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Coverage decreased (-38.3%) to 34.904% when pulling 210bb6cfd9b675e313e29a7ab095e14ebd5f0562 on donebit into b8ea53d58d04830a27b5e8aa9c72e78ce6bf0e82 on master.


chore(deps): update dependency babel-eslint to v10.1.0

This PR contains the following updates:

Package
Type
Update
Change

babel-eslint
devDependencies
minor
10.0.3 -> 10.1.0

Release Notes

babel/babel-eslint
v10.1.0
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Fix broken markdown for Youtube links with plain HTML

The Youtube links for the referred tutorial were broken, displaying some wrong chars in the rendered page, as well as not displaying the video cover.

[x] I have read freeCodeCamp's contribution guidelines.
[x] My pull request has a descriptive title (not a vague title like Update index.md)
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RuntimeError: only Tensors of floating point dtype can require gradients for QLoRA since transformers 4.40

System Info
transformers-4.40.1
peft-0.10.0
accelerate-0.30.0
bitsandbytes-0.43.1
evaluate-0.4.2

Who can help?
@pacman100
Information

[ ] The official example scripts
[X] My own modified scripts

Tasks

[ ] An officially supported task in the examples folder
[X] My own task or dataset (give details below)

Reproduction
This is the colab notebook for a simple fine-tuning of a DistilBERT model using QLoRA
The main code snippet of interest which is erroring out:
model_checkpoint = "distilbert/distilbert-base-uncased"

id2label = {0: "NEGATIVE", 1: "POSITIVE"}
label2id = {"NEGATIVE": 0, "POSITIVE": 1}

import torch
from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer, BitsAndBytesConfig

config = BitsAndBytesConfig(
    load_in_4bit=True, # quantize the model to 4-bits when you load it
    bnb_4bit_quant_type="nf4", # use a special 4-bit data type for weights initialized from a normal distribution
    bnb_4bit_use_double_quant=True, # nested quantization scheme to quantize the already quantized weights
    bnb_4bit_compute_dtype=torch.bfloat16, # use bfloat16 for faster computation,
)

model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint,
                                                           id2label=id2label,
                                                           label2id=label2id,
                                                           num_labels=2,
                                                           quantization_config=config)

from peft import prepare_model_for_kbit_training

model = prepare_model_for_kbit_training(model)

from peft import LoraConfig, get_peft_model, TaskType, replace_lora_weights_loftq

config = LoraConfig(
    r=8,
    lora_alpha=32,
    target_modules=["q_lin", "k_lin", "v_lin", "out_lin"],
    lora_dropout=0.05,
    bias="none",
    task_type=TaskType.SEQ_CLS)

peft_model = get_peft_model(model, config)
replace_lora_weights_loftq(peft_model)
print_trainable_parameters(peft_model)

Error happens in the line peft_model = get_peft_model(model, config) above when the PEFT model is being made. Error trace is as follows.
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
[<ipython-input-41-79fb3abcb23e>](https://localhost:8080/#) in <cell line: 19>()
     17     model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
     18
---> 19 peft_model = get_peft_model(model, config)
     20 replace_lora_weights_loftq(peft_model)
     21 print_trainable_parameters(peft_model)

5 frames
[/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py](https://localhost:8080/#) in requires_grad_(self, requires_grad)
   2433         """
   2434         for p in self.parameters():
-> 2435             p.requires_grad_(requires_grad)
   2436         return self
   2437

RuntimeError: only Tensors of floating point dtype can require gradients

Expected behavior
Ideally the model should get created and then fine-tuned. The same notebook used to work fine with transformers==4.38 but something might have changed as it is no longer working with transformers==4.40, have validated the same that when I downgrade this code still works. I want some help in figuring out if something is wrong in the code which I need to change \ fundamentally doing wrong or there is a deeper issue.

Yes, I can reproduce the error. The reason is that since transformers==4.40, the pre_classifier module of this model is converted to a bitsandbytes Linear4bit when instead of being a normal PyTorch nn.Linear. As this module as being added to the modules_to_save, PEFT tries to enable gradients on it, resulting in the error you see. We'll discuss this internally and think of an appropriate fix. In the meantime, if possible, downgrade to an earlier transformers version.

Hi @dipanjanS !
Thanks for the issue, I had a deeper look. Previously there was a silent bug in transformers that was quantizing the pre_classifier layer, which shouldn't happen as only the last layer should be quantized.
https://github.com/huggingface/transformers/pull/29958 introduced a fix for that and introduced this bug you are sharing which shouldn't be really a bug since the pre_classifier should be quantized at first place, only the last layer shouldn't be quantized.
To temporary fix your issue, can you load the 4-bit model with: llm_int8_skip_modules=["classifier", "pre_classifier"] ?
model_checkpoint = "distilbert/distilbert-base-uncased"

id2label = {0: "NEGATIVE", 1: "POSITIVE"}
label2id = {"NEGATIVE": 0, "POSITIVE": 1}

import torch
from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer, BitsAndBytesConfig

config = BitsAndBytesConfig(
    load_in_4bit=True, # quantize the model to 4-bits when you load it
    bnb_4bit_quant_type="nf4", # use a special 4-bit data type for weights initialized from a normal distribution
    bnb_4bit_use_double_quant=True, # nested quantization scheme to quantize the already quantized weights
    bnb_4bit_compute_dtype=torch.bfloat16, # use bfloat16 for faster computation,
+     llm_int8_skip_modules=["classifier", "pre_classifier"]
)

model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint,
                                                           id2label=id2label,
                                                           label2id=label2id,
                                                           num_labels=2,
                                                           quantization_config=config)

from peft import prepare_model_for_kbit_training

model = prepare_model_for_kbit_training(model)

from peft import LoraConfig, get_peft_model, TaskType, replace_lora_weights_loftq

config = LoraConfig(
    r=8,
    lora_alpha=32,
    target_modules=["q_lin", "k_lin", "v_lin", "out_lin"],
    lora_dropout=0.05,
    bias="none",
    task_type=TaskType.SEQ_CLS)

peft_model = get_peft_model(model, config)
replace_lora_weights_loftq(peft_model)
print_trainable_parameters(peft_model)

Awesome, can confirm this definitely works! Just wanted to check
going forward should I explicitly mention those classifier layers to be
skipped in the BnB configuration? or would this be automatically handled in
a future release. Based on your recommendation I will try to use that going
forward
On Mon, May 13, 2024 at 8:29 PM Younes Belkada @.***>
wrote:

Hi @dipanjanS https://github.com/dipanjanS !
Thanks for the issue, I had a deeper look. Previously there was a silent
bug in transformers that was quantizing the pre_classifier layer, which
shouldn't happen as only the last layer should be quantized.
huggingface/transformers#29958
https://github.com/huggingface/transformers/pull/29958 introduced a fix
for that and introduced this bug you are sharing which shouldn't be really
a bug since the pre_classifier should be quantized at first place, only
the last layer shouldn't be quantized.
To temporary fix your issue, can you load the 4-bit model with: llm_int8_skip_modules=["classifier",
"pre_classifier"] ?
model_checkpoint = "distilbert/distilbert-base-uncased"
id2label = {0: "NEGATIVE", 1: "POSITIVE"}
label2id = {"NEGATIVE": 0, "POSITIVE": 1}
import torch
from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer, BitsAndBytesConfig
config = BitsAndBytesConfig(
load_in_4bit=True, # quantize the model to 4-bits when you load it
bnb_4bit_quant_type="nf4", # use a special 4-bit data type for weights initialized from a normal distribution
bnb_4bit_use_double_quant=True, # nested quantization scheme to quantize the already quantized weights
bnb_4bit_compute_dtype=torch.bfloat16, # use bfloat16 for faster computation,+     llm_int8_skip_modules=["classifier", "pre_classifier"]
)
model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint,
id2label=id2label,
label2id=label2id,
num_labels=2,
quantization_config=config)
from peft import prepare_model_for_kbit_training
model = prepare_model_for_kbit_training(model)
from peft import LoraConfig, get_peft_model, TaskType, replace_lora_weights_loftq
config = LoraConfig(
r=8,
lora_alpha=32,
target_modules=["q_lin", "k_lin", "v_lin", "out_lin"],
lora_dropout=0.05,
bias="none",
task_type=TaskType.SEQ_CLS)
peft_model = get_peft_model(model, config)
replace_lora_weights_loftq(peft_model)
print_trainable_parameters(peft_model)
—
Reply to this email directly, view it on GitHub
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@.***>

Hi @dipanjanS
Thanks ! Yes, since there was a bug on previous transformers version, we will not automatically handle this in a future transformers release

Hi @dipanjanS ! Thanks for the issue, I had a deeper look. Previously there was a silent bug in transformers that was quantizing the pre_classifier layer, which shouldn't happen as only the last layer should be quantized.
huggingface/transformers#29958 introduced a fix for that and introduced this bug you are sharing which shouldn't be really a bug since the pre_classifier should be quantized at first place, only the last layer shouldn't be quantized.
To temporary fix your issue, can you load the 4-bit model with: llm_int8_skip_modules=["classifier", "pre_classifier"] ?
model_checkpoint = "distilbert/distilbert-base-uncased"

id2label = {0: "NEGATIVE", 1: "POSITIVE"}
label2id = {"NEGATIVE": 0, "POSITIVE": 1}

import torch
from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer, BitsAndBytesConfig

config = BitsAndBytesConfig(
    load_in_4bit=True, # quantize the model to 4-bits when you load it
    bnb_4bit_quant_type="nf4", # use a special 4-bit data type for weights initialized from a normal distribution
    bnb_4bit_use_double_quant=True, # nested quantization scheme to quantize the already quantized weights
    bnb_4bit_compute_dtype=torch.bfloat16, # use bfloat16 for faster computation,
+     llm_int8_skip_modules=["classifier", "pre_classifier"]
)

model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint,
                                                           id2label=id2label,
                                                           label2id=label2id,
                                                           num_labels=2,
                                                           quantization_config=config)

from peft import prepare_model_for_kbit_training

model = prepare_model_for_kbit_training(model)

from peft import LoraConfig, get_peft_model, TaskType, replace_lora_weights_loftq

config = LoraConfig(
    r=8,
    lora_alpha=32,
    target_modules=["q_lin", "k_lin", "v_lin", "out_lin"],
    lora_dropout=0.05,
    bias="none",
    task_type=TaskType.SEQ_CLS)

peft_model = get_peft_model(model, config)
replace_lora_weights_loftq(peft_model)
print_trainable_parameters(peft_model)

Thank you for the detailed answer.
I would like to ask, how could we know what layers are supposed to be quantized and what aren't to fix this issue in other models.
For my case i encoutered this error while trying to load Llama3-8B using the following config :
BitsAndBytesConfig {   "_load_in_4bit": true,   "_load_in_8bit": false,   "bnb_4bit_compute_dtype": "bfloat16",   "bnb_4bit_quant_storage": "bfloat16",   "bnb_4bit_quant_type": "nf4",   "bnb_4bit_use_double_quant": true,   "llm_int8_enable_fp32_cpu_offload": false,   "llm_int8_has_fp16_weight": false,   "llm_int8_skip_modules": null,   "llm_int8_threshold": 6.0,   "load_in_4bit": true,   "load_in_8bit": false,   "quant_method": "bitsandbytes" }


Feature/sb/omit msg body

Packages impacted by this PR
Issues associated with this PR
Describe the problem that is addressed by this PR
What are the possible designs available to address the problem? If there are more than one possible design, why was the one in this PR chosen?
Are there test cases added in this PR? (If not, why?)
Provide a list of related PRs (if any)
Command used to generate this PR:**(Applicable only to SDK release request PRs)
Checklists

[ ] Added impacted package name to the issue description
[ ] Does this PR needs any fixes in the SDK Generator?** (If so, create an Issue in the Autorest/typescript repository and link it here)
[ ] Added a changelog (if necessary)

API changes have been detected in @azure/service-bus. You can review API changes here
API changes
+     omitMessageBody?: boolean;

/azp run js - service-bus - tests

/azp run js - service-bus - tests

API change check for @azure/service-bus
API changes have been detected in @azure/service-bus. You can review API changes here
API changes
+     omitMessageBody?: boolean;

Just to make sure, all other languages are adding the same flag with the same name?
JS is doing this first in preview to unblock Portal team and to gather feedback.  It's useful for all languages as the service just  started to support large messages with size up to 100 MB.  We can decide on final naming before GA.

