
copying torchvision/ops/roi_align.py -> build/lib.linux-aarch64-3.6/torchvision/ops
copying torchvision/ops/init.py -> build/lib.linux-aarch64-3.6/torchvision/ops
copying torchvision/ops/roi_pool.py -> build/lib.linux-aarch64-3.6/torchvision/ops
copying torchvision/ops/new_empty_tensor.py -> build/lib.linux-aarch64-3.6/torchvision/ops
copying torchvision/ops/_utils.py -> build/lib.linux-aarch64-3.6/torchvision/ops
copying torchvision/ops/feature_pyramid_network.py -> build/lib.linux-aarch64-3.6/torchvision/ops
copying torchvision/ops/misc.py -> build/lib.linux-aarch64-3.6/torchvision/ops
copying torchvision/ops/ps_roi_pool.py -> build/lib.linux-aarch64-3.6/torchvision/ops
copying torchvision/ops/poolers.py -> build/lib.linux-aarch64-3.6/torchvision/ops
creating build/lib.linux-aarch64-3.6/torchvision/datasets
copying torchvision/datasets/cifar.py -> build/lib.linux-aarch64-3.6/torchvision/datasets
copying torchvision/datasets/folder.py -> build/lib.linux-aarch64-3.6/torchvision/datasets
copying torchvision/datasets/imagenet.py -> build/lib.linux-aarch64-3.6/torchvision/datasets
copying torchvision/datasets/caltech.py -> build/lib.linux-aarch64-3.6/torchvision/datasets
copying torchvision/datasets/sbu.py -> build/lib.linux-aarch64-3.6/torchvision/datasets
copying torchvision/datasets/kinetics.py -> build/lib.linux-aarch64-3.6/torchvision/datasets
copying torchvision/datasets/lsun.py -> build/lib.linux-aarch64-3.6/torchvision/datasets
copying torchvision/datasets/voc.py -> build/lib.linux-aarch64-3.6/torchvision/datasets
copying torchvision/datasets/mnist.py -> build/lib.linux-aarch64-3.6/torchvision/datasets
copying torchvision/datasets/init.py -> build/lib.linux-aarch64-3.6/torchvision/datasets
copying torchvision/datasets/sbd.py -> build/lib.linux-aarch64-3.6/torchvision/datasets
copying torchvision/datasets/utils.py -> build/lib.linux-aarch64-3.6/torchvision/datasets
copying torchvision/datasets/usps.py -> build/lib.linux-aarch64-3.6/torchvision/datasets
copying torchvision/datasets/hmdb51.py -> build/lib.linux-aarch64-3.6/torchvision/datasets
copying torchvision/datasets/phototour.py -> build/lib.linux-aarch64-3.6/torchvision/datasets
copying torchvision/datasets/coco.py -> build/lib.linux-aarch64-3.6/torchvision/datasets
copying torchvision/datasets/omniglot.py -> build/lib.linux-aarch64-3.6/torchvision/datasets
copying torchvision/datasets/cityscapes.py -> build/lib.linux-aarch64-3.6/torchvision/datasets
copying torchvision/datasets/fakedata.py -> build/lib.linux-aarch64-3.6/torchvision/datasets
copying torchvision/datasets/flickr.py -> build/lib.linux-aarch64-3.6/torchvision/datasets
copying torchvision/datasets/stl10.py -> build/lib.linux-aarch64-3.6/torchvision/datasets
copying torchvision/datasets/vision.py -> build/lib.linux-aarch64-3.6/torchvision/datasets
copying torchvision/datasets/ucf101.py -> build/lib.linux-aarch64-3.6/torchvision/datasets
copying torchvision/datasets/semeion.py -> build/lib.linux-aarch64-3.6/torchvision/datasets
copying torchvision/datasets/celeba.py -> build/lib.linux-aarch64-3.6/torchvision/datasets
copying torchvision/datasets/svhn.py -> build/lib.linux-aarch64-3.6/torchvision/datasets
copying torchvision/datasets/video_utils.py -> build/lib.linux-aarch64-3.6/torchvision/datasets
creating build/lib.linux-aarch64-3.6/torchvision/io
copying torchvision/io/init.py -> build/lib.linux-aarch64-3.6/torchvision/io
copying torchvision/io/_video_opt.py -> build/lib.linux-aarch64-3.6/torchvision/io
copying torchvision/io/video.py -> build/lib.linux-aarch64-3.6/torchvision/io
creating build/lib.linux-aarch64-3.6/torchvision/transforms
copying torchvision/transforms/functional.py -> build/lib.linux-aarch64-3.6/torchvision/transforms
copying torchvision/transforms/transforms.py -> build/lib.linux-aarch64-3.6/torchvision/transforms
copying torchvision/transforms/init.py -> build/lib.linux-aarch64-3.6/torchvision/transforms
copying torchvision/transforms/functional_tensor.py -> build/lib.linux-aarch64-3.6/torchvision/transforms
copying torchvision/transforms/_transforms_video.py -> build/lib.linux-aarch64-3.6/torchvision/transforms
copying torchvision/transforms/_functional_video.py -> build/lib.linux-aarch64-3.6/torchvision/transforms
creating build/lib.linux-aarch64-3.6/torchvision/models/segmentation
copying torchvision/models/segmentation/init.py -> build/lib.linux-aarch64-3.6/torchvision/models/segmentation
copying torchvision/models/segmentation/fcn.py -> build/lib.linux-aarch64-3.6/torchvision/models/segmentation
copying torchvision/models/segmentation/_utils.py -> build/lib.linux-aarch64-3.6/torchvision/models/segmentation
copying torchvision/models/segmentation/segmentation.py -> build/lib.linux-aarch64-3.6/torchvision/models/segmentation
copying torchvision/models/segmentation/deeplabv3.py -> build/lib.linux-aarch64-3.6/torchvision/models/segmentation
creating build/lib.linux-aarch64-3.6/torchvision/models/quantization
copying torchvision/models/quantization/googlenet.py -> build/lib.linux-aarch64-3.6/torchvision/models/quantization
copying torchvision/models/quantization/shufflenetv2.py -> build/lib.linux-aarch64-3.6/torchvision/models/quantization
copying torchvision/models/quantization/init.py -> build/lib.linux-aarch64-3.6/torchvision/models/quantization
copying torchvision/models/quantization/utils.py -> build/lib.linux-aarch64-3.6/torchvision/models/quantization
copying torchvision/models/quantization/resnet.py -> build/lib.linux-aarch64-3.6/torchvision/models/quantization
copying torchvision/models/quantization/inception.py -> build/lib.linux-aarch64-3.6/torchvision/models/quantization
copying torchvision/models/quantization/mobilenet.py -> build/lib.linux-aarch64-3.6/torchvision/models/quantization
creating build/lib.linux-aarch64-3.6/torchvision/models/video
copying torchvision/models/video/init.py -> build/lib.linux-aarch64-3.6/torchvision/models/video
copying torchvision/models/video/resnet.py -> build/lib.linux-aarch64-3.6/torchvision/models/video
creating build/lib.linux-aarch64-3.6/torchvision/models/detection
copying torchvision/models/detection/image_list.py -> build/lib.linux-aarch64-3.6/torchvision/models/detection
copying torchvision/models/detection/rpn.py -> build/lib.linux-aarch64-3.6/torchvision/models/detection
copying torchvision/models/detection/faster_rcnn.py -> build/lib.linux-aarch64-3.6/torchvision/models/detection
copying torchvision/models/detection/roi_heads.py -> build/lib.linux-aarch64-3.6/torchvision/models/detection
copying torchvision/models/detection/backbone_utils.py -> build/lib.linux-aarch64-3.6/torchvision/models/detection
copying torchvision/models/detection/init.py -> build/lib.linux-aarch64-3.6/torchvision/models/detection
copying torchvision/models/detection/_utils.py -> build/lib.linux-aarch64-3.6/torchvision/models/detection
copying torchvision/models/detection/mask_rcnn.py -> build/lib.linux-aarch64-3.6/torchvision/models/detection
copying torchvision/models/detection/generalized_rcnn.py -> build/lib.linux-aarch64-3.6/torchvision/models/detection
copying torchvision/models/detection/keypoint_rcnn.py -> build/lib.linux-aarch64-3.6/torchvision/models/detection
copying torchvision/models/detection/transform.py -> build/lib.linux-aarch64-3.6/torchvision/models/detection
creating build/lib.linux-aarch64-3.6/torchvision/datasets/samplers
copying torchvision/datasets/samplers/init.py -> build/lib.linux-aarch64-3.6/torchvision/datasets/samplers
copying torchvision/datasets/samplers/clip_sampler.py -> build/lib.linux-aarch64-3.6/torchvision/datasets/samplers
running build_ext
building 'torchvision._C' extension
creating build/temp.linux-aarch64-3.6
creating build/temp.linux-aarch64-3.6/jetson-inference
creating build/temp.linux-aarch64-3.6/jetson-inference/torchvision
creating build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision
creating build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision/csrc
creating build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision/csrc/cpu
creating build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision/csrc/cuda
aarch64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -DWITH_CUDA -I/jetson-inference/torchvision/torchvision/csrc -I/home/markw/.local/lib/python3.6/site-packages/torch/include -I/home/markw/.local/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -I/home/markw/.local/lib/python3.6/site-packages/torch/include/TH -I/home/markw/.local/lib/python3.6/site-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.6m -c /jetson-inference/torchvision/torchvision/csrc/vision.cpp -o build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision/csrc/vision.o -O0 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=C -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++11
In file included from /home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/boxing/KernelFunction.h:5:0,
from /home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/dispatch/DispatchTable.h:10,
from /home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/dispatch/OperatorEntry.h:3,
from /home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/dispatch/Dispatcher.h:3,
from /home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/TensorMethods.h:10,
from /home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:12,
from /home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
from /home/markw/.local/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /home/markw/.local/lib/python3.6/site-packages/torch/include/torch/script.h:3,
from /jetson-inference/torchvision/torchvision/csrc/vision.cpp:2:
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/boxing/kernel_functor.h: In instantiation of ‘typename c10::guts::infer_function_traits::type::return_type c10::detail::call_functor_with_args_from_stack(Functor, c10::Stack*, c10::guts::index_sequence<INDEX ...>) [with Functor = c10::detail::WrapRuntimeKernelFunctor_<long int ()(), long int, c10::guts::typelist::typelist<> >; bool AllowDeprecatedTypes = true; long unsigned int ...ivalue_arg_indices = {}; typename c10::guts::infer_function_traits::type::return_type = long int; c10::Stack = std::vector<c10::IValue, std::allocatorc10::IValue >; c10::guts::index_sequence<INDEX ...> = c10::guts::integer_sequence]’:
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/boxing/kernel_functor.h:202:77:   required from ‘typename c10::guts::infer_function_traits::type::return_type c10::detail::call_functor_with_args_from_stack(Functor, c10::Stack*) [with Functor = c10::detail::WrapRuntimeKernelFunctor_<long int ()(), long int, c10::guts::typelist::typelist<> >; bool AllowDeprecatedTypes = true; typename c10::guts::infer_function_traits::type::return_type = long int; c10::Stack = std::vector<c10::IValue, std::allocatorc10::IValue >]’
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/boxing/kernel_functor.h:234:91:   required from ‘static void c10::detail::wrap_kernel_functor_boxed<KernelFunctor, AllowDeprecatedTypes, typename std::enable_if<(! std::is_same<void, typename c10::guts::infer_function_traits::type::return_type>::value), void>::type>::call(c10::OperatorKernel, c10::Stack*) [with KernelFunctor = c10::detail::WrapRuntimeKernelFunctor_<long int ()(), long int, c10::guts::typelist::typelist<> >; bool AllowDeprecatedTypes = true; c10::Stack = std::vector<c10::IValue, std::allocatorc10::IValue >]’
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/boxing/KernelFunction.h:172:7:   required from ‘static c10::KernelFunction c10::KernelFunction::makeFromUnboxedFunctor(std::unique_ptrc10::OperatorKernel) [with bool AllowLegacyTypes = true; KernelFunctor = c10::detail::WrapRuntimeKernelFunctor_<long int ()(), long int, c10::guts::typelist::typelist<> >]’
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/boxing/KernelFunction.h:313:111:   required from ‘static c10::KernelFunction c10::KernelFunction::makeFromUnboxedRuntimeFunction(FuncType*) [with bool AllowLegacyTypes = true; FuncType = long int()]’
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/op_registration/op_registration.h:517:72:   required from ‘c10::guts::enable_if_t<(c10::guts::is_function_type::value && (! std::is_same<FuncType, void(c10::OperatorKernel*, std::vector<c10::IValue, std::allocatorc10::IValue >)>::value)), c10::RegisterOperators&&> c10::RegisterOperators::op(const string&, FuncType, c10::RegisterOperators::Options&&) && [with FuncType = long int(); c10::guts::enable_if_t<(c10::guts::is_function_type::value && (! std::is_same<FuncType, void(c10::OperatorKernel*, std::vector<c10::IValue, std::allocatorc10::IValue >*)>::value)), c10::RegisterOperators&&> = c10::RegisterOperators&&; std::__cxx11::string = std::__cxx11::basic_string]’
/jetson-inference/torchvision/torchvision/csrc/vision.cpp:52:57:   required from here
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/boxing/kernel_functor.h:191:22: warning: variable ‘num_ivalue_args’ set but not used [-Wunused-but-set-variable]
constexpr size_t num_ivalue_args = sizeof...(ivalue_arg_indices);
^~~~~~~~~~~~~~~
aarch64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -DWITH_CUDA -I/jetson-inference/torchvision/torchvision/csrc -I/home/markw/.local/lib/python3.6/site-packages/torch/include -I/home/markw/.local/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -I/home/markw/.local/lib/python3.6/site-packages/torch/include/TH -I/home/markw/.local/lib/python3.6/site-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.6m -c /jetson-inference/torchvision/torchvision/csrc/cpu/DeformConv_cpu.cpp -o build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision/csrc/cpu/DeformConv_cpu.o -O0 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++11
aarch64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -DWITH_CUDA -I/jetson-inference/torchvision/torchvision/csrc -I/home/markw/.local/lib/python3.6/site-packages/torch/include -I/home/markw/.local/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -I/home/markw/.local/lib/python3.6/site-packages/torch/include/TH -I/home/markw/.local/lib/python3.6/site-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.6m -c /jetson-inference/torchvision/torchvision/csrc/cpu/PSROIPool_cpu.cpp -o build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision/csrc/cpu/PSROIPool_cpu.o -O0 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++11

