chore: update eslint

Codecov Report
All modified and coverable lines are covered by tests :white_check_mark:

Comparison is base (c47d47a) 86.80% compared to head (1c94046) 86.80%.
Report is 3 commits behind head on main.

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Additional details and impacted files
@@           Coverage Diff           @@
##             main    #2426   +/-   ##
=======================================
  Coverage   86.80%   86.80%
=======================================
  Files          85       85
  Lines        9328     9328
  Branches      489      489
=======================================
  Hits         8097     8097
  Misses       1231     1231

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Track Ubuntu Bionic & Mint 19 incompatibilities

Mint 19 will be based on Ubuntu 18.04. As 18.04 starts to approach Feature Freeze and release, we should take notes of incompatibilities between the Ansible playbook and any betas (and subsequent releases). None of these should be acted on now since things may change before official release.
https://wiki.ubuntu.com/BionicBeaver/ReleaseSchedule
Current notes:

[ ]  Aptitude is not installed in 18.04. This may need to be installed manually because I don't think Ansible adds support for apt-get until 2.4.
[ ] The syslog user gets picked up as a valid user since homedir is /home/syslog and the shell is /usr/bin/nologin. We may need to adjust how we detect valid users.
[ ] gksu is not installed by default in 18.04. This package is no longer recommended and pkexec should probably be used instead

syslog should be easy enough to fix by adding a 3rd criteria:
- name: Build real user list
  set_fact:
    real_users: >-
      {{ real_users|default([]) +
      [ {'user': item.key, 'homedir': item.value.4,
      'uid': item.value.1, 'gid': item.value.2 } ] }}
  with_dict: "{{ getent_passwd }}"
  when:
    - item.value.4 | match ("^/home/")
    - item.value.5 != "/bin/false"

Yeah, item.value.5 != "/usr/sbin/nologin will probably do it. As would checking that uid is >= 1000. But I am uncertain if this user will make it into release Mint, so I want to avoid making any changes in the repo yet unless that binary exists in Mint 18.3.

If we look at going uid >=, it might better to use http://docs.ansible.com/ansible/latest/playbooks_lookups.html#the-ini-file-lookup against FIRST_UID in /etc/adduser.conf, or wherever else it's defined.

Updated top post with gksu issues

18.04 ships with Ansible 2.5.1. The aptitude issue is taken care of.

As of now, I believe all incompatibilities with Mint 19 have been found and several have been resolved. Related issues and PRs include:

#138
#146
#143
#144
#124
#139
#132

At this point I believe it is safe to close this issue and any remaining problems found can be opened as individual issues or PRs as we have done with anything found since this issue was created.


Update bond_zh_cov.py

add  one column Because the original data has more columns

Thanks for your PR, but you should check your code again in your local environment

i  has check


antd 3.26.13 to 4.3.3

upgrade to v4 using command npx -p @ant-design/codemod-v4 antd4-codemod src
delete Row component at L100 TestCompare/TestCompare.jsx and L11 TestCompare/TestInfo.jsx
Add form layout at L14 TestCompare/TestInfo.jsx
<span className="ant-divider"> to <Divider type="vertical"> at L47,63,101 Build/TestTable.jsx

Signed-off-by: Yixin Qian<EMAIL_ADDRESS>


[Plugins] General purpose plugin system

We need the core of MUD to be as small and as possible to make it easier to understand and easier to maintain, and eventually audit
At the same time we want to be able to support arbitrary complex use cases

→ We need a rock solid plugin system!
Plugins can:

modify the MUD config + types
modify the codegen / build / deploy pipeline
provide client libraries to interact with whatever the plugin provides
provide systems / tables / modules to add contract functionality

part of #1668 / #1561


feat: workflow_dispatch approach for GitLab

I was wondering if there is something planned to add the workflow_dispatch approach for GitLab, as it is with GitHub
-> https://github.com/fluxcd/notification-controller/blob/5500739269e6772343710405dbac9dc893e11c05/internal/notifier/github_dispatch.go#L110
During my research I found this issue: https://forum.gitlab.com/t/workflow-dispatch-approach-in-gitlab-ci/72426, which states triggering a pipeline with passed in environment variables (aka payload)
Update: or will the forwarder with a POST to trigger a pipeline / job via webhook do it: https://github.com/fluxcd/notification-controller/blob/5500739269e6772343710405dbac9dc893e11c05/internal/notifier/forwarder.go#L80 ?
Or does anyone have experience in implementing it? :)

We need for GitLab to tell us if the Pipeline Trigger is the right approach. CC @nagyv

Hi! GitLab PM here. One GitLab team is working on https://gitlab.com/groups/gitlab-org/-/epics/10866 with the specific intent of supporting Flux integrations. Please, add you voice to the linked epic!


feat: matcher backend

Introduction
Experimental draft PR for playing with a type encoded matcher similar to irregex. Ideally this wouldn't necessarily need to be used directly, though it could, but would serve more as an IR for building a NFA state graph for optimizing.
Linked Issues
#58
Dependencies
Test

[ ] Tested Locally
[ ] Documented

Review

[ ] Ready for review
[ ] Ready to merge

Deployment

I don't think I want to merge this. I should focus on the graph instead.


Adoption of Iris-grib

The grib interoperability functionality is being broken out into a separate library, with a different release cycle:
https://github.com/SciTools/iris-grib
The intent is that the Iris API is maintained and the current user experience continues.
This requires:

[ ] iris-grib release
[ ] iris to iris-grib interface
[ ] iris testing changes

This approach requires consideration by developers and users and testing; all involvement is gratefully received.
thank you
mark

@ajdawson your thoughts on this implementation would be really helpful

complete, as of #2087


Monad instance for either

In the Rust version of Nova, most of the functions return Result, a type the analogue of which is called Either in Haskell and other functional languages. For some cases, it's convenient to think of Either as a monad, so this PR introduces a Haskell-inspired monad instance for Either.

Can you refresh my memory why we're using Either instead of the native Sum?
/--
`Sum α β`, or `α ⊕ β`, is the disjoint union of types `α` and `β`.
An element of `α ⊕ β` is either of the form `.inl a` where `a : α`,
or `.inr b` where `b : β`.
-/
inductive Sum (α : Type u) (β : Type v) where
  | /-- Left injection into the sum type `α ⊕ β`. If `a : α` then `.inl a : α ⊕ β`. -/
    inl (val : α) : Sum α β
  | /-- Right injection into the sum type `α ⊕ β`. If `b : β` then `.inr b : α ⊕ β`. -/
    inr (val : β) : Sum α β

@[inheritDoc] infixr:30 " ⊕ " => Sum

I can't remember either. As far as I remember, it's been introduced when I've been working on megaparsec, but I can't recall the context now


[SQL] Fixed {securityAlertPolicyName} not defined for PUT

Generated from RestAPI PR: https://github.com/Azure/azure-rest-api-specs/pull/1349

Codecov Report

Merging #1269 into master will decrease coverage by 0.02%.
The diff coverage is 31.25%.

@@            Coverage Diff             @@
##           master    #1269      +/-   ##
==========================================
- Coverage   55.98%   55.96%   -0.03%
==========================================
  Files        2604     2605       +1
  Lines       69458    69515      +57
==========================================
+ Hits        38888    38901      +13
- Misses      30570    30614      +44

Impacted Files
Coverage Δ

.../azure/mgmt/sql/operations/databases_operations.py
18.98% <ø> (ø)
:arrow_up:

...e-mgmt-sql/azure/mgmt/sql/sql_management_client.py
100% <100%> (ø)
:arrow_up:

...ure/mgmt/sql/models/sql_management_client_enums.py
100% <100%> (ø)
:arrow_up:

...ure-mgmt-sql/azure/mgmt/sql/operations/__init__.py
100% <100%> (ø)
:arrow_up:

azure-mgmt-sql/azure/mgmt/sql/models/__init__.py
100% <100%> (ø)
:arrow_up:

.../azure/mgmt/sql/models/server_connection_policy.py
55.55% <55.55%> (ø)

...ql/azure/mgmt/sql/operations/servers_operations.py
38.69% <6.97%> (-3.85%)
:arrow_down:

Continue to review full report at Codecov.

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Looks good. Changelog:
0.7.1

Added support for server connection policies
Fixed error in databases_operations.create_or_update_threat_detection_policy


How to encode the Trellis Scatter Plot example

Apologies if I'm missing something obvious (I am using hvega rather than your module, so it could well be all my own fault ...), but I can't see how to encode the https://vega.github.io/vega-lite/examples/trellis_scatter.html example (which has changed since I last looked at it). The JSON is
{
  "$schema": "https://vega.github.io/schema/vega-lite/v3.json",
  "data": {"url": "data/movies.json"},
  "mark": "point",
  "columns": 2,
  "encoding": {
    "facet": {"field": "MPAA_Rating","type": "ordinal"},
    "x": {"field": "Worldwide_Gross","type": "quantitative"},
    "y": {"field": "US_DVD_Sales","type": "quantitative"}
  }
}

and the trouble I'm having is creating the facet entry in the encoding object. I can see column and row, but facet and facetFlow don't  fit here (since they're not ... -> List LabelledSpec -> List LabelledSpec).
The closest I could find is facet3 in elm-vegalite/test-gallery/src/GalleryFacet.elm, but that follows the "old" version of this plot (which had a column field instead of facet).

Thanks for raising this. The example you give is what I think the Vega-Lite team call a wrapped facet, which then gets translated into a spec that uses the facet operator pattern. In elm-vegalite, I would normally just code with the facet operator pattern directly:
facetTwoCols : Spec
facetTwoCols =
    let
        data =
            dataFromUrl "https://vega.github.io/vega-lite/data/movies.json"

        enc =
            encoding
                << position X [ pName "Worldwide_Gross", pMType Quantitative ]
                << position Y [ pName "US_DVD_Sales", pMType Quantitative ]
    in
    toVegaLite
        [ data []
        , columns (Just 2)
        , facetFlow [ fName "MPAA_Rating", fMType Ordinal ]
        , specification (asSpec [ enc [], point [] ])
        ]

(As an aside, none of the wrapped facet examples from the  Vega-Lite documentation pages are rendered correctly in Safari although they do render in Chrome.)
To me, the facet operator patterns feels neater in that it separates the specification for each faceted plot from the faceting specification. The question is, how useful/important would it be for elm-vegalite and hvega to support the wrapped facet pattern directly?

Could create a wrappedFacet function that serves this role. For example,
enc =
    encoding
        << position X [ pName "Worldwide_Gross", pMType Quantitative ]
        << position Y [ pName "US_DVD_Sales", pMType Quantitative ]
        << wrappedFacet [fName "MPAA_Rating", fMType Ordinal]

Pros
Cons

Provides a direct mapping to Vega-Lite JSON eqivalent
Not strictly necessary as can always replicate with facet operator pattern

Slightly more compact
Increases surface area of API

Possibly easier to remember than facet operator pattern
Yet another variation of faceting that added unnecessary complexity to 'faceting' as a process

What do you think?

I still don't have a feel for the full Vega-Lite schema/capabilities, so I am wary of suggesting API changes that I don't really understand! For the examples I am comparing the JSON output of hvega to that given on the example page, which is where I noticed the problem (essentially your first row in the table above), but as you said it is more important that the functionality be available, rather than a "bit-identical" mapping to the examples.

