Landingpage not accessible (KP in Azure)

Issue Description
Hello, I'm really now to Linux and KP and hope you can help. I created a VM in Azure. The installation went well. I can create a campaign and mail is being sent.
However, I'm not able tot create a landing page. I copied the "WWW" contents from a KP website template to /var/www and checked the server_config file. I also set require_id to false, but i still get ERR_CONNECTION_TIMED_OUT message. Also testing te url in the client results in an error message.
Should I be able to reach my landing page just by typing my IP adress and '/login' (The landing page of the template? (Credentials/Anti-virus_update)
Reproduction Steps

Try to reach my landing page

Environment Details
Host OS: Ubuntu 18.04 LTS in Azure
King Phisher Version: Latest from Github
Error Details / Stack Trace
There are no recent entries in the log (4/6/2018)
2018-05-31 20:04:29,682 KingPhisher.GeoIP                                  WARNING  the specified geoip database does not exist, downloading a new copy
2018-05-31 20:27:32,246 KingPhisher.Server.WebSocket.EventPublisher        WARNING  there was a socket error while processing web socket messages
2018-05-31 20:27:32,246 AdvancedHTTPServer                                 WARNING  IOError encountered in finish_request
2018-05-31 21:50:35,698 KingPhisher.Server                                 WARNING  processing shutdown request
2018-06-01 05:13:54,348 KingPhisher.Server                                 WARNING  processing shutdown request
2018-06-01 09:25:19,170 KingPhisher.Server                                 WARNING  processing shutdown request
2018-06-01 17:27:24,712 KingPhisher.Server                                 WARNING  processing shutdown request
2018-06-01 19:55:51,865 KingPhisher.Server.WebSocket.EventPublisher        WARNING  there was a socket error while processing web socket messages
2018-06-01 19:55:51,865 AdvancedHTTPServer                                 WARNING  IOError encountered in finish_request
Thanks!
Tim.

Couple of things. If you changed the require_id option to false that should help you trouble shoot the issue but the server must be restarted for the change to take affect. You didn't mention if you did this or not, but you'll need to restart the service whenever you make server config changes.
Secondly, what exactly are you seeing when you do try to visit the landing page? Does the connection fail or do you see a white 404 page saying the content wasn't found? Also check the server logs as you visit the landing page to see if anything shows up. You can increase the server verbosity by following these steps in the FAQ.

Thanks for your reply! I restarted the server, but doesn't help.
The server logs doesn't show information when visiting the landingpage.
When i visit the page I see a 504 Gateway time out. I cannot ping my Azure IP-adres either. It looks like the Azure machine is not reachable, but I can connnect with the King Phisher Client from my Windows Machine.

This is the result when I try to generate verbose log output:
./KingPhisherServer -f -L DEBUG server_config
DEBUG    king phisher version: 1.11.1 (rev: f6a12c21ae55) python version: 3.6.5
ERROR    failed to bind server to address (socket error #98)
ERROR    socket error #98 (Address already in use)
Traceback (most recent call last):
File "/opt/king-phisher/king_phisher/server/build.py", line 145, in server_from_config
server = KingPhisherServer(config, plugin_manager, handler_klass, addresses=addresses, ssl_certfile=ssl_certfile, ssl_keyfile=ssl_keyfile)
File "/opt/king-phisher/king_phisher/server/server.py", line 868, in init
super(KingPhisherServer, self).init(handler_klass, *args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/advancedhttpserver.py", line 1780, in init
server = server_klass((address[0], address[1]), handler_klass, config=self.__config)
File "/usr/local/lib/python3.6/dist-packages/advancedhttpserver.py", line 703, in init
super(ServerNonThreaded, self).init(*args, **kwargs)
File "/usr/lib/python3.6/socketserver.py", line 453, in init
self.server_bind()
File "/usr/local/lib/python3.6/dist-packages/advancedhttpserver.py", line 771, in server_bind
super(ServerNonThreaded, self).server_bind(*args, **kwargs)
File "/usr/lib/python3.6/http/server.py", line 136, in server_bind
socketserver.TCPServer.server_bind(self)
File "/usr/lib/python3.6/socketserver.py", line 467, in server_bind
self.socket.bind(self.server_address)
OSError: [Errno 98] Address already in use
CRITICAL server failed to build with error: socket error #98 (Address already in use)
INFO     unloading 0 plugins
g.ym

Ok so the 504 error would not be King Phisher, it wouldn't generate that. I'm not sure what to tell you about that but I'd guess it's an Azure configuration issue.
The error in your second log is showing that something is already running and it can't start the server. This would be the case if you started a second instance of King Phisher while another one was still running.

You were right. It was an Azure configuration issue. Got it working now by opening some ports on the Azure VM. Thanks for pointing me in the right direction!

Hooray, that's fantastic news. If it's not too much trouble, could you provide us a description of the changes you had to make and where we could find them? We could then add it to the wiki to help the next person that has the same problem.


Automated cherry pick of #2762: lbagents: deploy: 检查服务账号有效性

Cherry pick of #2762 on release/2.12.
#2762: lbagents: deploy: 检查服务账号有效性

Codecov Report

:exclamation: No coverage uploaded for pull request base (release/2.12@f91b7d3). Click here to learn what that means.
The diff coverage is 0%.

@@              Coverage Diff               @@
##             release/2.12   #2763   +/-   ##
==============================================
  Coverage                ?   7.23%
==============================================
  Files                   ?     472
  Lines                   ?   74429
  Branches                ?       0
==============================================
  Hits                    ?    5384
  Misses                  ?   68593
  Partials                ?     452

Flag
Coverage Δ

#aFlag
7.23% <0%> (?)

Impacted Files
Coverage Δ

pkg/compute/models/loadbalanceragents_deploy.go
0.38% <0%> (ø)

Continue to review full report at Codecov.

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Δ = absolute <relative> (impact), ø = not affected, ? = missing data
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/lgtm

/approve


Update index.ts

こっちも！

あ、こっちに投げられてたんですね。同じ機能の修正なので向こうのプルリクにまとめてください！

Codecov Report

Merging #81 into master will not change coverage.
The diff coverage is n/a.

@@           Coverage Diff           @@
##           master      #81   +/-   ##
=======================================
  Coverage   48.74%   48.74%
=======================================
  Files          36       36
  Lines        3990     3990
  Branches      787      787
=======================================
  Hits         1945     1945
  Misses       1625     1625
  Partials      420      420

Continue to review full report at Codecov.

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Δ = absolute <relative> (impact), ø = not affected, ? = missing data
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⚠ Jenkins Plugins Artifactory has degraded performance

In f937556, Jenkins Plugins Artifactory (https://get.jenkins.io/plugins/artifactory/) experienced degraded performance:

HTTP code: 200
Response time: 1203 ms

Resolved: Jenkins Plugins Artifactory performance has improved in a1321a2 after 56 minutes.


click event was triggered inside container when i moving element

i has a element inside the container, and i bind a click event on it. when i draging the container, the click event  was triggered. i has tried add a click event on the container and pass the $event to the handle function, but it not work. can anybody help me?

Please provide a basic example of the issue using https://jsfiddle.net/ or https://codepen.io/


Custom form data as HStore

If we upgrade to Django 1.8 (#443) and mandate PostgreSQL, we can use Django's new HStoreField for storing custom form data, which would be awesome and also would let us filter records by custom data using the special key lookups.

(Might also consider using the new DateRangeField for our date ranges, though that might not actually work that well considering how we treat dates and housing.)

I am 100% okay with us mandating Postgres. Not sure if that would work well for our date ranges - but it would be a good opportunity to re-evaluate our date handling. :-p


Suspicious login history

Summary
There is login history from <IP_ADDRESS> (localhost)

Expected Behavior
NO LOGIN FROM LOCALHOST
Actual Behavior
LOGIN FROM LOCALHOST
Steps to Reproduce

LOG INTO MY ACCOUNT

Environment
Instance: https://misskey.xyz/
OS: Windows10 Version 1803 (OS build: 17134.191)
Browser: Opera Beta Version 55.0.2994.34

There is no privacy policy, and there is no report huh?


Support SSH tunneling.

The only missing feature on all the vim SQL plugins is SSH tunneling. All modern database clients (e.g. DBeaver, sequelpro) allow you to add SSH tunneling options to the database connections.
I can create the tunnels locally on my machine and then configure SQHell to use those tunnels as connection endpoint but having the plugin manage it would be very useful.

Hey @hsanson
I'll look into this, thanks!


CUDA 9RC + cuDNN7

This is an unofficial and very not supported patch to make it possible to compile TensorFlow with CUDA9RC and cuDNN 7.  This should also works for CUDA 8 + cuDNN 7 but skip the EIGEN patch if using CUDA 8.
During testing on V100 (Volta) and ResNet-50 FP32  using CUDA 9RC + cuDNN 7 was significantly faster than CUDA 8 + cuDNN 6, which was not a surprise.  I am about to test on P100s.  I am sharing this patch informally so those that are interested can play with cuDNN 7 as well as CUDA 9RC before we have the official release.  As we have more interesting code, e.g FP16 models, I will share it in this issue.   I expect NVIDIA will start to submit official cuDNN 7 patches very soon.
Note:  This patch may work on more recent versions of TensorFlow but it will likely bit rot so keep that in mind.  Apply the cuDNN 7 patch and then fast-forwarding the branch might be the best approach.  My git skills are not strong so do what you think is best.

Download the patches

0001-CUDA-9.0-and-cuDNN-7.0-support.patch
eigen.f3a22f35b044.cuda9.diff

Clone the tensorflow repo
https://github.com/tensorflow/tensorflow.git

Checkout the revision that the TensorFlow patch can apply to:
git checkout db596594b5653b43fcb558a4753b39904bb62cbd~

Apply the TensorFlow patch:
git apply ~/Downloads/0001-CUDA-9.0-and-cuDNN-7.0-support.patch

Run ./configure. When it asks for the CUDA version, put 9.0. When it asks for the cudnn version, put 7.0.1 (Entering '7' worked fine for me). Make sure you put the paths to the right Cuda and cudnn versions and have your ldconfig or LD_LIBARY_PATH set to point to the CUDA 9 folder.
./configure

Attempt to build TensorFlow, so that Eigen is downloaded. This build will fail.
bazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package

Apply the Eigen patch:

    cd -P bazel-out/../../../external/eigen_archive
    patch -p1 < ~/Downloads/eigen.f3a22f35b044.cuda9.diff

Build TensorFlow successfully

    cd -
    bazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package

I have run this process myself on Ubuntu 14.04 with Python 2.7.
Thank for NVIDIA for the early patch and @reedwm who created most of these instructions.
If you are using Python 2.7 and gcc 4.8+ here is a .whl where I followed the instructions above and one with CUDA 8 and cuDNN 7 which I have yet to test.  Stress again, this was created by me and not the TensorFlow build team.  My/our goal is to engage with anyone that wants to try this out and try to have a little fun.  :-)

When I run cd -P bazel-out/../../../external/eigen_archive, I get No such file or directory. bazel-out is not a directory in my tensorflow dir. I followed the above steps exactly.

