add copy relative path as separate item like in other IDEs

Problem

Not obvious how to make Copy Relative Path appear
Even after finding out how you still need to do a lot of actions
The Copy Relative Path is used a lot, and hiding it is not very convenient

Solution
Add as a separate item in the tab and file explorer.

Thanks, convinced.


[Tracking] Make Emojis WhatsAppQuality

Old (unreviewed) doc with mock-ups
Outstanding WAQ

https://github.com/Expensify/App/issues/12578

Done WAQ

https://github.com/Expensify/App/issues/12691
https://github.com/Expensify/App/issues/8459
https://github.com/Expensify/App/issues/12804
https://github.com/Expensify/App/issues/13070
https://github.com/Expensify/App/issues/13160
https://github.com/Expensify/App/issues/12772

Downstream polish

https://github.com/Expensify/App/issues/14098
https://github.com/Expensify/App/issues/12188
https://github.com/Expensify/Expensify/issues/239100
https://github.com/Expensify/App/issues/12263
https://github.com/Expensify/App/issues/12189
https://github.com/Expensify/Expensify/issues/239099
https://github.com/Expensify/App/issues/4114
https://github.com/Expensify/App/issues/13818

At this point we're officially done with all our unheld WAQ issues, so awesome work so far! The next step we're embarking on it tackling the polish items listed above – starting with #14098 and #12188.

Both of those will be worked on by Margelo. Then I believe that @stitesExpensify is taking on #239100, which add an emoji category picker.

This is moving along! Margelo has assigned the 2 smaller issues, and are actively working on the design doc for Reactions!
Also, I created this GitHub Project (internal only unfortunately) for myself for visual tracking if it helps anybody else. I will keep the project status updated both there and in this issue.

Still moving forward! The detailed design doc is in progress for Emoji Reactions, and I asked for an update on auto-complete here https://github.com/Expensify/App/issues/12188#issuecomment-1408812294

Design doc for Emoji Reactions has released the detailed implementation and is being reviewed
Auto-complete and mobile search are both in review

Tons of progress happening in this issue. The category picker hit staging (maybe even production by now). A PR is in place for single emojis being larger, same for emoji code auto-suggestions. At that point, we'll be ready to pick up the remaining items on this list, though there are admittedly not many.

Largely the same as above. Lots of progress in the linked issues in the downstream polish section. Check those for the specific details.

Mobile Search - Actively being worked on and reviewed in PR
Emoji Autosuggestion - Has an approval, but 1 small bug was found
Stickers - I have not started this yet, not a priority
Efficiently store and search for emojis - Needs a design doc, not a priority
Make emojis larger when alone - In review, currently lots of conflicts
Add tooltip for category picker - Merged!
Add emoji reactions - Final review, should be merged first thing tomorrow morning to get it out for ExpensiconX

Hey! In the announcement email today, I see this is a known issue:

The hover bar of emojis has the wrong icon (no plus button)

That was also reported in this issue, and I can't see a reference to it here. Is there an existing bug report for it? If there isn't, I'll just use #15812 for that! Thanks!

Yep! I made my own issue for that here and already have a PR up :)

Thanks for confirming!

Emoji autosuggestions have hit staging.

I'm considering making the sticker issue external, or potentially having an expert contributor take it on since it is technically a new feature and not a bug

@shawnborton What's you'r stance on this one at this juncture? I tend to think we should approach this with a fresh set of eyes and even put this through p/s format. What do you think?

Building on that last comment, I think the last time we discussed this, we landed on stickers being an incredibly common way to communicate. There's like some insane number of users that send/receive stickers daily, I vaguely remember it being in the hundreds of millions. But whatever the case, I think before we do anything, it'd be good to level set on how we're approaching this.

I like that idea. I think I still consider this as being different from uploading custom emojis like Slack has. This is more like a sticker pack that's used as a way to express yourself via our new branding and brand universe. I think the UI can be similar to what we've proposed though, or we could even consider a separate sticker icon that's not part of the emoji selector. If we do that, we might want to consider a different compose box layout to support more icons, like these:

Super rough but you get the idea.

Awesome yeah that sounds great. I agree with this in particular.

I think I still consider this as being different from uploading custom emojis like Slack has.

Yep agreed. I think that as long as we provide the assets for stickers, it can be all front end and we can have a contributor (or expert contributor) take care of it since it should just be the same as adding attachments to a chat!

I still think I agree with Jason in that we should have a P/S for this, and we should consider a few things for a pre-design as there are a few ways we could implement this. For example, we could have a dedicated sticker button in the chat composer like I shared above (which adds another icon to the composer and thus might warrant moving things around), or we could just supplement the current emoji menu with a sticker section.
Either way, I don't think I personally have time to run this through the P/S process but I'm happy to help out with mocks if anyone feels passionate.

Also happy to help but don't have any extra bandwidth.

Still working through the items listed in the OP

Not exactly a top priority right now though see individual issues for the latest.

Our focus was switched to threads for a while, but I am coming back to this next week.

The only WAQ issue still open is on hold for an issue on hold for another issue so that will take a while. The rest is just polish

Same update as last week.

One issue knocked off the list. The emoji reactions issue shouldn't be far behind.

I think we should close this one out as soon as the issue for emoji reactions closes.

Agreed!

We are very very close! There are a couple of regressions that popped up from the big migration to the new way of emoji reacting, and then we can close this out!

The final regressions are still being taken care of here

Agreed, let's close it out.


Add "Shuumatsu no Valkyrie II Part 2"

Netflix puts both parts of Season 2 on the same season, unlike AL, MAL and Kitsu.

Also could someone tell me if I did it right please ?


Bug 2115280: Fix ci HCO version

/bugzilla refresh

/lgtm

/retest

/retest

/retest

@upalatucci , since CNV 4.11 (upstream 1.7.0), HCO is deploying the kubevirt console plugin and all of its associated resources.
Thus we're getting the failure on oc create of these resources. Not only this, but HCO will reconcile it back.
To test in the CI using a custom image of the console plugin built by the CI, you should change the env var KV_CONSOLE_PLUGIN_IMAGE of hco-operator.
Since this is being set by OLM, we need to change it in the CSV so OLM won't reconcile it back.
To do this, you can use this code instead of the existing at: https://github.com/kubevirt-ui/kubevirt-plugin/blob/main/test-prow-e2e.sh#L111-L120
echo "Modify the kubevirt console plugin image"
CSV=$(oc get csv -o name -n ${NS})
oc get $CSV -o json > /tmp/hco_csv.json
EXISTING_PLUGIN_IMAGE=$(jq -r '.spec.install.spec.deployments[] | select(.name=="hco-operator").spec.template.spec.containers[0].env[] | select(.name=="KV_CONSOLE_PLUGIN_IMAGE").value' /tmp/hco_csv.json)
sed -i -r "s|$EXISTING_PLUGIN_IMAGE|$KUBEVIRT_PLUGIN_IMAGE|" /tmp/hco_csv.json
oc apply -f /tmp/hco_csv.json

until \
  oc wait pods --for=jsonpath='{.spec.containers[0].image}'="quay.io/kubevirt-ui/kubevirt-plugin:latest" -l app=kubevirt-plugin
  do
    sleep 1
  done

sorry, forgot to specify the namespace.
third line should be:
oc get $CSV -n ${NS} -o json > /tmp/hco_csv.json

please also add -n ${NS} to the oc wait command couple of lines below.
(when i tested it, my context was pinned to the correct namespace and i forgot the namespace modifier in this case).

another correction, sorry:
in the oc wait command, please put $KUBEVIRT_PLUGIN_IMAGE instead of quay.io/kubevirt-ui/kubevirt-plugin:latest

also, the wait command in line 128 is not needed, since we're already made the deployment.

in the logs of kubevirt-plugin pod, after replacing it with the image built by the code in the PR, it is suggested that the file /usr/share/nginx/html/plugin-manifest.json is missing:
2022/09/01 08:35:35 [error] 19#0: *1 open() "/usr/share/nginx/html/plugin-manifest.json" failed (2: No such file or directory), client: <IP_ADDRESS>, server: , request: "GET /plugin-manifest.json HTTP/1.1", host: "kubevirt-plugin-service.kubevirt-hyperconverged.svc.cluster.local:9443", referrer: "https://console-openshift-console.apps.ci-op-gin3jzii-a8c7e.origin-ci-int-aws.dev.rhcloud.com/"
<IP_ADDRESS> - - [01/Sep/2022:08:35:35 +0000] "GET /plugin-manifest.json HTTP/1.1" 404 555 "https://console-openshift-console.apps.ci-op-gin3jzii-a8c7e.origin-ci-int-aws.dev.rhcloud.com/" "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Cypress/8.5.0 Chrome/91.0.4472.164 Electron/13.2.0 Safari/537.36"
2022/09/01 08:35:35 [info] 19#0: *1 client <IP_ADDRESS> closed keepalive connection

https://gcsweb-ci.apps.ci.l2s4.p1.openshiftapps.com/gcs/origin-ci-test/pr-logs/pull/kubevirt-ui_kubevirt-plugin/854/pull-ci-kubevirt-ui-kubevirt-plugin-main-kubevirt-e2e-aws/1565238855462817792/artifacts/kubevirt-e2e-aws/tests/artifacts/kubevirt-plugin-6559b7648b-sdtrq.logs

/lgtm


[Improve] Drop fake pages

"Comming Soon" without any associated effort is fake. Let's drop these pages.

agree


Add the host architecture Windows 10 SDK bin directory to the PATH, not the target

Fixes failures during cross-compilation when the host cannot execute the target architecture Windows 10 SDK tools.

