CSS does not load properly if there are any assert statements related to iframe

I have to test drag and drop functionality in a iframe. We can generate forms using this functionality.
The left side of the iframe holds the widgets and the empty container in the middile and a form to the right.
TestCase: Dropping a 'Text Area' or any widget should load a new form with radio buttons, few input fields and a unique string dynamically (using Jquery). This form varies from widget to widget.
This dynamic form is not loading properly in some cases. The radio buttons are displayed as input field of type text.
What I observed is that the rendering of this form fails if I am trying to perform any element presence or trying to use getText(), isPresent() or sendKeys() (on the elements inside the iframe) after switching into the iframe.
Scenario 1: OK
Form is loading properly
var widgets = element.all(by.css('css')); var emptyContainer = element.all(by.id('id'));  widgets.each(function(widget){ browser.actions().mouseDown(widget).mouseMove(emptyContainer).mouseUp().perform(); });
Scenario 2: FAIL
In this case, the form and it's components(radio buttons, unique number)  are loaded properly for the first iteration and for the rest(12) the radio buttons are loaded as input field of type text. You can see I am testing radio button presence for each widget.
browser.driver.switchTo() .frame('frameName').then(function(){ var widgets = element.all(by.css('css')); var emptyContainer = element.all(by.id('id'));  widgets.each(function(widget){ browser.actions() .mouseDown(widget) .mouseMove(emptyContainer) .mouseUp() .perform() then(function(){ var radioButton = element(by.css('css')); radioButton.isPresent().then(function(ispresent){ expect(ispresent).toBe(true); }); }); }) });
Scenario 3: FAIL
In this case I am trying to find if a widget is present or not. I am doing this before performing drag&drop.
browser.driver.switchTo() .frame('frameName').then(function(){
var widgets = element.all(by.css('css'));
var textAreaWidget = element(by.id('id'));
textAreaWidget .isPresent().then(function(ispresent){
expect(ispresent).toBe(true);
});
var emptyContainer = element.all(by.id('id'));
widgets.each(function(widget){
browser.actions()
.mouseDown(widget)
.mouseMove(emptyContainer)
.mouseUp()
.perform()
then(function(){
var radioButton = element(by.css('css'));
radioButton.isPresent().then(function(ispresent){
expect(ispresent).toBe(true);
});
});
})
});
The jquery execution is terminated somehow if I am trying to do anything other than dropping the widgets. Is there any way to wait for jquery to complete it's execution on  iframe?
Bug report

Node Version: 8.11.3
Protractor Version: 5.3.2
Browser(s): chrome 67.0.3396.99
webdriver-manager: 12.1.0

Hi, @autowala ! Could you please provide your config file and test example.

`var Jasmine2HtmlReporter = require('../node_modules/protractor-jasmine2-html-reporter');
var jasmineReporters = require('../node_modules/jasmine-reporters');
module.exports = {
//seleniumAddress: 'http://localhost:4444/wd/hub',
directConnect: true,
specs: [],
suites: {
//dummy: '../e2e/dummyspec.js',
//smoke: '../e2e/smokespec.js',
},
capabilities: {
browserName: 'chrome',
chromeOptions: {
prefs: {
'credentials_enable_service': false,
'profile': {
'password_manager_enabled': false
}
},
args: [
'--disable-extensions',
'--start-maximized',
'--disable-cache',
'--disable-infobars',
'--disable-application-cache',
'--disable-offline-load-stale-cache',
'--disk-cache-size=0',
'--v8-cache-options=off'
]
}
},
framework: 'jasmine2',
jasmineNodeOpts: {
onComplete: null,
isVerbose: false,
showColors: true,
includeStackTrace: true,
defaultTimeoutInterval: 7500000,
allScriptsTimeout: 5000000,
getPageTimeout: 200000,
rootElement: 'html',
stopSpecOnExpectationFailure: true,
restartBrowserBetweenTests: true
},
onPrepare: function () {
browser.driver.manage().window().maximize();
browser.manage().timeouts().implicitlyWait(5000);
browser.manage().timeouts().setScriptTimeout(90000);
browser.manage().timeouts().pageLoadTimeout(20000);
browser.driver.getCapabilities().then(function (caps) {
browser.browserName = caps.get('browserName');
browser.capabilities = caps;
});
return browser.getProcessedConfig().then(function (config) {
var browserName = config.multiCapabilities.browserName;
var suiteName = config.suite;
jasmine.getEnv().addReporter(new jasmineReporters.JUnitXmlReporter({
consolidateAll: true,
savePath: '../../testreports' + suiteName + '/',
filePrefix: browserName + '' + suiteName,
modifySuiteName: function (generatedSuiteName, suite) {
return browserName + '.' + generatedSuiteName;
}
}));
jasmine.getEnv().addReporter(new Jasmine2HtmlReporter({
savePath: '../../testreports' + '' + suiteName + '/',
filePrefix: browserName + '' + suiteName,
modifySuiteName: function (generatedSuiteName, suite) {
// this will produce distinct suite names for each capability,
// e.g. 'firefox.login tests' and 'chrome.login tests'
return browserName + '.' + generatedSuiteName;
}
}));
});
},
params: {
server: {
}
}
}
`
Examples are in initial comment.

Great! Can you share link to test application or app with similar structure? Because for me your config file looks good. Regarding isPresent and isDisplayed functions I can say that sometimes they are not applicable to elements and always return false for these elements for example. I faced such issues)

Thanks for the quick response. I am sorry I can not share the details you requested.
I just commented out the getText() assertions and it seems working. There is nothing wrong with isDisplayed and isPresent. The jquery  simply stops appending the content to the iframe if I use getText.

Thanks for the quick response. I am sorry I can not provide link.
I will share all the details that relates to the issue.
I am using isElementPresent instead of isPresent and the form is loading peoperly. I also commented out the getText assertions. I don't why using isPresent isDisplayed getText making the protractor not to wait for jquery.

@autowala What do you mean when say that protractor does not wait for jquery? What is jquery for you? You always could find source of isPresent etc. and take a look at code to figure out what is happen there :)

@CrispusDH jquery renders elements to the form in my case. When I am trying to use isPresent getText CSS classes are not loading properly for which I think jquery is execution is not completed.

@autowala , could you please provide these assertions (I mean related to mentioned isPresent and getText functions)?

var radioButton = element(by.css('css'));
radioButton.isPresent().then(function(ispresent){
expect(ispresent).toBe(true);

Please take a look at the initial comment to know where I am using this code.

@autowala , you can add additional wait to ensure that element was loaded and properly handled and then apply isPresent to it or use DOM element property offsetParent with additional wait.

@IgorSasovets  sharing the test application link will clearly give you the idea of this issue.
The iframe I am working on has three sections. The first section contains widgets(Draggable elements), the middle section is a empty container(Drop section), the third section contains a form. By default the page loads all the widgets. When I drag a widget on to the container the third section is loaded dynamically with a form of text input fields, radio buttons. One of this input fields holds a unique string which is generated after dropping the widget. So if two widgets are dropped, clicking on any widget will trigger the jquery to load(actually appends divs) it's related form in the third section.
wait may help if I am trying perform anything after dropping a widget.
The weird part is i'm using isPresent getText on the already loaded elements such as the widgets and then performing the dragAndDrop. The CSS fails to load properly. But if I remove
them it works fine. Below is the flow
**
CASE 1

switch to frame
verify presence of elements(eg., widgets) (isPresent, isDisplayed, getText) // if this code is removed
the form loads fine
drag and drop widgets**

In this case the form loads for the first widget and fails for the rest.
**
CASE 2

switch to frame
start for each
drag and drop a widget
verify if form is loaded with all the elements and a unique string // if this code is removed form loads
fine
end for each**

Any point if isPresent  getText isDisplayed is executed from then the form's CSS fails to load. The radio buttons are loaded as text input fields. The unique string is not generated.

Hm, how waiters can prevent loading?

@CrispusDH  hi, are we on same page?

@autowala , I mean browser.sleep function that will freeze test execution for some time (in your case time needed to upload third section components) and after that continue tests but with already loaded UI components. I know that using browser.sleep is not a good practice but sometimes it's really helpful.

@IgorSasovets  @raj8github  I do use sleep when it is taking more for the element to load.
The assertions will fail if I'm trying on elements which are not in DOM yet. In my case the third section loads if i'm not asserting anything. I am using for each loop to drop the elements. Even without any sleeps the third section is loading. It only fails to load when there are any assert statements with isPresent ``getText inside the for each or above the for each.
If wait is the solution in my case should I be placing that after every getText ``isPresent statements? Since the form loads without any sleeps.

also one more issue might be for loops and promises. please check on that as for loops might not wait for promises yo resolve so thats why you might be just seeing widgets being dragged but form elements not showing up.. not very sure on this please double check

and it seems to be more of placement of waits and assertions to cater the tests than issue with protractor itself. @IgorSasovets? might be a stackoverflow query?