Let's close it for now as there doesn't appear to be a compelling reason to introduce a wrapped facet function.


Add Tabs selector in devtools

Summary
Initial development setup for the privacy analysis extension setup with eslint, prettier and TypeScript configs. The extension add a "Privacy Panel" with 3 tabs as described in this issue.
https://github.com/GoogleChromeLabs/cookie-analysis-tool/assets/53055971/f05b7e27-5f95-461a-ac54-6305a120a553
Fixes #1

@ayushnirwal Lets also add some test coverage

@ayushnirwal Additionally added some more linting rules which were left in the initial setup PR


AnswerCorrectness does not pass llm to underlying faithfulness & similarity classes

Hi,
Happy with the new release and I'm eager to try out the new answer similarity and answer correctness metrics. One thing I noticed is that the llm argument you can pass to AnswerCorrectness is not used when instantiating the underlying faithfulness and similarity classes to calculate the metrics.
See below code
from ragas.metrics import AnswerCorrectness, AnswerSimilarity, Faithfulness
answer_similarity = AnswerSimilarity(llm=azure_model)
faithfulness = Faithfulness(llm=azure_model)
answer_correctness = AnswerCorrectness(llm=azure_model)

# code to create dataset, for this example called ragas_ds["eval"]

# calculating the metrics
f_score = faithfulness.score(ragas_ds["eval"])  # works!
s_score = answer_similarity.score(ragas_ds["eval"]) # works!
a_score = answer_correctness.score(ragas_ds["eval"])  # error! must provide engine or deployment_id

it does work when you pass the already instantiated faithfulness and similarity to correctness like the following:
answer_correctness = AnswerCorrectness(answer_similarity=answer_similarity, faithfulness=faithfulness)
a_score = answer_correctness.score(ragas_ds["eval"])  # works!

However, I would expect it to work as well when passing an llm as argument. I suspect the following block in answer_correctness.py needs a little tweak along the following lines
    def __post_init__(self: t.Self):
        if self.answer_similarity is None:
            self.answer_similarity = AnswerSimilarity(llm=<reference to self.llm or something like that>)
        if self.faithfulness is None:
            self.faithfulness = Faithfulness(llm=<reference to self.llm or something like that>)

this would also require a check that, if faithfulness and similarity arent passed to correctness, at least the llm argument should be passed. Hope it helps, keep up the good work!
Regards, Koen

thnks you so much for bringing this issue up - will make a patch for this asap and merge this in 😃


Github Actions CI: streamline build, export all artifacts required by testing, improve testing

This PR streamlines Github actions CI builds and makes build steps re-use the build container, saving some build time.
It also adds previously missing artifacts required by the tests. It makes artifact upload more granular, so build logs, images, dev container, and torcx packages can be downloaded individually. It exports torcx tarball and binary packages for successive re-use in the kola tests.
The change also updates the github actions kola test runner workflow to use the new, separated artifacts produced by ci.yaml.
Further, it adds a fix for the devcontainer tests. Devcontainer and bin packages used in the devcontainer tests are now served from a local temporary web server.
The change also adds the qemu_update test and provides the respective update payload.
Also, the tests now use a local torcx_manifest.json produced by ci.yaml, which points to a torcx tarball also served by the local temporary web server.
Lastly, the change adds Markdown export capabilities to tapfile_helper_lib.sh to create a test run summary suitable for ingestion by Github. Test result reports for each run are posted to PRs as comments.
How to use
Go to the "Run build" GH action, click on the "Run workflow" drop-down,  select branch "t-lo/speed-up-ci-build", and click on the green "Run workflow" button.
Testing done
Ran the workflow.
Cherry-pick and backport
This PR should be cherry-picked into all active maintenance branches (including LTS) as it would allow us to run automated builds and tests for auto-generated PRs (kernel, systemd, package refreshes).

In https://github.com/flatcar/scripts/pull/697/files I also added a new environment variable to the kola workflow. It will probably be a git conflict.

In https://github.com/flatcar/scripts/pull/697/files I also added a new environment variable to the kola workflow. It will probably be a git conflict.

Good catch @pothos, thank you! This looks like an easy merge though. I'll rebase on main when your PR was merged.

This should take care of the devcontainer test breakage: https://github.com/flatcar/flatcar-dev-util/pull/14 .

Rebased on latest main.

Included the emerge-gitclone fix from latest coreos-overlay:main.
Let's see if we can trigger a rebuild+retest by re-requesting reviews. This is one of the trigger actions for ci.yaml and is supposed to work...

Test results artifacts are 10GB each, this seems excessive. Taking a look.

Looks good! The coreos-overlay: bump ref to include emerge-gitclone fix commit needs to go before merging, as we would not want to backport this submodule reference update but have to do backports to the flatcar-MAJOR branches of coreos-overlay.

Good point. I'll split this out into a separate PR then.

A few things are still open.
TODO:

[ ] Split coreos-overlay bump into a separate PR and create respective PRs for flatcar-3033, flatcar-3549, flatcar-3510, flatcar-3374.
[ ] Use ${{ github.event.pull_request.head.sha }} for PR test runs in run-kola-tests.yaml in accordance with ci.yaml
[ ] Don't upload 10GB of OS image artifacts generated by the qemu update test.
[ ] For some reason the merged test runs TAP report is not present after testing concluded. Figure out why and archive the merged tests TAP report.

Manually triggered a new test run here: https://github.com/flatcar/scripts/actions/runs/4541175015

Everything should be in place now. Re-triggering a build/test run from scratch (by re-requesting review from pothos :) )

I was required to change quite a bit to make test results reporting usable for GitHub. Adding @krnowak as a reviewer because of his awesome bash skills and re-requested review from Kai.

I'll have a look tomorrow.

One thing to improve is that the amd64 and arm64 status should not be posted to the same comment. Now one has to klick edited to see the first one of the status reports.

One thing to improve is that the amd64 and arm64 status should not be posted to the same comment. Now one has to klick edited to see the first one of the status reports.

I'm a little confused it did that - but then I'm still learning the ropes with the add-pr-comment action.
Something we could do on our end to improve this is to merge both amd64 and arm64 into one report, using tapfile_helper_lib.sh. We could pass all TAP files to a separate merger action and use qemu_uefi (amd64) and qemu_uefi (arm64) as "vendors". This would have us end up with a report that includes all runs and would not require further changes to tapfile_helper_lib.sh. I'll have a look later.
I'll wait for Krzesimir's review (because I touched a lot of bash) and merge if I get his LGTM.

@pothos I've added test report merging in a separate job; we will now generate a single test report only, with amd64 and arm64 tests merged in (both qemu_uefi and qemu_update). I've kicked off a test to provide an example of a merged report, please have a look after it concluded: https://github.com/flatcar/scripts/actions/runs/4563204306

Merged and cherry-picked to flatcar-3033, flatcar-3549, flatcar-3510, flatcar-3374.


Add balances table and corresponding FK columns to archiver; make status column NOT NULL

Continuation of work at https://github.com/MinaProtocol/mina/pull/7674 which was closed for GitHub logistics reasons.
We need to add balance objects directly onto Extensional.Internal_command and Extensional.User_command but @psteckler will handle that after this PR (for now we have None/null where possible and dummy values in 2 places).
Testing:

Ran Rosetta test suite
Ran Rosetta CLI
Manually inspected balances table in Postgres while running (cd src/app/rosetta && ./start.sh FOREVER)

Do any of these need to be made required? In @mrmr1993's original schema (#7114 (comment)) fee_payer_balance is required. That also has status required on blocks_user_commands which I see now I missed.

Yes, status and fee-payer balance should both be required.

Do any of these need to be made required? In @mrmr1993's original schema (#7114 (comment)) fee_payer_balance is required. That also has status required on blocks_user_commands which I see now I missed.

Yes, status and fee-payer balance should both be required.

!ci-build-me

!ci-build-me

!ci-build-me

!ci-build-me

I found a bug where I was mixing up fee_transfer receiver and coinbase receiver PKs, testing fix now

I found a bug where I was mixing up fee_transfer receiver and coinbase receiver PKs, testing fix now

-                      let%bind receiver_id =
+                      let%bind fee_transfer_receiver_id =
                         Public_key.add_if_doesn't_exist
                           (module Conn)
-                          coinbase.receiver
+                          receiver_pk
                       in
                       let%bind receiver_balance_id =
                         Balance.add_if_doesn't_exist
                           (module Conn)
-                          ~public_key_id:receiver_id
+                          ~public_key_id:fee_transfer_receiver_id
                           ~balance:
                             (Option.value_exn
                                balances.fee_transfer_receiver_balance)
@@ -1246,7 +1246,7 @@ module Block = struct
                 let%bind id =
                   Coinbase.add_if_doesn't_exist (module Conn) coinbase
                 in
-                let%bind receiver_id =
+                let%bind coinbase_receiver_id =
                   Public_key.add_if_doesn't_exist
                     (module Conn)
                     coinbase.receiver
@@ -1254,7 +1254,7 @@ module Block = struct
                 let%bind receiver_balance_id =
                   Balance.add_if_doesn't_exist
                     (module Conn)
-                    ~public_key_id:receiver_id
+                    ~public_key_id:coinbase_receiver_id
                     ~balance:balances.coinbase_receiver_balance
                 in
                 let%map () =```

-                      let%bind receiver_id =
+                      let%bind fee_transfer_receiver_id =
                         Public_key.add_if_doesn't_exist
                           (module Conn)
-                          coinbase.receiver
+                          receiver_pk
                       in
                       let%bind receiver_balance_id =
                         Balance.add_if_doesn't_exist
                           (module Conn)
-                          ~public_key_id:receiver_id
+                          ~public_key_id:fee_transfer_receiver_id
                           ~balance:
                             (Option.value_exn
                                balances.fee_transfer_receiver_balance)
@@ -1246,7 +1246,7 @@ module Block = struct
                 let%bind id =
                   Coinbase.add_if_doesn't_exist (module Conn) coinbase
                 in
-                let%bind receiver_id =
+                let%bind coinbase_receiver_id =
                   Public_key.add_if_doesn't_exist
                     (module Conn)
                     coinbase.receiver
@@ -1254,7 +1254,7 @@ module Block = struct
                 let%bind receiver_balance_id =
                   Balance.add_if_doesn't_exist
                     (module Conn)
-                    ~public_key_id:receiver_id
+                    ~public_key_id:coinbase_receiver_id
                     ~balance:balances.coinbase_receiver_balance
                 in
                 let%map () =```

!ci-build-me

!ci-build-me

!ci-build-me

!ci-build-me

!approved-for-mainnet


Can not open any video

Hello,
when I want to try open any video I got an error in Kodi. In my log file I can see this:
2020-03-31 19:52:43.848 T:2402410704   ERROR: EXCEPTION Thrown (PythonToCppException) : -->Python callback/script returned the following error<--
- NOTE: IGNORING THIS CAN LEAD TO MEMORY LEAKS!
Error Type: <type 'exceptions.ValueError'>
Error Contents: time data 'Tue, 31 Mar 2020 17:50:09 GMT' does not match format '%a, %d %b %Y %H:%M:%S %Z'
Traceback (most recent call last):
File "/home/pi/.kodi/addons/plugin.video.ng/main.py", line 5, in <module>
plugin.run()
File "/home/pi/.kodi/addons/plugin.video.ng/resources/lib/plugin.py", line 275, in run
plugin.run()
File "/home/pi/.kodi/addons/script.module.routing/lib/routing.py", line 130, in run
self._dispatch(self.path)
File "/home/pi/.kodi/addons/script.module.routing/lib/routing.py", line 141, in _dispatch
view_func(**kwargs)
File "/home/pi/.kodi/addons/plugin.video.ng/resources/lib/plugin.py", line 212, in play_film
soup = ngs.get_html(url)
File "/home/pi/.kodi/addons/plugin.video.ng/resources/lib/search.py", line 133, in get_html
c.set(uri, r.content, r.headers)
File "/home/pi/.kodi/addons/script.module.cache/lib/cache.py", line 199, in set
httpdate_to_datetime(headers.get("date")),
File "/home/pi/.kodi/addons/script.module.cache/lib/cache.py", line 38, in httpdate_to_datetime
return datetime(*(time.strptime(input_date, HTTP_DATE_FORMAT)[0:6]))
File "/usr/lib/python2.7/_strptime.py", line 478, in _strptime_time
return _strptime(data_string, format)[0]
File "/usr/lib/python2.7/_strptime.py", line 332, in _strptime
(data_string, format))
ValueError: time data 'Tue, 31 Mar 2020 17:50:09 GMT' does not match format '%a, %d %b %Y %H:%M:%S %Z'
-->End of Python script error report<--

I don't if I have to old or to newest packages?
My kodi version:
$ kodi --version
18.6 Git:20200310-283bece277 Media Center Kodi
Copyright (C) 2005-2019 Team Kodi - http://kodi.tv

My python version:
$ python2.7 -V
Python 2.7.16

Do you need any more information?

