install xdebug failed on php5

Hi there,
I wanted to build a lamp docker with xdebug, but I haven't make it. And then I found your sharing. Thank you in advance.
When the script install XDebug, it failed:
error in 7.2.x:
Step 6/27 : RUN curl -sS https://getcomposer.org/installer | php -- --install-dir=/usr/local/bin --filename=composer
 ---> Running in 1a589ab3686b
All settings correct for using Composer
Downloading...

Composer (version 1.7.3) successfully installed to: /usr/local/bin/composer
Use it: php /usr/local/bin/composer

Removing intermediate container 1a589ab3686b
 ---> d4dec3a0d39b
Step 7/27 : RUN pecl install xdebug-2.6.0
 ---> Running in 89bd995b2a4d
No releases available for package "pecl.php.net/xdebug"
install failed
ERROR: Service 'webserver' failed to build: The command '/bin/sh -c pecl install xdebug-2.6.0' returned a non-zero code: 1

error in 5.6.x:
Installing shared extensions:     /usr/local/lib/php/extensions/no-debug-non-zts-20131226/
Installing header files:           /usr/local/include/php/
find . -name \*.gcno -o -name \*.gcda | xargs rm -f
find . -name \*.lo -o -name \*.o | xargs rm -f
find . -name \*.la -o -name \*.a | xargs rm -f
find . -name \*.so | xargs rm -f
find . -name .libs -a -type d|xargs rm -rf
rm -f libphp.la       modules/* libs/*
No releases available for package "pecl.php.net/xdebug"
install failed
ERROR: Service 'webserver' failed to build: The command '/bin/sh -c docker-php-ext-install curl     && docker-php-ext-install tokenizer     && docker-php-ext-install json     && docker-php-ext-install mcrypt     && docker-php-ext-install pdo_mysql     && docker-php-ext-install pdo_sqlite     && docker-php-ext-install mysqli     && docker-php-ext-install zip     && docker-php-ext-install -j$(nproc) intl     && docker-php-ext-install mbstring     && docker-php-ext-configure gd --with-freetype-dir=/usr/include/ --with-jpeg-dir=/usr/include/     && docker-php-ext-install -j$(nproc) gd     && pecl install xdebug-2.5.5 && docker-php-ext-enable xdebug     && echo "xdebug.remote_enable=1" >> /usr/local/etc/php/php.ini' returned a non-zero code: 1

lLook here , please
https://github.com/laradock/laradock/issues/1315#issuecomment-380492758

Hello, please let me know if I should open a new issue.
I'm trying to run PHP 7.2 using the PHP72/Dockerfile and I'm running into the same issue here. Docker Compose fails when trying to install xdebug from pecl.
Step 9/27 : RUN pecl install xdebug-2.6.0
 ---> Running in f95c58f229f5
No releases available for package "pecl.php.net/xdebug"
install failed
ERROR: Service 'webserver' failed to build : The command '/bin/sh -c pecl install xdebug-2.6.0' returned a non-zero code: 1

I tried following the instructions linked above in the laradock comment, and added the http_proxy variables to the args section and the Dockerfile but it didn't work for me.
The last few comments in the laradock issue mention running pecl with sudo, but if I try that I get an error back saying sudo is not found
/bin/sh: 1: sudo: not found

Hello, please let me know if I should open a new issue.
I'm trying to run PHP 7.2 using the PHP72/Dockerfile and I'm running into the same issue here. Docker Compose fails when trying to install xdebug from pecl.
Step 9/27 : RUN pecl install xdebug-2.6.0
 ---> Running in f95c58f229f5
No releases available for package "pecl.php.net/xdebug"
install failed
ERROR: Service 'webserver' failed to build : The command '/bin/sh -c pecl install xdebug-2.6.0' returned a non-zero code: 1

I tried following the instructions linked above in the laradock comment, and added the http_proxy variables to the args section and the Dockerfile but it didn't work for me.
The last few comments in the laradock issue mention running pecl with sudo, but if I try that I get an error back saying sudo is not found
/bin/sh: 1: sudo: not found


Use magic commands in LSP-friendly way

Jupytext support use magic commands like a comment, say,
# %time

2+3

would be regard as

But that does not work for jupynium

Could we have something similar to allow using magic commands without breaking LSP?

Sounds like a useful feature request. I'll look into it soon!

Done!


CUDA Out of Memory

Hi, I've been following your instructions on the README to train the model. However, I ran into CUDA out-of-memory issues even with a 16 GB GPU.
I have tried to solve the above with the following solutions, but none has worked so far:

Decreasing batch_size to 4 -> 2 -> 1
Decreasing num_data_workers to 2 -> 1
Use torch.cuda.empty_cache()
Use gc.collect()
Use Google Colab and Kaggle to run a Notebook version

For point 5, the training is able to run up until the end of validation at the first epoch before the entire website crashes.
The following is a more detailed log of the error I received:
-- Process 0 terminated with the following error: Traceback (most recent call last):   File "/home/chenweiyi/.conda/envs/grapher/lib/python3.9/site-packages/torch/multiprocessing/spawn.py", line 69, in _wrap     fn(i, *args)   File "/home/chenweiyi/.conda/envs/grapher/lib/python3.9/site-packages/pytorch_lightning/strategies/launchers/multiprocessing.py", line 139, in _wrapping_function     results = function(*args, **kwargs)   File "/home/chenweiyi/.conda/envs/grapher/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 624, in _fit_impl     self._run(model, ckpt_path=self.ckpt_path)   File "/home/chenweiyi/.conda/envs/grapher/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 1061, in _run     results = self._run_stage()   File "/home/chenweiyi/.conda/envs/grapher/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 1140, in _run_stage     self._run_train()   File "/home/chenweiyi/.conda/envs/grapher/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 1163, in _run_train     self.fit_loop.run()   File "/home/chenweiyi/.conda/envs/grapher/lib/python3.9/site-packages/pytorch_lightning/loops/loop.py", line 199, in run     self.advance(*args, **kwargs)   File "/home/chenweiyi/.conda/envs/grapher/lib/python3.9/site-packages/pytorch_lightning/loops/fit_loop.py", line 267, in advance     self._outputs = self.epoch_loop.run(self._data_fetcher)   File "/home/chenweiyi/.conda/envs/grapher/lib/python3.9/site-packages/pytorch_lightning/loops/loop.py", line 199, in run     self.advance(*args, **kwargs)   File "/home/chenweiyi/.conda/envs/grapher/lib/python3.9/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 214, in advance     batch_output = self.batch_loop.run(kwargs)   File "/home/chenweiyi/.conda/envs/grapher/lib/python3.9/site-packages/pytorch_lightning/loops/loop.py", line 199, in run     self.advance(*args, **kwargs)   File "/home/chenweiyi/.conda/envs/grapher/lib/python3.9/site-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 88, in advance     outputs = self.optimizer_loop.run(optimizers, kwargs)   File "/home/chenweiyi/.conda/envs/grapher/lib/python3.9/site-packages/pytorch_lightning/loops/loop.py", line 199, in run     self.advance(*args, **kwargs)   File "/home/chenweiyi/.conda/envs/grapher/lib/python3.9/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 200, in advance     result = self._run_optimization(kwargs, self._optimizers[self.optim_progress.optimizer_position])   File "/home/chenweiyi/.conda/envs/grapher/lib/python3.9/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 239, in _run_optimization     closure()   File "/home/chenweiyi/.conda/envs/grapher/lib/python3.9/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 147, in __call__     self._result = self.closure(*args, **kwargs)   File "/home/chenweiyi/.conda/envs/grapher/lib/python3.9/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 133, in closure     step_output = self._step_fn()   File "/home/chenweiyi/.conda/envs/grapher/lib/python3.9/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 406, in _training_step     training_step_output = self.trainer._call_strategy_hook("training_step", *kwargs.values())   File "/home/chenweiyi/.conda/envs/grapher/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 1443, in _call_strategy_hook     output = fn(*args, **kwargs)   File "/home/chenweiyi/.conda/envs/grapher/lib/python3.9/site-packages/pytorch_lightning/strategies/ddp_spawn.py", line 280, in training_step     return self.model(*args, **kwargs)   File "/home/chenweiyi/.conda/envs/grapher/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1190, in _call_impl     return forward_call(*input, **kwargs)   File "/home/chenweiyi/.conda/envs/grapher/lib/python3.9/site-packages/torch/nn/parallel/distributed.py", line 1040, in forward     output = self._run_ddp_forward(*inputs, **kwargs)   File "/home/chenweiyi/.conda/envs/grapher/lib/python3.9/site-packages/torch/nn/parallel/distributed.py", line 1000, in _run_ddp_forward     return module_to_run(*inputs[0], **kwargs[0])   File "/home/chenweiyi/.conda/envs/grapher/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1190, in _call_impl     return forward_call(*input, **kwargs)   File "/home/chenweiyi/.conda/envs/grapher/lib/python3.9/site-packages/pytorch_lightning/overrides/base.py", line 98, in forward     output = self._forward_module.training_step(*inputs, **kwargs)   File "/home/chenweiyi/Grapher/model/litgrapher.py", line 102, in training_step     logits_nodes, logits_edges= self.model(text_input_ids,   File "/home/chenweiyi/.conda/envs/grapher/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1190, in _call_impl     return forward_call(*input, **kwargs)   File "/home/chenweiyi/Grapher/model/grapher.py", line 58, in forward     output = self.transformer(input_ids=text,   File "/home/chenweiyi/.conda/envs/grapher/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1190, in _call_impl     return forward_call(*input, **kwargs)   File "/home/chenweiyi/.conda/envs/grapher/lib/python3.9/site-packages/transformers/models/t5/modeling_t5.py", line 1660, in forward     decoder_outputs = self.decoder(   File "/home/chenweiyi/.conda/envs/grapher/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1190, in _call_impl     return forward_call(*input, **kwargs)   File "/home/chenweiyi/.conda/envs/grapher/lib/python3.9/site-packages/transformers/models/t5/modeling_t5.py", line 1052, in forward     layer_outputs = layer_module(   File "/home/chenweiyi/.conda/envs/grapher/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1190, in _call_impl     return forward_call(*input, **kwargs)   File "/home/chenweiyi/.conda/envs/grapher/lib/python3.9/site-packages/transformers/models/t5/modeling_t5.py", line 684, in forward     self_attention_outputs = self.layer[0](   File "/home/chenweiyi/.conda/envs/grapher/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1190, in _call_impl     return forward_call(*input, **kwargs)   File "/home/chenweiyi/.conda/envs/grapher/lib/python3.9/site-packages/transformers/models/t5/modeling_t5.py", line 590, in forward     attention_output = self.SelfAttention(   File "/home/chenweiyi/.conda/envs/grapher/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1190, in _call_impl     return forward_call(*input, **kwargs)   File "/home/chenweiyi/.conda/envs/grapher/lib/python3.9/site-packages/transformers/models/t5/modeling_t5.py", line 520, in forward     scores = torch.matmul( torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 10.92 GiB total capacity; 10.48 GiB already allocated; 11.50 MiB free; 10.68 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF

We tested the code on V100 (32GB) and A100(40GB), and it works fine. I think it might be challenging to fit the model and training on 16GB GPU.

Thanks for the reply. In that case, do you have a pre-trained model available instead? From my understanding, the current model is not trained, but do correct me if I'm wrong.

Due to some internal policies we have not released the trained models yet. For now, please use the provided code to train your ow model.
To reduce the memory usage, try setting --pretrained_model t5-base instead of t5-large.

Thank you so much for your suggestion, I was able to train the model with t5-base.

Hi，IndexError: piece id is out of range.

Hi, I am trying to use t5-base, but I meet this problem:IndexError: piece id is out of range.

Hi, I am trying to use t5-base, but I meet this problem: IndexError: piece id is out of range. Mabye the input text contains a token that is not in the tokenizer vocabulary. Is there any way to solve it?

Have you been able to solve this problem?

Thank you so much for your suggestion, I was able to train the model with t5-base.

would you be able share the trained model..? it would really be a great help!

Have you been able to solve this problem?

Hi, I am trying to use t5-base, but I meet this problem: IndexError: piece id is out of range. Mabye the input text contains a token that is not in the tokenizer vocabulary. Is there any way to solve it?

