[flash_ctrl] ADD TEST FLASH CTRL ERROR PROG TYPE

Perform accesses in order to provoke the 'program type' error.
Test via the Software interface
Signed-off-by: TIM EWINS<EMAIL_ADDRESS>

Please could someone with Azure Access tell me what the error in CI is ?

Please could someone with Azure Access tell me what the error in CI is ?

I just reran the CI. I do not think your PR causes the failure.

Please do not merge until I push new changes

I have updated the following ....
Simplified the constraints, as suggested in simillar PR 12313
Ready for Review

Resubmitted

Some checks are failing ... seems a common proble.  Suspect issues outside this PR.

Please can someone retrigger the checkin checks to see if the errors have cleared ?

Updates pushed

Rebased and ready for merge
This merge will conflict with #12313
Depending on which is merged first the other will need rebasing

Checks need to be re-ran .. something is failing elsewhere

@weicaiyang Rebased and clean ... subject to further PR merges if they occur in the meantime


WIP: Feat/Stripe: Added more object of the stripe products categorie

NOTE: WIP Just looking for some feedback :)
What kind of change does this PR introduce?

Added new product objects: coupons, promotion_codes, tax_codes, tax_rates and shipping rates
Added missing tests for mandates and tokens (TODO)
Restructured docs and code to be grouped like the official stripe docs

What is the current behavior?
Added more object to make the wrapper more complete
What is the new behavior?
You can now access more information from stripe
Additional context
This is still a WIP so will place some comments on my own code to check some things.

Thanks for the PR! Happy to see more objects to be added to this FDW, let us know once it is ready.

hey @nielsbrakel, are you still working on this? I would like to add subscription_items and their usage record summaries to the list of supported objects and am wondering if I should start a new pr, or collaborate on this one?

@psteinroe I'm planning to continue working on it, but can not give a date or time.

closing due to inactivity. please re-open when ready


style(connections):fix some styles

PR Checklist
If you have any questions, you can refer to the Contributing Guide
What is the current behavior?
Please describe the current behavior and link to a relevant issue.
Issue Number
#1319
#1354
What is the new behavior?
Please describe the new behavior or provide screenshots.
Does this PR introduce a breaking change?

[ ] Yes
[x] No

Specific Instructions
Are there any specific instructions or things that should be known prior to review?
Other information

There is a bug in the autosize input of ElementUI, see this link .
I've fixed it.


Added New command spo group get

Adds 'spo group get' command. Closes https://github.com/pnp/office365-cli/issues/1692

Linked Issue

Closes https://github.com/pnp/office365-cli/issues/1692

Thank you! We'll review it shortly 👍

Coverage remained the same at 100.0% when pulling 129b6bf13b04fd9028809bce2b10a902d2546e48 on nanddeepn:master into b53d74b7e37ba66da0c22c68cb80be05832a15e1 on pnp:master.

Merged manually. Thank you!


Fixing error in 1497

Fixes the error described in https://github.com/biocore/qiita/issues/1497.
The warning require more thought since the problem is that for creation and update/extend we are using the same function, but the functionality of such method for update/extend is slightly different (i.e. it should warn if the data is not in the DB already). Doing this efficiently and w/o code duplication is a bit more tricky.

After a discussion with @antgonza we came up with a solution for the warnings too.
This now fully fixes #1497

:+1: if tests pass.

@ElDeveloper @mortonjt able to do a quick review?

Just a couple of very minor comments.  I think it looks good.

:+1:


Skkkdxkjs

[Enter feedback here]

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

ID: 75e6432b-c6ec-e208-5f1c-bbc4fde9929b
Version Independent ID: 9cbb940e-cddd-f82f-e8bb-ee62b17af794
Content: Latest supported Visual C++ Redistributable downloads
Content Source: docs/windows/latest-supported-vc-redist.md
Product: visual-cpp
Technology: cpp-windows
GitHub Login: @corob-msft
Microsoft Alias: corob

Jskd

@Ttioopd
This doesn't appear to be an actionable issue, so I'm closing it.


