500 - Internal Server Error Code: PROXY_MISSING_CONFIG

Issue: I get the error 500 - Internal Server Error Code: PROXY_MISSING_CONFIG when going to the output cloudfront_domain_name url
Versions: v1.0.0-canary.5,  v0.12.2
Example: complete
This is following the steps in the readme exactly, I do see the readme says version 1 isn't supported yet in the example. So I tried 0.12.2 but that still failed. Any ideas? I'd love to use this project and would even contribute the fixes if pointed in the right direction thanks!

Hi,
mixing the CLI version (v1.0.0-canary.5) and the Terraform module version (v0.12.2) will not work since the upcoming v1.0.0 release is incompatible with previous versions of this module.
I recommend to use the Atomic Deployments Example when you want to try the latest features (It is based on the complete example, but requires a custom domain).
Otherwise I recommend to use the previous release (v0.13.x) until we updated the other examples.
You can find the examples of the previous release in the branch v0.x: https://github.com/milliHQ/terraform-aws-next-js/tree/v0.x/examples

Hi I tried Atomic Deployments Example but in deploy step I am getting permission denied though I have administrator access while using
tf-next deploy --endpoint https://<api-id>.execute-api.<region>.amazonaws.com

Issue: I get the error 500 - Internal Server Error Code: PROXY_MISSING_CONFIG when going to the output cloudfront_domain_name url Versions: v1.0.0-canary.5, v0.12.2 Example: complete
This is following the steps in the readme exactly, I do see the readme says version 1 isn't supported yet in the example. So I tried 0.12.2 but that still failed. Any ideas? I'd love to use this project and would even contribute the fixes if pointed in the right direction thanks!

Hi, mixing the CLI version (v1.0.0-canary.5) and the Terraform module version (v0.12.2) will not work since the upcoming v1.0.0 release is incompatible with previous versions of this module.
I recommend to use the Atomic Deployments Example when you want to try the latest features (It is based on the complete example, but requires a custom domain).
Otherwise I recommend to use the previous release (v0.13.x) until we updated the other examples. You can find the examples of the previous release in the branch v0.x: https://github.com/milliHQ/terraform-aws-next-js/tree/v0.x/examples

Hi @ofhouse I tried Atomic Deployments Example but in deploy step I am getting permission denied error though I have administrator access while using
tf-next deploy --endpoint https://<api-id>.execute-api.<region>.amazonaws.com

Issue: I get the error 500 - Internal Server Error Code: PROXY_MISSING_CONFIG when going to the output cloudfront_domain_name url Versions: v1.0.0-canary.5, v0.12.2 Example: complete
This is following the steps in the readme exactly, I do see the readme says version 1 isn't supported yet in the example. So I tried 0.12.2 but that still failed. Any ideas? I'd love to use this project and would even contribute the fixes if pointed in the right direction thanks!

@zachfagerness Can you let me know how you ever able to successfully used this example

Hi, any news on this issue, I faced exact the same problem even I tried atomic-deployments in the link  https://github.com/milliHQ/terraform-aws-next-js/tree/main/examples/atomic-deployments

@caophuong2012 try
tf-next deploy --endpoint https://.execute-api..amazonaws.com --profile your_profile_name_with_access

It should not be the problem, your setting is about authentication to AWS.

@caophuong2012 did you change build folder to .next in next.config.js ?

Hi @kethan-drishya how to do it? Can you explain more please

Hi @caophuong2012 if you are using next.js for building your application then create a next.config.js file and use below lines

module.exports = withBundleAnalyzer({
  distDir:  '.next',
  poweredByHeader: false,
  trailingSlash: true,
  reactStrictMode: true,
  images: {
    domains: ['www.gravatar.com', 'i.picsum.photos'],
    loader: 'akamai',
    path: '',
  },

  target: 'serverless',
  webpack: (config, { isServer }) => {
    if (!isServer) {
      config.resolve.fallback.fs = false;
    }
    return config;
  }
});

See if it works for you

I am having the same issue. Has anyone ever figured the 500 Error out?

@kethan-drishya @caophuong2012 ever figured out how to work around this problem? I am trying to deploy using canary 5 but got blocked because of this issue

You can try above solution

Hi @caophuong2012 if you are using next.js for building your application then create a next.config.js file and use below lines

module.exports = withBundleAnalyzer({
  distDir:  '.next',
  poweredByHeader: false,
  trailingSlash: true,
  reactStrictMode: true,
  images: {
    domains: ['www.gravatar.com', 'i.picsum.photos'],
    loader: 'akamai',
    path: '',
  },

  target: 'serverless',
  webpack: (config, { isServer }) => {
    if (!isServer) {
      config.resolve.fallback.fs = false;
    }
    return config;
  }
});

See if it works for you

it did not work for me. what exactly is the problem here? why am I getting a proxy error here?

As per my understanding behind the scenes it is using vercel so build folder should be .next. are you using latest tf-next ?

@ofhouse so I followed the example step by step, I created a next app using npx create-next-app@latest --ts, I managed to deploy it following the atomic deployments example, however I am facing the following problems:

I have a domain hooked up to Route53, lets say its called example.com
When I deploy, I get some url .example.com and when I visit it I am able to see my app.. however, if I try to access the api route /api/hello, I am also getting 500 Internal Server Error with Code: PROXY_ERROR
If I try to access my original domain example.com I am getting 500 Internal Server Error with Code: PROXY_MISSING_CONFIG

I would really appreciate any input to figure this out as, and thank you for creating this in the first place, regards!

@ofhouse so I followed the example step by step, I created a next app using npx create-next-app@latest --ts, I managed to deploy it following the atomic deployments example, however I am facing the following problems:

I have a domain hooked up to Route53, lets say its called example.com
When I deploy, I get some url .example.com and when I visit it I am able to see my app.. however, if I try to access the api route /api/hello, I am also getting 500 Internal Server Error with Code: PROXY_ERROR
If I try to access my original domain example.com I am getting 500 Internal Server Error with Code: PROXY_MISSING_CONFIG

I would really appreciate any input to figure this out, and thank you for creating this in the first place, regards!
PS: I actually created a folder called terraform in the root of my nextjs app and I put my main.tf in there so it does not create any clutter, could that be part of the problem? I think it shouldnt as it actually manages to deploy the app, but I think its worth mentioning it..

any idea what to do if my domain is on cloudflare but not on route53? I can neither see hash.domain.com nor domain.com :/

@ofhouse so I followed the example step by step, I created a next app using npx create-next-app@latest --ts, I managed to deploy it following the atomic deployments example, however I am facing the following problems:

I have a domain hooked up to Route53, lets say its called example.com
When I deploy, I get some url .example.com and when I visit it I am able to see my app.. however, if I try to access the api route /api/hello, I am also getting 500 Internal Server Error with Code: PROXY_ERROR
If I try to access my original domain example.com I am getting 500 Internal Server Error with Code: PROXY_MISSING_CONFIG

I would really appreciate any input to figure this out, and thank you for creating this in the first place, regards!
PS: I actually created a folder called terraform in the root of my nextjs app and I put my main.tf in there so it does not create any clutter, could that be part of the problem? I think it shouldnt as it actually manages to deploy the app, but I think its worth mentioning it..

any idea what to do if my domain is on cloudflare but not on route53? I can neither see hash.domain.com nor domain.com :/

I figured it our. After creating the ns records on cloudflare, I can reach the app using the has.domain.com. No idea how to promote it to domain.com thoug.

@kethan-drishya @caophuong2012 ever figured out how to work around this problem? I am trying to deploy using canary 5 but got blocked because of this issue

Hi, sorry I missed your message, previously I just try to use the correct version then I can solve the above issue. The version I've used is:
version = "v0.13.2"

You can try above solution

Hi @caophuong2012 if you are using next.js for building your application then create a next.config.js file and use below lines

module.exports = withBundleAnalyzer({
  distDir:  '.next',
  poweredByHeader: false,
  trailingSlash: true,
  reactStrictMode: true,
  images: {
    domains: ['www.gravatar.com', 'i.picsum.photos'],
    loader: 'akamai',
    path: '',
  },

  target: 'serverless',
  webpack: (config, { isServer }) => {
    if (!isServer) {
      config.resolve.fallback.fs = false;
    }
    return config;
  }
});

See if it works for you

Getting this error, any idea how to fix this?

Build error occurred
ReferenceError: withBundleAnalyzer is not defined


What's the minimum size for a response to be compressed?

It looks like compression only happens when a response is over a (very small) size limit. What's that size limit if that's the case? Is it documented anywhere? If not, I can add it for everyone.

Must have missed it, sorry.

It's no problem at all :) I only pointed out where it was in the docs since you were asking about the docs in addition to what it was :)


[Bug] Memory leak somewhere, not sure where though.

Every second the memory usage increased by about 430kb, I was playing Recroom for an hour or so and the memory usage atm is 328mb.

Heya, thanks for the report!
A mem-leak, huh? Well, That's what I get for getting comfy with JavaScript!
I probably have a copied struct getting referenced while being passed or something, I'll poke around and see if I can find it!

(Hopefully) Fixed in 2.0! #9


Crash of STL based robot model on IFC6410

Using motoman_mh5_support package, rviz crashes on attempt of adding the robot model view, with bus error.
strace  https://gist.github.com/tfoote/8c7c925d2868e26babf3
gdb backtrace  https://gist.github.com/tfoote/4a9db2b2d7480a08b27d
Any STL file brings to same result.

For people who may not know, the IFC6410 is a quad-core ARM single board computer: http://www.inforcelive.com/index.php?route=product/product&product_id=53
This might have something to do with ARM as I cannot reproduce this on a normal amd64 desktop machine.
@ahendrix wasn't there a patch to assimp for ARM? Maybe that could be the source of the difference?

I also have the same issue on Jetson TK1 dev board. However when loading robot models constructed with Collada files (*.dae) rviz runs fine....

My best guess is that this crash is related to this part of the stacktrace:
Program received signal SIGBUS, Bus error.
0x40327984 in ogre_tools::STLLoader::load(unsigned char*) () from /opt/ros/indigo/lib/librviz.so
(gdb) bt full
#0  0x40327984 in ogre_tools::STLLoader::load(unsigned char*) () from /opt/ros/indigo/lib/librviz.so
No symbol table info available.
#1  0xbef0bf68 in ?? ()

Which suggests that this is a bug in OGRE's STL loader on arm.
Two things to try which can help confirm this:

Try to load a different robot model which contains STL meshes, to confirm that this affects most or all STL files.
Try to use the ORGE STL loading functions by themselves, outside of rviz and ROS. This will likely mean finding an OGRE sample that loads meshes, or writing your own.