/azp run js - service-bus - tests


place the examples under examples/robot_planning/ directly under examples/

Flat is better than nested. There is little reason to bury the examples so deep. Every time I want to open an example, I have to write "robot_planning", even though none of the two words is very descriptive of those examples.
If there are no objections, I will move these files.

I agree with the proposed change. Because it only concerns examples, I think that it can go into version 1.2.1.

Addressed with cce07057c85268bf97ffacde81b87a8283327b08.


Allow exclusion of specific guides..

@batson I made that change, but need to debug a bit as it's not giving me quite what I expected.


Add Azure specific fields to V5GetNodePoolResponseNodeSpec

Towards https://github.com/giantswarm/roadmap/issues/9.
What's in the box:

Adding Azure-specific fields to the API response when listing node pools

I believe these can be null, right?

You are correct!


请问可以设置设置四个点得拖动嘛

Make sure you compiled the latest version.(确保你编译了最新版本)
Describe the scenarios or operates when crash happened as much as possible(pictures would be better).(尽可能详细的描述崩溃时的场景或操作，最好有截图)
Tell me your device type and Android OS version is very helpful.(设备信息和系统版本也比较有用)
Paste your xml or java code.(粘贴你的代码)
Paste the crash log.(粘贴你的崩溃日志)
Please be polite.(请保持礼貌)

暂不支持


Excluded modules still on stats.json

Here is my config:
new StatsPlugin('stats.json', {
   chunkModules: true,
   exclude: [/node_modules/]
})

When I drop my stats.json at the analizer, it stills showing things from inside node_modules on the modules graph, like axios and react. Is this the way it is designed to work with or am I missing something?

If you look at the implementation of this plugin, it's just forwarding those options to the toJson method coming from Webpack itself. If you believe there's a bug there, I'd suggest you create a small repo that reproduces the problem, and post in https://github.com/webpack/webpack/issues.


Merge branches from Amethyst-szs

freeze-tag
sardines
...

Implemented with v1.6.0-rcl.8.

Pull request: https://github.com/CraftyBoss/SuperMarioOdysseyOnline/pull/74


Simple demo does not work

Reproduction repo

https://gitlab.com/michal-bryxi/bugreport/ember-heroicons-bugreport
See README.md

Steps to reproduce manually

Bootstrap new app:

EMBER_CLI_PNPM=true ember new ember-heroicons-bugreport --skip-npm --pnpm=true --typescript=true

Install ember-heroicons:

ember install ember-heroicons

Try to render any HeroIcon component:

<HeroIcon @icon="check-circle" />

See that respective place where hero icon should be is empty:

  -- START --

<svg>

</svg>

  -- END --

I'll try to look into this soon. One thing to note: I don't believe I have tested with Ember 5.x yet. I also have not tested with pnpm. If you end up having more time to investigate, a PR is welcome! Thanks!

Turns out it's a dependency issue. Following:
pnpm install --save-dev heroicons

Will solve it in my bugreport repro app.
Now the question is: Can it be so that the heroicons package will be installed / pulled in automagically when ember-heroicons is installed?

Oh and re the v5: When I install this package I get a warning that peer deps are broken because of version of ember. Might be also worth bumping.

Ha! I just got bitten by this again and found my original fix that is still working :)

Thinking out loud: I think that this is the original idea between peerDependencies. So I think adding heroicons package to package.json here should be the solution?

Turns out it's a dependency issue. Following:
pnpm install --save-dev heroicons

Will solve it in my bugreport repro app.
Now the question is: Can it be so that the heroicons package will be installed / pulled in automagically when ember-heroicons is installed?

Migrated one repo to pnpm and found the icons were missing in the production build. This did the trick, thanks!


Styling issues in Firefox

The following issues seem to be unique to Firefox (tried also with Brave but everything looks fine there).

The main form is not boxed properly in the Offset page

There is no margin at the bottom of the Bond and Info pages

Related to 2, I'd ensure the margin at the bottom is consistent across all pages. Currently even in the rest of the pages where there is some margin, it is not consistent.

fixed in #273


RLMNotVersioned has different values in 32- and 64-bit

RLMNotVersioned is initialized like so:
    const NSUInteger RLMNotVersioned = (NSUInteger)-1;

This results in RLMNotVersioned having the value<PHONE_NUMBER> in 32-bit, but<PHONE_NUMBER>3709551615 in 64-bit. This could result in incorrect behavior when moving a Realm that was created on 32-bit device to a 64-bit device.

I think this must be fixed before Rel 1.0, and thus be a P1.

Will this be a breaking change (will it change the file format)?

I don't think so. As @tgoyne noted in #1967, the schema version is always updated to an actual version before the initial transaction is committed. The differing values were only apparent in the commit log, since different entries were generated for 32- and 64-bit.

Please talk to @simonask about this. He already committed a fix here https://github.com/realm/realm-cocoa-private/commit/96d310f3418e3a94104976a60d61da4ecc46a9ba.


Upgrade to fp-ts 2

Do you plan it or should userland send a PR? I know the company which maybe will desperately need it.

Yes I do (as soon as I understand how to use the new bigint type introduced in TypeScript 3.2)

I too am investigating alternative libraries for currency manipulation. I tried money-ts some months ago and found the incompatibility with fp-ts 2 insurmountable. Here we are on the brink of fp-ts 3!
I haven't popped the hood to look at the internals yet but would value the ability to use this library highly. It sounds like there's some modernization required, in terms of bigint as well as the fp-ts peer dependency. What would be the best way to help?

@EricCrosson did you ever find a proper substitute for this library?

@EricCrosson did you ever find a proper substitute for this library?

No @ryanleecode I have not. I did "start" a tiny (unpublished) version locally for a project but it is still in a rough state.

No @ryanleecode I have not. I did "start" a tiny (unpublished) version locally for a project but it is still in a rough state.


Add DC Core to Exchange Core Ratio feature

add a different switch to the script for checking DC Cores to Exchange Server Core ratios.

Completed Feature for release


GroupChat UserProxyAgent improvements.

There are many subtleties in getting GroupChat to operate optimally. One of these subtleties is how GroupChat interacts with the UserProxyAgent (or similar). Specifically, agents are selected to speak based on their names and system messages -- but the UserProxyAgent has an empty system message because, in its default configuration, it doesn't call an LLM. This can lead to suboptimal orchestration. Possible corrective actions include:

[ ] Improve Group Chat Documentation, including a detailed description of how agents are selected, and how termination is handled
[ ] Add a warning or exception in cases where an agent with an empty name or system message is added to the Group Chat (since these are critical parameters)
[ ] Add a default system prompt to the UserProxyAgent. It won't be used in most cases, because it has no llm_config. But it's critical for GroupChat

We are closing this issue due to inactivity; please reopen if the problem persists.


Task 10, step 1 has wrong version no. I think?

The page is about upgrading v.14 to v.16, but the text says:
"(Multitenant only) Mount the tenant to the version 15 server instance."

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

ID: 5a8b2a8e-89a9-dda2-7d06-c433f2127044
Version Independent ID: 4a968a4c-8ad7-8d9b-b966-b08daf4bda9a
Content: Upgrading Customized C/AL Application to Microsoft Base Application - Business Central
Content Source: dev-itpro/upgrade/upgrade-to-microsoft-base-app.md
Service: dynamics365-business-central
GitHub Login: @jswymer
Microsoft Alias: jswymer

Thanks for the feedback! The version has been changed to version 16. The update will be published soon.


[LAD] Fix a typo in default Linux Diagnostic extension config

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[x] The PR has modified HISTORY.rst with an appropriate description of the change (see Modifying change log).

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[ ] Each command and parameter has a meaningful description.
[ ] Each new command has a test.

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Codecov Report

Merging #3746 into master will decrease coverage by 0.03%.
The diff coverage is n/a.

@@            Coverage Diff             @@
##           master    #3746      +/-   ##
==========================================
- Coverage   72.22%   72.19%   -0.04%
==========================================
  Files         421      421
  Lines       25970    25970
  Branches     3941     3941
==========================================
- Hits        18756    18748       -8
- Misses       6000     6006       +6
- Partials     1214     1216       +2

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...li/command_modules/vm/_vm_diagnostics_templates.py
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@yugangw-msft, could you please take a look at this PR? Thanks.


Fix releases for Xandra and Broadway packages

I added a fix for opentelemetry_broadway, where the version in mix.exs was not defined as @version and was screwing up the CI job to release 🙃


Allow GoViral user to remove ID from Open Humans [2]

In other words, if they have a connection to Open Humans, let them undo the GoViral ID sharing with Open Humans when they go to: http://www.goviralstudy.com/open-humans
If they do this, also give them the option to add it back without going through a new connection.
The page http://www.goviralstudy.com/open-humans shouldn't offer Open Humans connection to users that have already connected -- it should offer management of the connection to connected users.

Removing this as we're overhauling connections anyawy. If there's any UI/UX issues with the updated connection, they'll be found in #192.


Update to Pydantic 2.X

From fideslang created by ThomasLaPiana: IABTechLab/fideslang#159
Is your feature request related to a specific problem?
There are some big performance improvements in Pydantic 2, that we'll want to take advantage of with our FastAPI server
Will need to be careful see and see how bad any potential breaks might be, although recently moving to 2.0 should make this a bit less painful

Is there any reason why restricting to pydantic<=2.7.1 and not broader pydantic<3?


Think about renaming repo? Keystore typically is a repo of security certs...?

When I think of a "keystore" I typically think of either the Java KeyStore or a similar tool that stores credentials of some sort.
This project is awesome, using Cloud Datastore as a simple Key-Value storage system -- I wonder if it's worth renaming it to "gcloud-kvstore" or "gcloud-kv" ?
Might be at least worth the discussion...

Renamed!