Delay in loading the credentials

Describe the bug
Hi,
I'm using the AWSSDK.Core in a .NET 6 project and I noticed that in a production hosting environment there is a considerable delay (around 16 seconds) from the moment the AWS credentials are found until the controller action method is actually executed.
The credentials are stored in a shared credentials file, but I'm using a custom name and location.
Registering the AWS Services with a scoped lifetime in the DI container makes things even worse, as this delay can be noticed for each request.
Please see the attached logs and the sample project for more details.
Expected Behavior
When using a shared credentials file, I wouldn't expect any considerable delay between the moment the credentials are found and the action method being executed.
When running the referenced sample project locally on IIS Express I do not encounter this bug.
Please see the following logs from my local machine:
[28-07-2022 13:35:13.586 +02:00  INF]  Request starting HTTP/1.1 GET http://localhost:21956/WeatherForecast - -
[28-07-2022 13:35:13.595 +02:00  INF]  Executing endpoint 'AWS.SES.NET6.Controllers.WeatherForecastController.Get (AWS.SES.NET6)'
[28-07-2022 13:35:13.606 +02:00  INF]  Route matched with {action = "Get", controller = "WeatherForecast"}. Executing controller action with signature System.Collections.Generic.IEnumerable`1[AWS.SES.NET6.WeatherForecast] Get() on controller AWS.SES.NET6.Controllers.WeatherForecastController (AWS.SES.NET6).
[28-07-2022 13:35:13.649 +02:00  INF]  Found AWS credentials for the profile test-profile
[28-07-2022 13:35:13.792 +02:00  INF]  Executing ObjectResult, writing value of type 'AWS.SES.NET6.WeatherForecast[]'.
[28-07-2022 13:35:13.804 +02:00  INF]  Executed action AWS.SES.NET6.Controllers.WeatherForecastController.Get (AWS.SES.NET6) in 193.8871ms
[28-07-2022 13:35:13.807 +02:00  INF]  Executed endpoint 'AWS.SES.NET6.Controllers.WeatherForecastController.Get (AWS.SES.NET6)'
[28-07-2022 13:35:13.808 +02:00  INF]  Request finished HTTP/1.1 GET http://localhost:21956/WeatherForecast - - - 200 - application/json;+charset=utf-8 221.5434ms
Current Behavior
When deploying the app in production environment, there is a consistent delay each time a request is sent.
Please see the following logs from the staging environment:
[28-07-2022 07:05:21.802 -04:00  INF]  Request starting HTTP/1.1 GET http://localhost:89/weatherforecast - -
[28-07-2022 07:05:21.803 -04:00  INF]  Executing endpoint 'AWS.SES.NET6.Controllers.WeatherForecastController.Get (AWS.SES.NET6)'
[28-07-2022 07:05:21.804 -04:00  INF]  Route matched with {action = "Get", controller = "WeatherForecast"}. Executing controller action with signature System.Collections.Generic.IEnumerable`1[AWS.SES.NET6.WeatherForecast] Get() on controller AWS.SES.NET6.Controllers.WeatherForecastController (AWS.SES.NET6).
[28-07-2022 07:05:21.805 -04:00  INF]  Found AWS credentials for the profile test-profile
[28-07-2022 07:05:37.574 -04:00  INF]  Executing ObjectResult, writing value of type 'AWS.SES.NET6.WeatherForecast[]'.
[28-07-2022 07:05:37.575 -04:00  INF]  Executed action AWS.SES.NET6.Controllers.WeatherForecastController.Get (AWS.SES.NET6) in 15771.0262ms
[28-07-2022 07:05:37.575 -04:00  INF]  Executed endpoint 'AWS.SES.NET6.Controllers.WeatherForecastController.Get (AWS.SES.NET6)'
[28-07-2022 07:05:37.576 -04:00  INF]  Request finished HTTP/1.1 GET http://localhost:89/weatherforecast - - - 200 - application/json;+charset=utf-8 15773.1153ms
Reproduction Steps
I put up together a sample project which can be cloned from this repo
https://github.com/dumitrascuSorin/AwsCredentialsSample
Possible Solution
No response
Additional Information/Context
No response
AWS .NET SDK and/or Package version used
AWSSDK.Core <IP_ADDRESS>
AWSSDK.Extensions.NETCore.Setup 3.7.2
AWSSDK.SimpleEmail <IP_ADDRESS>
Targeted .NET Platform
.NET 6
Operating System and version
Local machine:
Windows 10 Enterprise
Version 21H2
OS build 19044.1826
Staging environment
Windows Server 2019 Standard
VErsion 1809
OS Build 17763.3165

FYI, I just downgraded the AWSSDK.Extensions.NETCore.Setup package to version 3.7.0 from 3.7.2 and the delay is no longer there

FYI, I just downgraded the AWSSDK.Extensions.NETCore.Setup package to version 3.7.0 from 3.7.2 and the delay is no longer there

Could be related to https://github.com/aws/aws-sdk-net/issues/2000. @dumitrascuSorin Could we close this issue as duplicate of https://github.com/aws/aws-sdk-net/issues/2000?

Sure, this can be closed as a duplicate of issue 2000


add validation to export page

🚀 Deployed on https://62d9eea03489d415f9e651b0--cozy-fox-0defec.netlify.app


[Feature Request] - Chaining templates

It would be great if we could chain templates to avoid cluttering and re-usability.
For example:
Snippet 1:
"This is the text for snippet 1.
[Signature A]"
Snippet 2:
"This is the text for snippet 2.
[Signature A]"
Signature A:
"Footer text"
So when you use Snippet 1 or 2 it would pull in the text from Signature A to be pasted from the clipboard.

