text:
Error Output is Unicode Encoded and Doesn't Respect New Lines

Expected behaviour
When I (or my team) insert a syntax error into my tests, karma reports the error with a stack trace that is legible.
Actual behaviour
When I (or my team) insert a syntax error into my tests, karma reports the error with a stack trace that is unicode encoded and does not respect new lines, making it illegible.
Environment Details

Karma version (output of karma --version): 3.1.4

Also using webpack 4.27.1, and webpack-karma 2.0.0
Changing versions of webpack-karma doesn't seem to solve anything

Relevant part of your karma.config.js file

I've removed all complex parts in order to isolate what is causing the issue. Here is my bare minimum karma config which still produces the issue.
{
    basePath: '',
    browsers: ['ChromeHeadless', 'FirefoxHeadless'],
    captureTimeout: 60000,
    colors: true,
    frameworks: [],
    files: [
      'spec/unit/spec_index.js'
    ],
    port: 9876,
    preprocessors: {
      'spec/unit/spec_index.js': ['webpack', 'sourcemap']
    },
    webpack: webpackConfig,
    webpackServer: {
      quiet: false
    }
  }

Steps to reproduce the behaviour
Insert a syntax error within the beforeEach() of any test.
Get encoded output such as:
  {
    "message": "Uncaught ReferenceError: beforeEach is not defined\nat spec/unit/spec_index.js:95055:3\n\nReferenceError: beforeEach is not defined\n    at _default (spec/unit/spec_index.js:95055:3)\n    at Object.<anonymous> (spec/unit/spec_index.js:231:34)\n    at __webpack_require__ (spec/unit/spec_index.js:64:30)\n    at spec/unit/spec_index.js:197:18\n    at spec/unit/spec_index.js:200:10",
    "str": "Uncaught ReferenceError: beforeEach is not defined\nat spec/unit/spec_index.js:95055:3\n\nReferenceError: beforeEach is not defined\n    at _default (spec/unit/spec_index.js:95055:3)\n    at Object.<anonymous> (spec/unit/spec_index.js:231:34)\n    at __webpack_require__ (spec/unit/spec_index.js:64:30)\n    at spec/unit/spec_index.js:197:18\n    at spec/unit/spec_index.js:200:10"
  }

A much more intense example I've run into:
{
    "message": "Uncaught Error: Module build failed (from ./node_modules/babel-loader/lib/index.js):\nSyntaxError: /Users/john/Documents/workspace/company/project/spec/unit/amp/components/amp_container_spec.js: Unexpected token, expected \";\" (31:23)\n\n\u001b[0m \u001b[90m 29 | \u001b[39m      }\u001b[0m\n\u001b[0m \u001b[90m 30 | \u001b[39m    }\u001b[33m;\u001b[39m\u001b[0m\n\u001b[0m\u001b[31m\u001b[1m>\u001b[22m\u001b[39m\u001b[90m 31 | \u001b[39m    welcome\u001b[33m.\u001b[39mto\u001b[33m.\u001b[39msyntax()errrrrr\u001b[33m;\u001b[39m\u001b[33m;\u001b[39m\u001b[33m;\u001b[39m\u001b[0m\n\u001b[0m \u001b[90m    | \u001b[39m                       \u001b[31m\u001b[1m^\u001b[22m\u001b[39m\u001b[0m\n\u001b[0m \u001b[90m 32 | \u001b[39m  })\u001b[33m;\u001b[39m\u001b[0m\n\u001b[0m \u001b[90m 33 | \u001b[39m\u001b[0m\n\u001b[0m \u001b[90m 34 | \u001b[39m  it(\u001b[32m'wraps the body in the IntlProvider'\u001b[39m\u001b[33m,\u001b[39m \u001b[36mfunction\u001b[39m() {\u001b[0m\n    at Object.raise (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:3834:17)\n    at Object.unexpected (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:5142:16)\n    at Object.semicolon (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:5130:40)\n    at Object.parseExpressionStatement (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:7726:10)\n    at Object.parseExpressionStatement (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:2003:18)\n    at Object.parseStatementContent (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:7326:19)\n    at Object.parseStatement (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:7199:17)\n    at Object.parseStatement (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:1976:26)\n    at Object.parseBlockOrModuleBlockBody (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:7757:25)\n    at Object.parseBlockBody (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:7744:10)\n    at Object.parseBlock (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:7733:10)\n    at Object.parseFunctionBody (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:6865:24)\n    at forwardNoArrowParamsConversionAt (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:1954:70)\n    at Object.forwardNoArrowParamsConversionAt (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:2148:16)\n    at Object.parseFunctionBody (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:1954:19)\n    at Object.parseArrowExpression (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:6818:10)\nat spec/unit/spec_index.js:104174:1\n\nError: Module build failed (from ./node_modules/babel-loader/lib/index.js):\nSyntaxError: /Users/john/Documents/workspace/company/project/spec/unit/amp/components/amp_container_spec.js: Unexpected token, expected \";\" (31:23)\n\n\u001b[0m \u001b[90m 29 | \u001b[39m      }\u001b[0m\n\u001b[0m \u001b[90m 30 | \u001b[39m    }\u001b[33m;\u001b[39m\u001b[0m\n\u001b[0m\u001b[31m\u001b[1m>\u001b[22m\u001b[39m\u001b[90m 31 | \u001b[39m    welcome\u001b[33m.\u001b[39mto\u001b[33m.\u001b[39msyntax()errrrrr\u001b[33m;\u001b[39m\u001b[33m;\u001b[39m\u001b[33m;\u001b[39m\u001b[0m\n\u001b[0m \u001b[90m    | \u001b[39m                       \u001b[31m\u001b[1m^\u001b[22m\u001b[39m\u001b[0m\n\u001b[0m \u001b[90m 32 | \u001b[39m  })\u001b[33m;\u001b[39m\u001b[0m\n\u001b[0m \u001b[90m 33 | \u001b[39m\u001b[0m\n\u001b[0m \u001b[90m 34 | \u001b[39m  it(\u001b[32m'wraps the body in the IntlProvider'\u001b[39m\u001b[33m,\u001b[39m \u001b[36mfunction\u001b[39m() {\u001b[0m\n    at Object.raise (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:3834:17)\n    at Object.unexpected (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:5142:16)\n    at Object.semicolon (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:5130:40)\n    at Object.parseExpressionStatement (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:7726:10)\n    at Object.parseExpressionStatement (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:2003:18)\n    at Object.parseStatementContent (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:7326:19)\n    at Object.parseStatement (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:7199:17)\n    at Object.parseStatement (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:1976:26)\n    at Object.parseBlockOrModuleBlockBody (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:7757:25)\n    at Object.parseBlockBody (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:7744:10)\n    at Object.parseBlock (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:7733:10)\n    at Object.parseFunctionBody (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:6865:24)\n    at forwardNoArrowParamsConversionAt (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:1954:70)\n    at Object.forwardNoArrowParamsConversionAt (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:2148:16)\n    at Object.parseFunctionBody (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:1954:19)\n    at Object.parseArrowExpression (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:6818:10)\n    at Object.<anonymous> (spec/unit/spec_index.js:104174:7)\n    at __webpack_require__ (webpack:///webpack/bootstrap:63 <- spec/unit/spec_index.js:64:30)\n    at webpackContext (webpack:///spec/unit%20sync%20_spec$:361 <- spec/unit/spec_index.js:99804:9)\n    at Array.forEach (<anonymous>)\n    at Object.<anonymous> (webpack:///spec/unit/spec_index.js:19 <- spec/unit/spec_index.js:236:21)\n    at __webpack_require__ (webpack:///webpack/bootstrap:63 <- spec/unit/spec_index.js:64:30)\n    at webpack:///webpack/bootstrap:196 <- spec/unit/spec_index.js:197:18\n    at spec/unit/spec_index.js:200:10",
    "str": "Uncaught Error: Module build failed (from ./node_modules/babel-loader/lib/index.js):\nSyntaxError: /Users/john/Documents/workspace/company/project/spec/unit/amp/components/amp_container_spec.js: Unexpected token, expected \";\" (31:23)\n\n\u001b[0m \u001b[90m 29 | \u001b[39m      }\u001b[0m\n\u001b[0m \u001b[90m 30 | \u001b[39m    }\u001b[33m;\u001b[39m\u001b[0m\n\u001b[0m\u001b[31m\u001b[1m>\u001b[22m\u001b[39m\u001b[90m 31 | \u001b[39m    welcome\u001b[33m.\u001b[39mto\u001b[33m.\u001b[39msyntax()errrrrr\u001b[33m;\u001b[39m\u001b[33m;\u001b[39m\u001b[33m;\u001b[39m\u001b[0m\n\u001b[0m \u001b[90m    | \u001b[39m                       \u001b[31m\u001b[1m^\u001b[22m\u001b[39m\u001b[0m\n\u001b[0m \u001b[90m 32 | \u001b[39m  })\u001b[33m;\u001b[39m\u001b[0m\n\u001b[0m \u001b[90m 33 | \u001b[39m\u001b[0m\n\u001b[0m \u001b[90m 34 | \u001b[39m  it(\u001b[32m'wraps the body in the IntlProvider'\u001b[39m\u001b[33m,\u001b[39m \u001b[36mfunction\u001b[39m() {\u001b[0m\n    at Object.raise (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:3834:17)\n    at Object.unexpected (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:5142:16)\n    at Object.semicolon (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:5130:40)\n    at Object.parseExpressionStatement (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:7726:10)\n    at Object.parseExpressionStatement (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:2003:18)\n    at Object.parseStatementContent (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:7326:19)\n    at Object.parseStatement (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:7199:17)\n    at Object.parseStatement (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:1976:26)\n    at Object.parseBlockOrModuleBlockBody (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:7757:25)\n    at Object.parseBlockBody (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:7744:10)\n    at Object.parseBlock (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:7733:10)\n    at Object.parseFunctionBody (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:6865:24)\n    at forwardNoArrowParamsConversionAt (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:1954:70)\n    at Object.forwardNoArrowParamsConversionAt (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:2148:16)\n    at Object.parseFunctionBody (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:1954:19)\n    at Object.parseArrowExpression (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:6818:10)\nat spec/unit/spec_index.js:104174:1\n\nError: Module build failed (from ./node_modules/babel-loader/lib/index.js):\nSyntaxError: /Users/john/Documents/workspace/company/project/spec/unit/amp/components/amp_container_spec.js: Unexpected token, expected \";\" (31:23)\n\n\u001b[0m \u001b[90m 29 | \u001b[39m      }\u001b[0m\n\u001b[0m \u001b[90m 30 | \u001b[39m    }\u001b[33m;\u001b[39m\u001b[0m\n\u001b[0m\u001b[31m\u001b[1m>\u001b[22m\u001b[39m\u001b[90m 31 | \u001b[39m    welcome\u001b[33m.\u001b[39mto\u001b[33m.\u001b[39msyntax()errrrrr\u001b[33m;\u001b[39m\u001b[33m;\u001b[39m\u001b[33m;\u001b[39m\u001b[0m\n\u001b[0m \u001b[90m    | \u001b[39m                       \u001b[31m\u001b[1m^\u001b[22m\u001b[39m\u001b[0m\n\u001b[0m \u001b[90m 32 | \u001b[39m  })\u001b[33m;\u001b[39m\u001b[0m\n\u001b[0m \u001b[90m 33 | \u001b[39m\u001b[0m\n\u001b[0m \u001b[90m 34 | \u001b[39m  it(\u001b[32m'wraps the body in the IntlProvider'\u001b[39m\u001b[33m,\u001b[39m \u001b[36mfunction\u001b[39m() {\u001b[0m\n    at Object.raise (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:3834:17)\n    at Object.unexpected (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:5142:16)\n    at Object.semicolon (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:5130:40)\n    at Object.parseExpressionStatement (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:7726:10)\n    at Object.parseExpressionStatement (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:2003:18)\n    at Object.parseStatementContent (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:7326:19)\n    at Object.parseStatement (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:7199:17)\n    at Object.parseStatement (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:1976:26)\n    at Object.parseBlockOrModuleBlockBody (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:7757:25)\n    at Object.parseBlockBody (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:7744:10)\n    at Object.parseBlock (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:7733:10)\n    at Object.parseFunctionBody (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:6865:24)\n    at forwardNoArrowParamsConversionAt (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:1954:70)\n    at Object.forwardNoArrowParamsConversionAt (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:2148:16)\n    at Object.parseFunctionBody (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:1954:19)\n    at Object.parseArrowExpression (/Users/john/Documents/workspace/company/project/node_modules/@babel/parser/lib/index.js:6818:10)\n    at Object.<anonymous> (spec/unit/spec_index.js:104174:7)\n    at __webpack_require__ (webpack:///webpack/bootstrap:63 <- spec/unit/spec_index.js:64:30)\n    at webpackContext (webpack:///spec/unit%20sync%20_spec$:361 <- spec/unit/spec_index.js:99804:9)\n    at Array.forEach (<anonymous>)\n    at Object.<anonymous> (webpack:///spec/unit/spec_index.js:19 <- spec/unit/spec_index.js:236:21)\n    at __webpack_require__ (webpack:///webpack/bootstrap:63 <- spec/unit/spec_index.js:64:30)\n    at webpack:///webpack/bootstrap:196 <- spec/unit/spec_index.js:197:18\n    at spec/unit/spec_index.js:200:10"
  }

