text:
Fails To Compile In Release Mode But Works In Debug Mode

I'm attempting to build a project in release mode and am getting a failure due to the followign compiler error, however it only happens in release mode not debug mode
error[E0412]: cannot find type `Liquidation_state_enum` in this scope
  --> db/src/schema.rs:11:30
   |
1  | / table! {
2  | |     use diesel::sql_types::*;
3  | |     use crate::models::Liquidation_state_enum;
4  | |
...  |
11 | |         liquidation_state -> Liquidation_state_enum,
   | |                              ^^^^^^^^^^^^^^^^^^^^^^ help: a struct with a similar name exists: `liquidation_state`
12 | |     }
13 | | }
   | |_- similarly named struct `liquidation_state` defined here
   |
   = note: consider importing this struct:
           crate::models::Liquidation_state_enum

warning: ignoring -C extra-filename flag due to -o flag

error: aborting due to previous error; 1 warning emitted

For more information about this error, try `rustc --explain E0412`.
error: could not compile `db`

To learn more, run the command again with --verbose.

models.rs file
use crate::schema::liquidation_state_transitions;
use crate::schema::obligation_accounts;
use crate::schema::user_farm_accounts;
use crate::schema::user_farm_manager_accounts;
use diesel::prelude::*;
use diesel_derive_enum;

#[derive(Queryable, Clone)]
pub struct ObligationAccount {
    pub id: i32,
    pub pubkey: String,
    pub authority: String,
    pub liquidated: bool,
}
#[derive(Queryable, Clone)]
pub struct UserFarmAccount {
    pub id: i32,
    pub pubkey: String,
    pub authority: String,
    pub farm_manager: String,
    pub obligations: Vec<String>,
    pub obligation_indexes: Vec<i32>,
}

#[derive(Queryable, Clone)]
pub struct UserFarmManagerAccount {
    pub id: i32,
    pub pubkey: String,
    pub authority: String,
    pub farm_key: i32,
}

#[derive(Debug, PartialEq, Clone, diesel_derive_enum::DbEnum)]
#[PgType = "Liquidation_state_enum"]
#[DieselType = "Liquidation_state_enum"]
pub enum LiquidationStateEnum {
    Start,
    Pull,
    Repay,
    Remove,
    Liquidate,
    End,
}

#[derive(Queryable, PartialEq, Clone)]
pub struct LiquidationStateTransistion {
    pub id: i32,
    pub temp_liquidation_account: String,
    pub authority: String,
    pub user_farm: String,
    pub obligation: String,
    pub liquidation_state: LiquidationStateEnum,
}

#[derive(Insertable)]
#[table_name = "obligation_accounts"]
pub struct NewObligationAccount<'a> {
    pub pubkey: &'a str,
    pub authority: &'a str,
    pub liquidated: bool,
}

#[derive(Insertable)]
#[table_name = "user_farm_manager_accounts"]
pub struct NewUserFarmManagerAccount<'a> {
    pub pubkey: &'a str,
    pub authority: &'a str,
    pub farm_key: i32,
}

#[derive(Insertable)]
#[table_name = "user_farm_accounts"]
pub struct NewUserFarmAccount<'a> {
    pub pubkey: &'a str,
    pub authority: &'a str,
    pub farm_manager: &'a str,
    pub obligations: Option<Vec<String>>,
    pub obligation_indexes: Option<Vec<i32>>,
}

#[derive(Insertable)]
#[table_name = "liquidation_state_transitions"]
pub struct NewLiquidationStateTransition<'a> {
    pub temp_liquidation_account: &'a str,
    pub authority: &'a str,
    pub user_farm: &'a str,
    pub obligation: &'a str,
    pub liquidation_state: LiquidationStateEnum,
}

schema.rs file
table! {
    use diesel::sql_types::*;
    use crate::models::Liquidation_state_enum;

    liquidation_state_transitions (id) {
        id -> Int4,
        temp_liquidation_account -> Varchar,
        authority -> Varchar,
        user_farm -> Varchar,
        obligation -> Varchar,
        liquidation_state -> Liquidation_state_enum,
    }
}

table! {
    use diesel::sql_types::*;
    use crate::models::Liquidation_state_enum;

    obligation_accounts (id) {
        id -> Int4,
        pubkey -> Varchar,
        authority -> Varchar,
        liquidated -> Bool,
    }
}

table! {
    use diesel::sql_types::*;
    use crate::models::Liquidation_state_enum;

    user_farm_accounts (id) {
        id -> Int4,
        pubkey -> Varchar,
        authority -> Varchar,
        farm_manager -> Varchar,
        obligations -> Array<Text>,
        obligation_indexes -> Array<Int4>,
    }
}

table! {
    use diesel::sql_types::*;
    use crate::models::Liquidation_state_enum;

    user_farm_manager_accounts (id) {
        id -> Int4,
        pubkey -> Varchar,
        authority -> Varchar,
        farm_key -> Int4,
    }
}

allow_tables_to_appear_in_same_query!(
    liquidation_state_transitions,
    obligation_accounts,
    user_farm_accounts,
    user_farm_manager_accounts,
);

diesel.toml file
# For documentation on how to configure this file,
# see diesel.rs/guides/configuring-diesel-cli

[print_schema]
file = "src/schema.rs"
import_types = ["diesel::sql_types::*", "crate::models::Liquidation_state_enum"]

migration up.sql file
-- Your SQL goes here
CREATE TABLE obligation_accounts (
  id SERIAL PRIMARY KEY NOT NULL,
  pubkey VARCHAR NOT NULL UNIQUE,
  -- the account field is owner but we're using authority here for consistency
  authority VARCHAR NOT NULL,
  liquidated BOOLEAN NOT NULL DEFAULT 'f'
);

CREATE TABLE user_farm_accounts (
  id SERIAL PRIMARY KEY NOT NULL,
  pubkey VARCHAR NOT NULL UNIQUE,
  authority VARCHAR NOT NULL,
  farm_manager VARCHAR NOT NULL,
  obligations TEXT[] NOT NULL DEFAULT '{}',
  obligation_indexes INTEGER[] NOT NULL DEFAULT '{}'
);

CREATE TABLE user_farm_manager_accounts (
  id SERIAL PRIMARY KEY NOT NULL,
  pubkey VARCHAR NOT NULL UNIQUE,
  authority VARCHAR NOT NULL,
  farm_key INTEGER NOT NULL DEFAULT 0
);

-- liquidation state transition enum
-- it marks the most recent phase in liquidation
-- allowing one to resume from that step
-- the one exception is "end" which indicates liquidation as finished
CREATE TYPE liquidation_state_enum AS ENUM ('start', 'pull', 'repay', 'remove', 'liquidate', 'end');

CREATE TABLE liquidation_state_transitions (
  id SERIAL PRIMARY KEY NOT NULL,
  temp_liquidation_account VARCHAR NOT NULL UNIQUE,
  authority VARCHAR NOT NULL,
  user_farm VARCHAR NOT NULL,
  obligation VARCHAR NOT NULL,
  liquidation_state liquidation_state_enum NOT NULL
);

dependencies

diesel = { version = "1.4.7", features = ["postgres"] }
diesel-derive-enum = { version = "1.1.1", features = ["postgres"] }
rustc 1.51.0 (2fd73fabe 2021-03-23)

Something interesting to note, after updating rustc it now fails to compile in release and debug with the same error

Hi, thanks for the bug report. I am unable to reproduce this with the code you have posted. Generally it is very unusual that something will compile in debug mode but not release, so I wonder if there is something amiss with your rustc installation. Perhaps try cargo clean, or re-installing rustc?

I suspect cargo clean will do the trick - sometimes cargo messes up with file edit times when it loads the source, then you edit the source, then it saves the binary for the previous source. File modification dates let it think it doesn't need to recompile the section.

authors: Ten0 adwhit bonedaddy

repo: adwhit/diesel-derive-enum


text:
Possible option to '-dontwarn' feature

I'm faced with 'small' problem. When I try build (assembleRelease) my library with (or not) enabled proguard configuration, the build process throws an exception because of some warnings. But I can't fix this issue from my own project, I need download your android-fat-aar.gradle file and edit by myself just adding the below code snippet on generateXClass method:
task generateXClass(type: ProGuardTask, dependsOn: generateRClass) {
    delete(post_r2x_dir)
    println generateXClass

    injars pree_r2x_dir, filter: '!**/R.java'
    outjars post_r2x_dir
    libraryjars android.getBootClasspath()
    libraryjars libraryPaths
    dontshrink
    dontoptimize
    dontpreverify
    dontwarn 'my.package.**' <<<
    dontwarn '**.R'
    dontwarn '**.R$*'
    keep 'class !**.R {*;}'
    keep 'class !**.R$* {*;}'
    applymapping "$base_r2x_dir/mapping-r.txt"
    printmapping "$base_r2x_dir/mapping.txt"
    printconfiguration "$base_r2x_dir/config.txt"
}

I believe that you can add a config file to create a way of to configure your internal proguard. Or I'm wrong and already exists a right way to do this?
Thanks in advance!

Hi,
If possible can you please share the warnings you get? Maybe that points to
a bug in fat-aar gradle file.
There isn't a way currently to include an external proguard file for
remapping the R class, although it would surely be possible to do so.
Theoretically I can add something like don't warn **, but that would be so
wrong 😊
Since we rename only one file per module, I was thinking that we should be
able to generate the exact config for our needs. If you can let us know the
errors, maybe we can fix or note that down in the known issues.
Cheers.

Hi,
I'll try explain, in the truth just now I understand my real problem.
I'm using your gradle script since begin of last year, but I stayed 15 days without to work on my Android project. Last week I formatted my ubuntu system, therefore I installed all newest versions of applications to work, such as Android Studio 2.2 and JDK 1.8. Today when I ran assembleReselease, I note that your script is running the proguard process again (unduly?).
My minifyEnabled is setted to false, but still running proguard process when I call assembleRelease. And this didn't happened before.
I have multiples projects that are joined by com.github.dcendents.android-maven plugin, in this projects really uses minifyEnabled setted as true, but on main project that generate final lib don't.
The warning generated:
Warning: com.package.Foo: can't find referenced class javax.persistence.Table
Warning: com.package.Bar: can't find referenced class org.apache.commons.codec.binary.Hex
After that I wrote text above, did some tests and now I can run assembleRelease with success by changing the gradle file to use:
'com.android.tools.build:gradle:2.1.3' (was 2.2.0)
'com.github.dcendents:android-maven-gradle-plugin:1.4.1' (was 1.5.0)
And:
apply from: 'https://raw.githubusercontent.com/adwiv/android-fat-aar/4308e779cd12b1398a67040d6ec6447c701bd0e2/fat-aar.gradle' (was master)

The older script did not use an internal proguard, we simply remapped the R classes of embedded projects to point to the library R file.
The 2.2 gradle created an issue by stripping any class named R and inner classes from the generated aar archive which the internal proguard tries to fix. The proguard wants to resolve all classes although it doesn't use them actually. Hence the error.
The javax.persistence.Table is not a part of android java library, hence the warning. From what I understand, this class belongs to java enterprise edition and one of the included jars has a reference to it. Same way, the script is not able to find the commons-codec library in the dependencies. How are you including it?
If you can share the names or dependencies of your embedded projects, I would try to replicate the issue.

Hi, Added the option to specify your own dontwarn options in file named fataar-rules.pro which is created automatically first time you run the script. Please check if it works as required.

fataar-rules.pro is like a common proguard configuration file? Because some java classes were obfuscated together with proguard process.

Yes.. it's included as a config file..
The obfuscation should not happen since we have the dontobsfucate option.
Can you check the generated config file to see what configuration is
applied. It's in the same location as mapping file.
On 07-Oct-2016 8:07 PM, "williamlopes-dev" notifications@github.com wrote:

fataar-rules.pro is like a common proguard configuration file? Because
some java classes were obfuscated together with proguard process.
—
You are receiving this because you modified the open/close state.
Reply to this email directly, view it on GitHub
https://github.com/adwiv/android-fat-aar/issues/33#issuecomment-252269523,
or mute the thread
https://github.com/notifications/unsubscribe-auth/AKfnZ8ESUqupzFLGL4_rDAkx02TrMDB2ks5qxlkYgaJpZM4KLWNo
.

When I tested my integration class has been obfuscated. I remember of add keep 'class foo.Bar' command to work correctly. I can compile without 'keep' command, but I can't use my library in that way.
Next week I must be able to test, after that, I'll back and tell if worked.