Have you been able to solve this problem?

Have you been able to solve this problem? thanks


Orgmode + Treesitter error on ORG_TOGGLE_CHECKBOX

Describe the bug
I have an item like
* TODO [ ] Do things
  SCHEDULED: <2023-09-08 Fri>

If I want to mark the checkbox, I would normally do <Leader>Space. Now that generates this error
[orgmode] ...ck/packer/start/orgmode/lua/orgmode/utils/treesitter.lua:99: attempt to index local 'node' (a nil value)

Occurs regardless if org is configured for treesitter.
Steps to reproduce
orgmode config
require('orgmode').setup_ts_grammar()
require('orgmode').setup({
  org_agenda_files = {'~/org/*'},
  org_default_notes_file = '~/org/refile.org',
  mappings = {
    note = {
        org_note_finalize = '<Leader>w',
    }
  },
})

treesitter config
require'nvim-treesitter.configs'.setup {
  ensure_installed = { "org", "vimdoc", "javascript", "typescript", "c", "lua", "rust" },
  sync_install = false,
  auto_install = true,
  highlight = {
    enable = true,
    additional_vim_regex_highlighting = {'org'},
  },
}

Expected behavior
Box gets checked
Emacs functionality
No response
Minimal init.lua

-- Enter your minimal_init.lua here

Screenshots and recordings
No response
OS / Distro
Debian 11
Neovim version/commit
0.9.1
Additional context
No response

That's not a checkbox, that's an empty priority. Only plain list items can have a checkbox.

It happens (in case of headlines) if you add the cookie afterwards. It
doesn't appear automatically in Emacs if I recall correctly.
Am 11. September 2023 17:27:29 schrieb Jonas Gollenz
@.***>:

Yes, I agree that the error message is ugly and not helpful to users 👍 I'm
wondering what the use case for manually refreshing the [/] is, because
this should™ happen automagically whenever changes that justify a
cookie-update occur. The only thing I can come up with is "something about
my progress-cookies is broken, so let's refresh them manually". That
certainly may be the case sometimes, but I would rather people report that
the progress-cookies are broken (so we can go fix them) than just update
them manually out of convenience (which means we might not learn about the
bug).
—
Reply to this email directly, view it on GitHub, or unsubscribe.
You are receiving this because you commented.Message ID:
@.***>

Indeed, that's a valid use-case. According to this, toggling a checkbox and updating a statistics cookie is both done with <C-c> <C-c>. So we actually should just map it to <C-Space>.

I would like to implement this

Are you talking about statistics cookies for headlines? If yes, see #572

Are you talking about statistics cookies for headlines? If yes, see https://github.com/nvim-orgmode/orgmode/pull/572
Yes I do and I actually recall this abandoned PR.
So we actually should just map it to .
You mean updating statistic cookies during "org-toggle-checkbox". I don't like hardcoded mappings (the user must be able to adjust them), but I'm confident, that you just used  as a synonym for the underlying function, didn't you?

You mean updating statistic cookies during org-toggle-checkbox

That is already happening. I meant doing it like we do for org-meta-return. Having both a mapping (be it <C-Space> or whatever) that does multiple things and allowing to override one of those multiple things sounds a bit complicated (although maybe doable?). I see these options:

have a dedicated mapping for updating the statistics cookie (which also means that it is adjustable by default)
introduce some sort of org-meta-space (since we already use  for checkbox toggling) similar to org-meta-return that handles the many things that are handled by <C-c> <C-c> in emacs orgmode. Of course, this would not enable users to override a specific functionality, only the mapping of org-meta-space.
we add both a dedicated mapping and org-meta-space. We do what we do in 2., but before actually dispatching to (in this case) the logic that updates the cookie, we check if users have overridden the mapping to something else than the default of <C-Space>. But from the top of my head I'm quite unsure if this is even possible. If we have a mapping for org-meta-space and org-toggle-checkbox that both map to <C-Space>, I would expect things to fall apart.

From the docs it seems that emacs orgmode offers both the <C-c> <C-c> that, based on the context, does a cookie update and a dedicated mapping

Yes I do and I actually recall this abandoned PR.

Before you start, please drop me a message. I started working on this myself a while ago and I'd like to discuss some things first :+1: thanks for you contributions :slightly_smiling_face:

@jgollenz I think option 2 seems to be viable while not being too complicated. I'm refering to org-toggle-checkbox because this is the action I would use for updating the statistic cookie manually and which would make the most sense.
What currently also isn't implemented is the statistic cookie for headlines in general. For checkboxes it seems to be working quite fine - although the manual update option would be convenient if you add it as an afterthought, so you don't have to artifical toggle a checkbox in the sublist twice to update it.
Do you have an entry-point to your implementation attempt - some branch on your fork or something?

option 2 seems to be viable

I started implementing it :+1: we can add a dedicated mapping for org-update-statistics-cookies later.

entry-point to your implementation attempt

Yes, somewhere :grin: Are you in the nvim-orgmode matrix channel by any chance?

Yes, somewhere 😁 Are you in the nvim-orgmode matrix channel by any chance?
No, I wasn't aware of that. Actually I have to admit, that I haven't used Matrix in the past, but if you can help me joining the channel I would be happy to do so.

can help me joining the channel

Oh, apparently the badge is broken atm:

Will fix that asap. Anyway, clicking on Chat on the main page (or follow the link in the README.md) should take you there. You will need a matrix account. Let me know if it's not working for you :slightly_smiling_face:


STM32 DMA/BDMA: Use interrupt flags instead of atomics

I have only tested the USART DMA example.
Any thoughts?
Code size USART DMA:
before (atomics)
   text    data     bss     dec     hex filename
   8804      48    1464   10316    284c usart_dma
after (interrput flags)
   text    data     bss     dec     hex filename
   8784      48    1464   10296    2838 usart_dma

Unfortunately I do not have any boards to test the BDMA changes, hope I did not miss anything. Overall changes are very similar to DMA.


[tensorflow ppc64le] Building tensorflow from source fails on power8 architecture machine

System information

Have I written custom code (as opposed to using a stock example script provided in TensorFlow): No
OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Red Hat Enterprise Linux Server 7.4 (Maipo)
Mobile devices: N/A
TensorFlow installed from (source or binary): source
TensorFlow version (use command below):
Python version: 3.5.6
Bazel version (if compiling from source): 0.14.0
GCC/Compiler version (if compiling from source): 5.4.0 (tried with 4.8.x as well)
CUDA/cuDNN version: 8.0/ 7.1.4
GPU model and memory: NVIDIA P100
Exact command to reproduce: bazel build //tensorflow/tools/pip_package:build_pip_package

Output of the tf_collect_env.sh
== cat /etc/issue ===============================================
Linux summitdev-login1 3.10.0-693.21.1.el7.ppc64le #1 SMP Fri Feb 23 14:02:56 EST 2018 ppc64le ppc64le ppc64le GNU/Linux
VERSION="7.4 (Maipo)"
VERSION_ID="7.4"
REDHAT_BUGZILLA_PRODUCT_VERSION=7.4
REDHAT_SUPPORT_PRODUCT_VERSION="7.4"
== are we in docker =============================================
No
== compiler =====================================================
c++ (GCC) 5.4.0
Copyright (C) 2015 Free Software Foundation, Inc.
This is free software; see the source for copying conditions.  There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
== uname -a =====================================================
Linux summitdev-login1 3.10.0-693.21.1.el7.ppc64le #1 SMP Fri Feb 23 14:02:56 EST 2018 ppc64le ppc64le ppc64le GNU/Linux
== check pips ===================================================
numpy      1.15.1
== check for virtualenv =========================================
False    (this is not true I created an environment and ran this script from inside that env)
== tensorflow import ============================================
== env ==========================================================
LD_LIBRARY_PATH /autofs/nccs-svm1_sw/summitdev/.swci/1-compute/opt/spack/20180406/linux-rhel7-ppc64le/gcc-5.4.0/spectrum-mpi-<IP_ADDRESS>-20180110-6keaqyzn74d6t7ilr5wcmd4ovxjkxue7/lib:/sw/summitdev/gcc/5.4.0new/lib64:/sw/summitdev/cuda/8.0.61-1/lib64:/autofs/nccs-svm1_home/shubhankar/magma_install/lib:/autofs/nccs-svm1_home1/shubhankar/magma_install/lib:/autofs/nccs-svm1_home/shubhankar/magma_install/lib:/autofs/nccs-svm1_home1/shubhankar/magma_install/lib:/opt/ibm/spectrumcomputing/lsf/10.1/linux3.10-glibc2.17-ppc64le-csm/lib:/autofs/nccs-svm1_home1/shubhankar/miniconda3/envs/pytorch/lib:/autofs/nccs-svm1_home/shubhankar/miniconda3/envs/pytorch/lib:/autofs/nccs-svm1_home/shubhankar/pytorch/build/lib:/autofs/nccs-svm1_home1/shubhankar/pytorch/build/lib:/autofs/nccs-svm1_home/shubhankar/libcudnn7_<IP_ADDRESS>-1+cuda8.0_ppc64el/lib/powerpc64le-linux-gnu:/sw/summitdev/cuda/8.0.61-1/extras/CUPTI/lib64:/autofs/nccs-svm1_home1/shubhankar/miniconda3/envs/pytorch/lib:/autofs/nccs-svm1_home/shubhankar/miniconda3/envs/pytorch/lib:/autofs/nccs-svm1_home/shubhankar/pytorch/build/lib:/autofs/nccs-svm1_home1/shubhankar/pytorch/build/lib:/sw/summitdev/cuda/9.0.69/extras/CUPTI/lib64
DYLD_LIBRARY_PATH is unset
== nvidia-smi ===================================================
Wed Sep 19 23:25:19 2018
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 390.46                 Driver Version: 390.46                    |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  Tesla P100-SXM2...  Off  | 00000002:01:00.0 Off |                    0 |
| N/A   32C    P0    29W / 300W |     18MiB / 16280MiB |      0%   E. Process |
+-------------------------------+----------------------+----------------------+
|   1  Tesla P100-SXM2...  Off  | 00000003:01:00.0 Off |                    0 |
| N/A   28C    P0    30W / 300W |     18MiB / 16280MiB |      0%   E. Process |
+-------------------------------+----------------------+----------------------+
|   2  Tesla P100-SXM2...  Off  | 00000006:01:00.0 Off |                    0 |
| N/A   32C    P0    28W / 300W |     18MiB / 16280MiB |      0%   E. Process |
+-------------------------------+----------------------+----------------------+
|   3  Tesla P100-SXM2...  Off  | 00000007:01:00.0 Off |                    0 |
| N/A   29C    P0    30W / 300W |     18MiB / 16280MiB |      0%   E. Process |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+
== cuda libs  ===================================================
Describe the problem
Building tensorflow from source fails on power architecture machine. I am following the instructions given here
Source code / logs
WARNING: Output base '/autofs/nccs-svm1_home1/shubhankar/.cache/bazel/_bazel_shubhankar/e74c0b81a4afde0b86e7c039eda6fc5e' is on NFS. This may lead to surprising failures and undetermined behavior.
ERROR: Skipping '//tensorflow/tools/pip_package:build_pip_package': error loading package 'tensorflow/tools/pip_package': Encountered error while reading extension file 'cuda/build_defs.bzl': no such package '@local_config_cuda//cuda': Traceback (most recent call last):
    File "/autofs/nccs-svm1_home1/shubhankar/tensorflow/third_party/gpus/cuda_configure.bzl", line 1166
        _create_local_cuda_repository(repository_ctx)
    File "/autofs/nccs-svm1_home1/shubhankar/tensorflow/third_party/gpus/cuda_configure.bzl", line 995, in _create_local_cuda_repository
        _get_cuda_config(repository_ctx)
    File "/autofs/nccs-svm1_home1/shubhankar/tensorflow/third_party/gpus/cuda_configure.bzl", line 750, in _get_cuda_config
        _cudnn_version(repository_ctx, cudnn_install_base..., ...)
    File "/autofs/nccs-svm1_home1/shubhankar/tensorflow/third_party/gpus/cuda_configure.bzl", line 464, in _cudnn_version
        _find_cudnn_header_dir(repository_ctx, cudnn_install_base...)
    File "/autofs/nccs-svm1_home1/shubhankar/tensorflow/third_party/gpus/cuda_configure.bzl", line 707, in _find_cudnn_header_dir
        auto_configure_fail(("Cannot find cudnn.h under %s" ...))
    File "/autofs/nccs-svm1_home1/shubhankar/tensorflow/third_party/gpus/cuda_configure.bzl", line 210, in auto_configure_fail
        fail(("\n%sCuda Configuration Error:%...)))

