Error when copying role for Data Team from the teams tab

Describe the bug

Data team is not able to copy the credentials for assuming role from the Env->Teams tab
As an Env Admin when we copy the credentials for data team it is incorrect and this makes it impossible for the consumption team to run aws commands. and hence this is a critical functional issue.

How to Reproduce
*P.S. Please do not attach files as it's considered a security risk. Add code snippets directly in the message body as much as possible.*

Expected behavior

Data team is able to copy the credentials for assuming role from the Env->Teams tab
Or  Env Admin can copy correct credentials for data team for the data team

Your project
No response
Screenshots
No response
OS
all
Python version
3.1
AWS data.all version
1.4.2
Additional context
No response

@manjulaK thanks for raising the issue! It is solved in the linked PR :)


[BEAM-4752] Add support for newer dill dependency

Please add a meaningful description for your change here

Follow this checklist to help us incorporate your contribution quickly and easily:

[ ] Format the pull request title like [BEAM-XXX] Fixes bug in ApproximateQuantiles, where you replace BEAM-XXX with the appropriate JIRA issue, if applicable. This will automatically link the pull request to the issue.
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Post-Commit Tests Status (on master branch)

Lang
SDK
Apex
Dataflow
Flink
Gearpump
Samza
Spark

Go

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Java

Python

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Retest this please

There are rat errors with unlicensed files, but this PR does not add any new files.

retest this please

I see a test error in https://builds.apache.org/job/beam_PostCommit_Py_ValCont/228
Dataflow logs contain error "No module named _dill"
Could not load main session: Traceback (most recent call last): File "/usr/local/lib/python2.7/site-packages/apache_beam/runners/worker/sdk_worker_main.py", line 125, in main _load_main_session(semi_persistent_directory) File "/usr/local/lib/python2.7/site-packages/apache_beam/runners/worker/sdk_worker_main.py", line 190, in _load_main_session pickler.load_session(session_file) File "/usr/local/lib/python2.7/site-packages/apache_beam/internal/pickler.py", line 260, in load_session return dill.load_session(file_path) File "/usr/local/lib/python2.7/site-packages/dill/dill.py", line 363, in load_session module = unpickler.load() File "/usr/local/lib/python2.7/pickle.py", line 864, in load dispatchkey File "/usr/local/lib/python2.7/pickle.py", line 1096, in load_global klass = self.find_class(module, name) File "/usr/local/lib/python2.7/site-packages/dill/dill.py", line 423, in find_class return StockUnpickler.find_class(self, module, name) File "/usr/local/lib/python2.7/pickle.py", line 1130, in find_class import(module) ImportError: No module named _dill Traceback (most recent call last): File "/usr/local/lib/python2.7/site-packages/apache_beam/runners/worker/sdk_worker_main.py", line 125, in main _load_main_session(semi_persistent_directory) File "/usr/local/lib/python2.7/site-packages/apache_beam/runners/worker/sdk_worker_main.py", line 190, in _load_main_session pickler.load_session(session_file) File "/usr/local/lib/python2.7/site-packages/apache_beam/internal/pickler.py", line 260, in load_session return dill.load_session(file_path) File "/usr/local/lib/python2.7/site-packages/dill/dill.py", line 363, in load_session module = unpickler.load() File "/usr/local/lib/python2.7/pickle.py", line 864, in load dispatchkey File "/usr/local/lib/python2.7/pickle.py", line 1096, in load_global klass = self.find_class(module, name) File "/usr/local/lib/python2.7/site-packages/dill/dill.py", line 423, in find_class return StockUnpickler.find_class(self, module, name) File "/usr/local/lib/python2.7/pickle.py", line 1130, in find_class import(module) ImportError: No module named _dill

Thank you @alanmyrvold. Sent https://github.com/apache/beam/pull/5946 to rollback.


[HotFix] [SVCS-894] Cache File During Contiguous Upload For Dropbox

Ticket
https://openscience.atlassian.net/browse/SVCS-894
Purpose
Dropbox's rate limit feature may cause uploading/copying/moving many files to fail with "too many write operations". Unlike GitHub, Dropbox doesn't provide detailed information on how they rate limit requests but simply asks clients to retry based on the "Retry-After" header in the 429 response. This doesn't work natively for upload (inter copy and move use upload internally) since the stream has been consumed when the request is finished. The solution is to cache the stream locally into a temporary file and stream from the file for both the initial request and following 429 retries.
Changes
TBD
Side effects
TBD
QA Notes
TBD
Deployment Notes
TBD

Coverage decreased (-0.06%) to 91.728% when pulling 2d2d14817de98aca9b21a95375d891add014a26f on cslzchen:hotfix/dropbox-upload-rate-limit into f86354750d66559819cc88cd77e1ddb6b6b55f68 on CenterForOpenScience:master.

@felliott

[x] Rebased to develop
[x] Make max retries a provider setting


Functional component in dev mode dont work

Version
3.0.0-beta.3
Reproduction link
https://github.com/vuejs/vue-next/blob/dece6102aa84c115a3d6481c6e0f27e5b4be3ef1/packages/runtime-core/src/helpers/resolveAssets.ts#L81
Steps to reproduce
Create a .vue component:
export default function(props) {}

What is expected?
component works
What is actually happening?
error in console:
TypeError: Cannot assign to read only property 'name' of function (function code)
at resolveAsset (link to a line of code in reproduction link)

In vue 3.0.0-beta.2 everything works

Fixed, but it's still better to name the functional component.


[FLINK-26968][state/heap] Bump CopyOnWriteStateMap entry version before write

What is the purpose of the change
CopyOnWriteStateMap copies the entry before returning it to the client
for update. This also updates its state and entry versions.

However, if the entry is NOT used by any snapshots, the versions
will stay the same despite that state is going to be updated.

With incremental checkpoints, this causes such updated version to
be ignored in the next snapshot.

This change bumps the state version in this case (entry version
stays the same).

Verifying this change
Multiple IT cases in https://github.com/rkhachatryan/flink/tree/flip-151-full,
e.g.

ProcessingTimeWindowCheckpointingITCase.testAggregatingTumblingProcessingTimeWindow
EventTimeWindowCheckpointingITCase.testSlidingTimeWindow
AggregateITCase.testWindowWithUnboundedAgg

Does this pull request potentially affect one of the following parts:

Dependencies (does it add or upgrade a dependency): no
The public API, i.e., is any changed class annotated with @Public(Evolving): no
The serializers: no
The runtime per-record code paths (performance sensitive): no
Anything that affects deployment or recovery: JobManager (and its components), Checkpointing, Kubernetes/Yarn/Mesos, ZooKeeper: no
The S3 file system connector: no

Documentation

Does this pull request introduce a new feature? no
If yes, how is the feature documented? no

Test failures unrelated, tests in private branch passed.


adds client lib from chart-operator

Towards https://github.com/giantswarm/giantswarm/issues/2737.

Fishing for LGTM.

Yeah boy.


V1.19.9 lightbits

create a makefile to generate new patched e2e.test binary and package it as docker image.
this image will be pushed to our registry so the test can pull it and use the patched binaries.
We should maintain such branch for each supported k8s version.
For more info about how to build and generate please look here
see:

https://github.com/LightBitsLabs/lb-csi/pull/124
https://github.com/LightBitsLabs/systests/pull/6902

just saw that i have a flaky situation in 1.19.9 as well and sometimes - very rare - first time i see it we might have a race on the snapshottable suite as well:
root@rack06-server67-vm02 [client_k8s_master]:~# kubectl describe pv
Name:            pvc-39d1b261-154e-48f9-8801-9cedcd0234bd
Labels:          <none>
Annotations:     pv.kubernetes.io/provisioned-by: csi.lightbitslabs.com
Finalizers:      [kubernetes.io/pv-protection external-attacher/csi-lightbitslabs-com]
StorageClass:    snapshotting-5167-e2e-scb79wn
Status:          Released
Claim:           snapshotting-5167/pvc-khc8r
Reclaim Policy:  Delete
Access Modes:    RWO
VolumeMode:      Filesystem
Capacity:        1Gi
Node Affinity:   <none>
Message:
Source:
    Type:              CSI (a Container Storage Interface (CSI) volume source)
    Driver:            csi.lightbitslabs.com
    FSType:            ext4
    VolumeHandle:      mgmt:<IP_ADDRESS>:443,<IP_ADDRESS>:443,<IP_ADDRESS>:443|nguid:2ce4c199-91c2-43f2-abf0-fcd9d32a46e7|proj:default|scheme:grpcs
    ReadOnly:          false
    VolumeAttributes:      storage.kubernetes.io/csiProvisionerIdentity=1648027482390-8081-csi.lightbitslabs.com
Events:
  Type     Reason              Age                   From                                                                            Message
  ----     ------              ----                  ----                                                                            -------
  Warning  VolumeFailedDelete  51m (x3 over 51m)     csi.lightbitslabs.com_lb-csi-controller-0_7b1531ab-e2a6-466e-8ff1-671e2acc35c0  persistentvolume pvc-39d1b261-154e-48f9-8801-9cedcd0234bd is still attached to node rack06-server69-vm01
  Warning  VolumeFailedDelete  2m40s (x18 over 51m)  csi.lightbitslabs.com_lb-csi-controller-0_7b1531ab-e2a6-466e-8ff1-671e2acc35c0  rpc error: code = Unauthenticated desc = no valid authentication header: Request unauthenticated with bearer

hence i add another commit with the same logic - wait for PV deletion after PVC deletion before SC deletion.


added support for editorConfig

We should add .editorconfig file to define consistent coding style.
Refer here

Good idea, thanks for PR. Merged.


Set a default build type

Set a default build type like we do in ASGarD
https://www.kitware.com/cmake-and-the-default-build-type/
https://github.com/openchemistry/tomviz/blob/master/cmake/BuildType.cmake

cmake definition notes:

-fconvert='big-endian'" used for nimrod
-malign-double used for AVX instructions


add support for uppercase and lowercase and Base32Hex

Just expanding on what was already here to add support for Base32Hex (RFC4648 §7) and also upper and lower case encodings of the RFC4648 alphabets separately.