In another thread, another method has been suggested which will remove the
need for internal proguard. I am hopeful it will get us out of the mess I
have created by using proguard internally 🙂

source: gharchive/issue

authors: adwiv williamlopes-dev

repo: adwiv/android-fat-aar

license: unlicense

license_type: permissive

license_source: bigquery


text:
Multi-card training problem

Hi！
when i run “dlhpcstarter -t cxrmate -c config/train/longitudinal_gt_prompt_tf.yaml --stages_module tools.stages --train”
But the speed is very slow. How should I set the training parameters and data batches to speed up the training process?
Only 4013MiB / 32768MiB of memory is used single gup
and my gpu card is 32G*2
Thank you very much!

Hi,
You can up the mini-batch size? Is your dataset on a solid state drive, or a disk? What is your iterations per second?

Hi,
You can up the mini-batch size? Is your dataset on a solid state drive, or on a hard disk? What is your iterations per second?

my mbatch_size is set 2，my dataset on a hard disk，2.77iterations per second

my gpu is v100*2

I get a pretty big speedup from using a solid state drive over a hard disk. A mini-batch size of up to 32 should be fine (so devices: 2 and mbatch_size: 16, which is a mini-batch size of 32 with ddp). You can also increase the number of workers for the dataloader (I think i have it already set high enough though with num_workers: 5 in the config).
I will have to dig up my number of iterations per second tomorrow when I am at my computer. Bare in mind this paper was done on P100 gpus, so it will be slower.

Also, I found the CvT encoder to be quite slow, if you see our later work, e.g,
https://huggingface.co/aehrc/cxrmate-ed
https://github.com/aehrc/anon
we use UniFormer which has sped up training quite a bit.

Yes, I saw this work, it is great, adding Auxiliary Patient Data. But I currently want to test multi-image input and Longitudinal Data. and I think I should read it carefully.

hi
I want to ask you how many epochs did you train with teacher forcing and train with Self-Critical Sequence Training (SCST) respectively?

hi
I want to ask you how many epochs did you train with teacher forcing and train with Self-Critical Sequence Training (SCST) respectively?

@anicolson hi, Are max_epochs set to 32 and 1 respectively? I saw this in the training configuration, but I don't know if it's accurate.

hi
I want to ask you how many epochs did you train with teacher forcing and train with Self-Critical Sequence Training (SCST) respectively?

@anicolson hi, Are max_epochs set to 32 and 1 respectively? I saw this in the training configuration, but I don't know if it's accurate.

Oh, I just saw the training parameters in the paper, thank you

authors: anicolson yihp

repo: aehrc/cxrmate

license_source: github-api


text:
NFR: Establish and Migrate New PA Repo

Context/Description
As a PartsAsist business owner I want to establish a new repository leveraging the Helix 5 migration and the existing structure of the PA website.
Acceptance Criteria
Adobe AC:
@dkuntze:
Set up the new repository for the PA migration
Remove static configs (helix-query.yaml, helix-sitemap.yaml and robots.txt)
Frontend AC:
@alexiscoelho:
Copy labels to the new repo
Do necessary doc changes
Remove hard coded market code
Remove and refactor hardcoded references to X
Review generation of the Atom Feed
Investigate and if possible remove all files under the folder files (afaik not possible atm)
Test i18N configs
Remove config-patches (.patch)
Test deployment on main
Review and fix the generation of the feed.xml for .us
Implement generation of the feed.xml for multiple locations (the logic used in MackTrucks should work fine)
Move github action variables:
@alexiscoelho:
Move all of the github action variables to a configuration per location
CONNECTOR_DOMAIN (moved to the new repo vars)
DOMAIN_URL (Not used)
NEWS_ENDPOINT
NEWS_FEED_INFO_ENDPOINT
NEWS_LOCALE (Not used)
NEWS_TARGET_DIRECTORY
NEWS_TARGET_FEED
UPSERT_ENDPOINT (Not used)
WEBSITE_NAME (moved to the new repo vars)
Repo changes/configs AC:
@alexiscoelho:
Give permissions to the team to the new repo
Content AC:
Out of Scope

[ ] #118

1 SP will roll over to Sprint 2 based on the dependancy from Darin

repo: aemsites/vg-partsasist-old


text:
branch and thread ?

Is the multi branch work with multi thread or process ?
I mean for example branch master work on thread A, and test for thread B at same time without lock ?

Hi!
It supports many connections to the same database on a single thread (as you can see in the tests).
But many threads writing to the db at the same time, probably not.
With LMDB it is suggested that the writes are serialized and implemented on a single thread.
It does not mean that it is impossible to implement multi-threading support. Writes would need to be redirected to a dedicated worker thread. Or maybe using a mutex.
Many processes using the same db: not tested. Maybe it can work because LMDB uses a lock file.

If there is one write thread, and multi read thread with diff branch. will this work ?

Yep, it should work. Although not tested yet.
You can make a multi-threaded test based on the test04_concurrent_access function from test.py

repo: aergoio/litetree

license: MIT


text:
re-render Sender examples after master secret renewal AGPUSH-1504

https://issues.jboss.org/browse/AGPUSH-1504
How to test this?

Renew master secret on Sender API tab
check that masterSecret was updated in the sender examples bellow

JVM exit reported again :-) Yay!

nice! works very well

source: gharchive/pull-request

authors: lfryc matzew

repo: aerogear/aerogear-unifiedpush-server


text:
Can't provision mobile CI/CD

Description
After running the installer I am attempting to provision mobile CI/CD. When navigating to the monitoring tab after a couple of minutes I can see a number of warnings, as in the screenshots below

When navigating back to the catalog I can't see the 'Mobile' tab anymore:

Environment
Operating system: Fedora 28
OpenShift versions: 3.9
Steps to reproduce

clone the mobile-core and checkout 1.0.0
run the installer
provision the Mobile CI/CD after the mobile tabs appear (tried with provisioning a Cordova app before provisioning this service as well as provisioning the service alone)
after a couple of minutes navigate to the 'Monitoring' tab for the project to see the errors as in the first two screenshots
navigate back to the 'Catalog' inside your project. No 'Mobile' tabs is present anymore (3rd screenshot)

@pawelpaszki from looking at the event logs, it looks like you're under disk space pressure, so kubernetes isn't scheduling things. You'll likely want to cleanup some space.

Hi @pawelpaszki,
Thanks for your feedback. This repo is now deprecated and is replaced by Mobile Developer Console. Could you please give it a try and if you still experience the same problem, please open a new issue in that project.
Thanks.

authors: jasonmadigan pawelpaszki wei-lee

repo: aerogear/mobile-core


text:
Running Android test on VirtualBox 6.1.6

Hey guys,
I have been testing what platforms selenoid could work on and within lot of threads I saw that it could work on VMWare Fusion but not on VirtualBox for Android tests.
With VirtualBox 6.0+ enabling nested virtualization is possible and when I try to run an Android test I see in the logs
"emulator: CPU Acceleration: working
emulator: CPU Acceleration status: KVM (version 12) is installed and usable."
but still get the following errors
"Segmentation fault      (core dumped) ANDROID_AVD_HOME=/root/.android/avd DISPLAY="$DISPLAY" /opt/android-sdk-linux/emulator/emulator ${EMULATOR_ARGS} -writable-system -no-boot-anim -no-audio -no-jni -avd android7.0-1 -sdcard /sdcard.img -skin "$SKIN" -skindir /opt/android-sdk-linux/platforms/android-24/skins/ -verbose -gpu swiftshader_indirect -ranchu -qemu -enable-kvm
error: no devices/emulators found
error: no devices/emulators found"
I saw that in one of the post that with Segmentation fault was to try rebuilding the image which I have tried to do, but, with that approach the VirtualBox Machine just restarts completely.
Do you know why this would be the case? Is running Android tests on VirtualBox just not possible, even with nested virtualization options?
Thank you.

@shj941013 have no experience running Android inside VirtualBox.

@vania-pooh Thank you for the response!
I actually tested it on VMware as well but getting the same issue. Do you By chance know if I have to run a specific ISO to make it work on VMWare then? I see on the other thread that it can work on VMware Fusion. (I have tried Ubuntu 18.04)

repo: aerokube/selenoid


text:
Support browsermob with new image

Feature required:
In order to record network deatils, I'm running standalone proxy by browsermob, works great with selenoid. Can Selenoid support creating proxy image on the fly? Like the way you're doing with video recording. Create new proxy image for per session, destroy it when session is over.
Thank you.

@jayden-xie we consider such feature outside of Selenoid scope. This should be usually done by tests code. Also locking to browsermob is not a good idea as other users may require features not supported by browsermob.

Indeed, there're other options, do you have any suggestion about which one is better?
I tried to find some...
1.python https://github.com/mitmproxy/mitmproxy
2.nodejs https://github.com/nodejitsu/node-http-proxy

@jayden-xie I personally have more Java experience, so browsermob is one of the best options for me. The majority of my colleagues are using it.

authors: jayden-xie vania-pooh


text:
fix runtime modification of lager hwm

Ref. https://www.pivotaltracker.com/story/show/154207930
Only apply on error_logger and the file_backend (and apply correctly).

I built locally package, deployed as per release notes, set hwm in epoch.yaml to 51 then at start I get:
14:43:44.082 [warning] lager_error_logger_h dropped 0 messages in the last second that exceeded the limit of 51 messages/sec

.... so 51 is picked.

Starting with ./bin/epoch console -noshell -noinput (as Docker image does) I managed to see that console sees the 51 limit:
14:53:11.102 [warning] lager_error_logger_h dropped 0 messages in the last second that exceeded the limit of 51 messages/sec

(I did not manage to hit this number outside startup.)

authors: lucafavatella uwiger

repo: aeternity/epoch

license: ISC


text:
updated the uk dictionary

changed from 69UkCities to uk_cities

😆

authors: LindaKivine martinezpl

repo: aflorithmic/apiaudio-python


text:
Fix parsing chained unwind info

There is no explicit size information for struct UNWIND_INFO and self.rest does not have a meaningful end pointer. Using new_aligned was causing the size check to fail. Replace this with
new_unaligned_from_prefix.

The Unity game engine is known to generate lots of chained unwind info, in case you need a test case. Though I think it should be observable with MSVC in general.

Right, I forgot about that. Thanks!

authors: afranchuk ishitatsuyuki

repo: afranchuk/pe-unwind-info


text:
其他下载器伪装功能-- 可在下载时伪装为其他下载器来跳过某些tracker以及用户设置屏蔽列表。

已经有TRACKER开始屏蔽MOTRIX用户。因其行为对其他用户及服务器负载过大。
我建议您编写一个下载器伪装功能来规避这些 具体实现方法为，

下载的时候可选择连接后其他用户会看到的下载器名字。大概只需修改连接后最前发布的包含连接到的用户信息的几个数据包。
因其行为与其他软件不同。建议在此模式下，只允许一个线程连接。
以及关于迅雷资源独占。
伪装成带有超级会员的迅雷客户端来骗过迅雷服务器-可能较难实现- 迅雷服务器较大可能性使用一些防盗版机制。

被tracker屏蔽或者被其他BT下载软件判定为吸血者并屏蔽主要原因是大量用户并不良好的下载习惯。BT下载最起码需要下载后开机做种10小时以上保证资源持续可用。因此屏蔽aria2于情于理都存在合理性，尤其是用PT站点时切勿使用使用aria2作为引擎。

被tracker屏蔽或者被其他BT下载软件判定为吸血者并屏蔽主要原因是大量用户并不良好的下载习惯。BT下载最起码需要下载后开机做种10小时以上保证资源持续可用。因此屏蔽aria2于情于理都存在合理性，尤其是用PT站点时切勿使用使用aria2作为引擎。

然而，我实在是没心思去搞更多的下载软件。我更希望用一个软件就能搞定我所需要的所有要求
这实际上是一种偏见。谁知道我会不会再开一会儿去做种。就因为我使用的软件比其他软件有功能下载更暴力一点。
就像你进一外国人店却被告知要滚出去，就因为你是中国人而他们以为你不会给小费就叫你滚出去

因为国内各种限制。导致你只能给几个国内的做种而连接不到很多外国服务器.虽然可以用某些加密协议v解决..... 但大多数人并不会用那种东西来下载BT 因为不用也可以下载到。用了的话反而会有个代理层之间的传输损耗。再加上范围更广的地区Ip限制....
国内P2P下载圈子才会这么烂。