Cuda Configuration Error: Cannot find cudnn.h under /autofs/nccs-svm1_home1/shubhankar/libcudnn7_<IP_ADDRESS>-1+cuda8.0_ppc64el/lib/powerpc64le-linux-gnu
WARNING: Target pattern parsing failed.
ERROR: error loading package 'tensorflow/tools/pip_package': Encountered error while reading extension file 'cuda/build_defs.bzl': no such package '@local_config_cuda//cuda': Traceback (most recent call last):
    File "/autofs/nccs-svm1_home1/shubhankar/tensorflow/third_party/gpus/cuda_configure.bzl", line 1166
        _create_local_cuda_repository(repository_ctx)
    File "/autofs/nccs-svm1_home1/shubhankar/tensorflow/third_party/gpus/cuda_configure.bzl", line 995, in _create_local_cuda_repository
        _get_cuda_config(repository_ctx)
    File "/autofs/nccs-svm1_home1/shubhankar/tensorflow/third_party/gpus/cuda_configure.bzl", line 750, in _get_cuda_config
        _cudnn_version(repository_ctx, cudnn_install_base..., ...)
    File "/autofs/nccs-svm1_home1/shubhankar/tensorflow/third_party/gpus/cuda_configure.bzl", line 464, in _cudnn_version
        _find_cudnn_header_dir(repository_ctx, cudnn_install_base...)
    File "/autofs/nccs-svm1_home1/shubhankar/tensorflow/third_party/gpus/cuda_configure.bzl", line 707, in _find_cudnn_header_dir
        auto_configure_fail(("Cannot find cudnn.h under %s" ...))
    File "/autofs/nccs-svm1_home1/shubhankar/tensorflow/third_party/gpus/cuda_configure.bzl", line 210, in auto_configure_fail
        fail(("\n%sCuda Configuration Error:%...)))

Cuda Configuration Error: Cannot find cudnn.h under /autofs/nccs-svm1_home1/shubhankar/libcudnn7_<IP_ADDRESS>-1+cuda8.0_ppc64el/lib/powerpc64le-linux-gnu
INFO: Elapsed time: 0.197s
FAILED: Build did NOT complete successfully (0 packages loaded)
    currently loading: tensorflow/tools/pip_package

NOTE: Don't have sudo access and can't modify /usr/local/* but have all dependencies installed.

Thank you for your post. We noticed you have not filled out the following field in the issue template. Could you update them if they are relevant in your case, or leave them as N/A? Thanks.
Mobile device

@gunan Nope I did.

the main problem is this
Cuda Configuration Error: Cannot find cudnn.h under /autofs/nccs-svm1_home1/shubhankar/libcudnn7_<IP_ADDRESS>-1+cuda8.0_ppc64el/lib/powerpc64le-linux-gnu

It is looking for the cudnn headers in the wrong place.

I guess cudnn.h usually lives inside /usr/local/cuda/ but I have cuda installed separately but I didn't find that file there either.

Hi @ghltshubh , I can help you get TensorFlow compiled on Power (or perhaps point you to a whl file where it is already compiled.)
Some questions:
Do you want to use CUDA 8.0 and cuDNN 7.0 as those versions are old, or are you just using those levels because that is what is documented at that website?
Can you share the contents of .tf_configure.bazelrc (in the root of your tensorflow directory) which will show me the paths you provided with the ./configure script.

I am using CUDA 8.0 and cuDNN 7.0 just because those are mentioned on the website. I have CUDA 9.0 and cuDNN 7.0 as well. Contents of the .tf_configure.bazelrc
build --action_env PYTHON_BIN_PATH="/ccs/home/shubhankar/miniconda3/envs/tensorflow/bin/python"
build --action_env PYTHON_LIB_PATH="/ccs/home/shubhankar/miniconda3/envs/tensorflow/lib/python3.5/site-packages"
build --python_path="/ccs/home/shubhankar/miniconda3/envs/tensorflow/bin/python"
build --define with_jemalloc=true
build:gcp --define with_gcp_support=true
build:hdfs --define with_hdfs_support=true
build:s3 --define with_s3_support=true
build:kafka --define with_kafka_support=true
build:xla --define with_xla_support=true
build:gdr --define with_gdr_support=true
build:verbs --define with_verbs_support=true
build --action_env TF_NEED_OPENCL_SYCL="0"
build --action_env TF_NEED_CUDA="1"
build --action_env CUDA_TOOLKIT_PATH="/ccs/home/shubhankar/8.0.61-1/"
build --action_env TF_CUDA_VERSION="8.0"
build --action_env CUDNN_INSTALL_PATH="/autofs/nccs-svm1_home1/shubhankar/libcudnn7_<IP_ADDRESS>-1+cuda8.0_ppc64el/lib/powerpc64le-linux-gnu"
build --action_env TF_CUDNN_VERSION="7"
build --action_env TF_NCCL_VERSION="1"
build --action_env TF_CUDA_COMPUTE_CAPABILITIES="3.5,5.2"
build --action_env LD_LIBRARY_PATH="/autofs/nccs-svm1_sw/summitdev/.swci/1-compute/opt/spack/20180406/linux-rhel7-ppc64le/gcc-4.8.5/spectrum-mpi-<IP_ADDRESS>-20180110-kfmznqmxgfqvpxzgtkuhlaggidefbkjw/lib:/sw/summitdev/cuda/8.0.61-1/lib64:/autofs/nccs-svm1_home/shubhankar/magma_install/lib:/autofs/nccs-svm1_home1/shubhankar/magma_install/lib:/opt/ibm/spectrumcomputing/lsf/10.1/linux3.10-glibc2.17-ppc64le-csm/lib:/autofs/nccs-svm1_home1/shubhankar/miniconda3/envs/pytorch/lib:/autofs/nccs-svm1_home/shubhankar/miniconda3/envs/pytorch/lib:/autofs/nccs-svm1_home/shubhankar/pytorch/build/lib:/autofs/nccs-svm1_home1/shubhankar/pytorch/build/lib:/autofs/nccs-svm1_home/shubhankar/libcudnn7_<IP_ADDRESS>-1+cuda8.0_ppc64el/lib/powerpc64le-linux-gnu:/sw/summitdev/cuda/8.0.61-1/extras/CUPTI/lib64"
build --action_env TF_CUDA_CLANG="0"
build --action_env GCC_HOST_COMPILER_PATH="/usr/bin/gcc"
build --config=cuda
test --config=cuda
build --define grpc_no_ares=true
build:opt --copt=-mcpu=power8
build:opt --copt=-mtune=power8
build:opt --define with_default_optimizations=true
build --strip=always

I myself have never built with anything below CUDA 9.0, but it should be fine to build with either version.
Make sure /autofs/nccs-svm1_home1/shubhankar/libcudnn7_<IP_ADDRESS>-1+cuda8.0_ppc64el/lib/powerpc64le-linux-gnu/include/cudnn.h and /autofs/nccs-svm1_home1/shubhankar/libcudnn7_<IP_ADDRESS>-1+cuda8.0_ppc64el/lib/powerpc64le-linux-gnu/lib64/libcudnn.so* exists on your system.
The build looks for the files in CUDNN_INSTALL_PATH/include and CUDNN_INSTALL_PATH/lib64. If CUDNN_INSTALL_PATH is not correct, correct it and try the build again.

These are the only contents of my libcudnn7_<IP_ADDRESS>-1+cuda8.0_ppc64el
drwxr-xr-x  0 root   root        0 Sep 16  2017 ./
drwxr-xr-x  0 root   root        0 Sep 16  2017 ./usr/
drwxr-xr-x  0 root   root        0 Sep 16  2017 ./usr/share/
drwxr-xr-x  0 root   root        0 Sep 16  2017 ./usr/share/doc/
drwxr-xr-x  0 root   root        0 Sep 16  2017 ./usr/share/doc/libcudnn7/
-rw-r--r--  0 root   root        0 Aug 31  2017 ./usr/share/doc/libcudnn7/copyright
-rw-r--r--  0 root   root      158 Sep 16  2017 ./usr/share/doc/libcudnn7/changelog.Debian.gz
drwxr-xr-x  0 root   root        0 Sep 16  2017 ./usr/share/lintian/
drwxr-xr-x  0 root   root        0 Sep 16  2017 ./usr/share/lintian/overrides/
-rw-r--r--  0 root   root       45 Sep 16  2017 ./usr/share/lintian/overrides/libcudnn7
drwxr-xr-x  0 root   root        0 Sep 16  2017 ./usr/lib/
drwxr-xr-x  0 root   root        0 Sep 16  2017 ./usr/lib/powerpc64le-linux-gnu/
-rw-r--r--  0 root   root 217439272 Sep 16  2017 ./usr/lib/powerpc64le-linux-gnu/libcudnn.so.7.0.3
lrwxrwxrwx  0 root   root         0 Sep 16  2017 ./usr/lib/powerpc64le-linux-gnu/libcudnn.so.7 -> libcudnn.so.7.0.3
NOTE: I don't have dpkg installed on my system and I don't have permissions to install it either. So I downloaded libcudnn7_<IP_ADDRESS>-1+cuda8.0_ppc64el from nvidia repo on my macbook, extracted .deb file and converted it to tar.gz which I then sent to my install machine.

No I can't build on docker as there is no root access. I will try these solutions and let you know.

@ghltshubh , Any luck with your build?

I renamed the build on Friday to include GPU in the name :) The URL is now:
https://powerci.osuosl.org/job/TensorFlow_Ubuntu_16.04_PPC64LE_GPU_Release_Build/

I don't get it why am I getting this error. I even created a symlink ln -s /autofs/nccs-svm1_home1/shubhankar/cuda/targets/ppc64le-linux/include/cudnn.h /autofs/nccs-svm1_home1/shubhankar/cuda/targets/ppc64le-linux/lib/cudnn.h
WARNING: Output base '/autofs/nccs-svm1_home1/shubhankar/.cache/bazel/_bazel_shubhankar/e74c0b81a4afde0b86e7c039eda6fc5e' is on NFS. This may lead to surprising failures and undetermined behavior.
ERROR: Skipping '//tensorflow/tools/pip_package:build_pip_package': error loading package 'tensorflow/tools/pip_package': Encountered error while reading extension file 'cuda/build_defs.bzl': no such package '@local_config_cuda//cuda': Traceback (most recent call last):
	File "/autofs/nccs-svm1_home1/shubhankar/tensorflow/third_party/gpus/cuda_configure.bzl", line 1166
		_create_local_cuda_repository(repository_ctx)
	File "/autofs/nccs-svm1_home1/shubhankar/tensorflow/third_party/gpus/cuda_configure.bzl", line 995, in _create_local_cuda_repository
		_get_cuda_config(repository_ctx)
	File "/autofs/nccs-svm1_home1/shubhankar/tensorflow/third_party/gpus/cuda_configure.bzl", line 750, in _get_cuda_config
		_cudnn_version(repository_ctx, cudnn_install_base..., ...)
	File "/autofs/nccs-svm1_home1/shubhankar/tensorflow/third_party/gpus/cuda_configure.bzl", line 464, in _cudnn_version
		_find_cudnn_header_dir(repository_ctx, cudnn_install_base...)
	File "/autofs/nccs-svm1_home1/shubhankar/tensorflow/third_party/gpus/cuda_configure.bzl", line 707, in _find_cudnn_header_dir
		auto_configure_fail(("Cannot find cudnn.h under %s" ...))
	File "/autofs/nccs-svm1_home1/shubhankar/tensorflow/third_party/gpus/cuda_configure.bzl", line 210, in auto_configure_fail
		fail(("\n%sCuda Configuration Error:%...)))