@raj8github In the actual code addwidget is a separate function which performs mouse functions. In the for each I am calling this function and in the callback function I am making the assertions. Only commenting out the assertions is helping.
I even tried this
function sleep(){
return browser.sleep(1000);
}

function addWidget(widget){
return browser.actions()
.mouseDown(widget)
.mouseMove(emptyContainer)
.mouseUp()
.perform()
.then(function(){
return sleep();
})
}

function addAllWidget(){
widgets.each(function(widget){
addWidget(widget).then(function(){
sleep().then(function(){
expect(someFormElement.getText()).toMatch('');
});
});
});
}

replacing isPresent  with browser.isElementPresent solved the issue partially. isPresent will wait for the angular if this is the problem I'm using coun() then calling count() should also cause the issue in my case. Currently i'm trying to find alternative for getText. It seems browser.getText is deprecated in the latest version.


[#2PlaysAMonth]: Customizable College Curriculum Manager

What's the Play about?
Title: "Customizable College Curriculum Manager"
Objective: The goal of this project is to create an interactive, user-friendly application that allows college students to manage their earned credits and create a customized course flow based on those credits.
The application will allow students to view their current credit balance and track their progress toward their degree. Students can search for courses offered by the college and view details about each course, including the number of credits it awards and the prerequisites required.
They can then add courses to their custom curriculum and arrange them in a personalized order that best fits their schedule and learning goals. The application will provide a visual representation of the student's progress and highlight any prerequisites that need to be fulfilled before they can take certain courses. Students will be able to collaborate with peers, share their custom curriculum with classmates, and receive feedback from instructors.
The application will be designed to be accessible and responsive on a variety of devices, including desktops, laptops, and mobile devices.
What ReactJS concept will be used to create this play?
This React project will be developed using the following technologies:
React.js
ReactFlow
JavaScript
HTML/CSS
A backend database for storing and retrieving data about students, courses, and curricula.
APIs for fetching course information and other relevant data from the college's systems.
Code of Conduct

[X] I agree to follow this project's Code of Conduct

Very interesting idea.


Support instantiating ES classes

I would expect this code example to work:
var di = require('didi');

class Car {
    start() {
        console.log('success!');
    }
}

Car.$inject = [];

var module = {
    'car': ['type', Car],
};

var injector = new di.Injector([module]);
injector.invoke(['car', function (car) {
    car.start();
}]);

Instead i get this error:
didi\dist\didi.js:316
    return fn.apply(context, dependencies);
              ^

TypeError: Class constructor Car cannot be invoked without 'new'

Which is pretty much self explanatory, apparently classes are not supported (yet).
This is contradicted in the readme however:

 // if an object asks for 'car', the injector will call new Car(...) to produce it
'car': ['type', Car],

Feature implemented via https://github.com/nikku/didi/commit/99bccd5912e523f417c8599180972d68ff6eee16.
Give it a try.

Thanks. This does work.


Add a CI with Git actions

Use git actions CI to test the PRs.

As part of #15 , I wanted to change test.yaml to run it on PRs. But only owners and members of the project can do so.
Here is the change I propose:
- on: [push]
+  push:
+    branches:
+      - master
+  pull_request:
+    branches:
+      - master

I have added that and PR now requires the tests to pass.
I guess I can close this ticket


Support default export in namespaced components syntax

What problem does this feature solve?
Since version 3.2.0 Vue supports Namespaced Components syntax. However, it expects all components to be exported as named exports, therefore this example does not work.
What does the proposed API look like?
I would simply like to use the following syntax:
// components/Dropdown.js
export default function Dropdown() {}
export function Toggle() {}

<script setup>
  import * as Dropdown from 'components/Dropdown'
</script>

<template>
  <Dropdown>
    <Dropdown.Toggle>click me</Dropdown.Toggle>
  </Dropdown>
</template>

instead of
<template>
  <Dropdown.default>...</Dropdown.default>
  <Dropdown.Dropdown>...</Dropdown.Dropdown>
</template>

Also, while we're on the subject. Why doesn't this syntax work when components are imported outside of script-setup (in a regular script block). is there any reason for this limitation? Thank you for your consideration.

I think this is not really feasable as it's ambiguous.  an non-namespaced Component means "use the value of the variable of this name as the component.
You now want it to mean: "Use the default property on the variable with this name as the component  if ...", well, if what?
It could be a runtime check, but then it would break this component:
export default defineComponent({
  default: /* something a user validly defined on the component using this key */
})

Also, what's wrong with named exports?

Why doesn't this syntax work when components are imported outside of script-setup (in a regular script block). is there any reason for this limitation? Thank you for your consideration.

Don't see a reason, could be a bug.


Trouble with fetch_openml

Hi! I was having trouble with fetch_openml. Here is the code line and the Error message :
Code : mnist = fetch_openml(dataname="MNIST original")
Error : TypeError: fetch_openml() got an unexpected keyword argument 'dataname'
I tried to change the name, but that didn't help.
Does anyone know how I can fix this Error?

@GiaGoswami if you want to use mnist you could do this mnist = fetch_openml('mnist_784', version = 1)

Thank you.

Never mind

Note that since Scikit-Learn 0.24, fetch_openml() returns a Pandas dataframe by default, which breaks the code in the book. So you need to add the argument as_frame=False to ask the function to return a NumPy array, as before.


Please merge #1f1592edce7122ff657d9538d58d9cce0cae46e6 into dev channel

commit(s) to merge: 1f1592edce7122ff657d9538d58d9cce0cae46e6
merge instructions: clean merge
This adds a Android manifest check for CrOS which we would like to get into the Flutter release.
/cc @dgrove @kevmoo @mit-mit @whesse @athomas

Merged to Version 2.3.0-dev.0.2


Bato fresh branch

Might have warning and stuff (but at least it's working)
I'll pr a issues fix later (or someone else can clean up)
Edit: Cleaned up
Checklist:

[x] Updated source's version for individual source changes
[x] Updated all sources' versions for template changes
[x] Set appropriate nsfw value
[x] Did not change id even if a source's name or language were changed
[x] Tested the modifications by running it on the simulator or a test device

can you add a domain switcher (like nepnep) for #112
wto.to is just bato.to but not blocked in some countries

Added support for mirror domain from issue #112

Added all the changes requested.


Describe the security limitation?

You pointed one of this limitation
This is not security. obfuscate_id was created to lightly mask record id numbers for the casual user. If you need to really secure your database ids (hint, you probably don't), you need to use real encryption like AES.
So it means the obfuscated ids can be reverse engineered to lookup the exact id in the db? From brute-force?
You also said, using AES would be much better but it isnt recommended. Thats contradicting on its own.
In gist, if the app needs to be secure, what are the measures?

I think what the author is getting at is that the obfuscated id produced is predictable. The gem behind the integer hashing is buy the same author and claims to be reversible, where as AES is not a hash and cannot be reverse, but an encryption that requires a key to decrypt making identifying the actual DB id nearly impossible.
In my opinion a plain ID, an obfuscated id and an encrypted id are all face the same security issues for a REST API or something. It's SQL injection you need to worry about. In which case AES would obviously be most affective at preventing an injection that could modify someones email address based on ID, but the real need to is to secure the app from SQL injection.

Hi @arjunmenon , this gem is not for crypto, it is about obfuscation.
Since this repository is kind deprecated, I created a gem with Hashids support, but it still about obfuscation: https://github.com/wbotelhos/idy
I would love your feedback.


Added LLM wrapper for the HuggingFace text-generation API

Added Feature for HF text-generation LLM wrapper
Basically just adds a feature (and simple tests) to allow usage of the HuggingFace text-generation LLM inference API. It works really well for both streaming and non-streaming outputs.

Fixes # (issue)

Issue 4433

Before submitting

Example Usage:
# Both of these examples assume you already have a local LLM server
# running. See the HuggingFace text-generation documentation for more
# information on how to do this.
from langchain.llms import HuggingFaceTextgen

prompt = "What is Deep Learning?"
host = "localhost"
port = 8080

# Basic example (no streaming)
llm = HuggingFaceTextgen(host=host, port=port)
print(llm(prompt))

# Streaming response example
from langchain.callbacks import streaming_stdout

callbacks = [streaming_stdout.StreamingStdOutCallbackHandler()]
llm = HuggingFaceTextgen(
    host=host,
    port=port,
    callbacks=callbacks,
    stream=True
)
print(llm(prompt))

Who can review?
Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested:

Feature added in this overlapping PR. Closing this PR now.


initial setup of troubleshoot executable

Issue #, if available:
Description of changes:
Initial setup for the use of troubleshoot executable.
This will be followed by porting over, piecemeal, the functionality of the support package to interface with this binary rather than using the troubleshoot go library.
The replicatedhq/troubleshoot binary is already built into our tools image -- see https://github.com/aws/eks-anywhere-build-tooling/pull/42
part of https://github.com/aws/eks-anywhere/issues/188
By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.