Also, this is the specific build error I receive:
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 "/home/tanmay/tensorflow/third_party/gpus/cuda_configure.bzl", line 1039
		_create_local_cuda_repository(repository_ctx)
	File "/home/tanmay/tensorflow/third_party/gpus/cuda_configure.bzl", line 976, in _create_local_cuda_repository
		_host_compiler_includes(repository_ctx, cc)
	File "/home/tanmay/tensorflow/third_party/gpus/cuda_configure.bzl", line 145, in _host_compiler_includes
		get_cxx_inc_directories(repository_ctx, cc)
	File "/home/tanmay/tensorflow/third_party/gpus/cuda_configure.bzl", line 120, in get_cxx_inc_directories
		set(includes_cpp)
depsets cannot contain mutable items
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 "/home/tanmay/tensorflow/third_party/gpus/cuda_configure.bzl", line 1039
		_create_local_cuda_repository(repository_ctx)
	File "/home/tanmay/tensorflow/third_party/gpus/cuda_configure.bzl", line 976, in _create_local_cuda_repository
		_host_compiler_includes(repository_ctx, cc)
	File "/home/tanmay/tensorflow/third_party/gpus/cuda_configure.bzl", line 145, in _host_compiler_includes
		get_cxx_inc_directories(repository_ctx, cc)
	File "/home/tanmay/tensorflow/third_party/gpus/cuda_configure.bzl", line 120, in get_cxx_inc_directories
		set(includes_cpp)
depsets cannot contain mutable items
INFO: Elapsed time: 4.619s
FAILED: Build did NOT complete successfully (0 packages loaded)
    currently loading: tensorflow/tools/pip_package```

One more note: When I install the whl file you've provided for CUDA 9 and cuDNN 7, I run import tensorflow, but it doesn't show me the GPU initialization. When I run a simple Keras NN, it looks like it's running on CPU - it refuses to run on GPU.

Thanks Toby. Looking forward to the performance numbers on P100.

Quick Question (sorry if it's not the right place to ask, and if I'm missing something really obvious on NVIDIA's Website): Do CUDA 9 & cuDNN 7 provide better performance on non-Volta (Pascal) GPUs? Are these noticeable when training Neural Nets?

I'm seeing a bit of a different result. With CUDA 8 & cuDNN 6, training a simple but deep Keras model took me 225 seconds, but with CUDA 9 & cuDNN 7, it took 243 seconds (on a P100).

My test was CUDA 8 + cuDNN 7 and CUDA 8 + cuDNN 6.  For CUDA 9RC + cuDNN 7, testing not apples to apples as I was using a slightly different versions of TF than I did for CUDA 8 + cuDNN 7 but I think likely perform the same, compared to CUDA 8 + cuDNN 7 Resnet-50 was about the same and I saw a regression with VGG16  at 8xP100 SMX running replicated nccl.  With 1 GPU I saw some differences but not large enough to be certain.  There are things to sort out

Is there a patch for CUDA 8 + cuDNN 7 as well?  I was planning on holding off on 9 for a while, but I'm very interested in cuDNN 7 support.

 I was planning on holding
off on 9 for a while, but I'm very interested in cuDNN 7 support.
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@terrylewis60 It looks like you are subscribed to the issue.  If you unsubscribe you will not get notifications.  I would also greatly appreciate it if you removed the profanity and childish name calling.  You are free to express yourself, although you are likely outside the community guidelines.  It just seems rather silly to complain about something you subscribed to.  Thank you.

Any updates on this? While the patch works with the specific tf-commit (also with Python3), the tf version says version is 1.2.1-rc1, so rather old (that version was incompatible with current bazel 0.5.3, so bazel needs to be downgraded in order to build the tf-patch version.) The cudnn 7 changes so far are not included in HEAD. Any possibility for a branch that supports the most recent released upstream software/drivers?

The commit point is Aug 1st, which is partially post 1.3. The patch works
with the 1.3 branch/tag as well.  The PRs are in progress from NVIDIA.
The eigen PR for CUDA 9RC is in review by the external eigen team.
On Aug 26, 2017 1:45 AM, "Dominik Schlösser"<EMAIL_ADDRESS>wrote:

Any updates on this? While the patch works with the specific tf-commit
(also with Python3), the tf version says version is 1.2.1-rc1, so
rather old (that version was incompatible with current bazel 0.5.3, so
bazel needs to be downgraded in order to build the tf-patch version.) The
cudnn 7 changes so far are not included in HEAD. Any possibility for a
branch that supports the most recent released upstream software/drivers?
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Bazel 5.3 had issues which as silly as it sounds were beyond our control
and immediate knowledge.  Our build servers do not run latest bazel.  I
also like to stay a step behind.  I think I was the first to hit the issue
having accidentally upgraded 1 hour after the release.  I wasted half a
day, which meant making up that time into the night.  I understand the
frustration.
On Aug 26, 2017 6:22 AM, "Toby Boyd"<EMAIL_ADDRESS>wrote:
The commit point is Aug 1st, which is partially post 1.3. The patch works
with the 1.3 branch/tag as well.  The PRs are in progress from NVIDIA.
The eigen PR for CUDA 9RC is in review by the external eigen team.
On Aug 26, 2017 1:45 AM, "Dominik Schlösser"<EMAIL_ADDRESS>wrote:

Any updates on this? While the patch works with the specific tf-commit
(also with Python3), the tf version says version is 1.2.1-rc1, so
rather old (that version was incompatible with current bazel 0.5.3, so
bazel needs to be downgraded in order to build the tf-patch version.) The
cudnn 7 changes so far are not included in HEAD. Any possibility for a
branch that supports the most recent released upstream software/drivers?
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The patch almost works with r1.3:  it fails on tensorflow/workspace.bzl:
error: patch failed: tensorflow/workspace.bzl:664
error: tensorflow/workspace.bzl: patch does not apply
[cmake anyone?  ;-) ]

I tested the patch on the 1.3 TAG v1.3.0-rc2 because I was doing
performance tests with the various versions.  With the env from the
original post.  The 1.3 final is not that different, you can look at the
log but I am fairly sure it was a few small cherry picks.  I am a bit
surprised it did not apply.  You can always apply to the TAG and then
merge.  If the merging does not get too messy you could merge all the way
to head.  Then make a branch, share it, and bask in some tasty karma.  :-)
On Sat, Aug 26, 2017 at 7:11 AM, Dominik Schlösser<EMAIL_ADDRESS>
wrote:

The patch almost works with r1.3: it fails on tensorflow/workspace.bzl:
error: patch failed: tensorflow/workspace.bzl:664
error: tensorflow/workspace.bzl: patch does not apply
[cmake anyone? ;-) ]
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For now I patched the patch so that it works with release r1.3:
0002-TF-1.3-CUDA-9.0-and-cuDNN-7.0-support.patch.txt
(note: the attached patch-file needs to be renamed, the extension .txt needs to be removed.)
This patch will only work with r1.3.  (workspace.bzl changes almost on a daily basis, which breaks the patch).

Big thank you.
On Aug 26, 2017 9:20 AM, "Dominik Schlösser"<EMAIL_ADDRESS>wrote:

For now I patched the patch so that it works with release r1.3:
0002-TF-1.3-CUDA-9.0-and-cuDNN-7.0-support.patch.txt
https://github.com/tensorflow/tensorflow/files/1253794/0002-TF-1.3-CUDA-9.0-and-cuDNN-7.0-support.patch.txt
(note: the attached patch-file needs to be renamed, the extension .txt
needs to be removed.)
This patch will only work with r1.3. (workspace.bzl changes almost on a
daily basis, which breaks the patch).
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Could somebody specify the required bazel version for this patch to work on r1.3?

I've built with tf r1.3, 0002-patch and bazel 0.5.3 (latest bazel release). The original 0001-patch (first post) did not build with bazel 0.5.3, but required a downgrade to 0.5.2.

Thank you @domschl !

Hi @tfboyd, how does the following command work?
cd -P bazel-out/../../../external/eigen_archive
Because there is no "external/eigen_archive" folder in tensorflow repo. There is only "third_party/eigen3/Eigen", so this command and your next patch command does not work.

You need to try to build tf once. That pulls the external dependencies like eigen3.

The real PRs are very close and I think some people have used those to get
things working as well.  Just an FYI. When they are all approved I will
update and close this issue.  Dominik is correct you have to build (then
fail) then do the Eigen step then build again.
On Wed, Aug 30, 2017 at 12:16 PM, Dominik Schlösser <
<EMAIL_ADDRESS>wrote:

You need to try to build tf once. That pulls the external dependencies
like eigen3.
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Hi @tfboyd, I tried to use your patches, but after the first build step, there is no "external/eigen_archive" folder at all. It means the command "cd -P bazel-out/../../../external/eigen_archive" failed. So when this folder is generated?

@hfutxrg  it must be broken now.  It will be in the official build in a few weeks or less.  Step #6 that mentions EIGEN is what creates that folder.  But if it did not work for you that is ok, this was for fun and it worked for a few people.

Hi @tfboyd, how did you benchmark the performance of P100 and V100? I used the benchmarks mentioned in https://www.tensorflow.org/performance/benchmarks. But it seems the performance results are incorrect. With real imagenet dataset, I got 905 images/sec with 1 P100 and 1234 images/sec with 4 P100. But that benchmark website has 219 images/sec with 1 P100 and 852 images/sec. So there are huge difference here.
My command to run the benchmark is:
 export CUDA_VISIBLE_DEVICES=0
python tf_cnn_benchmarks.py \
 --model_name=resnet50 \
 --batch_size=64 \
--num_batches=2000 \
 --num_gpus=1 \
 --data_dir=/cm/shared/scratch/database/tensorflow \
--data_name="imagenet"
Is there anything wrong with my command? Also that benchmark website has similar performance with synthetic data, but if I remove "--data_dir", the performance is 12866 images/sec which is incorrect. Any comments and suggestions are appreciated!
Thanks,
Rengan

You are running the trivial model not resnet50.   --model=resnet50  I was totally confused for a second.  I recently retested and has NVIDIA test on TensorFow 1.3 and there numbers are slightly different now on the DGX-1 but no more than 3-4%.  We made a few tweaks to the model as we worked to ensure it converged to match the paper.  Nothing huge just small changes.  You can also use the benchmarks from the staging branch but it requires 1.4.   tf_cnn_benchmarks does not worry about backwards compatibility as it is kind of a sandbox, just FYI.  I am working on a better way to publish updated code and accept external PRs more often.  It is more work than it looks.

Thanks @tfboyd.  After I changed to the correct option, the benchmark works correctly now.
Another question, how do we run the benchmark with 16-bit floating points (float16)? I didn't see any options to enable that.

fp16 is not ready just yet.  We still need batch_norm updated to support it and another push from the script.  Give me a few more weeks and I should have everything you need.  I (and we) very much want to share the code with you as soon as it functions even if it has some issues.  It will also be hard to use this patch as it is getting very stale so if all goes well in a few weeks, I hope less, we can have everything (Cuda9RC, cuDNN 7 and fp16 with something to play with) official.

Hi @tfboyd , in the benchmark code, one way for variable update is called "independent". This should never not be used in real training, right? Because in real training, all GPUs have to communicate each other to update the gradient. The page https://www.tensorflow.org/performance/performance_models also didn't mention "independent" variable update.

@hfutxrg Correct independent means graidents are not shared between the towers.  Only useful for testing hardware and no a "legit" way to test actual performance.
FYI, it looks like cuDNN 7 is mostly merged.  I have not tested it myself yet but just FYI.  Trying to keep the thread updated as I have time.
https://github.com/tensorflow/tensorflow/pull/12503

Will this be 1.4.0?
estimated release date? please?

