Google Places Autocomplete conflict

I am implementing a step-by-step form with Slick.  Some of my fields are location entries which I hooked up with Google Places API to do the autocomplete suggestions.
Google Places demo
There seems to be a conflict with Slick and Google's autocomplete list click event (when you click on a suggestion to fill the field).  When the field is INSIDE Slick, the click on Google's autocomplete list has no effect.  When the field is OUTSIDE slick, then all is normal.
What's really confusing is that Google appends their list element to the body, so it's no even inside Slick.  :-/
My work-around is to use a proxy field outside of Slick that overlays my field inside Slick on the focus event, and that proxy is the one tied to auto suggestions.
If you have any insight to the conflict, I'd love to hear about it!

I know there are some issues related to focus, but without a demo I couldn't say much more beyond that.

I'm facing the same issue

I'm facing the same issue. @lizsterine Did you fix this issue?

@tusharshuklaa I ended up copying the SlickSlider plugin and modifying it to remove some features that I didn't need for my particular form.
I'm not sure what specific features had the conflict, but here's a list of the things I removed to help get you started:

swipe related listeners and features
autoplay / pausing related code
dot navigation
arrow navigation
centerMode
as nav for
some focus related listeners and settings

Thanks a lot, will see if it fixes this issue.

I have created a fiddle if a demo of the issue is needed. https://jsfiddle.net/tusharshuklaa/ptd8z0ew/28/
If slick code is removed from my js then google autocomplete works fine.
P.S.: CSS is broken but it should not be a problem to locate the input box.

same issue here. is it already solved?

Found the solution!
Just had to remove the following line:

I think this is because this line is stopping the proppagation to the google-autocomplete.


S3 blobstore client ignores host option when downloading blobs

When the S3BlobstoreClient is initialized, it builds aws options based on the options passed into the initializer. It then ignores these options for reads, using the default s3 endpoint. Note that @options are used to generate @aws_options, which are then not used. Instead, the original @options is modified to set the :endpoint to S3BlobstoreClient::ENDPOINT.
https://github.com/cloudfoundry/bosh/blob/d75157676d0fdffd3aa56965d940fb69a28ad366/blobstore_client/lib/blobstore_client/s3_blobstore_client.rb#L30-L54
I am trying to build a local proxy with a cache, to remove the combined waiting time for my team when every workstation downloads the same blobs from s3 when developing on cf-release. I see that the blobstore configuration takes a :host key, which I would like to set to my cache/proxy. Instead, bosh directly downloads the files from s3.

@sax would it be simpler to keep ~/.bosh/cache folder in sync?

That might end up being the best solution. I had thought that it would be simpler to provide a cache, but something like BitTorrent Sync wind up being less difficult to manage.
Also, I realized that :host is only used when configuring a writable S3 blobstore. I can set :endpoint directly in my configuration to get the behavior that I was expecting.
Thank you!


North Star Badge Implementation

North Star Badge
The north star badge can be used to down vote a vote that would otherwise pass.
If at least 2 out of 3 north star badge holders vote against a proposal in the normal voting process, this is considered a veto and the vote fails.
A single north star badge "no" vote has no impact other than the negative votes of the badge holder which are counted as usual.

Additional info from gerardo
@Nikolaus Heger this is a simplified diagram of a proposal in the DG (Edges are in green). With this I can think on 2 different ways to handle the North Start functionality:
Method 1.- When user votes, (vote action) we can check for the user active Badges (check this on how to get active badges https://github.com/hypha-dao/dao-contracts/blob/6508e68d0178d3ccd6a1d8e17543c97bc1673af8/src/dao.cpp#L715, also you could extend this function to specify the badge name), then if the user has an active Badge Assignment of title North Start and voted fail we can create an extra Vote document (apart from the one created with the fail vote option) with the vote option as nsfail and power 1.0 or something like that, this should also handle the case when a voter change his/her vote from fail to something else by removing the extra Vote document. Then when the proposal is closed (closepropaction) in the didPass function we can check if the option nsfail in the votetally has a power >= 2.0 and return false.
Method 2.- When closeprop action is called, iterate all the vote edges that are fail and check for active North Start badge assignment on the owner of the vote, (same as in method 1) and just count them.
Method 1 requires storing more data, although is more efficient since it will look for badge assignments when someone votes (multiple transactions), but it has an small disadvantage: when a North Start badge assignment expires before the voting period ends but after the user voted, this would keep the nsfail vote. Also on the frontend side it would be trivial to check how many nsfail votes are already casted.
Method 2 Is easier to implement on the backend side, doesn't require extra data, and also doesn't have the problem described on Method 1, but could lead to transactions exceeding execution time when there are a lot of fail votes on a proposal. Also it would be relatively hard to count nsfail votes from the frontend side.

More info
"That's right we store all the total pass/fail/abstain power on the vote tally doc, although current implementation does iterate over all the votes when a new vote is casted (might need a refactor in the future to just add/substract from the vote tally), but on closeprop it just read the values from the and check if pass the alignment and quorum minimums.
"


feat timeout call

see https://github.com/kleros/kleros-api/issues/61

I update the branch with your comments


update now that all dependencies are auto loaded

LGTM

This is now out of date and no longer needed with the new site design, thanks for the PR though @jmkelly, appreciated!


Change proxy test URL to oversea servers

It doesn't quite make sense to have a domestic proxy test URL which will be used to test latency of oversea proxy endpoints. Ideally, the test servers should be distributed worldwide. So I chose Cloudflare's server (Google is good but may lead to human machine test when users attempt to access Google).

Good!


Use Python instead of PowerShell

Hi,
Since we need Python in dev instructions, why not use Python scripts for build/setup instead of PowerShell scripts, that force people install more things.
If all scripts is in Python, it work on all platforms and don't need install more one tool.
Thanks.

In the future, will only powershell


Support for structured_output

I wonder if it is possible to support structured_output from OpenAI
https://openai.com/index/introducing-structured-outputs-in-the-api/
It's very useful to work with json structured out for example.

OpenAIChat::setModelOption('response_format', ['type' => 'json_schema']);
That is all you need. You just need to craft your own JSON schema.

thank you for your help, I got it working.


Create a possibility to send gelfLog to more than one gelfServer.

Coverage increased (+0.2%) to 90.391% when pulling a79306f78f8eef7980e73bd45c8d93d9dccd8b82 on logs into 2d942ef1a6e911d67e1c0905d2a5b610c720e026 on master.

Coverage increased (+0.2%) to 90.391% when pulling a79306f78f8eef7980e73bd45c8d93d9dccd8b82 on logs into 2d942ef1a6e911d67e1c0905d2a5b610c720e026 on master.


Accuracy about the model

Hi justin,
Great work! I wonder do you have the accuracy result when you train the data? thanks.

And could you also provide the architecture of the network using for the emotion recognition model.

I think the test accuracy for FER2013 is 66% and the bundled architecture is defined here: https://github.com/oarriaga/face_classification/blob/master/src/models/cnn.py.

Closing issue.


/dev/shm or /tmp  for volatile.

https://wiki.archlinux.org/index.php/Anything-sync-daemon
Note: The default value of "/dev/shm" should work just fine for the VOLATILE setting. Be aware that using software such as bleachbit with asd can be dangerous since bleachbit likes to remove files stored in /tmp. This is why a value of /dev/shm is recommended.
But now default is in /tmp.

https://github.com/graysky2/anything-sync-daemon/blob/master/common/asd.conf
Define where data will reside in tmpfs.
This location must be mounted to tmpfs and MUST be writable and executable.

If using bleachbit, do NOT invoke it with the '--clean system.tmp' switch or
you will remove a key dot file (.foo) from /tmp that asd needs to keep track
of sync status.

Note that using a value of '/dev/shm' can cause problems with systemd's
NAMESPACE spawning only when users enable the overlayfs option.

#VOLATILE="/tmp"


Jvenstad/more general roles

@mpolden and @tokle please review. This is a first take on our general roles.

Need more roles. We should let users determine what roles are right for them.
Also, we need to find all GET stuff we haven’t covered yet, like the node repo, config, etc..

Agreed. I'll do another round with the policy names.

Will reopen against master.


Refactor/gjaskiewicz pill ts

Summary
Migrate Pill component to Typescript
Changes Made
Added typings to Pill component and Tests
Related Issues

Issue 1: [link to issue]

Testing Instructions

turbo test

Checklist

[ ] I have performed a self-review of my code.
[ ] I have commented my code, particularly in hard-to-understand areas.
[ ] I have added tests that prove my fix is effective or that my feature works.
[ ] New and existing unit tests pass locally with my changes.
[ ] I have made corresponding changes to the documentation (if applicable).
[ ] My changes generate no new warnings or errors.

Could you please merge actual main branch into your branch and make all changes green.


⚠ BeyondHD has degraded performance

In 041697a, BeyondHD ($BHD) experienced degraded performance:

HTTP code: 200
Response time: 4030 ms

Resolved: BeyondHD performance has improved in 4c276c7.