/retest


feat(licensed-users): add licensed users download link

What does this pull request do
Add licensed users download link to RBAC plugin
Which issue(s) does this PR fix

Fixes https://issues.redhat.com/browse/RHIDP-3603

Request that came from sprint review: Can you update the download text to "Download User List"  and also "hyperlink " or underline the text to allow users to know it is clickable.


Failed to compile Linux QT wallet

g++: error: /home/exp/wallets/GirlsToken-2.0/src/leveldb/libleveldb.a: No such file or directory
g++: error: /home/exp/wallets/GirlsToken-2.0/src/leveldb/libmemenv.a: No such file or directory
Makefile:320: recipe for target 'GirlsToken2.0.1' failed
make: *** [GirlsToken2.0.1] Error 1

will look into it immediately, however i am currently on  vacation using a 3g connection, so may take me a short while for the fix. Thank you for reporting this to me 😊

Ok, i have pushed a small fix, however am about 300 km away from my build machine. If you go to your GirlsToken-2.0 folder and do a "git pull" and then try building again all should work.
Please let me know 😊

Thanks. Better but still not ok
/usr/bin/ld : ne peut trouver -lboost_system-mgw49-mt-s-1_55
/usr/bin/ld : ne peut trouver -lboost_filesystem-mgw49-mt-s-1_55
/usr/bin/ld : ne peut trouver -lboost_program_options-mgw49-mt-s-1_55
/usr/bin/ld : ne peut trouver -lboost_thread-mgw49-mt-s-1_55
collect2: error: ld returned 1 exit status
make: *** [GirlsToken2.0.1] Erreur 1

I have the v1.58 of Boost and no problem to compile other wallets.

Ok if you look at that error, it is trying to use boost 1.55, which is what i used to compile. If you go into the girlstoken-qt.pro file you will see the dependancies that i used. You need to change the numbers to match the versions you are using.
On Jul 27, 2017 5:31 AM, anemol<EMAIL_ADDRESS>wrote:
Thanks. Better but still not ok
/usr/bin/ld : ne peut trouver -lboost_system-mgw49-mt-s-1_55
/usr/bin/ld : ne peut trouver -lboost_filesystem-mgw49-mt-s-1_55
/usr/bin/ld : ne peut trouver -lboost_program_options-mgw49-mt-s-1_55
/usr/bin/ld : ne peut trouver -lboost_thread-mgw49-mt-s-1_55
collect2: error: ld returned 1 exit status
make: *** [GirlsToken2.0.1] Erreur 1
I have the v1.58 of Boost and no problem to compile other wallets.
—
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I deleted and git clone again
qmake Linux-qt.pro
/media/SSD/Workspace/GirlsToken-2.0/Linux-qt.pro:141: Parse Error ('   �')
/media/SSD/Workspace/GirlsToken-2.0/Linux-qt.pro:141: Unterminated conditional block at end of file
Error processing project file: Linux-qt.pro

Ok, so I have uploaded a pre-built linux binary on the "releases" page ->
https://github.com/l8nit3tr0ubl3/GirlsToken-2.0/releases/download/<IP_ADDRESS>/GirlsToken2.0.1
However if you would like to build your own please follow the steps below:

ensure all dependencies are satisfied:
sudo apt-get install libdb5.1-dev libdb5.1++-dev libboost-all-dev libqrencode-dev qt4-qmake libqt4-dev build-essential libboost-dev libboost-system-dev libboost-filesystem-dev libboost-program-options-dev libboost-thread-dev libssl-dev automake
enter 'src' and build daemon to ensure leveldb is built correctly:
cd src && make -f makefile.unix
move back to main directory and build qt wallet:
cd ../ && qmake-qt4 linux-qt.pro && make

Hope this helps :)

Your fixes are good.
Deamon compilation  and Qt-wallet compilation are ok now.
Thanks :)

Glad i could get it fixed for you 😊 sorry for the delay


Support for elm-optimize-level-2

As far as I see it, there currently is no support for elm-optimize-level-2, right? It would be a bit of work to implement. Probably you'd want to not hard-code elm make anymore and make it elm-optimize-level-2, and possibly also support the --optimize-speed flag.
Or am I missing something?

Okay, I have made mkElmDerivation more generic. You can supply your own buildPhase and installPhase now, and they will run in an environment with all of the required elm packages installed.

Isn't elm-optimize-level-2 already packaged? I mean, it's in nixpkgs.elmPackages.elm-optimize-level-2.

Ahh, that would have been a good thing for me to check! I will try making a simple project and testing if my changes to mkElmDerivation make it suitable to package it.

Okay, I can get this to work correctly with my changes. I have an example derivation in the tests directory.

Is that a suitable solution for you? I think it is better to have an unopinionated approach that allows a user to be more flexible. Otherwise, I worry that I will end up with a lot of nix functions for software that I don't use, and will struggle to maintain.

Give me a few days to actually test this :)
Thanks for the work you put in thus far!

Phew! Finally tested this, successfully. Thanks for the hard work!


Prefab edits are reset on play (FTV-244)

Note: this bug only applies to prefabs, importing as a GameObject works as expected.
Repro:

Import Kitchen USD as prefab
Transform any object
Run the project

On run, the chair will return to the original position, but it should be transformed.

I believe this may have been resolved by PR #160 but we need to verify

Confirmed with @judubu that this bug is fixed in https://github.com/Unity-Technologies/usd-unity-sdk/pull/160. This issue had the wrong internal ID linked in the title (now updated).


Test sound

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  Misses            ?      439
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TensorFlowLite.h No such file or directory

Hi, may I know which TensorFlow Lite library did you include in the code. I have tried using Arduino_TensorFlowLite and TensorFlowLIte_ESP32 provided by Arduino IDE.

I think it is Arduino_TensorFlowLite 1.15.0-ALPHA.

It gave an error "am_bsp.h: No such file or directory" when I run the handwriting_recognizer sketch.

am_bsp.h is specific to the Sparkfun Artemis, try to comment out the include header in the arduino_command_responder.cpp.


Update action logic to work on commits

What does this PR do?
Work on updating the action logic to work based on pushes
Motivation
See https://github.com/DataDog/integrations-core/pull/3745
Review checklist (to be filled by reviewers)

[ ] PR title must be written as a CHANGELOG entry (see why)
[ ] Files changes must correspond to the primary purpose of the PR as described in the title (small unrelated changes should have their own PR)
[ ] PR must have changelog/ and integration/ labels attached
[ ] Feature or bugfix must have tests
[ ] Git history must be clean
[ ] If PR adds a configuration option, it must be added to the configuration file.

We found the bug


Automatic PR from RestAPI-PR454

Generated from RestAPI PR: https://github.com/Azure/azure-rest-api-specs/pull/454

Coverage decreased (-0.3%) to 58.371% when pulling 3e2d766967a2ff12d3e6803578429e21ea36dc73 on AutorestCI:RestAPI-PR454 into 84478ced9aa28555015a15b379e18d698b5ae3fe on Azure:dev.


Assert an enum variant exists

As per @robinkrahl's request (https://github.com/nvzqz/static-assertions-rs/issues/2#issuecomment-465547413), I made an attempt at this but it doesn't necessarily work. The only limited working form is if $v is an ident or $(tt)+ where only assert_variant!(Thing, A); works and neither of the others does. And even then, it still requires the #![feature(type_alias_enum_variants)] attribute for some reason on nightly.
Below is my current (failing) attempt at a solution:
#![feature(underscore_const_names)]
#![feature(type_alias_enum_variants)]

enum Thing {
    A,
    B { x: () },
    C(()),
}

macro_rules! assert_variant {
    ($e:ty, $v:pat) => {
        const _: fn($e) -> () = |e| match e {
            <$e>::$v => {},
            _ => {},
        };
    }
}

assert_variant!(Thing, A);
assert_variant!(Thing, B { .. });
assert_variant!(Thing, C(_));

fn main() {}

Ideally, the best solution would allow for:
assert_variant!(Thing, A);
assert_variant!(Thing, B);
assert_variant!(Thing, C);

assert_variant!(Thing, A, B, C);

I made an attempt to implement this:

macro_rules! assert_variant {
    ($e:ident: $($v:ident),+ $(,)?) => {
        const _: fn() = || {
            $(
                use $e::$v;
            )+
        };
    };
}

#[cfg(test)]
mod tests {

    enum Thing {
        A,
        B { x: () },
        C(()),
    }

    #[test]
    fn should_compile() {
        assert_variant!(Thing: A);
        assert_variant!(Thing: B);
        assert_variant!(Thing: C);

        assert_variant!(Thing: A, B, C);
    }
}


Error occured in Behavior

We are trying rollba-php v1.6.3 with CakePHP3.5.16 and PHP 7.0.21.
We were not able to find the CakePHP’s quick tutorial in docs.
https://docs.rollbar.com/docs/php#section-quick-start .
I edited bootstrap.php like that:
Rollbar::init(
  array(
    'access_token' => 'MY_TOKEN',
    'environment' => env('CAKE_ENV'),
    "root" => "/" . basename(dirname(APP))
  )
);

And I overrided ErrorHandler.
class AppErrorHandlerMiddleware extends ErrorHandlerMiddleware
{
    public function handleException($exception, $request, $response)
    {
        if (env('CAKE_ENV') == "production") {
            Rollbar::log(Level::ERROR, $exception, [$request, $response]);
        }
        parent::handleException($exception, $request, $response);
    }

    protected function handleInternalError($response)
    {
        if (env('CAKE_ENV') == "production") {
            Rollbar::log(Level::ERROR, $response);
        }
        parent::handleInternalError($response);
    }
}

Is that OK?