@d0niek sorry don't know how I missed this! It is because NG have changed how they stream content - will have a look to see if it can be fixed. Thanks for raising this!


Fixed diff-targets in release workflow

Current dependencies on/for this PR:

main

PR #18

PR #19

PR #20   👈

This comment was auto-generated by Graphite.


[admin] Use auto-complete relations instead of selects

The relations to subnets should be displayed as auto-complete fields instead of selects, because when the database is well populated loading all the subnets would become too slow.

I am interested in working on this issue please can someone guide me on how to get started? i am new

@pacicap https://docs.djangoproject.com/en/2.1/ref/contrib/admin/#django.contrib.admin.ModelAdmin.autocomplete_fields

Thanks nemesisdesign for the reply will work on that right away


fix: 🤔 Components package is not shipping all required dependencies

Checklist

[x] I have verified the bug exists in the latest version of @synergy-design-system/components (e.g. via pnpm install @synergy-design-system/components).
[x] I can provide a simple, reproducible example of the issue (e.g. via code-sandbox or sample code, ...).
[x] I would be willing to provide a PR for this issue.

Version
The bug occurred with the following version of the component library: ANY
Component
The issue arises when using the following component(s): ALL
Environment
The issue occurred with the following operating system(s): MacOS Ventura
The issue occurred in the following browser(s): All
Description
When trying to importing the components package, there are errors about missing dependencies. The problem is that some of the required shoelace-dependencies are not provided in our package.json dependencies list. Currently, @shoelace-style/localize is missing from this list, making it a manual step to install it.
Suggestion:
This should be fixed by adding the missing dependencies (and ones we will need in the future) to the components package.

Also, when not shipping with the types key, webpack based applications using typescript may break as they are not able to find the typescript declarations via the exports map. Suggestion: Add "types": "./dist/synergy.d.ts" for better compatibility


(PUP-7554) Don't error on v3 hiera.yaml in module

A v3 hiera.yaml in the root of a module directory should be a warning,
not an error. It should be roughly equivalent to not having a hiera.yaml
file there at all. This is to allow customers who currently have
hiera.yaml files in modules to install new versions of Puppet without
having to first refactor all their modules and CI systems.

Given this fix should go into LTS (currently 4.10.2), lets retarget at stable branch.
@hlindberg / @thallgren can we get some eyes on this?
Thanks!

CLA signed by all contributors.


SNOW-1709861: table clean up uses its own cursor

Which Jira issue is this PR addressing? Make sure that there is an accompanying issue to your PR.

Fixes SNOW-NNNNNNN

Fill out the following pre-review checklist:

[ ] I am adding a new automated test(s) to verify correctness of my new code

[ ] If this test skips Local Testing mode, I'm requesting review from @snowflakedb/local-testing

[ ] I am adding new logging messages
[ ] I am adding a new telemetry message
[ ] I am adding new credentials
[ ] I am adding a new dependency
[ ] If this is a new feature/behavior, I'm adding the Local Testing parity changes.

Please describe how your code solves the related issue.

auto table clean up could be kicked off by python gc at random timing, this caused flakiness in our async query execution as the random execution of async table clean up might overwrite the status of the shared session object leading to incorrect async query id being stored.
to improve, here we create a low level new connector cursor to execute drop temp table each time to avoid side effect

successful daily precommit running without any flaky test: https://github.com/snowflakedb/snowpark-python/actions/runs/11372006647


Minizinc global constraints "not supported" in the CentOS release

Try running, for example, bobs_sale.fzn with the CentOS fzn-or-tools binary. I get
 Not supported global_cardinality_low_up_closed
*** Check failure stack trace: ***
    @     0x7f4cfc819f92  google::LogMessage::Fail()
    @     0x7f4cfc819eee  google::LogMessage::SendToLog()
    @     0x7f4cfc819872  google::LogMessage::Flush()
    @     0x7f4cfc81c919  google::LogMessageFatal::~LogMessageFatal()
    @     0x7f4cfcd3e2b6  operations_research::sat::(anonymous namespace)::CpModelP
rotoWithMapping::FillConstraint()
    @     0x7f4cfcd413fc  operations_research::sat::SolveFzWithCpModelProto()
    @           0x402e63  main
    @     0x7f4cfa818c05  __libc_start_main
    @           0x402f57  (unknown)
    @              (nil)  (unknown)

The same seems to work fine for me on Ubuntu.

Hi skosch,
I generated the CentOS binaries using docker with the latest CentOS:7 image (yesterday).
-> Could you run ldd fzn-or-tools to check if all dependencies are found ?
Also try to update your distro if possible...

bobs was created for the CP solver, not the sat solver.
You can run it will fz --use_cp_sat=false.
Unfortunately, I cannot find the original mzn file to generate it for sat.
Laurent Perron | Operations Research |<EMAIL_ADDRESS>| (33) 1 42 68 53
00
2017-12-01 11:06 GMT+01:00 Mizux<EMAIL_ADDRESS>
Hi skosch,
I generated the CentOS binaries using docker with the latest CentOS:7
image (yesterday).
-> Could you run ldd fzn-or-tools to check if all dependencies are found ?
Also try to update your distro if possible...
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Merci beaucoup, à vous deux!
First off, apologies for a mistake in my original post (I'll edit it) – it turns out I wasn't running the latest release on Ubuntu even though I thought I was. This is an issue with the latest release, not with the platform at all.
--use_cp_sat=false works. I missed the change in default from CP to SAT solver (06dcefa). I'm surprised I'm the only one for whom suddenly everything stopped working without explanation; maybe an error message beyond "not supported" would be helpful.

The constraint is supported in minizinc.
But minizinc expands each mzn file into a solver specific fzn file.
Most of the files in examples/flatzinc are CP specific. So they may not
work directly with SAT.
But they should always work if we go back to the original mzn file.
Laurent Perron | Operations Research |<EMAIL_ADDRESS>| (33) 1 42 68 53
00
2017-12-01 15:50 GMT+01:00 Sebastian Kosch<EMAIL_ADDRESS>
Merci beaucoup, à vous deux!
First off, apologies for a mistake in my original post (I'll edit it) – it
turns out I wasn't running the latest release on Ubuntu even though I
thought I was. This is an issue with the latest release, not with the
platform at all.
--use_cp_sat=false works. I missed the change in default from CP to SAT
solver (06dcefa
https://github.com/google/or-tools/commit/06dcefa8e3ffd44d02ca1e50b3bb078047abdf8c).
I'm surprised I'm the only one for whom suddenly everything stopped working
without explanation; maybe an error message beyond "not supported" would be
helpful.
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Using Vim strings (vim.bo.ft), netrw state, custom buffer-specific configs and Windows tililng problem

There is this string vim.bo.filetype

I want a section with this string.. So I edited the premade pebble config... however it just shows an empty string (right side)

Full config
return function()
    require("staline").setup({
        sections = {
            left = {
                " ", "right_sep", "-mode", "left_sep", " ",
                "right_sep", "-file_name", "left_sep", " ",
                "right_sep", "-branch", "left_sep", " ",
            },
            mid  = { "lsp" },
            right =  {
                "right_sep", "- ", vim.bo.filetype:upper(), "- ", "left_sep", " ",
                "right_sep", "-line_column", "left_sep", " ",
            }
        },
        defaults = {
            fg = "#181a1f",
            left_separator = "",
            right_separator = "",
            true_colors = true,
            line_column = "%l:%c ~ %p%% ",
        },
        mode_colors = {
            n  = "#98c379",
            i  = "#d19a66",
            ic = "#181a23",
            c  = "#61afef",
            v  = "#c678dd",
            V  = "#9a6dd1",
            s  = "#123456",
            S  = "#000000",
            t  = "#ffffff",
            r  = "#971034",
            R  = "#546576"
        },
        mode_icons = {
            n = "Normal",
            i = "Insert",
            c = "Command",
            v = "Visual",
            no = "󰋜 ",
            niI = "󰋜 ",
            niR = "󰋜 ",
            niV = "󰋜 ",
            nov = "󰋜 ",
            noV = "󰋜 ",
            ic = "󰏫 ",
            ix = "󰏫 ",
            s = "󰏫 ",
            S = "󰏫 ",
            V = "󰈈 ",
            r = "󰛔 ",
            t = " " ,
            R = " ",
        }
    });
end

Heyyo @AXVIII3,
Have you tried wrapping it inside a function?
Something like:
-- ...
            right =  {
                "right_sep", "- ", function() return vim.bo.ft:upper() end, "- ", "left_sep", " ",
                "right_sep", "-line_column", "left_sep", " ",
            }
-- ...

the - inside the function isnt implemented. But we could return a table like {"HighlighName", "string"}.
In this case, we could do something like:
--- Since "StalineFill" is the highlight for filling bg

function() return { "StalineFill", vim.bo.filetype:upper() } end

@tamton-aquib
Heyy... that worked great... thanks for your help.... and this was quick too!
Great plugin btw! I found you via the reddit post you made years ago... Still find it amazing that you made this when you were only 12years (if I'm not wrong) into vim
Also one last question I'd like to bother you with.. sometimes the plugin reverts to the base nvim status line when using Netrw...  while sometimes it doesnt.. See video... Also I noticed that you had seperate configs for nvimtree, dashboard and packer... so is it possible to create different configs for different buffertypes?

I found you via the reddit post you made years ago.

That might not be me 😅 . This plugin was made maybe 4 years ago.

sometimes the plugin reverts to the base nvim status line when using Netrw

That is strange. I dont use netrw much since i'm still on nvim-tree, but I'll check.

is it possible to create different configs for different buffertypes

Buffer specific lines can be configured via special_table opt in setup.
For example:
require("staline").setup({
    special_table = {
        -- Where "netrw" key is the filetype.
        netrw = { 'NetRW', ' ' },
    },
}

That might not be me 😅 . This plugin was made maybe 4 years ago.

