Flux Dedistilled / fluxdev2pro support ?

I'm trying to use the amazing new Dedistilled models with the trainer
If you haven't tried them, they are groundbreaking : https://civitai.com/models/843551
For me it's the biggest thing in the Flux community since we're able to train LoRas.
They would allow training with CFG > 1 (Guidance > 1) thus probably allowing much better caption adherence during training / possible better prompt adherence later on
(Although it is not sure we can properly use a LoRa trained with CFG > 1 with distilled models. But if we can it would probably be amazing, that is why we need to try ASAP)
Currently I've just tried to replace flux1dev.sft by another file in the following parameter
--pretrained_model_name_or_path "C:\fluxgym\models\unet\flux1-dev.sft"
But I got this error which I haven't really investigated yet. I have the same using fluxdev2pro which is a fine-tuned dedistilled model enhancing training :
File "C:\fluxgym\sd-scripts\flux_train_network.py", line 519, in
trainer.train(args)
File "C:\fluxgym\sd-scripts\train_network.py", line 354, in train
model_version, text_encoder, vae, unet = self.load_target_model(args, weight_dtype, accelerator)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\fluxgym\sd-scripts\flux_train_network.py", line 82, in load_target_model
model = self.prepare_split_model(model, weight_dtype, accelerator)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\fluxgym\sd-scripts\flux_train_network.py", line 127, in prepare_split_model
flux_upper.to(accelerator.device, dtype=target_dtype)
File "C:\fluxgym\env\Lib\site-packages\torch\nn\modules\module.py", line 1340, in to
return self._apply(convert)
^^^^^^^^^^^^^^^^^^^^
File "C:\fluxgym\env\Lib\site-packages\torch\nn\modules\module.py", line 900, in _apply
module._apply(fn)
File "C:\fluxgym\env\Lib\site-packages\torch\nn\modules\module.py", line 900, in _apply
module._apply(fn)
File "C:\fluxgym\env\Lib\site-packages\torch\nn\modules\module.py", line 927, in _apply
param_applied = fn(param)
^^^^^^^^^
File "C:\fluxgym\env\Lib\site-packages\torch\nn\modules\module.py", line 1333, in convert
raise NotImplementedError(
NotImplementedError: Cannot copy out of meta tensor; no data! Please use torch.nn.Module.to_empty() instead of torch.nn.Module.to() when moving module from meta to a different device.
Traceback (most recent call last):
File "", line 198, in _run_module_as_main
File "", line 88, in run_code
File "C:\fluxgym\env\Scripts\accelerate.exe_main.py", line 7, in
File "C:\fluxgym\env\Lib\site-packages\accelerate\commands\accelerate_cli.py", line 48, in main
args.func(args)
File "C:\fluxgym\env\Lib\site-packages\accelerate\commands\launch.py", line 1174, in launch_command
simple_launcher(args)
File "C:\fluxgym\env\Lib\site-packages\accelerate\commands\launch.py", line 769, in simple_launcher
raise subprocess.CalledProcessError(returncode=process.returncode, cmd=cmd)
subprocess.CalledProcessError:

Really ? that's great. May I asked what differences have you noticed training stuff with it ?
How much CFG did you set ? Did it worked on distilled models with CFG = 1 afterwards ?
How were your results using the LoRa, both on distilled and dedistilled ?

You could check this disscusion: https://huggingface.co/nyanko7/flux-dev-de-distill/discussions/3 Dsienra is conducting some tests on this model and posting feedback.

@Ice-YY thank you, I didn't see that discussion on nyanko7's hugginface
This is extremely interesting. Still, have you tried yourself ?

I've been training a LoRA model on a dataset with several distinct art styles, each with its own unique trigger word. When training on the base model, the output doesn’t change much regardless of which trigger word I use for the different art styles. However, when I train the LoRA model using the de-distilled base model, it does show some ability to differentiate between the trigger words, although the results are still not ideal when compared to training LoRA on SDXL.
For now I'm experimenting with training using a larger (like 6.0) guidance scales to see if I can improve the results.

Ok so you've tried with guidance 1 using dedistilled for the moment ?
I am very curious to know if a LoRa trained on dedistilled with a guidance >= 4 (I mean not just 1.5 or 2) would work with distilled flux, meaning it is backward compatible

Ok so you've tried with guidance 1 using dedistilled for the moment ? Then did you generate images with it back with distilled ?

Yes. And now  I can confirm that LoRa trained on distilled guidance 6.0 works fine with the distilled Flux.

I've had incredible early success with this model in combination with ademix8bit.
In 326 steps it achieved the bulk of what it took 40,000 to get to. I used cfg of 1 and lr 1e-4 for training, then default cfg in forge for inference.

This very good news then, I get that training with de-distilled + guidance 1 improves prompt adherence when back with distilled models and that they're able to use Distilled guidance as usual. For LoRas at least (i'm currently asking questions to someone who has just finetuned a checkpoint, will update here).
Has anyone tried training with Dedistilled and guidance > 1 then back to using it with Distilled models ?

I changed like 152 in library/flux_utils.py:
scale = prompt_dict.get("scale", 1.0)
but the images are still completely distorted.
I'm guessing the problem is the model itself. Maybe you'd need to switch to a non-distilled model for inference.

Just thought to add my experience: On the latest pull of the SD3 branch, I can train on DevDedistilled. It's detected as a Schnell model because of the missing guidance blocks(line 77 in library/flux_utils.py): 'is_schnell = not ("guidance_in.in_layer.bias" in keys or "time_text_embed.guidance_embedder.linear_1.bias" in keys) and initialized as such. Training works but that's possibly why samples are broken? Forcing it to be detected as a dev model breaks it which makes sense. I imagine it might need some special consideration for full support. Unlike Dev2Pro, DevDedistilled fully removes the distilled guidance blocks.

@kohya-ss I apologize for the interruption, but I am also quite curious about this situation. Could you please help explain what issues might arise from classifying a de-distilled model as 'Schnell'? Are there any plans to support de-distilled models in the future?

Are there any best practices for training with de-distilled models cause this is all one big jungle tbh. Would be awesome to have a collection of best practices / experiments / knowledge sharing...
For example, some questions I have:

There are three de-distilled models that I'm aware of:

https://huggingface.co/nyanko7/flux-dev-de-distill
https://huggingface.co/ashen0209/Flux-Dev2Pro
https://huggingface.co/InstantX/flux-dev-de-distill-diffusers
--> are these all roughly equivalent?

When traininig with a de-distilled model, should you still train with "--guidance_scale = 1.0"?
What if you want to train a LoRA using de-distilled, but then run that LoRA on top of FLUX dev/schnell?
What about LoRA ranks? I currently see 4 being the default? Any reason to go higher?
There is also some interesting work on using better CLIP-l models: https://github.com/zer0int/ComfyUI-Long-CLIP which seems worth testing for training also
Currently, for single object/face LoRA's I'm getting the best results when training without any captions at all, but I am using a fairly detailed trigger word that visually describes the thing I'm trying to learn. There's a lot of confusing information on this topic: https://civitai.com/articles/7203/captions-vs-no-captions-a-deep-dive-into-effects-on-flux-lora-training
Right now I'm testing out masked training, which I remember made a huge difference in my SD15 / SDXL trainers (https://github.com/edenartlab/sd-lora-trainer)

Are there any best practices for training with de-distilled models cause this is all one big jungle tbh. Would be awesome to have a collection of best practices / experiments / knowledge sharing...
For example, some questions I have:

There are three de-distilled models that I'm aware of:

https://huggingface.co/nyanko7/flux-dev-de-distill
https://huggingface.co/ashen0209/Flux-Dev2Pro
https://huggingface.co/InstantX/flux-dev-de-distill-diffusers
--> are these all roughly equivalent?

When traininig with a de-distilled model, should you still train with "--guidance_scale = 1.0"?
What if you want to train a LoRA using de-distilled, but then run that LoRA on top of FLUX dev/schnell?
What about LoRA ranks? I currently see 4 being the default? Any reason to go higher?
There is also some interesting work on using better CLIP-l models: https://github.com/zer0int/ComfyUI-Long-CLIP which seems worth testing for training also
Currently, for single object/face LoRA's I'm getting the best results when training without any captions at all, but I am using a fairly detailed trigger word that visually describes the thing I'm trying to learn. There's a lot of confusing information on this topic: https://civitai.com/articles/7203/captions-vs-no-captions-a-deep-dive-into-effects-on-flux-lora-training
Right now I'm testing out masked training, which I remember made a huge difference in my SD15 / SDXL trainers (https://github.com/edenartlab/sd-lora-trainer)

https://huggingface.co/InstantX/flux-dev-de-distill-diffusers is just the diffusers version of https://huggingface.co/nyanko7/flux-dev-de-distill - it's a fully de-distilled model, with the distilled guidance blocks removed. It can be used for both training and inference and there are also finetunes of it. For inference, it uses classifier free guidance (CFG) instead of distilled guidance and thus takes about twice as long per step. Quality is slightly higher than base Flux dev and prompt adherence with CFG is tighter, some LORA work better too.

https://huggingface.co/ashen0209/Flux-Dev2Pro is only meant for training. It still contains distilled guidance blocks, but it has been trained to perform better with guidance_scale = 1 and thus be suitable as a training scaffold(i.e. you would train your LORA on Dev2Pro with the intent of using it on regular Dev).
So in summary, Dev2Pro is just a retrained FluxDev meant to perform better specifically for use during training, while dev-dedistilled is a true attempt to recreate Flux Pro both for training and inference.

All the tests that I've seen, and my own, have done so.

That's quite often the idea!

Definitely! I usually use between 16 and 32 depending on what I'm training. Generally as the complexity of what you're training goes up and the number of concepts you want the LORA to contain go up, the rank needs to go up too. There's no hard and fast rules but I've seen 4-8 recommended for style/anime stuff and simpler stuff like faces, 16-32 for full character multiple outfits/complex real world stuff. I've had my very best results at 24 and 32 but I don't tend to train super simple stuff.

In my experience Long CLIP slightly improves the quality of images created during inference(better shadows/details). I tried a while back to test it for training but at that time, kohya's scripts didn't like it. Dunno if that's changed.

"There's a lot of confusing information on this topic" - Indeed. Regardless of what anyone says, if something works well for you then go for it. I've gotten some of my best results with adafactor despite trying much newer stuff like adamwschedulefree... weird as that is. I train with detailed prose format captions personally, with custom highly unique tags to mark the main concepts I wanna be able to reproduce. I've not tried without captions so I can't speak to that.