But we’d like to use rollbar because it is very FANTASTIC, we think.
Do you have any idea?

@ArturMoczulski can you take a look at this?

@kaibadash When you initialize the Rollbar object. you need to turn of the auto handler
Rollbar::init($this->_options, false, false, false);

Thank you! I'll try that!

@kaibadash did turning off the autoloader resolve your issue?

Sorry, I was too busy to check...
I absolutely will do...

This is most likely due to CakePHP putting PDO objects in the $response or $request variables you are passing to \Rollbar\Rollbar::log. Please make sure, that you either remove those from the data passed Rollbar or convert them into non-PDO objects, like arrays.


wrong node relation in node status cause workflow stuck

Summary
We have a special workflow, still stuck.
Every leaf nodes have successed but one task group node keep staying at Running state.
I dont know how to fix this, pls help @alexec @simster7 @terrytangyuan
Diagnostics
Argo Workflow Version: v3.0.1
Node created by task Lv2-node-2 didn't link to others, means this DAG's node status is splited into two part.
When evaluate DAG's phase from root this node is ignored, but still it's one of root node's target tasks, so phase at this branch treated as Running, leads to DAG's Running phase and workflow stuck.
The relation of all tasks has shown below, note that this bug triggered when leaf node Lv2-node-1 run first, then Lv2-node-2. Leaf node sorted at here.

workflow:
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
  creationTimestamp: "2021-04-08T09:22:03Z"
  generateName: xxx-
  generation: 4
  labels:
    workflows.argoproj.io/creator: system-serviceaccount-argo-argo-server
    workflows.argoproj.io/phase: Running
  managedFields:
  - apiVersion: argoproj.io/v1alpha1
    fieldsType: FieldsV1
    fieldsV1:
      f:metadata:
        f:generateName: {}
        f:labels:
          .: {}
          f:workflows.argoproj.io/creator: {}
      f:spec:
        .: {}
        f:arguments: {}
        f:entrypoint: {}
        f:templates: {}
      f:status:
        .: {}
        f:finishedAt: {}
    manager: argo
    operation: Update
    time: "2021-04-08T09:22:03Z"
  - apiVersion: argoproj.io/v1alpha1
    fieldsType: FieldsV1
    fieldsV1:
      f:metadata:
        f:labels:
          f:workflows.argoproj.io/phase: {}
      f:status:
        f:artifactRepositoryRef: {}
        f:conditions: {}
        f:nodes: {}
        f:phase: {}
        f:progress: {}
        f:resourcesDuration: {}
        f:startedAt: {}
    manager: workflow-controller
    operation: Update
    time: "2021-04-08T09:22:38Z"
  name: xxx-xjx94
  namespace: argo
  resourceVersion: "205205"
  uid: d48a35fb-c5b1-472b-bb21-264b05e2e53d
spec:
  arguments:
    parameters:
    - name: dummy-params
      value: '[]'
    - name: params
      value: '[{"message":"whalesay bug reproducing"}]'
  entrypoint: main
  templates:
  - container:
      args:
      - '{{inputs.parameters.message}}'
      command:
      - cowsay
      image: docker/whalesay
      name: ""
      resources: {}
    inputs:
      parameters:
      - name: message
    metadata: {}
    name: whalesay
    outputs: {}
  - dag:
      tasks:
      - arguments:
          parameters:
          - name: message
            value: '{{inputs.parameters.message}}'
        name: task
        template: whalesay
    inputs:
      parameters:
      - name: message
    metadata: {}
    name: task-template
    outputs: {}
  - dag:
      tasks:
      - arguments:
          parameters:
          - name: message
            value: '{{item.message}}'
        name: Lv1-node-1
        template: task-template
        withParam: '{{workflow.parameters.dummy-params}}'
      - arguments:
          parameters:
          - name: message
            value: '{{item.message}}'
        name: Lv1-node-2
        template: task-template
        withParam: '{{workflow.parameters.dummy-params}}'
      - arguments:
          parameters:
          - name: message
            value: '{{item.message}}'
        dependencies:
        - Lv1-node-1
        - Lv1-node-2
        name: Lv2-node-1
        template: task-template
        withParam: '{{workflow.parameters.params}}'
      - arguments:
          parameters:
          - name: message
            value: '{{item.message}}'
        dependencies:
        - Lv1-node-1
        name: Lv2-node-2
        template: task-template
        withParam: '{{workflow.parameters.dummy-params}}'
    inputs: {}
    metadata: {}
    name: main
    outputs: {}
status:
  artifactRepositoryRef:
    default: true
  conditions:
  - status: "False"
    type: PodRunning
  finishedAt: null
  nodes:
    xxx-xjx94:
      children:
      - xxx-xjx94-3761629639
      - xxx-xjx94-3778407258
      displayName: xxx-xjx94
      finishedAt: null
      id: xxx-xjx94
      name: xxx-xjx94
      phase: Running
      progress: 1/1
      startedAt: "2021-04-08T09:22:03Z"
      templateName: main
      templateScope: local/xxx-xjx94
      type: DAG
    xxx-xjx94-967649888:
      boundaryID: xxx-xjx94
      children:
      - xxx-xjx94-3236009130
      displayName: Lv2-node-1
      finishedAt: "2021-04-08T09:22:38Z"
      id: xxx-xjx94-967649888
      name: xxx-xjx94.Lv2-node-1
      phase: Succeeded
      progress: 1/1
      resourcesDuration:
        cpu: 25
        memory: 25
      startedAt: "2021-04-08T09:22:03Z"
      templateName: task-template
      templateScope: local/xxx-xjx94
      type: TaskGroup
    xxx-xjx94-1017982745:
      boundaryID: xxx-xjx94
      displayName: Lv2-node-2
      finishedAt: "2021-04-08T09:22:03Z"
      id: xxx-xjx94-1017982745
      name: xxx-xjx94.Lv2-node-2
      phase: Succeeded
      startedAt: "2021-04-08T09:22:03Z"
      templateName: task-template
      templateScope: local/xxx-xjx94
      type: TaskGroup
    xxx-xjx94-3236009130:
      boundaryID: xxx-xjx94
      children:
      - xxx-xjx94-3600482713
      displayName: Lv2-node-1(0:message:whalesay bug reproducing)
      finishedAt: "2021-04-08T09:22:38Z"
      id: xxx-xjx94-3236009130
      inputs:
        parameters:
        - name: message
          value: whalesay bug reproducing
      name: xxx-xjx94.Lv2-node-1(0:message:whalesay bug reproducing)
      outboundNodes:
      - xxx-xjx94-3600482713
      phase: Succeeded
      progress: 1/1
      resourcesDuration:
        cpu: 25
        memory: 25
      startedAt: "2021-04-08T09:22:03Z"
      templateName: task-template
      templateScope: local/xxx-xjx94
      type: DAG
    xxx-xjx94-3600482713:
      boundaryID: xxx-xjx94-3236009130
      displayName: task
      finishedAt: "2021-04-08T09:22:29Z"
      hostNodeName: k3d-argo-server-0
      id: xxx-xjx94-3600482713
      inputs:
        parameters:
        - name: message
          value: whalesay bug reproducing
      name: xxx-xjx94.Lv2-node-1(0:message:whalesay bug reproducing).task
      outputs:
        exitCode: "0"
      phase: Succeeded
      progress: 1/1
      resourcesDuration:
        cpu: 25
        memory: 25
      startedAt: "2021-04-08T09:22:03Z"
      templateName: whalesay
      templateScope: local/xxx-xjx94
      type: Pod
    xxx-xjx94-3761629639:
      boundaryID: xxx-xjx94
      children:
      - xxx-xjx94-967649888
      displayName: Lv1-node-1
      finishedAt: "2021-04-08T09:22:03Z"
      id: xxx-xjx94-3761629639
      name: xxx-xjx94.Lv1-node-1
      phase: Succeeded
      progress: 1/1
      resourcesDuration:
        cpu: 25
        memory: 25
      startedAt: "2021-04-08T09:22:03Z"
      templateName: task-template
      templateScope: local/xxx-xjx94
      type: TaskGroup
    xxx-xjx94-3778407258:
      boundaryID: xxx-xjx94
      children:
      - xxx-xjx94-967649888
      displayName: Lv1-node-2
      finishedAt: "2021-04-08T09:22:03Z"
      id: xxx-xjx94-3778407258
      name: xxx-xjx94.Lv1-node-2
      phase: Succeeded
      progress: 1/1
      resourcesDuration:
        cpu: 25
        memory: 25
      startedAt: "2021-04-08T09:22:03Z"
      templateName: task-template
      templateScope: local/xxx-xjx94
      type: TaskGroup
  phase: Running
  progress: 1/1
  resourcesDuration:
    cpu: 25
    memory: 25
  startedAt: "2021-04-08T09:22:03Z"

Message from the maintainers:
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.

