Are panorama images streamed or served locally?

For the panorama images, can I serve them from my own server / S3 bucket or are they all live streamed from the Apple Maps API and proxied through my own server? Does provisioning servers closer to users make any difference in pano transitions?

For the panorama images, can I serve them from my own server / S3 bucket or are they all live streamed from the Apple Maps API and proxied through my own server?

Panorama faces are loaded directly from the internal Apple Maps API, but must be proxied through the backend to convert them and to bypass CORS.

Does provisioning servers closer to users make any difference in pano transitions?

Probably, but the most important thing is how much CPU power is available. Outside of Apple's walled garden, HEIC support is practically nonexistent, so for the majority of users, the backend has to convert the images to JPG first. Since these images are fairly large, and HEIC is also inherently slower to decode than many other formats, this is a very CPU-intensive task, so the faster your server is, the better the experience will be.

Thanks a lot for the detailed reply. After the initial conversation from HEIC to JPG, is it possible to cache the images in CDN or locally and then serve that for future users or does each new user have to re-request the conversion for each panorama again?
Can the JS embedding demo load the converted JPG from CDN for subsequent requests?

is it possible to cache the images in CDN or locally and then serve that for future users or does each new user have to re-request the conversion for each panorama again?

I'm a bit out of my depth when it comes to caching or CDNs, but the panoramas are completely static, so it should be pretty easy to add a caching layer somewhere.
That said, IMO this is only worth doing if you are certain that specific panoramas are frequently accessed. On my public instance, where people are just randomly exploring, the chance that two people happen to hit the exact same panorama before it leaves a hypothetical cache is very low; if you're embedding a specific place on a website, though, it may make sense to cache that.

Can the JS embedding demo load the converted JPG from CDN for subsequent requests?

You mean from a different URL? The viewer will always request images from  apiBaseUrl/pano/..., so if you want it to pivot to a cached file on a CDN if one exists, you'll need to add that functionality. Returning an HTTP redirect from that endpoint might work, but I haven't tested it, so no guarantees

You mentioned available CPU power, what sort of VPS requirements would you suggest would offer good performance? For example would you be able to share what sort of specs the current web demo is using?

Alternatively would it be feasible to preload for example a 100m radius of panoramas on each panorama transition? This way after a user arrives at one panorama, looks around and then decides to proceed to the next one, the images are already loaded instead of a delay between each transition?

Not really.
In the best case scenario, where the 100 m circle only contains one road that has been covered only once, that's ~35 panoramas. At zoom level 5, which is what the viewer loads initially, you'd be downloading 6 files per panorama at a size of ~140 kB each, so that's 210 files with a size of 29.4 MB.
Most of the time though, the user will be in a city, where you can expect ~300 panoramas to be in the circle. That's 1,800 files to download weighing 252 MB – and that's still just for the initial low quality load.
In the worst case scenario, which is a city center with dense backpack coverage, you're already in the thousands: a 100 m radius around [50.083556, 14.434191], which is in Prague, contains an incredible 6,796 panoramas. That'd be a download of 40,776 files or 5.71 GB (!).

I see, alternatively would it be feasible to, instead of pushing the HEIC files directly to JS viewer, to push the files to something like an S3 bucket and then have JS viewer fetch the HEIC files from the S3 link instead. This would allow CDN caching using AWS Cloudfront.

Yes, you could preemptively download all metadata and panoramas within a specific area, e.g. with the lookaround submodule, and then serve that from a CDN – either by modifiying routes/api.py as needed, or by writing a custom server which emulates those responses.

Instead of preemptively downloading all panoramas, would realtime pushing to CDN and JS pulling images from CDN be realistic? This might introduce a small delay between transitions for first time users but all subsequent requests should be from fetched from CDN.

If your users are primarily looking at one specific area, then I suppose that would work. Otherwise, you'd be accumulating a lot of files that will never actually be hit a second time

Okay thanks for the confirmation! Does the minified viewer.js use this three JS code?
Is there a way to pass in a custom value for the crossfade effect? Currently it’s very fast and the cross fade effect isn’t very visible.
https://github.com/sk-zk/lookaround-map/blob/main/js/viewer/LookaroundAdapter.js#L145

Okay thanks for the confirmation! Does the minified viewer.js use this three JS code?

You mean in general or the method you linked in particular? But the answer is yes to both

Is there a way to pass in a custom value for the crossfade effect? Currently it’s very fast and the cross fade effect isn’t very visible.

It's hardcoded atm, but I can add an option in createPanoViewer for this.

That would be really helpful! I think it would result in much smoother user transitions.

Would it be possible to add an additional forward motion to the camera during transitions? I think this forward motion coupled with the cross fade would at least provide users a sense of depth moving forward, somewhat approaching the native Apple Maps transition effect.

During the crossfade between transitions, there's often flashes of panorama images in between. Is there something that can be done to mitigate this or is it non-trivial?