Thanks for this! Would it be possible to add a test for this on CI?

I added a CI task targeting aarch64 -- hopefully did that right.
The tests already include running rc (which ships in the Windows Kits). Without this change, it will attempt to run the aarch64 version of the tool on the x86_64 host.
I also manually ran the aarch64 tests locally.

👍 Thanks!


Getting "BrokenPipeError", "ConnectionResetError", and "EOFError" errors for hifigan training.

I've put wav files at 48k SR into a folder using the following script: https://github.com/CherokeeLanguage/Cherokee-IMS-Toucan/blob/main/create_vocoder_files.py
I am using the following to call the training: https://github.com/CherokeeLanguage/Cherokee-IMS-Toucan/blob/main/HiFiGAN_combined.py
At preprocessing 55% it crashes, and I don't see what the cause is in the error log.
hifigan-crash.log
Assistance appreciated.
python=3.8.12
Python Environment: https://github.com/CherokeeLanguage/Cherokee-IMS-Toucan/blob/main/environment.yml

I used to get broken pipe errors when I ran the code over ssh and the connection failed at some point. Since I started calling every script with nohup I haven't seen a broken pipe error. So maybe it's a server/connection issue? Assuming it's not, here are my thoughts:
The error happens when a wave is added to the list of all eligible waves. The list is handled by a resource manager to avoid multiple processes interfering with each other. The call to librosa.resample in the same line returns the identity if the samplingrate is already the same as the desired one, so that is probably not the cause. Adding something to the resource-manager list is the only possible cause I see. But then it's weird that it would work 817 times and then suddenly not.
You could try to check if wave contains what you think it contains right as it crashes. Also you could reduce the loading_processes argument when creating a HiFiGAN dataset. The default is 40 which assumes that the server it is run on has a lot of CPU cores (58 cores in the case of the one I'm using).

Were you able to resolve the issue? If not, I'll add the option to create the HiFiGAN dataset without multiprocessing. Multiprocessing is not needed for this, it just makes it a lot quicker.

I haven't had the opportunity yet to try any changes.
It would be useful to have a single threaded version that reports the filename if any error occurs though.

Should be simple, I'll do it in the next few days.

There is a memory leak:
python HiFiGAN_combined.py
Preparing
57%|██████████████████████████████████████████████████████████▌                                            | 33778/59360 [00:50<07:38, 55.78it/s]
Killed
I have a 32 GB machine and python was up to 33 GB VIRT 92% MEM before getting killed.
No sure where to look to find the objects not being released after use...

Would this work as an alternative to the in memory list?
https://github.com/Belval/disklist

It's not a memory leak, it works as intended. It's just built for servers with a lot of memory, to make the training oders of magnitude faster than when using lazy loading.
Yes, the code is processing all the wavs and then holds them during training. The disklist you suggest sounds cool, I'll add an option to use it, however only during single processing, since it's incompatible with the resource manager. The decrease in speed during training might be ok, because the torch dataloader prepares batches in the background. The HiFiGAN authors suggest to train for a very long time, that's why I did everything I could think of to make the training faster, but the models become already really good after around 200,000 steps, so if the speed decrease is too bad, you can just train for less steps.
I'm also looking for a good way to share pretrained models, since HiFiGAN is pretty speaker independent, you can use the same HiFiGAN pretrained model for any purposes and wouldn't need to train one yourself.

I'm also looking for a good way to share pretrained models, since HiFiGAN is pretty speaker independent, you can use the same HiFiGAN pretrained model for any purposes and wouldn't need to train one yourself.

Use the release function of github, it allows adding binaries manually to the release.
Tag repo with a version. Go into release in github. Mark tag as release. Add binaries.

Thanks for the suggestion, I just did that for now.
I'll also be looking for ways to load models auomatically in the future.

Meh... I tried to put together a HiFiGANDataset_disklist variant, but is seems that DiskList isn't pickling either librosa numpy data nor torch.tensors at all. I'll try looking into alternatives when I can.

Ok, I just did a direct to disk file caching of the tensors using torch.save, my GPU usage ranges 93% to 97%. This is on a GTX 3090.
Is it safe to increase the batch size? It is currently only using 13GBs of RAM and I some to spare above that.
Here is my nvidia-smi output:
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 470.86       Driver Version: 470.86       CUDA Version: 11.4     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA GeForce ...  Off  | 00000000:01:00.0  On |                  N/A |
| 51%   74C    P2   328W / 350W |  13463MiB / 24259MiB |     93%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|    0   N/A  N/A      1333      G   /usr/lib/xorg/Xorg                207MiB |
|    0   N/A  N/A      2305      G   /usr/bin/telegram-desktop           4MiB |
|    0   N/A  N/A      2702      G   /usr/lib/firefox/firefox          189MiB |
|    0   N/A  N/A      2928      G   /usr/lib/firefox/firefox            4MiB |
|    0   N/A  N/A      2931      G   /usr/lib/firefox/firefox            4MiB |
|    0   N/A  N/A     64587      C   python                          13049MiB |
+-----------------------------------------------------------------------------+

Yes, if the GPU-Util is not at 100% yet it makes sense to increase the batch size. Beyond that however i'm not sure if there's a benefit, even if there is VRAM left. The gradient estimate will become better, but the batches will take a little bit longer and the gradient estimate was already good enough.
So I'd say a slight increase until GPU-Util hits 100% is the best trade-off

Batch size = 32
How does this look so far? Should Generator Loss be increasing?
Epoch:              18
Time elapsed:       562 Minutes
Steps:              31518
Generator Loss:     160.41
    Mel Loss:       11.005
    FeatMatch Loss: 147.153
    Adv Loss:       2.252
Discriminator Loss: 0.292
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████| 1854/1854 [33:02<00:00,  1.07s/it]
Epoch:              19
Time elapsed:       595 Minutes
Steps:              33372
Generator Loss:     164.81
    Mel Loss:       10.822
    FeatMatch Loss: 151.747
    Adv Loss:       2.241
Discriminator Loss: 0.294
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████| 1854/1854 [33:01<00:00,  1.07s/it]
Epoch:              20
Time elapsed:       628 Minutes
Steps:              35226
Generator Loss:     168.52
    Mel Loss:       10.689
    FeatMatch Loss: 155.577
    Adv Loss:       2.253
Discriminator Loss: 0.292
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████| 1854/1854 [33:11<00:00,  1.07s/it]
Epoch:              21
Time elapsed:       661 Minutes
Steps:              37080
Generator Loss:     173.101
    Mel Loss:       10.658
    FeatMatch Loss: 160.177
    Adv Loss:       2.265
Discriminator Loss: 0.29

It looks good to me. Generator loss increasing is fine, since the feature matching loss gets a lot higher very quickly while the discriminator is improving. When you look at the generator loss in the very long term you'll see that the generator loss increases a lot in the beginning and then the increase gets slower and slower. At the point where the generator loss starts going down, that's when I usually stop the training.
A good thing to look for is the Mel Loss. As long as that one is decreasing, the model gets better.

Update:
Epoch:              30
Time elapsed:       972 Minutes
Steps:              381924
Generator Loss:     395.299
    Mel Loss:       9.369
    FeatMatch Loss: 382.977
    Adv Loss:       2.953
Discriminator Loss: 0.177
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████| 1854/1854 [34:40<00:00,  1.12s/it]
Epoch:              31
Time elapsed:       1007 Minutes
Steps:              383778
Generator Loss:     393.296
    Mel Loss:       9.309
    FeatMatch Loss: 381.034
    Adv Loss:       2.953
Discriminator Loss: 0.177
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████| 1854/1854 [35:01<00:00,  1.13s/it]
Epoch:              32
Time elapsed:       1042 Minutes
Steps:              385632
Generator Loss:     392.862
    Mel Loss:       9.336
    FeatMatch Loss: 380.578
    Adv Loss:       2.947
Discriminator Loss: 0.178

Closing the issue, because it is stale. A new version of the toolkit that will come soon (hopefully latest by end of next week) includes a procedure that allows training hifiGAN with fewer RAM by loading only a random chunk of the data for a certain amount of epochs and then loading another random chunk.