Support the -v flag

Users might reasonably expect the -v to mean verbose output. Think about how it is possible to combine together with specifying log levels explicitly.

Clap (and thus presumably StructOp) supports counting how many times a flag is passed. Counting occurrences of -v (including compressed forms like -vv) could monotonically increase the verbosity. Example code:
let filters: Vec<LevelFilter> = [
  LevelFilter::Error,
  LevelFilter::Warn,
  LevelFilter::Info,
  LevelFilter::Debug,
  LevelFilter::Trace,
][..= std::cmp::min(args.occurrences_of("verbosity"), 4]
  .iter().cloned().collect();

Yes, I know, there's even a crate for that (clap-verbosity-flag, I think).
However, if I want to set the logging to debug, having to count how many -vs I need is just terrible UX, especially with daemons, which you don't want to run again and again, tweaking the debugging. Furthermore, the command line allows setting log levels for specific crates (which is often quite useful in larger projects ‒ which daemons often happen to be). Repetitive -v doesn't sound consistent with it.
So I'm more thinking about keeping the -l flag for logging level and -v would just set it to Debug, as a shortcut.


[admin] Publish

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Bump Dependencies #3362325279

Bumps dependencies for product-is. Link : https://github.com/wso2/product-is/actions/runs/3362325279

 Thank you for your submission! We really appreciate it. Like many open source projects, we ask that you sign our Contributor License Agreement before we can accept your contribution.jenkins-is-staging seems not to be a GitHub user. You need a GitHub account to be able to sign the CLA. If you have already a GitHub account, please add the email address used for this commit to your account.You have signed the CLA already but the status is still pending? Let us recheck it.


Error: Missing attribute value. attribute: {value}, parent:

Hello,
I'm receiving the following error when running collection using Newman with junitfull reporter:
/usr/local/lib/node_modules/newman-reporter-junitfull/node_modules/xmlbuilder/lib/XMLAttribute.js:14
        throw new Error("Missing attribute value. " + this.debugInfo(name));
        ^

Error: Missing attribute value. attribute: {value}, parent: <property>
    at new XMLAttribute (/usr/local/lib/node_modules/newman-reporter-junitfull/node_modules/xmlbuilder/lib/XMLAttribute.js:14:15)
    at XMLElement.module.exports.XMLElement.attribute (/usr/local/lib/node_modules/newman-reporter-junitfull/node_modules/xmlbuilder/lib/XMLElement.js:72:35)
    at XMLElement.module.exports.XMLElement.att (/usr/local/lib/node_modules/newman-reporter-junitfull/node_modules/xmlbuilder/lib/XMLElement.js:100:19)
    at /usr/local/lib/node_modules/newman-reporter-junitfull/lib/index.js:107:15
    at arrayEach (/usr/local/lib/node_modules/newman-reporter-junitfull/node_modules/lodash/lodash.js:516:11)
    at Function.forEach (/usr/local/lib/node_modules/newman-reporter-junitfull/node_modules/lodash/lodash.js:9342:14)
    at /usr/local/lib/node_modules/newman-reporter-junitfull/lib/index.js:104:7
    at arrayEach (/usr/local/lib/node_modules/newman-reporter-junitfull/node_modules/lodash/lodash.js:516:11)
    at Function.forEach (/usr/local/lib/node_modules/newman-reporter-junitfull/node_modules/lodash/lodash.js:9342:14)
    at EventEmitter.<anonymous> (/usr/local/lib/node_modules/newman-reporter-junitfull/lib/index.js:58:5)
    at EventEmitter.emit (/usr/local/lib/node_modules/newman/node_modules/eventemitter3/index.js:203:33)
    at done (/usr/local/lib/node_modules/newman/lib/run/index.js:280:29)
    at /usr/local/lib/node_modules/newman/node_modules/postman-runtime/lib/backpack/index.js:58:34
    at PostmanCollectionRun._process (/usr/local/lib/node_modules/newman/node_modules/postman-runtime/lib/runner/run.js:163:13)
    at PostmanCollectionRun.<anonymous> (/usr/local/lib/node_modules/newman/node_modules/postman-runtime/lib/runner/run.js:169:76)
    at ontimeout (timers.js:498:11)

I have 10 collection in my workspace and only one of them returns this error.
I even tried to execute a single request from the collection but still run is failing with the same error( all other collections are executed without any issues).

Same error

me too

i'm using a collection with about 150 Request/Tests when i suddenly ran into this error.
i created a one-request-only collection -> works fine, but it's a bit hard to analyse which request/test exactly is causing this error.
but i could attach the collection/env, if you need.
thanks

Same comment, I can provide evidence if needed. For me it happens evey time.

I got that error when I used a variable inside to define the test name.
pm.test(pm.info.requestName+"-Status validation")

Hello!
any update here?
I also encounter the same issue:
-when executing a collection with 10 requests => I encounter the error
-when splitting the requests into 10 collections => error doesn't appear anymore ?!
throw new Error("Missing attribute value. " + this.debugInfo(name));
Error: Missing attribute value. attribute: {value}, parent: <property>
    at new XMLAttribute (/usr/lib/node_modules/newman-reporter-junitfull/node_modules/xmlbuilder/lib/XMLAttribute.js:14:15)

Same error here. One collection, 280 requests, a couple of tests per request.
I have no new information, just following the thread to see if there is a resolution.
Simple JUnit reporter works perfectly fine, but is pretty useless since the tests are all aggregated

Same error here. One collection, 280 requests, a couple of tests per request.
I have no new information, just following the thread to see if there is a resolution.
Simple JUnit reporter works perfectly fine, but is pretty useless since the tests are all aggregated

Hello, I had the same issue.
After noticing that I only had this error on a specific environment (even on a simple request without tests), I fixed this issue by recreating the exact same environment.
Something to do with a corrupted environment json file ?

I identified 2 situations which the reporter cannot handle and lead to the above mentioned error.

Empty values for environment variables in the environment file, if they are represented by the null string.
Changing this in the json file to an empty string value with 2 double quotes, will fix the issue: null => "". Or regenerate the file with a recent Postman version, this should also generate the empty value with double quotes.

From within the test code, creating an environment variable (or changing the value of an existing, I guess), with the result of a function that does not return a value.

Example:

pm.environment.set("sessionid", pm.response.json().session);
Say the response json does not contain the session field. Then the sessionid environment variable will be set to null. And the reporter will fail.

A possible solution: test before you assign:

if (pm.response.json().session) {
pm.environment.set("sessionid", pm.response.json().session);
}

any update or workaround?

SOLVED FROM MY END!
The reason why it keeps on failing on my end is because there are requests from my collection that requires a "FILE". After fixing the path using work directory my Test Automation in Azure Pipeline is now working!

This is a really easy fix. I use a forked version of this repo where the same bug exists.
See my PR https://github.com/bhecquet/newman-reporter-xunit/pull/8
I'd suggest somebody who wants this repo fixed request the same here.

workaround: make your that you don't have any variable or environment variable that has a "NULL" value.
If only the XMLAttribute.js line#14 has this below line of code instead the current one, we would never have this kind of exception:
      if (name == null) {
        this.name = "unknown";
      }
      else {
 	this.name = this.stringify.attName(name);
      }
      if (value == null) {
        this.value = "null";
      }
      else {
        this.value = this.stringify.attValue(value);
      }

I identified 2 situations which the reporter cannot handle and lead to the above mentioned error.

Empty values for environment variables in the environment file, if they are represented by the null string.
Changing this in the json file to an empty string value with 2 double quotes, will fix the issue: null => "". Or regenerate the file with a recent Postman version, this should also generate the empty value with double quotes.
From within the test code, creating an environment variable (or changing the value of an existing, I guess), with the result of a function that does not return a value.

Example:

pm.environment.set("sessionid", pm.response.json().session);
Say the response json does not contain the session field. Then the sessionid environment variable will be set to null. And the reporter will fail.

A possible solution: test before you assign:

if (pm.response.json().session) {
pm.environment.set("sessionid", pm.response.json().session);
}

I experienced the exact same solution as this - maybe the smarter solution would be to post a proper warning message so we can clean up our testsuites?

SOLVED ON MY END!
The reason why it keeps on failing on my end is because there are requests from my collection that requires a "FILE". After fixing the path using work directory my Test Automation in Azure Pipeline is now working!

👆🏻 Solution


How make transaction from Extended privatekey

I did not find any example for making transaction from Extended key.
$factory = new HierarchicalKeyFactory();
$root = $factory->fromExtended("xprv...");

I don't know how to get Wif from extended private key, shall I do that?

Hi!
Extended private keys have a function called getPrivateKey and getPublicKey
https://github.com/Bit-Wasp/bitcoin-php/blob/1.0/src/Key/Deterministic/HierarchicalKey.php#L166
Does that help get you started?

Thanks.