就算连接到的外国服务器。国内能给的对外上传带宽也比较捉襟见肘。。资源失效等等问题实际上概率比百度盘更大。虽是非人为。

然而，我实在是没心思去搞更多的下载软件。我更希望用一个软件就能搞定我所需要的所有要求

你可以不这样认为，但是主流tracker服务器不会这样待你。违反了社区的规矩不被提供服务是自己的问题。

这实际上是一种偏见。谁知道我会不会再开一会儿去做种。就因为我使用的软件比其他软件有功能下载更暴力一点。

许多人同样抱有这类想法，这也是资源快速死亡的原因之一。下载后开机做种是社区礼仪，同样是为后来者铺路。

就像你进一店却被告知要滚出去，就因为你穿的衣服牌子他们不喜欢因为他们觉得穿这种衣服牌子的人都有病。我更愿意把我的衣服牌子遮住而不是再去买件新的衣服。

你可以不用，但是既然在使用开源软件或者其他BT软件，社区的礼仪请你遵守。就像你到回族聚集区，大谈大肉如何如何，会被大家所厌恶一样。遵守社区的规矩将得到更多人的认同和理解。

然而，我实在是没心思去搞更多的下载软件。我更希望用一个软件就能搞定我所需要的所有要求

你可以不这样认为，但是主流tracker服务器不会这样待你。违反了社区的规矩不被提供服务是自己的问题。

这实际上是一种偏见。谁知道我会不会再开一会儿去做种。就因为我使用的软件比其他软件有功能下载更暴力一点。

许多人同样抱有这类想法，这也是资源快速死亡的原因之一。下载后开机做种是社区礼仪，同样是为后来者铺路。

就像你进一店却被告知要滚出去，就因为你穿的衣服牌子他们不喜欢因为他们觉得穿这种衣服牌子的人都有病。我更愿意把我的衣服牌子遮住而不是再去买件新的衣服。

你可以不用，但是既然在使用开源软件或者其他BT软件，社区的礼仪请你遵守。就像你到回族聚集区，大谈大肉如何如何，会被大家所厌恶一样。遵守社区的规矩将得到更多人的认同和理解。

...的确，虽然我不能我完全认同,但是我想也没个更好的办法。因为国内网络服务商限制我没办法不吸血。使用代理软件可能犯法或上传速度并不会更好。
本来没有规矩也可以的事情被强行加规矩是一种让人讨厌的事情。你道理没什么问题但是吧, 我觉得你没有用对个例子。
.....穆斯林他们有的特权还少? 不合理教条还少? 搞的事情还少吗？世贸大楼911怎么搞的?。他们中的很多人想要的并不是各民族等之类的. 他们想要的是比你高的地位,你不应该忍受而是反击。认同和理解不是他们能够践踏你的权利的理由。虽然也不应该做的太过。

很多情况下，过了这个村就没这个店了。我不得不换个衣服。

不过Motrix默认设置上传速度限制256KB/s也的确不是符合规范的行为吧。

authors: Potato32 weearc zerofancy

repo: agalwood/Motrix


text:
⚠ AGEG website (prod) has degraded performance

In a4f3c68, AGEG website (prod) (https://www.ageg.ca/) experienced degraded performance:

HTTP code: 200
Response time: 1463 ms

Resolved: AGEG website (prod) performance has improved in 57f354f.


text:
Adds the missing points to the chin feature (issue #48)

Addressed the issue with the face_landmarks wrapper not including all points from the output of _raw_face_landmarks.

Replaced by #55 that includes the test fixes.

authors: ageitgey sbourigault

license: mit


text:
Performance different on X86 and ARM service

Hi, I wote some code and test their performance on X86 and ARM, and I got 50% performance enhance on X86, but 55% performance reduction on ARM. This did not meet my expectations and I expected the same performance enhance from them on different machines.
I use CMake to compile my project, and here are some code in my CMakeLists.txt
if(${ARCHITECTURE} STREQUAL "x86_64")
    add_compile_options(-DSSE42 -DFMA -march=skylake)
else()
    add_compile_options(-DAARCH64 -march=armv8.2-a)
endif()

I build nsimd withCMAKE_ARGS -Dsimd=SSE42 on X86 and  CMAKE_ARGS -Dsimd=AARCH64 on ARM.
To use a 128-bit register, I have some c++ code like this:
#if defined(NSIMD_X86)  // X86 platform
    using nsimd_t = nsimd::sse42;
#else   // ARM platform
    #if NSIMD_WORD_SIZE == 32
        using nsimd_t = nsimd::neon128;
    #else
        using nsimd_t = nsimd::aarch64;
    #endif
#endif

template <typename T>
using pack128_t = nsimd::pack<T, 1, nsimd_t>;

I run lscpu on the ARM service and I got this:
Architecture:          aarch64
Byte Order:            Little Endian
CPU(s):                128
On-line CPU(s) list:   0-127
Thread(s) per core:    1
Core(s) per socket:    64
Socket(s):             2
NUMA node(s):          4
Model:                 0
BogoMIPS:              200.00
L1d cache:             64K
L1i cache:             64K
L2 cache:              512K
L3 cache:              65536K
NUMA node0 CPU(s):     0-31
NUMA node1 CPU(s):     32-63
NUMA node2 CPU(s):     64-95
NUMA node3 CPU(s):     96-127
Flags:                 fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm jscvt fcma dcpop

Am I using the wrong compile options? Why it reduce performance on ARM service?

Hi @luoming17, it depends on so many things that it is impossible to answer you without more informations:

What is the algorithm you are running?
What is the Intel machine exactly?
What is the Arm machine exactly? (Raspberry Pi, Smartphone, server...)
What is your metric? What do you mean exactly by "50% performance enhance" and "55% performance reduction on ARM" ?

The only job of NSIMD is basically to ensure that what you type in your C or C++ code is properly translated into assembly. So if you write a + b the job of NSIMD is to be sure that it is translated into:

_mm_add_ps(a, b) for Intel SSE 4.2
vaddq_f32(a, b) for Aarch64.

You can check that by looking at the assembly of your binary. If it the case then NSIMD works properly. Getting good performances out of it requires some investigations. Moreover it is really surprising to expect the same level of performances from two chips that are completely different (Arm vs. Intel)

Hi @gquintin , because of confidentiality agreement, I cant post my code, I'm sorry.
I have solved my problem, I found out the reason was that a function was called billions of times, and it should add static inline declaration.

repo: agenium-scale/nsimd


text:
How can i run it from ip address?

We haven't any domain. I want to run from ip address
http://x.x.x.x/cockpit/auth/login?to=/

@uslualper how did you fix this?

authors: RanaOsamaAsif uslualper

repo: agentejo/cockpit


text:
Collection + set field : Message "Saving successful" but changes not applied

Changes are not saved although the message "saving successfull" appears when deleting an item from a Set field :

I sometimes need to delete an item 2 or 3 times.
Any idea on why this happen ?

@anthonyivol Do you happen to have required checked on those fields?

@Blistok I have removed it while testing today and with a blank new entry the problem is still here.
Actually I need 2 time delete + save and it works (not 3 as mentioned before).
This is the field setup :
{
  "fields": [
    {
      "type": "set",
      "name": "image",
      "label": "Image",
      "options": {
        "fields": [
          {
            "type": "select",
            "name": "size",
            "label": "Taille",
            "options": {
              "options": "XS, SM, MD, LG"
            }
          },
          {
            "type": "asset",
            "name": "image",
            "label": "Image"
          }
        ]
      }
    },
    {
      "type": "gallery",
      "name": "gallery_xs",
      "label": "Petite gallerie"
    },
    {
      "type": "gallery",
      "name": "gallery_l",
      "label": "Grande gallerie"
    }
  ]
}

I figure out when pasting that there was a nested image.image.
I renamed it but the problem is still there.

authors: Blistok anthonyivol


text:
fixed broken entry view with lang param

I  have two languages. default: English and de: Deutsch. If I try to open an entry with the new lang param
http://localhost:8080/cockpit/collections/entry/pages/5f92ce4b323938e4ab0002dd?lang=en, I have empty entries and I have js errors.
(intermediate value)._.find(...) is undefined riot.js:2:5377
<view-ed391dc2> {lang ? _.find(languages,{code:lang}).label:App.$data.languageDefaultLabel}

This fix takes care about querying the default language and querying non existent languages.

Wait. I messed that up. Now, the lang param doesn't work at all.

Now it works.

Thanks!

authors: aheinze raffaelj


text:
chapter 11 reusing only the lower layers

our original model  was having 5 hidden layer and we wanted to reuse only lower 3 hidden layer and build a hidden layer 4 on top of it so code in jupyter notebook was :
reset_graph()

n_hidden4 = 20  # new layer
n_outputs = 10  # new layer

saver = tf.train.import_meta_graph("./my_model_final.ckpt.meta")

X = tf.get_default_graph().get_tensor_by_name("X:0")
y = tf.get_default_graph().get_tensor_by_name("y:0")

hidden3 = tf.get_default_graph().get_tensor_by_name("dnn/hidden4/Relu:0")

new_hidden4 = tf.layers.dense(hidden3, n_hidden4, activation=tf.nn.relu, name="new_hidden4")
new_logits = tf.layers.dense(new_hidden4, n_outputs, name="new_outputs")

and original model graph in "dnn "was :

My question is why we have used "dnn/hidden4/Relu:0" in place of "dnn/hidden3/Relu:0" as this will give output of activation function of hidden3 ?

Good catch! Indeed, this is an error. If we want to reuse layers up to layer 3, we should get the output of layer 3, not 4.  I'll fix this, thanks!

authors: ageron nitml

repo: ageron/handson-ml


text:
Chapter 9 manual computation of gradients integer division...

Hi Aurelien,
Been getting through your (excellent) book, and I noticed that for the manual computation of the gradients section of chapter 9 I was getting gradients of 0 (no learning). It looked like the offending line was: gradients = 2/m * tf.matmul(tf.transpose(X), error) where 2/m is an integer division resulting in 0.
When I switched the 2 to float notation (so, 2.), the gradients were being updated accordingly and everything seemed to work.
Take care.
Michael

Hi @dataforager,
Thanks for your feedback!  Indeed, I wrote this code for Python 3, which defaults to float division. However, everything should work fine in Python 2 as well if you add the following line at the beginning of your Python files:
# To support both python 2 and python 3
from __future__ import absolute_import, division, print_function, unicode_literals

The Jupyter notebooks include this line at the very beginning, so they actually work fine in both Python 2 and 3.
Since Python 2 is now deprecated, I recommend switching to Python 3, or at least preparing for it. One way to prepare for migration is to start writing code that works on both Python 2 and Python 3. Some libraries like six or future can help a lot. In fact, I recommend you go through this excellent Python 2/3 compatibility cheat sheet. It shows how to write code that works on both Python 2 and 3.
The starting point is to add the from __future__ line above at the beginning of all your Python files, then run your tests and fix all the errors you find: you will likely need to replace a few / with // if you expect integer division, you will need to fix all your print statements (e.g. replace print "hi" with print("hi"), and so on), and most importantly you will need to fix a bunch of unicode vs byte string errors.  But once that's done your code will be much easier to migrate to Python 3, when the time comes.
That said, I think you are right that the book should mention this fact. Right now it just says "The latest version of Python 3 is recommended. Python 2.7+ should work fine too, but it is deprecated." I will add "If you use Python 2, you must add from __future__ import division, print_function, unicode_literals at the beginning of your Python files."
I hope this helps,
Aurélien

authors: ageron dataforager


text:
[Chapter 10] got 'loss:nan' while training the  model using funstional API

That's the code block
input = keras.layers.Input(shape = X_train_std.shape[1:])
hidden1 = keras.layers.Dense(30, activation = 'relu')(input)
hidden2 = keras.layers.Dense(30, activation = 'relu')(hidden1)
concat = keras.layers.Concatenate()([input, hidden2])
output = keras.layers.Dense(1)(concat)
model = keras.models.Model(inputs = [input], outputs = [output])

model.compile( loss = 'mean_squared_error')
history = model.fit(X_train_std, y_train, epochs = 50)

Got the output as:
Epoch 1/50
413/413 [==============================] - 1s 2ms/step - loss: nan
Epoch 2/50
413/413 [==============================] - 1s 2ms/step - loss: nan
Epoch 3/50
413/413 [==============================] - 1s 2ms/step - loss: nan
Epoch 4/50
413/413 [==============================] - 1s 2ms/step - loss: nan
Epoch 5/50
413/413 [==============================] - 1s 2ms/step - loss: nan
Epoch 6/50
413/413 [==============================] - 1s 2ms/step - loss: nan
Epoch 7/50
413/413 [==============================] - 1s 2ms/step - loss: nan