Merged without the version bump as 145447d06f307610cee2b2381b48606506d1b88e.


📎 Improve internal error reporting

Description
#2659 brought something to light for me: internal error reporting is uninformative for users and doesn't help them to understand the source of the issue or how to find a workaround.
Biome is a toolchain, a bug can come from any tool, and it is hard to tell without investment of the user.
It would be nice if we could specify which tool caused the error, and even in what unit of work (e.g. a rule for the linter), and the linted/formatted node - if any - (the queried node in the case of the linter).
Taking #2659 as an example.
Here is the current error:
Biome encountered an unexpected error

This is a bug in Biome, not an error in your code, and we would
appreciate it if you could report it to
https://github.com/biomejs/biome/issues/ along with the
following information to help us fixing the issue:

Source Location: crates/biome_js_semantic/src/semantic_model/scope.rs:115:33
Thread Name: biome::worker_0
Message: no entry found for key

types.ts internalError/panic  INTERNAL  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

✖ processing panicked: no entry found for key

⚠ This diagnostic was derived from an internal Biome error.
  Potential bug, please report it if necessary.

And here is an example of a better diagnostic:
types.ts internalError/panic  INTERNAL  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

✖ Biome encountered an internal error while linting the file with the
 rule suspicious/noRedeclare on the following code

    1 │
  > 2 │ type Element<T> = T extends Array<infer U> ? U : never
    3 │

ℹ This diagnostic was derived from the following internal Biome error:
 Source Location: crates/biome_js_semantic/src/semantic_model/scope.rs:115:33
 Thread Name: biome::worker_0
 Message: no entry found for key

ℹ This is a bug in Biome, not an error in your code, and we would
 appreciate it if you could report it to
 https://github.com/biomejs/biome/issues/

ℹ As a workaround try to disable the lint rule suspicious/noRedeclare
 or ignoring the file `types.ts`.

Have you checked if it's technically possible to retrieve the information you'd like to see?

No, I haven't checked how to proceed, it's an invitation to investigate. Perhaps I should clarify the issue description.
Because we have a multi-threaded architecture, panics are isolated in threads? I wonder if we could get some data from the main thread.

The only information we might be able to have, is the stack trace, however if it's not printed here, I doubt we have it.

I was thinking of doing this to get some information such as the currently running linter rule.

We can wrap evaluating rules with catch_unwind to catch panics, and that would allow us to surface the rule that was being evaluated. I'm not so sure about being able to surface the actual user code that resulted in the panic.

We already have that.
https://github.com/biomejs/biome/blob/main/crates%2Fbiome_cli%2Fsrc%2Fexecute%2Ftraverse.rs#L594

What if we did the catch_unwind here, where the rule is being evaluated?
https://github.com/biomejs/biome/blob/5088667ce337d34bc6f7a19887b07b06384d8fdc/crates/biome_analyze/src/registry.rs#L431
This function seems to have all the context we want in the new error messages.


the details of ResNet56

Hi,
Great work! You released the structures of ResNet nets in models.py except ResNet56, could you please make the definition of ResNet56 available as well? I'm aware of many online versions available, but I just would like to figure out some details, like whether bias=True or not in conv layers etc.. Thanks.

Thanks. The details of the ResNet56 can be found in preresnet.py.


fix(headless): allowed the constant query to be unset

https://coveord.atlassian.net/browse/KIT-450
The initial value of the constant query was an empty string. Unfortunately, while an empty string is often interpreted as "not set" for other values, for the constant query it meant "no query", or "don't filter", which meant a user explicitly setting the constant query to an empty string was interpreted as being equivalent to the user having never set the constant query at all. For most types of queries, this is fine, since nobody would want a non-empty default, but for the constant query this caused issues with the Tab controller, which would always overwrite an explicit "no query" with the constant query of any Tab controller whose "isActive" initial state was true.
In a nutshell, the following behaviour was possible even in a page with SearchParameterManager that properly reads and writes the URL:

Create a page with three tabs. "All" (cq=""), "Messages" (cq="...", initialState.isActive=true)
Load the page, the "Messages" tab is active
Select the "All" tab
Refresh the page, the "Messages" tab is active again

There is no difference between the cq and aq or q really, it is how we use it with the tabs which is different. We shouldn't make that difference explicit in the state by setting cq's type to string | null.
It also looks a bit weird to have #cq= in the url... I'm not so sure there is a "clean" solution here...
I like Sami's proposition for a possible fix of shouldInclude instead of the current null design for now. For it to be "clean", it comes back to have cq sets instead of a single string in the state perhaps... which is breaking, we were thinking of revisiting tabs anyways in the future.

I'm not sure we need both a wasSet and a default in the state, one of the do with good management of the reducer & shouldInclude should do it, no?

I'm not sure we need both a wasSet and a default in the state, one of the two with good management of the reducer & shouldInclude should do it, no?

Including only the default means that the tab must be registered before the search parameters manager calls restoreSearchParameters, which even in the React sample turned out not to be the case. Otherwise, whichever tab is being set in the URL would always be overwritten by the default.
Including only wasSet however looks like it may work, so long as we're ok with the behaviour of this PR (never removing the parameter if cq was explicitly set).


Change sync_es management command to use Celery

Description of change
This makes some changes to the sync_es management command. It:

makes the task run asynchronously by default (using Celery)
removes the --batch_size argument as it's not really used
adds a --sync argument for running it synchronously (mainly for convenience)

The reason for this change is to make things more consistent, and also as syncing (asynchronously) using Celery is generally faster due to parallelism. (This change will also benefit the staging refresh job.)
--sync is similar to running CELERY_TASK_ALWAYS_EAGER=True ./manage.py sync_es so we could potentially not bother adding --sync.
Checklist

[x] Has a new newsfragment been created? Check changelog/README.rst for instructions
[ ] Do any added or updated endpoints appear in the API documentation? See docs/Maintaining the API documentation.md for more details
[ ] Have any relevant search models been updated?
[ ] Have any relevant fixtures (fixtures/test_data.yaml) been updated?
[ ] Have any relevant select-/prefetch-related field lists in the views and search apps been updated?
[ ] Has the admin site been updated (for new models, fields etc.)?
[x] Has the README been updated (if needed)?

To the untrained eye, doing ./manage.py sync_es --sync looks like the --sync switch is actually telling the command to do the sync for real, which possibly suggests that omitting the switch would result in a dry run. This is I think a result of the command in that form making use of the word "sync" in two places; the first meaning "to synchronise ES" and the second meaning "synchronously". That differentiation in meaning isn't clear since both forms shorten to just "sync".
How about something like --foreground instead?

I agree that --sync switch may be confusing. Maybe we could just remove it?

I've renamed --sync to --foreground.


Any plans to add Opus support from SharpMP4?

I see SharpMP4 has Opus track capability, and that project seems to be more up to date than this one. Is SharpMP4 intended as a replacement for this project and should I use that instead for general MP4 writing?
Thanks

Eventually, yes. SharpMp4Parser is just a port of the Java code base, plus some bug fixes. No new features except for bugfixes are planned.
SharpMp4 is written from ground up in C#, has better support for newer codes, is stream in/stream out, but at the moment it is primarily intended and used for fragmented MP4 (fmp4). It is also pretty much work in progress.
Eventually, SharpMP4 will get MP4 support as well, but right now it's not tested on MP4 and when compared to this port it supports fewer MP4 boxes. I had some specific needs for fragmented mp4 that were not met by this project, so I've decided to rewrite everything from the ground up in C# and I've added some new features such as enhanced VPS/PPS/SPS parsers for H2655 and as you've noticed also Opus compatibility.

Thanks. No promises, but if I get a chance to work on Opus I'll put in a PR?

Of course, PRs are welcome!


Update cats-effect, cats-effect-std to 3.3.6

Updates

org.typelevel:cats-effect
org.typelevel:cats-effect-std

from 3.3.4 to 3.3.6.
GitHub Release Notes - Version Diff
I'll automatically update this PR to resolve conflicts as long as you don't change it yourself.
If you'd like to skip this version, you can just close this PR. If you have any feedback, just mention me in the comments below.
Configure Scala Steward for your repository with a .scala-steward.conf file.
Have a fantastic day writing Scala!

Ignore future updates
Add this to your .scala-steward.conf file to ignore future updates of this dependency:
updates.ignore = [ { groupId = "org.typelevel" } ]

labels: library-update, early-semver-patch, semver-spec-patch, commit-count:1

Superseded by #440.


Fixed tons of stuff

Removed unnecessary reflection, removed unnecessary code, performance improvements and credit standarization.

Thanks, :laughing:


Time to drop annotations

Discussed in https://github.com/thecodingmachine/graphqlite/discussions/673

Originally posted by fogrye March 29, 2024
Doctrine annotations was deprecated since php 8.0 release, should we drop support of them as php7 is not supported and there are no other reason not to use attributes?
Steps to do:

rm attributes from fixtures
polish annotation classes
rm lib from dependencies

I will create MR for this soon

Looking forward to it @fogrye


com.jio.myjio app is not working with HTTPS filtering enabled

@adguard-bot commented on Thu Jul 04 2019
Issue URL (Incorrect Blocking)
https://play.google.com/store/apps/details?id=com.jio.myjio
Comment
Username: @neodada

I need to add "@@||in.wzrkt.com^$app=com.jio.myjio" to user filter to check my account.

Screenshots

Screenshot 1

Screenshot 2

System configuration

Information
value

Platform:
And 8.1.0

AdGuard version:
v3.1.0 (1.3.347cl)

Stealth mode options:
Hide your search queries, Remove X-Client-Data header from HTTP requests, Self-destructing third-party cookies (180)

AdGuard mode:
VPN

Filtering quality:
High-quality

HTTPS filtering:
enabled

DNS requests blocking:
disabled

Filters:
AdGuard Mobile Ads,AdGuard Annoyances,AdGuard Base,AdGuard Tracking Protection,AdGuard Social Media

@neodada commented on Thu Jul 04 2019
I am wrong about user filter
"@@||in.wzrkt.com^$app=com.jio.myjio" doesn't work.
Https filtering is causing this.