I think Lv2-node-2 should link to a pod node created by Lv2-node-1 but when Lv2-node-2 initialize, this pod node cannot be found as outbound node

And there's another problem, this workflow cannot be stopped or terminated, dont know why. We have to delete it.

Can you please confirm if this is an issue in v2.12?

Can you please confirm if this is an issue in v2.12?

just confirmed, same issue in v2.12

hi @alexec
may i ask the progress about this issue


Provide capability to define autoscaling rules

Proposal
Allow end-users to specify how many instances they want to add/remove when one of the trigger criteria have been met. This allows them to have better control over the aggressiveness of their platform.
In terms of logic, the idea is to define the following:

When queue count is > 1000, add 2 instances and wait for 15 min to scale out more
When queue count is < 100, remove 1 instance and wait 5 min to scale in more

Or even more advanced by using multiple triggers:

When queue count is > 1000 (trigger 1) and DB CPU is <60 (trigger 2), add 2 instances
When queue count is < 100 (trigger 1) or DB CPU > 80 (trigger2), remove 1 instance

The important aspects here are:

Scale in/out can have different amount of replicas to add/remove
End-users can define cooldown periods during which no further scaler is added
Combination of triggers can be used, either with an OR or AND fashion.

This would work similarly to traditional autoscalers such as Azure Monitor Autoscale:

Use-Case
While today end-users can already define the triggers for scaling in/out, sometimes this can be confusing and people want to have more control about when scaling will happen and how many instances will be added.
For example, they might want to scale out more aggressively than KEDA does or scale in less aggressively.
Anything else?
I believe these 2 "scaling flavors" should be offered next to each other and am not sure if we should mix it with the current approach.
The current way of scaling definitely still has its place and should not be removed.

Is this something anyone is working on?
It would be a great addition to avoid flapping (https://learn.microsoft.com/en-us/azure/azure-monitor/autoscale/autoscale-flapping).

Not at the moment because we need to spec it out and probably will face issues since we rely on the HPA but I agree this would be a good one to have because of flapping indeed.
If it would be specced out, would you be interested to contributing to this?
@zroubalik We are blocked because of HPA, right?

I've never written a single line of go in my life but I would be happy to help in any way I can.

+1, If this is something getting prioritized, Will be happy to contribute

Definately open to contributions, @nagesh-salunke!


Hide y-scrollbar at rotate-down-animations

Cannot hide scrollbar because small devices need scrollbar. Bad animations are deleted.


Getting 500 internal server error from the Ebay API.

Here is the code
$sdk = new DTS\eBaySDK\Sdk([
    'apiVersion' => '1.13.0',
    'Finding'    => [
        'apiVersion' => '1.13.0'
    ],
    'credentials' => [
        'appId'  => '<my-key>',
        'devId'  => '<my-key>',
        'certId' => '<my-key>'
    ]
]);

$finding = $sdk->createFinding();

$request = new FindItemsByKeywordsRequest();

$request->keywords = 'Harry Potter';

$request->paginationInput = new PaginationInput();
$request->paginationInput->entriesPerPage = 25;
$request->paginationInput->pageNumber = 1;

$request->sortOrder = 'CurrentPriceHighest';

try {
    $response = $finding->findItemsByKeywords($request);
} catch (Exception $e) {
    var_dump($e->getMessage());
}

I get this error message... truncated.

Server error: POST http://svcs.ebay.com/services/search/FindingService/v1 resulted in a 500 Internal Server Error response:\n

I followed the example on getting started which gets the time from the API, which worked. However, this is giving me some issues. What am I doing wrong here?
Note: I dumped the $body variable in callOperationAsync() and got this:
1
25
CurrentPriceHighest
Harry Potter

Shouldn't this be in XML format?

Ensure that you credentials are not blank or contain spaces at the start and end. Also ensure that you are not passing your sandbox credentials by mistake.

Sandbox credentials don't work for this?

The SDK has no way to determine which environment the credentials belong to. By default it will assume you want to connect to the production site unless you use  the  [sandbox]http://devbay.net/sdk/guides/guide/configuration.html#sandbox) configuration option to tell it otherwise.

Ok it's all working great now. Thank you.
But I'm noticing it fails about 50% of the time due to timeout, is this a common issue with eBay servers?


OCPBUGS-23668: Bump Kubernetes dependencies

Tentatively address an information exposure through error stack trace.

/jira refresh

/jira refresh


Error & Critical level logging issue

Messages used in error and critical level logging are displayed separately before log message as logged in formatted manner
Sample output:
16:50:01.581 main . DEBU 001 debug ******
16:50:01.583 main . INFO 002 info
16:50:01.584 main . NOTI 003 notice
16:50:01.584 main . WARN 004 test
this is error level logging message
16:50:01.585 main . ERRO 005 this is error level logging message
this is critical level message
16:50:01.587 main . CRIT 006 this is critical level message
code example:
package main
import (
"os"
"github.com/op/go-logging"

)
var log = logging.MustGetLogger("example")
// Example format string. Everything except the message has a custom color
// which is dependent on the log level. Many fields have a custom output
// formatting too, eg. the time returns the hour down to the milli second.
var format = logging.MustStringFormatter(
"%{color}%{time:15:04:05.000} %{shortfunc} . %{level:.4s} %{id:03x}%{color:reset} %{message}",
)
// Password is just an example type implementing the Redactor interface. Any
// time this is logged, the Redacted() function will be called.
type Password string
func (p Password) Redacted() interface{} {
return logging.Redact(string(p))
}
func main() {
//test := "there are log of error i"
// For demo purposes, create two backend for os.Stderr.
backend1 := logging.NewLogBackend(os.Stderr, "", 0)
backend2 := logging.NewLogBackend(os.Stderr, "", 0)

// For messages written to backend2 we want to add some additional
// information to the output, including the used log level and the name of
// the function.
backend2Formatter := logging.NewBackendFormatter(backend2, format)

// Only errors and more severe messages should be sent to backend1
backend1Leveled := logging.AddModuleLevel(backend1)
backend1Leveled.SetLevel(logging.ERROR, "")

// Set the backends to be used.
logging.SetBackend(backend1Leveled, backend2Formatter)

log.Debug("debug %s", Password("secret"))
log.Info("info")
log.Notice("notice")
log.Warning("test")
log.Error("this is error level logging message")

//  log.Error("err")
log.Critical("this is critical level message")
}

Interesting - I'm noticing this for "INFO" as well...

Same for me...
Anybody to resolve that critical bug?

OK I found it...
This is because you don't have to copy/paste the example...
// Set the backends to be used.
    logging.SetBackend(backend1Leveled, backend2Formatter)

should be
// Set the backends to be used.
    logging.SetBackend(backend2Formatter)

Funny ;)

Great. Thanks @emilevauge


refactor(notification): add delay time for fetch notifications

Every time a page is loaded, the time elapsed since the last notification request is checked. The request will only be made if more than 5 minutes have passed since the last request.

If you want to force the request for notifications, you can do it by clicking on the bell and you will get notifications instantly.


Release version 0.0.17

Codecov Report
Patch and project coverage have no change.

Comparison is base (f5dc936) 100.00% compared to head (fe25812) 100.00%.

Additional details and impacted files
@@            Coverage Diff            @@
##              main      #223   +/-   ##
=========================================
  Coverage   100.00%   100.00%
=========================================
  Files           40        40
  Lines         1589      1589
  Branches       276       276
=========================================
  Hits          1589      1589

:umbrella: View full report in Codecov by Sentry.
:loudspeaker: Have feedback on the report? Share it here.


[React] Custom keyboard nav not working in sample

Open the keyboard-custom-navigation sample: https://staging.infragistics.com/react-demos/grids/grid/keyboard-custom-navigation
Editing a numeric cell with a value below 10 and pressing TAB key should result in an alert popping
Pressing the 'Enter' key should result in moving focus to a cell under the current cell

I am closing the issue as this is fixed already.