Oops... Sorry.. I used to use Lualine which was tremendously slow... So I have been researching many status lines today... so I mixed up some... this is a very good plugin nonetheless... love the customizability it provides

Buffer specific lines can be configured via special_table opt in setup.

So these special tables are similar to the sections table but for specific file types?

So these special tables are similar to the sections table but for specific file types?

Hmm nope, its just a table with 2 values. The text and the icon for the specific filetype key.
(I designed this 3-4 years ago so it isnt that good 😅 )

Hmm nope, its just a table with 2 values. The text and the icon for the specific filetype key. (I designed this 3-4 years ago so it isnt that good 😅 )

Yeah I figured that one out pretty soon..... And absolutely not... this is an amazing plugins... better than anything I (or most) could write

Heyyo @AXVIII3,
I think that behaviour is because of me not taking into account the laststatus option. I started assuming everyone moved to laststatus=3(aka global statusline).
I'll check it this/next week.

@tamton-aquib
Alright! Thanks a lot for your help

I think that behaviour is because of me not taking into account the laststatus option. I started assuming everyone moved to laststatus=3(aka global statusline).

I see... I actually like the concept of laststatus = 3.... I'm switching to that

I'll check it this/next week.

Sure sure.. no worries... take your time 👍🏻
Cheers :)

So hey @AXVIII3,
Im closing this issue for now. Feel free to re-open if there are any more issues 👍🏻

Ah yes sorry,
I did fix it but forgot to push.

I have pushed a fix, can you check? @AXVIII3 (No hurry btw)

It works great with NetRw now! No need to be sorry... I'm the one who dragged you into fixing a niche issue 😅

@tamton-aquib
I had some free time, so I decided to look into the code for a bit
In staline.nvim you have this function to "create" the status line
M.get_statusline = function(status)
    if conf.special_table[vim.bo.ft] ~= nil then
        local special = conf.special_table[vim.bo.ft]
        return "%#Staline#%=" .. special[2] .. special[1] .. "%="
    end

    M.sections = {}

    local mode = vim.api.nvim_get_mode()['mode']
    local fgColor = status and conf.mode_colors[mode] or t.inactive_color
    local bgColor = status and t.bg or t.inactive_bgcolor
    local modeIcon = conf.mode_icons[mode] or "󰋜 "

    local f_name = t.full_path and '%F' or '%t'
    -- TODO: original color of icon
    local f_icon = util.get_file_icon(vim.fn.expand('%:t'), vim.fn.expand('%:e'))
    local edited = vim.bo.mod and t.mod_symbol or ""
    -- TODO: need to support b, or mb?
    local size = ("%.1f"):format(vim.fn.getfsize(vim.api.nvim_buf_get_name(0))/1024)

    call_highlights(fgColor, bgColor)