Change from flake8-putty to per-file-ignores

Summary
Add flake8 per-file-ignores, in place of flake8-putty, so that we can support newer Python3 syntax like eg f-strings and type annotations.
Flake8 report:
samuelwilliams:~/git/digitalmarketplace-buyer-frontend [shw-flake8-per-file-ignores]$ make test-flake8
[ -z $VIRTUAL_ENV ] && [ ! -d venv ] && virtualenv -p python3 venv || true
/Users/samuelwilliams/git/digitalmarketplace-buyer-frontend/venv/bin/flake8 .
./app/main/views/__init__.py:0:1: X100 Superfluous per-file-ignores for C901
./app/main/views/crown_hosting.py:0:1: X100 Superfluous per-file-ignores for C901
./app/main/views/digital_services_framework.py:0:1: X100 Superfluous per-file-ignores for C901
./app/main/views/feedback.py:0:1: X100 Superfluous per-file-ignores for C901
./app/main/views/g_cloud.py:0:1: X100 Superfluous per-file-ignores for C901
./app/main/views/login.py:0:1: X100 Superfluous per-file-ignores for C901
./app/main/views/marketplace.py:0:1: X100 Superfluous per-file-ignores for C901
./app/main/views/suppliers.py:0:1: X100 Superfluous per-file-ignores for C901
./tests/main/helpers/test_search_helpers.py:185:13: F601 dictionary key 'page' repeated with different values
./tests/main/helpers/test_search_helpers.py:188:13: F601 dictionary key 'page' repeated with different values
make: *** [test-flake8] Error 1

Are we losing anything by dropping flake8-putty?

I don't believe so - we only use it to give us the ability to ignore flake8 errors on a per-file basis.

In which case have a 👍


Caching Database

Cache Database Info (tasks, requests etc) locally in variables
only update (connect to database) when new task is pushed, deleted, or server is started

caching should be able to get a huge amoutn of tasks (100? 1000?) for futureproofing scaling
when caching is done, i can maybe get a higher throughput/higher polling rate from the background worker -> 1 poll/second @ 10 tasks


Why need to await twice?

In the last example in this page:
https://learn.microsoft.com/en-us/dotnet/csharp/programming-guide/concepts/async/
Why do you write:
await finishedTask;
?
Because you already waited for this task in:
Task finishedTask = await Task.WhenAny(breakfastTasks);
I ran the code without "await finishedTask;" and it's working great...

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

ID: 17d27a0d-c072-8c9f-7e31-3de60bee9ae8
Version Independent ID: e0c09794-e712-19ff-cb9f-12648d60d4f6
Content: Asynchronous programming in C#
Content Source: docs/csharp/programming-guide/concepts/async/index.md
Product: dotnet-csharp
Technology: csharp-async
GitHub Login: @BillWagner
Microsoft Alias: wiwagn

Hi @Alonnj
In cases where the finished task completes successfully, you don't need to await it. However, in production code it's a good practice because of the possibility that the finishedTask faulted instead of completing successfully. In those cases, awaiting the task will re-throw the exception that caused the task to fail.
I've added this to our backlog. and added the "help-wanted" label in case anyone wants to update the text to match the code.

@BillWagner
I'm not sure I understood,
When we write:
Task finishedTask = await Task.WhenAny(breakfastTasks);
Because we wait for the task to finish, if the task failed, the exception will be thrown.
But now that we know that the task hasn't failed, it's already finished, so why do need to await it again?
await finishedTask;?

@BillWagner I'm not sure I understood, When we write: Task finishedTask = await Task.WhenAny(breakfastTasks);
Because we wait for the task to finish, if the task failed, the exception will be thrown. But now that we know that the task hasn't failed, it's already finished, so why do need to await it again: await finishedTask;?

Hi @Alonnj
There's a subtle difference that we need to update in this article:  The code hasn't awaited finishedTask. It awaited the Task returned by Task.WhenAny. The result of Task.WhenAny is the task that has completed (or faulted). You should await that task again, even though you know it's finished running. That's how you retrieve its result, or ensure that the exception causing it to fault gets thrown.
This refactoring might make it more clear:
Task<Task> whenAnyTask = Task.WhenAny(breakfastTasks);
Task finishedTask = await whenAnyTask;
await finishedTask;

@BillWagner
Ahhh now I get it, first to await Task<Task> to get its task, then to await Task to get its result.
Thanks!

@BillWagner I would like to work on this - please assign me so I have it on my list.

@BartoszKlonowski - May I recommend to you that you not wait for this to be assigned to you, but that instead you go ahead and make the change by creating a fork, update the code you want to update, and then submit a pull request.

I apologize @BartoszKlonowski
I missed seeing your earlier comment. I did just assign it to you. As @rhires points out, it's not necessary to wait for us to do that. We appreciate all the help!

@rhires @BillWagner Thank you!
Sure, it's not that I waited to be assigned, I just had a hard time finding a moment to actually work on this. I ask for an assignement to keep it on my list so I don't forget about it in such cases.

Hi @BartoszKlonowski
I'm in the middle of moving much of the async articles to a new location so they are easier to find. If you don't mind, I'll reassign this to me, as I'm in the article now, and it's going to move when I finish this PR.

Hello @BillWagner, of course, no problem, thank you for letting me know!
I'll jump to the other I have on me, then.


fix(curriculum): Typo in hint for Building a Dice Game - Step 14

Checklist:
Corrected typo in hints for building dice game.

[x] I have read and followed the contribution guidelines.
[x] I have read and followed the how to open a pull request guide.
[x] My pull request targets the main branch of freeCodeCamp.
[x] I have tested these changes either locally on my machine, or GitPod.

Closes #55937

Hi @TheRareFox. Congrats on your first pull request (PR)! 🎉
Thank you for your contribution to the page! 👍
We are happy to accept these changes and look forward to future contributions. 📝

Note for future - it is recommended to make changes on a separate branch, rather than the main branch. See more about it in point 3 at https://contribute.freecodecamp.org/how-to-contribute-to-the-codebase/#contributing-to-the-codebase.


⚠ ClamAV has degraded performance

In 26e9136, ClamAV (https://spamassassin.apache.org/) experienced degraded performance:

HTTP code: 200
Response time: 638 ms

Resolved: ClamAV performance has improved in a9e7ff7.


Update OMS vendored dependency

Use latest oms version with external source maps
Force babel compact mode: oms without inline maps is not compacted by default

regate


Fixed bug in config, refactored design matrix, made random forest mod…

…el class

Looks okay to me


Add FormatSpec to CountAggregation for proper symbol formatting

Instead of printing it as count() it will now be printed as count(*)
This is in preparation for CREATE VIEW for which we need to be able to
convert an AnalyzedStatement into a String that can be parsed again.

This is already merged? Sorry I missed that.

This is already merged? Sorry I missed that.

No, the simple-view branch contains this change as well, but I still would like to get this into master as a separate change.


[5.7] Add PHP 7.3 to Travis

What happened to https://github.com/laravel/framework/pull/26125 (the external dependencies)?

I think my tweak should fix the build.

Yeh, build fixed. Should be good to merge now. :)


Unable to quit the scanner view

The old ionic native barcode scanner plugin has a built-in button that can let users cancel the scan.
With this plugin, it is unable to do the same thing. There is no hardware back button for iPhone, how do I get back to the previous view when scanner view is active?
I also had to set the opacity of body element to 0 in order to make the scanner view showing.

Need help with this as well. IOS users are forced to quit app because they cannot escape scanner.

You need to create your own UI for this plugin, there are no UI elements provided by default.

You can simply render a button on top of your view. Setting the body opacity is not "the right way" since you won't be able to create your custom UI this way, background: transparent should be enough since the camera view is just rendered "behind" your application. Depending on your setup of course, maybe you have to set higher DOM elements to a transparent background as well :)

Thanks. I have figured it out right after submitting this issue.
I am using the Modal component which needs to be set to background: tranparent.
Then adding a button element to the page with position set to absolute and only display when isScanning = true;
This solves the problem.
This plugin indeed gives us more control over the UI that I can even put on a scanning area border on the view.


Opening file object in append mode fail.

Expected behavior and actual behavior.
Opening a file-descriptor (BytesIO, StringIO, or an openned file doesn't work with the append mode.
This is normal see implementation here: https://github.com/Toblerity/Fiona/blob/master/fiona/init.py#L212 and https://github.com/Toblerity/Fiona/blob/master/fiona/init.py#L212
Steps to reproduce the problem.
import fiona
from io import BytesIO

fp = BytesIO(b"some random valid geopackage bytes")
with fiona.open(fp, 'a', driver="Gpkg", schema={"something": "valid"}) as gpkg_file:
    pass

Operating system
Linux (Arch)
Fiona and GDAL version and provenance
1.8.20 manylinux1 wheel installed from PyPI using pip version 21.1.3.
GDAL 3.3, installed via package manager.

Is there an error message? What does it say?

@jsta we only support read-only and write-only access to datasets in a Python file object. I'm making this more clear in 55d039e.


Add Uint256 Hints

[ ] UInt256Add: Adds two uint256 integers, calculating the sum of the lower and higher parts separately, and includes overflow checks.
[ ] UInt256AddLow: Adds only the lower parts of two uint256 integers and checks for overflow.
[ ] Split64: Splits a uint256 integer into its lower 64 bits and the higher remaining bits.
[ ] UInt256Sub: Subtracts one uint256 integer from another, handling the high and low parts.
[ ] UInt256SignedNN: Checks if a uint256 integer is non-negative when considered as a signed number.
[ ] UInt256UnsignedDivRem: Performs division and modulus operations on uint256 integers, combining the high and low parts of the dividend and divisor.
[ ] Untt256ExpandedUnsignedDivRem: A variation of UINT256UnsignedDivRem that handles an expanded format for the divisor.
[ ] UInt256MulDivMod: Multiplies two uint256 integers, divides the result by another uint256 integer, and computes the remainder.
[ ] UInt256Sqrt: Calculates the square root of a uint256 integer by combining its high and low parts, and asserts that the root is less than 2^128.
[ ] UInt256SqrtFelt: Similar to UINT256Sqrt but assigns the root directly without splitting into high and low parts.

Already encompassed on #164


Adjust edge metrics collection config

What this PR does / why we need it:
Adjust edge metrics config
Which issue(s) this PR fixes:

Fixes #
Does this PR introduced a user-facing change?

None

Additional documentation, usage docs, etc.:

@wanjunlei @wenchajun @MDZZ110


[Redress][Now a slideshow]

Redress
The Ronald Reagan signing video is full of alot of extra material
What We Should Do to Fix it

[ ] A member of the team (probably not the developers) edits together audio from the youtube for use on the app.
[ ] Use images from the after section of photos to create a three image slideshow in place of the video.
[ ] insert Courtesy Ronald Reagan Presidential Library below the slideshow when it is implemented.

Copy And Paste-able Text
Courtesy Ronald Reagan Presidential Library

@KGTrammel I need you to be more specific about these photos in the after section. Which ones would you want me to include exactly?

@CCrawfordUOP I would really appreciate your input on this issue as there are two methods that can be utilized for this to be added. Either the background music gets swapped out with the audio track of redress (which makes it subjective to choppiness when switching scenes) or adding another separate audio source (which might throw more errors with having another audio listener present).

awaiting changes to audio scripts

@KGTrammel I need the audio for this to be a lot louder


[QUESTION] Clarification on function calling implementation with Agents

Hi,
I would like to clarify how function calling is implemented within agents.
Specifically, I am interested in understanding whether agents leverage the native function calling parameters specific to each LLM, or if it relies on the LLM's general intelligence to determine when a tool should be called.
To elaborate, each LLM that supports function calling has its own function calling format within its API. My concern is whether agents adhere to these specific function calling parameters when making API calls, or if it sends function calling instructions to the LLM as part of a regular chat interaction.
Thanks.