Techstack

Techstack of the website is upon discussion.
@aashimgarg @anubhavsingh16

I think logo is yet to be decided first.

Ur views regarding tech stack

[image: orgLogo.png]
On Wed, May 13, 2020 at 11:29 PM Aryaman Puri<EMAIL_ADDRESS>wrote:

Ur views regarding tech stack
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/Catalyst-SMVD/OpenSourceEvents-Frontend/issues/3#issuecomment-628152354,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/ANIP3P7J7RO4JSKTMHUOJI3RRLNXJANCNFSM4M77PNIA
.

HTML , CSS , VanillaJS finalised.


v3.63.0

@Br4nd0R Can you overwrite the docs/history.rst file with the contents of HISTORY.rst? They're out of sync until we get #3246 merged.


Crash with Quark+AutoRegLib

discovered these while trying to add Chiseled Me to a 1.12.2 Hexxit pack from Twitch.
I trimmed it down to just one mod that causes the crash:
Quark - crashed On Forge <IP_ADDRESS>54  (log attached)
chiseled-me-1.12.2-<IP_ADDRESS>.jar
Quark-r1.6-177.jar
AutoRegLib-1.3-32.jar   (required by Quark)
Chiseled-me, Quark, AutoregLib Crash.log

Was trying to add chiseled-me to a Hexxit 1.12.2 pack that I got from Twitch.  Did some binary searching to narrow the incompatibility down to this mod and its required library.

Quark? Oh, maybe makes sense, they must do something with bytecode too.
Will check this too.

Closing as duplicate of #32
Yeah I am going over the issues atm


[pull_request_merge] Remove co-authors and summaries from commit body

This change excludes bot co-authors from the pull request. We can use this method to slow down the number of bot contributions.
Here are some discussions about co-authors on the GitHub Maintainers community:

https://github.com/community/maintainers/discussions/354
https://github.com/community/maintainers/discussions/398

Those who have not made any substantial contribution are recorded as contributions on the pull request, which may make the PR author feel unfair.
Those summaries can also be removed. They don't have any real meaning; they are just lists generated by GitHub based on commits.

A better change would be to change the default merging strategy to use the PR description, since the commit list is indeed useless for all purposes

A better change would be to change the default merging strategy to use the PR description, since the commit list is indeed useless for all purposes

It's unnecessary to include so much information in the body. The PR number is already in the commit message.

Those who have not made any substantial contribution are recorded as contributions on the pull request, which may make the PR author feel unfair.

Are you talking about the raycast bot specifically or other users that spend the time to review a PR and suggest changes?

Are you talking about the raycast bot specifically or other users that spend the time to review a PR and suggest changes?

I'm talking about the bot. The bot co-authored our every contribution.

Why is it an issue that the raycast bot has contributions? Do you really care about it?

This information can be understood as redundant and can be removed.
But I respect your team's decisions. Feel free to close this PR.

I'd be willing to consider a change where it specifically omit the Raycast bot from the body - but I think it's important to preserve the rest of the information.
I'm not sure it's possible to do that with the GitHub CLI tho

We need another script to filter the bot line out.


Unexpected type conversion

Is there a way for the JSON document to be stored as is? For example, string "true" and string "42" are converted to boolean ant number respectively.
curl -H "Content-Type: application/json" -X POST -d '{"field1":"xyz","field2":"true","field3":"42","field4":"null"}' http://localhost:3000/example
{
  "field1": "xyz",
  "field2": true,
  "field3": 42,
  "field4": "null",
  "id": 1
}

While it may not be significant in javascript, the resulting document cannot be easily decoded to a map in Go (and maybe other languages) if the expected type is a string.
Thanks

Hi @rpdprd,
Not right now, but it's something that I'm working on (next branch).

I've published v0.9.0-beta.2 which should improve this. Auto convert has been removed.
If you want, you can give it a try using npm install json-server@next.
README for v0.9.0 can be found in the next branch https://github.com/typicode/json-server/tree/next
Feedback is welcome :)

Should be good in v0.9.0


Inject Page number in footer starting with arbitrary number

I would like to inject the page number into the pdf starting at at arbitrary number (so it doesn't necessarily start at page 1).
Is that possible?

How can I fork the package to add this functionality?

I hope this can be addressed

Hello @pjebs
Sorry for the late answer. Truth is I dunno how to do it..
Do you have a tool in mind that is capable of such task?

Couldn't you prepend

" for every page-number you want to skip and then use the nativePageRanges` option to exclude those from printing?

I'll have a try


REPLAY-1724 Create images screen in sample app

What does this PR do?
Created a screen in the sample app containing ninepatch, bundled, remote, statelist in various types of views -> View, TextView, Button, ImageButton, ImageView
Motivation
Necessary prerequisite for base64
Additional Notes
The link on the picture loading screen was broken, so I replaced it as part of this pr
Review checklist (to be filled by reviewers)

[ ] Feature or bugfix MUST have appropriate tests (unit, integration, e2e)
[ ] Make sure you discussed the feature or bugfix with the maintaining team in an Issue
[ ] Make sure each commit and the PR mention the Issue number (cf the CONTRIBUTING doc)

Codecov Report

Merging #1470 (d22c089) into feature/session-replay (d731cbf) will increase coverage by 0.01%.
The diff coverage is n/a.

@@                    Coverage Diff                     @@
##           feature/session-replay    #1470      +/-   ##
==========================================================
+ Coverage                   82.76%   82.77%   +0.01%
==========================================================
  Files                         407      407
  Lines                       14238    14238
  Branches                     2323     2323
==========================================================
+ Hits                        11783    11785       +2
  Misses                       1749     1749
+ Partials                      706      704       -2

see 14 files with indirect coverage changes


License and package review request (Python)

Ensure that the following changes are complete:

[x] Examine the license and decide whether it should be changed
[x] Discuss reasons for the license choice in issues
[x] Link to the R issue if the discussion is on that repository
[x] Initiate a review request by opening an issue using the template (https://github.com/UBC-MDS/software-review-2023)

Finished review request here: Issue

At the moment, both of our packages are licensed under the MIT License. This license is one of the most common. It allows offers users the most flexibility with using our package.  The requirements of using our package are that they include both copyright and license notices.
In a review of other options, I discovered that some options are:
Use a community license
GNU GPLv3
Apache
The Unlicense
Since we are not working within a community, we do not need to continue with a community or project license. This would be if we had either created a request for a previous project or were extending this project. Or if we were extending an existing project that required us to use
The GNU GPLv3 also is fairly open about what users can do with our package. Like the MIT License, they require both copyright and license notices. In addition to the MIT notice, the GPL requires users to use the same license. In addition, we would be required to grant patent rights. The Apache License works similarly to the GNU GPLv3 but also explicitly states that it does not permit trademark rights.
The Unlicense allows anyone to do anything with our project without restrictions on licenses or patent rights.
I believe that the current MIT License is the correct License for this project. This seems like the most straightforward strategy and allows for the most open-source collaboration with still including our copyright and license notices. The packages that we used in our package creation like Pillow used HPND License or their own licenses. So, I do not believe that we need to follow their licenses.
Sources:
https://choosealicense.com/

Thanks for doing that research, Shaun.  I agree that it makes sense to continue with the MIT license.

For the Python package, I think it makes sense to stick with the MIT License since our dependencies also use the same license. However for R, would it make more sense to adopt the GNU GPLv3 license? This would be because colorfindr uses the GPL license, which we have included in our package.

I agree that our python package, colourpycker, should remain under the MIT License. This aligns with the license of our dependency, extcolors, and allows for community benefits while protecting us from liability.
Also, as the R package, colorfindr, uses GPL (>= 2), and since we cannot convert it into MIT License or other license options, we might as well use GPLv3 to align with the latest improvements in the license.

I have updated the R package to adopt the GPLv3 license and will leave the Python package license as is.