Do you mean functions like AText or TextExpander? One Chinese guy made a kind of CodeExpander that seemed to do that, as did atext or TextExpander
Look at this:
https://textexpander.com/
https://codeexpander.com/en/
But I don't know how it was implemented in this project anymore :(


Add AKS-EE support instructions

AKS Edge Essentials does not provide a local-path storage that may cause issues with Dapr installation. Adding AKS-EE specific instructions.
Thank you for helping make the Dapr documentation better!
Please follow this checklist before submitting:

[ ] Commits are signed with Developer Certificate of Origin (DCO - learn more)
[ ] Read the contribution guide
[ ] Commands include options for Linux, MacOS, and Windows within codetabs
[ ] New file and folder names are globally unique
[ ] Page references use shortcodes instead of markdown or URL links
[ ] Images use HTML style and have alternative text
[ ] Places where multiple code/command options are given have codetabs

In addition, please fill out the following to help reviewers understand this pull request:
Description
Microsoft AKS Edge Essentials does not provide a local-path storage class by default, which may cause issues with Dapr deployments. The PR aims to add AKS-EE specific instructions to get some AKS-EE specific instruction to get Dapr working.
Issue reference

@msfussell  @hhunter-ms  who would merge this PR? Is there anything else needed I need to do?

@fcabrera23 can you look at the link validation failure here? https://github.com/dapr/docs/actions/runs/6164365582/job/16729931673?pr=3716

@yaron2 @hhunter-ms  - That link was not part of my commit, it was already there. I've only updated the AKS-EE part, not the AKS one.

@fcabrera23 I'm not sure how to fix that link - not sure why it's failing for you and it didn't fail for my PR with the same doc. I'll merge either way once it builds.


Mobaxterm issue

Why am I seeing this?
Also when I tried to login it showed this:

though after entering the password I was on this window:

root@pynqz2_dpu1:~# ls
install.sh  pkgs  samples
root@pynqz2_dpu1:~# cd zynq7020_dnndk_v3.1
-sh: cd: zynq7020_dnndk_v3.1: No such file or directory
root@pynqz2_dpu1:~# ./install.sh
-sh: ./install.sh: Permission denied

Hi!
It looks like the SD card image you are using does not include the DNNDK drivers. The zynq7020_dnndk_v3.1 folder contained them but I don't see that folder on there. You can dowload it here on this google drive. Then drag the folder to the left side of mobaXterm and it should be there ready to be accessed.
If the permission is denied use the chmod +x zynq7020_dnndk_v3.1 command and try again.
Good luck!

okay I did that

this seems alright right? should I ignore  /etc/modules: No such file or directory ?

UMM

So I have looked for every possible solution I could find, I even downloaded the plugin from mobaxterm official website

and placed it in the right folder:

The development.mxt3 is the plugin file for GCC and G++, but its still not working

Look, the problem seems to be that you don't have the correct SD card image. Try to use the one I made available as it already has all the necessary libraries.
gcc should be one of those packages and so, as it is not present there, it means the SD car image is not correct.
Download it here and then flash it on the micro SD card.
Good luck!

Certainly, I will do that and inform you accordingly. By the way, I have a
question regarding the implementation of ML, specifically YOLO, on PYNQ
(Z1/Z2). I have reviewed numerous projects and documents, but many of them
use a different approach than yours. For instance, some have created an IP
in Vivado HLS for YOLO, loaded it onto the board, and then used Jupyter to
run the model. While I don't fully understand that process, I am curious
about the rationale behind your approach. Could you please explain the
reasoning and any potential advantages of your method? And also can you
explain the flow used in this
https://iopscience.iop.org/article/10.1088/1742-6596/2405/1/012011/pdf
and this https://github.com/dhm2013724/yolov2_xilinx_fpga, if you know
about it. Thank you.
On Fri, Jul 26, 2024 at 2:05 AM André Araújo @.***>
wrote:

Look, the problem seems to be that you don't have the correct SD card
image. Try to use the one I made available as it already has all the
necessary libraries.
gcc should be one of those packages and so, as it is not present there, it
means the SD car image is not correct.
Download it here
https://drive.google.com/file/d/1ETyM51KSWX_h1DVq9ptHPIux89oTrNPy/view?usp=drive_linkand
then flash it on the micro SD card.
Good luck!
—
Reply to this email directly, view it on GitHub
https://github.com/andre1araujo/YOLO-on-PYNQ-Z2/issues/10#issuecomment-2251348930,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/A5RZEIUDY6CPI5BCMTTFZ6LZOFOPHAVCNFSM6AAAAABLOX6YMOVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDENJRGM2DQOJTGA
.
You are receiving this because you authored the thread.Message ID:
@.***>