You can see just from the code blocks that new lines aren't happening and my whole trace is on one unicode filled line.
I've attempted to hack my own formatError function to unencode everything, but even then although the output is unencoded the \ns are still are there. Something adds back in a slew of \ns into the output even though I've explicitly removed them.
  formatError: function(error) {
      var x = error;
      var r = /\\u([\d\w]{4})/gi;
      x = x.replace(/\n/g, "") // Remove '\n'
      x = x.replace(r, function (match, grp) {
        return String.fromCharCode(parseInt(grp, 16)); // Unencode unicode
      });

      return x;
    }

Any idea what could be causing such an odd issue?

I'm unsure where (how) you are seeing this output.  The 'reporter' plugins are responsible for formatting error messages and I don't see any in your config.
The first output is just a toString() rendering of the object created by karma client:
https://github.com/karma-runner/karma/blob/master/client/karma.js#L146
As such it looks correct, it has newline characters embedded because it has multiple lines in the string.
The second output has encoded unicode characters, I guess because that is what is in the source files...

authors: johnjbarton johnnyshankman


text:
Example for the dtype change for gelu kernels

This changes the iter speed for me from
total average iteration time: 45.785989 ms
to around:
total average iteration time: 44.766054 ms
Not ready for merging, just wanted to create this for discussions on the dtype changing for the memory limited kernels

converted to draft per comment

Can you explain briefly what is happening? It looks really wrong that these are int4, for floats.

So the high level issues previously is that there is too much latency from the read from memory instruction to the calculation and the return write. At least on my A100 you can in one instruction read float4 or int4 in a single read instruction, so this CR changes the kernel to read the memory as if it is a Int4 or a Float4 to read 8 at a time and write 8 at a time in a single instruction so theres less waiting for memory before doing calculations.

Went with different approach described in the PR here: https://github.com/karpathy/llm.c/pull/298

source: gharchive/pull-request

authors: ChrisDryden karpathy

license: MIT

license_type: permissive

license_source: github-api


text:
Fix MSVC compilation error with openMP

Fix MSVC compilation error with openMP
https://learn.microsoft.com/en-us/cpp/error-messages/compiler-errors-2/compiler-error-c3015?view=msvc-170
Besides, with this PR, openMP can also be disabled on windows paltform with 'NO_OMP=1' in makefile.

Not following exactly - are you saying the current code generates a C3015?

Our Windows CI and all current Windows builds are passing. What version of MSVC are you using? Please run: Cl /Bv

Our Windows CI and all current Windows builds are passing. What version of MSVC are you using? Please run: Cl /Bv. Also, what options are you using with Make?

Well, I configured this project with visual studio 2022, with MSVC 19.39.33523.0. I copyed all the c_flags and ld_flags from Makefile. Then I encounterd this error (C3015).

As you suggested, this time I tried the CI's way of building, which employs an external make.exe program. Yes, it succeeded. I don'y know what causes this difference.
Besides, to my knowlege, putting declaration of varariables which have possible impacted on OMP before the '#pragma omp parallel' macro could be much more safe without other drawbacks.

Glad it works for you now when using make and the Makefile!
As far as the other changes, let's discuss those over in the Issues area. Microsoft does have a compiler bug with OpenMP 5.2 support which I've filed with them.

I'm not 100% sure, but I thought putting the declarations in front of the openmp statements would actually totally break this code.
Aren't variables from outside the parallel region automatically shared?

I'm not 100% sure, but I thought putting the declarations in front of the openmp statements would actually totally break this code. Aren't variables from outside the parallel region automatically shared?

Thx for pointing out this. I checked the OMP usesage and found that I did have a misunderstanding of the memory model. declarations in front of the openmp statements will be implicitly handled as shared vars, not private. Adding the private(var) statement will make those vars private.
I have already fix the codes.

authors: avflyer ngc92 rosslwheeler


text:
Reworking loading concept and allow global properties

HI @kasperg, I see that you haven't touched your repository in a while. I'm planning on implementing your phing task in a project I'm working on.
For this I want to update:

the way people can install the package, through import file
the way people can use build properties on a global level
split up username/password system per authentication

I just want to ask if you have interest in picking up maintenance again. I plan to write some extra features as wel. If you can set aside some time to review incoming pull requests and discuss the possibility of new features, please let me know. Then I can estimate the extra amount of time for contribution instead of blindly forking without sending anything back.
A feature that I want to write:

a repository "fileset" where you can loop over multiple repositories

I will need help on this feature because it is not the simplest one when looking at the phing Fileset class. Right now I'm using a bash file to do this work. But it would be super nice to have a dedicated phing task that lets you include and exclude repositories through build.properties.
PS: this is a first draft. I haven't run it yet. I want to commit and install the composer package to test.
Best regards,
Alex

Hi @verbruggenalex,
Thanks for a very considerate PR. I really appreciate that.
You are right that this repository has not been updated for a couple of years. There are a couple of reasons for this:

This project matched the needs of my use case at the time.
I am not very active on the other project that used this at the time.
I do not use Phing for other projects.
This is the first ever PR for this project.

I am happy that you want to contribute and I would like to discuss changes with you if you can live with the fact that the will be a couple of days between followups.
If you are interested I am also open to either including you as a developer with push rights on the project or handing the project over to another organisation.
I have some initial feedback on the proposed change. Take them with a grain of salt. It has been some time since I looked at the project myself.

authors: kasperg verbruggenalex

repo: kasperg/phing-github

license: mit

license_source: bigquery


text:
Several Improvements

I adapted the code to my own preferences. But it includes fixes, e.g. for #22, that's why I made a PR.

Tini: sends TERM signal to all processes, so the container doesn't get killed after the timeout
Video compression: much smaller video files. Runs well on my server, but might cause performance issues on small instances
Outputs the terminal (from zoomrec.py) to docker
Reduce polling: Checking every second put some unnecessary load on my server

Feel free to cherry-pick

Add a flag for video compression and merge :)

repo: kastldratza/zoomrec


text:
Firefox compounding memory use on page refresh

I'm trying to debug an issue with Firefox & Wavesurfer.  I've noticed that as a user on my website refreshes a page that contains Wavesurfer, memory use in Firefox will slowly continue to creep up.  After enough refreshes & large enough audio files, memory use can become quite large.  I can replicate this on the Wavesurfer.fm homepage:
http://www.wavesurfer.fm/
Open a process explorer and continue hitting F5 on this page, you should see your memory increase after each refresh.  It doesn't look like it is ever released or GC.  Is there another workaround for this?

Duplicate of #77. Please see http://stackoverflow.com/q/19559720/352796 as well.

source: gharchive/issue


text:
update to version 1.3.5

update to version 1.3.5
+- Add a getImage method for MultiCanvas (#1012)
+- Add Playlist plugin (#1018)
+- Add Playlist plugin sample (#1019)
+- Add a new parameter barHeight (#1020)
+- Add a playBackRate method for MediaElement (#1022)

@entonbiba If you request a review from a specific user it means the PR cannot be merged until that user has agreed.

@mspae ok, updated :P

authors: entonbiba mspae


text:
notebook not working out of the box

I'm trying to just get the included toxicity notebook to work from a fresh clone and am having some issues:

Out of the box, the data & labels directory are pointing to the wrong place and the DataBunch is using filenames that are not part of the repo. These are fixed easily enough.

It would help if there was a pointer to where to get the PyTorch pretrained model uncased_L-12_H-768_A-12. There is a Google download which will not work with the from_pretrained_model cell:

FileNotFoundError: [Errno 2] No such file or directory: '../../bert/bert-models/uncased_L-12_H-768_A-12/pytorch_model.bin'

I have been able to get past this step by instead of using 'bert-base-uncased' instead of BERT_PRETRAINED_PATH as the model spec in the tokenizer and from_pretrained_model steps.

Once I get everything loaded, RuntimeError: CUDA out of memory. Tried to allocate 96.00 MiB (GPU 0; 7.43 GiB total capacity; 6.91 GiB already allocated; 10.94 MiB free; 24.36 MiB cached)

This is a standard 8G GPU compute engine instance on GCP. Advice on how to not run out of memory would help the tutorial a lot.