UAT | Payment Calculator Amount

Describe the bug
The payment calculator amounts are yet to be updated - advocate and advocate clerk welfare fund amounts.
To Reproduce
Steps to reproduce the behavior:

Go to 'File a Case' and under that to 'Review and Sign' on a fully signed case
Complete signature on the case file and submit the case to go to the success screen. Then go to the 'Make Payment' button to see the payment breakup

Expected behavior
The amounts for welfare funds should comply with the Rules shared and further, the petition fee amount should be in slabs.
Desktop (please complete the following information):

OS: [e.g. Windows]
Browser [Chrome]

Hi @deeshita  changes have been updated
below is the reference

Hi @deeshita @devarajd94 this issue is fixed

Hi @Ramu-kandimalla, verified in UAT env

url: https://dristi-kerala-uat.pucar.org/digit-ui/citizen/dristi/home/file-case/e-filing-payment?caseId=6567f02f-b3e0-4686-b0ba-172cfaa355ff

reference link: https://jam.dev/c/0c21afb9-0b31-4913-bd9b-783774f99a15
https://jam.dev/c/9603139a-3e51-4fd6-b294-99fb3651b858

Test cases:
1.verify that user able to see Petition Fee according following logic:
if Cheque Amount <= 50000 , then Petition fee is Rs 250
if Cheque Amount > 50000 && Cheque Amount <= 200000, then Petition fee is Rs 500
if Cheque Amount > 200000 && Cheque Amount <= 500000, then Petition fee is Rs 750
if Cheque Amount > 500000 && Cheque Amount <= 1000000, then Petition fee is Rs 1000
if Cheque Amount > 1000000 && Cheque Amount <= 2000000, then Petition fee is Rs 2000
if Cheque Amount > 2000000 && Cheque Amount <= 5000000, then Petition fee is Rs 5000
if Cheque Amount > 5000000, then Petition fee is Rs 10000
2.verify that Application Fee per Application = INR 2
3.verify that Vakalathnama Fee = INR 6
4.verify that Advocate Welfare Fund = INR 25
5.verify that Advocate Clerk’s Welfare Fund = INR 12
6.verify that if there is a delay of more than 1 month in filing a case for Delay Condonation Application, Delay Condonation Application Fee = INR 2

Verfied


Work around build issue when targeting node 11

Same kind of issue as bf45b46aadd80544eb792877eb73ac5781c14b34.
With this and lgeiger/node-abi#51, we can haz prebuilds for Node.js 11.x 🎉

@oleavr Hey btw. Long time no see!

@ralphtheninja HEY! Yeah way too long! How are you doing?

8.1.2


NullPointer when opening a flex maven project

All I did was open a maven flex project and I got this:

It is reproducible for me.

most likely a regression (not in production)

related to #1555


chore(apps/gcp/prow): update docker image of cherry-picker plugin to v2.4.4

Ref: https://github.com/ti-community-infra/tichi/pull/1188

/approve


Added a metadata attribute 'ssd_count' to spark_service.

@asaksena: can you fix your linter errors? They are causing Travis CI to fail. Run hooks/check-everything to see details.

@gareth-ferneyhough I have fixed the linter errors


Fix test.sh to not check CLUSTER_ID when deleting a cluster

Instead of checking CLUSTER_ID before deleting a cluster, which may interfere with CI jobs that set that env variable, define a separate variable to keep track of whether a cluster was created or not.

retest this please

retest this please

retest this please


installation issue

Hello,
magento 2.2.2
while trying to run the
composer require rubic/magento2-module-clean-checkout
I get the following error (php 7.1)
Using version ^1.6 for rubic/magento2-module-clean-checkout
./composer.json has been updated
Loading composer repositories with package information
Updating dependencies (including require-dev)
mmap() failed: [12] Cannot allocate memory
mmap() failed: [12] Cannot allocate memory
Fatal error: Out of memory (allocated 847257600) (tried to allocate 4096 bytes) in phar:///usr/local/bin/composer.phar/src/Composer/DependencyResolver/RuleWatchGraph.php on line 52
Thanks in advance for your support

Looks to me like some issue with composer, not this module. You can maybe try to temporarily raise your memory limit in the PHP configuration.

Going to assume this is unrelated to Clean Checkout. Feel free to re-open if you think that's not the case.


Sleep until next run time if we wake up too early

#132 was an attempt to fix this issue - but I think it's better to solve it at the source as opposed to the next run scheduler.
So this simply loops a very small sleep to ensure we are at the correct time. We're talking durations on the order of 1/2_000_000th of a second per loop
I appreciate this may sound trivial - but consider someone running a report on the first day of the month and the schedule is for direct at midnight. If we wake up early then we could be on the wrong month when the func is called.
If you add a fmt.Println("Sleeping") call inside the suggested loop and run the code from #132 I can get the loop firing sometimes 8-10+ times for a single call which means we certainly can run a fair amount of code in this interval (congrats to the raw speed of Go).

Thank for for taking the time to report and fix this issue @wishdev! I'll be looking into this in the weekend. There's another contributor that's helping with a big merge from and old PR at the moment, but as soon as we get this done we will start revisiting the scheduler code to make it more robust overall so cases like these stop happening.

@wishdev unfortunately I had to rollback these changes due to https://github.com/jasonlvhit/gocron/issues/135. I will be looking into this issue and find another way to fix it accordingly.


Determine how PDF scraper will interface with Elastic Search

PDF team and Elastic search team need to meet to discuss and make sure Elastic search can hold and return the information scraped from the PDFs

In discussion with Julienne, the scraper needs to output JSON format for elastic search to handle. This was the result of the discussion:
{
  "title": "GFC Meeting Materials 2015-05-09",
  "date": "2015-05-09",
  "committee": "GFC tuition committee",
  "subsection": "meeting minutes",
  "presenters": [ {"name": "Joe Doe"}, {"name": "Sally Jane"}]
  "pages": [
    {
      "text": "Committee meets to blah blah blah......"
      ("motions": [ and then a list of motion objects?])
      ("keywords": [ and then a list of scraped key words?])
    },
    {
      "text": "continuing from the previous page we motion to......"
    }
  ]
  "items": [
    {
      "index_in_pdf": 1 // How to refer to this issue from within the same pdf - should match the glossary
      "separate_index": 2983 // How to link this issue if it appears in multiple different pdfs
      "title": "Tuition discussion papers 2017"
      "pages_start": 4
      "pages_end": 8
    },
    {
      "text": "continuing from the previous page we motion to......"
    }
  ]
}

Excerpt from the discussion
Julienne - Today at 12:41 PM
Yeah that looks great! The only thing is that most of the meeting minutes have a field called presenters? Maybe that should be added?
Austin C - Today at 12:43 PM
Okay, then a list field of presenters, and a list for for items too perhaps? Theres going to be a lot of manual work here I feel but we will see what can be accomplished. I don't know how far Greg has got.
I'm studying for a monday midterm rn, and then my plan was to get cooking on those sprint 2 github issues like redo-ing the requirements, documentation, diagrams and addressing feedback. But if Greg can't get to this in time and you need it let me know and I'll get to that first instead.

Finalized
`{
"title": "GFC Faculties Council",
"date": "2015-05-09",
"committee": "General Faculties Council",
"time": "2:00PM - 4:00PM"
"location": "University Hall",
{
"subsection": "Opening Session",
{
"items": [
{
"index_in_pdf": 1 // How to refer to this issue from within the same pdf - should match the glossary
                    "separate_index": 2983 // How to link this issue if it appears in multiple different pdfs // is this part of our MVP?

                    "Title": "Tuition discussion papers 2017"

                    "pages_start": 4

                    "pages_end": 8

                    "Text": "continuing from the previous page we motion to......"

                    "Presenters": [ {"name": "Joe Doe"}, {"name": "Sally Jane"}]

                    "Motions": [ {"Motion": "That General Faculties Council approve the Agenda", "Carried": "True", "Content":"Materials before members are conained within"}])

                    "Keywords": [{"Keywords", "Of", "Interest"])

                    "Discussion": "Following an Indigneous welcome, etc")

                    "Purpose": "continuing from the previous page we motion to......"
                  } ]
    }
}

}
`


something questions about hyper parameters and experiment results

When i train diving-48 datasets with clip_base,i change videos_per_gps=4 because my machine limit(v100 32g). And i use 14 gpus to train a model so the batchsize is 56,close to 64.But finally i got top1_accuracy=85.3.This is my training logs,could you give me some advise? May be i should change some hyper parameters?
2023-05-31 00:40:47,429 - mmaction - INFO - Environment info:
sys.platform: linux
Python: 3.7.13 (default, Oct 18 2022, 18:57:03) [GCC 11.2.0]
CUDA available: True
GPU 0,1: Tesla V100-PCIE-32GB
CUDA_HOME: /share/soft/cuda-10.2
NVCC: Cuda compilation tools, release 10.2, V10.2.89
GCC: gcc (GCC) 7.5.0
PyTorch: 1.10.0+cu102
PyTorch compiling details: PyTorch built with:

GCC 7.3
C++ Version: 201402
Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications
Intel(R) MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)
OpenMP 201511 (a.k.a. OpenMP 4.5)
LAPACK is enabled (usually provided by MKL)
NNPACK is enabled
CPU capability usage: AVX512
CUDA Runtime 10.2
NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70
CuDNN 7.6.5
Magma 2.5.2
Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=10.2, CUDNN_VERSION=7.6.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON,

TorchVision: 0.11.0+cu102
OpenCV: 4.7.0
MMCV: 1.4.0
MMCV Compiler: n/a
MMCV CUDA Compiler: n/a
MMAction2: 0.15.0+unknown
2023-05-31 00:40:47,429 - mmaction - INFO - Distributed training: True
2023-05-31 00:40:47,986 - mmaction - INFO - Config: model = dict(
type='Recognizer3D',
backbone=dict(
type='ViT_CLIP',
input_resolution=224,
patch_size=16,
num_frames=32,
width=768,
layers=12,
heads=12,
drop_path_rate=0.2,
adapter_scale=0.5,
num_tadapter=1,
pretrained='openaiclip'),
cls_head=dict(
type='I3DHead',
in_channels=768,
num_classes=48,
spatial_type='avg',
dropout_ratio=0.5),
test_cfg=dict(average_clips='prob', max_testing_views=4))
checkpoint_config = dict(interval=10)
log_config = dict(interval=20, hooks=[dict(type='TextLoggerHook')])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
dataset_type = 'VideoDataset'
data_root = '/share/group/datanlpr_ai/wtyuan/diving48/videos'
data_root_val = '/share/group/datanlpr_ai/wtyuan/diving48//videos'
ann_file_train = '/share/group/datanlpr_ai/wtyuan/diving48/diving48_train_list_videos.txt'
ann_file_val = '/share/group/datanlpr_ai/wtyuan/diving48/diving48_val_list_videos.txt'
ann_file_test = '/share/group/datanlpr_ai/wtyuan/diving48/diving48_val_list_videos.txt'
img_norm_cfg = dict(
mean=[122.769, 116.74, 104.04], std=[68.493, 66.63, 70.321], to_bgr=False)
train_pipeline = [
dict(type='DecordInit'),
dict(
type='SampleFrames',
clip_len=32,
frame_interval=16,
num_clips=1,
frame_uniform=True),
dict(type='DecordDecode'),
dict(type='Resize', scale=(-1, 256)),
dict(type='RandomResizedCrop'),
dict(type='Resize', scale=(224, 224), keep_ratio=False),
dict(type='Flip', flip_ratio=0.5),
dict(
type='Normalize',
mean=[122.769, 116.74, 104.04],
std=[68.493, 66.63, 70.321],
to_bgr=False),
dict(type='FormatShape', input_format='NCTHW'),
dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
dict(type='ToTensor', keys=['imgs', 'label'])
]
val_pipeline = [
dict(type='DecordInit'),
dict(
type='SampleFrames',
clip_len=32,
frame_interval=16,
num_clips=1,
frame_uniform=True,
test_mode=True),
dict(type='DecordDecode'),
dict(type='Resize', scale=(-1, 256)),
dict(type='CenterCrop', crop_size=224),
dict(type='Flip', flip_ratio=0),
dict(
type='Normalize',
mean=[122.769, 116.74, 104.04],
std=[68.493, 66.63, 70.321],
to_bgr=False),
dict(type='FormatShape', input_format='NCTHW'),
dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
dict(type='ToTensor', keys=['imgs'])
]
test_pipeline = [
dict(type='DecordInit'),
dict(
type='SampleFrames',
clip_len=32,
frame_interval=16,
num_clips=1,
frame_uniform=True,
test_mode=True),
dict(type='DecordDecode'),
dict(type='Resize', scale=(-1, 224)),
dict(type='ThreeCrop', crop_size=224),
dict(type='Flip', flip_ratio=0),
dict(
type='Normalize',
mean=[122.769, 116.74, 104.04],
std=[68.493, 66.63, 70.321],
to_bgr=False),
dict(type='FormatShape', input_format='NCTHW'),
dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
dict(type='ToTensor', keys=['imgs'])
]
data = dict(
videos_per_gpu=4,
workers_per_gpu=2,
val_dataloader=dict(videos_per_gpu=1, workers_per_gpu=1),
test_dataloader=dict(videos_per_gpu=1, workers_per_gpu=1),
train=dict(
type='VideoDataset',
ann_file=
'/share/group/datanlpr_ai/wtyuan/diving48/diving48_train_list_videos.txt',
data_prefix='/share/group/datanlpr_ai/wtyuan/diving48/videos',
pipeline=[
dict(type='DecordInit'),
dict(
type='SampleFrames',
clip_len=32,
frame_interval=16,
num_clips=1,
frame_uniform=True),
dict(type='DecordDecode'),
dict(type='Resize', scale=(-1, 256)),
dict(type='RandomResizedCrop'),
dict(type='Resize', scale=(224, 224), keep_ratio=False),
dict(type='Flip', flip_ratio=0.5),
dict(
type='Normalize',
mean=[122.769, 116.74, 104.04],
std=[68.493, 66.63, 70.321],
to_bgr=False),
dict(type='FormatShape', input_format='NCTHW'),
dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
dict(type='ToTensor', keys=['imgs', 'label'])
]),
val=dict(
type='VideoDataset',
ann_file=
'/share/group/datanlpr_ai/wtyuan/diving48/diving48_val_list_videos.txt',
data_prefix='/share/group/datanlpr_ai/wtyuan/diving48//videos',
pipeline=[
dict(type='DecordInit'),
dict(
type='SampleFrames',
clip_len=32,
frame_interval=16,
num_clips=1,
frame_uniform=True,
test_mode=True),
dict(type='DecordDecode'),
dict(type='Resize', scale=(-1, 256)),
dict(type='CenterCrop', crop_size=224),
dict(type='Flip', flip_ratio=0),
dict(
type='Normalize',
mean=[122.769, 116.74, 104.04],
std=[68.493, 66.63, 70.321],
to_bgr=False),
dict(type='FormatShape', input_format='NCTHW'),
dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
dict(type='ToTensor', keys=['imgs'])
]),
test=dict(
type='VideoDataset',
ann_file=
'/share/group/datanlpr_ai/wtyuan/diving48/diving48_val_list_videos.txt',
data_prefix='/share/group/datanlpr_ai/wtyuan/diving48//videos',
pipeline=[
dict(type='DecordInit'),
dict(
type='SampleFrames',
clip_len=32,
frame_interval=16,
num_clips=1,
frame_uniform=True,
test_mode=True),
dict(type='DecordDecode'),
dict(type='Resize', scale=(-1, 224)),
dict(type='ThreeCrop', crop_size=224),
dict(type='Flip', flip_ratio=0),
dict(
type='Normalize',
mean=[122.769, 116.74, 104.04],
std=[68.493, 66.63, 70.321],
to_bgr=False),
dict(type='FormatShape', input_format='NCTHW'),
dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
dict(type='ToTensor', keys=['imgs'])
]))
evaluation = dict(
interval=5, metrics=['top_k_accuracy', 'mean_class_accuracy'])
optimizer = dict(
type='AdamW',
lr=0.0003,
betas=(0.9, 0.999),
weight_decay=0.05,
paramwise_cfg=dict(
custom_keys=dict(
class_embedding=dict(decay_mult=0.0),
positional_embedding=dict(decay_mult=0.0),
ln_1=dict(decay_mult=0.0),
ln_2=dict(decay_mult=0.0),
ln_pre=dict(decay_mult=0.0),
ln_post=dict(decay_mult=0.0))))
lr_config = dict(
policy='CosineAnnealing',
min_lr=0,
warmup='linear',
warmup_by_epoch=True,
warmup_iters=2.5)
total_epochs = 50
work_dir = 'work_dirs_vit/diving48/paper_sota_clstoken_fixmodelconfig'
find_unused_parameters = True
fp16 = None
optimizer_config = dict(
type='DistOptimizerHook',
update_interval=1,
grad_clip=None,
coalesce=True,
bucket_size_mb=-1,
use_fp16=True)
gpu_ids = range(0, 14)
omnisource = False
module_hooks = []
2023-05-31 00:40:49,149 - mmaction - INFO - load model from: openaiclip
2023-05-31 00:40:53,108 - mmaction - INFO - Missing keys: ['temporal_embedding', 'transformer.resblocks.0.MLP_Adapter.D_fc1.weight', 'transformer.resblocks.0.MLP_Adapter.D_fc1.bias', 'transformer.resblocks.0.MLP_Adapter.D_fc2.weight', 'transformer.resblocks.0.MLP_Adapter.D_fc2.bias', 'transformer.resblocks.0.S_Adapter.D_fc1.weight', 'transformer.resblocks.0.S_Adapter.D_fc1.bias', 'transformer.resblocks.0.S_Adapter.D_fc2.weight', 'transformer.resblocks.0.S_Adapter.D_fc2.bias', 'transformer.resblocks.0.T_Adapter.D_fc1.weight', 'transformer.resblocks.0.T_Adapter.D_fc1.bias', 'transformer.resblocks.0.T_Adapter.D_fc2.weight', 'transformer.resblocks.0.T_Adapter.D_fc2.bias', 'transformer.resblocks.1.MLP_Adapter.D_fc1.weight', 'transformer.resblocks.1.MLP_Adapter.D_fc1.bias', 'transformer.resblocks.1.MLP_Adapter.D_fc2.weight', 'transformer.resblocks.1.MLP_Adapter.D_fc2.bias', 'transformer.resblocks.1.S_Adapter.D_fc1.weight', 'transformer.resblocks.1.S_Adapter.D_fc1.bias', 'transformer.resblocks.1.S_Adapter.D_fc2.weight', 'transformer.resblocks.1.S_Adapter.D_fc2.bias', 'transformer.resblocks.1.T_Adapter.D_fc1.weight', 'transformer.resblocks.1.T_Adapter.D_fc1.bias', 'transformer.resblocks.1.T_Adapter.D_fc2.weight', 'transformer.resblocks.1.T_Adapter.D_fc2.bias', 'transformer.resblocks.2.MLP_Adapter.D_fc1.weight', 'transformer.resblocks.2.MLP_Adapter.D_fc1.bias', 'transformer.resblocks.2.MLP_Adapter.D_fc2.weight', 'transformer.resblocks.2.MLP_Adapter.D_fc2.bias', 'transformer.resblocks.2.S_Adapter.D_fc1.weight', 'transformer.resblocks.2.S_Adapter.D_fc1.bias', 'transformer.resblocks.2.S_Adapter.D_fc2.weight', 'transformer.resblocks.2.S_Adapter.D_fc2.bias', 'transformer.resblocks.2.T_Adapter.D_fc1.weight', 'transformer.resblocks.2.T_Adapter.D_fc1.bias', 'transformer.resblocks.2.T_Adapter.D_fc2.weight', 'transformer.resblocks.2.T_Adapter.D_fc2.bias', 'transformer.resblocks.3.MLP_Adapter.D_fc1.weight', 'transformer.resblocks.3.MLP_Adapter.D_fc1.bias', 'transformer.resblocks.3.MLP_Adapter.D_fc2.weight', 'transformer.resblocks.3.MLP_Adapter.D_fc2.bias', 'transformer.resblocks.3.S_Adapter.D_fc1.weight', 'transformer.resblocks.3.S_Adapter.D_fc1.bias', 'transformer.resblocks.3.S_Adapter.D_fc2.weight', 