Hi @nirjann ,
Thanks for your feedback. I just tried running notebook 10 on Colab, and I got the following output for the code your mentioned:
Epoch 1/20
363/363 [==============================] - 1s 2ms/step - loss: 1.9731 - val_loss: 3.3940
Epoch 2/20
363/363 [==============================] - 0s 1ms/step - loss: 0.7638 - val_loss: 0.9360
Epoch 3/20
363/363 [==============================] - 0s 1ms/step - loss: 0.6045 - val_loss: 0.5649
Epoch 4/20
363/363 [==============================] - 1s 1ms/step - loss: 0.5862 - val_loss: 0.5712
Epoch 5/20
363/363 [==============================] - 0s 1ms/step - loss: 0.5452 - val_loss: 0.5045
Epoch 6/20
363/363 [==============================] - 0s 1ms/step - loss: 0.5243 - val_loss: 0.4831
Epoch 7/20
363/363 [==============================] - 0s 1ms/step - loss: 0.5185 - val_loss: 0.4639
Epoch 8/20
363/363 [==============================] - 1s 1ms/step - loss: 0.4947 - val_loss: 0.4638
Epoch 9/20
363/363 [==============================] - 1s 1ms/step - loss: 0.4782 - val_loss: 0.4421
Epoch 10/20
363/363 [==============================] - 0s 1ms/step - loss: 0.4708 - val_loss: 0.4313
Epoch 11/20
363/363 [==============================] - 0s 1ms/step - loss: 0.4585 - val_loss: 0.4345
Epoch 12/20
363/363 [==============================] - 0s 1ms/step - loss: 0.4481 - val_loss: 0.4168
Epoch 13/20
363/363 [==============================] - 0s 1ms/step - loss: 0.4476 - val_loss: 0.4230
Epoch 14/20
363/363 [==============================] - 0s 1ms/step - loss: 0.4361 - val_loss: 0.4047
Epoch 15/20
363/363 [==============================] - 1s 1ms/step - loss: 0.4392 - val_loss: 0.4078
Epoch 16/20
363/363 [==============================] - 0s 1ms/step - loss: 0.4420 - val_loss: 0.3938
Epoch 17/20
363/363 [==============================] - 0s 1ms/step - loss: 0.4277 - val_loss: 0.3952
Epoch 18/20
363/363 [==============================] - 1s 1ms/step - loss: 0.4216 - val_loss: 0.3860
Epoch 19/20
363/363 [==============================] - 0s 1ms/step - loss: 0.4033 - val_loss: 0.3827
Epoch 20/20
363/363 [==============================] - 0s 1ms/step - loss: 0.3939 - val_loss: 0.4054
162/162 [==============================] - 0s 837us/step - loss: 0.4032

There's no nan here. Here are some potential solutions:

Ensure you are running the latest libraries, including TensorFlow 2.4.1. I recently updated environment.yml and requirements.txt, so you can use them to get a clean environment (see the installation instructions).
Ensure you are running the latest code (run git pull). I noticed that your code is slightly different from the one in the notebook (for example you use the default optimizer, which is "rmsprop", but the notebook uses SGD.
Ensure you run the cells in order, or else you may get unexpected results.
If you still get nan, try reducing the learning rate to a tiny amount.
Hope this helps

I found out I hadn't set the learning rate beforehand. That's why the error occurred. Thank you again. Big fan!

authors: ageron nirjann


text:
rpidistro-vlc: upgrade 3.0.12 -> 3.0.17

Connected to: https://github.com/agherzan/meta-raspberrypi/issues/1117
Update is also meant to fix bug where if x11
isn't define in DISTRO_FEATURES builds will
fail to compile.
Upgrades version of VLC to 3.0.17

Reason for update with version 3.0.12 compiling using GLESv2 and without x11/wayland defined in DISTRO_FEATURES it was proven to be difficult. Newer version of VLC appears to have better support for GLESv2 as there was far less hurdles.
SRCREV set to b276eb0d7bc3213363e97dbb681ef7c927be6c73

Updates to PACKAGECONFIG

Only include GLESv2 if opengl defined in DISTRO_FEATURES
Only include libnotify is x11 included in DISTRO_FEATURES. Not checking for wayland as well because wayland integration requires a separate MR.
remove ability to set certain flags as they require significant more development time to integrate. Probably best to include in separate MR. Flags for:

opencv
freerdp
samba
dvdread
vdpau
wayland
dca

Removal of
TARGET_CFLAGS:append = " -fcommon"
TARGET_CXXFLAGS:append = " -fcommon"

as define in configure.ac via the
3001-configure.ac-setup-for-OE-usage.patch patch
Removes extraneous sed operations in the do_configure takes. Appears to no longer be required.
Adds required flags to build that couldn't be set in configure.ac
TARGET_CFLAGS:append = " -I${STAGING_INCDIR}/drm"
TARGET_LDFLAGS:append = " ${@bb.utils.contains('DISTRO_FEATURES',  'opengl', '-lGLESv2', '', d)}"

Replaces patches with updated patches used in actual commit. Prepend patches that are from meta-openembedded/meta-multimedia to 2XXX-.patch. Adds nine new patches to fix ./configure, compile, runtime bugs.
PATCHES:

3001-configure.ac-setup-for-OE-usage.patch

Used to ensure crossed compilted OE libraries are used during compilation. Specifically for the mmal and omxil libraries.

3002-fix-EGL-macro-undeclared-and-EGLImageKHR.patch

Appears to be missing EGL preprocesor directives patch defines them. The function createImageKHR also appears to return EGLImageKHR not EGLImage patch updates. To accommodate for change.

3003-codec-omxil_core-replace-opt-vc-path-with-usr-lib.patch

Configures omxil_core.c for OE usages as libbcm_host.so and libopenmaxil.so are located in a different location.

3004-use-GLESv2-headers-over-GL-headers.patch

Ensures GLESv2 headers are used over OpenGL headers.

3005-modules-remove-glspectrum-usage.patch

Removes glspectrum plugin as it appears to require normal OpenGL and not GLESv2 for usage.

3006-codec-omxil_core.h-fix-multiple-definition-of.patch

Removes compile error due to function pointer in omxil_core.h header being defined multiple times as the header is included in multiple c source files.

3007-remove-xorg-related-link-libs.patch

Removes xorg related flags if x11 isn't defined in DISTRO_FEATURES variable.

3008-vo-Makefile.am-exclude-libgl_plugin.patch

Removes the libgl_plugin from being built if opengl not defined in DISTRO_FEATURES variable.

3009-vo-converter_vaapi-Fix-EGL-macro-undeclared.patch

Appears to be missing EGL preprocesor directives patch defines them.

@agherzan Sorry about the massive PR's Hopefully the commit message explains enough. Decided it would be best that way to save on unnecessary iterations.
Also, now it shouldn't matter what's set or not set in DISTRO_FEATURES variable. Should build just fine and display on screen, but the main distro features that I tested against where opengl x11 and wayland.
Have a branch for testing here https://github.com/EasyIP2023/meta-raspberrypi/tree/testing-two

repo: agherzan/meta-raspberrypi


text:
An example using the "real" nodes on different machines (or processes)

An example that shows how to use the distributed features on "real" nodes would be appreciated: how to create remote nodes, should listen be called only for the local node or for all nodes, how to wait until all the nodes connect, etc.

Alex:
There is a runnable example at: https://ide.c9.io/agocorona/transienttest
clone it.
execute three node processes by:
> cd transientinstaller
> ./init.sh

a cluster of three nodes will be running.
Enter in the first node at port 8080 by pointing the browser to:  http://http://transienttest-.c9users.io/
to enter in the second node: http://transienttest-agocorona.c9users.io:8081
to enter in the third node: http://transienttest-agocorona.c9users.io:8082
there you can execute a federated chat server and a map-reduce widget. Both widgets execute in the cluster.
there is a link to the source code in the example.

authors: AlexeyRaga agocorona

repo: agocorona/transient


text:
Support editing metadata without forking/cloning the project

This is to allow more people working on this without needing to know git/github and the complex command line setup of the project.

Allow editing metadata on the demo server
Simple login system to know who changed what
Automatically push the changed files to some branch/fork
Notifications (on slack?) so we know when to pull changes

Now only requires some documentation and let people know about this.


text:
Add land ownership transparency

So underlying basemap and data are still visible when it is turned on.

Would this be a fixed opacity?

Yes, fixed is fine since it's a reference layer. Same opacity value as the WRI app.

It'd be great to determine what those values are so we don't have to go hunting

I believe this is an upstream issue with the layer selector and will be fixed next dependency update.

Looks like this is good to go.

authors: nathankota steveoh

repo: agrc/plss


text:
Remove Geolocators from mapserv

Summary
http://mapserv.utah.gov/arcgis/rest/services/Geolocators
Mapserv has long hosted our geocoders in this folder. I wonder if they were originally used to back the web API geocoder? Now-a-days, the web API has it's own, dedicated ArcGIS Server instance and does not hit mapserv. No one has been updating these locators so they are likely years out-of-date. I propose that we deprecate them in favor of the web API or the downloadable locators on our website that Greg keeps up-to-date.
There is likely a bit of research that needs to be done to see what, if anything, is using these.

Triage

[x] Data Team Triage (@gregbunce)
[x] Dev Team Triage (@steveoh)
[x] Cadastre Team Triage (@rkelson)

Remove data from the following areas
~- [ ] Internal SGID (assigned to)~
~- [ ] SGID10 (assigned to)~

[x] Mapserv (@stdavis on 12/23/2022)

Update information in the following areas

[x] gis.utah.gov data pages (search for links on our website) (@gregbunce merged on 12/23/2022)
[x] Stewardship doc and sgid-index (@gregbunce on 12/12/2022)

Is there a map service?

[x] Stop (@stdavis on 12/09/2022)
[x] Delete (@stdavis on 12/22/2022)

Are there service dependencies?

[x] Validate that the web api does not query it (@steveoh)
[x] Validate that AGRC or applications widgets do not reference it (@steveoh)

Notification

[x] Intent Twitter (@steveoh)

My only concern is arcgis only clients that cannot access the web api

Great point. I forgot to mention that we may want to wait until masquerade is ready for prime time.

Let's block this until masquerade is available.
In the mean time should we start to maintain that locator?

Let's block this until masquerade is available.
In the mean time should we start to maintain that locator?

These are actually being maintained by forklift. Should we continue to provide them?

These are actually being maintained by forklift. Should we continue to provide them?

I feel like that masquerade will give much better results overall once it's up-to-snuff so we should deprecate these at that point.

I feel like that masquerade will give much better results overall once it's up-to-snuff so we should deprecate these at that point.

The next step for this might be to check the (perhaps unreliable?) ArcGIS Server usage stats for these services.

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:no_entry:

What is the appropriate messaging for this deprecation? Tweet + email? Here is some possible verbiage:

Heads up! We are deprecating our geolocator services "AddressPoints_AddressSystem" and "Roads_AddressSystem_STREET" on mapserv.utah.gov in favor of Masquerade (https://github.com/agrc/masquerade#readme) on Monday, December 5th. Please upgrade any of your projects before then.

@steveoh Can you help with getting this info out there? Does 4 weeks seem like a reasonable timeframe?

What email addresses would you use?
I'm fine to proceed without notification but a tweet seems normal and your message is good.
@brigsz, when is the next newsletter?

I don't think it is until January, around the week of the 17th is what I have on the calendar

I'm good with just a tweet. I'm not sure that the usage is enough to justify a separate spam to our newsletter email list.

/remind someone to remove the locators on december 5th

conductor results for tasks - 102

check
status

@gregbunce has completed 1 out of 3 tasks
:no_entry:

steveoh has completed 4 out of 4 tasks
:+1:

rkelson has completed 1 out of 1 tasks
:+1:

@stdavis has completed 0 out of 3 tasks
:no_entry:

I stopped the locators on both machines today.
/remind me to delete the locators in two weeks

I only saw a handful of failed requests in the logs. Hopefully everyone has migrated.

i'm reading that i can remove the reference to desktop locators on our website now.  is that correct?  sounds like we've made the appropriate notices and announcements.  just want to confirm before i do so.

verified that there were no references to the arcgis 10x locators/geocoders in the stewardship sheet and/or sgid index

PR 2174 removes references to the arcgis 10x locators from gis.utah.gov

@stdavis will you merge the above pr when you remove the locators on the 23rd? I won't be working then.

