How to send Logged in user object along with the Tokens?

I don't want to add changeable information with the token using TokenObtainPairSerializer such as username or profile picture url with the JWT token.

I don't want to add changeable information with the token using TokenObtainPairSerializer such as username or profile picture with the JWT token.

@Fahad-Md-Kamal Subclassing the TokenObtainPairSerializer is the only way. You can add data['username'] = "blah" and whatever else you'd like. You'd then have to subclass the view that goes along with it.
The serializer can be inherited from here: https://github.com/SimpleJWT/django-rest-framework-simplejwt/blob/master/rest_framework_simplejwt/serializers.py#L76
The view you need to subclass to use your own serializer can be found here: https://github.com/SimpleJWT/django-rest-framework-simplejwt/blob/master/rest_framework_simplejwt/views.py#L34
There is currently no other way, but, if anything, this is the suggested method. If I misinterpreted what you're asking, feel free to reopen the issue.

Hello @Andrew-Chen-Wang I know this question is kind of old.
But how about adding a setting to simple_jwt, so you could define which serializer is being used for the: TokenObtainPairView
You could leave it as is, but if you want to have a custom Serializer/data in your token, just add:
SIMPLE_JWT = {
    ...
    'PAIR_SERIALIZER': MyOwnTokenObtainPairSerializer, # if not defined, default to: TokenObtainPairSerializer
    ...
}

@jakobdo Is this only for "TokenObtainPairSerializer"? or any other serializers as well?

It's an idea I've considered before; just worried about performance. Specifically, the implementation will actually be using a string path to a serializer with import_string, which takes a performance hit, as this specification will be in your settings.py. I guess it's fine if you provide a class itself, but it's not desirable having an app related thing in your settings files.

@jakobdo Is this only for TokenObtainPairSerializer? or any other serializers as well?

I have not looked that much into the codebase yet, so I have am not sure how many places this is being used. So my input was just a "quick" one.


Create talk about "Disrupt the destruction of our natural world with open source"

Over the years we have collected tons of ideas and discussed how to "Disrupt the destruction of our natural world with open source". Let's be radical in our "open innovation" and create a talk just about the ideas we've created. Many ideas can already be found in the recommendation we created in the last report.

FOSDEM would be perfect to present this.
https://fosdem.org/2025/news/2024-11-03-call-for-presentations/

The deadline for submissions is 1st December.

Maybe we could also give out some cool stickers. It was a lot of fun last year with the ClimateTriage stickers.

Here the abstract for the FOSDEM 2025 application:
Disrupting the destruction of our natural world with openness
Despite the transformative power of free and open-source software (FOSS) across various sectors, its impact on one of the most urgent challenges of our time - environmental preservation - remains largely invisible. In response, the Open Sustainable Technology community has developed a range of strategies to make FOSS an essential factor in the fight against climate change and biodiversity loss. The number of possibilities we encountered was both inspiring and surprising, though many are only partially realizable within our current capacity as a community.
This talk marks the beginning of a global series exploring how FOSS can address critical challenges such as traceability, trust, and collaboration, especially concerning the natural commons in both economic and social systems. We’ll discuss the role of FOSS tools in areas like global carbon emission measurement, climate investment, sustainable branding, accountability, carbon offsets, and local government sustainability efforts, offering innovative ideas and strategies to tackle these issues head-on.
In addition, we will demonstrate how FOSS, combined with open science methods, can combat greenwashing in the sustainability sector. By applying open and transparent principles, we will show how these approaches can help ensure that environmental claims are accurate and meaningful. This will be illustrated through a number of projects currently under development worldwide, as well as new ideas that we will present and discuss with the broader FOSS community, inviting collaboration and further development. Finally, we will show why openness is the most important indicator of environmental sustainability and explain this using practical examples.

We are also invited to give this talk here at the 31.1 15:00 CET: https://geedevsnairobi.github.io/GEENairobi/


Velos: remove nulls, filter to unm persons, date-enrollment.

Nulls are done, but will have to filter further.

addressed in initial committ? https://github.com/UNMCCC/CRIIS_Source_Extracts_for_OMOP/commit/ed1e079c784f479e8c3b3e37b65e789385c10bee


zram?

Hello,
what about zram support? I couldn't find any info here, so I guess it isn't implemented (yet).
From what I can see in the swap graph on the HUB for the system below, zram isn't currently accounted for.

swapon -s
Filename                                Type            Size            Used            Priority
/mnt/swapfile                           file            511996          160952          -2
/dev/zram0                              partition       357856          0               100

https://wiki.debian.org/ZRam
Thanks

Nevermind, I needed to restart the agent service.


Test Bug Fixes - Part 2

Fixes what should be the last of Test Bugs due to recent product changes. All tests should now be passing 100%.

:tada: This PR is included in version 0.375.0 :tada:
The release is available on:

npm package (@next dist-tag)
GitHub release

Your semantic-release bot :package::rocket:


SwitchoffFile value '22' seems to be 'No request'

The value 22 is not yet used in the code, but it is used at my heat pump.
From dump_luxtronik.py:
Number: 106   Name: ID_WEB_Switchoff_file_Nr0                                    Type: SwitchoffFile        Value: 22
Number: 107   Name: ID_WEB_Switchoff_file_Nr1                                    Type: SwitchoffFile        Value: 22
Number: 108   Name: ID_WEB_Switchoff_file_Nr2                                    Type: SwitchoffFile        Value: 22
Number: 109   Name: ID_WEB_Switchoff_file_Nr3                                    Type: SwitchoffFile        Value: 22
Number: 110   Name: ID_WEB_Switchoff_file_Nr4                                    Type: SwitchoffFile        Value: 22
Number: 111   Name: ID_WEB_Switchoff_file_Time0                                  Type: Timestamp            Value: 2024-10-16 06:34:31
Number: 112   Name: ID_WEB_Switchoff_file_Time1                                  Type: Timestamp            Value: 2024-10-17 08:58:26
Number: 113   Name: ID_WEB_Switchoff_file_Time2                                  Type: Timestamp            Value: 2024-10-18 09:41:10
Number: 114   Name: ID_WEB_Switchoff_file_Time3                                  Type: Timestamp            Value: 2024-10-20 08:28:14
Number: 115   Name: ID_WEB_Switchoff_file_Time4                                  Type: Timestamp            Value: 2024-10-26 12:08:55
From datalog:
26.10.24 12:08 keine Anf.;
20.10.24 08:28 keine Anf.;
18.10.24 09:41 keine Anf.;
17.10.24 08:58 keine Anf.;
16.10.24 06:34 keine Anf.;

Yes, newer versions of the firmware have more switch off codes. This is what the firmware reports for the different IDs:
0 : "err. HP",
1 : "err. inst.",
2 : "OM ZWE",
3 : "EVU-block",
5 : "air defr.",
6 : "TOLmax",
7 : "TOLmin",
8 : "LOL",
9 : "no requ.",
10 : "ext.ener.",
11 : "flow rate",
12 : "p0_break",
13 : "u0_break",
14 : "I0_break",
15 : "D0_break",
16 : "mode of operation",
17 : "stop",
18 : "min.flow cooling",
19 : "PVmax",
20 : "h0_break",
21 : "DSH_break",
22 : "no requ.",
23 : "min. heat source out cooling",
24 : "LPC",
25 : "Restart",

As you can see, this are mostly abbreviations. In the manual, one can find some explanations:
err. HP
 -> heat pump fault
err. inst.
 -> system fault
OM ZWE
 -> mode of operation additional heating generator
EVU-block
 -> el. sup. blockade
Defr. air.
 -> air defrost
TPLmax
 -> temperature limits maximum of application
TPLmin
 -> temperature limits minimum of application
low lim
 -> lower limit of application
no requ.
 -> no request
TEE
 -> External energy source
flow
 -> Flow
p0_break
 -> Low pressure pause
u0_break
 -> Superheating pause
I0_break
 -> Inverter pause
DSH_break
 -> Overheating hot gas pause
h0_break
 -> Hot gas pause
D0_break
 -> Desuperheater pause
OpMode
 -> Operating mode for switching over
Stop
 -> Other shutdown
min. heat source out cooling
 -> Compressor switch off if TWA < 3°C during active cooling

Note that these do not exactly coincide with the abbreviations above.
Currently, this is the implemented conversion, see https://github.com/Bouni/python-luxtronik/blob/abd4752b66f8ce9deed676353ac3165eef86ca81/luxtronik/datatypes.py#L667C1-L680C6
    codes = {
        1: "heatpump error",
        2: "system error",
        3: "evu lock",
        4: "operation mode second heat generator",
        5: "air defrost",
        6: "maximal usage temperature",
        7: "minimal usage temperature",
        8: "lower usage limit",
        9: "no request",
        11: "flow rate",
        19: "PV max",
    }


Added cache for jcClass

Are there any proves that this change increases performance?

Types has less time to live period
request to find class by name is almost always hits class cache

@lehvolk jcClass is used in calculating the hashCode of the JcClassType, and we had met a situation when the hashCode of an instance of the JcClassType has changed. We assumed that happens because the jcClass getter sometimes returns different instances of JcClassOrInterface, which is quite unexpected for users.

@lehvolk jcClass is used in calculating the hashCode of the JcClassType, and we had met a situation when the hashCode of an instance of the JcClassType has changed.

