Fixes #17 - Support for Monorepos using Yarn Workspaces

I took a look how the path is defined when sourcing the binaries and this seems to keep looking up until the node_modules directory is found that also includes a bs-platform directory.
Let me know if anyone sees any issues with this, I tried this both on a monorepo project and a traditional project and it seems to work for both projects.

interesting, will need to try that. do you have any public yarn workspaces / rescript repo where I can test this myself?

I do not have any public repos using yarn workspaces, but I can certainly create an example repo if need be.
Let me know if that is something you would like me to do for testing purposes.

yeah, maybe create an examples/monorepo folder within this PR so we can keep it around for integration testing later on?

Just pushed up a quick and simple example for a monorepo @ryyppy - Let me know if this helps.
$ cd examples/monorepo
$ yarn install (this is done in the root dir
$ cd awesomeness
$ yarn re:clean && yarn re:watch
$ cd app
$ yarn re:clean && yarn re:watch
$ yarn start

Thanks.
I needed to simplify your example and add some other stuff, so I will close this PR in favour of #19

Thanks.
I needed to simplify your example and add some other stuff, so I will close this PR in favour of #19

Sounds good.

@dan003400 We still need to coordinate with Bob if this is the correct way to detect tooling from a compiler perspective. For now we will keep the aforementioned PR (#19) in a draft state.
Closing this PR.


Add local git bridge

Normally, we use datakit-github to monitor the state of a remote repository on GitHub and use that as the input to the CI. When getting started with DataKitCI it is more convenient to be able to monitor a local Git repository.
Implements #335.
TODO:

[ ] Test it.
[ ] Update the docs.

Please sign your commits following these rules:
https://github.com/docker/docker/blob/master/CONTRIBUTING.md#sign-your-work
The easiest way to do this is to amend the last commit:
$ git clone -b "local-bridge" git@github.com:talex5/datakit.git somewhere
$ cd somewhere
$ git commit --amend -s --no-edit
$ git push -f

Amending updates the existing PR. You DO NOT need to open a new one.


bootstrap theme support

it's an excellent project. could you please support bootstrap theme?

Not sure but I will look into it when I have free time

Looked into this and I don't believe its possible for now. But if you can come up with an implementation for it, I would gladly merge the PR


Training error on DynamicViT

Hi, I want to ask that when training on DynamicViT at keep ratio 0.3, the training will terminate due to loss nan problem, my training script is shown below.
Since I remember the original DynamicViT won't have this issue at keep ratio 0.3, but I do encountered this issue on other ViT token-pruning methods. Hopes to get your reply, thanks !

CUDA_VISIBLE_DEVICES=0
python train.py
--dataset imagenet
--data my dataset path ...
--batch-size 256
--lr 0.001
--epochs 30
--warmup-epochs 5
--lr_batch_normalizer 1024
--sched_in_steps
--use_amp
--grad_accum_steps 4
--output_dir output
--model dyvit_tiny_patch16_224
--reduction_loc 3 6 9
--keep_rate 0.3

Hi!
DynamicViT can at times be hard to train, especially with aggressive keep rates. I encountered this my self and through personal correspondence with the original authors had this confirmed. My suggestion would be to try and run with a new seed (using --seed) and with by constraining the gradient norm (using --clip-grad).
Hope this helps!

The gradient accumulation should be verified, as I built on the well established repos from timm and DeiT.

I encountered the same issue already in the forward pass, indicating that setting --clip-grad might not be the problem.
I then noticed that the nans come from PreditorLG when the policy is all 0s and that there is a bug when adding eps to avoid division by 0.
You might consider computing global_x as follows:
global_x = (x[:, :, C // 2 :] * policy).sum(dim=1, keepdim=True) / (torch.sum(policy, dim=1, keepdim=True) + self.eps)
I have not extensively tested it but nans are avoided in the first few iterations even with aggressive pruning rates.

@benbergner Thanks for your reply. I have observed that this problem may caused by the prediction module. If the prediction module predicts zero token to be preserved, it will cause loss nan. So my solution is to ensure that the prediction module at least preserve one token.

How exactly do you ensure this?

@benbergner I set a condition to check if the prediction module predicts zero token to be preserved, manually modify the decision mask to at least preserve one token.


I thought the medicne descriptions needed a rewritre

sorry if i did something wrong, i dont use git often and dont know what to do in this case. i just thought the chemicals could use better descriptions and stuff. not sayin the others were bad, just old. and there were a large number of overdose effects that just... never actually happened.
reagent-name-cryptobiolin = cryptobiolin
| reagent-desc-cryptobiolin = Not SDA Approved, Cryptobiolin has not yet been proven to do anything other than cause confusion, dizziness and violent depression. Although here at the 14th space station, we’ve been able to prove that Cryptobiolin in fact does not cause any effects.
  |
  | reagent-name-dylovene = dylovene
  | reagent-desc-dylovene = Originating from 2260s Starfleet, Dylovene is known as a broad-spectrum anti-toxin which is warned by the SDA to cause vomiting and dizziness during an overdose, the SDA had never specified the amount required to overdose and no evidence of a Dylovene overdosing has ever existed leading many colonies, cosmonauts and space station staff to believe the SDA had been lying to them. But wait a second! the government wouldn’t lie to us!
  |
  | reagent-name-diphenhydramine = diphenhydramine
  | reagent-desc-diphenhydramine = A drowsy antihistamine that reduces the effects of self harm inflicting allergic reactions caused by an increase of histamine, side effects may include a dry mouth or nose, double vision, rapid heart rate or difficulty disposing of bodily waste.
  |
  | reagent-name-arithrazine = arithrazine
  | reagent-desc-arithrazine = Originating from Starfleet, this unstable chemical compound causes slight pain to the patient but heals hyperthyroidism and protects thyroid glands by absorbing radiation.
  |
  | reagent-name-bicaridine = bicaridine
  | reagent-desc-bicaridine = The chemical compound known as bicardarine was named after the surname "Picardy" in northern France, It is now known for its ability to hinder internal or external blood hemorrhaging by increasing capillary endothelial resistance and promoting platelet adhesion.
  |
  | reagent-name-cryoxadone = cryoxadone
  | reagent-desc-cryoxadone = A super medicine that can only work in temperatures under 170K, The space-cryo-organization suggests this as a constant life-blood for cryogenically frozen staff.
  |
  | reagent-name-dermaline = dermaline
  | reagent-desc-dermaline = Dermaline is a skin care chemical using keratin to gather and group dead skin cells together while it uses allantoin and alpha hydroxy acids to moisturize the inner or outer layer of dead skin cells, forcing the cells to slowly wither and give a smoother new layer. The SDA Recently announced Dermaline might be more effective than Kelotane and is also recommended for general day to day skin care.
  |
  | reagent-name-dexalin = dexalin
  | reagent-desc-dexalin = Increases the oxygen intake in your lungs by forcing your lungs to take in around 3-4u more of carbon dioxide.
  |
  | reagent-name-dexalin-plus = dexalin plus
  | reagent-desc-dexalin-plus = Although rumored to cause a low mental state, These rumors were proven false, Said rumor started by a “Dr. Crusher” who had used an unspecified dosage on two patients that were already on many medicinal drugs. Dexalin-plus quickly heals asphyxiation damages through synthetic oxygen and hormones to induce a greater oxygen intake to dying cells with the added bonus of some bloodstream health benefits, much stronger than its inferior version, Dexalin.
  |
  | reagent-name-epinephrine = epinephrine
  | reagent-desc-epinephrine = A cardio stimulating super medicine made with oil that can resuscitate patients in a critical state by restarting their heart rhythm, Gives an extreme rush of adrenaline causing higher muscular speeds, cramped hands and reduced stuns. The SDA has warned just 30u is enough to overdose on.
  |
  | reagent-name-hyronalin = hyronalin
  | reagent-desc-hyronalin = A weak but cheap treatment for radiation, So weak in fact, all other medicinal drugs or just adrenaline could counter the radiation more than Hyronalin. Only really good if a patient can not produce or take adrenaline at the time.
  |
  | reagent-name-ipecac = ipecac
  | reagent-desc-ipecac = Causes vomiting of anything in your chemical stream or stomach, ironically when in your chemical stream or stomach. Anything that has not yet metabolized comes out with ease, recommended if your name is gus.
  |
  | reagent-name-inaprovaline = inaprovaline
  | reagent-desc-inaprovaline = A synaptic-cardiac stimulant that forces the brain to slow down the heart.
  |
  | reagent-name-kelotane = kelotane
  | reagent-desc-kelotane = Saturates and disinfects the skin, this has an effect on the speed of skin cell regeneration which reduces the time needed to fully heal to 50 seconds.
  |
  | reagent-name-leporazine = leporazine
  | reagent-desc-leporazine = Adjusts inner body pressure at high doses of around 110u and rapidly adjusts body temperature, seems to be negated by cryogenics
  |
  | reagent-name-barozine = barozine
  | reagent-desc-barozine = A potent drug that prevents pressure damage. Causes extreme pain and jittering. Very poisonous when overdosed. Reduces effects of barotrauma and pressure damage at just 2u but causes extreme pain and jittering due to your tissues stretching and sometimes your bones breaking, onto of this you might have a hard time keeping all your bodily fluids in and will have a large amount of blood not reaching the rest of your body, deadly if over 30u.
  |
  | reagent-name-phalanximine = phalanximine
  | reagent-desc-phalanximine = A 50 second cure for cancer assuming you metabolise 25u and are at 100 cellular damage, Phalanximine will also deal 2 Radiation for every 2 Cellular. The SDA Regulations demand at least 100u of a high strength radiation blocker nearby before treatment.
  |
  | reagent-name-romerol = romerol
  | reagent-desc-romerol = Reverses the effects of miasma-induced cellular resuscitation.
  |
  | reagent-name-pulped-banana-peel = pulped banana peel, reduces bleeding significantly.
  | reagent-desc-pulped-banana-peel = Pulped banana peels have some effectiveness against bleeding. Those monkeys know something we don’t.
  |
  | reagent-name-siderlac = siderlac
  | reagent-desc-siderlac = A powerful anti-caustic medicine derived from plants that reduces the effects of acidic substances.
  |
  | reagent-name-spaceacillin = spaceacillin
  | reagent-desc-spaceacillin = An antibiotic used to slow or even stop the progression of space diseases by blocking Voltage-Gated membrane channels.
  |
  | reagent-name-stellibinin = stellibinin
  | reagent-desc-stellibinin = A Natural antitoxin oil with a strong immunity against amotoxin, typically used by 40 year old soccer moms.
  |
  | reagent-name-synaptizine = synaptizine
  | reagent-desc-synaptizine = The SDA has banned this substance due to it’s ability to kill with just 2u. But we hate the SDA so it’s still legal here. Although deadly at 2u, at 1u it can remove hallucinations.
  |
  | reagent-name-tranexamic-acid = tranexamic acid
  | reagent-desc-tranexamic-acid = An anti heamoragging medicine to reduce blood loss from brute trauma or bloody bad times.
  |
  | reagent-name-tricordrazine = tricordrazine
  | reagent-desc-tricordrazine = A multi-purpose stimulator that branched from cordazine, It heals almost everything but very slowly and at a certain point, making other drugs are quicker and easier.
  |
  | reagent-name-lipozine = lipozine
  | reagent-desc-lipozine = Causes a chemical lipo-suction by using a secret chemical to convert fat into… umm… you’ll find it in your toilet later.
  |
  | reagent-name-omnizine = omnizine
  | reagent-desc-omnizine = A soothing milky liquid with an iridescent gleam. A well known conspiracy theory says that it's origins remain a mystery because knowing the secrets of its production would render most commercial pharmaceuticals obsolete. Consuming it gives a smooth and calming experience as you feel several types of damage and all of your toxicity fade away 4u at a time… ok we may have put a few illegal drugs in here but shush.
  |
  | reagent-name-ultravasculine = ultravasculine
  | reagent-desc-ultravasculine = Removes all toxins in the body but comes at the cost of your veins possibly exploding.
  |
  | reagent-name-oculine = oculine
  | reagent-desc-oculine = Heals eye damage and gives you 20/20 vision!
  |
  | reagent-name-ethylredoxrazine = ethylredoxrazine
  | reagent-desc-ethylredoxrazine = Useful at bars, this drug cancels out the effects of booze. Basically never used though, the last time this was bought was when an admin wanted to get drunk and then drunk again over and over.
  |
  | reagent-name-cognizine = cognizine
  | reagent-desc-cognizine = A mysterious chemical which is able to make any non-sentient creature sentient by giving it access to completely reliable and trustable google. Immediately terminate if said being attempts to google “google”, its gotten too smart.