During the transition crossfades, there are often flashes of panorama images in between that's sometimes quite jittery. Is there a way to mitigate this or is it non-trivial?
https://github.com/sk-zk/lookaround-map/assets/54892636/6d57c5c4-9983-4809-a893-db6fa246f3c7

Did you delete two comments or did GitHub swallow them?

I wanted to refactor my questions so I deleted them. After playing around with the code, I notice that the crossfade part is drawing the panorama to canvas and then fading that out on each transition. Have you noticed any crashing issues on mobile browsers with this approach? Would this be very resource heavy?
Also, I need to set the initial zoom to maxed out so that there's more panorama visibility in smaller mobile displays.  I notice there is a 'defaultZoomLvl' parameter but modifying it doesn't seem to affect the initial zoom level on load.

Thanks! Is there a 'loaded' event I can listen on? To know when the street view has been loaded and ready to navigate.

Thanks! So all of the methods, markers in the Photo Sphere documentation can be used as well right?

Most of what's in the core package should work, but because of the workarounds and monkeypatching I've had to do to make the library work for my use case, a few things don't. For example, you should call my navigateTo instead of the native setPanorama.
The marker and compass plugins are also the official ones, but the movement plugin I use is custom and not related to the VirtualTourPlugin.


Update e-invoicing-sa-onboarding.md

Updated documentation to capture changes in csr config file as well as powershell on-boarding script after recommendation from developer working on this.

:x: Organization membership check failed
Members of the MicrosoftDocs organization should use the private repo. Please move your changes to https://github.com/MicrosoftDocs/Dynamics-365-Operations, and close this pull request.

❌ Organization membership check failed
Members of the MicrosoftDocs organization should use the private repo. Please move your changes to https://github.com/MicrosoftDocs/Dynamics-365-Operations, and close this pull request.

Closing this as recommended


[BUG] bad_alloc when reading a PointIndexGrid from file

Environment
**Operating System: Ubuntu 18.04.6
**Version / Commit SHA: 10.0.2
**Other: c++17 gcc/g++ 9.4.0
Describe the bug
bad_alloc occurs when reading PointIndexGrid from a file with delay-loading turned OFF.
gdb backtraces the error to openvdb/io/Compression.h:260
To Reproduce
There is a simple example to reproduce the error:
#include <pcl/point_cloud.h>
#include <pcl/io/ply_io.h>

#include <openvdb/openvdb.h>
#include <openvdb/points/PointConversion.h>

using namespace std;

int main(int argc, char** argv)
{
    string pc_path = argv[1];

    pcl::PointCloud<pcl::PointXYZ>::Ptr pc(new pcl::PointCloud<pcl::PointXYZ>);
    if(pcl::io::loadPLYFile(pc_path, *pc) != 0){
        cout << "load " << pc_path << " failed!" << endl;
        return -1;
    }

    openvdb::initialize();
    float voxelSize = 0.06;
    openvdb::math::Transform::Ptr transform =
        openvdb::math::Transform::createLinearTransform(voxelSize);

    std::vector<openvdb::Vec3f> points(pc->size());
    for(int i=0;i<pc->size();++i) {
        points[i].x() = pc->at(i).x;
        points[i].y() = pc->at(i).y;
        points[i].z() = pc->at(i).z;
    }

    openvdb::points::PointAttributeVector<openvdb::Vec3f> pc_wrapper(points);

    openvdb::tools::PointIndexGrid::Ptr grid =
        openvdb::tools::createPointIndexGrid<openvdb::tools::PointIndexGrid>(pc_wrapper, *transform);

    grid->setName("Points");
    cout << "export ./test.vdb" << endl;
    openvdb::io::File("./test.vdb").write({grid});

    openvdb::io::File newFile("./test.vdb");
    newFile.open(false);
    openvdb::GridBase::Ptr baseGrid = newFile.readGrid("Points");
    auto testGrid = openvdb::gridPtrCast<openvdb::tools::PointIndexGrid>(baseGrid);
    testGrid->print();
}

Expected behavior
Throw a bad_alloc error when executing newFile.open(false);

Hi @leelegend94, I don't seem to be able to reproduce this. My setup is:

Mac M1
AppleClang <IP_ADDRESS>30022
Blosc ON, 1.21.1
OPENVDB_USE_DELAYED_LOADING ON and OFF (tested with both)

I've tested with a minimal point set as I don't have access to the point data you're reading in from your pc_path so it could be something in your dataset. Could you confirm what system you're testing on, compiler and how VDB was configured (if you're buildiung it yourself then the cmake options/output you're using

Closing due to no reply - please reopen if this is still an issue!


tests/index.js: Fix grammar for "clean up"

Signed-off-by: Bob Wombat Hogg<EMAIL_ADDRESS>I previously fixed these as in the PoC #155 and promised to split them out into a separate PR. I appear to have forgotten to do that until now.

Coverage remained the same at 95.787% when pulling 7eb7bf4185c1ce01a3205ab9984cf48bfb69c6b8 on rhoggSugarcrm:grammar-nitpicks-in-tests into fe63897f35f6423a2f3dd481c4919fbdf0325aa6 on sugarcrm:master.