I've been meaning to try out masking as well, please report how it goes!

Thank you Sanaria! I can now confirm that adding masks significantly reduces overfitting and maintains far greater promptability. I'm currently using very basic prompt-based CLIPseg masks from my SDXL trainer which work great!
https://github.com/edenartlab/sd-lora-trainer/blob/main/trainer/preprocess.py#L168

Thank you Sanaria! I can now confirm that adding masks significantly improves results by maintaining better promptability. I'm currently using very basic prompt-based CLIPseg masks from my SDXL trainer which work great! https://github.com/edenartlab/sd-lora-trainer/blob/main/trainer/preprocess.py#L168 (I simply added these masks as alpha channel into the training imgs and then activated --alpha_mask )
I've gotten great results training LoRA's on top of flux-dev-de-distill, going to test training on top of Flux-Dev2Pro now to see if I notice any differences.
One thing I'm still struggling with is training multiple concepts / faces / ... into a single LoRA, is that something you've succesfully done? Eg for faces I get a ton of bleeding (like most people report). I believe one main reason is that the kohya flux trainer doesn't support textual inversion which really is that way you'd wanna do multiple concepts.. I started a thread about Flux TI here with some good conversation, but I don't think anyone has cracked it yet...
In fact, there seems to be a lot of great experimentation going on in diffusers with TI that I havent tried yet, anyone have experience with these training scripts? https://huggingface.co/blog/linoyts/new-advanced-flux-dreambooth-lora

The one model I've trained that contained multiple faces they absolutely did bleed together. This was acceptable as it was meant to be a training of different facial imperfections and just more interesting faces than base Flux. I've never tried to train multiple /distinct/ faces I imagine that would be very tricky maybe reg images would help?
I will definitely give masking a shot then, I keep hearing really good things. I tried a few trains on Dev2Pro but that was so early on in my Flux experience that I don't think the data is relevant(it didn't go well, but nothing did at first XD) I've recently just been running tests at night changing up various parameters and seeing what works best. Most recently I've been experimenting with Huber loss but my results are mixed - better in some areas worse in others.

It makes sense that dev-dedistilled could benefit from guidance_scale > 1, I wondered about that when I mentioned I'd never seen anyone try it above. Have you compared directly to guidance_scale = 1 and seen improvement?
Ashen's Flux dev2pro was specifically retrained to perform better with guidance_scale = 1. This was before dev-dedistilled existed, and it was recognized that we're forced to train Flux Dev with guidance_scale = 1 while at the same time Flux Dev performs REALLY badly with guidance_scale = 1. It was thought that by training it to perform better in this specific scenario, we could get better results by training a LORA on Dev2Pro for use on regular Dev, just like how you might train a LORA on base SDXL to use on Turbo or one of the distilled SDXL models. It's only meant to be a training scaffold, it performs worse than normal Flux for all scenarios EXCEPT guidance_scale = 1. I've heard good things about it but when I tried it I was just starting out and nothing really worked yet, so I can't speak to more than the theoretical.
Dev-dedistilled came later and goes even further by modifying the model's architecture and actually removing the distilled guidance entirely from the model as well as retraining it to work with guidance. It's a more complete attempt at recreating Flux Pro from Flux Dev. Personally I think Dev-dedistilled supersedes Dev2Pro for most purposes since it's a more full conversion and is also highly useful for inference as well. However Dev2Pro does remain closer to the original Flux model, so there might be some benefit to that.
I think you can do multiconcept okay if the concepts are related enough. I've had luck with that a couple of different times. Highly unique tokens help a lot here and there will be some bleeding of concept but if it's a dataset that can tolerate that, it's definitely doable. However training multiple distinct faces in the same LORA would be a nightmare here I agree.

Woo so for the last few days I've been running test after test after test on my best dataset, changing only a single variable each time. After investigating other's LORAs above and discovering none were larger than Rank 16 I second guessed myself and did two back to back runs with the only difference being one was R16 and one R24. It's a complicated dataset and the R24 was clearly superior so yeah, increasing Rank above 16 can definitely still benefit. In fact with Adafactor + batch_size = 2, the R24 was my best LORA to that point(I also tested adamw8bit constant, cosine with restarts)
But the big win was last night - trained against Dev-dedistilled using guidance_scale = 4 with everything else the same. Wow!  For inference both on regular Flux and dedistilled this produced far and away the best output I've gotten by a huge margin. Prompt adherence is MUCH tighter, quality is much higher, just... it's so much better. HIGHLY RECOMMEND.

I personally use guidance 4 and rank 32 systematically
Maybe guidance 6 could be useful for dataset involving several concepts

Hi! I wonder if the Flux Trainer workflow for Flux on comfyui will work for de-distilled model lora training? Or I need to use a new cfg guidance logic to train it correctly? When I use the workflow with cfg=4 it looks fine, but idk if a correct workflow will make it better.

Let me preface this by saying there are two types of guidance at play here. CFG e.g. classifier free guidance is the same kind we all know and love from SDXL, SD, etc. Flux Pro (the paywalled version) also uses CFG. Flux Dev was distilled to use a more lightweight "FluxGuidance" to make it more approachable on consumer hardware. When you are inferencing with a normal Flux model you are generally using FluxGuidance and the image will break without it because of the distillation. You can also use CFG in LOW amounts but this will double the time per step(in exchange for slightly improved quality and negative prompt). Flux Dev dedistilled tries to return the model to the FluxPro state. It uses CFG only, takes longer per step and needs more step but has higher quality, prompt adherence, etc and it will break the image without CFG. Lastly, FluxGuidance is an inference only type of guidance.
So in short for inference:
Normal Flux - Requires FluxGuidance, CFG optional and must be kept very low(<1.4 w/o adaptive), lighter weight and great quality
Dedistilled Flux - Requires CFG, FluxGuidance not available, heavier weight but better quality and tighter control
For training, when we set --guidance_scale on the command-line, from my experiments and investigation that's applying CFG and that's why it generally needs to be 1 for normal Flux but is useful for Dedistilled. However the sample prompt generation is currently using FluxGuidance, so that's why samples are broken for Dedistilled. I looked into implementing CFG for sample generation and it's a bit beyond me, even though I am experienced with Python and AI.
So in short for training:
Normal Flux - use --guidance_scale = 1.0, samples will work correctly
Dedistilled Flux - use --guidance_scale > 1.0 with the caveats you mentioned taken into account.

Not so sure about that personally. I always train with captions but I've not compared to without I'd be interested to see comparison.

Not really. Normal Flux can use FluxGuidance from ~2.0-7.0 at least, and CFG of up to say 1.4 without issues(use perpendicular negative if you want negative with normal Flux or CFG will break your images!). Flux De-distilled can use CFG 2.0-7.0 as well but no FluxGuidance. More of either guidance = more strict prompt adherence but lower quality and diversity. As far as how the LORA's training method affects this... I find LORA trained on Dedistilled with CFG benefit from lower FluxGuidance when using normal Flux models but otherwise the same rules apply. Both types of guidance should be tweaked depending on how your output is going. I tend to stick around 3.5-4.5 for either type!

I hope this is all helpful!

@Sarania Thanks a lot! Your explanation about the differences between distill guidance and CFG, the actual use of CFG in flux trainer, and flux's tolerance for CFG < 1.4 has really clarified many of my doubts.
I have tried training under CFG = 1 with and without captions, but on two slightly different datasets. I didn’t observe significant differences. However, there are some articles discussing this.(https://civitai.com/articles/7203/captions-vs-no-captions-a-deep-dive-into-effects-on-flux-lora-training)
I might go take a look at differences between CFG and flux guidance in terms of inference/training code and network structure and tryout some more training. If I find anything, I’ll report back here.

After a lot more testing i'll share some of my current results / opinions:

getting better results training on top of flux-dev-de-distill-diffusers vs Fluxdev2pro (inference works really well with both Flux-Dev and Flux-Schnell!)
training with cfg=4.0 + proper captions (through GPT4-vision) did not improve my inference results, I'm still getting best results by training with cfg=1.0 and just a single, descriptive trigger text as the prompt for all training imgs.
using --lr_scheduler cosine_with_restarts with --lr_scheduler_num_cycles 3 improved results for me
Tested --min_snr_gamma=5.0 which may have improved results a tiny bit (from this paper)
also adding --noise_offset 0.1and --ip_noise_gamma 0.1 seemed to help a bit
adding proper masks def helps a lot. Currently using CLIPSeg masks which are very "leaky", need to experiment with eg Florence2 + SAM2 with near pixel perfect masks.
currently doing about 2000-4000 training steps at lr=0.5e-4 with lora_rank = 8 (my datasets are roughly 6 - 30 imgs, so very small!)

I'm def interested in experimenting more with cfg>1.0 training + proper captions cause it feels like thats the way forward here. Also, I'm curious if anyone has tried integrating FLUX Redux into a training pipeline? Essentially, Redux encodes an input image into the LLM token space, creating tokens that represent the image. So theoretically, it should be possible to do that for many training images, somehow average the token embeddings and use that instead of a manually chosen "trigger_text" as a basis for training a LoRA on top..
There are some tricky issues here though:

its not obvious how to average multiple Redux encodings since they are all multiple token sequences, and just averaging them in token space is prob not going to work (you really want to average their outputs at the LLM conditioning level that goes into the denoiser)
The Redux token sequence is very long, creating a very strong conditioning. This may hinder generalization of the model (eg making a cartoon version of a person etc). But there has been some great experimentation with modulating the redux conditioning.

I'm also very interested to play with OminiControl training as it may eventually replace LoRA's alltogether.

Thanks for your insights.
I'm surprised you didn't find substancial inferencing improvements when training with CFG > 1
I will keep on training with de-distilled CFG = 4 because I found some, but then I didn't test A vs B rigourously. Anyway, my point is it's definitely not worse, so every little bit helps.
I'm starting attemps to finetune then extracts which is supposed to make a big difference, but I don't have currently the rights settings for my 16 GB VRAM card. I have out of memory.
@aiXander What benefit do you think adding Florence2 on top of SAM2 would bring ?
I don't really get your part about FLUX Redux, it seems like you want to use it kinda like a VLM but the output wouldn't be a text prompt, right ?
I've never used Redux nor OmniControl yet.