While we appreciate the enthusiasm, this is an issue, not a change. Additionally, I'm not sure we really want any of these descriptions, they read less like descriptions and more like someone (a speaker) trying to tell jokes about the chemical.
You're looking for https://docs.spacestation14.io/en/getting-started/dev-setup


feat(aws-wafwebacl-apigateway): Implement New Construct

Issue #264 , if available:
Description of changes:
-Created README
By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.
fixes #264

AWS CodeBuild CI Report

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Commit ID: 5443b52517dfcf964f5b7727def1ee9aaf62900c
Result: FAILED
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CodeBuild project: codebuildgithubautobuildPro-fkVQbXRiQi6A
Commit ID: ce09c5929eb45d4c8a59bbb5e9b8409f2ae43928
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Referee and Gamecontroller are out of sync for the kickoff

Describe the Bug
During the second half, the game controller (and therefore the robots) thinks that another team has kickoff as the referee. This leads to the behavior that the teams correctly position themselves, but the robots of the kickoff team are removed by the game controller. It only happens in the beginning second half time and I suspect that the referee is confused due to goals scored in the game and the kickoffs related to the goals.
Steps to Reproduce

Simulate a game with goals
The kickoff in the second half is wrong in the referee ui vs the gamecontroller ui and this isn't limited to the ui.

E.g. second half of https://www.twitch.tv/videos/1065150793
Expected behavior
They should be synced

This also happens when no goal was scored, and the referee is right, the GameController is wrong.

The same happend in game Starkit vs RFC Berlin
The kick of side was not switched (our robots didn't receive any signal).
https://cloud.robocup.org/s/fTW228MqMMrS3fN

If it helps, here is a log of a game that I just simulated where this issue occurred: https://games.bit-bots.de/private/issue-260/game.html
The referee log is here: https://games.bit-bots.de/private/issue-260/referee_log.txt
According to the game controller, the red team has kick off both times, which is wrong.


⚠ knowtilus has degraded performance

In 985b9fe, knowtilus (https://www.knowtil.us) experienced degraded performance:

HTTP code: 503
Response time: 10666 ms

Resolved: knowtilus performance has improved in 2dcf11e after 7 minutes.


refactor: Add Guard Exports

Don't forget to export guards in index.ts

:tada: This PR is included in version 1.0.3 :tada:
The release is available on:

npm package (@latest dist-tag)
GitHub release

Your semantic-release bot :package::rocket:


Windows VM with accelerated networking

Hi Azure Team,
I found two different information about "Windows VM with accelerated networking"
The link below says "Windows VM with accelerated networking" is available only on:
Supported operating systems
The following distributions are supported out of the box from the Azure Gallery:

Windows Server 2016 Datacenter
Windows Server 2012 R2 Datacenter
https://docs.microsoft.com/en-us/azure/site-recovery/azure-vm-disaster-recovery-with-accelerated-networking#supported-operating-systems

But another link below says Accelerated Networking is available for windows server 2019 also.
Supported operating systems
Windows Server 2019 Datacenter
Windows Server 2016 Datacenter
Windows Server 2012 R2 Datacenter
https://docs.microsoft.com/en-us/azure/virtual-network/create-vm-accelerated-networking-powershell#supported-operating-systems

@romulobrandino, appreciate your feedback. The doc has been updated accordingly. It would take some time for the changes to get reflect over the doc.
We are closing this issue for now. If there are further questions regarding this matter, please reply and we will gladly continue the discussion.


Need to convert BB response as Json ,

Hi Team ,
I am not good at Python, so i am using talipp library in s flask rest service , and consuming in my client application.
When i am trying to conver  "BollingerBand" response to Json , so  i can consume in my client app.
BB response returned as a list of custom class.
By default custom classes are can not serialized , if i tries to cast as "talipp.indicators.BB.BBval"  but i am not able to find BBVal class BB has no BBval kind of error.
Please keep some sample code to ,convert response serialized , to i can send its as json to my client app.
below is sample code to which i am trying. Thank you
@dataclass
class BBVal:
lb: float = None
cb: float = None
ub: float = None
def encode_Bbaval(val):
if isinstance(val, BBVal):
print('Ima coming')
return {'lb': val.ub , 'cb': val.cb, 'ub': val.ub}
# else:

       close = numpy.asarray(request.json['close'])
        period = request.json['period']
        # deviation = request.json['deviation']
        # print(deviation)
        bbVals = BBVal(list(BB(period,2, close)))
        print(type(bbVals))
        print(bbVals)
        #for x in bbVals:
        #    print(type(BBVal(x)))
            #jsonfields = json.dumps(x, default=encode_Bbaval)
        # print(type(bbVals))
        # jsonfields = json.dumps(bbVals,default=encode_Bbaval)
        # print(jsonfields)
        #return bbVals
        # return numpy.array(testSample).tolist()

Hi @rakesh433jntu, there are multiple options:
1/ use directly to_lists() method which returns a list of of converted dataclasses (i.e. BBVal) into a dictionary which can be dumped into json easily.
bb = BB(5, 1, list(range(0, 100, 9)))
print(bb.to_lists())

{'lb': [5.272077938642145, 14.272077938642145, 23.272077938642145, 32.27207793864214, 41.27207793864214, 50.27207793864214, 59.27207793864214, 68.27207793864214],
'cb': [18.0, 27.0, 36.0, 45.0, 54.0, 63.0, 72.0, 81.0],
'ub': [30.727922061357855, 39.72792206135786, 48.72792206135786, 57.72792206135786, 66.72792206135786, 75.72792206135786, 84.72792206135786, 93.72792206135786]}

2/ you can use jsonpickle package to serialize whole indicator. Serializing and deserializing of whole indicator is demonstrated in examples/serialization.py.

Closed as inactive for long period.


Older seedlots not updating new BV data

Describe the Bug
Breeding values were update for Sitka spruce (Ss), so when I open the record, put it in edit mode and click on the recalculate button it should update the new breeding values from the oracle API and then recalculate the GW traits
Expected Behaviour
When I open a record in review mode and click recalculate, new BV should be pulled and the new GW should be recalculated
Actual Behaviour
The system is spinning and not pulling data

Opened PR for this request is here: https://github.com/bcgov/nr-spar/pull/1669

Previous changes in the PR didn't fix the issue. Ricardo found the following. @mgaseta would you be able to follow up on Ricardo's work and see if we can complete this issue?
here's the new issue:

Sure! I'll get right into it.

Hi @mgaseta I've tested your PR. The recalculate section is now working and doesn't get stuck :) However it looks like the form isn't pulling in new breeding values. These are the testing steps that I took:

created a new seedlot and registered it
Approved the seedlots
Opened old SPAR to update the breeding values for Sitka Spruce trees in orchard 172
Reopened the seedlot registration form in SPAR2
In the review and edit view, I clicked on recalculted

The Expected result is that new breeding values are brought up (see several GVO values updated from 2 to 10)
https://github.com/user-attachments/assets/53ebbc42-7075-433d-8043-97b52da6ca42

As agreed on our sprint planning, this issue will only tackle the infinite loading problem, the wrong breading value issue will be tackled on issue #1704


[Question] Time to read on Series page

Is it possible to display the time to read for a post on a series page?  It works really well on the post page.  thanks.

This is going to be closed in a day due to inactivity.


Ensembl gene download

( https://jira.sanger.ac.uk/browse/TOLIT-433 )
The test and test_full profiles are implemented and tested on GitHub. test corresponds to using command-line parameters, test_full to using a sample-sheet.
$ nextflow run src/ensemblgenedownload/ -profile test,singularity --outdir ensemblgenedownload
(...)
[23/06d5db] process > SANGERTOL_ENSEMBLGENEDOWNLOAD:ENSEMBLGENEDOWNLOAD:DOWNLOAD:ENSEMBL_GENESET_DOWNLOAD (GCA_907164935.1|2021_11)  [100%] 1 of 1 ✔
[87/549716] process > SANGERTOL_ENSEMBLGENEDOWNLOAD:ENSEMBLGENEDOWNLOAD:PREPARE_FASTA:TABIX_BGZIP (GCA_907164935.1.ensembl.2021_1... [100%] 3 of 3 ✔
[cf/38fb04] process > SANGERTOL_ENSEMBLGENEDOWNLOAD:ENSEMBLGENEDOWNLOAD:PREPARE_FASTA:CUSTOM_GETCHROMSIZES (GCA_907164935.1.ensem... [100%] 3 of 3 ✔
[c3/77107a] process > SANGERTOL_ENSEMBLGENEDOWNLOAD:ENSEMBLGENEDOWNLOAD:PREPARE_FASTA:SAMTOOLS_DICT (Osmia_bicornis_bicornis-GCA_... [100%] 3 of 3 ✔
[0c/afe4b9] process > SANGERTOL_ENSEMBLGENEDOWNLOAD:ENSEMBLGENEDOWNLOAD:PREPARE_GFF:TABIX_SORT_BGZIP (Osmia_bicornis_bicornis-GCA... [100%] 1 of 1 ✔
[73/f982f9] process > SANGERTOL_ENSEMBLGENEDOWNLOAD:ENSEMBLGENEDOWNLOAD:PREPARE_GFF:TABIX_TABIX_CSI (GCA_907164935.1.ensembl.2021... [100%] 1 of 1 ✔
[81/1d49e6] process > SANGERTOL_ENSEMBLGENEDOWNLOAD:ENSEMBLGENEDOWNLOAD:PREPARE_GFF:TABIX_TABIX_TBI (GCA_907164935.1.ensembl.2021... [100%] 1 of 1 ✔
[2f/6cfb4a] process > SANGERTOL_ENSEMBLGENEDOWNLOAD:ENSEMBLGENEDOWNLOAD:CUSTOM_DUMPSOFTWAREVERSIONS (1)                              [100%] 1 of 1 ✔
$ tree ensemblgenedownload/
ensemblgenedownload/
├── gene
│   └── ensembl
│       └── 2021_11
│           ├── GCA_907164935.1.ensembl.2021_11.cdna.fa.gz
│           ├── GCA_907164935.1.ensembl.2021_11.cdna.fa.gz.dict
│           ├── GCA_907164935.1.ensembl.2021_11.cdna.fa.gz.fai
│           ├── GCA_907164935.1.ensembl.2021_11.cdna.fa.gz.gzi
│           ├── GCA_907164935.1.ensembl.2021_11.cdna.fa.gz.sizes
│           ├── GCA_907164935.1.ensembl.2021_11.cds.fa.gz
│           ├── GCA_907164935.1.ensembl.2021_11.cds.fa.gz.dict
│           ├── GCA_907164935.1.ensembl.2021_11.cds.fa.gz.fai
│           ├── GCA_907164935.1.ensembl.2021_11.cds.fa.gz.gzi
│           ├── GCA_907164935.1.ensembl.2021_11.cds.fa.gz.sizes
│           ├── GCA_907164935.1.ensembl.2021_11.gff3.gz
│           ├── GCA_907164935.1.ensembl.2021_11.gff3.gz.csi
│           ├── GCA_907164935.1.ensembl.2021_11.gff3.gz.gzi
│           ├── GCA_907164935.1.ensembl.2021_11.gff3.gz.tbi
│           ├── GCA_907164935.1.ensembl.2021_11.pep.fa.gz
│           ├── GCA_907164935.1.ensembl.2021_11.pep.fa.gz.dict
│           ├── GCA_907164935.1.ensembl.2021_11.pep.fa.gz.fai
│           ├── GCA_907164935.1.ensembl.2021_11.pep.fa.gz.gzi
│           └── GCA_907164935.1.ensembl.2021_11.pep.fa.gz.sizes
└── pipeline_info
    ├── execution_report_2022-09-26_09-09-26.html
    ├── execution_timeline_2022-09-26_09-09-26.html
    ├── execution_trace_2022-09-26_09-09-26.txt
    ├── pipeline_dag_2022-09-26_09-09-26.html
    └── software_versions.yml
$ nextflow run src/ensemblgenedownload/ -profile test_full,singularity --outdir ensemblgenedownload
(...)
[f1/e033d0] process > SANGERTOL_ENSEMBLGENEDOWNLOAD:ENSEMBLGENEDOWNLOAD:PARAMS_CHECK:SAMPLESHEET_CHECK (samplesheet.csv)             [100%] 1 of 1 ✔
[bc/d6a2ac] process > SANGERTOL_ENSEMBLGENEDOWNLOAD:ENSEMBLGENEDOWNLOAD:DOWNLOAD:ENSEMBL_GENESET_DOWNLOAD (GCA_907164925.1|2022_02)  [100%] 5 of 5 ✔
[c8/674f0c] process > SANGERTOL_ENSEMBLGENEDOWNLOAD:ENSEMBLGENEDOWNLOAD:PREPARE_FASTA:TABIX_BGZIP (GCA_907164925.1.ensembl.2022_0... [100%] 15 of 15 ✔
[b1/f4ca17] process > SANGERTOL_ENSEMBLGENEDOWNLOAD:ENSEMBLGENEDOWNLOAD:PREPARE_FASTA:CUSTOM_GETCHROMSIZES (GCA_907164925.1.ensem... [100%] 15 of 15 ✔
[ca/ee04d0] process > SANGERTOL_ENSEMBLGENEDOWNLOAD:ENSEMBLGENEDOWNLOAD:PREPARE_FASTA:SAMTOOLS_DICT (Osmia_bicornis_bicornis-GCA_... [100%] 15 of 15 ✔
[25/553b73] process > SANGERTOL_ENSEMBLGENEDOWNLOAD:ENSEMBLGENEDOWNLOAD:PREPARE_GFF:TABIX_SORT_BGZIP (Osmia_bicornis_bicornis-GCA... [100%] 5 of 5 ✔
[a7/ecac78] process > SANGERTOL_ENSEMBLGENEDOWNLOAD:ENSEMBLGENEDOWNLOAD:PREPARE_GFF:TABIX_TABIX_CSI (GCA_907164925.1.ensembl.2022... [100%] 5 of 5 ✔
[14/3e333b] process > SANGERTOL_ENSEMBLGENEDOWNLOAD:ENSEMBLGENEDOWNLOAD:PREPARE_GFF:TABIX_TABIX_TBI (GCA_907164925.1.ensembl.2022... [100%] 5 of 5 ✔
[f7/a5fa7d] process > SANGERTOL_ENSEMBLGENEDOWNLOAD:ENSEMBLGENEDOWNLOAD:CUSTOM_DUMPSOFTWAREVERSIONS (1)                              [100%] 1 of 1 ✔