Cuda Configuration Error: Cannot find cudnn.h under /autofs/nccs-svm1_home1/shubhankar/cuda/targets/ppc64le-linux/lib
WARNING: Target pattern parsing failed.
ERROR: error loading package 'tensorflow/tools/pip_package': Encountered error while reading extension file 'cuda/build_defs.bzl': no such package '@local_config_cuda//cuda': Traceback (most recent call last):
	File "/autofs/nccs-svm1_home1/shubhankar/tensorflow/third_party/gpus/cuda_configure.bzl", line 1166
		_create_local_cuda_repository(repository_ctx)
	File "/autofs/nccs-svm1_home1/shubhankar/tensorflow/third_party/gpus/cuda_configure.bzl", line 995, in _create_local_cuda_repository
		_get_cuda_config(repository_ctx)
	File "/autofs/nccs-svm1_home1/shubhankar/tensorflow/third_party/gpus/cuda_configure.bzl", line 750, in _get_cuda_config
		_cudnn_version(repository_ctx, cudnn_install_base..., ...)
	File "/autofs/nccs-svm1_home1/shubhankar/tensorflow/third_party/gpus/cuda_configure.bzl", line 464, in _cudnn_version
		_find_cudnn_header_dir(repository_ctx, cudnn_install_base...)
	File "/autofs/nccs-svm1_home1/shubhankar/tensorflow/third_party/gpus/cuda_configure.bzl", line 707, in _find_cudnn_header_dir
		auto_configure_fail(("Cannot find cudnn.h under %s" ...))
	File "/autofs/nccs-svm1_home1/shubhankar/tensorflow/third_party/gpus/cuda_configure.bzl", line 210, in auto_configure_fail
		fail(("\n%sCuda Configuration Error:%...)))

Cuda Configuration Error: Cannot find cudnn.h under /autofs/nccs-svm1_home1/shubhankar/cuda/targets/ppc64le-linux/lib
INFO: Elapsed time: 0.258s
INFO: 0 processes.
FAILED: Build did NOT complete successfully (0 packages loaded)
    currently loading: tensorflow/tools/pip_package

In .tf_configure.bazelrc
build --action_env CUDNN_INSTALL_PATH="/autofs/nccs-svm1_home1/shubhankar/cuda/targets/ppc64le-linux/lib"
need to drop the /lib at the end and change to:
build --action_env CUDNN_INSTALL_PATH="/autofs/nccs-svm1_home1/shubhankar/cuda/targets/ppc64le-linux"
It looks for CUDNN_INSTALL_PATH/include/cudnn,h

I even copied cudnn.h to /autofs/nccs-svm1_home1/shubhankar/cuda/targets/ppc64le-linux/lib directory but it still can't find it.

The whl file was compiled on an Ubuntu 16.04 system with glibc 2.22. I'm asking around if there is a way to compile it for an older version of glibc.
For the message:
Invalid path to cuDNN 7 toolkit. None of the following files can be found:
/autofs/nccs-svm1_home1/shubhankar/cuda/targets/ppc64le-linux/lib64/libcudnn.so.7

it is looking for a lib64 (and not the lib directory) for libcudnn.so.7. Maybe symlink lib64 to lib?

Based on our experience with x86, there is no straightforward way to do it.
You can statically link glibc and libstdcxx into your binary, but that can cause other problems.

Thanks @gunan for the comment about building on ububtu 14, I'll see if i'm able to switch our build process to use an ubuntu 14 container.

@ghltshubh, that comment references compiling with a different level of glibc, the issue you have when using the whl file is at runtime.
From my stand point it will probably be easier to me to compile with an ubuntu 14.04 container then use those instructions. Thanks.

@wdirons Tried various permutations and combinations of bazel, tf, gcc, cuda and this is the least error I could achieve. Similar error showed up using different versions as well.. The following is using bazel 0.13.1, tf 1.8.0 and gcc 4.8.5 and cuda 9.0
ERROR: /autofs/nccs-svm1_home1/shubhankar/tensorflow/tensorflow/contrib/framework/BUILD:101:1: output 'tensorflow/contrib/framework/_objs/python/ops/_variable_ops_gpu/tensorflow/contrib/framework/kernels/zero_initializer_op_gpu.cu.pic.o' was not created
ERROR: /autofs/nccs-svm1_home1/shubhankar/tensorflow/tensorflow/contrib/framework/BUILD:101:1: not all outputs were created or valid
Target //tensorflow/tools/pip_package:build_pip_package failed to build
Use --verbose_failures to see the command lines of failed build steps.
INFO: Elapsed time: 232.502s, Critical Path: 97.67s
INFO: 3268 processes, local.
 Is their any recipe that I could follow to safely build it so that I can distribute it. Thanks.

oh ok got it. Btw which NCCL version does it use. Can I build it using NCCL 2.x?

From the contents of .tf_configure.bazelrc posted above, I see you answered nccl 1 in ./configure
NCCL2 will work, just download the os/agnostic package from: https://developer.nvidia.com/nccl/nccl-download and extract the file and use .configure to point at the directory containing the include and lib (or lib64) directory.

I tried linking nccl-2.2.13 but it failed I am not sure if it's path issue or legacy bazel version.
ERROR: /autofs/nccs-svm1_home1/shubhankar/tensorflow/tensorflow/contrib/nccl/BUILD:23:1: Linking of rule '//tensorflow/contrib/nccl:python/ops/_nccl_ops.so' failed (Exit 1)
/usr/bin/ld: skipping incompatible<EMAIL_ADDRESS>when searching for -l:libnccl.so.2
/usr/bin/ld: skipping incompatible<EMAIL_ADDRESS>when searching for -l:libnccl.so.2
/usr/bin/ld: cannot find -l:libnccl.so.2
collect2: error: ld returned 1 exit status
Target //tensorflow/tools/pip_package:build_pip_package failed to build
Use --verbose_failures to see the command lines of failed build steps.
INFO: Elapsed time: 988.531s, Critical Path: 771.31s
FAILED: Build did NOT complete successfully

We just upgraded the master to NCCL 2.3, which is opensource.
Could you retry?

I tried installing tf 1.8 NCCL 2.3 built from source but got the following error:
`ERROR: /autofs/nccs-svm1_home1/shubhankar/tensorflow/tensorflow/BUILD:541:1: Executing genrule //tensorflow:python_api_gen failed (Exit 1)
Traceback (most recent call last):
File "/autofs/nccs-svm1_home1/shubhankar/.cache/bazel/_bazel_shubhankar/e74c0b81a4afde0b86e7c039eda6fc5e/execroot/org_tensorflow/bazel-out/host/bin/tensorflow/tools/api/generator/create_python_api.runfiles/org_tensorflow/tensorflow/python/pywrap_tensorflow.py", line 58, in
from tensorflow.python.pywrap_tensorflow_internal import *
File "/autofs/nccs-svm1_home1/shubhankar/.cache/bazel/_bazel_shubhankar/e74c0b81a4afde0b86e7c039eda6fc5e/execroot/org_tensorflow/bazel-out/host/bin/tensorflow/tools/api/generator/create_python_api.runfiles/org_tensorflow/tensorflow/python/pywrap_tensorflow_internal.py", line 28, in
_pywrap_tensorflow_internal = swig_import_helper()
File "/autofs/nccs-svm1_home1/shubhankar/.cache/bazel/_bazel_shubhankar/e74c0b81a4afde0b86e7c039eda6fc5e/execroot/org_tensorflow/bazel-out/host/bin/tensorflow/tools/api/generator/create_python_api.runfiles/org_tensorflow/tensorflow/python/pywrap_tensorflow_internal.py", line 24, in swig_import_helper
_mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description)
File "/ccs/home/shubhankar/miniconda3/envs/tf_p3.5/lib/python3.5/imp.py", line 243, in load_module
return load_dynamic(name, filename, file)
File "/ccs/home/shubhankar/miniconda3/envs/tf_p3.5/lib/python3.5/imp.py", line 343, in load_dynamic
return _load(spec)
ImportError: /autofs/nccs-svm1_home1/shubhankar/.cache/bazel/_bazel_shubhankar/e74c0b81a4afde0b86e7c039eda6fc5e/execroot/org_tensorflow/bazel-out/host/bin/tensorflow/tools/api/generator/create_python_api.runfiles/org_tensorflow/tensorflow/python/_pywrap_tensorflow_internal.so: undefined symbol: LLVMInitializePowerPCTargetMC
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/autofs/nccs-svm1_home1/shubhankar/.cache/bazel/_bazel_shubhankar/e74c0b81a4afde0b86e7c039eda6fc5e/execroot/org_tensorflow/bazel-out/host/bin/tensorflow/tools/api/generator/create_python_api.runfiles/org_tensorflow/tensorflow/tools/api/generator/create_python_api.py", line 27, in
from tensorflow.python.util import tf_decorator
File "/autofs/nccs-svm1_home1/shubhankar/.cache/bazel/_bazel_shubhankar/e74c0b81a4afde0b86e7c039eda6fc5e/execroot/org_tensorflow/bazel-out/host/bin/tensorflow/tools/api/generator/create_python_api.runfiles/org_tensorflow/tensorflow/python/init.py", line 49, in
from tensorflow.python import pywrap_tensorflow
File "/autofs/nccs-svm1_home1/shubhankar/.cache/bazel/_bazel_shubhankar/e74c0b81a4afde0b86e7c039eda6fc5e/execroot/org_tensorflow/bazel-out/host/bin/tensorflow/tools/api/generator/create_python_api.runfiles/org_tensorflow/tensorflow/python/pywrap_tensorflow.py", line 74, in
raise ImportError(msg)
ImportError: Traceback (most recent call last):
File "/autofs/nccs-svm1_home1/shubhankar/.cache/bazel/_bazel_shubhankar/e74c0b81a4afde0b86e7c039eda6fc5e/execroot/org_tensorflow/bazel-out/host/bin/tensorflow/tools/api/generator/create_python_api.runfiles/org_tensorflow/tensorflow/python/pywrap_tensorflow.py", line 58, in
from tensorflow.python.pywrap_tensorflow_internal import *
File "/autofs/nccs-svm1_home1/shubhankar/.cache/bazel/_bazel_shubhankar/e74c0b81a4afde0b86e7c039eda6fc5e/execroot/org_tensorflow/bazel-out/host/bin/tensorflow/tools/api/generator/create_python_api.runfiles/org_tensorflow/tensorflow/python/pywrap_tensorflow_internal.py", line 28, in
_pywrap_tensorflow_internal = swig_import_helper()
File "/autofs/nccs-svm1_home1/shubhankar/.cache/bazel/_bazel_shubhankar/e74c0b81a4afde0b86e7c039eda6fc5e/execroot/org_tensorflow/bazel-out/host/bin/tensorflow/tools/api/generator/create_python_api.runfiles/org_tensorflow/tensorflow/python/pywrap_tensorflow_internal.py", line 24, in swig_import_helper
_mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description)
File "/ccs/home/shubhankar/miniconda3/envs/tf_p3.5/lib/python3.5/imp.py", line 243, in load_module
return load_dynamic(name, filename, file)
File "/ccs/home/shubhankar/miniconda3/envs/tf_p3.5/lib/python3.5/imp.py", line 343, in load_dynamic
return _load(spec)
ImportError: /autofs/nccs-svm1_home1/shubhankar/.cache/bazel/_bazel_shubhankar/e74c0b81a4afde0b86e7c039eda6fc5e/execroot/org_tensorflow/bazel-out/host/bin/tensorflow/tools/api/generator/create_python_api.runfiles/org_tensorflow/tensorflow/python/_pywrap_tensorflow_internal.so: undefined symbol: LLVMInitializePowerPCTargetMC
Failed to load the native TensorFlow runtime.
See https://www.tensorflow.org/install/install_sources#common_installation_problems
for some common reasons and solutions.  Include the entire stack trace
above this error message when asking for help.
Target //tensorflow/tools/pip_package:build_pip_package failed to build
Use --verbose_failures to see the command lines of failed build steps.
INFO: Elapsed time: 324.978s, Critical Path: 159.69s
FAILED: Build did NOT complete successfully`

I was able to build tf 1.8 with os agnostic NCCL 2.3 build from source with minor changes to NCCL.
Symlink LICENSE.txt in nccl dir to NCCL-SLA.txt


place some missing ";"

place some missing ";" to end of commands that create error when executing file.