1.4 binaries will most likely be CUDA 8 + cuDNN 6.  I suspect (which is not an estimate) that 1.5 will be built with CUDA 9 + cuDNN 7.  I am testing the build now and working through some issues with CUDA 9 and cuDNN 7.  Maybe I am doing something wrong, I will find out tomorrow.  I really want to have it CUDA 8 and cuDNN 7 building in 1.4 source but if we miss the release train we miss it.  There is no date for 1.4 but I suspect it will RC before the end of October.  It is being gated on finishing some features but again it most likely will not have prebuilt binaries for CUDA 9 and cuDNN 7.

@tfboyd Thanks for your great help for integrating cuda rc 9 + cudnn 7.  So, if I have a V100 machine, I should be able to compile tensorflow from the source codes (1.3 branch) based on your patches above and enjoy the speed up from V100, right?
Btw, will your patch work with the official cuda 9 (release few days ago) and cudnn 7? Or you will update the patch or you already did that?
Thanks again.

There are always errors for me to command eigen-...-diff file.
I can't command the code cd -P bazel-out/../../../external/eigen_archive correctly. I tried when terminal was in tensorflow file and tensorflow1.3 file. But it doesn't work. Could you please tell me what
bazel-out/../../../  means and what the file` /../../../  is? Can I just substitute the exact file of bazel-out/../../../ ?
I had tried to use this way: command cd -P bazel-out/(+the exact position of )external/eigen_archive  and then I execute the patch, the errors just happened at each @@ -236,7 +236,7@@(for example, others are just the same form) and the program says can't find file to patch at input line xx.
Do you know why these happen?

@tfboyd
There are always errors for me to command eigen-...-diff file.
I can't command the code cd -P bazel-out/../../../external/eigen_archive correctly. I tried when terminal was in tensorflow file and tensorflow1.3 file. But it doesn't work. Could you please tell me what
bazel-out/../../../ means and what the file` /../../../ is? Can I just substitute the exact file of bazel-out/../../../ ?
I had tried to use this way: command cd -P bazel-out/(+the exact position of )external/eigen_archive and then I execute the patch, the errors just happened at each @@ -236,7 +236,7@@(for example, others are just the same form) and the program says can't find file to patch at input line xx.
Do you know why these happen?

@tfboyd
There are always errors for me to command eigen-...-diff file.
I can't command the code cd -P bazel-out/../../../external/eigen_archive correctly. I tried when terminal was in tensorflow file and tensorflow1.3 file. But it doesn't work. Could you please tell me what
bazel-out/../../../ means and what the file**/../../../** is? Can I just substitute the exact file of bazel-out/../../../ ?
I had tried to use this way: command cd -P bazel-out/(+the exact position of )external/eigen_archive and then I execute the patch, the errors just happened at each @@ -236,7 +236,7@@(for example, others are just the same form) and the program says can't find file to patch at input line xx.
Do you know why these happen?

@zhangb223    @chunfuchen
I suggest taking TensorFlow from head and not using this patch.  I will update the original comment.
CUDA 9 with no other changes speeds up V100s over CUDA 8 in my testing of ResNet50 FP32.  I hope to share the teams FP16 code and some results but we are still working through a variety of items.  Technial the tf_cnn_benchmarks.py code currently published will do FP16 but I want to make sure it is running great with great convergence (high accuracy that matches or almost matches the FP32, e.e. ~75%) and scaling.

I marked this closed but as always (or mostly) I am happy to answer questions and share status when I have time.

Hello @tfboyd
I have another question. I don’t know what bazel-out mean. Is it a path or a command? I can’t find bazel-out in the bazel package.
In addition, when I execute ( cd -P tensorflow1.3/lib/python2.7/.../external/eigen_archive ) I can enter the path. However, if I add bazel-out after -P, the terminal report: No such file or dictionary. So I want know the function of bazel-out.
Thanks!

Hello @tfboyd
I want to offer more information about the first error . When I execute ( cd -P tensorflow1.3/lib/python2.7/.../external/eigen_archive ) I can enter the path. However, if I add bazel-out after -P, the terminal reports: No such file or dictionary. So I want to know the function of bazel-out.
Is it a path or a command? I can’t find bazel-out in the bazel package.
Thanks!

Hi, I am a newbie. I want some help regarding tensorflow installation.
My system has
OS - Windows 10
GPU - 940MX
I installed CudaTK 9 and updated driver and CuDNN 7 sucessfully.
even in Anaconda, tensorflow installation is also successful it shown. but still missing "dll" issues remain same.
I tried to install CUdatk8 but its telling h/w not supported for it. please help me with a suitable link or some suggestions so that I can make it work.
PC is Lenovo IdeaPad 320.
Thank you for your kind support

Hi, my gcc version is 5.4.0 (Ubuntu 16.04), so I compile and install gcc 5.3.0 into /opt/gcc-5.3.0 ( gcc dependentments have already write into /etc/ld.so.conf.d/gcc-5.3.0.conf, and $HOME/.bashrc except gcc), and I have soft connection all /opt/gcc-5.3.0/bin to /usr/bin except the file whose name begins with 'x86_64-unknown-linux-' because I do not know the name in /usr/bin.
So that, I fail bazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package with gcc: Error trying to exec 'cc1plus': Execvp: No Such File or Directory

@Arnab0989 did you find some solution? Because I have exactly the same situation and I didn't find solution except that to install the old nvidia driver + cudnn6 + cuda8

Looking through the benchmark code and it mentions NCCL not supporting FP16, however Caffe has NCCL with FP16 support.
Will this be added to Tensorflow for 1.5? - I'm currently getting worse results with FP16 on internal results. Current Hypotheses are it is either NCCL or TF.Layers. However, I've implemented a custom getter which I thought would circumvent the tf.layers issues.

@joeyearsley
Can you provide more specifics?  I assume V100 and where you are seeing FP16 worse than FP32 as far as throughput.  And single or multi-gpu as well as which CUDA and cuDNN.  That will help me to understand if and where the problem might be as well as get the right assistance and provide useful guidance.

@Arnab0989 and @plgkm6  I suggest opening another issue to get windows help.  This was just tracking getting CUDA 9 and cuDNN 7 support in the source.  I know that is a little nuanced but you will get more attention for the windows problem outside this thread with a separate issue.  I believe the team is still working though build issues with windows but I am not positive.

@tfboyd Thanks for the quick reply, it's an internal benchmark which mirrors the TF benchmark but with medical images.
GPUS: 8 P100s
CUDA: 9
cuDNN: 7
MGPU type: NCCL Replicated
General computation time, for example I can double by batch size to 8 (max that fits in memory) but I only get 85 img/s vs 100 img/s with FP32 and batch size of 4.

@joeyearsley
I would not advise FP16 on P100s for training unless it happens to work better for your situation.  It is ok for testing that it works.  I believe the issues is the V100 has the ability to do FP16 but do the accumulation in FP32.  P100 cannot do that so you have to cast and do other "stuff".  Apologies for being vague I am sharing what I have heard but I do not have direct experience.  I am pretty sure I am generally correct but my details are likely off in some way.  I will open an issue in the benchmark repo and link it here later today.  Maybe I can get more data from the perf team.

/CC @chsigg, who is planning on implementing FP16 nccl support. Not sure if this will be in by TF1.5 though.
@joeyearsley As @tfboyd mentioned, P100s do not get a huge gain from fp16, and there is currently no nccl support for fp16. I don't think tf.layers would slow things down in fp16. It might be nccl-related; you could try running without nccl for both fp16 and fp32 and compare.

@tfboyd @reedwm Thank you for the quick replies!
Having ran that test (with and without FP16 on a PS) it seems that it is mostly the casting and other stuff.
Once again, many thanks!


Break up the examples page into 4 pages

We should break up the examples page into 4 separate pages (one for each section on the page).
https://www.kubeflow.org/docs/examples/resources/
Also add an _index page, following the pattern of the other doc directories.
This will solve 2 problems:

The examples page is getting a little long.
The link to the examples page in the left-hand nav looks odd, because all other links are to sections and are therefore bold.

/cc @mameshini
/cc @DanSanche
This is a great issue for a tech writer to handle during the Kubeflow Doc Sprint. I'll publicize it as such in Slack.

I think @DanSanche is working on this issue. @DanSanche please would you confirm? If not, I may have a tech writer to work on it.

I believe @mameshini was looking at this? I'm happy to review, but not looking at this currently

/assign @mameshini


Branch list merging

## Overview
Fixes #

Closes #{issue number}
Description

Release notes

Notes:

P%N%

Gifs


Set initial view should be configured per each hotspot in that panorama

In my case the "initial view" should be a different view depending on where I am coming from to the panorama.
Assume there are 3 rooms - A B C and all have hotspot, both A -> B -> C and C -> B -> A
When going into B it matters if you come from A or from C therefore the initial view should be correlated to the used hotspot.

You are creating your viewer from Marzipano tools? Open data.js, and you will see each scene has a linkHotspots configuration. For example:
"linkHotspots": [         {           "yaw": -0.6333799649634333,           "pitch": 0.04287834953571057,           "rotation": 11.780972450961727,           "target": "1-angra"         } ]
You would need to manually adjust the yaw, pitch and rotation per scene. I'm pretty sure you can't adjust the view on a per-hotspot basis directly from the marzipano tools.

It's not possible to customize the view on a hotspot basis using the Marzipano Tool, but it can be done using the Javascript API. See this discussion, which is about doing something similar.

ok... from what I understand it is not possible - reason why I posted here in the first place.
Wouldn't it be easier if you made an extra option in the GUI that would end shipping the proper index.js depending on what the user wants ? "Keep orientation while surfing hotspots?" Yes/No
This would be very hackish of me : to update the javascript from the bundle generated by marzipano for every tour I create.

I had the problem and created a little tool to help me: marzipano-hotspot-view (github)


[fix] pass along set-cookie headers during SSR

fixes https://github.com/sveltejs/kit/issues/1198

Thanks Ben!


feat: add password confirm box at signup

This adds a "Confirm password" box in the join screen. As you might know, this is helpful for users who might mistype their password when making their account.

What happened to this PR @tnix100 @CST1229

What happened to this PR @tnix100 @CST1229

Will merge soon.

Closing as this has been re-implemented on the develop branch. I didn't merge because of too many merge conflicts, but I added you as a co-author on my commit so you were still credited.


[Snyk] Upgrade: @nestjs/common, @nestjs/core, @nestjs/platform-express

This PR was automatically created by Snyk using the credentials of a real user.Snyk has created this PR to upgrade multiple dependencies.
👯 The following dependencies are linked and will therefore be updated together.

:information_source: Keep your dependencies up-to-date. This makes it easier to fix existing vulnerabilities and to more quickly identify and fix newly disclosed vulnerabilities when they affect your project.