As far as general training settings go (and other things I've learned), my experiments have shown at least for me and my datasets:

"--min_snr_gamma 5.0" - possibly slightly helpful, definitely not worse
"--multires_noise_discount 0.3 --multires_noise_iterations 6" - BIG improvement in quality of smaller/fine details
"--noise_offset 0.1" - seems to be a small help
"--timestep_sampling sigmoid --discrete_flow_shift 3.0" produces overall better results than "--timestep_sampling shift --discrete_flow_shift 3.1582"
Increasing batch size definitely helps with quality and generalization. If low on VRAM, block swap can help! I've been using a batch size of 2 and that's definitely gotten me better results than a batch size of 1. You might have to boost your LR a little as batch size increases, depending on the optimizer.
Speaking of LR, it varies a lot. Small simple datasets I can get away with as high as 5e-4 for as little as 500 steps. Large, complex datasets benefit from as low as 2e-5 for many thousands of steps. There's a lot of interplay between other hyperparameters and LR.
At least when training on Dev-dedistilled(which is the only way I train now), I've been using network_alpha==network_dim and that seems to be better than alpha at half dim but I haven't tested rigorously.
Training the T5XXL produced worse results than identical training without it. Maybe with very explicit, highly verbose captions or edge cases it could be useful
Block Swap is AMAZING. I have a 4070 TI SUPER with 16GB VRAM which is just BARELY enough normally if I stick to lower memory optimizers and lighter settings. But with block swap I can go much further, using more advanced optimizers, training the freaking T5XXL if I want. The performance hit is there but at least on my system, it's not that bad.
Dataset quality is paramount

I haven't been running a lot of Flux tests recently because I got distracted by Hunyuan video and generative video in general XD

Thanks for your insights.
I'm surprised you didn't find substancial inferencing improvements when training with CFG > 1 I will keep on training with de-distilled CFG = 4 because I found some, but then I didn't test A vs B rigourously. Anyway, my point is it's definitely not worse, so every little bit helps.
I'm starting attemps to finetune then extracts which is supposed to make a big difference, but I don't have currently the rights settings for my 16 GB VRAM card. I have out of memory.
@aiXander What benefit do you think adding Florence2 on top of SAM2 would bring ?
I don't really get your part about FLUX Redux, it seems like you want to use it kinda like a VLM but the output wouldn't be a text prompt, right ?
I've never used Redux nor OmniControl yet.

Florence2 can generate bounding boxes based on a prompt and SAM2 is SOTA for box-based segmentation. So combining these two is the best way to generate pixel-perfect masks based on a textual description of the face/object/... you're training on.
Redux would be a potential way to do some kind of instant, training-free textual inversion, where you end up with token embeddings that already capture most of the visual look of what you're training on. But because the tokens are in LLM space, they are potentially more generalizeable / promptable. This would prob be non-trivial to integrate into kohya though


Disable FP64 emulation with tests

Description
Reopen https://github.com/oneapi-src/oneDAL/pull/2252 with some additions

/intelci: run

/intelci: run

/intelci: run

/intelci: run

/intelci: run

/intelci: run

/intelci: run

/intelci: run

/intelci: run

/intelci: run

/intelci: run

/intelci: run

Should CI be checked with infra #605?

i think they can be independently integrated - as long as this one is green we can integrate it and check with infra change

I would also suggest to add a compile time check for the float_algo_fixture template argument. It should always be a floating point type

I would also suggest to add a compile time check for the float_algo_fixture template argument. It should always be a floating point type

on this one i suggest to discuss offline and consider adding in next PR if needed


List inside list

Hi, I tried using this library to sort a list within a list and it seems to not be working. Are there any plans to support that in the future?

I'm afraid it's complicated to implement, but if you can kindly provide a test repo I will dig into it.


httpd.documentRoot: Create folder if missing

Issue description
Folder specified as services.httpd.documentRoot is not created automatically.
Apache2 fails to start afterwards, because its documentRoot is non-existing.

Auto creating the documentRoot was intentionally removed in  #21703 .
Is this really a bug?

(triage) Assuming this isn't a an issue anymore due to there being no response, and the change was apparently intentional. Don't hesitate to re-open if it is still an issue, hopefully with details about why this should be reverted back to creating a document root.


Use source instead of build number when filtering results in Importance by Component widget

Importance by Component widget uses the job name to fetch runs and the build number and component to group the results. However, there are cases where different jobs may have the same build number and component (for example, two different jobs for stage and prod that contains results for the same component).
This PR modifies the field used in the query from build_number to source which has more information to not mix runs.

@Fynardo overall I think it's important for us to move away from jenkins specific differentiation in the widgets, and support filtering of test results based on abstractions that allow for users to leverage Jenkins job metadata.

@mshriver If we forget for a moment about the fact that I just destroyed the branch (that is why you don't update things at 9pm), I simplified the SQL code that finds and filters the appropriate Jenkins runs.
I'll try to fix this tomorrow

@mshriver The code that creates the result dict could be a bit prettier but:

The complexity is fine, it runs through the result list just once, so at least it is linear.
I think we would like to address result objects construction using some sort of DTO definitions and make it more formal. However, that would require more design than the one scoped here. We could discuss it further in follow-up PRs

Local testing with compliance job data from an API job and UI job with the same build number and component is looking good, correct calculation and filtering of results from the relevant runs for the filtered job.


boilerplate-advancednode: npm i fails on Apple Silicon because of incompatible Node version

Note: I'm opening this issue here as it doesn't seem to be possible to open issues on the boilerplate-advancednode repository.
Affected page

https://www.freecodecamp.org/learn/quality-assurance/advanced-node-and-express/set-up-a-template-engine
https://github.com/freeCodeCamp/boilerplate-advancednode/
Your code

Running npm i on an Apple Silicon Mac results in the following error:
npm ERR! code 1
npm ERR! path /Users/ce-dev/Documents/dev/courses/freecodecamp-projects/06 Quality Assurance/boilerplate-advancednode/node_modules/node
npm ERR! command failed
npm ERR! command sh -c node installArchSpecificPackage
npm ERR! npm ERR! code ETARGET
npm ERR! npm ERR! notarget No matching version found for<EMAIL_ADDRESS>npm ERR! npm ERR! notarget In most cases you or one of your dependencies are requesting
npm ERR! npm ERR! notarget a package version that doesn't exist.
npm ERR!
npm ERR! npm ERR!      /Users/ce-dev/.npm/_logs/2022-01-21T14_43_20_296Z-debug.log
npm ERR! node:internal/modules/cjs/loader:936
npm ERR!   throw err;
npm ERR!   ^
npm ERR!
npm ERR! Error: Cannot find module 'node-darwin-arm64/package.json'
npm ERR! Require stack:
npm ERR! - /Users/ce-dev/Documents/dev/courses/freecodecamp-projects/06 Quality Assurance/boilerplate-advancednode/node_modules/node/installArchSpecificPackage.js
npm ERR!     at Function.Module._resolveFilename (node:internal/modules/cjs/loader:933:15)
npm ERR!     at Function.resolve (node:internal/modules/cjs/helpers:108:19)
npm ERR!     at ChildProcess.<anonymous> (/Users/ce-dev/Documents/dev/courses/freecodecamp-projects/06 Quality Assurance/boilerplate-advancednode/node_modules/node-bin-setup/index.js:18:27)
npm ERR!     at ChildProcess.emit (node:events:390:28)
npm ERR!     at maybeClose (node:internal/child_process:1064:16)
npm ERR!     at Process.ChildProcess._handle.onexit (node:internal/child_process:301:5) {
npm ERR!   code: 'MODULE_NOT_FOUND',
npm ERR!   requireStack: [
npm ERR!     '/Users/ce-dev/Documents/dev/courses/freecodecamp-projects/06 Quality Assurance/boilerplate-advancednode/node_modules/node/installArchSpecificPackage.js'
npm ERR!   ]
npm ERR! }

This is because<EMAIL_ADDRESS>which is listed as a dependency, does not run on Apple Silicon.
Expected behavior

Users should be able to install dependencies.
System

Device: MacBook Air M1
OS: MacOS 12.1
Browser: n/a

Additional context
Changing the dependencies to the following fixes the problem.
"dependencies": {
  "dotenv": "^8.2.0",
  "express": "^4.16.1",
  "mongodb": "^3.6.1",
  "node": "^17.3.1"
},

Everything still seems to work as I was able to pass the first challenge ("Set up a Template Engine") with these packages, but I have not done any testing beyond "seems to works on my system and on Heroku".

We should not be pinning node to version 17, IMHO.
odd-numbered releases are like "staging" releases, used for 6 months to allow developers to work with new features. Even numbered releases are stable releases and have LTS.
Node.js 16, however, does have Apple Silicon support AFAIK.

@ojeytonwilliams We migrated the example projects to node 16, should we do the same for the boilerplates?

With the dependencies:
"dependencies": {
    "dotenv": "^8.2.0",
    "express": "^4.16.1",
    "mongodb": "^3.6.1",
    "node": "^16.13.2"
},

npm i fails on node v16.14.0 (npm v8.3.1), but succeeds on node v14.18.3 (npm v6.14.15). Both are running through nvm on Apple Silicon. I already tried a clean install of node v16.14.0, but that didn't change anything. Full log of the failed install on node v16.14.0 is attached.
Completely removing node from the dependencies also works (dependencies install and server can be started), and this is what boilerplate-mongomongoose and the projects for the cert do, but I also understand the benefit of requiring a specific node version.
I'm not very knowledgeable about node and package management so I don't know how to give any more useful feedback than this, but happy to try anything someone suggests if it might help getting the project to install easily for other campers.
2022-02-15T20_50_26_538Z-debug-0.log

It seems there can be issues installing node on certain macs.  There are workarounds in that link.
What should we do?  Well, ideally we should recommend a specific node version, but not require  it, since we can't test in all environments.  In cases like this, where there's an incompatibility, strictness seems unhelpful.
So, engines seems useful, but engine-strict does not.

As far as I can tell boilerplate-advancednode is the only boilerplate that has node in the requirements at all.
Since this is the second boilerplate of the QA cert, and most people will already have done the API cert before that, it seems reasonable to assume that people who want to do the course on their own computer will have installed node either through binary or nvm by this point. I also didn't run into any issues running the boilerplate-advancednode with node 14, 16, or 17. Is there any major downside to just removing node from the requirements in package.json to bring it in line with other boilerplates?
Maybe there could be a suggestion on either the lesson page or in the readme to install the current LTS (or possibly be more specific and say 16.x, as unless there is another new CPU architecture 16.x should be ok for all popular architectures / operating systems for the foreseeable future)?

Is there any major downside to just removing node from the requirements in package.json to bring it in line with other boilerplates?