Cant use struct from other package

Describe the bug
I'm trying to put in a body param and in the failure a structure that are in other package, like in the next code:
`
package api
import (
"net/http"
"github.com/gin-gonic/gin"
_ "gitlab.com/kowiste/ms-common/notification"
_ "gitlab.com/kowiste/library-go/errorTx"

)
// setNotification godoc
// @Summary Create a new notification.
// @Tags Notification
// @Accept /
// @Param   	request     body    notification.Notification     true        "Create a new notification"
// @Produce json
// @Success 	202
// @Failure 	400 {object} errorTx.Error
// @Failure 	404
// @Failure 	500
// @Router /v1/notification/ [post]
func (a *API) setNotification(c *gin.Context) { // Set a new notification
}
`
and run
swag init -g main.go --output docs/
I have the follow error for notification and the same for errorTx:
2022/01/04 12:32:27 ParseComment error in file C:\code\gitlab.com\kowiste\gateway\handler\api\api-notification.go :cannot find type definition: notification.Notification
If I create the struct in the same package everything works fine so, What I'm doing wrong?
Your swag version
e.g. 1.7.8
Your go version
e.g. 1.17.3
Desktop (please complete the following information):

OS: Windows 11

Looking in this problem, I make an project , ,in which I have 3 endpoints:

Test1

An struct in other package with fields string and int, this is parse good

Test2

Using gin.Error as a return struct, doesn't works give error:
2022/01/10 08:39:33 ParseComment error in file C:\code\github.com\Kowiste\swag\func.go :cannot find type definition: gin.Error

Test3

Using ErrorTest struct inside the package, doesn't works give error:
2022/01/10 08:42:44 ParseComment error in file C:\code\github.com\Kowiste\swag\func.go :cannot find type definition: error

for ErrorTest you need to prefix it with the package name like this
// @success 200 {object} main.ErrorTest

For all of the 3 examples you need to have the "import packageName" in the top of the file otherwise swag won't be able to find the dependency.
For gin you may require to use the "--parseDependency" flag something like
swag init -g main.go --parseDependency --output docs/

Good luck

Ok thanks, I was looking other issues and find that error cant be parse because is an interface .
So I'm going to close this


Right axis isn't reflecting data

I think it just needs to be attached to columns. Or it's a C3.js bug. Reported by ProdOps as issue 1267081.

I think this is due to the issue indicated in masayuki0812/c3#895.

I'm putting this as on hold because Y2 just works way too weirdly in C3 at the moment. See masayuki0812/c3#914.

As per the discussion in masayuki0812/c3#914, the solution to making Y2 work like Y is to set all columns to y2 in config.data.axes.
Basically, C3 binds all columns to the Y axis by default and that axis' domain is set to the min/max of all data associated with it.
Possible ways to resolve this:

In each "Axis" panel, change the "column" dropdown control to a multiselect box.
Add an "axis" dropdown to each column in the "Data" panel.

Of these, I think I prefer the latter. It'll also make it easier to specify a default axis in the config file, meaning the "ST" design will be easier to create.


transform over predict

When we consider sklearn style, transform and fit_transform seem to be better terms (ex: used for PCA).

After looking at the description, I agree with this.
I generally dislike rehashing decisions we have made without significant reason, because that opens the door to scrupulosity and slows down the development process. I'm not saying we do this now, but I want to bring this point up now so that we don't fall into a pit.
I think fit/predict works fine, but fit/transform is more appropriate given the sklearn package. I'm okay with either, but I default to leaving it as is.

I think transform better describes the operation and is more obvious for the user.

I think that settles it then. Let's do transform.


Plugin Fix + some additions

Fixed Blockbench plugin (missing ] at the end of the script)
Added outlines for doors

outline takes up both upper and lower halves, like on bedrock

Added outlines for signs

fills entire sign properly & rotates

Added outlines for Dragon Head
Added outlines for sloped rails

some of these changes might be super subjective btw (like the rail or sign changes), so just keep that in mind if you decide to merge

 Thank you for your submission! We really appreciate it. Like many open source projects, we ask that you all sign our Contributor License Agreement before we can accept your contribution.0 out of 2 committers have signed the CLA.:x: ThatGravyBoat:x: MerpisMeYou have signed the CLA already but the status is still pending? Let us recheck it.

@MerpisMe can you sign the CLA and then I can accept this PR also just for future reference the plugin is now on the official blockbench repo

@MerpisMe can you sign the CLA and then I can accept this PR

done!

also just for future reference the plugin is now on the official blockbench repo

oh- alright, good to know :)


plan for policy in wasm

Istio mixer v2 is based on wasm loaded as envoy filters. Make a plan for this.

Is there any news about wasm envoy filter?


Cython build error, due to bad cache?

