text:
example-vite failed to build on Windows

Windows version: Windows 11 23H2 22631.3296
powersync-js: 68738c587f
The same build command (pnpm -F example-vite build) works on macOS.
The error output:
D:\repos\powersync-js> pnpm -F example-vite build

> example-vite@0.0.10 build D:\repos\powersync-js\demos\example-vite
> vite build

vite v5.1.4 building for production...
✓ 3 modules transformed.
x Build failed in 52ms
error during build:
Error: [commonjs--resolver] Failed to resolve entry for package "@journeyapps/powersync-sdk-web". The package may have incorrect main/module/exports specified in its package.json.
    at packageEntryFailure (file:///D:/repos/powersync-js/demos/example-vite/node_modules/vite/dist/node/chunks/dep-jDlpJiMN.js:48038:17)
    at resolvePackageEntry (file:///D:/repos/powersync-js/demos/example-vite/node_modules/vite/dist/node/chunks/dep-jDlpJiMN.js:48035:5)
    at tryNodeResolve (file:///D:/repos/powersync-js/demos/example-vite/node_modules/vite/dist/node/chunks/dep-jDlpJiMN.js:47805:20)
    at Object.resolveId (file:///D:/repos/powersync-js/demos/example-vite/node_modules/vite/dist/node/chunks/dep-jDlpJiMN.js:47568:28)
    at file:///D:/repos/powersync-js/demos/example-vite/node_modules/rollup/dist/es/shared/node-entry.js:18731:40
    at async PluginDriver.hookFirstAndGetPlugin (file:///D:/repos/powersync-js/demos/example-vite/node_modules/rollup/dist/es/shared/node-entry.js:18631:28)
    at async resolveId (file:///D:/repos/powersync-js/demos/example-vite/node_modules/rollup/dist/es/shared/node-entry.js:17298:26)
    at async ModuleLoader.resolveId (file:///D:/repos/powersync-js/demos/example-vite/node_modules/rollup/dist/es/shared/node-entry.js:17712:15)
    at async Object.resolveId (file:///D:/repos/powersync-js/demos/example-vite/node_modules/vite/dist/node/chunks/dep-jDlpJiMN.js:8171:10)
    at async PluginDriver.hookFirstAndGetPlugin (file:///D:/repos/powersync-js/demos/example-vite/node_modules/rollup/dist/es/shared/node-entry.js:18631:28)

Solved by clean and rebuild.

repo: powersync-ja/powersync-js


text:
Update golang/master build

This is an automated PR via build-bot

[APPROVALNOTIFIER] This PR is NOT APPROVED
This pull-request has been approved by: ltccci
To complete the pull request process, please assign  after the PR has been reviewed.
You can assign the PR to them by writing /assign  in a comment when ready.
The full list of commands accepted by this bot can be found here.

Needs approval from an approver in each of these files:

OWNERS

Approvers can indicate their approval by writing /approve in a comment
Approvers can cancel approval by writing /approve cancel in a comment

source: gharchive/pull-request

license_type: permissive

license_source: github-api


text:
Update golang/master build

This is an automated PR via build-bot

[APPROVALNOTIFIER] This PR is NOT APPROVED
This pull-request has been approved by: ltccci
To complete the pull request process, please assign
You can assign the PR to them by writing /assign  in a comment when ready.
The full list of commands accepted by this bot can be found here.

Needs approval from an approver in each of these files:

OWNERS

Approvers can indicate their approval by writing /approve in a comment
Approvers can cancel approval by writing /approve cancel in a comment


text:
Network error when converting rhel qcow2 to ova

When I'm trying to convert the rhel-8.3.qcow2 image to ova I'm facing a network error as shown below.
E0506 06:50:44.356184   20515 root.go:68] failed while preparing the image for rhel distro, err: script /setup.sh failed with exitstatus: 70, stdout: nameserver 9.9.9.9
Registering to: subscription.rhsm.redhat.com:443/subscription
, stderr: Network error, unable to connect to server. Please see /var/log/rhsm/rhsm.log for more information.

I've followed this solution idea, but the general connection seems to be available: https://access.redhat.com/solutions/1323123
At least from the host I can reach the subscription server:
[root@p639-met1 opt]# dig subscription.rhsm.redhat.com +short
209.132.183.108

Here is the full error log:
[root@p639-met1 opt]# pvsadm image qcow2ova  --image-name rhel-83 --image-url rhel-8.3.qcow2 --image-dist rhel --image-size 120 --rhn-user <myuser> --rhn-password <mypassword> --os-password 123456
I0506 06:49:17.351651   20515 validate.go:26] Checking: user
I0506 06:49:17.351699   20515 validate.go:26] Checking: platform
I0506 06:49:17.351706   20515 validate.go:26] Checking: image-name
I0506 06:49:17.351716   20515 validate.go:26] Checking: tools
I0506 06:49:17.351740   20515 tools.go:29] growpart found at /usr/bin/growpart
I0506 06:49:17.351758   20515 tools.go:29] qemu-img found at /usr/bin/qemu-img
I0506 06:49:17.351764   20515 validate.go:26] Checking: diskspace
I0506 06:49:17.351796   20515 diskspace.go:35] free: 836G, need: 170G
I0506 06:49:17.352066   20515 get-image.go:29] Copying rhel-8.3.qcow2 into /tmp/qcow2ova435243422/rhel-8.3.qcow2
I0506 06:49:36.938985   20515 get-image.go:33] Copy Completed!
I0506 06:49:36.939019   20515 qcow2ova.go:161] downloaded/copied the file at: /tmp/qcow2ova435243422/rhel-8.3.qcow2
I0506 06:49:36.939182   20515 qcow2ova.go:189] Converting Qcow2(/tmp/qcow2ova435243422/rhel-8.3.qcow2) image to raw(/tmp/qcow2ova435243422/ova-img-dir/disk.raw) format
I0506 06:49:39.885825   20515 qcow2ova.go:194] Conversion completed
I0506 06:49:39.885864   20515 qcow2ova.go:196] Resizing the image /tmp/qcow2ova435243422/ova-img-dir/disk.raw to 120G
WARNING: Image format was not specified for '/tmp/qcow2ova435243422/ova-img-dir/disk.raw' and probing guessed raw.
         Automatically detecting the format is dangerous for raw images, write operations on block 0 will be restricted.
         Specify the 'raw' format explicitly to remove the restrictions.
Image resized.
I0506 06:50:31.209047   20515 qcow2ova.go:201] Resize completed
I0506 06:50:31.209070   20515 qcow2ova.go:203] Preparing the image
/dev/loop8
CHANGED: partition=2 start=10240 old: size=16384000 end=16394240 new: size=251647967 end=251658207
xfs
meta-data=/dev/loop8p2           isize=512    agcount=4, agsize=512000 blks
         =                       sectsz=512   attr=2, projid32bit=1
         =                       crc=1        finobt=1, sparse=1, rmapbt=0
         =                       reflink=1
data     =                       bsize=4096   blocks=2048000, imaxpct=25
         =                       sunit=0      swidth=0 blks
naming   =version 2              bsize=4096   ascii-ci=0, ftype=1
log      =internal log           bsize=4096   blocks=2560, version=2
         =                       sectsz=512   sunit=0 blks, lazy-count=1
realtime =none                   extsz=4096   blocks=0, rtextents=0
data blocks changed from 2048000 to 31455995
nameserver 9.9.9.9
Registering to: subscription.rhsm.redhat.com:443/subscription
Network error, unable to connect to server. Please see /var/log/rhsm/rhsm.log for more information.
Error: failed while preparing the image for rhel distro, err: script /setup.sh failed with exitstatus: 70, stdout: nameserver 9.9.9.9
Registering to: subscription.rhsm.redhat.com:443/subscription
, stderr: Network error, unable to connect to server. Please see /var/log/rhsm/rhsm.log for more information.

Usage:
  pvsadm image qcow2ova [flags]

Flags:
      --image-name string       Name of the resultant OVA image
      --image-url string        URL or absolute local file path to the <QCOW2>.gz image
      --image-dist string       Image Distribution(supported: rhel, centos, coreos)
      --image-size uint         Size (in GB) of the resultant OVA image (default 120)
      --rhn-user string         RedHat Subscription username. Required when Image distribution is rhel
      --rhn-password string     RedHat Subscription password. Required when Image distribution is rhel
      --os-password string      Root user password, will auto-generate the 12 bits password(applicable only for redhat and cento distro)
  -t, --temp-dir string         Scratch space to use for OVA generation (default "/tmp")
      --prep-template string    Image preparation script template, use --prep-template-default to print the default template(supported distros: rhel and centos)
      --prep-template-default   Prints the default image preparation script template, use --prep-template to set the custom template script(supported distros: rhel and centos)
  -h, --help                    help for qcow2ova

Global Flags:
      --add_dir_header                   If true, adds the file directory to the header of the log messages
      --alsologtostderr                  log to standard error as well as files
  -k, --api-key string                   IBMCLOUD API Key(env name: IBMCLOUD_API_KEY)
      --audit-file string                Audit logs for the tool (default "pvsadm.log")
      --debug                            Enable PowerVS debug option(ATTENTION: dev only option, may print sensitive data from APIs)
      --log_backtrace_at traceLocation   when logging hits line file:N, emit a stack trace (default :0)
      --log_dir string                   If non-empty, write log files in this directory
      --log_file string                  If non-empty, use this log file
      --log_file_max_size uint           Defines the maximum size a log file can grow to. Unit is megabytes. If the value is 0, the maximum file size is unlimited. (default 1800)
      --logtostderr                      log to standard error instead of files (default true)
      --one_output                       If true, only write logs to their native severity level (vs also writing to each lower severity level
      --skip_headers                     If true, avoid header prefixes in the log messages
      --skip_log_headers                 If true, avoid headers when opening log files
      --stderrthreshold severity         logs at or above this threshold go to stderr (default 2)
  -v, --v Level                          number for the log level verbosity
      --vmodule moduleSpec               comma-separated list of pattern=N settings for file-filtered logging

E0506 06:50:44.356184   20515 root.go:68] failed while preparing the image for rhel distro, err: script /setup.sh failed with exitstatus: 70, stdout: nameserver 9.9.9.9
Registering to: subscription.rhsm.redhat.com:443/subscription
, stderr: Network error, unable to connect to server. Please see /var/log/rhsm/rhsm.log for more information.