Input parameters are the assembly accession, the Ensembl species name, and the geneset version.
PR checklist

[x] This comment contains a description of changes (with reason).
[ ] If you've fixed a bug or added code that should be tested, add tests!
[ ] If you've added a new tool - have you followed the pipeline conventions in the contribution docs
[x] Make sure your code lints (nf-core lint).
[x] Ensure the test suite passes (nextflow run . -profile test,docker --outdir <OUTDIR>).
[x] Usage Documentation in docs/usage.md is updated.
[x] Output Documentation in docs/output.md is updated.
[x] CHANGELOG.md is updated.
[x] README.md is updated (including new tool citations and authors/contributors).

This is half of the initial Ensembl download pipeline, but I was not happy with the way it was joined with the other half in one repository, so I decided to split them.
This half focuses on gene annotations, and reuses shared subworkflows from nf-core-pipeline.
The structure (e.g. param_checks subworkflow, download subworkflow) and the samplesheets try to use the same nomenclature as the other pipelines.


categories are empty!

i installed bludit latest release but all categories are empty (no page found)

Please complete the questions template when you create the issue.


Feature/add menu to open terminal

Added a new option in settings to add an additional menu item Open Terminal Here. When pressed a new Terminal window will be opened at given directory

This is super cool, love that you've included an icon too!
I haven't tried it out, but the implementation looks spot on, so I'll trust you that it works! 😄
I'll merge now, and when I get a min I'll do another release and new built binary (I'm sure there must be a way to automate that) 👍

Awesome, I forgot to attach a screenshot of it actually. I will add one to the PR and one in this email

On 29 Dec 2018, at 13:36, Steve Barnegren<EMAIL_ADDRESS>wrote:
This is super cool, love that you've included an icon too!
I haven't tried it out, but the implementation looks spot on, so I'll trust you that it works! 😄
I'll merge now, and when I get a min I'll do another release and new built binary (I'm sure there must be a way to automate that) 👍
—
You are receiving this because you authored the thread.
Reply to this email directly, view it on GitHub https://github.com/SteveBarnegren/TopDrawer/pull/4#issuecomment-450493766, or mute the thread https://github.com/notifications/unsubscribe-auth/ABtwokI4vpp1y3CmIvSngQ5E39yZ6QM7ks5u92_RgaJpZM4ZjWZF.


line 94 in lstm.py, return [tf.nn.rnn_cell.BasicLSTMCell(steps, state_is_tuple=True) for steps in layers]

Input to BasicLSTMCell is number of hidden unit not time steps. As far as i know they are both independent parameters.
Can someone elaborate the things if i am wrong?
@mouradmourafiq
@lepisma

@bhupendra36 yes the BasicLSTMCell expects num_units as an argument, time_steps is just the name of the variable I created. I am not sure what are you confused about, the number of units are the the hidden units that define the time/term/sequence dependencies.

@mouradmourafiq  , num_steps is used for Backpropagation through time ( defining the time dependencies). number of units increases the size of the layer.
Is there any specific reason for making num_steps and num_units equal ?

@bhupendra36 num_units is the size for rnn cell which are used for the backpropagation through time. Each layer is made out of an BasicLSTMCell. This is my understanding of the code
I think this is your source of confusion, there's no num_steps in the tensorflow code, I just named this variable to make it clearer.
Also currently there's no documentation for the rnn, I think it will be made public API in the next release.
You can also check other examples of rnn architecture used for NLP tasks, the num_units  is used as the size of the word embeddings.

@mouradmourafiq , From tensorflow website In  RNN tutorial

Truncated Backpropagation
In order to make the learning process tractable, it is a common practice to truncate the gradients for backpropagation to a fixed number (num_steps) of unrolled steps. This is easy to implement by feeding inputs of length num_steps at a time and doing backward pass after each iteration.

Time dependency can be taken in a way that how many time steps back RNN look while making a prediction. ( At each time step there will be input corresponding to that )
From this block of your code
X, y = generate_data(np.sin, np.linspace(0, 100, 10000), TIMESTEPS, seperate=False)
RNN is given TIMESTEPS previous number of inputs to make the prediction
So one can easily infer that num_steps ( as mentioned in RNN tutorial )== TIMESTEPS
So , what i was saying that num_units (number of such LSTM cell in a layer )are totally different and can be assigned different value than TIMESTEPS which is not done here

regressor = learn.TensorFlowEstimator(model_fn=lstm_model(TIMESTEPS, RNN_LAYERS, DENSE_LAYERS), n_classes=0,
verbose=2,  steps=TRAINING_STEPS, optimizer='Adagrad',
learning_rate=0.03, batch_size=BATCH_SIZE)

Exactly this is truncated backpropagation, which is optional and you can tweak/make it an option for learning in the model if you want.
Truncating backpropagation is connected with the learning and not with the length of the sequence, and this parameter can't be bigger than length of the sequence.
From this paper

This form of truncated backpropagation has been considered before for RNN language modelling [23], and found to speed up training (by reducing the sequence length and hence increasing the frequency of stochastic weight updates) without affecting the network’s ability to learn long-range dependencies.

But thanks for pointing it out, let me know if you try to use it and how does affect the learning in that case.

Thanks for the explanation.


[Feature] Alert server run some script or subprocess

Is your feature request related to a problem? Please describe.
Now, alert server just can send mail. But in some cases , alsert server is not allowed to connect Internet, which make users can't get alert.
So, I think adding more alert type can make it more easier.
Describe the solution you'd like

Alert server can run some script, such as shell or python .
Or alert server can call a created subprocess .

Yes ,We done it at https://github.com/apache/incubator-dolphinscheduler/issues/3049
Email, wechat, dingtalk, script ant http(https) have been completed so far.
You can see the code at https://github.com/apache/incubator-dolphinscheduler/tree/alert_plugin_design/dolphinscheduler-alert-plugin.

Yes ,We done it at https://github.com/apache/incubator-dolphinscheduler/issues/3049
Email, wechat, dingtalk, script ant http(https) have been completed so far.
You can see the code at https://github.com/apache/incubator-dolphinscheduler/tree/alert_plugin_design/dolphinscheduler-alert-plugin.


use dynamic mapper

Description of the change:
Using the dynamic rest mapper so ansible can create CRDs and then use that API
Motivation for the change:

@jmazzitelli

What is the reason ansible would need to create a CRD vs users creating one beforehand?

see the Kiali Operator and its runtime dashboards - Kiali operator needs to install a kiali runtime dashboards CRD first and then install the dashboard yamls. Not all CRDs are expected to be user-installed first - in this case, the Kiali Operator is going to manage this because it knows the runtime dashboards require one. The Kiali Operator has the appropriate roles to install CRDs so that isn't a problem..

@shawn-hurley et. al. will this be able to get merged in order to make it into the next ansible operator-sdk release?

@jmazzitelli it is a problem in the bigger picture. It requires your Operator to have global permissions to register CRDs, a very powerful and potentially dangerous capability on larger clusters with many Operators installed and users present. It's an uneven tradeoff on security for a little less inconvenience.

It requires your Operator to have global permissions to register CRDs, a very powerful and potentially dangerous capability on larger clusters with many Operators installed and users present.

So what is the solution? Reject this PR and demand users of operators to create the CRDs ahead of time? Or merge this PR and expect users to know about this permission that needs to be granted to the operator role?
I need to know how I move forward with my own operator - I'm either waiting on this to be merged, or I need to change documentation/scripts to tell users of my operator they need to do steps ahead of time before installing and using the operator.

I think it's generally good style to separate your CRDs and apply those before you stand-up your Operator. That way your Operator does not need permission to do this one-time thing and a central component like OLM can get awareness of your Operator's APIs and ensure they are properly lifecycled so you don't have to.

OK. I'm going to rip out the CRD creation from my operator and just have the user do it prior to installing the operator. I won't need this PR then.

Does OLM have the ability to install CRDs on behalf of someone requesting the Operator so that the Operator itself doesn't need to manage the CRD component? I haven't had a chance to use OLM (yet, but soon). I understand the concepts, but haven't used it in practice for much yet.
The ideal situation I can think of, is that the OLM has the ability to understand CRDs and manage those on behalf of the person installing the Operator. As mentioned previously, then OLM could even lifecycle manage the CRD as well.
When you start getting into more complicated setups where you're buildin a micro-service based application with multiple Operators joined together, having the OLM help with the lifecycle management of multiple Operator objects, their CRDs, and the dependency resolution among the various components seems like the proper approach here.
I'm afraid if we start seeing Operators starting to add functionality that I think should be at the orchestration layer, then all we've done is taken the magic out of the Operators, and made the development of them significantly more complicated.