I've looked at the code, and it seems that the JS code from the agent's plugin/tool is mapped and translated to match each LLM provider's function calling syntax.

OpenAI & Anthropic has specific formatting for tool calls, everything else uses Untooled, not you may be thinking - Ollama supports tool calls, so does Groq, why not just use that.

Through testing, we get more consistent tool responses via this method, which is also 100% provider agnostic and can easily be placed on any provider with zero lift instead of defining and reworking each provider to be agent capable. Groq's built in tool calling, for example, is quite bad. Untooled give groq much better coherence and tool calling


All the tasks in pool failed with CommandLaunchFailed after a while

Problem Description
We have a pool of one low priority Standard_NC6 as base vm which hosts a few long running tasks. After a while all the tasks on this pool failed with CommandLaunchFailed error, after retrying 1600 times (we use Azure batch-shipyard tool to specify the task with max_task_retries being -1).
Command line to launch is something like this:
/bin/bash -c 'set -e; set -o pipefail; pushd /mnt/batch/tasks/startup/wd; ./registry_login.sh; popd; [ -f .shipyard.envlist ] && export $(cat .shipyard.envlist | xargs); env | grep AZ_BATCH_ >> .shipyard.envlist; nvidia-docker run --rm --name task-00001 -v $AZ_BATCH_NODE_ROOT_DIR:$AZ_BATCH_NODE_ROOT_DIR -w $AZ_BATCH_TASK_WORKING_DIR --env-file .shipyard.envlist user_image_name:image_task bash -c "cd /workspace && python -m service.main.processor"; wait'

All the tasks have some code to ignore any exceptions happening at the global level inside the task, so I think maybe this is a server error.
No log or further information could be found.
Steps to Reproduce
This happens quite randomly. We experienced this twice on the same pool among a few others
Can anyone give us some leak what has possibly happened?

Here is the capture of the task status. I believe 0.27s is the running duration of last try.

I suspect we'll need node agent logs to investigate.
If using a recent version of Shipyard, can you execute:
shipyard diag logs upload --nodeid <node id where task failed> --generate-sas

It's best to create a support ticket with your account, region, pool id and with either the logs attached or the SAS URL generated. You can reference this GitHub issue in the ticket and ask support to escalate directly to PG if you can attach the logs.
cc @matthchr

Thanks. I have created a support request with attached log and the URL generated from shipyard diag logs. FYI I also attach the agent logs here.
agent-debug.log
agent-warn.log
controller-debug.log

Thanks for the logs - at first blush, there doesn't seem to be anything wrong on the compute node side. However, these logs don't seem to correspond with the timeframe of the initial issue; the logs begin at 20190408T043746.152Z.
We'll investigate further once we receive the support ticket on our end.

If you did not include the the following information in your support request, please add it to the request or mention it here if ok to do so publicly:

Region, Account, Pool Id
Node id, if possible
Job id and task id
Approximate time of issue

Hi, this issue happened again early morning today in the same node. Our Azure Batch account is created in West Europe region. I think the logs has begun before the issue today.

If this was not caused by the node, then it might be from our tasks. The thing is we couldn't find any log when containers were all removed from the node. I will add additional information to my support request.

@hieuhc given that the VMs have been reclaimed/deleted when the issue was reported, it's difficult to diagnose. We don't see anything currently wrong with the low priority node running. We will most likely need an active repro with the node up.
Is it possible to do the following:

Set your task retries to 0.
Set remove_container_after_exit to false.
Do not delete the VM (or allow autoscale to reclaim the VM; ensure that there is no autoscale formula applied to the pool and the target counts are statically set).

If you observe a repro, please do the following:

Capture any relevant stdout.txt and stderr.txt files for the failed task, if they exist, along with a screenshot as you have done above.
Add an SSH user to the pool and log in.
Run docker inspect on the container if the container launched (most likely it won't have gotten this far).

If at this point you can't figure out what's going wrong with your task, please provide us with the node id and the approximate time of repro. If you provide it here, the response time will be a bit faster, but you can certainly add it to the ticket.

Hi @alfpark, thanks for your suggestion. I didn't know that the VM has been reclaimed/deleted. I have been using the same node id to log in to this VM during the issues. Also in our auto scale formula, there is always a low priority node running as base node, like this:
taskPerNode = 4; maxNumberOfNodes = 65; num = avg($PendingTasks.GetSample(TimeInterval_Minute * 10, 5));$TargetLowPriorityNodes = 1;$TargetDedicated= min(maxNumberOfNodes , num == 0 ? 0 : num / (taskPerNode + 1) - $TargetLowPriorityNodes + 1);

I wonder if there is any case where the low priority node is reclaimed/deleted while the tasks are running? Does it get the same node id after being brought up again? What will happen to the running tasks on that node in that case, maybe they will be restarted continuously like in my case?
I will also submit a task with your suggestion settings, will provide further details if I can repro the issue.
Thanks

Low pri nodes are reclaimed if the node is preempted. Batch will goal seek and attempt to replace the preempted low pri node and the node id you observe will be the same. This is why even though the node id remains the same, the history (via node logs) is truncated. If a task is running and the node is preempted, Batch will automatically attempt to reschedule and run the task again. It's unlikely that you'd experience these continuous restarts at this level and not have a significant delay in processing time on the order of days.
We'll keep this issue open for a while longer in case you can repro and annotate the ticket as such. Thanks for your help and patience.

Hi. The issue has happened again today. Even though I have one task with max_task_retries being 0 and remove_container_after_exit being false, there is still no container log found, neither on Azure Portal nor in the node itself with docker inspect.
I think that this node has been reclaimed/deleted and brought up again, but not sure why our tasks were not re-run. Here are two captures, one for the task with max_task_retries and another without it. I also attach here the agent logs (with diag logs upload), captured around 30 minutes after the issue.

agent-debug.log
agent-warn.log
controller-debug.log
The pool id is gpu-vm -env prod, node id is tvm-2169193117_1-20180723t071411z-p. This issue happened around April 12, 2019 5:25 -> 5:35 UTC.

Your node was preempted at around 2019-04-12T05:21:35Z. Unfortunately the logs attached after the fact are of not much use - everything appears to be operating normally.
For the first screenshot, it looks like things worked as expected as your task exited with 125 and the system would not have retried after that. I'd advise reviewing your task/process on why it's failing.
For the second task where the task completed without an exit code, can you provide us with the exact job id and task id?

Due to the lack of log information from our Azure task and we have been running an old version of node agent, I will close this issue for now. I will upgrade the pool and better organize the logging, then will reopen the issue if it happens again with more information.
Thank you for your kind support during the process.


How do I do a Streming Response?

Hi,
I'm trying to implement a streaming response using
from streamlit_chat import message
Like in this blog post
https://medium.com/@avra42/how-to-stream-output-in-chatgpt-style-while-using-openai-completion-method-b90331c15e85
But I don't know if its possible.
In Gradio Chatbot you need to keep updating the same message.
https://gradio.app/creating-a-chatbot/#add-streaming-to-your-chatbot

Yes, it's possible. You can use the native Chat UI instead of installing streamlit_chat custom component. Here's an example:
import openai
import streamlit as st

# Initialize the chat messages history
openai.api_key = st.secrets.OPENAI_API_KEY
if "messages" not in st.session_state:
    st.session_state.messages = [{"role": "assistant", "content": "How can I help you?"}]

# Prompt for user input and save
if prompt := st.chat_input():
    st.session_state.messages.append({"role": "user", "content": prompt})

# display the existing chat messages
for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.write(message["content"])

# If last message is not from assistant, we need to generate a new response
if st.session_state.messages[-1]["role"] != "assistant":
    with st.chat_message("assistant"):
        response = ""
        resp_container = st.empty()
        for delta in openai.ChatCompletion.create(
            model="gpt-3.5-turbo",
            messages=st.session_state.messages,
            stream=True,
        ):
            response += delta.choices[0].delta.get("content", "")
            resp_container.markdown(response)

        st.session_state.messages.append({"role": "assistant", "content": response})

Hope it helps!


[Feature]: Limit how many movies/series are added to Radarr/Sonarr

Is your feature request related to a problem? Please elaborate.
If I create a large collection and I would like to add and search for missing titles, I would like to be able to limit the amount of titles that get added to sonarr on each run so that the missing titles from the collections can get populated gradually over time
Describe the solution you'd like
I'd like a field like radarr_add_limit: which could be set to an integer which would limit the amount of titles that get sent to radarr/sonarr for each collection on a given run
Additional Information
No response

Sorry @meisnate12, I noticed it was closed as completed. I hope this wasn't a nuisance issue, I couldn't see anything in the docs that indicated how to accomplish this


[fix] #3529: Fix topology for 3 or less peers

Description
Make Topology methods consistent with each other and add bunch of tests.

Linked issue

Closes #3529

Benefits
Work properly.

How to test

Run tests

cargo test --package iroha_core --lib -- sumeragi::network_topology::tests --nocapture

Startup iroha with 1-3 peers

# build iroha
cargo build

# run iroha network
./scripts/test_env 1-3

Checklist

[x]: Fix topology methods to be consistent
[x]: Add test to verify

Pull Request Test Coverage Report for Build<PHONE_NUMBER>

426 of 451 (94.46%)  changed or added relevant lines in 2 files are covered.
5322 unchanged lines in 89 files lost coverage.
Overall coverage decreased (-2.1%) to 57.367%

Changes Missing Coverage
Covered Lines
Changed/Added Lines
%

core/src/sumeragi/network_topology.rs
426
433
98.38%

core/src/sumeragi/main_loop.rs
0
18
0.0%

Files with Coverage Reduction
New Missed Lines
%

cli/src/main.rs
1
0%

cli/src/torii/pagination.rs
1
98.9%

config/base/src/runtime_upgrades.rs
1
51.72%

config/src/torii.rs
1
96.67%

core/src/smartcontracts/isi/block.rs
1
87.5%

crypto/src/merkle.rs
1
96.23%

data_model/derive/src/partially_tagged.rs
1
99.67%

data_model/src/account.rs
1
52.21%

data_model/src/domain.rs
1
47.37%

derive_primitives/src/lib.rs
1
0%

Totals

Change from base Build<PHONE_NUMBER>:
-2.1%

Covered Lines:
20479

Relevant Lines:
35698

💛  - Coveralls


setGeoLocation returns random info

The spec says we should be returning the location. If it is not possible in our new scheme to find the actual location, we should, if there is no error in setting, return the location that was requested.

The problem is here: https://github.com/appium/appium-android-driver/blob/master/lib/commands/network.js#L82
Rather than returning the value form the adb call, we should be awaiting that and returning other information.

