Unable to install the requirements

Hello,
I have installed Sockeye in an Anaconda (Conda 4.10.3 with Python 3.8.8) environment as explained here:
https://awslabs.github.io/sockeye/setup.html
But I can't install mxnet:
Could not find a version that satisfies the requirement mxnet==1.8.0.post0
I tried it with conda install -c anaconda mxnet and with pip install mxnet==1.8.0.post0,
but nothing could help.
Do you know why I can't install mxnet?
I want to train the model described here:
https://aws.amazon.com/blogs/machine-learning/train-neural-machine-translation-models-with-sockeye/

Hi! What do you get by running installation without a specific version requirement? pip install mxnet

Collecting mxnet
  Using cached mxnet-1.7.0.post2-py2.py3-none-win_amd64.whl (33.1 MB)
Collecting graphviz<0.9.0,>=0.8.1
  Using cached graphviz-0.8.4-py2.py3-none-any.whl (16 kB)
Requirement already satisfied: requests<2.19.0,>=2.18.4 in c:\users\rawilhel\anaconda3\lib\site-packages (from mxnet) (2.18.4)
Collecting numpy<1.17.0,>=1.8.2
  Using cached numpy-1.16.6.zip (5.1 MB)
Requirement already satisfied: urllib3<1.23,>=1.21.1 in c:\users\rawilhel\anaconda3\lib\site-packages (from requests<2.19.0,>=2.18.4->mxnet) (1.22)
Requirement already satisfied: idna<2.7,>=2.5 in c:\users\rawilhel\anaconda3\lib\site-packages (from requests<2.19.0,>=2.18.4->mxnet) (2.6)
Requirement already satisfied: chardet<3.1.0,>=3.0.2 in c:\users\rawilhel\anaconda3\lib\site-packages (from requests<2.19.0,>=2.18.4->mxnet) (3.0.4)
Requirement already satisfied: certifi>=2017.4.17 in c:\users\rawilhel\anaconda3\lib\site-packages (from requests<2.19.0,>=2.18.4->mxnet) (2020.12.5)
Building wheels for collected packages: numpy
  Building wheel for numpy (setup.py) ... error
  ERROR: Command errored out with exit status 1:
   command: 'C:\Users\rawilhel\Anaconda3\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\rawilhel\\AppData\\Local\\Temp\\pip-install-cxupm05t\\numpy_d1b4c2a57f2f4ec0848d631c1c5b7554\\setup.py'"'"'; __file__='"'"'C:\\Users\\rawilhel\\AppData\\Local\\Temp\\pip-install-cxupm05t\\numpy_d1b4c2a57f2f4ec0848d631c1c5b7554\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' bdist_wheel -d 'C:\Users\rawilhel\AppData\Local\Temp\pip-wheel-zwi25v13'
       cwd: C:\Users\rawilhel\AppData\Local\Temp\pip-install-cxupm05t\numpy_d1b4c2a57f2f4ec0848d631c1c5b7554\
  Complete output (264 lines):
  Running from numpy source directory.
  C:\Users\rawilhel\AppData\Local\Temp\pip-install-cxupm05t\numpy_d1b4c2a57f2f4ec0848d631c1c5b7554\numpy\distutils\misc_util.py:476: SyntaxWarning: "is" with a literal. Did you mean "=="?
    return is_string(s) and ('*' in s or '?' is s)
  blas_opt_info:
  blas_mkl_info:
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  customize MSVCCompiler
    libraries mkl_rt not found in ['C:\\Users\\rawilhel\\Anaconda3\\lib', 'C:\\', 'C:\\Users\\rawilhel\\Anaconda3\\libs']
    NOT AVAILABLE

  blis_info:
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  customize MSVCCompiler
    libraries blis not found in ['C:\\Users\\rawilhel\\Anaconda3\\lib', 'C:\\', 'C:\\Users\\rawilhel\\Anaconda3\\libs']
    NOT AVAILABLE

  openblas_info:
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  customize MSVCCompiler
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  customize MSVCCompiler
    libraries openblas not found in ['C:\\Users\\rawilhel\\Anaconda3\\lib', 'C:\\', 'C:\\Users\\rawilhel\\Anaconda3\\libs']
  get_default_fcompiler: matching types: '['gnu', 'intelv', 'absoft', 'compaqv', 'intelev', 'gnu95', 'g95', 'intelvem', 'intelem', 'flang']'
  customize GnuFCompiler
  Could not locate executable g77
  Could not locate executable f77
  customize IntelVisualFCompiler
  Could not locate executable ifort
  Could not locate executable ifl
  customize AbsoftFCompiler
  Could not locate executable f90
  customize CompaqVisualFCompiler
  Could not locate executable DF
  customize IntelItaniumVisualFCompiler
  Could not locate executable efl
  customize Gnu95FCompiler
  Could not locate executable gfortran
  Could not locate executable f95
  customize G95FCompiler
  Could not locate executable g95
  customize IntelEM64VisualFCompiler
  customize IntelEM64TFCompiler
  Could not locate executable efort
  Could not locate executable efc
  customize PGroupFlangCompiler
  Could not locate executable flang
  don't know how to compile Fortran code on platform 'nt'
    NOT AVAILABLE

  atlas_3_10_blas_threads_info:
  Setting PTATLAS=ATLAS
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  customize MSVCCompiler
    libraries tatlas not found in ['C:\\Users\\rawilhel\\Anaconda3\\lib', 'C:\\', 'C:\\Users\\rawilhel\\Anaconda3\\libs']
    NOT AVAILABLE

  atlas_3_10_blas_info:
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  customize MSVCCompiler
    libraries satlas not found in ['C:\\Users\\rawilhel\\Anaconda3\\lib', 'C:\\', 'C:\\Users\\rawilhel\\Anaconda3\\libs']
    NOT AVAILABLE

  atlas_blas_threads_info:
  Setting PTATLAS=ATLAS
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  customize MSVCCompiler
    libraries ptf77blas,ptcblas,atlas not found in ['C:\\Users\\rawilhel\\Anaconda3\\lib', 'C:\\', 'C:\\Users\\rawilhel\\Anaconda3\\libs']
    NOT AVAILABLE

  atlas_blas_info:
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  customize MSVCCompiler
    libraries f77blas,cblas,atlas not found in ['C:\\Users\\rawilhel\\Anaconda3\\lib', 'C:\\', 'C:\\Users\\rawilhel\\Anaconda3\\libs']
    NOT AVAILABLE

  accelerate_info:
    NOT AVAILABLE

  C:\Users\rawilhel\AppData\Local\Temp\pip-install-cxupm05t\numpy_d1b4c2a57f2f4ec0848d631c1c5b7554\numpy\distutils\system_info.py:639: UserWarning:
      Atlas (http://math-atlas.sourceforge.net/) libraries not found.
      Directories to search for the libraries can be specified in the
      numpy/distutils/site.cfg file (section [atlas]) or by setting
      the ATLAS environment variable.
    self.calc_info()
  blas_info:
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  customize MSVCCompiler
    libraries blas not found in ['C:\\Users\\rawilhel\\Anaconda3\\lib', 'C:\\', 'C:\\Users\\rawilhel\\Anaconda3\\libs']
    NOT AVAILABLE

  C:\Users\rawilhel\AppData\Local\Temp\pip-install-cxupm05t\numpy_d1b4c2a57f2f4ec0848d631c1c5b7554\numpy\distutils\system_info.py:639: UserWarning:
      Blas (http://www.netlib.org/blas/) libraries not found.
      Directories to search for the libraries can be specified in the
      numpy/distutils/site.cfg file (section [blas]) or by setting
      the BLAS environment variable.
    self.calc_info()
  blas_src_info:
    NOT AVAILABLE

  C:\Users\rawilhel\AppData\Local\Temp\pip-install-cxupm05t\numpy_d1b4c2a57f2f4ec0848d631c1c5b7554\numpy\distutils\system_info.py:639: UserWarning:
      Blas (http://www.netlib.org/blas/) sources not found.
      Directories to search for the sources can be specified in the
      numpy/distutils/site.cfg file (section [blas_src]) or by setting
      the BLAS_SRC environment variable.
    self.calc_info()
    NOT AVAILABLE

  'svnversion' is not recognized as an internal or external command,
  operable program or batch file.
  non-existing path in 'numpy\\distutils': 'site.cfg'
  lapack_opt_info:
  lapack_mkl_info:
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  customize MSVCCompiler
    libraries mkl_rt not found in ['C:\\Users\\rawilhel\\Anaconda3\\lib', 'C:\\', 'C:\\Users\\rawilhel\\Anaconda3\\libs']
    NOT AVAILABLE

  openblas_lapack_info:
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  customize MSVCCompiler
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  customize MSVCCompiler
    libraries openblas not found in ['C:\\Users\\rawilhel\\Anaconda3\\lib', 'C:\\', 'C:\\Users\\rawilhel\\Anaconda3\\libs']
    NOT AVAILABLE

  openblas_clapack_info:
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  customize MSVCCompiler
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  customize MSVCCompiler
    libraries openblas,lapack not found in ['C:\\Users\\rawilhel\\Anaconda3\\lib', 'C:\\', 'C:\\Users\\rawilhel\\Anaconda3\\libs']
    NOT AVAILABLE

  atlas_3_10_threads_info:
  Setting PTATLAS=ATLAS
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  customize MSVCCompiler
    libraries lapack_atlas not found in C:\Users\rawilhel\Anaconda3\lib
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  customize MSVCCompiler
    libraries tatlas,tatlas not found in C:\Users\rawilhel\Anaconda3\lib
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  customize MSVCCompiler
    libraries lapack_atlas not found in C:\
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  customize MSVCCompiler
    libraries tatlas,tatlas not found in C:\
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  customize MSVCCompiler
    libraries lapack_atlas not found in C:\Users\rawilhel\Anaconda3\libs
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  customize MSVCCompiler
    libraries tatlas,tatlas not found in C:\Users\rawilhel\Anaconda3\libs
  <class 'numpy.distutils.system_info.atlas_3_10_threads_info'>
    NOT AVAILABLE

  atlas_3_10_info:
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  customize MSVCCompiler
    libraries lapack_atlas not found in C:\Users\rawilhel\Anaconda3\lib
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  customize MSVCCompiler
    libraries satlas,satlas not found in C:\Users\rawilhel\Anaconda3\lib
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  customize MSVCCompiler
    libraries lapack_atlas not found in C:\
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  customize MSVCCompiler
    libraries satlas,satlas not found in C:\
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  customize MSVCCompiler
    libraries lapack_atlas not found in C:\Users\rawilhel\Anaconda3\libs
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  customize MSVCCompiler
    libraries satlas,satlas not found in C:\Users\rawilhel\Anaconda3\libs
  <class 'numpy.distutils.system_info.atlas_3_10_info'>
    NOT AVAILABLE

  atlas_threads_info:
  Setting PTATLAS=ATLAS
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  customize MSVCCompiler
    libraries lapack_atlas not found in C:\Users\rawilhel\Anaconda3\lib
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  customize MSVCCompiler
    libraries ptf77blas,ptcblas,atlas not found in C:\Users\rawilhel\Anaconda3\lib
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  customize MSVCCompiler
    libraries lapack_atlas not found in C:\
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  customize MSVCCompiler
    libraries ptf77blas,ptcblas,atlas not found in C:\
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  customize MSVCCompiler
    libraries lapack_atlas not found in C:\Users\rawilhel\Anaconda3\libs
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  customize MSVCCompiler
    libraries ptf77blas,ptcblas,atlas not found in C:\Users\rawilhel\Anaconda3\libs
  <class 'numpy.distutils.system_info.atlas_threads_info'>
    NOT AVAILABLE

  atlas_info:
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  customize MSVCCompiler
    libraries lapack_atlas not found in C:\Users\rawilhel\Anaconda3\lib
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  customize MSVCCompiler
    libraries f77blas,cblas,atlas not found in C:\Users\rawilhel\Anaconda3\lib
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  customize MSVCCompiler
    libraries lapack_atlas not found in C:\
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  customize MSVCCompiler
    libraries f77blas,cblas,atlas not found in C:\
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  customize MSVCCompiler
    libraries lapack_atlas not found in C:\Users\rawilhel\Anaconda3\libs
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  customize MSVCCompiler
    libraries f77blas,cblas,atlas not found in C:\Users\rawilhel\Anaconda3\libs
  <class 'numpy.distutils.system_info.atlas_info'>
    NOT AVAILABLE

  lapack_info:
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  customize MSVCCompiler
    libraries lapack not found in ['C:\\Users\\rawilhel\\Anaconda3\\lib', 'C:\\', 'C:\\Users\\rawilhel\\Anaconda3\\libs']
    NOT AVAILABLE