Does OLM have the ability to install CRDs on behalf of someone requesting the Operator so that the Operator itself doesn't need to manage the CRD component? I haven't had a chance to use OLM (yet, but soon). I understand the concepts, but haven't used it in practice for much yet.

Yes, it does install the CRDs on behalf of your Operator.

The ideal situation I can think of, is that the OLM has the ability to understand CRDs and manage those on behalf of the person installing the Operator. As mentioned previously, then OLM could even lifecycle manage the CRD as well.

Yes, it does that today, that's why you specify the CRDs that your Operator owns in the CSV and you ship the actual YAML manifests as part of the Operator bundle.

When you start getting into more complicated setups where you're buildin a micro-service based application with multiple Operators joined together, having the OLM help with the lifecycle management of multiple Operator objects, their CRDs, and the dependency resolution among the various components seems like the proper approach here.

You have just described one of OLM's main features :) - it does exactly that.

I'm afraid if we start seeing Operators starting to add functionality that I think should be at the orchestration layer, then all we've done is taken the magic out of the Operators, and made the development of them significantly more complicated.

That's exactly the mission of OLM - lifecycle the Operators and all required resources in a way that you can have dozens of Operators installed in the cluster.


Manage provisioning profiles locally

When you are added to 10+ iOS/OSX development teams and add those teams to Xcode you end up with a crazy amount of provisioning profiles. Currently I have 413 installed of which 124 are expired :disappointed:.
So I propose to add sigh manage for managing provisioning profiles locally.
Please bare with me since it is my first go at ruby :smile_cat:
Todo

[x] Update README.md
[x] sigh manage to list all installed provisioning profiles.
[x] sigh manage -e to delete expired provisioning profiles.
[x] sigh manage -p PATTERN to delete provisioning profiles matching a pattern.
[ ] tests?

@KrauseFx do you have any comments or suggestions?

Thanks, I went through the pull request from the top to bottom, looks good :+1:

@KrauseFx what do you think, does this satisfy your "concerns" ? :)

Looks great. Is it ready to merge from your side?

Yeah from my side it is ready.
On 13 Apr 2015 at 17:44:36, Felix Krause<EMAIL_ADDRESS>wrote:
Looks great. Is it ready to merge from your side?
—
Reply to this email directly or view it on GitHub.

Thanks @mbogh for working on this - sorry it took so long, I had too many GitHub tabs open.
I'll bundle a new release soon :+1:

I pushed a few commits after this:
https://github.com/KrauseFx/sigh/commit/47a8389428b7347b79f7af17c51b8d6300ce7dcd
https://github.com/KrauseFx/sigh/commit/e2e435b0003ba51fd3872cd733db441b579b199a?w=1
https://github.com/KrauseFx/sigh/commit/78128bd98697c2d84816a363e5920110f6e259f2
:+1:

Cool :)


Changing CSS Class of Month and Year Stops change of calendar while i pick year or month from DDL

Whenever i change the class
// Classes
klass: {
// Month & year labels
month: 'picker__month custom-select',
year: 'picker__year custom-select',
}
it add these classes in the year and month ddl, but it stops the change of calendar when i pick month or year from the DDL.
Can you please help.

Hi @ahmad6242,
thanks for reporting this.
Please provide some details about the pickadate version that you use.
Can you also provide a working codepen as reproducible testcase?

Sure i will send tonight... @DanielRuf


Switches and Checkboxes on iOS have accessibility issues

Internal: b/148045557
Switches and Checkboxes on iOS have accessibility issues.
In the following app / screenshot:

Seen result:
Tapping on 'Switch' just says 'Switch'.
Tapping on 'Checkbox' just says 'Checkbox'.
There's no indication that the field is on or off, or that it can be toggled with a double tap.
If the value is on, then this reads:
'Selected, Switch'
'Selected, Checkbox'
Expected result:
Based on how this works with normal iOS switches, I'd expect:
'Switch, off, double tap to toggle setting'
'Checkbox, off, double tap to toggle setting'
If they're on, I'd expect:
'Switch, on, double tap to toggle setting'
'Checkbox, on, double tap to toggle setting'
Full repro code:
import 'package:flutter/material.dart';

void main() => runApp(MyApp());

class MyApp extends StatefulWidget {
  @override
  State createState() => _MyAppState();
}

class _MyAppState extends State<MyApp> {
  bool _on = false;

  @override
  Widget build(BuildContext context) {
    return MaterialApp(
      title: 'Accessibility test',
      theme: ThemeData(
        colorScheme: ColorScheme.light(),
        primarySwatch: Colors.blue,
      ),
      darkTheme: ThemeData(
        colorScheme: ColorScheme.dark(),
        primarySwatch: Colors.blue,
      ),
      home: Scaffold(
        appBar: AppBar(title: Text('Accessibility test')),
        body: Column(
          children: [
            SwitchListTile(
              title: Text('Switch'),
              value: _on,
              onChanged: (value) => setState(() => _on = value),
            ),
            CheckboxListTile(
              title: Text('Checkbox'),
              value: _on,
              onChanged: (value) => setState(() => _on = value),
            )
          ],
        ),
      ),
    );
  }
}

fixed in https://github.com/flutter/engine/pull/16211


Offer to instructors a text box next to the option of removing the slides

Is your feature request related to a problem?
When instructors create a lecture and then choose to process the units automatically they can also remove "Break" or "Example solution" slides from the PDF file. But if the slides have different pattern or are in different language the removal process will not work as it does search the words  "Break" or "Example solution".
Describe the solution you'd like
In this #6895 PR it was suggested to offer to instructors a text box next to the option of removing the slides by @JohannesStoehr. So when instructors choose to remove slides they should see a text box alongside the option to remove slides, allowing users to input their desired text. If instructors fill the text box the slide to be removed will be compared with the entered string, otherwise it should be compared with default values: "Break" for break slides and "Example solution" for solution slides.
Describe alternatives you've considered
No response
Additional context
Possible solution is to get as request body 2 new fields (breakString and solutionString) and then pass to the split service that will be compared when removing slides.

Support a comma-separated list of key phrases


need to refactor motion correction workflow

the attempt to use the ants motion_correction workflow is problematic.  it doesn't seem possible to successfully save the transform files in a location other than /tmp, and this can be problematic on HPC systems since tmp can run out of space.
the current model does the full run on channel 1 and then separately transforms channel 2, which requires holding onto all of teh transforms. I propose to change to a model where the transform is computed for each timepoint, and then applied, so that the transform files can then be deleted before the next step.

Fixed in #21


Bug: Inconsistent Update log and update message

Cannot reproduce, must have been fixed a while ago.


Update scala-library to 2.13.14

About this PR
📦 Updates org.scala-lang:scala-library from 2.13.13 to 2.13.14
📜 GitHub Release Notes - Version Diff
Usage
✅ Please merge!
I'll automatically update this PR to resolve conflicts as long as you don't change it yourself.
If you'd like to skip this version, you can just close this PR. If you have any feedback, just mention me in the comments below.
Configure Scala Steward for your repository with a .scala-steward.conf file.
Have a fantastic day writing Scala!

🔍 Files still referring to the old version number
The following files still refer to the old version number (2.13.13).
You might want to review and update them manually.
.github/workflows/ci.yml
website/docs/reference/scala-versions.md
website/docs/release_notes.md

⚙ Adjust future updates
Add this to your .scala-steward.conf file to ignore future updates of this dependency:
updates.ignore = [ { groupId = "org.scala-lang", artifactId = "scala-library" } ]

Or, add this to slow down future updates of this dependency:
dependencyOverrides = [{
  pullRequests = { frequency = "30 days" },
  dependency = { groupId = "org.scala-lang", artifactId = "scala-library" }
}]

labels: library-update, early-semver-patch, semver-spec-patch, old-version-remains, commit-count:1

Superseded by #2882


Shouldn't the property shapes be also identified by IRIs?

Currently, property shapes of a Node shape are blank nodes, not identified by IRIs. This might have an effect on the usability of the resulting validation reports. We should consider pro's and con's of assigning IRIs also to property shapes.

Added creation of property nodes for all properties. There are no more blank nodes in the generated turtle. Closing the issue.


Archiving to GCS times out since v1.0.0

Version of Cadence server, and client(which language)
This is very important to root cause bugs.

Server version: v1.0.0
Client version: v1.0.0
Client langauge: Java

Describe the bug
We have archiving turned on, with archives backed by GCS buckets. Prior to v1.0.0, archiving worked fine with no problems. Since updating to v1.0.0, there are now timeout errors in connections to GCS. See stack traces below.
Note this is not an occasional error. It happens consistently any therefore blocks any workloads from being archived.
To Reproduce
Is the issue reproducible?

Yes

Steps to reproduce the behavior:

Enable archival history and visibility and set history/visibility URI to GCS buckets
Use v1.0.0, start cadence server and observe workloads to be archived will throw the stack traces below