Hmm I don't actually see a build error in there.  Are you sure the board isn't running out of memory during compilation?
You could keep an eye on sudo tegratstats to monitor the memory usage, or mount additional swap.

Thanks for the quick replies dusty-nv....I'll go use tegra to make sure I'm not running outta room.
I added the end of installation log below;
                                                                                ^

/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:322:1: note: declared here
T * data() const {
^ ~~
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIPool_cuda.cu:249:795: warning: ‘T* at::Tensor::data() const [with T = int]’ is deprecated [-Wdeprecated-declarations]
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
^
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:322:1: note: declared here
T * data() const {
^ ~~
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIPool_cuda.cu:249:918: warning: ‘T* at::Tensor::data() const [with T = float]’ is deprecated [-Wdeprecated-declarations]
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
^
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:322:1: note: declared here
T * data() const {
^ ~~
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIPool_cuda.cu:249:957: warning: ‘T* at::Tensor::data() const [with T = float]’ is deprecated [-Wdeprecated-declarations]
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
^
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:322:1: note: declared here
T * data() const {
^ ~~
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIPool_cuda.cu: In lambda function:
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIPool_cuda.cu:249:1194: warning: ‘T* at::Tensor::data() const [with T = c10::Half]’ is deprecated [-Wdeprecated-declarations]
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
^
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:322:1: note: declared here
T * data() const {
^ ~~
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIPool_cuda.cu:249:1227: warning: ‘T* at::Tensor::data() const [with T = int]’ is deprecated [-Wdeprecated-declarations]
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
^
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:322:1: note: declared here
T * data() const {
^ ~~
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIPool_cuda.cu:249:1354: warning: ‘T* at::Tensor::data() const [with T = c10::Half]’ is deprecated [-Wdeprecated-declarations]
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
^
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:322:1: note: declared here
T * data() const {
^ ~~
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIPool_cuda.cu:249:1397: warning: ‘T* at::Tensor::data() const [with T = c10::Half]’ is deprecated [-Wdeprecated-declarations]
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
^
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:322:1: note: declared here
T * data() const {
^ ~~
/usr/local/cuda/bin/nvcc -DWITH_CUDA -I/jetson-inference/torchvision/torchvision/csrc -I/home/markw/.local/lib/python3.6/site-packages/torch/include -I/home/markw/.local/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -I/home/markw/.local/lib/python3.6/site-packages/torch/include/TH -I/home/markw/.local/lib/python3.6/site-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.6m -c /jetson-inference/torchvision/torchvision/csrc/cuda/nms_cuda.cu -o build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision/csrc/cuda/nms_cuda.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=_C -D_GLIBCXX_USE_CXX11_ABI=1 -gencode=arch=compute_62,code=sm_62 -std=c++11
/home/markw/.local/lib/python3.6/site-packages/torch/include/c10/core/TensorTypeSet.h(44): warning: integer conversion resulted in a change of sign
/home/markw/.local/lib/python3.6/site-packages/torch/include/c10/core/TensorTypeSet.h(44): warning: integer conversion resulted in a change of sign
/jetson-inference/torchvision/torchvision/csrc/cuda/nms_cuda.cu: In lambda function:
/jetson-inference/torchvision/torchvision/csrc/cuda/nms_cuda.cu:93:104: warning: ‘c10::ScalarType detail::scalar_type(const at::DeprecatedTypeProperties&)’ is deprecated [-Wdeprecated-declarations]
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
^
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/Dispatch.h:31:1: note: declared here
inline at::ScalarType scalar_type(const at::DeprecatedTypeProperties &t) {
^~~~~~~~~~~
/usr/local/cuda/bin/nvcc -DWITH_CUDA -I/jetson-inference/torchvision/torchvision/csrc -I/home/markw/.local/lib/python3.6/site-packages/torch/include -I/home/markw/.local/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -I/home/markw/.local/lib/python3.6/site-packages/torch/include/TH -I/home/markw/.local/lib/python3.6/site-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.6m -c /jetson-inference/torchvision/torchvision/csrc/cuda/ROIAlign_cuda.cu -o build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision/csrc/cuda/ROIAlign_cuda.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=_C -D_GLIBCXX_USE_CXX11_ABI=1 -gencode=arch=compute_62,code=sm_62 -std=c++11
/home/markw/.local/lib/python3.6/site-packages/torch/include/c10/core/TensorTypeSet.h(44): warning: integer conversion resulted in a change of sign
/home/markw/.local/lib/python3.6/site-packages/torch/include/c10/core/TensorTypeSet.h(44): warning: integer conversion resulted in a change of sign
/jetson-inference/torchvision/torchvision/csrc/cuda/ROIAlign_cuda.cu: In lambda function:
/jetson-inference/torchvision/torchvision/csrc/cuda/ROIAlign_cuda.cu:340:98: warning: ‘c10::ScalarType detail::scalar_type(const at::DeprecatedTypeProperties&)’ is deprecated [-Wdeprecated-declarations]
AT_DISPATCH_FLOATING_TYPES_AND_HALF(input.type(), "ROIAlign_forward", [&] {
^
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/Dispatch.h:31:1: note: declared here
inline at::ScalarType scalar_type(const at::DeprecatedTypeProperties &t) {
^~~~~~~~~~~
/jetson-inference/torchvision/torchvision/csrc/cuda/ROIAlign_cuda.cu: In lambda function:
/jetson-inference/torchvision/torchvision/csrc/cuda/ROIAlign_cuda.cu:402:97: warning: ‘c10::ScalarType detail::scalar_type(const at::DeprecatedTypeProperties&)’ is deprecated [-Wdeprecated-declarations]
AT_DISPATCH_FLOATING_TYPES_AND_HALF(grad.type(), "ROIAlign_backward", [&] {
^
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/Dispatch.h:31:1: note: declared here
inline at::ScalarType scalar_type(const at::DeprecatedTypeProperties &t) {
^~~~~~~~~~~
/usr/local/cuda/bin/nvcc -DWITH_CUDA -I/jetson-inference/torchvision/torchvision/csrc -I/home/markw/.local/lib/python3.6/site-packages/torch/include -I/home/markw/.local/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -I/home/markw/.local/lib/python3.6/site-packages/torch/include/TH -I/home/markw/.local/lib/python3.6/site-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.6m -c /jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.cu -o build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.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=_C -D_GLIBCXX_USE_CXX11_ABI=1 -gencode=arch=compute_62,code=sm_62 -std=c++11
/home/markw/.local/lib/python3.6/site-packages/torch/include/c10/core/TensorTypeSet.h(44): warning: integer conversion resulted in a change of sign
/home/markw/.local/lib/python3.6/site-packages/torch/include/c10/core/TensorTypeSet.h(44): warning: integer conversion resulted in a change of sign
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.cu: In lambda function:
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.cu:345:342: warning: ‘T* at::Tensor::data() const [with T = double]’ is deprecated [-Wdeprecated-declarations]
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
^
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:322:1: note: declared here
T * data() const {
^ ~~
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.cu:345:467: warning: ‘T* at::Tensor::data() const [with T = double]’ is deprecated [-Wdeprecated-declarations]
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
^
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:322:1: note: declared here
T * data() const {
^ ~~
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.cu:345:508: warning: ‘T* at::Tensor::data() const [with T = double]’ is deprecated [-Wdeprecated-declarations]
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
^
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:322:1: note: declared here
T * data() const {
^ ~~
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.cu:345:541: warning: ‘T* at::Tensor::data() const [with T = int]’ is deprecated [-Wdeprecated-declarations]
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
^
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:322:1: note: declared here
T * data() const {
^ ~~
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.cu: In lambda function:
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.cu:345:769: warning: ‘T* at::Tensor::data() const [with T = float]’ is deprecated [-Wdeprecated-declarations]
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
^
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:322:1: note: declared here
T * data() const {
^ ~~
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.cu:345:893: warning: ‘T* at::Tensor::data() const [with T = float]’ is deprecated [-Wdeprecated-declarations]
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
^