Optimistic updates with a custom cache ID

Thanks for the great library!
I have a custom normalizerConfig that uses a contextId property on an object if it exists for the cache key, otherwise falls back to id.
Note that contextId is really only used as the cache ID; these objects also have a id property but it's not guaranteed to be unique across tenants in my multi-tenant use case; contextId is a compound key that makes it truly unique across all caches.
<QueryNormalizerProvider
  queryClient={queryClient}
  normalizerConfig={{
    getNormalizationObjectKey: (obj) => {
      if ('contextId' in obj && typeof obj.contextId === 'string') {
        return obj.contextId;
      }
      return typeof obj.id === 'string' ? obj.id : undefined;
    },
  }}
>
  ...
</QueryNormalizerProvider>

Now, when I go to optimistically update one of these entities keyed by contextId, I'm noticing that the id value of my entity is being overwritten by the id property returned from optimisticData:
onMutate: () => {
  return {
    optimisticData: {
      id: contextId,
      isFollowing: true,
    },
    rollbackData: {
      id: contextId,
      isFollowing: false,
    },
  };
},

What I thought this code was saying is "find the object in the cache keyed by the value contextId and set its isFollowing property to true". I did not expect the id property of the entity itself to also be overwritten, which in my case creates unintended UI bugs.
Is what I'm trying to achieve possible?

Thanks, that does work. How does the library know which record to find in the cache?
The fact that id isn't a special property and that it's able to find a record to optimistically update on contextId alone, while ignoring isFollowing: true as part of that lookup, is confusing to me.

Just by executing getNormalizationObjectKey.
This function is used when inspecting queries and normalizing data, as well as when reading mutation data, to know which objects to update.
For example, you could also have getNormalizationObjectKey: obj => obj._id as string | undefined,
Then, you would do:
onMutate: () => {
  return {
    optimisticData: {
      _id: 1,
      isFollowing: true,
    },
    rollbackData: {
      _id: 1,
      isFollowing: false,
    },
  };
},

Think about it in another way:

When having a response data from a mutation, you would get an object with normal id and normal contextId, not id as contextId, so if you manually prepare optimistic data, think what would you get from API
For this library to work, objects have to have consistent object structure, and using contextId as id is a violation for this rule.

update on contextId alone, while ignoring isFollowing: true as part of that lookup, is confusing to me.

To find a proper object to update, the object has to contain all attributes needed to calculate an unique identifier. It should also contain at least those attributes there, which could change, in your case isFollowing, which will be merged with the corresponding object in normalized store. Ofc this could be a nested obejct as well, or object containing arrays, even with other normalized nodes with own ids, it will work, as deep as you need, as long as the object structure is consistent.

That makes sense, thank you!


bound: Add Intersection helper for bound package

Add Intersection helper API for bound package
Updates: #939

@btracey PTAL!

@btracey @kortschak
I 've updated the CL which is reflected reviews.
PTAL

@kortschak @btracey
I've updated the CL as you comment
two APIs are added also.

Bound.IsEmpty()
Bound.IsValid()

Please take a look.

@kortschak I 've rebased commits
@btracey I remove IsEmpty method and update receiver of IsValid
@vladimir-ch I 've updated unit test names
Please take a look!

This probably should have been a rebase rather than a squash since it was nicely formatted to have them as separate commits.

Oh, shoot, sorry!


Create Route page in Monitoring app is crashing

Go to Create Route in the Monitoring app

Results:  App hangs.  Errors in console

fixed


Flags not being evaluated at the right time

Just tried to add -R to override the project root and ran into the fact that the flags are not evaluated till after the plugins.
After adding a bit of debugging *goroot, *goos, *goarch will only ever get set to the default values.
https://github.com/constabulary/gb/blob/master/cmd/gb/main.go#L77
vs flags evaluated on
https://github.com/constabulary/gb/blob/master/cmd/gb/main.go#L100

Can you check with the latest version, I think I fixed this problem last
night.
To be fair, -goos and -goarch aren't rrally tested, but I know -goroot works
On Sat, 2 May 2015 09:36 Mark Wolfe<EMAIL_ADDRESS>wrote:

Just tried to add -R to override the project root and ran into the fact
that the flags are not evaluated till after the plugins.
After adding a bit of debugging *goroot, *goos, *goarch will only ever
get set to the default values.
https://github.com/constabulary/gb/blob/master/cmd/gb/main.go#L77
vs flags evaluated on
https://github.com/constabulary/gb/blob/master/cmd/gb/main.go#L100
—
Reply to this email directly or view it on GitHub
https://github.com/constabulary/gb/issues/26.

I pulled down the latest changes and added.
	gb.Infof("goroot=%s goos=%s goarch=%s", *goroot, *goos, *goarch)
	tc, err := gb.NewGcToolchain(*goroot, *goos, *goarch)

Passed in arm as goarch and it is still set to amd64.
gb build -goarch=arm -goos=linux all

got
2015/05/02 10:18:38 INFO goroot=/usr/local/Cellar/go/1.4.2/libexec goos=darwin goarch=amd64

Yup, that's a bug. I haven't tested GOOS/GOARCH only changing the toolchain
via -goroot, there is a chance those former values are hard coded.
I'll fix it.
On Sat, 2 May 2015 10:19 Mark Wolfe<EMAIL_ADDRESS>wrote:

I pulled down the latest changes and added.
gb.Infof("goroot=%s goos=%s goarch=%s", *goroot, *goos, *goarch)
tc, err := gb.NewGcToolchain(*goroot, *goos, *goarch)

Passed in arm as goarch and it is still set to amd64.
gb build -goarch=arm -goos=linux all
got
2015/05/02 10:18:38 INFO goroot=/usr/local/Cellar/go/1.4.2/libexec goos=darwin goarch=amd64
—
Reply to this email directly or view it on GitHub
https://github.com/constabulary/gb/issues/26#issuecomment-98266215.

Note, unless you're using go1.5, to do cross compiles you'll have to do
that 'christen your cross compile toolchain dance', gb wont save you from
that, and in fact, it might make it worse as we always consider the
standard library to be up to date irrespective of the toolchain.
On Sat, 2 May 2015 10:22 Dave Cheney<EMAIL_ADDRESS>wrote:

Yup, that's a bug. I haven't tested GOOS/GOARCH only changing the
toolchain via -goroot, there is a chance those former values are hard
coded.
I'll fix it.
On Sat, 2 May 2015 10:19 Mark Wolfe<EMAIL_ADDRESS>wrote:

I pulled down the latest changes and added.
gb.Infof("goroot=%s goos=%s goarch=%s", *goroot, *goos, *goarch)
tc, err := gb.NewGcToolchain(*goroot, *goos, *goarch)

Passed in arm as goarch and it is still set to amd64.
gb build -goarch=arm -goos=linux all
got
2015/05/02 10:18:38 INFO goroot=/usr/local/Cellar/go/1.4.2/libexec goos=darwin goarch=amd64
—
Reply to this email directly or view it on GitHub
https://github.com/constabulary/gb/issues/26#issuecomment-98266215.

@wolfeidau what i'm going to do is close this issue by removing -goarch and -goos. I'll open a new issue to make them work properly.


Audio Chrome Problem

The sounds are all distorted when any key is pressed, this is only on chrome, but on other browsers the sounds are generally laggy, so chrome is the best for it, yet it's always coming up with bugs, any fixes?

Try switching from "Power Saver" to "Balanced" (or something like that, on 'Battery' on your desktop/computer.) If this doesn't work, then I don't know what does.


[Bug] front-end enum bug fixed

[Bug] front-end enum bug fixed
What changes were proposed in this pull request
Issue Number: close #xxx

Brief change log

Verifying this change

This change is a trivial rework / code cleanup without any test coverage.
(or)
This change is already covered by existing tests, such as (please describe tests).
(or)
This change added tests and can be verified as follows:

Does this pull request potentially affect one of the following parts

Dependencies (does it add or upgrade a dependency): no

cc @wangsizhu0504 PTAL, thanks


Perform RBAC user filter check on requested ids before allowing reque…

This addresses a vulnerability where a user can change the targeted ids in the browser console to allow operations like power on/off on VMs that he is not entitled access.
https://bugzilla.redhat.com/show_bug.cgi?id=1382756
(cherry picked from commit 9376fbdd058b92c42922482f09a792f18f75467f)

This is already on euwe via https://github.com/ManageIQ/manageiq/commit/9376fbdd058b92c42922482f09a792f18f75467f and on darga via https://github.com/ManageIQ/manageiq/commit/3add6e6fe433c7314b477dee9eb8cdec7174d7f8


公式显示问题

Checklist

[ ] 1. I have searched related issues but cannot get the expected help.
[ ] 2. The bug has not been fixed in the latest version.
[ ] 3. Please note that if the bug-related issue you submitted lacks corresponding environment info and a minimal reproducible demo, it will be challenging for us to reproduce and resolve the issue, reducing the likelihood of receiving feedback.