To be honest, I'm not sure why we have node as a dependency.  That's not how I ever set a node version in practice.  I vote we remove that.
It's useful to recommend compatible node versions (i.e. use engines), but only if we test them to make sure that's true.  We can defer that until we set up testing for the boilerplates - otherwise it's just annoying and misleading.  So, to qualify my previous statement: engines seems useful if we're running tests.
For now, I'd be fine with just deleting node from the dependencies.

Wanted to confirm the dependency wasn't present in other boilerplates before closing. Think we're good to go now.


bug(ui): html code block rendering

Environment
Device and OS: Ubuntu 22.04
App/package versions: main (latest)
Kubernetes distro being used: UDS K3d (latest)
Other: N/A
Steps to reproduce
Ask the LLM to write code related to HTML blocks
Expected result
A code block renders the HTML code blocks and the copy button puts the code into the user's clipboard
Actual Result
The code block does not render HTML and the copy button does put the proper HTML tags into the user's clipboard.
Visual Proof (screenshots, videos, text, etc)
Some example resulting code blocks:
Screenshot 2024-08-05 123931
Screenshot 2024-08-05 124017
Screenshot 2024-08-05 130152
Screenshot 2024-08-05 130323
Severity/Priority
N/A
Additional Context
N/A

this may be an LLM issue, not a UI issue. leaving in backlog for now


Vhuseinova/changelog fix

What

Why

How Tested

Process & policy checklist

[ ] I have updated the project documentation to reflect my changes if necessary.
[ ] I have read the CONTRIBUTING documentation.

Is this a breaking change?

[ ] This change causes current functionality to break.

An alternative to a fix for this happening in our repo, I'd like to allow beachball to specify the change-dir and remove all the hacky code we have around changing file names https://github.com/microsoft/beachball/pull/969/files

We should be able to close this in lieu of #5057

Closing as old. Please reopen if investigations continue here.


docker-maven-plugin 0.10.5 -> 0.11.2. requires 3.2.1 min maven version

Require Maven 3.2.1 to use the following plugin updates:
org.jolokia:docker-maven-plugin .............................. 0.11.2
We use org.jolokia:docker-maven-plugin 0.11.2 at work. Not sure if it's ok to raise the min maven version to 3.2.1 just for this though.

Well.. 3.2 has been superceeded by 3.3.1 so its not that new anymore. Fine by me.


BasicAuthenticator needs to write the auth type in replication options

In CivetWebSocket.cc (couchbase-lite-core), the property is used to decide how to do the authentication :
                slice authType = auth[kC4ReplicatorAuthType].asString();
                if (authType == slice(kC4AuthTypeBasic)) {

At the moment, any attempt to use basic auth fails with 401 Unsupported auth type.

Nice catch.  Sorry that no one has seen this PR in so long!


Add support for setting counts in iOS push notifications

This adds a new function get_apns_badge_count() to fetch count value for a user push notification and then sends that value with the APNs payload.
Once a message is read from the web app, the count is decremented accordingly and a push notification with event: remove is sent to the iOS clients.
Closes #10271

The changes so far lgtm.

@gnprice @mateuszmandera please have a look.

@timabbott this is ready for another review.

Just to note that this is not tested on a real device as I said in the public stream. So, it will better if someone tests the PR on a real device before merging.

@gnprice thanks for the review. I have updated the PR.

OK, I posted a batch of comments on this.  Since I think they are all issues with the tests, I'm going to stage this on chat.zulip.org to facilitate easier testing by folks on the mobile team who have iOS devices.

Staged on chat.zulip.org.  @gnprice @chrisbobbe FYI for doing mobile side testing.

OK, manual testing revealed some unrelated problems discussed here: https://chat.zulip.org/#narrow/stream/243-mobile-team/topic/iOS.20badge.20count/near/905509
But I believe the conclusion of that work is that this is likely good to merge once the testing issues discussed above are resolved.  (Further testing will likely still happen)

@timabbott this is ready for a review.

Posted one follow-up comment above; otherwise lgtm.

Merged, thanks @hashirsarwar!


remove gource

remove the broken gource links as the navbar is getting crowded and these are broken anyway

@MattIPv4 I think @ryankirkman says he'd try to get our Google account back so those links won't be dead, but I'm not sure.

Let's just comment it out for now then

@thomasdavis we have git, so we can just remove it, there is no reason to comment out with git.

@thomasdavis @PeterDaveHello Sounds good, I’lol make those changes in a bit. I’ll also chase the old pull request that was working on making the nav more mobile compatible.

@thomasdavis How does this look now?
What did you think of the other idea I sent you in my email?

@MattIPv4 not sure what's in the email but I believe everything can be discussed in public ;)

@PeterDaveHello Fair enough, was a proposition to create some merch for cdnjs. Any profits can go towards funding cdnjs and it allows the community to rep the service and support us at the same time :)


[Event Request] table 5741 "Transfer Line" - OnValidateVariantCodeOnBeforeCheckEmptyVariantCode

Can we please get the following event for the OnValidate Trigger of Table "Transfer Line"?
field(30; "Variant Code"; Code[10])
{
    trigger OnValidate()
    var
        ItemVariant: Record "Item Variant";
    begin
       ...

        OnValidateVariantCodeOnBeforeCheckEmptyVariantCode(Rec, xRec, CurrFieldNo);//NEW
        if "Variant Code" = '' then
            exit;

      ...
    end;
}

[IntegrationEvent(false, false)]
local procedure OnValidateVariantCodeOnBeforeCheckEmptyVariantCode(var TransferLine: Record "Transfer Line"; xTransferLine: Record "Transfer Line"; CurrentFieldNo: Integer)
begin
end;

Thanks for reporting this. We agree, and we’ll publish a fix asap, either in an update for the current version or in the next major release. We will update this issue with information about availability. Please do not reply to this, as we do not monitor closed issues. If you have follow-up questions or requests, please create a new issue where you reference this one.


ability to provide additional fields to append to json format #1310

We have the same issue as described in #1310. We need the option to configure serviceContext so we can filter error reports for service-name and version. Additionally it would be helpful to add custom json data to the log if needed.

i'm not sure if you would accept this feature also in 1.1.x. if you agree, i can open another pr for 1.1.x. thanks

@Bobsel Thanks for the PR submission. We can decide whether to add to 1.1.x after it's merged into master. Although I think it would be unlikely since it's not really a bug fix.


fix loading data with webViewer

closes #308

It's probably fine, but do we understand what sandbox we're disabling here and what the potential consequences are?

the WebViewer uses chromium for rendering the web pages. This disables the sandbox for the chromium renderer.
I did not find any alternative to fixing that issue.

Following some discussion in chat, this should be fine, but @RobertRosca wanted to have a look into what's happening.

After having spent another fair amount of time (unsuccessfully) trying to fix that issue, and having confirmation from Robert that the risk implied by this change is very low... I'll merge later today if there are no objections.

Thanks, LGTM

thanks!


Azure Batch: DeleteJob(async) fails

Hi,
I am currently using Docker Linux Containers ( microsoft/dotnet:2.1.1-aspnetcore-runtime) aund Azure Batch 8.1.2 and I am trying to delete an job like this:
  using (var bc = await BatchClient.OpenAsync(sharedKeyCredentials))
                {
                                     bc.JobOperations.DeleteJob(finishedJob);
                        }

The job exists, but I just get an BatchException which contains this message:
"Server failed to authenticate the request. Make sure the value of Authorization header is formed correctly including the signature.\nRequestId:fc2efe54-9b9e-441d-a9bf-3377d55fda88\nTime:2018-07-30T09:36:45.2244840Z"
When I delete this job through BatchLabs, there is no issue.

Hi @ChristianSauer - we're aware of this issue. It's due to how we implemented shared key auth and this issue in netcoreapp2.1
For now we're recommending folks target netcoreapp2.0. We will release a fix to this in the upcoming 9.0 release in the next few weeks.

Workaround. It's works for me.

You can also use an environment variable to opt out of using sockets implementations based on SocketsHttpHandler. To do this, set the DOTNET_SYSTEM_NET_HTTP_USESOCKETSHTTPHANDLER to either false or 0.
https://docs.microsoft.com/en-us/dotnet/core/whats-new/dotnet-core-2-1#sockets-improvements


你好，请问可否提供类似百度网盘这样的下载链接

The maximum size of a single fie in Baidu netdisk is 20GB, which is less than that of our rosbag file.


Add link references which were removed with #16011

Fix #16139

/assign @kbhawkey

hello @dsbrng25b. Thanks for this PR.
I do not see the links in the preview page?
Page Preview:
https://deploy-preview-16140--kubernetes-io-master-staging.netlify.com/docs/tasks/debug-application-cluster/audit/

/lgtm

I know the main PR has already merged (#16011)....  Falco has its own documentation. Even though this content doesn't exist in the Falco docs, it might be better located there and then linked to from the Kubernetes documentation. I know the Content Guide states that "...Adding content for kubernetes or kubernetes-sigs projects that don’t have their own instructional content" is allowed, so I need to reword and expand that sentence. The end-goal is to keep project documentation in the project's docs so the project's developers know about it and can update it with each release if needed, rather than their having to keep track of project documentation in two places (eg Falco docs and Kubernetes docs).

This felt to me like something that falls between two stools: it's about Falco and it's about Kubernetes.
(and that's why I was OK to see it merged)


User still appearing in search even though they've been deleted from the Commons

A user contacted us because we deleted their account, but they are still able to see it when searching on the Commons. This only occurs when using the search bar in the top nav, not from the user page. Clicking on the profile link goes back to the Commons homepage. Private Zenhub Image

ElasticPress used to be configured to completely reindex nightly. I removed that functionality for performance reasons (it was taking several hours and sometimes longer). An unanticipated side effect of this is that deleted members were never cleaned up. I have re-instituted a weekly cron job to re-index. It should run on Saturdays. Will check back next Monday to confirm that the user no longer appears in search results.

After scheduled re-index, the user no longer appears in search results.

Emailed user.


feat: support altering the target table’s columns of the sink.

I hereby agree to the terms of the RisingWave Labs, Inc. Contributor License Agreement.
What's changed and what's your intention?
Title: Enhance Sink Behavior to Handle Table Alterations and Add Associated Tests
Summary:
This pull request implements enhancements to the system's behavior concerning sinks when table alterations occur. The changes span across several areas of the codebase, including test scripts, protobuf definitions, and Rust source code. Here's a breakdown of the key additions and modifications:

A new end-to-end test script alter_column.slt has been added under e2e_test/sink/sink_into_table. This script validates the system's ability to manage sinks during various table alteration operations, including creating tables and sinks, inserting values, and adding or attempting to drop columns. It further checks if the sink tables reflect the correct state after these operations.