'transformer.resblocks.3.S_Adapter.D_fc2.bias', 'transformer.resblocks.3.T_Adapter.D_fc1.weight', 'transformer.resblocks.3.T_Adapter.D_fc1.bias', 'transformer.resblocks.3.T_Adapter.D_fc2.weight', 'transformer.resblocks.3.T_Adapter.D_fc2.bias', 'transformer.resblocks.4.MLP_Adapter.D_fc1.weight', 'transformer.resblocks.4.MLP_Adapter.D_fc1.bias', 'transformer.resblocks.4.MLP_Adapter.D_fc2.weight', 'transformer.resblocks.4.MLP_Adapter.D_fc2.bias', 'transformer.resblocks.4.S_Adapter.D_fc1.weight', 'transformer.resblocks.4.S_Adapter.D_fc1.bias', 'transformer.resblocks.4.S_Adapter.D_fc2.weight', 'transformer.resblocks.4.S_Adapter.D_fc2.bias', 'transformer.resblocks.4.T_Adapter.D_fc1.weight', 'transformer.resblocks.4.T_Adapter.D_fc1.bias', 'transformer.resblocks.4.T_Adapter.D_fc2.weight', 'transformer.resblocks.4.T_Adapter.D_fc2.bias', 'transformer.resblocks.5.MLP_Adapter.D_fc1.weight', 'transformer.resblocks.5.MLP_Adapter.D_fc1.bias', 'transformer.resblocks.5.MLP_Adapter.D_fc2.weight', 'transformer.resblocks.5.MLP_Adapter.D_fc2.bias', 'transformer.resblocks.5.S_Adapter.D_fc1.weight', 'transformer.resblocks.5.S_Adapter.D_fc1.bias', 'transformer.resblocks.5.S_Adapter.D_fc2.weight', 'transformer.resblocks.5.S_Adapter.D_fc2.bias', 'transformer.resblocks.5.T_Adapter.D_fc1.weight', 'transformer.resblocks.5.T_Adapter.D_fc1.bias', 'transformer.resblocks.5.T_Adapter.D_fc2.weight', 'transformer.resblocks.5.T_Adapter.D_fc2.bias', 'transformer.resblocks.6.MLP_Adapter.D_fc1.weight', 'transformer.resblocks.6.MLP_Adapter.D_fc1.bias', 'transformer.resblocks.6.MLP_Adapter.D_fc2.weight', 'transformer.resblocks.6.MLP_Adapter.D_fc2.bias', 'transformer.resblocks.6.S_Adapter.D_fc1.weight', 'transformer.resblocks.6.S_Adapter.D_fc1.bias', 'transformer.resblocks.6.S_Adapter.D_fc2.weight', 'transformer.resblocks.6.S_Adapter.D_fc2.bias', 'transformer.resblocks.6.T_Adapter.D_fc1.weight', 'transformer.resblocks.6.T_Adapter.D_fc1.bias', 'transformer.resblocks.6.T_Adapter.D_fc2.weight', 'transformer.resblocks.6.T_Adapter.D_fc2.bias', 'transformer.resblocks.7.MLP_Adapter.D_fc1.weight', 'transformer.resblocks.7.MLP_Adapter.D_fc1.bias', 'transformer.resblocks.7.MLP_Adapter.D_fc2.weight', 'transformer.resblocks.7.MLP_Adapter.D_fc2.bias', 'transformer.resblocks.7.S_Adapter.D_fc1.weight', 'transformer.resblocks.7.S_Adapter.D_fc1.bias', 'transformer.resblocks.7.S_Adapter.D_fc2.weight', 'transformer.resblocks.7.S_Adapter.D_fc2.bias', 'transformer.resblocks.7.T_Adapter.D_fc1.weight', 'transformer.resblocks.7.T_Adapter.D_fc1.bias', 'transformer.resblocks.7.T_Adapter.D_fc2.weight', 'transformer.resblocks.7.T_Adapter.D_fc2.bias', 'transformer.resblocks.8.MLP_Adapter.D_fc1.weight', 'transformer.resblocks.8.MLP_Adapter.D_fc1.bias', 'transformer.resblocks.8.MLP_Adapter.D_fc2.weight', 'transformer.resblocks.8.MLP_Adapter.D_fc2.bias', 'transformer.resblocks.8.S_Adapter.D_fc1.weight', 'transformer.resblocks.8.S_Adapter.D_fc1.bias', 'transformer.resblocks.8.S_Adapter.D_fc2.weight', 'transformer.resblocks.8.S_Adapter.D_fc2.bias', 'transformer.resblocks.8.T_Adapter.D_fc1.weight', 'transformer.resblocks.8.T_Adapter.D_fc1.bias', 'transformer.resblocks.8.T_Adapter.D_fc2.weight', 'transformer.resblocks.8.T_Adapter.D_fc2.bias', 'transformer.resblocks.9.MLP_Adapter.D_fc1.weight', 'transformer.resblocks.9.MLP_Adapter.D_fc1.bias', 'transformer.resblocks.9.MLP_Adapter.D_fc2.weight', 'transformer.resblocks.9.MLP_Adapter.D_fc2.bias', 'transformer.resblocks.9.S_Adapter.D_fc1.weight', 'transformer.resblocks.9.S_Adapter.D_fc1.bias', 'transformer.resblocks.9.S_Adapter.D_fc2.weight', 'transformer.resblocks.9.S_Adapter.D_fc2.bias', 'transformer.resblocks.9.T_Adapter.D_fc1.weight', 'transformer.resblocks.9.T_Adapter.D_fc1.bias', 'transformer.resblocks.9.T_Adapter.D_fc2.weight', 'transformer.resblocks.9.T_Adapter.D_fc2.bias', 'transformer.resblocks.10.MLP_Adapter.D_fc1.weight', 'transformer.resblocks.10.MLP_Adapter.D_fc1.bias', 'transformer.resblocks.10.MLP_Adapter.D_fc2.weight', 'transformer.resblocks.10.MLP_Adapter.D_fc2.bias', 'transformer.resblocks.10.S_Adapter.D_fc1.weight', 'transformer.resblocks.10.S_Adapter.D_fc1.bias', 'transformer.resblocks.10.S_Adapter.D_fc2.weight', 'transformer.resblocks.10.S_Adapter.D_fc2.bias', 'transformer.resblocks.10.T_Adapter.D_fc1.weight', 'transformer.resblocks.10.T_Adapter.D_fc1.bias', 'transformer.resblocks.10.T_Adapter.D_fc2.weight', 'transformer.resblocks.10.T_Adapter.D_fc2.bias', 'transformer.resblocks.11.MLP_Adapter.D_fc1.weight', 'transformer.resblocks.11.MLP_Adapter.D_fc1.bias', 'transformer.resblocks.11.MLP_Adapter.D_fc2.weight', 'transformer.resblocks.11.MLP_Adapter.D_fc2.bias', 'transformer.resblocks.11.S_Adapter.D_fc1.weight', 'transformer.resblocks.11.S_Adapter.D_fc1.bias', 'transformer.resblocks.11.S_Adapter.D_fc2.weight', 'transformer.resblocks.11.S_Adapter.D_fc2.bias', 'transformer.resblocks.11.T_Adapter.D_fc1.weight', 'transformer.resblocks.11.T_Adapter.D_fc1.bias', 'transformer.resblocks.11.T_Adapter.D_fc2.weight', 'transformer.resblocks.11.T_Adapter.D_fc2.bias']
2023-05-31 00:40:53,108 - mmaction - INFO - Unexpected keys: []
2023-05-31 00:40:53,109 - mmaction - INFO - => loaded successfully 'openaiclip'
2023-05-31 00:40:53,763 - mmaction - INFO - backbone.class_embedding: False
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.positional_embedding: False
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.temporal_embedding: True
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.conv1.weight: False
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.ln_pre.weight: False
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.ln_pre.bias: False
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.transformer.resblocks.0.attn.in_proj_weight: False
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.transformer.resblocks.0.attn.in_proj_bias: False
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.transformer.resblocks.0.attn.out_proj.weight: False
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.transformer.resblocks.0.attn.out_proj.bias: False
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.transformer.resblocks.0.ln_1.weight: False
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.transformer.resblocks.0.ln_1.bias: False
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.transformer.resblocks.0.mlp.c_fc.weight: False
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.transformer.resblocks.0.mlp.c_fc.bias: False
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.transformer.resblocks.0.mlp.c_proj.weight: False
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.transformer.resblocks.0.mlp.c_proj.bias: False
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.transformer.resblocks.0.ln_2.weight: False
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.transformer.resblocks.0.ln_2.bias: False
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.transformer.resblocks.0.MLP_Adapter.D_fc1.weight: True
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.transformer.resblocks.0.MLP_Adapter.D_fc1.bias: True
2023-05-31 00:40:53,764 - mmaction - INFO - backbone.transformer.resblocks.0.MLP_Adapter.D_fc2.weight: True
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2023-05-31 00:40:53,770 - mmaction - INFO - backbone.transformer.resblocks.8.mlp.c_proj.bias: False
2023-05-31 00:40:53,770 - mmaction - INFO - backbone.transformer.resblocks.8.ln_2.weight: False
2023-05-31 00:40:53,770 - mmaction - INFO - backbone.transformer.resblocks.8.ln_2.bias: False
2023-05-31 00:40:53,770 - mmaction - INFO - backbone.transformer.resblocks.8.MLP_Adapter.D_fc1.weight: True
2023-05-31 00:40:53,770 - mmaction - INFO - backbone.transformer.resblocks.8.MLP_Adapter.D_fc1.bias: True
2023-05-31 00:40:53,770 - mmaction - INFO - backbone.transformer.resblocks.8.MLP_Adapter.D_fc2.weight: True
2023-05-31 00:40:53,770 - mmaction - INFO - backbone.transformer.resblocks.8.MLP_Adapter.D_fc2.bias: True
2023-05-31 00:40:53,770 - mmaction - INFO - backbone.transformer.resblocks.8.S_Adapter.D_fc1.weight: True
2023-05-31 00:40:53,770 - mmaction - INFO - backbone.transformer.resblocks.8.S_Adapter.D_fc1.bias: True
2023-05-31 00:40:53,770 - mmaction - INFO - backbone.transformer.resblocks.8.S_Adapter.D_fc2.weight: True
2023-05-31 00:40:53,770 - mmaction - INFO - backbone.transformer.resblocks.8.S_Adapter.D_fc2.bias: True
2023-05-31 00:40:53,770 - mmaction - INFO - backbone.transformer.resblocks.8.T_Adapter.D_fc1.weight: True
2023-05-31 00:40:53,770 - mmaction - INFO - backbone.transformer.resblocks.8.T_Adapter.D_fc1.bias: True
2023-05-31 00:40:53,770 - mmaction - INFO - backbone.transformer.resblocks.8.T_Adapter.D_fc2.weight: True
2023-05-31 00:40:53,770 - mmaction - INFO - backbone.transformer.resblocks.8.T_Adapter.D_fc2.bias: True
2023-05-31 00:40:53,770 - mmaction - INFO - backbone.transformer.resblocks.9.attn.in_proj_weight: False
2023-05-31 00:40:53,770 - mmaction - INFO - backbone.transformer.resblocks.9.attn.in_proj_bias: False