This is quite strange because JcClassOrInterface designed to have proper equals method. Only case which I can imagine is that the same class located into two different jars. If you check if name and generics substitution is that same then only possible difference may occur into AbstractByteCodeLocation#jarOrFolder file


Implement Move command execute, Refactor EA Mode class and Assembly Parameter Enum

Implementation of the move command execution
still needs implementation of all of the EA modes

todo so I don't forget: instead of setting the length in the constructor for the assembly_parameter, should actually set it in the get and set methods, since it will be called by the opcode which knows what size to work with

Going for a merge @LinuxKid21 , I've applied the ERR changes and a quick bugfix on a return statement: the test issues and some of the other stuff we'll address further down the line


S0018 "Number of time plan" should be a list instead

The status S0018 "Number of time plans" is of limited use since it doens't tell which time plans that is possible to use - only the total number.
It would be better if this was a list of configured time plan in the controller, for instance "1,2,5,9".
Other protocols supports this

Already covered by S0022. S0018 could probably be deprecated

ok. closing this one and will open a new about deprecating S0018


[BUG] PR #1805 - "Danger" code review step has NodeJS crashing

In case the build output gets lost:
11: 0xecda4a v8::internal::Factory::NewFixedArrayWithFiller(v8::internal::Heap::RootListIndex, int, v8::internal::Object*, v8::internal::PretenureFlag) [/opt/hostedtoolcache/node/10.17.0/x64/bin/node]

12: 0xecdfd7 v8::internal::Factory::NewFixedArrayWithHoles(int, v8::internal::PretenureFlag) [/opt/hostedtoolcache/node/10.17.0/x64/bin/node]

13: 0xb98630  [/opt/hostedtoolcache/node/10.17.0/x64/bin/node]

14: 0xb98f67 v8::internal::Builtin_ArrayConcat(int, v8::internal::Object**, v8::internal::Isolate*) [/opt/hostedtoolcache/node/10.17.0/x64/bin/node]

15: 0x21bb55fdbf7d

Error:  Error: Got no results back from the Dangerfile evaluation, this is likely an issue with a custom sub-process exiting early.
    at Executor.<anonymous> (/home/runner/work/open-pixel-art/open-pixel-art/node_modules/danger/distribution/runner/Executor.js:140:35)
    at step (/home/runner/work/open-pixel-art/open-pixel-art/node_modules/danger/distribution/runner/Executor.js:32:23)
    at Object.next (/home/runner/work/open-pixel-art/open-pixel-art/node_modules/danger/distribution/runner/Executor.js:13:53)
    at /home/runner/work/open-pixel-art/open-pixel-art/node_modules/danger/distribution/runner/Executor.js:7:71
    at new Promise (<anonymous>)
    at __awaiter (/home/runner/work/open-pixel-art/open-pixel-art/node_modules/danger/distribution/runner/Executor.js:3:12)
    at Executor.handleResults (/home/runner/work/open-pixel-art/open-pixel-art/node_modules/danger/distribution/runner/Executor.js:134:16)
    at Object.<anonymous> (/home/runner/work/open-pixel-art/open-pixel-art/node_modules/danger/distribution/commands/utils/runDangerSubprocess.js:162:47)
    at step (/home/runner/work/open-pixel-art/open-pixel-art/node_modules/danger/distribution/commands/utils/runDangerSubprocess.js:43:23)
    at Object.next (/home/runner/work/open-pixel-art/open-pixel-art/node_modules/danger/distribution/commands/utils/runDangerSubprocess.js:24:53)
##[error]Process completed with exit code 1.

Hi @guilhermeblanco this seems to be an intermittent issue with GitHub Actions atm :( I hope this will be fixed by the time Actions goes GA. Thanks for flagging it!

It looks like this is still happening. 😿

Seems like this is an issue inside of Danger that is currently being investigated
https://github.com/danger/danger-js/issues/985

I'm creating a PR for Danger JS that will change the internal implementation of the JSON patch algorithm. If that PR gets merged we need to bump the Danger version and fix the Dangerfile to be compatible with the new version.
Otherwise, based on the implementation in rfc6902 we need to change from an array of pixels to an object. This would significantly improve the performance and remove the memory issue.


Exclude user from ownership list if he already entity owner

Describe the bug
If user was selected as data entity owner he should be excluded from subsequent ownership selections

This task will be resolved in scope of proper errors handling


feat: update dashboard insights to use API v2 with histograms

Description

Migrates the dashboard insights to the new /v2/histogram/pull-requests endpoint
Deletes previous useInsights hook
Needs https://github.com/open-sauced/api/issues/496 to be resolved first

What type of PR is this? (check all applicable)

[x] 🍕 Feature
[ ] 🐛 Bug Fix
[ ] 📝 Documentation Update
[ ] 🎨 Style
[ ] 🧑💻 Code Refactor
[ ] 🔥 Performance Improvements
[ ] ✅ Test
[ ] 🤖 Build
[ ] 🔁 CI
[ ] 📦 Chore (Release)
[ ] ⏩ Revert

Related Tickets & Documents

Mobile & Desktop Screenshots/Recordings

No visual changes, but different data.

Steps to QA

Click on https://deploy-preview-2516--oss-insights.netlify.app/pages/brandonroberts/5/dashboard?range=30
Note that PR insights are still displayed

Added to documentation?

[ ] 📜 README.md
[ ] 📓 docs.opensauced.pizza
[ ] 🍕 dev.to/opensauced
[ ] 📕 storybook
[ ] 🙅 no documentation needed

[optional] Are there any post-deployment tasks we need to perform?
[optional] What gif best describes this PR or how it makes you feel?

Looks like this closes https://github.com/open-sauced/app/issues/2508 ? 👏🏼


Android: tapping a message notification doesn't open the relevance message

When I tap on a message notification, Zulip opens but doesn't necessarily take me to the right server or the right message.

Does it take you to a blank white screen?  There's a bug in the latest release that results in that failure mode.

Not a blanket screen! Could be just the last channel I was in (e.g., a Fchat.fhir.org  publicchannel, when the message notification is for a private message at chat.zulip.org)


Scala Stream Collector: replace Location header with RawHeader to preserve double encoding

Making this change in order preserver double encoding URLs for use in
redirect chains. This also matches the behavior of the Clojure Collector.

@rbolkey has signed the Software Grant and Corporate Contributor License Agreement. Thanks so much


All tvOS specs are now green with the latest RM

RM 4.7 fixes the CGColor issue, so we can enable all the specs.

Will merge myself, as this is a trivial change again.

Thanks!  Just pushed 1.0.0 :smiley:


ODSP Driver: Two TreesLatest calls are observed when loading file

They have same callstack, and are happening next to each other.
There should be only one call loading the file. And even if summarizer is loaded right away (does not look like it), it should use cached version.
Reproducible in staging, on a new empty file.
| m | @ | bohemia.js:2
  | g | @ | bohemia.js:2
  | (anonymous) | @ | bohemia.js:2
  | (anonymous) | @ | bohemia.js:2
  | Promise.then (async) |   |
  | schedule | @ | bohemia.js:2
  | fetchAndParseAsJSON | @ | bohemia.js:2
  | (anonymous) | @ | bohemia.js:2
  | timedExecAsync | @ | bohemia.js:2
  | fetchSnapshotCore | @ | bohemia.js:2
  | async function (async) |   |
  | fetchSnapshotCore | @ | bohemia.js:2
  | fetchSnapshotWithTimeout | @ | bohemia.js:2
  | fetchSnapshot | @ | bohemia.js:2
  | (anonymous) | @ | bohemia.js:2
  | timedExecAsync | @ | bohemia.js:2
  | (anonymous) | @ | bohemia.js:2
  | p | @ | bohemia.js:2
  | getVersions | @ | bohemia.js:2
  | getVersions | @ | bohemia.js:2
  | getVersion | @ | bohemia.js:2
  | fetchSnapshotTree | @ | bohemia.js:2
  | load | @ | bohemia.js:2
  | async function (async) |

No, I am seeing 2 containers getting created having different containerId. So I don't think rate limiter has anything to do with this.

Here are the callstacks.
First one:

Second one:

Seems like when we try to load a component from an existing container, it is loading the container again.

It's late in my timezone, so I'll be getting offline. Can the FF team take point here for the initial investigation? I can pull in folks from the app side tomorrow if we have a root cause there

Yes, @sumedhb1995 , any chance you can take a first peak at where the problem is, and if possible, better understand where regression came from? Thanks!

It is something on the Fluid Preview side, we've escalated it as a livesite incident


Running a docker container (built from provided Dockerfile) fails

Error message provided:
$ winpty docker run -it -p 43839:43839 --platform=linux/amd64 --gpus all -v $MODEL_DIR:/workspace/instantmesh/models instantmesh

==========
== CUDA ==
==========

CUDA Version 12.4.1

Container image Copyright (c) 2016-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.

This container image and its contents are governed by the NVIDIA Deep Learning Container License.
By pulling and using the container, you accept the terms and conditions of this license:
https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license

A copy of this license is made available in this container at /NGC-DL-CONTAINER-LICENSE for your convenience.

Traceback (most recent call last):
  File "/workspace/instantmesh/app.py", line 5, in <module>
    import rembg
  File "/workspace/miniconda3/envs/instantmesh/lib/python3.10/site-packages/rembg/__init__.py", line 5, in <module>
    from .bg import remove
  File "/workspace/miniconda3/envs/instantmesh/lib/python3.10/site-packages/rembg/bg.py", line 6, in <module>
    import onnxruntime as ort
ModuleNotFoundError: No module named 'onnxruntime'

The following fixes worked for me in the end.
In the Docker file, at the end of Run pip install X lines, add:
RUN pip install onnxruntime
Then in requirements:
-accelerate
+accelerate==0.31.0


Onload event

Make use of:
window.addEventListener('DOMContentLoaded', init, false);

as an alternative to jQuery's on document ready.

Not case sensitive:
window.addEventListener('domcontentloaded', methods.init);


Updating download links

@jocaplan,
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

#sign-off

@jocaplan : Thanks for your contribution to the Azure documentation! The author, @minewiskan, has been notified to review your proposed change.

Change was already made as part of a different PR.

@jocaplan @Minewiskan Thanks for the contribution and review. Closing this pull request.


chore: codescan modifications

This PR addresses some new false positives picked up by our codescans.

@christiancompton It turns out that I'm in the process of updating this code with lots of changes due to some upstream design changes.   I'll incorporate your first change in my updated code, and the second change is no longer applicable as that code has been removed altogether.    I think you can just close out this PR.


Multiple SCSS files

I'm submitting a ...
[X ] bug report
[ ] feature request
[ ] question about the decisions made in the repository

Do you want to request  a bug?

What is the current behavior?
I have multiple pages, like in the starter. Each contains a .scss file.
My problem is now than I navigate trough the pages, each css file of the site gets loaded. This leads to double definitions and many errors.

Example:
On the dashboard page I got something like:
.cardHead {
position: absolute;
background-color: yellow;
}
and on the forms page:
.cardHead {
background-color: blue;
}
So if the user navigates from the dashboard to the forms page, it adds to the cardHead the property position: absolute;
Is there a way to separate the css files.

i hop it is :host /deep/
https://angular.io/guide/component-styles

@Newan doesn´t :host /deep/ enables the behaiviour I want to get ride off ?

Component styles normally apply only to the HTML in the component's own template.
Use the /deep/ shadow-piercing descendant combinator to force a style down through the child component tree

I want that it only applies on the components template html and not in all.

one Component one scss file. I have two pages with buttons. On one page the button is larger, so:
:host /deep/ {
  .btn-info {
    min-width: 155px;
  }
}

on the second it is the button default. i can navigate on and back from the site an all works fine. This stylesheet is only in this component active

It was a problem with view.Encapsulation, after removing it works now like I wanted.


load records for grid using data provider

Duplicates

[X] I have searched the existing issues

Summary
I am trying to load records for grid using data provider. But in my case i need to add additional filter values from external ui components.
I found a way to do that by storing them in js file properties that hold data provider logic. But i cant find a way to trigger data provider reload in this case, when my external component filter values change.
Is there any ways to do that?
Alternatively i can use direct rows source but then i dont know how to grab grid paging values. Maybe this have easier solution?
Thanks.
Examples
No response
Motivation
No response
Search keywords:

This currently isn't possible yet as all state related to filtering is localized to the datagrid. I see two potential ways we can move forward with this.

We expose the filter model as state on the datagrid and expose onChange/onCommit events on the form components. This will allow you to update the filter model programmatically.
We move the data provider concept at the page level, just like queries/actions and let it hold the filter state. We add UI that lets you bind page state to filter model items. This essentially creates global filter model state and is very similar to how queries/actions already work. I'm slightly more inclined towards this solution. I'll to write up a feature request for this idea this week.

Thank you for the quick reply and for the attention given to this issue.
Generally I think server-side filtering, like data provider is doing, is an excellent idea - it solves a lot of security and roles and permissions issues, enabling simpler code and system design. However we of course need to have a way to transfer state from the page to the server, as you mentioned in your response

This currently isn't possible yet as all state related to filtering is localized to the datagrid. I see two potential ways we can move forward with this.

We expose the filter model as state on the datagrid and expose onChange/onCommit events on the form components. This will allow you to update the filter model programmatically.
We move the data provider concept at the page level, just like queries/actions and let it hold the filter state. We add UI that lets you bind page state to filter model items. This essentially creates global filter model state and is very similar to how queries/actions already work. I'm slightly more inclined towards this solution. I'll try to write up a feature request for this idea this week.

Lifting/binding the filterModel state to the page level certainly seems a better way to solve the problem since it will allow other UI elements to manipulate its value and effectively implement filters outside the grid. Something like this one but built using the toolpad UI.


Getting Rid of In-Content Links

Fixing #1427 where some pages of older versions have broken links from the inline links to methods and props

NavigatorIOS also has been removed from the core but a bit later with version 0.58 so I have added the similar notice about that fact like on the Navigator page but to this component.
A little surprising was the fact that there was versioned page for the 0.58 docs for the NavigatorIOS but with the usual content when mentioned above commit clearly has been present in the 0.58 release.


Change section header for example answers in templates

Currently, when example answers are included in a template, the section is titled with the name of the organization owning the template.  For example, "NIH example answer." This is misleading since we make templates on behalf of funders, so the org name is the funder's name and makes it seem like the funder has supplied the example text, which they have not.
Please change the text to just "Example answer."

Changed to just read 'Example answer'.


New version: Discord.Discord.Canary version 1.0.411

Created by 🥟 Dumplings in workflow run #6496.

Log
Updated: 1.0.410 → 1.0.411
Submitting WinGet manifests
Creating manifests
Uploading manifests and making commits
Creating a pull request

Microsoft Reviewers: Open in CodeFlow

/azp run

https://github.com/microsoft/winget-pkgs/issues/142854


@Transient problem

Hi,
I have this entities configuration, User, Role and relationship between them with HasRole class:
import java.util.List;
import com.fasterxml.jackson.annotation.JsonIgnoreProperties;
import lombok.Data;
import org.neo4j.ogm.annotation.Index;
import org.neo4j.ogm.annotation.Relationship;
@Data
@NodeEntity
public class User  {
    String name;
    @Index(unique=true)
    String email;
    @JsonIgnoreProperties("user")
    @Relationship(type = "HAS_ROLE")
    List<HasRole> roles;
}

import java.util.List;
import org.neo4j.ogm.annotation.Relationship;
import com.fasterxml.jackson.annotation.JsonIgnoreProperties;
import lombok.Data;
@Data
@NodeEntity
public class Role {
    String name;
}

import java.util.Date;
import org.neo4j.ogm.annotation.EndNode;
import org.neo4j.ogm.annotation.RelationshipEntity;
import org.neo4j.ogm.annotation.StartNode;
import org.neo4j.ogm.annotation.Transient;
import org.neo4j.ogm.annotation.typeconversion.DateLong;
@Data
@RelationshipEntity(type = "HAS_ROLE")
public class HasRole {
    Long id;
    @Transient
    @StartNode
    User user;
    @EndNode
    Role role;
    @DateLong
    Date created;
}

Now when I save User, the OGM Library execute a recursive update despite I insert annotation @Transient on "user" property of HasRole class.

Thanks for reaching out to us.
I thought about your problem / feature request for a while. In the end, it would not work out to bring support for @Transient into the fields that are required for the relationship definition, namely @StartNode and @EndNode because Neo4j-OGM needs to know between which nodes the relationship needs to get created or updated.
Could you maybe explain more the use case and the problem you are trying to solve with this? Maybe we can figure out an alternative approach.

Hi meistermeier,
you can replicate the problem with follow example:
I have these entity with respective relactionship:
@Data
@NodeEntity
public class User  {
String name;
String email;
@Relationship(type = "HAS_ROLE")
List roles;
}
@Data
@NodeEntity
public class Role {
String name;
}
@Data
@RelationshipEntity(type = "HAS_ROLE")
public class HasRole {
Long id;
@StartNode
User user;
@EndNode
Role role;
@DateLong
Date created;
}
In attach my nodes...

now if I try to get an entity with this call:
Neo4jSessionFactory.getInstance().getSession().load(User.class, id, 1), where "id" is id of User
I get this error:
Infinite recursion (StackOverflowError) (through reference chain

I assume that you are calling something like toString on the User object.
This will, due to Lombok, create the StackOverflow, because the @Data annotation will also create a toString. It traverses through all connected objects and within those objects through all connected objects, etc.
I think that User -> HAS_ROLE -> Role (the -> are the toString traversal) is what you expect, but you get also HAS_ROLE->User which points back to the originating User.
If my assumption is right, you can fix this by adding an exclude to the HAS_ROLE class for the toString generation:
@Data
@ToString(exclude = "user")
@RelationshipEntity(type = "HAS_ROLE")
public class HasRole {
    Long id;
    @StartNode
    User user;
    @EndNode
    Role role;
    @DateLong
    Date created;
}

Hi Meistermeier,
your solution doesn't work, but you gave me an input to solve the problem, i basically serialize via jackson library, in this post i found the solution:
https://www.baeldung.com/jackson-bidirectional-relationships-and-infinite-recursion
this is the code:
@Data
@NodeEntity
public class User extends Entity {
String name;
String email;
@JsonManagedReference
@Relationship(type = "HAS_ROLE")
List roles;
}
@Data
@RelationshipEntity(type = "HAS_ROLE")
public class HasRole {
Long id;
@JsonBackReference
@StartNode
User user;
@EndNode
Role role;
@DateLong
Date created;
}
thank you
Dario


Adding existing features to refhost test pack

Can the following features please be added to the pack, to cover those smoke tests that we have tests for?

mgr_create_bootstrap_repo.feature
register_client.feature
salt_minion_details.feature
salt_minions_page.feature
test_reboot.feature
systems_needing_reboot.feature
salt_install_package.feature
salt_remote_cmds.feature
salt_action_pickedup.feature
lock_packages_on_client.feature

thx @0rnela ,
salt_action_pickedup is not even working for the normal testsuite,  atm
the rest could be added. we need to test if the feature are taking other depend. from other feature.

[ ] mgr_create_bootstrap_repo.feature
[ ] register_client.feature
[x] salt_minion_details.feature
[x] salt_minions_page.feature
[ ] test_reboot.feature
[ ] systems_needing_reboot.feature
[ ] salt_install_package.feature
[x] salt_remote_cmds.feature
[x] salt_action_pickedup.feature
[ ] lock_packages_on_client.feature

Bootstrap repo is disabled too...

fixed for refhost 30. head need rework, closing for moment.


Coinbase DEV environment keep connecting/disconnecting from WS

Hi,
I am trying the dockerized version. When I run the docker-compose up with the following env file:
# General config properties. Properties with `NULL` should be replaced with your own exchange account information.
TRIBECA_MODE=dev
EXCHANGE=Coinbase
TradedPair=BTC/USD
WebClientUsername=***
WebClientPassword=***
WebClientListenPort=3000
MongoDbUrl=mongodb://tribeca-mongo:27017/tribeca

NullGatewayTick=100

CoinbaseRestUrl=https://api-public.sandbox.gdax.com
CoinbaseWebsocketUrl=wss://ws-feed-public.sandbox.gdax.com
CoinbasePassphrase=***
CoinbaseApiKey=***
CoinbaseSecret=***
CoinbaseOrderDestination=Coinbase

Tribeca seems to be disconnecting.