Note ogre_tools are actually inside the rviz sources: http://docs.ros.org/indigo/api/rviz/html/c++/stl__loader_8cpp_source.html

The segfault is happening in rviz's code:
https://github.com/ros-visualization/rviz/blob/indigo-devel/src/rviz/ogre_helpers/stl_loader.cpp
Though those load functions are a bit scary, I don't see anything that jumps out at me. Without a debug build of rviz and a backtrace from that, I can't narrow it down to a particular line of code so I think there is little chance I can do anything about this.

Here's a minimal package which can reproduce the problem: https://www.dropbox.com/s/p3hcpeko4w3euc1/stlcrash.tgz?dl=0

Rviz only reads binary STL files, which the mh5 models are, and the stl loader in rviz is pretty hacky.
You guys are having issues on the IFC6410 and Jetson TK1, but not on x86 platforms.
Is this just endianness problem?

I am having issue with Rviz on Jetson-TK1 running on ubuntu 14.04 , ros indigo.
I enhanced the cpu performance to maximun but still the same issue. It did run once on Jetson but I cannot run it again. I have also set the GTK_IM_MODULE and tried deleting the ~/.rviz but nothing works. Can anyone suggest anything please?

@mehtajaghvi
Hi mehtajaghvi,
i met the same problem as you, but i used the tx1 instead, have you solved it? Can you give me some advice as i confused some days!

I met same problem on Rpi3 install Xubuntu 16.04 . When use mesh to load stl file in rviz , system dump and exit.


Rule suggestion: use-strict: "never"

As this question indicates, it's not entirely clear whether you should include "use strict" in your TypeScript source files. I lean towards "no" but others think it's a good idea.
Sounds like a good tslint rule! What about a use-strict rule which could enforce that either all or none your TS files have a "use strict" directive, depending on the settings.
The equivalent eslint rule is strict.

+1. I want to make sure I don't have extraneous 'use strict's in my ts files, this is exactly the sort of thing I would hope tslint can find.

Considering Typescript outputs code with 'use strict' automatically added (in alwaysStrict mode), I too would appreciate this rule

Incidentally I have developed a similar rule in my Fimbullinter project: https://github.com/fimbullinter/wotan/blob/master/packages/mimir/docs/no-useless-strict.md
You can find instructions on how to use it in the CLI docs: https://github.com/fimbullinter/wotan/blob/master/packages/wotan/README.md

Note: per #4534, this issue will be closed in less than a month if no PR is sent to add the rule. If you really need the rule, custom rules are always an option and can be maintained outside this repo!

💀 It's time! 💀
TSLint is being deprecated and no longer accepting pull requests for new rules. See #4534. 😱
If you'd like to see this rule implemented, you have two choices:

Recommended: Use typescript-eslint to lint your TypeScript code with ESLint ✅
Legacy: Write it as a custom TSLint rule 🤷♂

👋 It was a pleasure open sourcing with you!
If you believe this message was posted here in error, please comment so we can re-open the issue!


Beautiful IO of the tool

[x]  Use bold for default choices
[x]  Different colours
[x]  Input on separate line
[x]  Add more info messages
[ ] More improvements

I will be happy to see any ideas how to make it look better.

That's how the --help of the hs-init looks like:

And the work of the tool itself:

This issue will remain open until anybody propose better interface :sparkles:

@vrom911 Now we even have magician in comments! Maybe we close this issue? 🙂 🔮

You're right, thanks @ChShersh !


Json compatible serialization/desserialization?

I was just curious if this use case is already supported. There are a few other Yaml libraries similar to this one that support converting to/from json but typically they have flags to enable this compatibility. Is this something that is supported by default or is there a mechanism to support that as well in this library?

I answered this one myself. Seems like it does! Cheers :)


How can I choose python version with docker image?

How can I choose python version with docker image?  With "nvidia-docker run -ti --rm mxnet/python:gpu" I got default python 2.7. Any option to choose python version or should build specified image by myself? Thank you!

Thanks for submitting the issue @zhizec
@mxnet-label-bot[Question]
You can also post the question here link to get more help from community.

@mxnet-label-bot[question]

@zhizec I agree with you we should provide py2 and py3 installations, @nswamy can you please update the label to [feature request] ? Thanks!


Support OCI manifests

Checklist

[x] I've searched the issue queue to verify this is not a duplicate feature request.

Proposed Feature

Kargo should be able to use OCI registries for k8s manifests.
This includes the ability for a stage to subscribe to manifests from an OCI registry as well as to publish rendered manifests to an OCI registry.
Motivation
In the rendered manifest pattern, the rendered manifests are artifacts and, as such, should be published to an OCI registry (e.g., ghcr).
In addition, OCI registries support signed manifests.

Suggested Implementation
ArgoCD is adding first-class support for OCI
https://github.com/argoproj/argo-cd/blob/master/docs/proposals/native-ocp-support.md
Flux already supports OCI. The flux cli can push/pull manifests to/from OCI registries.
https://fluxcd.io/flux/cheatsheets/oci-artifacts/

Thanks for an interesting idea @joebowbeer. I think there's a lot to work through here, but I really like the idea.

Not stale.

The Argo CD link is https://github.com/argoproj/argo-cd/blob/master/docs/proposals/native-oci-support.md

Something being worked on atm with argoproj/argo-cd#18646

Is this still in progress?  We have some air gap use cases that would be nice to not have local git server dependency.

Update: The ArgoCD support is now beta
https://github.com/argoproj/argo-cd/pull/18646


Does not run on Windows Server 2008

sub.exe does not work on Windows Server 2008 operating system

Did you installed the latest version of dotnet framework ? Read the description   below " AsyncRAT requires the .Net Framework v4 (client) and v4.6+ (server) to run."

yes I did it by paying attention to the instructions.
Operating systems I've tested: Windows 7, Windows 8, Windows 8.1, Windows 10,
But I cannot communicate with the Server 2008 operating system.

Did you test the same stub on Windows 7, Windows 8, Windows 8.1, Windows 10, and Windows Server 2008 or different stub ? if the same stub works on ( Windows 7, Windows 8, Windows 8.1, Windows 10) and not works on windows server 2008 then may be it's a problem.otherwise,if you use different stub then i sense your settings or server problem

I do not understand what the problem is.
AsyncRAT-Sharp runs smoothly.
But client.exe did not work on server 2008.
just wanted to inform :)

Double-check everything.
Remember to disable some policies. assuming you know how to use WIN servers
It will be hard to detect the problem because this is a user fault, not a program fault.
https://prnt.sc/pj5t0p


Fix for Payment Request reorganizations

This PR prevents payment requests with invalid hashes (not set yet or out of the main chain) to count for the already requested balance of a proposal.

@aguycalled  I see that your travis-ci run for this PR on x86_64 linux is failing with similar error to my test, any ideas on what the cause is?

The issue this PR fixes can be reproduced as follows:

NODE1> staking false
NODE2> staking false
NODE2> addnode <IP_ADDRESS>:node1port onetry
NODE1> generate 300
NODE1> donatefund 100
NODE1> getnewaddress
Save output in %1
NODE2> getnewaddress
Save output in %2
NODE1> sendtoaddress %2 5000000
NODE1> createproposal %1 100 3600 test
Save hash in %3
NODE1> generate 1
NODE2> listproposals
Assert output.size > 0
NODE1> proposalvote %3 yes
NODE1> generate 250
NODE1> createpaymentrequest %3 100 test true
Save output in %4
NODE2> disconnectnode <IP_ADDRESS>:node1port
NODE1> forcetransactions [“%4”]
NODE2> forcetransactions [“%4”]
NODE1> staking true
Wait for 3+ blocks.
Store best block hash in %5
NODE1> staking false
NODE2> staking true
Wait for 1 block. Node 2 only has one output so it will only stake once.
NODE2> staking false
NODE2> addnode <IP_ADDRESS>:node1port onetry
Check that Node2.bestblockhash == %5

Tested succesfully with 2 nodes locally in devnet.
RPC test unit needs to be added.

compiling and starting to review this now.

compiling and starting to review this now.

Looks like the latest build has an issue with cfund-paymentrequest-state-reorg.py

Roger that.
On Thu, May 2, 2019, 14:26 proletesseract<EMAIL_ADDRESS>wrote:

@craigmacgregor commented on this pull request.
In qa/rpc-tests/cfund-paymentrequest-state-reorg.py
https://github.com/NAVCoin/navcoin-core/pull/456#discussion_r280298900:

   while self.nodes[1].getblockcount() == blockcount_1:

       time.sleep(1)

   print("nodes staked!")

   self.nodes[0].staking(False)

   self.nodes[1].staking(False)

   node_0_best_hash = self.nodes[0].getblockhash(self.nodes[0].getblockcount())

   node_1_best_hash = self.nodes[1].getblockhash(self.nodes[1].getblockcount())

   node_0_nbits = int(self.nodes[0].getblock(node_0_best_hash)["bits"], 16)

   node_1_nbits = int(self.nodes[1].getblock(node_1_best_hash)["bits"], 16)

   # Node 0 and Node 1 have forked. Difficulty in Node 0 must be higher (hence lower nbits)

   assert(node_0_best_hash != node_1_best_hash)

   assert(node_0_nbits < node_1_nbits)

Yeah, the hashes are different but the nbits are the same. im reverting
the target time to 30 seconds and seeing if it makes a difference.
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Roger that.
You mean ACK :P

Roger that.

You mean ACK :P

I see ACK on alot of opensource projects, what does it actually stand for? :)

Roger that.

You mean ACK :P

I see ACK on alot of opensource projects, what does it actually stand for? :)

It's just short for "Acknowledged" AFAIK.
https://github.com/bitcoin/bitcoin/issues/6100

I've changed the check so it looks at chainwork instead of nbits.
chainwork = prevblock.chainwork + nbits

Looks like final commit fixed the issue with the test.
@craigmacgregor ready to merge? :)

Travis failing again. Investigating.

it's failing again uniquely because of a random fee in the coldstaking_* tests

my local build and tests pass in Ubuntu 18.04


chore: upgrade version 0.4.0-alpha.2

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Allow allowing local access to mysqld socket

Currently the only way for non root/mysql users to access mysqld is via ip. This patch enables access to the socket (if desired). Default is to deny.

Looks good to me, though I'm thinking maybe it should be enabled by default? E.g. for Postgres, Unix domain socket access is enabled by default while TCP access is disabled.

Could do? I was going on the presumption of closed down by default (and not changing existing systems).

+1 to socket access by default.

Very well, changed.

So, I realised that if you changed your choice and switched with the old code, it would not update the permissions until you restarted (as mkdir will not update the permissions if the directory already exists).

Does this need anything else doing to get it merged?

Made the 0755 unconditional, as master already had this.

Clearly I am not awake yet, as the master already had the correct permissions unconditionally, so the merge should not have happened. Sorry about that.