Removal of a specific test case from basic.slt indicates an important change in the behavior of the system. The removed test case previously checked for an error when altering a table by adding a column with active incoming sinks. This change implies that the system may now support adding columns to a populated sink table.

The protobuf definition for a Sink within catalog.proto has been updated to incorporate a repeated field original_target_columns. This new field stores the initial structure of a sink's target table, facilitating the management of table alterations in relation to sinks.

The main body of Rust source code changes revolves around handling table alterations when sinks are involved:

The modules sink/catalog/desc.rs and sink/catalog/mod.rs have been updated to manage the new original_target_columns data.
A new function called default_column_exprs has been added to frontend/src/catalog/table_catalog.rs. This function generates default expressions for columns, which is essential for populating default values in rows added to altered tables.
The code within frontend/src/handler/alter_table_column.rs has been augmented to enable table alterations in the presence of sinks, highlighting improved support for adding columns to tables with active sinks.

The pull request also notes the use of Rust HashMap and Vec collections to maintain the newly introduced data points, signaling improvements in data handling, particularly for append-only tables engaged in sink operations.

This pull request presents a comprehensive enhancement to the system, focusing on improved sink interaction with table alterations. Reviewers can delve into the individual elements of this update for a more detailed understanding, and further discussions are encouraged to fine-tune these changes.
Please review these changes and provide feedback on any aspects that may benefit from further clarification
Checklist

[x] I have written necessary rustdoc comments
[x] I have added necessary unit tests and integration tests
[x] All checks passed in ./risedev check (or alias, ./risedev c)

Shall we try rust_min_stack


[WIP] Rotational angle cutoff for ensuring large rotational moves

Closing since we don't think this is an avenue worth pursuing.


PYTHONPATH not unsetting on deactivate.

I think this affects all env vars. Especially problematic when DJANGO_SETTINGS_MODULE leaks out from one inenv into another.

Yeah I think it happens on env switch and doesn't effect subprocess usage (e.g. gbin). If you deactivate first it should be fine (can you double check if this is true?). Maybe we should force a deactivate on every new switch? I thought it was cool do something like "inenv project" "inenv prod" to get the envvars of prod bound to project's venv. Not sure if thats generally undesirable though.

I think inenv project, inenv prod would be confusing, because I don't expect side-effects to carry over between inenv invocations. I'd vote against it.


Creating a Search Widget in Angular with No Map

I have managed to add a search widget to the Angular github sample. My modified code looks like:
    async initializeMap() {
    try {
      // Load the modules for the ArcGIS API for JavaScript
      const [EsriMap, EsriMapView, Search, Locator] = await loadModules([
        "esri/Map",
        "esri/views/MapView",
        "esri/widgets/Search",
        "esri/tasks/Locator"
      ]);
      const locator = new Locator({url: "https://geocode.arcgis.com/arcgis/rest/services/World/GeocodeServer"});

      const search = new Search(this._view);

      // Configure the Map
      const mapProperties: esri.MapProperties = {
        basemap: this._basemap
      };

      const map: esri.Map = new EsriMap(mapProperties);

      // Initialize the MapView
      const mapViewProperties: esri.MapViewProperties = {
        container: this.mapViewEl.nativeElement,
        center: this._center,
        zoom: this._zoom,
        map: map
      };

      this._view = new EsriMapView(mapViewProperties);
      this._view.ui.add(search, "top-right");
      await this._view.when();
      return this._view;

How would I add the search widget to the web page without a map?
I would also like my search widget to have the same styling (css?) as the rest of my inputs which are bootstrap.
How would I override the styling so all my inputs are consistent in look and theme?

Closing...


QUESTION: Does this client work with a V1 Dialogflow agent?

I'm asking this because I took a working V2 agent with the following:

a google service account configured with scopes for the Dialogflow API
a generated service key json file
and then just toggled the agent to use the V1 API on the Dialogflow console, and then used this Dialogflow client to try calling the detectIntent method off a newly instantiated SessionsClient, and it still worked and provided back the same response structure (different responseId).

Is this intended behavior? Shouldn't I at least be seeing different JSON response structures when sending to the agent configured as V1/V2?

@matthewayne we aren't really sure how to answer this one :) Can you take a look?

This library does not work with v1.  Please use this client library for Dialogflow v1.  I should note that the authentication, methods and expected requests/responses have changed from v1 to v2.


Player can see player locatin without login.

Hi.
Player can see locations withot login into account.
LoginSecurity teleports player after join split secondly on he last location. After teleports on spawn to login.
If anyone try to login from any name, another player can see his coordinates with https://wweclient.com/documentation/modules/TeleportFinder.php

I think this doesn't happen if you server uses paper

I think this doesn't happen if you server uses paper

im uses paper.

Any FIX?

@i-zet @Cyberyisus2g I will look into the issue


[Bug]: Headings not support HTML character entities or escape characers

Version
System:
    OS: macOS 14.0
    CPU: (10) arm64 Apple M2 Pro
    Memory: 79.34 MB / 32.00 GB
    Shell: 5.9 - /bin/zsh
  Browsers:
    Chrome: 118.0.5993.117
    Safari: 17.0
  npmPackages:
    rspress: ^1.5.0 => 1.5.0

Details
Headings and TOC does not support HTML character entities and escape characters.
What I expect to be:
User('sub')

But got:

Reproduce link
https://codesandbox.io/p/sandbox/awesome-sunset-fmx92l?file=%2Fdocs%2Foverview%2Findex.md%3A9%2C1
Reproduce Steps
```md
# 概览

## @User(\'abc\')

\'
'

## @User('abc')
```

You can unable this config to avoid this error.
I'll fix it at https://github.com/web-infra-dev/mdx-rs/pull/14


Add deep-clean to verify target

help CI machines with directory cleanup
this target is not normally used except on CI machines

/cc @pranavkm

:shipit:

Commit 731722ae36719baef3e137c56010ccbe10b1ceea


Facebook messenger cards not showed when Activity.Text is empty

Github issues should be used for bugs and feature requests. Use Stack Overflow for general "how-to" questions.
Version
What package version of the SDK are you using.
4.10.3
Describe the bug
I've upgraded the versions of Microsoft.Builder.Bot from version 4.2.2 to 4.10.3.
In the previous version the bot was able to send an activity with the field Text empty and with the channel data containing facebook's json for show a card like the one in the screenshots.
Now with the upgraded version the response from bot is empty, and if I set the Text property with a value, the card shows correctly.
To Reproduce
Steps to reproduce the behavior:

Create an Activity
Set the ChannelData property with some channel specific markup (facebook, telegram...)
Leave the Text property empty
See error

Expected behavior
The bot responds with the facebook messenger cards
Screenshots
Correct output

Incorrect output

Additional context
Add any other context about the problem here.

There is an error from my side, closing the issue.
Regards


KAFKA-5454: Add a new Kafka Streams example IoT oriented

Added a Kafka Streams example (IoT oriented) using "tumbling" window

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/cc @guozhangwang @dguy @mjsax  @bbejeck @enothereska

cc @miguno too

Thanks LGTM.

LGTM,  thanks for the example.

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@miguno I have updated the PR with your change requests. I agree on them.
@guozhangwang the same, instead of using toStream() and the filter() on that (so on KStream instead of a KTable). See my reply.
Thanks guys !

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@ppatierno Sorry for the late replies. I think the PR is good to go. Though there is a refactoring on the StreamsBuilder / Topology today which deprecates some of the public APIs and proposed a set of new APIs inside o.a.k.streams directly. Could you rebase this PR once again? Thanks!

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@guozhangwang I have rebased and updated the example using the new StreamBuilder. Now it should be ok ! Thanks ! ;)

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Merged to trunk, thanks!


[[DOCS]] Add a code of conduct

Coverage remained the same at 98.752% when pulling f0d9f732ab158bbf9782b526ed1110e668b47dbd on jugglinmike:code-of-conduct into e0b4e91ba601c316ffd64c724cf972c51e180f65 on jshint:master.


DOC: updates to the docs

Resolving some of the items listed in https://github.com/paris-saclay-cds/ramp-board/issues/146
cc @glemaitre @maikia

Ups I merged it.

Oh OK, interesting. Good to know.
On Fri, 15 Feb 2019 at 11:25, Joris Van den Bossche <
<EMAIL_ADDRESS>wrote:

@jorisvandenbossche commented on this pull request.
In doc/setup_server.rst
https://github.com/paris-saclay-cds/ramp-board/pull/149#discussion_r257179747
:

mkdir postgres_dbs

Well, it was noted in #146
https://github.com/paris-saclay-cds/ramp-board/issues/146 this was done
by the initdb of postgres (but didn't test it)
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INRIA Saclay - Parietal team
Center for Data Science Paris-Saclay
https://glemaitre.github.io/