Issue moved to appium/appium #7400 via ZenHub


Set Context.REFERRAL to "follow"

Hello!
This is not an issue but rather a question. I want to connect the catalog to our LDAP/Active Directory. Since our Active directory structure is a bit complex, I need the catalog to search from the root. In order to do that, I think that I simply must set env.put(Context.REFERRAL,"follow"); at some point, but I'm having a hard time finding where.  Also, I tried setting user-search-base="" thinking that it might be searching from the root, but no success (NameNotFoundException).
Any clues? --Thanks a lot!
Here a post that explains pretty well the situation.
Stack Overflow Post

If you want to set env.put(Context.REFERRAL,"follow"); , it can be done within the "connect" method of the following class (you'll need to recompile):
https://github.com/Esri/geoportal-server/blob/develop/geoportal/src/com/esri/gpt/framework/security/identity/ldap/LdapClient.java

hey thanks a lot! However, I meant to post this question in the geoportal-server-catalog project... sorry about that. I'll close this one and I already posted the same question on the other project.


rancher2.0 install failed

Rancher versions:
rancher/server or rancher/rancher: 2.0
**Docker version: 17.03.2-ce
**Operating system and kernel:

ubuntu 16.04
Linux ip-172-31-12-229 4.4.0-1060-aws #69-Ubuntu SMP Sun May 20 13:42:07 UTC 2018 x86_64 x86_64 x86_64 GNU/Linux

**Type/provider of hosts: AWS
**Setup details: single node rancher
**Environment Template: Kubernetes
Steps to Reproduce:
sudo docker run -d --restart=unless-stopped -p 80:80 -p 443:443 rancher/rancher
docker logs e95 -f

Results:
root@ip-172-31-12-229:/home/ubuntu# docker logs e95 -f
2018/06/06 11:45:16 [INFO] Listening on /tmp/log.sock
2018/06/06 11:45:16 [INFO] [certificates] Generating CA kubernetes certificates
2018/06/06 11:45:17 [INFO] [certificates] Generating Kubernetes API server certificates
***
***
***
2018/06/06 11:45:32 [INFO] uploading digitaloceanConfig to node schema
2018/06/06 11:45:32 [INFO] uploading vmwarevsphereConfig to node schema
2018/06/06 11:45:32 [INFO] uploading vmwarevsphereConfig to node schema
E0606 11:45:38.982324       1 core.go:70] Failed to start service controller: WARNING: no cloud provider provided, services of type LoadBalancer will fail.
E0606 11:45:39.156222       1 certificates.go:48] Failed to start certificate controller: error reading CA cert file "/etc/kubernetes/ca/ca.pem": open /etc/kubernetes/ca/ca.pem: no such file or directory
2018/06/06 11:45:54 [INFO] traversing files and generating templates
2018/06/06 11:45:54 [INFO] Updating catalog library
2018/06/06 11:45:54 [INFO] Creating template library-prometheus
2018/06/06 11:45:55 [INFO] Creating template library-redis
2018/06/06 11:45:55 [INFO] Creating template library-longhorn
2018/06/06 11:45:55 [INFO] Creating template library-memcached
2018/06/06 11:45:55 [INFO] Creating template library-mongodb-replicaset
2018/06/06 11:45:55 [INFO] Creating template library-mysql
2018/06/06 11:45:55 [INFO] Creating template library-mariadb
2018/06/06 11:45:55 [INFO] Creating template library-mongodb
2018/06/06 11:45:55 [INFO] Creating template library-wordpress
2018/06/06 11:45:55 [INFO] Creating template library-nfs-provisioner
2018/06/06 11:45:55 [INFO] Creating template library-etcd-operator
2018/06/06 11:45:55 [INFO] Creating template library-grafana
2018/06/06 11:45:55 [INFO] Creating template library-magento
2018-06-06 11:55:19.631773 I | mvcc: store.index: compact 1198
2018-06-06 11:55:19.633173 I | mvcc: finished scheduled compaction at 1198 (took 920.507µs)
***
***
2018/06/06 23:45:31 [INFO] Running cluster events cleanup
2018/06/06 23:45:31 [INFO] Done running cluster events cleanup
2018-06-06 23:50:20.099819 I | mvcc: store.index: compact 65618
2018-06-06 23:50:20.100893 I | mvcc: finished scheduled compaction at 65618 (took 633.146µs)
2018-06-06 23:55:20.103352 I | mvcc: store.index: compact 66068
2018-06-06 23:55:20.104455 I | mvcc: finished scheduled compaction at 66068 (took 698.482µs)
2018-06-07 00:00:20.107535 I | mvcc: store.index: compact 66519
2018-06-07 00:00:20.108586 I | mvcc: finished scheduled compaction at 66519 (took 648.45µs)
2018-06-07 00:05:20.112088 I | mvcc: store.index: compact 66970
2018-06-07 00:05:20.113204 I | mvcc: finished scheduled compaction at 66970 (took 721.359µs)
2018-06-07 00:10:20.115164 I | mvcc: store.index: compact 67419
2018-06-07 00:10:20.116207 I | mvcc: finished scheduled compaction at 67419 (took 697.953µs)
2018-06-07 00:15:20.118671 I | mvcc: store.index: compact 67870
2018-06-07 00:15:20.119770 I | mvcc: finished scheduled compaction at 67870 (took 696.612µs)
2018-06-07 00:20:20.121399 I | mvcc: store.index: compact 68321
2018-06-07 00:20:20.122572 I | mvcc: finished scheduled compaction at 68321 (took 694.312µs)
2018-06-07 00:25:20.125420 I | mvcc: store.index: compact 68771
2018-06-07 00:25:20.127133 I | mvcc: finished scheduled compaction at 68771 (took 1.167633ms)
2018-06-07 00:30:20.129448 I | mvcc: store.index: compact 69220
2018-06-07 00:30:20.130575 I | mvcc: finished scheduled compaction at 69220 (took 676.794µs)
2018-06-07 00:35:20.132825 I | mvcc: store.index: compact 69671
2018-06-07 00:35:20.133879 I | mvcc: finished scheduled compaction at 69671 (took 642.16µs)
2018-06-07 00:40:20.136230 I | mvcc: store.index: compact 70122
2018-06-07 00:40:20.137384 I | mvcc: finished scheduled compaction at 70122 (took 740.249µs)
2018-06-07 00:45:20.139217 I | mvcc: store.index: compact 70573
2018-06-07 00:45:20.140354 I | mvcc: finished scheduled compaction at 70573 (took 732.96µs)
2018/06/07 00:45:31 [INFO] Running cluster events cleanup
2018/06/07 00:45:31 [INFO] Done running cluster events cleanup

There is no error here, the error on certificate controller is described here: https://github.com/rancher/rancher/issues/12710
Please provide what problems you are experiencing so we can investigate.


add Flickr account type (Round 2)

Rebased the previous PR to a new branch to try not to lose all the comment history. The last two commits should contain all the changes from that PR to this one.
Still TODO add a unit test for get_comments

woo lgtm! thanks again for doing all this. feel free to merge whenever!
you're also welcome to deploy granary-demo. let me know if you don't have all the app keys and i'll send them.

Thanks so much for the feedback and sharp-eyed typo spotting.


Cannot compile hello_world on mac

I am running macOS Mojave 10.14.6
As was able to follow the install instructions but when I try to compile hello_world I see the following.
It looks like it thinks I want to run on Android, when I actually want to run on the desktop.
mediapipe commit: 8f69af91feb5944e130959dc9cc256b93d03abd2
bazel 3.5.0-homebrew
➜  mediapipe git:(master) bazel run --define MEDIAPIPE_DISABLE_GPU=1 mediapipe/examples/desktop/hello_world:hello_world
Starting local Bazel server and connecting to it...
INFO: SHA256 (https://github.com/bazelbuild/rules_foreign_cc/archive/master.zip) = f358144776d3dfc5a928ef32a0e4fbe93c99b55772d70cca77a6478e34d96aa7
DEBUG: Rule 'rules_foreign_cc' indicated that a canonical reproducible form can be obtained by modifying arguments sha256 = "f358144776d3dfc5a928ef32a0e4fbe93c99b55772d70cca77a6478e34d96aa7"
DEBUG: Repository rules_foreign_cc instantiated at:
  no stack (--record_rule_instantiation_callstack not enabled)
Repository rule http_archive defined at:
  /private/var/tmp/_bazel_nmiles/44f14cdbe8ed532118a38417b5444a80/external/bazel_tools/tools/build_defs/repo/http.bzl:336:31: in <toplevel>
INFO: SHA256 (https://github.com/bazelbuild/rules_cc/archive/master.zip) = 97ddba49baf32121709b8578db6b6a58a4b9ff192de14199e6c72b9c7f221c99
DEBUG: Rule 'rules_cc' indicated that a canonical reproducible form can be obtained by modifying arguments sha256 = "97ddba49baf32121709b8578db6b6a58a4b9ff192de14199e6c72b9c7f221c99"
DEBUG: Repository rules_cc instantiated at:
  no stack (--record_rule_instantiation_callstack not enabled)
Repository rule http_archive defined at:
  /private/var/tmp/_bazel_nmiles/44f14cdbe8ed532118a38417b5444a80/external/bazel_tools/tools/build_defs/repo/http.bzl:336:31: in <toplevel>
ERROR: Analysis of target '//mediapipe/examples/desktop/hello_world:hello_world' failed; build aborted: Either the path attribute of android_sdk_repository or the ANDROID_HOME environment variable must be set.
INFO: Elapsed time: 15.415s
INFO: 0 processes.
FAILED: Build did NOT complete successfully (8 packages loaded, 11 targets configured)
FAILED: Build did NOT complete successfully (8 packages loaded, 11 targets configured)
    currently loading: @bazel_tools//tools/python ... (3 packages)
    Fetching @local_config_cc_toolchains; Restarting.
    Fetching @local_execution_config_python; fetching

It's a Bazel 3.5.0 issue and will be solved in Bazel 3.6.0 (or you can go back to Bazel 3.4.0 if Bazel 3.6.0 is still not available). See https://github.com/google/mediapipe/issues/1094#issuecomment-690480208

That fixed it. Thanks! This is an amazing piece of technology.


how to use reranker model with langchain in retrievalQA case?

System Info
version 0.0.331
Who can help?
No response
Information

[ ] The official example notebooks/scripts
[ ] My own modified scripts

Related Components

[X] LLMs/Chat Models
[X] Embedding Models
[ ] Prompts / Prompt Templates / Prompt Selectors
[ ] Output Parsers
[ ] Document Loaders
[ ] Vector Stores / Retrievers
[ ] Memory
[ ] Agents / Agent Executors
[ ] Tools / Toolkits
[ ] Chains
[ ] Callbacks/Tracing
[ ] Async

Reproduction
omit
Expected behavior
I would like to use  BAAI/bge-reranker-large  model as the reranker to rerank the  intial retrieval results,
how to use this model with langchain ?
I have seen the demo with Cohere ranker,
but how to use ohter reranker models?
Does langchain support other reranker models except for CohereReranker?