  C:\Users\rawilhel\AppData\Local\Temp\pip-install-cxupm05t\numpy_d1b4c2a57f2f4ec0848d631c1c5b7554\numpy\distutils\system_info.py:639: UserWarning:
      Lapack (http://www.netlib.org/lapack/) libraries not found.
      Directories to search for the libraries can be specified in the
      numpy/distutils/site.cfg file (section [lapack]) or by setting
      the LAPACK environment variable.
    self.calc_info()
  lapack_src_info:
    NOT AVAILABLE

  C:\Users\rawilhel\AppData\Local\Temp\pip-install-cxupm05t\numpy_d1b4c2a57f2f4ec0848d631c1c5b7554\numpy\distutils\system_info.py:639: UserWarning:
      Lapack (http://www.netlib.org/lapack/) sources not found.
      Directories to search for the sources can be specified in the
      numpy/distutils/site.cfg file (section [lapack_src]) or by setting
      the LAPACK_SRC environment variable.
    self.calc_info()
    NOT AVAILABLE

  C:\Users\rawilhel\Anaconda3\lib\distutils\dist.py:274: UserWarning: Unknown distribution option: 'define_macros'
    warnings.warn(msg)
  running bdist_wheel
  running build
  running config_cc
  unifing config_cc, config, build_clib, build_ext, build commands --compiler options
  running config_fc
  unifing config_fc, config, build_clib, build_ext, build commands --fcompiler options
  running build_src
  build_src
  building py_modules sources
  creating build
  creating build\src.win-amd64-3.8
  creating build\src.win-amd64-3.8\numpy
  creating build\src.win-amd64-3.8\numpy\distutils
  building library "npymath" sources
  No module named 'numpy.distutils._msvccompiler' in numpy.distutils; trying from distutils
  error: Microsoft Visual C++ 14.0 or greater is required. Get it with "Microsoft C++ Build Tools": https://visualstudio.microsoft.com/visual-cpp-build-tools/
  ----------------------------------------
  ERROR: Failed building wheel for numpy
  Running setup.py clean for numpy
  ERROR: Command errored out with exit status 1:
   command: 'C:\Users\rawilhel\Anaconda3\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\rawilhel\\AppData\\Local\\Temp\\pip-install-cxupm05t\\numpy_d1b4c2a57f2f4ec0848d631c1c5b7554\\setup.py'"'"'; __file__='"'"'C:\\Users\\rawilhel\\AppData\\Local\\Temp\\pip-install-cxupm05t\\numpy_d1b4c2a57f2f4ec0848d631c1c5b7554\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' clean --all
       cwd: C:\Users\rawilhel\AppData\Local\Temp\pip-install-cxupm05t\numpy_d1b4c2a57f2f4ec0848d631c1c5b7554
  Complete output (10 lines):
  Running from numpy source directory.

  `setup.py clean` is not supported, use one of the following instead:

    - `git clean -xdf` (cleans all files)
    - `git clean -Xdf` (cleans all versioned files, doesn't touch
                        files that aren't checked into the git repo)

  Add `--force` to your command to use it anyway if you must (unsupported).

  ----------------------------------------
  ERROR: Failed cleaning build dir for numpy

If I try to import the mxnet library, then I receive
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Users\rawilhel\Anaconda3\lib\site-packages\mxnet\__init__.py", line 7, in <module>
    from .base import MXNetError
  File "C:\Users\rawilhel\Anaconda3\lib\site-packages\mxnet\base.py", line 52, in <module>
    _LIB = _load_lib()
  File "C:\Users\rawilhel\Anaconda3\lib\site-packages\mxnet\base.py", line 44, in _load_lib
    lib = ctypes.CDLL(lib_path[0], ctypes.RTLD_GLOBAL)
  File "C:\Users\rawilhel\Anaconda3\lib\ctypes\__init__.py", line 381, in __init__
    self._handle = _dlopen(self._name, mode)
FileNotFoundError: Could not find module 'C:\Users\rawilhel\Anaconda3\lib\site-packages\mxnet\libmxnet.dll' (or one of its dependencies). Try using the full path with constructor syntax.

 Can you use the Linux Subsystem instead? And see if
pip install mxnet==1.8.0.post0

works there? I'm actually not familiar with Windows machines, this is just a guess.
By the way, the error you encountered in your most recent comment is specific to Python 3.8:
https://github.com/apache/incubator-mxnet/issues/18276
Switching to e.g. Python 3.7 would be a fix for this problem I believe.

I switched to Python 3.7 and I receive this error message:

I switched to Python 3.7 and I received this message:
ERROR: Could not find a version that satisfies the requirement mxnet==1.8.0.post0 (from versions: 0.11.0, 0.12.0, 0.12.1, 1.0.0, 1.0.0.post1, 1.0.0.post3, 1.0.0.post4, 1.1.0.post0, 1.2.0, 1.2.1, 1.2.1.post1, 1.3.0, 1.3.1, 1.4.0, 1.4.0.post0, 1.4.1, 1.5.0, 1.6.0, 1.7.0.post1, 1.7.0.post2)
ERROR: No matching distribution found for mxnet==1.8.0.post0

Can you work inside WSL?

Interesting, it seems like 1.8 wasn't published for Windows. You should actually be able to use MXnet 1.7 as well so you could try installing 1.7.0.post2 instead.

Yes, on Windows try 1.7.0.post2 but with Python 3.7 (not 3.8) - although it seems there was one other unrelated error with building numpy.
Get Microsoft C++ Build Tools first.

I could install MXNet 1.7.0.post2 on Windows with Python 3.7.
With regards to using a WSL or Linux subsystem, I'll have to try it out first.
In my case, I will probably install Sockeye and the MXNet library on a Linux-based GPU container. I'll see tomorrow.

I can only run Sockeye on the CPU. If I want to install Sockeye-GPU on my Windows 10 computer with NVIDIA GeForce GTX960 2GB graphics (using Python 3.7):
pip install requirements.gpu-cu112.txt
I always receive

ERROR: Could not find a version that satisfies the requirement requirements.gpu-cu112.txt (from versions: none)
ERROR: No matching distribution found for requirements.gpu-cu112.txt

The same goes for the other requirement files.

Now you are just missing an -r:
pip install -r requirements.gpu-cu112.txt

Without this pip is just looking for a package called "requirements.gpu-cu112.txt".

ERROR: Could not find a version that satisfies the requirement mxnet-cu112==1.8.0.post0 (from versions: none)
ERROR: No matching distribution found for mxnet-cu112==1.8.0.post0

Is there a cuda version for mxnet==1.7.0.post2?

You can check existing packages yourself on Pypi if you like. Here is the page for mxnet-cu112:
https://pypi.org/project/mxnet-cu112/
I think the release history says that 1.8.0.post0 is the first version released for a build that supports CUDA 11.2.

According to the information received by the nvidia-smi command I have my GPU supports CUDA 11.6:

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 510.06       Driver Version: 510.06       CUDA Version: 11.6     |
|-------------------------------+----------------------+----------------------+
| GPU  Name            TCC/WDDM | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA GeForce ... WDDM  | 00000000:01:00.0  On |                  N/A |
| 30%   33C    P8    12W / 120W |    485MiB /  2048MiB |      1%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+

In my opinion this reports the driver version of CUDA, that is something else. Please run:
nvcc --version

This could mean that CUDA is in fact not installed on your system. (Having nvidia-smi does not mean that CUDA is installed.)

Maybe, this is just a guess, I don't have access to your machine and am missing a lot of information.

This is why I want to make this command run in order to give you more information.

Maybe, this is just a guess, I don't have access to your machine and am missing a lot of information.

Got it! This is the information I receive when I run nvcc --version:

nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2021 NVIDIA Corporation
Built on Mon_Sep_13_20:11:50_Pacific_Daylight_Time_2021
Cuda compilation tools, release 11.5, V11.5.50
Build cuda_11.5.r11.5/compiler.30411180_0

Ok, then I think your only choice is to work within the WSL and install Mxnet 1.8.0 with support for CUDA 10.1 (https://pypi.org/project/mxnet-cu101/1.8.0.post0/):
pip install mxnet-cu101==1.8.0.post0

Make sure that after installing everything, mxnet-cu101==1.8.0 is exactly what is installed. To check this:
pip show mxnet-cu101

(Technically you could still build mxnet from scratch with different dependencies that match your Windows machine exactly.)

Will try it out and let you know about it.

On the Ubuntu WSL2 I get the following information by using the command pip show mxnet-cu101:
Name: mxnet-cu101
Version: 1.8.0.post0
Summary: MXNet is an ultra-scalable deep learning framework. This version uses CUDA-10.1 and MKLDNN.
Home-page: https://github.com/apache/incubator-mxnet
Author: None
Author-email: None
License: Apache 2.0
Location: /home/rawilhel/.local/lib/python3.8/site-packages
Requires: requests, numpy, graphviz
Required-by:

Strange, after testing the first Sequence to Copy tutorial https://awslabs.github.io/sockeye/tutorials/seqcopy.html without --cpu-usage I received the message:

ERROR:root] Uncaught exception
Traceback (most recent call last):
File "/usr/lib/python3.8/runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/usr/lib/python3.8/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/home/rawilhel/.local/lib/python3.8/site-packages/sockeye/train.py", line 1151, in
main()
File "/home/rawilhel/.local/lib/python3.8/site-packages/sockeye/train.py", line 906, in main
train(args)
File "/home/rawilhel/.local/lib/python3.8/site-packages/sockeye/train.py", line 981, in train
context = utils.determine_context(device_ids=args.device_ids,
File "/home/rawilhel/.local/lib/python3.8/site-packages/sockeye/utils.py", line 362, in determine_context
check_condition(num_gpus >= 1,
File "/home/rawilhel/.local/lib/python3.8/site-packages/sockeye/utils.py", line 136, in check_condition
raise SockeyeError(error_message)
sockeye.utils.SockeyeError: No GPUs found, consider running on the CPU with --use-cpu

That's is odd.

check if a GPU is available independent of running Sockeye

check the setting of CUDA_VISIBLE_DEVICES:
echo $CUDA_VISIBLE_DEVICES

if that comes up empty, change the Sockeye call to
CUDA_VISIBLE_DEVICES="1" python -m sockeye.train [...]

assuming the ID of your GPU device is 1.
Alternatively export this environment variable.