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:322:1: note: declared here
T * data() const {
^ ~~
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.cu:345:933: warning: ‘T* at::Tensor::data() const [with T = float]’ is deprecated [-Wdeprecated-declarations]
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
^
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:322:1: note: declared here
T * data() const {
^ ~~
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.cu:345:966: warning: ‘T* at::Tensor::data() const [with T = int]’ is deprecated [-Wdeprecated-declarations]
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
^
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:322:1: note: declared here
T * data() const {
^ ~~
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.cu: In lambda function:
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.cu:345:1205: warning: ‘T* at::Tensor::data() const [with T = c10::Half]’ is deprecated [-Wdeprecated-declarations]
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
^
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:322:1: note: declared here
T * data() const {
^ ~~
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.cu:345:1333: warning: ‘T* at::Tensor::data() const [with T = c10::Half]’ is deprecated [-Wdeprecated-declarations]
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
^
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:322:1: note: declared here
T * data() const {
^ ~~
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.cu:345:1377: warning: ‘T* at::Tensor::data() const [with T = c10::Half]’ is deprecated [-Wdeprecated-declarations]
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
^
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:322:1: note: declared here
T * data() const {
^ ~~
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.cu:345:1410: warning: ‘T* at::Tensor::data() const [with T = int]’ is deprecated [-Wdeprecated-declarations]
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
^
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:322:1: note: declared here
T * data() const {
^ ~~
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.cu: In lambda function:
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.cu:415:344: warning: ‘T* at::Tensor::data() const [with T = double]’ is deprecated [-Wdeprecated-declarations]
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
^
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:322:1: note: declared here
T * data() const {
^ ~~
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.cu:415:377: warning: ‘T* at::Tensor::data() const [with T = int]’ is deprecated [-Wdeprecated-declarations]
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
^
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:322:1: note: declared here
T * data() const {
^ ~~
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.cu:415:517: warning: ‘T* at::Tensor::data() const [with T = double]’ is deprecated [-Wdeprecated-declarations]
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
^
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:322:1: note: declared here
T * data() const {
^ ~~
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.cu:415:557: warning: ‘T* at::Tensor::data() const [with T = double]’ is deprecated [-Wdeprecated-declarations]
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
^
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:322:1: note: declared here
T * data() const {
^ ~~
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.cu: In lambda function:
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.cu:415:788: warning: ‘T* at::Tensor::data() const [with T = float]’ is deprecated [-Wdeprecated-declarations]
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
^
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:322:1: note: declared here
T * data() const {
^ ~~
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.cu:415:821: warning: ‘T* at::Tensor::data() const [with T = int]’ is deprecated [-Wdeprecated-declarations]
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
^
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:322:1: note: declared here
T * data() const {
^ ~~
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.cu:415:960: warning: ‘T* at::Tensor::data() const [with T = float]’ is deprecated [-Wdeprecated-declarations]
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
^
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:322:1: note: declared here
T * data() const {
^ ~~
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.cu:415:999: warning: ‘T* at::Tensor::data() const [with T = float]’ is deprecated [-Wdeprecated-declarations]
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
^
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:322:1: note: declared here
T * data() const {
^ ~~
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.cu: In lambda function:
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.cu:415:1241: warning: ‘T* at::Tensor::data() const [with T = c10::Half]’ is deprecated [-Wdeprecated-declarations]
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
^
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:322:1: note: declared here
T * data() const {
^ ~~
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.cu:415:1274: warning: ‘T* at::Tensor::data() const [with T = int]’ is deprecated [-Wdeprecated-declarations]
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
^
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:322:1: note: declared here
T * data() const {
^ ~~
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.cu:415:1417: warning: ‘T* at::Tensor::data() const [with T = c10::Half]’ is deprecated [-Wdeprecated-declarations]
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
^
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:322:1: note: declared here
T * data() const {
^ ~~
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.cu:415:1460: warning: ‘T* at::Tensor::data() const [with T = c10::Half]’ is deprecated [-Wdeprecated-declarations]
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
^
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:322:1: note: declared here
T * data() const {
^ ~~
aarch64-linux-gnu-g++ -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-Bsymbolic-functions -Wl,-z,relro -Wl,-Bsymbolic-functions -Wl,-z,relro -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision/csrc/vision.o build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision/csrc/cpu/DeformConv_cpu.o build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision/csrc/cpu/PSROIPool_cpu.o build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision/csrc/cpu/ROIAlign_cpu.o build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision/csrc/cpu/PSROIAlign_cpu.o build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision/csrc/cpu/ROIPool_cpu.o build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision/csrc/cpu/nms_cpu.o build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision/csrc/cuda/ROIPool_cuda.o build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision/csrc/cuda/DeformConv_cuda.o build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIPool_cuda.o build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision/csrc/cuda/nms_cuda.o build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision/csrc/cuda/ROIAlign_cuda.o build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.o -L/usr/local/cuda/lib64 -lcudart -o build/lib.linux-aarch64-3.6/torchvision/_C.so
building 'torchvision.video_reader' extension
creating build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision/csrc/cpu/video_reader
aarch64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/jetson-inference/torchvision/torchvision/csrc/cpu/video_reader -I/usr/include -I/jetson-inference/torchvision/torchvision/csrc -I/home/markw/.local/lib/python3.6/site-packages/torch/include -I/home/markw/.local/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -I/home/markw/.local/lib/python3.6/site-packages/torch/include/TH -I/home/markw/.local/lib/python3.6/site-packages/torch/include/THC -I/usr/include/python3.6m -c /jetson-inference/torchvision/torchvision/csrc/cpu/video_reader/VideoReader.cpp -o build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision/csrc/cpu/video_reader/VideoReader.o -std=c++14 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=video_reader -D_GLIBCXX_USE_CXX11_ABI=1
In file included from /jetson-inference/torchvision/torchvision/csrc/cpu/video_reader/FfmpegDecoder.h:6:0,
from /jetson-inference/torchvision/torchvision/csrc/cpu/video_reader/VideoReader.cpp:6:
/jetson-inference/torchvision/torchvision/csrc/cpu/video_reader/FfmpegHeaders.h:4:10: fatal error: libavcodec/avcodec.h: No such file or directory
#include <libavcodec/avcodec.h>
^~~~~~~~~~~~~~~~~~~~~~
compilation terminated.
error: command 'aarch64-linux-gnu-gcc' failed with exit status 1

/jetson-inference/torchvision/torchvision/csrc/cpu/video_reader/FfmpegHeaders.h:4:10: fatal error: libavcodec/avcodec.h: No such file or directory
#include <libavcodec/avcodec.h>

I see, you must have ffmpeg executable tool installed on your Jetson, but not the libraries.
It looks like if torchvision finds the ffmpeg executable, it assumes the libraries are also installed:  https://github.com/pytorch/vision/blob/cca0c77a9ac5aa782b0811d850f246d73b0b4a1b/setup.py#L130
Try running sudo apt-get install libavcodec-dev first.