Any idea what's going wrong here?

@mgiessing  The error has nothing to do with the pvsadm tool, sometimes cdn network might have issues. please retry once , if it fails again please provide the contents of the mentioned files /etc/rhsm/rhsm.conf, /etc/hosts, /etc/resolv.conf.  and provide /var/log/rhsm/rhsm.log log file content as well

source: gharchive/issue

authors: bkhadars mgiessing


text:
Pretrained Weights

Would it be possible for you to share pretrained weights for resnet GN? Transferring directly from the pytorch BN weights gives very bad accuracy.

I don't see why would you expect a different model (with BN to work).
Pretrained weights in tensorflow was mentioned in https://github.com/ppwwyyxx/GroupNorm-reproduce/tree/master/ImageNet-ResNet-TensorFlow

The instructions are now updated to include the command to evaluate the above model on ImageNet.

Why would weights of a different model (with BN) work?

I wasn't expecting them to, but they're the only weights I could find. Especially for smaller models like ResNet18.
Do you happen to have pytorch weights? I can convert the TF weights if necessary, but I was trying to follow your Pytorch code.

There are also caffe2 weights at https://github.com/facebookresearch/Detectron/tree/master/projects/GN#pretrained-models-with-gn, but I don't have pytorch weights at the moment.

A pytorch model was uploaded at https://github.com/ppwwyyxx/GroupNorm-reproduce/releases/tag/v0.1

repo: ppwwyyxx/GroupNorm-reproduce


text:
Fractional scaling on Ubuntu breaks fullscreen

Enabling fractional scaling on Pop OS 20.10 makes it so Osu doesn't fullscreen properly. The window stays at bottom left with rest of screen black. Choosing the resolution matching PC-screen, which is 1440p, in Window-mode makes it render at that resolution but still much smaller, and is far from covering the whole screen. It looks like when I start reaching 5K in resolution, it covers the whole screen. There is also some artifacting with Osu when moving it around in windowed mode, which might be related.
I have two screens set up;
1440p 144hz screen on which I am playing the game. Scaled 100%
4K 60hz secondary screen. Scaled 150%
I think this is an universal issue for everyone running fractional scaling on Ubuntu-based systems. I doubt my logs will come in handy as I think it's easy to reproduce.
Screenshot of fulscreen, and video of Osu in 1440p winodwed mode glitching out and too small are in details. The video is in the zip.
osu!lazer version:
2020.1204.0
Logs:
database.log
network.log
performance.log
runtime.log

Zip with Osu in 1440p winodwed mode glitching out and too small

Sure it's fractional scaling, rather than just any scaling at all?

Yes. The game fullscreens fine when scaling to 200% without fractional scaling.

Would be useful to know what in particular is used to achieve this fractional scaling (plain xrandr? gnome-tweaks?)

It has been built into Ubuntu since April, which makes it even bigger of an issue imo

Thanks for the screenshot, but as an aside, saying that it's "even bigger of an issue" is not really helpful in any capacity,

Just gave some "dramatic context", but yeah, there isn't really much more useful stuff I can provide. It should be pretty easy to reproduce though, so that people more familiar with the Osu lazer code and the underlying works of fractional scaling can have a shot at fixing it.

This is embarrasing; I posted the issue in the wrong repo. I have no idea how I managed to do that. I apologize!!

Um, no, it's in the correct place now, I transferred it here on purpose.

Oh XD I didn't notice. Is there any more context I can give?

I think the information provided here from your side should be enough to work with if you're not willing to explore yourself - just need to set up an environment with the fractional scaling option depicted as above available.

Any update on this. I have the same problem. I discovered it on VLC. My screen stutters when in full screen and when moving the mouse pointer in full screen.

No.
What does VLC have to do with this?
"Screen stuttering" is a separate issue entirely.

No.
What does VLC have to do with this?
"Screen stuttering" is a separate issue entirely.

I am saying that I discovered this issue on VLC. I was watching a movie and the screen started flickering. And when I exited the full screen it happened again.

This is not the issue tracker for VLC. This is the issue tracker for osu!lazer and osu!framework-powered projects. Please do not stray off topic.

repo: ppy/osu-framework

license: MIT


text:
Limit the rate at which seeks are performed at bass' end

Previously seeks triggered by a drag operation could lag behind.

Potentially fixed by https://github.com/ppy/osu-framework/pull/894, but I'd like to get this in sooner rather than later so a local fix works bets for now IMO.


text:
changed method to get dataset to match ioh-guest in rc script

this fixes bug where only vm created first with name "foobar" would be started when bootprop is set to 1 if there is a vm created after with name "foo" as grep foo matches foobar

I need to stop naming my guests foobar.
:sunglasses:

license_source: bigquery


text:
Materiais para o site

Nessa pasta -> https://drive.google.com/drive/u/0/folders/1HQ8xXPLlBfsx5XI6WaRl-3PfbsgzKCie, vocês encontrarão as versões finalizadas das cartilhas produzidas até então. Elas estão em PDF e prontas para irem para o site.
@Dovyski

Precisamos atualizar as cartilhas que estão no site. Vamos colocar elas em cdn.uffs.cc, não no repositório do site (arquivos binários ocupam muito espaço no git). @CleissonVieira, se estiver usando Windows, instalar o WinSCP (se for Linux não precisa fazer nada) e me contatar no Telegram para receber instruções de acesso ao cdn.uffs.cc.

Cartilhas atualizadas e alocadas no cdn.uffs.cc.

repo: practice-uffs/programa


text:
Criação de identidade visual - Masculinidades e Saúde Entre Homens da Fronteira Sul - I

Demandante: Jeferson Araujo do curso de enfermagem da UFFS
Projeto de pesquisa guarda-chuva --> Masculinidades e Saúde Entre Homens da Fronteira Sul - I

DESCRIÇÃO DO PROJETO - Masculinidades e Saúde Entre Homens da Fronteira Sul - I
Este projeto, configurado no formato guarda-chuva, contempla diferentes projetos de pesquisa em pós-graduação (strictu e lato-sensu), bem como de iniciação científica, trabalhos de conclusão de curso e de outras naturezas que serão orientados pelo proponente. Tem por fundamentação como grande área a Enfermagem frente a discussão de cuidados a população masculina. A pesar da temática sobre homens e cuidados de saúde encontrar-se em constante expansão, evidências sobre a região da fronteira sul são escassas. A fornteira sul do pais apresenta uma cultura diversificada que reforça diversas praticas masculinizantes em relação a saúde. As identidades masculinas sofrem alterações com o fluxo de vida, a incorporação de determinados preceitos hegemônicos como a crença de ser forte, invulnerável, detentor de um corpo que não adoece, entre outros, configuram-se como ideologias que ameaçam a promoção de saúde dessa população. Portanto, conhecer as masculinidades vivenciadas pelos homens que residem nesta região de fronteira e analisar as suas relações com a promoção de saúde, certamente é um ponto de partida para a avaliação de saúde e o fortalecimento de programas aos homens como Política Nacional de Atenção Integral à Saúde do Homem e Politica Nacional de Promoção da Saúde. Destacamos a importância dos profissionais de Enfermagem no apreendizado a atenção a saúde do homem e seu contexto, numa perspectiva ampla que relacione agravos de saúde da população residente em zonas de fronteira, as politicas de saúde, suas masculinidades, sua cultura e seus significados. Assim, este projeto gurada-chuva tem por objetivo investigar as manifestações das masculinidades em homens residentes na fronteira sul do Brasil, visando analisar as relações estabelecida por estes no processo saúde e doença. Sua execução ocorrerá durante outo anos e será desenvolvido por múltiplas metodologias. Espera-se que esta pesquisa produza resultados efetivos que corroborem para o crescimento científico dos alunos, profissionais, comunidade em geral e dos membros do Grupo de Pesquisa EDUFES. Que as evidências venham subsidiar conhecimentos as demandas enfrentadas no cuidado a saúde dos homens potencializando a tomada de decisões.
Palavras-chave: Masculinidade; Saúde do Homem; Pesquisa em Enfermagem; Saúde na Fronteira; Educação em Saúde.

O professor tem interesse que o PRACTICE crie a identidade visual para o projeto guarda-chuva, @estelavilasboas já solicitei para o demandante responder o formulário, qualquer dúvida podemos enviar e-mail para tal.

Pasta.

Primeiro esboço concluído pela @TaynaraCh . @VIF-Franca favor verificar para o envio ao solicitante.

Aguardando feedback do cliente.

FEEDBACK DO CLIENTE

"...Gostei bastante da paleta de cores e a ideia adotada para a construção da identidade visual é bem significativa. Contudo, para termos opções de escolha será que teria como no lugar do lenço nós adicionarmos um chimarrão, um homem gaucho ou mesmo outro vetor simbólico da região, pois assim poderíamos ampliar o olhar para outros horizontes que envolvem a temática. O lenço é bem interessante mas ao apresentarmos em forma de laço, o conflitamos com uma identidade visual já nichada/criada pelo INCA e bem conhecida, como vc mesmo comentou, do novembro azul (o qual arremete a prevenção do câncer de próstata). Assim, se tivermos outras opções poderíamos tentar escolher uma que expressasse uma identidade única ao nosso trabalho.
Desde já agradeço a atenção e vós parabenizo pelo trabalho e pela excelente criatividade..."

sendo assim, já podemos voltar a elaborar outras alternativas, certo?

Sim, temos que elaborar mais alternativas para esta identidade. Lembrando que temos limite de propostas a serem enviadas, nesse caso são três.