Describe the bug

尝尝遇到公式输出失败的问题
Reproduction
公式显示问题
Environment
公式显示问题

Error traceback
No response

您好，感谢您的反馈。我们已经安排了同学去看前端显示的问题。


Some questions about GPU and clip_length?

Hello, I would like to ask some related issues about R(2+1)d paper
①Did you use 64 GPUs when training K400? What is the specific number and model of GPU?
② Is the clip_length of the pre-training model used by UCF101 and HMDB51 8 frames or 16 frames?

Thanks for your interest in the paper.
1, You can use any number of GPUs for training models on Kinetics. Remember to adjust your batch size and learning rate accordingly.
2, We use 32 frames which give better results on UCF101 and HMDB51.

Thanks for your interest in the paper. 1, You can use any number of GPUs for training models on Kinetics. Remember to adjust your batch size and learning rate accordingly. 2, We use 32 frames which give better results on UCF101 and HMDB51.

In addition, how many clips are used for average prediction for ucf101 and hmdb51?


[BattlePass] Raid Dens not Counting

Describe the bug
When Defeating a pokemon in a Raid Den, it does not count towards the BattlePass Challenges
To Reproduce
Steps to reproduce the behavior:

Have an open task to defeat a pokemon
Defeat it in the Raid Den
Not Register as completing the Task

Expected behavior
It should count for the task when defeating the Pokemon
Username: Ultamate
Server name (all applicable): All
Additional context
This is similar to the Horde Bug

This is a Pixelmon bug, and is out of our hands, just like the Horde Bug

This can be forwarded to the internal tracker, we can likely do something hacky on our side to force Hordes and Raids to count as catch normally @WisconsinWinters


 If that isn't so, feel free to close.
With the temporary fix mentioned in https://github.com/hopsoft/stimulus_reflex/issues/81 and https://github.com/hopsoft/stimulus_reflex/pull/82 I was able to install stimulus_reflex,
but the pack couldn't be compiled.
Describe the bug
Apparently babel-plugin-transform-object-rest-spread doesn't like the line
controller.StimulusReflex = { ...options, channel, room }
Output of bin/rails webpacker:compile
ERROR in ./node_modules/stimulus_reflex/stimulus_reflex.js
Module parse failed: Unexpected token (168:32)
You may need an appropriate loader to handle this file type.
|   const channel = 'StimulusReflex::Channel'
|   const room = options.room || controller.element.dataset.room || ''
|   controller.StimulusReflex = { ...options, channel, room }
|   extendStimulusController(controller)
|   createSubscription(controller)
 @ ./app/javascript/packs/application.js 14:0-45

To Reproduce
Set up a rails 5.2 application as outlined here: https://github.com/hopsoft/stimulus_reflex/issues/81 and try to bin/rails webpacker:compile
Expected behavior
The pack should compile.
Versions
Ruby: ruby 2.6.3p62 (2019-04-16 revision 67580) [x86_64-darwin18]
Rails: 5.2.3
Webpacker: 3.6.0
Node: v10.15.3
Yarn: 1.19.1

@rails/webpacker:
stimulus_reflex_test@ {...}/stimulus_reflex_test
└──<EMAIL_ADDRESS>
Is bin/webpack present?: true
Is bin/webpack-dev-server present?: true
Is bin/yarn present?: true

I'm not even sure stimulus_reflex is intended to be backwards compatible.

This is great feedback. I'd like to support Rails apps 5.2+ with webpacker.

I'd be glad to help out, but I'm no webpack wizard :-/

@julianrubisch any chance you have a reproduction example app?

affirmative: https://github.com/julianrubisch/stimulus_reflex_rails5_2_webpacker_3_test

We had the same issue.
Following the guide to upgrade webpack to version 4.x https://github.com/rails/webpacker/blob/master/docs/v4-upgrade.md solved the problem for us.

@nicpalard I'm sure of that! However, with the app I'm currently working at, that's just not an option :(

@hopsoft I need a hand with testing this but we may be able to just include the correct babel plugin and make the code backwards compatible.
  "plugins": [
        '@babel/plugin-proposal-object-rest-spread', { useBuiltIns: true },
    ],

It may be worth noting that stimulus' docs have instructions that are not compatible by default with Webpacker 3.5 (see here).
@julianrubisch I am curious why migrating to webpacker 4 is not an option? It is a relatively painless upgrade. I am sure you have your reasons, I am just curious as to what they are exactly from an educational standpoint 🙂

@andrewmcodes oh that‘s easy: it breaks other dependencies that aren’t webpacker 4 ready (in my specific case a problem with web workers)

While I do think that ES6 + webpack + yarn/npm are an improvement on the jQuery era in general (ie. "when it all works") its stories like @julianrubisch's that make me angsty about what Sam Stephenson aptly referred to as "an unfortunate step backwards for developer ergonomics" generally associated with bikeshedding in the SPA era.

Couldn’t agree more. Transpile all the things!

Where does this issue stand, given that v3 is now Rails 6 only?

Without prescribing anything, how do we feel about closing this issue for now?

Closing this for now. Just housekeeping. Please re-open if it's on your radar.


IIS AppPool identity incorrect mapping for username and password

When using IIS AppPoolIdentity set as SpecificUser. Username will be mapped as password and Password as Username.
This should be easily fixable:

@gunnarthors thank you for the find! I fixed this as soon as I could.


fixes #541, add namespace to root item in builder

This PR Closes #541
In xml2js, you can't add attribute to a node if its child nodes form an array. In this case, you can only use "-" to add text nodes.
I reckon it would be better to open the builder object since this can largely increase the possibilities to manipulate with xml2js for more particular cases. Also, Parser can access the Parser object through parser.saxParser.
And the problem mentioned in #541 can be solved this way:
    const urls = [{ url: "node1" }, { url: "node2" }, { url: "node3" }];

    const root = {
        urlset: {
            $: { xmlns: 'http://www.sitemaps.org/schemas/sitemap/0.9' }
        }
    }

    const builder = new xml2js.Builder();
    const rootNode = builder.build(root);

    const xmlString = rootNode.ele(urls).end(xml2js.defaults["0.2"].renderOpts);

    console.log(xmlString);

output:
<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
  <url>node1</url>
  <url>node2</url>
  <url>node3</url>
</urlset>

Currently, xml2js only supports adding attribute to XML in this form:
    const root = {
        urlset: {
            $: { xmlns: 'http://www.sitemaps.org/schemas/sitemap/0.9' },
            url: "node1",
            url2: "node2",
            url3: "node3"
        }
    };

    const builder = new xml2js.Builder();
    console.log(builder.buildObject(root));

output:
<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
  <url>node1</url>
  <url2>node2</url2>
  <url3>node3</url3>
</urlset>

Coverage remained the same at 97.727% when pulling 23c218a4987415aaca809a2ad28e612935491ea7 on Omega-Ariston:fix-issue541 into 8fc5b926846cd4ef9a2dbccd411705e0c110a708 on Leonidas-from-XIV:master.


Allow passing an object as options to advancedInstance

advancedInstance has too many options to put them all as arguments to the function. It's error prone, and using an options object instead would be better.
Instead of using the following pattern:
PageScrollInstance.advancedInstance(this.document, '#myId', null, null, true, 10, true, null, 300);

I should (also) be able to write:
PageScrollInstance.advancedInstance({
    document: this.document,
    scrollTarget: '#myId',
    isVerticalScrolling: true,
    pageScrollOffset: 10,
    pageScrollInterruptible: true,
    pageScrollDuration: 300,
});

It makes users less likely to make errors, since they no longer have to remember the order of the options. It is also easier to implement new options moving forward.
Would you be interested in a pull request with these changes?