Thanks so much dusty-nv....
I ran the install above and ran clean.....still getting the same error below. I'm looking into the tag you gave me above. Best,
                                                                                ^

/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:322:1: note: declared here
T * data() const {
^ ~~
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.cu:415:999: warning: ‘T* at::Tensor::data() const [with T = float]’ is deprecated [-Wdeprecated-declarations]
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
^
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:322:1: note: declared here
T * data() const {
^ ~~
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.cu: In lambda function:
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.cu:415:1241: warning: ‘T* at::Tensor::data() const [with T = c10::Half]’ is deprecated [-Wdeprecated-declarations]
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
^
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:322:1: note: declared here
T * data() const {
^ ~~
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.cu:415:1274: warning: ‘T* at::Tensor::data() const [with T = int]’ is deprecated [-Wdeprecated-declarations]
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
^
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:322:1: note: declared here
T * data() const {
^ ~~
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.cu:415:1417: warning: ‘T* at::Tensor::data() const [with T = c10::Half]’ is deprecated [-Wdeprecated-declarations]
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
^
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:322:1: note: declared here
T * data() const {
^ ~~
/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.cu:415:1460: warning: ‘T* at::Tensor::data() const [with T = c10::Half]’ is deprecated [-Wdeprecated-declarations]
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
^
/home/markw/.local/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:322:1: note: declared here
T * data() const {
^ ~~
aarch64-linux-gnu-g++ -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-Bsymbolic-functions -Wl,-z,relro -Wl,-Bsymbolic-functions -Wl,-z,relro -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision/csrc/vision.o build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision/csrc/cpu/DeformConv_cpu.o build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision/csrc/cpu/PSROIPool_cpu.o build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision/csrc/cpu/ROIAlign_cpu.o build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision/csrc/cpu/PSROIAlign_cpu.o build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision/csrc/cpu/ROIPool_cpu.o build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision/csrc/cpu/nms_cpu.o build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision/csrc/cuda/ROIPool_cuda.o build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision/csrc/cuda/DeformConv_cuda.o build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIPool_cuda.o build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision/csrc/cuda/nms_cuda.o build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision/csrc/cuda/ROIAlign_cuda.o build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision/csrc/cuda/PSROIAlign_cuda.o -L/usr/local/cuda/lib64 -lcudart -o build/lib.linux-aarch64-3.6/torchvision/_C.so
building 'torchvision.video_reader' extension
creating build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision/csrc/cpu/video_reader
aarch64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/jetson-inference/torchvision/torchvision/csrc/cpu/video_reader -I/usr/include -I/jetson-inference/torchvision/torchvision/csrc -I/home/markw/.local/lib/python3.6/site-packages/torch/include -I/home/markw/.local/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -I/home/markw/.local/lib/python3.6/site-packages/torch/include/TH -I/home/markw/.local/lib/python3.6/site-packages/torch/include/THC -I/usr/include/python3.6m -c /jetson-inference/torchvision/torchvision/csrc/cpu/video_reader/VideoReader.cpp -o build/temp.linux-aarch64-3.6/jetson-inference/torchvision/torchvision/csrc/cpu/video_reader/VideoReader.o -std=c++14 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=video_reader -D_GLIBCXX_USE_CXX11_ABI=1
In file included from /jetson-inference/torchvision/torchvision/csrc/cpu/video_reader/FfmpegDecoder.h:6:0,
from /jetson-inference/torchvision/torchvision/csrc/cpu/video_reader/VideoReader.cpp:6:
/jetson-inference/torchvision/torchvision/csrc/cpu/video_reader/FfmpegHeaders.h:5:10: fatal error: libavformat/avformat.h: No such file or directory
#include <libavformat/avformat.h>
^~~~~~~~~~~~~~~~~~~~~~~~
compilation terminated.
error: command 'aarch64-linux-gnu-gcc' failed with exit status 1

1000 thanks dusty-nv....not sure why but I'm successfully importing torchvision with python3...I believe problem solved!!!!!!!!!!!!!!!

ah.....looks like torchvision is successfully importing but torchvision sub calls are not working. I'll continue with the FFMPEG libraries

dusty-nv, thanks so much.....its working fine.....I'm able to get to all the libraries, working well
I simply removed ffmpeg from my system, ran the clean and the torchvision installation worked perfectly as described in your string.
I hope this will help someone else. Maybe bypass the ffmpeg loop in that installation call?
Best wishes, MW


Add National Ilan University.

@kc5408 I am sorry, the domain ems.niu.edu.tw is removed from our trusted domains list because it was abused in the past. If you are the student of this school, please enroll to GitHub Student Developer Program. Then apply for JetBrains educational subscription with your GitHub account (GitHub tab at https://www.jetbrains.com/shop/eform/students).


Make better use of strictParsing for SGroups

PR scope

eliminate some documentation ambiguity about the role of the strictParsing flag
fix some inconsistencies between SGroup parsing function prototype declarations and implementations
add a workaround for accepting malformed V2000 'M  SAP' entries affecting older version of MarvinJS (only if strictParsing is set to false)
if strictParsing is set to false, malformed V2000/V3000 SGroups are ignored rather than causing the parsing to fail
fix a couple typos in warnings

PR motivation
The motivation for this PR is that older versions of MarvinJS (MJ2009-MJ2011, and possibly later ones; MJ2021 is not affected) are affected by a bug in the way the M  SAP entry is output.
For example, the MDL molblock generated for this molecule:

is:

  MJ201100

 10 10  0  0  0  0  0  0  0  0999 V2000
   -1.7411    0.8920    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
   -2.4555    0.4795    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
   -2.4555   -0.3455    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
   -1.7411   -0.7580    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
   -1.0266   -0.3455    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
   -1.0266    0.4795    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
    0.1003    1.6065    0.0000 F   0  0  0  0  0  0  0  0  0  0  0  0
   -0.3121    0.8920    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
    0.1003    0.1775    0.0000 F   0  0  0  0  0  0  0  0  0  0  0  0
   -1.1371    0.8920    0.0000 F   0  0  0  0  0  0  0  0  0  0  0  0
  1  2  2  0  0  0  0
  2  3  1  0  0  0  0
  3  4  2  0  0  0  0
  4  5  1  0  0  0  0
  5  6  2  0  0  0  0
  6  1  1  0  0  0  0
  6  8  1  0  0  0  0
  7  8  1  0  0  0  0
  8  9  1  0  0  0  0
  8 10  1  0  0  0  0
M  STY  1   1 SUP
M  SAL   1  4   7   8   9  10
M  SMT   1 CF3
M  SBL   1  1   7
M  SAP   1  1   8
M  END

The M  SAP line according to specs should read (and it does in MarvinJS 2021):
M  SAP   1  1   8   6

As a consequence, the RDKit chokes on this molecule and fails to read it altogether:
In [4]: Chem.MolFromMolBlock(molblock)
[11:30:42] SGroup SAP line too short: 'M  SAP   1  1   8' on line 29

Also the Indigo toolkit fails to read it.
ChemDraw reads it and displays the abbreviated CF3 group without problems.
In fact, the missing lvIdx can be easily inferred from the single M  SBL entry.
So I implemented this simple workaround in the RDKit V2000 parser, such that the offending molecule can be parsed correctly with the correct SGroup in place if strictParsing is set to false:
In [5]: Chem.MolFromMolBlock(molblock, strictParsing=False)
[11:31:56] SGroup SAP line too short: 'M  SAP   1  1   8' on line 29
Out[5]: <rdkit.Chem.rdchem.Mol at 0x7f9a0066f4e0>

If strictParsing is set to true, the molecule still fails to read as it did before this PR.
In [6]: Chem.MolFromMolBlock(molblock, strictParsing=True)
[11:32:47] SGroup SAP line too short: 'M  SAP   1  1   8' on line 29

PR further growth
After I introduced the above workaround, I thought it was worth taking a further step, and avoiding that malformed SGroups may cause the whole parsing to fail. With the proposed changes, if strictParsing is set to false, malformed SGroups will be ignored and the molecule is still parsed with conforming SGroups in place.
If strictParsing is set to true, the molecule still fails to read as it did before this PR.
I introduced three macros which I think are useful to keep the code tidier and more concise.

@greglandrum I think I have done all of the changes you suggested.

@greglandrum I think I have done all of the changes you suggested.


Update todomvc-app-install-and-overview.mdx

Updated the correct file locations of practice.js files

@BathiyaL thank you :)


Implement caching of code storage

It seems like it would be trivial to implement caching of code in storage by using the storage key as an ETag.

Fixed in 4f3c3a1fd71b8ec19336ea9b20b546e3a44fbe2c


Cached errors are incorrectly rised for file checked with newer TS before

🔎 Search Terms
"errors from different version", "cached errors when changing TS version", "cached errors from newer tsserver", "older TS incorrectly shows errors rised by VSCode running newer TS"
🕗 Version & Regression Information

I was unable to test this on prior versions because it occurs when switching between TS versions

⏯ Playground Link
https://www.typescriptlang.org/play?noImplicitAny=false&ts=4.8.4#code/MYGwhgzhAECCA8AVAfNA3gWAFDV9AHgBQCU62eF0ATgKYAuArlQHbR0AWAljJHPGgQBc0CHSqdmAc2gBfZAG5yeGdiW4AnoXXDEpTFjWzsKg1mY0A7nBIA6IsRuaBFqgHspwgAyzi86AHp-Ni4YbmgwVhoqNypoCWgAVhsAJhToACMGOjiYZldsiOgomLjWABYbAA4bMqA
💻 Code
class A<T> {
    x() {
        return this as A<{ x: string }>;
    }

    y(y: T) {

    }
}

new A().x().y({ wrong: 0 }); // this is expected to be an error in 5.2.2 and expected to not be an error in 4.8.4

🙁 Actual behavior
First of all, the important thing is that this example code is correct in 4.8.4 and correctly rises an error in 5.2.2. When you open a project with this code that has a TS 4.8.4 installed, using VSCode with TypeScript version set to internal TS (5.2.2 in my case) instead of workspace TS (4.8.4), editor correctly shows an error in this file. However, when you run node_modules/.bin/tsc --noEmit from the terminal, which runs TS 4.8.4, it still outputs the same error, even though it shouldn't.
I made sure that TSC version 4.8.4 is being run in terminal using node_modules/.bin/tsc -v.
The error should not exist in 4.8.4 as this code is valid in 4.8.4 (with noImplicitAny set to false). No matter how many times you rerun tsc (4.8.4) is fails with errors cached from tsserver (5.2.2, run by VSCode) unless you change the file.
Even if you now change the TS version used by VSCode to the workspace (4.8.4), the errors to that file are still being shown when running tsc without changing the file in question.
This behavior was noticed by several developers on MacOS and Windows.
🙂 Expected behavior
Typechecking code with tsc 4.8.4 should not raise errors cached from tsserver 5.2.2 that checked that file before and rised errors.
Playground links showing that the same code is:

correctly not an error on 4.8.4
correctly an error on 5.2.2

Additional information about the issue
No response

TS Server doesn't write anything to disk, or at least shouldn't, so this bug as described doesn't sound physically possible.
Can you share a repo that demonstrates this behavior?

Does the problem disappear if you run npm i (or equivalent)?