  .
  .
  .
  .
  .
    end
    ```

The if statement in the first few lines blocks the execution of the `call_highlights()` function which comes later if the buffer is something like the dashboard or netrw. So I pushed that function above the if statement. This seemed to fix my issue. But I am not sure if any other issues would be created. What do you think about this?

```lua
    local mode = vim.api.nvim_get_mode()['mode']
    local fgColor = status and conf.mode_colors[mode] or t.inactive_color
    local bgColor = status and t.bg or t.inactive_bgcolor

    call_highlights(fgColor, bgColor)

    if conf.special_table[vim.bo.ft] ~= nil then
        local special = conf.special_table[vim.bo.ft]
        return "%#Staline#%=" .. special[2] .. special[1] .. "%="
    end

    M.sections = {}

    local modeIcon = conf.mode_icons[mode] or "󰋜 "

    local f_name = t.full_path and '%F' or '%t'
    -- TODO: original color of icon
    local f_icon = util.get_file_icon(vim.fn.expand('%:t'), vim.fn.expand('%:e'))
    local edited = vim.bo.mod and t.mod_symbol or ""
    -- TODO: need to support b, or mb?
    local size = ("%.1f"):format(vim.fn.getfsize(vim.api.nvim_buf_get_name(0))/1024)

    call_highlights(fgColor, bgColor)

Reopened just to gain your attention 😉 WIll close soon

Hi @AXVIII3,
Could you open a new issue for this? 😅
Since the original issue and this new issue seems to be different, it would be better to track in a new issue.

Sure!


Multiple Attachment Support

Seems like there is only a field for one attachment (and only one stored), despite storing how many attachments a packet has. Was there a reason for this @1c3t3a or is it just not implemented yet?
TODO:

[ ] Change binary data structure to a list
[ ] Consume as many packets as required

It's actually not yet implemented. That's defenetly something that should be added. In the current setup this would lead to just having the last binary payload as binary_payload of a packet.


Add spm support

A nicer package manager: http://spmjs.io
Documentation: http://spmjs.io/documentation
http://spmjs.io/package/chart.js

It is a package manager based on Sea.js which is a popular module loader in China.
spmjs focus in browser side. We supply a complete lifecycle managment of package by using spm.

If you need ownership of chart.js in spmjs, I can add it for your account after signing in http://spmjs.io.

spmjs/spm#781

I have no objections to this for 1.1

+1


TD-111 - migrate from pipenv to poetry

deployment and review apps:

add https://github.com/moneymeets/python-poetry-buildpack.git as buildpack

@amureki this is a  first experiment (also it was the last app using pipenv),
could you review it?
Then we can see


feat(ui): add streamer mode setting to hide sensitive informations

Fixes #3856

Pull Request Test Coverage Report for Build<PHONE_NUMBER>7
Details

0 of 5 (0.0%)  changed or added relevant lines in 1 file are covered.
No unchanged relevant lines lost coverage.
Overall coverage decreased (-0.006%) to 21.023%

Changes Missing Coverage
Covered Lines
Changed/Added Lines
%

src/stores/base.js
0
5
0.0%

Totals

Change from base Build<PHONE_NUMBER>8:
-0.006%

Covered Lines:
3899

Relevant Lines:
19726

💛  - Coveralls


check if attrs defined before using

[x] prevents a nilPointer in coopr_packages when using an OS without default attributes assigned

lgtm

Bump the patch level on this cookbook before merging.


solo: Inject right environment in integration test

Instead of looking at DOCKER_HOST and stuff from the environment of the
test runner, we need to be getting it from the environment of the host on
which the helios-solo job is run.
This fixes test failures when running HeliosSoloIT against a remote
Helios cluster (as opposed to a local helios-solo instance).

LGTM. I'm also checking i can mvn package locally

Check out the latest update. I realized that getEnvironment() doesn't actually return what I think it does. It returns the --env flags that are set when starting the Helios agent, not the environment variables for the Helios agent itself.
So to get the Docker host and cert path from the agent, I'm now having it explicitly report those.

So much yak shaving.


Reinstate numba in setup.py because why was it removed

Is numba actually installable via pip?
I also had some trouble with the openmm dependency in setup.py because when
a version constraint is used, pip doesn't recognize the conda installed
version.


Increase customizability - JSON

Is your feature request related to a problem? Please describe.
If the game required different number of guesses, disabled recommendations or otherwise, these currently have to be manipulated in the main codebase, which is risky, complex and inefficient.
Describe the solution you'd like
A goldle-config.json could be used to add parameters that can customize the Goldle game for the player.
A few starting parameters could be:

Number of guesses
Recommendations active?

This Issue is closed with PR #76.


Rainbow Brush - Test with more people (thanks to Discord's Mayron#6196, PureDarkRain#9449 and Jumble, Halloween Edition#1092 for testing it :) )

I have the advantage of knowing the percentages of the relic and how they change, so I'm heavily biased here.
So, gonna ask for some trustable people to test the relic:

(Mayron#6196) (PureDarkRain#9449) Test with one char, using Rainbow Brush from Act 1, without the Starting relic.
(Jumble, Halloween Edition#1092) Test with one char + modded chars loaded, using Rainbow Brush as the Boss Relic chosen at the end of Act 1.

Feedback:

Gk: too much text, arbitrary limitations.
Mayron: cut the cost of all cards created by one. Also, 7/10 cards were Curses. .-.
JohnnyBazooka: cut the cost of all cards created by one.

What to do:

Remove "bad card enforcing"? (tentative change)
Reduce cost of all made cards by one.
Check Hubris' Sloth to see how I can recognize it as a Curse.

Done.


Google maps instead of apple maps

Any way I can use this with google maps instead of apple maps?

Yeah, but this lib highly tied with Apple Maps. You can send PR though.
On Mon, Jun 25, 2018 at 6:57 PM Hardik Amal<EMAIL_ADDRESS>wrote:

Any way I can use this with google maps instead of apple maps?
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[Google Drive] get format/download source even the video still processing

Checklist

[X] I'm reporting a site feature request
[X] I've verified that I'm running yt-dlp version 2022.01.21. (update instructions)
[X] I've checked that all provided URLs are alive and playable in a browser
[X] I've searched the bugtracker for similar issues including closed ones. DO NOT post duplicates
[X] I've read the guidelines for opening an issue
[ ] I've read about sharing account credentials and I'm willing to share it if required

Region
No response
Example URLs
https://drive.google.com/file/d/1Yu3I73IWqzEX4GzfxpLZ-AFhE6nQfVWk/view
Description
hello, can you make get source format for download? even that video is still processing by google drive.
Verbose log
C:\Users\Administrator>yt-dlp -Uv https://drive.google.com/file/d/1Yu3I73IWqzEX4GzfxpLZ-AFhE6nQfVWk/view
[debug] Command-line config: ['-Uv', 'https://drive.google.com/file/d/1Yu3I73IWqzEX4GzfxpLZ-AFhE6nQfVWk/view']
[debug] Encodings: locale cp1252, fs utf-8, out utf-8, err utf-8, pref cp1252
[debug] yt-dlp version 2022.01.21 [f20d607] (win_exe)
[debug] Python version 3.8.10 (CPython 64bit) - Windows-10-10.0.19042-SP0
[debug] exe versions: ffmpeg 5.0-full_build-www.gyan.dev (setts), ffprobe 4.4.1-full_build-www.gyan.dev, phantomjs 2.1.1
[debug] Optional libraries: Cryptodome, mutagen, sqlite, websockets
[debug] Proxy map: {}
Latest version: 2022.01.21, Current version: 2022.01.21
yt-dlp is up to date (2022.01.21)
[debug] [GoogleDrive] Extracting URL: https://drive.google.com/file/d/1Yu3I73IWqzEX4GzfxpLZ-AFhE6nQfVWk/view
[GoogleDrive] 1Yu3I73IWqzEX4GzfxpLZ-AFhE6nQfVWk: Downloading webpage
ERROR: [GoogleDrive] 1Yu3I73IWqzEX4GzfxpLZ-AFhE6nQfVWk: We're processing this video. Please check back later.
  File "yt_dlp\extractor\common.py", line 612, in extract
  File "yt_dlp\extractor\googledrive.py", line 176, in _real_extract

C:\Users\Administrator>yt-dlp -F https://drive.google.com/file/d/1Yu3I73IWqzEX4GzfxpLZ-AFhE6nQfVWk/view
[GoogleDrive] 1Yu3I73IWqzEX4GzfxpLZ-AFhE6nQfVWk: Downloading webpage
ERROR: [GoogleDrive] 1Yu3I73IWqzEX4GzfxpLZ-AFhE6nQfVWk: We're processing this video. Please check back later.

C:\Users\Administrator>yt-dlp -f source https://drive.google.com/file/d/1Yu3I73IWqzEX4GzfxpLZ-AFhE6nQfVWk/view
[GoogleDrive] 1Yu3I73IWqzEX4GzfxpLZ-AFhE6nQfVWk: Downloading webpage
ERROR: [GoogleDrive] 1Yu3I73IWqzEX4GzfxpLZ-AFhE6nQfVWk: We're processing this video. Please check back later.

@pukkandan can you look it? i can't download source/original video.
C:\Users\Administrator>youtube-dl -vf source "https://drive.google.com/file/d/1R7GSWmmZEMUMDdLv6cVWtKCrFUOTzBxA/view"
[debug] Command-line config: ['-vf', 'source', 'https://drive.google.com/file/d/1R7GSWmmZEMUMDdLv6cVWtKCrFUOTzBxA/view']
[debug] Encodings: locale cp1252, fs utf-8, out utf-8, err utf-8, pref cp1252
[debug] yt-dlp version 2022.02.04 [c1653e9] (win_exe)
[debug] Python version 3.8.10 (CPython 64bit) - Windows-10-10.0.19042-SP0
[debug] exe versions: ffmpeg 5.0-full_build-www.gyan.dev (setts), ffprobe 4.4.1-full_build-www.gyan.dev, phantomjs 2.1.1
[debug] Optional libraries: Cryptodome, mutagen, sqlite, websockets
[debug] Proxy map: {}
[debug] [GoogleDrive] Extracting URL: https://drive.google.com/file/d/1R7GSWmmZEMUMDdLv6cVWtKCrFUOTzBxA/view
[GoogleDrive] 1R7GSWmmZEMUMDdLv6cVWtKCrFUOTzBxA: Downloading webpage
ERROR: [GoogleDrive] 1R7GSWmmZEMUMDdLv6cVWtKCrFUOTzBxA: Unable to play this video at this time. The number of allowed playbacks has been exceeded. Please try again later.
  File "yt_dlp\extractor\common.py", line 612, in extract
  File "yt_dlp\extractor\googledrive.py", line 176, in _real_extract

@pukkandan hello, can you make the yt-dlp download url as directlink without detect format?
from yt-dlp command
C:\Users\Administrator>youtube-dl -v --no-check-formats "https://drive.google.com/uc?id=1uSCsStYuZBCYsv1xNMwkc8N1uprGzu2x&export=download&confirm=t" --cookies "cookies.txt"
[debug] Command-line config: ['-v', '--no-check-formats', 'https://drive.google.com/uc?id=1uSCsStYuZBCYsv1xNMwkc8N1uprGzu2x&export=download&confirm=t', '--cookies', 'cookies.txt']
[debug] Encodings: locale cp1252, fs utf-8, out utf-8, err utf-8, pref cp1252
[debug] yt-dlp version 2022.02.04 [c1653e9] (win_exe)
[debug] Python version 3.8.10 (CPython 64bit) - Windows-10-10.0.19042-SP0
[debug] exe versions: ffmpeg 5.0-full_build-www.gyan.dev (setts), ffprobe 4.4.1-full_build-www.gyan.dev
[debug] Optional libraries: Cryptodome, mutagen, sqlite, websockets
[debug] Proxy map: {}
[debug] [GoogleDrive] Extracting URL: https://drive.google.com/uc?id=1uSCsStYuZBCYsv1xNMwkc8N1uprGzu2x&export=download&confirm=t
[GoogleDrive] 1uSCsStYuZBCYsv1xNMwkc8N1uprGzu2x: Downloading webpage
ERROR: [GoogleDrive] 1uSCsStYuZBCYsv1xNMwkc8N1uprGzu2x: We're processing this video. Please check back later.
  File "yt_dlp\extractor\common.py", line 612, in extract
  File "yt_dlp\extractor\googledrive.py", line 176, in _real_extract

from aria2c command, event google drive still process encoding the video
C:\Users\Administrator>aria2c "https://drive.google.com/uc?id=1uSCsStYuZBCYsv1xNMwkc8N1uprGzu2x&export=download&confirm=t" --load-cookies="cookies.txt"

03/02 13:17:56 [NOTICE] Downloading 1 item(s)
[#01dcab 0B/0B CN:1 DL:0B]
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Bug: VSCode Intellisense doesn't show custom type properties via dot notation after lambda function

Bicep version
VSCode Bicep extension: 0.13.1
Describe the bug
The Bicep extension page boasts of dot-property access for Bicep templates - namely that if I have a resource or module, I can hit period after the identifier and it'll show a list of valid properties I can type next. This is very useful in ensuring the properly cased-named values are used for properties.
This works fine when custom types are used (today in an experimental state) and again, helps in eliminating possible errors at development time.
However, if I pass a custom typed object through a lambda function (e.g. to assign to a variable within a module), I lose all this Intellisense typing on everything downstream of that operation. It doesn't matter if I create an array via map or filter or a single object via first, all downstream uses of this variable lack any of the useful dot notation I've come to rely on for other property access.
To Reproduce
Sample (real-world) bicep template to reproduce issue:
@description('Used to identify a Key Vault and where it\'s deployed to')
type keyVaultIdentifier = {
  @description('The name of the Key Vault')
  name: string
  @description('The ID of the subscription the Key Vault is associated with')
  subscriptionId: string
  @description('The name of the resource group the Key Vault is associated with')
  resourceGroupName: string
}

@description('Used to identify the details of a specific certificate')
type certificateMapping = {
  @description('The subject value of the certificate')
  subject: string
  @description('The name of the secret in the Key Vault')
  secretName: string
  @description('The thumbprint of the certificate')
  thumbprint: string
  @description('The identifier of the Key Vault instance')
  keyVault: keyVaultIdentifier
}

param CertificateSubjects certificateMapping[]
param ManagementEndpointSubject string

var endpointCertificate = first(filter(CertificateSubjects, c => c.subject == ManagementEndpointSubject))

//Any downstream use of endpointCertificate lacks any of the expected properties:
// > subject
// > secretName
// > thumbprint
// > keyVault (itself a custom type)

It looks like the return type for first can be improved. It will currently return a value of type any, which doesn't provide any completions:

@jeskew I'd like to take this up. Is there an example in the code base of another built-in function that returns a custom type that I could reference?