Expected behavior
Sufficient timeout so that we don't see error messages and workflows get successfully archived
Additional context
2023-10-01 23:02:15 {"level":"info","ts":"2023-10-02T06:02:15.136Z","msg":"Task list manager state changed","service":"cadence-matching","component":"matching-engine","wf-task-list-name":"sticky:Freds-MacBook-Pro-Work.local:6b40d852-82bf-4f29-9da9-92f201506249","wf-task-list-type":0,"lifecycle":"Stopped","logging-call-at":"taskListManager.go:217"}
2023-10-01 23:02:47 {"level":"error","ts":"2023-10-02T06:02:47.021Z","msg":"Unregistered field.","service":"cadence-worker","component":"indexer","component":"indexer-processor","es-field":"ShardID","wf-domain-id":"7c232933-fdc5-41c2-940f-28b8693276d1","logging-call-at":"processor.go:255","stacktrace":"github.com/uber/cadence/common/log/loggerimpl.(*loggerImpl).Error\n\t/cadence/common/log/loggerimpl/logger.go:137\ngithub.com/uber/cadence/service/worker/indexer.(*indexProcessor).dumpFieldsToMap\n\t/cadence/service/worker/indexer/processor.go:255\ngithub.com/uber/cadence/service/worker/indexer.(*indexProcessor).generateESDoc\n\t/cadence/service/worker/indexer/processor.go:235\ngithub.com/uber/cadence/service/worker/indexer.(*indexProcessor).addMessageToES\n\t/cadence/service/worker/indexer/processor.go:213\ngithub.com/uber/cadence/service/worker/indexer.(*indexProcessor).process\n\t/cadence/service/worker/indexer/processor.go:179\ngithub.com/uber/cadence/service/worker/indexer.(*indexProcessor).messageProcessLoop\n\t/cadence/service/worker/indexer/processor.go:161"}
2023-10-01 23:02:47 {
"level": "error",
"ts": "2023-10-02T06:02:47.398Z",
"msg": "Archive method encountered a transient error.",
"service": "cadence-history",
"archival-request-domain-id": "7c232933-fdc5-41c2-940f-28b8693276d1",
"archival-request-domain-name": "WORKFLOWS_PRIMARY",
"archival-request-workflow-id": "CXO00000.BOERSE_BERLIN_XEQT_SECURITIES_TICK_LADDER.2023-10-02T05:55:20Z",
"archival-request-run-id": "b295cc80-a113-4eb2-b9ab-0e7098729276",
"archival-request-workflow-type": "BasicWorkflow::run",
"archival-request-close-timestamp":<PHONE_NUMBER>376504600,
"archival-request-close-status": "COMPLETED",
"archival-URI": "gs://cadence-archival-stg/cadence_archival/visibility",
"archival-archive-fail-reason": "failed to write history to google storage",
"error": "context deadline exceeded",
"logging-call-at": "visibilityArchiver.go:133",
"stacktrace": "github.com/uber/cadence/common/log/loggerimpl.(*loggerImpl).Error\n\t/cadence/common/log/loggerimpl/logger.go:137\ngithub.com/uber/cadence/common/archiver/gcloud.(*visibilityArchiver).Archive\n\t/cadence/common/archiver/gcloud/visibilityArchiver.go:133\ngithub.com/uber/cadence/service/worker/archiver.(*client).archiveVisibilityInline\n\t/cadence/service/worker/archiver/client.go:271"
}
2023-10-01 23:02:47 {"level":"info","ts":"2023-10-02T06:02:47.399Z","msg":"failed to perform visibility archival inline","service":"cadence-history","shard-id":1,"address":"<IP_ADDRESS>:7934","archival-caller-service-name":"cadence-history","archival-archive-attempted-inline":true,"archival-request-domain-id":"7c232933-fdc5-41c2-940f-28b8693276d1","archival-request-domain-name":"WORKFLOWS_PRIMARY","archival-request-workflow-id":"CXO00000.BOERSE_BERLIN_XEQT_SECURITIES_TICK_LADDER.2023-10-02T05:55:20Z","archival-request-run-id":"b295cc80-a113-4eb2-b9ab-0e7098729276","archival-URI":"gs://cadence-archival-stg/cadence_archival/development","error":"retriable error","logging-call-at":"client.go:256"}
2023-10-01 23:02:47 {
"level": "error",
"ts": "2023-10-02T06:02:47.696Z",
"msg": "failed to send signal to archival system workflow",
"service": "cadence-history",
"shard-id": 1,
"address": "<IP_ADDRESS>:7934",
"archival-caller-service-name": "cadence-history",
"archival-archive-attempted-inline": true,
"archival-request-domain-id": "7c232933-fdc5-41c2-940f-28b8693276d1",
"archival-request-domain-name": "WORKFLOWS_PRIMARY",
"archival-request-workflow-id": "CXO00000.BOERSE_BERLIN_XEQT_SECURITIES_TICK_LADDER.2023-10-02T05:55:20Z",
"archival-request-run-id": "b295cc80-a113-4eb2-b9ab-0e7098729276",
"wf-id": "cadence-archival-46",
"error": "code:unavailable message:connection closed before server preface received",
"logging-call-at": "client.go:317",
"stacktrace": "github.com/uber/cadence/common/log/loggerimpl.(*loggerImpl).Error\n\t/cadence/common/log/loggerimpl/logger.go:137\ngithub.com/uber/cadence/service/worker/archiver.(*client).sendArchiveSignal\n\t/cadence/service/worker/archiver/client.go:317\ngithub.com/uber/cadence/service/worker/archiver.(*client).Archive\n\t/cadence/service/worker/archiver/client.go:193\ngithub.com/uber/cadence/service/history/task.(*transferTaskExecutorBase).recordWorkflowClosed\n\t/cadence/service/history/task/transfer_task_executor_base.go:341\ngithub.com/uber/cadence/service/history/task.(*transferActiveTaskExecutor).processCloseExecutionTaskHelper\n\t/cadence/service/history/task/transfer_active_task_executor.go:461\ngithub.com/uber/cadence/service/history/task.(*transferActiveTaskExecutor).processCloseExecution\n\t/cadence/service/history/task/transfer_active_task_executor.go:285\ngithub.com/uber/cadence/service/history/task.(*transferActiveTaskExecutor).Execute\n\t/cadence/service/history/task/transfer_active_task_executor.go:131\ngithub.com/uber/cadence/service/history/task.(*taskImpl).Execute\n\t/cadence/service/history/task/task.go:223\ngithub.com/uber/cadence/common/task.(*parallelTaskProcessorImpl).executeTask.func1\n\t/cadence/common/task/parallelTaskProcessor.go:156\ngithub.com/uber/cadence/common/backoff.(*ThrottleRetry).Do\n\t/cadence/common/backoff/retry.go:172\ngithub.com/uber/cadence/common/task.(*parallelTaskProcessorImpl).executeTask\n\t/cadence/common/task/parallelTaskProcessor.go:176\ngithub.com/uber/cadence/common/task.(*parallelTaskProcessorImpl).taskWorker\n\t/cadence/common/task/parallelTaskProcessor.go:146"
}
2023-10-01 23:02:47 {
"level": "error",
"ts": "2023-10-02T06:02:47.697Z",
"msg": "Fail to process task",
"service": "cadence-history",
"shard-id": 1,
"address": "<IP_ADDRESS>:7934",
"component": "transfer-queue-processor",
"cluster-name": "cluster0",
"queue-task-id": 9437264,
"queue-task-visibility-timestamp":<PHONE_NUMBER>383000000,
"xdc-failover-version": -24,
"queue-task-type": 2,
"wf-domain-id": "7c232933-fdc5-41c2-940f-28b8693276d1",
"wf-id": "CXO00000.BOERSE_BERLIN_XEQT_SECURITIES_TICK_LADDER.2023-10-02T05:55:20Z",
"wf-run-id": "b295cc80-a113-4eb2-b9ab-0e7098729276",
"error": "code:unavailable message:connection closed before server preface received",
"lifecycle": "ProcessingFailed",
"logging-call-at": "task.go:332",
"stacktrace": "github.com/uber/cadence/common/log/loggerimpl.(*loggerImpl).Error\n\t/cadence/common/log/loggerimpl/logger.go:137\ngithub.com/uber/cadence/service/history/task.(*taskImpl).HandleErr\n\t/cadence/service/history/task/task.go:332\ngithub.com/uber/cadence/common/task.(*parallelTaskProcessorImpl).executeTask.func1\n\t/cadence/common/task/parallelTaskProcessor.go:157\ngithub.com/uber/cadence/common/backoff.(*ThrottleRetry).Do\n\t/cadence/common/backoff/retry.go:172\ngithub.com/uber/cadence/common/task.(*parallelTaskProcessorImpl).executeTask\n\t/cadence/common/task/parallelTaskProcessor.go:176\ngithub.com/uber/cadence/common/task.(*parallelTaskProcessorImpl).taskWorker\n\t/cadence/common/task/parallelTaskProcessor.go:146"
}
2023-10-01 23:02:47 {
"level": "error",
"ts": "2023-10-02T06:02:47.748Z",
"msg": "Unregistered field.",
"service": "cadence-worker",
"component": "indexer-processor",
"es-field": "ShardID",
"wf-domain-id": "7c232933-fdc5-41c2-940f-28b8693276d1",
"logging-call-at": "processor.go:255",
"stacktrace": "github.com/uber/cadence/common/log/loggerimpl.(*loggerImpl).Error\n\t/cadence/common/log/loggerimpl/logger.go:137\ngithub.com/uber/cadence/service/worker/indexer.(*indexProcessor).dumpFieldsToMap\n\t/cadence/service/worker/indexer/processor.go:255\ngithub.com/uber/cadence/service/worker/indexer.(*indexProcessor).generateESDoc\n\t/cadence/service/worker/indexer/processor.go:235\ngithub.com/uber/cadence/service/worker/indexer.(*indexProcessor).addMessageToES\n\t/cadence/service/worker/indexer/processor.go:213\ngithub.com/uber/cadence/service/worker/indexer.(*indexProcessor).process\n\t/cadence/service/worker/indexer/processor.go:179\ngithub.com/uber/cadence/service/worker/indexer.(*indexProcessor).messageProcessLoop\n\t/cadence/service/worker/indexer/processor.go:161"
}

If this helps anyone, here's how we resolved it:
My understanding of the problem: When a workload gets archived, it can take two routes- either synchronously (inline) or asynchronously. History sizes below the default of 512K get archived synchronously, with no timeouts in that path. History above 512K get archived async, and that has a timeout; if we have a large workloads and a stringent timeout, we'll be hitting those errors. Some of our histories go up to 1MB and those were the remaining ones that couldn't get archived. Looks like Cadence had put in the async path since our last version update.
The solution is to increase the threshold on what gets archived synchronously. Fortunately, the 512K default is a dynamic config history.timerProcessorHistoryArchivalSizeLimit. I bumped that up to 10MB  to force all the history to go through the sync route. It might put more strain on the workers, but in the grand scheme of things, I don't think we're archiving super intensively and our cadence_history workers are scalable anyway.


tiny: change shell execution style in Makefile

I know this != is supposed to work, but some of environments (e.g RHEL7) seems to hate this style. (It looks like space has the problem(?))
[root@knakayam-storage-server2 torus]# make
Makefile:2: *** recipe commences before first target.  Stop.