@Alex-302 commented on Thu Jul 04 2019
@neodada please check without filters and without custom DNS

@neodada commented on Thu Jul 04 2019
@Alex-302 tried with all filters disabled and dns disabled. Not working.

@Alex-302 commented on Mon Jul 08 2019
@neodada got it. Moved to correct repository.

@Alex-302 commented on Mon Jul 08 2019
@TheHasagi please check it.

@TheHasagi commented on Mon Jul 29 2019
Can not reproduce this issue with the latest nightly. I guess the problem is solved already. @neodada please, check with this version.

@neodada commented on Mon Jul 29 2019
Doesn't work with https filtering enabled on latest nightly. There is no log either of blocking related to https requests.

@TheHasagi commented on Mon Jul 29 2019
@neodada
As I can see you are using VPN mode and enabled HTTPS filtering for this application, am I right?

@neodada commented on Mon Jul 29 2019
Yes, i have enabled https filtering through magisk module.

@TheHasagi commented on Mon Jul 29 2019
@neodata
Please, gather the log files regarding this article and send it to<EMAIL_ADDRESS>
@neodada commented on Mon Jul 29 2019
Sent.

@ameshkov commented on Fri Aug 02 2019
@neodada temporary solution: add jio.com to the list of HTTPS filtering exclusions.
Meanwhile, it seems to be a CoreLibs bug of some sort so I'll move this task

Please find the logs in NextCloud.
It seems to me that we cannot properly process the response with status code 308 + non-zero content-length.


Can't download any documents

Can't download any documents please help

what file type are you downloading, what's happening? Does the app have storage permissions?

I have the same problem since 1.7.3 on the same Samsung phone. 1.7.2 was fine. All permissions are OK.
When I tap downloas button of any media file such as images, nothing happens.
Can you fix it?


Improve finding the control for entering the search expression

To avoid the "full add" issue with Contact Assoc Applet when LabelFind had a HTML Type text.
Maybe: 1) check if field exist 2) check if set value could be read
Also document that it should be used with caution.
UPD: if list applet, always check only list columns??

changed the description


Added delayed messages in Prometheus when using namespace-level metrics aggregation

Motivation
Metrics for delayed messages in Prometheus are only emitted when topic-level aggregation is configured. We also need to report it when doing namespace level aggregation.

run java8 tests

run cpp tests


Reads "Date created" field from event markers

I found that from AcqKnowledge v4.4.0 on, the date a marker is created is included with the marker.
With some reversed-engineering, I figured out it is encoded in 'Uknnown3' (note the typo):
https://github.com/uwmadison-chm/bioread/blob/4641503e0c97df51551be224ceddcbad6963080f/bioread/headers.py#L791
This PR adds a new property date_created_utc for the class EventMarker, with the date it was created in UTC.  With a minor modification it could be modified to return it in the local timezone.
I think this solves #21 (for files created with AcqKnowledge v4.4.0 or later).

Hi again! Thanks for your patience here, life has been more hectic than normal so I haven't gotten a chance to think about this much.
Thank you for finding this — getting some kind of date created out of .acq files is one of the most common requests.
What would you think about adding a method to biopac.Datafile itself that would guess at the created date for the file, if available? Something like:
def created_at_guess(self):
    # return the minimum created_date from the event markers, or None if there are no markers
    # or our markers don't have created_date

I feel like there's probably a better name for this method, but it's not coming to me now.
Also, returning the date in UTC is, I think, completely fine. Folks can convert that to local time themselves if they want.

I don't have access to Acq right now, but... why do we need to subtract the position of the marker out from the date created? If the field in the marker really is the date/time the marker was created, that's the time we're interested in.
I think it's important to also have the Datafile's access method specify that this is a guess -- if someone deletes the standard markers and then later creates new ones during processing (and I bet people do this!) we'll happily return a valid date, but the wrong one.
probably a better method name is earliest_marker_created_at or something.

I'm merging this as-is; I'll look at the other things yet in master

I... I wonder if this data is in the unknown parts of the graph header as well

I don't have access to Acq right now, but... why do we need to subtract the position of the marker out from the date created? If the field in the marker really is the date/time the marker was created, that's the time we're interested in.

Well, for the EventMarker, yes, you probably want the date/time the marker was created. However, for the created_at (or created_at_guess) my intent was to find the date/time the recording (the file) was created. For that purpose, supposing that the earliest marker was created automatically during the recording, I'm subtracting the time from the beginning of the recording up to the creation of the marker (delta_time_first_event). If, from what I can tell, there is a marker created automatically by AcqKnowledge at the beginning of the recording (since v4.4.0), delta_time_first_event will be zero.

I think it's important to also have the Datafile's access method specify that this is a guess -- if someone deletes the standard markers and then later creates new ones during processing (and I bet people do this!) we'll happily return a valid date, but the wrong one.
probably a better method name is earliest_marker_created_at or something.

Yes, this is totally possible. I don't have much experience processing BioPac data. I'm just trying to synchronize physio responses to MRI images acquired at the same time. Feel free to change if you think it makes more sense.


cmd/cdi: update/fix go.{mod,sum}.

Update cmd/cdi/go.{mod,sum} to fix unbuildable cmd/cdi.

Is there something that builds the CLI in our PRs?

Yes, there is. The sanity workflow verifies among other things that (test) binaries build on multiple platforms.


404 error on recreation

I'm sure this is simply a beginner error, but I tried to recreate your project from zip and get a 404 error with the source saying that it can't GET piskel-packaged-min.js or piskel-style-packaged.css with piskel-boot.js. From the following lines:
var script;
if (window.location.href.indexOf('pack') != -1) {
script = 'js/piskel-packaged' + version + '.js';
} else {
script = 'js/piskel-packaged-min' + version + '.js';
}
loadStyle('css/piskel-style-packaged' + version + '.css');
Anyone know what I am missing?

Hi !
By default, piskel tries to load minified versions of the statics. You can
append ?debug to the URL you target to use the exploded source files.
Have a look at
https://github.com/juliandescottes/piskel/wiki/Development-Environment for
more info :)
Le 17 mai 2015 06:39, "ChrisKlinglerOhsome"<EMAIL_ADDRESS>a
écrit :

I'm sure this is simply a beginner error, but I tried to recreate your
project from zip and get a 404 error with the source saying that it can't
GET piskel-packaged-min.js or piskel-style-packaged.css with
piskel-boot.js. From the following lines:
var script;
if (window.location.href.indexOf('pack') != -1) {
script = 'js/piskel-packaged' + version + '.js';
} else {
script = 'js/piskel-packaged-min' + version + '.js';
}
loadStyle('css/piskel-style-packaged' + version + '.css');
Anyone know what I am missing?
—
Reply to this email directly or view it on GitHub
https://github.com/juliandescottes/piskel/issues/283.