Thank you! I also added you on the end of the github page as a thank you for your help on this project!
About the way I did this project, it was based on wutianze's  work. I was assigned to do this project and after months, I found his work and tried to replicate it. For someone with basically no experience as me, I had contact him on some parts, had to trie to look for other simillar implementations and so on. What I am basically saying is that even I don't fully understand the flow haha.
In general, it goes like this:

We first implement the hardware called DPU. This is a Intellectual Property (IP) core from Xilinx that can basically implement any type of CNN on the FPGA part of the board. We do this part in Vivado so we place the DPU and connect it to the Processing System (PL) which has the CPU of the PYNQ-Z2 and make other connections. We basically describe how the hardware should be layed out on the FPGA so that we can later use the DPU to make inference on any neural network we want.
Then the Petalinux only serves as a way to create a SD card image so when we insert the micro SD card on the board, it constructs the Hardware we designed previously. It creates the DPU and the connections. The SD card image also contains Linux so we can use c++ programs and access the DPU the way Xilinx desired.
We have the board ready to deploy any neural network we desire but to deploy for example YOLO, we have to use the Xilinx tool DNNDK. This tool is equivalent to the Vitis AI of today but an older version. Whith this tool, we select a neural network, quantize it and the create a file that later comunicates with the DPU and tells it how to arrange for deploying our desirde neural network.
Finally we just insert the neural network file on the board Linux environment, add a c++ script that uses the DPU libraries and we can make inference with our model.

Sorry for the long explanation. Both the approaches you linked use soemething called HLS which is High Level Synthesis. I never went on that area but what I think it is, is basically program the FPGA using High Level code like c++. So, you create a c++ program for a neural network you want and then point some parts where the program can be parallel, just like multithreading. This approach is not typically as optimized as programming in VHDL or Verilog your own neural network but surelly is a lot easier and faster. Their process is not as different as mine as it might seem:

They design the neural network they want using HLS language. This allows them to create a IP block like the DPU but this one only implements one neural network.
Then they connect their IP block to the board PS so it communicates with the CPU. Normally this is done for shceduling the communication between data channels and so on. It also is necessary so they can send images from the PS to the FPGA, where the IP is present.
I am not sure about the next step but they might use Petalinux to use a Linux OS so they can control the CPU better. Using a c++ script like on my project is easier to send images to the IP block.

I think the process of using HLS is a broad area of its own and might be really complicated. I don't have that experience so I can't tell for sure. But the process is a little different than what I did but kind of the same on some points haha.
The DPU solution from Xilinx seems to be the easiest for unexperienced people like me. It is generally a lot more easy on other boards with Zynq Ultrascale+ chips because now Vitis AI and DPU are a lot more optimized and easier to use. The challenge for the PYNQ-Z2 and other zynq-7000 familly chips is that Vitis AI and the DPU are no longer supported for them and that is why I used DNNDK which was a older version.
My masters degree thesys might be to create a better and more accessible solution for these boards as they seem to be forgotten by Xilinx on the CNN acceleration. My approach will not be HLS but RTL. RTL includes more specific languages like VHDL and Verilog. Essentially do the same as what the people on the paper did with HLS but more optimized. But we will see,
Sorry about the long text but I hope you understand a little bit the different approaches. There are no good (accessible) solutions for deploying object detection in real time on these boards yet.
Good luck!

Haha, I don't think there was any need to include my name, since you had
already completed the project and I was just learning. I had many doubts
along the way, and you helped me each time. I could say I was the Andre to
your Wutianze. I had been working on this project for a long time; when I
started, I didn't even know how to use Vivado and generate IP. Over time, I
learned to use HLS and Verilog/Verilog HDL to create IPs for image resizing
and other tasks.
Initially, I couldn't figure out the flow for implementing object
identification on an FPGA. I knew I needed to create an IP and a model, and
that I could use Jupyter to access it, but I wasn't sure how to proceed. I
talked to several seniors, and most had written Verilog code or used the
HLS method, writing YOLO v2 code in C++ because it was available online and
widely used.
My goal was to implement any model on an FPGA, so I was exploring different
flows. I encountered a method that involved converting the trained model
into a hardware-understood file, essentially HDL, and then calling an API
on Jupyter to implement the model. I wasn't sure how this worked or if it
would work at all. What I don't understand is how a trained model is
deployed on the board, IP is just for the neural network, but how are the
model weights being dumped on the board is something I haven't figured out
till date in the HLS or RTL process flows.
Then, I came across your GitHub and read the complete documentation, which
gave me some clarity. It seems both flows (yours and the HLS one) aim to
achieve the same purpose. The difference might be that, in your flow, you
are creating an OS for the entire system, focusing on object detection. In
the HLS flow, it seems like one has to write code for every task on Jupyter
to run the model.
When you mention RTL, do you mean writing the neural network using RTL?
Regards
Aryan Shah

Don't worry, you helped me validate the instructions of the tutorial and that is really helpfull.
I didn't know you had some experience with HLS but that is good to know because I might disturb you one day with that haha.
Yes, I meant writting the neural network in RTL. Seems like a suicide mission but lets try haha. There are lots of work on this field and I will try to implement the most used architectures and see where it goes. I didn't finish the research but it seems that the CNN accelerators follow all some simillar architectures. Because Xilinx never broght the DPU back to the Zynq-7000 chip, I am going to try and make a object detector with newer optimization techniques to see if I can achieve some decent real time performance on this low cost board.