Name
Versions
Released on

@nestjs/commonfrom 9.0.0 to 9.0.11
11 versions ahead of your current version
a month agoon 2022-08-16

@nestjs/corefrom 9.0.0 to 9.0.11
11 versions ahead of your current version
a month agoon 2022-08-16

@nestjs/platform-expressfrom 9.0.0 to 9.0.11
11 versions ahead of your current version
a month agoon 2022-08-16

Release notes

Package name: @nestjs/common

9.0.11 - 2022-08-16v9.0.11
Bug fixes

microservices

#10122 fix(microservices): close underlying server once if already terminated (@ kamilmysliwiec)
#10110 fix(microservice): grpc-client serialize error (@ gilre)

common

#10106 fix: add a check if the res is destroyed before sending response (@ jmcdo29)

Dependencies

#10097 chore(deps-dev): bump mongoose from 6.5.1 to 6.5.2 (@ dependabot[bot])
#10099 chore(deps-dev): bump apollo-server-express from 3.10.0 to 3.10.1 (@ dependabot[bot])
#10102 chore(deps-dev): bump amqp-connection-manager from 4.1.5 to 4.1.6 (@ dependabot[bot])
#10103 chore(deps-dev): bump @ nestjs/typeorm from 9.0.0 to 9.0.1 (@ dependabot[bot])
#10116 chore(deps-dev): bump imports-loader from 4.0.0 to 4.0.1 (@ dependabot[bot])
#10118 chore(deps-dev): bump @ grpc/grpc-js from 1.6.8 to 1.6.10 (@ dependabot[bot])
#10119 chore(deps-dev): bump @ types/node from 18.7.1 to 18.7.5 (@ dependabot[bot])

Committers: 4

Gil Reifan (@ gilre)
Jay McDoniel (@ jmcdo29)
Kamil Mysliwiec (@ kamilmysliwiec)
Pooyan Khanjankhani (@ TheKhanj)

9.0.10 - 2022-08-16v9.0.10

9.0.9 - 2022-08-11v9.0.9

9.0.8 - 2022-08-03v9.0.8

9.0.7 - 2022-07-28Enhancements

microservices

#10042 feat(microservices): add "maxConnectionAttempts" option to RMQ, hide urls log (@ kamilmysliwiec)

Docs

#8952 chore: fix Readme.md translation (@ rikapo)

Dependencies

#10028 chore(deps-dev): bump mongoose from 6.4.7 to 6.5.0 (@ dependabot[bot])
#10027 chore(deps-dev): bump amqplib from 0.10.0 to 0.10.1 (@ dependabot[bot])

Committers: 2

Kamil Mysliwiec (@ kamilmysliwiec)
rikapo (@ rikapo)

9.0.6 - 2022-07-28v9.0.6 (2022-07-28)
Features

common

#9935 feat(common): pass options to nested async modules (@ random42)

Bug fixes

common

#10025 Fix/10017 parse file pipe builder (@ thiagomini)

microservices

#9996 fix(microservices): prevent kafka parser from modifying received message (@ jDzames)

Enhancements

microservices

#10022 fix(microservices): pass options to rmq deserialize (@ bangbang93)

core

#9836 feat(core): add option to disable logs on LazyModuleLoader#load (@ micalevisk)

Dependencies

Other

#10033 chore(deps-dev): bump graphql-tools from 8.3.0 to 8.3.1 (@ dependabot[bot])
#10035 chore(deps): bump terser from 5.14.0 to 5.14.2 in /sample/05-sql-typeorm (@ dependabot[bot])
#10036 chore(deps): bump terser from 5.14.1 to 5.14.2 in /sample/07-sequelize (@ dependabot[bot])
#10037 chore(deps): bump terser from 5.10.0 to 5.14.2 in /sample/04-grpc (@ dependabot[bot])
#10038 chore(deps): bump terser from 5.10.0 to 5.14.2 in /sample/06-mongoose (@ dependabot[bot])
#10039 chore(deps): bump terser from 5.14.1 to 5.14.2 in /sample/02-gateways (@ dependabot[bot])
#10040 chore(deps): bump terser from 5.14.1 to 5.14.2 in /sample/03-microservices (@ dependabot[bot])
#10041 chore(deps): bump terser from 5.14.1 to 5.14.2 in /sample/01-cats-app (@ dependabot[bot])
#9977 chore(deps): bump terser from 5.10.0 to 5.14.2 in /sample/18-context (@ dependabot[bot])
#9978 chore(deps): bump terser from 5.10.0 to 5.14.2 in /sample/16-gateways-ws (@ dependabot[bot])
#9979 chore(deps): bump terser from 5.14.1 to 5.14.2 in /sample/17-mvc-fastify (@ dependabot[bot])
#9980 chore(deps): bump terser from 5.14.1 to 5.14.2 in /sample/13-mongo-typeorm (@ dependabot[bot])
#9981 chore(deps): bump terser from 5.10.0 to 5.14.2 in /sample/15-mvc (@ dependabot[bot])
#9982 chore(deps): bump terser from 5.10.0 to 5.14.2 in /sample/11-swagger (@ dependabot[bot])
#9983 chore(deps): bump terser from 5.10.0 to 5.14.2 in /sample/14-mongoose-base (@ dependabot[bot])
#9984 chore(deps): bump terser from 5.10.0 to 5.14.2 in /sample/12-graphql-schema-first (@ dependabot[bot])
#9985 chore(deps): bump terser from 5.10.0 to 5.14.2 in /sample/08-webpack (@ dependabot[bot])
#9986 chore(deps): bump terser from 5.14.1 to 5.14.2 in /sample/10-fastify (@ dependabot[bot])
#10034 chore(deps-dev): bump lerna from 2.11.0 to 5.3.0 (@ dependabot[bot])
#10024 chore(deps-dev): bump core-js from 3.23.5 to 3.24.0 (@ dependabot[bot])
#9993 chore(deps-dev): bump @ nestjs/apollo from 10.0.17 to 10.0.19 (@ dependabot[bot])
#9994 chore(deps-dev): bump @ types/sinon from 10.0.12 to 10.0.13 (@ dependabot[bot])
#9997 chore(deps): bump undici from 5.6.1 to 5.8.0 in /sample/33-graphql-mercurius (@ dependabot[bot])
#10000 chore(deps-dev): bump @ grpc/grpc-js from 1.6.7 to 1.6.8 (@ dependabot[bot])
#10011 chore(deps-dev): bump @ nestjs/graphql from 10.0.18 to 10.0.21 (@ dependabot[bot])
#10012 chore(deps-dev): bump ioredis from 5.2.1 to 5.2.2 (@ dependabot[bot])
#10013 chore(deps-dev): bump @ fastify/static from 6.4.0 to 6.4.1 (@ dependabot[bot])
#10020 chore(deps-dev): bump @ types/node from 18.0.6 to 18.6.1 (@ dependabot[bot])
#10021 chore(deps-dev): bump mongoose from 6.4.5 to 6.4.7 (@ dependabot[bot])
#9958 chore(deps): bump terser from 5.10.0 to 5.14.2 in /sample/33-graphql-mercurius (@ dependabot[bot])
#9968 chore(deps): bump terser from 5.10.0 to 5.14.2 in /sample/26-queues (@ dependabot[bot])
#9969 chore(deps): bump terser from 5.14.1 to 5.14.2 in /sample/22-graphql-prisma (@ dependabot[bot])
#9970 chore(deps): bump terser from 5.10.0 to 5.14.2 in /sample/27-scheduling (@ dependabot[bot])
#9971 chore(deps): bump terser from 5.14.1 to 5.14.2 in /sample/23-graphql-code-first (@ dependabot[bot])
#9972 chore(deps): bump terser from 5.10.0 to 5.14.2 in /sample/25-dynamic-modules (@ dependabot[bot])
#9973 chore(deps): bump terser from 5.10.0 to 5.14.2 in /sample/24-serve-static (@ dependabot[bot])
#9974 chore(deps): bump terser from 5.10.0 to 5.14.2 in /sample/21-serializer (@ dependabot[bot])
#9975 chore(deps): bump terser from 5.10.0 to 5.14.2 in /sample/19-auth-jwt (@ dependabot[bot])
#9976 chore(deps): bump terser from 5.10.0 to 5.14.2 in /sample/20-cache (@ dependabot[bot])

platform-fastify

#10023 chore(deps): bump light-my-request from 5.2.0 to 5.3.0 (@ dependabot[bot])
#10001 chore(deps): bump fastify from 4.2.1 to 4.3.0 (@ dependabot[bot])

Committers: 7

Antonio T. alias Tony (@ Tony133)
Jozef Džama (@ jDzames)
Micael Levi L. Cavalcante (@ micalevisk)
Pooyan Khanjankhani (@ TheKhanj)
Roberto Sero (@ random42)
Thiago Valentim (@ thiagomini)
bangbang93 (@ bangbang93)

9.0.5 - 2022-07-20 Read more

9.0.4 - 2022-07-18v9.0.4

9.0.3 - 2022-07-12v9.0.3

9.0.2 - 2022-07-11v9.0.2
Bug fixes

common

#9904 fix(common): Fix CacheModule registerAsync (@ tugascript)

Enhancements

core

#9902 refactor(core): replace our own 1-level flatten by the native one (@ micalevisk)

Dependencies

#9906 chore(deps-dev): bump mongoose from 6.4.3 to 6.4.4 (@ dependabot[bot])
#9908 chore(deps-dev): bump cache-manager from 4.0.1 to 4.1.0 (@ dependabot[bot])
#9910 chore(deps-dev): bump @ nestjs/graphql from 10.0.16 to 10.0.18 (@ dependabot[bot])
#9911 chore(deps-dev): bump core-js from 3.23.3 to 3.23.4 (@ dependabot[bot])

Committers: 3

Afonso Barracha (@ tugascript)
Antonio T. alias Tony (@ Tony133)
Micael Levi L. Cavalcante (@ micalevisk)

9.0.1 - 2022-07-08

9.0.0 - 2022-07-08

    from @nestjs/common GitHub release notes

Package name: @nestjs/core

9.0.11 - 2022-08-16 Read more

9.0.10 - 2022-08-16v9.0.10

9.0.9 - 2022-08-11v9.0.9

9.0.8 - 2022-08-03v9.0.8

9.0.7 - 2022-07-28Enhancements

microservices

#10042 feat(microservices): add "maxConnectionAttempts" option to RMQ, hide urls log (@ kamilmysliwiec)

Docs

#8952 chore: fix Readme.md translation (@ rikapo)

Dependencies

#10028 chore(deps-dev): bump mongoose from 6.4.7 to 6.5.0 (@ dependabot[bot])
#10027 chore(deps-dev): bump amqplib from 0.10.0 to 0.10.1 (@ dependabot[bot])

Committers: 2

Kamil Mysliwiec (@ kamilmysliwiec)
rikapo (@ rikapo)

9.0.6 - 2022-07-28 Read more

9.0.5 - 2022-07-20 Read more

9.0.4 - 2022-07-18v9.0.4

9.0.3 - 2022-07-12v9.0.3

9.0.2 - 2022-07-11 Read more

9.0.1 - 2022-07-08

9.0.0 - 2022-07-08

    from @nestjs/core GitHub release notes

Package name: @nestjs/platform-express

9.0.11 - 2022-08-16 Read more

9.0.10 - 2022-08-16v9.0.10

9.0.9 - 2022-08-11v9.0.9

9.0.8 - 2022-08-03v9.0.8

9.0.7 - 2022-07-28 Read more

9.0.6 - 2022-07-28 Read more

9.0.5 - 2022-07-20 Read more

9.0.4 - 2022-07-18v9.0.4

9.0.3 - 2022-07-12v9.0.3

9.0.2 - 2022-07-11 Read more

9.0.1 - 2022-07-08

9.0.0 - 2022-07-08

    from @nestjs/platform-express GitHub release notes

Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open upgrade PRs.
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@akroon3r - I see snyk attempting to update even the bug fix releases - I am wondering this might introduce unforeseen issues later on. This contradicts our package.json 's fixed version ideology. What's are your thoughts here?