@TaynaraCh acha que consegue elaborar mais uma proposta com a paleta de cores que o solicitante aprovou? Talvez se basear nas ideias  da #223 (pasta) ?

Estou de intrometida aqui
Gostei muito da terceira opção da pasta da issue #223, a com a silhueta de uma cuia de chimarrão, ficou muito elegante.

@TaynaraCh acha que consegue elaborar mais uma proposta com a paleta de cores que o solicitante aprovou? Talvez se basear nas ideias da #223 (pasta) ?

Consigo sim!

Lembrando que as respostas do solicitante estão na pasta entrada dessa issue.   :)

Adicionei mais dois exemplos na pasta do Drive, https://drive.google.com/drive/folders/15dLiY57tpGYe6eLZoSIbkeNbC3XMfaYx

Adicionei mais três exemplos na pasta

O que eu mais gostei foi o ex2-03, porém acho que pra logo redonda ficar legal, seria necessário completar a silhueta no centro, pois assim cortada passa a impressão de estar incompleta.

O ex3-04 e 2-03 foram os que mais gostei. E concordando com a Alessandra, eu digo que a silhueta cortada me dá um pouco de agonia :sweat_smile:

Corrigi a silhueta e adicionei na pasta o arquivo corrigido

conforme solicitado foram realizadas alterações na segunda versão e uma das opções poderá ser escolhida para ajustes finais. @VIF-Franca tu já podes encaminhar para o solicitante o arquivo.

@mariagelain, os arquivos já foram enviados para o cliente, aguardamos o feedback dele

FEEDBACK DO CLIENTE

Boa noite Vinícius, Obrigado pelo retorno.
Gostei bastante da arte que apresenta o homem gaucho com o chimarrão, acredito que representa bem o trabalho. Escolho então esta para os ajustes finais.
Desde já agradeço pela "praticidade" e auxilio.
Com os melhores cumprimentos,
Jeferson S. Araújo.

feedback do cliente

No momento, não consigo vislumbrar criativamente mudanças. Contudo se a equipe conseguir observar algo que fortaleça a criação da identidade visual, ficaria muito feliz, pois é sempre bom.

@TaynaraCh já pode exportar as versões em diferentes resoluções, juntamente da apresentação explicativa, e colocar na pasta saída.

Tudo finalizado na pasta!

Enviado para o CLIENTE !!!

Pessoal, reabria a issue segundo uma demanda do cliente.

Se possível, teria como você me encaminhar essa marca d'água em um arquivo em formato diferente do PDF pois ao transformar em imagem, pra divulgação em mídias sociais, o mesmo acaba perdendo a qualidade.
Com muitas felicidades pelo trabalho, cumprimento todos os colegas que colaboraram de forma direta e indireta na criação.

@VIF-Franca já está na pasta!

Enviado.

authors: TaynaraCh VIF-Franca alessandrapedrotti estelavilasboas mariagelain


text:
Project with NGraphis 0.4.0 or 0.3.1 does not pass Windows Store certification

Our application using NGraphics and SharpDX does not pass Windows Store certification because, as I understand it, its linked with debug version of the library:
The binary NGraphics.WindowsStore.dll is built in debug mode.
The binary NGraphics.dll  is built in debug mode.

Also the SharpDX library is using some incompatible API:
D2D1GetGradientMeshInteriorPointsFromCoonsPatch in d2d1.dll is not supported for this application type. SharpDX.Direct2D1.dll calls this API.
API D3D11On12CreateDevice in d3d11.dll is not supported for this application type. SharpDX.Direct3D11.dll calls this API.

Tried with NGraphics 0.3.1 and 0.4.0. Any clue how to pass Windows Store certification with NGraphics, please?

Did you build a Windows 8 or Windows 10 (UWP) app?

Windows 8.1 Store App.

Sorry about this, I'll update the scripts to build Release.

authors: Blackhex TomSoderling praeclarum

repo: praeclarum/NGraphics

license: mit


text:
title subtitle wrap + colon

Subtitle now wraps from title, colon only shows then a subtitle is visible
@Mitso review please
Before:

After:

@projectmushroom  :cake:

authors: Mitso projectmushroom

repo: praekelt/molo-babycenter


text:
Trying to pretrain mBART model.

Hi,
I'm trying to pre-train mBART model using the following parameters:
!python pretrain_nmt.py -n 1 -nr 0 -g 1 --use_official_pretrained --fp16 --pretrained_model "facebook/mbart-large-50" --model_path "facebook/mbart-large-50" --tokenizer_name_or_path "facebook/mbart-large-50" --mono_src "/content/yanmtt/cleaned_Sanskrit_text_for_LM.txt" --shard_files --batch_size 16
I'm getting this error.
`Using label smoothing of 0.1
Using gradient clipping norm of 1.0
Using softmax temperature of 1.0
Masking ratio: 0.3
Training for: ['']
Shuffling corpus!
Zero size batch due to an abnormal example. Skipping empty batch.
Zero size batch due to an abnormal example. Skipping empty batch.
Zero size batch due to an abnormal example. Skipping empty batch.
Saving the model
Loading from checkpoint
Traceback (most recent call last):
File "pretrain_nmt.py", line 888, in
run_demo()
File "pretrain_nmt.py", line 885, in run_demo
mp.spawn(model_create_load_run_save, nprocs=args.gpus, args=(args,files,train_files,))         #
File "/usr/local/lib/python3.7/dist-packages/torch/multiprocessing/spawn.py", line 199, in spawn
return start_processes(fn, args, nprocs, join, daemon, start_method='spawn')
File "/usr/local/lib/python3.7/dist-packages/torch/multiprocessing/spawn.py", line 157, in start_processes
while not context.join():
File "/usr/local/lib/python3.7/dist-packages/torch/multiprocessing/spawn.py", line 118, in join
raise Exception(msg)
Exception:
-- Process 0 terminated with the following error:
Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/torch/multiprocessing/spawn.py", line 19, in _wrap
fn(i, *args)
File "/content/yanmtt/pretrain_nmt.py", line 488, in model_create_load_run_save
lprobs, labels, args.label_smoothing, ignore_index=tok.pad_token_id
File "/content/yanmtt/common_utils.py", line 130, in label_smoothed_nll_loss
nll_loss = -lprobs.gather(dim=-1, index=target)
RuntimeError: Size does not match at dimension 1 expected index [1, 13, 1] to be smaller than src [1, 12, 250054] apart from dimension 2`

Hi,
Your command is bound to give errors. You are missing a few things:
--langs hi_IN (since there's no language token for Sanskrit you may have to use the one for Hindi. Since you don't provide a language token the code bugs out as it uses some default language token that mbart tokenizer doesn't recognize and segments it weirdly.)
Don't use fp16 if you use multiple GPUs. Regardless I've had mixed results with fp16 so I'd avoid it.
--batch_size 16 (I think you meant this to be 16 sentences. So you need the flag --batch_size_indicates_lines. By default this means number of tokens in batch.)
Additionally, be careful about learning rates and dropouts etc. Good luck!

Hi,
Thanks for your reply.
I added the arguments.
!python pretrain_nmt.py -n 1 -nr 0 -g 1 --use_official_pretrained --langs hi_IN --batch_size_indicates_lines --pretrained_model "facebook/mbart-large-50" --model_path "facebook/mbart-large-50" --tokenizer_name_or_path "facebook/mbart-large-50" --mono_src "/content/yanmtt/cleaned_Sanskrit_text_for_LM.txt" --shard_files --batch_size 1
You see,I've put the batch size as 1 and even them I'm getting this:
RuntimeError: CUDA out of memory. Tried to allocate 978.00 MiB (GPU 0; 15.90 GiB total capacity; 14.69 GiB already allocated; 337.75 MiB free; 14.76 GiB reserved in total by PyTorch)
GPU:

Hi,
I can't really help with the GPU memory issue. 16 GBs is a tab bit small. The only thing you can try is limit the maximum sequence length via the --hard_truncate_length option. Find out what's the average sequence length in your corpus and then play with that argument. Btw why not try IndicBart which is much about a third of mbarts size and is more suited for Indic languages? Since its compact you will not run into memory issues.

Sure, Thanks.

authors: nikhilbyte prajdabre

repo: prajdabre/yanmtt


text:
JavaScript Game

**  Dear @prateekj7777 ,
I want to request the addition of my JavaScript project to your repository. The project will be designed using Javascript, HTML, and CSS. This game will be like Mario ☺, please assign this issue to me.    **

Describe the project you want to add with tech stack
Project Name: Flying Eagle Game
Project Description:  It is a eagle flight game.In this game you have to save eagle from obstacles.You have to use (up,down,right,left) arrow keys of your keyboard. You will get 5 pts.  for each obstacle to save eagle.
Tech Stack: HTML , CSS & JS.
Expected behavior
Benefits: This application will makes users to have fun and increase their focus and increase their gaming ability.
Screenshot:

Additional:
I have added sound effects in this application.Also more the time you spent, the speed of obstacle increase and it increase the hardness level.

Project you want to add with tech stack in GSSOC' 23
Project Name :- Dragon-Dino Game
Description :- It's dragon-dino game in which dino have to run and jump over the dragon. A user can use up, down, left and right arrow button to run the dino. Whenever a dino jumps over a dragon a score board is automatically increased by 1. If dino comes in contact with dragon then game over. I had added sound feature for better experience.
Tech Stack :- HTML, CSS, JS.
Screenshot :-

Project Link :- https://dragon-dino-game.netlify.app/
Please assign it to me.

can u assign me this isssue

repo: pranjay-poddar/Dev-Geeks


text:
add a new project of digital clock

Describe the project you want to add with tech stack

Expected behavior
It's a project in java to make digital clock using swings.
please assign me this issue under GSSO'c'2023 so that i can work on it.

Screenshots (optional)

Additional context (optional )

Hello there ! Please assign me this task .


text:
Random Jokes Generator

Title and Issue number

Close #2605 <issue_no>

Checklist:

[x] I have mentioned the issue number in my Pull Request.