The optimization techniques normally used to compress neural networks is quantization and prunning. DNNDK did not use prunning. They explicitly said it was not supported on that version. I suspect they now use prunning on Vitis AI.
There are some frameworks for accelerating neural networks on the Zynq-7000 chips like FINN from xilinx or Brevitas also from Xilinx. They are more easy to work with but I suspect the results will be slighly worse. But we never know!
About the numbers above the boxes on the detections, it represents the accuracy of the detection. 0,99 is 99% accuracy.
I hope this helps!

Hey Andre,
Deployment on PYNQ z2 board has been successful however we have to repeat
this
https://andre-araujo.gitbook.io/yolo-on-pynq-z2/dpu-implementation/implementing-the-dpu-on-a-sd-card-image#sd-card-image
steps, after every time we power off the board. Like for example if I have
copied those image files on SD card, then setup USB connection using putty,
and everything is working fine even on MobaXterm. But after few runs if I
power off the board and remove all systems and try again connecting to
putty or MobaXterm that doesn't work. I will have to format the SD card
again , repeat the step mentioned here
https://andre-araujo.gitbook.io/yolo-on-pynq-z2/dpu-implementation/implementing-the-dpu-on-a-sd-card-image#sd-card-image,
and then again setup the whole system then it works. Does that make sense?
Is that normal? Do I have to do that again and again every time I want to
perform the task?
Regards
Aryan Shah
On Mon, Jul 29, 2024 at 8:14 PM André Araújo @.***>
wrote:

The optimization techniques normally used to compress neural networks is
quantization and prunning. DNNDK did not use prunning. They explicitly said
it was not supported on that version. I suspect they now use prunning on
Vitis AI.
There are some frameworks for accelerating neural networks on the
Zynq-7000 chips like FINN https://github.com/Xilinx/finnfrom xilinx or
Brevitas https://github.com/Xilinx/brevitas also from Xilinx. They are
more easy to work with but I suspect the results will be slighly worse. But
we never know!
About the numbers above the boxes on the detections, it represents the
accuracy of the detection. 0,99 is 99% accuracy.
I hope this helps!
—
Reply to this email directly, view it on GitHub
https://github.com/andre1araujo/YOLO-on-PYNQ-Z2/issues/10#issuecomment-2256136463,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/A5RZEIQIYQIICIO7PPIHLHDZOZIM7AVCNFSM6AAAAABLOX6YMOVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDENJWGEZTMNBWGM
.
You are receiving this because you authored the thread.Message ID:
@.***>

Hello Aryan.
No, that is not normal behaviour at all. The image shuld work evry time.
I don't see why that problem is happening, but I might suspect that due to poor description in the tutorial, you are not executing these commands each time you power up the board:
cd zynq7020_dnndk_v3.1
./install.sh

If that is the case let me know. If you already did those commands every time you restart the board and the problem pressists let me know so we can try to figure out the problem.
Good luck!

The problem is I am not able to establish connection after powering it up
again, like putty glitches up, can't reconnect even on MobaXterm so I am
not even able to go further and run those commands.
On Fri, Aug 9, 2024 at 12:49 PM André Araújo @.***>
wrote:

Hello Aryan.
No, that is not normal behaviour at all. The image shuld work evry time.
I don't see why that problem is happening, but I might suspect that due to
poor description in the tutorial, you are not executing these commands each
time you power up the board:
cd zynq7020_dnndk_v3.1
./install.sh
If that is the case let me know. If you already did those commands every
time you restart the board and the problem pressists let me know so we can
try to figure out the problem.
Good luck!
—
Reply to this email directly, view it on GitHub
https://github.com/andre1araujo/YOLO-on-PYNQ-Z2/issues/10#issuecomment-2277308286,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/A5RZEIT3YWHAOVEKOE2XLWDZQRUQ7AVCNFSM6AAAAABLOX6YMOVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDENZXGMYDQMRYGY
.
You are receiving this because you authored the thread.Message ID:
@.***>

That is really strange. I never saw that behaviour before.
Can you confirm that the green DONE LED lights up? It probably is something wrong with the SD card image. Can you try it with a different micro SD card?
If you see what LEDs light when the board crahses, it may help identify the cause. I will have to look it up.