@levimatheri Poking around myself in some of @jeskew 's recent commits, it seems the offending code is here. I would refer you to this example of another lambda keyword (map) in the same class.

@levimatheri A function can be assigned a dynamic return type via the WithReturnResultBuilder method on a function overload builder (such as on this line of the flatten definition).
Looking at the implementation of first in ARM, only the first overload of first needs to be updated. ARM will raise an error if the argument provided is not a string or array, so the cases that need to be handled are:

If the argument is of type any, the return type will need to be determined at runtime, so return any.
If the argument is an array, return the type of an item within the array (e.g., string[] => string). If the array is empty at runtime, first() will return null, so it should probably actually be string[] => string | null.


feat(theme-default): heading-sidebar

Before submitting the PR, please make sure you do the following

[X] Read the Contributing Guidelines.
[X] Provide a description in this PR that addresses what the PR is solving. If this PR is going to solve an existing issue, please reference the issue (e.g. close vuepress/core#123).

What is the purpose of this pull request?

[ ] Bug fix
[X] New feature
[ ] Other

Description
The auto sidebar feature of the default theme is quite useful, especially in cases where the pages are autogenerated documentation from something like TypeDoc.
Currently you can only use the auto feature on all pages or manually specify the sidebar for every page. It would be great if you could turn the feature on per page as well.
This PR also contains the initial setup of the e2e tests for the default theme and a couple e2e tests of this new feature.
Screenshots

Before
After
closes #17

I created a docs PR for these changes.  https://github.com/vuepress/docs/pull/9


Support for ISO datetime strings in casting Timex.Ecto.Date

Hi there,
Thanks for the great library. I was wondering if there would be interest in a PR to expand how strings are cast by the Timex.Ecto.Date type. In particular, when dealing with JSON, it would be convenient to accept ISO datetime strings as well as well as dates, and simply discard the time portion (similar to this in Ecto)
If you'd be up for adding this functionality, I'd be happy to submit a PR.

I'm noticing that #13 is an open PR for this issue... so I'm closing this in favor the PR.

@tokafish Just FYI, #13 has been merged, I'll publish a new version today.


Warning with React 0.14.0

Warning: cloneWithProps(...) is deprecated. Please use React.cloneElement instead.
Warning: Tab.getDOMNode(...) is deprecated. Please use ReactDOM.findDOMNode(instance) instead.

For the second warning there is already a PR #30

These warnings will be fixed with the next release.


Style

Moved things around, no functional changes

/api -> apps
/apps -> apps/impl
/store -> apps/store

Small style fixes

Codecov Report

Merging #33 (b8ee67b) into lev-fullApp (9b615a3) will decrease coverage by 0.03%.
The diff coverage is 1.86%.

@@              Coverage Diff               @@
##           lev-fullApp     #33      +/-   ##
==============================================
- Coverage         8.33%   8.30%   -0.04%
==============================================
  Files               15      16       +1
  Lines              552     554       +2
==============================================
  Hits                46      46
- Misses             505     507       +2
  Partials             1       1

Impacted Files
Coverage Δ

server/apps/app.go
0.00% <ø> (ø)

server/apps/binding.go
0.00% <ø> (ø)

server/apps/context.go
0.00% <0.00%> (ø)

server/apps/impl/client.go
0.00% <0.00%> (ø)

server/apps/impl/expand.go
0.00% <0.00%> (ø)

server/apps/impl/install.go
0.00% <0.00%> (ø)

server/apps/impl/provision.go
0.00% <0.00%> (ø)

server/apps/impl/proxy.go
0.00% <0.00%> (ø)

server/apps/impl/service.go
0.00% <0.00%> (ø)

server/apps/impl/subscriptions.go
0.00% <0.00%> (ø)

... and 6 more

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update 9b615a3...b8ee67b. Read the comment docs.


Use a hash router

This allows us to ship the web app as a static site that can be hosted by anyone anywhere, without any special server config required to support URL rewriting.

lmao

delete firebase config y/n

we do have some dynamic imports that i think need vite.config update


Try to fix intermittent CI failures in sslapitest

Checklist

[ ] documentation is added or updated
[x] tests are added or updated

That seems to be unrelated, the assertion is not violated in the test_multiblock_write.
This PR is mostly targeting the test_pluggable_signature test case which fails very often,
this test case uses the "xorgroup" dummy algorithm from the tls-provider, and my theory is,
the problem is triggered because the xorgroup id is different between client and server
handshake.

@nhorman, @t-j-h, @romen please review this - it's very similar to #23765 which you already approved with the addition of an assert. It also correctly specifies the target branches.
Note though that I think a separate PR for 3.1/3.0 will be required since it does not cherry-pick cleanly there.

This pull request is ready to merge

Merged to master and 3.2 branches.  Thanks!


Different results in Python and  CPP

Hello,
Thanks a lot for writing this repo. I am trying to replicate this in my system but the results from python inference and the cpp inference that you have provided varies. Can you please help?
Please find the inference values from python and c++ respectively,
Python :
[[1.8702552e-07 3.4026144e-08 1.3844835e-05 1.8523424e-06 1.1318865e-08
1.6930930e-09 4.8130622e-10 9.9993885e-01 1.3360306e-08 4.5223489e-05]]
C++ :
Confidence of this number being 0: 3.81422%
Confidence of this number being 1: 12.9407%
Confidence of this number being 2: 10.4153%
Confidence of this number being 3: 6.08555%
Confidence of this number being 4: 16.9787%
Confidence of this number being 5: 7.98519%
Confidence of this number being 6: 17.4515%
Confidence of this number being 7: 12.9483%
Confidence of this number being 8: 9.55298%
Confidence of this number being 9: 1.82757%
Best,
Sanga

Hello, this project was a toy project for me to learn C++. As you can see, it's been years since the last commit. I don't recommend using it!

Ah ok. Thanks a lot for the info.


Add a way to override channel arguments for server creation

Can one of the admins verify this patch?

Can one of the admins verify this patch?


SwiftMailer::setReturnPath() in yii\mail\MessageInterface?

As I can see there is no way to set custom Return-Path by built-in yii2-swiftmailer or how to do that?

You can access the Swift message object,
$headers = $message->getSwiftMessage()->getHeaders();
$headers->addPathHeader('Return-Path', 'person@example.org');

http://swiftmailer.org/docs/headers.html#path-headers
Or you could also extend yii\swiftmailer\Message to add the method yourself.
'components' => [
//....
    'mailer' => [
        'class' => 'yii\swiftmailer\Mailer',
        'messageClass' => 'myMessageClass'
    ],
//...
],


[back end] add concert to user

when adding concert, save to user db and display on profile/feed

Half finished! Now just need to get it to display on profile/feed

2/3 finished. Now to get them to display to the profile page

Finished. Going to close this, but open up a new issue with the problems that came up while doing this


Tabular: Added initial daal4py support

Issue #, if available:
Description of changes:
Added daal4py support to KNN: https://intelpython.github.io/daal4py/
This results in an average of 25x speed-up of KNN inference on intel CPUs compared to native sklearn with minimal to no accuracy loss. Combined with the optimized LOO stacking strategy, mainline with daal will be 250x+ faster than v0.1.0 for KNN models.
All that is needed to get the speedup is to install daal4py:
pip install autogluon
pip install daal4py

Benchmark result (only KNN models):
valid_tasks: 550
                                           framework  time_train_s  metric_error  time_infer_s  bestdiff  loss_rescaled  time_infer_s_rescaled      rank  rank=1_count  rank=2_count  rank=3_count  rank>3_count  error_count
0  AutoGluon_bestquality_1h_2021_03_20_knn_v3_KNe...      0.959381      1.196857     13.041495  0.009988       0.235755              25.233313  2.009091           101           310            68            71           54
1  AutoGluon_bestquality_1h_2021_03_31_daal_KNeig...      0.346045      1.209153      0.790459  0.015466       0.288210               1.054045  2.068182           132           259            39           120           11
2  AutoGluon_bestquality_1h_2021_03_20_knn_v3_KNe...      0.912026      1.191187     13.051080  0.030370       0.709032              25.411448  2.907273            74            88            80           308           42
3  AutoGluon_bestquality_1h_2021_03_31_daal_KNeig...      0.523245      1.195930      0.789863  0.033754       0.751086               1.241980  3.015455            29           104           118           299            2

Very slight accuracy loss probably from floating points, but a 25x speedup on inference. This speed-up is even larger on big datasets, where non-daal didn't even train KNN due to running out of time.
By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.

Job PR-1049-1 is done.
Docs are uploaded to http://autogluon-staging.s3-website-us-west-2.amazonaws.com/PR-1049/1/index.html

How are users supposed to know to install daal4py?

Hi @Innixma, I'm one of daal4py maintainers. I appreciate that you are adding this into the project, I'm happy to help you with the activity :)
You mentioned that you have some issues with kNN and RF, it's interesting. What version of daal4py did you use? These issues are expected to be solved in the latest version 2021.2. Do you have some tests/benchmarks to reproduce the issues?
Also, I want to notice that we are extracting scikit-learn acceleration from daal4py to Intel Extension for Scikit-learn package. At the moment daal4py contains such functionality, but it will be removed in the future and will be available in Intel Extension for Scikit-learn only. There is no difference in functionality and performance between daal4py and Intel Extension for Scikit-learn (simply uses daal4py underneath). I prefer to use this package instead of daal4py, hopefully, it's easy to do :)
Installation:
pip install scikit-learn-intelex

Also, the main feature of Intel Extension for Scikit-learn - you don't need to change existing Scikit-learn code, but just call patch_sklearn function. Without arguments (patch_sklearn()) it patches all Scikit-learn algorithms which can be accelerated, but we can specify concrete estimators what we want to accelerate. The patches are just runtime, they don't change scikit-learn package permanently. It might look like this:
from sklearnex import patch_sklearn
patch_sklearn("knn_classifier")
patch_sklearn("knn_regressor")
from sklearn.neighbors import KNeighborsClassifier as _KNeighborsClassifier, KNeighborsRegressor as _KNeighborsRegressor

List of available algorithm to patch this way

Hi @SmirnovEgorRu,
Thanks to you and your team for the excellent package! I will have to take a look at scikit-learn-intelex and likely update this PR to use it.
I have a few questions regarding DAAL:

Say I wanted to train 2 models side-by-side, one with DAAL and one without. I'm assuming I would want to avoid monkey-patching in this case, is that correct?
Can I convert a trained DAAL model to a sklearn model? (say I am deploying to a machine that lacks DAAL's requirements)
What is the best way to check within python if the machine satisfies the requirements for DAAL acceleration? (How do I tell if I have a valid CPU?)
Is there a way to dynamically install scikit-learn-intelex from pip install autogluon iff the user has a valid intel CPU? If not, would you recommend an optional dependency option such as pip install autogluon[daal]?
Is there any benefit of scikit-learn-intelex when running on alternative CPUs? Does it error?

I also have a few questions regarding the acceleration:

I noticed random forest used sklearn version for predict_proba. Why is this? Generally, we only call predict_proba in AutoGluon for the individual models as we do a variety of post-processing steps such as weighted averaging.
When I did a raw test of model.predict on DAAL vs non-DAAL random-forest, DAAL was slower. Is this expected? Do you have an example notebook / script that shows DAAL speeding up RF? This would be of great interest to us.
I noticed that KNN had slightly worse accuracy when accelerated with DAAL (see main comment). Is this expected? Is it due to floating point error?

@Innixma, thank you!

Is there any benefit of scikit-learn-intelex when running on alternative CPUs? Does it error?

It works on any x86 architectures, so it works on AMD as well as on Intel CPUs and also provides performance gains, but expected to be better on Intel CPUs (we have AVX-512 branches of code and other staff). ARM isn't supported and we don't provide built packages for this, so we just can't import Intel Extension for Scikit-learn package on ARM.
Does this behavior satisfy your requirements?

What is the best way to check within python if the machine satisfies the requirements for DAAL acceleration? (How do I tell if I have a valid CPU?)

Our patching is a "seamless way" to accelerate scikit-learn. If we don't accelerate some Scikit-learn functionality or don't support some algorithms parameters - we just use original Scikit-learn implementation inside the patching. So, all Scikit-learn algorithms work always as expected, but acceleration is achieved if parameters allow this.
We have this table which show what we are able to accelerate and some limitations.
To check an algorithm was accelerated or not - we provide a env variable (SKLEARNEX_VERBOSE=INFO) which enables printing info such:
INFO: sklearn.cluster.DBSCAN.fit: uses Intel(R) oneAPI Data Analytics Library solver
or
INFO: sklearn.cluster.DBSCAN.fit: uses original Scikit-learn solver