@valkryhx Do you have any idea to solve?

from future import annotations
from typing import Dict, Optional, Sequence
from langchain.schema import Document
from langchain.pydantic_v1 import Extra, root_validator
from langchain.callbacks.manager import Callbacks
from langchain.retrievers.document_compressors.base import BaseDocumentCompressor
from sentence_transformers import CrossEncoder
from config import bge_reranker_large
参照文件改写 https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/retrievers/document_compressors/cohere_rerank.py
class BgeRerank(BaseDocumentCompressor):
"""Document compressor that uses BAAI/bge-reranker."""
model_name:str = bge_reranker_large
"""Model name to use for reranking."""
top_n: int = 10
"""Number of documents to return."""
model:CrossEncoder = CrossEncoder(model_name)
"""CrossEncoder instance to use for reranking."""

def bge_rerank(self,query,docs):

    model_inputs =  [[query, doc] for doc in docs]
    scores = self.model.predict(model_inputs)
    results = sorted(enumerate(scores), key=lambda x: x[1], reverse=True)

    return results[:self.top_n]

class Config:
    """Configuration for this pydantic object."""

    extra = Extra.forbid
    arbitrary_types_allowed = True

def compress_documents(
    self,
    documents: Sequence[Document],
    query: str,
    callbacks: Optional[Callbacks] = None,
) -> Sequence[Document]:
    """
    Compress documents using Cohere's rerank API.

    Args:
        documents: A sequence of documents to compress.
        query: The query to use for compressing the documents.
        callbacks: Callbacks to run during the compression process.

    Returns:
        A sequence of compressed documents.
    """
    if len(documents) == 0:  # to avoid empty api call
        return []
    doc_list = list(documents)
    _docs = [d.page_content for d in doc_list]
    results = self.bge_rerank(query, _docs)
    final_results = []
    for r in results:
        doc = doc_list[r[0]]
        doc.metadata["relevance_score"] = r[1]
        final_results.append(doc)
    return final_results

参照文件改写 https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/retrievers/document_compressors/cohere_rerank.py
##code `
from __future__ import annotations
from typing import Dict, Optional, Sequence
from langchain.schema import Document
from langchain.pydantic_v1 import Extra, root_validator

from langchain.callbacks.manager import Callbacks
from langchain.retrievers.document_compressors.base import BaseDocumentCompressor

from sentence_transformers import CrossEncoder
from config import bge_reranker_large

class BgeRerank(BaseDocumentCompressor):
    model_name:str = 'bge_reranker_large_model_path'
    """Model name to use for reranking."""
    top_n: int = 10
    """Number of documents to return."""
    model:CrossEncoder = CrossEncoder(model_name)
    """CrossEncoder instance to use for reranking."""

    def bge_rerank(self,query,docs):
        model_inputs =  [[query, doc] for doc in docs]
        scores = self.model.predict(model_inputs)
        results = sorted(enumerate(scores), key=lambda x: x[1], reverse=True)
        return results[:self.top_n]

    class Config:
        """Configuration for this pydantic object."""

        extra = Extra.forbid
        arbitrary_types_allowed = True

    def compress_documents(
        self,
        documents: Sequence[Document],
        query: str,
        callbacks: Optional[Callbacks] = None,
    ) -> Sequence[Document]:
        """
        Compress documents using BAAI/bge-reranker models.

        Args:
            documents: A sequence of documents to compress.
            query: The query to use for compressing the documents.
            callbacks: Callbacks to run during the compression process.

        Returns:
            A sequence of compressed documents.
        """
        if len(documents) == 0:  # to avoid empty api call
            return []
        doc_list = list(documents)
        _docs = [d.page_content for d in doc_list]
        results = self.bge_rerank(query, _docs)
        final_results = []
        for r in results:
            doc = doc_list[r[0]]
            doc.metadata["relevance_score"] = r[1]
            final_results.append(doc)
        return final_results

`

@zhangjiekui  how to resolve config  in from config import bge_reranker_large Please advise

nice

from config import bge_reranker_large

这个是用来干嘛的大佬

就是你存在在本地的模型地址，或者是模型的官方名称（如HuggingFace），可以删除那一行，我是从我的代码文件中直接拷贝过来的，我把模型名称地址放到配置文件里了。

谢谢大佬
发自我的iPhone
------------------ Original ------------------
From: zhangjiekui @.>
Date: Thu,Dec 14,2023 8:47 PM
To: langchain-ai/langchain @.>
Cc: whm233 @.>, Comment @.>
Subject: Re: [langchain-ai/langchain] how to use reranker model with langchainin retrievalQA case? (Issue #13076)
就是你存在在本地的模型地址，或者是模型的官方名称（如HuggingFace），可以删除那一行，我是从我的代码文件中直接拷贝过来的，我把模型名称地址放到配置文件里了。
—
Reply to this email directly, view it on GitHub, or unsubscribe.
You are receiving this because you commented.Message ID: @.***>

参照文件改写 https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/retrievers/document_compressors/cohere_rerank.py
##code `
from __future__ import annotations
from typing import Dict, Optional, Sequence
from langchain.schema import Document
from langchain.pydantic_v1 import Extra, root_validator

from langchain.callbacks.manager import Callbacks
from langchain.retrievers.document_compressors.base import BaseDocumentCompressor

from sentence_transformers import CrossEncoder
from config import bge_reranker_large

class BgeRerank(BaseDocumentCompressor):
    model_name:str = 'bge_reranker_large_model_path'
    """Model name to use for reranking."""
    top_n: int = 10
    """Number of documents to return."""
    model:CrossEncoder = CrossEncoder(model_name)
    """CrossEncoder instance to use for reranking."""

    def bge_rerank(self,query,docs):
        model_inputs =  [[query, doc] for doc in docs]
        scores = self.model.predict(model_inputs)
        results = sorted(enumerate(scores), key=lambda x: x[1], reverse=True)
        return results[:self.top_n]

    class Config:
        """Configuration for this pydantic object."""

        extra = Extra.forbid
        arbitrary_types_allowed = True

    def compress_documents(
        self,
        documents: Sequence[Document],
        query: str,
        callbacks: Optional[Callbacks] = None,
    ) -> Sequence[Document]:
        """
        Compress documents using BAAI/bge-reranker models.

        Args:
            documents: A sequence of documents to compress.
            query: The query to use for compressing the documents.
            callbacks: Callbacks to run during the compression process.

        Returns:
            A sequence of compressed documents.
        """
        if len(documents) == 0:  # to avoid empty api call
            return []
        doc_list = list(documents)
        _docs = [d.page_content for d in doc_list]
        results = self.bge_rerank(query, _docs)
        final_results = []
        for r in results:
            doc = doc_list[r[0]]
            doc.metadata["relevance_score"] = r[1]
            final_results.append(doc)
        return final_results

`

@zhangjiekui how to resolve config in from config import bge_reranker_large Please advise

Just the bge_reranker_large model name or local path (I put it in the config.py), be free to del that line of code

is there any tools for Text Embeddings Inference rerank models server like HuggingFaceHubEmbeddings instead of local running rerank models ?

So how can I use this bge rerank model in RetrievalQA?

So how can I use this bge rerank model in RetrievalQA?

from langchain.retrievers.document_compressors.BgeRerank import BgeRerank
from langchain.chains import RetrievalQA
from langchain.retrievers import ContextualCompressionRetriever
compressor = BgeRerank()
compression_retriever = ContextualCompressionRetriever(
base_compressor=compressor, base_retriever=db_retriever
)
kc = RetrievalQA.from_llm(llm=qwllm, retriever=compression_retriever, prompt=prompt)

@whm233 Thanks!

hi @whm233
which version of langchain that can use this
from langchain.retrievers.document_compressors.BgeRerank import BgeRerank

I install langchain=0.1.13 and can not found BgeRerank
Thank.

May I ask if the parameter model_name in the modification method you provided can accept the path of the local model? compressor = BgeRerank(model_name='D:\bge_reranker_base')
I am unable to load the local model using this assignment method. The code will automatically connect to the code repository of the huggingface and attempt to download the model. Finally, this error will be displayed:
OSError: We couldn't connect to 'https://huggingface.co' to load this file, couldn't find it in the cached files and it looks like bge_reranker_large_model_path is not the path to a directory containing a file named config.json.
Checkout your internet connection or see how to run the library in offline mode at 'https://huggingface.co/docs/transformers/installation#offline-mode'.

hi @whm233 which version of langchain that can use this
from langchain.retrievers.document_compressors.BgeRerank import BgeRerank

I install langchain=0.1.13 and can not find BgeRerank Thank.

you should make BgeRerank.py flie and copy the code in it

May I ask if the parameter model_name in the modification method you provided can accept the path of the local model? compressor = BgeRerank(model_name='D:\bge_reranker_base')
I am unable to load the local model using this assignment method. The code will automatically connect to the code repository of the huggingface and attempt to download the model. Finally, this error will be displayed:
OSError: We couldn't connect to 'https://huggingface.co' to load this file, couldn't find it in the cached files and it looks like bge_reranker_large_model_path is not the path to a directory containing a file named config.json. Checkout your internet connection or see how to run the library in offline mode at 'https://huggingface.co/docs/transformers/installation#offline-mode'.

this is my code
from future import annotations
from typing import Dict, Optional, Sequence
from langchain.schema import Document
from langchain.pydantic_v1 import Extra, root_validator
from langchain.callbacks.manager import Callbacks
from langchain.retrievers.document_compressors.base import BaseDocumentCompressor
from sentence_transformers import CrossEncoder
#from config import bge_reranker_large
class BgeRerank(BaseDocumentCompressor):
model_name:str = '/data/shared/Qwen/filedir/bge-reranker-base'
"""Model name to use for reranking."""
top_n: int = 3
"""Number of documents to return."""
model:CrossEncoder = CrossEncoder(model_name)
"""CrossEncoder instance to use for reranking."""
def bge_rerank(self,query,docs):
    model_inputs =  [[query, doc] for doc in docs]
    scores = self.model.predict(model_inputs)
    results = sorted(enumerate(scores), key=lambda x: x[1], reverse=True)
    return results[:self.top_n]

May I ask if the parameter model_name in the modification method you provided can accept the path of the local model? compressor = BgeRerank(model_name='D:\bge_reranker_base')
I am unable to load the local model using this assignment method. The code will automatically connect to the code repository of the huggingface and attempt to download the model. Finally, this error will be displayed:
OSError: We couldn't connect to 'https://huggingface.co' to load this file, couldn't find it in the cached files and it looks like bge_reranker_large_model_path is not the path to a directory containing a file named config.json. Checkout your internet connection or see how to run the library in offline mode at 'https://huggingface.co/docs/transformers/installation#offline-mode'.

Perhaps you should set the address of the model directly in the source code

请问您提供的修改方法中model_name的参数是否可以接受局部模型的路径？compressor = BgeRerank(model_name='D:\bge_reranker_base')
我无法使用此赋值方法加载本地模型。代码将自动连接到 huggingface 的代码库并尝试下载模型。最后，将显示此错误：
OSError：我们无法连接到“https://huggingface.co”来加载此文件，在缓存文件中找不到它，并且看起来bge_reranker_large_model_path不是包含名为 config.json 的文件的目录的路径。在“https://huggingface.co/docs/transformers/installation#offline-mode”中签出您的互联网连接或查看如何在离线模式下运行库。

也许你应该直接在源代码中设置模型的地址

Thanks, Modify the address in the source code is the correct solution

参照文件改写 https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/retrievers/document_compressors/cohere_rerank.py
##code `
from __future__ import annotations
from typing import Dict, Optional, Sequence
from langchain.schema import Document
from langchain.pydantic_v1 import Extra, root_validator

from langchain.callbacks.manager import Callbacks
from langchain.retrievers.document_compressors.base import BaseDocumentCompressor

from sentence_transformers import CrossEncoder
# from config import bge_reranker_large

class BgeRerank(BaseDocumentCompressor):
    model_name:str = 'bge_reranker_large_model_path'
    """Model name to use for reranking."""
    top_n: int = 10
    """Number of documents to return."""
    model:CrossEncoder = CrossEncoder(model_name)
    """CrossEncoder instance to use for reranking."""

    def bge_rerank(self,query,docs):
        model_inputs =  [[query, doc] for doc in docs]
        scores = self.model.predict(model_inputs)
        results = sorted(enumerate(scores), key=lambda x: x[1], reverse=True)
        return results[:self.top_n]

    class Config:
        """Configuration for this pydantic object."""

        extra = Extra.forbid
        arbitrary_types_allowed = True

    def compress_documents(
        self,
        documents: Sequence[Document],
        query: str,
        callbacks: Optional[Callbacks] = None,
    ) -> Sequence[Document]:
        """
        Compress documents using BAAI/bge-reranker models.

        Args:
            documents: A sequence of documents to compress.
            query: The query to use for compressing the documents.
            callbacks: Callbacks to run during the compression process.

        Returns:
            A sequence of compressed documents.
        """
        if len(documents) == 0:  # to avoid empty api call
            return []
        doc_list = list(documents)
        _docs = [d.page_content for d in doc_list]
        results = self.bge_rerank(query, _docs)
        final_results = []
        for r in results:
            doc = doc_list[r[0]]
            doc.metadata["relevance_score"] = r[1]
            final_results.append(doc)
        return final_results

`

谢谢，我跟你有同样的想法，很有参考价值。
但是想请教下要如何理解在 BaseDocumentCompressor 里的注释说：
“    Note users should favor using a RunnableLambda
instead of sub-classing from this interface.”


Add coral-fingerprint option of displaying categories relative to their mean

Sometimes a feature has a category that will always have an overwhelming occurrence. E.g., android app dataset has a version number feature, where the category "varies by device" is always much higher than all the others. This also leads to this feature being a "defining feature" which always appears as one of the first rows.
To address this problem, it would be useful to have an option to use categories relative to their mean occurrence. E.g., "varies by device" might be the category for 80% of all datapoints, but for your current selection it is 81% - which is not that defining or different from the rest of the data. In fact it is only 1.25% above the average occurrence of this category.

this is now possible in storytelling and cluster/selection comparison
to get the comparison of a selection to the "overall mean" the other selection would just have to be the entire dataset


Fix python creation script

When running the script without these changes I get the following errors:
  File "pb_theme_creator.py", line 137, in <module>
  File "pb_theme_creator.py", line 22, in get_pbcolors_srgb
    self.hex_l = self._ask_hex('lightmode')
  File "pb_theme_creator.py", line 51, in _ask_hex
    is_valid = self._is_valid_hex(ask_hex)
  File "pb_theme_creator.py", line 80, in _is_valid_hex
    current_character = valid_hex[i].lower()

I ended up fixing this by looping over characters in the string instead, since earlier it was comparing with string[string] rather than string[index].
edited for clarity*

This was something I accidentally changed when formatting the script. Thank you for the PR!


Add FluxCD dashboards

Towards giantswarm/giantswarm#19449 and giantswarm/giantswarm#19455
This PR

adds FluxCD dashboards

Checklist

[x] Update changelog in CHANGELOG.md in an end-user friendly language.

Source: https://github.com/fluxcd/flux2/tree/v0.21.0/manifests/monitoring/grafana/dashboards
I mentioned it in a commit message as well.

Tested on ginger.

We should somehow think about keeping these up to date with upstream but I have no easy solution for this


Escape symbols for code snippets

Describe the feature or improvement you're requesting
How can I ensure that a response containing JavaScript code is always escaped? I am receiving a response from Open in the form of a text stream which contains both strings and pieces of JavaScript code. The problem is that sometimes the JavaScript code is not properly escaped with triple tilde characters. Is there any special option for that?
Additional context
No response

You might try the new function calling feature: https://openai.com/blog/function-calling-and-other-api-updates


Optimize Apple HomeKit?

Describe the bug
Using the Camera UI directly my cameras are very responsive.  They appear in 1-2 seconds.  However,  the same Camera using Apple Home can sometimes take >15 seconds.  What is Apple doing that causes this to be so slow and is there a way to speed it up?
This is the FFMPEG command I find when Camera UI streams to my phone via HomeKit (with IPs and Users and Passwords removed)
/usr/local/bin/ffmpeg -rtsp_transport udp -i rtsp://user:password@IP:554/h264Preview_01_sub -an -sn -dn -codec:v copy -pix_fmt yuv420p -color_range mpeg -f rawvideo -payload_type 99 -ssrc 16558985 -f rtp -srtp_out_suite AES_CM_128_HMAC_SHA1_80 -srtp_out_params wjJh5MqqZhD7+91mj7CCYLM4LY53Mx20u0A5vRor srtp://IP:54318?rtcpport=54318&pkt_size=1316 -vn -sn -dn -codec:a libfdk_aac -profile:a aac_eld -flags +global_header -f null -ar 16k -b:a 24k -ac 1 -payload_type 110 -ssrc 11860871 -f rtp -srtp_out_suite AES_CM_128_HMAC_SHA1_80 -srtp_out_params vLamlj3vEvU2kux8HCGU0SceMrtBZ+jNCdmF1GDT srtp://IP:5450?rtcpport=55450&pkt_size=188 -loglevel level+verbose -progress pipe:1
To Reproduce
Steps to reproduce the behavior
Expected behavior
A clear and concise description of what you expected to happen.
Logs
If available, attach the log where the bug is vissible
Screenshots
If applicable, add screenshots to help explain your problem.
Environment

Node.js Version: v14.18
Homebridge Version: vX.X
Camera UI Version: v4.2
Operating System: Raspberry PI 4
Browser: XXX

Fixed in v5.0.0-beta.36
sudo npm i -g<EMAIL_ADDRESS>

Demo:
https://user-images.githubusercontent.com/34152761/147686500-eb53d35c-167a-450a-9a31-85bf6ecd4af1.MP4


Add GraphQL syntax highlighting

Would like to see support for GraphQL syntax highlighting.

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

ID: d876ecc9-4bd3-7939-e085-45ee004d2ca4
Version Independent ID: 01ce33da-da13-b0f5-a153-3e1d41d848f5
Content: How to include code in docs - Contributor guide
Content Source: Contribute/code-in-docs.md
Product: non-product-specific
GitHub Login: @tdykstra
Microsoft Alias: tdykstra

We do have support for GraphQL, it was just left out of the list in this article. I'm adding it in Pull Request #794

Awesome, thank you!


[language] remove diem-types dependency from move-coverage

This removes the diem-types dependency from move-coverage.

Wait, unit test failed?
FAIL [   0.297s]                                     diem-mempool  tests::multi_node_test::test_failover

/land

Wait, unit test failed?
FAIL [ 0.297s] diem-mempool tests::multi_node_test::test_failover

Probably a flake :/

Already filed an issue for the flaky test: #8118. Any idea who I should assign it to?

:broken_heart: Test Failed - ci-test-success

/land


[ci] use github actions #5

Remove functionality which was already migrated.

Red diff - good diff


[Bug] Azure.Core getLroMetadata result in a Model as finalResult, for a delete operation

Describe the bug
A clear and concise description of what the bug is.
To Reproduce
Steps to reproduce the behavior:
TypeSpec source (deviceupdate): https://github.com/Azure/azure-rest-api-specs-pr/blob/0788c5a7d5375a141d66f2cb82266f7c8190550d/specification/deviceupdate/Microsoft.DeviceUpdate/routes.tsp#L80-L102
compiler 0.51.0
typespec-azure-core 0.37.1

This deleteUpdate is almost exactly same as the importUpdate above (while importUpdate would have "void" as finalResult).
Expected behavior
A clear and concise description of what you expected to happen.
A delete operation have more reason to have "void" as finalResult.
Additional context
Add any other context about the problem here.
PS: for the standard LRO in Azure.Core LongRunningResourceDelete, it does have "void" as finalResult.

@weidongxu-microsoft   I believe this is because the 'delete' operation is not decorated with @deletesResource, which should be fixed by the upcoming PR enabling LRO support in ARM specs.

@weidongxu-microsoft I believe this is because the 'delete' operation is not decorated with @deletesResource, which should be fixed by the upcoming PR enabling LRO support in ARM specs.

I tried on DevCenter SDK the DeleteDevBox operation. The problem can be fixed by adding @deletesResource(DevBox).

closing as fixed.


DS-3116 : Only log errors if service bean cannot be located.

Fix for https://jira.duraspace.org/browse/DS-3116
Only log errors if service bean cannot be located.  Provide INFO messages on status of finding bean by name. Ensure that an ERROR is logged if the bean cannot be found.

+1  The old behavior is quite confusing.

This is an obvious minor fix. I'm merging it, as there has been no objections for over a week.


IA-1888: Org unit location and catchment edition

Initial bug was that you can edit a marker and a shape in the same time while editing a location of a org unit.
We discovered a lots of other bugs while fixing it.
I'll describe the correct behaviour instead of describe all the errors we got.

if you don't have a a geo location, you can add a marker or a shape
while adding a shape, a marker or a catchment, you cannot do something else (including cancel or save)
Org unit shape and marker should use primary color
catchment shape should use secondary color
make sure the marker is not disappearing

Related JIRA tickets : IA-1888
Self proofreading checklist

[x] Did I use eslint and black formatters
[ ] Is my code clear enough and well documented
[x] Are my typescript files well typed
[ ] Did I add translations
[ ] My migrations file are included
[ ] Are there enough tests

Changes
The main issue is due to the EditGroup Class object that was not refreshing his value while react did it in his state.
How to test
Open the detail of a org unit.
Go to map tab, click on edit
Play with the location, shape and catchment tools.
Print screen / video
https://user-images.githubusercontent.com/12494624/222453897-edf02166-3d75-44bd-affc-a1185c40924f.mov

Yes this is the current behaviour already on prod, the tab is not deep linked


Could you please share the paper so that I can study it?

Thanks for your interest.
The paper link can be found on the main page.

thank you very much!


Rewrite clients with react query

This is one part of a rewrite of the "clients" feature to support having a similar feature for clients: https://github.com/codeforpdx/PASS/issues/351. This PR implements 3 main features:

Renames the 'client' feature to 'contacts' within the codebase. This will let the feature better reflect the design proposed in the issue -- have one universal page that can store all types of contacts for all types of users (e.g. clients, case managers, coworkers, organizations)
Rewrites the 'contacts' page to use react query instead of contexts. This is to serve as a demo of how it can work in PASS. It seems to work substantially better than our current system of contexts and helper functions. We've been able to remove a substantial amount of code from the system and push it into react query instead.
Added podUrl into the session context so we don't need to use SignedInUserContext as extensively.

The change list is long, but a lot of it is do to renaming and deletions of unnecessary files. The most significant change I did was combining the Clients page and the ClientsList component into one Contacts page.