[x] I have created a helpful and easy to understand README.md

I have completed the project . Please review it and merge if possible.
Thank you

@pranjay-poddar Hey I have completed the project. Can you please review it??

@pranjay-poddar plz look into it..


text:
Frontend Project "Netflix Clone" Improved Styling

Title and Issue number
Frontend Project "Netflix Clone" Improved Styling
Close #3709
Output
Before

After

Checklist:

[x] I have mentioned the issue number in my Pull Request.
[x] I have commented my code, particularly in hard-to-understand areas
[ ] I have created a helpful and easy to understand README.md

@MohitGupta121 Kindly review this pull request.

@MohitGupta121 Kindly review this pull request.

@MohitGupta121

@MohitGupta121 Kindly review this pull request.

@MohitGupta121 @TusharKesarwani Kindly review this pull request. It has been open for 3 days.


text:
Improving documentation

@prasmussen U really need improve this documentation. The CLI is awesome, but i'm having a hard time to figure out things..
I have revoked the access of gdrive in my google account, and i can't figure it out how to connect again.
gdrive --config (dir) isn't working, and i can't find the path for the token in my Ubuntu. Can u help?
Thanks!

You can delete the .gdrive directory in your home directory to reset all state. The path to the config directory is also shown in the description for the --config flag if you write for example gdrive help list.

authors: celicoo prasmussen

repo: prasmussen/gdrive


text:
🛑 ChatGPT Beyond 2021 is down

In e19e7ac, ChatGPT Beyond 2021 (https://chatgpt-beyond-2021.onrender.com/) was down:

HTTP code: 0
Response time: 0 ms

Resolved: ChatGPT Beyond 2021 is back up in 43d0380 after 4 minutes.

repo: prateekralhan/status


text:
🛑 Prateek Ralhan-Resume is down

In c87d296, Prateek Ralhan-Resume (https://prateek-online-resume-render.onrender.com/) was down:

HTTP code: 0
Response time: 0 ms

Resolved: Prateek Ralhan-Resume is back up in 552dfe4 after 32 minutes.


text:
Permutations for N People to Occupy R Seats In A Classroom

Feature ✅
Description

Write a code to find all the possible permutations in which N people can occupy R number of seats in a classroom
N students are looking to find R seats in a classroom. Some of the seats are already occupied and only a few can be accommodated in the classroom. The available seats are assumed as R and N number of people are looking to accommodate within the room.
Hint :
nPr = n! / (n-r)!
Example

Input:
Enter the num of people: 9
Enter the number of seats available: 5
Output:
Num of ways people can be seated: 15120
Checklist:

[x] I have read the project guidelines.
[x] I have checked all the existing projects, before submitting a new project issue.
[x] This issue will be meaningful for the project.
[x] I will contribute more in future

Contributors are supposed to mention their coding language while asking for assignment

I would like to do this in C please.

@pallavivaswani @pratik-choudhari i would like to do this in Java. Can you assign it to me?

@therupdeep You are assigned to two issues which is max limit for this repository.

Hello @pratik-choudhari I would like to do this in Java. Could you assign it to me?

I would like to do this in c++

@pratik-choudhari I can do this in C#

@maityankan10 C/C++ is already assigned to @cianopiano

@pratik-choudhari please Assign this to me. I will do in python.

repo: pratik-choudhari/AlgoCode


text:
Zoom To Center of Image

I am trying to attach a slider component to trigger zoom in & out, I use it to update the scale value however when that happens it zooms into (0,0) (top, left) corner of the image. Is there an easy way to have this automatically zoom to the center of the image?

Hi there, im going out on a limb, as im quite new to the libairy myself, but i believe you should be able to set a center point with setPositionX  and setPositionY
these methods return from the TransformWrapper. Similar to zoomIn, and ZoomOut from the example

Hi @kylegoines, i have met this issue, too. Could you please share your workaround here?
I use setPositionX  and  setPositionY  before setScale but it seems not working and the transform point still (0, 0).
Many thanks.
onClick={() => {
                            setPositionX(500)
                            setPositionY(100)

                            setScale(1)
                        }}

Hi, sorry I'm really struggling here.
I noticed that the zoom buttons already have this functionality. However they use a bunch of stuff that doesn't seem to be exposed.
This is as far as I got, from: https://github.com/prc5/react-zoom-pan-pinch/blob/f78c546414b13c61a68368db74ed9fd9fb2e185c/src/store/zoom/index.ts#L268
            const wrapperBounds = wrapperRef?.current?.getBoundingClientRect();
            resetTransform();
            const x = (wrapperBounds.width / 2) / defaultScale;
            const y = (wrapperBounds.height / 2) / defaultScale;
            setTransform(x, y, defaultScale);

Is anyone able to tell me how I can zoom to the center?
Seems like everyone on github (including the suggestion from @kylegoines) seems to be hardcoding the center point, which doesn't work for my case as I'm being passed a dynamic image:
https://github.com/search?l=JavaScript&q=react-zoom-pan-pinch+center&type=Code

authors: chienweiluo kylegoines petya-v shawnbro


text:
No parallels query for a worker ?

Hi,
from what I understand from the code, if we want to send multiples queries to a worker, the queries will be done one after the other ? I am correct ?  I understand this from the call  to serviceWorkerUnwait, and also the use of self.lock which seems even more restrictive ( One request by service ? )
If so, is there any good reason to justify that ? This seems to me to be counter intuitive, specially when using Node.JS.

worker.rid seems also to be a problem to address.

I think you're right.
The lock is a mess and I'm going to fix it.
Fixed the lock you should be able to send concurrent requests for a single service because dispatch is called every time a message arrives.

the worker.rid is fine
A worker can handle 1 request per time.

the worker.rid is fine
A worker can handle 1 request per time

Why is that ?
The unit test I made seems to prove otherwise

@prdn is there any problem making a worker type that handles multiple requests at a time? Right now we are using both dealer and router sockets so multiple requests can theoretically be handles like http://zguide.zeromq.org/java:mtserver

From what i understand, the lock is already useless, since the call to
 handle(workerId, worker, done);

will call 'done' after the message is send, but not after the response is received.
As say just before, i thinks worker already can handle multiple requests at a time

@maxired It seems that in your test you're using only 1 worker.
If you have 3 workers and 100 requests the 3 workers work in parallel (the lock is messed so I'm going to fix it).
However we have to choose if a worker should or shouldn't handle multiple requests in parallel.
Handling more requests in parallel sure we can better use the I/O time to serve other requests.
Let think about any issue and if no one can't find it let's do it.
@maxired yes I'm going to remove it.

ps in the meantime the lock has gone

If we do handle things in parallel a max queued messages would be good so
you can limit the number of pending queries (I think this is more an edge
case though).
On Thu, Jan 22, 2015 at 10:37 AM, Paolo Ardoino notifications@github.com
wrote:

ps in the meantime the lock has gone
—
Reply to this email directly or view it on GitHub
https://github.com/prdn/pigato/issues/14#issuecomment-71050545.

I think that each worker should tell how many requests it is able to handle at the same time

As long as Infinity is an option that seems fine.
On Thu, Jan 22, 2015 at 10:39 AM, Paolo Ardoino notifications@github.com
wrote:

I think that each worker should tell how many requests it is able to
handle at the same time
—
Reply to this email directly or view it on GitHub
https://github.com/prdn/pigato/issues/14#issuecomment-71051076.

Yes sure.
A worker may vary that value depending on its load

This could be a good feature.
I am not sure of your usage of this library, but in my case, a default option with unlimited seems good to me.

@maxired we currently have limits to prevent memory usage from peaking, but I think we are a in the minority

Ok let's design a little bit this thing.
I probably want to design a possible implementation during the weekend.
There will be a major design of Worker and some changes to the Broker

I'll also try an implementation on top of my current changes.
@prdn , why do you think there is lot of work on the Worker ?

@maxired because the Worker is very small now and adding that feature should change its overall structure. Nothing hard however

I've committed a first version of the parallel workers. Now they can receive up to 100 concurrent requests but I'm going to add an infinite default limit and the possibility to configure that limit.

ps. there are few other changes.
now we use node-uuid instead of shortid and moved to zmq-2.10.0 and readable-stream 1.1.13.

Hi,
Do you have any plan to add/integre units test for this part. The one from https://github.com/prdn/pigato/pull/15 might sill be a good start.

Do you mean this one?
https://github.com/maxired/pigato/commit/d52b597b24bd83759e3b75df96612cd04e9f844a

@prdn , I was more thinking about the one from https://github.com/maxired/pigato/commit/d6c59b1ea230fa423693c5b2e37b799003f9c53c , if you remove the wildcard  part

Ok, I'm going to add it.
Thank you :-)

Test integrated.
I've fixed a couple of bugs and we have also a concurrency configurable parameter that is delivered by worker heartbeats called "concurrency".
You can use -1 for infinite concurrency, default is 100
new PIGATO.worker(location, 'echo', { concurrency: -1 });

Awesome, i would have done it the same way

Thank you.
I added you in the collaborators list.

I'm going to update the npm version tomorrow

authors: bmeck maxired prdn

repo: prdn/pigato


text:
building placment

can still make buildings even if you dont have the resources
it just starts going into minus numbers

Fixed

repo: preatomicprince/sturdy-waffle


text:
Adkernel: documenting global_sun alias

🏷 Type of documentation

[x] update bid adapter

📋 Checklist

[x] Related pull requests in prebid.js or server are linked -> https://github.com/prebid/Prebid.js/pull/11986

this was done and released back in PBJS 9.6. merging doc


text:
Generated Packages are not PEP8 compliant when initially created.

I need to go through and fix the templates so that they produce PEP8 compliant files and actually pass the pylint and pycodestyle checks via pytest and python setup.py pytest.