It's been 4 years, why hasn't this PR been accepted?

@ChrisSaxonOra is this something you can review?

Approved


Relative URLs not expanded

Posts from http://githubengineering.com/atom.xml result in broken links, because they are relative and are not expanded.
My stringer is currently on a state from last September, so this might very well be fixed already, but I got no time to check right now. I will upgrade this week and then see again unless someone already knows for sure if this is a still-existing bug.

Links from the GitHub Engineering blog work fine for me. I'm running 64ab6e81c296a5d4857be0108a949969da115943 (2015-05-01).

Removed the feed, updated Stringer and re-added the feed. Story headline links to http:/scripts-to-rule-them-all/ which is wrong.

Oh, I didn't look at the headlines, I just checked links in the post bodies. I see what you mean now.

This is possibly another case of #222, caused by the use of relative URLs for all link elements, even for the base ones nested directly under feed. Without a base absolute URL, Feedjira, which we use for parsing, cannot expand the URLs.
I'm closing this, as this is not an issue specific to Stringer, but rather with the feed itself. It doesn't pass W3C's Feed Validation Service at the time of this writing.
For reference, here's the start of the feed in question:
<?xml version="1.0" encoding="utf-8"?>
<feed xmlns="http://www.w3.org/2005/Atom">
 <title>GitHub Engineering</title>
 <link href="/atom.xml" rel="self"/>
 <link href=""/>
 <updated>2015-07-01T01:56:14+00:00</updated>
 <id></id>
 <author>
   <name>GitHub Engineering</name>
 </author>

 <entry>
   <title>Scripts to Rule Them All</title>
   <link href="/scripts-to-rule-them-all/"/>
   <updated>2015-06-30T00:00:00+00:00</updated>
   <id>/scripts-to-rule-them-all</id>

Ok, thanks. Will report it back upstream then.

FYI: it's fixed. https://twitter.com/aroben/status/616641212980817924


Blurred overlay appears with no survey

Version: MixPanel 4.3.0
Platform: Android 4.4
Device: Nexus 5
Steps to reproduce

Open app that uses MixPanel surveys
Click a notification from a different app, opening the other app.
Using the back button, go back to the first app.

Here, a MixPanel survey attempts to show
Expected Result: The screen blurs and the survey is shown.
Actual Result: The screen blurs, but no survey appears. Clicking the back button closes the blurred screen.
Additional Details
I have only experienced this once, but did very recently update to 4.3.0 to incorporate App Links.

Please re-open if you still have this issue.


Implement OpenTelemetry tracing

Background
Logging provides a limited view into Fabric's operation. We can use existing APM best practices to collect and export events during Fabric's run time, allowing users to send the telemetry into external collectors in their environments.
This feature will also allow us to see into Fabric's code when it is integrated into a bigger application.
Design

integrate logging with OpenTelemetry tracing
increase logging coverage (with sensible debug / info / warn split) to all core components of Fabric

References

https://opentelemetry.io/docs/languages/go/
https://opentelemetry.io/docs/specs/otel/logs/
https://github.com/remychantenay/slog-otel

@dobarx with #184, this one can be closed, right?


WIP: Try to build lib separately

Towards #25

Hi! This is the friendly automated conda-forge-linting service.
I just wanted to let you know that I linted all conda-recipes in your PR (recipe) and found it was in an excellent condition.

looking for _i_give_permission_to_break_this_code_default_unk_piece_ seems to produce a lead: https://github.com/google/sentencepiece/issues/593

.... or not. The mentioned test.bat just builds static libraries... 😑

Really starting to wonder if it's worth the effort to build a shared lib for windows. The upstream situation is just such an impenetrable mix of libraries, vendoring and custom glue code that I'm really struggling.

Ah, it seems they should be coming from here using GOOGLE_PROTOBUF_EXTENSION_ACCESSORS, which doesn't seem to be working on windows. Every come across this @xhochy?

Any experience with GOOGLE_PROTOBUF_EXTENSION_ACCESSORS (+win) @xhochy?

No, never had to deal with them. Do you know where that macro is defined? I have a hard time googling for it.

No, never had to deal with them. Do you know where that macro is defined? I have a hard time googling for it.

Found the definition in the sentence-piece third party sources. It's pretty big...

@xhochy
Nothing particularly windows-specific stands out to me in the definition of GOOGLE_PROTOBUF_EXTENSION_ACCESSORS (link above) - any ideas?

Really starting to wonder if it's worth the effort to build a shared lib for windows. The upstream situation is just such an impenetrable mix of libraries, vendoring and custom glue code that I'm really struggling to debug some very cryptic errors.

Going through the logs again, I would say no. Seems like the GOOGLE_PROTOBUF_EXTENSION_ACCESSORS won't be the only hurdle.

Did you try building here with the EXPORT_ALL_SYMBOLS flag on?

Did you try building here with the EXPORT_ALL_SYMBOLS flag on?

Did that now and it then fails at a different point:
spm_decode_main.obj : error LNK2019: unresolved external symbol "class absl::Flag<int> FLAGS_minloglevel" (?FLAGS_minloglevel@@3V?$Flag@H@absl@@A) referenced in function main [D:\bld\sentencepiece-split_1640913561144\work\build\src\spm_decode.vcxproj]

I've found the following snippet upstream
#ifdef _USE_EXTERNAL_ABSL
// Naive workaround to define minloglevel on external absl package.
// We want to define them in other cc file.
#include "third_party/absl/flags/flag.h"
#include "third_party/absl/flags/parse.h"
ABSL_FLAG(int32, minloglevel, 0,
          "Messages logged at a lower level than this don't actually.");
#endif

but even setting _USE_EXTERNAL_ABSL didn't help. Interestingly, that guard isn't used elsewhere.
The relevant abseil-macros (ABSL_FLAG / ABSL_DECLARE_FLAG) are actually what is recommended usage by abseil, but it looks like sentencepiece (p)redeclares them.

Nevermind, the flags things is a bit of a red herring at the moment; I hadn't noticed that it still runs into
     Creating library D:/bld/sentencepiece-split_1640913561144/work/build/src/Release/sentencepiece_train_import.lib and object D:/bld/sentencepiece-split_1640913561144/work/build/src/Release/sentencepiece_train_import.exp
trainer_interface.obj : error LNK2019: unresolved external symbol "private: static class google::protobuf::internal::LazyString const sentencepiece::TrainerSpec::_i_give_permission_to_break_this_code_default_unk_piece_" (?_i_give_permission_to_break_this_code_default_unk_piece_@TrainerSpec@sentencepiece@@0VLazyString@internal@protobuf@google@@B) referenced in function "public: bool __cdecl <lambda_e29d05afb1d6eeb86afbc2647f25bd32>::operator()(class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const &,enum sentencepiece::ModelProto_SentencePiece_Type)const " (??R<lambda_e29d05afb1d6eeb86afbc2647f25bd32>@@QEBA_NAEBV?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@W4ModelProto_SentencePiece_Type@sentencepiece@@@Z) [D:\bld\sentencepiece-split_1640913561144\work\build\src\sentencepiece_train.vcxproj]
sentencepiece_trainer.obj : error LNK2001: unresolved external symbol "private: static class google::protobuf::internal::LazyString const sentencepiece::TrainerSpec::_i_give_permission_to_break_this_code_default_unk_piece_" (?_i_give_permission_to_break_this_code_default_unk_piece_@TrainerSpec@sentencepiece@@0VLazyString@internal@protobuf@google@@B) [D:\bld\sentencepiece-split_1640913561144\work\build\src\sentencepiece_train.vcxproj]
trainer_interface.obj : error LNK2019: unresolved external symbol "private: static class google::protobuf::internal::LazyString const sentencepiece::TrainerSpec::_i_give_permission_to_break_this_code_default_bos_piece_" (?_i_give_permission_to_break_this_code_default_bos_piece_@TrainerSpec@sentencepiece@@0VLazyString@internal@protobuf@google@@B) referenced in function "public: bool __cdecl <lambda_e29d05afb1d6eeb86afbc2647f25bd32>::operator()(class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const &,enum sentencepiece::ModelProto_SentencePiece_Type)const " (??R<lambda_e29d05afb1d6eeb86afbc2647f25bd32>@@QEBA_NAEBV?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@W4ModelProto_SentencePiece_Type@sentencepiece@@@Z) [D:\bld\sentencepiece-split_1640913561144\work\build\src\sentencepiece_train.vcxproj]
sentencepiece_trainer.obj : error LNK2001: unresolved external symbol "private: static class google::protobuf::internal::LazyString const sentencepiece::TrainerSpec::_i_give_permission_to_break_this_code_default_bos_piece_" (?_i_give_permission_to_break_this_code_default_bos_piece_@TrainerSpec@sentencepiece@@0VLazyString@internal@protobuf@google@@B) [D:\bld\sentencepiece-split_1640913561144\work\build\src\sentencepiece_train.vcxproj]
trainer_interface.obj : error LNK2019: unresolved external symbol "private: static class google::protobuf::internal::LazyString const sentencepiece::TrainerSpec::_i_give_permission_to_break_this_code_default_eos_piece_" (?_i_give_permission_to_break_this_code_default_eos_piece_@TrainerSpec@sentencepiece@@0VLazyString@internal@protobuf@google@@B) referenced in function "public: bool __cdecl <lambda_e29d05afb1d6eeb86afbc2647f25bd32>::operator()(class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const &,enum sentencepiece::ModelProto_SentencePiece_Type)const " (??R<lambda_e29d05afb1d6eeb86afbc2647f25bd32>@@QEBA_NAEBV?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@W4ModelProto_SentencePiece_Type@sentencepiece@@@Z) [D:\bld\sentencepiece-split_1640913561144\work\build\src\sentencepiece_train.vcxproj]
sentencepiece_trainer.obj : error LNK2001: unresolved external symbol "private: static class google::protobuf::internal::LazyString const sentencepiece::TrainerSpec::_i_give_permission_to_break_this_code_default_eos_piece_" (?_i_give_permission_to_break_this_code_default_eos_piece_@TrainerSpec@sentencepiece@@0VLazyString@internal@protobuf@google@@B) [D:\bld\sentencepiece-split_1640913561144\work\build\src\sentencepiece_train.vcxproj]
trainer_interface.obj : error LNK2019: unresolved external symbol "private: static class google::protobuf::internal::LazyString const sentencepiece::TrainerSpec::_i_give_permission_to_break_this_code_default_pad_piece_" (?_i_give_permission_to_break_this_code_default_pad_piece_@TrainerSpec@sentencepiece@@0VLazyString@internal@protobuf@google@@B) referenced in function "public: class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const & __cdecl sentencepiece::TrainerSpec::pad_piece(void)const " (?pad_piece@TrainerSpec@sentencepiece@@QEBAAEBV?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@XZ) [D:\bld\sentencepiece-split_1640913561144\work\build\src\sentencepiece_train.vcxproj]
sentencepiece_trainer.obj : error LNK2001: unresolved external symbol "private: static class google::protobuf::internal::LazyString const sentencepiece::TrainerSpec::_i_give_permission_to_break_this_code_default_pad_piece_" (?_i_give_permission_to_break_this_code_default_pad_piece_@TrainerSpec@sentencepiece@@0VLazyString@internal@protobuf@google@@B) [D:\bld\sentencepiece-split_1640913561144\work\build\src\sentencepiece_train.vcxproj]
trainer_interface.obj : error LNK2019: unresolved external symbol "const sentencepiece::ModelProto::`vftable'" (??_7ModelProto@sentencepiece@@6B@) referenced in function "private: class sentencepiece::util::Status __cdecl sentencepiece::TrainerInterface::SaveModel(class absl::string_view)const " (?SaveModel@TrainerInterface@sentencepiece@@AEBA?AVStatus@util@2@Vstring_view@absl@@@Z) [D:\bld\sentencepiece-split_1640913561144\work\build\src\sentencepiece_train.vcxproj]
unigram_model_trainer.obj : error LNK2001: unresolved external symbol "const sentencepiece::ModelProto::`vftable'" (??_7ModelProto@sentencepiece@@6B@) [D:\bld\sentencepiece-split_1640913561144\work\build\src\sentencepiece_train.vcxproj]
sentencepiece_trainer.obj : error LNK2001: unresolved external symbol "const sentencepiece::ModelProto::`vftable'" (??_7ModelProto@sentencepiece@@6B@) [D:\bld\sentencepiece-split_1640913561144\work\build\src\sentencepiece_train.vcxproj]
unigram_model_trainer.obj : error LNK2019: unresolved external symbol "struct sentencepiece::TrainerSpecDefaultTypeInternal sentencepiece::_TrainerSpec_default_instance_" (?_TrainerSpec_default_instance_@sentencepiece@@3UTrainerSpecDefaultTypeInternal@1@A) referenced in function "public: virtual bool __cdecl sentencepiece::ModelInterface::ByteFallbackEnabled(void)const " (?ByteFallbackEnabled@ModelInterface@sentencepiece@@UEBA_NXZ) [D:\bld\sentencepiece-split_1640913561144\work\build\src\sentencepiece_train.vcxproj]
sentencepiece_trainer.obj : error LNK2019: unresolved external symbol "private: static class google::protobuf::internal::LazyString const sentencepiece::TrainerSpec::_i_give_permission_to_break_this_code_default_unk_surface_" (?_i_give_permission_to_break_this_code_default_unk_surface_@TrainerSpec@sentencepiece@@0VLazyString@internal@protobuf@google@@B) referenced in function "class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > __cdecl sentencepiece::PrintProto(class sentencepiece::TrainerSpec const &,class absl::string_view)" (?PrintProto@sentencepiece@@YA?AV?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@AEBVTrainerSpec@1@Vstring_view@absl@@@Z) [D:\bld\sentencepiece-split_1640913561144\work\build\src\sentencepiece_train.vcxproj]
sentencepiece_trainer.obj : error LNK2019: unresolved external symbol "const sentencepiece::TrainerSpec::`vftable'" (??_7TrainerSpec@sentencepiece@@6B@) referenced in function "public: static class sentencepiece::util::Status __cdecl sentencepiece::SentencePieceTrainer::Train(class std::unordered_map<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,struct std::hash<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >,struct std::equal_to<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >,class std::allocator<struct std::pair<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const ,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > > > > const &,class sentencepiece::SentenceIterator *,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > *)" (?Train@SentencePieceTrainer@sentencepiece@@SA?AVStatus@util@2@AEBV?$unordered_map@V?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@V12@U?$hash@V?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@@2@U?$equal_to@V?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@@2@V?$allocator@U?$pair@$$CBV?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@V12@@std@@@2@@std@@PEAVSentenceIterator@2@PEAV?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@6@@Z) [D:\bld\sentencepiece-split_1640913561144\work\build\src\sentencepiece_train.vcxproj]
sentencepiece_trainer.obj : error LNK2019: unresolved external symbol "const sentencepiece::NormalizerSpec::`vftable'" (??_7NormalizerSpec@sentencepiece@@6B@) referenced in function "public: static class sentencepiece::NormalizerSpec __cdecl sentencepiece::SentencePieceTrainer::GetNormalizerSpec(class absl::string_view)" (?GetNormalizerSpec@SentencePieceTrainer@sentencepiece@@SA?AVNormalizerSpec@2@Vstring_view@absl@@@Z) [D:\bld\sentencepiece-split_1640913561144\work\build\src\sentencepiece_train.vcxproj]
D:\bld\sentencepiece-split_1640913561144\work\build\src\Release\sentencepiece_train.dll : fatal error LNK1120: 9 unresolved externals [D:\bld\sentencepiece-split_1640913561144\work\build\src\sentencepiece_train.vcxproj]