Deprecate convert_to_singleton

As noted in #689, convert_to_singleton doesn't produce statedicts with compatible keys (for some unknown reason).
Since reshard_mp can do the same job, without the GPU node requirement of convert_to_singleton, we should deprecate convert_to_singleton.
TODO: Work out dependencies on covert_to_singleton, and identify any special cases it can handle that reshard_mp can't (such as separating out qkv weights, as noted by @tangbinh)

#695

Since reshard_mp can do the same job, without the GPU node requirement of convert_to_singleton, we should deprecate convert_to_singleton.

@andrewPoulton Is this true though? I was unable to convert 8 shards successfully to restored.pt using:
python -m metaseq.scripts.reshard_mp \
--input "opt/shards/reshard-model_part-*.pt" \
--output "opt/pt/reshard_no_os_mp8/reshard-model_part-{i}.pt" \
--num-output-parts 1

I was unable to convert 8 shards successfully to restored.pt using:

@ayeeyecorp Can you share the stack trace? I suspect it might be related to the fact that the checkpoints available on the OPT page are flattened, which are are not compatible with reshard.mp.

@tangbinh let's add a flat param check to reshard_*, and raise an error unless user specifically wants to unflatten. I'll create an issue to track in a bit. Happy to own as well.

@andrewPoulton I was adding an option to split the KVQ weights in reshard_mp, but I think this is probably not needed for 2 reasons:

This weight splitting has already been included in the script convert_opt_original_pytorch_checkpoint_to_pytorch.py. Previously, there was a bug that basically turned this off, but it has been fixed (see https://github.com/huggingface/transformers/pull/22526).
Previously, we support both transformer_lm and transformer_lm_megatron models, but the the former has been removed in #633. Therefore, there's no need to split KVQ weights within Metaseq.

Once we fixed #625, I think we can safely remove convert_to_singleton.py as users are able to load OPT checkpoints using reshard_mp.py and convert_opt_original_pytorch_checkpoint_to_pytorch.py.

I was unable to convert 8 shards successfully to restored.pt using:

@ayeeyecorp Can you share the stack trace? I suspect it might be related to the fact that the checkpoints available on the OPT page are flattened, which are are not compatible with reshard.mp.

Here is a snippet of the stack trace:
    raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( RuntimeError: Error(s) in loading state_dict for FlattenParamsWrapper:         Missing key(s) in state_dict: "_fpw_module.decoder.layers.0._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.1._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.2._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.3._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.4._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.5._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.6._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.7._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.8._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.9._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.10._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.11._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.12._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.13._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.14._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.15._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.16._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.17._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.18._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.19._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.20._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.21._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.22._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.23._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.24._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.25._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.26._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.27._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.28._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.29._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.30._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.31._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.32._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.33._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.34._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.35._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.36._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.37._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.38._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.39._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.40._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.41._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.42._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.43._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.44._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.45._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.46._fsdp_wrapped_module.flat_param_0", "_fpw_module.decoder.layers.47._fsdp_wrapped_module.flat_param_0".         Unexpected key(s) in state_dict: "_fpw_module.decoder.layers.0._fsdp_wrapped_module._fpw_module.self_attn.qkv_proj.weight", "_fpw_module.decoder.layers.0._fsdp_wrapped_module._fpw_module.self_attn.qkv_proj.bias", "_fpw_module.decoder.layers.0._fsdp_wrapped_module._fpw_module.self_attn.out_proj.weight", "_fpw_module.decoder.layers.0._fsdp_wrapped_module._fpw_module.self_attn.out_proj.bias", "_fpw_module.decoder.layers.0._fsdp_wrapped_module._fpw_module.self_attn_layer_norm.weight", "_fpw_module.decoder.layers.0._fsdp_wrapped_module._fpw_module.self_attn_layer_norm.bias", "_fpw_module.decoder.layers.0._fsdp_wrapped_module._fpw_module.fc1.weight", "_fpw_module.decoder.layers.0._fsdp_wrapped_module._fpw_module.fc1.bias", "_fpw_module.decoder.layers.0._fsdp_wrapped_module._fpw_module.fc2.weight", "_fpw_module.decoder.layers.0._fsdp_wrapped_module._fpw_module.fc2.bias", "_fpw_module.decoder.layers.0._fsdp_wrapped_module._fpw_module.final_layer_norm.weight", "_fpw_module.decoder.layers.0._fsdp_wrapped_module._fpw_module.final_layer_norm.bias", "_fpw_module.decoder.layers.1._fsdp_wrapped_module._fpw_module.self_attn.qkv_proj.weight", "_fpw_module.decoder.layers.1._fsdp_wrapped_module._fpw_module.self_attn.qkv_proj.bias", "_fpw_module.decoder.layers.1._fsdp_wrapped_module._fpw_module.self_attn.out_proj.weight", "_fpw_module.decoder.layers.1._fsdp_wrapped_module._fpw_module.self_attn.out_proj.bias", "_fpw_module.decoder.layers.1._fsdp_wrapped_module._fpw_module.self_attn_layer_norm.weight", "_fpw_module.decoder.layers.1._fsdp_wrapped_module._fpw_module.self_attn_layer_norm.bias", "_fpw_module.decoder.layers.1._fsdp_wrapped_module._fpw_module.fc1.weight", "_fpw_module.decoder.layers.1._fsdp_wrapped_module._fpw_module.fc1.bias", "_fpw_module.decoder.layers.1._fsdp_wrapped_module._fpw_module.fc2.weight", "_fpw_module.decoder.layers.1._fsdp_wrapped_module._fpw_module.fc2.bias", "_fpw_module.decoder.layers.1._fsdp_wrapped_module._fpw_module.final_layer_norm.weight", "_fpw_module.decoder.layers.1._fsdp_wrapped_module._fpw_module.final_layer_norm.bias", "_fpw_module.decoder.layers.2._fsdp_wrapped
The 992 shards were first converted to reshard-model_part-$j.pt
for j in {0..7}; do
    python3 -m ./metaseq/metaseq/scripts/reshard_fsdp \
    --input-glob-pattern "./checkpoint_last-model_part-$j-shard*.pt" \
    --output-shard-name "./reshard/reshard-model_part-$j.pt" \
    --num-output-shards 1 --skip-optimizer-state True --unflatten-weights True
done

Should I have set --unflatten-weights **False** in order for ./metaseq/metaseq/scripts/convert_to_singleton to work correctly?

@ayeeyecorp Just so I'm clear - you first ran reshard_fsdp on the shards (with unflatten-weights=true), then tried running convert_to_singleton on the consolidated shards?  If that's so, then can you try running reshard_mp on the consolidated shards instead?

@andrewPoulton
you first ran reshard_fsdp on the shards (with unflatten-weights=true), then tried running convert_to_singleton on the consolidated shards?
Correct, this resulted in the state_dict error
If that's so, then can you try running reshard_mp on the consolidated shards instead?
Will do that again shortly and post stack trace results.

@ayeeyecorp May I ask why you want to convert the 8 MP parts of OPT 175B into a singleton? I don't think you would not be able to load the singleton into any GPU considering its size, which is about 350GB.

Should I have set --unflatten-weights False in order for metaseq.scripts.convert_to_singleton and metaseq.scripts.reshard_mp to work correctly?

convert_to_singleton expects flattened weights; that's probably why you got Missing key(s) in state_dict. However, reshard_mp expects unflattened weights. As suggested by @andrewPoulton, please try to use reshard_mp instead, as we're deprecating convert_to_singleton.

@andrewPoulton
I started over earlier today from the 992 shards (resetting my environment per the instructions here using Python3.8) and verified that the 8 consolidated FSDP shards had the correct md5sum. Upon confirmation, I converted the checkpoints, to eliminate use of MP, to 1 with the reshard_mp.py script, with no issues this time, using:
python -m metaseq.scripts.reshard_mp \
    --input "/path/to/resharded/checkpoints/reshard-model_part-*.pt" \
    --output "/path/to/mp/resharded/checkpoints/reshard-model_part-{i}.pt" \
    --num-output-parts 1