@RyanCavanaugh Oh, so the reason must be entirely different than I thought, sorry for the confusion ;o But the fact is that tsc 5.2.2 was never run by that user, only tsserver was run in 5.2.2, yet tsc 4.8.4 was raising errors exclusive to typescript 5.2.2, that why I thought it's definitely some cache.
I cannot share the original repo this happened in so I'll try preparing a small one with this issue isolated. However, the additional problem is that it's inconsistent, several devs saw it (both Mac and Win), but for example on my own PC it didn't happen at all. But I'll try anyway and we'll see, the issue is super strange.
I think we didn't try it but I'll ask again. We only made sure that there's only TS 4.8.4 installed in repo by running npm list typescript.


client: make getJWT and verifyJWT async

This modifies both getJWT and verifyJWT to return promises.  getJWT has also been updated to check the store for the token again to support environments that don’t load cookies immediately.
Also renames the getJWT util function to retrieveJWT to avoid ambiguity.

Didn't we want to create an Authentication class for this (we can always map some of its methods directly to the app object)? I can do that, too when wrapping this up though.

@daffl I've updated it to use a class.


Move photo metadata

Creates a new table photo_metadata for storing what's currently in photos.metadata

The main reason is to make the photos table bytewise smaller to speed up backup and recovery time
The metadata in the new table is stored compressed in a PostgreSQL bytea type column, ultimately taking about 45% of the space of current text column
Metadata gets YAML-dumped and Zlib-deflated before saving, and can be Zlib-inflated and YAML-loaded to fully restore the original data types, like EXIFR::TIFF::Degrees

This PR also removes setting default values for the native_* fields on photos like native_username or native_page_url, in another effort to save space and prevent stale data

There are already methods to fallback to photo.user.login for example for native_username, so we shouldn't need to denormalize those values

There is a method that can be used in delayed jobs to retroactively move metadata and strip native_* fields where applicable

Once all metadata has moved, there will need to be another commit to use the new photo_metadata table in place of photos.metadata so we can remove the photos.metadata column

FWIW, this looks good to me. Out of curiosity, did you look into using a jsonb column instead of zipped yaml in a binary column? My guess is that it would take up less space than our current text column, but probably wouldn't save as much as zipping.

I did try a json column, but not a jsonb column as it seems like json might end up needing less space, and we just store and retrieve metadata, never query it. I made 3 separate tables, one with a text column, one with a json column, and one with a bytea column. Normalized to the size of the text table the sizes were: text: 100%, json: 78%, and bytea: 48% with a test of about 5.3 million rows.
We also use get some pretty specific datatypes from EXIF parsing like EXIFR::TIFF::Degrees which get kind of mucked up when trying to convert to JSON and back. We can cast them as floats, but that'll change how we use them. I was also hitting encoding errors converting to JSON, which could be overcome, but but leaving the data as compressed YAML we can avoid those in this process.
I'm doing some timing of dump/restore right now, and it's actually not looking great for the compressed table. Saving disk space in the running DB might be costing time and space on dump/restore. If that hold up, I may want to yank the custom compressed serializer and stick with the regular YAML serializer but keep everything else


Making meter-widget adjustable

Adding width and height for the Meter-Widget

This change is also in #333 -- this PR should be closed as it is superseded by #332.


I am running SP2019 on an Azure SQL Managed Instance and the Example code that you provide on this page errors with this error:  Remove : The term 'Remove' is not recognized as the name of a cmdlet, function, script  file, or operable program. Check the spelling of the name, or if a path was included,  verify that the path is correct and try again.

[Enter feedback here]

Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: af7eded9-6781-5917-4c8b-26b7fc29798e
Version Independent ID: f791183e-71ea-e450-4137-5833d4d9161a
Content: Remove-SPSecureStoreApplication (SharePointServer)
Content Source: sharepoint/sharepoint-ps/sharepoint-server/Remove-SPSecureStoreApplication.md
Product: sharepoint-2016-powershell
GitHub Login: @Techwriter40
Microsoft Alias: pamgreen

Thanks for your feedback, the example has a typo, fixing thru a pull request.


spotify: <IP_ADDRESS>9.g5ffabd56-26 -> <IP_ADDRESS>.g6bd7cc73-35

Motivation for this change
Things done

[x] Tested using sandboxing (nix.useSandbox on NixOS, or option build-use-sandbox in nix.conf on non-NixOS)
Built on platform(s)

[x] NixOS
[ ] macOS
[ ] other Linux distributions

[ ] Tested via one or more NixOS test(s) if existing and applicable for the change (look inside nixos/tests)
[ ] Tested compilation of all pkgs that depend on this change using nix-shell -p nox --run "nox-review wip"
[x] Tested execution of all binary files (usually in ./result/bin/)
[x] Fits CONTRIBUTING.md.

Duplicate of #36161 with the same problem as mentioned there

@globin rebased to use 1.0.69 https://github.com/sternenseemann/nixpkgs/commit/7d7d25fb3b3a79e49211e98320f7da8a2bacab21


[Feature]: Support elasticsearch data stream

Requirement
https://www.elastic.co/guide/en/elasticsearch/reference/current/data-streams.html
Problem
I has created a data stream index template, and I tried --es.use-aliases=true but not work.
Proposal
No response
Open questions
No response

Is this because the client library used for connecting to Elasticsearch is outdated? Namely, I see in the logs of version 1.54 of the collector that the client is<EMAIL_ADDRESS>This client is from July 27th, 2021. The client is also not being updated anymore, it would appear. Namely, it was last updated to version 7.0.32 on Mar 19th, 2022. This is almost two years ago now. Elastic has its own go library now: https://github.com/elastic/go-elasticsearch.

I see that the OpenSearch project has the same feature-request.

I am currently working on this feature, but I prefer to implement it in jaeger v2

@JaredTan95 +2 for v2, but what specifically is the difference? Our storage implementations are currently identical in v1 and v2 (with the exception of configuration)

that doesn't really address my question - all ES-specific details are hidden behind es.Factory, which is used by both Jaeger v1 and v2, so I don't see a difference
Data streams also need to be set up once, just like index mappings. We want to do that on collector start-up, with some coordination between multiple collector instances (if creation operation are idempotent then it's less of an issue)


Using IPv6 to access React dev server throws websocket error

Trying to access react dev server via IPv6 throws an exception while trying to connect to WebpackDevServer via a socket.

SyntaxError: Failed to construct 'WebSocket': The URL 'ws://[[::1]]/sockjs-node' is invalid.

Found this issue on a quick search, but didn't get much information there.

Environment
npx create-react-app --info
System:
  OS: Linux 4.15 Ubuntu 16.04.6 LTS (Xenial Xerus)
  CPU: (4) x64 Intel(R) Core(TM) i7-5600U CPU @ 2.60GHz
Binaries:
  Node: 12.16.1 - /usr/bin/node
  Yarn: Not Found
  npm: 6.13.4 - /usr/bin/npm
Browsers:
  Chrome: Not Found
  Firefox: 74.0
npmPackages:
  react: ^16.13.1 => 16.13.1
  react-dom: ^16.13.1 => 16.13.1
  react-scripts: 3.4.1 => 3.4.1
npmGlobalPackages:
  create-react-app: 3.3.0

I use Nginx reverse proxy to access dev server. To access dev server via IPv6 and port i.e., http://[::1]:3000, I had to modify package.json:
{
  ...
  "scripts": {
    "start": "HOST=0:0:0:0:0:0:0:1 react-scripts start",
    ...
  }
}

Expected behavior
The React dev server should connect to WebpackDevServer via socket and should work like how it works for IPv4
Actual behavior
Throws WebSocket error while trying to access dev server.

Well, I modified node_modules/react-dev-utils/webpackHotDevClient.js file to remove square brackets around the IPv6 address in the hostname.
Line#63 window.location.hostname ---> window.location.hostname.replace(/^\[(.*)\]$/, "$1")
Old WebSocket connection
var connection = new WebSocket(
  url.format({
    protocol: window.location.protocol === 'https:' ? 'wss' : 'ws',
    hostname: process.env.WDS_SOCKET_HOST || window.location.hostname,
    port: process.env.WDS_SOCKET_PORT || window.location.port,
    // Hardcoded in WebpackDevServer
    pathname: process.env.WDS_SOCKET_PATH || '/sockjs-node',
    slashes: true,
  })
);

Modified WebSocket connection
var connection = new WebSocket(
  url.format({
    protocol: window.location.protocol === 'https:' ? 'wss' : 'ws',
    hostname: process.env.WDS_SOCKET_HOST || window.location.hostname.replace(/^\[(.*)\]$/, "$1"),
    port: process.env.WDS_SOCKET_PORT || window.location.port,
    // Hardcoded in WebpackDevServer
    pathname: process.env.WDS_SOCKET_PATH || '/sockjs-node',
    slashes: true,
  })
);

Now it's working fine.


Add pex --docs and several pex3 docs options.

The pex --docs shortcut should provide an ergonomic way to open the
local docs for most Pex CLI users (undoubtedly few use pex3). The
pex3 docs additional options allow for most of the control needed
though, and the help for the pex --docs option points there.

Thanks for taking a look @zmanji.

I am not sure on the whole server/pidfile logic but everything lgtm.

That is just moved code in this PR (so pex --docs could share the pex3 docs infra), and its still ~copied code from testing.devpi which powers the --devpi option for the tests and integration tests. Both CI and me have been hammering that logic for a while now; so it may be buggy, but it appears to be solid; so the bugs likely lie in rare corner cases.


[Rust] Keep track of in-progress Write on the Fragment level

To be able to identify unsuccessful write in a distributed environment, to allow later recover / garbage collection, we should allow the Fragment::Write() to track the progress of writing.
For example, we could set a fragment.json.inprocess when the write process starts, and delete it when the write finishes.

Done in #1146


You shouldn't be using pid_utils with a script name of 'ctl'

pid_utils redirects the output to /var/vcap/monit/<script_name>.{out,err}.log, so if other releases do the same thing, everything ends up in one logfile.
Probably you should either:

name the logfile after the job's name, not the script's name
include the job's name in the script's name, like e.g. cf-release or concourse do

Removed pid_utils entirely.