Works perfectly. Thanks Julian


在AIStudio中报错undefined symbol: dnnl_layer_normalization_v2_forward_desc_init

温馨提示：根据社区不完全统计，按照模板提问，可以加快回复和解决问题的速度

环境
AIStudio， 新建一个项目， paddle是2.4.1
问题日志及出现问题的操作流程
新建AiStudio， 安装FastDeploy， import fastdeploy就报错
aistudio@jupyter-227854-5797577:~$ pip list | grep paddle
paddle-bfloat                  0.1.7
paddle2onnx                    1.0.0
paddlefsl                      1.1.0
paddlehub                      2.3.0
paddlenlp                      2.4.2
paddlepaddle-gpu               2.4.1.post112
tb-paddle                      0.3.6
x2paddle                       1.4.0
aistudio@jupyter-227854-5797577:~$ pip install numpy opencv-python fastdeploy-gpu-python -f https://www.paddlepaddle.org.cn/whl/fastdeploy.html
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Looking in links: https://www.paddlepaddle.org.cn/whl/fastdeploy.html
Requirement already satisfied: numpy in /opt/conda/envs/python35-paddle120-env/lib/python3.9/site-packages (1.23.5)
Requirement already satisfied: opencv-python in /opt/conda/envs/python35-paddle120-env/lib/python3.9/site-packages (<IP_ADDRESS>)
Collecting fastdeploy-gpu-python
  Downloading https://bj.bcebos.com/fastdeploy/release/wheels/fastdeploy_gpu_python-1.0.5-cp39-cp39-manylinux1_x86_64.whl (1353.4 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.4/1.4 GB 410.0 kB/s eta 0:00:00
Requirement already satisfied: pyyaml in /opt/conda/envs/python35-paddle120-env/lib/python3.9/site-packages (from fastdeploy-gpu-python) (5.1.2)
Requirement already satisfied: wheel in /opt/conda/envs/python35-paddle120-env/lib/python3.9/site-packages (from fastdeploy-gpu-python) (0.36.2)
Requirement already satisfied: tqdm in /opt/conda/envs/python35-paddle120-env/lib/python3.9/site-packages (from fastdeploy-gpu-python) (4.64.1)
Collecting fastdeploy-tools>=0.0.5
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/19/27/527bb120f18b72d79b280bc7a810e67a00e0a0a4a273d4543feede0e812f/fastdeploy_tools-0.0.5-py3-none-any.whl (10 kB)
Requirement already satisfied: fastapi in /opt/conda/envs/python35-paddle120-env/lib/python3.9/site-packages (from fastdeploy-gpu-python) (0.90.1)
Requirement already satisfied: requests in /opt/conda/envs/python35-paddle120-env/lib/python3.9/site-packages (from fastdeploy-gpu-python) (2.24.0)
Collecting uvicorn==0.16.0
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/a0/68/effcc7c39d79fbae86ad621ebcf28d63a3b9c111a885bc2b74c576ba8dc4/uvicorn-0.16.0-py3-none-any.whl (54 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 54.9/54.9 kB 1.0 MB/s eta 0:00:00
Requirement already satisfied: h11>=0.8 in /opt/conda/envs/python35-paddle120-env/lib/python3.9/site-packages (from uvicorn==0.16.0->fastdeploy-tools>=0.0.5->fastdeploy-gpu-python) (0.14.0)
Collecting asgiref>=3.4.0
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/8f/29/38d10a47b322a77b2d12c2b79c789f52956f733cb701d4d5157c76b5f238/asgiref-3.6.0-py3-none-any.whl (23 kB)
Requirement already satisfied: click>=7.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.9/site-packages (from uvicorn==0.16.0->fastdeploy-tools>=0.0.5->fastdeploy-gpu-python) (8.0.4)
Requirement already satisfied: starlette<0.24.0,>=0.22.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.9/site-packages (from fastapi->fastdeploy-gpu-python) (0.23.1)
Requirement already satisfied: pydantic!=1.7,!=1.7.1,!=1.7.2,!=1.7.3,!=1.8,!=1.8.1,<2.0.0,>=1.6.2 in /opt/conda/envs/python35-paddle120-env/lib/python3.9/site-packages (from fastapi->fastdeploy-gpu-python) (1.10.4)
Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.9/site-packages (from requests->fastdeploy-gpu-python) (1.25.11)
Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/envs/python35-paddle120-env/lib/python3.9/site-packages (from requests->fastdeploy-gpu-python) (2019.9.11)
Requirement already satisfied: chardet<4,>=3.0.2 in /opt/conda/envs/python35-paddle120-env/lib/python3.9/site-packages (from requests->fastdeploy-gpu-python) (3.0.4)
Requirement already satisfied: idna<3,>=2.5 in /opt/conda/envs/python35-paddle120-env/lib/python3.9/site-packages (from requests->fastdeploy-gpu-python) (2.8)
Requirement already satisfied: typing-extensions>=4.2.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.9/site-packages (from pydantic!=1.7,!=1.7.1,!=1.7.2,!=1.7.3,!=1.8,!=1.8.1,<2.0.0,>=1.6.2->fastapi->fastdeploy-gpu-python) (4.3.0)
Requirement already satisfied: anyio<5,>=3.4.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.9/site-packages (from starlette<0.24.0,>=0.22.0->fastapi->fastdeploy-gpu-python) (3.6.1)
Requirement already satisfied: sniffio>=1.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.9/site-packages (from anyio<5,>=3.4.0->starlette<0.24.0,>=0.22.0->fastapi->fastdeploy-gpu-python) (1.3.0)
Installing collected packages: asgiref, uvicorn, fastdeploy-tools, fastdeploy-gpu-python
  Attempting uninstall: uvicorn
    Found existing installation: uvicorn 0.20.0
    Uninstalling uvicorn-0.20.0:
      Successfully uninstalled uvicorn-0.20.0
Successfully installed asgiref-3.6.0 fastdeploy-gpu-python-1.0.5 fastdeploy-tools-0.0.5 uvicorn-0.16.0

[notice] A new release of pip available: 22.1.2 -> 23.0.1
[notice] To update, run: pip install --upgrade pip
aistudio@jupyter-227854-5797577:~$ python
Python 3.9.16 (main, Jan 11 2023, 16:05:54)
[GCC 11.2.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import fastdeploy
Traceback (most recent call last):
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.9/site-packages/fastdeploy/c_lib_wrap.py", line 164, in <module>
    from .libs.fastdeploy_main import *
ImportError: /opt/conda/envs/python35-paddle120-env/lib/python3.9/site-packages/fastdeploy/libs/third_libs/paddle_inference/paddle/lib/libpaddle_inference.so: undefined symbol: dnnl_layer_normalization_v2_forward_desc_init

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.9/site-packages/fastdeploy/__init__.py", line 53, in <module>
    from .c_lib_wrap import (
  File "/opt/conda/envs/python35-paddle120-env/lib/python3.9/site-packages/fastdeploy/c_lib_wrap.py", line 166, in <module>
    raise RuntimeError("FastDeploy initalized failed!")
RuntimeError: FastDeploy initalized failed!
>>>
aistudio@jupyter-227854-5797577:~$ conda --version
conda 4.10.1
aistudio@jupyter-227854-5797577:~$ which python
/opt/conda/envs/python35-paddle120-env/bin/python
aistudio@jupyter-227854-5797577:~$ which pip
/opt/conda/envs/python35-paddle120-env/bin/pip
aistudio@jupyter-227854-5797577:~$ python --version
Python 3.9.16

将paddlepaddle升级到develop版最新

将paddlepaddle升级到develop版最新 也报错 ImportError: /home/yscredit/anaconda3/envs/paddle/lib/python3.8/site-packages/fastdeploy/libs/third_libs/paddle_inference/paddle/lib/libpaddle_inference.so: undefined symbol: dnnl_layer_normalization_v2_forward_desc_init

@magicly @clSpider
请直接修改下这个代码文件
/home/yscredit/anaconda3/envs/paddle/lib/python3.8/site-packages/fastdeploy/__init__.py

将里面的try: import paddle代码逻辑注释掉，即可使用

@jiangjiajun  注释之后 还是报错：
WARNING:root:Failed to create a symbolic link pointing to /home/yscredit/anaconda3/envs/paddle/lib/python3.8/site-packages/fastdeploy/libs/third_libs/tensorrt/lib/libnvcaffe_parser.so.8 by an unprivileged user. It may failed when you use Paddle TensorRT backend. Please use administator privilege to import fastdeploy at first time.
Traceback (most recent call last):
File "/home/yscredit/anaconda3/envs/paddle/lib/python3.8/site-packages/fastdeploy/c_lib_wrap.py", line 164, in
from .libs.fastdeploy_main import *
ImportError: /home/yscredit/anaconda3/envs/paddle/lib/python3.8/site-packages/fastdeploy/libs/third_libs/paddle_inference/paddle/lib/libpaddle_inference.so: undefined symbol: dnnl_layer_normalization_v2_forward_desc_init
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "fastpipeline.py", line 7, in
import fastdeploy as fd
File "/home/yscredit/anaconda3/envs/paddle/lib/python3.8/site-packages/fastdeploy/init.py", line 53, in
from .c_lib_wrap import (
File "/home/yscredit/anaconda3/envs/paddle/lib/python3.8/site-packages/fastdeploy/c_lib_wrap.py", line 166, in
raise RuntimeError("FastDeploy initalized failed!")
RuntimeError: FastDeploy initalized failed!

遇到同样的报错，把同一个环境下的paddlepaddle-gpu卸载了，我这就能执行了，不知道是不是这两个会冲突？


Error while installing

I executed this command:
$ docker build -t ark-analysis .
It ran for sometime and stopped in Step 5/8 : RUN pip install -r /opt/ark-analysis/requirements.txt
ERROR: THESE PACKAGES DO NOT MATCH THE HASHES FROM THE REQUIREMENTS FILE. If you have updated the package versions, please update the hashes. Otherwise, examine the package contents carefully; someone may have tampered with them.
...
The command '/bin/sh -c pip install -r /opt/ark-analysis/requirements.txt' returned a non-zero code: 1
FYI I'm running Docker Toolbox (Docker Quickstart Terminal) on Win10.

Hi there!  There may have been a problem while downloading some dependencies.  Could you try running the command again?  If that doesn't work, can you post a screenshot(s) of the error message and/or specify the packages it lists as not having matching hashes?

It worked after several attempt. However, the link is not working.

when you ran bash start_docker.sh, did you keep the window open or close it?  The terminal window should be kept open (not exited out) while using the notebooks.

The window was open. Also, the "ark-analysis" image was running on Docker as well.

Gotcha.  Ok, after a little more research, it looks like this is an issue with Docker Toolbox.  I'd recommend updating to Docker Desktop for Windows in the future, but there is an immediate workaround that we can try:

Run the command docker-machine ip default in the docker terminal.  This should print an ip address that looks something like xxx.xxx.xxx.xxx.  Remember this ip address.
When you copy/paste the link from the terminal into the browser, replace the <IP_ADDRESS> in the url with the ip address output by step 1, xxx.xxx.xxx.xxx

It worked! Thanks a lot.

No problem! Happy to help

However, there were still some warnings. Any suggestion?

Yeah these are all fine, it'll still work


Integrate gongmattree instead of bespoke side-gong-diagrams component

sidebar-gong-diagrams is difficult to maintain (>1 kSloc for ts and 270 sloc for html). Use of a
gongmattree seems appropriate (maybe as an internal model)
Steps:

[x] add node/tree to the models
[x] add the tree component to the gongdocspecific lib

[x] add a paramter with tree name

[x] display the tree component below the sidebar-gong
[x] when a diagram is selected, populate the tree with the correct items

[x] add an interception for detecting which node is selected among diagram nodes
[x] unselect the diagram that was not selected
[x] in the front, have the classdiagramm poll the commitNbFromBack

[ ] implement user interactions on the tree for struct

[ ] in case a classdiagram is selected, update the tree of gong

[ ] implement user interactions on the tree for enums
[ ] implement user interactions on the tree for notes

Problem with the addition of a diagram sequence:

add a diagram
"New Diagram" appears
save the name

Unexpected:

edit button present

Next step is removing gongdocCommand from the front. However, this is not straightforward since update of position for:

classshapes (via positions)
notes
link middlevertice

are updated via the gongdoc command.
this idea is to switch to the associated services

Now, the remaining gongdoc  command use is for the selection of a classshape
case DIAGRAM_GONGSTRUCT_SELECT:
				case DIAGRAM_GONGSTRUCT_SELECT:
					log.Println("UML Shape selected ", GongdocCommandSingloton.StructName)
					gongStruct, ok := Stage.GongStructs_mapString[GongdocCommandSingloton.StructName]
					if ok {
						if GongdocCommandSingloton.GongdocCommandCallback != nil {
							GongdocCommandSingloton.GongdocCommandCallback.HasSelected(gongStruct.Name)
						}
					}

Now, deal with the enum nodes.