Move privatekey of test accounts to .env file

@manhlx3006 @Zohar97
all good to go, plz take a look
ty


Fine-tune llama2-7b base(not using lora) with 8-A100 GPU(AWS ml.p4de.24xlarge) is not available.

I got the following error when I tried to train llama2 7b model without LORA(base).
Please help me with this problem.
SYSTEM ENV:
Sagemaker ml.p4de.24xlarge AWS instance
pytorch_p310 image
ERROR:
[3/3] c++ cpu_adam.o custom_cuda_kernel.cuda.o -shared -lcurand -L/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/lib -lc10 -lc10_cuda -ltorch_cpu -ltorch_cuda -ltorch -ltorch_python -L/usr/local/cuda-11.8/lib64 -lcudart -o cpu_adam.so
Loading extension module cpu_adam...
Time to load cpu_adam op: 46.40223288536072 seconds
Loading extension module cpu_adam...
Loading extension module cpu_adam...
Loading extension module cpu_adam...
Loading extension module cpu_adam...

Time to load cpu_adam op: 46.11113476753235 seconds
Loading extension module cpu_adam...
Time to load cpu_adam op: 46.442068576812744 seconds
Loading extension module cpu_adam...
Time to load cpu_adam op: 46.448280572891235 seconds
Adam Optimizer #0 is created with AVX512 arithmetic capability.
Config: alpha=0.000050, betas=(0.900000, 0.999000), weight_decay=0.000000, adam_w=1
Adam Optimizer #0 is created with AVX512 arithmetic capability.
Config: alpha=0.000050, betas=(0.900000, 0.999000), weight_decay=0.000000, adam_w=1
Adam Optimizer #0 is created with AVX512 arithmetic capability.
Config: alpha=0.000050, betas=(0.900000, 0.999000), weight_decay=0.000000, adam_w=1
Adam Optimizer #0 is created with AVX512 arithmetic capability.
Config: alpha=0.000050, betas=(0.900000, 0.999000), weight_decay=0.000000, adam_w=1
Adam Optimizer #0 is created with AVX512 arithmetic capability.
Config: alpha=0.000050, betas=(0.900000, 0.999000), weight_decay=0.000000, adam_w=1
Adam Optimizer #0 is created with AVX512 arithmetic capability.
Config: alpha=0.000050, betas=(0.900000, 0.999000), weight_decay=0.000000, adam_w=1
Adam Optimizer #0 is created with AVX512 arithmetic capability.
Config: alpha=0.000050, betas=(0.900000, 0.999000), weight_decay=0.000000, adam_w=1
Adam Optimizer #0 is created with AVX512 arithmetic capability.
Config: alpha=0.000050, betas=(0.900000, 0.999000), weight_decay=0.000000, adam_w=1
Finding best initial lr:   0%|                               | 0/100 [00:00<?, ?it/s]Traceback (most recent call last):
File "/home/ec2-user/SageMaker/train.py", line 24, in
model.finetune(dataset=dataset)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/xturing/models/causal.py", line 119, in finetune
trainer.fit()
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/xturing/trainers/lightning_trainer.py", line 187, in fit
self.trainer.fit(self.lightning_model)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 545, in fit
call._call_and_handle_interrupt(
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 43, in _call_and_handle_interrupt
return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 102, in launch
return function(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 581, in _fit_impl
self._run(model, ckpt_path=ckpt_path)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 970, in _run
call._call_callback_hooks(self, "on_fit_start")
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 208, in _call_callback_hooks
fn(trainer, trainer.lightning_module, *args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/callbacks/lr_finder.py", line 126, in on_fit_start
self.lr_find(trainer, pl_module)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/callbacks/lr_finder.py", line 110, in lr_find
self.optimal_lr = _lr_find(
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/tuner/lr_finder.py", line 275, in _lr_find
_try_loop_run(trainer, params)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/tuner/lr_finder.py", line 515, in _try_loop_run
loop.run()
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 202, in run
self.advance()
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 359, in advance
self.epoch_loop.run(self._data_fetcher)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 136, in run
self.advance(data_fetcher)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 240, in advance
batch_output = self.automatic_optimization.run(trainer.optimizers[0], batch_idx, kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 187, in run
self._optimizer_step(batch_idx, closure)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 265, in _optimizer_step
call._call_lightning_module_hook(
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 157, in _call_lightning_module_hook
output = fn(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/core/module.py", line 1282, in optimizer_step
optimizer.step(closure=optimizer_closure)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/core/optimizer.py", line 151, in step
step_output = self._strategy.optimizer_step(self._optimizer, closure, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/strategies/ddp.py", line 263, in optimizer_step
optimizer_output = super().optimizer_step(optimizer, closure, model, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/strategies/strategy.py", line 230, in optimizer_step
return self.precision_plugin.optimizer_step(optimizer, model=model, closure=closure, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py", line 117, in optimizer_step
return optimizer.step(closure=closure, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/optim/lr_scheduler.py", line 69, in wrapper
return wrapped(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/optim/optimizer.py", line 280, in wrapper
out = func(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/deepspeed/ops/adam/cpu_adam.py", line 150, in step
assert p.device == device, f"CPUAdam param is on {p.device} and must be 'cpu', make "
AssertionError: CPUAdam param is on cuda:0 and must be 'cpu', make sure you enabled 'offload_optimizer': 'cpu' in your ZeRO config.
Traceback (most recent call last):
File "/home/ec2-user/SageMaker/train.py", line 24, in
model.finetune(dataset=dataset)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/xturing/models/causal.py", line 119, in finetune
trainer.fit()
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/xturing/trainers/lightning_trainer.py", line 187, in fit
self.trainer.fit(self.lightning_model)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 545, in fit
call._call_and_handle_interrupt(
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 43, in _call_and_handle_interrupt
return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 102, in launch
return function(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 581, in _fit_impl
self._run(model, ckpt_path=ckpt_path)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 970, in _run
call._call_callback_hooks(self, "on_fit_start")
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 208, in _call_callback_hooks
fn(trainer, trainer.lightning_module, *args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/callbacks/lr_finder.py", line 126, in on_fit_start
self.lr_find(trainer, pl_module)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/callbacks/lr_finder.py", line 110, in lr_find
self.optimal_lr = _lr_find(
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/tuner/lr_finder.py", line 275, in _lr_find
_try_loop_run(trainer, params)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/tuner/lr_finder.py", line 515, in _try_loop_run
loop.run()
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 202, in run
self.advance()
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 359, in advance
self.epoch_loop.run(self._data_fetcher)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 136, in run
self.advance(data_fetcher)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 240, in advance
batch_output = self.automatic_optimization.run(trainer.optimizers[0], batch_idx, kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 187, in run
self._optimizer_step(batch_idx, closure)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 265, in _optimizer_step
call._call_lightning_module_hook(
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 157, in _call_lightning_module_hook
output = fn(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/core/module.py", line 1282, in optimizer_step
optimizer.step(closure=optimizer_closure)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/core/optimizer.py", line 151, in step
step_output = self._strategy.optimizer_step(self._optimizer, closure, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/strategies/ddp.py", line 263, in optimizer_step
optimizer_output = super().optimizer_step(optimizer, closure, model, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/strategies/strategy.py", line 230, in optimizer_step
return self.precision_plugin.optimizer_step(optimizer, model=model, closure=closure, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py", line 117, in optimizer_step
return optimizer.step(closure=closure, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/optim/lr_scheduler.py", line 69, in wrapper
return wrapped(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/optim/optimizer.py", line 280, in wrapper
out = func(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/deepspeed/ops/adam/cpu_adam.py", line 150, in step
assert p.device == device, f"CPUAdam param is on {p.device} and must be 'cpu', make "
AssertionError: CPUAdam param is on cuda:3 and must be 'cpu', make sure you enabled 'offload_optimizer': 'cpu' in your ZeRO config.
Traceback (most recent call last):
File "/home/ec2-user/SageMaker/train.py", line 24, in
model.finetune(dataset=dataset)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/xturing/models/causal.py", line 119, in finetune
trainer.fit()
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/xturing/trainers/lightning_trainer.py", line 187, in fit
self.trainer.fit(self.lightning_model)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 545, in fit
call._call_and_handle_interrupt(
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 43, in _call_and_handle_interrupt
return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 102, in launch
return function(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 581, in _fit_impl
self._run(model, ckpt_path=ckpt_path)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 970, in _run
call._call_callback_hooks(self, "on_fit_start")
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 208, in _call_callback_hooks
fn(trainer, trainer.lightning_module, *args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/callbacks/lr_finder.py", line 126, in on_fit_start
self.lr_find(trainer, pl_module)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/callbacks/lr_finder.py", line 110, in lr_find
self.optimal_lr = _lr_find(
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/tuner/lr_finder.py", line 275, in _lr_find
_try_loop_run(trainer, params)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/tuner/lr_finder.py", line 515, in _try_loop_run
loop.run()
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 202, in run
self.advance()
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 359, in advance
self.epoch_loop.run(self._data_fetcher)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 136, in run
self.advance(data_fetcher)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 240, in advance
batch_output = self.automatic_optimization.run(trainer.optimizers[0], batch_idx, kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 187, in run
self._optimizer_step(batch_idx, closure)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 265, in _optimizer_step
call._call_lightning_module_hook(
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 157, in _call_lightning_module_hook
output = fn(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/core/module.py", line 1282, in optimizer_step
optimizer.step(closure=optimizer_closure)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/core/optimizer.py", line 151, in step
step_output = self._strategy.optimizer_step(self._optimizer, closure, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/strategies/ddp.py", line 263, in optimizer_step
optimizer_output = super().optimizer_step(optimizer, closure, model, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/strategies/strategy.py", line 230, in optimizer_step
return self.precision_plugin.optimizer_step(optimizer, model=model, closure=closure, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py", line 117, in optimizer_step
return optimizer.step(closure=closure, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/optim/lr_scheduler.py", line 69, in wrapper
return wrapped(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/optim/optimizer.py", line 280, in wrapper
out = func(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/deepspeed/ops/adam/cpu_adam.py", line 150, in step
assert p.device == device, f"CPUAdam param is on {p.device} and must be 'cpu', make "
AssertionError: CPUAdam param is on cuda:6 and must be 'cpu', make sure you enabled 'offload_optimizer': 'cpu' in your ZeRO config.
Traceback (most recent call last):
File "/home/ec2-user/SageMaker/train.py", line 24, in
model.finetune(dataset=dataset)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/xturing/models/causal.py", line 119, in finetune
trainer.fit()
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/xturing/trainers/lightning_trainer.py", line 187, in fit
self.trainer.fit(self.lightning_model)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 545, in fit
call._call_and_handle_interrupt(
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 43, in _call_and_handle_interrupt
return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 102, in launch
return function(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 581, in _fit_impl
self._run(model, ckpt_path=ckpt_path)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 970, in _run
call._call_callback_hooks(self, "on_fit_start")
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 208, in _call_callback_hooks
fn(trainer, trainer.lightning_module, *args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/callbacks/lr_finder.py", line 126, in on_fit_start
self.lr_find(trainer, pl_module)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/callbacks/lr_finder.py", line 110, in lr_find
self.optimal_lr = _lr_find(
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/tuner/lr_finder.py", line 275, in _lr_find
_try_loop_run(trainer, params)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/tuner/lr_finder.py", line 515, in _try_loop_run
loop.run()
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 202, in run
self.advance()
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 359, in advance
self.epoch_loop.run(self._data_fetcher)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 136, in run
self.advance(data_fetcher)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 240, in advance
batch_output = self.automatic_optimization.run(trainer.optimizers[0], batch_idx, kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 187, in run
self._optimizer_step(batch_idx, closure)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 265, in _optimizer_step
call._call_lightning_module_hook(
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 157, in _call_lightning_module_hook
output = fn(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/core/module.py", line 1282, in optimizer_step
optimizer.step(closure=optimizer_closure)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/core/optimizer.py", line 151, in step
step_output = self._strategy.optimizer_step(self._optimizer, closure, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/strategies/ddp.py", line 263, in optimizer_step