This is completed.


text:
mistralai/Mistral-7B-Instruct-v0.2 doesn't work with larger than 8192 total tokens

System Info
ghcr.io/predibase/lorax:8ff0bf5
Information

[ ] Docker
[ ] The CLI directly

Tasks

[ ] An officially supported command
[ ] My own modifications

Reproduction
run the v2 mistral model and try to query with max new tokens above 8192
Expected behavior
should work

I am running Mixtral model in 4 shards and got transport error when prompt size is 1731.
sudo docker run --gpus='"device=4,5,6,7"' --shm-size 1g -p 8080:80 -v $PWD/data:/data \
    ghcr.io/predibase/lorax:latest --model-id mistralai/Mixtral-8x7B-Instruct-v0.1 --num-shard 4 --sharded true \
--max-input-length 4095 \
   --max-total-tokens 4096\
   --max-batch-prefill-tokens 65536\
   --waiting-served-ratio 1.2 \
   --max-waiting-tokens 20 \
   --max-stop-sequences 10 \
   --cuda-memory-fraction 0.99

client
from lorax import Client
client = Client("http://127.0.0.1:8080")
prompt="""some string with 1731 tokens"""
print(client.generate(prompt, max_new_tokens=20, stop_sequences=["\n\n"]).generated_text)

Errors
  File "/home/hayley/lorax/.venv/lib/python3.8/site-packages/lorax/client.py", line 192, in generate
    raise parse_error(resp.status_code, payload)
lorax.errors.GenerationError: Request failed during generation: Server error: transport error

2024-03-27T23:59:34.118606Z ERROR lorax_launcher: interceptor.py:41 Method Prefill encountered an error.
Traceback (most recent call last):
  File "/opt/conda/bin/lorax-server", line 8, in <module>
    sys.exit(app())
  File "/opt/conda/lib/python3.10/site-packages/typer/main.py", line 311, in __call__
    return get_command(self)(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/click/core.py", line 1157, in __call__
    return self.main(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/typer/core.py", line 778, in main
    return _main(
  File "/opt/conda/lib/python3.10/site-packages/typer/core.py", line 216, in _main
    rv = self.invoke(ctx)
  File "/opt/conda/lib/python3.10/site-packages/click/core.py", line 1688, in invoke
    return _process_result(sub_ctx.command.invoke(sub_ctx))
  File "/opt/conda/lib/python3.10/site-packages/click/core.py", line 1434, in invoke
    return ctx.invoke(self.callback, **ctx.params)
  File "/opt/conda/lib/python3.10/site-packages/click/core.py", line 783, in invoke
    return __callback(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/typer/main.py", line 683, in wrapper
    return callback(**use_params)  # type: ignore
  File "/opt/conda/lib/python3.10/site-packages/lorax_server/cli.py", line 89, in serve
    server.serve(
  File "/opt/conda/lib/python3.10/site-packages/lorax_server/server.py", line 324, in serve
    asyncio.run(
  File "/opt/conda/lib/python3.10/asyncio/runners.py", line 44, in run
    return loop.run_until_complete(main)
  File "/opt/conda/lib/python3.10/asyncio/base_events.py", line 636, in run_until_complete
    self.run_forever()
  File "/opt/conda/lib/python3.10/asyncio/base_events.py", line 603, in run_forever
    self._run_once()
  File "/opt/conda/lib/python3.10/asyncio/base_events.py", line 1909, in _run_once
    handle._run()
  File "/opt/conda/lib/python3.10/asyncio/events.py", line 80, in _run
    self._context.run(self._callback, *self._args)
  File "/opt/conda/lib/python3.10/site-packages/grpc_interceptor/server.py", line 165, in invoke_intercept_method
    return await self.intercept(
> File "/opt/conda/lib/python3.10/site-packages/lorax_server/interceptor.py", line 38, in intercept
    return await response
  File "/opt/conda/lib/python3.10/site-packages/opentelemetry/instrumentation/grpc/_aio_server.py", line 82, in _unary_interceptor
    raise error
  File "/opt/conda/lib/python3.10/site-packages/opentelemetry/instrumentation/grpc/_aio_server.py", line 73, in _unary_interceptor
    return await behavior(request_or_iterator, context)
  File "/opt/conda/lib/python3.10/site-packages/lorax_server/server.py", line 96, in Prefill
    generations, next_batch = self.model.generate_token(batch)
  File "/opt/conda/lib/python3.10/contextlib.py", line 79, in inner
    return func(*args, **kwds)
  File "/opt/conda/lib/python3.10/site-packages/lorax_server/models/flash_causal_lm.py", line 927, in generate_token
    raise e
  File "/opt/conda/lib/python3.10/site-packages/lorax_server/models/flash_causal_lm.py", line 924, in generate_token
    out = self.forward(batch, adapter_data)
  File "/opt/conda/lib/python3.10/site-packages/lorax_server/models/flash_mixtral.py", line 430, in forward
    logits = model.forward(
  File "/opt/conda/lib/python3.10/site-packages/lorax_server/models/custom_modeling/flash_mixtral_modeling.py", line 979, in forward
    hidden_states = self.model(
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
    return forward_call(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/lorax_server/models/custom_modeling/flash_mixtral_modeling.py", line 922, in forward
    hidden_states, residual = layer(
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
    return forward_call(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/lorax_server/models/custom_modeling/flash_mixtral_modeling.py", line 868, in forward
    moe_output = self.moe(normed_attn_res_output)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
    return forward_call(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/lorax_server/models/custom_modeling/flash_mixtral_modeling.py", line 718, in forward
    return self.sparse_forward(x)
  File "/opt/conda/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/lorax_server/models/custom_modeling/flash_mixtral_modeling.py", line 616, in sparse_forward
    x = ops.padded_gather(x, indices, bin_ids, bins, padded_bins,
  File "/opt/conda/lib/python3.10/site-packages/torch/autograd/function.py", line 553, in apply
    return super().apply(*args, **kwargs)  # type: ignore[misc]
  File "/opt/conda/lib/python3.10/site-packages/stk/backend/autocast.py", line 28, in decorate_fwd
    return fwd(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/megablocks/ops/padded_gather.py", line 14, in forward
    return kernels.padded_gather(
  File "/opt/conda/lib/python3.10/site-packages/megablocks/backend/kernels.py", line 118, in padded_gather
    output_rows = padded_bins[-1].cpu().item()
RuntimeError: CUDA error: an illegal memory access was encountered
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

2024-03-27T23:59:34.119496Z ERROR batch{batch_size=1}:prefill:prefill{id=0 size=1}:prefill{id=0 size=1}: lorax_client: router/client/src/lib.rs:34: Server error: Unexpected <class 'RuntimeError'>: CUDA error: an illegal memory access was encountered
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

2024-03-27T23:59:34.629248Z ERROR batch{batch_size=1}:prefill:prefill{id=0 size=1}:prefill{id=0 size=1}: lorax_client: router/client/src/lib.rs:34: Server error: transport error
2024-03-27T23:59:34.939148Z ERROR shard-manager: lorax_launcher: Shard complete standard error output:

[W Utils.hpp:133] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function getCvarInt)
Warmup to max_total_tokens: 100%|██████████| 1/1 [00:11<00:00, 11.96s/it]
terminate called after throwing an instance of 'c10::Error'
  what():  CUDA error: an illegal memory access was encountered
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