[ ] checkbox disabled only is needed

Manage serialization/deserialization of enums

[ ] when serializing,

{
			Struct: &(models.OCADE_EtatSIC{}),
			Position: &uml.Position{
				X: 20.000000,
				Y: 790.000000,
			},
			Width:  240.000000,
			Heigth: 48.000000,
		},
		{
			Struct: new(models.OCADE_NiveauxMaintenanceEnum),
			Position: &uml.Position{
				X: 850.000000,
				Y: 30.000000,
			},
			Width:

deal now with gong enums fields

[ ] callback


Implement payloads endpoint

Implement rosetta payloads endpoint

in general: we don't use jira's names in the commits, as the repository is open sourced but our internal jira project is closed.
that being said, please just write a commit name corresponding to the changes, the jira ticket is not needed

@marcinwadon @TheMMaciek
Are there any outstanding issues with this PR that I've missed or that you'd like me to address?

@marcinwadon @TheMMaciek
Are there any outstanding issues with this PR that I've missed or that you'd like me to address?

We've been working whole team mostly on the download logic today. I'll make a re-review tomorrow morning are working hours.


Update Hungarian translation - pt. 2

Continuation of #5965

@yaichenbaum please don't merge this yet, I'll let you know when I'm done

@yaichenbaum I'm done for now and the pipeline seemed to work for the changes previously caused(?) it to fail... 🤔


Add Semantic Release

Add the semantics release github action
Add necessary description to https://github.com/rog-golang-buddies/README

@shukra-in-spirit , I would like to take this forward, if that's fine.

Thats great @adityaprakash-bobby . Thank you.


K8SSAND-1318 ⁃ Helm install docs are missing step to add K8ssandra helm repo

What happened?
Running through the local single-cluster Helm install docs I got an error when running the command to install the k8ssandra-operator helm chart:
helm install k8ssandra-operator k8ssandra/k8ssandra-operator -n k8ssandra-operator --create-namespace

I hadn't added the k8ssandra helm repo, because there's no step in the v2 docs that say to do so.
It looks like the v1 docs do list this information, and running helm repo add k8ssandra https://helm.k8ssandra.io/stable is all I needed to do for the install to work.
Did you expect to see something different?
We should list the steps for adding the k8ssandra helm repo to the operator installation docs before telling users to perform the helm install.
How to reproduce it (as minimally and precisely as possible):
helm repo remove k8ssandra
helm install k8ssandra-operator k8ssandra/k8ssandra-operator -n k8ssandra-operator --create-namespace
┆Issue is synchronized with this Jira Task by Unito
┆friendlyId: K8SSAND-1318
┆priority: Medium

Fixing in https://github.com/k8ssandra/k8ssandra/pull/1349.

Done


[ADP-1963] fix bech32 prefix for extra_signatures to req_signer_vkh

changed  addr_keyhash to req_signer_vkh in json encoding of extra_signatures field

Comments

Issue Number

ADP-1963

bors r+


Small coverage improvements

While checking what we do here to help setup coverage on metatensor-models, I found a couple of issues.
Contributor (creator of pull-request) checklist

[ ] ~Tests updated (for new features and bugfixes)?~
[ ] ~Documentation updated (for new features)?~
[ ] ~Issue referenced (for PRs that solve an issue)?~

Reviewer checklist

[ ] CHANGELOG updated with public API or any other important changes?

📚 Documentation preview 📚: https://metatensor--531.org.readthedocs.build/en/531/

Interesting that combine is not able to find the file for  'python/metatensor-learn/.coverage'. Can you maybe check if the file is created locally?

It was not created on CI, we where only running a subset of tests there.

Coverage still seems to be missing for -learn: https://app.codecov.io/gh/lab-cosmo/metatensor/commit/74ddd5bc99029f5f22125ac20ed4f86214bd3f31/blob/python/metatensor-learn/metatensor/learn/data/collate.py


[DOCS] Unstyled autocomplete is unreadable in dark theme

[x] The issue is present in the latest release.
[x] I have searched the issues of this repository and believe that this is not a duplicate.

Current Behavior 😯

Expected Behavior 🤔
text should be black to be able to read

Steps to Reproduce 🕹

https://material-ui.com/components/autocomplete/#customized-hook
Context 🔦

Your Environment 🌎

Tech
Version

Material-UI
v5.?.?

React

Browser
Chrome 81

TypeScript
4.0

etc.

Duplicate of #22491


Added if option for presence constraints

This lets you say
add_presence_constraint :books, :isbn, if: "status = 'published'"

And it will generate
CHECK (NOT (status = 'published') OR (isbn !~ '^\s*$'))

The PR includes a test and README update.

This needs a rebase.

Rebased! :-)


Add files via upload https://www.ippt.pan.pl/

Domain for:
Instytut Podstawowych Problemów Techniki Polskiej Akademii Nauk
Institute of Fundamental Technological Research Polish Academy of Sciences

Pull request merged.
Please notice that free educational subscription for JetBrains tools is for students and teachers only, and is valid for using the tools for educational purpose only. If employees of this Institute who are not students, nor teachers, also need a license for JetBrains tools, they are eligible for an academic discount for the tools. They can get the discount by email to<EMAIL_ADDRESS>Thank you!


Training a simple XOR network yields incorrect, undeterministic behaviour

Bug description
Hi, I am trying to train a simple DNN to solve the XOR problem. This can be trivially solved with a pure torch implementation. I cannot replicate the same simple model in lightning. Instead the trained model oscillates between different states, never managing to correctly produce XOR.
What version are you seeing the problem on?
v2.1
How to reproduce the bug
# import libraries
import torch
import torch.nn as nn
from torch.autograd import Variable

class XOR(nn.Module):
    def __init__(self):
        super(XOR, self).__init__()
        self.linear_sigmoid_stack = nn.Sequential(
            nn.Linear(2, 2),
            nn.Sigmoid(),
            nn.Linear(2, 1)
        )

    def forward(self, x):
        return self.linear_sigmoid_stack(x)

if __name__ == "__main__":
    # create data
    Xs = torch.Tensor([[0., 0.],
                       [0., 1.],
                       [1., 0.],
                       [1., 1.]])

    y = torch.Tensor([0., 1., 1., 0.]).reshape(Xs.shape[0], 1)

    xor_network = XOR()

    epochs = 1000
    mseloss = nn.MSELoss()
    optimizer = torch.optim.Adam(xor_network.parameters(), lr=0.03)
    all_losses = []
    current_loss = 0
    plot_every = 50

    for epoch in range(epochs):

        # input training example and return the prediction
        yhat = xor_network.forward(Xs)

        # calculate MSE loss
        loss = mseloss(yhat, y)

        # backpropogate through the loss gradiants
        loss.backward()

        # update model weights
        optimizer.step()

        # remove current gradients for next iteration
        optimizer.zero_grad()

        # append to loss
        current_loss += loss
        if epoch % plot_every == 0:
            all_losses.append(current_loss / plot_every)
            current_loss = 0

        # print progress
        if epoch % 500 == 0:
            print(f'Epoch: {epoch} completed')

I tried to use Lightning to simplify away the boilerplate code like so:
import torch
from torch import nn
import torch.nn.functional as F
import lightning as L
from torch.utils.data import TensorDataset, DataLoader

class XORNetwork(L.LightningModule):
    def __init__(self):
        super(XORNetwork, self).__init__()
        self.linear_sigmoid_stack = nn.Sequential(
            nn.Linear(2, 2),
            nn.Sigmoid(),
            nn.Linear(2, 1)
        )

    def training_step(self, batch, batch_idx):
        # training_step defines the train loop.
        x, y = batch
        yhat = self.forward(x)
        loss = F.mse_loss(yhat, y)
        return loss

    def forward(self, x):
        return self.linear_sigmoid_stack(x)

    def configure_optimizers(self):
        optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
        return optimizer

if __name__ == "__main__":
    X = torch.Tensor([[0., 0.], [0., 1.], [1., 0], [1., 1]])
    labels = torch.Tensor([0., 1., 1., 0])
    dataset = TensorDataset(X, labels)
    dataloader = DataLoader(dataset, batch_size=1, shuffle=True)

    xor_network = XORNetwork()

    # train model
    trainer = L.Trainer(max_epochs=500, accelerator="cpu")
    trainer.fit(model=xor_network, train_dataloaders=dataloader)

    xor_network.eval()
    with torch.no_grad():
        test_output = xor_network(X)
        print(test_output.round())

### Error messages and logs

_No response_

### Environment

<details>
  <summary>Current environment</summary>

* CUDA:
	- GPU:               None
	- available:         False
	- version:           None
* Lightning:
	- lightning:         2.1.1
	- lightning-utilities: 0.9.0
	- pytorch-lightning: 2.1.1
	- torch:             2.1.0
	- torchmetrics:      1.2.0
* Packages:
	- aiohttp:           3.8.6
	- aiosignal:         1.3.1
	- async-timeout:     4.0.3
	- attrs:             23.1.0
	- certifi:           2023.7.22
	- charset-normalizer: 3.3.2
	- filelock:          3.13.1
	- frozenlist:        1.4.0
	- fsspec:            2023.10.0
	- idna:              3.4
	- jinja2:            3.1.2
	- lightning:         2.1.1
	- lightning-utilities: 0.9.0
	- markupsafe:        2.1.3
	- mpmath:            1.3.0
	- multidict:         6.0.4
	- networkx:          3.2.1
	- numpy:             1.26.1
	- packaging:         23.2
	- pip:               22.3.1
	- pytorch-lightning: 2.1.1
	- pyyaml:            6.0.1
	- requests:          2.31.0
	- setuptools:        65.5.1
	- sympy:             1.12
	- torch:             2.1.0
	- torchmetrics:      1.2.0
	- tqdm:              4.66.1
	- typing-extensions: 4.8.0
	- urllib3:           2.0.7
	- wheel:             0.38.4
	- yarl:              1.9.2
* System:
	- OS:                Darwin
	- architecture:
		- 64bit
		-
	- processor:         arm
	- python:            3.10.13
	- release:           23.0.0
	- version:           Darwin Kernel Version 23.0.0: Fri Sep 15 14:41:34 PDT 2023; root:xnu-10002.1.13~1/RELEASE_ARM64_T8103

</details>

### More info

_No response_

@Fohlen In your Lightning code,

You didn't choose the same learning rate
You didn't run for the same number of epochs

And in the pytorch code you are missing the test code:
    xor_network.eval()
    with torch.no_grad():
        test_output = xor_network(Xs)
        print(test_output.round())

To make both of them the same, the hyperparameters need to be the same of course. Can you try again? I get the correct predictions after these fixes.
In addition, to make it fully deterministic you can set the seed
from lightning import seed_everything

seed_everything(0)

Hi @awaelchli, this indeed produces the correct result. I can get the code to converge correctly within 100 epochs or less with pure Torch, any idea why that wouldn't be the case with lightning?