Can we cancel or merge this PR and address the topic of scheduled dependency updates at another time?


Custom first day of week in month view

Currently the first day of the week in month view is Sunday. This feature request would make it possible to choose which day to set as the first day of the week.
Let me know if I missed something in the documentation and this is already possible. Also, I'd be happy to work on this one myself.

As the calendar uses moment for everything you can just set this option in moment like so:
moment.locale('en', {
  week : {
    dow : 1 // Monday is the first day of the week
  }
});


Revert "Github Actions CodeBuild shim"

Reverts awslabs/s2n#1511
Credential problems- GHA claims no credentials were provided, but they exist in both the fork and the base.
##[error]Input required and not supplied: aws-access-key-id
##[error]Node run failed with exit code 1

Codecov Report

Merging #1516 into master will decrease coverage by 1.24%.
The diff coverage is n/a.

@@            Coverage Diff             @@
##           master    #1516      +/-   ##
==========================================
- Coverage   87.72%   86.48%   -1.25%
==========================================
  Files         133      140       +7
  Lines        9663    11097    +1434
==========================================
+ Hits         8477     9597    +1120
- Misses       1186     1500     +314


Changes to run e2e for private cluster

Fixes #N/A
Description
How was this change tested?
Does this change impact docs?

[ ] Yes, PR includes docs updates
[ ] Yes, issue opened: #
[x] No

By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.

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This pull request's base commit is no longer the HEAD commit of its target branch. This means it includes changes from outside the original pull request, including, potentially, unrelated coverage changes.

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For a quick fix, rebase this PR at GitHub. Your next report should be accurate.

Details

0 of 0   changed or added relevant lines in 0 files are covered.
No unchanged relevant lines lost coverage.
Overall coverage remained the same at 82.3%

Totals

Change from base Build<PHONE_NUMBER>:
0.0%

Covered Lines:
5352

Relevant Lines:
6503

💛  - Coveralls


Only pick up project output files that exactly match assembly filename. Fixes #750

Fix for #750. Ideally a unit test around this could be added but it's a bit beyond my F# skill at the moment. PackageProcessSpecs.fs looks like a likely spot to add something...

/cc @mavnn

thanks. please try 0.38.6

Works for me, cheers!


update requirements with detailed versions

this pull request will fix this issue

yes, the online version is still running fine since we built the image before the packages changed (I actually got the new requirements.txt by running pip freeze from inside the running container)


Remove step load feature

Resolves https://github.com/locustio/locust/issues/1575

Nice! But can you give an error when someone tries to use step load? Like the --slave parameter is handled atm.

@max-rocket-internet Any chance you'll have time to finish this soon? :)

@cyberw pushed changes, I think that's it 🙂

thanks!


GPG Support

As discussed on Discord, I think it would be nice to have some sort of GPG support.
Here how I envision it could work:

New hotkey in the TUI to enable Encryption.
Lookup the user's GPG keychain for a match against the recipient.
Offer to lookup the recipient's public key (on a key server like keys.openpgp.org) if not found in the keychain.
An environment variable like POP_PGP that could be set to Automatic, Manual...

A third option would be to ask the user to manually select a GPG file from the drive.


[KED-1451] Upgrade pylint to 2.4 for Kedro-Viz

Description

We have GitHub issues in other Kedro repositories
Here is the link to this one: https://github.com/quantumblacklabs/kedro-viz/issues/120

Fixed in https://github.com/quantumblacklabs/kedro-viz/pull/135


Feeds layout

Joined the team earlier this looks nice.

You need to start Squashing your Commit and Merge.


New: Added Chrome Traces to cdp connector

Chrome Traces has much more information about performance and
rules can use the new events - tracing::data and tracing::end
for trace information.
This information can be used for new rules like firstPaint,
total time taken to parseHTML, long frames, rendering, etc.

@axemclion Thanks for the PR!
Can you sign the CLA? Thanks!

Cc: @molant @sarvaje

Sorry, I think I sent the PR using a wrong account. I work for Microsoft.

@panarasi you still need to sign the CLA. The code now belongs to the JS Foundation 😊

@panarasi the build is failing because of some eslint errors

@sarvaje  Fixed eslint issues.

Also in line 551, I am not using reject since the whole block is wrapped inside a setTimeout anyway. Is that ok ?

Closing this as there's more work that needs to happen to enable it in all connectors but definitely something we want to add in the future.


Incorrect output when a map-to is decomposed into multiple pieces

The following code produces the wrong output if I swap the uncommented-commented lines as per instructions
#define _POSIX_C_SOURCE 200809L
#include "stdlib.h"
#include "math.h"
#include "sys/time.h"
#include "omp.h"
#include "pthread.h"

#include <stdio.h>

int main(int argc, char* argv[])
{
  int u[10][14][14];
  int grad[12][12] = {{0}};

  for (int i = 0; i < 10; i++)
    for (int j = 0; j < 14; j++)
      for (int k = 0; k < 14; k++)
        u[i][j][k] = i;

  #pragma omp target enter data map(to: grad[0:12][0:12])

  for (int time = 0, t0 = (time)%(2), t1 = (time + 1)%(2); time < 9; time++, t0 = (time)%(2), t1 = (time + 1)%(2))
  {
    #pragma omp target enter data map(to: u[time:2][0:14][0:14])

    // UNCOMMENT THE TWO LINES BELOW, COMMENT THE PRAGMA ABOVE
    //#pragma omp target enter data map(to: u[time:1][0:14][0:14])
    //#pragma omp target enter data map(to: u[time+1:1][0:14][0:14])

    #pragma omp target teams distribute parallel for collapse(2)
    for (int j = 0; j < 10; j += 1)
    {
      for (int k = 0; k < 10; k += 1)
      {
        grad[j+1][k+1] = u[time][j+2][k+2] + u[time+1][j+2][k+2];
      }
    }

    #pragma omp target exit data map(delete: u[time:2][0:14][0:14])

    // UNCOMMENT THE TWO LINES BELOW, COMMENT THE PRAGMA ABOVE
    //#pragma omp target exit data map(delete: u[time:1][0:14][0:14])
    //#pragma omp target exit data map(delete: u[time+1:1][0:14][0:14])
  }

  #pragma omp target update from(grad[0:12][0:12])
  #pragma omp target exit data map(release: grad[0:12][0:12])

  for (int j = 0; j < 10; j += 1)
  {
    for (int k = 0; k < 10; k += 1)
    {
      printf("%d ", grad[j+1][k+1]);
    }
    printf("\n");
  }

  return 0;
}

This was compiled with:
clang -O3 -g -fPIC -Wall -Wno-unused-result -Wno-unused-variable -ffast-math -fopenmp -fopenmp-targets=nvptx64-nvidia-cuda mfe.c -o mfe

With clang11. The same exact issue with AOMP.
context: this was extrapolated from code automatically generated by devito. Some lines may actually be unnecessary, but the code should be sufficiently small and easily understandable to highlight the issue.

We (possibly I) should see if this reproduces on trunk, and post it to bugzilla for wider visibility if it does.

It seems the problem is with the 2nd exit data . The first one appears to have already deleted the data.   Here is my attributed code
#include "sys/time.h"
#include "omp.h"
#include "pthread.h"
#include <stdio.h>
int main(int argc, char* argv[])
{
  int u[10][14][14];
  int grad[12][12] = {{0}};
  for (int i = 0; i < 10; i++)
    for (int j = 0; j < 14; j++)
      for (int k = 0; k < 14; k++)
        u[i][j][k] = i;

  #pragma omp target enter data map(to: grad[0:12][0:12])
  for (int time = 0, t0 = (time)%(2), t1 = (time + 1)%(2); time < 9; time++, t0 = (time)%(2), t1 = (time + 1)%(2))
  {
    printf("\ncalling target enter at data time %d\n",time);
    #pragma omp target enter data map(to: u[time:2][0:14][0:14])
    printf("DONE calling target enter at data time %d\n",time);
    // UNCOMMENT THE TWO LINES BELOW, COMMENT THE PRAGMA ABOVE
    //#pragma omp target enter data map(to: u[time:1][0:14][0:14])
    //#pragma omp target enter data map(to: u[time+1:1][0:14][0:14])
    #pragma omp target teams distribute parallel for collapse(2)
    for (int j = 0; j < 10; j += 1)
    {
      for (int k = 0; k < 10; k += 1)
      {
        grad[j+1][k+1] = u[time][j+2][k+2] + u[time+1][j+2][k+2];
      }
    }
    printf("Kernel done, now calling exit data at time %d\n",time);
    //#pragma omp target exit data map(delete: u[time:2][0:14][0:14])
    // UNCOMMENT THE TWO LINES BELOW, COMMENT THE PRAGMA ABOVE
    #pragma omp target exit data map(delete: u[time:1][0:14][0:14])
    printf("calling 2nd exit data at time %d\n",time);
    #pragma omp target exit data map(delete: u[time+1:1][0:14][0:14])
    printf("DONE calling exit data at time %d\n",time);
  }

  printf("\ncalling target update \n");
  #pragma omp target update from(grad[0:12][0:12])
  printf("calling exit data \n");
  #pragma omp target exit data map(release: grad[0:12][0:12])
  printf("DONE calling exit data \n\n");
  for (int j = 0; j < 10; j += 1)
  {
    for (int k = 0; k < 10; k += 1)
    {
      printf("%d ", grad[j+1][k+1]);
    }
    printf("\n");
  }
  return 0;
}

Here is the output run with LIBOMPTARGET_KERNEL_TRACE=2.   LIBOMPTARGET_DEBUG=4 does nothing so we should fix the Libomptarget error message.
export LIBOMPTARGET_KERNEL_TRACE=2
grodgers@tenjin:~/git/aomp13/aomp/test/smoke/issue177$ ./issue177
Call         __tgt_rtl_number_of_devices:        0us              1 )
Call           __tgt_rtl_is_valid_binary:        1us              1 (0x000000407da0)
Call             __tgt_rtl_init_requires:        0us              1 (             1)
Call               __tgt_rtl_init_device:        3us              0 (             0)
Call               __tgt_rtl_load_binary:     4052us 0x00000101c530 (             0, 0x000000407da0)
Call                __tgt_rtl_data_alloc:        1us 0x7f09dce06000 (             0,            576, 0x7ffcf7e36cb0)
Call               __tgt_rtl_data_submit:      200us              0 (             0, 0x7f09dce06000, 0x7ffcf7e36cb0,            576)

calling target enter at data time 0
Call                __tgt_rtl_data_alloc:        1us 0x7f09dce07000 (             0,           1568, 0x7ffcf7e36ef0)
Call               __tgt_rtl_data_submit:      113us              0 (             0, 0x7f09dce07000, 0x7ffcf7e36ef0,           1568)
DONE calling target enter at data time 0
DEVID: 0 SGN:0 ConstWGSize:256  args: 3 teamsXthrds:(   1X 256) reqd:(   0X   0) n:__omp_offloading_10302_2523a3b_main_l24
Call    __tgt_rtl_run_target_team_region:       31us              0 (             0, 0x00000107fea0, 0x0000010808f0, 0x000001080390,              3,              0,              0,            100)
Kernel done, now calling exit data at time 0
Call               __tgt_rtl_data_delete:        1us              0 (             0, 0x7f09dce07000)
calling 2nd exit data at time 0
Libomptarget error: Section to delete (hst addr 0x00007ffcf7e37200) does not exist in the allocated memory
Libomptarget error: Deallocating data from device failed.
Libomptarget error: Run with LIBOMPTARGET_DEBUG=4 to dump host-target pointer mappings.
issue177.c:37:5: Libomptarget fatal error 1: failure of target construct while offloading is mandatory
Aborted (core dumped)

Sorry to delete my last message.  I will repost with correction in a few minutes.