CUDA_VISIBLE_DEVICES: command not found

No matter if I specify the parameter 0 or 1, nothing happens. The same error message as before.
Also the export of this environment variable didn't help.

Check if a GPU is available independent of running Sockeye:
https://mxnet.apache.org/versions/1.4.1/install/validate_mxnet.html

>>> a = mx.nd.ones((2, 3), mx.gpu())

[16:41:11] ../src/imperative/./imperative_utils.h:93: GPU support is disabled. Compile MXNet with USE_CUDA=1 to enable GPU support.
Traceback (most recent call last):
File "", line 1, in
File "/home/rawilhel/.local/lib/python3.8/site-packages/mxnet/ndarray/ndarray.py", line 3295, in ones
return _internal._ones(shape=shape, ctx=ctx, dtype=dtype, **kwargs)
File "", line 39, in _ones
File "/home/rawilhel/.local/lib/python3.8/site-packages/mxnet/_ctypes/ndarray.py", line 82, in _imperative_invoke
check_call(_LIB.MXImperativeInvokeEx(
File "/home/rawilhel/.local/lib/python3.8/site-packages/mxnet/base.py", line 246, in check_call
raise get_last_ffi_error()
mxnet.base.MXNetError: Traceback (most recent call last):
File "../src/imperative/imperative.cc", line 81
MXNetError: Operator _ones is not implemented for GPU.

How to compile MXNet to USE_CUDA=1?

----------Python Info----------
Version      : 3.8.10
Compiler     : GCC 9.3.0
Build        : ('default', 'Sep 28 2021 16:10:42')
Arch         : ('64bit', 'ELF')
------------Pip Info-----------
Version      : 20.0.2
Directory    : /usr/lib/python3/dist-packages/pip
----------MXNet Info-----------
No MXNet installed.
----------System Info----------
Platform     : Linux-<IP_ADDRESS>-microsoft-standard-WSL2-x86_64-with-glibc2.29
system       : Linux
node         : DESKTOP-A9OEPPR
release      : <IP_ADDRESS>-microsoft-standard-WSL2
version      : #1 SMP Fri Apr 2 22:23:49 UTC 2021
----------Hardware Info----------
machine      : x86_64
processor    : x86_64
Architecture:                    x86_64
CPU op-mode(s):                  32-bit, 64-bit
Byte Order:                      Little Endian
Address sizes:                   39 bits physical, 48 bits virtual
CPU(s):                          8
On-line CPU(s) list:             0-7
Thread(s) per core:              2
Core(s) per socket:              4
Socket(s):                       1
Vendor ID:                       GenuineIntel
CPU family:                      6
Model:                           94
Model name:                      Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz
Stepping:                        3
CPU MHz:                         3407.999
BogoMIPS:                        6815.99
Virtualization:                  VT-x
Hypervisor vendor:               Microsoft
Virtualization type:             full
L1d cache:                       128 KiB
L1i cache:                       128 KiB
L2 cache:                        1 MiB
L3 cache:                        8 MiB
Vulnerability Itlb multihit:     KVM: Mitigation: VMX disabled
Vulnerability L1tf:              Mitigation; PTE Inversion; VMX conditional cache flush
es, SMT vulnerable
Vulnerability Mds:               Vulnerable: Clear CPU buffers attempted, no microcode;
SMT Host state unknown
Vulnerability Meltdown:          Mitigation; PTI
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prct
l and seccomp
Vulnerability Spectre v1:        Mitigation; usercopy/swapgs barriers and __user pointe
r sanitization
Vulnerability Spectre v2:        Mitigation; Full generic retpoline, IBPB conditional,
IBRS_FW, STIBP conditional, RSB filling
Vulnerability Srbds:             Unknown: Dependent on hypervisor status
Vulnerability Tsx async abort:   Vulnerable: Clear CPU buffers attempted, no microcode;
SMT Host state unknown
Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge m
ca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht sysc
all nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xt
opology cpuid pni pclmulqdq vmx ssse3 fma cx16 pcid ss
e4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hyp
ervisor lahf_lm abm 3dnowprefetch invpcid_single pti s
sbd ibrs ibpb stibp tpr_shadow vnmi ept vpid ept_ad fs
gsbase bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed
adx smap clflushopt xsaveopt xsavec xgetbv1 xsaves fl
ush_l1d arch_capabilities
----------Network Test----------
Setting timeout: 10
Timing for MXNet: https://github.com/apache/incubator-mxnet, DNS: 0.0038 sec, LOAD: 0.6027 sec.
Timing for Gluon Tutorial(en): http://gluon.mxnet.io, DNS: 0.0562 sec, LOAD: 0.4735 sec.
Error open Gluon Tutorial(cn): https://zh.gluon.ai, <urlopen error [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: certificate has expired (_ssl.c:1131)>, DNS finished in 0.11693787574768066 sec.
Timing for FashionMNIST: https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz, DNS: 0.0241 sec, LOAD: 1.2617 sec.
Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.0130 sec, LOAD: 0.5648 sec.
Error open Conda: https://repo.continuum.io/pkgs/free/, HTTP Error 403: Forbidden, DNS finished in 0.03590869903564453 sec.
----------Environment----------

Could you post this as a code block? The part I'd like to look at is currently not present ("build features").

This line:
No MXNet installed.

says that Mxnet is not installed at all. Do you perhaps have different Pythons installed (example: "python3" and "python" link to different Python executables) or different virtual python environments? Did you mix pip with conda?

No, I have no Anaconda installed #

This line:
No MXNet installed.

says that Mxnet is not installed at all. Do you perhaps have different Pythons installed (example: "python3" and "python" link to different Python executables) or different virtual python environments? Did you mix pip with conda?

I have no Anaconda / Conda installed. If I type "python", I only get

Command 'python' not found, did you mean:
command 'python3' from deb python3
command 'python' from deb python-is-python3

Sorry, I believe you edited your post and now this line about Mxnet not being installed is gone.
I think you probably have two different mxnet packages installed. Could you run this:
pip freeze | grep "mxnet"

(I am soon going to give up, last try :) )

Yes, I run this command as root and I run it again as normal user.
Sorry for that, but I tried everything, but Mxnet-GPU won't just work. I'm very thankful for your help and time.
Here is the output:

username@DESKTOP-A9OEPPR:/$ pip freeze | grep "mxnet"
mxnet==1.8.0.post0
mxnet-cu101==1.8.0.post0

So, you do have two mxnet packages installed at the same time, that is the cause of your issue.
mxnet-cu101==1.8.0.post0 is the package you need, mxnet==1.8.0.post0 should not be installed.
pip uninstall mxnet

and after that make sure that mxnet-cu is still installed. Then try running the mxnet post-installation validation check (or the build diagnostics) again.

pip freeze | grep "mxnet"

mxnet-cu101==1.8.0.post0

python3

Python 3.8.10 (default, Sep 28 2021, 16:10:42)
[GCC 9.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.

import sockeye
import mxnet as mx
Traceback (most recent call last):
File "", line 1, in
ModuleNotFoundError: No module named 'mxnet'

New build diagnostics:

----------Python Info----------
Version      : 3.8.10
Compiler     : GCC 9.3.0
Build        : ('default', 'Sep 28 2021 16:10:42')
Arch         : ('64bit', 'ELF')
------------Pip Info-----------
Version      : 20.0.2
Directory    : /usr/lib/python3/dist-packages/pip
----------MXNet Info-----------
No MXNet installed.
----------System Info----------
Platform     : Linux-<IP_ADDRESS>-microsoft-standard-WSL2-x86_64-with-glibc2.29
system       : Linux
node         : DESKTOP-A9OEPPR
release      : <IP_ADDRESS>-microsoft-standard-WSL2
version      : #1 SMP Fri Apr 2 22:23:49 UTC 2021
----------Hardware Info----------
machine      : x86_64
processor    : x86_64
Architecture:                    x86_64
CPU op-mode(s):                  32-bit, 64-bit
Byte Order:                      Little Endian
Address sizes:                   39 bits physical, 48 bits virtual
CPU(s):                          8
On-line CPU(s) list:             0-7
Thread(s) per core:              2
Core(s) per socket:              4
Socket(s):                       1
Vendor ID:                       GenuineIntel
CPU family:                      6
Model:                           94
Model name:                      Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz
Stepping:                        3
CPU MHz:                         3407.999
BogoMIPS:                        6815.99
Virtualization:                  VT-x
Hypervisor vendor:               Microsoft
Virtualization type:             full
L1d cache:                       128 KiB
L1i cache:                       128 KiB
L2 cache:                        1 MiB
L3 cache:                        8 MiB
Vulnerability Itlb multihit:     KVM: Mitigation: VMX disabled
Vulnerability L1tf:              Mitigation; PTE Inversion; VMX conditional cache flush
es, SMT vulnerable
Vulnerability Mds:               Vulnerable: Clear CPU buffers attempted, no microcode;
SMT Host state unknown
Vulnerability Meltdown:          Mitigation; PTI
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prct
l and seccomp
Vulnerability Spectre v1:        Mitigation; usercopy/swapgs barriers and __user pointe
r sanitization
Vulnerability Spectre v2:        Mitigation; Full generic retpoline, IBPB conditional,
IBRS_FW, STIBP conditional, RSB filling
Vulnerability Srbds:             Unknown: Dependent on hypervisor status
Vulnerability Tsx async abort:   Vulnerable: Clear CPU buffers attempted, no microcode;
SMT Host state unknown
Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge m
ca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht sysc
all nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xt
opology cpuid pni pclmulqdq vmx ssse3 fma cx16 pcid ss
e4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hyp
ervisor lahf_lm abm 3dnowprefetch invpcid_single pti s
sbd ibrs ibpb stibp tpr_shadow vnmi ept vpid ept_ad fs
gsbase bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed
adx smap clflushopt xsaveopt xsavec xgetbv1 xsaves fl
ush_l1d arch_capabilities
----------Network Test----------
Setting timeout: 10
Timing for MXNet: https://github.com/apache/incubator-mxnet, DNS: 0.0119 sec, LOAD: 0.4856 sec.
Timing for Gluon Tutorial(en): http://gluon.mxnet.io, DNS: 0.0152 sec, LOAD: 0.1396 sec.
Error open Gluon Tutorial(cn): https://zh.gluon.ai, <urlopen error [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: certificate has expired (_ssl.c:1131)>, DNS finished in 0.020061969757080078 sec.
Timing for FashionMNIST: https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz, DNS: 0.0252 sec, LOAD: 1.2152 sec.
Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.0139 sec, LOAD: 0.4264 sec.
Error open Conda: https://repo.continuum.io/pkgs/free/, HTTP Error 403: Forbidden, DNS finished in 0.014934062957763672 sec.
----------Environment----------