Locators have been removed from mapserv and the associated gis.utah.gov PR has been merged.

thanks for checking the box for me, @stdavis.  you beat me to it.
gis.utah.gov data pages (search for links on our website)

authors: agrc-conductor brigsz gregbunce stdavis steveoh

repo: agrc/porter


text:
🛑 SIHE UMS is down

In 122f315, SIHE UMS (https://student.sheridan.edu.au) was down:

HTTP code: 0
Response time: 0 ms

Resolved: SIHE UMS is back up in e591e16 after 14 minutes.

repo: agrez/upptime


text:
input value getting set to an empty string on blur

Description
ng-currency is setting the field to an empty value on blur
Expected outcome
The field contains the parsed number
Actual outcome
The field is emptied
Live Demo
Live Demo
Steps to reproduce

focus the field
click off the field

Browsers Affected

[x] Chrome
[ ] Firefox
[ ] Safari 9
[ ] Safari 8
[ ] Safari 7
[ ] Edge
[ ] IE 11
[ ] IE 10

Seems this only happens when you provide a min and max value.
I'll look into this tonight.

Not entirely resolved, the value is still blanked out when updateOn is set to blur:
Live Demo

repo: aguirrel/ng-currency


text:
Add a Gitter chat badge to README.md

ahaasler/hexo-multilingual now has a Chat Room on Gitter
@ahaasler has just created a chat room. You can visit it here: https://gitter.im/ahaasler/hexo-multilingual.
This pull-request adds this badge to your README.md:

If my aim is a little off, please let me know.
Happy chatting.
PS: Click here if you would prefer not to receive automatic pull-requests from Gitter in future.

I don't like this badge.

authors: ahaasler gitter-badger

repo: ahaasler/hexo-multilingual


text:
:rocket: Make the gitconfig section more accurate

:rocket: Make the gitconfig section more accurate

@ahmadawais Please review this PR

@Rizwanjamal you are not using Emoji log for commits. I cannot in right mind accept this in a repo of Emoji log. Also, I want you to use actual emoji instead of codes like :rocket: since that's a GitHub only feature.

I don't see why any of the changes here are required. So, closing the PR. Doesn't add anything useful but extra Windows related details, you can put all that in an issue and I can link it there.

Thanks a lot for taking out time :)

authors: Rizwanjamal ahmadawais

repo: ahmadawais/Emoji-Log


text:
Validation issues that were reported on Wordpress support

Hey,
I had some free time, so I've been answering some questions, found someone with some validation issues on the Wordpress support page and thought that I would add it to Github for ease.
https://wordpress.org/support/topic/help-fixing-errors/#post-8217485

Help Fixing Errors

Activated your plugin but when tested in Chrome /?amp version only shows blog posts (no other content) and chrome AMP validator notes 9 errors.

http://www.falmerinc.com

The validation issues can be found here: https://validator.ampproject.org/#url=http%3A%2F%2Fwww.falmerinc.com%2F.
I have a feeling that a lot of people might have some of these issues, partularly with the stray tags for script and style, so this might be a big issue.
Here are all of the invalidations, just in case he fixes the page:
Fonts from a custom share plugin:
The attribute 'href' in tag 'link rel=stylesheet for fonts' is set to the invalid value 'http://www.falmerinc.com/wp-content/plugins/custom-share-buttons-with-floating-sidebar/css/csbwfs.css?ver=4.6.1'.
Stray inline CSS added in the page:
The mandatory attribute 'amp-custom' is missing in tag 'style amp-custom'.
Stray script link:
The tag 'script' is disallowed except in specific forms.
Stray inline script:
The tag 'script' is disallowed except in specific forms.
DNS prefetch:
The attribute 'rel' in tag 'link rel=' is set to the invalid value 'dns-prefetch'

I think these might have been fixed so I am closing the issue. I am unsure though.

repo: ahmedkaludi/Accelerated-Mobile-Pages


text:
issue #90: update fertiscan inspection

[x] metric_type as an enum with values weight, density and volume
[x] erd update
[ ] modular functions used in update_inspection (wip)
[ ] update_inspection function
[ ] tests
[ ] documentation

closing this because this has diverted too much from #77.

repo: ai-cfia/ailab-datastore


text:
As a dev, I want the GET /inspections/<inspection_id> to return properly formatted JSON instead of a JSON string

Description
Bug Summary:
The GET /inspections/<inspection_id> endpoint currently returns a response where the JSON object is incorrectly serialized as a string. This causes issues when trying to consume the API, as clients expect the response to be a parsed JSON object, not a JSON string.
Steps to Reproduce:

Make a GET request to the /inspections/<inspection_id> endpoint.
Observe that the response contains the entire JSON payload as a string (escaped with backslashes), rather than as a properly formatted JSON object.

Expected Behavior:
The endpoint should return a properly formatted JSON object that can be directly consumed by the client without requiring additional parsing.
Actual Behavior:
The endpoint returns the JSON as a string, which requires manual deserialization to extract the data.

This will be fixed in #182.

repo: ai-cfia/fertiscan-backend


text:
Chisel&Bits/ForgeMultipart not functioning properly with Mekanism

Issue description:
When trying to use Chisel&Bits (v13.14) or ForgeMultipart (v2.2.0.41) in conjunction with Mekanism (v9.3.4-fix) for Minecraft 1.11.2, Forge version 13.20.1.2476, I get improper functionality with cables, and pipes.

When attempting to apply a 'cover' or 'bit' it simply does not work as it should/used to
Steps to reproduce:

Place Pipe/Cable
Attempt to apply ForgeMultipart or Chisel&Bits parts
Failure

Version (make sure you are on the latest version before reporting):
**Forge: 13.20.1.2476
**Mekanism: v9.3.4-fix
**Chisel&Bits: v13.14
**ForgeMultipart 2.2.0.41

What exactly does not work?

The ''Multipart'' in ForgeMultipart for example. The covers would normally snap onto the cable, but as you can see in the top picture above, I can only get the cover to snap just outside of the cable. This is the same issue with Chisel&Bits, where I can't place the bits on or around the cable

The multipart of FMP isn't supported, we only support MCMP (Mcmultipart) . The C&B is a bit weird, as that's supposed to work as it's MCMP.

I updated my Mekanism to 9.3.5.206 and reinstalled my McMultipart. But now stuff like this happens on our server

Is the rendering failing to look connected only when C&B is on the part?

Unfortunately, no. I recorded this Video Demonstration of the effect, to allow you to better understand the effect it is having when I try linking universal cables together.
The same effect can be seen when connecting to machinery as well, and stretches across all types of cable. WIth the logistical cables however, it actually stops the item from progressing through the pipe

Any chance you've tried this with just Mek/MCMultipart/C&B installed? I've not played any of Mek in 1.11 so can't say I've seen it.

Well.. That's a first.. I've played almost every version (singelplayer and multiplayer) an this is something I haven't seen before. It looks like a mod or something is messing with the blockstates causing it not to render the connection correctly.
Is the power being transferred or is it only a render glitch?

No, power has not 'apparently' been affected by this glitch. Logistic Pipes, however, as you can see in the video above, actually stops the pipe from transferring items, until said pipe is fixed. I should also remind that this is only an issue on multiplayer, and though I forgot to add before since I didn't think it was important, I am running SpongeForge 6.1.0-BETA with Nucleus 1.1.3, PEx 2.0, VoxelSniper Sponge 8.4.1, FastAsyncWorldEdit 17.09.24 and Worldedit Forge 6.1.6

UPDATE: This is definitely an issue caused directly by sponge. After temporarily removing it, the cables and pipes worked properly, as they should. I'm not sure, however, what to do now that I've figured this out

Oh good, I don't have to suggest trying without Sponge  xD
I would suggest reporting to them, as Sponge is a very invasive core mod that does things forge mods don't anticipate. That's why it gets a lot of hate.

authors: Xopharios thiakil thommy101

repo: aidancbrady/Mekanism


text:
Corrected grammar in quickstart.mdx

"are" -> "is"

thanks :) lgtm

authors: aidenybai digimunk

repo: aidenybai/million


text:
Would it make sense to add address index?

I wonder if it'd make sense to add an optional (non-default) index of all addresses?

Hi @unsystemizer,
I am currently experimenting with adding LMDB functionality to piocoin. Id this probes to be beneficial it means it would be much easier to add functionality as suggested above.

Hi @aido - that's exactly why I thought to ask.
I'd be interested to help with testing.

authors: aido unsystemizer

repo: aido/picocoin


text:
tuple index out of range

Training a Go playing AI. This means 361 features. I can fit just fine on both random forest and neural network, and random forest predicts predict_prob just fine, but I get tuple index out of range with this package (with predict_prob)
CODE:
/home/MYNAME/anaconda/lib/python2.7/site-packages/sknn/mlp.pyc in _create_specs(self, X, y)
---> 67         self.unit_counts = [numpy.product(X.shape[1:]) if self.is_convolution else X.shape[1]]
IndexError: tuple index out of range

Training set size is 150,000 samples.
envoke:
		X_train, y_train, X_test, y_test = self.load_data(move_number)
		nnet_layers = [Layer("Maxout", units=100, pieces=2), Layer('Softmax')]
		model = Classifier(layers=nnet_layers, learning_rate=0.001, n_iter=25)
		model = model.fit(X_train, y_train.astype(int)) # 0 or 1
		pickle.dump( model, open( "model"+str(move_number)+".pkl", "wb" ))

Thanks for reporting this. Can you print X_train.shape and y_train.shape and let me know what they are?

Ya let's take a look...
import cPickle as pickle
X_train = np.array( pickle.load( open( "X_train"+str(5)+".pkl", "rb" )))
y_train = np.array( pickle.load( open( "y_train"+str(5)+".pkl", "rb" )))
print X_train.shape
print X_train[0][:80]
print y_train.shape
print y_train[:80]
(132102, 361)
[ 0.5  0.5  0.5  0.5  0.5  0.5  0.5  0.5  0.5  0.5  0.5  0.5  0.5  0.5  0.5
0.5  0.5  0.5  0.5  0.5  0.5  0.5  0.5  0.5  0.5  0.5  0.5  0.5  0.5  0.5
0.5  0.5  0.5  0.5  0.5  0.5  0.5  0.5  0.5  0.5  0.5  0.5  0.5  0.5  0.5
0.5  0.5  0.5  0.5  0.5  0.5  0.5  0.5  0.5  0.5  0.5  0.5  0.5  0.5  0.5

0.5  0.5  0.5  0.5  0.5  0.5  0.5  0.5  0.5  0.5  1.   0.5  0.   0.5
0.5  0.5  0.5  0.5  0.5]
(132102,)
[ 1.  0.  1.  1.  0.  1.  1.  1.  1.  1.  1.  1.  0.  0.  1.  1.  0.  1.

I don't understand how X.shape[1] can be out of range given the shape you just showed, since it has two elements.  Are you running the latest version from PIP?

So I reinstalled everything as per these instruction:
https://github.com/aigamedev/scikit-neuralnetwork
I am using Anaconda.
Attached is a screenshot of the exact error
On Fri, Sep 25, 2015 at 10:02 AM, Alex J. Champandard <
notifications@github.com> wrote:

I don't understand how X.shape[1] can be out of range given the shape you
just showed, since it has two elements. Are you running the latest version
from PIP?
—
Reply to this email directly or view it on GitHub
https://github.com/aigamedev/scikit-neuralnetwork/issues/115#issuecomment-143284247
.

--
<(ò.Ó<)  ^(ô.ô)^  (>Ò.ó)>

I am running the latest version
On Sat, Sep 26, 2015 at 10:45 AM, Matthew Bird birdmw@gmail.com wrote:

So I reinstalled everything as per these instruction:
https://github.com/aigamedev/scikit-neuralnetwork
I am using Anaconda.
Attached is a screenshot of the exact error
On Fri, Sep 25, 2015 at 10:02 AM, Alex J. Champandard <
notifications@github.com> wrote:

I don't understand how X.shape[1] can be out of range given the shape you
just showed, since it has two elements. Are you running the latest version
from PIP?
—
Reply to this email directly or view it on GitHub
https://github.com/aigamedev/scikit-neuralnetwork/issues/115#issuecomment-143284247
.

--
<(ò.Ó<)  ^(ô.ô)^  (>Ò.ó)>

--
<(ò.Ó<)  ^(ô.ô)^  (>Ò.ó)>

On line 67 of anaconda/lib/python2.7/site-packages/sknn/mlp.py, can you print(X.shape) and confirm it's the same shape as the one you reported above?

Did you manage to get around this? I can't reproduce it with what I have on my end...

Hey Alex,
Thanks for circling back. You can close the issue if nobody else has raised
it. I am not sure what the problem was, it very well may have been on my
end. When I go to use your package again, I will make sure to contact you
if it arises again. Thanks again.
-Matthew Bird
On Wed, Nov 18, 2015 at 2:48 AM, Alex J. Champandard <
notifications@github.com> wrote:

Did you manage to get around this? I can't reproduce it with what I have
on my end...
—
Reply to this email directly or view it on GitHub
https://github.com/aigamedev/scikit-neuralnetwork/issues/115#issuecomment-157676851
.