optimizer_output = super().optimizer_step(optimizer, closure, model, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/strategies/strategy.py", line 230, in optimizer_step
return self.precision_plugin.optimizer_step(optimizer, model=model, closure=closure, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py", line 117, in optimizer_step
return optimizer.step(closure=closure, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/optim/lr_scheduler.py", line 69, in wrapper
return wrapped(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/optim/optimizer.py", line 280, in wrapper
out = func(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/deepspeed/ops/adam/cpu_adam.py", line 150, in step
assert p.device == device, f"CPUAdam param is on {p.device} and must be 'cpu', make "
AssertionError: CPUAdam param is on cuda:1 and must be 'cpu', make sure you enabled 'offload_optimizer': 'cpu' in your ZeRO config.
Traceback (most recent call last):
File "/home/ec2-user/SageMaker/train.py", line 24, in
model.finetune(dataset=dataset)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/xturing/models/causal.py", line 119, in finetune
trainer.fit()
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/xturing/trainers/lightning_trainer.py", line 187, in fit
self.trainer.fit(self.lightning_model)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 545, in fit
call._call_and_handle_interrupt(
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 43, in _call_and_handle_interrupt
return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 102, in launch
return function(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 581, in _fit_impl
self._run(model, ckpt_path=ckpt_path)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 970, in _run
call._call_callback_hooks(self, "on_fit_start")
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 208, in _call_callback_hooks
fn(trainer, trainer.lightning_module, *args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/callbacks/lr_finder.py", line 126, in on_fit_start
self.lr_find(trainer, pl_module)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/callbacks/lr_finder.py", line 110, in lr_find
self.optimal_lr = _lr_find(
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/tuner/lr_finder.py", line 275, in _lr_find
_try_loop_run(trainer, params)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/tuner/lr_finder.py", line 515, in _try_loop_run
loop.run()
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 202, in run
self.advance()
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 359, in advance
self.epoch_loop.run(self._data_fetcher)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 136, in run
self.advance(data_fetcher)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 240, in advance
batch_output = self.automatic_optimization.run(trainer.optimizers[0], batch_idx, kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 187, in run
self._optimizer_step(batch_idx, closure)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 265, in _optimizer_step
call._call_lightning_module_hook(
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 157, in _call_lightning_module_hook
output = fn(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/core/module.py", line 1282, in optimizer_step
optimizer.step(closure=optimizer_closure)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/core/optimizer.py", line 151, in step
step_output = self._strategy.optimizer_step(self._optimizer, closure, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/strategies/ddp.py", line 263, in optimizer_step
optimizer_output = super().optimizer_step(optimizer, closure, model, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/strategies/strategy.py", line 230, in optimizer_step
return self.precision_plugin.optimizer_step(optimizer, model=model, closure=closure, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py", line 117, in optimizer_step
return optimizer.step(closure=closure, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/optim/lr_scheduler.py", line 69, in wrapper
return wrapped(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/optim/optimizer.py", line 280, in wrapper
out = func(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/deepspeed/ops/adam/cpu_adam.py", line 150, in step
assert p.device == device, f"CPUAdam param is on {p.device} and must be 'cpu', make "
AssertionError: CPUAdam param is on cuda:2 and must be 'cpu', make sure you enabled 'offload_optimizer': 'cpu' in your ZeRO config.
Finding best initial lr:   0%|                               | 0/100 [00:22<?, ?it/s]
Traceback (most recent call last):
File "/home/ec2-user/SageMaker/train.py", line 24, in
model.finetune(dataset=dataset)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/xturing/models/causal.py", line 119, in finetune
trainer.fit()
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/xturing/trainers/lightning_trainer.py", line 187, in fit
self.trainer.fit(self.lightning_model)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 545, in fit
call._call_and_handle_interrupt(
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 43, in _call_and_handle_interrupt
return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 102, in launch
return function(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 581, in _fit_impl
self._run(model, ckpt_path=ckpt_path)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 970, in _run
call._call_callback_hooks(self, "on_fit_start")
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 208, in _call_callback_hooks
fn(trainer, trainer.lightning_module, *args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/callbacks/lr_finder.py", line 126, in on_fit_start
self.lr_find(trainer, pl_module)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/callbacks/lr_finder.py", line 110, in lr_find
self.optimal_lr = _lr_find(
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/tuner/lr_finder.py", line 275, in _lr_find
_try_loop_run(trainer, params)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/tuner/lr_finder.py", line 515, in _try_loop_run
loop.run()
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 202, in run
self.advance()
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 359, in advance
self.epoch_loop.run(self._data_fetcher)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 136, in run
self.advance(data_fetcher)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 240, in advance
batch_output = self.automatic_optimization.run(trainer.optimizers[0], batch_idx, kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 187, in run
self._optimizer_step(batch_idx, closure)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 265, in _optimizer_step
call._call_lightning_module_hook(
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 157, in _call_lightning_module_hook
output = fn(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/core/module.py", line 1282, in optimizer_step
optimizer.step(closure=optimizer_closure)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/core/optimizer.py", line 151, in step
step_output = self._strategy.optimizer_step(self._optimizer, closure, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/strategies/ddp.py", line 263, in optimizer_step
optimizer_output = super().optimizer_step(optimizer, closure, model, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/strategies/strategy.py", line 230, in optimizer_step
return self.precision_plugin.optimizer_step(optimizer, model=model, closure=closure, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py", line 117, in optimizer_step
return optimizer.step(closure=closure, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/optim/lr_scheduler.py", line 69, in wrapper
return wrapped(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/optim/optimizer.py", line 280, in wrapper
out = func(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/deepspeed/ops/adam/cpu_adam.py", line 150, in step
assert p.device == device, f"CPUAdam param is on {p.device} and must be 'cpu', make "
AssertionError: CPUAdam param is on cuda:5 and must be 'cpu', make sure you enabled 'offload_optimizer': 'cpu' in your ZeRO config.
Traceback (most recent call last):
File "/home/ec2-user/SageMaker/train.py", line 24, in
model.finetune(dataset=dataset)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/xturing/models/causal.py", line 119, in finetune
trainer.fit()
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/xturing/trainers/lightning_trainer.py", line 187, in fit
self.trainer.fit(self.lightning_model)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 545, in fit
call._call_and_handle_interrupt(
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 43, in _call_and_handle_interrupt
return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 102, in launch
return function(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 581, in _fit_impl
self._run(model, ckpt_path=ckpt_path)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 970, in _run
call._call_callback_hooks(self, "on_fit_start")
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 208, in _call_callback_hooks
fn(trainer, trainer.lightning_module, *args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/callbacks/lr_finder.py", line 126, in on_fit_start
Finding best initial lr:   0%|                               | 0/100 [00:22<?, ?it/s]
self.lr_find(trainer, pl_module)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/callbacks/lr_finder.py", line 110, in lr_find
self.optimal_lr = _lr_find(
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/tuner/lr_finder.py", line 275, in _lr_find
_try_loop_run(trainer, params)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/tuner/lr_finder.py", line 515, in _try_loop_run
loop.run()
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 202, in run
self.advance()
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 359, in advance
self.epoch_loop.run(self._data_fetcher)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 136, in run
self.advance(data_fetcher)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 240, in advance
batch_output = self.automatic_optimization.run(trainer.optimizers[0], batch_idx, kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 187, in run
self._optimizer_step(batch_idx, closure)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 265, in _optimizer_step
call._call_lightning_module_hook(
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 157, in _call_lightning_module_hook
output = fn(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/core/module.py", line 1282, in optimizer_step
optimizer.step(closure=optimizer_closure)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/core/optimizer.py", line 151, in step
step_output = self._strategy.optimizer_step(self._optimizer, closure, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/strategies/ddp.py", line 263, in optimizer_step
optimizer_output = super().optimizer_step(optimizer, closure, model, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/strategies/strategy.py", line 230, in optimizer_step
return self.precision_plugin.optimizer_step(optimizer, model=model, closure=closure, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py", line 117, in optimizer_step
return optimizer.step(closure=closure, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/optim/lr_scheduler.py", line 69, in wrapper
return wrapped(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/optim/optimizer.py", line 280, in wrapper
out = func(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/deepspeed/ops/adam/cpu_adam.py", line 150, in step
assert p.device == device, f"CPUAdam param is on {p.device} and must be 'cpu', make "
AssertionError: CPUAdam param is on cuda:7 and must be 'cpu', make sure you enabled 'offload_optimizer': 'cpu' in your ZeRO config.
Traceback (most recent call last):
File "/home/ec2-user/SageMaker/train.py", line 24, in
model.finetune(dataset=dataset)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/xturing/models/causal.py", line 119, in finetune
trainer.fit()
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/xturing/trainers/lightning_trainer.py", line 187, in fit
self.trainer.fit(self.lightning_model)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 545, in fit
call._call_and_handle_interrupt(
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 43, in _call_and_handle_interrupt
return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 102, in launch
return function(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 581, in _fit_impl
self._run(model, ckpt_path=ckpt_path)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 970, in _run
call._call_callback_hooks(self, "on_fit_start")
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 208, in _call_callback_hooks
fn(trainer, trainer.lightning_module, *args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/callbacks/lr_finder.py", line 126, in on_fit_start
self.lr_find(trainer, pl_module)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/callbacks/lr_finder.py", line 110, in lr_find
self.optimal_lr = _lr_find(
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/tuner/lr_finder.py", line 275, in _lr_find
_try_loop_run(trainer, params)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/tuner/lr_finder.py", line 515, in _try_loop_run
loop.run()
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 202, in run
self.advance()
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 359, in advance
self.epoch_loop.run(self._data_fetcher)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 136, in run
self.advance(data_fetcher)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 240, in advance
batch_output = self.automatic_optimization.run(trainer.optimizers[0], batch_idx, kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 187, in run
self._optimizer_step(batch_idx, closure)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 265, in _optimizer_step
call._call_lightning_module_hook(
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 157, in _call_lightning_module_hook
output = fn(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/core/module.py", line 1282, in optimizer_step
optimizer.step(closure=optimizer_closure)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/core/optimizer.py", line 151, in step
step_output = self._strategy.optimizer_step(self._optimizer, closure, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/strategies/ddp.py", line 263, in optimizer_step
optimizer_output = super().optimizer_step(optimizer, closure, model, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/strategies/strategy.py", line 230, in optimizer_step
return self.precision_plugin.optimizer_step(optimizer, model=model, closure=closure, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py", line 117, in optimizer_step
return optimizer.step(closure=closure, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/optim/lr_scheduler.py", line 69, in wrapper
return wrapped(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/optim/optimizer.py", line 280, in wrapper
out = func(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/deepspeed/ops/adam/cpu_adam.py", line 150, in step
assert p.device == device, f"CPUAdam param is on {p.device} and must be 'cpu', make "
AssertionError: CPUAdam param is on cuda:4 and must be 'cpu', make sure you enabled 'offload_optimizer': 'cpu' in your ZeRO config.
Finding best initial lr:   0%|                               | 0/100 [00:22<?, ?it/s]
Finding best initial lr:   0%|                               | 0/100 [00:22<?, ?it/s]
Finding best initial lr:   0%|                               | 0/100 [00:23<?, ?it/s]
Finding best initial lr:   0%|                               | 0/100 [00:23<?, ?it/s]
Finding best initial lr:   0%|                               | 0/100 [00:23<?, ?it/s]
Finding best initial lr:   0%|                               | 0/100 [00:23<?, ?it/s]