Add GitHub Actions job that redeploys snapshots to get consistent timestamps

As required for most build tools other than Maven, that is Gradle, sbt, etc.
On GitHub Actions, the result looks like this at
https://github.com/saudet/tensorflow-java/runs/559706945
which ultimately deployed those files:
tensorflow-core-api-0.1.0-20200404.015837-100-linux-x86_64-gpu.jar.md5 | Sat Apr 04 01:58:54 UTC 2020 | 32 |
tensorflow-core-api-0.1.0-20200404.015837-100-linux-x86_64-gpu.jar.sha1 | Sat Apr 04 01:58:54 UTC 2020 | 40 |
tensorflow-core-api-0.1.0-20200404.015837-100-linux-x86_64-mkl-gpu.jar | Sat Apr 04 01:58:54 UTC 2020 | 152881432 |
tensorflow-core-api-0.1.0-20200404.015837-100-linux-x86_64-mkl-gpu.jar.md5 | Sat Apr 04 01:59:01 UTC 2020 | 32 |
tensorflow-core-api-0.1.0-20200404.015837-100-linux-x86_64-mkl-gpu.jar.sha1 | Sat Apr 04 01:59:01 UTC 2020 | 40 |
tensorflow-core-api-0.1.0-20200404.015837-100-linux-x86_64-mkl.jar | Sat Apr 04 01:59:01 UTC 2020 | 39131128 |
tensorflow-core-api-0.1.0-20200404.015837-100-linux-x86_64-mkl.jar.md5 | Sat Apr 04 01:59:02 UTC 2020 | 32 |
tensorflow-core-api-0.1.0-20200404.015837-100-linux-x86_64-mkl.jar.sha1 | Sat Apr 04 01:59:02 UTC 2020 | 40 |
tensorflow-core-api-0.1.0-20200404.015837-100-linux-x86_64.jar | Sat Apr 04 01:59:02 UTC 2020 | 36061068 |
tensorflow-core-api-0.1.0-20200404.015837-100-linux-x86_64.jar.md5 | Sat Apr 04 01:59:03 UTC 2020 | 32 |
tensorflow-core-api-0.1.0-20200404.015837-100-linux-x86_64.jar.sha1 | Sat Apr 04 01:59:03 UTC 2020 | 40 |
tensorflow-core-api-0.1.0-20200404.015837-100-macosx-x86_64-mkl.jar | Sat Apr 04 01:59:03 UTC 2020 | 46506587 |
tensorflow-core-api-0.1.0-20200404.015837-100-macosx-x86_64-mkl.jar.md5 | Sat Apr 04 01:59:04 UTC 2020 | 32 |
tensorflow-core-api-0.1.0-20200404.015837-100-macosx-x86_64-mkl.jar.sha1 | Sat Apr 04 01:59:04 UTC 2020 | 40 |
tensorflow-core-api-0.1.0-20200404.015837-100-macosx-x86_64.jar | Sat Apr 04 01:59:05 UTC 2020 | 45371550 |
tensorflow-core-api-0.1.0-20200404.015837-100-macosx-x86_64.jar.md5 | Sat Apr 04 01:59:06 UTC 2020 | 32 |
tensorflow-core-api-0.1.0-20200404.015837-100-macosx-x86_64.jar.sha1 | Sat Apr 04 01:59:06 UTC 2020 | 40 |
tensorflow-core-api-0.1.0-20200404.015837-100.jar | Sat Apr 04 01:58:37 UTC 2020 | 2267498 |
tensorflow-core-api-0.1.0-20200404.015837-100.jar.md5 | Sat Apr 04 01:58:37 UTC 2020 | 32 |
tensorflow-core-api-0.1.0-20200404.015837-100.jar.sha1 | Sat Apr 04 01:58:37 UTC 2020 | 40 |
tensorflow-core-api-0.1.0-20200404.015837-100.pom | Sat Apr 04 01:58:37 UTC 2020 | 416 |
tensorflow-core-api-0.1.0-20200404.015837-100.pom.md5 | Sat Apr 04 01:58:37 UTC 2020 | 32 |
tensorflow-core-api-0.1.0-20200404.015837-100.pom.sha1 | Sat Apr 04 01:58:37 UTC 2020 | 40 |

https://oss.sonatype.org/content/repositories/snapshots/org/tensorflow/tensorflow-core-api/0.1.0-SNAPSHOT/
Also remove from GPU platform artifacts unavailable dependencies for Mac
/cc @frankfliu

Thanks @saudet ,
I'm not too sure this is the right approach to fix our snapshot issues. Unless I'm missing something, we'll continue to deploy snapshots artifacts individually and then we have an extra step to fetch back those deployed artifacts to a single machine and redeploy them at once.
The problems I'm foreseeing is that between the time the first artifact is deployed and the redeploy step is executed, our users might have trouble to download the snapshots (this period of time may vary between a few minutes or a few hours I'd guess, depending on the speed of the individual GitHub machines).
Also, the process of fetching back the artifacts by resolving Maven dependencies may fail if some snapshots artifacts have already been discarded by desynchronized timestamps from the individual deployments, the same way we observe right now.
Can we think of a solution that would imply artifact staging, like was proposing initially @frankfliu? At a first glance, the Sonatype Nexus maven plugin sounds promising with its server-side staging feature.
What do you think?

I actually not even sure this re-download can work. The timestamp already
messed up when 2nd deploy happen.
Instead of deployment artifact in the first round of build, we can upload
artifacts to github, And download them back at final publish job.
Thanks,
Frank
On Sun, Apr 5, 2020 at 8:27 AM Karl Lessard<EMAIL_ADDRESS>wrote:

Thanks @saudet https://github.com/saudet ,
I'm not too sure this is the right approach to fix our snapshot issues.
Unless I'm missing something, we'll continue to deploy snapshots artifacts
individually and then we have an extra step to fetch back those deployed
artifacts to a single machine and redeploy them at once.
The problems I'm foreseeing is that between the time the first artifact is
deployed and the redeploy step is executed, our users might have trouble
to download the snapshots (this period of time may vary between a few
minutes or a few hours I'd guess, depending on the speed of the individual
GitHub machines).
Also, the process of fetching back the artifacts by resolving Maven
dependencies may fail if some snapshots artifacts have already been
discarded by desynchronized timestamps from the individual deployments, the
same way we observe right now.
Can we think of a solution that would imply artifact staging, like was
proposing initially @frankfliu https://github.com/frankfliu? At a first
glance, the Sonatype Nexus maven plugin sounds promising with its
server-side staging feature.
What do you think?
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I actually not even sure this re-download can work. The timestamp already
messed up when 2nd deploy happen.

Yes, that's what I was pointing out as well in my second concern.

Instead of deployment artifact in the first round of build, we can upload
artifacts to github

I think that should work too, I'm sharing here the github actions to upload/download artifacts you have previously mentioned in the mailing thread:
actions/upload-artifact@v1, actions/download-artifact@v1

I'm 100% sure it works fine. I never had any issues at all with this for
over 5 years. If you feel differently, at least please provide proof!

BTW, Nexus provides no synchronization when uploading snapshots, so if
Gradle isn't able to wait for all artifacts to be uploaded, it's going to
fail anyway until everything is uploaded.
In any case, I keep telling you guys. I'm fine with whatever gets the job
done. If you know how to fix this exactly like you want it, please do it. I
don't intend to spend more time fixing something that doesn't need fixing,
especially when it's Gradle who should be fixing their code, not us.

And yes, I meant that the developers of Gradle do not want to fix their code, see https://github.com/gradle/gradle/issues/2882.

Thanks for detailed explanation. Just for my understanding:

Existing publishing should work for maven since it keeps the
snapshotVersion history. But gradle will fail to download it.
The re-deploy is purely workaround for gradle

Am I right?
Thanks,
Frank
On Sun, Apr 5, 2020 at 6:37 PM Samuel Audet<EMAIL_ADDRESS>wrote:

And yes, I meant that the developers of Gradle do not want to fix their
code, see gradle/gradle#2882
https://github.com/gradle/gradle/issues/2882.
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If this works with Maven, then I would like to understand why we missed macosx artifacts in Nexus a few days ago while GitHub was saying that they have been deployed successfully. If it is not because of timestamp desynchronization, could have they been deleted by mistake?
@saudet , I think you also have the credentials for deploying TF snapshots to the repository, are you aware of doing something that might have deleted those artifacts on your side?
Here is the last job that was ran before the artifacts were missing: https://github.com/tensorflow/java/actions/runs/63013370

Ok so here is what I suggest then: We leave the deployments scripts as they are now and see if the problem occurs again. If it does not, then we classify this issue as an unresolved mystery and move on. If it does, then investigate more about it, maybe requesting some help from Sonatype. Wdyt?
@saudet do you still want to make adjustments to disable OS X GPU artifacts though?

Thanks for detailed explanation. Just for my understanding:

Existing publishing should work for maven since it keeps the
snapshotVersion history. But gradle will fail to download it.
The re-deploy is purely workaround for gradle

Am I right?

Yes, that's correct. Maven has always supported that, and yeah it's only a workaround for Gradle and others like sbt too, but it's simple and it works.

If this works with Maven, then I would like to understand why we missed macosx artifacts in Nexus a few days ago while GitHub was saying that they have been deployed successfully. If it is not because of timestamp desynchronization, could have they been deleted by mistake?

It's not by mistake, it's just a setting of Nexus. Sonatype OSSRH configured their servers to leave only the 4 latest versions of snapshots, and anything else that is older than 2 days gets removed automatically (whenever the scheduled task actually runs though, so typically after 3 days):
https://issues.sonatype.org/browse/OSSRH-33316?focusedCommentId=422828&page=com.atlassian.jira.plugin.system.issuetabpanels%3Acomment-tabpanel#comment-422828
Redeploying like that though prevents this from happening because Nexus considers them as 1, so it's also a workaround for that.
BTW, GitHub Packages seems to be even worse in that regard: https://github.community/t5/GitHub-API-Development-and/GithHub-Packages-Older-artifact-downloaded-when-using-maven/m-p/43873

All right, thanks for the explanations @saudet, let's merge it then.


Rhino Automation - Reload test cases button doesn't work (no errors console/networking error)

According to the image it is working (log status is correct).
Reload Test Cases updates the test case cache on client site when working with multiple tabs.


Update scheduler-setup.md

Fixed URL

@JohanFreelancer9 - kindly do the copy editor review for this PR. Thanks


Displaying battery data without i2c Lcd

Hi Martin,
I'm a Arduino newb, living in Cape Town, South Africa, with an electrical and electronics background.
Have acquired all the hardware and software except the 20 x 4 i2c lcd display. Will order the display today. Have installed the Arduino IDE software for windows and downloaded the Github zipped file. Loaded the ino file onto the Arduino Uno.
Hardware:
Arduino Uno board.
12v Dc transformer to provide 12v to battery enable
All leads/wires from Uno to battery have be prepared and connected, with labels to the pins on Uno and battery sides.
Will connect the 20 x 4 i2c LCD display to the 5v supply leads and SCL and SDA pins on the Uno.
Until the display arrives is there any way to display the voltages, temps, state of charge and Serial number on my laptop?
Hope you can assist.
Regards
Moejaahied Bardien

By default, the code displays everything on a serial port.


msvc STATICLIB build example error

in example.pro line 54
PRE_TARGETDEPS += $$OUT_PWD/../bin/Toou2D/lib$${LIBNAME}.a
but msvc don`t output *.a.
when I modify code:
PRE_TARGETDEPS += $$OUT_PWD/../bin/Toou2D/$${LIBNAME}.lib
QQmlApplicationEngine failed to load component
qrc:/main.qml:3:1: plugin cannot be loaded for module "Toou2D": ??????F:/Qt/5.15.2/msvc2019/qml/Toou2D/Toou2Dd.dll?????????????Qt??(????????????????汾??????汾??)

Hi, have you solved this problem, I have the same problem


interract with the crop

Description of the new feature / enhancement
Using the new function "crop and lock".
When I do win+ctrl+shift+t
I would like to interact with the little windows :
either :

or the click and keyboard in the little windows will be reported to the real window in the background
or double click and the little windows show the real windows on the screen with the focus.