I can get the code to converge correctly within 100 epochs or less with pure Torch

The code that you posted can't actually converge in 100 epochs. Please share what you changed to make that possible.

Sorry for the imprecise wording. After some experimentation with epochs I could produce the correct result at epoch=250 (not convergence). However, this appears to be extremely sensitive to the seed one uses when training. I find this interesting. According to the Deep Learning book, the correct weights should be learned with a single pass of this network. However, this behaviour is not lightning-specific. Thanks for your help, I will keep on digging in torch to find out the reason for this behaviour 👍


Provide multi-platform Docker images

The Docker image in Docker Hub mwader/postfix-relay currently supports only OS/arch of type linux/amd64 .
It would be great to support multiple architectures (=platforms), in particular linux/arm64 , as ARM processors are widely used.
How to reproduce
If you run in an ARM machine:
$ dpkg --print-architecture
arm64

You get the following error:
$ docker run -it --rm mwader/postfix-relay:latest bash

WARNING: The requested image's platform (linux/amd64) does not match the detected host platform (linux/arm64/v8) and no specific platform was requested
standard_init_linux.go:219: exec user process caused: exec format error

Usecases

My usecase is to deploy the container in a RaspberryPi, which has an ARM processor.
Any other ARM machine.

Suggested solution
You can use buildx. It can build and push a multi-platform image without modifying the Dockerfile.
It integrates very well with CI/CD pipelines:
Example of GitHub Actions: https://github.com/marketplace/actions/build-and-push-docker-images#multi-platform-image
Tests
I tested a multi-architecture build with buildx, following the steps here: https://docs.docker.com/docker-for-mac/multi-arch/
$ docker buildx create --name mybuilder
mybuilder
$ docker buildx use mybuilder
$ docker buildx build --platform linux/amd64,linux/arm64 -t forzagreen/postfix-relay:multi --push .

The resulting image is here: forzagreen/postfix-relay:multi
I tested in both amd64 and arm64.
Docker pulls the correct image based on the machine architecture.

Hi! yes would like to do this also for this image and some other images that I have. Thanks for the links and suggestion, i guess I will move image building from docker hub to github actions somehow, seems to be the most flexible.

Hi! yes would like to do this also for this image and some other images that I have. Thanks for the links and suggestion, i guess I will move image building from docker hub to github actions somehow, seems to be the most flexible.

Hi again, could you try again now? now the image is built for platforms linux/amd64, linux/arm/v7 and linux/arm64, same as official debian and ubuntu images minus ppc and s390.
Workflow is here https://github.com/wader/postfix-relay/blob/master/.github/workflows/ci.yml there were some minor issues that got solved but might be done better:

How to map master tag to latest tag, now done with if
How to update docker hub description, now done with a dummy build in docker hub (action to do it does not work with 2FA https://github.com/peter-evans/dockerhub-description/issues/10)

Let me know if you have suggestions

Hi again, could you try again now? now the image is built for platforms linux/amd64, linux/arm/v7 and linux/arm64, same as official debian and ubuntu images minus ppc and s390.
Workflow is here https://github.com/wader/postfix-relay/blob/master/.github/workflows/ci.yml there were some minor issues that got solved but might be done better:

How to map master tag to latest tag, now done with if
How to update docker hub description, now done with a dummy build in docker hub (action to do it does not work with 2FA https://github.com/peter-evans/dockerhub-description/issues/10)

Let me know if you have suggestions

And feel tree to close issue if you think thinks seems ok!

And feel tree to close issue if you think thinks seems ok!

Thank you !
I tested with the new image in my Raspberry Pi (with arm64 architecture). It works great 👍
For suggestions, I have some ideas:

Add releases and tags to provide fixed versions, for both Github code and the image in Docker Hub (like mwader/postfix-relay:1.0.0 )
For all push and pull requests: build image without push
Only for tags (and maybe master branch too): build and push image.

I will try to test it and start a PR.

Thank you !
I tested with the new image in my Raspberry Pi (with arm64 architecture). It works great 👍
For suggestions, I have some ideas:

Add releases and tags to provide fixed versions, for both Github code and the image in Docker Hub (like mwader/postfix-relay:1.0.0 )
For all push and pull requests: build image without push
Only for tags (and maybe master branch too): build and push image.

I will try to test it and start a PR.

Great!
Tags could be a good idea to make it possible to stay at a version but don't think i would want to maintain some kind of version semver compatilbity, but of course i already try hard not to break things for the latest tag.
I think it should do a test builds for pull requests now

Great!
Tags could be a good idea to make it possible to stay at a version but don't think i would want to maintain some kind of version semver compatilbity, but of course i already try hard not to break things for the latest tag.
I think it should do a test builds for pull requests now


disable packages to install when provisioning

I am trying to setup on local using docker, how can I disable it to not install cert-manager when creating cluster?
after install if I use this I get error but the package is installed
$ eksctl anywhere delete package --cluster dev-cluster cert-manager
Error: Error from server (NotFound): packages.packages.eks.amazonaws.com "cert-manager" not found

found an answer here but I still wanted to have more control on cert-manager in same cluster

Cert-manager in your docker management cluster is not installed as "package", so you cannot use "delete package" command to remove it.
There is no option to disable it. You can remove it manually by deleting its k8s deplolyments, but your management cluster will perhaps not be able to "manage" itself or other "workload" clusters. So I don't recommend doing that.

I was able to delete all cert-manager's manifests manually and then reinstalled it how I wanted, and everything seems to works fine.
I understand the concept of workload cluster but I want to have the ability to just manage it on single cluster. It would have been great if I had more control on this rather than forced to use cert-manager preinstalled.


Make path to image-formats.xml configurateable

Tasks:

[ ] test coverage
[ ] gather feedback for my changes
[x] submit changes to the documentation
[x] added changelog line

Informations:

Q
A

Tests pass?
yes

Fixed tickets
fixes #454

BC Breaks
no

Doc
https://github.com/sulu-io/docs/blob/master/detail-specification/500-assets/DET-506-ImageFormats.md#change-path-to-formats-xml

@danrot PR updated

@danrot I did also update the sulu image formats to use the xml format instead of write the array in the DependencyInjection

Can you fix the merge conflicts?

Squashed to one commit and merged in https://github.com/sulu-io/sulu/commit/7659303476f3160de6bedef097b68378f3abc971


Implement Heart Rate transformation.

@brandonpollett,
Thanks for your contribution as a Microsoft full-time employee or intern. You do not need to sign a CLA.
Thanks,
Microsoft Pull Request Bot

    public static readonly Coding BodyWeight = new Coding()

are you missing the () for HeartRate coding?

Refers to: Microsoft.HealthVault.Fhir/Codes/HealthVault/HealthVaultVitalStatisticsCodes.cs:21 in a916e6e. [](commit_id = a916e6e8a9454479d7d28497f597e0729e3bbae2, deletion_comment = False)

    public static CodeableConcept BodyWeight = new CodeableConcept()

same as before

Refers to: Microsoft.HealthVault.Fhir/Constants/HealthVaultVocabularies.cs:35 in a916e6e. [](commit_id = a916e6e8a9454479d7d28497f597e0729e3bbae2, deletion_comment = False)

:shipit:

:shipit:


New version: JetBrains.IntelliJIDEA.Community version 213.7172.25

Auto-updated by vedantmgoyal2009/winget-pkgs-automation in workflow run 454
Microsoft Reviewers: Open in CodeFlow

/azp run


Improve xDS logging consistency

Slightly inconsistencies in the logs make it hard to debug issues
Cherry pick of https://github.com/istio/istio/pull/12772 from 1.1, which was approved but we decided to push this back to 1.2

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/test integ-pilot-k8s-presubmit-tests-master

prow/e2e-simpleTests.sh
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prow/e2e-bookInfoTests-v1alpha3.sh
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prow/istio-pilot-e2e-envoyv2-v1alpha3.sh
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prow/integ-mixer-k8s-presubmit-tests.sh
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prow/e2e-mixer-no_auth.sh
e8b5d68869c9039d1dfd688663f37e66f4ba558d
link
/test e2e-mixer-no_auth-master

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prow/integ-pilot-k8s-presubmit-tests.sh
e8b5d68869c9039d1dfd688663f37e66f4ba558d
link
/test integ-pilot-k8s-presubmit-tests-master

prow/e2e-bookInfoTests-v1alpha3.sh
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link
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prow/e2e-mixer-no_auth.sh
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KAFKA-16252: Dynamic KRaft network manager and channel

See https://github.com/apache/kafka/pull/15986 for description and all comment history, this addresses some of the comments from that PR (anything with 👀)
Specifically, https://github.com/apache/kafka/pull/15986#discussion_r1619520419, https://github.com/apache/kafka/pull/15986#discussion_r1619518236, and

I don't think we want to change all of the tests to use controller.quorum.bootstrap.servers. We still need to support the old configuration. Let's make it so that tests using IBP_3_8_IV0 or newer use the new thing, and tests using an older MV use the old configuration. That way we will have good coverage.

are not addressed in this PR. I think it would be alright for them to be addressed in a followup.
Committer Checklist (excluded from commit message)

[ ] Verify design and implementation
[ ] Verify test coverage and CI build status
[ ] Verify documentation (including upgrade notes)

There are some test failures in KafkaRaftClientTestand StorageToolTest.