Not sure what the original problem was. The md5sum of the single checkpoint (325.2 GB) was:
When I went to convert to HF using:
python3 transformers.src.transformers.models.opt.convert_opt_original_pytorch_checkpoint_to_pytorch --pytorch_dump_folder_path ~/opt_meta/hugging/ --hf_config config.json --fairseq_path ~/opt_meta/single_shard/reshard-model_part-0.pt, the operation failed after 1+ hour with the following stack trace:
        size mismatch for decoder.layers.8.final_layer_norm.weight: copying a param with shape torch.Size([12288]) from checkpoint, the shape in current model is torch.Size([768]).         size mismatch for decoder.layers.8.final_layer_norm.bias: copying a param with shape torch.Size([12288]) from checkpoint, the shape in current model is torch.Size([768]).         size mismatch for decoder.layers.9.self_attn.k_proj.weight: copying a param with shape torch.Size([12288, 12288]) from checkpoint, the shape in current model is torch.Size([768, 768]).         size mismatch for decoder.layers.9.self_attn.k_proj.bias: copying a param with shape torch.Size([12288]) from checkpoint, the shape in current model is torch.Size([768]).         size mismatch for decoder.layers.9.self_attn.v_proj.weight: copying a param with shape torch.Size([12288, 12288]) from checkpoint, the shape in current model is torch.Size([768, 768]).         size mismatch for decoder.layers.9.self_attn.v_proj.bias: copying a param with shape torch.Size([12288]) from checkpoint, the shape in current model is torch.Size([768]).         size mismatch for decoder.layers.9.self_attn.q_proj.weight: copying a param with shape torch.Size([12288, 12288]) from checkpoint, the shape in current model is torch.Size([768, 768]).         size mismatch for decoder.layers.9.self_attn.q_proj.bias: copying a param with shape torch.Size([12288]) from checkpoint, the shape in current model is torch.Size([768]).         size mismatch for decoder.layers.9.self_attn.out_proj.weight: copying a param with shape torch.Size([12288, 12288]) from checkpoint, the shape in current model is torch.Size([768, 768]).         size mismatch for decoder.layers.9.self_attn.out_proj.bias: copying a param with shape torch.Size([12288]) from checkpoint, the shape in current model is torch.Size([768]).         size mismatch for decoder.layers.9.self_attn_layer_norm.weight: copying a param with shape torch.Size([12288]) from checkpoint, the shape in current model is torch.Size([768]).         size mismatch for decoder.layers.9.self_attn_layer_norm.bias: copying a param with shape torch.Size([12288]) from checkpoint, the shape in current model is torch.Size([768]).         size mismatch for decoder.layers.9.fc1.weight: copying a param with shape torch.Size([49152, 12288]) from checkpoint, the shape in current model is torch.Size([3072, 768]).         size mismatch for decoder.layers.9.fc1.bias: copying a param with shape torch.Size([49152]) from checkpoint, the shape in current model is torch.Size([3072]).         size mismatch for decoder.layers.9.fc2.weight: copying a param with shape torch.Size([12288, 49152]) from checkpoint, the shape in current model is torch.Size([768, 3072]).         size mismatch for decoder.layers.9.fc2.bias: copying a param with shape torch.Size([12288]) from checkpoint, the shape in current model is torch.Size([768]).         size mismatch for decoder.layers.9.final_layer_norm.weight: copying a param with shape torch.Size([12288]) from checkpoint, the shape in current model is torch.Size([768]).         size mismatch for decoder.layers.9.final_layer_norm.bias: copying a param with shape torch.Size([12288]) from checkpoint, the shape in current model is torch.Size([768]).         size mismatch for decoder.layers.10.self_attn.k_proj.weight: copying a param with shape torch.Size([12288, 12288]) from checkpoint, the shape in current model is torch.Size([768, 768]).         size mismatch for decoder.layers.10.self_attn.k_proj.bias: copying a param with shape torch.Size([12288]) from checkpoint, the shape in current model is torch.Size([768]).         size mismatch for decoder.layers.10.self_attn.v_proj.weight: copying a param with shape torch.Size([12288, 12288]) from checkpoint, the shape in current model is torch.Size([768, 768]).         size mismatch for decoder.layers.10.self_attn.v_proj.bias: copying a param with shape torch.Size([12288]) from checkpoint, the shape in current model is torch.Size([768]).         size mismatch for decoder.layers.10.self_attn.q_proj.weight: copying a param with shape torch.Size([12288, 12288]) from checkpoint, the shape in current model is torch.Size([768, 768]).         size mismatch for decoder.layers.10.self_attn.q_proj.bias: copying a param with shape torch.Size([12288]) from checkpoint, the shape in current model is torch.Size([768]).         size mismatch for decoder.layers.10.self_attn.out_proj.weight: copying a param with shape torch.Size([12288, 12288]) from checkpoint, the shape in current model is torch.Size([768, 768]).         size mismatch for decoder.layers.10.self_attn.out_proj.bias: copying a param with shape torch.Size([12288]) from checkpoint, the shape in current model is torch.Size([768]).         size mismatch for decoder.layers.10.self_attn_layer_norm.weight: copying a param with shape torch.Size([12288]) from checkpoint, the shape in current model is torch.Size([768]).         size mismatch for decoder.layers.10.self_attn_layer_norm.bias: copying a param with shape torch.Size([12288]) from checkpoint, the shape in current model is torch.Size([768]).         size mismatch for decoder.layers.10.fc1.weight: copying a param with shape torch.Size([49152, 12288]) from checkpoint, the shape in current model is torch.Size([3072, 768]).         size mismatch for decoder.layers.10.fc1.bias: copying a param with shape torch.Size([49152]) from checkpoint, the shape in current model is torch.Size([3072]).         size mismatch for decoder.layers.10.fc2.weight: copying a param with shape torch.Size([12288, 49152]) from checkpoint, the shape in current model is torch.Size([768, 3072]).         size mismatch for decoder.layers.10.fc2.bias: copying a param with shape torch.Size([12288]) from checkpoint, the shape in current model is torch.Size([768]).         size mismatch for decoder.layers.10.final_layer_norm.weight: copying a param with shape torch.Size([12288]) from checkpoint, the shape in current model is torch.Size([768]).         size mismatch for decoder.layers.10.final_layer_norm.bias: copying a param with shape torch.Size([12288]) from checkpoint, the shape in current model is torch.Size([768]).         size mismatch for decoder.layers.11.self_attn.k_proj.weight: copying a param with shape torch.Size([12288, 12288]) from checkpoint, the shape in current model is torch.Size([768, 768]).         size mismatch for decoder.layers.11.self_attn.k_proj.bias: copying a param with shape torch.Size([12288]) from checkpoint, the shape in current model is torch.Size([768]).         size mismatch for decoder.layers.11.self_attn.v_proj.weight: copying a param with shape torch.Size([12288, 12288]) from checkpoint, the shape in current model is torch.Size([768, 768]).         size mismatch for decoder.layers.11.self_attn.v_proj.bias: copying a param with shape torch.Size([12288]) from checkpoint, the shape in current model is torch.Size([768]).         size mismatch for decoder.layers.11.self_attn.q_proj.weight: copying a param with shape torch.Size([12288, 12288]) from checkpoint, the shape in current model is torch.Size([768, 768]).         size mismatch for decoder.layers.11.self_attn.q_proj.bias: copying a param with shape torch.Size([12288]) from checkpoint, the shape in current model is torch.Size([768]).         size mismatch for decoder.layers.11.self_attn.out_proj.weight: copying a param with shape torch.Size([12288, 12288]) from checkpoint, the shape in current model is torch.Size([768, 768]).         size mismatch for decoder.layers.11.self_attn.out_proj.bias: copying a param with shape torch.Size([12288]) from checkpoint, the shape in current model is torch.Size([768]).         size mismatch for decoder.layers.11.self_attn_layer_norm.weight: copying a param with shape torch.Size([12288]) from checkpoint, the shape in current model is torch.Size([768]).         size mismatch for decoder.layers.11.self_attn_layer_norm.bias: copying a param with shape torch.Size([12288]) from checkpoint, the shape in current model is torch.Size([768]).         size mismatch for decoder.layers.11.fc1.weight: copying a param with shape torch.Size([49152, 12288]) from checkpoint, the shape in current model is torch.Size([3072, 768]).         size mismatch for decoder.layers.11.fc1.bias: copying a param with shape torch.Size([49152]) from checkpoint, the shape in current model is torch.Size([3072]).         size mismatch for decoder.layers.11.fc2.weight: copying a param with shape torch.Size([12288, 49152]) from checkpoint, the shape in current model is torch.Size([768, 3072]).         size mismatch for decoder.layers.11.fc2.bias: copying a param with shape torch.Size([12288]) from checkpoint, the shape in current model is torch.Size([768]).         size mismatch for decoder.layers.11.final_layer_norm.weight: copying a param with shape torch.Size([12288]) from checkpoint, the shape in current model is torch.Size([768]).         size mismatch for decoder.layers.11.final_layer_norm.bias: copying a param with shape torch.Size([12288]) from checkpoint, the shape in current model is torch.Size([768]). (venv) [ec2-user@ip-172-31-28-254 opt]$
I followed @patrickvonplaten conversion instructions found here and generated a config.json with the following:
from transformers import OPTConfig num_layers = 12 num_heads = 12 d_model = 768 config = OPTConfig(hidden_size=d_model, num_attention_heads=num_heads, num_hidden_layers=num_layers, ffn_dim=4*d_model) config.save_pretrained("./")  # <- this will create a config.json in your current folder
Thoughts? I will re-run the operation overnight and log the full failure stack trace.
@tangbinh - thank you for the clarification. I am converting the 8 MP parts of OPT 175B into a singleton to run experiments against recently release 3-bit kernels from GPTQ allowing me to run OPT-175B on 1xA100 80GB.

@ayeeyecorp For OPT 175B, we should have num_layers = 96, num_heads = 96, and d_model = 12288.

@ayeeyecorp For OPT 175B, we should have num_layers = 96, num_heads = 96, and d_model = 12288.

@tangbinh that was quick! brilliant, will give that a go now. I blindly used values from HF...

After updating instance to 1.5TB of RAM... I successfully generated the file using transformers.src.transformers.models.opt.convert_opt_original_pytorch_checkpoint_to_pytorch!
@tangbinh Final question, when running
python -m metaseq.scripts.reshard_mp \
--input "opt/shards/reshard-model_part-*.pt" \
--output "opt/pt/reshard_no_os_mp8/reshard-model_part-{i}.pt" \
--num-output-parts 1

Do the dependency versions (pytorch, etc.) impact the files being generated? I ask because I previously ran into an issue  of not getting the correct md5sum when converting the 992 shards to the 8 consolidated FSDP shards. Until I test this pytorch model with 3-bit kernels, I am not certain if the file generated by reshard_mp is valid. The md5sum of the single checkpoint (325.2 GB) after running reshard_mp was: 06e7e7ed424db3834ccd1a776d82ff14. Not sure if anyone can confirm this?


Do not include YEAR in generated boilerplate

What type of PR is this?
/kind documentation
What this PR does / why we need it:
We do not have to include the YEAR in the boilerplate header. This means
we can use a custom header for generated golang code, which should not
change next year.
Which issue(s) this PR fixes:

None
Does this PR have test?

None
Special notes for your reviewer:
Does this PR introduce a user-facing change?

None

/lgtm

Shouldn't this file be removed/changed too?
Why is the copyright year not required anymore?

Shouldn't this file be removed/changed too?

Nope, we still wanna preserve the YEAR for manually edited files.

Why is the copyright year not required anymore?

Right now the verify boilerplate script skips generated files:
https://github.com/kubernetes/repo-infra/blob/5597dcdfb69ef4f4c7d994c2176034b9d6aa4721/hack/verify_boilerplate.py#L116
I think we should fix that somehow that the script only skips the YEAR instead of skipping the whole test.


[BUG] Error using vector search with ImageVectorFieldNames

Library name and version
Azure.AI.OpenAI <IP_ADDRESS>
Describe the bug
When I use vector search with ImageVectorFieldNames, an error occurs

ChatCompletionOptions options = new();

options.AddDataSource(new AzureSearchChatDataSource()
{
	Endpoint = _optionsAzureAISearch.Endpoint,
	IndexName = _optionsAzureAISearch.IndexName,
	Authentication = DataSourceAuthentication.FromApiKey(_optionsAzureAISearch.Key),
	QueryType = DataSourceQueryType.VectorSemanticHybrid,
	SemanticConfiguration = _optionsAzureAISearch.IndexName + "-semantic-configuration",
	VectorizationSource =  DataSourceVectorizer.FromDeploymentName(_optionsAzureOpenAIOptions.EmbeddingsModelDeploymentName),
	InScope = true,
	TopNDocuments = 3,
	FieldMappings = new DataSourceFieldMappings()
	{
		ContentFieldNames = { "chunk" },
		FilePathFieldName = "metadata_storage_name",
		TitleFieldName = "title",
		UrlFieldName = "metadata_storage_path",
		VectorFieldNames = { "text_vector" },
		ImageVectorFieldNames = { "image_vector" }
	},

});

However, if I don't use ImageVectorFieldNames, no error occurs.
Expected behavior
That you can search for image vectors
Actual behavior
Cannot search for image vectors
Reproduction Steps
The exception is raised from the code provided above
Environment
No response

Hi @druidajes ,
Thank you for raising this issue! Please hang tight while we take a look and feel free to add more details if you have any.


Add AzureSiteExtension tag and packageType

#51
This generates:
    <tags>aspnet logging aspnetcore AzureSiteExtension</tags>
    <packageTypes>
      <packageType name="AzureSiteExtension" />
    </packageTypes>

As long as it indeeds generates what you state in the nuspec, looks good!