Also, I seem to be getting different predictions for the learned model and one that is realoaded as a predictor. The following code cell, added to the bottom of the sample notebook.
from fast_bert.prediction import BertClassificationPredictor

texts = [ "this is strange." ]

# the learned model
print(learner.predict_batch(texts))
learner.save_and_reload(MODEL_PATH, "toxic-example-classifier")

# the learned model restored as a predictor
predictor = BertClassificationPredictor(model_path=MODEL_PATH/"toxic-example-classifier.bin", pretrained_path='bert-base-uncased',
                                        label_path=LABEL_PATH, multi_label=False)
print(predictor.predict_batch(texts))

produces the following output:
[[('toxic', 0.05682373046875), ('insult', 0.033843994140625), ('obscene', 0.0301055908203125), ('threat', 0.026397705078125), ('severe_toxic', 0.022064208984375), ('identity_hate', 0.0198822021484375)]]

05/24/2019 01:00:03 - INFO - pytorch_pretrained_bert.modeling -   loading archive file https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz from cache at /home/schmillm_ae_com/.pytorch_pretrained_bert/9c41111e2de84547a463fd39217199738d1e3deb72d4fec4399e6e241983c6f0.ae3cef932725ca7a30cdcb93fc6e09150a55e2a130ec7af63975a16c153ae2ba
05/24/2019 01:00:03 - INFO - pytorch_pretrained_bert.modeling -   extracting archive file /home/schmillm_ae_com/.pytorch_pretrained_bert/9c41111e2de84547a463fd39217199738d1e3deb72d4fec4399e6e241983c6f0.ae3cef932725ca7a30cdcb93fc6e09150a55e2a130ec7af63975a16c153ae2ba to temp dir /tmp/tmpgw4vq6hi
05/24/2019 01:00:07 - INFO - pytorch_pretrained_bert.modeling -   Model config {
  "attention_probs_dropout_prob": 0.1,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 768,
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "max_position_embeddings": 512,
  "num_attention_heads": 12,
  "num_hidden_layers": 12,
  "type_vocab_size": 2,
  "vocab_size": 30522
}

05/24/2019 01:00:12 - INFO - pytorch_pretrained_bert.tokenization -   loading vocabulary file https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt from cache at /home/schmillm_ae_com/.pytorch_pretrained_bert/26bc1ad6c0ac742e9b52263248f6d0f00068293b33709fae12320c0e35ccfbbb.542ce4285a40d23a559526243235df47c5f75c197f04f37d1a0c124c32c9a084
05/24/2019 01:00:12 - INFO - pytorch_pretrained_bert.modeling -   loading archive file https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz from cache at /home/schmillm_ae_com/.pytorch_pretrained_bert/9c41111e2de84547a463fd39217199738d1e3deb72d4fec4399e6e241983c6f0.ae3cef932725ca7a30cdcb93fc6e09150a55e2a130ec7af63975a16c153ae2ba
05/24/2019 01:00:12 - INFO - pytorch_pretrained_bert.modeling -   extracting archive file /home/schmillm_ae_com/.pytorch_pretrained_bert/9c41111e2de84547a463fd39217199738d1e3deb72d4fec4399e6e241983c6f0.ae3cef932725ca7a30cdcb93fc6e09150a55e2a130ec7af63975a16c153ae2ba to temp dir /tmp/tmprltaxjan
05/24/2019 01:00:16 - INFO - pytorch_pretrained_bert.modeling -   Model config {
  "attention_probs_dropout_prob": 0.1,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 768,
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "max_position_embeddings": 512,
  "num_attention_heads": 12,
  "num_hidden_layers": 12,
  "type_vocab_size": 2,
  "vocab_size": 30522
}

[[('toxic', 0.30697527527809143), ('insult', 0.17855168879032135), ('obscene', 0.15811719000339508), ('threat', 0.13821621239185333), ('severe_toxic', 0.11494892090559006), ('identity_hate', 0.10319076478481293)]]

You will have to pass multi-label as True while instantiating the predictor as this is a multilabel problem. Looking at the output of learner, you have trained a multilabel classifier.
On the cuda out of memory, just reduce the batch size.

Regarding "out of memory", I set max_seq_length = 64 and successfully run with 8GB GTX1070.

Hi, I am getting this error.
Target size (torch.Size([0, 6])) must be the same as input size (torch.Size([32, 6]))
databunch = BertDataBunch('fast-bert/sample_data/multi_label_toxic_comments/data', 'fast-bert/sample_data/multi_label_toxic_comments/label', tokenizer,
train_file='train_sample.csv', val_file='val_sample.csv',label_file='labels.csv',label_col=None,
bs=args['train_batch_size'], maxlen=args['max_seq_length'],
multi_gpu=multi_gpu, multi_label=True)
metrics = []
metrics.append({'name': 'accuracy', 'function': accuracy})
learner = BertLearner.from_pretrained_model(databunch, 'https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz', metrics, device, logger=None,
finetuned_wgts_path=None,
is_fp16=args['fp16'], loss_scale=args['loss_scale'],
multi_gpu=multi_gpu,  multi_label=True)
learner.fit(1, lr=args['learning_rate'],
schedule_type="warmup_cosine_hard_restarts")

FileNotFoundError: [Errno 2] No such file or directory: '/home/kuru/Desktop/uncased_L-12_H-768_A-12/pytorch_model.bin'
how did you guys overcome this setp

learner = BertLearner.from_pretrained_model(databunch, 'https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz', metrics, device, logger=None,
finetuned_wgts_path=None,
is_fp16=args['fp16'], loss_scale=args['loss_scale'],
multi_gpu=multi_gpu, multi_label=True)learner = BertLearner.from_pretrained_model(databunch, 'https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz', metrics, device, logger=None,
finetuned_wgts_path=None,
is_fp16=args['fp16'], loss_scale=args['loss_scale'],
multi_gpu=multi_gpu, multi_label=True)
This is what i did to load the pretrained model.

thanks. anyone got issue with apex installation ? .it would be helpfull if you kindly share link links for anaconda apex that supports fast-bert

!git clone https://github.com/NVIDIA/apex.git
%cd apex
!python setup.py install --cuda_ext --cpp_ext

@kuruparan - You can also just indicate the pre-trained model like this:
learner = BertLearner.from_pretrained_model(databunch, 'bert-base-uncased', metrics, device, logger=None,
finetuned_wgts_path=None,
is_fp16=args['fp16'], loss_scale=args['loss_scale'],
multi_gpu=multi_gpu, multi_label=True)

The pytorch_pretrained_bert library handles download and caching of the pretrained model - see https://github.com/huggingface/pytorch-pretrained-BERT#doc

@kuruparan - https://github.com/NVIDIA/apex#quick-start

I am unable to save databunch.
It throws this error
unsupported operand type(s) for /: 'str' and 'str' python
at this line
tmp_path = self.data_dir/'tmp'.

First of all, really a great job. Thank you!
I am having an issue with the DistributedSampler in my local machine.
What an trying to do is the following:
DistributedSampler(train_data, num_replicas = 1, rank=1)
as it was raising an error: RuntimeError("Requires distributed package to be available")
Does such a change cause any problems when training? Should I change anything in the batch size?

Do you have Apex installed? What GPU are you using?
On Thu, 30 May 2019, 19:04 oltip, notifications@github.com wrote:

First of all, really a great job. Thank you!
I am having an issue with the DistributedSampler in my local machine.
What an trying to do is the following:
DistributedSampler(train_data, num_replicas = 1, rank=1)
as it was raising an error: RuntimeError("Requires distributed package to
be available")
Does such a change cause any problems when training? Should I change
anything in the batch size?
—
You are receiving this because you commented.
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I am just trying first on my local machine with no Apex and a slight GPU, Nvidia GeForce (smth).
So no  multi-gpu,  no distributed package.

Anyway, I solved that ===>maybe others might encounter the same issue.
The point was that the pytorch v1.0.1 in windows  doesn't have the distribution stuff.
I moved to ubuntu 18.4 and it works.

I encountered the apex error in windows - this solved it https://github.com/NVIDIA/apex/issues/429.
However, I am running into issues now with RuntimeError: CUDA out of memory. even after changing max_seq_length = 64. Also odd is that I am getting this error now when creating the learner object. Previously it was when I was fitting the learner.

I encountered the apex error in windows - this solved it NVIDIA/apex#429.
However, I am running into issues now with RuntimeError: CUDA out of memory. even after changing max_seq_length = 64. Also odd is that I am getting this error now when creating the learner object. Previously it was when I was fitting the learner.

How much memory does your GPU have?

I managed to solve this issue by restarting my jupyter kernel before running the model. I also used the gradient_accumulation = 8 setting + batch_size = 8 to get batches of 64 that fit into my GPU memory.

I managed to solve this issue by restarting my jupyter kernel before running the model. I also used the gradient_accumulation = 8 setting + batch_size = 8 to get batches of 64 that fit into my GPU memory. I was able to keep sequence length ar 256 with these settings.

Which method is using those arguments?

If I understand correctly, I'm using Bert base uncased and it worked for
both multilabel and multiclass.
On Thu, 21 Nov 2019 at 04:54, Enzo Lebrun notifications@github.com wrote:

I managed to solve this issue by restarting my jupyter kernel before
running the model. I also used the gradient_accumulation = 8 setting + batch_size
= 8 to get batches of 64 that fit into my GPU memory. I was able to keep
sequence length ar 256 with these settings.
Which method is using those arguments?
—
You are receiving this because you commented.
Reply to this email directly, view it on GitHub
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authors: DanyalAndriano EnzoTheBrown Mandark27 Pawel-Kranzberg kaushaltrivedi kuruparan mschmill oltip

repo: kaushaltrivedi/fast-bert


text:
Configure the cache directory used by LLaMA C++

Need to specify the cache directory so that LLaMA C++ caches models inside the project directory (and not some arbitrary place in the user's home directory. Maybe this requires setting some environment variable?

Closed by #29.


text:
stack.cpp

Title/Heading < Edit this part>
Please include a summary of the change and which issue is fixed. Please also include relevant motivation and context. List any dependencies that are required for this change. < Edit this part>
Fixes # (issue)
Type of change
Please delete options that are not relevant.

[ ] Bug fix (non-breaking change which fixes an issue)
[x] New feature (non-breaking change which adds functionality)
[ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
[ ] This change requires a documentation update

Checklist:

[x] My code follows the style guidelines of this project
[x] I have performed a self-review of my own code
[x] I have commented my code, particularly in hard-to-understand areas
[ ] I have made corresponding changes to the documentation
[ ] My changes generates no new warnings
[ ] I have added tests that prove my fix is effective or that my feature works
[ ] New and existing unit tests pass locally with my changes
[ ] Any dependent changes have been merged and published in downstream modules

violation of guidelines:

Issue not assigned to you
changes from master branch


text:
Callback / Promise return from migration

I want to use this within my tests to allow me to create a test database and run my tests against the test database using all of the migrations in the script. However, I run into a problem in that migration() does not take a callback argument or return a promise enabling it to be used in a situation where I need to not run following code until the migration completes.
The way I'd like to use this:
const migration = require('mysql-migrations');

before((done) => {
 const connection = mysql.createPool({
    connectionLimit: 10,
    host: process.env.MYSQL_HOST,
    user: process.env.MYSQL_USER,
    password: process.env.MYSQL_PASSWORD,
    database: process.env.MYSQL_DATABASE,
  });

  migration.init(connection, path.resolve(__dirname, '../migrations'), done);
}

I'm actually using await/async syntax but a callback will allow me the functionality I need.

One thing I noticed is that my example above is not entirely correct. I need to modify argv via process.argv = ['test', 'test', 'up']; since the migrations.init assumes it is being run on the command line.


text:
FR: Python2.x

Привет!
Очень понравилась библиотека.
Планируется ли поддержка  python {2.6,..}?
Это было бы очень удобно для написания 'smartctl-less' агентов для мониторинга.