Here is my annotated code .
grodgers@tenjin:~/git/aomp13/aomp/test/smoke/issue177$ cat issue177.c
#include "sys/time.h"
#include "omp.h"
#include "pthread.h"
#include <stdio.h>
int main(int argc, char* argv[])
{
  int u[10][14][14];
  int grad[12][12] = {{0}};
  for (int i = 0; i < 10; i++)
    for (int j = 0; j < 14; j++)
      for (int k = 0; k < 14; k++)
        u[i][j][k] = i;

  #pragma omp target enter data map(to: grad[0:12][0:12])
  for (int time = 0, t0 = (time)%(2), t1 = (time + 1)%(2); time < 9; time++, t0 = (time)%(2), t1 = (time + 1)%(2))
  {
    printf("\ncalling target enter at data time %d\n",time);
    //#pragma omp target enter data map(to: u[time:2][0:14][0:14])
    // UNCOMMENT THE TWO LINES BELOW, COMMENT THE PRAGMA ABOVE
    #pragma omp target enter data map(to: u[time:1][0:14][0:14])
    printf("calling 2nd target enter at data time %d\n",time);
    #pragma omp target enter data map(to: u[time+1:1][0:14][0:14])
    printf("DONE calling 2nd target enter at data time %d\n",time);
    #pragma omp target teams distribute parallel for collapse(2)
    for (int j = 0; j < 10; j += 1)
    {
      for (int k = 0; k < 10; k += 1)
      {
        grad[j+1][k+1] = u[time][j+2][k+2] + u[time+1][j+2][k+2];
      }
    }
    printf("Kernel done, now calling exit data at time %d\n",time);
    //#pragma omp target exit data map(delete: u[time:2][0:14][0:14])
    // UNCOMMENT THE TWO LINES BELOW, COMMENT THE PRAGMA ABOVE
    #pragma omp target exit data map(delete: u[time:1][0:14][0:14])
    printf("calling 2nd exit data at time %d\n",time);
    #pragma omp target exit data map(delete: u[time+1:1][0:14][0:14])
    printf("DONE calling exit data at time %d\n",time);
  }

  printf("\ncalling target update \n");
  #pragma omp target update from(grad[0:12][0:12])
  printf("calling exit data \n");
  #pragma omp target exit data map(release: grad[0:12][0:12])
  printf("DONE calling exit data \n\n");
  for (int j = 0; j < 10; j += 1)
  {
    for (int k = 0; k < 10; k += 1)
    {
      printf("%d ", grad[j+1][k+1]);
    }
    printf("\n");
  }
  return 0;
}

Here is the build command.
/home/grodgers/rocm/aomp/bin/clang -O3 -g -fPIC -Wall -Wno-unused-result -Wno-unused-variable -ffast-math -fopenmp -fopenmp-targets=amdgcn-amd-amdhsa -Xopenmp-target=amdgcn-amd-amdhsa -march=gfx906 issue177.c -o issue177
Here is the output showing incorrect results.  This was run with LIBOMTARGET_KERNEL_TRACE=2
export LIBOMPTARGET_KERNEL_TRACE=2
grodgers@tenjin:~/git/aomp13/aomp/test/smoke/issue177$ ./issue177
Call         __tgt_rtl_number_of_devices:        0us              1 )
Call           __tgt_rtl_is_valid_binary:        0us              1 (0x000000407da0)
Call             __tgt_rtl_init_requires:        0us              1 (             1)
Call               __tgt_rtl_init_device:        3us              0 (             0)
Call               __tgt_rtl_load_binary:     3434us 0x0000018fd530 (             0, 0x000000407da0)
Call                __tgt_rtl_data_alloc:        0us 0x7fa764606000 (             0,            576, 0x7ffde47e5e50)
Call               __tgt_rtl_data_submit:      132us              0 (             0, 0x7fa764606000, 0x7ffde47e5e50,            576)

calling target enter at data time 0
Call                __tgt_rtl_data_alloc:        0us 0x7fa764607000 (             0,            784, 0x7ffde47e6090)
Call               __tgt_rtl_data_submit:       92us              0 (             0, 0x7fa764607000, 0x7ffde47e6090,            784)
calling 2nd target enter at data time 0
Call                __tgt_rtl_data_alloc:        1us 0x7fa764608000 (             0,            784, 0x7ffde47e63a0)
Call               __tgt_rtl_data_submit:       92us              0 (             0, 0x7fa764608000, 0x7ffde47e63a0,            784)
DONE calling 2nd target enter at data time 0
DEVID: 0 SGN:0 ConstWGSize:256  args: 3 teamsXthrds:(   1X 256) reqd:(   0X   0) n:__omp_offloading_10302_2523a3e_main_l25
Call    __tgt_rtl_run_target_team_region:       27us              0 (             0, 0x000001960ea0, 0x0000019618f0, 0x000001961390,              3,              0,              0,            100)
Kernel done, now calling exit data at time 0
Call               __tgt_rtl_data_delete:        0us              0 (             0, 0x7fa764607000)
calling 2nd exit data at time 0
Call               __tgt_rtl_data_delete:        0us              0 (             0, 0x7fa764608000)
DONE calling exit data at time 0

calling target enter at data time 1
Call                __tgt_rtl_data_alloc:        0us 0x7fa764607000 (             0,            784, 0x7ffde47e63a0)
Call               __tgt_rtl_data_submit:       94us              0 (             0, 0x7fa764607000, 0x7ffde47e63a0,            784)
calling 2nd target enter at data time 1
Call                __tgt_rtl_data_alloc:        0us 0x7fa764608000 (             0,            784, 0x7ffde47e66b0)
Call               __tgt_rtl_data_submit:       86us              0 (             0, 0x7fa764608000, 0x7ffde47e66b0,            784)
DONE calling 2nd target enter at data time 1
DEVID: 0 SGN:0 ConstWGSize:256  args: 3 teamsXthrds:(   1X 256) reqd:(   0X   0) n:__omp_offloading_10302_2523a3e_main_l25
Call    __tgt_rtl_run_target_team_region:       23us              0 (             0, 0x000001960ea0, 0x0000019618f0, 0x000001961390,              3,              0,              0,            100)
Kernel done, now calling exit data at time 1
Call               __tgt_rtl_data_delete:        0us              0 (             0, 0x7fa764607000)
calling 2nd exit data at time 1
Call               __tgt_rtl_data_delete:        0us              0 (             0, 0x7fa764608000)
DONE calling exit data at time 1

calling target enter at data time 2
Call                __tgt_rtl_data_alloc:        0us 0x7fa764607000 (             0,            784, 0x7ffde47e66b0)
Call               __tgt_rtl_data_submit:       91us              0 (             0, 0x7fa764607000, 0x7ffde47e66b0,            784)
calling 2nd target enter at data time 2
Call                __tgt_rtl_data_alloc:        0us 0x7fa764608000 (             0,            784, 0x7ffde47e69c0)
Call               __tgt_rtl_data_submit:      120us              0 (             0, 0x7fa764608000, 0x7ffde47e69c0,            784)
DONE calling 2nd target enter at data time 2
DEVID: 0 SGN:0 ConstWGSize:256  args: 3 teamsXthrds:(   1X 256) reqd:(   0X   0) n:__omp_offloading_10302_2523a3e_main_l25
Call    __tgt_rtl_run_target_team_region:       23us              0 (             0, 0x000001960ea0, 0x0000019618f0, 0x000001961390,              3,              0,              0,            100)
Kernel done, now calling exit data at time 2
Call               __tgt_rtl_data_delete:        0us              0 (             0, 0x7fa764607000)
calling 2nd exit data at time 2
Call               __tgt_rtl_data_delete:        0us              0 (             0, 0x7fa764608000)
DONE calling exit data at time 2

calling target enter at data time 3
Call                __tgt_rtl_data_alloc:        0us 0x7fa764607000 (             0,            784, 0x7ffde47e69c0)
Call               __tgt_rtl_data_submit:       91us              0 (             0, 0x7fa764607000, 0x7ffde47e69c0,            784)
calling 2nd target enter at data time 3
Call                __tgt_rtl_data_alloc:        0us 0x7fa764608000 (             0,            784, 0x7ffde47e6cd0)
Call               __tgt_rtl_data_submit:       83us              0 (             0, 0x7fa764608000, 0x7ffde47e6cd0,            784)
DONE calling 2nd target enter at data time 3
DEVID: 0 SGN:0 ConstWGSize:256  args: 3 teamsXthrds:(   1X 256) reqd:(   0X   0) n:__omp_offloading_10302_2523a3e_main_l25
Call    __tgt_rtl_run_target_team_region:       21us              0 (             0, 0x000001960ea0, 0x0000019618f0, 0x000001961390,              3,              0,              0,            100)
Kernel done, now calling exit data at time 3
Call               __tgt_rtl_data_delete:        0us              0 (             0, 0x7fa764607000)
calling 2nd exit data at time 3
Call               __tgt_rtl_data_delete:        0us              0 (             0, 0x7fa764608000)
DONE calling exit data at time 3

calling target enter at data time 4
Call                __tgt_rtl_data_alloc:        0us 0x7fa764607000 (             0,            784, 0x7ffde47e6cd0)
Call               __tgt_rtl_data_submit:       90us              0 (             0, 0x7fa764607000, 0x7ffde47e6cd0,            784)
calling 2nd target enter at data time 4
Call                __tgt_rtl_data_alloc:        0us 0x7fa764608000 (             0,            784, 0x7ffde47e6fe0)
Call               __tgt_rtl_data_submit:       89us              0 (             0, 0x7fa764608000, 0x7ffde47e6fe0,            784)
DONE calling 2nd target enter at data time 4
DEVID: 0 SGN:0 ConstWGSize:256  args: 3 teamsXthrds:(   1X 256) reqd:(   0X   0) n:__omp_offloading_10302_2523a3e_main_l25
Call    __tgt_rtl_run_target_team_region:       22us              0 (             0, 0x000001960ea0, 0x0000019618f0, 0x000001961390,              3,              0,              0,            100)
Kernel done, now calling exit data at time 4
Call               __tgt_rtl_data_delete:        0us              0 (             0, 0x7fa764607000)
calling 2nd exit data at time 4
Call               __tgt_rtl_data_delete:        0us              0 (             0, 0x7fa764608000)
DONE calling exit data at time 4