I think I'm coming full circle to the idea that (e.g. for GOOGLE_PROTOBUF_EXTENSION_ACCESSORS to work) we're probably gonna need https://github.com/conda-forge/libprotobuf-feedstock/pull/100 for protobuf.

Hi! This is the friendly automated conda-forge-linting service.
I was trying to look for recipes to lint for you, but it appears we have a merge conflict.
Please try to merge or rebase with the base branch to resolve this conflict.
Please ping the 'conda-forge/core' team (using the @ notation in a comment) if you believe this is a bug.

Hi! This is the friendly automated conda-forge-linting service.
I just wanted to let you know that I linted all conda-recipes in your PR (recipe) and found it was in an excellent condition.

Nothing particularly windows-specific stands out to me in the definition of GOOGLE_PROTOBUF_EXTENSION_ACCESSORS (link above) - any ideas?

Well, one big reason is that libprotobuf 3.18.0 removed that macro, but then I still don't understand why linux/osx manage to get through...

I noticed that all the problematic ones were static const here. Feels like this might have something to do with it.

@xhochy

I noticed that all the problematic ones were static const here. [...might be related to] better link

So, from what I can see the static data members are correctly defined in exactly one translation unit, but something seems to be messing with the initialisation of LazyString.
Notably, this class has no explicit constructor, and - I admit I'm fully out of my depth here - the definition seems to do brace initialisation of a union of structs. Could it be that this somehow doesn't work correctly (in combination with static) on MSVC?

occurrences of LazyString in sentencepiece sources
>findstr /L /S /N "LazyString" *.*
src\builtin_pb\sentencepiece_model.pb.cc:394:const ::PROTOBUF_NAMESPACE_ID::internal::LazyString TrainerSpec::_i_give_permission_to_break_this_code_default_unk_piece_{{{"<unk>", 5}}, {nullptr}};
src\builtin_pb\sentencepiece_model.pb.cc:395:const ::PROTOBUF_NAMESPACE_ID::internal::LazyString TrainerSpec::_i_give_permission_to_break_this_code_default_bos_piece_{{{"<s>", 3}}, {nullptr}};
src\builtin_pb\sentencepiece_model.pb.cc:396:const ::PROTOBUF_NAMESPACE_ID::internal::LazyString TrainerSpec::_i_give_permission_to_break_this_code_default_eos_piece_{{{"</s>", 4}}, {nullptr}};
src\builtin_pb\sentencepiece_model.pb.cc:397:const ::PROTOBUF_NAMESPACE_ID::internal::LazyString TrainerSpec::_i_give_permission_to_break_this_code_default_pad_piece_{{{"<pad>", 5}}, {nullptr}};
src\builtin_pb\sentencepiece_model.pb.cc:398:const ::PROTOBUF_NAMESPACE_ID::internal::LazyString TrainerSpec::_i_give_permission_to_break_this_code_default_unk_surface_{{{" \342\201\207 ", 5}}, {nullptr}};
src\builtin_pb\sentencepiece_model.pb.h:958:  static const ::PROTOBUF_NAMESPACE_ID::internal::LazyString _i_give_permission_to_break_this_code_default_unk_surface_;
src\builtin_pb\sentencepiece_model.pb.h:960:  static const ::PROTOBUF_NAMESPACE_ID::internal::LazyString _i_give_permission_to_break_this_code_default_unk_piece_;
src\builtin_pb\sentencepiece_model.pb.h:962:  static const ::PROTOBUF_NAMESPACE_ID::internal::LazyString _i_give_permission_to_break_this_code_default_bos_piece_;
src\builtin_pb\sentencepiece_model.pb.h:964:  static const ::PROTOBUF_NAMESPACE_ID::internal::LazyString _i_give_permission_to_break_this_code_default_eos_piece_;
src\builtin_pb\sentencepiece_model.pb.h:966:  static const ::PROTOBUF_NAMESPACE_ID::internal::LazyString _i_give_permission_to_break_this_code_default_pad_piece_;
third_party\protobuf-lite\arenastring.cc:50:const std::string& LazyString::Init() const {
third_party\protobuf-lite\arenastring.cc:120:std::string* ArenaStringPtr::Mutable(const LazyString& default_value,
third_party\protobuf-lite\arenastring.cc:241:void ArenaStringPtr::ClearToDefault(const LazyString& default_value,
third_party\protobuf-lite\google\protobuf\arenastring.h:56:class PROTOBUF_EXPORT LazyString {
third_party\protobuf-lite\google\protobuf\arenastring.h:58:  // We explicitly make LazyString an aggregate so that MSVC can do constant
third_party\protobuf-lite\google\protobuf\arenastring.h:148://   `const LazyString&` for it.
third_party\protobuf-lite\google\protobuf\arenastring.h:206:  std::string* Mutable(const LazyString& default_value, ::google::protobuf::Arena* arena);
third_party\protobuf-lite\google\protobuf\arenastring.h:247:  void ClearToDefault(const LazyString& default_value, ::google::protobuf::Arena* arena);
third_party\protobuf-lite\google\protobuf\arenastring.h:295:  // Variadic to support 0 args for EmptyDefault and 1 arg for LazyString.

The compilers might get it, but I'm having trouble wrapping my head around how three values (e.g. {{{"<unk>", 5}}, {nullptr}}) are supposed to initialize what looks like 5 values (4 if counting the union as one);
 public:
  struct InitValue {
    const char* ptr;
    size_t size;
  };
  // We keep a union of the initialization value and the std::string to save on
  // space. We don't need the string array after Init() is done.
  union {
    mutable InitValue init_value_;
    alignas(std::string) mutable char string_buf_[sizeof(std::string)];
  };
  mutable std::atomic<const std::string*> inited_;

My god, I did not plan to get so deep into initializers... Anonymous unions, aggregate initialization (which has special rules for static members), union initialization rules etc... 😅

damn, that it is one deep rabbit hole... I managed to get rid of the previous errors of
error LNK2019: unresolved external symbol "private: static class google::protobuf::internal::LazyString const sentencepiece::TrainerSpec::_i_give_permission_to_break_this_code_default_unk_piece_"

etc. (by moving the initializer to the header, getting a fortuitous clang warning on osx-arm - msvc's warning was useless - and then turning it to constexpr).
Nowe there's still external symbol errors with missing virtual function tables
  Auto build dll exports
     Creating library D:/bld/sentencepiece-split_1644334940326/work/build/src/Release/sentencepiece_train_import.lib and object D:/bld/sentencepiece-split_1644334940326/work/build/src/Release/sentencepiece_train_import.exp
trainer_interface.obj : error LNK2019: unresolved external symbol "const sentencepiece::ModelProto::`vftable'" (??_7ModelProto@sentencepiece@@6B@) referenced in function "private: class sentencepiece::util::Status __cdecl sentencepiece::TrainerInterface::SaveModel(class absl::string_view)const " (?SaveModel@TrainerInterface@sentencepiece@@AEBA?AVStatus@util@2@Vstring_view@absl@@@Z) [D:\bld\sentencepiece-split_1644334940326\work\build\src\sentencepiece_train.vcxproj]
unigram_model_trainer.obj : error LNK2001: unresolved external symbol "const sentencepiece::ModelProto::`vftable'" (??_7ModelProto@sentencepiece@@6B@) [D:\bld\sentencepiece-split_1644334940326\work\build\src\sentencepiece_train.vcxproj]
sentencepiece_trainer.obj : error LNK2001: unresolved external symbol "const sentencepiece::ModelProto::`vftable'" (??_7ModelProto@sentencepiece@@6B@) [D:\bld\sentencepiece-split_1644334940326\work\build\src\sentencepiece_train.vcxproj]
unigram_model_trainer.obj : error LNK2019: unresolved external symbol "class sentencepiece::TrainerSpecDefaultTypeInternal sentencepiece::_TrainerSpec_default_instance_" (?_TrainerSpec_default_instance_@sentencepiece@@3VTrainerSpecDefaultTypeInternal@1@A) referenced in function "public: virtual bool __cdecl sentencepiece::ModelInterface::ByteFallbackEnabled(void)const " (?ByteFallbackEnabled@ModelInterface@sentencepiece@@UEBA_NXZ) [D:\bld\sentencepiece-split_1644334940326\work\build\src\sentencepiece_train.vcxproj]
sentencepiece_trainer.obj : error LNK2019: unresolved external symbol "const sentencepiece::TrainerSpec::`vftable'" (??_7TrainerSpec@sentencepiece@@6B@) referenced in function "public: static class sentencepiece::util::Status __cdecl sentencepiece::SentencePieceTrainer::Train(class std::unordered_map<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> >,struct std::hash<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >,struct std::equal_to<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >,class std::allocator<struct std::pair<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > const ,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > > > > const &,class sentencepiece::SentenceIterator *,class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > *)" (?Train@SentencePieceTrainer@sentencepiece@@SA?AVStatus@util@2@AEBV?$unordered_map@V?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@V12@U?$hash@V?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@@2@U?$equal_to@V?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@@2@V?$allocator@U?$pair@$$CBV?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@V12@@std@@@2@@std@@PEAVSentenceIterator@2@PEAV?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@6@@Z) [D:\bld\sentencepiece-split_1644334940326\work\build\src\sentencepiece_train.vcxproj]
sentencepiece_trainer.obj : error LNK2019: unresolved external symbol "const sentencepiece::NormalizerSpec::`vftable'" (??_7NormalizerSpec@sentencepiece@@6B@) referenced in function "public: static class sentencepiece::NormalizerSpec __cdecl sentencepiece::SentencePieceTrainer::GetNormalizerSpec(class absl::string_view)" (?GetNormalizerSpec@SentencePieceTrainer@sentencepiece@@SA?AVNormalizerSpec@2@Vstring_view@absl@@@Z) [D:\bld\sentencepiece-split_1644334940326\work\build\src\sentencepiece_train.vcxproj]
D:\bld\sentencepiece-split_1644334940326\work\build\src\Release\sentencepiece_train.dll : fatal error LNK1120: 4 unresolved externals [D:\bld\sentencepiece-split_1644334940326\work\build\src\sentencepiece_train.vcxproj]

Based on some googling, this answer gives some good explanation/context. I even found a method without a body anywhere in the sources, but even adding that hasn't fixed it.