At some point we are bound to solve this problem :D.
It's possible that on your machine "pip" and "pip3" (and "python" and "python3") are not the same executables. Run this:
which pip
which pip3
which python
which python3

and general advice: never work with the system-level Python directly. Do not install packages there. Never use root privileges with pip.
Create a virtual environment, for instance with venv (https://docs.python.org/3/library/venv.html):
python3 -m venv /path/to/new/virtual/environment

and activate with
source /path/to/new/virtual/environment/bin/activate

then install everything from scratch.

username@DESKTOP-A9OEPPR:/$ which pip
/usr/bin/pip
username@DESKTOP-A9OEPPR:/$ which pip3
/usr/bin/pip3
username@DESKTOP-A9OEPPR:/$ which python
username@DESKTOP-A9OEPPR:/$ which python3
/usr/bin/python3
username@DESKTOP-A9OEPPR:/$

about

and general advice: never work with the system-level Python directly. Do not install packages there. Never use root privileges >with pip

I understand and acknowledged that. You have given me a very important advice.
Here are the results after activating my virtual environment:
 import mxnet as mx
 a = mx.nd.ones((2, 3), mx.gpu())

[21:04:09] ../src/imperative/./imperative_utils.h:93: GPU support is disabled. Compile MXNet with USE_CUDA=1 to enable GPU support.
Traceback (most recent call last):
File "", line 1, in
File "/home/rawilhel/env/lib/python3.8/site-packages/mxnet/ndarray/ndarray.py", line 3295, in ones
return _internal._ones(shape=shape, ctx=ctx, dtype=dtype, **kwargs)
File "", line 39, in _ones
File "/home/rawilhel/env/lib/python3.8/site-packages/mxnet/_ctypes/ndarray.py", line 82, in _imperative_invoke
check_call(_LIB.MXImperativeInvokeEx(
File "/home/rawilhel/env/lib/python3.8/site-packages/mxnet/base.py", line 246, in check_call
raise get_last_ffi_error()
mxnet.base.MXNetError: Traceback (most recent call last):
File "../src/imperative/imperative.cc", line 81
MXNetError: Operator _ones is not implemented for GPU.

Even in the virtual environment if I only install mxnet-cu101 the CPU-version will be installed anyway. The same problems remain, after I uninstalled the CPU version. :(
pip3 freeze

certifi==2021.10.8
charset-normalizer==2.0.7
graphviz==0.8.4
idna==3.3
mxnet-cu101==1.8.0.post0
numpy==1.21.4
portalocker==2.3.2
PyYAML==6.0
requests==2.26.0
sacrebleu==1.4.14
sockeye==2.3.24
urllib3==1.26.7

The build diagnostics remain the same. MXNet can't drill through the wood. :(
Sorry my for annoying problems. I'm afraid that I can completely forget about Sockeye on a GPU. :'(

Okay, I think I'm almost out of ideas now.
The only way I could possibly help you is if you post a complete list of all the exact commands you run, in order, starting with creating a new virtual environment. Example: in your last comment you do import mxnet as mx, but I don't know which Python executable you used to start the Python interpreter.
Some more things you could run:
python -V
python3 -V

realpath python
realpath python3

I'm really sorry that for my trouble. WSL2 seems to be totally different than a usual Computer with Ubuntu as OS.
Here is my output:

(env) username@DESKTOP-A9OEPPR:~$ python -V
Python 3.8.10
(env) username@DESKTOP-A9OEPPR:~$ python3 -V
Python 3.8.10
(env) username@DESKTOP-A9OEPPR:~$ realpath python
/home/username/python
(env) username@DESKTOP-A9OEPPR:~$ realpath python3
/home/username/python3
(env) username@DESKTOP-A9OEPPR:~$

If even the next advice doesn't help, I will start a new attempt with WSL2. Otherwise, it seems I have to resign myself to the fact that my computer is just not made for MXNet or Ubuntu.
I'm infinitely grateful to you, @bricksdont, for taking a lot of time and patience for me.

I thank you for your, and especially for @bricksdont's, help. I'm going to close this issue because I deleted the Ubuntu kernel from my computer again. It wouldn't have worked because of my memory capacity, for one thing, and because I have come to the realization that my computer simply doesn't meet certain requirements. I find that @bricksdont has written down important tips that I will apply to another computer.
First and foremost, it's important that you have enough free space on your Windows system hard drive. The WSL2 installations can even take over 30 GB of disk space.
I believe that Sockeye is only a tool for Linux users whose computers have NVIDIA graphics cards. Sockeye and MXNet are just unnecessarily complicated and you need an insane amount of disk space.


test: Adding NetworkFence capability to test tool

Adding the missing NetworkFence capability to the
cmd/csi-addons admin/test tool.
Fixes: #204

Keeping it as WIP. Yet to test this and do the doc updates. Reviews are welcome though ;-)

@karthik-us the codespell failures were from the 1st commit itself, it didn't already existed in the code. So you may have to squash the commits.

Test outputs:
karthik:~$ kr exec -c csi-addons csi-rbdplugin-provisioner-f88868fb7-7bprx -- csi-addons -operation NetworkFence -clusterid rook-ceph -secret rook-ceph/csi-rbd-secret -cidrs <ip-addr>/20 -endpoint unix:///csi/csi-addons.sock
Network fence successful
kr exec -c csi-addons csi-rbdplugin-provisioner-f88868fb7-7bprx -- csi-addons -operation NetworkUnFence -clusterid rook-ceph -secret rook-ceph/csi-rbd-secret -cidrs <ip-addr>/20 -endpoint unix:///csi/csi-addons.sock
Network unfence successful


[WIP]fixing slice_axis kernel to int32

Description
improves performance of SSD on GPU with Large tensor support.
Checklist
Essentials
Please feel free to remove inapplicable items for your PR.

[x] Changes are complete (i.e. I finished coding on this PR)
[x] To the best of my knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change

Results
---- will be updated later

Is it possible to re-implement without mshadow?


v3 fixes

You can leave this open, I'll probably add more commits to this PR. Just fixing some unrelated stuff here while I'm playing around with this.

I know you planned to do more fixed in this PR, but turns out this fix is important, so thanks and sorry for merging

Just one note -- do not forget to build project before committing with npm run build

Yeah, I have npm watch running, but I forgot to commit the stuff in /lib...


FIX: Want to enhance the nav bar and footer of the author page

The nav bar of the author page needs to be adjusted in order to make it look more attractive and also the footer section of that page needs to me adjusted a little bit.

Please assign the labels of hactorberfest and good first issue


Do you have the training DTU dataset at high resolution?

Hi there,
It seems that the DTU training dataset that you provide isn't at the original resolution. I was wondering if you could share the training dataset at the original resolution. Thank you!

Hi Ignasi,
You can download the original resolution depth maps at:
https://drive.google.com/open?id=1LVy8tsWajG3uPTCYPSxDvVXFCdIYXaS-

Thank you so much @YoYo000 ! I see that in the link you shared with me there is just the depth maps. Would it be possible to have also access to the high res rectified images?  Thanks!


add explicit

add explicit and format code

why we need explicit?

"A single-argument constructor may be used as an implicit conversion
function, unless it is marked "explicit" (see [ISO/IEC 14882-2003
https://www.securecoding.cert.org/confluence/display/cplusplus/AA.+Bibliography#AA.Bibliography-ISO/IEC14882-2003]
Section 12.3.1, "Conversion by constructor"). Such implicit conversions may
be unexpected, and lead to undesirable behavior."
according to
https://www.securecoding.cert.org/confluence/pages/viewpage.action?pageId=3416
it's not forced, but it maybe more better? :) anyway, it's up to your
opinion.
2017-08-26 22:46 GMT+08:00 pezy chen<EMAIL_ADDRESS>
why we need explicit?
—
You are receiving this because you authored the thread.
Reply to this email directly, view it on GitHub
https://github.com/Mooophy/Cpp-Primer/pull/585#issuecomment-325134405,
or mute the thread
https://github.com/notifications/unsubscribe-auth/ATuZSnUMFmp44wUYEtFTwrfTz_Z1zA4Cks5scC-0gaJpZM4PDU0c
.


Default AKS CLI extension to SLB+VMSS when possible

This checklist is used to make sure that common guidelines for a pull request are followed.

For new extensions:

[ ] My extension description/summary conforms to the Extension Summary Guidelines.

If this PR is for a new extension or change to an existing extension, use the following to try out the changes in this PR:
docker run -it microsoft/azure-cli:latest
export EXT=<NAME>
pip install --upgrade --target ~/.azure/cliextensions/$EXT "git+https://github.com/xuto2/azure-cli-extensions.git@SLBVMSSDefault#subdirectory=src/$EXT&egg=$EXT"


Day 7 Subfolder Name Can Be in Many Paths

Relate to fb3b1a7.
For example,
?- dir("vbzr").
true ;
true ;
true ;
true .

This issue was solved in 10eaf91.


Added new cuda kernel for encoder forwards using three dimensional kernels

Spent the afternoon trying to understand how the multi dimensional cuda kernel instantiation works and came up with an example for the encoder forwards but I'm having the issue that for large block sizes its slower. Would love for someone with more understanding of how this works to take a look.
This is to try to apply the advice that @ngc92 gave for this topic: https://github.com/karpathy/llm.c/issues/406
Kernel 3:
block_size   32 | time 0.0309 ms | bandwidth 3257.16 GB/s
block_size   64 | time 0.0293 ms | bandwidth 3436.13 GB/s
block_size  128 | time 0.0291 ms | bandwidth 3463.98 GB/s
block_size  256 | time 0.0294 ms | bandwidth 3428.10 GB/s
block_size  512 | time 0.0293 ms | bandwidth 3436.86 GB/s
block_size 1024 | time 0.0300 ms | bandwidth 3351.67 GB/s
Kernel 4:
block_size   32 | time 0.0306 ms | bandwidth 3286.23 GB/s
block_size   64 | time 0.0295 ms | bandwidth 3416.79 GB/s
block_size  128 | time 0.0292 ms | bandwidth 3444.44 GB/s
block_size  256 | time 0.0295 ms | bandwidth 3413.71 GB/s
block_size  512 | time 0.0315 ms | bandwidth 3194.80 GB/s
block_size 1024 | time 0.0518 ms | bandwidth 1942.42 GB/s
I'm having a hard time intuitively understanding why it could be slower since its removes all of the modulo, and division operations

Figured out that I needed to change the block size dynamically based off of the value of C and the current block size and it is now around .0020ms faster!
block_size   32 | time 0.0346 ms | bandwidth 2911.14 GB/s
block_size   64 | time 0.0319 ms | bandwidth 3153.09 GB/s
block_size  128 | time 0.0309 ms | bandwidth 3256.19 GB/s
block_size  256 | time 0.0311 ms | bandwidth 3235.72 GB/s
block_size  512 | time 0.0311 ms | bandwidth 3234.76 GB/s
block_size 1024 | time 0.0310 ms | bandwidth 3251.12 GB/s
block_size   32 | time 0.0350 ms | bandwidth 2876.90 GB/s
block_size   64 | time 0.0314 ms | bandwidth 3207.94 GB/s
block_size  128 | time 0.0313 ms | bandwidth 3211.82 GB/s
block_size  256 | time 0.0312 ms | bandwidth 3227.00 GB/s
block_size  512 | time 0.0318 ms | bandwidth 3163.65 GB/s
block_size 1024 | time 0.0329 ms | bandwidth 3058.42 GB/s


⚠ Mobitopup has degraded performance

In ed764c9, Mobitopup (https://napcard.voz.tech) experienced degraded performance:

HTTP code: 200
Response time: 6058 ms

Resolved: Mobitopup performance has improved in e5e99fd.