Эта библиотека была написана для решения конкретной проблемы. Проблема решена. Дальнейшее развитие библиотеки пока под вопросом.

authors: kamaev kazenniy

repo: kazenniy/atapt


text:
Values doesn't implement Debug

Rust Version

rustc 1.26.1 (827013a31 2018-05-25)

Affected Version of clap

clap 2.31.2

Expected Behavior Summary
Debug printing of clap::Values should be allowed.
Actual Behavior Summary
Compilation error:
error[E0277]: `clap::Values<'_>` doesn't implement `std::fmt::Debug`

Agreed. However adding it to the 2.x branch is a breaking change (however small), so it probably won't happen until 3.x since that is actively being worked on and the goal is to have it released in the coming months.

Just for curiosity - why is deriving Debug a breaking change? Can it break existing code?

Ah I think you're correct! I wasn't thinking about the orphan rules. I was thinking if someone impl'd Debug for Values, however I forgot that's not possible without a newtype.
I can move this into an easy 2.x issue :+1:

authors: drrlvn kbknapp

repo: kbknapp/clap-rs


text:
feat(arg_aliases): Ability to alias arguments

There are some cases where you need to have an argument to have an
alias, an example could be when you deprecate one option in favor of
another one.
Now you are going to be able to alias arguments as follows:
Arg::with_name("opt")
    .long("opt")
    .short("o")
    .takes_value(true)
    .alias("invisible")
    .visible_alias("visible")

Closes #669
Also you can alias flags as follow:
Arg::with_name("flg")
    .long("flag")
    .short("f")
    .alias("not_visible_flag")
    .visible_alias("awesome_v_flag")

This is the first iteration of this feature! 🎉
I am sharing it at this point so I can have some feedback. If this is something you guys are happy with let me know and if not, I am open to suggestions, comments, etc.
I will continue with some more testing and documentation!

Uhmm 🤔  I think the pipeline is broken, at least for windows since appveryor is not even compiling the binary, I wonder if I need to look into it? cc/ @Vinatorul @kbknapp

If you look at the travis details, the nightly failed too.
There was an issue with the previous "nightly", it was fixed on master five days ago, but there have not been a nightly since then until today.
Just close and reopen to trigger a rebuild.

@tormol Thank you for that advice! 👍
The new nightly rust version has some new warning that I already fixed sooo we are green now! ✅

@afiune this looks great! Once #670 merges could you do a quick rebase onto master and this should be good for a merge!
@tormol thank for the assistance :wink:
I've been traveling a lot lately so I haven't had much time, but I'm finally home :tada:

@kbknapp Fantastic! I'm on it!!

@kbknapp Rebased and test passing.. I think we are ready to merge!!!

@homu r+

:pushpin: Commit 905d3b9 has been approved by kbknapp

:hourglass: Testing commit 905d3b9 with merge 4e7466c...

:sunny: Test successful - status

@kbknapp Thank you for approving this!! 😄 I have one last question, when/how is the release process to be able to use this code? Anything I can do to help?
Thanks again!! 🎉

@afiune I'll be releasing the new version to crates.io tonight when I get home. :wink:

authors: afiune homu kbknapp tormol


text:
browser.NewPageAsync() dies in IIS

I have an Angular Client App with a C# backend. I want to take a screenshot of my webpage on the server.
When I run the following code on my Client (Win10, Visual Studio Professional 2017) everything works fine:
await new BrowserFetcher().DownloadAsync(BrowserFetcher.DefaultRevision);
try
{
var browser = await Puppeteer.LaunchAsync(new LaunchOptions  { Headless = true });
var page = await browser.NewPageAsync();
await page.GoToAsync(url);
await page.ScreenshotAsync(outputFile);
}
catch (Exception e)
{
SystemLoggerFa...Error(SourceName, null, "Puppeteer failed", e);
}
I then deploy the application to IIS on our Windows Server 2012 R2. Here the program dies at
var page = await browser.NewPageAsync();
with
at System.Runtime.ExceptionServices.ExceptionDispatchInfo.Throw()
at System.Runtime.CompilerServices.TaskAwaiter.HandleNonSuccessAndDebuggerNotification(Task task)
at PuppeteerSharp.CDPSession.d__26.MoveNext()
--- End of stack trace from previous location where exception was thrown ---
at System.Runtime.ExceptionServices.ExceptionDispatchInfo.Throw()
at System.Runtime.CompilerServices.TaskAwaiter.HandleNonSuccessAndDebuggerNotification(Task task)
at PuppeteerSharp.Page.d__157.MoveNext()
--- End of stack trace from previous location where exception was thrown ---
at System.Runtime.ExceptionServices.ExceptionDispatchInfo.Throw()
at System.Runtime.CompilerServices.TaskAwaiter.HandleNonSuccessAndDebuggerNotification(Task task)
at PuppeteerSharp.Target.d__16.MoveNext()
--- End of stack trace from previous location where exception was thrown ---
at System.Runtime.ExceptionServices.ExceptionDispatchInfo.Throw()
at System.Runtime.Compile
I tried to run it in Visual Studio on the server: works
I used runas to use the IIS service account, to start Visual Studio : works
Only when in IIS, the program dies
Any ideas?
regards
Norbert

We have another report related to the NewPageAsync here #374
You can inject a Logger into the LaunchAsync function. Maybe if you can get those logs into a file, that might be helpful.
I'm thinking how can I reproduce it.

Is your url something I can navigate to? or is a private site?

Still on our dev server behind firewall, sorry
Will try on another server tomorrow

@norbertK could you try 1.5?

Closed due to inactivity. Feel free to reopen it if needed.

This bug continues to exist, i'm having this issue now, unfortunatly, i dont know how to make a logger so i could know a bit more

authors: kblok norbertK taz4270

repo: kblok/puppeteer-sharp


text:
Rename gem eclaircir

The gem has to change its name as there is already a clarifier gem on rubygems. I mistakenly didn't see it. I should have checked more.
Candidates were:

clarificare
clarifunk
eclaircir

I chose the latter cause it's a french word and I like gems with french words.

Pull Request Test Coverage Report for Build 14

89 of 89 (100.0%) changed or added relevant lines in 36 files are covered.
No unchanged relevant lines lost coverage.
Overall coverage decreased (-0.0007%) to 99.621%

Totals

Change from base Build 9:
-0.0007%

Covered Lines:
526

Relevant Lines:
528

💛  - Coveralls

authors: coveralls kbogtob

repo: kbogtob/clarifier


text:
Stutters

RE: kbz's comment

I think I found it. Need a bit more testing though.
So basically every time the tap sound plays the SoundPool code runs through the permission check in appOps to see if sound playback is allowed. And for whatever reason one of the checks throws an error (which happens quite often as evident in the stutters) it defaults to false (ie. yolo we'll play the sound anyway) while throwing an expensive error.
This is platform specific to Android since Sound.java implements AndroidSound which uses SoundPool.
Proposed solution: implement a custom play() method and check appops permission on app load.
Thoughts?

Does this happen only when there's simultaneous taps? Or does it happen on normal hits as well? A work-around would be to add some kind of sound handler and add cool-downs to sounds so that they can't happen at the same time.

Apologies about the late reply. Been a busy week.
Ran some more tests..

Don't think the try block above in SoundPool.isRestricted() is the main cause of stutters since it's consistently throwing exceptions every time (<3ms).
"Lag spikes" that occur without tap and hint sounds enabled never exceeded 1ms during my test runs.
Not related to sync since stutters are present even though I have sync disabled.
Stutters seem to occur consistently at specific parts of some songs. This is interesting.
Tried adding 'flags' to implement the cool-down thing you mentioned but it doesn't seem to help much (still lag spikes/stutter)
Does not have anything to do with new accuracy markers and popup obj creations at WorldController.hit()

So yea.. Quite sure it's something to do with sound.
Might see if running tap sounds on a different thread helps as a temporary workaround (won't eliminate audio issues but it's better than having >100ms stutters on the gui thread). Kinda want to solve this before having to dig into Android libs and sacrifice all the libgdx portability. Worst case scenario I'm happy to poke around Android AudioTrack and writing tap sounds directly into the audio buffer, but would like to make sure this stutter isn't something to do with the current codebase.
Looks like there are some interesting threads over at libgdx forums. Research time!
Android audio why is this such a pita? Why can LLSIF do this properly and we can't :/
Throughs, comments and suggestions on what else to test would be awesome!

Another way may be playing the song from a separate thread (as well as the tap sounds) so that the GUI is partially isolated from the backend.

repo: kbz/SIFTrain


text:
compiling with node.js 4.1.2 will give warning at dependencies

../lib/kerberosgss.c:509:13: warning: implicit declaration of function
'gss_acquire_cred_impersonate_name' is invalid in C99
[-Wimplicit-function-declaration]

See #194

authors: jdesboeufs kuehn-sba

repo: kcbanner/connect-mongo


text:
Add nounspace MOR20 blog post to the website

Nounspace is working on a blog on why they used MOR20 Fair Launch.
Once they post it, update the link.

Added a link out to the Nounspace fair launch page as a placeholder for the Blog
Commit Here

repo: kcdenman/accmor


text:
fix: stream result

1. Does this PR affect any open issues?(Y/N) and add issue references (e.g. "fix #123", "re #123".):

[ ] N
[ ] Y

2. What is the scope of this PR (e.g. component or file name):

3. Provide a description of the PR(e.g. more details, effects, motivations or doc link):

[ ] Affects user behaviors
[ ] Contains syntax changes
[ ] Contains variable changes
[ ] Contains experimental features
[ ] Performance regression: Consumes more CPU
[ ] Performance regression: Consumes more Memory
[ ] Other

4. Are there any breaking changes?(Y/N) and describe the breaking changes(e.g. more details, motivations or doc link):

[ ] N
[ ] Y

5. Are there test cases for these changes?(Y/N) select and add more details, references or doc links:

[ ] Unit test
[ ] Integration test
[ ] Benchmark (add benchmark stats below)
[ ] Manual test (add detailed scripts or steps below)
[ ] Other

Pull Request Test Coverage Report for Build 8002681980
Details

-44 of 71 (38.03%)  changed or added relevant lines in 1 file are covered.
No unchanged relevant lines lost coverage.
Overall coverage decreased (-0.3%) to 62.923%

Changes Missing Coverage
Covered Lines
Changed/Added Lines
%

pkg/kcl/api.go
27
71
38.03%

Totals

Change from base Build 8002043776:
-0.3%

Covered Lines:
3143

Relevant Lines:
4995

💛  - Coveralls

authors: Peefy coveralls

repo: kcl-lang/kcl-go


text:
✨ Rebase kcp modifications onto v0.16.2

This PR applies the modifications from the kcp-1.26 branch onto the most recent version (0.16.2), plus it bumps dependencies to Kubernetes 1.28.
Fixes #41

/lgtm
/approve

We can rename the branch to kcp-1.28 and make it the default.

I'll rename the branch once this is merged.

authors: sttts xrstf

repo: kcp-dev/controller-runtime


text:
Implement a CLI command to bind a user workspace to some compute.