[root@knakayam-storage-server2 torus]# make --version
GNU Make 3.82
Built for x86_64-redhat-linux-gnu
Copyright (C) 2010  Free Software Foundation, Inc.
License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>
This is free software: you are free to change and redistribute it.
There is NO WARRANTY, to the extent permitted by law.
~~~


[Mktg-Call Steward] co-host for March

Hi @aeiras! Re. what you mentioned on the call today – I'd be happy to help co-hosting the call in March.

Thank you for choosing to step in to contribute, Rustam, @mehmandarov!
You have edit access to the Stewards' sheet and can help adjust stuff, especially responsibilities. Please take the time to navigate responsibilities.  It doesn't mean you have to do the tasks. It means you are a watcher to move stuff forward. https://docs.google.com/spreadsheets/d/1gb2D8wPoXIrvmndMt0fP4jDYC61YuvBwVEFqVskywU8/edit#gid=0
Let's leave this ticket open. The important part is for you to help document stuff as you onboard into navigating the business side of MP.
Use the MP Drive Community to create documents relevant to the tasks that might be missing. If you help write any of the documents, they will become wikis and welcome PRs, as you saw today. :)
Abrazos,

Awesome! Thanks!
I created a doc explaining how to get started as a steward and I intend add some info about the onboarding process as we go. Feel free to add any content: https://docs.google.com/document/d/1ayqaFoXGeTZdneL6U9oekaZP3LPe7PapTlg7jh7eD3M/edit

Hola Rustam @mehmandarov,
Do you have the copy/paste the agenda template for the next call either today or tomorrow?
MP work is fluid via the tickets, therefore the agenda is recommended within 24hrs after the call is completed. :) This is part of the documentation. 👍   Questions, welcomed. And nope, you are not expected to search stuff in git, what we need is the template so that closed tickets and work in progress tickets can be listed in the agenda sections.
When we delay the proactive agenda write it, the work gets lost as 2 weeks pass. The agenda needs utmost care & update by anyone who is doing stuff. As a Steward, it is your job to make sure minutes are there welcoming input.

Hola Rustam,
Today, I copied/paste the agenda minutes so that the many tickets that have closed since the last meeting are traced without fail.
Do note that early placement of the agenda is recommended, either the same day after the meeting ends or the day after. :) This  ought to be added to the onboarding wiki you are helping write 👍
@mehmandarov,
Since you will lead next week's mktg call, you need to also own the send of of the minutes to the Mktg forum on Thursday. Ideally, on that day or the day before you have gotten enough time to see what else might be missing from the conversation based on what you have seen in the mktg git, mktg forum & MP Community forum/. :)

@@mehmandarov,
1 of the monthly tasks is to help publish content in MP Wordpress. Do you want to own it? Learn how to do it?
You are already a Contributor who has submitted 1 blog.
I will upgrade you to Editor to enable you to review and maybe push content written by others. We ought to not push your own content or at least get other Editors to publish it for you.
You have became the 12 Content Editor, today! :)
Saying no to own the task is OK. Note that I am adjusting stuff to enable you to own responsibilities. Yet always keep in mind that you can ask others to help you as well. Being assign to a month doesn't mean you must do the work, it just means that you follow the process and make sure that stuff gets done in any way that brings collaboration and scales beyond you. : )
https://github.com/eclipse/microprofile-marketing/issues/452

Sure, I can do that, @aeiras! Do we have guidelines for the content that we publish?

Blog Guidelines- Blog Call to Action space

@mehmandarov Hola!
Would you like to do the honors on copy/pasting the agenda minutes for the March 29th call?
If yes, how does tomorrow sound? Ideally, the agenda is created within 24hrs after the marketing call. The MP work doesn't stop, as you can see, we use git issues and when closed, we tend to add them to the agenda to make sure that tracing is there. There is no agenda, so that will be problematic to older tickets that are closed. \
Thanks for your help,

adding it as a topic item to next Monday agenda. Retrospective fun. Closing the ticket :)


TinyMCE does not follow STATIC_URL changes controlled through django-storages

<head>
  <script src="/admin/jsi18n/"></script>
  <script src="https://storage.googleapis.com/final-sigma-website-resources/admin/js/vendor/jquery/jquery.min.js?X-Goog-Algorithm=GOOG4-RSA-SHA256&a ... 9819cfd"></script>
  <script src="/static/tinymce/tinymce.min.js"></script>
  <script src="https://storage.googleapis.com/final-sigma-website-resources/admin/js/jquery.init.js?X-Goog-Algorithm=GOOG4-RSA-SHA256&a ...969db42"></script>
</head>

In development on my own machine, where collectstatic delivers all static files to a standard staticfiles/ folder in my project directly, TinyMCE loads just fine when editing fields in localhost:8000/admin interface.
In production, I use django-storages[google] to host static & media files from Google Cloud. The above code is from the <head> of my admin page in production. As you can see, all static-files urls are properly changed except TinyMCE.
Django Storages for Google Cloud Storage
My Project

Implemented a solutions in my settings.py (similar to this):
#Tiny MCE fix
if config('CODE_ENVIRONMENT') == 'production':
    from django.contrib.staticfiles.storage import staticfiles_storage

    TINYMCE_JS_URL = staticfiles_storage.url('tinymce/tinymce.min.js')
    TINYMCE_JS_ROOT = staticfiles_storage.url('tinymce')

Now tinymce.min.js and tinymce/ are pulling from Google Cloud Storage like everything else:
<script src="https://storage.googleapis.com/final-sigma-website-resources/tinymce/tinymce.min.js?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=final-sigma-website%40final-sigma-website-357112.iam.gserviceaccount.com%2F20220725%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20220725T033134Z&a ... b79fd9"></script>

However, now where the editor should load in my admin page, there's just an empty space.

You may (re-)read https://docs.djangoproject.com/en/4.0/intro/tutorial06/ which shows how you should refer to static files in your templates with the static tag. However, including {{ form.media }} in your form templates should even take care of including tinymce static files.

Thanks, @claudep, what about for django's built-in admin app where I'm not personally designing the templates?

The admin app calls {{ form.media }}, so with the normal flow, the proper JS paths should be output without the need to do anything else. (Media.render_js should call the static template tag, which should call staticfiles_storage.url(path)).

I think I found it.
The request to download tinymce/theme.min.js goes to the right Google Cloud Storage bucket, but it does not include the authentication parameters.
Right now looks like this (and returns a 403 error):
Request URL: https://storage.googleapis.com/final-sigma-website-resources/tinymce/themes/silver/theme.min.js

When it should look like this:
Request URL: https://storage.googleapis.com/final-sigma-website-resources/django_tinymce/init_tinymce.js?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=final-sigma-website%40final-sigma-website-357112.iam.gserviceaccount.com%2F20220725%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20220725T123730Z&X-Goog-Expires=86400&X-Goog-SignedHeaders=host&X-Goog-Signature=4dbeb4944b654d1 ... b199ddfb6

I can do some digging to find where that url is defined, unless @claudep you know off-hand.

I guess this might be a django-storages usage or bug issue. I'm not using this package so unfortunately cannot help much.


Migrate from React.PropTypes, close #34

Coverage remained the same at 100.0% when pulling e48d80d0eb6d781e2fbb169d72e255e581f98682 on feature/prop-types into 39f772eae4b19ee5375866b96ba3f1e89c3d4137 on develop.

Coverage remained the same at 100.0% when pulling 11d5fd2ee9b179804166ab11e253a6d5a5338d65 on feature/prop-types into 45c4f6737abe2feb42cbc370424e6260912fd259 on develop.


Plugin not recognized on install

Hi there, excited to install but when I follow the normal plugin install procedure (upload contents of .zip to craft/plugins folder, then go to Settings/Plugins) the plugin does not display.  Same for 'export' plugin.

Realized folder had "-develop" appended to it, so Craft wasn't recognizing.  Perhaps put a note on this in readme for inexperienced craft devs such as myself? :)  Thank you for the awesome plugin!

Actually this is in the readme:
Important:
The plugin's folder should be named "import"


Sample app crash with API 10: Preference Dialogs

java.lang.NoSuchMethodError: com.afollestad.materialdialogssample.PreferenceActivity.getFragmentManager
at com.afollestad.materialdialogssample.PreferenceActivity.onCreate(PreferenceActivity.java:23)
at android.app.Instrumentation.callActivityOnCreate(Instrumentation.java:1047)
at android.app.ActivityThread.performLaunchActivity(ActivityThread.java:1660)
at android.app.ActivityThread.handleLaunchActivity(ActivityThread.java:1716)
at android.app.ActivityThread.access$1500(ActivityThread.java:124)
at android.app.ActivityThread$H.handleMessage(ActivityThread.java:968)
at android.os.Handler.dispatchMessage(Handler.java:99)
at android.os.Looper.loop(Looper.java:130)
at android.app.ActivityThread.main(ActivityThread.java:3806)
at java.lang.reflect.Method.invokeNative(Native Method)
at java.lang.reflect.Method.invoke(Method.java:507)
at com.android.internal.os.ZygoteInit$MethodAndArgsCaller.run(ZygoteInit.java:839)
at com.android.internal.os.ZygoteInit.main(ZygoteInit.java:597)
at dalvik.system.NativeStart.main(Native Method)

The other two are fixed, this one is a little harder since PreferenceFragment cannot be used on lower API levels.

@Kuitsi I should mention that this isn't a Material Dialogs issue. This is an issue specific to the sample project.

This is not working either. App does not crash but the screen is empty, only ActionBar is visible.

Not sure if there's much I can do about this then, I might have to disable the Settings Activity on API 10. The preferences themselves aren't affected, but there's no AppCompat variation of PreferenceActivity (which extends ActionBarActivity) so I have to use PreferenceFragments.

We have in Bitbeaker something that may help. It can fall back to old style preferences but it can use fragments too.

Well like I said above, PreferenceActivity can't be used because it won't work correctly with AppCompat theming.

The sample will use PreferenceActivity on API 10 and below, which won't display an action bar due to AppCompat not supporting it. Above API 10, the regular solution (ActionBarActivity with API 11+ PreferenceFragments) will be used.
The sample project is a reflection of the library, obviously, but this PreferenceActivity sample is independent from the Material preferences themselves.

Nope. The first crash is a classic
android.content.ActivityNotFoundException: Unable to find explicit activity class {com.afollestad.materialdialogssample/com.afollestad.materialdialogssample.PreferenceActivityCompat}; have you declared this activity in your AndroidManifest.xml?

After it is fixed, the next crash is
java.lang.RuntimeException: Unable to start activity ComponentInfo{com.afollestad.materialdialogssample/com.afollestad.materialdialogssample.PreferenceActivityCompat}: android.view.InflateException: Binary XML file line #13: Error inflating class com.afollestad.materialdialogs.prefs.MaterialMultiSelectListPreference

Your MaterialMultiSelectListPreference requires API 11 as stated in it's source code. I tried what happens if I just comment it out from res/xml/preferences.xml. Everything seems to be ok until you click the last one, MaterialEditTextPreference.
java.lang.ClassCastException: android.widget.TextView
        at com.afollestad.materialdialogs.MaterialDialog.canViewOrChildScroll(MaterialDialog.java:366)
        at com.afollestad.materialdialogs.MaterialDialog.canViewOrChildScroll(MaterialDialog.java:366)
        at com.afollestad.materialdialogs.MaterialDialog.invalidateCustomViewAssociations(MaterialDialog.java:206)
        at com.afollestad.materialdialogs.DialogInit$1.onGlobalLayout(DialogInit.java:266)

I didn't register then activity for API 10 in the manifest. Feel free to do
it yourself, my personal life is more busy on the weekend.
On Sat, Mar 28, 2015, 4:28 AM Kuitsi<EMAIL_ADDRESS>wrote:

Nope. The first crash is a classic
android.content.ActivityNotFoundException: Unable to find explicit activity class {com.afollestad.materialdialogssample/com.afollestad.materialdialogssample.PreferenceActivityCompat}; have you declared this activity in your AndroidManifest.xml?
After it is fixed, the next crash is
java.lang.RuntimeException: Unable to start activity ComponentInfo{com.afollestad.materialdialogssample/com.afollestad.materialdialogssample.PreferenceActivityCompat}: android.view.InflateException: Binary XML file line #13: Error inflating class com.afollestad.materialdialogs.prefs.MaterialMultiSelectListPreference
Your MaterialMultiSelectListPreference requires API 11 as stated in it's
source code. I tried what happens if I just comment it out from
res/xml/preferences.xml. Everything seems to be ok until you click the
last one, MaterialEditTextPreference.
java.lang.ClassCastException: android.widget.TextView
at com.afollestad.materialdialogs.MaterialDialog.canViewOrChildScroll(MaterialDialog.java:366)
at com.afollestad.materialdialogs.MaterialDialog.canViewOrChildScroll(MaterialDialog.java:366)
at com.afollestad.materialdialogs.MaterialDialog.invalidateCustomViewAssociations(MaterialDialog.java:206)
at com.afollestad.materialdialogs.DialogInit$1.onGlobalLayout(DialogInit.java:266)
—
Reply to this email directly or view it on GitHub