Problems of using Tabnine in. Remote-ssh machine

please complete the following information:

OS version: ssh to remote machine(ubuntu)
Editor version [e.g. 1.45.0-insider]:
Programming language: python
TabNine extension version: 3.1.5

I have installed Tabnine in my ssh-linked machines like this

but I can't open the Tabnine config, and the autocomplete function didn't work very well

See https://tabnine.com/faq#browser_failed


Put data preview in south pane

Issue: https://github.com/airbnb/caravel/issues/1446
Before: data preview of a selected table appears as a modal, but for
some cases users may want to view data and edit sql at the same time

After:

data preview of a selected table pops up a new tab in South Pane
data are saved to local state and flushed in global store in
ResultSet component

Done:

Added queries to SouthPane component (mapped from state)
Save results locally in ResultSet once loaded and clear them from global store
Added tabs in SouthPane for data preview of tables

Todo:

Add pagination to ResultSet

needs-review @mistercrunch @ascott

Here's an idea that helps simplifying things quite a bit: you can just store previewQueryId in the table object itself. From that point, you can simply pass tables to SouthPane, you already have the queries array there.
Note that the queries are an object in the store and an array in the component (that was by design). I could be changed to be an object in the component to avoid looping to find the query with the right id, though I think that's fine too.

Done:

Moved dataPreviewQueryId to tables object, selectStar query is fired once a table is added.

However I'm wondering whether it's still better to keep the 'data preview' icon for each table, instead of fetching table data each time a table is selected. There could be large tables where user only needs to view column names to run a query, keeping the data preview icon will give user flexibility option to view table data, as well as saving us time for fetching the data when not needed. @mistercrunch

I think we're almost there! So what happens when the user refreshes the page? Is the tab still there and its empty?
Seems like we should just remove the tab and the user would have to click the preview button to view the data.

Done:

a link will be displayed under preview tab after user refreshes the page
clicking on the link will re-fetch the data by running a new query
runQuery is moved after fetching table metadata call in actions (so that table metadata can be displayed before preview data is fetched)

@mistercrunch

LGTM


fix: bufferline and 'No Name' bufs

fixes #389
suggestion by @agronskiy there works like a charm.

How does this fix work with the other popup buffers? It seems very strange that the handle returned from nvim_create_buf is one index off.
What seems more likely is that two buffers are created and the last one is used, but the first one if forgotten, or something similar

My "fix" in the linked thread was rather intended to surface the potential problem and invite someone knowledgable for deeper investigation, TBH I would also not push it per se, since it seems too much of a hack.


Separate authorization page

Is it possible to have a separate authorization page, for example, when a client logs in, they are redirected to an Amber page to authorize signing, NIP4/NIP44 encryption/decryption, and no further authorization is required for subsequent operations?

In the login page you can send some default permissions to be authorized for ever and the user can press the adjust button to deny and authorize manually.
I forgot to update the docs but you need to send in the intent an array of objects
The object contains the type of the event and the kind

        val permissions = listOf(
            Permission(
                "sign_event",
                22242
            ),
            Permission(
                "nip04_encrypt"
            ),
            Permission(
                "nip04_decrypt"
            ),
            Permission(
                "nip44_encrypt"
            ),
            Permission(
                "nip44_decrypt"
            ),
            Permission(
                "decrypt_zap_event"
            ),
        )

Great, thx :)

Here's some screenshots:


publish not working unless you're already logged in

<EMAIL_ADDRESS>first time running publish i get ENEEDAUTH
run npm adduser
fill out stuff
get the following
npm ERR! Unexpected token C
npm ERR! Cannot PUT /-/user/org.couchdb.user:mattkrick

currently switching back whenever i need to publish, which isn't great, but sure beats the heck out of waiting 5 mins to run install thanks to npm3  :+1:

Yeah, currently npm publish only works if you are already logged in. This is something worth fixing, though! :)

Same issue here, and npm login not work either (got error messages the same as above).
Do I have to switch my registry back to https://registry.npmjs.org to log in?

@gera2ld What I've normally done is npm login in one registry, then copy the .npmrc credentials from one to the other when I switch back. Of course you shouldn't have to do that, but in the meantime, it's a workaround.

So now that we have tests in place, this should be a fairly easy issue to reproduce. You would just need to add a test for npm login and confirm that the app at least redirects you to the main npm (no need to expose your credentials in the test itself, just reproduce a 403 or something...).

Fixed by https://github.com/nolanlawson/local-npm/pull/95.


Improve Tests

This is an Epic issue containing new tests to be added:

[ ] unit test static.config.js options

[ ] matches expected output
[ ] executes expected functionality

[ ] unit test Components

[ ] matches expected output
[ ] executes expected functionality

[ ] unit test Methods

[ ] executes expected functionality

[ ] unit test Node API

[ ] executes expected functionality

*Use this issue to track progress, this ticket will be updated in the future to reflect updated changes

Fantastic!

I can help out here. What is priority for testing? is that list in order?

I've reordered them to reflect priority :)

bad bot!


Example not running due to CORS problems

Followed the tutorial, but I am not able to see the alert in in iTwin Viewer.
After some investigation I see following error in chrome debugger
Access to fetch at 'http://localhost/5000/imjs_extensions/sample/index.js' from origin 'http://localhost:3000' has been blocked by CORS policy: No 'Access-Control-Allow-Origin' header is present on the requested resource. If an opaque response serves your needs, set the request's mode to 'no-cors' to fetch the resource with CORS disabled.
Tried as well to copy extension inside the build folder of iTwin viewer but have same error except with port number 3000.
So I believe the command npx serve --cors .lib is not working as expected.
Any tip where I can modify the code to modify the header would be help ful.
Thank you

Apologies it is working.
Made a typing error in configuration code / -> :
http://localhost/5000/imjs_extensions/sample/index.js
Must be
http://localhost:5000/imjs_extensions/sample/index.js


No way to create syntax theme

Description
No way to create syntax theme via command (⌘+Shift+P)
Steps to Reproduce

Go to create a Syntax Theme by hitting ⌘+Shift+P.
Select "Generate Syntax Theme"...
hit enter and nothing happens.
It just sits there with this box

Expected behavior:

Actual behavior:

Reproduces how often:

Every time
Versions

Atom    : 1.53.0
Electron: 6.1.12
Chrome  : 76.0.3809.146
Node    : 12.4.0
Mac OS Mojave

Prerequisites

[x] Put an X between the brackets on this line if you have done all of the following:

Reproduced the problem in Safe Mode: https://flight-manual.atom.io/hacking-atom/sections/debugging/#using-safe-mode
Followed all applicable steps in the debugging guide: https://flight-manual.atom.io/hacking-atom/sections/debugging/
Checked the FAQs on the message board for common solutions: https://discuss.atom.io/c/faq
Checked that your issue isn't already filed: https://github.com/issues?utf8=✓&q=is%3Aissue+user%3Aatom
Checked that there is not already an Atom package that provides the described functionality: https://atom.io/packages

I'm sorry - this is actually a much bigger problem than I realized. It happens with all dialogue boxes, even ones I coded myself in python. You hit enter and nothing happens.

Wait - i figured it out. Okay so this is the default keymapping file (⌘+,)
# Here's an example taken from Atom's built-in keymap:

# 'atom-text-editor':
#   'enter': 'editor:newline'

I uncommented those lines and then it was mapping Enter to "newline.' Is there a reason that's in the default file? Probably not best to include an example that breaks Atom. I recommented them and it fixed the issue.

What's the status on this


Create pull_request_template.md

Coverage remained the same at 100.0% when pulling b7789487005c2780a0c4653da1e3e869fd0917a6 on pr-template into 9073cb26b347b63f44c8086f0240205a001a4871 on main.


Prepare intl_translation for release support intl 0.19

This enables support for Flutter 3.22 which pins package:intl:0.19.
Also takes advantage of unnamed library directives.

It looks like the only semantic changes here are moving the dep on package:intl from ^0.18.0 to ^0.19.0, and requiring Dart 3.0.0. I'm a little surprised that this package doesn't yet include 0.19.0 in its dep ranges (i.e., >=0.18.0 <0.20.0).
I believe that the source-of-record for intl_translation is google3. In any case, we should wait for a review from @mosuem before merging and publishing.

@parlough - I'm going to land a portion of this internally (https://github.com/dart-lang/i18n/pull/839); that change will be mirrored out to github and we can publish from there.

Sounds great, thanks @devoncarew!


Use custom uploaded artwork for local files

I'm pretty sure Cider had a feature like that and it worked well, is it possible you could implement it into this as well?

At first glance this would require to

get the music track location in the filesystem (AppleScript?)
extract the cover art (ffmpeg can do it - maybe use a wasm version to avoid the need to install another dependency on the user side)
push it to a hosting site (???)

I'm quite lazy to work on it right now but it looks doable. If anyone wants to develop it feel free to open a pr.

You should use a music database (such as MusicBrainz) to find the cover album first since it's not very ideal to push the image to a hosting site everytime.


Decoder for FixedBytes

Transactions which call a function designed to accept a parameter of FixedBytes<x> but include an argument of less than x bytes are successfully processed into a block.
Currently the decode function will only successfully decode a FixedBytes<x> parameter if there are x bytes in the tx input. Since the solidity code for the contract will accept less than x bytes, we need to be able to decode less than x bytes into a FixedBytes<x>.
To achieve this, we parse up to x bytes for aFixedBytes<x> param only if it is the last argument in the transaction input. Otherwise, if the param is not the last in the tx input, we still require all x bytes.

This works, but feels like a very specific special case for a generalized decoder function.  I would lean towards processing the data before it gets to the decode function, so decode can expect the data to be formatted as specified in the ABI.
Open question moving forward: are there cases where this special casing will "hide" bad data because we are now allowing shorter byte arrays than expected?

The more I think about this, the more I am leaning towards preprocessing data before we pass it in, and just pad it with 0s so the data always has a byte length that is a multiple of 32.
I think the only concern about that was that it means we pass data to mappings that is not 1:1 on chain. But we're talking about broken encodings, and I think it's ok to fix those before.
@olibearo If you agree, I can add the padding logic in graph-node and verify the affected subgraph(s) work with that; then we shouldn't need this PR.

After a discussion yesterday we have decided to keep the decoder the way it is, apart from one modification:

Allow a Fn(&ParamType, &[u8]) -> Option<[u8]> (or similar) to be passed to the decoder.
Whenever a value fails to decode, the decoder will call this function to allow it to transform the data and return it to the decoder. If None is returned, the decoder fails with the original error. If Some is returned, the decoder tries to decode the transformed data.
In graph-node, we can then selectively pad data to multiples of 32 or do other things like log warnings.