Hi @WL0118,
What is the data set size you are using?
Probably better for your case is to use one A100 and LoRA, It will reduce costs and be much faster.
We will also check the issue with gpu's, but most probable reason - our configuration is for data=parallel finetuning, not for splitting model between GPU's.

Hi @WL0118, What is the data set size you are using? Probably better for your case is to use one A100 and LoRA, It will reduce costs and be much faster. We will also check the issue with gpu's, but most probable reason - our configuration is for data=parallel finetuning, not for splitting model between GPU's.

Hi,
Thank you for the comment.
I used a small text file(<20MB)
Since I wanted to change the model weight directly, I decided not to use LORA.
Personally, I solved this problem by using the 'Deepspeed' directly instead of using Xturing training.
I am gonna close this issue since it looks like I am the only guy who needs this function.
Thank you.


feat: add sections into listing detail page

Issue

Closes #1278
Addresses #1278
[ ] This change is a dependency for another issue
[ ] This change has a dependency from another issue

Description
Adds missing detail page sections.
How Can This Be Tested/Reviewed?

Run partners and fill the "Additional Eligibility Rules" fields in a listing page
Open preview and check if these sections exist

Checklist:

[x] My code follows the style guidelines of this project
[x] I have performed a self-review of my own code
[x] I have reviewed the changes in a desktop view
[x] I have reviewed the changes in a mobile view
[x] I have commented my code, particularly in hard-to-understand areas
[ ] I have made corresponding changes to the documentation
[x] My changes generate no new warnings
[ ] I have added tests that prove my fix is effective or that my feature works
[x] New and existing unit tests pass locally with my changes
[ ] Any dependent changes have been merged and published in downstream modules
[x] I have assigned reviewers
[ ] I have run yarn generate:client and/or created a migration if I made backend changes that require them
[ ] I have exported any new pieces added to ui-components
[x] My commit message(s) is/are polished, and any breaking changes are indicated in the message and are well-described
[ ] Commits made across packages purposefully have the same commit message/version change, else are separated into different commits

Reviewer Notes:
Steps to review a PR:

Read and understand the issue
Review the code itself from a style point of view
Pull the changes down locally and test that the acceptance criteria is met
Also review the acceptance criteria on the Netlify deploy preview (noting that these do not yet include any backend changes made in the PR)
Either explicitly ask a clarifying question, request changes, or approve the PR if there are small remaining changes but the PR is otherwise good to go

On Merge:
If you have one commit and message, squash. If you need each message to be applied, rebase and merge.

@seanmalbert I'm not sure about the getOccupancyDescription function.
In core, it uses listing.unitsSummarized to determine text, but in Detroit, a listing doesn't have this property. I used /listings/meta endpoint to fetch unitTypes - is that correct?