Got this error on my home PC after pulling and running ubuntu_setup.sh after a few weeks. Not sure exactly what commit it was on before that.
Looks like there's some dependency not setup properly in our SConscripts. @maxime-desroches is this the same as what you were seeing in CI?
build_error.txt
common/transformations/transformations.cpp:3841:13: error: use of undeclared identifier 'PyDataType_FLAGS'
 3841 |   __pyx_r = PyDataType_FLAGS(__pyx_v_self);
      |             ^
common/transformations/transformations.cpp:3875:13: error: use of undeclared identifier 'PyArray_MultiIter_NUMITER'
 3875 |   __pyx_r = PyArray_MultiIter_NUMITER(__pyx_v_self);
      |             ^
common/transformations/transformations.cpp:3909:13: error: use of undeclared identifier 'PyArray_MultiIter_SIZE'; did you mean 'PyArrayMultiIterObject'?
 3909 |   __pyx_r = PyArray_MultiIter_SIZE(__pyx_v_self);
      |             ^
.venv/lib/python3.12/site-packages/numpy/core/include/numpy/ndarraytypes.h:1263:3: note: 'PyArrayMultiIterObject' declared here
 1263 | } PyArrayMultiIterObject;
      |   ^
common/transformations/transformations.cpp:3943:13: error: use of undeclared identifier 'PyArray_MultiIter_INDEX'
 3943 |   __pyx_r = PyArray_MultiIter_INDEX(__pyx_v_self);
      |             ^
common/transformations/transformations.cpp:3977:13: error: use of undeclared identifier 'PyArray_MultiIter_NDIM'
 3977 |   __pyx_r = PyArray_MultiIter_NDIM(__pyx_v_self);
      |             ^
common/transformations/transformations.cpp:4011:13: error: use of undeclared identifier 'PyArray_MultiIter_DIMS'
 4011 |   __pyx_r = PyArray_MultiIter_DIMS(__pyx_v_self);
      |             ^
common/transformations/transformations.cpp:4045:13: error: use of undeclared identifier 'PyArray_MultiIter_ITERS'; did you mean 'PyArrayMultiIterObject'?
 4045 |   __pyx_r = PyArray_MultiIter_ITERS(__pyx_v_self);
      |             ^
.venv/lib/python3.12/site-packages/numpy/core/include/numpy/ndarraytypes.h:1263:3: note: 'PyArrayMultiIterObject' declared here
 1263 | } PyArrayMultiIterObject;
      |   ^
12 errors generated.
scons: *** [msgq_repo/msgq/visionipc/visionipc_pyx.o] Error 1

Needed to do a full clean to recover.

Yes, same type of errors as https://github.com/commaai/openpilot/actions/runs/9670296819/job/26679640212#step:11:283

Downgrade numpy to 1.x

@maxime-desroches is this fixed?

Yes, the cython builds are now dependent on the numpy version


ExistingAttributesPersistence model collisions

All models use $ExistingAttributesPersistence_tableColumns defined as class variable in Mortimer\Poignant\Model to store their existing column names. When the model eloquent.saving event handler tries to access the variable later, $ExistingAttributesPersistence_tableColumns contains the columns of the last model that called bootExistingAttributesPersistence.
As a result, ExistingAttributesPersistence trait deletes all model attributes in most cases.

Good catch! Thanx for the smart fix also.


10x faster AtomicBool implementation

It's very straightforward to use actual atomic instead of mutex. And it's paying off in nearly 10x margin in terms of performance
goos: linux
goarch: amd64
pkg: github.com/junegunn/fzf/src/util
BenchmarkMutex_Set-4            20000000                69.4 ns/op
BenchmarkMutex_Get-4            20000000                68.9 ns/op
BenchmarkAtomic_Set-4           200000000                7.66 ns/op
BenchmarkAtomic_Get-4<PHONE_NUMBER>               0.42 ns/op

Also, AtomicBool is used only here https://github.com/junegunn/fzf/blob/master/src/matcher.go#L150 for, basically, context cancellation. I plan later to use proper methods for that task. As explained here https://blog.golang.org/context, for example

I'm think that code in latest commit is much better and shouldn't get any complains from linter. Please review

Merged, thanks.


Add new option "failOnWarning".

This option causes the maven-compiler-plugin to treat warnings as errors
and fail accordingly.
Simply adds "-Werror" to the compiler command line. It may be nice to
add this to the plexus-compiler-api proper but as the sonatype repo only
has tags to 2.4 and the current plugin references 2.6 (and I have no
idea where that comes from), I went the easy route. Happy to refactor
it if wanted.

Is this PR still relevant. If so, is there any reason not to pass to compilerArgs?

Hm. No comment for almost a year and then one comment and closed within 8 days.
Yes, it is still relevant. As an option, I can expose this as a property with
...
   <failOnWarning>${failWarningSwitch}</failOnWarning>
...

so this can be overridden from the command line. If it gets added to , there is no way to control this dynamically from the command line.

plexus compiler api 2.8.1 supports failOnWarning directly. I may simply redo this patch to leverage this.

Let's open, Can you reopen, can you provide a PR?