Fix marlin quantization using BaseQuantizeConfig from _pretrained #581

Fix Issue #581
Main branch cannot perform marlin quantization as show in example below due to is_marlin_format not read/applied from quantization_config:
quantize_config = BaseQuantizeConfig(
    bits=4,  # quantize model to 4-bit
    group_size=128,  # it is recommended to set the value to 128
    desc_act=False,  # desc_act and group size only works on triton
    damp_percent=0.005,
    is_marlin_format=True
)
# load un-quantized model, the model will always be force loaded into cpu
model = AutoGPTQForCausalLM.from_pretrained(pretrained_model_dir, quantize_config=quantize_config)

# quantize model, the examples should be list of dict whose keys can only be "input_ids" and "attention_mask"
# with value under torch.LongTensor type.
model.quantize(traindataset, use_triton=False)

# save quantized model
model.save_quantized(quantized_model_dir)

Also fixed qlinear_marlin.py not correctly handling linear.bias == None.
@chu-tianxiang  @fxmarty

@fxmarty  Please review this as I consider this a high-priority bug fix. Anyone attempting to quantize Marlin using quantize() api on main will end up with a fake-success (quantize config says marlin but format is actually non-marlin) causing subsequent model load in this project and others that use marlin to fail.  The silent fail bug is even worse than a crash since most the end-user is using correct quantize code as documented.


fix: ensure route.path is a string

This fixes a potential crash/DDOS.

+1

#152 should have fixed this.

This is merged in 5.3.0 and 7.0.0.


Description for owner input is confusing

I'm looking to migrate to this action from peter-murray/workflow-application-token-action (currently waiting on #111 for feature parity) and I found the documentation for the owner input confusing:
https://github.com/actions/create-github-app-token/blob/f2acddfb5195534d487896a656232b016a682f3c/action.yml#L22-L24
To me, the documentation implies that this should be the owner of the GitHub app itself, whereas if you trace through the code it's actually the owner for which the GitHub app is installed (i.e. the place you want to give the app access to).
https://github.com/actions/create-github-app-token/blob/f2acddfb5195534d487896a656232b016a682f3c/lib/main.js#L112-L129

the owner for which the GitHub app is installed

that is correct. Sorry for the confusion. We should definitely update the input description. Pull request welcome!


[Bug]: ValueError: Queue <multiprocessing.queues.Queue object at 0x7f5703d2d0f0> is closed;zipfile.BadZipFile: Bad magic number for file header

Your current environment

my device: cuda 11.8
vllm version: vllm-0.5.5
torch is suitable with cuda and  vllm
python 3.10

🐛 Describe the bug
My env is ready, Only!!!!! it works successfully at first time。
If running after time, there always got the error bellow:
INFO 09-03 15:47:02 config.py:813] Defaulting to use mp for distributed inference
INFO 09-03 15:47:02 llm_engine.py:184] Initializing an LLM engine (v0.5.5) with config: model='/root/paddlejob/workspace/env_run/output/ckpts/qwen2-7b-instruct', speculative_config=None, tokenizer='/root/paddlejob/workspace/env_run/output/ckpts/qwen2-7b-instruct', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=4, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None, collect_model_forward_time=False, collect_model_execute_time=False), seed=0, served_model_name=/root/paddlejob/workspace/env_run/output/ckpts/qwen2-7b-instruct, use_v2_block_manager=False, enable_prefix_caching=False)
INFO 09-03 15:47:02 custom_cache_manager.py:17] Setting Triton cache manager to: vllm.triton_utils.custom_cache_manager:CustomCacheManager
(VllmWorkerProcess pid=85762) Process VllmWorkerProcess:
(VllmWorkerProcess pid=85762) Traceback (most recent call last):
(VllmWorkerProcess pid=85762)   File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
(VllmWorkerProcess pid=85762)     self.run()
(VllmWorkerProcess pid=85762)   File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
(VllmWorkerProcess pid=85762)     self._target(*self._args, **self._kwargs)
(VllmWorkerProcess pid=85762)   File "/usr/local/lib/python3.10/dist-packages/vllm/executor/multiproc_worker_utils.py", line 210, in _run_worker_process
(VllmWorkerProcess pid=85762)     worker = worker_factory()
(VllmWorkerProcess pid=85762)   File "/usr/local/lib/python3.10/dist-packages/vllm/executor/gpu_executor.py", line 23, in create_worker
(VllmWorkerProcess pid=85762)     wrapper.init_worker(**kwargs)
(VllmWorkerProcess pid=85762)   File "/usr/local/lib/python3.10/dist-packages/vllm/worker/worker_base.py", line 436, in init_worker
(VllmWorkerProcess pid=85762)     load_general_plugins()
(VllmWorkerProcess pid=85762)   File "/usr/local/lib/python3.10/dist-packages/vllm/plugins/__init__.py", line 21, in load_general_plugins
(VllmWorkerProcess pid=85762)     discovered_plugins = entry_points(group='vllm.general_plugins')
(VllmWorkerProcess pid=85762)   File "/usr/lib/python3.10/importlib/metadata/__init__.py", line 1021, in entry_points
(VllmWorkerProcess pid=85762)     return SelectableGroups.load(eps).select(**params)
(VllmWorkerProcess pid=85762)   File "/usr/lib/python3.10/importlib/metadata/__init__.py", line 459, in load
(VllmWorkerProcess pid=85762)     ordered = sorted(eps, key=by_group)
(VllmWorkerProcess pid=85762)   File "/usr/lib/python3.10/importlib/metadata/__init__.py", line 1018, in <genexpr>
(VllmWorkerProcess pid=85762)     eps = itertools.chain.from_iterable(
(VllmWorkerProcess pid=85762)   File "/usr/lib/python3.10/importlib/metadata/_itertools.py", line 16, in unique_everseen
(VllmWorkerProcess pid=85762)     k = key(element)
(VllmWorkerProcess pid=85762)   File "/usr/lib/python3.10/importlib/metadata/__init__.py", line 943, in _normalized_name
(VllmWorkerProcess pid=85762)     or super()._normalized_name
(VllmWorkerProcess pid=85762)   File "/usr/lib/python3.10/importlib/metadata/__init__.py", line 622, in _normalized_name
(VllmWorkerProcess pid=85762)     return Prepared.normalize(self.name)
(VllmWorkerProcess pid=85762)   File "/usr/lib/python3.10/importlib/metadata/__init__.py", line 617, in name
(VllmWorkerProcess pid=85762)     return self.metadata['Name']
(VllmWorkerProcess pid=85762)   File "/usr/lib/python3.10/importlib/metadata/__init__.py", line 606, in metadata
(VllmWorkerProcess pid=85762)     or self.read_text('PKG-INFO')
(VllmWorkerProcess pid=85762)   File "/usr/lib/python3.10/importlib/metadata/__init__.py", line 927, in read_text
(VllmWorkerProcess pid=85762)     return self._path.joinpath(filename).read_text(encoding='utf-8')
(VllmWorkerProcess pid=85762)   File "/usr/lib/python3.10/zipfile.py", line 2380, in read_text
(VllmWorkerProcess pid=85762)     with self.open('r', encoding, *args, **kwargs) as strm:
(VllmWorkerProcess pid=85762)   File "/usr/lib/python3.10/zipfile.py", line 2361, in open
(VllmWorkerProcess pid=85762)     stream = self.root.open(self.at, zip_mode, pwd=pwd)
(VllmWorkerProcess pid=85762)   File "/usr/lib/python3.10/zipfile.py", line 1537, in open
(VllmWorkerProcess pid=85762)     raise BadZipFile("Bad magic number for file header")
(VllmWorkerProcess pid=85762) zipfile.BadZipFile: Bad magic number for file header
ERROR 09-03 15:47:02 multiproc_worker_utils.py:120] Worker VllmWorkerProcess pid 85762 died, exit code: 1
INFO 09-03 15:47:02 multiproc_worker_utils.py:123] Killing local vLLM worker processes
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/vllm/executor/multiproc_worker_utils.py", line 169, in _enqueue_task
    self._task_queue.put((task_id, method, args, kwargs))
  File "/usr/lib/python3.10/multiprocessing/queues.py", line 88, in put
    raise ValueError(f"Queue {self!r} is closed")
ValueError: Queue <multiprocessing.queues.Queue object at 0x7f5703d2d0f0> is closed

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/root/paddlejob/workspace/env_run/output/baidu/personal-code/llm-edu/llm_edu/model_eval/evaluate_qwen15_test.py", line 10, in <module>
    llm = LLM(
  File "/usr/local/lib/python3.10/dist-packages/vllm/entrypoints/llm.py", line 175, in __init__
    self.llm_engine = LLMEngine.from_engine_args(
  File "/usr/local/lib/python3.10/dist-packages/vllm/engine/llm_engine.py", line 473, in from_engine_args
    engine = cls(
  File "/usr/local/lib/python3.10/dist-packages/vllm/engine/llm_engine.py", line 270, in __init__
    self.model_executor = executor_class(
  File "/usr/local/lib/python3.10/dist-packages/vllm/executor/distributed_gpu_executor.py", line 25, in __init__
    super().__init__(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/vllm/executor/executor_base.py", line 46, in __init__
    self._init_executor()
  File "/usr/local/lib/python3.10/dist-packages/vllm/executor/multiproc_gpu_executor.py", line 137, in _init_executor
    self._run_workers("init_device")
  File "/usr/local/lib/python3.10/dist-packages/vllm/executor/multiproc_gpu_executor.py", line 186, in _run_workers
    worker_outputs = [
  File "/usr/local/lib/python3.10/dist-packages/vllm/executor/multiproc_gpu_executor.py", line 187, in <listcomp>
    worker.execute_method(method, *args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/vllm/executor/multiproc_worker_utils.py", line 176, in execute_method
    self._enqueue_task(future, method, args, kwargs)
  File "/usr/local/lib/python3.10/dist-packages/vllm/executor/multiproc_worker_utils.py", line 172, in _enqueue_task
    raise ChildProcessError("worker died") from e
ChildProcessError: worker died

is this a big bug ?????
Before submitting a new issue...

[X] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the documentation page, which can answer lots of frequently asked questions.

I also meet this problem.

It works fine with 'tensor_parallel_size=1' and 'LLama-3.1-8B-Instruct', but I got the same problem when setting 'tensor_parallel_size>1' such as 8.
My env: CUDA==12.3, torch==2.4.0, vllm==2.6.1. I also try vllm==2.6.2 or 2.6.3, but I still got this problem.

It works fine with 'tensor_parallel_size=1' and 'LLama-3.1-8B-Instruct', but I got the same problem when setting 'tensor_parallel_size>1' such as 8.
My env: python3.10, CUDA==12.3, torch==2.4.0, vllm==0.6.1. I also try vllm==0.6.2 or 0.6.3, but I still got this problem.