Scenario when this would be used?
To understand, It will be usefull with clockify website, to keep on the top the button "start/stop".

And not having to find it in my many windows...
Supporting information
No response

To clarify, what you're saying is that for certain apps that you'd like to crop into, there are still meaningful buttons that wouldn't necessarily be in that crop that you'd like to find a solution to still be able to access those other buttons?

/needinfo

I apologize, I hadn't seen the earlier notification.
I would like that when I crop using the shortcut Ctrl + Shift + Win + T, the buttons appearing in the crop are still clickable. The idea is that click and keyboard events are propagated to the original program.

Oups, I put the answer with an other account. the comment with esi-deploy is my too. I forgot I was logged with an other account.
Did you need some other information ?

you can do this with the reparent option.  the window can be interacted with


[SuperEditor] Drag to select skips one character in the upstream direction

When creating a text selection in the upstream direction with the mouse, the selection base will start one character further upstream than expected. See the below video for a couple examples:
https://user-images.githubusercontent.com/1316184/167518847-9ce9baae-b543-4d26-bba6-16910809a8e3.mov
I tracked the cause down to the the following line in text.dart
    // Rework the textPosition so that it reports a "downstream" affinity because
    // the editor doesn't support "upstream" positions, yet.
    //
    // Applying the "-1" to switch from upstream to downstream works everywhere, except
    // when the position is at the very end of the text. In that case, we leave the offset
    // alone.
    return TextNodePosition.fromTextPosition(textPosition.affinity == TextAffinity.downstream
        // The textPosition is already "downstream", leave it alone.
        ? textPosition
        // The textPosition if "upstream", adjust it to become "downstream", unless
        // the position sits at the very end of the text.
        : TextPosition(
            offset: textPosition.offset < widget.text.text.length ? textPosition.offset - 1 : textPosition.offset,
            affinity: TextAffinity.downstream,
          ));

Revising that code to always use textPosition without any modifications fixes the drag to select issue but presumably breaks other forms of selection elsewhere in the codebase (though no tests fail).

The first step here is probably to write an exhaustive set of tests that tap and drag for text selection. Then we can adjust this behavior and see what happens to the tests.
At first glance, the - 1 looks correct, because the - 1 is only applied when the incoming TextAffinity is upstream. An upstream position at x is the same as a downstream position at x - 1. This may be a red herring, and the root cause is somewhere else entirely.

What happens when you look up offsets from a RenderParagraph? We don't use Flutter TextFields in Flutter, so to the extent that those text fields are massaging the TextPosition, we may or may not be doing that in super_editor. However, super_editor relies heavily on RenderParagraph for mapping to/from TextPositions.
So if you pass the same TextPosition into RenderParagraph, one with upstream and another with downstream, do you get the same Offset returned for that position?

So if you pass the same TextPosition into RenderParagraph, one with upstream and another with downstream, do you get the same Offset returned for that position?

Yes. Test case:
import 'package:flutter/rendering.dart';
import 'package:flutter_test/flutter_test.dart';

void main() {
  test('RenderParagraph', () {
    final renderParagraph = RenderParagraph(
      const TextSpan(text: 'abcdefg'),
      textDirection: TextDirection.ltr,
    );
    renderParagraph.layout(const BoxConstraints(
      minWidth: 300,
      maxWidth: 300,
      minHeight: 100,
      maxHeight: 100,
    ));

    const downstreamPosition =
        TextPosition(offset: 3, affinity: TextAffinity.downstream);
    final upstreamPosition = TextPosition(
        offset: downstreamPosition.offset, affinity: TextAffinity.upstream);
    expect(
      renderParagraph.getOffsetForCaret(downstreamPosition, Rect.zero),
      renderParagraph.getOffsetForCaret(upstreamPosition, Rect.zero),
    );
  });
}

Output:
$ flutter test -r expanded
00:00 +0: RenderParagraph
00:00 +1: All tests passed!

Ok, thanks. We'll need to re-evaluate all of our usages of TextPosition throughout super_editor.


Durability/full inventory alerts

I suggest adding durability/full inventory alerts if that comply the Purpur theme. It is writes a message in the chat or action/title bar if the item in the hand is almost broken or the inventory is full. Also, so that it is configurable on what residual durability it will warn, and separately enable/disable these both functions + choose where the notifications will be in the chat or action/title bar.

This is best left up to a tiny 5 line plugin or skript


[B8] Flux dissipates very quickly

I needed to discover Vitium so I went somewhere secluded and dumped 5 or 6 stacks of cobble in a crucible.  As expected, flux skyrocketed, I got the entry level flux research, and some taint-related things started happening.
After observing for a while I swapped back to my thaumometer and noticed I was reading no flux, despite the meter being completely maxed out before.  I went back to base and got my Dioptra, and... yep.  No flux anywhere.  The taint is still there (including the seed) but no flux at all.  This explains why my base read as completely clean despite having definitely created some flux earlier on.
As a side note, when the flux was still showing up on the thaumometer it didn't seem to be spreading into nearby chunks.  It was only in one chunk.
Should tainted pigs not give new scan results?

I noticed this as well. I wanted to test the effects of serious taint in the environment, so I filled the crucible to the brim and emptied it twice, then held out my thaumometer. Of course, some extreme taint popped up pretty quickly, but I never even saw the flux before or after. Maybe the flux clears out of the environment after overflowing?

After further experimentation it's almost as if the taint eats the flux as it grows.  Could just be correlation though.
I did manage to get some flux readings from adjacent chunks but only after repeated dumpings of cobble, which seemed to stick around.  Maybe taint does eat flux?  It seems to eat too much of it in that case though.
The dioptra's flux setting showed the slightest of bumps near the area where I dumped flux.  I think it needs to be more sensitive for it to be useful.

Oh, from the Thauminomicon:
"If Flux builds high enough it seem to cause a tear in the very fabric of magic itself. The flux escapes through this tear(...)"

Taint does indeed 'eat' flux to grow, but at the same time it creates just enough flux to be able to continue growing. This results in there being an initial big burst of taint growth once it happens and then just slow and steady growth as long as no further flux is added to the system.

Also flux levels need to be fairly high before it starts spreading to nearby chunks. If you think of the aura as liquid then Vis flows like water while flux flows like thick jam.


pytorch fails signature tests with odd error message

This was discussed on slack. There are errors like
FAILED array_api_tests/test_signatures.py::test_func_signature[max] - hypothesis.errors.InvalidArgument: Could not create full array of dtype=torch.uint8 with fill value 0
FAILED array_api_tests/test_signatures.py::test_func_signature[mean] - hypothesis.errors.InvalidArgument: Could not create full array of dtype=torch.float32 with fill value 0.0
FAILED array_api_tests/test_signatures.py::test_func_signature[min] - hypothesis.errors.InvalidArgument: Could not create full array of dtype=torch.uint8 with fill value 0
FAILED array_api_tests/test_signatures.py::test_func_signature[prod] - hypothesis.errors.InvalidArgument: Could not create full array of dtype=torch.uint8 with fill value 0
FAILED array_api_tests/test_signatures.py::test_func_signature[std] - hypothesis.errors.InvalidArgument: Could not create full array of dtype=torch.float32 with fill value 0.0
FAILED array_api_tests/test_signatures.py::test_func_signature[sum] - hypothesis.errors.InvalidArgument: Could not create full array of dtype=torch.uint8 with fill value 0
FAILED array_api_tests/test_signatures.py::test_func_signature[var] - hypothesis.errors.InvalidArgument: Could not create full array of dtype=torch.float32 with fill value 0.0

which are not easy to read
The problem is that hypothesis tries to pass an integer to full, but torch only supports a tuple. I think a simple fix would be if hypothesis only tried passing a tuple to full to avoid this issue.

This error actually occurs throughout the test suite, not just in the signature tests.

I think a simple fix would be if hypothesis only tried passing a tuple to full to avoid this issue.

Not a big fan of changes like this, where hypothesis.extra.array_api is nominally built with the mindset to happily do whatever the spec allows (such as passing ints as the shape arg). But given this is probably not going to change anytime soon for pytorch, most likely a worthy change beyond just for this test suite, so I'll submit a patch.

For onlookers, ideally we could fallback to using inspect, but alas

https://github.com/pytorch/pytorch/issues/28233

so we instead pass arrays for un-inspectable functions. They could be manual examples, but at the time I found it convenient to use Hypothesis, as it could easily translate a functions valid input dtypes and create an array from that... using manual examples could be revisited too, esp. if there's some blocker to changing Hypothesis behaviour.


i18n(zh-cn): Update 5-astro-api/2.mdx

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Update 5-astro-api/2.mdx
To make the zh-cn translation status change, I review the doc and find other typos to commit.
Related issues & labels (optional)

#8907
Suggested label: i18n

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How to improve performance ?

i have about 20 WebImage in a lazy vgrid
now the app janks a lot when scrolling
i instrumented it and it shows WebImage was taking very long to draw
so is there a way to improve the performance ?

@leogiroux i have cropped images to small size, and yet the issue is still there
i think the problem is each WebImage has its own network module
which is against SwiftUI's design principle to keep views light
i might have to eventually ditch this framework and just use Image instead

Hello  @brightfuture @leogiroux  try lazyvgrid to simple vgrid.

"janks a lot" 🤣 I hope you made this up, because I'm stealing it. And I was just talking about this with a buddy. It would be nice if this library was updated to use the new Swift Concurrency features, it would be so smooth, @dreampiggy! There are a lot of things that the new AsyncImage cannot do, so it would be great if this library wrapped it.

I'm just going to put out there that I was having this exact same issue. I have 2 LazyHGrids and 1 LazyVGrid on the same screen all loading images. I was getting a horrible framerate and CPU pegged at 100%. Decided to make a Release build to see what the performance would be like in production. Smooth as butter, CPU sitting at around 2-3% unless the user is scrolling, but it never goes above 50%. So this seems to be a Debug-mode related issue with LazyV/HGrid and likely has nothign to do with SDWebImage


[MAIN] CI conda runner

coverage: 86.649% (-0.3%) from 86.983% when pulling c20f137fc4a9c9579edb6d32df2554e87d9e1059 on INM-6:fix/conda_CI into 0354d0ed384592f2bf9aee21a559a4a7b4b59d48 on NeuralEnsemble:master.