We should implement a KCP CLI command that allows "bind" a user workspace to some compute.
More precisely this command would allow providing:

at least the location workspace that contains locations and sync targets the user workspace resources should be synced to
optionally the location selectors, namespace selectors
optionally a list of workload APIExports of resources we want to sync.

Command line could something like:
kubectl-kcp bind compute <locationWorkspace name> --location-selector=<locationSelector1> --location-selector=<locationSelector2> --apiexport=<apiExport1> --apiexport=<apiExport2> --namespace-selector=<namespaceSelector>

Based on these provided information:

a Placement would be created,
APIBindings would be created in the user workspace to the corresponding APIExports. We should check that the every requested APIExport is effectively part of the supportedAPIExports list of at least one SyncTarget of the location Workspace.

If no APIExport is mentioned, then for both the local kubernetes and global kubernetes APIExports, if it is supported in at least one SyncTarget of the location Workspace, automatically create a binding to it.
When creating an APIBinding, set the apis.kcp.dev/kind label to the workload value, in order to identify this binding as a workload-related APIBinding later on.
In this task we should also remove the defaultplacement_controller.go, since this command would replace it.

Creating the APIExport - not sure. I could see a flow like:

oh, I have a workspace
cool, let me add in k8s 1.24 + whatever else
great, now let's see which locations I can schedule to

We might also think about what happens when someone upgrades their export from k8s-1.24 to k8s-1.25 - a CLI that can handle existing exports & do validation for you might help there too.

@stevekuznetsov I understand your point, and I don't think that implementing this command would prevent the flow you're proposing. It wouldn't prevent you to create a binding without running this CLI command.
Similarly it wouldn't prevent additional (future) code to present you all the locations compatible with a given APIExport, and use the result of such a search when applying this CLI command. However the code that present available & compatible locations is part of the EPIC stretched goals, but is not required in the context of this issue.
For now users know what locationWorkspace they plan to use: it is the one inside which they (or some admin user) ran the workload sync command (In the currently flow, users always explicitly bind to the local kubernetes APIExport in this location workspace).
So the purpose of this issue is to provide, with a CLI command, the simplest and most straightforward way to bind to this already-known location Workspace. It makes it :

simpler because it manages both the Placement and the Workload-related APIBindings, with appropriate defaults.
safer since it also checks that at least one SyncTarget of this locationWorkspace supports the bound workload-APIExports.

should it be kubectl kcp workload bind? we currently do not have a kubectl kcp bind command, or should there be a common bind command that will also be used for other bind related functions?

We do now 😄 https://github.com/kcp-dev/kcp/pull/2027

should it be kubectl kcp workload bind? we currently do not have a kubectl kcp bind command, or should there be a common bind command that will also be used for other bind related functions?

From a user POV it should be kubectl kcp bind workload (== a subcommand of the bind command).
But implementation-wise it might also have been a kubectl-kcp-bind-workload binary.
However based on the just-merged PR @ncdc mentioned, adding a sub-command to the bind command, and completing the bind command help with the workload use-case would probably provide a better UX.

authors: davidfestal ncdc qiujian16 stevekuznetsov

repo: kcp-dev/kcp


text:
Enable avoiding using podman

Summary
Allow using the CreateKindClusters.sh script without  podman, even though podman is installed on the system (for example on linux boxes where you have both docker and podman)

don't care much about this little chance. Overall, IMO this does not really belong into the repository.

/lgtm
/approve

/retest

authors: davidfestal sttts


text:
UPNP_Device discover.py -> no output

Hello,
here is my output :
C:\Users\mtourlouse\AppData\Local\Programs\Python\Python37-32\Lib\site-packages\samsungctl-0.8.0b0-py3.7.egg\samsungctl\upnp\UPNP_Device
λ python discover.py > discover_output.txt
SSDP: 239.255.255.250
M-SEARCH * HTTP/1.1
ST: upnp:rootdevice
MAN: "ssdp:discover"
HOST: 239.255.255.250:1900
MX: 1
Content-Length: 0
SSDP: 239.255.255.250
M-SEARCH * HTTP/1.1
ST: upnp:rootdevice
MAN: "ssdp:discover"
HOST: 239.255.255.250:1900
MX: 1
Content-Length: 0
But I dont have any discover_output.txt :/
Thks,
matt

working now

repo: kdschlosser/samsungctl


text:
Make Browser.click/2 return the parent element

This addresses issue #134

Looks great! Thanks :tada:

authors: aellispierce keathley

repo: keathley/wallaby


text:
NNTF support

JIRA: https://keboola.atlassian.net/browse/PST-1420

@ondrajodas tohle už je taky ready

repo: keboola/db-extractor-mysql


text:
KBC-2903 Synapse columns comparison case insensitive

JIRA: https://keboola.atlassian.net/browse/KBC-2903

Spadnuly enom TD testy, protože nejel server.

👁

authors: martinjunger zajca

repo: keboola/php-db-import-export


text:
Increase version

And one more thing. 😃

BTW. Is it necessary? The package is installed directly from github and not a package registry like pip, so maybe it isn't?

repo: keboola/sandboxes-notebook-utils


text:
KEDA scaler for Redis Streams

Redis Streams, a data type introduced with Redis 5.0 is an append-only log data structure. One of its features includes "consumer groups" that allows a group of clients to co-operate consuming a different portion of the same stream of messages.
This scaler can check the Pending Entries List (see XPENDING https://redis.io/commands/xpending) for a specific Consumer Group of a Redis Stream and scale-out the Deployment as per the count returned (by XPENDING command). If you have a Redis Streams consumer application that is unable to keep up with the rate of production, KEDA can help by auto-scaling the no. of instances (part of the same consumer group) so that it can start sharing the processing workload.

Scaler Source: Redis Streams (Pending Entries List aka PEL)
Authentication: Password-based authentication supported by Redis

If this sounds interesting, I can continue working on the implementation

Here are some additional points:

Consumer application must acknowledge the processes message (XACK https://redis.io/commands/xack) else the “Pending Entry List” will keep increasing indefinitely if messages continue to be inserted in the stream. The scaler can keep a cap on the no. of instances by using maxReplicaCount
If a consumer instance has failed or crashed - in this case, simply scaling out your application may not help (since Redis treats consumer groups as a stateful entity and stores the processing state of each consumer instance, including the messages which were received but not acknowledged - see https://redis.io/topics/streams-intro#consumer-groups). This needs to be handled - examples below (each has its own caveats)

Explicitly reading the history (using 0-0 ID) to process previous messages which were received but not acknowledged (see Ruby example here https://redis.io/topics/streams-intro#creating-a-consumer-group)
Using XCLAIM (https://redis.io/topics/streams-intro#recovering-from-permanent-failures) to take ownership of such messages and process them (probably as an out-of-band process)

These points have been mentioned since these characteristics are specific to Redis Streams based consumer applications irrespective of the scaler

The ScaledObject for this scaler will look something like this:
apiVersion: keda.k8s.io/v1alpha1
kind: ScaledObject
metadata:
  name: redis-streams-scaledobject
  namespace: default
  labels:
    deploymentName: redis-streams-consumer
spec:
  scaleTargetRef:
    deploymentName: redis-streams-consumer
  pollingInterval: 15
  cooldownPeriod: 200
  maxReplicaCount: 25
  minReplicaCount: 1
  triggers:
    - type: redis-streams
      metadata:
        address: REDIS_HOST
        stream: my-stream
        consumerGroup: consumer-group-1
        pendingEntriesCount: "10"
      authenticationRef:
        name: keda-redis-stream-triggerauth

stream and consumerGroup are names of the stream and a consumer group respectively and pendingEntriesCount is the threshold for the count of Pending Entries (obtained using XPENDING) for that consumer group in the stream

I've added it to today's standup meeting but think this would be a good addition - Will discuss and let you know!
Feel free to join if you want as well.

Thanks for the work. I wonder why you have chosen the count of Pending Entries as the trigger for scaling. Based my own understanding, the count should equal to the number of consumers (each will read an entry, process and acknowledge it). The increase of Pending Entries correlate to the increase of the number of consumers, it seems to does the opposite as the trigger for scaler.

Can I use this for a job queue? From my understanding and testing, when a message is added using XADD, the consumer's Pending Entries List (PEL) does not grow. It's only when a consumer executes XREADGROUP that the message is added to the PEL. This this makes this unusable, since it's specifically that consumer I want to scale.
Is this correct?
For now, I'll try using Redis lists.

@accnops is right. If I have an application adding messages to a stream, they are not delivered to any consumer group until an XREADGROUP is issued over that consumer group. This means that if the Job that KEDA is trying to scale consumes messages, it has to wait for someone to consume messages (!!!) using XREADGROUP, so that the messages are moved to the PEL of that group. This leaves Redis Streams pretty much useless with KEDA because if some other application has already fetched the messages, how would I retrieve them in my KEDA application that I am trying to scale?
Why not have an option to create a Job without a consumer group? This way any new messages added to a stream would trigger a Job and the user would be able to do their processing over the newly added messages.
Keda developers, please let us know if there is a workaround to this issue.

@nakumgaurav could you please open a separate issue for this?

Thank you @zroubalik I filed the new issue #2044

authors: abhirockzz accnops nakumgaurav tomkerkhove yifan zroubalik

repo: kedacore/keda


text:
Disable response compression for k8s restAPI in client-go

Keda-operator timing out for large number of configmaps and secrets. Hence disabling compression in client-go.
https://github.com/kedacore/keda/issues/3863
Checklist

[ ] When introducing a new scaler, I agree with the scaling governance policy
[x] Commits are signed with Developer Certificate of Origin (DCO - learn more)
[ ] Tests have been added
[ ] A PR is opened to update our Helm chart (repo) (if applicable, ie. when deployment manifests are modified)
[x] A PR is opened to update the documentation on (repo) (if applicable)
[ ] Changelog has been updated and is aligned with our changelog requirements

Fixes #

Relates to #

have added documentation here:
https://github.com/kedacore/keda-docs/pull/978

@zroubalik all feedback incorporated. Please check

/run-e2e aws*

/run-e2e aws*

/run-e2e

authors: JorTurFer ckuduvalli


text:
fix(mongodb): escape username and password

Signed-off-by: Jorge Turrado jorge_turrado@hotmail.es

If either the username or password contains a character in the set  :/?#[]@  ,  it must be percent-encoded or the scaler will fail.
Checklist

[x] Tests have been added
[x] Commits are signed with Developer Certificate of Origin (DCO - learn more)

Fixes https://github.com/kedacore/keda/issues/3992

/run-e2e mongo*


text:
Upgrade vendor to openshift v3.6.1

We have the vendor update script, I think peroidic upgrades will help us validate the script works. One more thing I saw that kompose is facing right now is that since kompose vendor has not been updated for long they are having hard time moving to newer Openshift.