Can you just tell me what  "zynq7020_dnndk_v3.1" is and where it came from?
Like how did you generate that file? I am using the file from your github
itself.
[image: image.png]

Hi!
Sorry for the late response.
That zynq7020_dnndk_v3.1 folder comes from the DNNDK package you downloaded for the second chapter of the tutorial. It should contain a folder related to the devices and one of them is the "ZedBoard". I used that folder because that board has the same chip as the PYNQ-Z2. Then I renamed the folder to "zynq7020_dnndk_v3.1". That contains the libraries for DNNDK.
The problem you have might be related to something on the process of the SD card image build or the problem you mentioned on this issue.
I can try to look that up but it will be easier for you to make experiments.
Sorry about that. I will try to think about the problem as soon as i can and then I make a suggestion.
Good luck!

Hi!
I will be closing this issue due to inactivity. If you need further assistance make sure to open a new one or just contact me.
Thank you for understanding! :)


Abort trap: 6

After upgrading to 1.0.0 I'm getting fairly inconsistent errors along the lines of:
Assertion failed: (handle->flags & UV_CLOSING), function uv__finish_close, file ../deps/uv/src/unix/core.c, line 171.
Abort trap: 6

Seems to be an issue with node-sass that has persisted into the pre-release sass-loader is using?
https://github.com/sass/node-sass/issues/713

I'm also getting this.

Yep, I was also getting these. You need to stick with sass-loader 0.x until this is fixed. :unamused:

So close! The brief 30 seconds of custom importers was bliss.

+1 for this. =)

+1

Also seeing the same behaviour.

+1
had to back up to<EMAIL_ADDRESS>because 0.5.0 has a dependency on<EMAIL_ADDRESS>which doesn't exist

Thanks for the fix here @andycmaj, struggling with the same issue!

I've managed to write a version of sass-loader that provides the same features while depending on node-sass 2.1.1. It's on a separate branch and can be installed via npm i<EMAIL_ADDRESS>Please note, that sass-loader 0.6.0 is listing node-sass as peerDependency as well.

@jhnns I'm still seeing this problem even with<EMAIL_ADDRESS>and<EMAIL_ADDRESS>Is there some feature I shouldn't be using or something?

Just wanted to iterate that this loader has been extremely troublesome for me. Slowing me down massively. Sometimes it works but mostly it crashes. At least i know what's causing it.

For those still looking for a workaround<EMAIL_ADDRESS>does not work neither as it depends on<EMAIL_ADDRESS>which, according to the comments in https://github.com/sass/node-sass/issues/713, is also one of the versions that provoke these errors.
The latest working version seems to be<EMAIL_ADDRESS>but even explicitely depending on<EMAIL_ADDRESS>as a peer dependency is not a workaround because it does not satisfy ^2.1.1.

Afaik<EMAIL_ADDRESS>is the last version that does not bother you with errors

Unfortunately<EMAIL_ADDRESS>has dependency "node-sass": "^2.0.1" which means that the latest<EMAIL_ADDRESS>gets installed.

For those of you trying to get this to work, you need to use sass/node-sass#v3.0.0-alpha.1, with<EMAIL_ADDRESS>. You can see a working version of it here in a project I was recently working on https://github.com/alexhawkins/UberDoku  Look at my package.json and webpack.config

Same here; I had to downgrade to 0.4.2 (…and not 0.6.0, as recommended in the README) to get rid of the errors.
sass/node-sass@fce099346a0c6e6fc6b9c8d4543fbbf077025118 allegedly fixes this issue, however even when using it, I kept getting random crashes with both<EMAIL_ADDRESS>and 1.0.1.

@raphaelsaunier I've updated the README

This seems totally stable for me now with the latest node-sass release - and pulling "sass-loader": "1.0.0" from npm gets the updated<EMAIL_ADDRESS>despite sass-loader still specifying the beta.
I haven't tested with a large codebase full of imports yet, but I will likely get to it over the weekend.

@haustraliaer that are good news. Please let me know if everything works as expected. I'll remove the note in the README then.

This should probably be a separate issue, but after upgrading to 1.0.2 webpack will just completely hang.

Yeah I'm on 1.0.0 so haven't seen that bug - you should log it separately.

@alexduan this does not happen in our tests, so please try to narrow it down and open a separate issue.