Exception raised from c10_cuda_check_implementation at ../c10/cuda/CUDAException.cpp:44 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7fc433581d87 in /opt/conda/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::string const&) + 0x64 (0x7fc43353275f in /opt/conda/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #2: c10::cuda::c10_cuda_check_implementation(int, char const*, char const*, int, bool) + 0x118 (0x7fc433cfe8a8 in /opt/conda/lib/python3.10/site-packages/torch/lib/libc10_cuda.so)
frame #3: <unknown function> + 0x1d40e (0x7fc433cc940e in /opt/conda/lib/python3.10/site-packages/torch/lib/libc10_cuda.so)
frame #4: <unknown function> + 0x1f744 (0x7fc433ccb744 in /opt/conda/lib/python3.10/site-packages/torch/lib/libc10_cuda.so)
frame #5: <unknown function> + 0x1fb6d (0x7fc433ccbb6d in /opt/conda/lib/python3.10/site-packages/torch/lib/libc10_cuda.so)
frame #6: <unknown function> + 0x540210 (0x7fc4324f7210 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_python.so)
frame #7: <unknown function> + 0x649bf (0x7fc4335669bf in /opt/conda/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #8: c10::TensorImpl::~TensorImpl() + 0x21b (0x7fc43355fc8b in /opt/conda/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #9: c10::TensorImpl::~TensorImpl() + 0x9 (0x7fc43355fe39 in /opt/conda/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #10: <unknown function> + 0x802b98 (0x7fc4327b9b98 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_python.so)
frame #11: THPVariable_subclass_dealloc(_object*) + 0x2f6 (0x7fc4327b9f16 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_python.so)
frame #12: <unknown function> + 0x13d5a7 (0x55923e51c5a7 in /opt/conda/bin/python3.10)
frame #13: <unknown function> + 0x14db76 (0x55923e52cb76 in /opt/conda/bin/python3.10)
frame #14: <unknown function> + 0x14dbd3 (0x55923e52cbd3 in /opt/conda/bin/python3.10)
frame #15: <unknown function> + 0x14dbd3 (0x55923e52cbd3 in /opt/conda/bin/python3.10)
frame #16: <unknown function> + 0x14dbd3 (0x55923e52cbd3 in /opt/conda/bin/python3.10)
frame #17: <unknown function> + 0x14dbd3 (0x55923e52cbd3 in /opt/conda/bin/python3.10)
frame #18: <unknown function> + 0x14dbd3 (0x55923e52cbd3 in /opt/conda/bin/python3.10)
frame #19: <unknown function> + 0x14dbd3 (0x55923e52cbd3 in /opt/conda/bin/python3.10)
frame #20: <unknown function> + 0x14dbd3 (0x55923e52cbd3 in /opt/conda/bin/python3.10)
frame #21: <unknown function> + 0x14dbd3 (0x55923e52cbd3 in /opt/conda/bin/python3.10)
frame #22: <unknown function> + 0x14dbd3 (0x55923e52cbd3 in /opt/conda/bin/python3.10)
frame #23: <unknown function> + 0x14dbd3 (0x55923e52cbd3 in /opt/conda/bin/python3.10)
frame #24: <unknown function> + 0x14dbd3 (0x55923e52cbd3 in /opt/conda/bin/python3.10)
frame #25: <unknown function> + 0x14dbd3 (0x55923e52cbd3 in /opt/conda/bin/python3.10)
frame #26: <unknown function> + 0x14dbd3 (0x55923e52cbd3 in /opt/conda/bin/python3.10)
frame #27: <unknown function> + 0x14dbd3 (0x55923e52cbd3 in /opt/conda/bin/python3.10)
frame #28: <unknown function> + 0x15262b (0x55923e53162b in /opt/conda/bin/python3.10)
frame #29: <unknown function> + 0x1525e7 (0x55923e5315e7 in /opt/conda/bin/python3.10)
frame #30: <unknown function> + 0x563095 (0x7fc21caff095 in /opt/conda/lib/python3.10/site-packages/grpc/_cython/cygrpc.cpython-310-x86_64-linux-gnu.so)
frame #31: <unknown function> + 0x56b815 (0x7fc21cb07815 in /opt/conda/lib/python3.10/site-packages/grpc/_cython/cygrpc.cpython-310-x86_64-linux-gnu.so)
frame #32: <unknown function> + 0x60ae0f (0x7fc21cba6e0f in /opt/conda/lib/python3.10/site-packages/grpc/_cython/cygrpc.cpython-310-x86_64-linux-gnu.so)
frame #33: <unknown function> + 0x56a20b (0x7fc21cb0620b in /opt/conda/lib/python3.10/site-packages/grpc/_cython/cygrpc.cpython-310-x86_64-linux-gnu.so)
frame #34: <unknown function> + 0x5cbc29 (0x7fc21cb67c29 in /opt/conda/lib/python3.10/site-packages/grpc/_cython/cygrpc.cpython-310-x86_64-linux-gnu.so)
frame #35: <unknown function> + 0x14f3bd (0x55923e52e3bd in /opt/conda/bin/python3.10)
frame #36: PyObject_VectorcallMethod + 0x85 (0x55923e53dc85 in /opt/conda/bin/python3.10)
frame #37: <unknown function> + 0xae1eb (0x55923e48d1eb in /opt/conda/bin/python3.10)
frame #38: <unknown function> + 0x7bf6 (0x7fc434080bf6 in /opt/conda/lib/python3.10/lib-dynload/_asyncio.cpython-310-x86_64-linux-gnu.so)
frame #39: <unknown function> + 0x143d2a (0x55923e522d2a in /opt/conda/bin/python3.10)
frame #40: <unknown function> + 0x25f22c (0x55923e63e22c in /opt/conda/bin/python3.10)
frame #41: <unknown function> + 0xfda7b (0x55923e4dca7b in /opt/conda/bin/python3.10)
frame #42: <unknown function> + 0x13c1b3 (0x55923e51b1b3 in /opt/conda/bin/python3.10)
frame #43: _PyEval_EvalFrameDefault + 0x5d5d (0x55923e51916d in /opt/conda/bin/python3.10)
frame #44: _PyFunction_Vectorcall + 0x6c (0x55923e5238cc in /opt/conda/bin/python3.10)
frame #45: _PyEval_EvalFrameDefault + 0x72c (0x55923e513b3c in /opt/conda/bin/python3.10)
frame #46: _PyFunction_Vectorcall + 0x6c (0x55923e5238cc in /opt/conda/bin/python3.10)
frame #47: _PyEval_EvalFrameDefault + 0x72c (0x55923e513b3c in /opt/conda/bin/python3.10)
frame #48: _PyFunction_Vectorcall + 0x6c (0x55923e5238cc in /opt/conda/bin/python3.10)
frame #49: _PyEval_EvalFrameDefault + 0x72c (0x55923e513b3c in /opt/conda/bin/python3.10)
frame #50: _PyFunction_Vectorcall + 0x6c (0x55923e5238cc in /opt/conda/bin/python3.10)
frame #51: _PyEval_EvalFrameDefault + 0x72c (0x55923e513b3c in /opt/conda/bin/python3.10)
frame #52: _PyFunction_Vectorcall + 0x6c (0x55923e5238cc in /opt/conda/bin/python3.10)
frame #53: _PyEval_EvalFrameDefault + 0x4c12 (0x55923e518022 in /opt/conda/bin/python3.10)
frame #54: _PyFunction_Vectorcall + 0x6c (0x55923e5238cc in /opt/conda/bin/python3.10)
frame #55: _PyEval_EvalFrameDefault + 0x4c12 (0x55923e518022 in /opt/conda/bin/python3.10)
frame #56: _PyFunction_Vectorcall + 0x6c (0x55923e5238cc in /opt/conda/bin/python3.10)
frame #57: PyObject_Call + 0xbc (0x55923e52fd9c in /opt/conda/bin/python3.10)
frame #58: _PyEval_EvalFrameDefault + 0x2d84 (0x55923e516194 in /opt/conda/bin/python3.10)
frame #59: _PyFunction_Vectorcall + 0x6c (0x55923e5238cc in /opt/conda/bin/python3.10)
frame #60: PyObject_Call + 0xbc (0x55923e52fd9c in /opt/conda/bin/python3.10)
frame #61: _PyEval_EvalFrameDefault + 0x2d84 (0x55923e516194 in /opt/conda/bin/python3.10)
frame #62: <unknown function> + 0x150402 (0x55923e52f402 in /opt/conda/bin/python3.10)
frame #63: PyObject_Call + 0xbc (0x55923e52fd9c in /opt/conda/bin/python3.10)
 rank=3
2024-03-27T23:59:34.939198Z ERROR shard-manager: lorax_launcher: Shard process was signaled to shutdown with signal 6 rank=3
2024-03-27T23:59:35.003043Z ERROR lorax_launcher: Shard 3 crashed
2024-03-27T23:59:35.003070Z  INFO lorax_launcher: Terminating webserver
2024-03-27T23:59:35.003088Z  INFO lorax_launcher: Waiting for webserver to gracefully shutdown
2024-03-27T23:59:35.003172Z  INFO lorax_router::server: router/src/server.rs:1187: signal received, starting graceful shutdown
2024-03-27T23:59:35.100045Z ERROR shard-manager: lorax_launcher: Shard complete standard error output:

[W Utils.hpp:133] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function getCvarInt)
Warmup to max_total_tokens: 100%|██████████| 1/1 [00:11<00:00, 11.94s/it]
[rank2]:[E ProcessGroupNCCL.cpp:1182] [Rank 2] NCCL watchdog thread terminated with exception: CUDA error: an illegal memory access was encountered
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

Exception raised from c10_cuda_check_implementation at ../c10/cuda/CUDAException.cpp:44 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7fc387781d87 in /opt/conda/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::string const&) + 0x64 (0x7fc38773275f in /opt/conda/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #2: c10::cuda::c10_cuda_check_implementation(int, char const*, char const*, int, bool) + 0x118 (0x7fc387fb28a8 in /opt/conda/lib/python3.10/site-packages/torch/lib/libc10_cuda.so)
frame #3: c10d::ProcessGroupNCCL::WorkNCCL::finishedGPUExecutionInternal() const + 0x6c (0x7fc33d7fe3ac in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
frame #4: c10d::ProcessGroupNCCL::WorkNCCL::isCompleted() + 0x58 (0x7fc33d8024c8 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
frame #5: c10d::ProcessGroupNCCL::workCleanupLoop() + 0x15a (0x7fc33d805bfa in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
frame #6: c10d::ProcessGroupNCCL::ncclCommWatchdog() + 0x119 (0x7fc33d806839 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
frame #7: <unknown function> + 0xd3e95 (0x7fc387af0e95 in /opt/conda/bin/../lib/libstdc++.so.6)
frame #8: <unknown function> + 0x94ac3 (0x7fc389319ac3 in /usr/lib/x86_64-linux-gnu/libc.so.6)
frame #9: clone + 0x44 (0x7fc3893aaa04 in /usr/lib/x86_64-linux-gnu/libc.so.6)

terminate called after throwing an instance of 'c10::DistBackendError'
  what():  [Rank 2] NCCL watchdog thread terminated with exception: CUDA error: an illegal memory access was encountered
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

Exception raised from c10_cuda_check_implementation at ../c10/cuda/CUDAException.cpp:44 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7fc387781d87 in /opt/conda/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::string const&) + 0x64 (0x7fc38773275f in /opt/conda/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #2: c10::cuda::c10_cuda_check_implementation(int, char const*, char const*, int, bool) + 0x118 (0x7fc387fb28a8 in /opt/conda/lib/python3.10/site-packages/torch/lib/libc10_cuda.so)
frame #3: c10d::ProcessGroupNCCL::WorkNCCL::finishedGPUExecutionInternal() const + 0x6c (0x7fc33d7fe3ac in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
frame #4: c10d::ProcessGroupNCCL::WorkNCCL::isCompleted() + 0x58 (0x7fc33d8024c8 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
frame #5: c10d::ProcessGroupNCCL::workCleanupLoop() + 0x15a (0x7fc33d805bfa in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
frame #6: c10d::ProcessGroupNCCL::ncclCommWatchdog() + 0x119 (0x7fc33d806839 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
frame #7: <unknown function> + 0xd3e95 (0x7fc387af0e95 in /opt/conda/bin/../lib/libstdc++.so.6)
frame #8: <unknown function> + 0x94ac3 (0x7fc389319ac3 in /usr/lib/x86_64-linux-gnu/libc.so.6)
frame #9: clone + 0x44 (0x7fc3893aaa04 in /usr/lib/x86_64-linux-gnu/libc.so.6)