Very good suggestion, no idea why I didn't come up with this initially.
PR would be awesome


CentOS - failed call to cuInit: CUresult(-1)

So, again me - I'm on centOS on a cluster. Have used this hack for bypassing and using a different GLIBC. Now, however, there seems to be some weird issue with Tensorflow not wanting to run my GPU by failing something on the CUDA side.
Some general info:
LSB Version:    :base-4.0-amd64:base-4.0-noarch:core-4.0-amd64:core-4.0-noarch:graphics-4.0-amd64:graphics-4.0-noarch:printing-4.0-amd64:printing-4.0-noarch
Distributor ID: CentOS
Description:    CentOS release 6.5 (Final)
Release:        6.5
Codename:       Final
CUDA version: 8.0
CUDNN version: 5.1
Python version: 3.6.0
LD_LIBRARY_PATH=/share/apps/barber/system/lib/:/share/apps/barber/system/lib64/:/opt/gridengine/lib/linux-x64:/opt/openmpi/lib/:/share/apps/barber/cuda/lib64/:/share/apps/barber/cuda/nvvm/lib64/:/share/apps/barber/cuda/extras/CUPTI/lib64:/share/apps/barber/cudnn/lib64/:/share/apps/barber/arrayfire-3/lib/:/share/apps/python-3.6.0-shared/lib/

The actual error I get from my code is (first 2 lines are printed by me):
Running tool: tensorflow
/gpu:0
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcublas.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcudnn.so.5 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcufft.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcurand.so.8.0 locally
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could spe
ed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could s
peed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could s
peed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could spee
d up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could spe
ed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could spee
d up CPU computations.
E tensorflow/stream_executor/cuda/cuda_driver.cc:509] failed call to cuInit: CUresult(-1)
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:158] retrieving CUDA diagnostic information for host: tesla2.local
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:165] hostname: tesla2.local
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:189] libcuda reported version is: Not found: was unable to find libcuda.so DSO loaded into this program
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:363] driver version file contents: """NVRM version: NVIDIA UNIX x86_64 Kernel Module  367.48  Sat Sep  3 18:21:08 PD
T 2016
GCC version:  gcc version 4.4.7 20120313 (Red Hat 4.4.7-16) (GCC)
"""
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:193] kernel reported version is: 367.48.0
Traceback (most recent call last):
  File "/share/apps/barber/system/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1022, in _do_call
    return fn(*args)
  File "/share/apps/barber/system/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1000, in _run_fn
    self._extend_graph()
  File "/share/apps/barber/system/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1049, in _extend_graph
    self._session, graph_def.SerializeToString(), status)
  File "/share/apps/python-3.6.0-shared/lib/python3.6/contextlib.py", line 89, in __exit__
    next(self.gen)
  File "/share/apps/barber/system/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py", line 469, in raise_exception_on_not_ok_status
    pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: Cannot assign a device to node 'gradients/ff_1/add_grad/BroadcastGradientArgs': Could not satisfy explicit d
evice specification '/device:GPU:0' because no devices matching that specification are registered in this process; available devices: /job:localhost/replica:0/task:0/cpu:
0
         [[Node: gradients/ff_1/add_grad/BroadcastGradientArgs = BroadcastGradientArgs[T=DT_INT32, _device="/device:GPU:0"](gradients/ff_1/add_grad/Shape, gradients/ff_1/
add_grad/Shape_1)]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "bench.py", line 23, in <module>
    execute_all(**vars(parser.parse_args()))
  File "/home/abotev/work/python/deep-bench/deep_bench/executor.py", line 40, in execute_all
    wide=w, depth=d, batch=b)
  File "/home/abotev/work/python/deep-bench/deep_bench/executor.py", line 123, in single_experiment
    f(model, device, temp_folder, **kwargs)
  File "/home/abotev/work/python/deep-bench/deep_bench/frameworks/tensorflow/executor.py", line 80, in execute_experiment
    session.run(init)
  File "/share/apps/barber/system/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 767, in run
    run_metadata_ptr)
  File "/share/apps/barber/system/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 965, in _run
    feed_dict_string, options, run_metadata)
  File "/share/apps/barber/system/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1015, in _do_run
    target_list, options, run_metadata)
  File "/share/apps/barber/system/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1035, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Cannot assign a device to node 'gradients/ff_1/add_grad/BroadcastGradientArgs': Could not satisfy explicit d
evice specification '/device:GPU:0' because no devices matching that specification are registered in this process; available devices: /job:localhost/replica:0/task:0/cpu:
0
         [[Node: gradients/ff_1/add_grad/BroadcastGradientArgs = BroadcastGradientArgs[T=DT_INT32, _device="/device:GPU:0"](gradients/ff_1/add_grad/Shape, gradients/ff_1/
add_grad/Shape_1)]]

Caused by op 'gradients/ff_1/add_grad/BroadcastGradientArgs', defined at:
  File "bench.py", line 23, in <module>
    execute_all(**vars(parser.parse_args()))
  File "/home/abotev/work/python/deep-bench/deep_bench/executor.py", line 40, in execute_all
    wide=w, depth=d, batch=b)
  File "/home/abotev/work/python/deep-bench/deep_bench/executor.py", line 123, in single_experiment
    f(model, device, temp_folder, **kwargs)
  File "/home/abotev/work/python/deep-bench/deep_bench/frameworks/tensorflow/executor.py", line 75, in execute_experiment
    x_in, y_in, inference, train, cost = build_model(batch)
  File "/home/abotev/work/python/deep-bench/deep_bench/frameworks/tensorflow/ff.py", line 21, in build_ff_net
    train = optimizer.minimize(cost)
  File "/share/apps/barber/system/lib/python3.6/site-packages/tensorflow/python/training/optimizer.py", line 288, in minimize
    grad_loss=grad_loss)
  File "/share/apps/barber/system/lib/python3.6/site-packages/tensorflow/python/training/optimizer.py", line 354, in compute_gradients
    colocate_gradients_with_ops=colocate_gradients_with_ops)
  File "/share/apps/barber/system/lib/python3.6/site-packages/tensorflow/python/ops/gradients_impl.py", line 482, in gradients
    in_grads = grad_fn(op, *out_grads)
  File "/share/apps/barber/system/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py", line 586, in _AddGrad
    rx, ry = gen_array_ops._broadcast_gradient_args(sx, sy)
  File "/share/apps/barber/system/lib/python3.6/site-packages/tensorflow/python/ops/gen_array_ops.py", line 411, in _broadcast_gradient_args
    name=name)
  File "/share/apps/barber/system/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 763, in apply_op
    op_def=op_def)
  File "/share/apps/barber/system/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 2395, in create_op
    original_op=self._default_original_op, op_def=op_def)
  File "/share/apps/barber/system/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1264, in __init__
    self._traceback = _extract_stack()

...which was originally created as op 'ff_1/add', defined at:
  File "bench.py", line 23, in <module>
    execute_all(**vars(parser.parse_args()))
[elided 2 identical lines from previous traceback]
  File "/home/abotev/work/python/deep-bench/deep_bench/frameworks/tensorflow/executor.py", line 75, in execute_experiment
    x_in, y_in, inference, train, cost = build_model(batch)
  File "/home/abotev/work/python/deep-bench/deep_bench/frameworks/tensorflow/ff.py", line 14, in build_ff_net
    h = net.dense_layer(h, shape=(arch_specs[i-1], arch_specs[i]), nonlinearity="tanh")
  File "/home/abotev/work/python/deep-bench/deep_bench/frameworks/tensorflow/net.py", line 40, in dense_layer
    return f(tf.matmul(x_in, w) + tf.reshape(b, [1, -1]))
  File "/share/apps/barber/system/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py", line 884, in binary_op_wrapper
    return func(x, y, name=name)
  File "/share/apps/barber/system/lib/python3.6/site-packages/tensorflow/python/ops/gen_math_ops.py", line 73, in add
    result = _op_def_lib.apply_op("Add", x=x, y=y, name=name)
  File "/share/apps/barber/system/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 763, in apply_op
    op_def=op_def)
  File "/share/apps/barber/system/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 2395, in create_op
    original_op=self._default_original_op, op_def=op_def)
  File "/share/apps/barber/system/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1264, in __init__
    self._traceback = _extract_stack()

InvalidArgumentError (see above for traceback): Cannot assign a device to node 'gradients/ff_1/add_grad/BroadcastGradientArgs': Could not satisfy explicit device specific
ation '/device:GPU:0' because no devices matching that specification are registered in this process; available devices: /job:localhost/replica:0/task:0/cpu:0
         [[Node: gradients/ff_1/add_grad/BroadcastGradientArgs = BroadcastGradientArgs[T=DT_INT32, _device="/device:GPU:0"](gradients/ff_1/add_grad/Shape, gradients/ff_1/
add_grad/Shape_1)]]

One thing slightly suspicious is that it is picking up the default GCC (4.4) rather than the local user one (4.9), however I don't see how this relates to CUDA.
However, the second error about libcuda version not found has been reported before in #4267, while a similar issue seems to be #2882. At this stage, there is not much I can really say why this is happening.
Also for reference, both Theano and Pytorch worked out of the box, no errors, no complaints directly linking to both CUDA and CuDNN, so this is not a CUDA installation issue. Potentially the fact I use a separate GLIBC might be an issue, but again I don't see why myself.
PS: This potentially could be some problem of the alternative GLIBC not detecting the GPUs, which to be more general than tensorflow, but I will need to talk with the system admin.