I haven't dug into any of this yet, BUT:
I'm not sure we want a Contacts page for the caseworkers.  I think they need quick dedicated access to their Clients.  I'm unsure what other contacts caseworkers would have - that they'd have the authority to set perms to.
Like, let's say they have other caseworkers in their contacts list, do we see them trying to give them perms to a clients files? Would they have authority to do so?
But I haven't thought about this much honestly. I know we need to have changes to the contactsList so we can add caseworkers, organizations, etc - and not only have clients.  But, I'm unsure about changing any UI of the clients page due to this yet.
Could you explain a little about this thought process?

@xscottxbrownx I think you're conflating two behaviors: permission setting and connection. We can handle permissions setting separately. None of the changes in this PR affect permissions, or limit how we can approach it.
Goal of this PR is to allow us to reuse code across multiple very similar features. A client will need to contact multiple case managers and possibly other clients (like family members). A case manager may need to talk with not just clients, but other related case managers and organizations. This ability to 'contact' people is functionality that we can reuse in several places.

But, I'm unsure about changing any UI of the clients page due to this yet. Could you explain a little about this thought process?
Thought process for updating the UI is because right now we don't have a way to actually differentiate between a client and a case worker, so I wanted to create a UI that would be equally usable by both. I think a more custom UI could be useful, but for now my thought is to just filter contacts by type, and make a table on the contacts page for each type. Then we can customize the tables to better suit each type. If we decide that certain contact types need different, more dedicated flows, then we can factor those tables out into separate flows.

Distinguishing between a client and a case worker is also non-trivial. What happens if we have a case worker on food stamps? They're a case worker for one organization, but a client for another.

@xscottxbrownx @leekahung Thanks for the eyes on this! I think it's ready for another pass.

Hey @timbot1789 had started reviewing the PR again. Before diving into the codebase itself, I've started with a visual check with npm run dev for the new Contacts page. However, it looks like there could still be some issue with the contacts list as seen in the error from the clip below. The page crashes every time it enters it.
https://github.com/codeforpdx/PASS/assets/14917816/9cbef5cd-13eb-47a5-a278-b0da9d366c38
Other parts of the site remains unaffected, but again, it'll break once it re-enters the contacts page.
https://github.com/codeforpdx/PASS/assets/14917816/b70682e9-9bdc-49b2-b758-38133d91e6a4

@leekahung delete your existing userlist.ttl and try testing again. Let me know if the issue persists

@leekahung delete your existing userlist.ttl and try testing again. Let me know if the issue persists

That fixes it.


chore: upgrade node version for website

Fixes https://github.com/weaveworks/weave-gitops/actions/runs/12493666162/job/34862378667

What changed?

Why was this change made?

How was this change implemented?

How did you validate the change?

Release notes

Documentation Changes

Superseeded by https://github.com/weaveworks/weave-gitops/pull/4280


It is failed to query CnchMergeTree from the remote object disk because of the part_id of part is zero.

Hello, all.
In my ByConity system, CnchMergeTree data is stored on remote object storage. After data is imported, data can be queried from ByConity.
However, occasionally querying data fails. I have not find a pattern of reappearance.
The part_id of the failed part is zero, so it is failed to query from server. But why the part_id of part save to zero?
Briefly describe the bug
It is failed to query from server, this is error log:
Code: 499. DB::Exception: Received from <IP_ADDRESS>:9000. DB::Exception: DB::Exception: Encounter exception when request s3, HTTP Code: 404, RemoteHost: , RequestID: , ExceptionName: , ErrorMessage: No response body., Extra: SQLSTATE: HY000.: While executing Remote SQLSTATE: HY000.

And we can find these failed log from default-worker:
AWSClient[121] : Make request to: http://{endpoint}/{bucket}/{path}/{table_uuid}/00000000-0000-0000-0000-000000000000/data
AWSClient[104] : Request returned error. Attempting to generate appropriate error codes from response
AWSClient[104] : HTTP response code: 404
Resolved remote host IP address:
Request ID:
Exception name:
Error message: No response body.
12 response headers:
connection : close
content-length : 0
content-type :
date : xxxxxxx
server : xxxxxxxxx
x-amz-error-code : NoSuchKey
x-amz-error-detail-key : {path}/{table_uuid}/00000000-0000-0000-0000-000000000000/data
x-amz-error-message : The specified key does not exist.

This is stack info:
CnchWorkerService[195] : virtual void DB::CnchWorkerServiceImpl::sendQueryDataParts(google::protobuf::RpcController *, const Protos::SendDataPartsReq *, Protos::SendDataPartsResp *, google::protobuf::Closure *): Code: 499, e.displayText() = DB::S3::S3Exception: Encounter exception when request s3, HTTP Code: 404, RemoteHost: , RequestID: , ExceptionName: , ErrorMessage: No response body., Extra: SQLSTATE: HY000, Stack trace (when copying this message, always include the lines below):
Poco::Exception::Exception(std::__1::basic_string<char, std::__1::char_traits, std::__1::allocator > const&, int) @ 0x1840f4b2 in /opt/byconity/byconity/bin/clickhouse
DB::Exception::Exception(std::__1::basic_string<char, std::__1::char_traits, std::__1::allocator > const&, int, bool) @ 0xac43120 in /opt/byconity/byconity/bin/clickhouse
DB::S3::S3Exception::S3Exception(Aws::S3::S3Error const&, std::__1::basic_string<char, std::__1::char_traits, std::__1::allocator > const&) @ 0x12d79b97 in /opt/byconity/byconity/bin/clickhouse
DB::S3::S3Util::headObject(std::__1::basic_string<char, std::__1::char_traits, std::__1::allocator > const&) const @ 0x12d7f020 in /opt/byconity/byconity/bin/clickhouse
DB::S3::S3Util::getObjectSize(std::__1::basic_string<char, std::__1::char_traits, std::__1::allocator > const&) const @ 0x12d7eb43 in /opt/byconity/byconity/bin/clickhouse
DB::DiskByteS3::getFileSize(std::__1::basic_string<char, std::__1::char_traits, std::__1::allocator > const&) const @ 0x1361d5e6 in /opt/byconity/byconity/bin/clickhouse
DB::MergeTreeDataPartCNCH::loadPartDataFooter() const @ 0x145013fc in /opt/byconity/byconity/bin/clickhouse
DB::MergeTreeDataPartCNCH::loadChecksums(bool) @ 0x14506b9e in /opt/byconity/byconity/bin/clickhouse
DB::IMergeTreeDataPart::getChecksums() const @ 0x1442eeea in /opt/byconity/byconity/bin/clickhouse
DB::MergeTreeDataPartCNCH::loadColumnsChecksumsIndexes(bool, bool) @ 0x14500732 in /opt/byconity/byconity/bin/clickhouse
void std::__1::__function::__policy_invoker<void (std::__1::shared_ptrDB::IMergeTreeDataPart&)>::__call_impl<std::__1::__function::__default_alloc_func<DB::MergeTreeCloudData::loadDataPartsInParallel(std::__1::vector<std::__1::shared_ptrDB::IMergeTreeDataPart, std::__1::allocator<std::__1::shared_ptrDB::IMergeTreeDataPart > >&)::$_0, void (std::__1::shared_ptrDB::IMergeTreeDataPart&)> >(std::__1::__function::__policy_storage const*, std::__1::shared_ptrDB::IMergeTreeDataPart&) @ 0x14478513 in /opt/byconity/byconity/bin/clickhouse
ThreadPoolImpl::worker(std::__1::__list_iterator<ThreadFromGlobalPool, void*>) @ 0xac7e0cf in /opt/byconity/byconity/bin/clickhouse
ThreadFromGlobalPool::ThreadFromGlobalPool<void ThreadPoolImpl::scheduleImpl(std::__1::function<void ()>, int, std::__1::optional)::'lambda0'()>(void&&, void ThreadPoolImpl::scheduleImpl(std::__1::function<void ()>, int, std::__1::optional)::'lambda0'()&&...)::'lambda'()::operator()() @ 0xac7fe6c in /opt/byconity/byconity/bin/clickhouse
ThreadPoolImplstd::__1::thread::worker(std::__1::__list_iterator<std::__1::thread, void*>) @ 0xac7abc5 in /opt/byconity/byconity/bin/clickhouse
void* std::__1::__thread_proxy<std::__1::tuple<std::__1::unique_ptr<std::__1::__thread_struct, std::__1::default_deletestd::__1::__thread_struct >, void ThreadPoolImplstd::__1::thread::scheduleImpl(std::__1::function<void ()>, int, std::__1::optional)::'lambda0'()> >(void*) @ 0xac7f0da in /opt/byconity/byconity/bin/clickhouse
? @ 0x8f3b in /usr/lib64/libpthread-2.28.so
clone @ 0xf8840 in /usr/lib64/libc-2.28.so
After I restart all processes of ByConity, I fase the same problem.

The part_id of this part is zero. ClickHouse-server query part metadata from KV store, the part_id of this part is zero "00000000-0000-0000-0000-000000000000".
Version
<IP_ADDRESS>
Does any one face the sampe problem?

@zhaojintaozhao hi we will close this issue since it has been a while with no clue and cannot reproduce for over a week.


[queues] Take cdw and timestamp from consumer meta in queue agent

I hereby agree to the terms of the CLA available at: https://yandex.ru/legal/cla/?lang=en

LGTM modulo small comments above.


add package.json; bump version

Adds a package.json file to allow installation via npm (e.g., for use with Browserify):
npm install troywarr/uservoice-trigger-directive

If you'd like, you can register the npm module uservoice-trigger-directive to allow installation very similar to Bower:
npm install uservoice-trigger-directive

Also includes a minor version bump.

Sure. Looks good. I'll publish this to npm now.

It's now published on npm. :ship:
https://www.npmjs.com/package/uservoice-trigger-directive
Cheers

Excellent - thank you!


Sort Helper not being displayed

Hey,
When I drag an element the element shows the Sort Helper without any of the CSS that should be applied to the Element because the nested css is very specific to the parent(s) of the Sort Helper. Currently you are appending it to the body element and this is not even the root of the application. Would it be possible to allow me to choose which element the Sort Helper should be appended to.
Thanks

This isn't really an issue per-se, going to close and we can discuss in the PR instead.

I created a pull request for this, #194
On Wed, Apr 26, 2017, 9:47 PM Claudéric Demers<EMAIL_ADDRESS>wrote:

This isn't really an issue per-se, going to close and we can discuss in
the PR instead.
—
You are receiving this because you authored the thread.
Reply to this email directly, view it on GitHub
https://github.com/clauderic/react-sortable-hoc/issues/192#issuecomment-297588522,
or mute the thread
https://github.com/notifications/unsubscribe-auth/AGEzSnYQjfdpSXrlGQiff4JP9vbVXewKks5rz_PAgaJpZM4NCJRm
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--
Faried Amani


[ObjC] add privacy manifests to boringssl grpc

BoringSSL-GRPC uses sources from https://github.com/google/boringssl.git, which doesn't contain PrivacyInfo.xcprivacy file.
This PR embeds the manifest data into the podspec and extract during pod install, similar to file boringssl_prefix_symbols.h
--
Will need to regenerate the podspec after #35542 merged.

@HannahShiSFB Ahh there's a conflict now! Would you mind fixing it?