Thanks, closing.

authors: alexjc birdmw

repo: aigamedev/scikit-neuralnetwork


text:
AssertionError: Mismatch between dataset size and units in output layer

I want to do a NN classification, I have 5 classes, so in the output layer , I have units=5 ibut I am getting this error: Mismatch between dataset size and units in output layer,
I reshaped my y_train and applied "Sigmoid" function to the output layer according to the documentation:

If you want to do multi-label classification, simply fit using a y array of integers that has multiple dimensions, e.g. shape (N, 3) for three different classes. Then, make sure the last layer is Sigmoid instead.

y_train shape is : (2115, 5)
X_train shape is : (2115, 343)
This is the code:
import sknn.mlp as mlp
from sknn.mlp import Classifier
ip_layer = mlp.Layer('Sigmoid', units=1)
hidden_layer = mlp.Layer('Tanh', units=100)
op_layer = mlp.Layer('Sigmoid', units=5)

nn = Classifier(
    [ip_layer, hidden_layer, op_layer],
    n_iter=10000
)
nn.fit(X_train, y_train)

It may be easier if you just leave out the number of output units. Can you paste the full error and tracebak?

Here is the full error:

AssertionError                            Traceback (most recent call last)
 in ()
2 clf=Classifier(([Layer("Sigmoid", units=100),
3                              Layer("Sigmoid", units=5)]), learning_rate=0.1,n_iter=100)
----> 4 clf.fit(X_train, y_train)
c:\users\sameh\scikit-neuralnetwork\sknn\mlp.py in fit(self, X, y, w)
395
396         # Now train based on a problem transformed into regression.
--> 397         return super(Classifier, self)._fit(X, yp, w)
398
399     def partial_fit(self, X, y, classes=None):
c:\users\sameh\scikit-neuralnetwork\sknn\mlp.py in _fit(self, X, y, w)
211
212         if not self.is_initialized:
--> 213             X, y = self._initialize(X, y, w)
214
215         log.info("Training on dataset of {:,} samples with {} total size.".format(data_shape[0], data_size))
c:\users\sameh\scikit-neuralnetwork\sknn\mlp.py in _initialize(self, X, y, w)
36         assert not self.is_initialized,
37             "This neural network has already been initialized."
---> 38         self._create_specs(X, y)
39
40         backend.setup()
c:\users\sameh\scikit-neuralnetwork\sknn\mlp.py in _create_specs(self, X, y)
63         else:
64             assert y is None or self.layers[-1].units == y.shape[1],
---> 65                 "Mismatch between dataset size and units in output layer."
66
67         # Then compute the number of units in each layer for initialization.
AssertionError: Mismatch between dataset size and units in output layer.

authors: alexjc sameh-habboubi


text:
modelscope issues? Failed to obtain Lora after training, please check the training process.

Hello training fails and I am seeing a lot of "modelscope" related warnings. I pasted below some portions of the log. I have updated Automatic1111 with following:  version: v1.6.0  •  python: 3.10.6  •  torch: 2.0.1+cu118  •  xformers: 0.0.17  •  gradio: 3.41.2  •  checkpoint: 18d1c095b6
2023-10-09 01:11:01,193 - modelscope - INFO - PyTorch version 2.0.1+cu118 Found.
2023-10-09 01:11:01,195 - modelscope - INFO - TensorFlow version 2.14.0 Found.
2023-10-09 01:11:01,195 - modelscope - INFO - Loading ast index from C:\Users*USER*.cache\modelscope\ast_indexer
2023-10-09 01:11:01,230 - modelscope - INFO - Loading done! Current index file version is 1.8.4, with md5 c03d7f5c980f6e54d254d7718291a800 and a total number of 902 components indexed
2023-10-09 01:11:03,388 - modelscope - INFO - Use user-specified model revision: v1.0.3
2023-10-09 01:11:04,381 - modelscope - WARNING - ('PIPELINES', 'face_recognition', 'face_recognition') not found in ast index file
2023-10-09 01:11:04,381 - modelscope - INFO - initiate model from C:\Users*USER*.cache\modelscope\hub\bubbliiiing\cv_retinafce_recognition
2023-10-09 01:11:04,381 - modelscope - INFO - initiate model from location C:\Users*USER*.cache\modelscope\hub\bubbliiiing\cv_retinafce_recognition.
2023-10-09 01:11:04,383 - modelscope - INFO - initialize model from C:\Users*USER*.cache\modelscope\hub\bubbliiiing\cv_retinafce_recognition
2023-10-09 01:11:04,414 - modelscope - WARNING - ('MODELS', 'face_recognition', 'face_recognition') not found in ast index file
2023-10-09 01:11:05,374 - modelscope - INFO - Model revision not specified, use the latest revision: v2.0.2
2023-10-09 01:11:07,511 - modelscope - INFO - initiate model from C:\Users*USER*.cache\modelscope\hub\damo\cv_resnet50_face-detection_retinaface
2023-10-09 01:11:07,511 - modelscope - INFO - initiate model from location C:\Users*USER*.cache\modelscope\hub\damo\cv_resnet50_face-detection_retinaface.
2023-10-09 01:11:07,516 - modelscope - WARNING - No preprocessor field found in cfg.
2023-10-09 01:11:07,516 - modelscope - WARNING - No val key and type key found in preprocessor domain of configuration.json file.
2023-10-09 01:11:07,516 - modelscope - WARNING - Cannot find available config to build preprocessor at mode inference, current config: {'model_dir': 'C:\Users\USER\.cache\modelscope\hub\damo\cv_resnet50_face-detection_retinaface'}. trying to build by task and model information.
2023-10-09 01:11:07,516 - modelscope - WARNING - Find task: face-detection, model type: None. Insufficient information to build preprocessor, skip building preprocessor
2023-10-09 01:11:07,517 - modelscope - INFO - loading model from C:\Users*USER*.cache\modelscope\hub\damo\cv_resnet50_face-detection_retinaface\pytorch_model.pt
D:\sda1111\venv\lib\site-packages\torchvision\models_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
warnings.warn(
D:\sda1111\venv\lib\site-packages\torchvision\models_utils.py:223: UserWarning: Arguments other than a weight enum or None for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing weights=None.
warnings.warn(msg)
2023-10-09 01:11:07,957 - modelscope - INFO - load model done
2023-10-09 01:11:08,614 - modelscope - INFO - load facefusion models done
2023-10-09 01:11:08,614 - modelscope - INFO - init done
2023-10-09 01:11:08,617 - modelscope - WARNING - No preprocessor field found in cfg.
2023-10-09 01:11:08,617 - modelscope - WARNING - No val key and type key found in preprocessor domain of configuration.json file.
2023-10-09 01:11:08,617 - modelscope - WARNING - Cannot find available config to build preprocessor at mode inference, current config: {'model_dir': 'C:\Users\USER\.cache\modelscope\hub\bubbliiiing\cv_retinafce_recognition'}. trying to build by task and model information.
2023-10-09 01:11:08,617 - modelscope - WARNING - No preprocessor key ('face_recognition', 'face_recognition') found in PREPROCESSOR_MAP, skip building preprocessor.
2023-10-09 01:11:08,619 - modelscope - INFO - image face recognition model init done
2023-10-09 01:11:09,600 - modelscope - INFO - Use user-specified model revision: v2.0.2
2023-10-09 01:11:12,023 - modelscope - INFO - initiate model from C:\Users*USER*.cache\modelscope\hub\damo\cv_resnet50_face-detection_retinaface
2023-10-09 01:11:12,023 - modelscope - INFO - initiate model from location C:\Users*USER*.cache\modelscope\hub\damo\cv_resnet50_face-detection_retinaface.
2023-10-09 01:11:12,026 - modelscope - WARNING - No preprocessor field found in cfg.
2023-10-09 01:11:12,026 - modelscope - WARNING - No val key and type key found in preprocessor domain of configuration.json file.
2023-10-09 01:11:12,027 - modelscope - WARNING - Cannot find available config to build preprocessor at mode inference, current config: {'model_dir': 'C:\Users\USER\.cache\modelscope\hub\damo\cv_resnet50_face-detection_retinaface'}. trying to build by task and model information.
2023-10-09 01:11:12,027 - modelscope - WARNING - Find task: face-detection, model type: None. Insufficient information to build preprocessor, skip building preprocessor
2023-10-09 01:11:12,028 - modelscope - INFO - loading model from C:\Users*USER*.cache\modelscope\hub\damo\cv_resnet50_face-detection_retinaface\pytorch_model.pt
2023-10-09 01:11:12,335 - modelscope - INFO - load model done
2023-10-09 01:11:13,315 - modelscope - INFO - Use user-specified model revision: v1.0.0
2023-10-09 01:11:13,741 - modelscope - INFO - initiate model from C:\Users*USER*.cache\modelscope\hub\damo\cv_u2net_salient-detection
2023-10-09 01:11:13,741 - modelscope - INFO - initiate model from location C:\Users*USER*.cache\modelscope\hub\damo\cv_u2net_salient-detection.
2023-10-09 01:11:13,743 - modelscope - INFO - initialize model from C:\Users*USER*.cache\modelscope\hub\damo\cv_u2net_s

sorry，the log relate to modelscope seems to be WARNING and INFO, you may provide more details about the training function stack

Thanks for your response, so you don't think that's why it failed? The log on A1111 seems empty. Any other place where I can get more details?

close modelscope INFO & WARNING， you could add  code below after import modelscope by yourself

from modelscope.utils.logger import get_logger
logger = get_logger()
logger.setLevel(logging.ERROR)

I mean i haven't  found the truely error in your log， if that‘s all， your could provide snapshot or your terminal screen,. Help us to find the BUG