calling target enter at data time 5
Call                __tgt_rtl_data_alloc:        0us 0x7fa764607000 (             0,            784, 0x7ffde47e6fe0)
Call               __tgt_rtl_data_submit:       94us              0 (             0, 0x7fa764607000, 0x7ffde47e6fe0,            784)
calling 2nd target enter at data time 5
Call                __tgt_rtl_data_alloc:        0us 0x7fa764608000 (             0,            784, 0x7ffde47e72f0)
Call               __tgt_rtl_data_submit:       86us              0 (             0, 0x7fa764608000, 0x7ffde47e72f0,            784)
DONE calling 2nd target enter at data time 5
DEVID: 0 SGN:0 ConstWGSize:256  args: 3 teamsXthrds:(   1X 256) reqd:(   0X   0) n:__omp_offloading_10302_2523a3e_main_l25
Call    __tgt_rtl_run_target_team_region:       23us              0 (             0, 0x000001960ea0, 0x0000019618f0, 0x000001961390,              3,              0,              0,            100)
Kernel done, now calling exit data at time 5
Call               __tgt_rtl_data_delete:        0us              0 (             0, 0x7fa764607000)
calling 2nd exit data at time 5
Call               __tgt_rtl_data_delete:        0us              0 (             0, 0x7fa764608000)
DONE calling exit data at time 5

calling target enter at data time 6
Call                __tgt_rtl_data_alloc:        0us 0x7fa764607000 (             0,            784, 0x7ffde47e72f0)
Call               __tgt_rtl_data_submit:       89us              0 (             0, 0x7fa764607000, 0x7ffde47e72f0,            784)
calling 2nd target enter at data time 6
Call                __tgt_rtl_data_alloc:        0us 0x7fa764608000 (             0,            784, 0x7ffde47e7600)
Call               __tgt_rtl_data_submit:       88us              0 (             0, 0x7fa764608000, 0x7ffde47e7600,            784)
DONE calling 2nd target enter at data time 6
DEVID: 0 SGN:0 ConstWGSize:256  args: 3 teamsXthrds:(   1X 256) reqd:(   0X   0) n:__omp_offloading_10302_2523a3e_main_l25
Call    __tgt_rtl_run_target_team_region:       20us              0 (             0, 0x000001960ea0, 0x0000019618f0, 0x000001961390,              3,              0,              0,            100)
Kernel done, now calling exit data at time 6
Call               __tgt_rtl_data_delete:        0us              0 (             0, 0x7fa764607000)
calling 2nd exit data at time 6
Call               __tgt_rtl_data_delete:        0us              0 (             0, 0x7fa764608000)
DONE calling exit data at time 6

calling target enter at data time 7
Call                __tgt_rtl_data_alloc:        0us 0x7fa764607000 (             0,            784, 0x7ffde47e7600)
Call               __tgt_rtl_data_submit:       90us              0 (             0, 0x7fa764607000, 0x7ffde47e7600,            784)
calling 2nd target enter at data time 7
Call                __tgt_rtl_data_alloc:        0us 0x7fa764608000 (             0,            784, 0x7ffde47e7910)
Call               __tgt_rtl_data_submit:       83us              0 (             0, 0x7fa764608000, 0x7ffde47e7910,            784)
DONE calling 2nd target enter at data time 7
DEVID: 0 SGN:0 ConstWGSize:256  args: 3 teamsXthrds:(   1X 256) reqd:(   0X   0) n:__omp_offloading_10302_2523a3e_main_l25
Call    __tgt_rtl_run_target_team_region:       21us              0 (             0, 0x000001960ea0, 0x0000019618f0, 0x000001961390,              3,              0,              0,            100)
Kernel done, now calling exit data at time 7
Call               __tgt_rtl_data_delete:        0us              0 (             0, 0x7fa764607000)
calling 2nd exit data at time 7
Call               __tgt_rtl_data_delete:        0us              0 (             0, 0x7fa764608000)
DONE calling exit data at time 7

calling target enter at data time 8
Call                __tgt_rtl_data_alloc:        0us 0x7fa764607000 (             0,            784, 0x7ffde47e7910)
Call               __tgt_rtl_data_submit:       90us              0 (             0, 0x7fa764607000, 0x7ffde47e7910,            784)
calling 2nd target enter at data time 8
Call                __tgt_rtl_data_alloc:        0us 0x7fa764608000 (             0,            784, 0x7ffde47e7c20)
Call               __tgt_rtl_data_submit:       89us              0 (             0, 0x7fa764608000, 0x7ffde47e7c20,            784)
DONE calling 2nd target enter at data time 8
DEVID: 0 SGN:0 ConstWGSize:256  args: 3 teamsXthrds:(   1X 256) reqd:(   0X   0) n:__omp_offloading_10302_2523a3e_main_l25
Call    __tgt_rtl_run_target_team_region:       17us              0 (             0, 0x000001960ea0, 0x0000019618f0, 0x000001961390,              3,              0,              0,            100)
Kernel done, now calling exit data at time 8
Call               __tgt_rtl_data_delete:        0us              0 (             0, 0x7fa764607000)
calling 2nd exit data at time 8
Call               __tgt_rtl_data_delete:        0us              0 (             0, 0x7fa764608000)
DONE calling exit data at time 8

calling target update
Call             __tgt_rtl_data_retrieve:       91us              0 (             0, 0x7ffde47e5e50, 0x7fa764606000,            576)
calling exit data
Call               __tgt_rtl_data_delete:        0us              0 (             0, 0x7fa764606000)
DONE calling exit data

-1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578
-1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578
-1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578
-1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578
-1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578
-1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578
-1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578
-1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578
-1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578
-1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578 -1094795578
Call           __tgt_rtl_is_valid_binary:        4us              1 (0x000000407da0)
grodgers@tenjin:~/git/aomp13/aomp/test/smoke/issue177$

I added some more instrumentation to the code and found that this does not work because with multiple map clauses, the sections of array u on the target are no more contiguous. Here's the run log:
calling target enter at data time 0
Call                __tgt_rtl_data_alloc:        0us 0x7f09b5008000 (             0,            784, 0x7ffe2312ac50)
Call               __tgt_rtl_data_submit:      123us              0 (             0, 0x7f09b5008000, 0x7ffe2312ac50,            784)
calling 2nd target enter at data time 0
Call                __tgt_rtl_data_alloc:        0us 0x7f09b5009000 (             0,            784, 0x7ffe2312af60)
Call               __tgt_rtl_data_submit:       79us              0 (             0, 0x7f09b5009000, 0x7ffe2312af60,            784)
DONE calling 2nd target enter at data time 0
GPU: u ptrs/values: 0x7f09b50081d8: 0 0x7f09b50084e8: -1094795586   <---------- Here's the problem
So the first map occurs at 0x7f09b5008000 and the second at 0x7f09b5009000 on the target. I printed out the actual accesses (within the loop) made on the target prefixed above by "GPU". The 1st is accessed correctly at 0x7f09b50081d8 but the 2nd one is incorrectly accessed at 0x7f09b50084e8. Using a single map would solve this issue.

I think we concluded that this was expected behaviour, not a bug. Requiring distinct maps to be contiguous would be disproportionately expensive.

AOMP team, lets review this ticket on our Monday meeting.    What is status of this on AOMP 13.0-2?

@FabioLuporini  The real issue is the following: When the compiler generates code for
the kernel, there is a distinct argument for every unique variable
that is captured. If there is an array A and multiple sections of A
are mapped, the runtime does not have any way to pass in the base
address of the distinct sections because the compiler generated code
has only one captured argument for A. I looked at what the runtime does. The sections
are indeed mapped correctly but the base address passed as argument
corresponds to only one of the sections. So any memory access in the
target region that corresponds to the section actually passed still
accesses a valid location but for all other sections, an invalid
memory location is accessed.
Using a distinct pointer for every array section works because then
the pointer becomes a captured variable and is passed to the kernel as
an argument. So the runtime has a place to pass the proper base
address for every array section.
Closing this issue.


Propose updates to decouple panel match client from v2alpha API

This PR proposes a set of updates to decouple the panel match client libraries from the v2alpha API.
This is NOT intended to be merged as-is, but rather to prove the feasibility of decoupling and to gather some initial feedback on the overall approach. If there are no major concerns, I will split this into several smaller PRs and send those for separate review.

Summary of changes:

Introduces panel match internal versions of ExchangeWorkflow and ExchangeStepAttempt protobuf messages.

Exchange task logic now uses the internal ExchangeWorkflow exclusively.
Relevant tools have been updated to accept a --exchange-workflow-format flag to be compatible with both the v2alpha messages and the new internal ones. Supported formats are:

V2ALPHA - For exchanges that continue to use the Kingdom-based protocol with v2alpha messages.
KINGDOMLESS - For exchanges that have adopted the upcoming Kingdom-less protocol with panel match internal messages.

Code related to claiming / validating / launching exchange tasks updated as follows:

ApiClient.ClaimedExchangeStep - Updated to remove references to v2alpha concepts. Notable changes:

The attemptKey for a step is now a panel match internal ExchangeStepAttemptKey with mostly the same info as before but no references to a "resource name".
Rather than creating a panel match internal ExchangeStep message, instead ported the necessary fields directly into ClaimedExchangeStep. These are: exchangeDate, stepIndex, and workflow.
New field: workflowFingerprint. When a claimed exchange step is being validated, the existing logic compares the serialized workflow returned by the Kingdom against the serialized workflow in private storage. I'm proposing that we now compare fingerprints of the serialized workflows. These may be fingerprints of v2alpha workflows or the new panel match internal workflow - depending on the concrete implementation of ApiClient.

With the removal of ExchangeStep, the exchange task validator now accepts a ApiClient.ClaimedExchangeStep.

This change is


Yarn: Use cwd instead of ~/

Fix https://github.com/mafintosh/prebuild/pull/150#discussion_r91936707

5.0.3

Woo! Thank you.

Just checked, it works now with


Merge multiple paths for error creation

Background
This PR is created out of the discussion in secureworks/errors#2 PR where we contemplated on merging the multiple paths for creating errors, while maintaining compatibility with the standard errors library.
Summary or the change
This PR essentially merges most public APIs into one API: the errors.New(...) function, which creates new errors that always capture context (stack-trace, frames) and optionally wrap other errors, and can also augment or replace the error message while retaining the error chain.
Notes
Admittedly this refactor has gone farther than what I've wanted it to, but every change along the way made sense to me so I decided to bring it to completion and gather feedback from there.
It should be noted from the top that this PR breaks backwards compatibility - but that can be restored before merging it as a layer on top of the proposed changes (as part of this PR). I am intentionally submitting it before so that the changes themselves can be gauged and reviewed.
Changes summary
APIs merged

withStackTrace

NewWithStackTrace (to be restored as part of a backwards compatibility change later on)
WithStackTrace (to be restored as part of a backwards compatibility change later on)

withFrames

NewWithFrame (to be restored as part of a backwards compatibility change later on)
WithFrame (to be restored as part of a backwards compatibility change later on)
NewWithFrameAt (to be restored as part of a backwards compatibility change later on)
WithFrameAt (to be restored as part of a backwards compatibility change later on)
NewWithFrames (to be restored as part of a backwards compatibility change later on)
WithFrames (to be restored as part of a backwards compatibility change later on)

withMessage

WithMessage (to be restored as part of a backwards compatibility change later on)

APIs removed

FramesFrom: removed since it never represents an entire chain. If we want to restore it, I'm not sure what the logic should be; needs discussion.
Errorf: replaced by the refactored New function, but without the restrictions it had before (to be restored as part of a backwards-compatibility change later on)

Underlying changes
The above merged & removed APIs converge into a single type called errorImpl and a single API to create new errors: errors.New(...).
The New function creates a new error from either:

No arguments: creates an empty error with no message, no cause/underlying error, but with the stack-trace that led to its creation.
String argument followed by zero or more additonal arguments: in this case, the first (string) argument is considered the message format, and the following arguments will be used as formatting arguments to it; they can optionally include one error argument which will signal the cause/underlying error.
The resulting error will wrap it.
The resulting error captures the stack-trace that led to its creation.
Single error argument: resulting error will wrap the given error, and will have no message of its own.
The resulting error captures the stack-trace that led to its creation.