Make docker-gc work as non-root

Description
docker-gc can only be run as root because it wants to write its process ID into /var/run/, which writable by root only. This was added in #39.
How to reproduce
Run docker-gc as non-root user.
What do you expect
docker-gc works if the current user is part of the docker group.
What happened instead
$ LC_ALL=C docker-gc --help
/usr/sbin/docker-gc: line 67: /var/run/dockergc: Permission denied

Software:

docker-gc: git version 89b5248a5c9b43b7ad16bfcfa499c04c5ee250b8
ubuntu 16.04
docker 1.12.1

Launch it like this PID_DIR="$XDG_RUNTIME_DIR" docker-gc

If I do that, I get this:
-bash: docker-gc: command not found
Non-root users do not usually have /usr/sbin in the PATH. To be really non-root-friendly, the executable should be in /usr/bin.


Keep drag within container?

Hi there, was hoping to find out if there is a way to keep my dragable item within its container, essentially the equivalent to this: http://api.jqueryui.com/sortable/#option-containment? I couldn't see anything in the documentation, apologies if I missed it.
Thanks,
James :)

Hey @jam3sn,
so you want the item to stop at the boundary of the parent? This is currently not possible.
I would love to accept a PR though, if you send one in.


Slack would like to help contribute and maintain this package

Hi @mjpearson, I saw the note in your README about how you could use some help and I'd like to discuss what you have in mind. Thanks for carrying this package forward for as long as you have. It's clear that you put a lot of care and work into it.
My name is Ankur and I work on developer tools at Slack. Our team builds and maintains packages and tools in collaboration with the community. OAuth plays a pretty critical role in how developers deal with user identity in their app (Sign in with Slack) and handle installation (Add to Slack) - and we think this package is important to how developers implement these features.
I've got a little bit of experience working with passport, OAuth, and Slack's APIs. I maintain another implementation of a Slack passport strategy. I'd be excited to help, in almost any way that you are open to. There's also an opportunity to deliver the next generation of this package in a way that supports workspace apps as a first class citizen.
Here's what my vision looks like: I'd like to triage the current open issues and pull requests so we make sure we know what users of this package need. Next, we create a plan to merge my implementation with this one. Maybe we write some tests. Finally, if you're comfortable with it, we can take lead by moving this repo over to the slackapi github organization and we could gain publish access to the passport-slack package on npm.
If you want to chat outside of this issue, feel free to email me<EMAIL_ADDRESS>

Hi Ankur, sorry for the delay, i'll get to this soon. Which owner on NPM
should I transfer it to, is that @slackapi also or @slack?
regards
mike p
On Mon, Sep 10, 2018 at 7:22 PM Ankur Oberoi<EMAIL_ADDRESS>wrote:

@mjpearson https://github.com/mjpearson my work email is
<EMAIL_ADDRESS>we don't use slack.com for employee email addresses
(origin separation). if you'd like to verify who i am, feel free to take a
look at my github profile and org memberships: github.com/aoberoi.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/mjpearson/passport-slack/issues/38#issuecomment-420093246,
or mute the thread
https://github.com/notifications/unsubscribe-auth/AAIg2AYv56CMVVpR2EJAJPvbsYlEuMPrks5uZvRGgaJpZM4WiUbw
.

--
http://www.linkedin.com/in/mjpearson
+1<PHONE_NUMBER>

For NPM: I don’t think we should transfer the package to anyone, just add myself as an owner on the package (my npm username is aoberoi). This way existing users of this package will not break. I’m happy to keep you on, but if you prefer to be removed, that’s fine with me!
For GitHub: we could transfer this repo to the @slackapi org. Before we do this, I’d like to make several updates so that the status of the project is clear. For now, you can add me as a collaborator on the repo and when everything is ready I can transfer it to the org.

Ok thanks, I'll get to it tomorrow.
On Thu, Sep 13, 2018, 4:45 PM Ankur Oberoi<EMAIL_ADDRESS>wrote:

For NPM: I don’t think we should transfer the package to anyone, just add
myself as an owner on the package (my npm username is aoberoi). This way
existing users of this package will not break. I’m happy to keep you on,
but if you prefer to be removed, that’s fine with me!
For GitHub: we could transfer this repo to the @slackapi
https://github.com/slackapi org. Before we do this, I’d like to make
several updates so that the status of the project is clear. For now, you
can add me as a collaborator on the repo and when everything is ready I can
transfer it to the org.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/mjpearson/passport-slack/issues/38#issuecomment-421147304,
or mute the thread
https://github.com/notifications/unsubscribe-auth/AAIg2I-hmDCTcSE4ZRRXyk7ZDHsaqwhKks5uasQCgaJpZM4WiUbw
.

Hi, I added you to the repo. Will look out for PR's, let me know when it's
good to be delagated in npm
-mike
On Thu, Sep 13, 2018, 6:42 PM Michael Pearson<EMAIL_ADDRESS>wrote:

Ok thanks, I'll get to it tomorrow.
On Thu, Sep 13, 2018, 4:45 PM Ankur Oberoi<EMAIL_ADDRESS>wrote:

For NPM: I don’t think we should transfer the package to anyone, just add
myself as an owner on the package (my npm username is aoberoi). This way
existing users of this package will not break. I’m happy to keep you on,
but if you prefer to be removed, that’s fine with me!
For GitHub: we could transfer this repo to the @slackapi
https://github.com/slackapi org. Before we do this, I’d like to make
several updates so that the status of the project is clear. For now, you
can add me as a collaborator on the repo and when everything is ready I can
transfer it to the org.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/mjpearson/passport-slack/issues/38#issuecomment-421147304,
or mute the thread
https://github.com/notifications/unsubscribe-auth/AAIg2I-hmDCTcSE4ZRRXyk7ZDHsaqwhKks5uasQCgaJpZM4WiUbw
.

No worries, thanks I added you as owner.
I know its only a few lines of code but thanks for showing it love and
finding it a proper home.
-mike
On Mon, Sep 17, 2018 at 1:24 AM Ankur Oberoi<EMAIL_ADDRESS>wrote:

You can add me as an owner now. This command line command should do it. npm
owner add aoberoi passport-slack.
I can land the PRs over the next couple days, and when everything is all
set I’ll need your help once more to move the repo on GutHub under the
@slackapi https://github.com/slackapi Org.
Thanks!
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+1<PHONE_NUMBER>


Make sure nav bar is visible in every view

[ ] Nav bar should be visible in all views: Timeline, Goals, Reports, Settings
[ ] Nav bar paddings from left and right should be: 16pt from right and 8pt from left
[ ] Superday text should be 12pt from the Superday icon
[ ] Show Calendar icon only in Timeline view (Not in Goals or Reports).
[ ] Calendar icon colour should be #B7B9BB

About second point: it should be 16pt from left and 8pt from right 🙈

Calendar icon colour should be #B7B9BB
@pe0ny please update the one in Zeplin

@georgmay

It's available in here: https://zpl.io/bPqLQJD

Icon is not a font, it's this circle thingy (so distance between icon/logo and text should be 12pt):
Sorry, for not making that one clear.

@pe0ny
Superday text should be 12pt from the Superday icon
Ah you meant the distance between the text and icon? Gotcha.


Issue: can i define an agent using a llm chain or a conversationcain as a too?

Issue you'd like to raise.
can i define an agent using a llm chain or a conversationcain as a too?
Suggestion:
No response

🤖
Hello @xasxin! Nice to see you back here. Hope you're doing well.
Yes, you can define an agent using an LLM (Language Learning Model) chain in LangChain. The ChatAgent class in LangChain has a class method from_llm_and_tools which constructs an agent from an LLM and tools. This method validates the tools, creates a prompt, and then creates an LLMChain with the LLM, prompt, and callback manager. The LLMChain is then used to create the agent.
Here is the relevant code:
@classmethod
def from_llm_and_tools(
    cls,
    llm: BaseLanguageModel,
    tools: Sequence[BaseTool],
    callback_manager: Optional[BaseCallbackManager] = None,
    output_parser: Optional[AgentOutputParser] = None,
    system_message_prefix: str = SYSTEM_MESSAGE_PREFIX,
    system_message_suffix: str = SYSTEM_MESSAGE_SUFFIX,
    human_message: str = HUMAN_MESSAGE,
    format_instructions: str = FORMAT_INSTRUCTIONS,
    input_variables: Optional[List[str]] = None,
    **kwargs: Any,
) -> Agent:
    """Construct an agent from an LLM and tools."""
    cls._validate_tools(tools)
    prompt = cls.create_prompt(
        tools,
        system_message_prefix=system_message_prefix,
        system_message_suffix=system_message_suffix,
        human_message=human_message,
        format_instructions=format_instructions,
        input_variables=input_variables,
    )
    llm_chain = LLMChain(
        llm=llm,
        prompt=prompt,
        callback_manager=callback_manager,
    )
    tool_names = [tool.name for tool in tools]
    _output_parser = output_parser or cls._get_default_output_parser()
    return cls(
        llm_chain=llm_chain,
        allowed_tools=tool_names,
        output_parser=_output_parser,
        **kwargs,
    )

This method is part of the ChatAgent class in the libs/langchain/langchain/agents/chat/base.py file in the LangChain repository.
However, there is no mention of a "conversation chain" in the provided context, so it's unclear whether such a concept exists in LangChain or how it would be used to define an agent. If you could provide more details about what you mean by a "conversation chain", I might be able to provide a more accurate answer.
As for the LLMChain, it is a class in the LangChain codebase designed to run queries against Language Models (LLMs). It takes a prompt and a language model as inputs and generates responses based on the input prompt. You can find more details about the LLMChain class in the libs/langchain/langchain/chains/llm.py file in the LangChain repository.
I hope this helps! If you have any more questions, feel free to ask.
Sources

Issue: Can an LLM be used as a tool?
libs/langchain/langchain/agents/chat/base.py
libs/langchain/langchain/chains/llm.py

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A llm chain can take variables other than user_input and put them in a custom prompt template. Can I create an agent based on that so the agent can take multiple variables rather than a user input?