My situation is the same with the @Jiangchenglin521. After pip install vllm==0.6.2, first run with tensor_parallel_size=8 is OK. But it will always throws the bug after the first run.

The problem is because the vllm manages the distributed runtime with either Ray or python native multiprocessing (by default) package. In my case, there is something wrong with the python native multiprocessing, so changing the 'distributed-executor-backend' to 'ray' solved my problem.
You can change this setting via the LLM class distributed-executor-backend argument or --distributed-executor-backend API server argument, as mentioned by the vllm documention. For instance, set distributed_executor_backend="ray" when initializing LLM engine:
from transformers import AutoTokenizer
from vllm import LLM, SamplingParams

model_name = "openbmb/MiniCPM3-4B"
prompt = [{"role": "user", "content": "推荐5个北京的景点。"}]

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
input_text = tokenizer.apply_chat_template(prompt, tokenize=False, add_generation_prompt=True)

llm = LLM(
    model=model_name,
    trust_remote_code=True,
    tensor_parallel_size=1,
   distributed_executor_backend="ray"
)
sampling_params = SamplingParams(top_p=0.7, temperature=0.7, max_tokens=1024, repetition_penalty=1.02)

outputs = llm.generate(prompts=input_text, sampling_params=sampling_params)

print(outputs[0].outputs[0].text)


 is not rendered in case of exception

When I pass callback(err, null) in the renderer async-fragment-error is not rendered.

For uncaught exceptions that is the expected behavior. There are no try...catch calls in the marko runtime and for async things they wouldn't help. If the job of the user data provider function to catch any errors. However, if you want exceptions to be caught then I suggest using promises instead of Node.js-style callbacks. This is because Promise implementations do use try...catch blocks during every step of the async promise chain.

Hey @SunnyGurnani, did that answer your question? If not, please provide more details or share code to make the problem more clear. In the mean time I'll be adding more test cases around error handling.
I'm going to close this issue for now, but please feel to reopen if you are not satisfied with the answer. Thanks!


Illustration: How everything fits together

For Java

Maven
JUnit
Cucumber
Feature files
Step definitions
The application

Not a priority


Fixed the example with-react-intl, cannot read property 'locale' of undefined

In line 26 of examples/with-react-intl/components/PageWithIntl.js:
const {locale, messages} = req || window.__NEXT_DATA__.props.initialProps

Actuall the field initialProps doesn't exist, instead, locale and messages are in window.__NEXT_DATA__.props, so the solution is quite simple, just remove initialProps:
const {locale, messages} = req || window.__NEXT_DATA__.props

My Environment

Tech
Version

next
4.1.4

node
v8.9.1

OS
macOS High Sierra

browser
Chrome Version 62.0.3202.94 (Official Build) (64-bit)

I find this quite strange, do you run dev or production build since I actually added it in there in order to get it work in production mode. Just curious. I dont mind the change in anyway!

@ptomasroos Yeah,  I see your commit here https://github.com/zeit/next.js/commit/629d0a261b2957daaf72bbb5ef308d5d81366cc0 that specifically adds initialProps.
But I tried to run the example  a few times, still the same error.

npm run build then npm run start, go to http://localhost:3000 and click either link, will get an error An unexpected error has occurred. and the console throws out red error TypeError: Cannot read property 'locale' of undefined

npm run dev, go to http://localhost:3000 and click either link then the page will crash with stack trace.

What's the status here?

Thanks @soulmachine

@timneutkens No problem

It should be ⬇ for now.
const {locale, messages} = req || window.__NEXT_DATA__.props.pageProps


Dev support identity

python3 benchmarks/instant_id.py --repo ~/repos/InstantID/ --face-analysis-root ~/repos/face-analysis-root/

Traceback (most recent call last):
  File "/home/cheng/repos/onediff/src/onediff/infer_compiler/with_oneflow_compile.py", line 172, in wrapper
    return func(self, *args, **kwargs)
  File "/home/cheng/repos/onediff/src/onediff/infer_compiler/utils/graph_management_utils.py", line 82, in wrapper
    ret = func(self, *args, **kwargs)
  File "/home/cheng/repos/onediff/src/onediff/infer_compiler/with_oneflow_compile.py", line 255, in __call__
    output = dpl_graph(*args, **kwargs)
  File "/home/cheng/repos/oneflow/python/oneflow/nn/graph/graph.py", line 292, in __call__
    return self._dynamic_input_graph_cache(*args, **kwargs)
  File "/home/cheng/repos/oneflow/python/oneflow/nn/graph/cache.py", line 116, in __call__
    return graph(*args, **kwargs)
  File "/home/cheng/repos/oneflow/python/oneflow/nn/graph/graph.py", line 295, in __call__
    self._compile(*args, **kwargs)
  File "/home/cheng/repos/oneflow/python/oneflow/nn/graph/graph.py", line 865, in _compile
    return self._compile_new(*args, **kwargs)
  File "/home/cheng/repos/oneflow/python/oneflow/nn/graph/graph.py", line 884, in _compile_new
    _, eager_outputs = self.build_graph(*args, **kwargs)
  File "/home/cheng/repos/oneflow/python/oneflow/nn/graph/graph.py", line 1428, in build_graph
    outputs = self.__build_graph(*args, **kwargs)
  File "/home/cheng/repos/oneflow/python/oneflow/nn/graph/graph.py", line 1576, in __build_graph
    outputs = self.build(*lazy_args, **lazy_kwargs)
  File "/home/cheng/repos/onediff/src/onediff/infer_compiler/with_oneflow_compile.py", line 322, in build
    return self.model(*args, **kwargs)
  File "/home/cheng/repos/oneflow/python/oneflow/nn/graph/proxy.py", line 188, in __call__
    result = self.__block_forward(*args, **kwargs)
  File "/home/cheng/repos/oneflow/python/oneflow/nn/graph/proxy.py", line 238, in __block_forward
    result = unbound_forward_of_module_instance(self, *args, **kwargs)
  File "/home/cheng/repos/onediff/src/infer_compiler_registry/register_diffusers/unet_2d_condition_oflow.py", line 358, in forward
    sample, res_samples = downsample_block(
  File "/home/cheng/repos/oneflow/python/oneflow/nn/graph/proxy.py", line 188, in __call__
    result = self.__block_forward(*args, **kwargs)
  File "/home/cheng/repos/oneflow/python/oneflow/nn/graph/proxy.py", line 238, in __block_forward
    result = unbound_forward_of_module_instance(self, *args, **kwargs)
  File "/home/cheng/pyvenv/oneflow/lib/python3.10/site-packages/diffusers/models/unet_2d_blocks.py", line 1160, in forward
    hidden_states = attn(
  File "/home/cheng/repos/oneflow/python/oneflow/nn/graph/proxy.py", line 188, in __call__
    result = self.__block_forward(*args, **kwargs)
  File "/home/cheng/repos/oneflow/python/oneflow/nn/graph/proxy.py", line 238, in __block_forward
    result = unbound_forward_of_module_instance(self, *args, **kwargs)
  File "/home/cheng/repos/onediff/src/infer_compiler_registry/register_diffusers/transformer_2d_oflow.py", line 1329, in forward
    hidden_states = block(
  File "/home/cheng/repos/oneflow/python/oneflow/nn/graph/proxy.py", line 188, in __call__
    result = self.__block_forward(*args, **kwargs)
  File "/home/cheng/repos/oneflow/python/oneflow/nn/graph/proxy.py", line 238, in __block_forward
    result = unbound_forward_of_module_instance(self, *args, **kwargs)
  File "/home/cheng/pyvenv/oneflow/lib/python3.10/site-packages/diffusers/models/attention.py", line 329, in forward
    attn_output = self.attn1(
  File "/home/cheng/repos/oneflow/python/oneflow/nn/graph/proxy.py", line 188, in __call__
    result = self.__block_forward(*args, **kwargs)
  File "/home/cheng/repos/oneflow/python/oneflow/nn/graph/proxy.py", line 238, in __block_forward
    result = unbound_forward_of_module_instance(self, *args, **kwargs)
  File "/home/cheng/repos/onediff/src/infer_compiler_registry/register_diffusers/attention_processor_oflow.py", line 364, in forward
    return self.processor(
  File "/home/cheng/repos/oneflow/python/oneflow/nn/graph/proxy.py", line 188, in __call__
    result = self.__block_forward(*args, **kwargs)
  File "/home/cheng/repos/oneflow/python/oneflow/nn/graph/proxy.py", line 238, in __block_forward
    result = unbound_forward_of_module_instance(self, *args, **kwargs)
  File "/home/cheng/repos/oneflow/python/oneflow/nn/modules/module.py", line 200, in forward
    raise NotImplementedError()
NotImplementedError

RTX 4090
python3 benchmarks/instant_id.py --repo ~/repos/InstantID/ --face-analysis-root ~/repos/face-analysis-root/ --seed 0 --output-image tmp/instant_id.jpg

Unoptimized
Inference time: 5.806s
Iterations per second: 5.453
CUDA Mem after: 20.447GiB
Host Mem after: 7.249GiB

Optimized
Inference time: 3.221s
Iterations per second: 10.149
CUDA Mem after: 20.155GiB
Host Mem after: 8.458GiB

Hi @chengzeyi ,

Hi @chengzeyi , Could you provide a set of revisions for oneflow/onediff/onediffx for which compilation of the InstantID pipeline works? I cannot manage to make it work on my script.

You need to use InstantID with diffusers==0.25.1.

HI @chengzeyi , I got this error when run instantid with onediff
NotImplementedError: Transform failed of <class 'diffusers.models.unets.unet_2d_condition.UNet2DConditionModel'>: Transform failed of <class 'torch.nn.modules.container.ModuleList'>: Transform failed of <class 'diffusers.models.unets.unet_2d_blocks.CrossAttnDownBlock2D'>: Transform failed of <class 'torch.nn.modules.container.ModuleList'>: Transform failed of <class 'diffusers.models.transformers.transformer_2d.Transformer2DModel'>: Transform failed of <class 'torch.nn.modules.container.ModuleList'>: Transform failed of <class 'diffusers.models.attention.BasicTransformerBlock'>: Transform failed of <class 'diffusers.models.attention_processor.Attention'>: Transform failed of <class 'ip_adapter.attention_processor.AttnProcessor2_0'>: super(type, obj): obj must be an instance or subtype of type


[PAY ON 7/1] New Feature: Make lonely emojis bigger in E.cash

If you haven’t already, check out our contributing guidelines for onboarding and email<EMAIL_ADDRESS>to request to join our Slack channel!