Exception raised from ncclCommWatchdog at ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1186 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7fc387781d87 in /opt/conda/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #1: <unknown function> + 0xdf6b11 (0x7fc33d55cb11 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
frame #2: <unknown function> + 0xd3e95 (0x7fc387af0e95 in /opt/conda/bin/../lib/libstdc++.so.6)
frame #3: <unknown function> + 0x94ac3 (0x7fc389319ac3 in /usr/lib/x86_64-linux-gnu/libc.so.6)
frame #4: clone + 0x44 (0x7fc3893aaa04 in /usr/lib/x86_64-linux-gnu/libc.so.6)
 rank=2
2024-03-27T23:59:35.100095Z ERROR shard-manager: lorax_launcher: Shard process was signaled to shutdown with signal 6 rank=2
2024-03-27T23:59:35.322172Z ERROR batch{batch_size=1}:prefill:prefill{id=0 size=1}:prefill{id=0 size=1}: lorax_client: router/client/src/lib.rs:34: Server error: transport error
2024-03-27T23:59:35.343506Z ERROR batch{batch_size=1}:prefill:prefill{id=0 size=1}:prefill{id=0 size=1}: lorax_client: router/client/src/lib.rs:34: Server error: transport error
2024-03-27T23:59:35.343683Z ERROR batch{batch_size=1}:prefill:clear_cache{batch_id=Some(0)}:clear_cache{batch_id=Some(0)}: lorax_client: router/client/src/lib.rs:34: Server error: error trying to connect: Connection refused (os error 111)
2024-03-27T23:59:35.343698Z ERROR batch{batch_size=1}:prefill:clear_cache{batch_id=Some(0)}:clear_cache{batch_id=Some(0)}: lorax_client: router/client/src/lib.rs:34: Server error: error trying to connect: Connection refused (os error 111)
2024-03-27T23:59:35.343707Z ERROR batch{batch_size=1}:prefill:clear_cache{batch_id=Some(0)}:clear_cache{batch_id=Some(0)}: lorax_client: router/client/src/lib.rs:34: Server error: error trying to connect: Connection refused (os error 111)
2024-03-27T23:59:35.343720Z ERROR batch{batch_size=1}:prefill:clear_cache{batch_id=Some(0)}:clear_cache{batch_id=Some(0)}: lorax_client: router/client/src/lib.rs:34: Server error: error trying to connect: Connection refused (os error 111)

@magdyksaleh need more details to repro the issue. I ran a test the the following params:
--max-input-length 32767 --max-total-tokens 32768 --max-batch-prefill-tokens 60000

And had no issues prompting the model with various values of max_new_tokens (including leavng it empty).

Okay, seems the issue here is specific to use of --compile with long contexts. Will take a look.

Hey @hayleyhu, the transport error is interesting, I have not seen that one before. Would you mind creating a separate issue to track that?

authors: hayleyhu magdyksaleh tgaddair

repo: predibase/lorax


text:
lock-file not up-to-date with the project with Python package from Github.

Checks

[x] I have checked that this issue has not already been reported.

[x] I have confirmed this bug exists on the latest version of pixi, using pixi --version.

Reproducible example
Hi,
when installing from Github, I cannot lock my environment.
[project]
channels = ["conda-forge"]
name = "repro"
platforms = ["osx-arm64", "linux-64"]
version = "0.1.0"

[tasks]

[dependencies]
python = "*"

[pypi-dependencies]
inspect-ai = { git = "https://github.com/tobiasraabe/inspect_ai", rev = "enable-structured-output"}

$ pixi --version
pixi 0.39.3

Running
$ pixi install
$ pixi run --locked python

always leads to an lock-file not up-to-date with the project.
pixi.lock
Issue description

Expected behavior

Thanks for the issue @tobiasraabe.
I can reproduce the problem on my machine.

authors: Hofer-Julian tobiasraabe

repo: prefix-dev/pixi


text:
Websocket example should be updated

Hey @preichenberger,
you have to send the subscribe message each < 5 seconds to avoid a disconnection to the websocket.
To begin receiving feed messages, you must first send a subscribe message to the server indicating which channels and products to receive. This message is mandatory — you will be disconnected if no subscribe has been received within 5 seconds.

I think we should update the example https://github.com/preichenberger/go-gdax#websockets

I believe you have to send it once within 5 seconds of connecting, not every 5 seconds.

Yea but not just once after the dial. You have to send it regularly each < 5 seconds. I test this since a few days and only this way works for long time running processes. If u don't do this u will receive exactly 60 times per minute (1 error message per minute) the message websocket: close 1006 (abnormal closure): unexpected EOF. I also tried ping/pong messages and so on..

Does adding the heartbeat channel help?

I was going to suggest the same. I had same problem before, and after subscribing to heartbeat channel the problem is gone.

print out the time field in the message you are receiving and check that the GDAX server time is not of sync.
also, you need some logic to reconnect when you don't see a hearbeat within a second or so.
im my experience, they don't arrive within a second. you might need some error bound, not sure.

@loeffel-io any update?

authors: loeffel-io mbana preichenberger zhengyangfeng00

repo: preichenberger/go-gdax


text:
niLang/niCurl: Fetch request override from JavaScript code

Description, Motivation and Context
The web client needs the option to implement a JavaScript function that overrides the call of niCurl fetch function. It can basically intercept the call and override it with anything it wants. One use case is to connect to a Crypto wallet. This function can handle call the wallet API and only return when all the transactions have been finalized. From the point of view of the niCURL client this all happens transparently.
To have access to all the emscripten environment this function can be implemented in the post_js_0.js file. The function module path is: Module.niCURL.handleFetchOverride
Example:
handleFetchOverride: function () {
  console.log("Module.niCURL: handleFetchOverride");
  return `
      {
        "url": "http://example.com",
        "headers": {
          "Content-Type": "application/json",
          "Accept": "application/json"
        },
        "payload": {
          "status": "OK",
          "data": "this will be send back to client in cml format"
        }
      }`
}

This function would just generate a json reply and return it to the client.
See the file post_js_0.js of this PR for more details.

Link to issue: https://github.com/reddoordigital/reddoor/issues/575

How Has This Been Tested?
For Linux or MacOS:
cd niLang/sources/niCurl
. hat emscripten
./ham all

How to test:
cd $WORK
simple-http-server regular .

Now open a web browser and go to this URL:
http://localhost:8123/niLang/bin/web-js/Test_niCURL_ra.html?niCURL.TraceFetch=1```

For Windows. There is not emscripten, so you can test only the standard niCURL:
How to Build
cd niLang/sources/niCURL
. hat
./ham Run_Test_niCURL

Types of changes

[ ] 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 change)
[ ] Demos or Packages (adds examples or modifies a release package)
[ ] Documentation (code docs, comments, or changes to the doc systems)
[ ] Testing/Build (test coverage or the test/build subsystems themselves)

In the handleFetchOverride() javascript function, we need to optionally override some API endpoints while skipping the override for other endpoints. In order to achieve this, I think handleFetchOverride() needs to receive the URL being fetched as a parameter so we can add this custom override behavior only when fetching specific URLs.

authors: JavierFerrerGomez jamesrdd

repo: prenaux/niLang


text:
Invite-link generation is faulty

nothing relevant in the logs (neither creation of them nor errors)

So what exactly is faulty then?

:man_shrugging: don't know yet

maybe nothing

authors: BjoernPetersen torbencarstens

repo: preparingforexams/hhh-diff-bot


text:
🛑 Beta is down

In 72afcb7, Beta (https://beta.presquelabs.com) was down:

HTTP code: 400
Response time: 685 ms

Resolved: Beta is back up in 22018f7.

repo: presquelabs/upptime


text:
Refactor TestFileBasedSystemAccessControl to use Java objects instead of files

@kokosing @findepi
Opening this issue to track the below refactoring suggestion.
https://github.com/prestosql/presto/pull/3766#discussion_r427119728

kokosing @findepi
Please assign this issue to me.

I think we will still need stored files, so we can verify that we are not breaking backwards compatibility.

I think we will still need some stored files tests, so we can verify that we are not breaking backwards compatibility.

That's correct. Especially we should test what is written in docs.

we should test what is written in docs.

That's actually pretty easy. We do this for resource groups' docs.

we should test what is written in docs.

That's actually pretty easy. We do this for resource groups' docs.

@findepi - Can you point me to the code location

@haldes see io.prestosql.plugin.resourcegroups.TestFileResourceGroupConfigurationManager#testDocsExample
and io.prestosql.plugin.base.security.TestFileBasedSystemAccessControl#testDocsExample
for examples

authors: dain findepi haldes kokosing

repo: prestosql/presto


text:
Pushing down reducing projections in a more generic manner

Several optimizations focus on achieving this general goal for specific node patterns:

Let’s say there is a planNode A, with a list of outputSymbols L1.  A.getSources() returns a single-element list B. Say B’s list of outputSymbols is L2. Optimizers identify that there is “reduction in datatypes” going from L2 to L1. For example, losing unreferenced columns, losing unused fields from nested structures etc.
Then the “reduction” is pushed down as much as possible in order to get rid of tossing around unnecessary data early.

Rather than implementing separate rules in an ad hoc manner for every such optimization, it may be worth considering the possibility of implementing something generic that looks for such reductions and pushes them down.
The reductions that I can think of are:

child’s output symbol not used by the parent (implemented in PruneUnreferencedOutputs)
All implementation of ProjectOffPushDownRule (Removing symbols from the child’s output columns)
Subscript pushdown efforts: https://github.com/prestodb/presto/pull/12534/
Efforts in #187 to push down dereferences
Possible unnest optimizations to extract only required fields and subfields from a deeply nested structure (maps, arrays) while unnesting, subscripting, dereferencing
substr, regexp_extract, slice (of an array), functions that return a boolean (e.g. like, contains), most functions that return a numeric type (e.g. length of a string), etc. (We need to be a bit careful here, since there can be a bigger cputime price to pay to execute cpu intensive udfs)

As described above, many of the current optimizations and possible future optimizations can be thought of as specific implementations of the general idea of “reduction” optimization.
It can be worth considering the idea of implementing a generic mechanism that can be extended to detect such “reductions” and then push them further down. That way the implementation of specific projection pushdowns can be more systematic. @martint Do you have any thoughts on this?