I don't really care, it's a mocked username and password, there are no money and it's on 10 minutes email...

I agree, no harm when using sandbox keys (just make sure your WebClientUsername and WebClientPassword aren't used elsewhere).
I have the exact same problem using dockerized version on COINBASE DEV.

1st. Great program MichaelGrosner :)
2nd "cannot compute a quote since no position report exists!" Issue
Without going into a tons of possible reasons this can happen, first understand you are using a low latency program. So need to have a reasonable computer to run on and good internet connection. If you have believe to be in good spec then, check the logs to see if connection was accepted or if you get invalid api key, deny, timeout, etc. If you have issues there check your api and env settings again and/or firewall/isp network issues. 90% of the time that fixes the issues:)
However if you believe your API, computer, network connections are all good and your still  not getting your values to show on the url, and still seeing "cannot compute a quote since no position report exists!" Issue, then it could computer timing:) NTP If virtual make sure your host and guest systems are in sync. Also ensure both are in sync with internet time i.e nist.time.gov
Let me know if that helps:)


Clear the session cache manually

hi everybody, am using laravel 5.0 with file session driver ,   I am having a  problem while trying to clear the session cache upon request (on demand), more precisely, how to make a manual call  to the function gc() of the class 'FileSessionHandler.php' in order to clear expired sessions , thx in advance .

This repo is for bug tracking. Use the forums or slack channel for solving your issue


deletion: file deletion leaves a stale index

I noticed that during deletion of a file that existed remotely and whose index was saved to disk, the index was not cleared out. It is important to clean out stale indices otherwise the user's indices dir gets way too many files and wastes memory.

PR https://github.com/odeke-em/drive/pull/256 addresses this issue.

Addressed by PR https://github.com/odeke-em/drive/pull/256


UNAVAILABLE: Internal: HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/models/stable_diffusion/1/stable_diffusion/1/tokenizer/'. Use repo_type argument if needed.

@xunfeng2zkj Which branch and diffusers version inside container?

v2 branch, try pip package with (diffusers==0.11.1,transformers==4.25.1) and default in nvcr.io/nvidia/tritonserver:22.06-py3

this is convert onnx logs:
etching 15 files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15/15 [00:00<00:00, 12345.87it/s] /home/lzcs/Documents/code/tools/stable-diffusion-tritonserver/venv/lib/python3.10/site-packages/transformers/models/clip/modeling_clip.py:754: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.   mask.fill_(torch.tensor(torch.finfo(dtype).min)) /home/lzcs/Documents/code/tools/stable-diffusion-tritonserver/venv/lib/python3.10/site-packages/transformers/models/clip/modeling_clip.py:280: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!   if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): /home/lzcs/Documents/code/tools/stable-diffusion-tritonserver/venv/lib/python3.10/site-packages/transformers/models/clip/modeling_clip.py:288: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!   if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len): /home/lzcs/Documents/code/tools/stable-diffusion-tritonserver/venv/lib/python3.10/site-packages/transformers/models/clip/modeling_clip.py:320: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!   if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): /home/lzcs/Documents/code/tools/stable-diffusion-tritonserver/venv/lib/python3.10/site-packages/torch/onnx/symbolic_opset9.py:5408: UserWarning: Exporting aten::index operator of advanced indexing in opset 16 is achieved by combination of multiple ONNX operators, including Reshape, Transpose, Concat, and Gather. If indices include negative values, the exported graph will produce incorrect results.   warnings.warn( /home/lzcs/Documents/code/tools/stable-diffusion-tritonserver/venv/lib/python3.10/site-packages/diffusers/models/unet_2d_condition.py:369: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!   if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]): /home/lzcs/Documents/code/tools/stable-diffusion-tritonserver/venv/lib/python3.10/site-packages/diffusers/models/resnet.py:182: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!   assert hidden_states.shape[1] == self.channels /home/lzcs/Documents/code/tools/stable-diffusion-tritonserver/venv/lib/python3.10/site-packages/diffusers/models/resnet.py:187: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!   assert hidden_states.shape[1] == self.channels /home/lzcs/Documents/code/tools/stable-diffusion-tritonserver/venv/lib/python3.10/site-packages/diffusers/models/resnet.py:109: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!   assert hidden_states.shape[1] == self.channels /home/lzcs/Documents/code/tools/stable-diffusion-tritonserver/venv/lib/python3.10/site-packages/diffusers/models/resnet.py:122: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!   if hidden_states.shape[0] >= 64: /home/lzcs/Documents/code/tools/stable-diffusion-tritonserver/venv/lib/python3.10/site-packages/diffusers/models/unet_2d_condition.py:470: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!   if not return_dict: /home/lzcs/Documents/code/tools/stable-diffusion-tritonserver/venv/lib/python3.10/site-packages/torch/onnx/_internal/jit_utils.py:258: UserWarning: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function. (Triggered internally at ../torch/csrc/jit/passes/onnx/shape_type_inference.cpp:1884.)   _C._jit_pass_onnx_node_shape_type_inference(node, params_dict, opset_version) /home/lzcs/Documents/code/tools/stable-diffusion-tritonserver/venv/lib/python3.10/site-packages/torch/onnx/utils.py:687: UserWarning: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function. (Triggered internally at ../torch/csrc/jit/passes/onnx/shape_type_inference.cpp:1884.)   _C._jit_pass_onnx_graph_shape_type_inference( /home/lzcs/Documents/code/tools/stable-diffusion-tritonserver/venv/lib/python3.10/site-packages/torch/onnx/utils.py:1178: UserWarning: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function. (Triggered internally at ../torch/csrc/jit/passes/onnx/shape_type_inference.cpp:1884.)   _C._jit_pass_onnx_graph_shape_type_inference( /home/lzcs/Documents/code/tools/stable-diffusion-tritonserver/venv/lib/python3.10/site-packages/diffusers/models/vae.py:570: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!   if not return_dict: /home/lzcs/Documents/code/tools/stable-diffusion-tritonserver/venv/lib/python3.10/site-packages/torch/onnx/_internal/jit_utils.py:258: UserWarning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied. (Triggered internally at ../torch/csrc/jit/passes/onnx/constant_fold.cpp:179.)   _C._jit_pass_onnx_node_shape_type_inference(node, params_dict, opset_version) /home/lzcs/Documents/code/tools/stable-diffusion-tritonserver/venv/lib/python3.10/site-packages/torch/onnx/utils.py:687: UserWarning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied. (Triggered internally at ../torch/csrc/jit/passes/onnx/constant_fold.cpp:179.)   _C._jit_pass_onnx_graph_shape_type_inference( /home/lzcs/Documents/code/tools/stable-diffusion-tritonserver/venv/lib/python3.10/site-packages/torch/onnx/utils.py:1178: UserWarning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied. (Triggered internally at ../torch/csrc/jit/passes/onnx/constant_fold.cpp:179.)   _C._jit_pass_onnx_graph_shape_type_inference( /home/lzcs/Documents/code/tools/stable-diffusion-tritonserver/venv/lib/python3.10/site-packages/diffusers/models/vae.py:607: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!   if not return_dict: /home/lzcs/Documents/code/tools/stable-diffusion-tritonserver/venv/lib/python3.10/site-packages/torch/onnx/utils.py:617: UserWarning: ONNX Preprocess - Removing mutation from node aten::index_put_ on block input: '1'. This changes graph semantics. (Triggered internally at ../torch/csrc/jit/passes/onnx/remove_inplace_ops_for_onnx.cpp:335.)   _C._jit_pass_onnx_remove_inplace_ops_for_onnx(graph, module) ONNX pipeline saved to stable-diffusion-onnx ONNX pipeline is loadable

this is run docker logs with sudo docker run -it --rm --gpus device=0 -p8000:8000 -p8001:8001 -p8002:8002 --shm-size 16384m   -v $PWD/models:/models tritonserver tritonserver --model-repository /models/
=============================
== Triton Inference Server ==
NVIDIA Release 22.12 (build 50109463)
Triton Server Version 2.29.0
Copyright (c) 2018-2022, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
Various files include modifications (c) NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
This container image and its contents are governed by the NVIDIA Deep Learning Container License.
By pulling and using the container, you accept the terms and conditions of this license:
https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license
WARNING: CUDA Minor Version Compatibility mode ENABLED.
Using driver version 515.86.01 which has support for CUDA 11.7.  This container
was built with CUDA 11.8 and will be run in Minor Version Compatibility mode.
CUDA Forward Compatibility is preferred over Minor Version Compatibility for use
with this container but was unavailable:
[[Forward compatibility was attempted on non supported HW (CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE) cuInit()=804]]
See https://docs.nvidia.com/deploy/cuda-compatibility/ for details.
I0118 15:03:03.731989 1 pinned_memory_manager.cc:240] Pinned memory pool is created at '0x7f5638000000' with size 268435456
I0118 15:03:03.732169 1 cuda_memory_manager.cc:105] CUDA memory pool is created on device 0 with size 67108864
I0118 15:03:03.733787 1 model_lifecycle.cc:459] loading: text_encoder:1
I0118 15:03:03.733801 1 model_lifecycle.cc:459] loading: vae_decoder:1
I0118 15:03:03.733809 1 model_lifecycle.cc:459] loading: unet:1
I0118 15:03:03.733820 1 model_lifecycle.cc:459] loading: stable_diffusion:1
I0118 15:03:03.735066 1 onnxruntime.cc:2459] TRITONBACKEND_Initialize: onnxruntime
I0118 15:03:03.735075 1 onnxruntime.cc:2469] Triton TRITONBACKEND API version: 1.10
I0118 15:03:03.735080 1 onnxruntime.cc:2475] 'onnxruntime' TRITONBACKEND API version: 1.10
I0118 15:03:03.735083 1 onnxruntime.cc:2505] backend configuration:
{"cmdline":{"auto-complete-config":"true","min-compute-capability":"6.000000","backend-directory":"/opt/tritonserver/backends","default-max-batch-size":"4"}}
I0118 15:03:03.742998 1 onnxruntime.cc:2563] TRITONBACKEND_ModelInitialize: vae_decoder (version 1)
I0118 15:03:03.743316 1 onnxruntime.cc:666] skipping model configuration auto-complete for 'vae_decoder': inputs and outputs already specified
I0118 15:03:03.743573 1 onnxruntime.cc:2563] TRITONBACKEND_ModelInitialize: unet (version 1)
I0118 15:03:03.743845 1 onnxruntime.cc:666] skipping model configuration auto-complete for 'unet': inputs and outputs already specified
I0118 15:03:03.744204 1 onnxruntime.cc:2563] TRITONBACKEND_ModelInitialize: text_encoder (version 1)
I0118 15:03:03.744397 1 onnxruntime.cc:666] skipping model configuration auto-complete for 'text_encoder': inputs and outputs already specified
I0118 15:03:06.019240 1 onnxruntime.cc:2606] TRITONBACKEND_ModelInstanceInitialize: vae_decoder (GPU device 0)
I0118 15:03:06.046037 1 onnxruntime.cc:2640] TRITONBACKEND_ModelInstanceFinalize: delete instance state
I0118 15:03:06.046073 1 onnxruntime.cc:2586] TRITONBACKEND_ModelFinalize: delete model state
E0118 15:03:06.046083 1 model_lifecycle.cc:597] failed to load 'vae_decoder' version 1: Internal: unable to create stream: the provided PTX was compiled with an unsupported toolchain.
I0118 15:03:06.046082 1 onnxruntime.cc:2606] TRITONBACKEND_ModelInstanceInitialize: unet (GPU device 0)
I0118 15:03:06.063183 1 onnxruntime.cc:2640] TRITONBACKEND_ModelInstanceFinalize: delete instance state
I0118 15:03:06.063195 1 onnxruntime.cc:2606] TRITONBACKEND_ModelInstanceInitialize: text_encoder (GPU device 0)
I0118 15:03:06.063198 1 onnxruntime.cc:2586] TRITONBACKEND_ModelFinalize: delete model state
E0118 15:03:06.063234 1 model_lifecycle.cc:597] failed to load 'unet' version 1: Internal: unable to create stream: the provided PTX was compiled with an unsupported toolchain.
I0118 15:03:06.080165 1 onnxruntime.cc:2640] TRITONBACKEND_ModelInstanceFinalize: delete instance state
I0118 15:03:06.080180 1 onnxruntime.cc:2586] TRITONBACKEND_ModelFinalize: delete model state
E0118 15:03:06.080204 1 model_lifecycle.cc:597] failed to load 'text_encoder' version 1: Internal: unable to create stream: the provided PTX was compiled with an unsupported toolchain.
I0118 15:03:06.080210 1 python_be.cc:1856] TRITONBACKEND_ModelInstanceInitialize: stable_diffusion (GPU device 0)
E0118 15:03:06.097037 1 model_lifecycle.cc:597] failed to load 'stable_diffusion' version 1: Internal: unable to create stream: the provided PTX was compiled with an unsupported toolchain.
I0118 15:03:06.097091 1 server.cc:563]
+------------------+------+
| Repository Agent | Path |
+------------------+------+
+------------------+------+
I0118 15:03:06.097121 1 server.cc:590]
+-------------+-----------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Backend     | Path                                                            | Config                                                                                |
+-------------+-----------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------+
| onnxruntime | /opt/tritonserver/backends/onnxruntime/libtriton_onnxruntime.so | {"cmdline":{"auto-complete-config":"true","min-compute-capability":"6.000000","backend-directory":"/opt/tritonserver/backends","default-max-batch-size":"4"}} |
| python      | /opt/tritonserver/backends/python/libtriton_python.so           | {"cmdline":{"auto-complete-config":"true","min-compute-capability":"6.000000","backend-directory":"/opt/tritonserver/backends","default-max-batch-size":"4"}} |
+-------------+-----------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------+
I0118 15:03:06.097153 1 server.cc:633]
+------------------+---------+--------------------------------------------------------------------------------------------------------------+
| Model            | Version | Status                                                                                |
+------------------+---------+--------------------------------------------------------------------------------------------------------------+
| stable_diffusion | 1       | UNAVAILABLE: Internal: unable to create stream: the provided PTX was compiled with an unsupported toolchain. |
| text_encoder     | 1       | UNAVAILABLE: Internal: unable to create stream: the provided PTX was compiled with an unsupported toolchain. |
| unet             | 1       | UNAVAILABLE: Internal: unable to create stream: the provided PTX was compiled with an unsupported toolchain. |
| vae_decoder      | 1       | UNAVAILABLE: Internal: unable to create stream: the provided PTX was compiled with an unsupported toolchain. |
+------------------+---------+--------------------------------------------------------------------------------------------------------------+
I0118 15:03:06.123809 1 metrics.cc:864] Collecting metrics for GPU 0: NVIDIA GeForce RTX 3060
I0118 15:03:06.124020 1 metrics.cc:757] Collecting CPU metrics
I0118 15:03:06.124125 1 tritonserver.cc:2264]
+----------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Option                           | Value                                                                                |
+----------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| server_id                        | triton                                                                                |
| server_version                   | 2.29.0                                                                                |
| server_extensions                | classification sequence model_repository model_repository(unload_dependents) schedule_policy model_configuration system_shared_memory cuda_shared_memory binary_tensor_data statistics trace logging |
| model_repository_path[0]         | /models/                                                                                |
| model_control_mode               | MODE_NONE                                                                                |
| strict_model_config              | 0                                                                                |
| rate_limit                       | OFF                                                                                |
| pinned_memory_pool_byte_size     | 268435456                                                                                |
| cuda_memory_pool_byte_size{0}    | 67108864                                                                                |
| response_cache_byte_size         | 0                                                                                |
| min_supported_compute_capability | 6.0                                                                                |
| strict_readiness                 | 1                                                                                |
| exit_timeout                     | 30                                                                                |
+----------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
I0118 15:03:06.124138 1 server.cc:264] Waiting for in-flight requests to complete.
I0118 15:03:06.124142 1 server.cc:280] Timeout 30: Found 0 model versions that have in-flight inferences
I0118 15:03:06.124146 1 server.cc:295] All models are stopped, unloading models
I0118 15:03:06.124149 1 server.cc:302] Timeout 30: Found 0 live models and 0 in-flight non-inference requests
error: creating server: Internal - failed to load all models

=============================
== Triton Inference Server ==
NVIDIA Release 22.06 (build 39726160)
Triton Server Version 2.23.0
Copyright (c) 2018-2022, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
Various files include modifications (c) NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
This container image and its contents are governed by the NVIDIA Deep Learning Container License.
By pulling and using the container, you accept the terms and conditions of this license:
https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license
I0119 06:05:07.785478 1 pinned_memory_manager.cc:240] Pinned memory pool is created at '0x7fbebe000000' with size 268435456
I0119 06:05:07.785635 1 cuda_memory_manager.cc:105] CUDA memory pool is created on device 0 with size 67108864
I0119 06:05:07.786661 1 model_repository_manager.cc:1191] loading: text_encoder:1
I0119 06:05:07.887112 1 model_repository_manager.cc:1191] loading: stable_diffusion:1
I0119 06:05:07.902154 1 onnxruntime.cc:2466] TRITONBACKEND_Initialize: onnxruntime
I0119 06:05:07.902221 1 onnxruntime.cc:2476] Triton TRITONBACKEND API version: 1.10
I0119 06:05:07.902249 1 onnxruntime.cc:2482] 'onnxruntime' TRITONBACKEND API version: 1.10
I0119 06:05:07.902264 1 onnxruntime.cc:2512] backend configuration:
{"cmdline":{"auto-complete-config":"false","min-compute-capability":"6.000000","backend-directory":"/opt/tritonserver/backends","default-max-batch-size":"4"}}
I0119 06:05:07.921227 1 onnxruntime.cc:2568] TRITONBACKEND_ModelInitialize: text_encoder (version 1)
I0119 06:05:07.921968 1 onnxruntime.cc:2611] TRITONBACKEND_ModelInstanceInitialize: text_encoder (GPU device 0)
I0119 06:05:07.987297 1 model_repository_manager.cc:1191] loading: vae_decoder:1
I0119 06:05:08.087460 1 model_repository_manager.cc:1191] loading: unet:1
I0119 06:05:08.907431 1 model_repository_manager.cc:1345] successfully loaded 'text_encoder' version 1
I0119 06:05:08.917194 1 onnxruntime.cc:2568] TRITONBACKEND_ModelInitialize: vae_decoder (version 1)
I0119 06:05:08.917444 1 onnxruntime.cc:2568] TRITONBACKEND_ModelInitialize: unet (version 1)
I0119 06:05:08.917712 1 onnxruntime.cc:2611] TRITONBACKEND_ModelInstanceInitialize: unet (GPU device 0)
I0119 06:05:11.131345 1 onnxruntime.cc:2611] TRITONBACKEND_ModelInstanceInitialize: vae_decoder (GPU device 0)
I0119 06:05:11.131984 1 model_repository_manager.cc:1345] successfully loaded 'unet' version 1
I0119 06:05:11.252130 1 python_be.cc:1774] TRITONBACKEND_ModelInstanceInitialize: stable_diffusion (GPU device 0)
I0119 06:05:11.252149 1 model_repository_manager.cc:1345] successfully loaded 'vae_decoder' version 1
0119 06:05:12.212904 116 pb_stub.cc:309] Failed to initialize Python stub: HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/models/stable_diffusion/1/stable_diffusion/1/tokenizer/'. Use repo_type argument if needed.
At:
/usr/local/lib/python3.8/dist-packages/huggingface_hub/utils/_validators.py(166): validate_repo_id
/usr/local/lib/python3.8/dist-packages/huggingface_hub/utils/_validators.py(114): _inner_fn
/usr/local/lib/python3.8/dist-packages/transformers/utils/hub.py(466): cached_file
/usr/local/lib/python3.8/dist-packages/transformers/tokenization_utils_base.py(1760): from_pretrained
/models/stable_diffusion/1/model.py(70): initialize
E0119 06:05:12.355376 1 model_repository_manager.cc:1348] failed to load 'stable_diffusion' version 1: Internal: HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/models/stable_diffusion/1/stable_diffusion/1/tokenizer/'. Use repo_type argument if needed.
At:
/usr/local/lib/python3.8/dist-packages/huggingface_hub/utils/_validators.py(166): validate_repo_id
/usr/local/lib/python3.8/dist-packages/huggingface_hub/utils/_validators.py(114): _inner_fn
/usr/local/lib/python3.8/dist-packages/transformers/utils/hub.py(466): cached_file
/usr/local/lib/python3.8/dist-packages/transformers/tokenization_utils_base.py(1760): from_pretrained
/models/stable_diffusion/1/model.py(70): initialize
I0119 06:05:12.355465 1 server.cc:556]
+------------------+------+
| Repository Agent | Path |
+------------------+------+
+------------------+------+
I0119 06:05:12.355497 1 server.cc:583]
+-------------+-----------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Backend     | Path                                                            | Config                                                                                |
+-------------+-----------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------+
| onnxruntime | /opt/tritonserver/backends/onnxruntime/libtriton_onnxruntime.so | {"cmdline":{"auto-complete-config":"false","min-compute-capability":"6.000000","backend-directory":"/opt/tritonserver/backends","default-max-batch-size":"4"}} |
| python      | /opt/tritonserver/backends/python/libtriton_python.so           | {"cmdline":{"auto-complete-config":"false","min-compute-capability":"6.000000","backend-directory":"/opt/tritonserver/backends","default-max-batch-size":"4"}} |
+-------------+-----------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------+
I0119 06:05:12.355545 1 server.cc:626]
+------------------+---------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Model            | Version | Status                                                                                |
+------------------+---------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| stable_diffusion | 1       | UNAVAILABLE: Internal: HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/models/stable_diffusion/1/stable_diffusion/1/tokenizer/'. Use repo_type argument if needed. |
|                  |         |                                                                                |
|                  |         | At:                                                                                |
|                  |         |   /usr/local/lib/python3.8/dist-packages/huggingface_hub/utils/_validators.py(166): validate_repo_id                                                                                |
|                  |         |   /usr/local/lib/python3.8/dist-packages/huggingface_hub/utils/_validators.py(114): _inner_fn                                                                                |
|                  |         |   /usr/local/lib/python3.8/dist-packages/transformers/utils/hub.py(466): cached_file                                                                                |
|                  |         |   /usr/local/lib/python3.8/dist-packages/transformers/tokenization_utils_base.py(1760): from_pretrained                                                                                |
|                  |         |   /models/stable_diffusion/1/model.py(70): initialize                                                                                |
| text_encoder     | 1       | READY                                                                                |
| unet             | 1       | READY                                                                                |
| vae_decoder      | 1       | READY                                                                                |
+------------------+---------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
I0119 06:05:12.380740 1 metrics.cc:650] Collecting metrics for GPU 0: NVIDIA GeForce RTX 3060
I0119 06:05:12.380895 1 tritonserver.cc:2159]
+----------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Option                           | Value                                                                                |
+----------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| server_id                        | triton                                                                                |
| server_version                   | 2.23.0                                                                                |
| server_extensions                | classification sequence model_repository model_repository(unload_dependents) schedule_policy model_configuration system_shared_memory cuda_shared_memory binary_tensor_data statistics trace |
| model_repository_path[0]         | /models/                                                                                |
| model_control_mode               | MODE_NONE                                                                                |
| strict_model_config              | 1                                                                                |
| rate_limit                       | OFF                                                                                |
| pinned_memory_pool_byte_size     | 268435456                                                                                |
| cuda_memory_pool_byte_size{0}    | 67108864                                                                                |
| response_cache_byte_size         | 0                                                                                |
| min_supported_compute_capability | 6.0                                                                                |
| strict_readiness                 | 1                                                                                |
| exit_timeout                     | 30                                                                                |
+----------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
I0119 06:05:12.380914 1 server.cc:257] Waiting for in-flight requests to complete.
I0119 06:05:12.380921 1 server.cc:273] Timeout 30: Found 0 model versions that have in-flight inferences
I0119 06:05:12.380925 1 model_repository_manager.cc:1223] unloading: unet:1
I0119 06:05:12.380945 1 model_repository_manager.cc:1223] unloading: text_encoder:1
I0119 06:05:12.380966 1 model_repository_manager.cc:1223] unloading: vae_decoder:1
I0119 06:05:12.380995 1 server.cc:288] All models are stopped, unloading models
I0119 06:05:12.380999 1 server.cc:295] Timeout 30: Found 3 live models and 0 in-flight non-inference requests
I0119 06:05:12.381028 1 onnxruntime.cc:2645] TRITONBACKEND_ModelInstanceFinalize: delete instance state
I0119 06:05:12.381028 1 onnxruntime.cc:2645] TRITONBACKEND_ModelInstanceFinalize: delete instance state
I0119 06:05:12.381068 1 onnxruntime.cc:2645] TRITONBACKEND_ModelInstanceFinalize: delete instance state
I0119 06:05:12.393729 1 onnxruntime.cc:2591] TRITONBACKEND_ModelFinalize: delete model state
I0119 06:05:12.393754 1 model_repository_manager.cc:1328] successfully unloaded 'vae_decoder' version 1
I0119 06:05:12.406201 1 onnxruntime.cc:2591] TRITONBACKEND_ModelFinalize: delete model state
I0119 06:05:12.406409 1 model_repository_manager.cc:1328] successfully unloaded 'text_encoder' version 1
I0119 06:05:12.433237 1 onnxruntime.cc:2591] TRITONBACKEND_ModelFinalize: delete model state
I0119 06:05:12.433264 1 model_repository_manager.cc:1328] successfully unloaded 'unet' version 1
I0119 06:05:13.381187 1 server.cc:295] Timeout 29: Found 0 live models and 0 in-flight non-inference requests
error: creating server: Internal - failed to load all models

@xunfeng2zkj
Thanks for sharing the info, could you please also share the model name to reproduce the issue

@kamalkraj thank you for answering me so quickly all pipeline only follow v2 readme.

@kamalkraj thank you for answering me so quickly all pipeline only follow v2 readme.

runwayml/stable-diffusion-v1-5

@xunfeng2zkj
Unable to reproduce the issue.
Please clone the repo again and try

thx, Is it related to permission verification? "Internal: HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/models/stable_diffusion/1/stable_diffusion/1/tokenizer/'. Use repo_type argument if needed"

share screenshot of tree view of models folder

tree.txt

├── convert_stable_diffusion_checkpoint_to_onnx.py
├── copy_files.sh
├── Dockerfile
├── Inference.ipynb
├── LICENSE
├── models
│   ├── stable_diffusion
│   │   ├── 1
│   │   │   ├── model.py
│   │   │   ├── pycache
│   │   │   │   └── model.cpython-38.pyc
│   │   │   ├── scheduler
│   │   │   │   └── scheduler_config.json
│   │   │   └── tokenizer
│   │   │       ├── merges.txt
│   │   │       ├── special_tokens_map.json
│   │   │       ├── tokenizer_config.json
│   │   │       └── vocab.json
│   │   └── config.pbtxt
│   ├── text_encoder
│   │   ├── 1
│   │   │   └── model.onnx
│   │   └── config.pbtxt
│   ├── unet
│   │   ├── 1
│   │   │   └── model.onnx
│   │   └── config.pbtxt
│   └── vae_decoder
│       ├── 1
│       │   └── model.onnx
│       └── config.pbtxt
├── README.md
├── requirements.txt
├── stable-diffusion-onnx
│   ├── feature_extractor
│   │   └── preprocessor_config.json
│   ├── model_index.json
│   ├── safety_checker
│   │   └── model.onnx
│   ├── scheduler
│   │   └── scheduler_config.json
│   ├── text_encoder
│   │   └── model.onnx
│   ├── tokenizer
│   │   ├── merges.txt
│   │   ├── special_tokens_map.json
│   │   ├── tokenizer_config.json
│   │   └── vocab.json
│   ├── unet
│   │   └── model.onnx
│   ├── vae_decoder
│   │   └── model.onnx
│   └── vae_encoder
│       └── model.onnx
└── tree.txt

@kamalkraj  start the  images cannot at proj dir

NVIDIA Release 22.06 (build 39726160)
Triton Server Version 2.23.0
Copyright (c) 2018-2022, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
Various files include modifications (c) NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
This container image and its contents are governed by the NVIDIA Deep Learning Container License.
By pulling and using the container, you accept the terms and conditions of this license:
https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license
I0121 08:20:14.657010 1 pinned_memory_manager.cc:240] Pinned memory pool is created at '0x7f9d4e000000' with size 268435456
I0121 08:20:14.657219 1 cuda_memory_manager.cc:105] CUDA memory pool is created on device 0 with size 67108864
I0121 08:20:14.657493 1 server.cc:556]
+------------------+------+
| Repository Agent | Path |
+------------------+------+
+------------------+------+
I0121 08:20:14.657504 1 server.cc:583]
+---------+------+--------+
| Backend | Path | Config |
+---------+------+--------+
+---------+------+--------+
I0121 08:20:14.657512 1 server.cc:626]
+-------+---------+--------+
| Model | Version | Status |
+-------+---------+--------+
+-------+---------+--------+
I0121 08:20:14.683162 1 metrics.cc:650] Collecting metrics for GPU 0: NVIDIA GeForce RTX 3060
I0121 08:20:14.683292 1 tritonserver.cc:2159]
+----------------------------------+----------------------------------------------------------------------------------------------------------+
| Option                           | Value                                                                                |
+----------------------------------+----------------------------------------------------------------------------------------------------------+
| server_id                        | triton                                                                                |
| server_version                   | 2.23.0                                                                                |
| server_extensions                | classification sequence model_repository model_repository(unload_dependents) schedule_policy model_confi |
|                                  | guration system_shared_memory cuda_shared_memory binary_tensor_data statistics trace                     |
| model_repository_path[0]         | /models/                                                                                |
| model_control_mode               | MODE_NONE                                                                                |
| strict_model_config              | 1                                                                                |
| rate_limit                       | OFF                                                                                |
| pinned_memory_pool_byte_size     | 268435456                                                                                |
| cuda_memory_pool_byte_size{0}    | 67108864                                                                                |
| response_cache_byte_size         | 0                                                                                |
| min_supported_compute_capability | 6.0                                                                                |
| strict_readiness                 | 1                                                                                |
| exit_timeout                     | 30                                                                                |
+----------------------------------+----------------------------------------------------------------------------------------------------------+
I0121 08:20:14.683998 1 grpc_server.cc:4587] Started GRPCInferenceService at <IP_ADDRESS>:8001
I0121 08:20:14.684148 1 http_server.cc:3303] Started HTTPService at <IP_ADDRESS>:8000
I0121 08:20:14.724943 1 http_server.cc:178] Started Metrics Service at <IP_ADDRESS>:8002

NVIDIA Release 22.06 (build 39726160)
Triton Server Version 2.23.0
Copyright (c) 2018-2022, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
Various files include modifications (c) NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
This container image and its contents are governed by the NVIDIA Deep Learning Container License.
By pulling and using the container, you accept the terms and conditions of this license:
https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license
I0121 08:20:14.657010 1 pinned_memory_manager.cc:240] Pinned memory pool is created at '0x7f9d4e000000' with size 268435456
I0121 08:20:14.657219 1 cuda_memory_manager.cc:105] CUDA memory pool is created on device 0 with size 67108864
I0121 08:20:14.657493 1 server.cc:556]
+------------------+------+
| Repository Agent | Path |
+------------------+------+
+------------------+------+
I0121 08:20:14.657504 1 server.cc:583]
+---------+------+--------+
| Backend | Path | Config |
+---------+------+--------+
+---------+------+--------+
I0121 08:20:14.657512 1 server.cc:626]
+-------+---------+--------+
| Model | Version | Status |
+-------+---------+--------+
+-------+---------+--------+
I0121 08:20:14.683162 1 metrics.cc:650] Collecting metrics for GPU 0: NVIDIA GeForce RTX 3060
I0121 08:20:14.683292 1 tritonserver.cc:2159]
+----------------------------------+----------------------------------------------------------------------------------------------------------+
| Option                           | Value                                                                                |
+----------------------------------+----------------------------------------------------------------------------------------------------------+
| server_id                        | triton                                                                                |
| server_version                   | 2.23.0                                                                                |
| server_extensions                | classification sequence model_repository model_repository(unload_dependents) schedule_policy model_confi |
|                                  | guration system_shared_memory cuda_shared_memory binary_tensor_data statistics trace                     |
| model_repository_path[0]         | /models/                                                                                |
| model_control_mode               | MODE_NONE                                                                                |
| strict_model_config              | 1                                                                                |
| rate_limit                       | OFF                                                                                |
| pinned_memory_pool_byte_size     | 268435456                                                                                |
| cuda_memory_pool_byte_size{0}    | 67108864                                                                                |
| response_cache_byte_size         | 0                                                                                |
| min_supported_compute_capability | 6.0                                                                                |
| strict_readiness                 | 1                                                                                |
| exit_timeout                     | 30                                                                                |
+----------------------------------+----------------------------------------------------------------------------------------------------------+
I0121 08:20:14.683998 1 grpc_server.cc:4587] Started GRPCInferenceService at <IP_ADDRESS>:8001
I0121 08:20:14.684148 1 http_server.cc:3303] Started HTTPService at <IP_ADDRESS>:8000
I0121 08:20:14.724943 1 http_server.cc:178] Started Metrics Service at <IP_ADDRESS>:8002

OK, I solve it, just self.scheduler_config_path = current_name + "/scheduler/"

@xunfeng2zkj
Hi, did you solve the error of Stub process is unhealthy and it will be restarted.?
How do you deal with that?


Fix date format on certificates

We need to change the date format on our certificates. Currently it's
January, 15th 2016
It should be:
January 15, 2016
Note that the comma needs to be moved, and the date should just be a natural number, not an ordinal number.

Can i try this issue ??

@sanskar-modi, this issue has already got 2 Pull Requests! Checkout our other help wanted issues here: https://github.com/FreeCodeCamp/FreeCodeCamp/labels/help wanted.


508: Emissions Workspace UI Focus Issues

[ ] When a user hits the Select Configurations button with the keyboard, it should stay on that button

I'm still seeing a loss of focus. for example if I have chikasaw open and active, then hit select configurations, focus is lost..... if I tab again its on the facility header

@aprematta This ticket was accidentally moved to ready for testing earlier. It should have the updates now

Just rechecked, looks good.... closing ticket


75% of german translations by thomas

Question: "20 minute, quickcode" --> "20 Minuten gültig"
Valid for 20 minutes?

Yes. Did I missunderstand this? All the different vouchers have to be checked anyway. It's still a bit abstract for me.

Let me see where that comment sits first. I think it's misleading in DE and EN

OK. But I think that the translations are as good as possible today. When we test the site in german, there will be a lot of corrections. So don't delay the process for just a few wrong or dubious translations, please.

I've created a note about this for attention later.
Naming it 'valid for 20 minutes' isn't correct and will interfere with the description.


Update common

@howardjohn: The following test failed, say /retest to rerun them all:

Test name
Commit
Details
Rerun command

gencheck_installer
7ef4c8ff62ed6cc69494e637f73225e4ffd18b91
link
/test gencheck_installer

Instructions for interacting with me using PR comments are available here.  If you have questions or suggestions related to my behavior, please file an issue against the kubernetes/test-infra repository. I understand the commands that are listed here.


stackage-curator: thread blocked indefinitely in an MVar operation

We believe this can be avoided by removing cyclic dependencies from the build plan. (e.g. a test suite that is used by its own dependencies.)

Fixed by bf21ceaa115070cfeacfad1c4c2a6c4097d6532b


Scrubber and a few other elements do not render on android 5.1

I believe this is a similar issue to things not rendering correctly in 5.0.  Looking at the scrubber_control_selector i see it directly uses the *_reference drawables.  This no longer works ion 5.1

Actually, intercepting drawables on 5.1 doesn't work anymore. But color intercepting does.
The new approach is to use the material themes for android 5.0+, while keeping the benefits of HoloAccent. Please have a look at the 'dev' branch and let me know if it is working correctly.
I have been working on a new library too. It follows the same principles as HoloAccent and it backports the Material themes to android 4.0+. It is called GreenMatter and it is in development stage, but I suggest that you consider replacing HoloAccent by it:
https://github.com/negusoft/GreenMatter

Thats not true.  level-list still can get intercepted (i been using it for intercepting for awhile)
My users want holo so GreenMatter is not an option.  Anyway i just posted this to let you know its broken


Moves things around the FilterAdapter

Classes out of FilterAdapter
Functionality from the FilterAnimator
Unused methods from FilterAdapter are removed

It looks like this  change is just cutting up FilterAdapter into multiple files (+ removing the unused methods), is that right?
👍 lgtm if so, I don't want to add comments to change existing code if it's not relevant.

It looks like this change is just cutting up FilterAdapter into multiple files (+ removing the unused methods), is that right?
👍 lgtm if so, I don't want to add comments to change existing code if it's not relevant.

Exactly! Just moving things around, to make it easier to refactor later


sim: enable selecting clang native compiler on MacOS.

Summary
Enabling selecting native compiler suite (clang) on MacOS via config:
X86_64 based hosts stay on current (gcc/binutils) configuration for backward compatibility.
ARM64 based hosts will set clang compiler by default.
This change is step ahead to get MacOS simulation harmonized to use native compiler.
Impact
Simulation on MacOS/ARM64.
Testing
MacBook Pro Late 2013 running Big Sur (X86_64)
Testing on MacBook Air 2020 running Monterey (ARM64) will be part of separate PR.

X86_64 based hosts stay on current (gcc/binutils) configuration for backward compatibility.

i guess you misunderstand the situation.
i have always been using clang to develop sim/macOS/x86-64.
i have not used gcc for the purpose.
i don't know if it's possible to build sim/macOS with gcc.
also, i usually use clang to build sim/Linux/x86-64 without problems.
so i'm not sure why this option is necessary.

Native compiler on MacOS doesn't have objcopy installed. I assume, you have additional software installed on you MacOS which (maybe as a side-effect) installs objcopy. Can you check your system regarding this point, please.

Native compiler on MacOS doesn't have objcopy installed. I assume, you have additional software installed on your MacOS which (maybe as a side-effect) installs objcopy. Can you check your system regarding this point, please. If current MacOS/X86 is designed to use clang, then the option is most likely not needed, as it was just introduced for backward compatibility.

yes, it requires objcopy from binutils.
see https://github.com/apache/incubator-nuttx/pull/1817
maybe we forgot to document it.
at that point, the llvm version of objcopy didn't work well.
can you explain why objcopy is not necessary for clang?

@ptka does llvm-objcpy and llvm-ojdump exist on macOS?

@ptka does llvm-objcpy and llvm-objdump exist on macOS?

in my environment with Monterey x86-64, Xcode 13.2.1,
neither Xcode nor CommandLineTools has them.
llvm from homebrew has them.

maybe we can consider to revert https://github.com/apache/incubator-nuttx/pull/1817 .
it's awkward to require binutils.

@ptka found that objcopy isn't really needed, I think.