🤖
To modify the from_llm_and_tools method in the ChatAgent class to accept multiple variables, you can add a new parameter to the method that accepts a dictionary of variables. Then, you can modify the create_prompt method call within from_llm_and_tools to pass these variables to the input_variables parameter. Here's how you can do it:
@classmethod
def from_llm_and_tools(
    cls,
    llm: BaseLanguageModel,
    tools: Sequence[BaseTool],
    callback_manager: Optional[BaseCallbackManager] = None,
    output_parser: Optional[AgentOutputParser] = None,
    system_message_prefix: str = SYSTEM_MESSAGE_PREFIX,
    system_message_suffix: str = SYSTEM_MESSAGE_SUFFIX,
    human_message: str = HUMAN_MESSAGE,
    format_instructions: str = FORMAT_INSTRUCTIONS,
    input_variables: Optional[List[str]] = None,
    additional_variables: Optional[dict] = None,  # New parameter for additional variables
    **kwargs: Any,
) -> Agent:
    """Construct an agent from an LLM and tools."""
    cls._validate_tools(tools)
    if additional_variables is not None:  # If additional variables are provided
        if input_variables is None:  # If input_variables is None, initialize it as an empty list
            input_variables = []
        input_variables.extend(additional_variables.keys())  # Add the keys of the additional variables to input_variables
    prompt = cls.create_prompt(
        tools,
        system_message_prefix=system_message_prefix,
        system_message_suffix=system_message_suffix,
        human_message=human_message,
        format_instructions=format_instructions,
        input_variables=input_variables,
    )
    llm_chain = LLMChain(
        llm=llm,
        prompt=prompt,
        callback_manager=callback_manager,
    )
    tool_names = [tool.name for tool in tools]
    _output_parser = output_parser or cls._get_default_output_parser()
    return cls(
        llm_chain=llm_chain,
        allowed_tools=tool_names,
        output_parser=_output_parser,
        **kwargs,
    )

In this modification, the additional_variables parameter is a dictionary where the keys are the names of the variables and the values are the values of the variables. The keys of this dictionary are added to the input_variables list, which is then passed to the create_prompt method. This allows the ChatAgent to accept multiple variables, not just user input.
The input_variables parameter in the ChatPromptTemplate class of the LangChain Python framework is a list of input variables in template messages. It is used for validation and to determine which variables are needed to format the template messages in the chat template.
In the ChatPromptTemplate class, the input_variables parameter is used in several methods:

In the __add__ method, it is used to combine two prompt templates.
In the validate_input_variables method, it is used to validate the input variables. If input_variables is not set, it will be set to the union of all input variables in the messages.
In the from_messages method, it is used to automatically infer input variables from messages.
In the format_messages method, it is used to format the chat template into a list of finalized messages.
In the partial method, it is used to get a new ChatPromptTemplate with some input variables already filled in.

Here is the code snippet that shows the usage of input_variables:
class ChatPromptTemplate(BaseChatPromptTemplate):
    """A prompt template for chat models."""

    input_variables: List[str]
    """List of input variables in template messages. Used for validation."""

    @root_validator(pre=True)
    def validate_input_variables(cls, values: dict) -> dict:
        """Validate input variables.

        If input_variables is not set, it will be set to the union of
        all input variables in the messages.

        Args:
            values: values to validate.
Returns:
            Validated values.
        """
        messages = values["messages"]
        input_vars = set()
        for message in messages:
            if isinstance(message, (BaseMessagePromptTemplate, BaseChatPromptTemplate)):
                input_vars.update(message.input_variables)
        if "input_variables" in values and values.get("validate_template"):
            if input_vars != set(values["input_variables"]):
                raise ValueError(
                    "Got mismatched input_variables. "
                    f"Expected: {input_vars}. "
                    f"Got: {values['input_variables']}"
                )
        else:
            values["input_variables"] = sorted(input_vars)
        return values

I hope this helps! If you have any more questions, feel free to ask.
Sources

Problem in understanding Langchain SequentialChain with prompts  and output_variables
libs/langchain/langchain/agents/structured_chat/base.py
libs/core/langchain_core/prompts/chat.py

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.
Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.
If you want to continue the conversation, start your reply with @dosu-bot.

how can i initialize the agent?