This method was previously "preserved" as a compatibility layer with the standard library's errors.New function. This has now changed and the error returned by github.com/secureworks/errors.New, retains API compatibility but is not created using the standard library's errors.New.
Features
Simplicity
This change simplifies the library significantly, as the user only needs to learn one major API: the errors.New function, at least for basic usage.
Opportunity to merge multi-errors into the same API
This change also presents a great opportunity (not yet taken in this PR though) to merge multi-errors into that single API as well! Imagine this:
root := errors.New("omg") // happened in task 1
errTask1 := errors.New("task1 failed: %w", root) // task 1 returned a wrapper around root
errTask2 := errors.New("task2 failed: %s", dbMessage) // task 2 failed with this simple error
merr := errors.New("syncGroup done", errTask1, errTask2)

By simply passing more than one error to the errors.New function, we can change it to automatically create multi-error automagically.
Seamless masking of message while retaining context
The message format passed to errors.New may omit the %w verb, even when an error is provided in the additional arguments; the New function will still wrap the given error, but will not present the wrapped error's message in its own message. This is useful when you want to omit the technical message of the wrapped error, but preserve it as context.
NOTE: this was possible before, but didn't capture the context in a suitable way for a full chain.
Presenting the full context of an error chain
This implementation, when formatted via the %+v verb, prints the stack traces of the entire error chain, providing maximal traceability of the path that created an error.
Tasks

[ ] Restore backwards compatibility

Let me run gofmt on main we gotta clean it up so I can read this...

OK, @arikkfir thank you for all of the focused work on this. I am reading through it and hope to offer feedback as soon as I can.

Sorry to have ghosted on this. In essence, it's a different approach to error management. Feel free to fork this repo and go in whichever path you feel best.


support loadBalancerSourceRange in gatewayapi spec

What would you like to be added:
add loadBalancerSourceRange into the gateway spec.listeners using the same definition that exist in the service spec:
LoadBalancerSourceRanges []string json:"loadBalancerSourceRanges,omitempty" protobuf:"bytes,9,opt,name=loadBalancerSourceRanges"
Why this is needed:
load-balancer services sourceRange are important for security. but when a gateway is involved, it is the one creating
the load-balancer service.
so when the gateway operator creates it's load-balancer service, the sourceRange is carried to the load-balancer service.
this is a related to https://github.com/kubernetes-sigs/gateway-api/issues/450, except in 450, the text says 'service' where it should say 'gateway'.
This goes with this cilium request: https://github.com/cilium/cilium/issues/32478

not needed


Call DataFrame.map instead of DataFrame.applymap

Fix https://github.com/Stonesjtu/pytorch_memlab/issues/54

LGTM, thanks for fixing it.


$kissenc not defined

Had an error appeared today. I actually managed to make the script work yesterday on firefox. Idk what happened after I tried today. Here's the log.
Loading scripts ...
/Scripts/common.js
/Scripts/aes.js
/Scripts/sha256.min.js
/Scripts/kissenc.min.js?v=2
Starting Kissasian Batch Downloader script...
Starting to fetch links..
Fetching listing 1 [Running Man (Game-Show) Episode 1]
ReferenceError: $kissenc is not defined [Learn More]
Tried it on chrome as well no luck. Also with chrome yesterday it was giving me blank text files. I am logged in with an account on kissasian. Please do help thank you :)

Maybe the kiss* sites codebase has changed a bit.
I am out right now so can't have a full look at it. Will work on it in 4-5 days.
Thank you for your patience. In the meanwhile, please follow #16

thank you :) cheers

Same error as dodieboy.
jquery17.min.js?v=1:4 Uncaught ReferenceError: $kissenc is not defined
at eval (eval at success (:95:5), :1:20)
at Object.success (:95:5)
at o (http://kissasian.com/Scripts/jquery17.min.js?v=1:2:14733)
at Object.fireWith [as resolveWith] (http://kissasian.com/Scripts/jquery17.min.js?v=1:2:15502)
at w (http://kissasian.com/Scripts/jquery17.min.js?v=1:4:12472)
at d (http://kissasian.com/Scripts/jquery17.min.js?v=1:4:18318)
at Object.send (http://kissasian.com/Scripts/jquery17.min.js?v=1:4:18357)
at Function.ajax (http://kissasian.com/Scripts/jquery17.min.js?v=1:4:15044)
at :82:9


Jupyter-like focus indicator missing when selecting notebook layout

Do you want to see an "inactive" (gray color) focus indicator or do you want to see the blue indicator all the time even when the editor is not focused? Do you want to see it when the editor is focused? Trying to figure out how the behavior compares to the border indicator. I can't decide what I want.

Do you want to see it when the editor is focused

this is tracked in https://github.com/microsoft/vscode/issues/125338

Do you want to see an "inactive" (gray color) focus indicator or do you want to see the blue indicator all the time even when the editor is not focused

Right now we didn't show the inactive focus indicator at all when the editor is blurred, which should be fixed.
Verifier, please test with following settings
"notebook.cellFocusIndicator": "gutter",
"workbench.colorCustomizations": {
        "notebook.inactiveFocusedCellBorder": "#ff0000"
    },

click on a cell, focus outside of the notebook editor, and make sure you see the red cell border.


unittesting failed

I am trying to get familar with this library under win10 64 bit ,but failed to pass unittest.So was I missing something?
Thanks in advance.
Launching build\testrun.exe
------------------ TESTS ------------------
Testing xml_test::xml_file_parsing1 .. [error]

 Error: Assert violation
  - in xml_file_parsing1 main.c3:14.

Testing xml_test::xml_file_parsing2 .. [error]

 Error: Unexpected fault 'OUT_OF_MEMORY' was unwrapped!
  - in ensure_capacity mem_allocator.c3:85.

Testing xml_test::xml_file_parsing3 .. [error]

 Error: Assert violation
  - in xml_file_parsing3 main.c3:53.

3 tests run.

Test Result: FAILED. 0 passed, 3 failed.
Program completed with exit code 1.

Will try to check under Windows, works fine currently on linux, so some windows oddity happening here

@LukyGuyLucky
It's fixed now on Windows, please give it a try

Thank you for taking care of this.
Sorry,it failed as the same as previous one does.

@LukyGuyLucky
Okay, I tried to increase the buffer capacity at here
https://github.com/tonis2/xml.c3/blob/main/parser.c3#L183
Can you try to pull and run unit tests again if you get a chance ?
I might need to use dynamic allocator there, but not sure if this is the error causation, cause it somehow doesn't pop-up at my machine :/

Thank you.
I just tested and got below result:
Launching build\testrun.exe
------------------ TESTS ------------------
Testing xml_test::xml_file_parsing1 .. [error]

 Error: Assert violation
  - in xml_file_parsing1 test.c3:14.

Testing xml_test::xml_file_parsing2 .. [error]

 Error: Unexpected fault 'OUT_OF_MEMORY' was unwrapped!
  - in ensure_capacity mem_allocator.c3:85.

Testing xml_test::xml_file_parsing3 .. [error]

 Error: Assert violation
  - in xml_file_parsing3 test.c3:54.

3 tests run.

Test Result: FAILED. 0 passed, 3 failed.
Program completed with exit code 1.


Unitprice precision incorrect due ommited numbers in one-time charges

An incorrect amount is shown in some edge cases when checking out.
For example
A product is €1.00 with 21% tax. Without the tax this will results 0,8264462809917355. When applying the required formula * 100 for cashier results in 82,64462809917355. Adding this result in ChargeItemBuilder will ommit every number after the comma resulting in the price €0,99 when checking out.
Is there a reason why mollie only includes 2 numbers after the comma?

Hi @KumaVolt ,
Can you share a code sample so we can reproduce this issue?
Note that cashier mollie performs all calculations locally.

    public function __invoke(Request $request)
    {
        $user = User::orderBy('id', 'desc')->first();

        $product_price = 1.00;
        $tax_rate = 0.21; // 21% tax rate

        // create a new charge item
        $item = new ChargeItemBuilder($user);
        $item->unitPrice(money($this->priceWithoutTax($product_price, $tax_rate) * 100, 'EUR'));
        $item->description('Product description');
        $item->quantity(1);
        $item->taxPercentage($tax_rate * 100);
        $chargeItem = $item->make();

        // dd($chargeItem); // charge item object omitted numbers that are after the comma

        // generate checkout link and navigate to it
        $charge = $user->newFirstPaymentChargeThroughCheckout();
        $charge->addItem($chargeItem);
        $result = $charge->create();

        // expecting to pay 1 euro
        if(is_a($result, \Laravel\Cashier\Http\RedirectToCheckoutResponse::class)) {
            return $result;
        }
    }

    private function priceWithoutTax($price, $tax_rate)
    {
        return $price / (1 + ($tax_rate));
    }

I made a demo repository for this as wel.
https://github.com/KumaVolt/mollieissue

@sandervanhooft Did you had the time to look into this yet?

On further inspection i found an open pull request in the moneyphp/money library which addresses the issues i am currently facing with calculating the precision in this library.

Hi @KumaVolt,
We have this on the radar, but since it's an edge case / related to MoneyPHP it's not high on the backlog. I am happy to accept a PR if you see a way to fix this sooner.

@sandervanhooft It's not an edge case, we noticed a similar issue and it has nothing to do with MoneyPHP but your helper on line 34 force typecasting every value to integer and thereby hiding the error which the built in MoneyPHP type checking would otherwise throw if you give it something which is not an integer. And how can a servious rounding issue be an edge case in the first place for a payment provider? It's your main purpose that the numbers match.
This is a one line fix and got not addresse for months as I can see now. Just remove the int typecasting and let MoneyPHP throw and error if you don't give it an integer value.
@KumaVolt The workaround we used for now, is a simple round() call before passing the value, since the round() call actually rounds and not just cuts of everything after the "." what the typecasting does.

@Master-Minder as mentioned, I am happy to accept a PR.

@sandervanhooft I can offer you a PR which is absolutly untested, or just tested for this scenario, or do you expect me to do full regression testing? Since the money() helper is used all over the place in your code and I have no idea if this will impact anywhere else.
With a quick look at some of your automated test scripts I also didn't find any test cases covering the scenario that the value passed to the money() helper is not an int, so those tests won't find possible impacts anyway.


(JS-0043) Invalid variable usage

Description
Variables should be used inside of their binding context. This helps avoid difficult bugs with variable hoisting. It is a bad practice to use var declarations because variables declared using var can be accessed in a function-wide scope.
Occurrences
There are 120 occurrences of this issue in the repository.
See all occurrences on DeepSource → app.deepsource.com/gh/EDM115/website/issue/JS-0043/occurrences/

https://www.youtube.com/watch?v=Hd2ZvzwAxio


download the heat log as a csv

In order to share my reading history with others
As a tenant or an advocate
I want to download a CSV file from my tenant page
Adapted from #48 thanks to @oliverbarnes suggestions.

:+1: