[📑 Docs]: determine page outline for Generator Tool docs

What Dev Docs changes are you proposing?
Determine outline for Generator Tool docs
Before any TW investment is done toward adding the Generator Tool docs in our /website repo, we need to agree on the content outline we'll use for this tool documentation. After the outline is agreed upon, the migration and re-writing efforts will make more sense. (cc @pratik2315 @Florence-Njeri)
Proposed Generator Tool docs outline

Overview (what use cases/scenarios this tool solves)
Installation guide (download/install instructions)
CLI Command reference (list of commands & what they do)
Workflows (functionality) (Show us how to use the tool and it’s functionality. ex: generate a template, author a template)

URLs for new tool docs page and section hyperlinks
If we use the above sample outline, then we'd in turn end up with URLs as follows:

docs/tools/generator
docs/tools/generator#overview
docs/tools/generator#installation-guide
docs/tools/generator#cli-command-reference
docs/tools/generator#workflows

Code of Conduct

[X] I agree to follow this project's Code of Conduct

I'm not convinced about CLI Command reference and Workflows because:

Generator is mainly a library. Current CLI will be soon deprecated and people will use generator in the main asyncapi CLI. So we should have a main section like Usage or Integration where we explain both, CLI of course, but also how to use generator on JavaScript API level ( + find a way how to integrate current generator API.md in the whole story)
Instead of Workflows we need separate section with a set of tutorials on how to create code generator with AsyncAPI Geneerator.

In my 1:1 onboarding call with @alequetzalli , we had a brainstorming practice session about where the generator tutorials should belong. The conclusion was that, since we already have a tutorials section with "Streetlights" and "Streetlights - Interactive (Alpha)" content buckets, we could maybe create similar content buckets for the Generator stuff? So one possible hyperlinks architecture could be for example: docs/tutorials/generatorTutorial1 🤔

We need a place where we can explain concepts in generator, basically what is AsyncAPI generator and what is Template. What are render engines and what is context

Okay! So if I am not wrong this information could go to: docs/tools/generator#overview ?

We need a place where we can explain reference info about all possible Configurations that one can do in their code generators
Even though we will have tutorials on writing a code generator, we still need a reference docs of all the Generator features, where we discuss all possible options (while in tutorial you always cover just some)

is done toward adding the Generator Tool docs in our /website repo

Source of documentation, markdown files will be stored and developed in generator repository and just pushed to website to render them nicely in the UI, thus moving this issue to generator repo so it is visible to all it's users and maintainers, so they can also share their opinion.

I also had a suggestion I wanted to get reviewed😅
What do you guys think about a separate section for FAQ's related to generator tool where we list common errors faced while using the generator tools and ways to deal with them? or does that conflict with our current information architecture?🤔
what do you think @derberg @alequetzalli @Florence-Njeri !

In my 1:1 onboarding call with @alequetzalli , we had a brainstorming practice session in which we discussed where the generator tutorials should belong. The conclusion was that, since we already have a tutorials section with "Streetlights" and "Streetlights - Interactive (Alpha)" content buckets, we could maybe create similar content buckets for the Generator stuff? So one possible hyperlinks architecture could be for example: docs/tutorials/generatorTutorial-1

yes, we have current streetlight tutorial but, even though it uses AsyncAPI Generator, its goal is more about showing what you can get out from writing AsyncAPI document. In case of Generator tutorials, we will have more step by step tutorials showing how to create a template, and using a template to generate something will be one of the tutorial set

Okay! So if I am not wrong this information could go to: docs/tools/generator#overview ?

tbh I think we will end up with docs/tools/generator/concepts/overview 🤔

What do you guys think about a separate section for FAQ's related to generator tool where we list common errors faced while using the generator tools and ways to deal with them? We can also collect and list some of the most commonly asked questions about the asyncapi community and most used tools and try to answer them there. Does that sound good ! or does that conflict with our current information architecture?🤔

I love FAQs, we just need to be careful to not put there everything. Sometimes asked community question should convert into update in existing doc or additional concept document, not quick new-FAQ-entry

I also had a suggestion I wanted to get reviewed😅 What do you guys think about a separate section for FAQ's related to generator tool where we list common errors faced while using the generator tools and ways to deal with them? We can also collect and list some of the most commonly asked questions about the asyncapi community and most used tools and try to answer them there. Does that sound good ! or does that conflict with our current information architecture?🤔

@pratik2315 So we won't have FAQs in the Tools docs, because it's not a blog post or stand-alone page. Rather.. think about how documenting limitations or common errors fit within out new IA. In this case, we will have the how-to section added as a content bucket for writing guides to common user problems/error scenarios. The tool limitations would also require a well-thought-out content outline header for those topics.
To further illustrate the issue I want us to avoid, let me quote Tom Johnson:

"All too often the FAQ format is abused by non-writers who want an easy way to write. The list of random questions grows with each incoming question until it's a ridiculous hodgepodge of information thrown together, with no larger story or narrative."

I also had a suggestion I wanted to get reviewed😅 What do you guys think about a separate section for FAQ's related to generator tool where we list common errors faced while using the generator tools and ways to deal with them? We can also collect and list some of the most commonly asked questions about the asyncapi community and most used tools and try to answer them there. Does that sound good ! or does that conflict with our current information architecture?🤔

@pratik2315 So we won't have FAQs in the Tools docs, because it's not a blog post or stand-alone page. Rather.. think about how documenting limitations or common errors fit within our new IA. In our new IA, we'll have a how-to section added as a NEW content bucket for writing guides to common user problems/error scenarios. The tool limitations also require a well-thought-out content outline header for those topics.
To further illustrate the issue I want us to avoid, let me quote Tom Johnson:

"All too often the FAQ format is abused by non-writers who want an easy way to write. The list of random questions grows with each incoming question until it's a ridiculous hodgepodge of information thrown together, with no larger story or narrative."

That makes sense! Thanks for the input💪😄


Model Clipping in Autodesk VRED

So I'm using my Quest and ALVR to view my 3D models in Vred. It works really well btw. Good Job!!
My issue. I'm seeing some clipping of the model when I get close to it in the scene. I can also notice this happening to the scene in my periphery. Any ideas what causes this?
Also is there a way to change the button programming on the touch controllers? I'd like to access the VR menu in Vred but it is mapped to the Menu button on the Rift and Vive. Thanks

Perhaps it is caused by settings of the application. Can you find configuration like "near clipping distance"?

Hmm, I haven't seen a setting for that. I use the same file and setup with my Rift and have never noticed anything like this.. With my rift setup I'd have to put my head right through the Geometry to get a similar affect. I'll dig around and see if I can come up with anything.
On another note the same thing happened when I tried it with Virtual Desktop VR streaming function.. Thanks for your help..


Implement a Grid View showing multiple assets.

As a user I want to be able to look at multiple assets at once. Therefore a grid view should be implemented showing multiple assets next to each other.
In a first stage the name and status should be displayed for every asset.
Dependecies
#30 (asstes need to be accessed)
Definition of Done
[ ] Multiple Assets can be shown in a Grid view.


How to import & use generated queries?

First off - fantastic work on this. I will say the readme is a little short :)
We have a GraphQL server and It's pulling in the schema just fine. And what is more is I get that nice  "GraphQL Schemas" at the bottom of the project. But it is un-useable.
How in the world do I have it generate that same file for me in a specified location so I can use it with React/Typescript ie:
import { slopes } from './queries.graphql';
Instead of having to manually create queries (before I found the plugin):
export const SLOPES_QUERY = gql{ slopes { name orderNumber skillTracks { name orderNumber } } };
In the past I've used:
 #apollo-codegen generate **/*.graphql --schema schema.json --target typescript --output types.ts
And was hoping the plugin did the same thing, but not a line of instruction anywhere specifying how to do this.
Could you point me in the right direction?

Hi Mark.
Thanks for using the plugin.
The plugin focuses on the GraphQL language and makes no assumptions about how you will be accessing the GraphQL scheme types in your code, other than the use of gql, Relay.QL, and graphql tagged templates which activates the plugin.
Some people use JS, some with Flow, others use TypeScript. So it's up to you how and if to use code generation. Using apollo-codegen should work fine in combination with the plugin. In my own team, we use an internal tool to generate a Schema.ts based on the GraphQL schema, and that also works fine with the plugin. In our React components we simply import the relevant types from Schema.ts and use that when accessing the GraphQL types.
I hope this makes things more clear.

Ok, so this plugin does not automate the generation of code for the languages that apollo-codegen supports. Got it. Well now I'm wondering of of the advantage of the plugin and writing pure GraphQL models, mostly because Apollo Client does a ton of the heavy lifting. I thought it would be pretty awesome to hit a url, and then have code generated for the client project.
Can you help me understand why and when this plugin would be useful? For instance, on our API we have our schemas created in our TypeScript code, attached to mongodb models.
export const slopeType = new GraphQLObjectType({
    name: 'Slope',
    description: 'Slope',
    fields: () => ({
	    id: { type: GraphQLID },
        name: { type: GraphQLString },
	    orderNumber: { type: GraphQLInt },
        skillTracks: {
	        type: new GraphQLList(skillTrackType),
	        resolve(parentValue, args) {
				return getSkillTracksForSlope(parentValue.id);
	        }
        },
    }),
});

Can you help me understand how your plugin can add value to our workflow?


djlint: 1.32.1 -> 1.34.1

Description of changes
Changelog: https://github.com/djlint/djLint/releases
Things done

Built on platform(s)

[x] 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
[x] Tested basic functionality of all binary files (usually in ./result/bin/)
24.05 Release Notes (or backporting 23.05 and 23.11 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.

Result of nixpkgs-review pr 277309 run on x86_64-linux 1

2 packages built:

djlint
djlint.dist

This package is broken after the update, nixos has pathspec version 0.11 and djlint now wants version at least 0.12


Python - Not all headings are found

Bug Description
When searching the python docs I'd would expect that I can find every heading. But this is not the case e.g. Built-in Types are not found via the workflow
Relevant Screenshot
No response
To Reproduce
No response
Debugging Log
log.txt
Workflow Configuration

Checklist

[X] I have updated to the latest version of this workflow.
[X] I am using Alfred 5. (Older versions are not supported anymore.)

Well, the current implementation of the Python Docs Search only looks for titles of pages, in your case "stdtypes". It's not ideal for sure, but technically not a bug, but rather a FR 😅
Implementing searching of headings in the python docs would take quite a bit of extra effort. If anyone wants to give it a try, it's this file you'd have to work with.

Okay, I figured out a way to correctly fetch all headings in the python docs. Added in the latest release 1.14.1
https://github.com/chrisgrieser/alfred-docs-searches/releases/download/1.14.1/alfred-docs-searches.alfredworkflow


Remove unnecessary ring_buffer template-id

A minor tweak for C++20 compatability that doesn't really fit into any other category: on GCC, the redundant template-id in ring_buffer got treated as an error.

This is covered by:

https://github.com/godotengine/godot/pull/75310

And isn't relevant without that as a whole IMO, I don't think we should take parts of it piecemeal

To an extent I'd agree, but that proposal makes a lot of changes that I don't think actually address some of the core issues in play.
With that said, I might as well just put that up as a draft to tentatively pull from, instead of having this as a redundant entry point.


not all dns requests dump to stdout

I have a new coredns install that only has the dump plugin running on a new Ubuntu 20.04 VM (<IP_ADDRESS>). When I run this command from the host system (<IP_ADDRESS>), it doesn't seem to print out every request.  Roughly 55% is dumped.
for i in {1..100}; do dig @<IP_ADDRESS> -p 4444 helloworld$i.com; sleep 1; done
As a test, I modified the dump.go file by commenting out the Fprintln and unused vars/imports.  I then added a line that simple prints out hello.  After building, the change, hello is printed, but again it seems to only print about 55% of the time.
Any idea what might cause this?  I have noticed buffering slowing down output in golang before, but this seems like a different issue.  I am happy to dig further into the code to find the root cause and look forward to your insight / perspective on this.

Closing, I had multiple pids running...

Closing, I had multiple pids running...


[SMALLFIX]Fix logging message of PlanInfo

What changes are proposed in this pull request?
Please outline the changes and how this PR fixes the issue.
Why are the changes needed?
Please clarify why the changes are needed. For instance,

Fix Alluxio/new-contributor-tasks#635
Close Alluxio/new-contributor-tasks#635

Does this PR introduce any user facing changes?
Please list the user-facing changes introduced by your change, including

change in user-facing APIs
addition or removal of property keys
webui

alluxio-bot, merge this please


Add caching to HttpForkSource

What :computer:

Adds a caching layer to the http fork source.
Adds --cache command line parameter to either:

none - disable caching
memory - in-memory caching
disk - persisted on-disk caching

Adds --cache-dir & --reset-cache options to disk caching to select the cache location and whether to purge cache on startup, respectively

Why :hand:

During testing it is quite often intended to run similar requests multiple times. Having a cache reduces the required number of network calls to be made.

Evidence :camera:

Notes :memo:

Fixes #106

merged in upstream


azure-cosmosdb-tables does not support CosmosDB Tables Service

This is a case of a unfortunately named library that may lead you to believe you could use it to connect to the CosmosDB Table API.
Do not be misled by the links on the CosmosDB Table API page: https://docs.microsoft.com/en-us/azure/cosmos-db/table-introduction
A rename of the project is certainly in order.
I hope that a future version will support the new API soon.
Thank you to @srinathnarayanan for confirming this with me.

Thank you. I will.
-Clay
From: Srinath Narayanan<EMAIL_ADDRESS>Sent: Tuesday, December 5, 2017 2:04 PM
To: Azure/azure-cosmosdb-python<EMAIL_ADDRESS>Cc: Hagler, Clay<EMAIL_ADDRESS>Author<EMAIL_ADDRESS>Subject: Re: [Azure/azure-cosmosdb-python] azure-cosmosdb-tables does not support CosmosDB Tables Service (#9)
With patch 1.0.1, You can now use the azure-cosmosdb-table sdk to connect to the CosmosDB Table API using the connection string provided for your table api on the portal. Please check out the fix and feel free to close the issue.
—
You are receiving this because you authored the thread.
Reply to this email directly, view it on GitHubhttps://github.com/Azure/azure-cosmosdb-python/issues/9#issuecomment-349440864, or mute the threadhttps://github.com/notifications/unsubscribe-auth/ALV9lvmzKVQwDJvyokLx-DxDSS65WRs6ks5s9a_bgaJpZM4QFP0B.

I am closing this issue as it has been addressed. Feel free to reopen it if you are running into a similar issue. Thanks!


Cannot run falcon_180B benchmark using fp8

Description:
I am trying to run the falcon_180B model using TensorRT-LLM with fp8 precision, but I am getting the following error:
#OMPI_ALLOW_RUN_AS_ROOT=1 OMPI_ALLOW_RUN_AS_ROOT_CONFIRM=1 mpirun -n 8 python benchmark.py -m falcon_180b --mode plugin     --batch_size "64"     --input_output_len "2048,128" --max_input_len=2048 --max_output_len=2048 --enable_fp8 --dtype float16

Traceback (most recent call last):
  File "/app/tensorrt_llm/TensorRT-LLM/benchmarks/python/benchmark.py", line 322, in <module>
    main(args)
  File "/app/tensorrt_llm/TensorRT-LLM/benchmarks/python/benchmark.py", line 219, in main
    benchmarker = GPTBenchmark(
  File "/app/tensorrt_llm/TensorRT-LLM/benchmarks/python/gpt_benchmark.py", line 142, in __init__
    engine_buffer = self.build()
  File "/app/tensorrt_llm/TensorRT-LLM/benchmarks/python/gpt_benchmark.py", line 370, in build
    tensorrt_llm_model = fp8_quantize(tensorrt_llm_model,
  File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/models/quantized/quant.py", line 371, in fp8_quantize
    return _default_fp8_quantize(model, quant_mode, quant_scales)
  File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/models/quantized/quant.py", line 354, in _default_fp8_quantize
    assert model.quant_mode == quant_mode, "Quant setting not consistent with model init setting"
AssertionError: Quant setting not consistent with model init setting
Traceback (most recent call last):
  File "/app/tensorrt_llm/TensorRT-LLM/benchmarks/python/benchmark.py", line 322, in <module>
    main(args)
  File "/app/tensorrt_llm/TensorRT-LLM/benchmarks/python/benchmark.py", line 219, in main
    benchmarker = GPTBenchmark(
  File "/app/tensorrt_llm/TensorRT-LLM/benchmarks/python/gpt_benchmark.py", line 142, in __init__
    engine_buffer = self.build()
  File "/app/tensorrt_llm/TensorRT-LLM/benchmarks/python/gpt_benchmark.py", line 370, in build
    tensorrt_llm_model = fp8_quantize(tensorrt_llm_model,
  File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/models/quantized/quant.py", line 371, in fp8_quantize
    return _default_fp8_quantize(model, quant_mode, quant_scales)
  File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/models/quantized/quant.py", line 354, in _default_fp8_quantize
    assert model.quant_mode == quant_mode, "Quant setting not consistent with model init setting"
AssertionError: Quant setting not consistent with model init setting
Traceback (most recent call last):
  File "/app/tensorrt_llm/TensorRT-LLM/benchmarks/python/benchmark.py", line 322, in <module>
    main(args)
  File "/app/tensorrt_llm/TensorRT-LLM/benchmarks/python/benchmark.py", line 219, in main
    benchmarker = GPTBenchmark(
  File "/app/tensorrt_llm/TensorRT-LLM/benchmarks/python/gpt_benchmark.py", line 142, in __init__
    engine_buffer = self.build()
  File "/app/tensorrt_llm/TensorRT-LLM/benchmarks/python/gpt_benchmark.py", line 370, in build
    tensorrt_llm_model = fp8_quantize(tensorrt_llm_model,
  File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/models/quantized/quant.py", line 371, in fp8_quantize
    return _default_fp8_quantize(model, quant_mode, quant_scales)
  File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/models/quantized/quant.py", line 354, in _default_fp8_quantize
    assert model.quant_mode == quant_mode, "Quant setting not consistent with model init setting"
AssertionError: Quant setting not consistent with model init setting
Traceback (most recent call last):
  File "/app/tensorrt_llm/TensorRT-LLM/benchmarks/python/benchmark.py", line 322, in <module>
    main(args)
  File "/app/tensorrt_llm/TensorRT-LLM/benchmarks/python/benchmark.py", line 219, in main
    benchmarker = GPTBenchmark(
  File "/app/tensorrt_llm/TensorRT-LLM/benchmarks/python/gpt_benchmark.py", line 142, in __init__
    engine_buffer = self.build()
  File "/app/tensorrt_llm/TensorRT-LLM/benchmarks/python/gpt_benchmark.py", line 370, in build
    tensorrt_llm_model = fp8_quantize(tensorrt_llm_model,
  File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/models/quantized/quant.py", line 371, in fp8_quantize
    return _default_fp8_quantize(model, quant_mode, quant_scales)
  File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/models/quantized/quant.py", line 354, in _default_fp8_quantize
    assert model.quant_mode == quant_mode, "Quant setting not consistent with model init setting"
AssertionError: Quant setting not consistent with model init setting
Traceback (most recent call last):
  File "/app/tensorrt_llm/TensorRT-LLM/benchmarks/python/benchmark.py", line 322, in <module>
    main(args)
  File "/app/tensorrt_llm/TensorRT-LLM/benchmarks/python/benchmark.py", line 219, in main
    benchmarker = GPTBenchmark(
  File "/app/tensorrt_llm/TensorRT-LLM/benchmarks/python/gpt_benchmark.py", line 142, in __init__
    engine_buffer = self.build()
  File "/app/tensorrt_llm/TensorRT-LLM/benchmarks/python/gpt_benchmark.py", line 370, in build
    tensorrt_llm_model = fp8_quantize(tensorrt_llm_model,
  File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/models/quantized/quant.py", line 371, in fp8_quantize
    return _default_fp8_quantize(model, quant_mode, quant_scales)
  File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/models/quantized/quant.py", line 354, in _default_fp8_quantize
    assert model.quant_mode == quant_mode, "Quant setting not consistent with model init setting"
AssertionError: Quant setting not consistent with model init setting
Traceback (most recent call last):
  File "/app/tensorrt_llm/TensorRT-LLM/benchmarks/python/benchmark.py", line 322, in <module>
    main(args)
  File "/app/tensorrt_llm/TensorRT-LLM/benchmarks/python/benchmark.py", line 219, in main
    benchmarker = GPTBenchmark(
  File "/app/tensorrt_llm/TensorRT-LLM/benchmarks/python/gpt_benchmark.py", line 142, in __init__
    engine_buffer = self.build()
  File "/app/tensorrt_llm/TensorRT-LLM/benchmarks/python/gpt_benchmark.py", line 370, in build
    tensorrt_llm_model = fp8_quantize(tensorrt_llm_model,
  File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/models/quantized/quant.py", line 371, in fp8_quantize
    return _default_fp8_quantize(model, quant_mode, quant_scales)
  File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/models/quantized/quant.py", line 354, in _default_fp8_quantize
    assert model.quant_mode == quant_mode, "Quant setting not consistent with model init setting"
AssertionError: Quant setting not consistent with model init setting
Traceback (most recent call last):
  File "/app/tensorrt_llm/TensorRT-LLM/benchmarks/python/benchmark.py", line 322, in <module>
    main(args)
  File "/app/tensorrt_llm/TensorRT-LLM/benchmarks/python/benchmark.py", line 219, in main
    benchmarker = GPTBenchmark(
  File "/app/tensorrt_llm/TensorRT-LLM/benchmarks/python/gpt_benchmark.py", line 142, in __init__
    engine_buffer = self.build()
  File "/app/tensorrt_llm/TensorRT-LLM/benchmarks/python/gpt_benchmark.py", line 370, in build
    tensorrt_llm_model = fp8_quantize(tensorrt_llm_model,
  File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/models/quantized/quant.py", line 371, in fp8_quantize
    return _default_fp8_quantize(model, quant_mode, quant_scales)
  File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/models/quantized/quant.py", line 354, in _default_fp8_quantize
    assert model.quant_mode == quant_mode, "Quant setting not consistent with model init setting"
AssertionError: Quant setting not consistent with model init setting
Traceback (most recent call last):
  File "/app/tensorrt_llm/TensorRT-LLM/benchmarks/python/benchmark.py", line 322, in <module>
    main(args)
  File "/app/tensorrt_llm/TensorRT-LLM/benchmarks/python/benchmark.py", line 219, in main
    benchmarker = GPTBenchmark(
  File "/app/tensorrt_llm/TensorRT-LLM/benchmarks/python/gpt_benchmark.py", line 142, in __init__
    engine_buffer = self.build()
  File "/app/tensorrt_llm/TensorRT-LLM/benchmarks/python/gpt_benchmark.py", line 370, in build
    tensorrt_llm_model = fp8_quantize(tensorrt_llm_model,
  File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/models/quantized/quant.py", line 371, in fp8_quantize
    return _default_fp8_quantize(model, quant_mode, quant_scales)
  File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/models/quantized/quant.py", line 354, in _default_fp8_quantize
    assert model.quant_mode == quant_mode, "Quant setting not consistent with model init setting"
AssertionError: Quant setting not consistent with model init setting
--------------------------------------------------------------------------
Primary job  terminated normally, but 1 process returned
a non-zero exit code. Per user-direction, the job has been aborted.
--------------------------------------------------------------------------
--------------------------------------------------------------------------
mpirun detected that one or more processes exited with non-zero status, thus causing
the job to be terminated. The first process to do so was:

  Process name: [[36290,1],6]
  Exit code:    1
--------------------------------------------------------------------------

Environment:

TensorRT-LLM version: release/0.5.0
GPU: 4 H100
CUDA version: 12.2
Driver version: 535.104.12
Python version: 3.10.12

Please investigate this issue and provide a solution. Thank you.

Hi @tohaowu , thanks for reporting the issue. Can you share the build.py command that you're using, please? Also, does it also fail with run.py? As soon as we have the command-lines, we will see if we can reproduce the issue. Thanks, Julien

The code path is https://github.com/NVIDIA/TensorRT-LLM/blob/release/0.5.0/benchmarks/python/benchmark.py.
The command to reproduce is OMPI_ALLOW_RUN_AS_ROOT=1 OMPI_ALLOW_RUN_AS_ROOT_CONFIRM=1 mpirun -n 8 python benchmark.py -m falcon_180b --mode plugin     --batch_size "64"     --input_output_len "2048,128" --max_input_len=2048 --enable_fp8

Thank @tohaowu for reporting this bug. To fix it, we need to add quant_mode=self.quant_mode in constructing a falcon model here. Like this,
            tensorrt_llm_model = tensorrt_llm.models.FalconForCausalLM(
                num_layers=self.num_layers,
                num_heads=self.num_heads,
                num_kv_heads=self.num_kv_heads,
                hidden_size=self.hidden_size,
                vocab_size=self.vocab_size,
                max_position_embeddings=self.n_positions,
                dtype=kv_dtype,
                bias=self.bias,
                quant_mode=self.quant_mode,    # <-- this option.
                use_alibi=self.use_alibi,
                new_decoder_architecture=self.new_decoder_architecture,
                parallel_attention=self.parallel_attention,
                mapping=tensorrt_llm.Mapping(world_size=self.world_size,
                                             tp_size=self.world_size))

This fixes the issue on my end. (It will take time due to engine build.)
Besides that, you need to change --batch_size 64 to --batch_size 8 or add --max_batch_size 64 to set the maximum batch size. Otherwise, you will see this message
[WARNING] check batch_size(64) <= max_batch_size(8) failed, skipping.

It's because the benchmark of falcon-180b sets max_batch_size=8 by default.
With --batch_size 8, I could run the benchmark as below.
$ OMPI_ALLOW_RUN_AS_ROOT=1 OMPI_ALLOW_RUN_AS_ROOT_CONFIRM=1 mpirun -n 8 --oversubscribe python benchmark.py -m falcon_180b --mode plugin --batch_size "8" --input_output_len "2048,128" --max_input_len=2048 --max_output_len=2048 --enable_fp8 --dtype float16
.....
[BENCHMARK] model_name falcon_180b world_size 8 num_heads 232 num_kv_heads 8 num_layers 80 hidden_size 14848 vocab_size 65024 precision float16 batch_size 8 input_length 2048 output_length 128 gpu_peak_mem(gb) 31.44 build_time(s) 910.02 tokens_per_sec 265.8 percentile95(ms) 3862.287 percentile99(ms) 3862.287 latency(ms) 3852.586 compute_cap sm90

We will fix it soon. Thanks for the report.

It works. Thanks


Linux custom OS configuration settings tables with false "Setting" values

The four tables in the "Linux custom OS configuration settings" section do not specify the correct property names to put into the linuxosconfig.json file. The property names have to be provided in camelCase, not with - and _.
For example, to set vm.max_map_count, the correct property label is vmMaxMapCount.
Best regards,
Thomas

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

ID: 380f1a40-3494-7d98-216d-9fd1cd624314
Version Independent ID: 2ac3f4f8-0bf3-68a7-cb78-ff9a252ecd8b
Content: Customize the node configuration for Azure Kubernetes Service (AKS) node pools - Azure Kubernetes Service
Content Source: articles/aks/custom-node-configuration.md
Service: azure-kubernetes-service
GitHub Login: @palma21
Microsoft Alias: jpalma

@palma21
Can you please check and add your comments on this doc update request as applicable.

@thesse1
I'm going to assign this to the document author so they can take a look at it accordingly.
@palma21
Can you please check and add your comments on this doc update request as applicable.


onRender prop not being called

Hi, I've been playing around with this component trying to get it to render my list but I've hit a snag where it seems to just not call its onRender prop.
React 16.3.1
react-dynamic-virtual-list 1.2.2
Here's my jsx for the component:
<DVL
                        onRender={item =>
{this.renderRow(item)}
}
                        items={this.state.employees}
                        windowContainer={true}
/>

and the renderRow function:
 renderRow(item){
        console.log("render row");
        return (
            <Item
                key={item.id}
                id={item.id}
                checked={selected === item.id}
                onChange={this.onChange}
            >
                {item.first_name + ' ' + item.last_name + ' ' + ((this.props.currentUser === item.id) ? '(Me)': '')}
            </Item>
        );
    }

I never see the "render now" log leading me to believe this function isn't being called. Any idea what might be happening?

Not sure, every which way I test it things seem to work fine.
A few things to try:

Replace this.state.employees with an array of fixed values, see if that works
Replace the renderRow method with a super simple inline function.

Basically remove complexity until we get a result, then we'll what's causing the issue.
You might just drop the below code from the readme into your app and see if that at least renders, then add elements back one at a time.
<DVL
    onRender={item =>
{item.name}
}
    items={[{name: "Billy"}, {name: "Joel"}]}
    windowContainer={true}
/>

Interesting,  are there any console errors?
If you're comfortable with sharing your source code with me I'd be happy to take a look and see whats going on.
I also just pushed an update, v1.2.3 with some changes to the rendering scripts.  You might give it a try.

No console errors. It's as if the component isn't there. Very weird. I unfortunately can't share the code, but if I can replicate the error in a simple project on Monday I'll be able to share that.

I was just thinking... are you using babel by chance?  Might try playing with the import statement, one of these will probably end up working:
import * as DVL from "react-dynamic-virtual-list";
import DVL from "react-dynamic-virtual-list";
import { DVL } from "react-dynamic-virtual-list";

I normally use Typescript but I've noticed Babel imports are sometimes different for some reason...

Marking this resolved since it's been inactive for so long, feel free to reopen if you find further info.


Enable ELB attributes to be retrieved from knife or a recipe

for example, the public DNS name

IMO we should go the other way, and make the resources two-way.  But either way, it needs to be easy to get this information.

It's worth noting that the new aws_object method does not seem to apply to the load_balancer resource (because that's in core provisioning?).  Trying to retrieve settings via lazy attributes on ELBs produces a NoMethodError.
NoMethodError
-------------
undefined method `aws_object' for Chef::Resource::LoadBalancer

@gmiranda23 How to get the aws_object from the base provisioning resources is documented here:
https://github.com/chef/chef-provisioning-aws#looking-up-chef-provisioning-resources
But I'd say +1 for a uniform way of getting at aws_object across all resources (and of associating them with existing real AWS resources).

@christinedraper It seems boilerplate enough to consider a uniform approach to lookup options.  @jkeiser 's original direction was that it should be easy to get this information.  I agree that the data_bag approach is less preferable.  But regardless, it should be accessible in a consistent manner.

The discussion I had re this with @tyler-ball during office hours was about changing chef provisioning base resources so that providers can directly extend them. Then an aws_object method could be directly accessible on both base resources like machine, and AWS specific resources. This would be a significant change, though, so would need to be a major version change.
I believe/hope the same change could also make it easier to associate a machine resource with an existing AWS instance, which is another thing I've been running into.

:+1:

+1

Do the instructions at https://github.com/chef/chef-provisioning-aws#looking-up-chef-provisioning-resources still work as a workaround until we can get something better?

+1

:+1:

:+1:

+1

:thumbsup:

@tyler-ball Look up of instances works via the method in the README.  Lookup of ELBs results in the same issue seen in #242

This will be fixed by adding the aws_object method the machine, machine_batch and load_balancer resources as part of https://github.com/chef/chef-provisioning-aws/pull/346 - see https://github.com/chef/chef-provisioning-aws/pull/346#issuecomment-144447869 for an example of what the recipes would look like.


Bug: [Trace] attribute hides stacktrance

Expected Behaviour
Not sure if I am missing something, but I would expect that when an exception is thrown inside a method which is marked with the [Trace] attribute I should see the full stack trace for that exception
Current Behaviour
The stack trace shows this instead of the actual location of the error
Stack Trace:
TracingAspectHandler.OnException[T](AspectEventArgs eventArgs, Exception exception)
MethodAspectAttribute.WrapAsync[T](Func`2 target, Object[] args, AspectEventArgs eventArgs)
 line 154
Code snippet
Please see this repo
https://github.com/jaknor/aws-lambda-powertools-tracing-stack-trace-bug/blob/main/README.md

Note, I've only reproduced it in unit test here, but we have seen the same behaviour when deployed to a Lambda.

Possible Solution
No response
Steps to Reproduce
Please see repo https://github.com/jaknor/aws-lambda-powertools-tracing-stack-trace-bug/blob/main/README.md
Powertools for AWS Lambda (.NET) version
latest
AWS Lambda function runtime
dotnet6
Debugging logs
No response

Hi @jaknor thanks for opening an issue and the detailed repro and images.
We have some work in progress, not all utilities to bubble up the full stack trace.
We will track the work in this issue and update when we have made progress.

Hi @jaknor thanks again for reporting this issue and for the sample project.
Pull request in progress #357

Hi @jaknor thank you for your support of the project. A fix for this issue will be released in the next release. #357

Thanks! Will keep an eye out for the release


mergerfs.fsck does not report filename collisions

mergerfs.fsck detects but does not report when there are two different files with the same name in the pool.
Possible fix, add a print statement:
def check_consistancy(fullpath,verbose,size,fix):
    ...
    if (size and not size_equal(stats)):
>       if verbose:
>           print("%s (filename collision)" % fullpath)
        return

I think what should be done is that if the sizes are different then don't ignore the files if the stats are the same. And perhaps change '--size' to something which checks if they are the same file.

You mean fix the file content instead of only reporting the inconsistency? Mergerfs.fsck is for fixing meta-data, not the file content, right?

Yes. mergerfs.fsck only focuses on metadata but could be extended to look for data inconsistencies as well.

Should use mergerfs.dedup for this usecase.


Deeplink to Linkedin app for authentication without password

I managed to run the ExampleApp.
I have the LinkedIn app installed on my phone, I don't understand why I don't get redirected to the Linkedin app to avoid typing my password. I see that some apps succeed in doing that (http://www.shapr.co/)
I'm I supposed to get redirected to Linkedin app if it's installed ?
Thanks,

Hi @botmane , I have same issue now. Did you find a solution to this?

Hello,
Yes, I managed to make it work by following this tutorial: https://developer.linkedin.com/docs/ios-sdk#
You need to update the manifest and declare your bundle id to linkedin in their developer website.
Otmane


REDMINE_PORT not working

According to entrypoint.sh , the REDMINE_PORT env should be put into the nginx conf file which at assets/config/nginx/redmine .
However, the conf file has a hard code 80.
The fix should be easy and I'm lazy, so I would not give a pull request.

@acabin the REDMINE_PORT variable does not alter the port on which the internal nginx server listens on. It merely indicates the port on which the redmine application will be accessible to the outside world.

But what does entrypoint.sh:279 do?
sed 's/{{REDMINE_PORT}}/'"${REDMINE_PORT}"'/' -i /etc/nginx/sites-enabled/redmine

It configures http -> https redirection.

but it seems have not any effect -- according to the origin conf file
发自我的 iPhone

在 2015年8月27日，17:41，Sameer Naik<EMAIL_ADDRESS>写道：
It configures http -> https redirection.
—
Reply to this email directly or view it on GitHub.

It only kicks in when you try to enable SSL. The sed command will have
effect only when the redmine-ssl nginx template is picked.
On Thursday 27 August 2015 06:34 PM, Garveen wrote:

but it seems have not any effect -- according to the origin conf file
发自我的 iPhone

在 2015年8月27日，17:41，Sameer Naik<EMAIL_ADDRESS>写道：
It configures http -> https redirection.
—
Reply to this email directly or view it on GitHub.

—
Reply to this email directly or view it on GitHub
https://github.com/sameersbn/docker-redmine/issues/138#issuecomment-135418232.

--
Sameer Naik
w: http://www.damagehead.com e<EMAIL_ADDRESS>


Crash when opening app

I am not sure what changed but my app started running into this issue
https://github.com/mixpanel/mixpanel-iphone/issues/937
And it looks like the solution is to just upgrade the mixpanel pod since this package is depending on 3.6 we need to bump it to 3.9

@ToyboxZach hi, make fork from this repo, fix this line in index.js:
const instance = new MixpanelInstance(apiToken, optOutTrackingDefault)
to
const instance = new MixpanelInstance(apiToken, optOutTrackingDefault, trackCrashes, automaticPushTracking, launchOptions)
Init MP like this: Mixpanel.sharedInstanceWithToken(TOKEN, false, true, false, null)

I actually decided to switch to the mixpanel supported one, seems like a better move going forward.
I would probably suggest this package just mention the supported package now
https://github.com/mixpanel/mixpanel-react-native

@ToyboxZach oh nice, I didn't know about that. Fixed that issue all day long.


failed to solve: changes out of order: "ca-certificates/.gitkeep" "auth.json"

Description:

Expected Behavior:

Context information:
Output of git rev-parse HEAD
{paste here}

Output of docker version
{paste here}

Output of docker-compose version
{paste here}

System info: Mac, Windows or Linux. Include which disto/version
{paste here}

Steps to reproduce the issue:

Stacktrace & Additional info:
{paste here}

Hi, did you figure it out? I have the same problem after running docker compose according to the installation instructions.


Installation instructions contain errors

Great project, but as a n00b trying to install Genie for the first time, I came across some errors/typos in installation instructions.
https://wiki.genie.stanford.edu/getting-started/installation-guide/standalone-server
https://github.com/stanford-oval/genie-server
First, both URLs above links to podman.io installation instructions. The Ubuntu instructions on podman.io don't work (e.g. contain nonsensical commands like ". /etc/os-release")
Second, on https://wiki.genie.stanford.edu/getting-started/installation-guide/standalone-server there is a typo in:
podman run --name genie -p 3000:3000
-v /dev/shm:/dev/shm
-v $XDG_RUNTIME_DIR/pulse:/run/pulse
v $XDG_CONFIG_HOME/genie-server:/var/lib/genie-server
--security-opt label=disable
stanfordoval/almond-server:latest
I'm guessing the fourth line should start with -v
Third, the podman command shown on https://wiki.genie.stanford.edu/getting-started/installation-guide/standalone-server is different than the podman command on https://github.com/stanford-oval/genie-server

If you find an error in your wiki, apologies, feel free to fix it, the wiki is editable by anyone with a Github account. I corrected the wiki now and both wiki and readme have the same instructions.
As for the podman install instructions, I recommend you report the problem to the podman developers.


pkmountTest: ensure tests don't share cache

pkmountTest: ensure tests don't share cache

Also introduce PERKEEP_CACHE_DIR so we can deprecate CAMLI_CACHE_DIR
at some point.

I had a couple of cache issues while working on my previous fs patch. At some point I reverted my entire patch trying to debug why my tests were still failing until I realised that the tests were using my camlicache, causing unexpected behaviors.
This ensures that all mount tests have their own cache.

cc @bradfitz  for a second round of review


add saved button w local storage

Save button working with local storage. Some typescript issues to clean up still, but I needed to get this in for a final project.

closes #103

@brandonfcohen1 noted. will leave until later--not high priority, so we can come back to it.


Astype operation support

We will also need it in NYC taxi-q4

https://github.com/intel-go/modin/pull/29. For now it is supposed to cover needs for taxi queries so in general it requires more testing

HDK engine is deprecated and will be removed in a future version.


Change max zoom level on pmtiles for public map to 24

On running 'export-map' job to update the zoom level led to this bug

I think we ought to discuss this one. That introduces a lot of size and duplication for very little benefit.

tldr; We decided this change was not needed


Increase number of char possible in ref: paper_url

Currently at varchar(255), but need an increase because some paper have a longer url.

transfered field into type TEXT, done with commit (32b1f95)


Support DDL for SQLServer with visitor

For #4318.

Codecov Report

:exclamation: No coverage uploaded for pull request base (master@bdeb9ed). Click here to learn what that means.
The diff coverage is 0%.

@@           Coverage Diff            @@
##             master   #4326   +/-   ##
========================================
  Coverage          ?   55.5%
  Complexity        ?     352
========================================
  Files             ?    1032
  Lines             ?   18723
  Branches          ?    3420
========================================
  Hits              ?   10393
  Misses            ?    7675
  Partials          ?     655

Impacted Files
Coverage Δ
Complexity Δ

...e/sql/parser/visitor/impl/SQLServerDDLVisitor.java
0% <0%> (ø)
0 <0> (?)

...phere/sql/parser/visitor/impl/MySQLDDLVisitor.java
0% <0%> (ø)
0 <0> (?)

...impl/ddl/column/DropColumnDefinitionExtractor.java
8.33% <0%> (ø)
1 <0> (?)

...here/sql/parser/visitor/impl/OracleDDLVisitor.java
0% <0%> (ø)
0 <0> (?)

...e/sql/parser/visitor/impl/SQLServerTCLVisitor.java
0% <0%> (ø)
0 <0> (?)

.../sql/parser/visitor/impl/PostgreSQLDDLVisitor.java
0% <0%> (ø)
0 <0> (?)

...er/impl/ddl/column/DropColumnDefinitionFiller.java
33.33% <0%> (ø)
0 <0> (?)

Continue to review full report at Codecov.

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Powered by Codecov. Last update bdeb9ed...73d9d94. Read the comment docs.


error TS2307: Cannot find module 'path'.

ts-docs-gen 0.2.0
error TS2307: Cannot find module 'path'.

but i have @types/node in my node_modules

Could you write here your tsconfig.json?

{
  "compilerOptions": {
    "module": "CommonJS",
    "target": "es2017",

    "moduleResolution": "node",
    "allowJs": false,

    "declaration": true,
    "experimentalDecorators": true,

    "allowSyntheticDefaultImports": true,

    "strict": false,

    "locale": "zh-TW",
    "pretty": true,

    "traceResolution": true,
    "newLine": "lf"
  },

  "jsdoc": "jsdoc",

  "exclude": [
    "node_modules",
    "test"
  ]
}

@MartynasZilinskas
can output now
but looks like still something wrong
when i confing this
{
 "entryFile": [
   "index.ts",
   "lib/loader/index.ts",
   "lib/loader/segment/index.ts"
  ]
}

the  output dir only has one md file
and it is api about lib/loader/segment/index.ts

when i confing this
{
 "entryFile": [
   "index.ts",
  ]
}

api only has index.ts it self (not include any other require...)

i trying use on node-segment-dict

What TypeScript version do you use? Because I don't see it in package.json. I installed newest TypeScript 2.8.3 and got errors while trying to compile it.
lib/loader/opencc/index.ts:46:16 - error TS2503: Cannot find namespace 'libLoader'.

46 export default libLoader.load;
                  ~~~~~~~~~

lib/loader/segment/index.ts:115:16 - error TS2503: Cannot find namespace 'Loader'.

115 export default Loader.load;
                   ~~~~~~

@bluelovers Could you make a separate branch with ts-docs-gen installed? So I could inspect tools behavior.

@bluelovers Thanks.
ts-docs-gen log:
[13:23:44] warn: D:/Projects/Temp/node-segment-dict/index.ts(10,8): Declaration "ImportClause" is not supported yet.
[13:23:44] warn: D:/Projects/Temp/node-segment-dict/index.ts(11,10): Exported item does not exist.
[13:23:44] warn: D:/Projects/Temp/node-segment-dict/index.ts(10,25): Imported item does not exist.
[13:23:44] warn: D:/Projects/Temp/node-segment-dict/index.ts(13,8): Declaration "ImportClause" is not supported yet.
[13:23:44] warn: D:/Projects/Temp/node-segment-dict/index.ts(14,10): Exported item does not exist.
[13:23:44] warn: D:/Projects/Temp/node-segment-dict/index.ts(20,1): Declaration "ExportAssignment" is not supported yet.
[13:23:44] warn: ./index.ts(11,9): ApiItems are missing in "requireLoader"?
[13:23:44] warn: ./index.ts(10,24): ApiItems are missing in "requireModule"?
[13:23:44] warn: ./index.ts(14,9): ApiItems are missing in "getDictPath"?

I have inspected your code. Some TypeScript's declarations are not supported yet by our ts-extractor. We haven't encountered this usage, because we don't use default exports/imports.
I will reproduce your project cases and add them as tests.
For now, we are busy rewriting ts-extractor with a new and better structure, then it will be turn for ts-docs-gen.


Probe listing incorrect for BEGIN/END probes

# bpftrace -l 'BEGIN'
special::
# bpftrace -l 'END'
special::

@ajor, can you please help me to understand what is expected output?

What I was thinking when I opened this issue was probably:
# bpftrace -l 'BEGIN'
BEGIN
# bpftrace -l 'END'
END

But it's probably not something people will ever use and was only really for completeness. It might conflict with your other changes in #3111 (which are definitely more useful), so maybe we just close this one

ok. closing :-)

ahm... I don't have permission it seems.


Bump C# samples dependencies and take use new constructor for BotFrameworkHttpAdapter

Sample information

Sample type: samples and solutions
Sample language: dotnetcore and webapi
Sample name: All samples and solutions

Describe the fix

[ ] Bump C# botbuilder SDK dependencies to ≥ 4.4.5 in samples
[ ] Update the samples using BotFrameworkHttpAdapter to use the constructor that takes an IConfiguration and ILogger (see below C# SDK PR).

Additional context
C# PR for new .netcore constructor: microsoft/botbuilder-dotnet#1926
[bug]

Example code from EchoBot in Ibiza: AdapterWithErrorHandler.cs
// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.

using System;
using Microsoft.Bot.Builder.Integration.AspNet.Core;
using Microsoft.Extensions.Configuration;
using Microsoft.Extensions.Logging;

namespace Microsoft.Bot.Builder.EchoBot
{
    public class AdapterWithErrorHandler : BotFrameworkHttpAdapter
    {
        public AdapterWithErrorHandler(IConfiguration configuration, ILogger<BotFrameworkHttpAdapter> logger, ConversationState conversationState = null)
            : base(configuration, logger)
        {
            OnTurnError = async (turnContext, exception) =>
            {
                // Log any leaked exception from the application.
                logger.LogError($"Exception caught : {exception.Message}");

                // Send a catch-all apology to the user.
                await turnContext.SendActivityAsync("Sorry, it looks like something went wrong.");

                if (conversationState != null)
                {
                    try
                    {
                        // Delete the conversationState for the current conversation to prevent the
                        // bot from getting stuck in a error-loop caused by being in a bad state.
                        // ConversationState should be thought of as similar to "cookie-state" in a Web pages.
                        await conversationState.DeleteAsync(turnContext);
                    }
                    catch (Exception e)
                    {
                        logger.LogError($"Exception caught on attempting to Delete ConversationState : {e.Message}");
                    }
                }
            };
        }
    }
}

@cleemullins I pulled this back onto my plate Chris, as there's a PR I've issued that has the fixes for this.


[iOS] Native video fixes

Fixes video view not being correctly displayed and fixes orientation and frame change.
Can be cherry-picked for 3.2

As discussed in #43811 this might become relevant only for 3.2 if we're changing this API in 4.0, but since the PR is ready, let's merge and cherry-pick as is.

Thanks!

Cherry-picked for 3.2.4.


Ensure that SSL certificates up to date on Mono

This seems to break build.sh on Windows. For starters, there is no mozroots there.

Sorry, that was not a good implementation. For one thing mozroots should be guarded by the value of $OS. I had thought that the $LOADER prefix would just be ignored if it evaluated to the empty string, but that is clearly not the case. I will submit another PR to fix these things.

Great, thanks


Duplicate are not saved to DB

Even if -d is not specified, duplicates are not logged into the DB.
I don't know if it could qualify as a bug, or if it is more a feature request.
The feature request is to log the duplicates into the DB.

Thank you for the feature request!
I implemented it in commit 97131e8a2764d5b1968d783655efcc5b7db0cadd.


Do not create scope when conflicting method exists

This allows i.e. to use a finite state machine with a status "new".

Thanks!


➕Improve "Testimonial Section" for Enhanced Interactivity !!

Describe the feature
Hey @Its-Aman-Yadav and @Kalivarapubindusree
here is the feature I want to suggest -
Current State:

The testimonial page currently displays "only" images of people.
Clicking on the images reveals the testimonial text, but the transition is not smooth and the styling too.
The coloring and styling of the text and images need significant improvement

Here as you can see-
https://github.com/user-attachments/assets/9618835f-7773-447e-b900-af770e568af9
I want To revamp the testimonial page to make it more interactive and engaging, encouraging users to leave their testimonials.
Please assign me this task
thankyou!
Add ScreenShots
.
Record

[X] I agree to follow this project's Code of Conduct
[X] I'm a GSSOC'24 contributor
[X] I want to work on this issue

@Its-Aman-Yadav @Kalivarapubindusree , I want to work on this, please assign me :D


Problems with packaging and distribution

Hi,
thank you for share the code.
Based on your project, I have created my custom app and all works perfect, if I use it with VS Code.
But if I try to package the app (with command npm run electron:build as you suggest in the infos) to test the distribution, when I run the exe file I receive the following error:

I tried also to package with electron-forge but when I run the app, It doesn't find the index.html file.
I also saw that with command npm run electron:build it build with target=portable but I would to create a setup file to install it.

Can you help me to understand how can correctly setup and package the app for distribution?
The configuration files (package.json, angular.json, electron-builder.json are the same as your project)
A last question...when I'll install for the first time the app, I think that it create also a new db file sqlite3.
When I'll must update/upgrade the app, how I can sure that the new installation doesn't overwright the db?
Thank you in advance for the support

谢谢你分享的问题，这个打包后找不到第三方包依赖的问题也是之前困扰了我很久很久的问题。
Thank you for sharing the problem. This problem of not being able to find third-party package dependencies after packaging is also a problem that has troubled me for a long time.
首先，请确认这个仓库直接克隆到本地后，不做任何改动也是可以成功打包且打包后是可运行的，以此来排除版本和环境问题。
First, please confirm that after cloning this repository directly to the local computer, it can be packaged successfully without any changes and can be run after packaging, so as to rule out version and environment problems.
然后，需要注意的是，angular 所需要的第三方依赖应该在根目录下的package.json文件中添加。app目录下的package.json文件是用来添加主进程（即electron）所需要的依赖的。
Then, it should be noted that the third-party dependencies required by angular should be added in the package.json file in the root directory. The package.json file in the app directory is used to add the dependencies required by the main process (i.e. electron).

如果对应依赖添加的位置不对，就会出现打包后找不到模块的错误。
If the corresponding dependency is added to the wrong location, an error will occur that the module cannot be found after packaging.
关于想要创建安装文件的问题，我想这应该是打包工具使用的相关内容，印象中我曾经在electron-build官方网站上查到过如何配置去打包成安装文件，但是当时的项目并没有这个需求，所以并没有将它实践，以至于这里没能将这个内容包含进去，我很抱歉。但我认为你可以从electron-build官方网站找到这个问题的解决办法。
Regarding the issue of wanting to create installation files, I think this should be related to the use of packaging tools. I remember that I once found how to configure to package into installation files on the electron-build official website, but the project at that time did not have this requirement, so I did not put it into practice, so I was not able to include this content here. I am sorry. But I think you can find a solution to this problem from the electron-build official website.
新的安装会覆盖数据库这个问题我之前确实没有考虑过，刚刚我在本地做了测试，发现确实在重新打包后之前的数据都会被清空，这是个棘手的且需要解决的问题。
I had never considered the issue that the new installation would overwrite the database. I just tested it locally and found that all previous data would be cleared after repackaging. This is a tricky problem that needs to be solved.
最后，感谢你的使用和收藏，感谢你分享的问题！希望我能够帮助到你。
Finally, thank you for your use and collection, and thank you for sharing your questions! I hope I can help you.

@afabri73 你好
就找不到 ../../src/app/shared/functions/sharedfunctions.globals 模块的问题，有一个并不理想的方法就是把这个文件放到主进程的app文件夹，然后在相关的文件（应该是login.repository）内修改引用路径。
因为主进程app以外的文件似乎无法直接被引用到。希望能解决问题。


Issue with tiff2pdf conversionnodejs

Hi white ,
I'm trying to do  a conversion from tiff to pdf file using the module tiff2pdf(1.0.2)
PDF file is not generating with the sample test code and i'm getting some error.I have tried with the test.js(in downloaded package) and still getting the same error.In console it  is saying  successful but pdf file is not generating.I have printed the result object and getting the same error as mentioned below.
{ message: 'error',
code:
{ Error: spawn tiff2pdf ENOENT
at _errnoException (util.js:1031:13)
at Process.ChildProcess._handle.onexit (internal/child_process.js:201:19)
at onErrorNT (internal/child_process.js:389:16)
at _combinedTickCallback (internal/process/next_tick.js:138:11)
at process._tickCallback (internal/process/next_tick.js:180:9)
at Function.Module.runMain (module.js:684:11)
at startup (bootstrap_node.js:191:16)
at bootstrap_node.js:613:3
errno: 'ENOENT',
code: 'ENOENT',
syscall: 'spawn tiff2pdf',
path: 'tiff2pdf',
spawnargs: [ '-o', '\pdfs\test.pdf', 'test.tiff' ] } }
{ message: 'close', code: -4058 }
please let me know if there is any dependency in npm version or OS.
OS windows 7 64 bit
npm 5.5.1
IDE- Webstorm(Evaluation Version)
Code -
var tiff2pdf=require("tiff2pdf")
tiff2pdf('test.tiff', '/pdfs', function(result){
console.log(result);
});
I would be very much  grateful to you if you can help me at the earliest.
Regards,
Manuel George

@manuelgeorge make sure the path is correct to your file and output directory, and that the output directory exists.

Same issue

Same issue


Add Alibaba Cloud DingTalk Webhook binding doc

Thank you for helping make the Dapr documentation better!
Please follow this checklist before submitting:

[x] Read the contribution guide
[x] Commands include options for Linux, MacOS, and Windows within codetabs
[x] New file and folder names are globally unique
[x] Page references use shortcodes instead of markdown or URL links
[x] Images use HTML style and have alternative text
[x] 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
Added docs for Alibaba Cloud DingTalk Webhook pubsub
Issue reference
#1404

Waiting on https://github.com/dapr/components-contrib/issues/811 to merge


CUDA out of memory

Report Issue: H2O LLM Studio at http://<IP_ADDRESS>:10101/
q.app
q.user
q.client
home: False
report_error: True
q.events
q.args
home: False
report_error: True
stacktrace
Traceback (most recent call last):
File “/home/garret/h2o-llmstudio/./llm_studio/app_utils/handlers.py”, line 320, in handle await experiment_display(q)
File “/home/garret/h2o-llmstudio/./llm_studio/app_utils/sections/experiment.py”, line 984, in experiment_display await chat_tab(q)
File “/home/garret/h2o-llmstudio/./llm_studio/app_utils/sections/chat.py”, line 56, in chat_tab cfg, model, tokenizer = load_cfg_model_tokenizer(
File “/home/garret/h2o-llmstudio/./llm_studio/app_utils/sections/chat.py”, line 194, in load_cfg_model_tokenizer model = cfg.architecture.model_class(cfg)
File “/home/garret/h2o-llmstudio/./llm_studio/src/models/text_causal_language_modeling_model.py”, line 32, in init self.backbone, self.backbone_config = create_nlp_backbone(
File “/home/garret/h2o-llmstudio/./llm_studio/src/utils/modeling_utils.py”, line 754, in create_nlp_backbone backbone = model_class.from_pretrained(
File “/home/garret/llmstudio/lib/python3.10/site-packages/transformers/models/auto/auto_factory.py”, line 566, in from_pretrained return model_class.from_pretrained(
File “/home/garret/llmstudio/lib/python3.10/site-packages/transformers/modeling_utils.py”, line 3694, in from_pretrained ) = cls._load_pretrained_model(
File “/home/garret/llmstudio/lib/python3.10/site-packages/transformers/modeling_utils.py”, line 4079, in _load_pretrained_model state_dict = load_state_dict(shard_file)
File “/home/garret/llmstudio/lib/python3.10/site-packages/transformers/modeling_utils.py”, line 510, in load_state_dict return safe_load_file(checkpoint_file)
File “/home/garret/llmstudio/lib/python3.10/site-packages/safetensors/torch.py”, line 310, in load_file result[k] = f.get_tensor(k)
File “/home/garret/llmstudio/lib/python3.10/site-packages/torch/utils/_device.py”, line 77, in torch_function return func(*args, **kwargs)
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 32.00 MiB. GPU 0 has a total capacty of 10.00 GiB of which 0 bytes is free. Including non-PyTorch memory, this process has<PHONE_NUMBER>4.00 GiB memory in use. Of the allocated memory 9.29 GiB is allocated by PyTorch, and 1.60 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
Error
None
Git Version
015c75209e7c8b548b0de5006cdffdc4aedf99ee

LLM Studio needs quite a bit of GPU memory to fine-tune the large language models.
We employ state of the art techniques to reduce the amount of VRAM required for training.
Make sure to use LoRA, gradient checkpointing, low batch size (1) and int4 quantization.
May I ask which model you were trying to fine-tune and which GPU you have? With 10 GB VRAM and the above settings, it may be possible to fine-tune a 7B model, but not larger.

I am trying to reproduce what could be different in the chat than for fine-tune. Both use the same logic, and locally, I see the memory peaking at around 15 GB for both when loading the checkpoints.
The batch_size is not used during inference/chat. It seems that the model exceeds max VRAM when loading the checkpoint.

There is a small memory leak after training, so if the weights are very tight it could oom.
Did you try restarting the whole application and then navigating to the Chat tab?

Yes, I did restart the application and restarted the machine just to be sure.

Maybe we can init the model with empty weights before loading the checkpoint, haven't tested the workflow yet.
https://huggingface.co/docs/accelerate/v0.11.0/en/big_modeling


How to insert sqrtm into CNN model forward()?

I inserted sqrtm in the forward pass of CNN model, using feature map as input.
But it return error that sqrtm is in device cpu, and simply add .cuda() after sqrtm didn't work.
I am thinking about how to into sqrtm CNN model forward() properly?

Hi. I just pushed an update that can work with CUDA tensors. Note that the core computation of this implementation is done on CPU using scipy.


GITOPS-1000 Parse the image only if git repository is provided

What type of PR is this?

Uncomment only one  /kind line, and delete the rest.
For example, > /kind bug would simply become: /kind bug

/kind bug
What does this PR do / why we need it:
Added git repo url nil check for image parsing process.
Have you updated the necessary documentation?

[ ] Documentation update is required by this PR.
[ ] Documentation has been updated.

Which issue(s) this PR fixes:
Fixes #
How to test changes / Special notes to the reviewer:
kam service add command with no --git-repo-url flag is successful on OCP 4.8

@ciiay Running make gofmt should fix the format

/test v4.8-integration-e2e

/retest

/test v4.8-integration-e2e

/test v4.8-integration-e2e

In pr https://github.com/redhat-developer/kam/pull/273 I am observing the below error
Command stderr:  ✗  failed to bootstrap resources: failed to read Docker config "/var/run/kam-data/kam-test-secrets/*******-kambot-auth.json" : open /var/run/kam-data/kam-test-secrets/*******-kambot-auth.json: no such file or directory

I am retesting this pr for cross verification
/test all

/test v4.8-integration-e2e

/test v4.8-integration-e2e

/test v4.8-integration-e2e


Improved json test

This PR improves the readability of the json test by using multi-line string formatting. It also asserts on the output rather than using println!.
One thing I'd like to fix but I'm not sure how, is the input string must end in a null byte '\0` or else the parsing fails. This behavior was carried over from the previous implementation.

Coverage decreased (-0.04%) to 87.543% when pulling 52aded733363a81b3620eaa632893658fad08e3c on JoshMcguigan:json-example into 49a20d4f2bca4b893108096905bb91ac47193b91 on Geal:master.

the solution would probably require the code at https://github.com/Geal/parser_benchmarks/blob/master/json/nom/src/main.rs#L130-L140
ie not using ws! for the root parser


[Bug] Convert wind speed from OWM from 10m to 2m

Describe the bug
PyETO requires a wind velocity at 2m height above ground (https://pyeto.readthedocs.io/en/latest/api.html#evapotranspiration). The OWM API returns wind speeds at 10m height, resulting in an overestimated wind speed due to the atmospheric boundary layer, yielding an overestimated evapotranspiration value in the process.
I found HASmartIrrigation to greatly overestimate the ET0 values, after debugging I noticed the wind speeds were extremely high compared to experienced wind. I checked the values from the API with KNMI measurements for the Netherlands and other measurement sites to confirm.
Expected behavior

Convert the OWM API wind speed to the right value using PyETO's wind_speed_2m (https://pyeto.readthedocs.io/en/latest/api.html#wind-speed).
Add documentation for sensor users to convert their wind measurements to the right height.

@mathijsmeulendijks just wanted to check in to see if this worked for you and improved the situation :)

Hey Jeroen,
I think this definitely helps, wind speeds change quite a bit because of the atmospheric boundary layer. After updating to the latest version I immediately saw a decrease in evapotranspiration for my setup.
I started to look at the input values for the ETo calculation because I felt like the system overestimated my evapotranspiration. I'm using it to determine irrigation times for approx 2 hectares, overestimates quickly leads to very long watering times and thus higher energy consumption. In the long run I should probably switch to sensor based calculations ;).
In the meantime I'll stick to data based, even though evapotranspiration stays high. For a day like today near Eindhoven the system forecasts ETo=4.23mm, which seems high compared to reference values for mid summer (3.45mm) with higher temperatures and sun. I'm looking for a source to verify the calculated values, but haven't found a decent one yet.
A further improvement might be to use average hourly wind speeds: looking at the raw OWM data, I see that the predicted wind daily is equal to the maximum wind speed in the hourly forecast. So a single windy hour increases the ETo for the whole day significantly. An alternative might be to use the average hourly wind speeds, but I don't know if that works for Penman Monteith.
For today, the maximum values for wind speed (which is also used for the daily forecast) is 5.64 ms^-1. The average value of hourly wind speeds is 2.94 ms^-1. Using ETo calculator I calculated the expected evapotranspiration, resulting in a difference of 0.5mm.
Taking this even further, another improvement would be to determine the daily adjusted values based on observations instead of forecasts. OWM's onecall API also provides historical data for the lasts 5 days. We could use these values to calculate the hourly ETo, sum them and use the result for further calculations. This way, the daily adjusted runtime is based on actual measurements (which seem to match the KNMI weather station measurements for my region). For the hourly adjusted values we can use observations for past timestamps and hourly forecasts for the future. This gives us increasing accuracy as the day progresses, but retains the property of predicting the whole day. Sidenote: add properties to the sensors to debug this, so show observed ETo from midnight till now and expected ETo based on forecasts separately.

thanks - I use my system with actual measurements (my own weatherstation) so not entirely interested in doing black magic with forecasts, but feel free to make any suggestions / code changes - it's open source for a reason :)


0.9.0-rc5 traefik can not be deployed "unable to retrieve the complete list of server APIs: metrics.k8s.io/v1beta1: the server is currently unable to handle the request"

Version
v0.9.0-rc5
Architecture
x86_64
Describe the bug
Traefik can not be deployed "unable to retrieve the complete list of server APIs: metrics.k8s.io/v1beta1: the server is currently unable to handle the request".
I guess the issue is that the API is not beta anymore and was renamed?
To Reproduce
Install a new Virtual Machine with k3os 0.9.0.
Did work well with 0.8.0
Expected behavior
Traefik should be installed after creating a standalone k3os kubernetes.
Actual behavior
helm-install-traefik pod fails with error:
"unable to retrieve the complete list of server APIs: metrics.k8s.io/v1beta1: the server is currently unable to handle the request"
Additional context
If you could give a hint or documentation to understand how the chart is provisioned into the iso, I could try to fix it myself and provide pull-request.
Anyway I am not a fan of Helm and would prefer deployment with kustomize (now also included in kubectl), as it is much better (human-) readable and more clear what happens.

Issue must be something else:
$ kubectl get --raw /apis/metrics.k8s.io/v1beta1 Error from server (ServiceUnavailable): the server is currently unable to handle the request

@amiga23 how are you running k3OS? Installation that was installed from ISO or something else? If you are running with an overlay installation please provide the kernel that you are running with.

Seems to be an issue with multimaster. If I install a single instance, master and agent in one, without any k3s_args, it works fine. But when installing multi-master with dqlite, with 3 masters and 1 worker, the metricsserver is not reachable via api but does also not show an error.

Okay, my mistake, sorry:
time="2020-02-07T08:41:39.954556606Z" level=error msg="--disable-agent is not compatible with dqlite"

Might be worthwhile to update the documentation to point that out (or add an external database in the sample). I was using it as a blueprint to deploy my server. Guess what the helm didn't work... :P
I am new to kubernetes so that would definately help me and I guess a lot of other people as well.


Editor window resizing on Wayland doesn't work

Tested versions

Reproducible in: 4.3-beta3
Not reproducible in: 4.3-beta2

System information
Fedora 40 - Godot 4.3-beta3 (custom built 64 bit  with c# enabled)- Vulkan (forward +) - Wayland - AMD Ryzen™ 7 6800HS with Radeon™ Graphics × 16
Issue description
A bunch of
platform/linuxbsd/wayland/wayland_thread.cpp:2053 - Parameter "ws" is null.
message appears in the editor output, and I cannot resize the editor window anymore.
I don't know what else is broken, I'm still testing, but this "feature" :smile: arrived with pr #94021.
Steps to reproduce
I just compiled a custom version from the fresh Godot 4.3-beta3, and starting the editor the problem appeared.
This issue also appeared on the released binaries.
Minimal reproduction project (MRP)
I don't know if this thing appears on Ubuntu or other Linux distribution. But on Fedora 40 (workstation) just download the binary from Godot builds.

Thanks for the verbose log stuff
wayland-debug.log

Sure, here is the beta-3

And this is beta-2

This is the two editor window-related settings next to each other

and the multi-window settings

As you see one of the windows has a blueish "header" I can move out that window from the editor, that's beta-2, but the beta-3 is completely themed and I cannot move out the settings window from the editor. but As I see the settings are the same.

For comparison, I did it with both versions.
wayland-debug-beta-2.log
wayland-debug-beta-3.log

Okay, I'm not familiar with this log but I'm sure beta-2 is not wayland :D

probably, because these lines are not familiar from the time when I built my beta-2, my biggest fear is that, after this build, beta-2 won't work properly too :D
[  2%] Generating Wayland client header: "platform/linuxbsd/wayland/protocol/fractional_scale.gen.h"
[  2%] Generating Wayland client header: "platform/linuxbsd/wayland/protocol/idle_inhibit.gen.h"
Generating Wayland client header: "platform/linuxbsd/wayland/protocol/pointer_constraints.gen.h"
[  2%] Generating Wayland client header: "platform/linuxbsd/wayland/protocol/pointer_gestures.gen.h"
[  2%] Generating Wayland client header: "platform/linuxbsd/wayland/protocol/primary_selection.gen.h"
[  2%] Generating Wayland client header: "platform/linuxbsd/wayland/protocol/relative_pointer.gen.h"
[  2%] Generating Wayland client header: "platform/linuxbsd/wayland/protocol/tablet.gen.h"
[  2%] Generating Wayland client header: "platform/linuxbsd/wayland/protocol/text_input.gen.h"
[  2%] Generating Wayland client header: "platform/linuxbsd/wayland/protocol/viewporter.gen.h"
[  2%] Generating Wayland client header: "platform/linuxbsd/wayland/protocol/wayland.gen.h"
[  2%] Generating Wayland client header: "platform/linuxbsd/wayland/protocol/xdg_activation.gen.h"
[  2%] Generating Wayland client header: "platform/linuxbsd/wayland/protocol/xdg_decoration.gen.h"
[  2%] Generating Wayland client header: "platform/linuxbsd/wayland/protocol/xdg_foreign.gen.h"
[  2%] Generating Wayland client header: "platform/linuxbsd/wayland/protocol/xdg_shell.gen.h"

beta-3 full screen (I'm using a Nordic (gtk?) theme if this is the interesting part)

Here is a full screenshot (it's quite big)

Okay, I found something, and at this point it's makes everything way more trickier.
There is a way, to arrange windows in gnome, if I grab them and pull to the edge of the monitor, then the window will be resized exactly to the half of the screen
Here is a video about this
https://youtu.be/R2k9PG-TXFE?si=Lp25EwsDeiXKtA8T&t=57
So if I do this with the Godot editor (since I have a fresh build if beta-2, it's a beta version independent issue) I lost the ability to resize it. If I put a different window to the other side, I can resize them together (I can set them up to cover different amount from the screen. So if I put the window this position I cannot resize it.
And if I don't do this just, I can resize the editor, but it's like 2FPS and
https://github.com/user-attachments/assets/7f1d010b-69df-4cfa-b40f-96a1a5342957

LTO should help considerably with performance once it's done making your laptop a very expensive space heater xD

lto=thin allows you to get most of the performance benefits of LTO, but with much faster linking times and lower RAM usage. It's not supported on all platforms and compilers though.

I built a new version of beta-3, with the command below.
scons platform=linuxbsd bits=64 module_mono_enabled=yes precision=double target=editor production=yes use_llvm=yes lto=thin
Resizing is still slow,
Then I removed the custom theming from the os and nothing changed.
Then I reset the editor theming to the default and no improvement.
Here is a fresh log
wayland-debug.log

2fps slow, the video is about xwayland
https://github.com/user-attachments/assets/28269054-29f8-46f7-a716-ceda5e3e40e5

Yes I will create a new one :)

I built a version from the rc-1 and the resizing is still 2fps
Here is the log (if it shows anything without the --verbose)
I've used this command.
WAYLAND_DEBUG=1 ./bin/godot.linuxbsd.editor.double.x86_64.llvm.mono > wayland-debug.log 2>&1
wayland-debug.log


refactor(header): Implement HttpDate, a wrapper for dates

Using time::Tm directly in HTTP header fields requires special handling to parse and format
the header values., this stops us from using the header macros. By wrapping time::Time in a
HttpDate, we can use the FromStr and Display traits of HttpDate like for most other values.
BREAKING_CHANGE: All code using one of the Date, Expires, If-Modified-Since,
If-Unmodified-Since, Last-Modified header fields needs to wrap time::Tm
with HttpDate. Removed FromStr trait of Date, If-Modified-Sice and If-Unmodified-Sice,
implementing the trait here is inconsistent with other headers.

I love it.


GetOrgnameAndOnlineRegionFromServiceUri or alternative?

I am working on an upgrade for a D365 deployment framework in order to ditch the now not supported ADAL that is utilized by the older Microsoft.Xrm.Tooling.Connector library. - This means a complete upgrade to the latest Dataverse SDK (Microsoft.PowerPlatform.DataVerse.Client).
All good and it seems as if it will be relatively easy.... if not for this bastard: Microsoft.Xrm.Tooling.Connector.Utilities.GetOrgnameAndOnlineRegionFromServiceUri(serviceUri, out onlineRegion, organizationName, isOnPrem)
I reverse-engineered it and the logic is also relatively easy to re-implement, but I'd rather not ;-)
Is there anything like this in the new SDK? or somewhere else? or has it just been forgotten? ;-)
I'd hate to keep using it from the old SDK as it will put dependencies on the old ADAL libraries.

The code is still present but was taken internal as there are future updates that will likely alter how this is done.
it can be found here: https://github.com/microsoft/PowerPlatform-DataverseServiceClient/blob/b483aee3feb4cd79095764e7c59131393b62c6d2/src/GeneralTools/DataverseClient/Client/Utils/Utils.cs#L57
It was built to support our login controls only,
What are you trying to use it for, as that information may be available elsewhere.

Closing this thread as there has been no feedback,


Image component doesn't work if expo-image is installed

While testing library I've noticed that Image component was causing app to crash with ViewManagerAdapter_Image was not found in the UIManager error. Initially, I've thought it is related to whole library, but other components were working as expected. After that, I've completely removed expo-image package and Image from swiftui-react-native was working as expected - which means that these two libraries somehow overlap each other.
Expo SDK version: 51.0.31
expo-image version: 1.12.15

Hey sorry for the late response @Vadko. It's likely because they have the same name on the native side, which is causing conflicts. Have you figured out a workaround or is it still an issue?

I'm experiencing the same issue with expo-image and this library at the current time I haven't found a workaround.

I can see if I can easily update the name of the image component so it doesn't conflict. will keep you posted

Published a fix for this in<EMAIL_ADDRESS>Can you please try and let me know if it works? Install the new version, prebuild, and run your app. @Vadko @samducker

many thanks @andrew-levy


Assistance with 1080 download (Windows)

Excuse me if this is not posted in the right place but everything aside from 1080 downloading works.
I have all the required modules installed
Ran pip install ffmpeg-python
I am unsure what to add to the environment variables to enable 1080p downloading
The -q 1080 switch doesn't work, downloading a course without switches will proceed at 720.
This is running on a windows 10 machine. Thank you for your assistance.

@scapman2k -q switch with 1080 will only work if:

ffmpeg is installed and path is added windows env.
video should have 1080p.
Otherwise udemy-dl will fallback to internal downloading functionality which only works with mp4 (<=720p) videos not streamed one.

By Installing ffmpeg i mean you need to download the exe file for windows from ffmpeg.org.
This article will help you configure ffmpeg so that udemy-dl can use it. install-ffmpeg-windows-10
Let me know if you still face issue with downloading 1080p and if this information helps you out please do close the issue.


Fix broken link

Going to push what I think is a fix. Though I'm a little confused, because this looks like it might be the result of a bug in markdown parsing.

Fixed. There was space in between the anchor [] and url ().


Fixes bug in FileChooserBuilderTest

The tmpdir is loaded but the temporary file is not created in it, resulting on macOS in a Permission denied IOException because java cannot write on root (/). The fix just places the temporary file in java.io.tmpdir as it was probably intended.

I agree with your interpretation, but I'm missing cleanup of the created file in this test. What do you think about this alternative approach:

use java.io.File#createTempFile to create a test file at the beginning of the test
wrap the test procedure in to a try {} finally {} and delete the temp file in the finally clause

You are right about the cleanup, of course. I've pushed another commit that uses getWorkDir() instead. This seems to be the standard. Cleanup is not assumed unless clearWorkDir is called.

Looks good. the workdir is below nb.build.dir and this is cleaned up by the clean task.

Committed.


Add collector for systemd service status.

Utilizes dbus API to determine state of services managed by systemd. It will track the state of services listed under [[SystemdService]] entries in the config (modeled off of processes_windows collector).
It writes metric information to linux.systemd.unit, and to os.service.running. The systemd metric tracks each possible ActiveState of a systemd service unit. The os metric simply tracks whether the service is running or not.
If WatchProc was set to true in the config, it also attempts to lookup the service process and sends process metrics via the processes_linux collector methods.
Example config:
[[SystemdService]]
   Name = "scollector"
   WatchProc = true

[[SystemdService]]
   Name = "redis-.*"

I would add osServiceRunningDesc = "0: active, 1: inactive" as a const in scollector/collectors/collecors.go and use that for the os.service.running description. That way it can be shared with windows. You can also move the metric name to a const in that same file (ex: osServiceRunning = "os.service.running")

@captncraig  / @gbrayut Requested changes made.
I pushed the followup commits as-is to make review easier. I plan to squash things down before merge.

LGTM... will try and test it tomorrow.

Squished down. I left the NameMatches change separate because my changes no longer require it, but it should still be done.


Removed omitempty meta tag from RateLimitPerUser

This patches #809

Note that this deactivates rate limits, unless you specify it every time

That's true, but it seems like one way or another we will have to remove omitempty

I think the solution is a redesign tbh. Introducing side effects to idiomatic code is not going to be the solution.


For contributing what branch should be targeted?

I just created PR #190 which is WIP on a bs-carousel that uses contextual components. I targeted the  Release 1.0 branch, but that seems wrong (it adds a ton of other commits that I did not create).
Anyway. Like I said the bs-carousel is a WIP and I made this a PR not to have it merged (yet), but for proof of concept reasons.
Note: The code format is not standard, but it makes it so I can read it. When ready to actually merge I'll uglify the code. So coding format critique is not useful. An appraisal of the UI and back-end logic is useful. :)

Please target the master branch. The release-1.0 branch is not needed any more, as it is merged to master. Will review your PR hopefully soon, thanks so far!


Rendering is not faster than (diff-gaussian-rasterization).

Hi Den,
Thanks for your great work~
I have tried to run the fast-gauss and calculate the total render function time as following sample code. Compared with the original (diff-gaussian-rasterization), the spent time in all is a bit longer than (diff-gaussian-rasterization)  in my pre-trained 3DGS model.
Is that making sense?
I'm running on Ubuntu20.04 with 4090 nvidia, (EasyVolcap is missing)
for outer_idx in range(outer_loop_count):
    for idx, view in enumerate(tqdm(views, desc="Rendering progress")):
        start_time = time.time()
        rendering = render(view, gaussians, pipeline, background)["render"]
        end_time = time.time()
        # gt = view.original_image[0:3, :, :]
        # torchvision.utils.save_image(rendering, os.path.join(render_path, '{0:05d}'.format(idx) + ".png"))
        # torchvision.utils.save_image(gt, os.path.join(gts_path, '{0:05d}'.format(idx) + ".png"))

        global frame_count
        frame_count += 1
        fps = calculate_fps(end_time - start_time, frame_count)

        if frame_count % 30 == 0:
            print(f"Current FPS: {fps:.2f}")

print(f"Final after all frames: {elapsed_time:.2f} seconds | Total Frames: {frame_count}")

My question is, how can I achieve the 3~5x faster rendering speed?
Thanks for your great work~

Here is the running result for fast_gauss vs diff_gauss

I met the same problem as you and my env is Win11+ Nvidia 4090...