Closed in #448

authors: kadel surajssd

repo: kedgeproject/kedge


text:
feat: Ethereum RPC " chain_id" call

Description
Implement "chain_id" Ethereum RPC call
Helios implmentation: https://github.com/a16z/helios/blob/94bf458d941f30e34faeaa7d2484898dba54234d/client/src/rpc.rs#L67
Acceptance Criteria

Beerus can query a Ethereum's block number
Associated cli command
Associated rest api wrapper
Helios integration
Units tests
Integration tests

Taking this one :)

Ok 👍

Implemented

authors: abdelhamidbakhta danilowhk

repo: keep-starknet-strange/beerus


text:
Addition of first ethereum endpoint: feat/ethereum_blockNumber

Addition of first ethereum endpoint: feat/ethereum_blockNumber
Pull Request type

Please check the type of change your PR introduces:

[ ] Bugfix
[X] Feature
[ ] Code style update (formatting, renaming)
[ ] Refactoring (no functional changes, no API changes)
[ ] Build-related changes
[ ] Documentation content changes
[ ] Testing
[ ] Other (please describe):

What is the current behavior?
Ethereum endpoints are not implemented

Issue Number: #290
What is the new behavior?

The endpoint has been added + boilerplate to add other eth endpoints
Does this introduce a breaking change?

[ ] Yes
[X] No

Other information

Addition of test boilerplate for eth endpoints is needed to be able to test these new endpoints

I have added the first eth_endpoint: ethereum_blockNumber, also i have renamed the existing endpoints to starknet_ to be able to defferentiate between eth and stark.
Next step would be to add tests.

If possible I prefer to keep the namespaced version of the RPC server. I’m fairly certain jsonrpsee allows you to extend the server methods with another namespace

Resolved conflicts, ready to merge 👍

authors: drspacemn phklive


text:
Update starknet-rs

Update starknet-rs to latest version.
Pull Request type
Please check the type of change your PR introduces:

[ ] Bugfix
[x] Feature
[ ] Code style update (formatting, renaming)
[ ] Refactoring (no functional changes, no API changes)
[ ] Build-related changes
[ ] Documentation content changes
[ ] Testing
[ ] Other (please describe):

What is the current behavior?
Beerus is using an old version of starknet-rs that does not support Serializaion / Deserialization datatypes that reflect the ecosystem and needs updating.
Issue Number: #251
What is the new behavior?

Update starknet-rs version
Support Sierra Contract and deprecate Legacy datatype
Add support for V1 and V2 datatypes

Does this introduce a breaking change?

[ ] Yes
[ ] No

I think you have a problem with the associated issue imo it's not #251 right ?

I think you have a problem with the associated issue imo it's not #251 right ?

It was a blocker, but not directly related. Created a new issue and linked, thanks!

Changed to draft so I could do some additional testing.

lgtm merging


text:
bug: flaky tests 🔨

Bug Report
Madara version:

https://github.com/keep-starknet-strange/madara/commit/3db1adafd06259090a2df80cae6617a904b318c3
Current behavior:

There are some flaky integration tests.
Get transaction by blockId and index
Expected behavior:

Tests should always pass.
Steps to reproduce:

Run the integration tests with npm run test-seq in the tests/ folder.
Related code:

We get the following error :
LibraryError: -32601: Method not found

Other information:

I do have errors locally on main branch but not the same ones

indeed, see related job https://github.com/keep-starknet-strange/madara/actions/runs/5043252152/jobs/9045546080

but I'm also on latest main, how come we (I) don't have the same error?

but I'm also on latest main, how come we (I) don't have the same error?

Not sure why, I'm on node v20.2.0 and did npm i before running the test, that's a bit strange we have different errors 😨

I have
$ npm install

up to date, audited 992 packages in 10s

$ node -v
v20.0.0

and git log
commit 3db1adafd06259090a2df80cae6617a904b318c3 (HEAD -> main, madara/main)
Author: Abdel @ StarkWare <45264458+abdelhamidbakhta@users.noreply.github.com>
Date:   Mon May 22 09:58:13 2023 +0200

    📝 Add doc badges (#425)

I managed to replicate this, I did cargo build --release and ran the tests npm run test-seq afterwards

I do have errors locally on main branch but not the same ones

That's weird, I have every test working on my machine

That's weird, I have every test working on my machine

I think this can be closed now, @greged93 did a fix with #435  💪

I think we have two separate issues: the above failing tests mentioned by @ClementWalter and @ftupas which were fixed with #435, but we still have some flaky CI tests (see here). So for me this remains open.

I think we have two separate issues: the above failing tests mentioned by @ClementWalter and @ftupas which were fixed with #435, but we still have some flaky CI tests (see here). So for me this remains open.

Ah gotcha! Thanks sir 🫡

authors: ClementWalter EvolveArt ftupas greged93 sparqet

repo: keep-starknet-strange/madara


text:
Support for gulp-better-rollup

Any chance you could make a version of this that would play well with gulp and it's gulp-better-rollup plugin?

Please tell me any errors or outputs you saw on trying to do something with this plugin.

authors: Elvanos kei-ito

repo: kei-ito/rollup-plugin-glob-import


text:
Zoomed page looks jaggy on some devices.

System information

MangaView version: 1.0.0
Android version: Android 9.0(API Level 28)
Device: Lenovo TB-7305F(Lenovo Tab M7)

Describe the current behavior

Launch the sample app.
Zoom page.
The zoomed pages(images) are jagged.

Lenovo Tab M7

Pixel 4a

Describe the expected behavior
The page is processed anti-alias.

Already fixed 44523925af317b8eb5406396b0fc23bc5012aa12

repo: keiji/mangaview


text:
pixel obs

Hi @keiohta , thanks for sharing such a great project!
I'm wondering if I want to set observation to be image-based using tf2rl, do you have any advice?

I'm not sure what you want to do but I guess Atari example with DQN might help you.
https://github.com/keiohta/tf2rl/blob/master/examples/run_dqn_atari.py

hi @keiohta, sorry for the late reply.
basically, I'd like to use GAIfO+SAC on image-based dm_control and robosuite tasks.
What I'm trying to say is that does tf2rl have any support of image-based RL/IL. or let's say can I pull a request, for example, SAC+AE working on the image-based dm_conttrol suite. https://sites.google.com/view/sac-ae/home. also something like CURL. https://arxiv.org/abs/2004.04136
example of IL, the experiment of the original GAIfO paper is also based on image-based tasks, it uses CNN policy and CNN discriminator. also, VAIL explored video imitation in their experiments.
if there could be a CNN-GAIfO or VAE-GAIfO-SAC. that would be great!
thanks!
Haoyu

Hi @haoyu-x , sorry I completely forgot to answer your question...
Current tf2rl does not officially support image-based RL/IL (I mean I don't provide no example of such scripts under examples), but we've already implemented the codes to do that.
For example, I think you can solve an easy image-based RL (let's say Pendulum-v0) using SAC + VAE, or do IRL in similar way.
I strongly suggest you use VAE instead of directly inputting image to your policy. Refs are:

Improving Sample Efficiency in Model-Free Reinforcement Learning from Images
Learning to drive in a day

Of course it would be awesome if you can send the PR that supports CURL or other image-based RL/IL!

Thank you！
Kei Ohta notifications@github.com于2020年8月12日 周三21:30写道：

Hi @haoyu-x https://github.com/haoyu-x , sorry I completely forgot to
answer your question...
Current tf2rl does not officially support image-based RL/IL (I mean I
don't provide no example of such scripts under examples
https://github.com/keiohta/tf2rl/tree/master/examples), but we've
already implemented the codes to do that.
For example, I think you can solve an easy image-based RL (let's say
Pendulum-v0) using SAC
https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/sac.py + VAE
https://github.com/keiohta/tf2rl/blob/master/tf2rl/tools/vae.py, or do
IRL in similar way.
I strongly suggest you use VAE instead of directly inputting image to your
policy. Refs are:

Improving Sample Efficiency in Model-Free Reinforcement Learning
from Images https://arxiv.org/abs/1910.01741
Learning to drive in a day https://arxiv.org/abs/1807.00412

Of course it would be awesome if you can send the PR that supports CURL or
other image-based RL/IL!
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/keiohta/tf2rl/issues/93#issuecomment-672871430, or
unsubscribe
https://github.com/notifications/unsubscribe-auth/APACPZSDJ4VEWI5IABFFTYTSAKKOHANCNFSM4PQGTADQ
.

I close this issue since it seems to be resolved.
Please reopen this issue if you find other problem related to this.

authors: haoyu-x keiohta


text:
Converting SCP to EADF works less accurately than FS-UAE.

I haven't looked closely into what is going on here, but I just got a Greaseweazle F1 and was testing it on some really old disks formatted using Diskspare.device on the Amiga (so 82 tracks in custom format).
I can convert them to EADF using raw_dd format since disk-analyse does not seem to support the Diskspare.device format, but the resulting images had a lot of read errors when accessed from FS-UAE.
If I just feed the SCP files directly to FS-UAE, I still get a few read errors but a lot less. Is some part of the decoding of the flux less accurate in disk-analyse than in FS-UAE?

Conversion is a single revolution index-to-index. AmigaDOS disks are often not index aligned and have sectors straddling the index mark. Such sectors will be corrupted by the conversion. However, such disks are usually pure AmigaDOS and can be converted using --format=amigados.

authors: DagAgren keirf

repo: keirf/Disk-Utilities

license: unlicense


text:
Reaper Scans FR broken

Source information
fr.reaperscans@1.4.36
Source language
French
Steps to reproduce

Launch

Expected behavior
See mangas
Actual behavior
"Cloudflare error" (this is not a cloudflare error, the domain name changed and new backend.
Mihon/Tachiyomi version
0.17.0
Android version
Android 14
Other details
I will write the PR.
Acknowledgements

[X] I have searched the existing issues and this is a new ticket, NOT a duplicate or related to another open or closed issue.
[X] I have written a short but informative title.
[X] I have updated the app to version 0.15.3.
[X] I have updated all installed extensions.
[X] I have tried the troubleshooting guide.
[X] If this is an issue with the app itself, I should be opening an issue in the app repository.
[X] I will fill out all of the requested information in this form.

Add a :+1: reaction to issues you find important.

Ok, for stranges reasons, my app wasn't updated...

repo: keiyoushi/extensions-source


text:
question

is nocode compatible with noscript?

@rickteuthof The nocode framework is written in No programming language, and there's no compatibility problem!

Double click 666

repo: kelseyhightower/nocode


text:
Request to build a version for Linux

Hello,
Please build a version for a Linux OS (like Ubuntu).

A Linux version would be great.

Thanks for request. @omouren was gracious enough to make a pull request (https://github.com/kelyvin/Google-Allo-For-Desktop/pull/2) that comes with a linux build.
Please feel free to download and give it a try. Unforunately I am not near a stable internet connection nor do I have a current Linux VM to test this on, so if you guys can give it a shot it would be greatly appreciated!  I will update both the site and the release with this build once it has been verified.
Thanks!

Thanks for the quick response! I tested it on Ubuntu 16.04 and it's working fine. There's an error that shows up in the terminal about:
Received TURN CreatePermission error response, code=403; pruned connection.

But other than the above message, the app itself is working fine.