I got the same error after I finished training the model.
Steps: 100%|█████████████████████████████████████████████| 800/800 [36:27<00:00,  2.63s/it, lr=5e-5, step_loss=0.00327] saving checkpoint: outputs\easyphoto-user-id-infos\Lea_1\user_weights\checkpoint-800.safetensors 03/06/2024 21:23:54 - INFO - __main__ - Saved state to outputs\easyphoto-user-id-infos\Lea_1\user_weights\checkpoint-800.safetensors, outputs\easyphoto-user-id-infos\Lea_1\user_weights\checkpoint-800 Steps: 100%|█████████████████████████████████████████████| 800/800 [36:28<00:00,  2.63s/it, lr=5e-5, step_loss=0.00271] saving checkpoint: outputs\easyphoto-user-id-infos\Lea_1\user_weights\pytorch_lora_weights.safetensors UNet2DConditionModel: 64, 8, 768, False, False HTTP Request: POST http://127.0.0.1:7860/api/predict "HTTP/1.1 200 OK" HTTP Request: POST http://127.0.0.1:7860/api/predict "HTTP/1.1 200 OK" loading u-net: <All keys matched successfully> loading vae: <All keys matched successfully> loading text encoder: <All keys matched successfully> HTTP Request: POST http://127.0.0.1:7860/api/predict "HTTP/1.1 200 OK" You have disabled the safety checker for <class 'diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint.StableDiffusionInpaintPipeline'> by passing safety_checker=None. Ensure that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered results in services or applications open to the public. Both the diffusers team and Hugging Face strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling it only for use-cases that involve analyzing network behavior or auditing its results. For more information, please have a look at https://github.com/huggingface/diffusers/pull/254 . You have loaded a UNet with 4 input channels which. {'variance_type', 'sample_max_value', 'timestep_spacing', 'solver_order', 'euler_at_final', 'prediction_type', 'lambda_min_clipped', 'use_karras_sigmas', 'dynamic_thresholding_ratio', 'lower_order_final', 'use_lu_lambdas', 'final_sigmas_type', 'algorithm_type', 'thresholding', 'solver_type'} was not found in config. Values will be initialized to default values. 03/06/2024 21:24:03 - INFO - __main__ - Running validation error, skip it.Error info: UNet2DConditionModel.forward() got an unexpected keyword argument 'added_cond_kwargs'. HTTP Request: POST http://127.0.0.1:7860/api/predict "HTTP/1.1 200 OK" 2024-03-06 21:24:06,141 - modelscope - INFO - Use user-specified model revision: v1.0.3 2024-03-06 21:24:06,667 - modelscope - WARNING - ('PIPELINES', 'face_recognition', 'face_recognition') not found in ast index file 2024-03-06 21:24:06,668 - modelscope - INFO - initiate model from C:\Users\patho\.cache\modelscope\hub\bubbliiiing\cv_retinafce_recognition 2024-03-06 21:24:06,668 - modelscope - INFO - initiate model from location C:\Users\patho\.cache\modelscope\hub\bubbliiiing\cv_retinafce_recognition. 2024-03-06 21:24:06,671 - modelscope - INFO - initialize model from C:\Users\patho\.cache\modelscope\hub\bubbliiiing\cv_retinafce_recognition 2024-03-06 21:24:06,676 - modelscope - WARNING - ('MODELS', 'face_recognition', 'face_recognition') not found in ast index file HTTP Request: POST http://127.0.0.1:7860/api/predict "HTTP/1.1 200 OK" 2024-03-06 21:24:08,721 - modelscope - INFO - Model revision not specified, use revision: v2.0.2 2024-03-06 21:24:10,537 - modelscope - INFO - initiate model from C:\Users\patho\.cache\modelscope\hub\damo\cv_resnet50_face-detection_retinaface 2024-03-06 21:24:10,537 - modelscope - INFO - initiate model from location C:\Users\patho\.cache\modelscope\hub\damo\cv_resnet50_face-detection_retinaface. 2024-03-06 21:24:10,544 - modelscope - WARNING - No preprocessor field found in cfg. 2024-03-06 21:24:10,545 - modelscope - WARNING - No val key and type key found in preprocessor domain of configuration.json file. 2024-03-06 21:24:10,545 - modelscope - WARNING - Cannot find available config to build preprocessor at mode inference, current config: {'model_dir': 'C:\\Users\\patho\\.cache\\modelscope\\hub\\damo\\cv_resnet50_face-detection_retinaface'}. trying to build by task and model information. 2024-03-06 21:24:10,545 - modelscope - WARNING - Find task: face-detection, model type: None. Insufficient information to build preprocessor, skip building preprocessor 2024-03-06 21:24:10,547 - modelscope - INFO - loading model from C:\Users\patho\.cache\modelscope\hub\damo\cv_resnet50_face-detection_retinaface\pytorch_model.pt 2024-03-06 21:24:11,075 - modelscope - INFO - load model done HTTP Request: POST http://127.0.0.1:7860/api/predict "HTTP/1.1 200 OK" 2024-03-06 21:24:12,003 - modelscope - INFO - load facefusion models done 2024-03-06 21:24:12,003 - modelscope - INFO - init done 2024-03-06 21:24:12,009 - modelscope - WARNING - No preprocessor field found in cfg. 2024-03-06 21:24:12,009 - modelscope - WARNING - No val key and type key found in preprocessor domain of configuration.json file. 2024-03-06 21:24:12,009 - modelscope - WARNING - Cannot find available config to build preprocessor at mode inference, current config: {'model_dir': 'C:\\Users\\patho\\.cache\\modelscope\\hub\\bubbliiiing\\cv_retinafce_recognition'}. trying to build by task and model information. 2024-03-06 21:24:12,009 - modelscope - WARNING - No preprocessor key ('face_recognition', 'face_recognition') found in PREPROCESSOR_MAP, skip building preprocessor. 2024-03-06 21:24:12,011 - modelscope - INFO - image face recognition model init done 2024-03-06 21:24:12,017 - modelscope - WARNING - task face_recognition input definition is missing C:\Users\patho\.cache\modelscope\modelscope_modules\cv_retinafce_recognition\image_face_recognition\matlab_cp2tform.py:81: FutureWarning: rcondparameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions. To use the future default and silence this warning we advise to passrcond=None, to keep using the old, explicitly pass rcond=-1`.
r, _, _, _ = lstsq(X, U)
2024-03-06 21:24:13,332 - modelscope - INFO - model inference done
2024-03-06 21:24:13,332 - modelscope - WARNING - task face_recognition output keys are missing
2024-03-06 21:24:14,183 - modelscope - INFO - model inference done
HTTP Request: POST http://127.0.0.1:7860/api/predict "HTTP/1.1 200 OK"
2024-03-06 21:24:14,776 - modelscope - INFO - model inference done
2024-03-06 21:24:15,177 - modelscope - INFO - model inference done
2024-03-06 21:24:15,638 - modelscope - INFO - model inference done
2024-03-06 21:24:16,182 - modelscope - INFO - model inference done
2024-03-06 21:24:16,728 - modelscope - INFO - model inference done
HTTP Request: POST http://127.0.0.1:7860/api/predict "HTTP/1.1 200 OK"
2024-03-06 21:24:17,573 - modelscope - INFO - model inference done
2024-03-06 21:24:18,409 - modelscope - INFO - model inference done
2024-03-06 21:24:19,078 - modelscope - INFO - model inference done
2024-03-06 21:24:19,862 - modelscope - INFO - model inference done
HTTP Request: POST http://127.0.0.1:7860/api/predict "HTTP/1.1 200 OK"
2024-03-06 21:24:20,563 - modelscope - INFO - model inference done
2024-03-06 21:24:20,854 - modelscope - INFO - model inference done
2024-03-06 21:24:21,430 - modelscope - INFO - model inference done
error at: OpenCV(4.9.0) D:\a\opencv-python\opencv-python\opencv\modules\imgproc\src\imgwarp.cpp:2748: error: (-215:Assertion failed) src.cols > 0 && src.rows > 0 in function 'cv::warpAffine'
2024-03-06 21:24:22,076 - modelscope - INFO - model inference done
2024-03-06 21:24:22,186 - modelscope - INFO - model inference done
2024-03-06 21:24:22,240 - modelscope - INFO - model inference done
2024-03-06 21:24:22,292 - modelscope - INFO - model inference done
2024-03-06 21:24:22,346 - modelscope - INFO - model inference done
2024-03-06 21:24:22,398 - modelscope - INFO - model inference done
2024-03-06 21:24:22,449 - modelscope - INFO - model inference done
2024-03-06 21:24:22,559 - modelscope - INFO - model inference done
2024-03-06 21:24:22,669 - modelscope - INFO - model inference done
2024-03-06 21:24:22,771 - modelscope - INFO - model inference done
2024-03-06 21:24:22,871 - modelscope - INFO - model inference done
2024-03-06 21:24:22,949 - modelscope - INFO - model inference done
2024-03-06 21:24:23,001 - modelscope - INFO - model inference done
2024-03-06 21:24:23,052 - modelscope - INFO - model inference done
error at: OpenCV(4.9.0) D:\a\opencv-python\opencv-python\opencv\modules\imgproc\src\imgwarp.cpp:2748: error: (-215:Assertion failed) src.cols > 0 && src.rows > 0 in function 'cv::warpAffine'
Dectect no face in training data, move last weights and validation image to best_outputs
Traceback (most recent call last):
File "E:\AI\stable-diffusion-webui\stable-diffusion-webui\extensions\sd-webui-EasyPhoto\scripts\train_kohya\train_lora.py", line 1390, in
main()
File "E:\AI\stable-diffusion-webui\stable-diffusion-webui\extensions\sd-webui-EasyPhoto\scripts\train_kohya\utils\gpu_info.py", line 195, in wrapper
result = func(*args, **kwargs)
File "E:\AI\stable-diffusion-webui\stable-diffusion-webui\extensions\sd-webui-EasyPhoto\scripts\train_kohya\train_lora.py", line 1362, in main
copyfile(t_result_list[0][1], os.path.join(best_outputs_dir, os.path.basename(t_result_list[0][1])))
IndexError: list index out of range
Steps: 100%|█████████████████████████████████████████████| 800/800 [36:56<00:00,  2.77s/it, lr=5e-5, step_loss=0.00271]
HTTP Request: POST http://127.0.0.1:7860/api/predict "HTTP/1.1 200 OK"
Traceback (most recent call last):
File "C:\Users\patho\AppData\Local\Programs\Python\Python310\lib\runpy.py", line 196, in _run_module_as_main
return _run_code(code, main_globals, None,
File "C:\Users\patho\AppData\Local\Programs\Python\Python310\lib\runpy.py", line 86, in _run_code
exec(code, run_globals)
File "E:\AI\stable-diffusion-webui\stable-diffusion-webui\venv\lib\site-packages\accelerate\commands\launch.py", line 989, in
main()
File "E:\AI\stable-diffusion-webui\stable-diffusion-webui\venv\lib\site-packages\accelerate\commands\launch.py", line 985, in main
launch_command(args)
File "E:\AI\stable-diffusion-webui\stable-diffusion-webui\venv\lib\site-packages\accelerate\commands\launch.py", line 979, in launch_command
simple_launcher(args)
File "E:\AI\stable-diffusion-webui\stable-diffusion-webui\venv\lib\site-packages\accelerate\commands\launch.py", line 628, in simple_launcher
raise subprocess.CalledProcessError(returncode=process.returncode, cmd=cmd)
subprocess.CalledProcessError: Command '['E:\AI\stable-diffusion-webui\stable-diffusion-webui\venv\Scripts\python.exe', 'E:\AI\stable-diffusion-webui\stable-diffusion-webui\extensions\sd-webui-EasyPhoto\scripts\train_kohya/train_lora.py', '--pretrained_model_name_or_path=extensions\sd-webui-EasyPhoto\models\stable-diffusion-v1-5', '--pretrained_model_ckpt=models\Stable-diffusion\Chilloutmix-Ni-pruned-fp16-fix.safetensors', '--train_data_dir=outputs\easyphoto-user-id-infos\Lea_1\processed_images', '--caption_column=text', '--resolution=512', '--random_flip', '--train_batch_size=1', '--gradient_accumulation_steps=4', '--dataloader_num_workers=0', '--max_train_steps=800', '--checkpointing_steps=100', '--learning_rate=0.0001', '--lr_scheduler=constant', '--lr_warmup_steps=0', '--train_text_encoder', '--seed=535413', '--rank=128', '--network_alpha=64', '--validation_prompt=easyphoto_face, easyphoto, 1person', '--validation_steps=100', '--output_dir=outputs\easyphoto-user-id-infos\Lea_1\user_weights', '--logging_dir=outputs\easyphoto-user-id-infos\Lea_1\user_weights', '--enable_xformers_memory_efficient_attention', '--mixed_precision=fp16', '--template_dir=extensions\sd-webui-EasyPhoto\models\training_templates', '--template_mask', '--merge_best_lora_based_face_id', '--merge_best_lora_name=Lea_1', '--cache_log_file=E:\AI\stable-diffusion-webui\stable-diffusion-webui\outputs/easyphoto-tmp/train_kohya_log.txt', '--validation']' returned non-zero exit status 1.
2024-03-06 21:24:25,349 - EasyPhoto - Error executing the command: Command '['E:\AI\stable-diffusion-webui\stable-diffusion-webui\venv\Scripts\python.exe', '-m', 'accelerate.commands.launch', '--mixed_precision=fp16', '--main_process_port=3456', 'E:\AI\stable-diffusion-webui\stable-diffusion-webui\extensions\sd-webui-EasyPhoto\scripts\train_kohya/train_lora.py', '--pretrained_model_name_or_path=extensions\sd-webui-EasyPhoto\models\stable-diffusion-v1-5', '--pretrained_model_ckpt=models\Stable-diffusion\Chilloutmix-Ni-pruned-fp16-fix.safetensors', '--train_data_dir=outputs\easyphoto-user-id-infos\Lea_1\processed_images', '--caption_column=text', '--resolution=512', '--random_flip', '--train_batch_size=1', '--gradient_accumulation_steps=4', '--dataloader_num_workers=0', '--max_train_steps=800', '--checkpointing_steps=100', '--learning_rate=0.0001', '--lr_scheduler=constant', '--lr_warmup_steps=0', '--train_text_encoder', '--seed=535413', '--rank=128', '--network_alpha=64', '--validation_prompt=easyphoto_face, easyphoto, 1person', '--validation_steps=100', '--output_dir=outputs\easyphoto-user-id-infos\Lea_1\user_weights', '--logging_dir=outputs\easyphoto-user-id-infos\Lea_1\user_weights', '--enable_xformers_memory_efficient_attention', '--mixed_precision=fp16', '--template_dir=extensions\sd-webui-EasyPhoto\models\training_templates', '--template_mask', '--merge_best_lora_based_face_id', '--merge_best_lora_name=Lea_1', '--cache_log_file=E:\AI\stable-diffusion-webui\stable-diffusion-webui\outputs/easyphoto-tmp/train_kohya_log.txt', '--validation']' returned non-zero exit status 1.
Applying attention optimization: Doggettx... done.`

authors: Patholog-CZ k8kiss wuziheng

repo: aigc-apps/sd-webui-EasyPhoto


text:
chore(tests): Replace svelte-client.patch with --conditions=browser

Using --conditions=browser with Bun makes it import client-sided libraries instead of server-sided libraries, namely the Svelte runtime.