Native compiler on MacOS doesn't have objcopy installed. I assume, you have additional software installed on your MacOS which (maybe as a side-effect) installs objcopy. Can you check your system regarding this point, please. If current MacOS/X86 is designed to use clang, then the option is most likely not needed, as it was just introduced for backward compatibility.

yes, it requires objcopy from binutils. see #1817 maybe we forgot to document it. at that point, the llvm version of objcopy didn't work well.
can you explain why objcopy is not necessary for clang?

Afaik: objcopy is used to hide the NuttX world from the HOST world to avoid, that the wrong functions are called. Before linking the final host executable, objcopy renames a list of NuttX symbols. Overall this is a kind of a workaround as in principle is should work in a different way: Compiling code with "-fvisibility=hidden" should mark functions as hidden. Hidden means, they are not exported from any 'ld' created object file. All symbols, which needs to exported, can overwrite the visibility in the code itself. This way 'main()' and 'up_doirq()' gets visible in the HOST world although they are defined in the NuTTX world. On MacOS/clang the compiler/linker behaves exactly that way and objcopy is thus not needed.

Native compiler on MacOS doesn't have objcopy installed. I assume, you have additional software installed on your MacOS which (maybe as a side-effect) installs objcopy. Can you check your system regarding this point, please. If current MacOS/X86 is designed to use clang, then the option is most likely not needed, as it was just introduced for backward compatibility.

yes, it requires objcopy from binutils. see #1817 maybe we forgot to document it. at that point, the llvm version of objcopy didn't work well.
can you explain why objcopy is not necessary for clang?

Afaik: objcopy is used to hide the NuttX world from the HOST world to avoid, that the wrong functions are called. Before linking the final host executable, objcopy renames a list of NuttX symbols. Overall this is a kind of a workaround as in principle is should work in a different way: Compiling code with "-fvisibility=hidden" should mark functions as hidden. Hidden means, they are not exported from any 'ld' created object file. All symbols, which needs to exported, can overwrite the visibility in the code itself. This way 'main()' and 'up_doirq()' gets visible in the HOST world although they are defined in the NuTTX world. On MacOS/clang the compiler/linker behaves exactly that way and objcopy is thus not needed.

ok. thank you for the explanation.

i guess more functions need to be exported. eg. netdriver_setmacaddr
i don't think it's arm specific.
if it's clang-specific (i dunno) it might be simpler to use ifneq "$(shell $(CC) --version | grep clang)" "" than a kconfig
i don't understand the change in up_vfork_x86_64.S. is it somehow tied to the objcopy part of the PR? otherwise, i'd suggest to leave it for a separate PR.

maybe we can consider to revert #1817 .
it's awkward to require binutils.

I think, we need to distinguish between clang and gcc/binutils based HOST compiler suites (maybe it's even just an MacOS issue). On gcc/binutils based HOST compiler suites objcopy is needed to avoid wrong function calls between the NuttX and the HOST world within the overall simulation. In my opinion we cannot simply revert mentioned PR.

maybe we can consider to revert #1817 .
it's awkward to require binutils.

I think, we need to distinguish between clang and gcc/binutils based HOST compiler suites (maybe it's even just an MacOS issue). On gcc/binutils based HOST compiler suites objcopy is needed to avoid wrong function calls between the NuttX and the HOST world within the overall simulation. In my opinion we cannot simply revert mentioned PR.

as far as your approach works, there is no point to revert.

If @ptka finding is right, I would prefer that we remove objcopy step for macOS regardless whether it is run on x64 or arm64.

Native compiler on MacOS doesn't have objcopy installed. I assume, you have additional software installed on your MacOS which (maybe as a side-effect) installs objcopy. Can you check your system regarding this point, please. If current MacOS/X86 is designed to use clang, then the option is most likely not needed, as it was just introduced for backward compatibility.

yes, it requires objcopy from binutils. see #1817 maybe we forgot to document it. at that point, the llvm version of objcopy didn't work well.
can you explain why objcopy is not necessary for clang?

Afaik: objcopy is used to hide the NuttX world from the HOST world to avoid, that the wrong functions are called. Before linking the final host executable, objcopy renames a list of NuttX symbols. Overall this is a kind of a workaround as in principle is should work in a different way: Compiling code with "-fvisibility=hidden" should mark functions as hidden. Hidden means, they are not exported from any 'ld' created object file. All symbols, which needs to exported, can overwrite the visibility in the code itself. This way 'main()' and 'up_doirq()' gets visible in the HOST world although they are defined in the NuTTX world. On MacOS/clang the compiler/linker behaves exactly that way and objcopy is thus not needed.

ok. thank you for the explanation.

i guess more functions need to be exported. eg. netdriver_setmacaddr
i don't think it's arm specific.
if it's clang-specific (i dunno) it might be simpler to use ifneq "$(shell $(CC) --version | grep clang)" "" than a kconfig
i don't understand the change in up_vfork_x86_64.S. is it somehow tied to the objcopy part of the PR? otherwise, i'd suggest to leave it for a separate PR.

This ifneq "$(shell $(CC) --version | grep clang)" "" could be a good approach to explicitly set CC=clang in the Makefilesc and depend on the search path order for 'cc'.

@ptka does llvm-objcpy and llvm-objdump exist on macOS?

in my environment with Monterey x86-64, Xcode 13.2.1, neither Xcode nor CommandLineTools has them.
llvm from homebrew has them.

I would like to see MacOS based simulation independent from homebrew. There's nothing wrong with homebrew, but native compiler suite should be good enough and there's no need for this dependencies from compiler perspective.

more symbol need add default visibility: _up_cpu_started, _up_init_ipi, _netdriver_setmacaddr.

more symbol need add default visibility: _up_cpu_started, _up_init_ipi, _netdriver_setmacaddr.

that's the easy part ... the critical part is getting the modules compiled ... there's a hard dependency:
MODULECC = x86_64-elf-gcc
MODULELD = x86_64-elf-ld
to get the elf modules build.
This could be really a show-stopper (or at least an not-easy to fix dependency to homebrew based additional software needed on MacOS).

Let me park this PR completely ... it's not working, they way it was intended ... I need to re-think.

I installed the packages via homebrew to fulfill:
MODULECC = x86_64-elf-gcc
MODULELD = x86_64-elf-ld

Additionally, I removed all changed except the "objcopy" part and the related symbol export changes. Making this PR focus on a single topic.


stub Select.filter and Select.filter_by

PR for https://github.com/dropbox/sqlalchemy-stubs/issues/254
New PR because of the unset GitHub user.email

@ilevkivskyi Is the project still supported?

There is only minimal amount of maintenance. Since SQLAlchemy 2.0 supports types natively it is recommended to use it instead.


Suggestion - using OpenDalle/DPO model as default

Hello,
thank you for your work and making it public, it's a very nice idea.
I just wanted to suggest you to use OpenDalle https://civitai.com/models/238116?modelVersionId=275681 or other DPO finetuned mdoel https://civitai.com/models/239836?modelVersionId=270839
In my experiments OpenDalle is better. I follows prompt better than regular SD XL models.

Hello, thank you for your work and making it public, it's a very nice idea.
I just wanted to suggest you to use OpenDalle https://civitai.com/models/238116?modelVersionId=275681 or other DPO finetuned mdoel https://civitai.com/models/239836?modelVersionId=270839
In my experiments OpenDalle is better. I follows prompt better than regular SD XL models.

Thanks for your constructive suggestions. We will consider to try these models in next update.


Configure excludes polish

Related to #4007
Steps to Reproduce:

Have a large workspace, generate a jsconfig.json that does not exclude anything, click on Configure Excludes in the status bar
You get the attached message
I propose the following changes:

Rename "Configure excludes..." action to "Configure Excludes"
In this particular case the suggestion is not very precise, why are we tryign to be smart here, the user should decide what to exclude himself imho
The title of the project seems to be wrong - why not use the same we show in the explorer header, in this case 'vscode'

:+1:
While the algorithm to find large folders is nice. A user that configures the excludes knows where the generated js resources are. Therefore do not add the guessed folders to the message.

I disagree, folks won't know to exclude things like temp folders which become giant because of a transient build step. node_modules et al are the obvious but we have been hurt more than once with those unusual folders

Rename "Configure excludes..." action to "Configure Excludes"

I put the ... because it opens a new UI element which I believe is the rule?

In this particular case the suggestion is not very precise, why are we tryign to be smart here, the user should decide what to exclude himself imho

see above

The title of the project seems to be wrong - why not use the same we show in the explorer header, in this case 'vscode'

Agree, it's the filename of the configuration but the action already takes you there...

I put the ... because it opens a new UI element which I believe is the rule?

Not sure where else in UI we use this rule? AFAIK we do not follow it in debug and task world
@stevencl opinions?

Like here for instance. Unsure if it's applies to message-actions?

Yeah i was thinking more of action labels

removed in the ... with #4248

I disagree, folks won't know to exclude things like temp folders which become giant because of a transient build step. node_modules et al are the obvious but we have been hurt more than once with those unusual folders

Good point but then let's use heuristics and not show just all folders including many files. Only mention the suspects 'temp' and 'tmp' if they are big.

I'd approach it the other way around and not show folders like src or test and if that results to a case in which I have no folder left to propose exclusion for a generic message will show.
And on the other hand tsserver will generate a similar message which blames the last folder (https://github.com/Microsoft/TypeScript/blob/release-1.8/src/compiler/program.ts#L1117)

Excluding well known source folders like src and test works for me.

excluding src, test, and tests now

Nice!


Added tests for enabled/disabled plugins, added comments to clarify o…

#584 Created some tests for enabled.txt and disabled.txt in the plugins folder
#615 Added more documentation for tool build modes.

Coverage increased (+0.03%) to 72.933% when pulling 28a4f3a4c59e35dbf87e332da999b7d9a384d9a5 on feature/issue-584-615 into 1c123bb1cdbc3d1afedfd2f8a1c76532fe850166 on develop.

@garyluu Looks good, let's follow-up with an issue for creating end-user documentation for plugins (with the dockstore-ui documentation focusing on end-users and the GitHub documentation continuing to focus on developers)