Docker Volumes Usage

Hello @cytopia
It's not really a bug report, and most probably be labeled enhancement, and as it's also a little question: should be in forum. Sorry, if it's not in good place.
With your recent works, especially on ELK stack, I've noticed usage of Docker Volumes.
Could you make a summary about usage, and if possible a link to current doc.
I've also noticed that some docker-compose.override.yaml still use volume folder bind.
https://github.com/cytopia/devilbox/blob/master/compose/docker-compose.override.yml-rabbitmq#L18
https://github.com/cytopia/devilbox/blob/master/compose/docker-compose.override.yml-varnish#L19
Thanks in advance to clarify the situation !

I am slowly migrating the Devilbox to use Docker Volumes instead of simple directory mounts (where possible). This brings several advantages, performance improvements and fixes a couple of issues on Windows and MacOS.
To ensure I will not break all setups out there at once, I do it gradually, by implementing parts of it into the override container. The full migration will be handled in the upcoming release: https://github.com/cytopia/devilbox/pull/416
This includes the full switch to Docker volumes.
I do however encourage everybody to already use the mentioned release branch, as many other bugfixes already went into it.

Latest features, improvements and smaller bugfixes are available in the master branch first (e.g.: Varnish, ELK stack, etc). I will rebase this around every week into the release branch. So feature-wise its a bit behind, but should be the new standard.

Why am I not merging the release branch right away?
The release branch will make all your persistent data unavailable. So one must first backup all databases, switch to the release branch and import them again. Merging this without enough visibility will most likely make people unhappy and think they might have lost their data.
So I am still in the process to raise visibility for that branch and that's why I also encourage people to use this one (migrate to it) first.

Thanks for this reply and clarify the situtation !

You're welcome. I will close this issue then. As a side note: You've already mentioned it above, that this kind of stuff is better kept in the Forum and I also fully agree. This is important information and should not be hidden in closed issues. ;-)
So please add similar questions to the Forum in future, as it will persist there for everybody to pick up.


transport oci layouts instead of registry backends

transporting content using the non-standard distribution backend provided a lot of convenience for serving, but it wastes the potential of the OCI Layout spec for all sorts of other things.
Evaluate transporting oci layouts instead, and then transferring into registry content at serve time or something similar

This would be part of a much larger conversation around in-transit formats alongside of disk requirements at runtime.
The benefits of using the distribution backend for transit allowed an package taking up 100GB on disk (uncompressed) to only require 100GB of disk space in the air gap.
In contrast, an OCI layout taking up 75G of space (uncompressed) on disk representing a distribution backend that takes up 100GB of space when serving the OCI registry needs 175GB of peak disk space, since there will be a non-trivial time period where the full OCI layout and the full distribution backend will exist on disk together.


Add Railfence/Zig-Zag cracker

Hello spoooopyyy hackers :jack_o_lantern:
This is a Hacktoberfest only issue! :ghost:
Find our contributing guidelines here, this walks you through how to add a decoder / cracker!
https://github.com/Ciphey/Ciphey/wiki#adding-your-own-crackers--decoders
Don't worry if it looks hard, we will walk you through everything! :)
Write this issue in C++!
Links
These links will help you understand how this works!
https://en.wikipedia.org/wiki/Rail_fence_cipher
https://crypto.interactive-maths.com/rail-fence-cipher.html
https://www.dcode.fr/rail-fence-cipher

Hi, I would like to contribute to this issue if possible :) I have alredy joined your Discord server too (stanislav). Can you please assign it to me ?

I plan on taking this as my first project here. What would be the amount of iterations I am limited to keep performance smooth?

I'm working on this with @Mr-Salme. They will add detailed documentation on how to add a C++ cracker after I complete it.

Hi @lukasgabriel and @bee-san,
I've noticed that this was not implemented last year so I decided to give it a go (https://github.com/Ciphey/Ciphey/pull/699). Let me know what you think.


how can I add props hasMaskedValue to native object

Now I am write  Inputmask({ mask: '99/99/9999' }).mask(input).
but I want to use hasMaskedValue,what need to do

i saw it，just not support

@cyhwinner ,
What do you mean exactly?

originally, because of my careless i want to use hasMaskedValue via Inputmask class,  then i saw this method  only works in jquery ,sry

@cyhwinner ,
It also work without jquery, you need to call it on the inputmask object.
input.inputmask.hasMaskedValue()


Trouble building on Raspberry PI 4

I get the following error with whichever way I try to build...
cmake version 3.26.4
python version 3.11.3
(I've confirmed the same issue with python 3.11.4)
[89/402] BuildTest.WrongOutputInDepfileCausesRebuild
*** Failure in /home/pi/Downloads/ninja-python-distributions/Ninja-src/src/build_test.cc:574
build_res
[386/402] TokenPoolTest.IgnoreWithJNninja: warning: -jN forced on command line; ignoring GNU make jobserver.
[402/402] ElideMiddle.ElideInTheMiddle
failed
make[2]: *** [CMakeFiles/build_ninja.dir/build.make:120: build_ninja-prefix/src/build_ninja-stamp/build_ninja-run_ninja_test_suite] Error 1
make[1]: *** [CMakeFiles/Makefile2:111: CMakeFiles/build_ninja.dir/all] Error 2
make: *** [Makefile:136: all] Error 2
Any help would be appreciated.

This is fixed by using: python3 -m pip install ninja==1.10.0

pip should now download a binary wheel.


Faster CS decomposition for equipartitioned 2x2 unitary matrices.

This implements the CS decomposition in the base case of an equipartitioned 2x2 unitary matrix.
The function csd-2x2-basic is significantly faster and less memory-hungry than the call to lapack-csd as shown below:
MAGICL> (let ((p 1)
              (q 1)
              (u (magicl:random-unitary 2)))

          (sb-ext:gc :full t)

          (time
           (dotimes (i 100000)
             (when (= p q 1)
               (csd-2x2-basic u p q))))

          (sb-ext:gc :full t)

          (time
           (dotimes (i 100000)
             (lapack-csd u p q))))
Evaluation took:
  0.144 seconds of real time
  0.143749 seconds of total run time (0.130993 user, 0.012756 system)
  [ Run times consist of 0.003 seconds GC time, and 0.141 seconds non-GC time. ]
  100.00% CPU
  390,120,550 processor cycles
  103,980,336 bytes consed

Evaluation took:
  0.507 seconds of real time
  0.506777 seconds of total run time (0.488778 user, 0.017999 system)
  [ Run times consist of 0.014 seconds GC time, and 0.493 seconds non-GC time. ]
  100.00% CPU
  1,376,727,302 processor cycles
  246,394,256 bytes consed

This hasn't received attention in a while. Shall we merge it @jmbr ?

This hasn't received attention in a while. Shall we merge it @jmbr ?

Yes.


Remove encryption module from nodejs-skynet

This module includes the skykeys stuff that ended up not being relevant to Skynet at all. It's merely an artifact from the earliest of the days of this SDK.

Hello MSevey,
Here's the PR: https://github.com/SkynetLabs/nodejs-skynet/pull/194
You can control it and give info.

removed in https://github.com/SkynetLabs/nodejs-skynet/pull/194


Rewrite the @extend section

Added in https://github.com/HugoGiraudel/sass-guidelines/commit/e9bc0331735b3e70a1fe6a0b09a03d2575ec2ae6.


Update readme with deferred instructions

Deferred Payment Flow Instructions
Add the deferred payment flow section to the readme giving a brief explanation of what the deferred payment flow actually is, explain how to activate it and add a link for more information to the Afterpay deferred payment flow documentation.
fix #9

@garciajordy you could have used the #5 PR to add this commit. No need for a separate PR.


Bound to a Directory Service

Under "Bound to a Directory Service" I have 130 clients Bound and 1 Not Bound.    If I click on Not Bound button it lists all the computers.   Seems to be the same for if i click on Bound as well in that it shows all the computers instead of only the ones bound.  Running version <IP_ADDRESS>0.   If I click on "Not Bound" it should only show the one machine that isn't bound to a directory correct?   If I click on "Bound" it should only show the machines bound to a directory correct?

Yes, you're right, someone should write a filter for those.


fix typo in doc of signal.peak_widths

Fix a minor typo in the docstring of signal.peak_widths

Let me know when the CIs are happy and I'll merge

@larsoner all checks have passed.

Since @larsoner approved already merging. Thanks @anubhavp28


Langmuir

What changes does this PR introduce?
The Langmuir Circulation (LC) parameterization for GGL90 mixing model
What is the current behaviour?
The absence of LC parameterization
What is the new behaviour
The option for the inclusion of the LC parameterization (The parameterizaiton adjusts the mixing length scale when the LC can change the vertical mixing.) Additionally, the stokes velocity term is included in the momentum equation.
This parameterization was tested in "verification/vermix".
Does this PR introduce a breaking change?
No, this PR is the introduction of a new parameterization to the existing mixing model.
Other information:
This LC parameterization is discussed in the manuscript by Tak et al. submitted to Ocean Modelling. It is currently under review.
Suggested addition to tag-index
(To avoid unnecessary merge conflicts, please don't update tag-index. One of the admins will do that when merging your pull request.)

@hajsong thanks for this great contribution. I see that you have added all of the code changes to vermix/code. I think it would be great if you could add the changes (embedded in the corresponding ifdef's) to the main code (ie. pkg/ggl90, the pkg/mom-directories, except for the specific GGL90_OPTIONS.h file, which belongs in the code directory) instead. Then these changes would also be available for other experiments.
Further, I like that you added a test for the scheme, but I we should think about whether a new sub-test of vermix would be a better option?

@mjlosch, thank you for checking the pull request. As you suggested, I added the modified source code to ggl90, mom_fluxform and mom_vecinv.
I put GGL90_OPTIONS.h in ggl90 as well because it has an option for this parameterization.
Please let me know if you have more suggestions.
Thank you!

Thanks, now I think that most of the files in vermix/code are no longer necessary (because there are copies in pkg/ggl90 and pkg/mom_*), there's also one experiment failing (aim.5l_cs). Do you have any idea why?
I'll have a closer look once these first order problems are solved.

Sure, I  removed the files related to the LC parameterization from vermix/code.
I also saw the failure. It is not clear now why it happened. Probably my modification to mom_vi_coriolis.F caused an issue. I haven't done testreport yet. Let me fix that issue and request the push tomorrow when I can think clearly. (It is quite late here :) ).

I accidentally deleted one line in mom_vecinv/mom_vi_coriolis.F when adding the stokes velocity term, which caused the failure. I fixed that and saw the NORMAL END in aim.5l_cs test case where I used to have an error.
Please let me know if you see other issues.
Thank you!

I fixed the error in the codes in mom_* package and made sure testreport does not give any error messages. I will address other suggestions tomorrow. I appreciate it!

@mjlosch, I revised the codes following your suggestions. I could see why you made those suggestions; the code becomes much easier to read. Also, I was able to find a couple of bugs. I really appreciate your input!
Please let me know if you see room to improve the code.