https://github.com/afollestad/material-dialogs/issues/361#issuecomment-87197594
.

Nevermind already got it with the latest release
https://github.com/afollestad/material-dialogs/releases/tag/v<IP_ADDRESS>

I guess you did not see my commit https://github.com/Kuitsi/material-dialogs/commit/a446b59f11747ea0ee9fb9dea02cabde2d0cef5c (in my fork, referenced in this issue but I did not create a pull request for it yet) from this morning. You can't even try to load MaterialMultiSelectListPreference in preferences.xml when opening PreferenceActivityCompat or the app will crash.

Oh sorry, good catch.
Fixed that by using xml-v11 instead: https://github.com/afollestad/material-dialogs/commit/8ac9c0374bd06ee5e15d0aaa99750a6102b3c2d3

Thanks. No more issues at this point :)

Cool :) thanks for your bug reports


closes #7

Codecov Report

Merging #8 into master will decrease coverage by 7.32%.
The diff coverage is n/a.

@@            Coverage Diff             @@
##           master       #8      +/-   ##
==========================================
- Coverage   98.78%   91.46%   -7.33%
==========================================
  Files           7        7
  Lines         165      164       -1
==========================================
- Hits          163      150      -13
- Misses          2       14      +12

Impacted Files
Coverage Δ

src/data_utils.jl
100% <ø> (ø)
:arrow_up:

src/model_traits.jl
58.62% <0%> (-41.38%)
:arrow_down:

src/metadata_utils.jl
100% <0%> (ø)
:arrow_up:

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update f45bf0f...127fcc8. Read the comment docs.


add a test that verifies merge transactions

fixes https://github.com/omgnetwork/omg-childchain-v2/issues/65

what are the fees when this test is executed? I see you're not paying them, but can you confirm they're set for the currency you're transacting with?
we should test that fees are not being paid.

@InoMurko I checked that the Merge validator was triggered. Because of it, the test didn't charge any fees


Add Easy-Email editor to the documentation.

Easy Email is React.js Drag-and-Drop Email Editor based on MJML.
I hope more people can use it. Can I add it to the documentation?

Hi !
Can you add this to the tooling section instead ? I

Hi !
Can you add this to the tooling section instead ? I

Moved to tooling section.


Globe dissappears during browser resize.

The globe window contents disappears when the left sidebar is collapsed and the browser width is decreased.

FireFox, Chrome and Safari.

See issue: https://github.com/nathancahill/Split.js/issues/109

Added the following code fragment sidestep the issue:
                $(window).resize(function () {
                    // Force re-calc of size percentages when collapsed.
                    // The collapsed window has a 'fixed' size (0), but the
                    // size percentages vary during the resize. We need to
                    // re-calc the size percentages during resizing to a
                    // smaller window, else the remaining split-pane's size
                    // could be fractionally to big to fit into its container
                    // and cause the layout to fail.
                    if ($(horzSplitPanes[0]).width() < 5) {
                        sidebarSplitter.collapse(0)
                    }
                });


⚠ Bofrost.fr has degraded performance

In 2db0fb6, Bofrost.fr (https://www.bofrost.fr) experienced degraded performance:

HTTP code: 200
Response time: 1049 ms

Resolved: Bofrost.fr performance has improved in 04fc690 after 6 minutes.


[SDXL test]IndexError: tuple index out of range

It's maybe fixed. Please try it out on the sdxl branch.

It's maybe fixed. Please try it out on the sdxl branch.

thank u for replying！it fixed but there is another error

I fixed it at 4b19877cadf058825163576c7d6c0cf9bd8a8ffd.  (Sorry for the inconvenience because I don't have enough GPU resources for testing...)

I fixed it at 4b19877. (Sorry for the inconvenience because I don't have enough GPU resources for testing...)

i can make a pr with Paged optimizer which can save 75% VRAM if you need.
it needs gradit checkpoint and bitsandbytes over 0.39.1.

I fixed it at 4b19877. (Sorry for the inconvenience because I don't have enough GPU resources for testing...)

OK,it works well now.
only it suddenly uses bigger VRAM in dyn res...
i had to use batch size 4 for almost 40G VRAM.
i think maybe dyn res is not use resulation for middle squre.
for example
resulation: 1024
it still use 512X576 to finetune

SDXL result is nothing changed....
SD 1.5 with same prompt works well

In previous test (https://huggingface.co/p1atdev/leco/blob/main/elon_musk_sdxl_09.safetensors), it only worked when applied at very high weight (such as 10.0). Please try higher weight if you haven't tried.
I don't know if it will work, but anyway it seems that its effect is less on SDXL than on SDv1.x or SDv2.x. (https://twitter.com/gcem156/status/1682020714579365891)

In previous test (https://huggingface.co/p1atdev/leco/blob/main/elon_musk_sdxl_09.safetensors), it only worked when applied at very high weight (such as 10.0). Please try higher weight if you haven't tried.
I don't know if it will work, but anyway it seems that its effect is less on SDXL than on SDv1.x or SDv2.x. (https://twitter.com/gcem156/status/1682020714579365891)

i use weight 10 in comfyui(in comfy_ui,it max weight is 10) but it changed a little alsmot cant see
Should i use higher guidance_scale?
i will test for more result.


Sometimes failing to retrieve Application Default Credentials on GKE Autopilot in googleapis auth library

Mirror question here in Stack Overflow: https://stackoverflow.com/questions/68854640/sometimes-failing-to-retrieve-application-default-credentials-on-gke-autopilot-i

Some pods in my GKE Autopilot cluster aren't able to grab the Application Default Credentials to call other GCP services.
I will apply a new deployment, and 1 or 2 out of the 3 pods won't be able to authenticate using the googleapis (google-auth-library) npm package (tried with version v73.0.0 and the latest v84.0.0).
I get:
Error: Could not load the default credentials. Browse to https://cloud.google.com/docs/authentication/getting-started for more information.     at GoogleAuth.getApplicationDefaultAsync (/node_modules/google-auth-library/build/src/auth/googleauth.js:173:19)
I am using this code and retrying on failure:

const {google} = require('googleapis');

const setGoogleAuth = async () => {
    try {
        const auth = new google.auth.GoogleAuth({
            // Scopes can be specified either as an array or as a single, space-delimited string.
            scopes: ['https://www.googleapis.com/auth/cloud-platform'],
        });

        // Acquire an auth client, and bind it to all future calls
        const authClient = await auth.getClient();
        google.options({auth: authClient});
    } catch (e) {
        console.error(e)

        //retry

        //sleep for 3 seconds
        await sleep(3000)

        await setGoogleAuth()
    }

}

Calling the metadata server manually from a troubled pod via curl --location --request POST 'http://metadata/computeMetadata/v1/instance/service-accounts/default/identity?audience=my-gcp-endpoint' \ --header 'Metadata-Flavor: Google' \ --data-raw '{}' returns a valid token
Sometimes killing the pod and having them recreated works (using minReplicas in Horizontal Pod Autoscaler) and sometimes no matter how many times I kill the troubled pods the issue persists. Other times, I'll redeploy and have no problems.  The behaviour seems very non-deterministic.
I also tried running another node process inside the troubled pod, and did not get the error

Any help would be appreciated, thank you!
Possible related issues
https://github.com/googleapis/google-auth-library-nodejs/issues/798
https://github.com/googleapis/google-auth-library-nodejs/issues/786
https://github.com/googleapis/google-auth-library-nodejs/issues/526
Environment details

GKE Version: 1.20.8-gke.900
Node.js version: v10.24.1
npm version: 6.14.12
google-auth-library version: tried with v73.0.0 and v84.0.0 (latest)

Steps to reproduce

Difficult to reproduce as behaviour is non deterministic

@mikegin could you your cluster with the environment variable DEBUG_AUTH set to true, and provide and provide any additional error information ?

@bcoe
Here is some more output with the DEBUG_AUTH set to true
{ FetchError: network timeout at: http://metadata.google.internal./computeMetadata/v1/instance
    at Timeout.<anonymous> (/node_modules/node-fetch/lib/index.js:1448:13)
    at ontimeout (timers.js:436:11)
    at tryOnTimeout (timers.js:300:5)
    at listOnTimeout (timers.js:263:5)
    at Timer.processTimers (timers.js:223:10)
  message:
   'network timeout at: http://metadata.google.internal./computeMetadata/v1/instance',
  type: 'request-timeout',
  config:
   { url:
      'http://metadata.google.internal./computeMetadata/v1/instance',
     headers: { 'Metadata-Flavor': 'Google' },
     retryConfig:
      { noResponseRetries: 0,
        currentRetryAttempt: 0,
        retry: 3,
        httpMethodsToRetry: [Array],
        statusCodesToRetry: [Array] },
     responseType: 'text',
     timeout: 3000,
     paramsSerializer: [Function: paramsSerializer],
     validateStatus: [Function: validateStatus],
     method: 'GET' } }

@mikegin could you try setting DETECT_GCP_RETRIES=3, and see if this stops the behavior? This will hopefully give GKE Autopilot more time to initialize.

@bcoe for me it helped, thanks. 🎉 Our GKE is not in autopilot mode, I had the same issue in our staging environment, where everything is scaled down, on our production system fortunately I haven't had this issue. So my first thought was that it didn't get enough CPU, or something...

@bcoe I will try that. So far I found setting K_SERVICE=true (based on the code in the gcp-metadata library) has helped. I redeployed 10 times or so to test and haven't experienced the issue.

@mikegin @bvarga any thoughts on where we could document this setting, so it might have jumped out at you? Would you have noticed documentation calling out this setting in the README for this repo?

Adding a "Troubleshooting" section in the README with the issue and solution would be good. Even mentioning the DEBUG_AUTH would be great, as it helped with further diagnosis.

We also hit this today and spent quite a while chasing shadows before eventually finding this thread.
While documentation is good and an improvement...
As a Google Cloud customer, I would like the Google Cloud SDKs to work on Google Cloud by default. It is not ideal to have to find somewhat obscure env vars hidden in the READMEs of internal SDK libraries in order to make things work reliably.
It seems like there is already handling to speed up the metadata checks for those who are running the SDK outside of GCP, e.g. the failFast here. Perhaps defaulting to more tries would be OK?
Also, there are also some (apparently) GCE-specific checks that control the failFast behaviour, here, but from a quick test, the env vars they use don't seem to be present for GKE workloads (i.e. inside a pod). Maybe those could be extended in some way to detect GKE as well as GCE?

@jdleesmiller the folks who maintain this auth library don't work directly with the folks who maintain GKE Autopilot (this is one of many GCP environments we make an effort to support, Cloud Run, Cloud Build,  Cloud Functions, people running from their laptop, etc.). I'm going to reach out to someone on the GKE Autopilot team, and surface this issue with them.
I've seen these issues in the past in GKE environments; perhaps it relates to too many containers coming up at once and all hammering the metadata server? or, perhaps the metadata server isn't available for the first few seconds of a container being provisioned?
I'll see if we can figure out a workaround, with the team.

Many thanks @bcoe .
In case it helps, a bit more about our case:

Like https://github.com/googleapis/google-auth-library-nodejs/issues/1236#issuecomment-915457074, we are not actually on GKE Autopilot (just GKE Standard), but I think the common thread may be workload identity, which is enabled by default on GKE Autopilot and also for the service in which we saw this problem on GKE Standard.
The service in question was a redis-backed message queue worker (bull) --- with a non-empty queue, it starts up quite quickly, pulls its first message from redis (so the network must be up to at least the extent required for it to connect to redis) and then tries to connect to BigQuery using its service account, which fails with the above error. We think that is indeed racing somehow with the metadata server becoming available --- adding retries helped, as apparently did a 5s delay on startup.
There were only ~2 of these worker pods (one container each), and it was just those pods starting up at the time, so I don't think anything was hammering the metadata server, but if there are any metrics we should look at for that we can see what they say.

Thanks again!

which GKE version were you using?

Thanks @bcoe. I was facing the same issue with a deployment in a standard GKE cluster. Setting DETECT_GCP_RETRIES=3 did work, but it would be great to have this documented somewhere easier to find, maybe in the GKE Workload Identity troubleshooting section?

Want to give an update to this issue - this PR should resolve this issue, however we're syncing internally on its implementation and uniformity across languages: https://github.com/googleapis/gcp-metadata/pull/528

We've merged a fix! Happy to share this is fixed as of gcp-metadata v5.1.0, which should be automatically picked up by future installations of this library.

https://github.com/googleapis/gcp-metadata/pull/528


Messages: Explode partitions list in API

What
Within #47 API has been changed and multiple partitions are passed like &partitions=1&partitions=2&partitions=3 to backend. We need to use explode keyword in OpenAPI spec to make it possible to use &partitions=1,2,3 instead.