Adding hdfs support

Adding hdfs support that allows load templates from an HDFS path.
Tests:

The output is also included in the new notebook.

Tested the pyarrow Filesystem interface with both hdfs and local paths in the same list and it worked.


Squiz/MultiLineFunctionDeclaration: bug fix - skip over attributes

Description
The sniff looks for T_COMMA tokens to find the start of the next parameter and skips over parenthesis sets and square brackets sets (like short arrays) to prevent mismatching on a T_COMMA which is not a parameter separator.
This logic did not take parameter attributes into account, which can contain multiple comma-separated attributes, so should also be skipped over.
Fixed now. Includes plenty of tests.
Also includes minor stability fix for the parentheses/square brackets skipping.
Notes:

It could be argued that the sniff should use the File::getMethodParameters() method to do the parameter parsing instead. This could be done in a future iteration, but will need to be evaluated carefully for side-effects.
This sniff extends the PEAR FunctionDeclaration sniff. It has been verified that that sniff is not affected by this bug.

Suggested changelog entry
Fixed Squiz.Functions.MultiLineFunctionDeclaration did not skip over (parameter) attributes leading to false positives.
Related issues/external references
Fixes #608
Types of changes

[x] Bug fix (non-breaking change which fixes an issue)

Rebased without changes. Merging once the build has passed.


Issue with Ikea bulbs with cold/warm light.

Issue description
Error messages for Ikea light bulbs with cold/warm light.
I think the messages started maybe 2 weeks ago. About the same time as the "effect" switches were introduced.
Additional information
Zigbee2MQTT version: v.1.17.1-dev
Python version: 3.7.3
Domoticz version: 2020.2 (build 12934)
Plugin version:  latest build 10/2-21
Logs
2021-02-10 20:15:18.299 Error: Python Plugin System: (Zigbee2MQTT) Matbord: can not process numeric item "color_temp_startup"
2021-02-10 20:15:18.305 Error: Python Plugin System: (Zigbee2MQTT) Trappa: can not process numeric item "color_temp_startup"

Duplicate of #569


test code

Hi,
I was wondering if you had any test code/benchmarking code ?
I add some CMake logic to easily add bunches of gtest tests...
I would like to add an istream interface to LineReader, but before starting any coding,
it would nice if there are some tests already I could add.
Cheers
Jiri

I was wondering if you had any test code/benchmarking code ?

None that is in a state that I'd put it in a public repro. ;)

I add some CMake logic to easily add bunches of gtest tests...

I would really prefer not to add further dependencies just for tests. Currently neither cmake nor gtest is needed.

I would like to add an istream interface to LineReader,

Newest version has support for this.

well :) that sounds great!
Regarding tests, cmake and gtest.
This is just something that is orthogonal to the library.
Usage of the library doesn't require any dependency.
I will push an example up to my fork.

https://github.com/jkhoogland/fast-cpp-csv-parser
has an example of how this could work.

orthogonal to your lib
automatically downloads gtest
automatically picks up new test files

My current cmake is older than the required 3.0 so I cannot run your script. Perhaps I should finally get around upgrading my distro. However, I'm not going to do this because of some csv test set.
Also I'm not convinced that gtest is superior for this particular application to a simple:
template<class A, class B>
inline expect_eq_check(const A&a, const B&b, const char*a_label, const char*b_label,int line_num){
	if(a != b){
		cout << "line " << line_num << " : " << a_label << " != "<< b_label << endl
			<< "value of left side : " << (a) << endl
			<< "value of right side : " << (b) << endl;
	}
}

#define EXPECT_EQ2(a,b,a_label,b_label, line_num) \
	expect_eq_check(a, b, a_label, b_label, line_num)

#define EXPECT_EQ(a, b) \
	EXPECT_EQ2(a,b,#a, #b, __LINE__)
int main(){
  // my tests
}

For a large software project with thousands of tests I buy that gtest has its advantages as for example testing does not abort at the first error but to test a single 1200 line header file...

sure, you don't have to use gtest at all.
It is just what i tend to use for my stuff. It can be discarded if you want.
The benefit I like is that I can add a new test by adding
TEST( And, AnotherTest )
{
  // test code...
}

CMake will pick up the test when regenerating the make file
and I can use gtest to handle the main, it will register the new test.


Remove winget and chocolately

I recently bought the .exe wrapper of this software because I thought that would remove the requirement of download both winget and chocolately but it still did install it. Can there please be a separate version without winget or chocolately, I don't use the download section, I only use the tweaks scripts. I already bought the program so I hope this can happen.

Winget and Chocolately are required for Winutil's program installs to function.


UDP stream

Hello
do you know how I can output / broadcast a video stream in udp://@IPADRESS:PORT with nginx ?
Thanks

Seems like more of a task for ffmpeg.
‘udp://hostname:port[?options]’

Thanks @rmens!
@Korben00, can you try the suggestion above?


Can‘t  install GroundingDINO

`Installing collected packages: groundingdino
Running setup.py develop for groundingdino
error: subprocess-exited-with-error
× python setup.py develop did not run successfully.
│ exit code: 1
╰─> [103 lines of output]
    fatal: not a git repository (or any of the parent directories): .git
    Building wheel groundingdino-0.1.0
    Compiling with CUDA
    running develop
    running egg_info
    writing groundingdino.egg-info\PKG-INFO
    writing dependency_links to groundingdino.egg-info\dependency_links.txt
    writing requirements to groundingdino.egg-info\requires.txt
    writing top-level names to groundingdino.egg-info\top_level.txt
    reading manifest file 'groundingdino.egg-info\SOURCES.txt'
    adding license file 'LICENSE'
    writing manifest file 'groundingdino.egg-info\SOURCES.txt'
    running build_ext
    building 'groundingdino._C' extension
    Emitting ninja build file D:\code\python\acupoint_detection\Grounded-Segment-Anything-main\GroundingDINO\build\temp.win-amd64-cpython-38\Release\build.ninja...
    Compiling objects...
    Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)
    C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\setuptools\command\develop.py:40: EasyInstallDeprecationWarning: easy_install command is deprecated.
    !!

            ********************************************************************************
            Please avoid running ``setup.py`` and ``easy_install``.
            Instead, use pypa/build, pypa/installer, pypa/build or
            other standards-based tools.

            See https://github.com/pypa/setuptools/issues/917 for details.
            ********************************************************************************

    !!
      easy_install.initialize_options(self)
    C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\setuptools\_distutils\cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated.
    !!

            ********************************************************************************
            Please avoid running ``setup.py`` directly.
            Instead, use pypa/build, pypa/installer, pypa/build or
            other standards-based tools.

            See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.
            ********************************************************************************

    !!
      self.initialize_options()
    C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\torch\utils\cpp_extension.py:346: UserWarning: Error checking compiler version for cl: [WinError 2] 系统找不到指定的文件。
      warnings.warn(f'Error checking compiler version for {compiler}: {error}')
    ninja: error: Stat(D:/code/python/acupoint_detection/Grounded-Segment-Anything-main/GroundingDINO/build/temp.win-amd64-cpython-38/Release/code/python/acupoint_detection/Grounded-Segment-Anything-main/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.obj): Filename longer than 260 characters
    Traceback (most recent call last):
      File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\torch\utils\cpp_extension.py", line 1808, in _run_ninja_build
        subprocess.run(
      File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\subprocess.py", line 512, in run
        raise CalledProcessError(retcode, process.args,
    subprocess.CalledProcessError: Command '['ninja', '-v']' returned non-zero exit status 1.

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

    Traceback (most recent call last):
      File "<string>", line 2, in <module>
      File "<pip-setuptools-caller>", line 34, in <module>
      File "D:\code\python\acupoint_detection\Grounded-Segment-Anything-main\GroundingDINO\setup.py", line 200, in <module>
        setup(
      File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\setuptools\__init__.py", line 107, in setup
        return distutils.core.setup(**attrs)
      File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\setuptools\_distutils\core.py", line 185, in setup
        return run_commands(dist)
      File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\setuptools\_distutils\core.py", line 201, in run_commands
        dist.run_commands()
      File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\setuptools\_distutils\dist.py", line 969, in run_commands
        self.run_command(cmd)
      File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\setuptools\dist.py", line 1244, in run_command
        super().run_command(command)
      File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\setuptools\_distutils\dist.py", line 988, in run_command
        cmd_obj.run()
      File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\setuptools\command\develop.py", line 34, in run
        self.install_for_development()
      File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\setuptools\command\develop.py", line 115, in install_for_development
        self.run_command('build_ext')
      File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\setuptools\_distutils\cmd.py", line 318, in run_command
        self.distribution.run_command(command)
      File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\setuptools\dist.py", line 1244, in run_command
        super().run_command(command)
      File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\setuptools\_distutils\dist.py", line 988, in run_command
        cmd_obj.run()
      File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\setuptools\command\build_ext.py", line 84, in run
        _build_ext.run(self)
      File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\setuptools\_distutils\command\build_ext.py", line 345, in run
        self.build_extensions()
      File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\torch\utils\cpp_extension.py", line 765, in build_extensions
        build_ext.build_extensions(self)
      File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\setuptools\_distutils\command\build_ext.py", line 467, in build_extensions
        self._build_extensions_serial()
      File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\setuptools\_distutils\command\build_ext.py", line 493, in _build_extensions_serial
        self.build_extension(ext)
      File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\setuptools\command\build_ext.py", line 246, in build_extension
        _build_ext.build_extension(self, ext)
      File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\setuptools\_distutils\command\build_ext.py", line 548, in build_extension
        objects = self.compiler.compile(
      File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\torch\utils\cpp_extension.py", line 738, in win_wrap_ninja_compile
        _write_ninja_file_and_compile_objects(
      File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\torch\utils\cpp_extension.py", line 1487, in _write_ninja_file_and_compile_objects
        _run_ninja_build(
      File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\torch\utils\cpp_extension.py", line 1824, in _run_ninja_build
        raise RuntimeError(message) from e
    RuntimeError: Error compiling objects for extension
    [end of output]

note: This error originates from a subprocess, and is likely not a problem with pip.

error: subprocess-exited-with-error
× python setup.py develop did not run successfully.
│ exit code: 1
╰─> [103 lines of output]
fatal: not a git repository (or any of the parent directories): .git
Building wheel groundingdino-0.1.0
Compiling with CUDA
running develop
running egg_info
writing groundingdino.egg-info\PKG-INFO
writing dependency_links to groundingdino.egg-info\dependency_links.txt
writing requirements to groundingdino.egg-info\requires.txt
writing top-level names to groundingdino.egg-info\top_level.txt
reading manifest file 'groundingdino.egg-info\SOURCES.txt'
adding license file 'LICENSE'
writing manifest file 'groundingdino.egg-info\SOURCES.txt'
running build_ext
building 'groundingdino._C' extension
Emitting ninja build file D:\code\python\acupoint_detection\Grounded-Segment-Anything-main\GroundingDINO\build\temp.win-amd64-cpython-38\Release\build.ninja...
Compiling objects...
Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)
C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\setuptools\command\develop.py:40: EasyInstallDeprecationWarning: easy_install command is deprecated.
!!
        ********************************************************************************
        Please avoid running ``setup.py`` and ``easy_install``.
        Instead, use pypa/build, pypa/installer, pypa/build or
        other standards-based tools.