Apart from my inline comment, I have a few things that I could push (more ifs out of loops, integer to floats ...). One addition is related to automatic differentiation by TAF. Would you like me to push those changes?

@mjlosch, it would be great if you can make any appropriate modifications and push them. Please let me know if you have any other suggestions.

@mjlosch, is there anything that can be done from my end?

@hajsong sorry, accidentally I did not post my post last week, which said that I will need some time for this, because I am busy with other stuff. Maybe you can make the parameterisation equations available (some link or so)? I have no idea about Langmuir parameterisations.

@hajsong , regarding implementation within coriolis S/R: I can see @mjlosch point. I am going to see if we could get a simpler set of changes and will let you know soon.

@hajsong I took a very quick look at changes in coriolis S/R and have few comments:

My main problem:
if I use the flux-form, the stokes-drift is added to the eulerien velocity to evaluate
the coriolis term, and as far as I can see, nothing else.
if I use vector invariant, the stokes-drift is added to the eulerien velocity
to evaluate both the coriolis term and the relative vorticity advection term.
This seems strange to me, because I expect both formulations (flux-form or vecinv) to
solve the same set of equations and here it does not seem to be the case.
Or may be I missed something ?

Using the option "useAbsVorticity=T" is not what I would recommend, mainly because
we may want to use a refine advection scheme for relative vorticity (currently:
highOrderVorticity, upwindVorticity but more options might be added in the future)
whereas a centered scheme seems the only reasonable option to use for the planetary
vorticity (i.e., coriolis).
We should probably add a warning somewhere for the case:
useAbsVorticity.AND.(highOrderVorticity.OR.upwindVorticity)
but this will be better in a different PR.
It's still nice to have this "useAbsVorticity" option available when using a centered
scheme to combine the relative and planetary vorticity and advect the absolute vorticity.
Now for the Langmuir feature, I would suggest, at least for now, to add a stop
if useLANGMUIR & vectorInvariantMomentum & useAbsVorticity
and focus on the case useAbsVorticity=F in the pkg/mom_vecinv/ implementation.
And once this is sorted out, we could reassess if it's worth to bring back
useLANGMUIR & useAbsVorticity.

@jm-c, thank you for reviewing the codes and making suggestions. I was not through enough to check how the stokes velocity is working under the flux form. I will see if I can fix the issue #1, first.

@jm-c, I was not fully understanding the code for the vector invariant formulation when adding the stokes velocity. Now I understand better what you meant. The #1 and #2 in your comments are not connected!
I think that mom_vecinv/mom_vi_coriolis.F is currently consistent with mom_fluxform/mom_u_coriolis.F and 'mom_fluxform/mom_u_coriolis.F', right? Then, I agree with your suggestion. We can stop the model if useLANGMUIR is with vector invariant and absolute vorticity. If this means that we do not need modification in mom_vi_u_coriolis.F, mom_vi_v_coriolis.F, mom_vi_u_coriolis_c4.F and mom_vi_v_coriolis_c4.F, please let me know. I will remove the Langmuir part and make a commit. Thank you again for reviewing the codes!

@hajsong I don't know the details of how the stokes drift should affect the momentum tendencies, but if it does it only through the coriolis term (consistent with your changes in
pkg/mom_fluxform) then we only need to keep the changes in mom_vi_coriolis.F and leave the other pkg/mom_vecinv routines unchanged (providing we stay away from useAbsVorticity=T, which is OK for now). So, yes, you are right.

@jm-c, Thank you for your comments. By the way, in my previous posting, the sentence "The No.1 and No.2 in your comments are not connected!" should be "The No.1 and No.2 in your comments are connected!". Sorry for the confusion.
I referred to equations (9) and (10) in Noh et al. (2016) when adding the stokes velocity to the momentum equation (attached). So your suggestion makes perfect sense. I will make the necessary modifications to the code and make a commit.
Noh2016_JPO.pdf

I also attach the manuscript under review regarding this Langmuir circulation parameterization.
Langmuir_LLC90-5.pdf

@jm-c, I added the stop in mom_vecinv.F when useLANGMUIR .AND. useAbsVorticity. It would be great if you could check that part. Please make any changes if you think necessary. Thank you!

Hi @jm-c and @mjlosch, just let you know that the manuscript describing this parameterization is now accepted. Happy Thanksgiving!

@hajsong  I am going to push (incomplete) changes: adding a small S/R that just compute Stokes drift to be used for Coriolis contribution. This should work with original mom_u,v,vi_coriolis.F but will not (at least just now) put back these 3 original versions (so yes, accounted 2 times) so that you can see and decide which way to go. If we decide to go this way then I will put back the 3 original S/R version.

There is one major difference between the new way to compute Stokes drift (now in new short S/R) and the previous one (still inside the 3 mom_*_coriolis.F for now): I multiply by the corresponding mask so that it's zero inside the ground. I thought it would be better this way.

@jm-c , thank you for making Stokes drift part neat and clean. I am happy with your modification!

@jm-c, I am happy with your changes in ggl90_check.F (warning message for  useLANGMUIR .AND. useAbsVorticity).
For ggl90_calc.F,

Agreed
LC_Gamma(the amplification of the mixing length scale in the presence of LC) and LC_num(Langmuir number = (u*/U_s)^0.5 where u* and U_s are the frictional velocity and Stokes drift velocity) are dimensionless, while LC_lambda(wavelength used in the stokes velocity vertical profile) is in meter.
I agree with you. Please let me know if I can help with that.

Thank you again for thoroughly evaluating the PR!

@hajsong  I added/updated new Langmuir parameter description (following a little bit what you wrote) and simplified a little bit the Stokes drift calculation in ggl90_calc.F (similar to ggl90_add_stokesdrift.F).
I left a comment regarding the loop range fix. I think this might answer @mjlosch comment:

I do not understand why the verification experiment vermix has zero agreement of cg2d on my laptop (MacBook with gfortran):
since now, in output.txt,  I get exactly zero for wVel, etaN and cg2d. I updated the reference output.

One last thing: I am going to rename "input.ggl90LC" to simply "input.gglLC", a little bit shorter, like the other additional test dir in vermix dir.

@jm-c, thank you for reviewing the code once again!
I agree with your change from "input.ggl90LC" to "input.gglLC".
If there is anything else that I can do, please let me know.
Happy new year, Jean-Michel and Martin!

@hajsong Thanks and Happy new year, to you !
From my side, everything is good and I think this PR is ready to be merged. But I will wait until @mjlosch gives his approval.

Hi, happy new year everyone. I think that this looks good now. My "vermix" issue is fixed. Let's merge this and then think about creating a separate routine for the mixing-length computation.

@hajsong I am going to open a new PR to add back 1 line that got accidentally removed in ggl90_calc.F (around line 786). This is  withing #ifdef ALLOW_GGL90_HORIZDIFF so it does not get tested.

@hajsong, in the context of PR #742, I am currently having another close look at the mixing length computation and I am a little puzzled by the Langmuir code. Can I assume that you never tried the p-coordinate part? As this requires the coordinate system to be upside down, the level k-1 is no longer above level k, but below, so one would have to use k+1 instead, also the order of the loops is usually reversed for p-coordinates. For the mxlMaxFlag=2 and 3, this does not look right to me, because neither the loop directions are reversed nor, the k-indices are different between z- and p-coordinates.
For the LCmixingLength you probably only need a downward sweep, right? If you agree, I can try to fix this in PR #742.


Mesos only detects storage mounted on root dir

This is a problem on ec2 where the majority of storage is on /mnt/ephemeral or /mnt/persistent. My current workaround is to specify the actual storage capacity when starting the mesos slave, which seems to be working but needs real testing. This won't work when we want to mount EBS volumes to a running slave.
I have asked on the mesos user group, and hopefully there is an easy solution to this issue.

The user group already got back to me.
<EMAIL_ADDRESS>Unfortunately, there is no easy solution. Mesos only supports management of 1 disk (or storage device). However because cgcloud mesos_tools mounts any specified EBS before starting mesos on the node, this problem isn't as big as I thought. This is something to keep in mind for the future though.


Bigbutton

I know it is a big button but perhaps it's time to call it something more relevant like crank toggle ? I know there's already a mycrank so maybe mycranktoggle or mycrankswitch ?

Sorry one more
for consistency rename mystring and mylowstring to myhistring or myhichant and mylostring or mylochant ????
this will then make them consistent with what you're calling them in the default tunings file???

I'll probably go through at some point and do a larger refactoring of the code and change the "my" names to something more professional sounding.  Will include this then.
I'm setting it to the 2.0 milestone because I want to limit what I add before making a v1.5 that I push to the main branch (v1.0 is not worth using anymore).  I'll refactor after that probably.

Once you're at a point when you'd like a hand with it,let me know, spent a lot of time working on the ergonomics of systems, tech manuals and instructions of things I've and others have built. Close these issues and perhaps add to a discussion?

Coming back to this... now that the code is split among lots of files, the "my" thing has kind of standardized into objects I create in the code, and it lets me over-use simple terms like "crank", for example.  Probably won't change that.
I dunno.  I sorta like bigbutton, sorry.  But myBigButton to keep with the scheme established.  Which would require changing others.... I'm so on the fence.
2.2, kick the can!  I'll keep chewing on making something that's both consistent, but that I also like when coding.


Open ssl 1 1 1w+quic

Checklist

[ ] documentation is added or updated
[ ] tests are added or updated

ping?

Taking a look now, will take me a day or two most likely


Issue with entry names being hashed

Operating System: Ubuntu 18.10
Node Version: v14.15.4
NPM Version: 6.14.10
webpack Version: ^5.23.0
karma-webpack Version: ^5.0.0

Feature Proposal
Hello,
i have an issue when updating karma-webpack, my entry names are not respected,
if for exemple i say that my entry names are :
[
'abc',
'abcd'
]

a hash get happended and it breaks everything :
[
'abc.1491000527',
'abcd.2882415412'
]

Feature Use Case
I want to disable hash and have my entry names as is.
something like that :
function getPathKey(filePath, withExtension = false, hash = false) {
  const pathParts = path.parse(filePath);
  let key = `${pathParts.name}`;
  if(hash) {
   key += `.${hash(filePath)}`
  }
  return withExtension ? `${key}${pathParts.ext}` : key;
}

edit:
and there is an issue with the hash itself, if the project is stored at a different location, the hash will change, so it's not predictable.
thanks.

Why is this necessary in your case, is there reasoning beyond preference?

well if there is a hash i can't predict the file location, and i can't use the file,
for example, i have this in my html:

This seems like an edge case that I don't think we are going to support. Can you shed more light as to why you might want to hard code references to the temporary karma-webpack files? Are you trying to create / reference variables on the global object?

I don't want to hardcode anything, i just want to use the file, aka execute the file in a browser via karma.
I don't understand, karma-webpack is used to test an application ? how you can even test an application if you can't load it because you don't know where the build files are and how they are named ?

Could you provide me with a minimal example of how you might test in this way?
My usual setup would be something like
import AdvancedMath from './js-source-example';

describe('bundled js content', () => {
  it('should be able to bundle js content', () => {
    assert.equal(!!AdvancedMath.add, true);
  });
  it('should be able to interpret bundled js content', () => {
    assert.equal(AdvancedMath.add(1, 1), 2);
  });
});

This is actually taken directly from one of the integration tests. All of the imports are relative and there is no need to interact with the created temporary files that karma outputs.

I have a .spect.html file and a .spec.ts file :

        <link href="/absolute/tmp/_karma_webpack_/abcd.bundle.css" rel="stylesheet" />

describe("abcd e2e tests", () => {
    beforeAll(function(done) {
        jasmine.addMatchers(DOMCustomMatchers);

        karmaHTML.abcd.onstatechange = function(ready: Boolean) {
            if (ready) {
                done();
            }
        };
        karmaHTML.abcd.open({ width: "1920", height: "1080" });
    });

    it("has correct dimmensions", () => {
       let someElement = karmaHTML["abcd"].document.querySelector("#someElement");

        expect(someElement.innerText).toBe('SomeText');
    });
});

i made a fork to resolve my issue : https://github.com/lk77/karma-webpack
until it gets resolved here.

As karma is now deprecated and coming up on EOL, we are no longer planning on any significant enhancements to this project and are instead going to focus on security updates, stability, and a migration path forward as karma's lifecycle comes to an end.
Thank you for supporting and using this project!


Add v1alpha1 ExternalDNS Operator API.

Adds a preliminary (v1alpha1) ExternalDNS Operator
CRD under the operator.openshift.io group name.
This commit is in support of
https://issues.redhat.com/browse/NE-640.
Enhancement proposal available here:
https://github.com/openshift/enhancements/blob/master/enhancements/dns/externaldns-operator.md

Keeping track of review comments that could use input from API reviewers...
https://github.com/openshift/api/pull/956#discussion_r660160116
https://github.com/openshift/api/pull/956#discussion_r660164271
https://github.com/openshift/api/pull/956#discussion_r660751021
https://github.com/openshift/api/pull/956#pullrequestreview-694425953

/assign @deads2k

superseded by https://github.com/openshift/external-dns-operator/pull/12

/close


Configuring github actions to run on live api

https://github.com/gbif/pygbif/issues/101

Does this create downloads? If so, at least that part should continue to use the UAT API.

I am trying to configure now to skip any downloads tests on live API.

I can't find any tests that make a live download, but @CecSve can tell me if I am wrong.

I am pretty sure no tests makes downloads

Codecov Report
Attention: Patch coverage is 0% with 1 lines in your changes are missing coverage. Please review.

Project coverage is 80.17%. Comparing base (59960ff) to head (45064d0).
Report is 3 commits behind head on master.

Files
Patch %
Lines

pygbif/caching.py
0.00%
1 Missing :warning:

:exclamation: Your organization needs to install the Codecov GitHub app to enable full functionality.
Additional details and impacted files
@@            Coverage Diff             @@
##           master     #128      +/-   ##
==========================================
+ Coverage   80.00%   80.17%   +0.17%
==========================================
  Files          26       26
  Lines         675      671       -4
==========================================
- Hits          540      538       -2
+ Misses        135      133       -2

:umbrella: View full report in Codecov by Sentry.
:loudspeaker: Have feedback on the report? Share it here.

Codecov Report
Attention: Patch coverage is 0% with 1 lines in your changes are missing coverage. Please review.

Project coverage is 80.17%. Comparing base (1180c24) to head (2c06c5a).

Files
Patch %
Lines

pygbif/caching.py
0.00%
1 Missing :warning:

:exclamation: Your organization needs to install the Codecov GitHub app to enable full functionality.
Additional details and impacted files
@@           Coverage Diff           @@
##           master     #128   +/-   ##
=======================================
  Coverage   80.17%   80.17%
=======================================
  Files          26       26
  Lines         671      671
=======================================
  Hits          538      538
  Misses        133      133

:umbrella: View full report in Codecov by Sentry.
:loudspeaker: Have feedback on the report? Share it here.


integration tests fails (grid-client)

fix nightly workflow for grid-client
add a mnemonic for the repo for all networks

@Eslam-Nawara what is the progress here?

@xmonader

the accounts used in integration tests needs to be funded and rawda is working on it
some of the tests fails due to nodes don't respond and the connection times out
there's no enough resources on devnet to run some tests

WIP:

updated all the tests to return if any assertion failed
updated the tests to not update the existing node filter and use a copy of it instead
allowed the tests to use any farm, not only free farm
rename all networks in tests to stop old conflicts

WIP:
All failing tests now, fails with context deadline exceeded messages even after increasing the context time and the nodes are actually up and reachable

WIP:

Used a random string generator to stop the error global workload with the same name exists: conflict
Set test timeout in the nightly workflow to 30 min as it was the default 10 and not all the tests had chance to finish

Problems:

Some tests have the same behavior as the last comment, they are up and reachable but the requests timeout when sent to them
BatchVm test fails due to ssh connection refused, sound like ygg problem


sbtd initialized in wrong place - breaks SAN manager

In file syno_enable_dedupe.sh,
Line 882 “sbtd=support_tiny_btrfs_dedupe”
Should be moved up by line 859, since the variable sbtd is used on lines 872-876.
Without that, the synoinfo.conf gets messed up with an extra “= no” line near the end.
On my 1522+ this corruption somehow kills SAN manager, which in turn prevents Virtual Machine Manager from running.

Thank you.

I've moved “sbtd=support_tiny_btrfs_dedupe” up to underneath "sbd=support_btrfs_dedupe".
I've also added some checks that $sbd and $sdtb are not empty before synosetkeyvalue is used.

I've released a new version v1.2.15
I just tried synosetkeyvalue with an unset variable as the keyname and while it did return 1 as the exist code instead 0 it didn't create an ="no" line.

After applying v1.2.14 I also experiened this problem. I tried using the --restore arg and rebooting but it didn't allow me to start or repair SAN Manager. I also tried applying v1.2.15 and rebooting but that also didn't help. There isn't a rogue = no line near the end of my synoinfo.conf.
I don't use iSCSI so there are no targets or snapshots currently. If there's a quick way to clear the package config entirely so that I can get the package working I'd be open to that as a solution.
Attaching journalctl output when attempting to repair the SAN Manager and synoinfo.conf.
journalctl.log
synoinfo.conf.txt

The fix worked for me, thank you revad.
Gallivanter, I did have to reboot after manually restoring the synoinfo.conf.bak file once for SAN manager to be happy. Just using the —restore did not work because it left an unclean conf for me because of the extra “=no”. Give that a try.
I’m going to close, and though, because the initial issue is fixed.

If there's a quick way to clear the package config entirely so that I can get the package working I'd be open to that as a solution.

Download https://global.synologydownload.com/download/Package/spk/ScsiTarget/1.0.10-0314/ScsiTarget-x86_64-1.0.10-0314.spk
Then uninstall SAN Manager with the following command.
sudo synopkg uninstall ScsiTarget
Finally in Package Center do a "Manual Install" of the ScsiTarget-x86_64-1.0.10-0314.spk you downloaded.

Thanks for the suggestions @darthstrydre and @007revad .
I manually restored synoinfo.conf from synoinfo.conf.bak, rebooted and attempted to manually remove the ScsiTarget package and reinstall using the manual install method. Unfortunately SAN Manager still won't start. The relevant log lines appear to be:
Dec 29 22:06:45 nas01 systemd[1]: Starting iSCSI Service...
Dec 29 22:06:45 nas01 synocheckiscsitrg[6570]: iSCSI:iscsi_repair.cpp:80:SYNOiSCSIRepair Total 0 issue(s) are detected.
Dec 29 22:06:45 nas01 S78iscsitrg.sh[6542]: :: Running lunbackup garbage collection ... [  OK  ]
Dec 29 22:06:45 nas01 synoiscsiep[6575]: iSCSI:iscsi_lun_service_start_all.cpp:105:SYNOiSCSILunServiceStartAllWithoutLock mkdir(/config/target/core/iblock_0, 448), err=No such file or directory
Dec 29 22:06:45 nas01 synoiscsiep[6575]: iSCSI:iscsi_start_all.cpp:22:SYNOiSCSIStartAllWithoutLock SYNOiSCSILunServiceStartAllWithoutLock(), err=Failed to create directory
Dec 29 22:06:45 nas01 synoiscsiep[6575]: iSCSI:iscsi_start_all.cpp:115:SYNOiSCSIStartAll SYNOiSCSIStartAllWithoutLock(), err=Failed to create directory
Dec 29 22:06:45 nas01 synoiscsiep[6579]: iSCSI:iscsi_lun_service_start_all.cpp:105:SYNOiSCSILunServiceStartAllWithoutLock mkdir(/config/target/core/iblock_0, 448), err=No such file or directory
Dec 29 22:06:45 nas01 synoiscsiep[6579]: iSCSI:vhost_scsi_start_all.cpp:13:SYNOiSCSIVhostStartAllWithoutLock SYNOiSCSILunServiceStartAllWithoutLock(), err=Failed to create directory
Dec 29 22:06:45 nas01 synoiscsiep[6579]: iSCSI:vhost_scsi_start_all.cpp:39:SYNOiSCSIVhostStartAll SYNOiSCSIVhostStartAllWithoutLock(), err=Failed to start service
Dec 29 22:06:45 nas01 synoiscsiep[6598]: iSCSI:iscsi_lun_service_start_all.cpp:105:SYNOiSCSILunServiceStartAllWithoutLock mkdir(/config/target/core/iblock_0, 448), err=No such file or directory
Dec 29 22:06:45 nas01 synoiscsiep[6598]: iSCSI:iscsi_lun_service_start_all.cpp:145:SYNOiSCSILunServiceStartAll SYNOiSCSILunServiceStartAllWithoutLock(), err=Failed to create directory
Dec 29 22:06:45 nas01 synoiscsiep[6602]: iSCSI:iscsi_lun_service_start_all.cpp:105:SYNOiSCSILunServiceStartAllWithoutLock mkdir(/config/target/core/iblock_0, 448), err=No such file or directory
Dec 29 22:06:45 nas01 synoiscsiep[6602]: iSCSI:iscsi_loopback_start_all.cpp:29:SYNOiSCSILoopbackStartAll SYNOiSCSILunServiceStartAllWithoutLock(), err=Failed to create directory
Dec 29 22:06:45 nas01 synoiscsiep[6606]: iSCSI:iscsi_lun_service_start_all.cpp:105:SYNOiSCSILunServiceStartAllWithoutLock mkdir(/config/target/core/iblock_0, 448), err=No such file or directory
Dec 29 22:06:45 nas01 synoiscsiep[6606]: iSCSI:fc_start_all.cpp:53:SYNOFCStartAll SYNOiSCSILunServiceStartAllWithoutLock(), err=Failed to create directory
Dec 29 22:06:45 nas01 systemd[1]: pkg-iscsi.service: main process exited, code=exited, status=221/CONFIRM
Dec 29 22:06:45 nas01 systemd[1]: Failed to start iSCSI Service.