2023-05-31 00:40:53,770 - mmaction - INFO - backbone.transformer.resblocks.9.attn.out_proj.weight: False
2023-05-31 00:40:53,770 - mmaction - INFO - backbone.transformer.resblocks.9.attn.out_proj.bias: False
2023-05-31 00:40:53,770 - mmaction - INFO - backbone.transformer.resblocks.9.ln_1.weight: False
2023-05-31 00:40:53,770 - mmaction - INFO - backbone.transformer.resblocks.9.ln_1.bias: False
2023-05-31 00:40:53,770 - mmaction - INFO - backbone.transformer.resblocks.9.mlp.c_fc.weight: False
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.9.mlp.c_fc.bias: False
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.9.mlp.c_proj.weight: False
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.9.mlp.c_proj.bias: False
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.9.ln_2.weight: False
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.9.ln_2.bias: False
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.9.MLP_Adapter.D_fc1.weight: True
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.9.MLP_Adapter.D_fc1.bias: True
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.9.MLP_Adapter.D_fc2.weight: True
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.9.MLP_Adapter.D_fc2.bias: True
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.9.S_Adapter.D_fc1.weight: True
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.9.S_Adapter.D_fc1.bias: True
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.9.S_Adapter.D_fc2.weight: True
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.9.S_Adapter.D_fc2.bias: True
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.9.T_Adapter.D_fc1.weight: True
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.9.T_Adapter.D_fc1.bias: True
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.9.T_Adapter.D_fc2.weight: True
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.9.T_Adapter.D_fc2.bias: True
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.10.attn.in_proj_weight: False
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.10.attn.in_proj_bias: False
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.10.attn.out_proj.weight: False
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.10.attn.out_proj.bias: False
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.10.ln_1.weight: False
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.10.ln_1.bias: False
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.10.mlp.c_fc.weight: False
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.10.mlp.c_fc.bias: False
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.10.mlp.c_proj.weight: False
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.10.mlp.c_proj.bias: False
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.10.ln_2.weight: False
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.10.ln_2.bias: False
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.10.MLP_Adapter.D_fc1.weight: True
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.10.MLP_Adapter.D_fc1.bias: True
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.10.MLP_Adapter.D_fc2.weight: True
2023-05-31 00:40:53,771 - mmaction - INFO - backbone.transformer.resblocks.10.MLP_Adapter.D_fc2.bias: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.10.S_Adapter.D_fc1.weight: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.10.S_Adapter.D_fc1.bias: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.10.S_Adapter.D_fc2.weight: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.10.S_Adapter.D_fc2.bias: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.10.T_Adapter.D_fc1.weight: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.10.T_Adapter.D_fc1.bias: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.10.T_Adapter.D_fc2.weight: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.10.T_Adapter.D_fc2.bias: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.attn.in_proj_weight: False
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.attn.in_proj_bias: False
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.attn.out_proj.weight: False
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.attn.out_proj.bias: False
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.ln_1.weight: False
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.ln_1.bias: False
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.mlp.c_fc.weight: False
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.mlp.c_fc.bias: False
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.mlp.c_proj.weight: False
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.mlp.c_proj.bias: False
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.ln_2.weight: False
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.ln_2.bias: False
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.MLP_Adapter.D_fc1.weight: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.MLP_Adapter.D_fc1.bias: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.MLP_Adapter.D_fc2.weight: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.MLP_Adapter.D_fc2.bias: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.S_Adapter.D_fc1.weight: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.S_Adapter.D_fc1.bias: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.S_Adapter.D_fc2.weight: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.S_Adapter.D_fc2.bias: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.T_Adapter.D_fc1.weight: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.T_Adapter.D_fc1.bias: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.T_Adapter.D_fc2.weight: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.transformer.resblocks.11.T_Adapter.D_fc2.bias: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.ln_post.weight: True
2023-05-31 00:40:53,772 - mmaction - INFO - backbone.ln_post.bias: True
2023-05-31 00:40:53,773 - mmaction - INFO - cls_head.fc_cls.weight: True
2023-05-31 00:40:53,773 - mmaction - INFO - cls_head.fc_cls.bias: True
2023-05-31 00:40:53,774 - mmaction - INFO - Number of total parameters: 96512304, tunable parameters: 10714416
2023-05-31 00:40:58,009 - mmaction - INFO - Start running, host: ai_zhuxinxin@g28, work_dir: /share/home/ai_zhuxinxin/wtyuan/git/aim3/aim3/work_dirs_vit/diving48/paper_sota_clstoken_fixmodelconfig
2023-05-31 00:40:58,009 - mmaction - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH   ) CosineAnnealingLrUpdaterHook
(ABOVE_NORMAL) DistOptimizerHook
(NORMAL      ) CheckpointHook
(NORMAL      ) DistEvalHook
(VERY_LOW    ) TextLoggerHook
before_train_epoch:
(VERY_HIGH   ) CosineAnnealingLrUpdaterHook
(NORMAL      ) DistSamplerSeedHook
(NORMAL      ) DistEvalHook
(LOW         ) IterTimerHook
(VERY_LOW    ) TextLoggerHook
before_train_iter:
(VERY_HIGH   ) CosineAnnealingLrUpdaterHook
(NORMAL      ) DistEvalHook
(LOW         ) IterTimerHook
after_train_iter:
(ABOVE_NORMAL) DistOptimizerHook
(NORMAL      ) CheckpointHook
(NORMAL      ) DistEvalHook
(LOW         ) IterTimerHook
(VERY_LOW    ) TextLoggerHook
after_train_epoch:
(NORMAL      ) CheckpointHook
(NORMAL      ) DistEvalHook
(VERY_LOW    ) TextLoggerHook
before_val_epoch:
(NORMAL      ) DistSamplerSeedHook
(LOW         ) IterTimerHook
(VERY_LOW    ) TextLoggerHook
before_val_iter:
(LOW         ) IterTimerHook
after_val_iter:
(LOW         ) IterTimerHook
after_val_epoch:
(VERY_LOW    ) TextLoggerHook
after_run:
(VERY_LOW    ) TextLoggerHook
2023-05-31 00:40:58,009 - mmaction - INFO - workflow: [('train', 1)], max: 50 epochs
2023-05-31 00:40:58,009 - mmaction - INFO - Checkpoints will be saved to /share/home/ai_zhuxinxin/wtyuan/git/aim3/aim3/work_dirs_vit/diving48/paper_sota_clstoken_fixmodelconfig by HardDiskBackend.
2023-05-31 00:41:34,264 - mmaction - INFO - Epoch [1][20/269]	lr: 3.763e-05, eta: 6:45:30, time: 1.812, data_time: 0.949, memory: 20587, top1_acc: 0.0455, top5_acc: 0.1830, loss_cls: 3.7788, loss: 3.7788
''''''''''''''''''''''''''''''''''''''''''''''
2023-05-31 04:07:05,175 - mmaction - INFO - Epoch [50][260/269]	lr: 2.960e-07, eta: 0:00:07, time: 0.761, data_time: 0.001, memory: 20590, top1_acc: 0.9313, top5_acc: 0.9839, loss_cls: 0.2157, loss: 0.2157
2023-05-31 04:07:11,845 - mmaction - INFO - Saving checkpoint at 50 epochs
2023-05-31 04:08:10,086 - mmaction - INFO - Evaluating top_k_accuracy ...
2023-05-31 04:08:10,093 - mmaction - INFO -
top1_acc	0.8594
top5_acc	0.9888
2023-05-31 04:08:10,093 - mmaction - INFO - Evaluating mean_class_accuracy ...
2023-05-31 04:08:10,094 - mmaction - INFO -
mean_acc	0.7878
2023-05-31 04:08:10,095 - mmaction - INFO - Epoch(val) [50][141]	top1_acc: 0.8594, top5_acc: 0.9888, mean_class_accuracy: 0.7878
2023-05-31 04:09:08,388 - mmaction - INFO - Testing results of the last checkpoint
2023-05-31 04:09:08,388 - mmaction - INFO - top1_acc: 0.8533
2023-05-31 04:09:08,388 - mmaction - INFO - top5_acc: 0.9873
2023-05-31 04:09:08,388 - mmaction - INFO - mean_class_accuracy: 0.7874