Yeah looks like those are existing failures. Looks like Jose didn't take any of the changes, closing this out.


Transfer partial page-fit functions from zoom to view parameter in URL

This is the follow up fix for #10773  as the previous pull request is closed.
Changes are eliminated based on former feedback.

I still keep "FitR" in zoom as it's not included in the documentation of view and I'm not sure how it is described in other standards.


swift: added "--cluster" to rgw-admin command for multisite support

This needs testing but I wanted to get it out so I don't forget about it
Signed-off-by: Ali Maredia<EMAIL_ADDRESS>

This should be good to merge and seems to be a barrier to testing the rest of the multi-site changes


Get xtool sln running under cmd

Currently blocked on bazelbuild/bazel#2768. Things run under cygwin, but the paths are all for cygwin's bazel output path, which is not good for Visual Studio.

With the workaround this is now possible, but it's nasty (directly touching .py files instead of using py_binary, etc).

Fixed in HEAD bazel. Our workaround is good enough for now.


[OF-1503] test: Automatically generate test accounts when tests run

Use the existing CreateTestUser function to create test user accounts on the fly. Also move said function to the same place as the LogIn functions.
IIRC, @MAG-AdamThorn mentioned that test users are cleaned up automatically and regularly, therefore I have not made use of CleanupTestUser(), because only the super user has the privileges required to do so (see organization tests).
Also, because the super user credentials are required in organization tests (to clean up test users, which we can potentially get rid of, see above, but also to create an organization) and that I am not aware of a method to create such credentials on the fly, we cannot quite get rid of the credentials file just yet.
I have updated all the other C++ tests to generate test accounts as needed. This change excludes the CSharp tests and the Multiplayer Test Client.
Usage: if you need to log in only once and don't need to know the email address, use LogInAsNewUser(). If you need to log in, out and back in, or need to know the email address, use Profile TestUser = CreateTestUser(); LogIn(UserSystem, TestUserId, TestUser.Email, GeneratedTestAccountPassword);

IIRC, @MAG-AdamThorn mentioned that test users are cleaned up automatically and regularly, therefore I have not made use of CleanupTestUser(), because only the super user has the privileges required to do so (see organization tests).

How certain are you both of this? I can see a world where this is true, but the scale of the amount of test users we will make on CHS with this change being unacceptable to them. If you think there's any chance that this might be a surprise to them, we should check in an make sure they're okay with, i dunno, hundreds of throwaway accounts per day? Maybe thousands?


SSL Backend Support

This PR does the following:

Changes the default ports for HAProxy and Nginx to be the same: 80 and 443
HAProxy: add support for ssl backends
Nginx: add support for ssl backends

/cc @normalfaults for the default haproxy port change -- it will come in version 0.3.0.

nice :+1:

:)

On Aug 18, 2015, at 2:42 PM, Matt Bentley<EMAIL_ADDRESS>wrote:
nice
—
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Change the colour scheme based on temperature / weather type

Change the websites appearance to match the weather conditions. The Apple Weather app has some cool examples in iOS 15, check them out here.

Me and @n00bmaster2810 would like to work on this.

#29 fixes this issue.
@yashika51 this can be closed.


Windows on arm64

I have windows on ARM64, so can test if you pump out a release for that.

you already have it in the GitHub releases - my mistake
It's not on the Web page. thats why I missed it.

Sorry about that, yeah it's not in the MSI version because I can't cross-build CGo for it yet!

What’s the cgo blocker ?  DB ?

Not quite - I just haven't set up WINE on ARM64 yet, and I'm not sure how cross-compilation would work for that platform tbh!

this is for GitHub CI ?


merge package from Kronos-Tools/npm-package-template-minimal

package.json

chore(package<EMAIL_ADDRESS>chore(package): add module from template

Coverage remained the same at 14.543% when pulling 06ae9c69b89fb94623e318e01929cc5c561cdfd5 on template-sync-1 into f7277f69b86d5cf998d86c962c3aa11bc8fbb226 on master.


Sensitive output

When output is generated sensitive data should not displayed or at least it should be masked. Talking about passwords (data connections).
But first some analysis should be made to identify the possible operations that are affected + how to solve this - is it by masking the data or just "ignoring" it and not sowing these properties?

this is not a problem of CLI. Closing this issue in favour of https://github.com/Informatiqal/automatiqal/issues/177


ollama support?

Can we deploy Omost services using Ollama and use api to use omost?

u can try ollama+gguf+Comfyui-IF_AI+Comfyui_omost


High CPU usage by droned process

I am running a drone docker container integrated with GitLab with Drone Version: 0.3.0-alpha-1436428588.
The CPU utilization reaches 99.9% generally after 24 hours of uptime of 'droned' process. Following is output of 'top' from the 'Ubuntu 14.04.2 LTS, Trusty Tahr' host running drone.
top - 18:09:56 up 1 day, 17:32,  1 user,  load average: 1.00, 1.01, 0.97
Tasks:  75 total,   3 running,  72 sleeping,   0 stopped,   0 zombie
%Cpu0  : 99.0 us,  0.7 sy,  0.0 ni,  0.0 id,  0.0 wa,  0.0 hi,  0.3 si,  0.0 st
KiB Mem:   2050060 total,   813368 used,  1236692 free,   262560 buffers
KiB Swap:        0 total,        0 used,        0 free.   349076 cached Mem
PID USER      PR  NI    VIRT    RES    SHR S %CPU %MEM     TIME+ COMMAND
2724 root      20   0  487928  17616   7676 R 99.9  0.9  55:54.18 droned
There hardly 2-3 builds running in a day so most of the time drone is inactive, in spite of inactivity it utilizes 99% of CPU.
Please look into this issue. I can provide you more details if needed regarding this issue for debug.

Have you tried using http://www.sysdig.org/ utility to gather more debug info about the droned process while it's running.

Following is the output of sysdig process trace, I am getting when CPU utilization is at 99%.
32 01:24:45.558975540 0 droned (11153) < clock_gettime
33 01:24:45.559026435 0 droned (11153) > switch next=5830(sysdig) pgft_maj=12 pgft_min=1772 vm_size=568116 vm_rss=33116 vm_swap=0
34 01:24:45.561364006 0 droned (11150) < select res=0
35 01:24:45.561365538 0 droned (11150) > clock_gettime
36 01:24:45.561365860 0 droned (11150) < clock_gettime
37 01:24:45.561366467 0 droned (11150) > epoll_wait maxevents=128
38 01:24:45.561367111 0 droned (11150) < epoll_wait res=0
39 01:24:45.561367883 0 droned (11150) > select
40 01:24:45.561370586 0 droned (11150) > switch next=11153(droned) pgft_maj=0 pgft_min=26 vm_size=568116 vm_rss=33116 vm_swap=0
41 01:24:45.561479761 0 droned (11153) > clock_gettime
42 01:24:45.561480348 0 droned (11153) < clock_gettime
43 01:24:45.561546667 0 droned (11153) > clock_gettime
44 01:24:45.561546907 0 droned (11153) < clock_gettime
45 01:24:45.561677894 0 droned (11153) > clock_gettime
46 01:24:45.561678224 0 droned (11153) < clock_gettime
47 01:24:45.561743967 0 droned (11153) > clock_gettime
48 01:24:45.561744246 0 droned (11153) < clock_gettime
49 01:24:45.561875380 0 droned (11153) > clock_gettime
50 01:24:45.561875623 0 droned (11153) < clock_gettime
51 01:24:45.561941489 0 droned (11153) > clock_gettime
52 01:24:45.561941700 0 droned (11153) < clock_gettime

I think I hit this issue too. I'm using the latest 0.3.0-alpha release and after drone running 1 day long, the page loading costs more than 1 minute, even for a user login.
I see that angular.min.js says get user costs more than 15 seconds and see /api/user return 401 in chrome debug console.
However, after I added some debug logs into GetUserCurrent(), there's no such logs output, seems the angular.min.js didn't access /api/user at all.
Weird! But if I click login to login, GetUserCurrent() does print some logs.

em, seems there are some magic in go web server, even I run http GET http://host/api/user multiple times, GetUserCurrent only print logs at the first request, seems there are some cache in web server, filtered the following request out directly.

OK, it's first handled by RequireUser.

No magic on the server side. The angular app caches the user for duration of the session:
https://github.com/drone/drone/blob/master/server/app/scripts/services/auth.js#L5

I'm using sqlite3 and below is a chrome debugger screenshot.

We have seen this behavior, too. I created a build w/Go the profiler and didn't have much luck identifying the problem. I'd been intending to do some instrumentation of the code to try to identify where it might be occurring, but haven't gotten around to it.
In the meantime, I've been hoping that 0.4 won't exhibit the same behavior, and look to aggressively move to that soon.

@benschumacher agreed, the problem may not even exist in 0.4 which is why I haven't pursued it more

FYI,
I am running Version: 0.3.0-alpha-1437596183 now & I don't see high CPU usage. The 'droned' process is up since last 13 days without any spike on CPU usage.

After having a day long running, didn't reproduce this issue today, I'll wait for another day.

marking as fixed in 0.4


Show request details in response body when calling /redirect/:n

When we test the max number of redirect used by our HTTP clients, we need to access the request data the server received at each redirect.

Can you provide more information? What request data do you need returned?

The information as returned when calling http://httpbin.org/get should be just enough.