Why
Having topics with a considerable number of partitions can lead to problems. Imagine having 1000 partitions and having a need to search messages in all partitions but one, this will exceed the max URL length easily.

Adjust backend API spec
Update frontend accordingly

@germanosin wants to check if we are using generated requests rather than manually built ones?


Configure Github Actions for TDD

As a Developer
I need to create a tdd.yml file and configure Github Actions
So that we can perform unit tests everytime we pull a request push to master
Details and Assumptions
"Following TDD practices, place your test cases in a ./tests folder"
"Start developing by creating the TDD test_routes.py and test_models.py
files that contains your Unit Tests to test your service routes and your model
respectively"
Acceptance Criteria
Given developer pushes to master branch
When the automated build fails
Then abort the merge

Given developer submit a PR
When the automated build fails
Then dont create PR

Given developer pushes to master branch or submit a PR
When the automated build passes
Then respective actions are carried out successfully

Same as #72


Open Source Project?

What an amazing state-of-the-art project!
Do you plan to transform this great paper and code into an open source project/ecosystem like FinGPT? or is this just meant to be an implementation of the paper for demonstration purposes?

Hi, we are glad you like it. The code in this GitHub is the implementation of our proposed memory structure.
You can see more details at Here. We included everything we could except the data we obtained from other vendors, which we cannot publicize.

Thank you very much.
Of course.
Working memory appears to be the next frontier for improving agents however it is quite underrated given its potential. This paper seems to be the first in-depth article on the subject:
https://arxiv.org/abs/2312.17259
Hope that your implementation of memory structure would inspire general purpose agent developers to build a working memory module.
It is much needed indeed.

Thank you very much.
Of course.
Working memory appears to be the next frontier for improving agents however it is quite underrated given its potential. This paper seems to be the first in-depth article on the subject:
https://arxiv.org/abs/2312.17259
Hope that your implementation of memory structure would inspire general purpose agent developers to build a working memory module. It is much needed indeed.

Thanks for your reminder. We will add it in the citation list while we are doing the further development.


pkgs/go: godep unversioned -> v60

Things done:

[ ] Tested using sandboxing (nix-build --option build-use-chroot true or nix.useChroot on NixOS)
Built on platform(s)

[x] NixOS
[ ] OS X
[ ] Linux

[ ] Tested compilation of all pkgs that depend on this change using nix-shell -p nox --run "nox-review wip"
[x] Tested execution of all binary files (usually in ./result/bin/)
[ ] Fits CONTRIBUTING.md.

Would you mind changing the commit subject to godep: 2015-10-15 -> 60 per the guidelines?

@joachifm done


get the trained model to work with gcloud local predict

in /src/prediction/_model.py
s/'SERVING'/tag_constants.SERVING/g
that will help in making the output model work with gcloud local predict

I thought it was oddly quite painful to refer to the saved_model APIs, based on the ways they were organized... so opted for choosing the more legible literal value.
I don't think using the literal should get in the way of using gcloud... since matching strings should suffice.
I did fix the other issue where in glcoud the model would not be initialized. Perhaps that was getting in the way.

iris model trained from your master branch does not work with gcloud local predict.
gcloud ml-engine local predict --model-dir TOUT/model/ --text-instances iris/data/eval.csv
Traceback (most recent call last):
File "lib/googlecloudsdk/command_lib/ml/local_predict.py", line 133, in
main()
File "lib/googlecloudsdk/command_lib/ml/local_predict.py", line 128, in main
instances=instances)
File "/usr/local/google/home/brandondutra/cml-virtualenv/local/lib/python2.7/site-packages/google/cloud/ml/prediction/_prediction_lib.py", line 737, in local_predict
client = SessionClient(*load_model(model_dir))
File "/usr/local/google/home/brandondutra/cml-virtualenv/local/lib/python2.7/site-packages/google/cloud/ml/prediction/_prediction_lib.py", line 338, in load_model
model_path)
File "/usr/local/google/home/brandondutra/cml-virtualenv/local/lib/python2.7/site-packages/google/cloud/ml/session_bundle/_session_bundle.py", line 66, in load_session_bundle_from_path
meta_graph_filename)
RuntimeError: Expected meta graph file missing TOUT/model/export.meta
This is the code that fails
  try:
    from tensorflow.contrib.session_bundle import bundle_shim  # pylint: disable=g-import-not-at-top
    from tensorflow.python.saved_model import tag_constants  # pylint: disable=g-import-not-at-top
    # We expect that the customer will export saved model and use
    # tag_constants.SERVING for serving graph. This assumption also extends to
    # model server.
    session, meta_graph = (
        bundle_shim.load_session_bundle_or_saved_model_bundle_from_path(
            model_path, tags=[tag_constants.SERVING]))
  except Exception:  # pylint: disable=broad-except
    session, meta_graph = session_bundle.load_session_bundle_from_path(
        model_path) # line 338

  if session is None:
    raise PredictionError(PredictionError.FAILED_TO_LOAD_MODEL,
                          "Could not load model from %s" % model_path)

Because it got to line 338, the try failed. My guess is because of tag_constants.SERVING, which I think is 'serve'. You use 'SERVING'. If I manually edit my gcloud files to use 'SERVING', I get an error later on:
_get_interface
"map.".format(INPUTS_KEY)))
google.cloud.ml.prediction._prediction_lib.PredictionError: (5, 'Invalid value for collection: inputs. Should be a tensor alias map.')
which makes we realize we might be calling tf.add_to_collection wrong in _ff.py. The old iris sample called it like so: tf.add_to_collection(OUTPUTS_KEY, json.dumps(outputs))
I'm confused by these parts, because I am not directly calling tf.add_to_collection in the structured data package, so maybe some util function I call does this for me.

$ gcloud ml-engine versions create v1 --model tfx --origin gs://cloud-ml-dev_bdt/TOUT/model
Creating version (this might take a few minutes)......failed.
ERROR: (gcloud.ml-engine.versions.create) Create Version failed. Model validation failed: SavedModel must contain at least one MetaGraphDef with serve tag

Got gcloud local predict working with this proof of concept:
def _build_signature(inputs, outputs):
  def tensor_alias(tensor):
    local_name = tensor.name.split('/')[-1]
    return local_name.split(':')[0]

  input_map = {}
  output_map = {}
  for tensor in inputs:
    input_map[tensor_alias(tensor)] = tensor
  for tensor in outputs:
    output_map[tensor_alias(tensor)] = tensor

  outputs_ref = tf.get_collection_ref('outputs')
  inputs_ref = tf.get_collection_ref('inputs')

  del outputs_ref[:]
  del inputs_ref[:]

  return tf.saved_model.signature_def_utils.predict_signature_def(
                input_map,
                output_map)

Will research what gcloud is doing with the output collection if it is defined on Monday

I was able to update just 'SERVING' to 'serve' and make things work.
Note that I actually had to change gcloud code -- for some reason the v1 version of gcloud is loading prediction_lib_beta in my setup of gcloud. Odd. However, the real test is running this on the service.
Maybe there should be a "tfx predict" as well as a CLI wrapper around tensorfx.predictions.Model.load() and .predict().

Getting gcloud local predict is a real test too: that's the first thing a ml-engine user will try!
Also, did your last update undo your table init work? Because gcloud predict did not work for me.
gcloud ml-engine local predict --model-dir /tmp/tensorfx/iris/csv/model --text-instances iris/data/eval.csv
ERROR:root:expected string or buffer
Traceback (most recent call last):
  File "/Users/brandondutra/google-cloud-sdk/lib/googlecloudsdk/command_lib/ml/local_predict.py", line 133, in <module>
    main()
  File "/Users/brandondutra/google-cloud-sdk/lib/googlecloudsdk/command_lib/ml/local_predict.py", line 128, in main
    instances=instances)
  File "/Users/brandondutra/cml-virtualenv/lib/python2.7/site-packages/google/cloud/ml/prediction/_prediction_lib.py", line 731, in local_predict
    client = SessionClient(*load_model(model_dir))
  File "/Users/brandondutra/cml-virtualenv/lib/python2.7/site-packages/google/cloud/ml/prediction/_prediction_lib.py", line 351, in load_model
    signature = _get_legacy_signature(graph)
  File "/Users/brandondutra/cml-virtualenv/lib/python2.7/site-packages/google/cloud/ml/prediction/_prediction_lib.py", line 361, in _get_legacy_signature
    input_map, output_map = _get_interfaces(graph)
  File "/Users/brandondutra/cml-virtualenv/lib/python2.7/site-packages/google/cloud/ml/prediction/_prediction_lib.py", line 292, in _get_interfaces
    "map.".format(INPUTS_KEY)))
google.cloud.ml.prediction._prediction_lib.PredictionError: (5, 'Invalid value for collection: inputs. Should be a tensor alias map.')

Did your changes remove your table init work?
gcloud ml-engine predict  ...
{
  "error": "Prediction failed: Exception during model execution: AbortionError(code=StatusCode.FAILED_PRECONDITION, details=\"Table not initialized.\n\t [[Node: output/labels/label = LookupTableFind[Tin=DT_INT64, Tout=DT_STRING, _class=[\"loc:@output/labels/label_table/hash_table\"], _output_shapes=[[-1]], _device=\"/job:localhost/replica:0/task:0/cpu:0\"](output/labels/label_table/hash_table, output/labels/arg_max, output/labels/label_table/hash_table/Const)]]\")"
}

The line to make saved model work is still there: https://github.com/TensorLab/tensorfx/blob/master/src/training/_model.py#L256
Some how I think you've got an older version of gcloud (or there is a major bug in the GA version of gcloud). The trace indicates its trying to load a model with 'inputs', and 'outputs' collection keys whose value is a json-serialized dictionary of tensor aliases to tensor names (search for _get_legacy_signature in code search). You'll find it in prediction_lib_beta.py ... and I am puzzled how that is in the path if you're using the GA command rather than the beta command.

FWIW, added a command line tool to do local predictions in af8beccb0900fe7847e1f444b03db7933deacb62.
Definitely need to validate with the service as the ultimate test. Hopefully it isn't trying to old models with the old json collections.


Z-wave print node issue

hass 0.58.1
python 3.5.3
2017-10-11-Hassbian
ZW090 Z-Stick Gen5 US
Everytime I try "PRINT NODE" in Z-WAVE node management console spits lot's tons of
"Warning, Exception: Manager.cpp:2499 - 102 - ValueID passed to GetValueFloatPrecision is not a Decimal Value" and no other info about the node."
Currently I have only TRANE thermostat and ZW100 MultiSensor 6 and the issue happens with both.

Works here, gives a dict output of the node in homeassistant.log:
https://hastebin.com/igowezulil.py

Same error here (in OZW Log):
"Warning, Exception: Manager.cpp:2499 - 102 - ValueID passed to GetValueFloatPrecision is not a Decimal Value"

The print_node output is not in OZW_Log.txt, it is in homeassistant.log
The error you see in OZW is something that comes when reading the values. If you look at the OZW log, you will see this numerous other places as well.

Same error here. I don't see any output in homeassistant.log either.

Same issue here also.  hass 0.65.3, it occurs on every device and there is no output in the homeassistant.log


Pound £ currency

Hi,
I couldn't find the way to add not a dollar but a £ as a currency on the visualization in databricks platform.

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

ID: c37c940b-2646-8c8a-573f-9d8ed63a299d
Version Independent ID: c37c940b-2646-8c8a-573f-9d8ed63a299d
Content: Format numeric types in visualizations - Azure Databricks
Content Source: databricks/visualizations/format-numeric-types.md
Service: azure-databricks
GitHub Login: @mssaperla
Microsoft Alias: saperla

@kateglee-db
Could you please review add comments on this, update as appropriate.

@NikolettaPakalidou1
Thank you for bringing this to our attention.
I've delegated this to content author @mssaperla, who will review it and offer their insightful opinions.

Thanks for providing feedback that helps improve our documentation. We've created an internal work item (DOC-13193) to address your feedback. The timeline for resolution varies based on resourcing.
#please-close


"className" property not working on Label.

When installing a component that uses Label, some of the required libraries may not be installed.
Adding the @radix-ui/react-primitive library fixes it.
npm install @radix-ui/react-primitive

This issue has been automatically closed because it received no activity for a while. If you think it was closed by accident, please leave a comment. Thank you.

just installed the shadcn  label , using latest

Type '{ key?: Key | null | undefined; ref: ForwardedRef<HTMLLabelElement>; className: string; }' is not assignable to type 'IntrinsicAttributes & LabelProps & RefAttributes<HTMLLabelElement>'.
  Property 'className' does not exist on type 'IntrinsicAttributes & LabelProps & RefAttributes<HTMLLabelElement>'.ts(2322)

in label.tsx

const Label = React.forwardRef<
React.ElementRef<typeof LabelPrimitive.Root>,
React.ComponentPropsWithoutRef<typeof LabelPrimitive.Root> &
  VariantProps<typeof labelVariants>
>(({ className, ...props }, ref) => (
<LabelPrimitive.Root
  ref={ref}
  className={cn(labelVariants(), className)}
  {...props}
/>
))
Label.displayName = LabelPrimitive.Root.displayName

export { Label }

The same happened to me ,
This worked for me.
pnpm add @radix-ui/react-primitive .