Hi, thanks for using my code!
As mentioned in the readme, this implementation benefits more from high-resolution cases (much more pixels than Gaussians) and can be quite underperforming in offline rendering compared to online rendering. If possible, you could try setting up a GUI (or maybe just use the EasyVolcap GUI).
For offline rendering, one way to speed things up is to slightly mitigate the copying overhead from GL to CUDA but switching the output buffer to uint8 (which I recently supported in the repo, just change the initialization parameter!).


Add(parse) repo reference URL for the module

New URL information added to the References
What did you implement:

Associate the msf module with GitHub URL
Test for GitHub URL formating

How Has This Been Tested?
$ make test
fatal: No names found, cannot describe anything.
GO111MODULE=off go get -u golang.org/x/lint/golint
golint github.com/vulsio/msfdb-list-updater github.com/vulsio/msfdb-list-updater/git github.com/vulsio/msfdb-list-updater/log github.com/vulsio/msfdb-list-updater/models github.com/vulsio/msfdb-list-updater/msf github.com/vulsio/msfdb-list-updater/utils
echo github.com/vulsio/msfdb-list-updater github.com/vulsio/msfdb-list-updater/git github.com/vulsio/msfdb-list-updater/log github.com/vulsio/msfdb-list-updater/models github.com/vulsio/msfdb-list-updater/msf github.com/vulsio/msfdb-list-updater/utils | xargs env GO111MODULE=on go vet || exit;
gofmt -d git/git.go; gofmt -d log/log.go; gofmt -d main.go; gofmt -d models/type.go; gofmt -d msf/msf.go; gofmt -d msf/msf_test.go; gofmt -d utils/last_updated.go; gofmt -d utils/utils.go;
GO111MODULE=on go test -cover -v ./... || exit;
?   	github.com/vulsio/msfdb-list-updater	[no test files]
?   	github.com/vulsio/msfdb-list-updater/git	[no test files]
?   	github.com/vulsio/msfdb-list-updater/log	[no test files]
?   	github.com/vulsio/msfdb-list-updater/models	[no test files]
=== RUN   TestParse
=== RUN   TestParse/test_000.rb
=== RUN   TestParse/test_001.rb
=== RUN   TestParse/test_002.rb
=== RUN   TestParse/test_003.rb
=== RUN   TestParse/test_004.rb
--- PASS: TestParse (0.00s)
    --- PASS: TestParse/test_000.rb (0.00s)
    --- PASS: TestParse/test_001.rb (0.00s)
    --- PASS: TestParse/test_002.rb (0.00s)
    --- PASS: TestParse/test_003.rb (0.00s)
    --- PASS: TestParse/test_004.rb (0.00s)
=== RUN   TestFormatDescription
--- PASS: TestFormatDescription (0.00s)
=== RUN   TestFormatReferences
--- PASS: TestFormatReferences (0.00s)
=== RUN   TestFormatModuleURL
--- PASS: TestFormatModuleURL (0.00s)
PASS
coverage: 55.6% of statements
ok  	github.com/vulsio/msfdb-list-updater/msf	(cached)	coverage: 55.6% of statements
?   	github.com/vulsio/msfdb-list-updater/utils	[no test files]

Others
msfdb-list
$ bat msfdb-list/rapid7/2020/CVE-2020-4429.json
───────┬──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
       │ File: msfdb-list/rapid7/2020/CVE-2020-4429.json
───────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
   1   │ [
   2   │   {
   3   │     "Name": "ibm_drm_download.rb",
   4   │     "Title": "IBM Data Risk Manager Arbitrary File Download",
   5   │     "Description": "IBM Data Risk Manager (IDRM) contains two vulnerabilities that can be chained by an unauthenticated attacker to download arbitrary files off the system. The first is an unauthenticated bypass, followed by a pa
       │ th traversal. This module exploits both vulnerabilities, giving an attacker the ability to download (non-root) files. A downloaded file is zipped, and this module also unzips it before storing it in the database. By default this
       │ module downloads Tomcat's application.properties files, which contains the database password, amongst other sensitive data. At the time of disclosure, this is was a 0 day, but IBM later patched it and released their advisory. Ver
       │ sions 2.0.2 to 2.0.4 are vulnerable, version 2.0.1 is not.",
   6   │     "CveIDs": [
   7   │       "CVE-2020-4427",
   8   │       "CVE-2020-4429"
   9   │     ],
  10   │     "References": [
  11   │       "https://github.com/pedrib/PoC/blob/master/advisories/IBM/ibm_drm/ibm_drm_rce.md",
  12   │       "https://seclists.org/fulldisclosure/2020/Apr/33",
  13   │       "https://www.ibm.com/blogs/psirt/security-bulletin-vulnerabilities-exist-in-ibm-data-risk-manager-cve-2020-4427-cve-2020-4428-cve-2020-4429-and-cve-2020-4430/",
  14   │       "https://github.com/rapid7/metasploit-framework/blob/master/modules/auxiliary/admin/http/ibm_drm_download.rb"
  15   │     ]
  16   │   },
  17   │   {
  18   │     "Name": "ibm_drm_rce.rb",
  19   │     "Title": "IBM Data Risk Manager Unauthenticated Remote Code Execution",
  20   │     "Description": "IBM Data Risk Manager (IDRM) contains three vulnerabilities that can be chained by an unauthenticated attacker to achieve remote code execution as root. The first is an unauthenticated bypass, followed by a co
       │ mmand injection as the server user, and finally abuse of an insecure default password. This module exploits all three vulnerabilities, giving the attacker a root shell. At the time of disclosure this was an 0day, but it was later
       │  confirmed and patched by IBM. The authentication bypass works on versions <= <IP_ADDRESS>, but the command injection should only work on versions <= 2.0.4 according to IBM.",
  21   │     "CveIDs": [
  22   │       "CVE-2020-4427",
  23   │       "CVE-2020-4428",
  24   │       "CVE-2020-4429"
  25   │     ],
  26   │     "References": [
  27   │       "https://github.com/pedrib/PoC/blob/master/advisories/IBM/ibm_drm/ibm_drm_rce.md",
  28   │       "https://seclists.org/fulldisclosure/2020/Apr/33",
  29   │       "https://www.ibm.com/blogs/psirt/security-bulletin-vulnerabilities-exist-in-ibm-data-risk-manager-cve-2020-4427-cve-2020-4428-cve-2020-4429-and-cve-2020-4430/",
  30   │       "https://github.com/rapid7/metasploit-framework/blob/master/modules/exploits/linux/http/ibm_drm_rce.rb"
  31   │     ]
  32   │   },
  33   │   {
  34   │     "Name": "ibm_drm_a3user.rb",
  35   │     "Title": "IBM Data Risk Manager a3user Default Password",
  36   │     "Description": "This module abuses a known default password in IBM Data Risk Manager. The 'a3user' has the default password 'idrm' and allows an attacker to log in to the virtual appliance via SSH. This can be escalate to ful
       │ l root access, as 'a3user' has sudo access with the default password. At the time of disclosure this was an 0day, but it was later confirmed and patched by IBM. Versions <= <IP_ADDRESS> are confirmed to be vulnerable.",
  37   │     "CveIDs": [
  38   │       "CVE-2020-4429"
  39   │     ],
  40   │     "References": [
  41   │       "https://github.com/pedrib/PoC/blob/master/advisories/IBM/ibm_drm/ibm_drm_rce.md",
  42   │       "https://seclists.org/fulldisclosure/2020/Apr/33",
  43   │       "https://www.ibm.com/blogs/psirt/security-bulletin-vulnerabilities-exist-in-ibm-data-risk-manager-cve-2020-4427-cve-2020-4428-cve-2020-4429-and-cve-2020-4430/",
  44   │       "https://github.com/rapid7/metasploit-framework/blob/master/modules/exploits/linux/ssh/ibm_drm_a3user.rb"
  45   │     ]
  46   │   }
  47   │ ]
───────┴──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────

LGTM


refactor: New sql_etl flow for managing extraction and loading data from/to big query and snowflake

Goal
What changed? What is the business/product goal?
common-utils/pyproject.toml

updating configuration for unit testing and version bump

common-utils/src/common/databases/snowflake_utils.py

made CommonSettings global to reduce duplicate code
added defaults for warehouse and schema
forcing warehouse and schema to be defined via envars to reduce complexity
created richer snowflake gcs stage model
created a task for returning a SnowflakeConnector block with ability to override warehouse and schema if needed.  This is useful for creating the connector once and then passing it to downstream tasks.  Especially important for flows which use multiprocessing.

common-utils/src/common/deployment/__init__.py

fix issue with envars getting wrapped in single quotes

common-utils/src/common/settings.py

added helper to use for having deployment type aware values

common-utils/src/common/testing_utils.py

removing unused imports

common-utils/tests/databases/unit/test_snowflake_utils.py
common-utils/tests/unit/test_settings.py
common-utils/tests/unit/test_settings.py

adding test coverage

data-products/docs

added base code for eventual docs site

data-products/poetry.lock
data-products/pyproject.toml

updating deps and version bump

data-products/src/snowflake_query_extraction

transformed into new sql_etl app

data-products/src/shared/utils.py

new helper module for creating sql jobs

data-products/src/sql_etl/run_jobs_flow.py

new sql_etl app

data-products/src/sql_etl/sql/backend_events_for_mozilla
data-products/src/sql_etl/sql/impression_stats_v1

moving original sql for bq_to_snowflake to new app and adding jinja2 templating

data-products/tests/shared/test_utils.py
data-products/tests/sql_etl/unit/sql/

adding unit test coverage

data-products/tests/snowflake_query_extraction/unit

removing old unit tests

data-products/tests/test.json

this fake gcp credential file used for testing.

Implementation Decisions
Merge work being done for bq_to_snowflake and nimbus data extraction into single flow to reduce code duplication and user experience.
Deployment steps

[X] Database migrations?
[X] Secrets?
[N/A] FOR POCKET DEVELOPERS ONLY - Post in #changelog:

Write a #changelog message using our best practices if this PR (potentially) impacts users. Describe the 'why' and 'what'. @mention relevant teams. Link to this PR.

References
JIRA ticket:
DIS-729
Documentation:
https://getpocket.atlassian.net/wiki/spaces/PE/pages/2839904307/Prefect+v2+Migration
Will be adding project docs static site via DIS-808

@chelseybeck had to apply some final fixes....tests in staging are now passing
https://app.prefect.cloud/account/20e6b12d-ffc1-4de6-b27b-c38b0bbe68f7/workspace/9ca53df3-2b17-44a6-a7d2-bdcc9d29e09c/flow-runs/flow-run/d156ece1-ce16-4cd2-b62c-635ca2d54079

https://app.prefect.cloud/account/20e6b12d-ffc1-4de6-b27b-c38b0bbe68f7/workspace/9ca53df3-2b17-44a6-a7d2-bdcc9d29e09c/flow-runs/flow-run/5db81404-cc82-4e45-bee6-af417576e4ab

for impression stats counts continue to match...this is from staging run starting at 2023-06-25
2023-06-26	SF - 111839801 BQ - 111839801
2023-06-25	SF - 56686656 BQ - 56686656


Evovle[Doc]: Reference Shopify's Liquid and Rust's regex

2 of 3 addressing issues #607 and #609

I meanwhile I've merged some changes only related to the references but not related to the description changes.