Fix dnsimple for explore.ipld.io

I noticed that  /ipns/explore.ipld.io  and https://explore.ipld.io  were not updated for some time due to dnsimple error:
https://ci.ipfs.team/job/IPFS Shipyard/job/ipld-explorer/job/master/4/console:
[ipyard_ipld-explorer_master-KNVJNOVICU45J2MBH2J3FIY2FYTWMPVT47THE2IQQPKJPDJECPLA] Running shell script
+ ./dnslink-dnsimple explore.ipld.io /ipfs/QmVDBPHPXiaexukv88rYiBWLvAYRVLH1t9FYJo3YVR12pb _dnslink
error in createRecord: POST https://api.dnsimple.com/v2/zones/explore.ipld.io/records: 404 Not Found

cc @olizilla

yep, is a known issue, related https://github.com/ipfs/dev-team-enablement/issues/162
this should be working now, I need to trigger a new build for explore.ipld.io

@VictorBjelkholm same issue for explore.ipld.io as well

This has been fixed, closing. see: https://ci.ipfs.team/job/IPFS Shipyard/job/ipld-explorer/job/master/5/console
#454 is staying open for now until a PR and a postmortem has been completed.


Update years in LICENSE

WHY
We have to indicate the valid years of LICENSE.
WHAT
Update years in LICENSE file

Thanks!!


Updating controller-gen.yaml and zz_generated_controller.go

Hi,
i'm playing around with the code and trying to extend it's functionality but i'm stuck when it comes to updating controller-gen.yaml and zz_generated_controller.go files.
It seems that some tool/script is used to generate zz_generated_controller.go file from controller-gen.yaml. I cannot find it in this repo nor in any comments in source code.
Could you point me to the tool/script used for generating this controller file?

Hi,
the tool is at https://github.com/iljaweis/kube-controller-gen .

Thank, it worked!


Execute on iOS/Android

Hi,
First congratulation for the work!
Before investing too much time into it, would it be possible to run the VM from an iOS or Android application? This would allow to run wasm programs and interrop with it!
Thanks!

It won't work right now, as there's a small amount of assembly code needs to be ported to ARM. With that assembly code ported to ARM, it should work, but further changes to WAVM are probably necessary to make it practical.
In particular, WAVM doesn't have any capability to compile WASM->native code ahead of time and distribute it. It can only compile the WASM byte code to native code when you want to run it (i.e. JIT). I understand that the iOS app store prohibited JIT compilation in the past, but that it might be allowed in limited circumstances now. Still, Apple would probably prefer that you don't do it.
Additionally, WAVM uses LLVM to generate native code, which is relatively slow for a JIT, but produces high quality code. On a mobile-class CPU, it's probably painfully slow for any non-trivial WebAssembly module.
I would like to add the capability to cache the compiled native code on disk, so it doesn't need to be recompiled every time you run WAVM. However, I'm not actively working on that right now, so you shouldn't count on it getting done.

Okay, thanks for clarifying all of that!
I'm thinking of a unified way to run an application(compiled to wasm) from various platforms and interpreting a tree structure out of it for rendering it. So basically, you would have to subscribe to a callback with the tree updates (from wasm), and a channel for sending (to wasm) simple actions (user input, layout size changes...).
Another question, how does this project compare to wasmint?

I created an issue to port WAVM to AArch64 hosts: https://github.com/WAVM/WAVM/issues/76. AFAIK that's the only issue blocking running on iOS and Android.


Fix meltano analysis when no model is present

Migrated from GitLab: https://gitlab.com/meltano/meltano/-/issues/658
Originally created by @mbergeron on 2019-05-28 14:24:07

There is currently an Error if there is no model in the Meltano project.

View 2 previous comments from the original issue on GitLab


Image Colorization

Thank you for sharing your work. I want to know how to train a colorization model on my own dataset.

Thank you for sharing your work. I want to know how to train a colorization model on my own dataset.

The training procedure is similar to SR. You should change control_type to grayscale and rewrite the checkpoints/stable-diffusion-v1-5/schedule/scheduler_config.json. Please make sure to set rescale_betas_zero_snr=True and prediction_type=v_prediction.
In PASD, we only train the newly added layers due to limited data and gpus. I would recommend to finetune the whole model if possible.

Thank you for sharing your work. I want to know how to train a colorization model on my own dataset.

The training procedure is similar to SR. You should change control_type to grayscale and rewrite the checkpoints/stable-diffusion-v1-5/schedule/scheduler_config.json. Please make sure to set rescale_betas_zero_snr=True and prediction_type=v_prediction.
In PASD, we only train the newly added layers due to limited data and gpus. I would recommend to finetune the whole model if possible.

Thanks for your reply. I have another question. When making my own data set, do I still need the txt file corresponding to the image? If necessary, how to generate the corresponding text?

Thank you for sharing your work. I want to know how to train a colorization model on my own dataset.

The training procedure is similar to SR. You should change control_type to grayscale and rewrite the checkpoints/stable-diffusion-v1-5/schedule/scheduler_config.json. Please make sure to set rescale_betas_zero_snr=True and prediction_type=v_prediction.
In PASD, we only train the newly added layers due to limited data and gpus. I would recommend to finetune the whole model if possible.

If we need to generate captions, do we use grayscale images to generate captions during training and inference?

@yangxy
How can i train the colorization model based on sd-2.1

Thank you for sharing your work. I want to know how to train a colorization model on my own dataset.

The training procedure is similar to SR. You should change control_type to grayscale and rewrite the checkpoints/stable-diffusion-v1-5/schedule/scheduler_config.json. Please make sure to set rescale_betas_zero_snr=True and prediction_type=v_prediction.
In PASD, we only train the newly added layers due to limited data and gpus. I would recommend to finetune the whole model if possible.

Do i need to set rescale_betas_zero_snr=True  when trainning? or  set rescale_betas_zero_snr=True  in both training and test time?

@yangxy

Thank you for sharing your work. I want to know how to train a colorization model on my own dataset.

The training procedure is similar to SR. You should change control_type to grayscale and rewrite the checkpoints/stable-diffusion-v1-5/schedule/scheduler_config.json. Please make sure to set rescale_betas_zero_snr=True and prediction_type=v_prediction.
In PASD, we only train the newly added layers due to limited data and gpus. I would recommend to finetune the whole model if possible.

Do i need to set rescale_betas_zero_snr=True when trainning? or set rescale_betas_zero_snr=True in both training and test time?

In both training and test time. sd2.1 works.

Thank you for sharing your work. I want to know how to train a colorization model on my own dataset.

The training procedure is similar to SR. You should change control_type to grayscale and rewrite the checkpoints/stable-diffusion-v1-5/schedule/scheduler_config.json. Please make sure to set rescale_betas_zero_snr=True and prediction_type=v_prediction.
In PASD, we only train the newly added layers due to limited data and gpus. I would recommend to finetune the whole model if possible.

Do i need to set rescale_betas_zero_snr=True when trainning? or set rescale_betas_zero_snr=True in both training and test time?

In both training and test time. sd2.1 works.

If I only do the colorization task on controlnet, do I also need to set these two parts during training and testing? In addition, if I use COCO2017 for training, how long does it usually take to train it to work? Or how many steps? I run in 8*RTX4090,batch=10

Thank you for sharing your work. I want to know how to train a colorization model on my own dataset.

The training procedure is similar to SR. You should change control_type to grayscale and rewrite the checkpoints/stable-diffusion-v1-5/schedule/scheduler_config.json. Please make sure to set rescale_betas_zero_snr=True and prediction_type=v_prediction.
In PASD, we only train the newly added layers due to limited data and gpus. I would recommend to finetune the whole model if possible.

Do i need to set rescale_betas_zero_snr=True when trainning? or set rescale_betas_zero_snr=True in both training and test time?

In both training and test time. sd2.1 works.

If I only do the colorization task on controlnet, do I also need to set these two parts during training and testing? In addition, if I use COCO2017 for training, how long does it usually take to train it to work? Or how many steps? I run in 8*RTX4090,batch=10

Yes, I think so. This is because the color leaking issue during training. Please refer to the Supplementary Materials of our paper. And rescale_betas_zero_snr=True is critical while prediction_type=v_prediction is optional.
I am not sure about that training time based on your settings. Maybe you should conduct some exps.


Input has weird behavior when text transform is applied (android)

react-native, react and native-base version
Expo SDK 23, native-base 2.3.3
Expected behaviour
The Input should work correctly in all cases when I do onChangeText={text => this.setState({text: text.toLowerCase()})}
Actual behaviour
On Android, as it tries to capitalize the first letter of the input, you end up typing "Abcdef", expecting the input value to become "abcdef" (lowercased). But if you type fast, it actually does start to duplicate the letters.
Instead of "abcdef" you end up with "aabcaabcdeaabcaabcdef"
Steps to reproduce (code snippet or screenshot)
import React, { Component } from 'react';
import { Input } from 'native-base'; // 2.3.4
import { View } from 'glamorous-native'; // 1.2.0

export default class App extends Component {
  state = {
    value: '',
  };
  render() {
    const {value} = this.state;
    return (
      <View width="100%" height="100%" alignItems="center" justifyContent="center">
        <View width="90%" height={30}>
          <Input
            style={{borderColor: 'red', borderWidth: 1}}
            value={value}
            onChangeText={text => {
              this.setState({value: text.toLowerCase()})
            }}
          />
        </View>
      </View>
    );
  }
}

It is published on Expo
Is the bug present in both ios and android or in any one of them?
As far as I know it's only Android but maybe it depends on your iOS keyboard settings/devices, don't know.
Any other additional info which would help us debug the issue quicker.
I think this bug was not present in former version of our app (native base 2.2.1), we noticed it after upgrading to Expo SDK23 / React 16
Using regular TextInput from ReactNative does work fine, and removing the toLowerCase transform also works fine

@slorber Sharing expo link help us to know the issue
Appreciated!
We will get back to you in this

@slorber tried your code. I was getting the same result with React Native <TextInput/>.
Setting autoCapitalize='none' can prevent this to some extend.
Gif

hmmm thanks for the workaround.
Maybe I didn't type fast enough when trying with the regular RN TextInput, sorry for bad reporting, it may be a bug related to RN50 instead then

Closing this issue (Not a Native Base Issue).