Enable cookie popup management by default

Task/Issue URL: https://app.asana.com/0/715106103902962/1206285795453160/f
Description:
Changes the default user setting value for cookie autoconsent to enabled. Since the setting is enabled by default there is no point in showing the dialog.
The dialog was disabled anyway due to https://app.asana.com/0/1203936086921904/1204496002772588/f so the UI code was dead anyway.
Steps to test this PR:
Test enabled by default

Make sure cookie popup setting was not toggled or remove the app
Run updated version
Go to Settings, “Manage Cookie Pop-ups” should be enabled

Test user setting is respected

Disable CPM
Visit webpage with manageable cookie prompt (e.g. facebook.com)
Ensure cookies were not managed automatically
Enable CPM
Reload webpage
Ensure cookies were managed automatically

Converted back to draft due to additional circumstances mentioned in https://app.asana.com/0/0/1206430574685321/1206433715557448/f


MY_GITHUB_TOKEN not defined

I have been trying to automate the process of assigning issues to an organization project from a repository, here's my main.yml :
name: Auto Assign to Project

on:
  issues:
    types: [opened, labeled]
  pull_request:
    types: [opened, labeled]
env:
  MY_GITHUB_TOKEN: ${{ secrets.MY_GITHUB_TOKEN }}

jobs:
  assign_one_project:
    runs-on: ubuntu-latest
    name: Assign to One Project
    steps:
    - name: Assign NEW issues and NEW pull requests to project Global Feature progress
      uses<EMAIL_ADDRESS>      if: github.event.action == 'opened'
      with:
        project: 'https://github.com/orgs/7map/projects/1'
        column_name: 'To do'

I have created a personal token for the organization (from my user settings) and added it to the repository secrets but every time I run the job, it fails with a MY_GITHUB_TOKEN not defined error message.
Some help would be greatly appreciated 😄

I have the same problem when trying to assign a general project not for a specific repository.

I have no idea what I did but it somehow solved itself after a couple of days of successive failures.
As a message for future lost people ending up on this thread, here's the current state of my repository : I have a token I generated from my own account (I'm the organization's owner) added as a repository secret (Settings -> Secrets -> Actions -> Repository secrets) and it apparently kind of works.


Arbitrary User Input?

I'm curious - what's pydot's policy on data sanitization? Should I trust pydot to sanitize fields (like the label) or should I do that myself?
Sanitizing data is always a good idea anyway, but I just wanted to check

I think it's safe to assume for the user that we don't sanitize input at all, as pydot is just a wrapper.
Maybe it's a good idea to put this information in the README.

That's a good assumption to know about. Maybe it should go in secuirty.md? I think that's a matter of preference, unless you put it somewhere near the bottom of the readme.
https://github.com/pydot/pydot/blob/d8645e72a3c97bdfd8a4abeaa7118090e1723e3c/src/pydot/core.py#L218-L226 was what I was most worried about, but per https://stackoverflow.com/a/52022409/10442089 , this should be safe from injection

Oh, that's what you meant. If I remember correctly, there is no user input in that command, all parameters are "fixed".
But still, it's a good thing to look at and document.

Yeah, my current use case is a adding this to my storywriting Discord bot. I'm planning on using this to let users create character graphs to keep track of what's going on. Given how insane the stories generally end up being (the bot chooses random server members and to continue writing one big story, so it bounces around a bunch), I think this will be a huge help for a lot of servers:

My concern is whether allowing users arbitrary access to create custom labels will open me up to command injection, or whether I should sanitize user input before passing it to Graphviz
This library has been doing a quite a bit of heavy lifting for that feature btw! You (along with the past maintainers and the Graphviz developers) have done a great job with this library!

I think you should be "as safe as graphviz is" from the possibility of code injection, which should be pretty safe.
The subprocess call, as you noted, is safe (and as @lkk7 said, it includes no user input in the arguments).
And even graphviz's "HTML-like labels" (which we don't even parse other than as opaque string values, so there's no chance of us executing anything untoward) — are referred to that way specifically because they implement, "by hand" in the graphviz source, only a very small subset of HTML. Nobody's going to manage any script injections or anything like that, simply because it doesn't support any scripting at all, and will cheerfully ignore any tags other than the dozen or so it supports. The most advanced markup available is <TABLE>.


[Backport release-24.05] forgejo: 7.0.10 -> 7.0.11

Release Notes: https://codeberg.org/forgejo/forgejo/milestone/8609
Cherry Pick check is expected to fail

Things done

Built on platform(s)

[ ] x86_64-linux
[ ] aarch64-linux
[ ] x86_64-darwin
[ ] aarch64-darwin

For non-Linux: Is sandboxing enabled in nix.conf? (See Nix manual)

[ ] sandbox = relaxed
[ ] sandbox = true

[ ] Tested, as applicable:

NixOS test(s) (look inside nixos/tests)
and/or package tests
or, for functions and "core" functionality, tests in lib/tests or pkgs/test
made sure NixOS tests are linked to the relevant packages

[ ] Tested compilation of all packages that depend on this change using nix-shell -p nixpkgs-review --run "nixpkgs-review rev HEAD". Note: all changes have to be committed, also see nixpkgs-review usage
[ ] Tested basic functionality of all binary files (usually in ./result/bin/)
25.05 Release Notes (or backporting 24.11 and 25.05 Release notes)

[ ] (Package updates) Added a release notes entry if the change is major or breaking
[ ] (Module updates) Added a release notes entry if the change is significant
[ ] (Module addition) Added a release notes entry if adding a new NixOS module

[ ] Fits CONTRIBUTING.md.