@xhochy
OK, making some more headway after managing to patch the protoc-generated sources, and figuring out that ~the cake~ SPM_USE_EXTERNAL_ABSL is a lie - i.e. there were lots of unconditional #include "third_party/absl/... everywhere.
After several false starts and finally placing the sentencepiece-specific glue code that pretends to be third-party code in the actual sources (I'm gonna try to upstream this eventually), I'm now back to green on linux/osx (though OSX was missing some symbols with the current abseil baseline, but passes with the newest one for which the migration just started...).
On windows, there was also a new set of errors (pervious compilation errors were most likely due to the above inclusion collision shenanigans), down to "only" linker errors, because protoc (and/or CMake) somewhere set sentencepiece.pb.obj to use static linking against the MSVC MultiThread runtime (despite -DPROTOBUF_USE_DLLS and any other seemingly relevant option I could find).
In any case, I'm now thinking that the remaining errors are because abseil on windows isn't compiled with BUILD_SHARED_LIBS from what I can tell. I looked at the first error and doing
#include "absl/strings/numbers.h"
[...]
int main() {
    int32 freq = 1;
    CHECK_OR_RETURN(absl::SimpleAtoi(some_string, &freq)
}

fails with
error LNK2019: unresolved external symbol "bool __cdecl absl::lts_20211102::numbers_internal::safe_strto32_base(class std::basic_string_view<char,struct std::char_traits<char> >,int *,int)" (?safe_strto32_base@numbers_internal@lts_20211102@absl@@YA_NV?$basic_string_view@DU?$char_traits@D@std@@@std@@PEAHH@Z) referenced in function "bool __cdecl absl::lts_20211102::SimpleAtoi<int>(class std::basic_string_view<char,struct std::char_traits<char> >,int *)" (??$SimpleAtoi@H@lts_20211102@absl@@YA_NV?$basic_string_view@DU?$char_traits@D@std@@@std@@PEAH@Z) [D:\bld\sentencepiece-split_1645439189158\work\build\src\sentencepiece.vcxproj]

even though that's clearly part of the public API, and the internal symbol shouldn't concern us. I'll try to raise a PR on the abseil-feedstock and we can discuss there...

With the changes in abseil_dev, I'm down to one missing symbol:
private: static void const * const absl::lts_20211102::hash_internal::MixingHashState::kSeed

This one is hard to make constexpr, because it dereferences an uninitialized pointer to get some random value...


The default header size of nginx ingress controller is not enough for large k8s CA data

Hello, I try to import an existed CCE (huawei cloud) kubernetes cluster into my rancher (installed using rke and helm):

First I get the websocket: bad handshake error from cattle-cluster-agent.
After curl with the register wss endpoint, I got 400 with header too large error.
Pass it by modify the nginx configmap with large-client-header-buffers: 4 64k.

Then I got the http2: server sent GOAWAY and close the connection error.
Solved it by modify the nginx configmap with http2-max-field-size: 32k and http2-max-header-size: 64k

The problem seems to be the kubernets ca.crt file I have is 12kib (base64), which will be passed as http header, and exceed the header size limits of the nginx ingress controller.
Hope it helps other who faced the similar issues, thanks!

Great help,
thank you for your point, that do really save the time in the ha mode during the POC

I experienced same issue when migrated single-node rancher deployment to HA one. Thanks a ton!

Amazing! This work thanks you.

Reproduced in v2.3.4 with Rancher Ingress CA certificate approx. 8KB in size.

Just had this issue, too with Rancher 2.5.7 in RKE2 cluster and with a CA crt size of ~2k.. is there a general fix for this issue planned for RKE2 / Rancher ?


Confirm testing branch is equal to berkeley + pending PRs

Done: https://gist.github.com/georgeee/693950459af576f101bf56b2760b9b48


Add more context to cudf2lp errors

If we could inform the line of parser error, would be helpful.
ie: like when we forgot to add space after request:

ERROR: syntax error::

The cudf format makes it hard to add location tracking using standard lexer/parser generators. If there wouldn't be the line continuations with leading blanks, I would have implemented better error messages. I am sure it would be possible to implement something but it is not straight forward.


aquatone-gather

i cant use this one although i have nodejs installed on my system.

i solved it by installing nodejs-legacy.. however, i can't install the nightmare.js .. maybe i can install it manually.. can you help me?

tried installing latest node?
#Install NodeJS
curl -sL https://deb.nodesource.com/setup_8.x | bash -
apt-get install -qq -y nodejs

``


Document the docker image production

(especially the qw tagging magic)

Each merge on the main branch triggers the build of a new Docker image available on DockerHub at quickwit/quickwit:edge. Tagging a commit also creates a new image quickwit/quickwit: if the tag name starts with v* or qw*. The Docker images are based on Debian.


Orb Cuda computeAsync not using updated keypoints

I am using the ORB cuda CV2 detector. I need to filter the keypoint returned from detect and then compute the descriptors using the updated keypoints.
Unfortunately it seems that the computeAsync uses the keypoints received from the detect.
The number of keypoints returned from detectAsync is 3000 (max number defined in orb cuda create). The filtered keypoints is 1700 but the number of descriptors is 3000.
I know that I can use detectAndComputeAsync and filter after but is much time consuming.
I am using opencV 4.8.0 with python 3.8 on windows 64bit PC.
thanks,
Iliya
Code example
##################################
#detect keypoints
cuMat1 = cv2.cuda_GpuMat()
cuMat1.upload(image)
keypoints_detected_GPU = detector.detectAsync(cuMat1, mask=None)
keypoints_detected = detector.convert(keypoints_detected_GPU)
        #filter keypoints
        keypoints = FilterFunction(keypoints_detected )

        #upload filtered keypoints
        keypoints_np = np.array([
            [kp.pt[0] for kp in keypoints],  # x-coordinate
            [kp.pt[1] for kp in keypoints],  # y-coordinate
            [kp.response for kp in keypoints],  # response
            [kp.angle for kp in keypoints],  # angle
            [kp.octave for kp in keypoints],  # octave
            [kp.size for kp in keypoints],  # size
        ], dtype=np.float32)

        keypointsGpu = cv2.cuda_GpuMat()
        keypointsGpu.upload(keypoints_np)

        #Compute descriptor
        keypointsGpuCompute, descriptorsGpuCompute = detector.computeAsync(cuMat1, keypointsGpu)
        keypoints_np1 = keypointsGpuCompute.download()

        keypoints_list = []
        num_keypoints = keypoints_np1.shape[1]

        for i in range(num_keypoints):
            x = keypoints_np1[0, i]
            y = keypoints_np1[1, i]
            response = keypoints_np1[2, i]
            angle = keypoints_np1[3, i]
            octave = keypoints_np1[4, i]
            size = keypoints_np1[5, i]

            # Create cv2.KeyPoint object and append to the list
            kp = cv2.KeyPoint(x, y, size, angle, response, int(octave), -1)
            keypoints_list.append(kp)
        keypoints = keypoints_list

        #download descriptors
        descriptors = descriptorsGpuCompute.download()

This issue appears to be present in the underlying OpenCV CUDA ORB implementation and not only in the Python binding. There is an issue open for it on the OpenCV repo as well (https://github.com/opencv/opencv/issues/9151) that was opened in 2017.


Change/Update the amount of the products in the shop cart by typing in direct amount

As a User,
I need to be able to change the amount of product I added to the shop cart,
So that I could buy the desired amount of product.
Details and Assumptions
Assumption: Assume there is a product that I want to change its amount in the shop cart.
When a user adds a product to the shop cart, the default number would be 1. If users want to buy 5 of that product, they could directly change the number by typing in the amount in the amount box.
Acceptance Criteria
Given there is a product in the shop cart,
When a user modifies the amount of product by typing a number in the amount box,
Then the amount of product in the shop cart would change to the user's desired amount.

This ticket can be picked up when UI work is started


Update testcontainers-scala-kafka, ... to 0.40.1

Updates

com.dimafeng:testcontainers-scala-kafka
com.dimafeng:testcontainers-scala-scalatest

from 0.40.0 to 0.40.1.
GitHub Release Notes - Version Diff
I'll automatically update this PR to resolve conflicts as long as you don't change it yourself.
If you'd like to skip this version, you can just close this PR. If you have any feedback, just mention me in the comments below.
Configure Scala Steward for your repository with a .scala-steward.conf file.
Have a fantastic day writing Scala!

Ignore future updates
Add this to your .scala-steward.conf file to ignore future updates of this dependency:
updates.ignore = [ { groupId = "com.dimafeng" } ]

labels: test-library-update, early-semver-minor, semver-spec-patch, commit-count:1

Superseded by #115.


ponytest/test_helper: fix link, line width

ℹ This was missing a t in AmbienAuth.

good catch!
fyi, please put a [skip ci] in the commit title or the PR description to avoid running all jobs for docs changes only.

@mfelsche will do, next time 😉 thanks for the quick reaction 😃


Support for muxing AV1

WebCodecs supports encoding AV1. It would be nice if we could use that to generate WebM files with the AV1 codec. Currently it doesn't work.
It's hard to come by technical details about AV1 in WebM but the best reference I have found is: https://github.com/ietf-wg-cellar/matroska-specification/blob/master/codec/av1.md
This is apparently a de-facto standard for WebM too, as browsers support things like WebM files with AV1.
Here's a minimal repro:
videoTest.zip
This can successfully encode a VP9 video from a canvas. The only changes are to change the WebM codec_id to V_AV1 and configure WebCodecs to encode AV1. This fails with a few warnings and errors including:

AddGenerocFrame failed.
Failed to mux frame: -6
no video, no audio track
TypeError: Cannot read properties of undefined (reading 'dataEnd')

Repro'd. Yes it'd be good to support AV, I'll look into it when I get some time.

OK I got this working, just need to tidy it up before I push the changes. Might be a few days.

Sounds great, thanks for looking in to it!

Should be working now. @AshleyScirra let me know if you try it.

I tried it out and it works! Thanks, that's really cool.
I was looking at some diffs though and saw some code that looked odd around here: https://github.com/davedoesdev/webm-muxer.js/blob/main/webm-worker.js#L200
First I think there's a typo in the VP9 section - it reads metadata.video.chrome_subsampling whereas I think it was meant to be chroma_subsampling.
Secondly all the AV1 parameters are read off metadata rather than metadata.video as with VP9, so specifying the AV1 params in metadata.video will return undefined for all of the read values and coerce them all to 0.
Despite that it still works, but might be worth a look over that code.

Yes my bad, and it needs a test to vary the params

I've fixed those issues, appreciate the heads up.
I also added checking for different private data to the tests.

All looks good now. Thanks again!