I think the biggest challenge is how to pull this off without ending up with a monolithic optimization that needs to understand every possible node type. In the current design of the optimizer (Rules + PlanNodes), the decoupling of the hierarchy of nodes from how optimizations are structured makes it possible to (in the future) support an extensible IR and optimization rules supplied by connectors.
Also, as we continue evolving the optimizer to support a Memo that 1) can represent multiple simultaneous plans and 2) can reason about cost intrinsically, there is a benefit in optimization rules being more granular. The optimizer can selectively apply some but not others depending on whether the transformations are productive.
That's not to say we can't come up with "utility" functions/classes/libraries that make it easier to implement the optimizations you described above.

I see. I understand the point of keeping things granular for selective application of transformation. In that case, I believe we could somehow keep track of reducing transformations (using utility functions) while creating the plan tree, and then implement a common Rule<? extends PlanNode> with different captures.


text:
Alert component

Per https://app.zeplin.io/project/596d11bc29e9db6303116850/screen/59ce7dfc54b85ec584d3df7e
Could be composed of the Flex, Text, and Icon components

@jxnblk, how do I get invited to that Zeplin project?

@Emuentes added!

@chrisdroukas Thanks, brotha.

Now called the Banner and in PR #111

authors: Emuentes chrisdroukas jxnblk

repo: pricelinelabs/design-system


text:
OmniFaces CombinedResourceHandler PF Convenience Classes

We heavily use the Omnifaces CombinedResourceHandler to combine our CSS and JS to just 1 script each in our PrimeFaces apps.
See: https://showcase.omnifaces.org/resourcehandlers/CombinedResourceHandler
However PrimeFaces hard codes some resources in the HeadRenderer which leave it outside the scope of being combined like the theme.css, beanvalidation.js etc etc.
Of course @BalusC has an excellent solution provided on his WIKI here: https://github.com/omnifaces/omnifaces/wiki/Combine-hardcoded-PrimeFaces-resources-using-CombinedResourceHandler
So rather than make people copy and paste and change with every PF version I thought I would include the classes here for PrimeFaces Extensions starting with 10.0.
faces-config.xml
<application>
    <system-event-listener>
        <system-event-listener-class>org.primefaces.extensions.application.PrimeFacesScriptProcessor</system-event-listener-class>
        <system-event-class>javax.faces.event.PostAddToViewEvent</system-event-class>
        <source-class>javax.faces.component.UIViewRoot</source-class>
     </system-event-listener>
</application>

<lifecycle>
    <phase-listener>org.primefaces.extensions.application.PrimeFacesResourceProcessor</phase-listener>
</lifecycle>

Don't forget to disable PF's HeadRenderer:
<render-kit>
    <renderer>
        <component-family>javax.faces.Output</component-family>
        <renderer-type>javax.faces.Head</renderer-type>
        <renderer-class>com.sun.faces.renderkit.html_basic.HeadRenderer</renderer-class>
    </renderer>
</render-kit>

In case you're using MyFaces, use org.apache.myfaces.renderkit.html.HtmlHeadRenderer as .

OK this is checked in and I tested it locally and its working great.

OK this is checked in and I tested it locally and its working great.

Just a word of warning I have found one issue in my testing and that is with CKEditor because of the way it dynamically loads scripts.  The URL's all look wrong and get 404's.  Need to investigate.

Just a word of warning I have found one issue in my testing and that is with CKEditor because of the way it dynamically loads scripts.  The URL's all look wrong and get 404's.  Need to investigate.

Tested this (without CKEditor) and it is working flawlessly in my application. Thanks a lot!

Tested this (without CKEditor) and it is working flawlessly in my application. Thanks a lot!

Nice!  I was able to fix CKEditor as well!

Nice!  I was able to fix CKEditor as well!

There is no page for this feature in the showcase, right?

No there is not. I should make one. I will open a ticket.

authors: jepsar melloware stolp


text:
Module not found: error when using

code example:
import Link from "@primer/components/src/Link";
import React from "react";

interface ExternalLinkProps {
  src: string;
  text: string;
  newTab?: boolean;
}

export const ExternalLink = (props: ExternalLinkProps) => {
  const target: string = props.newTab ? `_blank` : `_self`;
  const rel: string | undefined = props.newTab
    ? `noreferrer noopener`
    : undefined;

  return (
    <Link href={props.src} target={target} rel={rel}/>
  );
};

While trying to run npm start in console, it won't render, but show the following error:
Module not found: Can't resolve './utils/elementType' in '{my path}/node_modules/@primer/components/src'

Hey @assapir thanks for the 🐛 report! We're aware of this issue and shipping a fix in 19.0.0 which should go out any day now :) Here's the tracking PR if you'd like to follow along: #764

Thanks for that. The problem is that in mean while it's completely unusable :(

Can you do:
import {Link} from "@primer/components";
as a work-around?

yes, that works. but the bundle size is much bigger then.
Waiting for 19.0.0!

This is now released in v19.0.0. See detailed instructions at https://primer.style/components/getting-started

Thannks for the follow up!

authors: BinaryMuse assapir dmarcey emplums

repo: primer/components


text:
fix(bind): output directory outputs to src/bindings

With the arbiter config "bindings_workspace", it's taking the workspace as the root directory and adding "src/bindings", then at the end of the function it adds another "/bindings", so output for the workspace "simulation" is "simulation/src/bindings/bindings".
I remove the first addition of bindings so we rely on the join at the end of the function.

Clean, yeah i was noticing this. Thanks for shipping this <3

repo: primitivefinance/arbiter


text:
Two pointer indicator

Two pointer indicator is required, in order to slide start and end.
Imagine if you have a price range, and u wanna select between 2,000 and 10,000

@rohamj it's not based on canvas/svg. I do have an implementation for such support. But I built that for one of my clients and I have to keep it private for 2 years as per our verbal agreement.  Look for canvas or svg based solution 👍

authors: princejwesley rohamj

repo: princejwesley/angular-circular-slider


text:
Technical question about point cloud condition

I have a technical question: When using PointNet for feature extraction on partial point clouds to use them as conditions, is your approach to directly extract features using a pre-trained model, or to train PointNet simultaneously with the main diffusion network?

We trained from scratch and simultaneously with the diffusion network.

We trained from scratch and simultaneously with the diffusion network.

Ok, thanks for your reply.


text:
No video on firefox

On Firefox most of the times I get this error:

Invalid URI. Load of media resource  failed.

and sometimes a still Image not video! it works fine on chrome.
using Firefox 54.0.1 (x64)

Cannot reproduce.

authors: MrDevIr hthetiot

repo: priologic/easyrtc


text:
Disabling World edit logs ?

Hi there,
I wonder if there is any ways to disable prism logs with world edit commands ?
I have a loooots of entries made by my builders and my DB is growing really fast. Let me know if there is a way to configure that

this was added recently https://github.com/prism/Prism/pull/162
go to the blacklist setting in the config and add "worldedit" as a plugin id

authors: FrancoisMoraine phit

repo: prism/Prism


text:
Are there plan to upgrade deps?

I want to update subscriptions-transport-ws and it seems like PR already exist by Renovate.
https://github.com/prismagraphql/prisma-binding/pull/165
and many other updating deps PRs are, do you have any plan or policy to merge them?

Hey 👋, I believe this issue is no longer in sync with the current version of prisma-binding. In need for gaining a better overview of the issues facing the current version, I'll close it.
Feel free to reopen the issue if you believe we should further discuss its content. 🙂

authors: joe-re maticzav

repo: prisma/prisma-binding


text:
Slow startup time when creating a new client

I just started using the prisma client and I started a small project following the Getting Started guide.  After introspecting my database, I copied in the script from the docs and ran it with ts-node; however, it takes about 50 seconds for the line const prisma = new PrismaClient(); to execute.  The database introspected has a decent amount of tables, but nothing crazy.  Is it normal for this step take this long on every start up?
Details

script.ts

import { PrismaClient } from '@prisma/client';
console.log("started");
const prisma = new PrismaClient();
console.log("created");
async function main() {
  const users = await prisma.loop_user.findMany({
    where: {
      email: {
        contains: "maadmin"
      }
    }
  });
  console.log(JSON.stringify(users));
}

main()
  .catch(e => {
    throw e;
  })
  .finally(async () => {
    await prisma.disconnect();
  });

package.json

{
  "name": "prisma-test",
  "version": "1.0.0",
  "main": "index.js",
  "license": "MIT",
  "scripts": {
    "start": "ts-node src/script.ts"
  },
  "devDependencies": {
    "@types/node": "^13.7.7",
    "prisma2": "2.0.0-preview022",
    "ts-node": "^8.6.2",
    "typescript": "^3.8.3"
  },
  "dependencies": {
    "@prisma/client": "2.0.0-preview022"
  }
}

We are aware of some problems with the size, which can also affect the loading time: https://github.com/prisma/prisma-client-js/issues/524
Can you share the number of tables of your schema?
Or possibly even the schema itself? (If you can not share it publicly, we are happy to sign an NDA: https://github.com/prisma/prisma2/issues/757 Having actual real world schemas that show specific problems makes it much easier for us to reproduce and optimize)

We have 94 tables in our schema. I'll chat with my manager about sharing the schema.  We'd love to help optimize the project!

@gibsonjoshua55 please try out the latest alpha - we did a major improvement, which should reduce the loading time a lot!

Did the recent releases improve the situation @gibsonjoshua55?

Closing this due to inactivity

authors: gibsonjoshua55 janpio timsuchanek

repo: prisma/prisma-client-js