        See https://github.com/pypa/setuptools/issues/917 for details.
        ********************************************************************************

!!
  easy_install.initialize_options(self)
C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\setuptools\_distutils\cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated.
!!

        ********************************************************************************
        Please avoid running ``setup.py`` directly.
        Instead, use pypa/build, pypa/installer, pypa/build or
        other standards-based tools.

        See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.
        ********************************************************************************

!!
  self.initialize_options()
C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\torch\utils\cpp_extension.py:346: UserWarning: Error checking compiler version for cl: [WinError 2] 系统找不到指定的文件。
  warnings.warn(f'Error checking compiler version for {compiler}: {error}')
ninja: error: Stat(D:/code/python/acupoint_detection/Grounded-Segment-Anything-main/GroundingDINO/build/temp.win-amd64-cpython-38/Release/code/python/acupoint_detection/Grounded-Segment-Anything-main/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.obj): Filename longer than 260 characters
Traceback (most recent call last):
  File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\torch\utils\cpp_extension.py", line 1808, in _run_ninja_build
    subprocess.run(
  File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\subprocess.py", line 512, in run
    raise CalledProcessError(retcode, process.args,
subprocess.CalledProcessError: Command '['ninja', '-v']' returned non-zero exit status 1.

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

Traceback (most recent call last):
  File "<string>", line 2, in <module>
  File "<pip-setuptools-caller>", line 34, in <module>
  File "D:\code\python\acupoint_detection\Grounded-Segment-Anything-main\GroundingDINO\setup.py", line 200, in <module>
    setup(
  File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\setuptools\__init__.py", line 107, in setup
    return distutils.core.setup(**attrs)
  File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\setuptools\_distutils\core.py", line 185, in setup
    return run_commands(dist)
  File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\setuptools\_distutils\core.py", line 201, in run_commands
    dist.run_commands()
  File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\setuptools\_distutils\dist.py", line 969, in run_commands
    self.run_command(cmd)
  File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\setuptools\dist.py", line 1244, in run_command
    super().run_command(command)
  File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\setuptools\_distutils\dist.py", line 988, in run_command
    cmd_obj.run()
  File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\setuptools\command\develop.py", line 34, in run
    self.install_for_development()
  File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\setuptools\command\develop.py", line 115, in install_for_development
    self.run_command('build_ext')
  File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\setuptools\_distutils\cmd.py", line 318, in run_command
    self.distribution.run_command(command)
  File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\setuptools\dist.py", line 1244, in run_command
    super().run_command(command)
  File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\setuptools\_distutils\dist.py", line 988, in run_command
    cmd_obj.run()
  File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\setuptools\command\build_ext.py", line 84, in run
    _build_ext.run(self)
  File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\setuptools\_distutils\command\build_ext.py", line 345, in run
    self.build_extensions()
  File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\torch\utils\cpp_extension.py", line 765, in build_extensions
    build_ext.build_extensions(self)
  File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\setuptools\_distutils\command\build_ext.py", line 467, in build_extensions
    self._build_extensions_serial()
  File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\setuptools\_distutils\command\build_ext.py", line 493, in _build_extensions_serial
    self.build_extension(ext)
  File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\setuptools\command\build_ext.py", line 246, in build_extension
    _build_ext.build_extension(self, ext)
  File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\setuptools\_distutils\command\build_ext.py", line 548, in build_extension
    objects = self.compiler.compile(
  File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\torch\utils\cpp_extension.py", line 738, in win_wrap_ninja_compile
    _write_ninja_file_and_compile_objects(
  File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\torch\utils\cpp_extension.py", line 1487, in _write_ninja_file_and_compile_objects
    _run_ninja_build(
  File "C:\Users\lynnd\Anaconda3\envs\ground_seg\lib\site-packages\torch\utils\cpp_extension.py", line 1824, in _run_ninja_build
    raise RuntimeError(message) from e
RuntimeError: Error compiling objects for extension
[end of output]

note: This error originates from a subprocess, and is likely not a problem with pip.`

Try running this:
which c++
export CXX=g++

try to use C++9 and G++9

I have the same problem...were you able to solve this problem? I am using Ubuntu 22.04

try to use C++9 and G++9

(ground_seg) PS D:\code\python\acupoint_detection\Grounded-Segment-Anything-main> g++ -v
Using built-in specs.
COLLECT_GCC=C:\msys64\ucrt64\bin\g++.exe
COLLECT_LTO_WRAPPER=C:/msys64/ucrt64/bin/../lib/gcc/x86_64-w64-mingw32/13.1.0/lto-wrapper.exe
Target: x86_64-w64-mingw32
Configured with: ../gcc-13.1.0/configure --prefix=/ucrt64 --with-local-prefix=/ucrt64/local --build=x86_64-w64-mingw32 --host=x86_64-w64-mingw32 --target=x86_64-w64-mingw32 --with-native-system-header-dir=/ucrt64/include --libexecdir=/ucrt64/lib --enable-bootstrap --enable-checking=release --with-arch=nocona --with-tune=generic --enable-languages=c,lto,c++,fortran,ada,objc,obj-c++,jit --enable-shared --enable-static --enable-libatomic --enable-threads=posix --enable-graphite --enable-fully-dynamic-string --enable-libstdcxx-filesystem-ts --enable-libstdcxx-time --disable-libstdcxx-pch --enable-lto --enable-libgomp --disable-libssp --disable-multilib --disable-rpath --disable-win32-registry --disable-nls --disable-werror --disable-symvers --with-libiconv --with-system-zlib --with-gmp=/ucrt64 --with-mpfr=/ucrt64 --with-mpc=/ucrt64 --with-isl=/ucrt64 --with-pkgversion='Rev5, Built by MSYS2 project' --with-bugurl=https://github.com/msys2/MINGW-packages/issues --with-gnu-as --with-gnu-ld --disable-libstdcxx-debug --with-boot-ldflags=-static-libstdc++ --with-stage1-ldflags=-static-libstdc++
Thread model: posix
Supported LTO compression algorithms: zlib zstd
gcc version 13.1.0 (Rev5, Built by MSYS2 project)

It seem that my g++ version is higher than G++9

I have the same problem...were you able to solve this problem? I am using Ubuntu 22.04

I meet this question in windows，and I try it again in ubuntu 22.04，this bug doesn't appear again，but when I run   python grounded_sam_demo.py I meet new bug：
‘’‘
/home/lia/anaconda3/envs/sam/lib/python3.8/site-packages/torch/functional.py:478: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2894.)
return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
final text_encoder_type: bert-base-uncased
('Connection aborted.', ConnectionResetError(104, 'Connection reset by peer'))
Traceback (most recent call last):
File "/home/lia/anaconda3/envs/sam/lib/python3.8/site-packages/urllib3/connectionpool.py", line 714, in urlopen
httplib_response = self._make_request(
File "/home/lia/anaconda3/envs/sam/lib/python3.8/site-packages/urllib3/connectionpool.py", line 403, in _make_request
self._validate_conn(conn)
File "/home/lia/anaconda3/envs/sam/lib/python3.8/site-packages/urllib3/connectionpool.py", line 1053, in validate_conn
conn.connect()
File "/home/lia/anaconda3/envs/sam/lib/python3.8/site-packages/urllib3/connection.py", line 419, in connect
self.sock = ssl_wrap_socket(
File "/home/lia/anaconda3/envs/sam/lib/python3.8/site-packages/urllib3/util/ssl.py", line 449, in ssl_wrap_socket
ssl_sock = ssl_wrap_socket_impl(
File "/home/lia/anaconda3/envs/sam/lib/python3.8/site-packages/urllib3/util/ssl.py", line 493, in _ssl_wrap_socket_impl
return ssl_context.wrap_socket(sock, server_hostname=server_hostname)
File "/home/lia/anaconda3/envs/sam/lib/python3.8/ssl.py", line 500, in wrap_socket
return self.sslsocket_class._create(
File "/home/lia/anaconda3/envs/sam/lib/python3.8/ssl.py", line 1040, in _create
self.do_handshake()
File "/home/lia/anaconda3/envs/sam/lib/python3.8/ssl.py", line 1309, in do_handshake
self._sslobj.do_handshake()
ConnectionResetError: [Errno 104] Connection reset by peer
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/lia/anaconda3/envs/sam/lib/python3.8/site-packages/requests/adapters.py", line 486, in send
resp = conn.urlopen(
File "/home/lia/anaconda3/envs/sam/lib/python3.8/site-packages/urllib3/connectionpool.py", line 798, in urlopen
retries = retries.increment(
File "/home/lia/anaconda3/envs/sam/lib/python3.8/site-packages/urllib3/util/retry.py", line 550, in increment
raise six.reraise(type(error), error, _stacktrace)
File "/home/lia/anaconda3/envs/sam/lib/python3.8/site-packages/urllib3/packages/six.py", line 769, in reraise
raise value.with_traceback(tb)
File "/home/lia/anaconda3/envs/sam/lib/python3.8/site-packages/urllib3/connectionpool.py", line 714, in urlopen
httplib_response = self._make_request(
File "/home/lia/anaconda3/envs/sam/lib/python3.8/site-packages/urllib3/connectionpool.py", line 403, in _make_request
self._validate_conn(conn)
File "/home/lia/anaconda3/envs/sam/lib/python3.8/site-packages/urllib3/connectionpool.py", line 1053, in validate_conn
conn.connect()
File "/home/lia/anaconda3/envs/sam/lib/python3.8/site-packages/urllib3/connection.py", line 419, in connect
self.sock = ssl_wrap_socket(
File "/home/lia/anaconda3/envs/sam/lib/python3.8/site-packages/urllib3/util/ssl.py", line 449, in ssl_wrap_socket
ssl_sock = ssl_wrap_socket_impl(
File "/home/lia/anaconda3/envs/sam/lib/python3.8/site-packages/urllib3/util/ssl.py", line 493, in _ssl_wrap_socket_impl
return ssl_context.wrap_socket(sock, server_hostname=server_hostname)
File "/home/lia/anaconda3/envs/sam/lib/python3.8/ssl.py", line 500, in wrap_socket
return self.sslsocket_class._create(
File "/home/lia/anaconda3/envs/sam/lib/python3.8/ssl.py", line 1040, in _create
self.do_handshake()
File "/home/lia/anaconda3/envs/sam/lib/python3.8/ssl.py", line 1309, in do_handshake
self._sslobj.do_handshake()
urllib3.exceptions.ProtocolError: ('Connection aborted.', ConnectionResetError(104, 'Connection reset by peer'))
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/lia/anaconda3/envs/sam/lib/python3.8/site-packages/transformers/modeling_utils.py", line 1329, in from_pretrained
resolved_archive_file = cached_path(
File "/home/lia/anaconda3/envs/sam/lib/python3.8/site-packages/transformers/file_utils.py", line 1776, in cached_path
output_path = get_from_cache(
File "/home/lia/anaconda3/envs/sam/lib/python3.8/site-packages/transformers/file_utils.py", line 2040, in get_from_cache
http_get(url_to_download, temp_file, proxies=proxies, resume_size=resume_size, headers=headers)
File "/home/lia/anaconda3/envs/sam/lib/python3.8/site-packages/transformers/file_utils.py", line 1886, in http_get
r = requests.get(url, stream=True, proxies=proxies, headers=headers)
File "/home/lia/anaconda3/envs/sam/lib/python3.8/site-packages/requests/api.py", line 73, in get
return request("get", url, params=params, **kwargs)
File "/home/lia/anaconda3/envs/sam/lib/python3.8/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/home/lia/anaconda3/envs/sam/lib/python3.8/site-packages/requests/sessions.py", line 589, in request
resp = self.send(prep, **send_kwargs)
File "/home/lia/anaconda3/envs/sam/lib/python3.8/site-packages/requests/sessions.py", line 703, in send
r = adapter.send(request, **kwargs)
File "/home/lia/anaconda3/envs/sam/lib/python3.8/site-packages/requests/adapters.py", line 501, in send
raise ConnectionError(err, request=request)
requests.exceptions.ConnectionError: ('Connection aborted.', ConnectionResetError(104, 'Connection reset by peer'))
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "grounded_sam_demo.py", line 169, in
model = load_model(config_file, grounded_checkpoint, device=device)
File "grounded_sam_demo.py", line 42, in load_model
model = build_model(args)
File "/home/lia/projects/Grounded-Segment-Anything-main/GroundingDINO/groundingdino/models/init.py", line 17, in build_model
model = build_func(args)
File "/home/lia/projects/Grounded-Segment-Anything-main/GroundingDINO/groundingdino/models/GroundingDINO/groundingdino.py", line 372, in build_groundingdino
model = GroundingDINO(
File "/home/lia/projects/Grounded-Segment-Anything-main/GroundingDINO/groundingdino/models/GroundingDINO/groundingdino.py", line 108, in init
self.bert = get_tokenlizer.get_pretrained_language_model(text_encoder_type)
File "/home/lia/projects/Grounded-Segment-Anything-main/GroundingDINO/groundingdino/util/get_tokenlizer.py", line 23, in get_pretrained_language_model
return BertModel.from_pretrained(text_encoder_type)
File "/home/lia/anaconda3/envs/sam/lib/python3.8/site-packages/transformers/modeling_utils.py", line 1351, in from_pretrained
raise EnvironmentError(msg)
OSError: Can't load weights for 'bert-base-uncased'. Make sure that:

'bert-base-uncased' is a correct model identifier listed on 'https://huggingface.co/models'
(make sure 'bert-base-uncased' is not a path to a local directory with something else, in that case)

or 'bert-base-uncased' is the correct path to a directory containing a file named one of pytorch_model.bin, tf_model.h5, model.ckpt
‘’‘

try to use C++9 and G++9

(ground_seg) PS D:\code\python\acupoint_detection\Grounded-Segment-Anything-main> g++ -v
Using built-in specs.
COLLECT_GCC=C:\msys64\ucrt64\bin\g++.exe
COLLECT_LTO_WRAPPER=C:/msys64/ucrt64/bin/../lib/gcc/x86_64-w64-mingw32/13.1.0/lto-wrapper.exe
Target: x86_64-w64-mingw32
Configured with: ../gcc-13.1.0/configure --prefix=/ucrt64 --with-local-prefix=/ucrt64/local --build=x86_64-w64-mingw32 --host=x86_64-w64-mingw32 --target=x86_64-w64-mingw32 --with-native-system-header-dir=/ucrt64/include --libexecdir=/ucrt64/lib --enable-bootstrap --enable-checking=release --with-arch=nocona --with-tune=generic --enable-languages=c,lto,c++,fortran,ada,objc,obj-c++,jit --enable-shared --enable-static --enable-libatomic --enable-threads=posix --enable-graphite --enable-fully-dynamic-string --enable-libstdcxx-filesystem-ts --enable-libstdcxx-time --disable-libstdcxx-pch --enable-lto --enable-libgomp --disable-libssp --disable-multilib --disable-rpath --disable-win32-registry --disable-nls --disable-werror --disable-symvers --with-libiconv --with-system-zlib --with-gmp=/ucrt64 --with-mpfr=/ucrt64 --with-mpc=/ucrt64 --with-isl=/ucrt64 --with-pkgversion='Rev5, Built by MSYS2 project' --with-bugurl=https://github.com/msys2/MINGW-packages/issues --with-gnu-as --with-gnu-ld --disable-libstdcxx-debug --with-boot-ldflags=-static-libstdc++ --with-stage1-ldflags=-static-libstdc++
Thread model: posix
Supported LTO compression algorithms: zlib zstd
gcc version 13.1.0 (Rev5, Built by MSYS2 project)

It seem that my g++ version is higher than G++9

use 9.4.0, must 9.4.0

请问如何解决

Hey, I met this issue but fixed it successfully.
Here are some steps I tried that may help:

the previous g++ version is 11.4.0, I re-install the required version 9.4.0;
export correct path export CUDA_HOME=/path/to/cuda-11.3/ follow README.md, and check torch and cuda versions are matched;
pip install --no-build-isolation -e GroundingDINO

Hey, I met this issue but fixed it successfully. Here are some steps I tried that may help:

the previous g++ version is 11.4.0, I re-install the required version 9.4.0;
export correct path export CUDA_HOME=/path/to/cuda-11.3/ follow README.md, and check torch and cuda versions are matched;
pip install --no-build-isolation -e GroundingDINO

How to install the required version 9.4.0, I can't get this version, but only the version 9.5. And it seems not working.

i use windows and my c++ is (ground_seg) PS D:\code\python\acupoint_detection\Grounded-Segment-Anything-main> which c++ /c/msys64/ucrt64/bin/c++

Hi @Leestronger I have your same problem, but if I run which c++, I get:
"which" is not recognized as an internal or external command,  an executable program or a batch file.

Any suggestion?
Which are your torch, CUDA and Visual Studio version? @yiningsutd @Zhangzhixiang-laugh

windows 10, wsl2:
torch=2.3.1
cuda=12.1
cudnn=8.9
This configuration works well, might be also appropriate for you.

i use windows and my c++ is (ground_seg) PS D:\code\python\acupoint_detection\Grounded-Segment-Anything-main> which c++ /c/msys64/ucrt64/bin/c++

Hi @Leestronger I have your same problem, but if I run which c++, I get: "which" is not recognized as an internal or external command, an executable program or a batch file.

Any suggestion?
Which are your torch, CUDA and Visual Studio version? @yiningsutd @Zhangzhixiang-laugh

windows 10, wsl2:
torch=2.3.1
cuda=12.1
cudnn=8.9
This configuration works well, might be also appropriate for you.

@Zhangzhixiang-laugh  I'm working on windows 11.
Do you think is mandatory cudnn to run Grounded-Segment-Anything?

G++的版本调整为9.4.0就能安装成功了

I fixed this issue by installing an older cuda version (try cuda 11.X.X).
I set cuda to CUDA/11.8.0


Assertion Failed: You can only pass attributes (such as name=value) not bare values to a helper for a View found in 'ember-cli-project@component:property-print

I can get stuff to display in ember-admin using master, but when I use ember-admin-bootstrap 0.0.2, I get the following error:
Uncaught Error: Assertion Failed: You can only pass attributes (such as name=value) not bare values to a helper for a View found in 'ember-cli-project@component:property-print:' vendor.js:17039
Ember.assert vendor.js:17039
(anonymous function) vendor.js:17970
helperMissingHelper vendor.js:20188
program10 ember-cli-project/templates/admin/index/default.js:78
prog vendor.js:10895
CoreView.extend.render vendor.js:55439
EmberRenderer_createElement vendor.js:52666
Renderer_renderTree vendor.js:23793
merge.ensureChildrenAreInDOM vendor.js:53911
View.extend._ensureChildrenAreInDOM vendor.js:53876
DeferredActionQueues.invoke vendor.js:13853
DeferredActionQueues.flush vendor.js:13923
Backburner.end vendor.js:13309
Backburner.run vendor.js:13364
run vendor.js:31375
hash.success vendor.js:66880
fire vendor.js:3237
self.fireWith vendor.js:3349
done vendor.js:9393
callback

Things to note:  I import bootstrap separately in my brocfile like so:
app.import('bower_components/bootstrap/dist/css/bootstrap.css');
app.import('bower_components/bootstrap/dist/js/bootstrap.js');

So I'm not sure if that interferes.
Googling hasn't returned a similar error elsewhere.  Maybe it has something to do with using ember-admin master while ember-admin-bootstrap has a dependency on 0.0.8?  I'm just guessing here.

FYI, I'm still getting this error when ember-admin-bootstrap is installed:
Uncaught Error: Assertion Failed: You can only pass attributes (such as name=value) not bare values to a helper for a View found in 'app@component:property-print:'
Note, that I'm sure a related error is thrown when not using this addon (just stock ember-admin).
See the issue here: https://github.com/dockyard/ember-admin/issues/50
DEBUG: -------------------------------
DEBUG: Ember      : 1.12.0-beta.1
DEBUG: Ember Data : 1.0.0-beta.16.1
DEBUG: Firebase   : 2.2.4
DEBUG: EmberFire  : 0.0.0
DEBUG: jQuery     : 1.11.3
DEBUG: -------------------------------


Provide samples in Components and Validators documentation in FileVault DocView format

Currently both https://wcm.io/wcm/ui/granite/components.html and https://wcm.io/wcm/ui/granite/validation.html use JSON serialisation format (primarily used in Sling context). I think FileVault DocView format is more common in the AEM ecosystem.

it's currently common practice thorough the whole wcm.io documentation to use the JSON notation. but you're right that switching to filevault would be more user-friendly (or provide something like view the content structure in one or the other way).
i will take a note, but i will probably take a while to change this.

i've updated the documentation to provide code snippets for both FileVault and JSON view (the former is derived automatically from the lattern).
https://github.com/wcm-io/io.wcm.wcm.ui.granite/pull/14
https://github.com/wcm-io/io.wcm.maven.skins.reflow-maven-skin/pull/2

@stefanseifert Thanks, looks great.


neural report refactor + verify votes

For neural report votes
Check the grc address is valid
Check if they are eligible to vote.
Notes:
Neural report votes only come from neural network participants however there are no checks at this point in time to verify such are legit votes. This is in the direction of verifying votes as best we can much like the superblock check PR. The checks only need to be done in neural report. The current neural report is all neural network nodes hashes (even thou the sync every 500 blocks) and everyone participates in that however its the neural report thats trusted for superblock and thats where the consensus_hash is pulled from.
@tomasbrod this may be something to add with the superblock one in your v8 for mandatory.

#492

ill update this to brods block times

there you go boss


memory limits seem weird?

  The second set of numbers show ~6GB ram which seems like it might be something your machine might have.  I guess two observations:

"exceed job resource limits" is a confusing message given the name of the previous config blog
If we set the limits higher than the available resources, maybe a warning but carry on anyway?

I think this is partly a naming issue - DefaultJobResourceLimits might be better named DefaultResources. This is the limit that jobs will get when they don't specify any resource limits themselves.
So in your case, you are saying "by default I want jobs to have 8 CPUs and 8Gibibytes of RAM" and Bacalhau is saying "you only have 1.6 CPUs and 6GB of RAM, so your jobs won't be accepted if they use these defaults".