@zheng-xq  Any ideas what might be going wrong here?  StreamExecutor claims to load libcuda, but then cuInit fails... Maybe the dynamic loader state being a mess?  (There seems to be a lot of dubious library hackery going on, so it may be pretty much impossible for us to reproduce.  )

This can be closed, @prb12 thanks for the fast response, it turned out there was some issue with my GLIBC installation, which however I did not manage to nail down and understand. We had a new one reinstalled a new and now it works!.


Export js module that can be accessed externally

This is necessary to be able to consume the generated JS types easily. Not the ideal setup because I'm adding the index file to this repo while we still download the generated code that the index depends on, but we can clean that up when we automate the release.

Ah yeah, makes sense... can you bump common/version.go to rc41?

Done


Support @link

Another useful tag which we could support is {@link}.

I know some markup is supported. The [text](url) syntax may already support URLs

There is discussion of @link on #79. There is commit mentioning that it was implemented but I haven't tried it. Using double square brackets [[SymbolName]] has been supported for a while.

The {@link} seems to work with symbols (e.g. {@link my_symbol}).

Linking directly to fields works, too ({@link MyClass.myMethod})


Repeat jumps in either direction with f / f

Contributing guidelines

[X] I have read CONTRIBUTING.md
[X] I have read CODE_OF_CONDUCT.md

Module(s)
mini.jump
Description
In clever-f.vim, there is an option that after doing a backwards jump with 'F', you can repeat the jump (still moving backwards) with 'f'. In other words, when you are in the middle of a chain of jumps, 'f' means to keep going in the same direction as the initial jump of the chain, and 'F' means to go in the opposite direction. Similar logic applies to 't' and 'T'.
Not having to go back to holding the shift key after the initial backward jump is more ergonomic, and makes repeated jumps quicker and easier. It would be nice to see this functionality in mini.jump.

Thanks for the suggestion! There is a repeat_jump mapping, which is by default ;. So you can, for example, type Fx to search backwards for x and then repeat backward jump with ;.  The same goes with t and T. Is this good enough for you?
At the moment I like current design better for its simplicity: f and t always jump forward, F and T always jump backward, ; always repeats previous jump.

The repeat map will work well enough, perhaps remapped to another key for my preferences. I do appreciate the logic of keeping the directions absolute.
By chance is there any status variable set by the plugin, where a user could detect if there is a jump chain in progress, and remap 'f' during a chain if desired? I tried to have a look at the code but I'm pretty new at Lua.
BTW, so far I'm  loving mini.nvim, and have replaced several standby plugins with your modules. You've covered most of my "essentials" with nice minimal versions. Big thumbs up, and I'm looking forward to mini-vim-lion.

Thank you for your kind words!
At the moment there is some stored helper information about the process (if "inside jumping", if "backward", etc.), but it is not exported to the user. I'll think about exporting it, as it does seem like there is an interesting use case.

I think I'll close this in favor of a newly created one.

@rogersai, I've created an exported MiniJump.state table which should have enough information for the custom remap you were looking for. Sorry for this much delay, wanted to cover module with tests before making any changes. Hope it helps.


Actions workflow for building and uploading package to PyPi.

Changes Proposed
This pull request aims to eliminate the need to run "pip install -r requirements.txt" and "pip install .", simplifying the installation process to just "pip install self-operating-computer" through the implementation of a GitHub Actions workflow for automatic package upload to PyPI.
The specific changes include:
・Creation of .github/workflows/upload-package.yml, defining a GitHub Actions workflow for automating the package upload to PyPI.
・Setting the Python version to 3.8, considering compatibility with the current version of the project.
Next Steps
Necessary changes to setup.py will be made in additional commits. This will facilitate an easier setup and execution process for users.
Notes
・These changes are in response to the request listed in CONTRIBUTING.md to "Remove necessity for pip install .: I think by uploading packages to PyPi we can reduce the installation code steps by consolidating pip install -r requirements.txt and pip install .. If that's possible that'd be great."
・The changes are aimed at enhancing the usability and maintainability of the project.

@Daisuke134 thanks for this PR. As you were probably planning in your next steps I learned we could add the code below to setup.py to avoid the pip install -r requirements.txt. I'm going to try adding that. I thought I'd add that first since it will be required for PyPi. I'll follow up on this PR in a little
with open("requirements.txt") as f:
    required = f.read().splitlines()

Ok, I think I've completed the configuration on the PyPi side. I'm going to merge this PR in.

@joshbickett Thank you so much. I will check🙇♂

sg, let me know what you find @Daisuke134

@joshbickett I finished checking and pip install worked fine. I will make new PR to change read.md. I don't have a twitter account, so you can tweet without my name. Thank you so much!!

Sounds good!

Oh I didn't even notice the numbering issue, thanks for catching that @Daisuke134


Python Code

Can you kindly share the python code ....?

Hi @MisbahAF ,
This book, as the title says, is based on R and hence it would be difficult to share python code for the whole book at this time. We might redo this book in python sometime in future but there are no plans as of now.
If the concepts in the book were helpful, you should be able to translate the same in python with ease.


feat(journeys): add single integration suite to run all tests

Deploy preview for hopeful-hamilton-4090b9 ready!
Built with commit 968caf8207bd841a342e4c2654fd6f59787a3b2e
https://deploy-preview-666--hopeful-hamilton-4090b9.netlify.com


Launch a UserControl view from the corresponding ViewModel

Dear Antony,  most of my views are UserControls except the MainShell which is a Window.   During the development, sometimes I like to launch a UserControl individually to focus on just that item.  So I am trying to use the WindowManager.showWindow and feed it with a ViewModel to launch the View.  But it seems can only launch a window. Do you have a way to launch a UserControl or let me know how to find the view from the ViewModel?  I know how to launch a UserControl directly. Many thanks.   Frank

You can easily create a Window which bad a corresponding VM. The VM just owns a child VM, and the Window just shows the View for that child VM. Then do e.g.WindowManager.ShowWindow(new WindowViewModel(new My UserControlView Model)) (or wrap that up in an extension method).
Then you can control exactly how the UserControl is shown in the window: where window title comes from, how it's sized, etc.
Make sense?

This should work. Make sense.  I will give it a try.

This seems to be a little complicated.  Because the WindowViewModoel has a MyUserControlViewModel as an input, so every time I try to launch a new user control, I need to mod this WindowViewModel to input the new UserControlViewModel.
What I used to do is to create a set of methods and each method will launch a user control, something like (if using stylet):
var vmfactory = IoC.Get<AssignGroupViewModelFactory>();
 var vm = vmfactory.CreateAssignGroupViewModel(lci);   //(lci some runtime data)
_windowManager.ShowWindow(vm);                                 // I wish something will work here

Before Stylet, I would just new up the ViewModel and then create the view from ViewModel
Then launch it. like:
var view = new AssignGroupView(viewModel);
NavigationController.NavigatePopMaxToUserControl(view, "Assign Group View");

I don't mind creating a window to host a user control, but to create a window for each user control I am trying to launch seems too much work.   Also, the code to feed runtime data will have to go to the main window's ViewModel code and could be potentially different for each user control.
I am not sure if extension method will help here.  Am I missing something?

Here is the code I used to launch a UserControl
public static void NavigatePopMaxToUserControl(UserControl target, string title)
        {
            var popwindow = new Window
            {
                Title = title,
                WindowStartupLocation = WindowStartupLocation.CenterOwner,
                WindowState = WindowState.Maximized
            };

            var mainWindow = Application.Current.MainWindow;
            if (mainWindow != null && mainWindow.IsLoaded) popwindow.Owner = mainWindow;
            if (target != null) popwindow.Content = target;

            popwindow.ShowDialog();
        }

If I know how to call the function that finds the view from ViewModel and bind the content, I can use the above code to accomplish the task.   Thanks!! :-)

I think I may be solved this problem. the following example method successfully launched a UserControl view.
 public void AssignGroupView()
        {
            var lci = new LaunchClass()
            {
                LaunchClassId = 1
            };

            var vmfactory = IoC.Get<AssignGroupViewModelFactory>();
            var vm = vmfactory.CreateAssignGroupViewModel(lci);
            var viewManager = IoC.Get<IViewManager>();
            var view =(UserControl) viewManager.CreateAndBindViewForModelIfNecessary(vm);
            NavigationController.NavigatePopMaxToUserControl(view, "Assign Group View");
        }

Am I using the right function to get the view?
this is just for testing a view in the development stage.
In the application itself, I understand I should use conductor/screen with Child ViewModel as parameters to show child views.  Thanks!