Cannot make conditional validations to work

I have simple form and I want to validate participant2 params only if param[:single] is false. single is a boolean checkbox.
I had an ideas but cannot make this to work, could you help me with that?
require 'dry-types'
Dry::Types.load_extensions(:maybe)
module Types
  include Dry::Types.module

end

module Events
  module Competitions
    module SignUps
      class CreateForm < Dry::Validation::Schema::Form
        configure do
          config.messages_file = 'app/components/events/competitions/sign_ups/errors.yml'
          config.type_specs = true
        end

        define! do
          if optional(:single).value(:false?)
            required(:participant_name_2, Types::String).filled
          end

         required(:participant_name_1, Types::String).filled
         required(:terms_of_service, Types::Form::Bool).value(:true?)
        end
      end
    end
  end
end

sorry for reopening old issue, but how to achieve this behavior today, in version 1.6 ?

Check out docs on rules or ask a question on our forum with a sample piece of code.


about "API Proxying During Development" in docs

i think our docs made a mistake with describing the result about Proxying
in this docs
The above example will proxy the request /api/posts/1 to http://jsonplaceholder.typicode.com/posts/1.

maybe it should be
http://jsonplaceholder.typicode.com/api/posts/1
in docs from http-proxy-middleware
var express = require('express');
var proxy = require('http-proxy-middleware');

var app = express();

app.use('/api', proxy({target: 'http://www.example.org', changeOrigin: true}));
app.listen(3000);

// http://localhost:3000/api/foo/bar -> http://www.example.org/api/foo/bar

Except the example in the docs uses pathRewrite:
pathRewrite: {
  '^/api': ''
 }

sorry, forget it.
i'll close this


Update macOS-Development-Setup.rst

Add command to enable Command Line Tools to prevent error("xcode-select: error: tool 'xcodebuild' requires Xcode, but active developer directory '/Library/Developer/CommandLineTools' is a command line tools instance").

Since this trivial I'm just going to merge it. Unfortunately I don't have a mac to test it on.


Add more examples

It would also be great to have a demo link to see the functionality in action without needing to download and build, if possible!
The library looks very intriguing, thanks for putting it out there!


Add stake pool metadata to swagger.yaml

Relates to #1063.
Overview
Adds types, API JSON instances, and swagger spec for stake pool metadata.

bors r+

bors r+


chore(deps): upgrade dependencies

Upgrades project dependencies. See details in workflow run.

Automatically created by projen via the "upgrade-main" workflow

Codecov Report

Merging #402 (af7df48) into main (03aae66) will not change coverage.
The diff coverage is n/a.

@@           Coverage Diff           @@
##             main     #402   +/-   ##
=======================================
  Coverage   95.69%   95.69%
=======================================
  Files           1        1
  Lines         395      395
  Branches       53       53
=======================================
  Hits          378      378
  Misses         17       17

Continue to review full report at Codecov.

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How to contribute guide references requirements.txt, which is not in the repo

📓 Documentation Update
The contributing guide has an 'install requirements' step that references requirements.txt, but there is no requirements.txt at the top level of the repo.

The requirement.txtis not needed, they are mentioned in settings.ini file. I remebering cleaning this.

Yeah, that's the thing, we removed the usage of it but forgot to clean the documentation

Implemented in the PR #403


requesting an intra frame while recording

Hello Developers,
I want to request a key frame (intra frame) while video recording is in progress. What is the correct way of doing this?
Many thanks for answering this quite a basic question.

Currently picamera doesn't provide a means for doing this via it's API but there is an MMAL call for it. Frankly, I ought to add a method for this and use it in split_recording. I'll mark this as a future enhancement - not sure if I can squeeze it into 1.11, but I'll try.


[Question] How to deploy dashboard ?

Search before asking

[X] I had searched in the issues and found no similar issues.

Question
When I wanted to run and deploy dashboard on my computer, I had some problems.

After I studied the tutorial (https://eventmesh.apache.org/docs/instruction/runtime-with-docker), I deployed runtime in a virtual machine (it works fine. I wrote a demo based on the http protocol that also works well.)
Then I opened the dashboard project source code using the IDE (VS Code) and executed the command npm run dev.
Then use google browser to visit http://localhost:3000/ as the following picture shows.

I don't know how to do the next step, what is http://localhost:10106? What should I fill in?

Help me, thanks.

@HadesKing If you are using the 1.4.0 version of EventMesh Docker, you may have trouble connecting to the dashboard because the image does not expose port 10106 for admin access. The current solution is to build a Docker image using the latest EventMesh code and run the updated image, which should allow you to connect to the dashboard.

docker run on WSL.
The current dashboard does not load the admin address through the local configuration file, but the address can be modified in the Endpoint.tsx file.

Dockerfile
FROM openjdk:8u111-jdk

MAINTAINER mxsm<EMAIL_ADDRESS>
WORKDIR /data
RUN mkdir /data/app
ADD eventmesh-1.8.0-release.tar.gz  /data/app/eventmesh
WORKDIR /data/app/eventmesh/bin

EXPOSE 10000
EXPOSE 10105
EXPOSE 10106

ENV DOCKER true

CMD bash start.sh

Thank you for your reply!
What is it ? What files are in this package ?

@HadesKing Thanks for this documentation. In my case I have reproduce those steps but I have an error:

Is your Dockerfile is in the docker folder?

@HadesKing Thanks for this documentation. I am on Windows and in my case I have reproduced those steps but I have an error:

Is your Dockerfile is in the docker folder or in the root folder?

@xwm1992  Yes.
@HattoriHenzo you need make the tar.gz package and the dockerfile same directory.

@xwm1992 it works fine on my Windows pc. Thanks

Has anyone succeeded in making this work with an HTTPS connection? I deployed event mesh on K8S and exposed it over Istio. The API endpoints are working fine but it keeps on returning an HTTP 404 when I try to configure it in the dashboard.
Not sure what the problem is so before I debug it a bit more, I would like to know if others faced issues with this.
Bram

@brampurnot May you please provide some debug logs? It will be helpful.
Dashboard now only supports localhost connection. It will support connecting to any instance in the near future.

Hi @Pil0tXia ,
It's a bit strange but it actually works. I think something might be wrong with the error message in the dashboard. When you enter the URL and click on save, it shows the error as you can see on the screenshot:

However after a while, I noticed that the configuration and the metrics started populating so it seems to work. Could it be that the /client endpoint does not exist? Because that's the endpoint it calls when you click on save:

Bram

@brampurnot It is a known issue for us and we have already assigned a develeoper to resolve it. For now the only way to use the dashboard properly is to access it from a machine that has Runtime started.

Ok thanks for the info!


Add debug code to makeUnionType

This is to track down the caller that passes two structs with the
same name.

TBR
Landing when bot is happy

hmm... does not seem ready yet

PTAL


[ISSUE #8816] Fix log typo.

Which Issue(s) This PR Fixes

Fixes #8816

Codecov Report
All modified and coverable lines are covered by tests :white_check_mark:

Project coverage is 47.45%. Comparing base (11f0002) to head (f20e892).
Report is 1 commits behind head on develop.

Additional details and impacted files
@@              Coverage Diff              @@
##             develop    #8817      +/-   ##
=============================================
- Coverage      47.48%   47.45%   -0.04%
+ Complexity     11599    11591       -8
=============================================
  Files           1283     1283
  Lines          89911    89911
  Branches       11577    11577
=============================================
- Hits           42692    42664      -28
- Misses         41983    42013      +30
+ Partials        5236     5234       -2

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swamup talk link does not contain more information

I followed the
'For more information, see this talk.'
but I did not notice any new information or link to presentations or video at the destination:
https://swampup2017.sched.com/event/A6CW/distroless-docker-containerizing-apps-not-vms

@patflynn I'm not sure the video is online yet.

Aha. I'm just noticing the date on it.


Fix #815: CodeScanner: add start, stop

Fix https://github.com/primefaces-extensions/primefaces-extensions.github.com/issues/815

I bumped the pom.xml from ECMASCRIPT5 to ECMASCRIPT_2016 and it seems to be compressing now!  Let me test the showcase make sure nothing is broken.

Everything looking good so do an update and then a mvn clean install -Pcompress to test.

OK nope its breaking Layout and other components compiling for ECMASCRIPT_2016.  Can you just rewrite the one section:
 this.codeReader.listVideoInputDevices()
                .then((videoInputDevices) => {
                    var deviceId = videoInputDevices[0].deviceId;
                    $this.codeReader.decodeFromVideoDevice(deviceId, $this.video, (result, err) => {
                        if (result) {
                            handleResult(result);
                        }
                        if (err && !(err instanceof ZXing.NotFoundException)) {
                            console.error(err);
                        }
                    })
                })
                .catch((err) => {
                    console.error(err)
                });

I think that is the only thing failing.  Make sure to test by building with -Pcompress and testing the showcase.

Yeah, I'll rewrite it.

No worries I just checked in a fix.  I turned compression OFF for the codescanner so it just gets combined not compressed and its working.
                            <aggregations>
                                <aggregation>
                                    <inputDir>${resources.dir.compressed}</inputDir>
                                    <outputFile>${resources.dir.compressed}/codescanner/codescanner.js</outputFile>
                                    <withoutCompress>true</withoutCompress>
                                </aggregation>
                            </aggregations>

This fix seems to work for now.


Help wanted: update CEF version on Linux, help needed on OSX and Windows

The version of CEF on Linux is lagging behind and I would like to try to improve this situation.
To do so there are two ways:

fix the crash on quit on https://github.com/adobe/brackets-shell/pull/499
update the portion of cefclient used on the brackets shell which hopefully fix the crash

We tryed the first thing and asked for help without results.
Now I'm tryng the second way, but before I need some cleanup PR: I'm asking if someone of you could try to compile and run them on OSX and Windows and report the results.
If you will try, could you report in the PR mentioned which OS you used (Windows, OSX) and the compiler (GCC, Visual Studio 2015, ...)?
Thank you
PS: Here you can find how build the shell: https://github.com/adobe/brackets-shell/wiki/Building-brackets-shell

Could you look at https://github.com/adobe/brackets-shell/pull/561?

Ficristo, it is fantastic news you are working on this! Thanks so much! Is there any news on your progress?

@Learn-Digital-Audio I'll update this thread with any news.
Right now I have a couple of PR in the works, nothing big, but they are build one on top of each other.
So I need adobe/brackets-shell#561 before posting another one. And that PR has yet to be tested on OSX.
In general there is still a lot of work to do (especially since I'm not really a native developer)
Since Linux version is mostly community driven, as of now, if I receive more help I can do (a little) more.

Anyone know if there is a workaround? I'm running brackets on my desktop which is Ubuntu 14.04 without a problem. On my laptop with 16.04 I get the libgcrypt11 issue.

@kirkins have you searched it on other issues?
By the way there are some workarounds in #12380 and #10255 for the libgcrypt11 issue.

Another, but much more ambitious way, to fix all problems on Linux would be to fix this electron issue and switch to that shell.

As a work around I used the repackaged version of 1.6 found at the bottom of this page:
https://brbsix.github.io/2015/10/29/fix-missing-libgcrypt11-for-brackets-text-editor/

Another cleanup https://github.com/adobe/brackets-shell/pull/570 ready to be looked at.

https://github.com/adobe/brackets-shell/pull/571

I'm not sure if I still continue on this given that @zaggino has released brackets-electron.
See this comment for a bit more context: https://github.com/adobe/brackets/issues/10255#issuecomment-243854796

~~https://github.com/adobe/brackets-shell/pull/578~~
Merged

https://github.com/adobe/brackets-shell/pull/581

https://github.com/adobe/brackets-shell/pull/583

~~https://github.com/adobe/brackets-shell/pull/584~~
Merged

https://github.com/adobe/brackets-shell/pull/585
https://github.com/adobe/brackets-shell/pull/586

https://github.com/adobe/brackets-shell/pull/588

There are still some problems, but at least now I can run the application on Linux with the latest CEF.
https://github.com/adobe/brackets-shell/pull/589

https://github.com/adobe/brackets-shell/pull/591

With #562 and #589 I can compile and run master on Linux, I don't know how much is stable but could be a start.
That said this work was too much time consuming for me so I gave up.
For people on Linux the best alternative is simply to use brackets-electron.