Hi, I remember that clip_base should be able to fit into a 32G GPU. If it doesn't, you could increase 'update_interval' to 2/4/8. This is for gradient accumulation. update_interval=2 means that the batchsize is 8/2=4, and gradients are accumulated 2 steps for backward pass. So it is equivalent to batchsize=8, but may slow down the training a little bit.
If you use 14 gpus, you may need to scale up the learning rate by 14/8 times.


Pod count as load metrics in BurstMetrics Service

Type of PR

[X] Code changes

PR Checklist

[X] All new and existing tests passed.
[X] My code follows the code style of this project.
[X] I ran lint checks locally prior to submission.
[X] Threw exception manually to test 204-NoContent

Purpose of PR
Change the load type from CPU metrics to Pod count from HPA.
Does this introduce a breaking change

[X] YES
[ ] NO

Previously BurstMetrics Service returned CPU percentage as load metrics.
With this PR, BurstMetrics will return POD count as load metrics.
Validation

[X] Ran local tests and integration tests
[X] Update documentation or issue referenced above

Issues Closed

Closes https://github.com/retaildevcrews/wcnp/issues/280
Closes https://github.com/retaildevcrews/wcnp/issues/294
Closes https://github.com/retaildevcrews/wcnp/issues/299

blocked until after Spark Encore presentations


ultralist: rename from todolist, update

Motivation for this change
todolist package was renamed to ultralist.
Here is announcement: ultralist/ultralist#136.
Also updated package to the latest version
Things done

[x] Tested using sandboxing (nix.useSandbox on NixOS, or option sandbox in nix.conf on non-NixOS linux)
Built on platform(s)

[x] NixOS
[ ] macOS
[ ] other Linux distributions

[ ] Tested via one or more NixOS test(s) if existing and applicable for the change (look inside nixos/tests)
[ ] Tested compilation of all pkgs that depend on this change using nix-shell -p nixpkgs-review --run "nixpkgs-review wip"
[x] Tested execution of all binary files (usually in ./result/bin/)
[ ] Determined the impact on package closure size (by running nix path-info -S before and after)
[x] Ensured that relevant documentation is up to date
[x] Fits CONTRIBUTING.md.

This pull request has been mentioned on NixOS Discourse. There might be relevant details there:
https://discourse.nixos.org/t/prs-ready-for-review/3032/336

Result of nixpkgs-review pr 100447 run on x86_64-linux 1

1 package built:

ultralist

Result of nixpkgs-review pr 100447 run on x86_64-darwin 1

1 package built:

ultralist

Result of nixpkgs-review pr 100447 run on x86_64-linux 1

1 package built:

ultralist

Result of nixpkgs-review pr 100447 run on x86_64-darwin 1

1 package built:

ultralist

@uvNikita please fix the merge conflict.

@uvNikita please fix the merge conflict.

This is a semi-automatic executed nixpkgs-review which does not build all packages (e.g. lumo, tensorflow or pytorch)
If you find some bugs or got suggestions for further things to search or run please reach out to SuperSandro2000 on IRC.
Result of nixpkgs-review pr 100447 run on x86_64-darwin 1

1 package built:

ultralist

This is a semi-automatic executed nixpkgs-review which does not build all packages (e.g. lumo, tensorflow or pytorch)
If you find some bugs or got suggestions for further things to search or run please reach out to SuperSandro2000 on IRC.
Result of nixpkgs-review pr 100447 run on x86_64-darwin 1

1 package built:

ultralist

This is a semi-automatic executed nixpkgs-review which does not build all packages (e.g. lumo, tensorflow or pytorch)
If you find some bugs or got suggestions for further things to search or run please reach out to SuperSandro2000 on IRC.
Result of nixpkgs-review pr 100447 run on x86_64-linux 1

1 package built:

ultralist

This is a semi-automatic executed nixpkgs-review which does not build all packages (e.g. lumo, tensorflow or pytorch)
If you find some bugs or got suggestions for further things to search or run please reach out to SuperSandro2000 on IRC.
Result of nixpkgs-review pr 100447 run on x86_64-linux 1

1 package built:

ultralist


Remove title field from user address form

Fixes #3696

Passed all tests in integration\address\test_models.py and integration\address\test_forms.py

On it.
I also found title field in dashboard.orders.forms.ShippingAddressForm and payment.forms.BillingAddressForm. So removing these as well.


UUF updates

More informative messages for 500 errors.
Error page 'uuf.page.error' revamped.
Removed 'uuf.unit.sign-in' unit by moving its content to 'uuf.page.sign-in' page.
theme-wso2 update (removing unnecessary CSS classes)

Smoke tested with IoT server pack from 2015/01/06, no issues found.