/config exists but is empty.

There is no /config folder on my DS1821+ with DSM 7.2.1. Where are seeing the config folder?
What DSM version are you using?
Another alternative is to reinstall the same DSM version: https://github.com/007revad/Synology_DSM_reinstall

Interesting, you don't have a /config directory on your NAS? My DS1821+ has a /config dir:
admin@nas01:~$ ls -la /config
total 4
drwxr-xr-x  2 root root    0 Dec 29 22:20 .
drwxr-xr-x 26 root root 4096 Dec 29 22:21 ..

I'm running DSM 7.2.1-69057 Update 3. If that /config path definitely shouldn't exist I'm tempted to remove it since it's empty, before trying anything more drastic like re-installing DSM.

Checking if /config was referenced anywhere before removal, I found that it's actually mounted as configfs.
admin@nas01:~$ mount |grep config
configfs on /sys/kernel/config type configfs (rw,nosuid,nodev,noexec,relatime)
none on /config type configfs (rw,nosuid,nodev,noexec,relatime)

Reinstalling the same DSM version is no different than upgrading DSM. All your files and settings etc are retained.
ScsiTarget-x86_64-1.0.10-0314 is the version included in DSM 7.2.1-69057 Update 3.
Oh, I do have /config
I was looking for a ScsiTarget config folder in /var/packages/ScsiTarget/target and /usr/local/packages/@appstore/ScsiTarget
My DS1812+'s /config folder contains a bunch folders and files:
# ls -lR /config
/config:
total 0
drwxr-xr-x 2 root root 0 Dec 30 10:40 pool
drwxr-xr-x 2 root root 0 Dec 30 10:40 rodsys
drwxr-xr-x 6 root root 0 Dec 30 10:41 target
drwxr-xr-x 2 root root 0 Dec 21 18:35 volume
drwxr-xr-x 2 root root 0 Dec 28 14:46 vsys

/config/pool:
total 0
-r--r--r-- 1 root root 4096 Dec 30 10:42 cache_info
-rw-r--r-- 1 root root 4096 Dec 30 10:42 ext_jnl_cache_cnt
-rw-r--r-- 1 root root 4096 Dec 30 10:42 ext_jnl_enable
-rw-r--r-- 1 root root 4096 Dec 30 10:42 ext_jnl_path
-rw-r--r-- 1 root root 4096 Dec 30 10:42 ext_jnl_suspend
-r--r--r-- 1 root root 4096 Dec 30 10:42 help
-rw-r--r-- 1 root root 4096 Dec 30 10:42 preread_control

/config/rodsys:
total 0
-r--r--r-- 1 root root 4096 Dec 30 10:42 help
-r--r--r-- 1 root root 4096 Dec 30 10:42 local_key
-rw-r--r-- 1 root root 4096 Dec 30 10:42 throttle_interval_msec

/config/target:
total 0
drwxr-xr-x 8 root root    0 Dec 21 18:35 core
-rw-r--r-- 1 root root 4096 Dec 30 10:42 dbroot
-r--r--r-- 1 root root 4096 Dec 30 10:42 dump_qos_lists
-rw-r--r-- 1 root root 4096 Dec 30 10:42 io_delay_msec
-rw-r--r-- 1 root root 4096 Dec 30 10:42 io_delay_per_io
drwxr-xr-x 3 root root    0 Dec 21 18:35 iscsi
-r--r--r-- 1 root root 4096 Dec 30 10:42 lio_event
drwxr-xr-x 3 root root    0 Dec 21 18:35 loopback
-rw-r--r-- 1 root root 4096 Dec 30 10:42 target_iops
-r--r--r-- 1 root root 4096 Dec 30 10:42 version
drwxr-xr-x 3 root root    0 Dec 21 18:35 vhost

/config/target/core:
total 0
drwxr-xr-x 3 root root 0 Dec 21 18:35 alua
drwxr-xr-x 2 root root 0 Dec 21 18:35 epio_0
drwxr-xr-x 2 root root 0 Dec 21 18:35 epio_1
drwxr-xr-x 2 root root 0 Dec 21 18:35 fileio_0
drwxr-xr-x 2 root root 0 Dec 21 18:35 iblock_0
drwxr-xr-x 2 root root 0 Dec 21 18:35 multifile_0

/config/target/core/alua:
total 0
drwxr-xr-x 3 root root 0 Dec 21 18:35 lu_gps

/config/target/core/alua/lu_gps:
total 0
drwxr-xr-x 2 root root 0 Dec 21 18:35 default_lu_gp

/config/target/core/alua/lu_gps/default_lu_gp:
total 0
-rw-r--r-- 1 root root 4096 Dec 30 10:42 lu_gp_id
-r--r--r-- 1 root root 4096 Dec 30 10:42 members

/config/target/core/epio_0:
total 0
-r--r--r-- 1 root root 4096 Dec 30 10:42 hba_info
-rw-r--r-- 1 root root 4096 Dec 30 10:42 hba_mode
-r--r--r-- 1 root root 4096 Dec 30 10:42 odx_info
-rw-r--r-- 1 root root 4096 Dec 30 10:42 odx_mode

/config/target/core/epio_1:
total 0
-r--r--r-- 1 root root 4096 Dec 30 10:42 hba_info
-rw-r--r-- 1 root root 4096 Dec 30 10:42 hba_mode
-r--r--r-- 1 root root 4096 Dec 30 10:42 odx_info
-rw-r--r-- 1 root root 4096 Dec 30 10:42 odx_mode

/config/target/core/fileio_0:
total 0
-r--r--r-- 1 root root 4096 Dec 30 10:42 hba_info
-rw-r--r-- 1 root root 4096 Dec 30 10:42 hba_mode
-r--r--r-- 1 root root 4096 Dec 30 10:42 odx_info
-rw-r--r-- 1 root root 4096 Dec 30 10:42 odx_mode

/config/target/core/iblock_0:
total 0
-r--r--r-- 1 root root 4096 Dec 30 10:42 hba_info
-rw-r--r-- 1 root root 4096 Dec 30 10:42 hba_mode
-r--r--r-- 1 root root 4096 Dec 30 10:42 odx_info
-rw-r--r-- 1 root root 4096 Dec 30 10:42 odx_mode

/config/target/core/multifile_0:
total 0
-r--r--r-- 1 root root 4096 Dec 30 10:42 hba_info
-rw-r--r-- 1 root root 4096 Dec 30 10:42 hba_mode
-r--r--r-- 1 root root 4096 Dec 30 10:42 odx_info
-rw-r--r-- 1 root root 4096 Dec 30 10:42 odx_mode

/config/target/iscsi:
total 0
drwxr-xr-x 2 root root    0 Dec 21 18:35 discovery_auth
-rw-r--r-- 1 root root 4096 Dec 30 10:42 global_np_enable
-r--r--r-- 1 root root 4096 Dec 30 10:42 lio_version

/config/target/iscsi/discovery_auth:
total 0
-r--r--r-- 1 root root 4096 Dec 30 10:42 authenticate_target
-rw-r--r-- 1 root root 4096 Dec 30 10:42 enforce_discovery_auth
-rw-r--r-- 1 root root 4096 Dec 30 10:42 password
-rw-r--r-- 1 root root 4096 Dec 30 10:42 password_mutual
-rw-r--r-- 1 root root 4096 Dec 30 10:42 userid
-rw-r--r-- 1 root root 4096 Dec 30 10:42 userid_mutual

/config/target/loopback:
total 0
drwxr-xr-x 2 root root    0 Dec 21 18:35 discovery_auth
-r--r--r-- 1 root root 4096 Dec 30 10:42 version

/config/target/loopback/discovery_auth:
total 0

/config/target/vhost:
total 0
drwxr-xr-x 2 root root    0 Dec 21 18:35 discovery_auth
-rw-r--r-- 1 root root 4096 Dec 30 10:42 throttle_interval_msec
-r--r--r-- 1 root root 4096 Dec 30 10:42 version

/config/target/vhost/discovery_auth:
total 0

/config/volume:
total 0

/config/vsys:
total 0
-r--r--r-- 1 root root 4096 Dec 30 10:42 help

I bit the bullet and used Synology_DSM_reinstall to reinstall the current version of DSM. My /config path now looks like yours and SAN Manager is working as expected. Thanks for all the help!


Add support for IDE versions 2023

Current latest build version is 231.* (2023.1)
Aware that last time out this triggered a bunch of work with minimum version upgrades, etc though.
ref: https://plugins.jetbrains.com/docs/intellij/build-number-ranges.html#intellij-platform-based-products-of-recent-ide-versions

Plugin fails on GoLand 2023.1 with following error: Plugin 'Jsonnet Language Server' (version '0.1.1') is not compatible with the current version of the IDE, because it requires build 223.* or older but the current build is GO-231.8109.199

Closed by: https://github.com/zzehring/intellij-jsonnet/releases/tag/v0.1.2


create text facet include

parallel to gallery. basically a gallery for text.

Closing bc wax-facets exists? But worth revisiting for 2.0 theme!


Create diagram showing system architecture and deployment plan

What Issue Are You Seeing or What Feature Needs To Be Added?
Come up with an explicit plan showing the design of the application from starting it to deploying it.
Are There Any Links That Would Be Helpful?

Server: https://otree.readthedocs.io/en/latest/server/intro.html

Ohhh, this work is mad useful:

https://ie.pubpub.org/pub/otree/release/1
https://github.com/SciEcon/Intelligent-Economics-XAI-Autumn-2021

Login information: https://github.com/prijatelilab/PrijateliTree/issues/4#issuecomment-1696310768

As far as I can tell, we are currently at the create/customize the application step. Based off of my conversations with @FedericoDM, I think we are going the route of using the text editor. With that in mind, I created the barebones app in this PR: https://github.com/prijatelilab/PrijateliTree/pull/9

For deploying projects, we can use this application: https://www.otreehub.com/
It will require us syncing our credentials in with otreehub, but it seems that it's safe enough.

The next steps will be taking @FedericoDM's game diagrams and plotting those out in pseudo-code and then implementing dummy files to map out the paths (html for x function, etc.).

The next steps will be taking @FedericoDM's game diagrams and plotting those out in pseudo-code and then implementing dummy files to map out the paths (html for x function, etc.).

We decided to use FastAPI instead of oTree, so we're gonna scrap this task.


how to use it in flutter windows application

I'm trying to using this dependence in a flutter windows application but is not working.

This package predates flutter windows. I just updated it for flutter 2.0 and null safety. I haven't tested windows but it should now work there.