Linux Build works perfectly even desktop notifications work on elementary os! Thanks so much!

authors: DGxInfinitY kelyvin mikeadamz rigved

repo: kelyvin/Google-Allo-For-Desktop


text:
Misc Changes

Some more misc changes, see commit messages for details

Hi,
Thank you for your time, it means a lot.
Implementing equality and unhashability is great
The is_definite_disjoint method doesn't make sense because this is a memory optimized Bloom filter where k > 1 (usually) bits are flipped for an added element, which leads to the underlying bitline to fill up with 1s very quickly. A quick test populating two Bloom(10_000, 0.01) with only 100 differing elements each showed that is_define_disjoint was only able to prove disjunction only <3% of the time. the subset and superset methods make sense but should correspond to the python set signature because even one element difference takes the false positive rate down to the same as for contains so unlike disjoint this should be expected behavior for users. It would probably be prudent to also implement <, >, <=, >= when subsets are implemented. I will then document in the readme that this is also probablistic. The only reason I strayed from the signature with approx_items instead of len was because len isn't supposed to return floats.
I think the implementations of union and intersection should clone self and call update and intersection_update respectively.
Using a Box<[Word]> is a great idea! It doesn't really make a difference but it's definitely more elegant

I unfortunately only now had the time to review your updated changes, but they were absolutely excellent, thank you so much! I will be updating the README and preparing a new minor release!

authors: Dr-Emann kenbyte

repo: kenbyte/rbloom


text:
[other]:  如何关闭大核和 rtthread

Other
我们当前不需要使用大核与 rt-thread ，所以，如何把这两个东西关闭或者禁用，以将低功耗？

大核有单独的电源域控制，可以在uboot中讲大核电源域关闭来达到降低功耗目标。目前SDK尚未支持这个功能，会在之后的版本中增加。

7天以上未反馈结果，问题先关闭；希望已帮助您解决问题，谢谢！

authors: Qinka chenhb3 zhangxiaojingCAN


text:
getConnectedDevice returns No bluetooth device connected when device is connected.

When I connect to a bluetooth device using the connect() method in the app, getConnectedDevice  returns the connected device as expected.
But when I close the app and open it again, with the device still connected, the method returns No bluetooth device connected.

I'm using Android.

Ya, that's how things work currently.  Connected in terms of the library does NOT meant Connected in terms of the platform.  Connected in the library means there is currently RFCOMM session with open sockets available for communication.  When you close the app, this session is closed and therefore the device is no longer "connected" in the eyes of the application.
There is no way to maintain connection across Activity life cycles (that I'm aware of) as the objects required are not Serializable and therefore not Bundleable.
I get around this by:

Saving the most recently connected (RFCOMM) device
Upon start up I see if list().find(device) exists and automatically re-connect to it.

Remember:

PAIRED = Android (and to a lesser extend IOS) know about the device.  Devices are available with list()
CONNECTED = Library has an open RFCOMM (socket) with the device.  Device is available with getConnectedDevice()

authors: kenjdavidson thiago-carrilho


text:
後でやります

なんかエライ忙しくなってしまって Problems まで手が回ってないんですけど、やる気はあるので、ちゃんとプルリクもマージしてリリースします
すんません

時間とれたのでちょい作業します！

repo: kenkoooo/AtCoderProblems


text:
Autocomplete value onchange is ignoring options

The drop down works with these options:
const options = {
types: ['address'],
componentRestrictions: {country: 'uk'}
}
However the onChange value from the typed in text remains in the USA until the dropdown is clicked.

@timfisher Thank you for reporting!
Could you provide more details?

Kenny, please see the video here:
https://sendvid.com/idj0zws3

I see. Would you mind providing me a code snippet for rendering these input fields?

Hi Kenny,
I think the problem is in geocodeByAddress in utils.js, I've had to replace the geocode with:
  geocoder.geocode({'address': address + ', United Kingdom' }, function (results, status) {

The problem is it doesn't match the first result (I would rather it matched), could you help with that?

Hi guys, I am having the same issue here. I need to restrict the dropdown result to AU only.
@pjho I have tried your post, doesn't seem to be working. The result dropdown list still not filtering by country.
@kenny-hibino Any advice on how to add the componentRestrictions supports?
#114 also want the same thing.
Thank you all!

Actually I found where I went wrong, a silly mistake: I used ComponentRestrictions instead of componentRestrictions. It is case sensitive.
Below option setting works with the original function   geocodeByAddress  from the util:
const options = {   componentRestrictions: { country: ['AU'] },   types: ['establishment'] };

prop searchOptions wasn't working for me, options did.
"react-places-autocomplete": "^6.1.3",
Since they don't keep a changelog idk if this was changed on the newer versions.

authors: AndreiCalazans jinjingbo kenny-hibino timfisher

repo: kenny-hibino/react-places-autocomplete


text:
🛑 FG_Caricuao is down

In 158c421, FG_Caricuao (caricuao.fortiddns.com) was down:

HTTP code: 0
Response time: 0 ms

Resolved: FG_Caricuao is back up in ae7d760 after 8 minutes.

repo: kennysolorzano/upptime


text:
About the accuracy of 94.5% on UCF101 using the Resnext101?

Hello, can you tell me how can achieve the the accuracy of 94.5% on UCF101 using the Resnext101?
I use your code, the same network architecture(Resnext101) and your pretrained parameters(resnext-101-64f-kinetics-UCF101_split1.pth) by the manner of sliding windows over the sequeence of videos. But I can only got the accuracy of 90.35%.
I set somthing wrong?

How do you evaluate the accuracy?
If you get the accuracy in val.log, the accuracy is the clip-level accuracy whereas the accuracy reported in my paper is the video-level accuracy, which can be evaluated using val.json and eval_ucf101.py.

Oh, sorry, I used the 16-frames input so got the low accuracy. But if use the 64-frames input, how to deal with the video with the lower number of frames than 64? zero padding?

Hello @Wei2Huang could you send the config you use (python main.py ...) for running testing on UCF101?

Hello @Wei2Huang could you send the config you use (python main.py ...) for running testing on UCF101?
I have read your paper in details later and I found your method,  so now I got the accuracy of 94.7%, thank you very much!

@Katou2  Could you please share the details of your config file which you used for fine-tuning using resnext-100 and resnext-101-64f version of the models on UCF-101.
I want to know the value of the hyper parameters used in the training. With the help of the resenext-101 64f fine tuned model provided by the author I could achieve 93 percent accuracy of the Split1 of UCF-101. However, using my own fine-tuned model the accuracy hardly reaches 90 %.

There is another issue in the code. Once you generate the val.json, the file contains prediction results of 1 video less than what contained in the val list of videos. I mean if I have 100 validation videos, the the generated val.json will have prediction results of only 99 videos. I still wonder, what could be the possible reason for such behaviour.

@sumeetssaurav I just utilized the default hyperparameters. Are you sure you tested at video level accuracy?
For the second problem, I think you are right. The reason is that the last sample would not be tested in test.py. To settle this problem, you can add the following code at the end of dataloader function in test.py.
if i == len(data_loader) - 1:
    test_results = calculate_video_results(output_buffer, previous_video_id,
                                        test_results, class_names)

@Katou2 Thanks for the quick reply. Still wondering where I have to put the loop suggested by you. Could you please highlight it in the test.py code.

Hello @Wei2Huang could you send the config you use (python main.py ...) for running testing on UCF101?
I have read your paper in details later and I found your method,  so now I got the accuracy of 94.7%, thank you very much!

excuse me， how to get 94.7 on validation？and could you tell me what is the difference between video level and clip level?

@Katou2  Could you please share the details of your config file which you used for fine-tuning using resnext-100 and resnext-101-64f version of the models on UCF-101.
I want to know the value of the hyper parameters used in the training. With the help of the resenext-101 64f fine tuned model provided by the author I could achieve 93 percent accuracy of the Split1 of UCF-101. However, using my own fine-tuned model the accuracy hardly reaches 90 %.

hello, can you tell me how to achieve 93? I use the model, but i can only reach 90

@ilovekj Hi, utilizing the default config is enough. The key point is that you should calculate the video-level accuracy, namely using utils/eval_ucf101.py to calculate the accuracy.

@ilovekj Hi, utilizing the default config is enough. The key point is that you should calculate the video-level accuracy, namely using utils/eval_ucf101.py to calculate the accuracy.

so ，what is the difference between clip level and video level? i do not understand, can you tell me?

@ilovekj Hi, utilizing the default config is enough. The key point is that you should calculate the video-level accuracy, namely using utils/eval_ucf101.py to calculate the accuracy.

and when we validate on dataset, the accuracy is clip-level acc, right?

@ilovekj Yes, validation is the clip-level accuracy. Video-level accuracy is utilizing the average of the scores of all the clips of a video to get the accuracy, while clip-level accuracy is based on the score of a clip.

@Katou2 however we just average of the scores , how can we get higher accuracy, that is quite incredible

@ilovekj Try to think about it. In some cases, it can make it.

@Katou2 could you explain to me more clearly? thank you

@ilovekj Try to think about it. In some cases, it can make it.

and can you give me your WeChat or facebook?

@Katou2 excuse me? this question is very important for me, and i cannot get it

@Katou2 @kenshohara
Great discussion.What I want to ask is how do I get the video accuracy and what is input with eval_ucf101.py, I don't see val.json.I only have val.log here

@kenshohara @Katou2
Is there a way I can resume training on my fine-tuned model. I used the following command to train till the 20th epoch.

python main.py --root_path C:\Users\purav\Downloads\Study\Project\data --video_path extracted_jpg --annotation_path ucf101_01.json --result_path results --dataset ucf101 --n_classes 101 --n_pretrain_classes 400 --pretrain_path C:\Users\purav\Downloads\Study\Project\3D-ResNets-PyTorch\models\resnet-50-kinetics.pth --ft_begin_module fc --model resnet --model_depth 50 --batch_size 128 --checkpoint 5

Then I had to stop the training for other important task and now when I try to start the training again using the saved model using the following command:
`

python main.py --root_path C:\Users\purav\Downloads\Study\Project\data --video_path extracted_jpg --annotation_path ucf101_01.json --result_path results --dataset ucf101 --resume_path C:\Users\purav\Downloads\Study\Project\data\results\save_20.pth --n_classes 101 --model_depth 50 --batch_size 16 --checkpoint 5

`
I face the following error :
Traceback (most recent call last):
  File "main.py", line 428, in <module>
    main_worker(-1, opt)
  File "main.py", line 363, in main_worker
    opt.resume_path, opt.begin_epoch, optimizer, scheduler)
  File "main.py", line 105, in resume_train_utils
    optimizer.load_state_dict(checkpoint['optimizer'])
  File "C:\Users\purav\anaconda3\envs\vision\lib\site-packages\torch\optim\optimizer.py", line 111, in load_state_dict
    raise ValueError("loaded state dict has a different number of "
ValueError: loaded state dict has a different number of parameter groups

Am I missing some parameters while resuming the training again?
Please Advise

@Purav-Zumkhawala See this issue #42.
You need to keep referencing the pretrained info and just add the resume path. Try this:
python main.py --root_path C:\Users\purav\Downloads\Study\Project\data --video_path extracted_jpg --annotation_path ucf101_01.json --result_path results --dataset ucf101 --n_classes 101 --n_pretrain_classes 400 --pretrain_path C:\Users\purav\Downloads\Study\Project\3D-ResNets-PyTorch\models\resnet-50-kinetics.pth --ft_begin_module fc --model resnet --model_depth 50 --batch_size 128 --checkpoint 5 --resume_path C:\Users\purav\Downloads\Study\Project\data\results\save_20.pth

@Katou2 @kenshohara Great discussion.What I want to ask is how do I get the video accuracy and what is input with eval_ucf101.py, I don't see val.json.I only have val.log here

hello,Are you using the code for the master branch? There are no val.json and eval_ucf101.py files in this branch


text:
Update cisco-asa.yml

Updated Memory OIDs. @thezackm can you please approve

@thezackm, we noticed that mempooltype is 10 i.e. virtual memory. so we can go with the better one

@thezackm hi, made changes as requested

repo: kentik/snmp-profiles


text:
Change font style

https://github.com/kenu/okdevtv/pull/97와 동일한 PR입니다!