Merged into #40

repo: aigis-llm/aigis


text:
Improve deploy-related scripts

Change summary:

Remove unnecessary -R option from chown and chmod
Add _gcp suffix to gcp ip address files
Update node param-related scripts for onprem hosting
Rename port number directory in blockchain data uploading

Related issues:

https://github.com/ainblockchain/ain-blockchain/issues/1285

Thanks for the review! @jiyoung-an Now merging..

repo: ainblockchain/ain-blockchain


text:
EHLO command returns all bunch of 250- data, even when the hooks returns otherwise

In the current state of the code, some data are pushed BEFORE calling the hook, including
    250-{hostname}
    250-SIZE ...
    250-8BITMIME
    250-SMTPUTF8
    250-STARTTLS

And AFTER that, the hook is called.
The issue is that if the hook doesn't return a 2xx response, the server sends a mixed response and makes the clients fails.
The EHLO command should call the hook, and depending on the response, send all the push commands afterward.

Just trying to understand here - is there a case where you would want to return a different response to the EHLO and still succeed?
I know personally I haven't had a need for that, but I'm not sure what the valid use cases are.

I just did some searching and I haven't been able to find a really great reason that EHLO should be returning anything besides a 250.
The only reason I can think of that you might be returning something different is that you've got some kind of broken setup - which indicates that you need to fix your code, not that there's an issue here.
But if there's some case that I have considered, I'd love to hear about it!

Hi @waynew ! I do have an example :)
When the server connects to the SMTP daemon, the HELO/EHLO command can allow many checks that can lead to refusal:

checking if the IP has a PTR record
checking that the hostname matches the PTR of that the SPF records allow the IP to send emails
Checking that the sending IP is not on a blacklist (Spamhaus for instance)

If one of those checks fails, you can return a 4xx/5xx to refuse the connection, before going further.
But with the EHLO command, aiosmtpd has already returned 250-* messages before sending that 4xx/5xx code, so it's not good.
Hope that makes sense :)

Hey @cnicodeme yeah, thanks! That definitely makes sense. Do you have any ideas about how we would want to change this? Pull requests of course are always welcome :)

I've sent a PR (#157). Let me know what you think.
The PR also kind of join the idea of having None returned for the hooks instead of MISSING. (see #154 )

If there is no way to override the 250 response, how can I test that my SMTP client can fall back to HELOwhen EHLO fails?

Answering my own question, overriding smtp_EHLO() is the only way.

authors: agronholm cnicodeme waynew

repo: aio-libs/aiosmtpd


text:
Organize 4.0.0 change list

Keeping all changes in a single list means users only have to look
in one place when they upgrade.
In this case, the "Removed" item was really obvious, but in general
a single list is a good practice, I think.
A single list is also recommended by http://keepachangelog.com/
which I'm a big fan of.
[ci skip]

Ben, just wanted to run this by you in case you had strong feelings about "Removed" vs. "Changed"

I don't actually have a strong preference one way or the other. I guess I just wanted to put the "Removed" piece in bold to help make it stand out in case any users ran into this.  Last time something was removed from PaperTrail there was a lot of questions filed in the issues tracker about what happened with it if I remember correctly. Then again, I didn't really start following good semantic versioning practices until prep for 4.0.0

authors: batter jaredbeck


text:
The comment of appendAt in MvRxExtensions.kt is wrong

The example gives in the comment of appendAt is wrong.

@luozejiaqun This PR has a ton of changes that seem unrelated.

Sorry, my fault. I will close the PR. This is just a reminder. You can correct the error yourself.

authors: gpeal luozejiaqun

repo: airbnb/MvRx


text:
Remove Kotlin-Reflect entirely

I was able to fully remove kotlin-reflect without changing any functionality, annotation processors, or separating @PersistState to its own module. It also doesn't rely on Kotlin's @Metadata annotation at all.
These were all trivial except for @PersistState. I tried a few methods including using the kotlinx-metadata-jvm library which is significantly smaller than kotlin-reflect. I got it to work but was able to remove the need for even that.
The comments in PersistState.kt should be clear enough to understand what it is doing. If it is not, let me know and I can add clarifying details.
@rossbacher This seems to work with proguard. I added a keystore to the counter app and ran proguard on it and it still worked. You are the expert so please let me know if there are any concerns here. The only thing this relies on is the copy$default static function and componentN functions to exist and have their original name. I think this will also make https://github.com/airbnb/MvRx/pull/331 redundant.

App
Size (kb)

App with AppCompat
2240

w/ RxJava + RxAndroid
2912

w/ MvRx + kotlin-reflect (1.3.0)
3848

w/ MvRx + kotlin-metadata-jvm
3340

w/ MvRx + this change
3000

Thanks to @ZacSweers for pointing me in the direction of copy$default and the property mask.
Fixes https://github.com/airbnb/MvRx/issues/274

@gpeal Yes, I'll have a deeper look in a bit into this but you are right that this will make #331 obsolete.

@gpeal I think we can also get rid of some of the config that is now no needed anymore, I already made some progress today with that (which made the sample app a bit smaller as fewer things actually need to be kept).
But I ran into a few instances where I don't understand yet, why the rules are still needed (e.g. for the cache prewarming)
I need to dig a bit deeper on Monday.

@rossbacher The cache warning shouldn't be needed anymore and was removed in this PR
https://github.com/airbnb/MvRx/pull/334/files#diff-d6da0afe1caeee8562010718cd2c4257L83

@gpeal Exactly that confused me. Looking into that.

@gpeal I updated the proguard config of sample and the lib itself to reflect the removal of Kotlin reflection This should again help to make apps smaller. Though very little in the demo apps as they only have very few States and Models.

Thanks @rossbacher!
@BenSchwab @elihart want to also review this?

Thanks @rossbacher!
@BenSchwab @elihart want to also review this?

Actually I has missed @ZacSweers comment, just addressed it and updated the config.

authors: gpeal rossbacher


text:
Use reconfigure API to apply updates CollectionView on iOS 15

As discussed in this thread:

When using cell prefetching, watch out for cases where the data for a cell changes after the cell has been prefetched, but before it becomes visible (thus it won't be included in visibleCells).

The current logic relies on cellForItem(at:), which is documented as:

Gets the visible cell object at the specified index path.

As such, this method may not necessarily return a cell instance dequeued via prefetching when applying updates. We should verify whether this is the case or not, as this could potentially result in inconsistent cell data.

Clarified with Tyler Fox in a follow-up—we should be good since cellForItem(at:) will return prefetched but not visible cells.

repo: airbnb/epoxy-ios


text:
Fix example project

The Podfile in the example project referenced lottie-ios version 1.0.0 but in 1.0.1 we changed the prefix from "LA" to "LOT."" So currently the example project doesn't build.
I've added a Gemfile and updated the npm command to run CocoaPods via bundle exec, which ensures the right version of CocoaPods.
There is some risk that user doesn't have Bundler but I think it should be fairly straightforward as to how to get it.
I ran the example project. It looks odd on iPad but I expect that was the case before. Here it is on iPhone

@bachand I just updated this in  #98 (which this is now conflicting with), although I didn't add a gemfile and changes to use the bundler. Think you can rebase on top of these changes?

@lelandrichardson I rebased and then ran through the steps again and it seems to be working!

Thanks!

authors: bachand lelandrichardson

repo: airbnb/lottie-react-native


text:
No way to set a "title" attribute on input fields

In many browsers, when you hover over an input field, text that looks similar to alt text appears (see the "Search" text in the tooltip below the search field):

https://www.w3.org/WAI/tutorials/forms/labels/#using-the-title-attribute
On images this is the alt attribute. On input fields, it is the title attribute. Currently there is no way to set a title attribute on the Start or End date fields.
By default in Chrome, the text in the tooltip defaults to "Please fill out this field" if nothing is set. When implementing the Date Range Picker for a client, they requested we set custom text for this; I had to tell them this was not currently possible.
Adding this issue here as I am thinking of drafting a PR for this, but would like to get some outside input, especially from an accessibility perspective. I've not been able to find a ton of information about the accessibility concerns regarding this, but will leave the following resource to get the conversation started:
https://stackoverflow.com/questions/56947455/specifying-both-aria-label-and-title

I have started a PR for this. It still needs Tests & Documentation:
https://github.com/airbnb/react-dates/pull/1862
@majapw would love to get some input from you on this, as I'm sure you've got a bit more accessibility experience :)

@majapw and @ljharb this now has a PR with full Tests & Storybook Documentation

repo: airbnb/react-dates


text:
chore(package): update react-with-styles to version 3.2.3

Previous version is breaking (resolving to 3.2.2)

For context for anyone else: https://github.com/airbnb/react-dates/issues/1720

@majapw @ljharb Could either of you please take a look when you have the chance? This dependency is breaking the master build of a large product

Coverage remained the same at 84.916% when pulling 37b282542adb88a78b0a80e5ce7318cef8dee8d4 on greenkeeper/react-with-styles-3.2.3 into 121fc443631efaea3e2dbee80a5695c0666599ea on master.

This isn’t a chore, and isn’t necessary, since 3.2.3 is included in the semver range for ^3.2.0.
What problem is this solving for you that an updated lockfile wouldn’t?

Greenkeeper provided the commit message and I figured it was configured well enough for this task. You've got a point there, and we don't have a lockfile to begin with, so I surmised that whatever built entity we're pulling from ends up old.
I'll see about fixes that don't necessitate this change on our end, though I don't see much harm in cutting a new minor to explicitly avoid a broken dep.

It’d be a new patch, and it’s fine to do the update; but if you don’t have a lockfile then rm -rf node_modules && npm install should be installing the latest version of r-w-s.

Yes, sorry, misspoke (:s/minor/patch). I think our issue is coming from our use of artifactory.

I’ve definitely seen issues where artifactory fails to pick up public updates; if that’s the case then this PR wouldn’t fix your problem, it would make react-dates unusable.

I don't follow that conclusion, but yes I do think seeing about refreshing artifactory should be my focus.

The conclusion is because the implication is that your artifactory doesn't contain v3.2.3 of r-w-s at all.

authors: coveralls ljharb oshanley scottsheffield


text:
Cant drag map on ios apple maps when using markers with image/custom view

If I use a marker with image or a custom view, then i can not drag the map if clicking on the marker. This becomes a bigger problem if there are many markers on the map, and there isn't much space left to drag the map.
I created a sample repo to demonstrate the problem.
https://github.com/alexbuicescu/react-native-maps-example

+1 here
Unfortunately it's a problem you don't notice it until you have 10+ marker on the map 😢

Same problem here, under Android everything works well.
React Native: v0.47.1
Maps: v0.16.0

I confirm this only affects iOS not Android

I made some test, and the issue starts after 045ec7eee5f90d06b00f1eb624453d6a7d02df92.
But you will note it only after this one dd1b8aa9499769dda008cbba464ea9b7a5c21369, because the above commit will cause compilation errors.
cc @Benjamin-Dobell @foyarash @christopherdro
https://github.com/airbnb/react-native-maps/pull/1360 https://github.com/airbnb/react-native-maps/pull/1485

@delvedor - I doubt that the issue starts with the commits you mention. I Have this behavior for a year now. Initially I switched to google maps - there we don't have the issue, but we have another bug - high CPU usage, so it's a no go on iOS :(...
Is this something we can fix? You don't need to have 100 markers for this. Even if you have one custom marker and you won't be able to drag the map when the drag initiates on the marker view. So I guess that nearly everyone is affected by this, just very few are noticing this.

Same problem
any solutions ?

For some unknown reason it works now fine for me.
react-native-maps: 0.15.3 react-native:0.46.4

@mtt87 - okay strange. I'm on 0.16.3 with rn 0.47.2 and I experience this. And as I said above -we always had this...

@compojoom yeah it's really strange, I can't tell why it works now and it's been a month I think already.
I haven't done any change to the code regarding maps.
Anyway I will soon try https://github.com/mapbox/react-native-mapbox-gl as I've heard it's much more stable and working as expected.

Having this issue as well. Very frustrating. Need to find a workaround for iOS because markers can't be used, it makes it so userunfriendly..

Also having this issue on react-native: 0.58.6 and react-native-maps: 0.22.1

authors: alexbuicescu compojoom delvedor hkxicor mtt87 ruankranz vemundeldegard

repo: airbnb/react-native-maps


text:
Add diagnostics to discovery announcer

Sometimes there are gaps in the discovery announcment, so add logging to
determine where gaps are happening.

Looks good

Assuming travis passes, looks good.

authors: dain electrum nileema

repo: airlift/airlift