[elasticsearch-exported] Update README to reflect #14748

Just a simple update to add a parameter to the README that was added in #14748

Not sure if the version bump was necessary given I only changed the readme but I've done it :)

Could you signoff commit (see DCO)?


chore: enhancing app theme capability by changing adding colors

This PR enhances the theme capability of the app by introducing more colors. And this PR generalizes primary, secondary, and disabled button styles for the whole app.
Changes For openedX Theme
While generalizing the button styles, the button styles will change at some places for the openedX theme. Other than the highlighted changes below nothing will change in openedX theme.

New
Old

@sdaitzman Please review the changes mentioned-above for button changes.

This PR enhances the theme capability of the app by introducing more colors. And this PR generalizes primary, secondary, and disabled button styles for the whole app.
Changes For openedX Theme
While generalizing the button styles, the button styles will change at some places for the openedX theme. Other than the highlighted changes below nothing will change in openedX theme.
New	Old

@sdaitzman Please review the changes mentioned-above for button changes.

@saeedbashir  Last two screens you show in the Description have unreadable button for Light theme for my opinion. All other look good to me.

@saeedbashir Last two screens you show in the Description have unreadable button for Light theme for my opinion. All other looks good to me.

That is a disbalanced button state. In the disabled state, the opacity/transparency is 30%, and in the active state, they will look like normal buttons.

Hi @saeedbashir, thanks for tagging me for review! The first two screens look great to me. I'm okay with switching to using the accent color there.
Like @rnr mentioned, the buttons are hard to read in the final two screens. In those screenshots, the contrast ratio of ~1.14 is much too low. Even in the disabled state, users should be able to read the text to understand what action is not currently possible. I also think a grayed-out style is more clear for the disabled button state. Was there a design/usability rationale for switching to the transparent accent color for that button?

For reference, this tool can help check the contrast ratio against WCAG 2.1 values.

Are the images correct for before / after for the disabled buttons relating to the discussion examples? Both of those look the same to me

Are the images correct for before / after for the disabled buttons relating to the discussion examples? Both of those look the same to me

@marcotuts They mixed up while updating the images. Corrected now.

Copying from Slack, and adding a note about the second screen:
For the default OpenEdX mobile theme, we should continue using the grayed-out style inactive buttons (Figma component here) which are a bit more similar to mobile platform inactive button patterns. There are some pending updates on our end to adjust the color scheme and improve contrast ratios all around, currently working to document those changes in a new ticket.
Just noting here: both the EdX Paragon theme and the OpenEdX (grayed-out) inactive button style sometimes have a contrast ratio of ~2-3. This is still low, but in a better range than the inactive buttons currently proposed in this PR, and WCAG 2.2 allows it for inactive controls.
Lastly, I was wondering about the rationale for switching the "Leave" button in the second screen to use the accent color rather than the warning color. Since "Leave" is technically a destructive action (discards current changes), the use of the warning color there may make more sense.

@sdaitzman thanks for the feedback, Sam! I feel we can take up these edge cases in our design syncs better to discuss and address them in detail as it looks like it needs more attention.
For now, let's move forward with the current PR to save time. As we are replicating edX's theme which is tried and tested, we have fewer risks going in with it. Once we finalize our design approach we can do a fast follow on this.

All good @sdaitzman @marcotuts @moiz994 @ekangedx ?

I think we're good to go with this.
We discussed this in our design weekly this week as well. We decided to move forward with the PR for the sake of MVP but we will come back with more research and solid requirements to standardize the approach for buttons post-MVP. A solution was to add button types in the theme instead of colors on buttons but as mentioned a proper ticket will be created post-MVP.


Some questions about the current file / folder structure (v0.4.0)

why is format.ts in 'src/core/impl' but parseDate.ts in 'src/core'? For a consistent structure, parseDate.ts should be renamed to parse.ts and moved to src/core/impl.

constant.ts should be named as plural (constants.ts), as this file contains several constants.

unit.ts should IMHO also be spelled as plural, as it contains several unit types. In the current structure, the unit constants from constant.ts (from 'MS' to 'Date') could also be moved to units.ts without violating the naming scheme.

as the constants from src/common/index.ts are very often imported as import * as C from constant, why not export all constants as default export (in addition to the existing named exports). This would make the import avoid the wildcard (import C from constant.
Besides this, as you know already, I prefer "speaking" names and C says nothing to me 😄

The constant already exported as C in common/index.ts. And I agree with you about 1-3. It's helpful if you can provide a pr about those.

concerning '4':
the question was, if we shouldn't create a default export already in 'constant.ts'.
This way the '* as C' (which I only see for used js-only dependencies) in 'index.ts' could be replaced with 'import C from constant.ts'.

It's reasonable for me. You could request a pr.


must be a better way to reuse my functions...

See bmrxml:rda_iri_slug and bmrxml:rda_iri_slug_templates

Eliminated use of functions, using a single attribute value template pattern to output rdf:nodeIDs for ordering and defining PTs. See #24


Implement From<Option> for Value

Coverage remained the same at 94.946% when pulling e1d26fbe514f1b1fbb40d0a30f2264850b1f6e22 on dfaust:master into 5222ef2a7f61b352719f992e0af677511cc8db2b on AlexPikalov:master.

Codecov Report

Merging #116 into master will not change coverage.
The diff coverage is 100%.

@@           Coverage Diff           @@
##           master     #116   +/-   ##
=======================================
  Coverage   95.56%   95.56%
=======================================
  Files          18       18
  Lines        1870     1870
=======================================
  Hits         1787     1787
  Misses         83       83

Impacted Files
Coverage Δ

src/types/value.rs
96.12% <100%> (ø)
:white_check_mark:

Continue to review full report at Codecov.

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Allow opening links in a browser

I run Zulip electron on the mac, and have noticed recently that when I click links to external pages (not images), the electron app opens a chrome-less window that doesn't let me open the page in a browser.
Steps to reproduce:

Post the link https://example.com on a zulip instance
Click that link
=> A window inside zulip-electron pops up

Expected result: my browser should open a new tab.
Alternative result: The window should have a button to open the page in a browser, or show a URL.
I noticed that the electron app no longer shows a context menu when right-clicking on these links, which makes getting a copy-able URL from them even harder...

@antifuchs thanks for opening this. It's https://github.com/zulip/zulip-electron/issues/11 again 😭
I'll send a fix.

@acrefoot which version of zulip-electron are you using? Could you please try it on latest master?

I noticed that the electron app no longer shows a context menu when right-clicking on these links, which makes getting a copy-able URL from them even harder...

It's there in the latest release. See -

Oh dang, I wasn't on a release when repo'ing this: I reported against eac2b92cb671fba290ef4865bba4c03033edc077.
The latest HEAD (531973194c4e8c018079e8a068b25800232c997b) now does open links in a new browser tab, but I am not able to open the context menu: I only have a touchpad available, and neither two-finger-tapping (as configured system-wide) nor control-clicking opens a context menu for me.

@antifuchs ohh, I haven't consider the touchpad case yet 😆
I'll be adding this in next release.

@antifuchs I cannot reproduce it on my Mac. Urls work well with tapping and control clicking.
@akashnimare Has this been fixed, and shall we close it?

@geeeeeeeeek let's just wait and see if @antifuchs could reproduce this again.

@antifuchs can you verify whether the latest Zulip electron works fine on your touchpad?  We're closing this issue for now, since we cannot reproduce.

Thanks for the update - I just tested and two-finger-tap on the touchpad totally works now!


Updated deprecated Tensorflow syntax. Runtime warnings are fixed.

Updated Tensorflow syntax to address runtime warnings. Confirmed everything runs as before.
Pasting an excerpt of warnings for reference
From worker.py:26 in run.: all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.
Instructions for updating:
Please use tf.global_variables instead.
From worker.py:27 in run.: initialize_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.
Instructions for updating:
Use tf.variables_initializer instead.
From worker.py:28 in run.: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.
Instructions for updating:
Use tf.global_variables_initializer instead.
WARNING:tensorflow:From worker.py:38 in run.: init (from tensorflow.python.training.summary_io) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.FileWriter. The interface and behavior is the same; this is just a rename.
[2016-12-27 10:30:49,343] From worker.py:38 in run.: init (from tensorflow.python.training.summary_io) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.FileWriter. The interface and behavior is the same; this is just a rename.

Closed by https://github.com/openai/universe-starter-agent/commit/a670bdaba284bccb7b96d60a4d7a0d9dc231685b


[BUG] - Input placeholder disappearing

Describe the bug
If you select an input that has a placeholder then  the placeholder will disappear
Steps to Reproduce the Bug or Issue

Select an Input with a "placeholder" field
Click a Link with a href that goes to another page
The placeholder will disappear if the page uses a shared Input component

Expected behavior
I expected the placeholder to reappear after un-selecting the Input
Screenshots or Videos
https://user-images.githubusercontent.com/116610914/210851140-e9df96a0-4c6f-4995-8e88-107febfe43e3.mov
Operating System Version
macOS
Browser
Chrome

Created Code Sandbox based on replication steps, unable to reproduce.
Link:
https://codesandbox.io/s/modest-galileo-8isd79?file=/components/TestInput.jsx

Hey guys sorry for the delay please upgrade to V2.
https://nextui.org/docs/components/input


How properly change compiler?

Please follow the general troubleshooting steps first:

[x] Ran brew update and retried your prior step?
[x] Ran brew doctor, fixed as many issues as possible and retried your prior step?
[x] If you're seeing permission errors tried running sudo chown -R $(whoami) $(brew --prefix)?

Bug reports:
logs - http://pastebin.com/iMT9sDT3 ,
gcc/cc outputs - http://pastebin.com/8TQs04Db
i'm trying to install new glibc on ubuntu 12.04, but threre's bug with glibc and gcc-4.7. I tried to change compiler with -cc option, but have no luck. Unfortunately, i haven't sudo on this server. Any idea, how can i fix it?

You need two hyphens. brew install --cc=gcc-4.6
Please report brew gist-logs glibc to https://github.com/Linuxbrew/homebrew-core/issues


handle null activePipeline

Please check if the PR fulfills these requirements

[ ] The commit message follows our guidelines
[ ] Have you checked to ensure there aren't other open Pull Requests for the same update/change?
[ ] Tests for the changes have been added (for bug fixes / features)
[ ] Passes e2e testing
[ ] Passes lint
[ ] Docs have been added / updated if necessary (for bug fixes / features)

What kind of change does this PR introduce? (Bug fix, feature, docs update, ...)
bug fix

What is the current behavior? (You can also link to an open issue here)
closes _issue #_1175

What is the new behavior (if this is a feature change)?

Does this PR introduce a breaking change? (What changes might users need to make in their application due to this PR?)

Other information:

All tests pass locally