감사합니다. 폰트 가독성이 부드러워졌습니다. 🙇♂

혹시 public/js/index.js에서 changeFont 함수가 반드시 필요한건가요?
필요없다면 삭제해도 크게 문제는 없을 것 같습니다!

https://font.okdevtv.com 에서 괜찮은 폰트를 적용한 것이 changeFont() 입니다.

크게 중요한 것은 아닌데, 그래서 숨겨놓은 기능입니다.
감사합니다. 🙇♂

authors: kenu rivercory

repo: kenu/youtb


text:
$( document ).on( "ready", ... ) removed in jquery 3.0.0

Slick Slider relies on the "ready" event on multiple occasions and is therefore not compatible with jQuery 3.0+.
https://jquery.com/upgrade-guide/3.0/#breaking-change-on-quot-ready-quot-fn-removed
https://api.jquery.com/ready#entry-longdesc
https://github.com/jquery/jquery-migrate/blob/1.x-stable/warnings.md#user-content-jqmigrate-ready-event-is-deprecated
https://github.com/jquery/jquery/issues/2264

@leggomuhgreggo we should get a release cut for this

now jquery 3.1.1 is released. Join for waiting for slick update ;)

@kenwheeler @programmer403 sure seems low impact, and straight forward

Closing. Thanks!

Which version of slick has this fix?

@zheoreh  v1.7.1+
https://github.com/kenwheeler/slick/releases

authors: DmitryGonchar KolyaKorruptis kenwheeler leggomuhgreggo programmer403 zheoreh

repo: kenwheeler/slick


text:
[bug]: Wrong redirect link for documentation

Is there an existing issue for this?

[X] I have searched the existing issues

Current behavior
The link to documentation redirects to keploy's github repository actually it should redirect to keploys docs
Steps to reproduce

Run Keploy Server
Visit localhost:8081
Click on documentation link.

Environment
No response
Version
Local

I am working on this!

@nehagup i have created a pull request for #59 issue! pls review it . Thanks :)

repo: keploy/ui


text:
LoRa device doesn't start after softboot

When performing a softboot (Ctrl-D in REPL) there won't be any join. No data is received on the gateway either so it seems the LoRa chip is in an undefined state. Check th ecommunity if this is a know and avoidable problem as is makes debugging hard,

Digging through the LoPy forum on Pycom did not reveal a solution.

repo: keptenkurk/pyTTNmap


text:
SLI display name - Fallback to raw name is not implemented

I'm using keptn-0.8.1-alpha, with the SLO file:
---
spec_version: "1.0"
comparison:
  aggregate_function: "avg"
  compare_with: "single_result"
  include_result_with_score: "pass"
  number_of_comparison_results: 1
filter:
objectives:
  - sli: "response_time_p95"
    key_sli: false
    pass:             # pass if (relative change <= 10% AND absolute value is < 600ms)
      - criteria:
          - "<=+10%"  # relative values require a prefixed sign (plus or minus)
          - "<600"    # absolute values only require a logical operator
    warning:          # if the response time is below 800ms, the result should be a warning
      - criteria:
          - "<=800"
    weight: 1
total_score:
  pass: "90%"
  warning: "75%"

In this SLO, I have not displayName defined.

Consequently, no name is shown in the Heatmap / SLI breakdown:

Expected behavior: I see the "raw" SLI name which is response_time_p95

FYI: When setting the displayName, it works nice:

The original issue is here: https://github.com/keptn/keptn/issues/3345

@johannes-b could you add also the payload of the evaluation.finished event?

authors: ermin-muratovic johannes-b

repo: keptn/keptn


text:
docs: v1alpha3 has changed so Keptn on non-k8s tutorial fails

The documented example for running Keptn in non-k8s no longer works because the spec for v1alpha3 has changed.
The existing YAML for a KeptnTask in the example looks like this:
apiVersion: lifecycle.keptn.sh/v1alpha3
kind: KeptnTask
metadata:
  name: runhelloworld1
spec:
  workload: "my-workload"
  workloadVersion: "1.0.0"
  appVersion: "1.0.0"
  app: "my-app"
  taskDefinition: helloworldtask
  context:
    appName: "my-app"
    appVersion: "1.0.0"
    objectType: ""
    taskType: ""
    workloadName: "my-workload"
    workloadVersion: "1.0.0"

But the v1alpha3 spec has now dropped spec.workload, spec.workloadVersion, spec.app and spec.appVersion to remove the redundancy and thus only pass these values in spec.context.
I think all that's required here is to remove the spec.... fields and things should be picked up from the context.... fields now.
Definition of Done
The example on the page above should work.

Confirmed that removing the spec... fields from the above does work. @StackScribe this should be a quick fix.

repo: keptn/lifecycle-toolkit


text:
chore: release 3.0.1

:robot: I have created a release *beep* *boop*
3.0.1 (2022-06-14)
Bug Fixes

cannot find command (d597bdb)

This PR was generated with Release Please. See documentation.

:robot: Release is at https://github.com/keq-request/keq-cli/releases/tag/v3.0.1 :sunflower:

repo: keq-request/keq-cli


text:
Addition of GroupNormalization

Adds Group Normalization + associated tests from the paper Group Normalization
By extention, setting the parameter "groups" to 1 should make it equivalent to Instance Normalization and settng "groups" to the number of input channels will apply layer normalization.

CNTK tests fail due to library issues, however the Keras code for mean and standard calculation remains the same for all backends, so I feel it can be skipped in the short term.

This looks nice. I bet it is something that is comparatively likely to be accepted into Keras upstream. Should we check for interest there first?

@ahundt Sure, I'll add a request there. For the time being however, I think I'll have to switch to manually computing the mean and variance if I push to Keras. It doesnt have the equivalent K.moments() op, which we added to Contrib quite some time ago.
There are performance repercussions in this implementation. While it follows the code from the paper quite closely (with some modifications for how Keras handles different data formats etc), K.batch_normalization() and the internal code of batchnorm checks for and uses fused batchnorm ops when available.
Those won't be available for either contrib or Keras proper until the appropriate ops are added to TF itself (and all this is under the hope that GroupNormalization gains enough favor in the community to warrant an additional fused op in TF).
Edit: I forgot to say, I think we can keep this version here, since it uses the contrib moments op anyway.

Can't builds won't pass, either here or in Keras proper. I'm investigating the reason currently but have no plausible source of error yet.

Thanks, @titu1994. I started merging this into 0.2.0 of Keras-ResNet:
https://github.com/broadinstitute/keras-resnet/pull/40
Would you mind if I added you to the AUTHORS.rst?

Sure, I don't mind, but know that grouped convolutions as I have implemented here do not work on CNTK backend. TF and Theano show no problems, and I've been using this frequently for the past few weeks.
I would say, unless you are fine tuning a task, or have small batch sizes, this is inferior to batch norm + 128 batch size. That's just my observation on very select tests though so I won't comment on overall benefit.

@titu1994 I’ll work on fixing the CNTK issues. I’ve actually seen worse results with typical batch sizes, so it won’t replace BatchNormalization as the default normalization layer. However, the Keras-ResNet is used by a few packages for image segmentation and object detection where batch sizes are usually one or two and group normalization performs dramatic performance benefits.

Yeah I've noticed similar results. It's surprisingly powerful for small batch sizes, even beating instance norm with just groups of 2. It's weird that it would have such a significant difference.
I'm any case, I've been using it as a drop in replacement for instance norm in a few style transfer tasks, and it performs on par with actual instance norm at 16 batch size so it's useful enough for me.

Btw, if you do get a fix for the CNTK issues, please send a fix here as well.

@titu1994 Of course. 👍 Ideally, it won’t stay in Keras-ResNet for too long.

@ahundt I may have a potential solution to the CNTK issue, which is to use the private backend function _moments from the CNTK backend directly and wrap it in KC.moments().
Also, added the comments about how altering groups will affect the behavior of the normalization.

@ahundt I dunno if its just my setup or it's an actual issue, but Keras 2.2.0 combined with TF 1.8 always segfaults when I use K.clear_session() and follow up with building another model and then calling fit.
Perhaps that is another source of test errors. I feel we should create a PR that mimics Keras and get these tests passing at least.

@ahundt This is ready for merging as well, and passes CNTK tests now.
Are there any other corrections I should make, or can we merge this?

repo: keras-team/keras-contrib


text:
[WIP] Implements map_coordinates op in keras-core

Adds the map_coordinates op as described in https://github.com/keras-team/keras-core/pull/477 .

Considering the PR is abandoned -- will set this as an "request for contributions" item.

authors: fchollet mihirparadkar

repo: keras-team/keras-core


text:
ValueError: Weights for model sequential have not yet been created. Weights are created when the Model is first called on inputs or build() is called with an input_shape.

How to fix the problem when I try to add Conv2D operations :ValueError: Weights for model sequential have not yet been created. Weights are created when the Model is first called on inputs or build() is called with an input_shape.

You might be getting this error because you haven't provided the input shape, Could you please provide the code where you are getting this error?

Hi @linfengYang ,
If you are using a simple sequential model then you need to pass input_shape to the first layer.Refer a simple code snippet below.
model = keras.Sequential()
model.add(keras.layers.Dense(2, activation="relu"))

model.summary()

#raises ValueError: This model has not yet been built. Build the model first by calling build()or by calling the model on a batch of data.
The below code works fine when added input_shape

model = keras.Sequential()
model.add(keras.layers.Dense(2, activation="relu", input_shape=(4,)))

model.summary()

Alternatively you can also call the model with some input value and model will infer the input_shape from the provided input automatically.
from keras import layers
model = keras.Sequential(
    [
        layers.Dense(2, activation="relu"),
        layers.Dense(3, activation="relu"),
        layers.Dense(4),
    ]
)  # No weights at this stage!

# At this point, you can't do this:
# model.weights

# You also can't do this:
# model.summary()

# Call the model on a test input
x = tf.ones((1, 4))
y = model(x)
print("Number of weights after calling the model:", len(model.weights))
model.summary()

Please refer the attached gist also for verifying the results above.
Thanks!

authors: SuryanarayanaY Warlord-K linfengYang

repo: keras-team/keras-io