Action Performed:
Emoji with no text are sent
Expected Result:
Emoji to be larger than they normally are
Actual Result:
Emoji are small
Platform:
All (I assume)
Version Number:
Logs: https://stackoverflow.com/c/expensify/questions/4856
Notes/Photos/Videos:
From earlier post, providing for more context.
Problem
Emojis by themselves in E.cash are small and sometimes hard to see.
Why is this important?
Having bigger emojis is a nice design feature and improves the UX of our chat app. This also provides more parity with Slack.
Solution
If the entire content of a message is emojis, then increase the font size so the emojis appear bigger. Here is an example of what this looks like on Slack:

Expensify/Expensify Issue URL:
https://github.com/Expensify/Expensify/issues/160734#
View all open jobs on Upwork

Triggered auto assignment to @cdraeger11 (AutoAssignerTriage), see https://stackoverflow.com/c/expensify/questions/4749 for more details.

Triggered auto assignment to @kevinksullivan (External), see https://stackoverflow.com/c/expensify/questions/8582 for more details.

Triggered auto assignment to @NikkiWines (Exported), see https://stackoverflow.com/c/expensify/questions/7972 for more details.

Posted https://www.upwork.com/jobs/~01c7f72d3d3dd13a97

Proposal

We create isSingleEmoji, a really long regex test to see if a string is composed of a single emoji.
In our ReportAction of type TEXT, we change the fontSize when the string passes our isSingleEmoji test.

If we don't want the trouble of maintaining a regex for emojis (new ones are added all the time), we can use an external library to do that for us, like grapheme-breaker.

Hmm, yeah we could use regex to try and identify singular emojis. There's a short article which provides a detailed regex for parsing emojis based on a regex string from Lodash.js .
@stitesExpensify what are your thoughts on this approach?

Yep makes sense to me! That was my plan too :)

Nice, @mallenexpensify all good to hire @rdjuric for this issue

Hired @rdjuric in Upwork (I had the wrong title, updated that too).

Will pay on 7/1 if no regressions

Trying to pay via Upwork but their site is acting up. Will try to play later today @rdjuric

Paid! Thanks for the help @rdjuric


Remove unnecessary and incorrect find('.menu').toggle()

Follows:

#22950

The dropdown menu works well without these codes.
The reason is that the event bubbling still works for the dropdown menu, the Fomantic UI dropdown menu module will hide the menu correctly if an item is clicked.

Codecov Report

Merging #22987 (2b1dba4) into main (c8093a1) will increase coverage by 0.09%.
The diff coverage is 51.39%.

:mega: This organization is not using Codecov’s GitHub App Integration. We recommend you install it so Codecov can continue to function properly for your repositories. Learn more
@@            Coverage Diff             @@
##             main   #22987      +/-   ##
==========================================
+ Coverage   47.29%   47.39%   +0.09%
==========================================
  Files        1113     1128      +15
  Lines      150159   150651     +492
==========================================
+ Hits        71023    71396     +373
- Misses      70700    70786      +86
- Partials     8436     8469      +33

Impacted Files
Coverage Δ

cmd/admin.go
0.00% <ø> (ø)

cmd/admin_user_change_password.go
0.00% <0.00%> (ø)

cmd/admin_user_create.go
0.00% <0.00%> (ø)

cmd/admin_user_delete.go
0.00% <0.00%> (ø)

cmd/admin_user_generate_access_token.go
0.00% <0.00%> (ø)

cmd/admin_user_list.go
0.00% <0.00%> (ø)

cmd/admin_user_must_change_password.go
0.00% <0.00%> (ø)

models/activities/action.go
48.43% <0.00%> (ø)

models/asymkey/gpg_key_import.go
54.54% <ø> (ø)

models/git/protected_branch.go
37.04% <0.00%> (ø)

... and 162 more

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add support for hung pcm process

Hey @evancohen This has been here for awhile, do you want to take a look?

There is a real issue with the underlying recording library. When using arecord, the process dies silently, but the binary output (chunk size) reliably changes at the time of the crash.
The ideal fix here would be to monitor chunk size, restart the process when it crashes, and repipe the audio internally.

The ideal fix here would be to monitor chunk size, restart the process when it crashes, and repipe the audio internally.

working on it..

see PR 93 which handles it as suggested... this one will be closed... wrong solution


error type given

I use  v2.0.3 in shaarli and get this error when refreshing thumbnails:
[Thu Nov 05 18:34:21.146472 2020] [php7:notice] [pid 29009] [client <IP_ADDRESS>:38716] TypeError: Argument 1 passed to WebThumbnailer\\Utils\\ImageUtils::isImageString() must be of the type string, bool given, called in /var/www/shaarli.agentcobra.net/vendor/arthurhoaro/web-thumbnailer/src/WebThumbnailer/Finder/DefaultFinder.php on line 59, referer: https://shaarli.agentcobra.net/admin/thumbnails

it referer to :
https://github.com/ArthurHoaro/web-thumbnailer/blob/39bfd4f3136d9e6096496b9720e877326cfe4775/src/WebThumbnailer/Finder/DefaultFinder.php#L59
function definition is here :
https://github.com/ArthurHoaro/web-thumbnailer/blob/39bfd4f3136d9e6096496b9720e877326cfe4775/src/WebThumbnailer/Utils/ImageUtils.php#L177

I use:

Linux

No LSB modules are available.
Distributor ID: Debian
Description:    Debian GNU/Linux 10 (buster)
Release:        10
Codename:       buster

Apache2

Server version: Apache/2.4.38 (Debian)
Server built:   2020-08-25T20:08:29

PHP

PHP 7.3.19-1~deb10u1 (cli) (built: Jul  5 2020 06:46:45) ( NTS )
Copyright (c) 1997-2018 The PHP Group
Zend Engine v3.3.19, Copyright (c) 1998-2018 Zend Technologies
    with Zend OPcache v7.3.19-1~deb10u1, Copyright (c) 1999-2018, by Zend Technologies

shaarli
master (commit 48df9f45b8c4b2995c1e04146071628668531b37)

ping @ArthurHoaro

Sorry for the very late answer. Thanks for the very clear report :+1:
This can happen if the remote content could not be retrieved. I'll send a PR to fix this and add proper checks before calling this method.


WITH ROLLUP Support

I want to write the NyQL equivalent for following MySQL query that contains WITH ROLLUP. Is there anyway I can achieve this? Could not find this in NyQL wiki.
SELECT year, SUM(profit) AS profit
FROM sales
GROUP BY year ASC WITH ROLLUP;

At the moment i am working around this with a native injection as follows.
GROUP_BY("year ASC WITH ROLLUP")

The proposed syntax is; appending ROLLUP() function immediately after GROUP BY function.
GROUP_BY (columns...) ROLLUP()

The function syntax () will be used to provide possible subset defining option in the future.


Add NUMBER and LIST parsing to cloudformation/parsing.py

Resolves #3109.
Thanks @bblommers for the suggestion on where to start. This PR adds "NUMBER" and "LIST" type parsing to cloudformation/parsing.py. Since CloudFormation "NUMBER" parameters can be either ints or floats, this PR uses a function that first tries to convert to int and then falls back to float if that doesn't work. I added tests to cover the additions and I think the new tests also cover the pre-existing "LIST*" if statement.

Coverage decreased (-0.05%) to 94.337% when pulling 0a40ce0d33dd1fa163e02d488e7283747ade1e15 on richford:parse-cloudformation-number-params into 81be4b37a125d62586ab8429e8d98bb002ce7154 on spulec:master.


gcloud.storage.get_bucket throws AttributeError 'api_rquest

I'm testing an installation of Google SDK on Fedora 22 (a fresh new installation). And when I test the installation by running this Python script:
$ python
Python 2.7.8 (default, Apr 15 2015, 09:26:43)
[GCC 4.9.2 20150212 (Red Hat 4.9.2-6)] on linux2
Type "help", "copyright", "credits" or "license" for more information.

import gcloud
from gcloud import storage
storage.set_default_bucket("ccb_ci_acct")
storage.set_default_project('applied-algebra-93003')
storage_conn = storage.get_connection()
storage.set_default_connection(storage_conn)
bucket=storage.get_bucket("ccb_ci_acct","applied-algebra-93003")
Traceback (most recent call last):
File "", line 1, in
File "/usr/lib/python2.7/site-packages/gcloud/storage/api.py", line 165, in get_bucket
bucket.reload()
File "/usr/lib/python2.7/site-packages/gcloud/storage/_helpers.py", line 57, in reload
self._properties = self.connection.api_request(
AttributeError: 'str' object has no attribute 'api_request'

The Python shell returns the above AttributeError message. My guess is that the installed host is missing either some Python module, or, in the above code, a mandatory configuration parameter is not being set.
Any suggestions to fix this problem are appreciated.

This looks like an issue with gcloud-python. Can you file the issue there instead?


[opentitantool] Fix rescue for c2d2 interface

This commit does the following:

Changes fn write for C2d2ResetPin implementation to expect a single line of output on console after h1_reset [01] cmd
Adds builtin h1dx_devboard_c2d2.json configuration that allow end-users to use rescue without specifying any external json

Now the following command works to rescue when C2D2 is connected
./opentitantool --interface=c2d2  bootstrap -p rescue /path/to.bin

@jesultra can you take a take a look at this too?
@cfrantz are you the correct person to approve?

Anything else I need to do before this gets merged? (Sorry this is my first PR for OT repo)


The train dataset release plan

Hi, Thanks for your wonderful work.
Since only the test dataset released, I'd like to ask would you like to share the train dataset?

Hi, we will release the training dataset after our paper is accepted.


dev: dired and magit support

The settings for these faces have been adopted from doom-themes, with the support for darkening/lightening/blending that doom does removed so it is directly compatible.
Addresses issue #22, as well as more magit goodness

Any dramas or suggestion, let me know and will be happy to help fix anything.

Looks great!