I was thinking just:
WindowViewModel.cs:
public class WindowViewModel : Screen
{
    public IScreen Child { get; }

    public WindowViewModel(IScreen child)
    {
        Child = child;
        Child.ConductWith(this);
    }
}

WindowView.xaml:
<Window x:Class="Stylet.Samples.Hello.WindowView"
        xmlns="http://schemas.microsoft.com/winfx/2006/xaml/presentation"
        xmlns:s="https://github.com/canton7/Stylet"
        Title="{Binding Child.DisplayName}" Height="450" Width="800">
    <ContentControl s:View.Model="{Binding Child}"/>
</Window>

Then you can write:
this.windowManager.ShowWindow(new WindowViewModel(new TestViewModel()));

Or you can create an extension method:
public static class WindowExtensions
{
    public static void ShowUserControlAsWindow(this IWindowManager windowManager, object viewModel)
    {
        windowManager.ShowWindow(new WindowViewModel(viewModel));
    }
}

Thanks Antony!!! This looks great. Appreciate!


dyrector.io Hacktoberfest Swag 2023 🏆

Hello contributors!👋
We're participating in Hacktoberfest in 2023, as well! Last year's Hacktoberfest was a riot, we were happy to see all the excitement surrounding the platform from all the contributors, so we were waiting for this year's open-source festivities like it's Christmas.
We already rolled out a few issues for contributors, and we plan to publish a lot more in the coming weeks to give more contributors a chance to complete the challenge.
Last year we offered swag for one issue. Since Hacktoberfest t-shirts are no longer a thing, we decided to offer a swag pack to our best contributors. Our plan is to ship packs in Europe, but we go to conferences often, so if you earn a swag pack, we're happy to meet you at KubeCon EU, Web Summit, and other events and give you the pack in person.
Here's what we sent last year to one of our contributors:

You can pick an issue in the particular issue's comments, and if you need assistance, you can ask for help on Discord at the link below:

Discord: https://discord.gg/pZWbd4fxga

Let's goooo 🔥

Hactoberfest!!!!!!!!

cool

Fantastic

Let's go!!!!!!!!!

Closing the issue rn, concerning Hacktoberfest is now closed for 2023. Looking forward to seeing you all in 2024! 🥳


WebsocketSession.readFrame() should batch read payload bytes.

This is much faster than calling socket.read() to read byte-by-byte.
I believe this is the same issue raised here: https://github.com/httpswift/swifter/issues/314

Hello, I didn't see any contribution guidelines but made this fix that I think others may find useful. Let me know if there's anything you'd like me to change about the PR.
Also: Thanks for making such a nice library and sharing it with everyone!

Hey, @lynaghk Thanks for taking the time to create this PR and for your kind words ! Is pretty similar to #314 and it was solved in #362  but only for the body and headers.


remove alpinejs from npm install

I can confirm that this now successfully builds on cloudflare and github pages without npm install alpinejs


Some solution using maxflow

It should just replace my some solution as it is very slow

Done, please re-review

Fix #3


Testing a LightGBM model on fresh data

Apologies if I have posted this in the wrong section. This is more of a conceptual question. I performed combinatorial CV on 20 folds (5 choose 2, times 2) using early stopping. There are two labels and the positive class of interest is about 25% of the sample).  The model is very promising.
I now have to prove this model to management using out of sample data. What is the recommended procedure here to determine the polished final model?
(1) Train on all 5 folds (how do I avoid overfitting with no early stopping set...)
(2) Choose one of the 20 boosters from the CV exercise at random
(3) Use all 20 boosters and go with the majority decision?
(4) Other..?
Many thanks

As a general rule, cross validation is not intended to produce a final model that's used for prediction in your application.
It's used to:

choose between competing sets of hyperparameters
get an estimate of the generalization error (how well the model will perform on unseen data)

Given only the details you've provided, I recommend that you:

reserve a portion of your total dataset as a validation set (X_valid)
perform cross-validation on the remaining data (X_train), to choose the best hyperparameters
retrain on all of (X_train), using those chosen hyperparameters

use early stopping, and pass X_valid as the validation set

This is not really a LightGBM-specific question, so if you have additional not-specific-to-LightGBM questions, please try the following:

review scikit-learn's excellent tutorial on cross-validation: https://scikit-learn.org/stable/modules/cross_validation.html
search https://stats.stackexchange.com/, e.g. review some of these answers:

Thank you, I appreciate your help. Apologies for the generic question


Some minor improvements to the code

I have changed the following things (untested):

Moved all module imports to the top of the code
Corrected spelling error in one of the functions
Added a requirements-section in the README

Hey @tctovsli thanks for your contribution ♥
Since the commits of this PR got changed (now outdated) while merging other PRs i decided not to merge this PR but i will keep this PR open because it’s never nice to reject someone’s work. Please don't be discouraged to contribute in the future even though this PR won't get merged. Thank you for understanding.


Update dui3/alpha with changes from dev

Low priority, but should go in this week.

Closing, will fix conflicts on a temp branch.


INFO events are written to ~/.xsession-errors

Bug
Quite a lot of INFO events are written to ~/.xsession-errors - ledger and download progress among other. E.g:
Daemon: 2021-09-22 18:12:41,783 INFO     lbry.wallet.ledger:150: lbc_mainnet: added BlockHeightEvent(height=1033294, change=1) header blocks, final height 1033294
Daemon: 2021-09-22 18:12:49,501 INFO     lbry.wallet.ledger:150: lbc_mainnet: added BlockHeightEvent(height=1033295, change=1) header blocks, final height 1033295

To Reproduce
No clue...
Expected behavior
~/.xsession-errors should only contain errors.
Screenshots
If applicable, add screenshots to help explain your problem.
System Configuration

LBRY Daemon version: 0.99.0
LBRY App version: 0.51.2
LBRY Installation ID:
Operating system: 5.13.12-1-MANJARO

Installed from https://aur.archlinux.org/packages/lbry-desktop-git/

Looks like this may be where the electron based app is putting all it's logging - we'd need to find out why that is for the arch case. Thanks for opening and sorry about the issue!

in electron/daemon.js
 this.subprocess.stderr.on('data', data => console.error(Daemon: ${data}));
The sdk outputs almost everything on stderr, and that's piped to the app.


Make it work with webpack v2?

Hi,
I'm trying to make imagemin works with webpack v2 but I'm having trouble with it.
First I struggled with the error Can't resolve 'fs' in 'imagemin/index.js'
After googling it, I've found that it could be fixed with transform-loader and brfs.
I've set up my webpack config like this:
var path = require("path");

module.exports = {
  devtool: "cheap-module-eval-source-map",
  entry: ["./src/index"],
  output: {
    path: path.join(__dirname + "/public", "dist"),
    filename: "bundle.js"
  },
  module: {
    rules: [
      {
        include: path.resolve(__dirname, "node_modules/imagemin"),
        loader: "transform-loader?brfs",
        enforce: "post"
      },
      {
        test: /\.js$/,
        loader: "babel-loader",
        options: {
          presets: ["env"]
        },
        include: path.join(__dirname, "public"),
        exclude: [/node_modules/]
      }
    ]
  },
  resolve: {
    modules: ["node_modules", path.resolve(__dirname, "src")],
    extensions: [".js", ".css"]
  }
};

But I'm getting another error and I don't know how to fix it:
Error: tried to statically call { readFile: [Function: readFile], readFileSync: [Function: readFileSync], readdir: [Function: readdir], readdirSync: [Function: readdirSync] } as a function in node_modules/static-modules/index.js
Any idea how to make imagemin works with webpack?
Thanks!

Imagemin isn't webpack'able. You should just exclude. This isn't the place to get Webpack support though.


Remove --fp8 flag from script

What does this PR do?

Fixes # (issue)
Before submitting

[ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
[ ] Did you make sure to update the documentation with your changes?
[ ] Did you write any new necessary tests?

@Yantom1 , did you inform QA to remove the arg in the tests?

https://github.com/huggingface/optimum-habana/pull/962


HDF5 File

Hii
It is great that you are helping in diagnosing COVID-19 virus in individuals in such a small time. But I am concerned about the hdf5 file that you have created and wanted some information that how much images have you taken to build it and the procedure you have opted to build this hdf5 file, so that it can be clear for others on how to do such things.

Hii
It is great that you are helping in diagnosing COVID-19 virus in individuals in such a small time. But I am concerned about the hdf5 file that you have created and wanted some information that how much images have you taken to build it and the procedure you have opted to build this hdf5 file, so that it can be clear for others on how to do such things.

Hello.
My apologies for the late reply.

I had mentioned in the repository in the code setup section, that the dataset (containing 188 images) was available on a google drive link of mine.

However, recently, a much larger dataset had been made available by another author, as I pointed out elsewhere.