Add a :+1: reaction to pull requests you find important.

vm tests pass
nix-build -A forgejo.tests
/nix/store/rqnaxlnzyb8pyffhv3qidgwjz92jrp2i-vm-test-run-forgejo-mysql
/nix/store/9rmc0bf3sy8r78ll55brymzsxb62rrpb-vm-test-run-forgejo-postgres
/nix/store/3kijxxqqaa7ilba0mx8hvb14gdhlj46y-vm-test-run-forgejo-sqlite3

nixpkgs-review result
Generated using nixpkgs-review.
Command: nixpkgs-review pr 356533

x86_64-linux

:white_check_mark: 2 packages built:

forgejo
forgejo.data

# nix-build -A forgejo.tests
/nix/store/rqnaxlnzyb8pyffhv3qidgwjz92jrp2i-vm-test-run-forgejo-mysql
/nix/store/9rmc0bf3sy8r78ll55brymzsxb62rrpb-vm-test-run-forgejo-postgres
/nix/store/3kijxxqqaa7ilba0mx8hvb14gdhlj46y-vm-test-run-forgejo-sqlite3

This failed for me a few times on aarch64-linux, in the check phase. I'll keep an eye on hydra to make sure it succeeds.
       > --- FAIL: TestWorkerPoolQueueActiveWorkers (2.04s)
       >     workerqueue_test.go:200:
       >         	Error Trace:	/build/source/modules/queue/workerqueue_test.go:200
       >         	Error:      	Not equal:
       >         	           	expected: 0
       >         	           	actual  : 1
       >         	Test:       	TestWorkerPoolQueueActiveWorkers
       >     workerqueue_test.go:216:
       >         	Error Trace:	/build/source/modules/queue/workerqueue_test.go:216
       >         	Error:      	Not equal:
       >         	           	expected: 0
       >         	           	actual  : 3
       >         	Test:       	TestWorkerPoolQueueActiveWorkers


[Feature Request] Auto Support list

Can we have auto support list like autoskill list? or merged with it? like for farming qp I like to use auto skill slot 1 w/ support with mlb monalisa but for event I use autoskill slot 2 w/ mlb event ce's
thanks

I'm personally against it. The settings file would get too bloated as there are many parameters for support selection. Just make separate files.

I had made this modular before where you could select support and skill lists in similar ways at run time. But when I had friends give those a try, they complained that starting the script was a pain because you had to navigate so many menus to get it working. Thus, I never tried to pull it into the main. Best I think we could do is allow players to list CEs and Servants to be used in the config file and then allowing them to select which they want to use on that specific run.

Duplicate of #101


Update dropwizard-core, dropwizard-forms, ... to 2.0.23

Updates

io.dropwizard:dropwizard-core
io.dropwizard:dropwizard-forms
io.dropwizard:dropwizard-testing

from 1.3.29 to 2.0.23.
GitHub Release Notes - Release Notes - Version Diff
I'll automatically update this PR to resolve conflicts as long as you don't change it yourself.
If you'd like to skip this version, you can just close this PR. If you have any feedback, just mention me in the comments below.
Configure Scala Steward for your repository with a .scala-steward.conf file.
Have a fantastic day writing Scala!

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

labels: library-update, semver-major

Superseded by #1177.


Fixes #2441 - Make TaskReplaceActor wait after killing

Comments addresed

Addressed the rest of the commits.

I like it! Thanks!


Improves robustness of sanitizer parsing, fixes memsan parsing bug.

Parsing everything via string manipulation is messy, sigh.  This strictly improves the likelihood of sanitizer report parsing success by stripping whitespace, and wraps a couple of stray casts in try/catches (that weren't previously).

Apologies for the branch name --- I had started it with the intention of doing the thing I said I was doing, then realized there was some subtlety in some of the sanitizer report parsing that should be improved.  So I did that, made that a PR, and then can get back to the other thing.

Hold up, sigh.


Image name mixup

If I use the alias below to make a native image that includes support for js, I somehow end up with an executable called js instead of the expected name (optikon). This is minor but it may indicate some error in the parsing of the command line parameters:
{:native-image {:extra-deps {clj.native-image {:git/url "https://github.com/taylorwood/clj.native-image.git"
                                               :sha     "498baa963e914fd817dbf33ea251729efd0c8f95"}}
                :main-opts  ["-m clj.native-image optikon.core"
                             "-H:Name=optikon"
                             "--language:js"]}
 :jvm-opts   ["-Dclojure.compiler.direct-linking=true"]}

This is apparently by design with the native-image --language flag: https://github.com/oracle/graal/issues/1032
If you swap the argument order it should work, or at least it did in my quick local test:
:main-opts ["-m clj.native-image foo.core"
            "--language:js"
            "-H:Name=foo"]