Do you want to know what implementation is called exactly in python code rather than printing?

Can I convert a trained DAAL model to a sklearn model? (say I am deploying to a machine that lacks DAAL's requirements)

At the moment - no. If you use pickle to serialize an estimator - it's expected to have Intel Extension for Scikit-learn on deploy stage as well. Is it critical?

Is there a way to dynamically install scikit-learn-intelex from pip install autogluon iff the user has a valid intel CPU? If not, would you recommend an optional dependency option such as pip install autogluon[daal]?

If you split python packages to x86 and ARM specific: the extension works on any x86 architectures and can be used installed for this for x86 packages only.
Instead of autogluon[daal] I think better to use autogluon[skex] or autogluon[onedal]. Because oneDAL is a new name of DAAL and extend it to run this on Intel GPUs.

I noticed random forest used sklearn version for predict_proba. Why is this? Generally, we only call predict_proba in AutoGluon for the individual models as we do a variety of post-processing steps such as weighted averaging.

We want to add patching of predict_proba in the next release :)
To better answer on questions about kNN and RF - let me provide run some benchmarks and provide results. But my expectation is that mentioned issues have been solved in version 2021.2 recently released. The same for training 2 models side-by-side, let me think how to better to do it.

@SmirnovEgorRu
I've created a script which shows the behavior I was seeing in regards to performance for random forest. I also tried to create a Colab notebook, but for some reason the notebook failed to detect daal4py / sklearnex.
From the output below, the difference in score between DAAL and sklearn random forest is much larger than what would be caused by chance. Also, logistic regression appears to not use DAAL during training.
Output (ran on a p3.2xlarge, not using GPU, latest versions of daal/sklearnex/sklearn):
Sklearn RF (Native):
Score:             0.81
Training Time (s): 1.4285519123077393
Infer Time (s):    0.20345067977905273

Sklearn RF (DAAL):
INFO:root:sklearn.ensemble.RandomForestClassifier.fit: uses Intel(R) oneAPI Data Analytics Library solver
INFO:root:sklearn.ensemble.RandomForestClassifier.predict: uses Intel(R) oneAPI Data Analytics Library solver
Score:             0.7842
Training Time (s): 0.6954841613769531
Infer Time (s):    0.09650731086730957

Sklearn LR (Native):
Score:             0.704
Training Time (s):<PHONE_NUMBER>6080017
Infer Time (s):    0.0036160945892333984

Sklearn LR (DAAL):
INFO:root:sklearn.linear_model.LogisticRegression.predict: uses Intel(R) oneAPI Data Analytics Library solver
Score:             0.704
Training Time (s):<PHONE_NUMBER>985199
Infer Time (s):    0.00391840934753418

Sklearn KNN (Native):
Score:             0.759
Training Time (s): 0.0025246143341064453
Infer Time (s):    5.370389938354492

Sklearn KNN (DAAL):
INFO:root:sklearn.neighbors.KNeighborsMixin.kneighbors: uses Intel(R) oneAPI Data Analytics Library solver
INFO:root:sklearn.neighbors.KNeighborsClassifier.predict: uses Intel(R) oneAPI Data Analytics Library solver
Score:             0.759
Training Time (s): 0.005175352096557617
Infer Time (s):    0.07428693771362305

In regards to your comments:

Does this behavior satisfy your requirements?

Yes, we don't expect ARM support yet, and it seems most other CPUs should be supported.
Regarding How do I tell if I have a valid CPU?, it seems the previous answer that sklearnex works on nearly all CPU types is enough to answer that question, I don't think we need to check if they have an ARM CPU.

At the moment - no. If you use pickle to serialize an estimator - it's expected to have Intel Extension for Scikit-learn on deploy stage as well. Is it critical?

This is fine. The fact that daal works on most CPUs means this isn't much of a concern.

We want to add patching of predict_proba in the next release :)

Great! Are you also planning to add ExtraTrees support? The sklearn models we most frequently use are RF, KNN, and XT (ExtraTrees). Also, sklearn now has a MLP model. Are there any plans for CPU acceleration of the MLP model in sklearn?

@jwmueller

How are users supposed to know to install daal4py?

They aren't yet. This is the initial PR adding support, we'd want to do more benchmarking and add more algorithms before mentioning to users. It is possible this could be added to the default installation.

Job PR-1049-2 is done.
Docs are uploaded to http://autogluon-staging.s3-website-us-west-2.amazonaws.com/PR-1049/2/index.html

Job PR-1049-3 is done.
Docs are uploaded to http://autogluon-staging.s3-website-us-west-2.amazonaws.com/PR-1049/3/index.html

@Innixma, I reproduced the issue with RF. It's unexpected. I ran my several benchmarks for RF and accuracy is similar to scikit-learn. Let me investigate the case more precisely. Results what I have on my benchmarks:

Dataset
Original Scikit-learn, accuracy
Intel Extesnion for Scikit-learn, accuracy
Ratio

ijcnn
99.515
99.460
0.9994

a9a
82.967
82.956
0.9999

gisette
95.000
95.500
1.0053

klaverjas
88.027
88.011
0.9998

connect
82.564
82.889
1.0039

mnist
95.770
95.880
1.0011

sensit
84.396
84.441
1.0005

skin_segmentation
99.929
99.929
1.0000

codrnanorm
98.328
98.230
0.9990

Average

1.0010045

About Logistic Regression acceleration. I used this code to accelerate Scikit-learn and if works as expected (training is optimized):
from sklearnex import patch_sklearn
patch_sklearn("logistic")
from sklearn.linear_model import LogisticRegression as LogisticRegression # estimator is optimized now
fit_model(LogisticRegression, **fit_args)

To enable default behavior of scikit-learn back - you can use:
from sklearnex import unpatch_sklearn
unpatch_sklearn()                                   # or unpatch_sklearn("logistic") for log reg only
from sklearn.linear_model import LogisticRegression # re-importing to use original Scikit-learn
fit_model(LogisticRegression, **fit_args)

Great! Are you also planning to add ExtraTrees support? The sklearn models we most frequently use are RF, KNN, and XT (ExtraTrees). Also, sklearn now has a MLP model. Are there any plans for CPU acceleration of the MLP model in sklearn?

ExtraTrees were planned for the second part of the year, MLP is out of plans at the moment. But we are able re-prioritize plans.  My understanding that ExtraTrees are quite fast already (if compare with RF for example). Are these algorithms hotspots in your automl in terms of execution time?
Also, we have quite fast SVM algorithm. It's not represented in your framework, but anyway it's powerful algorithm. This blog contains some insights of our implementation.

@SmirnovEgorRu, regarding the patching, I believe the re-import of LogisticRegression after unpatching does not actually have any effect, as the previously imported LogisticRegression was updated. my understanding is:
from sklearnex import patch_sklearn
patch_sklearn("logistic")
from sklearn.linear_model import LogisticRegression # estimator is optimized now
model_daal = fit_model(LogisticRegression, **fit_args)
from sklearnex import unpatch_sklearn
unpatch_sklearn("logistic")
from sklearn.linear_model import LogisticRegression # re-importing to use original Scikit-learn
model_sklearn = fit_model(LogisticRegression, **fit_args)

# model_daal is accelerated
# model_sklearn is not accelerated
# this is ideal

However,
from sklearnex import patch_sklearn
patch_sklearn("logistic")
from sklearn.linear_model import LogisticRegression # estimator is optimized now
unpatch_sklearn("logistic") # I want to limit the scope of patching, for example maybe I don't want to init the model right away.
# estimator is no longer optimized despite not re-importing
model_daal = fit_model(LogisticRegression, **fit_args)
from sklearn.linear_model import LogisticRegression # re-importing to use original Scikit-learn
model_sklearn = fit_model(LogisticRegression, **fit_args)

# model_daal is not accelerated
# model_sklearn is not accelerated
# this is not ideal

A code example of this:
from sklearn.linear_model import LogisticRegression

print('Sklearn LR (Native):')
fit_model(LogisticRegression, **fit_args)

patch_sklearn('logistic')
from sklearn.linear_model import LogisticRegression

print('Sklearn LR (DAAL):')
fit_model(LogisticRegression, **fit_args)

print('Sklearn LR 2 (DAAL):')
fit_model(LogisticRegression, **fit_args)

unpatch_sklearn('logistic')
print('Sklearn LR 3 (Native -> Should be DAAL):')
fit_model(LogisticRegression, **fit_args)

from sklearn.linear_model import LogisticRegression
print('Sklearn LR 4 (Native):')
fit_model(LogisticRegression, **fit_args)

and output:
Sklearn LR (Native):
Score:             0.7256
Training Time (s): 6.198185443878174
Infer Time (s):    0.0037772655487060547

Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)
Sklearn LR (DAAL):
INFO:root:sklearn.linear_model.LogisticRegression.fit: uses Intel(R) oneAPI Data Analytics Library solver
Score:             0.7256
Training Time (s): 0.5205447673797607
Infer Time (s):    0.010569334030151367

Sklearn LR 2 (DAAL):
INFO:root:sklearn.linear_model.LogisticRegression.fit: uses Intel(R) oneAPI Data Analytics Library solver
Score:             0.7256
Training Time (s): 0.47805118560791016
Infer Time (s):    0.0067026615142822266

Sklearn LR 3 (Native -> Should be DAAL):
Score:             0.7256
Training Time (s): 6.1781089305877686
Infer Time (s):    0.003770589828491211

Sklearn LR 4 (Native):
Score:             0.7256
Training Time (s): 6.188448429107666
Infer Time (s):    0.0038619041442871094

In this sense, I want to be able to have an equivalent to from sklearnex.foo import LogisticRegression (DAAL accelerated)
However, it seems it is different:
Currently:
# This is effectively identical to just importing sklearn LogisticRegression

patch_sklearn("logistic")
from sklearn.linear_model import LogisticRegression # daal
unpatch_sklearn("logistic")  # -> silently reverts LogisticRegression to sklearn

from sklearn.linear_model import LogisticRegression # sklearn
patch_sklearn("logistic")  # -> does nothing until LogisticRegression is re-imported

Ideally:
# unpatch should function in the same manner as patch: does not alter already imported modules
patch_sklearn("logistic")
from sklearn.linear_model import LogisticRegression # estimator is optimized now
unpatch_sklearn("logistic")  # -> does nothing until LogisticRegression is re-imported
model_daal = fit_model(LogisticRegression, **fit_args)  # fits DAAL accelerated
from sklearn.linear_model import LogisticRegression # sklearn version
model_sklearn = fit_model(LogisticRegression, **fit_args)

This would be nice for our use-case, as we may do steps inbetween determining the model class and actually initializing the model. If we were to run into an exception between those two steps, we wouldn't have had a chance to call unpatch, meaning the DAAL patch will propagate to future model training, even if they didn't intend to use DAAL.
This could be solved by having an explicit from sklearnex.foo import LogisticRegression as an alternative to the patching API (personally I would prefer this in most cases).

@SmirnovEgorRu Regarding model priorities,
We are definitely interested in trying out algorithms which have gotten significant speedups via DAAL (SVM, linear) which we had previously discarded / disabled due to being too slow.
In terms of CPU acceleration, the priority of model speedups are as follows:
Neural Networks > LightGBM > KNN > CatBoost > XGBoost > RandomForest > ExtraTrees > Linear > Others
KNN has been optimized by 250x since our last release (25x by DAAL, 10x by a separate optimization), so it went from the most costly inference model to one of the least.
Now, the most costly models are neural networks (training speed / inference speed) and RandomForest/ExtraTrees (inference speed). Of course, any improvements to the GBM models' training speed would be highly important, but I suspect they are already heavily optimized. While we don't yet use the sklearn NN model, it is apparently quite good (2x faster training than TensorFlow equivalent). If something were to somehow give a 25x speedup (or even a 2x speedup!) to tabular NN models which don't benefit much from GPUs due to their small dimensions, this would dramatically improve AutoGluon.
Of course, given a big enough speedup (such as 100x+ with SVM), other models could obviously jump in priority as well.

Thank you, for the comprehensive report! We have never looked at MLP in sklearn. Let us do it and understand feasibility to improve this.

Hopefully, my team is accelerating training of CatBoost (2 recent releases 0.24.4 and 0.25) and XGBoost (blog) by commits in mainlines as well :)
For faster prediction of LGBM and XGBoost - yes, we have model convertors from daal4py (blog, xgboost example, lgbm example). Current limitation - we don't support missing values and categorical features.

This could be solved by having an explicit from sklearnex.foo import LogisticRegression as an alternative to the patching API (personally I would prefer this in most cases).

Yep, we are planning to move this feature from daal4py to Intel Extension for Scikit-learn in the next release as well :)

Couple additional comments

Intel Extension for Scikit-learn also provides Intel GPU support - it's might be not so relevant for cloud environment at the moment(although all current GPU devices are supported already), but going forward this would be easiest way  to get Scikit-learn enabled there.

For NN cases - there is oneDNN library that provides optimizations for TensorFlow and PyTorch. So they can be used for massive perf gains
Intel® Optimization for TensorFlow
Intel® Optimization for PyTorch


Remove full refresh from provisioning flow

This patch is a bigger effort to reduce number of full refreshes which are very expensive for larger rhv environments.

@miq-bot assign @borod108

@agrare @durandom please review

cc @gmcculloug @bdunne

Please see related failing test.