Problems with mtcnn？

Hi,
Run successfully at the terminal‘ trt_mtcnn.py ’.
But I can't use mtcnn. The procedures and problems are as follows：
codes：
mtcnn = TrtMtcnn()
'''
path1="./data/"
i=0
for filename in os.listdir(path1):
    path=path1+filename
    print(i,path)

    img_cv=cv2.imread(path)
    img_cv=cv2.resize(img_cv,(640,480))
    dets, landmarks = mtcnn.detect(img_cv, minsize=40)
    print('{} face(s) found'.format(len(dets)))
    img = show_faces(img_cv, dets, landmarks)
    cv2.imwrite("./result/"+str(i)+".jpg",img)
    i=i+1
'''
#del(mtcnn)

problems：
Deserialize required 1226718 microseconds.
Current optimization profile is: 0. Please ensure there are no enqueued operations pending in this context prior to switching profiles
Assertion fail in file 'trtNet.cpp' line 186: bad dims for 'data'
terminate called after throwing an instance of 'std::exception'
what():  std::exception
Aborted (core dumped)

Actually, you don't need to resize the image before calling mtcnn.detect().  The TrtMtcnn preprocessing code would take care of image resizing before feeding it into TensorRT engine for inference.
Nonetheless, I tested roughly the same code as the snippet you've shown.  It worked.  Please check out a clean copy of the code from this repo and try again.

@jiagh2010  Do you have any update on this issue?


no longer ignore tests in box config

No that tests have been moved out of the src directoriy, there is no
need to ignore the test files anymore (as there are no inside src/).

Good catch :+1:


can't debug py file

Environment data
VS Code version:  1.19.3
Python Extension version:  0.9.1
Python Version:   Python 2.7 64bit
OS and version:    Win10 Chinese Simplified
the folder path has chinese string
C:\Users\Name\.vscode\extensions\ms-python.python-0.9.1\pythonFiles\PythonTools\visualstudio_py_debugger.py:1669: UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal
  if module.filename.lower() == path.abspath(bp.filename).lower():
C:\Users\Name\.vscode\extensions\ms-python.python-0.9.1\pythonFiles\PythonTools\visualstudio_py_debugger.py:1089: UnicodeWarning: Unicode unequal comparison failed to convert both arguments to Unicode - interpreting them as being unequal
  if filename != frame.f_code.co_filename:

{
    // Use IntelliSense to learn about possible attributes.
    // Hover to view descriptions of existing attributes.
    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
    "version": "0.2.0",
    "configurations": [
        {
            "name": "Python",
            "type": "python",
            "request": "launch",
            "stopOnEntry": false,
            "pythonPath": "${config:python.pythonPath}",
            "program": "${file}",
            "cwd": "${workspaceFolder}",
            "env": {},
            "envFile": "${workspaceFolder}/.env",
            "debugOptions": [
                "RedirectOutput"
            ]
        },
        {
            "type": "node",
            "request": "launch",
            "name": "nodejs",
            "program": "${file}"
        }
    ]
}

@kzhui125, thanks for letting us know about this problem.  We're looking into it.

@kzhui125, could you check if you have the same problem under Python 3?

FTR:

https://github.com/Microsoft/vscode-python/blob/master/pythonFiles/PythonTools/visualstudio_py_debugger.py#L1089
https://github.com/Microsoft/vscode-python/blob/master/pythonFiles/PythonTools/visualstudio_py_debugger.py#L1669

I expect that this is not a problem under Python 3.  Under Python 2 it is problably a matter of coercing bytes to unicode (e.g. in the code that calls add_breakpoint()).

@ericsnowcurrently worth checking whether this is an issue in the experimental debugger.

@ericsnowcurrently no problem using Python 3
Thanks.

I just encountered this issue while trying to debug a Python script that misbehaves with non-ASCII paths. 🤦♂
With Python Experimental, breakpoints don't work at all. With normal Python debugger, breakpoints work but they show up as "Unknown Source".

File upstream issue https://github.com/Microsoft/ptvsd/issues/421

This has been fixed in the latest version of the experimental debugger.


Wrapper around song list

This a feature of a wrapper round the song list as per the new UI mocks on figma.

torvalds.dev is analyzing the pull request


Setup Alerts on system metrics + model prediction metrics (accuracy) vs. current model

PagerDuty, here we come!

moving to Advanced Edition:  http://pipeline.ai/products


Revert "add CPU autotp UT (#4263)"

This reverts commit 388c84834fca87465aff8bb8f6d85be88fa82ba6.

@delock and @Yejing-Lai do you have any idea on the problem with #4263? Thanks!

@delock and @Yejing-Lai do you have any idea on the problem with #4263? Thanks!

Hi @tjruwase @loadams Do you have a description to the problem with #4263 ?

@delock and @Yejing-Lai do you have any idea on the problem with #4263? Thanks!

Hi @tjruwase @loadams Do you have a description to the problem with #4263 ?

@delock - it appears after that PR was merged we see the cpu_inference tests either failing or failing and timing out at the 6 hour mark.  I'm not sure why/how this passed on the merge queue, but reverting the PR fixes it, so I'm trying to isolate what the change was that caused this, or if these are new test failures that should be skipped.

Hi @loadams, sorry for breaking the workflow.  From the error message (https://github.com/microsoft/DeepSpeed/actions/runs/6341645987/job/17235544538), it means the UT tries to start test with FP16 data format which is not supported on CPU.  How did it escaped merge CI is not clear to me.  A proper fix should skip these tests for FP16.
From the error log, TestModelTask should have the following check as other checks.  I need to run a few tests in my fork to verify.
if dtype not in get_accelerator().supported_dtypes():
    pytest.skip(f"Acceleraor {get_accelerator().device_name()} does not support {dtype}.")

@delock and @Yejing-Lai do you have any idea on the problem with #4263? Thanks!

Hi @tjruwase @loadams Do you have a description to the problem with #4263 ?

@delock - it appears after that PR was merged we see the cpu_inference tests either failing or failing and timing out at the 6 hour mark. I'm not sure why/how this passed on the merge queue, but reverting the PR fixes it, so I'm trying to isolate what the change was that caused this, or if these are new test failures that should be skipped. Let me know if you have any thoughts on this?
This looks to be when it ran in the merge queue, it seems to have failed but somehow GitHub marked that as a pass? Its also running more tests than the previous one that ran in the merge queue before it. Is it worth skipping these tests for now, or should we revert then re-apply this PR?

@loadams @tjruwase This PR fix the not implemented error
https://github.com/microsoft/DeepSpeed/pull/4430
This fix is verified on my local repo, cpu_inference can pass.
https://github.com/delock/DeepSpeedSYCLSupport/pull/15

Hi @loadams, sorry for breaking the workflow. From the error message (https://github.com/microsoft/DeepSpeed/actions/runs/6341645987/job/17235544538), it means the UT tries to start test with FP16 data format which is not supported on CPU. How did it escaped merge CI is not clear to me. A proper fix should skip these tests for FP16.
From the error log, TestModelTask should have the following check as other tests. I need to run a few tests in my fork to verify.
if dtype not in get_accelerator().supported_dtypes():
    pytest.skip(f"Acceleraor {get_accelerator().device_name()} does not support {dtype}.")

@delock and @Yejing-Lai do you have any idea on the problem with #4263? Thanks!

Hi @tjruwase @loadams Do you have a description to the problem with #4263 ?

@delock - it appears after that PR was merged we see the cpu_inference tests either failing or failing and timing out at the 6 hour mark. I'm not sure why/how this passed on the merge queue, but reverting the PR fixes it, so I'm trying to isolate what the change was that caused this, or if these are new test failures that should be skipped. Let me know if you have any thoughts on this?
This looks to be when it ran in the merge queue, it seems to have failed but somehow GitHub marked that as a pass? Its also running more tests than the previous one that ran in the merge queue before it. Is it worth skipping these tests for now, or should we revert then re-apply this PR?

Thanks, @delock - I'll close this PR and we can move any other discussion to your PR.  I'm still not sure how this showed as passed on the GitHub checks, but thanks for looking into this.


Skip AQE-join-DPP tests for [databricks] 14.3

Fixes #11527.
This is a temporary workaround to handle the failures in AQE/Join/DPP tests, as listed in #11527.
The AQE plan for broadcast hash join seems to have changed between 13.3 and 14.3 Databricks versions.
The actual fix for the problem will be tracked in #11643.  This commit disables the test so that the CI test can run through.

Build

Thank you for the review, @revans2.  I've merged this change.
I'll chase down #11643 ASAP.


[New Metric] Hinge losses

🚀 Feature

Add Hinge losses to classification package:
https://scikit-learn.org/stable/modules/generated/sklearn.metrics.hinge_loss.html#sklearn.metrics.hinge_loss
possibly with an added parameter squared to also calculated squared hinge loss
Motivation

Pitch

Alternatives

Additional context

Hi @SkafteNicki, I'd like to take a look at adding hinge_loss to torchmetrics :) Could you assign me to the issue?

@ethanwharris Nice, thanks for wanting to contribute :]