I suggest adding a comment in the code to explain why the refresh was removed.  This would prevent someone from accidentally re-inserting the logic without understanding why it was removed in the first place.

 Targeted refresh improvements

@gmcculloug I added comment and fixed the tests. Please review

@pkliczewski I do not have access to the trello card.  Is this meant for euwe release?  (Please label euwe/yes or euwe/no)
Any other comments or ack for this PR?

Ilanit tested this patch on large env with 39 hosts and 2171 vms. According to her measurements now it takes ~1 min to perform vm provisioning whereas it takes ~17 mins to perform full refresh on the same env.
I am not sure who to label this PR but it needs to be euwe/yes

@gmcculloug LGTM :+1:
@pkliczewski :tada: great news on the speed-up

With this change you now need to run a full server in dev mode if you want to provision VMs on RHEV.
We can add a dev mode workaround later :+1: LGTM

With this change you now need to run a full server in dev mode if you want to provision VMs on RHEV.

@bdunne interesting, can you explain what server (miq or rhev) and why?

@bdunne interesting I always thought you had to run the full server anyway to test provisioning, you'd need at least a generic worker and a refresh worker right?

@bdunne is referring to running the ManageIQ server and using simulate_queue_worker to process the items off the queue.  Using this you do not get an event monitor so you would never see the new VM.  You would need to manually refresh the provider.

Yes, to elaborate on what @gmcculloug said, I usually just test with a rails server for the UI and a rails console running simulate_queue_worker for the backend.  Obviously this is a dev mode only solution.  I find it very helpful when troubleshooting provisioning issues because you can ensure that you start with an empty queue and you'll only queue things required for what you're doing.  Troubleshooting is faster, logs are cleaner (no noise from worker sync and other activities),  or dealing with unrelated provider refresh.  It is also much more resource efficient on my laptop since it is two processes rather than 1000 workers.
That being said, this PR is much better for production mode and I'll find a workaround for dev later.
Thanks @pkliczewski

@bdunne cool :heart: the thorough explanation

@miq-bot add_label blocker

@gmcculloug I've set a blocker label and the flag is set on the bug. Can this be backported?
Thanks for the quick response so far!

Euwe Backport details:
$ git log -1
commit 35beb414e86aa93ec32ee267605005bd2373b9ea
Author: Greg McCullough<EMAIL_ADDRESS>Date:   Fri Nov 11 08:33:27 2016 -0500

    Merge pull request #12529 from pkliczewski/master

    Remove full refresh from provisioning flow
    (cherry picked from commit e1f05e60fd251a450efbb5dbb44e8e83f82552db)

    https://bugzilla.redhat.com/show_bug.cgi?id=1388679


[Str] add ability to supply string encoding and use internal encodig instead of string encoding by default

Now Psl\Str can be complete replacement for mb_str* and str* functions 🎉

Pull Request Test Coverage Report for Build 499

62 of 62 (100.0%)  changed or added relevant lines in 27 files are covered.
No unchanged relevant lines lost coverage.
Overall coverage remained the same at 100.0%

Totals

Change from base Build 497:
0.0%

Covered Lines:
1655

Relevant Lines:
1655

💛  - Coveralls


Android Native UI Component doesn't respect the native view height

Description
I have issues with React Native respecting the height of an Android Native UI Component. Below I have created a very simple use-case to demonstrate the issue.
React Native version
System:
    OS: macOS Mojave 10.14.6
    CPU: (16) x64 Intel(R) Core(TM) i9-9980HK CPU @ 2.40GHz
    Memory: 3.31 GB / 32.00 GB
    Shell: 5.3 - /bin/zsh
  Binaries:
    Node: 10.16.3 - ~/.nvm/versions/node/v10.16.3/bin/node
    Yarn: 1.19.1 - ~/.nvm/versions/node/v10.16.3/bin/yarn
    npm: 6.9.0 - ~/.nvm/versions/node/v10.16.3/bin/npm
    Watchman: 4.9.0 - /usr/local/bin/watchman
  Managers:
    CocoaPods: 1.8.4 - /usr/local/bin/pod
  SDKs:
    iOS SDK:
      Platforms: iOS 13.2, DriverKit 19.0, macOS 10.15, tvOS 13.2, watchOS 6.1
    Android SDK:
      API Levels: 26, 28, 29
      Build Tools: 28.0.2, 28.0.3, 29.0.2
      System Images: android-21 | Google APIs Intel x86 Atom_64, android-23 | Google APIs Intel x86 Atom, android-23 | Google APIs Intel x86 Atom_64, android-24 | Intel x86 Atom, android-25 | Google APIs Intel x86 Atom, android-27 | Google APIs Intel x86 Atom, android-28 | Android TV Intel x86 Atom, android-28 | Google APIs Intel x86 Atom, android-29 | Google APIs Intel x86 Atom, android-29 | Google Play Intel x86 Atom
      Android NDK: Not Found
  IDEs:
    Android Studio: 3.6 AI-192.7<IP_ADDRESS>92135
    Xcode: 11.3.1/11C504 - /usr/bin/xcodebuild
  Languages:
    Java: 1.8.0_212 - /usr/bin/javac
    Python: 2.7.10 - /usr/bin/python
  npmPackages:
    @react-native-community/cli: Not Found
    react: 16.11.0 => 16.11.0
    react-native: 0.62.2 => 0.62.2
  npmGlobalPackages:
    *react-native*: Not Found

Steps To Reproduce

Create a React Native ViewManager in the Android Project:

public class CustomTextViewManager extends SimpleViewManager<TextView> {
    @NonNull
    @Override
    protected TextView createViewInstance(@NonNull ThemedReactContext reactContext) {
        return new TextView(reactContext);
    }

    @NonNull
    @Override
    public String getName() {
        return "CustomTextView";
    }

    @ReactProp(name = "text")
    public void setText(TextView view, String text) {
        view.setText(text);
    }
}

Add the ViewManager via a ReactPackage to the Packages list.

Bind the native view in JavaScript:

import {requireNativeComponent} from 'react-native';

const CustomTextView = requireNativeComponent('CustomTextView')

export default CustomTextView;

Create a simple app:

import React from 'react';
import CustomTextView from './CustomTextView';

const App: () => React$Node = () => {
  return (
    <CustomTextView
      text={'Some test text'}
    />
  );
};

export default App;

Expected Results
When running this code nothing is shown. I actually expected that the height of the Android View would be reflected in React Native. Apparently this is not the case. I have tried this with different native view types, with no luck.
After adding a fixed height with style={{height: 100}} the view is shown at the provided height.
Height "auto" doesn't work either.
I have found similar issues but they are all more complex. My example should work out of the box I suppose.
I hope that someone can tell me what I'm missing.
Snack, code example, screenshot, or link to a repository
Check-out this repo which demonstrates the issue:
https://github.com/BenGroot/react-native-native-ui-component-issue

This information is already attached.

Label "Needs: Environment Info" can be removed right?

https://github.com/facebook/react-native/blob/5cde6c5e7d440e62d21ef858d77ed185df8a2da0/ReactAndroid/src/main/java/com/facebook/react/uimanager/LayoutShadowNode.java#L27-L36
https://github.com/facebook/react-native/blob/5cde6c5e7d440e62d21ef858d77ed185df8a2da0/ReactAndroid/src/main/java/com/facebook/react/uimanager/LayoutShadowNode.java#L148-L169
You need to find where they set the default height...

any solutions for this? running into this issue now

@BenGroot @hannzern guys,  did you find a solution fro this?

Check this answer on StackOverflow

the solution from Maik Peschutter https://stackoverflow.com/a/65024470/11452286 and related blog https://nicholasmarais1158.github.io/2017/07/React-Native-Custom-Measuring may solve this issue
this discussion is more appropriate on Stackoverflow rather then Github
Github is used to report bugs/issues in facebook/react-native
If you head over to the docs you can read the info about Different component for ex. TextInput and their props for example selectionColor https://reactnative.dev/docs/textinput#selectioncolor
if the prop selectionColor does not behave as specified in https://reactnative.dev/docs/textinput#selectioncolor
then you can open an issue on Github to keep track of this problem and/or publish a pull request to fix the issue
currently we have some guides on how to implement your Native UI Component ..
BUT
this classes are not part of the public api for example like done with rails https://api.rubyonrails.org/
currently the are some generic guildelines but not clear specification for me to read
It is the responsability of the developer implementing the Native UI Component to read the reactnative java sourcecode, understand the reactnative framework built on top of Android Open Source Project and apply the required solutions
I think it makes no sense having a documented public api (like https://api.rubyonrails.org/) of our Java Classes to be then used by third party devs to implements their Native UI Components as we are making lots of changes in ReactNative so it would become outdated relatively quickly
In my opinion makes more sense that react-native developers contribute to react-native and understand the sourcecode, follow the changes in the framework...
We can not open issues on github like.. I inherited from ReactRootView and the functionality does not work... otherwise we would risk having 10.000 issues in our repositories (hundreds of issues for each Public Java API/class)
The opensource contributors would invest time to read 1000s of issues that are not relevant for react-native, just generic discussions that belongs to StackOverflow
I believe this type of assistance is provided in StackOverflow with the bounty system or Upwork/Codementor hiring a freelancer that assist you in your work.
I believe react-native developer needs to be a specialist and needs to have good knowledge of the OpenSource project.
In the end we can not document every API and every method of the framework, it is easier that you contribute and learn the sourcecode rather then investing time to write step by step guide
This is my experience, I contribute since 6 months to rn and I can say my understanding of react-native really improved.
I believe I will reach full understanding of ReactNative (iOS and Android) sourcecode in August 2021.
Thanks :peace_symbol: :pray:

any solution?

you need to look into the react shadow node measure logic


feat(website): RPC connect Metamask Networks

It seems that
window.ethereum

is causing the preview build to fail.

There seems to be other dependency issues after running
npm i @metamask/providers

Maybe package.json wasn't updated correctly?

Running
pnpm add @metamask/providers

inside
packages/website

fixed the GitHub deployment.
However, the Vercel deployment seems to be failing.

Closing and starting with a new branch without any pnpm imports.


WIP: First attempt at successor

This doesn't compile at the moment, but I felt it was easier to get pointers on code than hypotheticals in Discord.
The current compile error is:
error[E0599]: no method named `poll` found for type `Fut` in the current scope
  --> src/stream/stream/successor.rs:51:52
   |
51 |         match self.as_mut().successor()(self.next).poll(cx) {
   |                                                    ^^^^ method not found in `Fut`
   |
   = help: items from traits can only be used if the type parameter is bounded by the trait
help: the following trait defines an item `poll`, perhaps you need to restrict type parameter `Fut` with it:
   |
41 | impl <F, Fut: core::future::future::Future, T> Stream for Successor<F, Fut, T>
   |          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

which confuses me because I thought I had that trait bound on the where?

I think the error is saying you're calling .poll(cx) on a Fut whereas you should be calling it on a Pin<&mut Fut>, i.e. the future is not pinned.
We should store the future returned by successor(next) into the Successor struct and then poll it. Note that if polling returns Poll::Pending, later on we'll want to poll that same future again instead of asking successor(next) to give a fresh future.

Anything I’d we would need to do to get this merged? 🤩

Created a backup of the async impl here for future reference (e.g. when we get async closures): https://gist.github.com/yoshuawuyts/32fb44959c0bd66a683008ee1d540500. Thanks!


<EMAIL_ADDRESS>

https://github.com/electron-vite/vite-plugin-electron/compare/0.7.0

🥳


[sdk-v2] add Vue example

This adds a Vue.js example to the 2.0.0 release of the javascript SDK.

depends on #233 due to https://github.com/fermyon/spin-js-sdk/pull/233/commits/7d71a89e7c0fd62c3ab56b0e09322a0e38d0cfa9

@bacongobbler I have updated the PR to the released SDK.


Add search to Microsite

Preferably this should be Algolia

Pending Docsearch Application response

Fixed in 0.4.x

Fixed


Add introductory python material

@martinjrobins if you could merge this and deploy in the morning that would be great!
The one thing I can't figure out right now is why the images aren't working...... as far as I can tell I've done exactly the same as in this page:
https://gutenberg.fly.dev/material/software_project_management/collaboration/issues
but in my pages the images don't show up. Any ideas?


Images in Terms explanations are replaced

Nice plugin, but I have the problem, that I want to explain some terms with images as well.
I put images inside a subfolder where the md files are and not in the static folder.
Because of that, these images are replaced as well with a link and not the image.
Is it possible to prevent replacing images?

Hello @h3rbst you can check if the v1.0.1 fixes the problem you mentioned.
We have also upgraded the docusaurus version to v3. So, if you want to check it, install v2.0.0-rc.


data-vtside Doesn't override

Hi Ryan,
Great library! I'm trying to override the sides based on some condition in my code as you declared in your docs data-vtside="right" or data-vtside="left" but nothing happens and it just alternates by default.

Thank you! I've pushed a small change to address that bug and tested it, everything seems to be working. Let me know if you're still running into problems and I'll dig deeper. Sorry about that.