🤖
Based on the modifications you've made to the from_llm_and_tools method in the ChatAgent class, you can initialize the agent with additional variables as follows:
from langchain_core.language_models import YourLanguageModel
from langchain.tools import YourTool
from langchain.callbacks.base import YourCallbackManager
from langchain.agents.structured_chat.output_parser import YourOutputParser

# Initialize your language model, tools, callback manager, and output parser
llm = YourLanguageModel()
tools = [YourTool()]
callback_manager = YourCallbackManager()
output_parser = YourOutputParser()

# Define your additional variables
additional_variables = {
    "variable1": "value1",
    "variable2": "value2",
    # Add more variables as needed
}

# Initialize the agent
agent = StructuredChatAgent.from_llm_and_tools(
    llm=llm,
    tools=tools,
    callback_manager=callback_manager,
    output_parser=output_parser,
    prefix="Your Prefix",
    suffix="Your Suffix",
    human_message_template="Your Human Message Template",
    format_instructions="Your Format Instructions",
    input_variables=["Your Input Variables"],
    additional_variables=additional_variables,  # Pass the additional variables here
)

Please replace YourLanguageModel, YourTool, YourCallbackManager, YourOutputParser, and other placeholders with your actual implementations. The additional_variables parameter is used to pass any extra variables that you want to include in the prompt creation process. These variables are added to the input_variables list, which is then passed to the create_prompt method.
As for the purpose of the additional_variables parameter, it seems to be a way to extend the functionality of the from_llm_and_tools method by allowing it to accept more variables for the prompt creation process. However, without more context or documentation, it's hard to say for sure. If you could provide more information about how you intend to use these additional variables, I might be able to give a more precise answer.
Sources

Separate LLMs for Tool Selection and Chatting in Agent Initialization
libs/langchain/langchain/agents/structured_chat/base.py

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Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.
If you want to continue the conversation, start your reply with @dosu-bot.

Thank you very much. I decided not only to use a llm chain but any llm models. I would like to create an agent with a custom prompt template that takes multiple variables whenever user makes input. like agent_executor.invoke({"input": "what is my name", "example":example, "user_profile":user_profile})
the custom prompt template looks like """
user input: {input}
example: {example}
user_profile : {user_profile}
"""

🤖
To create an agent in the LangChain Python framework that uses a custom prompt template and can handle multiple variables such as "input", "example", and "user_profile", you need to modify the CustomPromptTemplate class and the format_messages method within it.
First, add "example" and "user_profile" to the input_variables list when initializing the CustomPromptTemplate instance:
prompt = CustomPromptTemplate(
    template=template,
    tools=tools,
    input_variables=["input", "intermediate_steps", "example", "user_profile"]
)

Then, modify the format_messages method to handle these new variables:
def format_messages(self, **kwargs) -> str:
    # Get the intermediate steps (AgentAction, Observation tuples)
    # Format them in a particular way
    intermediate_steps = kwargs.pop("intermediate_steps")
    thoughts = ""
    for action, observation in intermediate_steps:
        thoughts += action.log
        thoughts += f"\nObservation: {observation}\nThought: "
    # Set the agent_scratchpad variable to that value
    kwargs["agent_scratchpad"] = thoughts
    # Create a tools variable from the list of tools provided
    kwargs["tools"] = "\n".join([f"{tool.name}: {tool.description}" for tool in self.tools])
    # Create a list of tool names for the tools provided
    kwargs["tool_names"] = ", ".join([tool.name for tool in self.tools])
    # Add the new variables to the template
    kwargs["example"] = kwargs.get("example", "")
    kwargs["user_profile"] = kwargs.get("user_profile", "")
    formatted = self.template.format(**kwargs)
    return [HumanMessage(content=formatted)]

Finally, modify the template string to include placeholders for the new variables:
template = """Complete the objective as best you can. You have access to the following tools:

{tools}

Use the following format:

Question: the input question you must answer
Example: {example}
User Profile: {user_profile}
Thought: you should always think about what to do
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question

These were previous tasks you completed:

Begin!

Question: {input}
{agent_scratchpad}"""

This way, when a user makes an input, the agent will be able to handle the "input", "example", and "user_profile" variables. You can find more information about this in the LangChain documentation.
As for the ChatAgent class, it has been updated to StructuredChatAgent and has several methods and properties that you might find useful. You can find more details about this class in the LangChain codebase.
Sources

docs/docs/modules/agents/how_to/custom_llm_chat_agent.mdx
libs/langchain/langchain/agents/chat/base.py
libs/langchain/langchain/agents/structured_chat/base.py

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.
Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.
If you want to continue the conversation, start your reply with @dosu-bot.

Look great! can I add memory to it?


problem running it

tried it...my r session just broke on me.

Could you please supply details of:

R version
OS
Model you are trying to run
details of function call which lead to error.

Apologies, not sure what happened before. it runs now but it is very slow. Details below.
library(rllama)

# Initialise llama.cpp with built-in model
ctx <- llama_init("Llama/ggml-vic7b-q5_0.bin")
system.time(llama(ctx, prompt = "The apple said to the banana", n = 10))
, "You're not as smart as I   user  system elapsed
  95.06    0.00   23.83

My system
R version 4.2.2 (2022-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)

System is: I9, 32


python 2.6.5 fails to install on Ubuntu 11.10

running build
running build_ext
Traceback (most recent call last):
File "./setup.py", line 1942, in
main()
File "./setup.py", line 1937, in main
'Lib/smtpd.py']
File "/.pythonbrew/build/Python-2.6.5/Lib/distutils/core.py", line 152, in setup     dist.run_commands()   File "/.pythonbrew/build/Python-2.6.5/Lib/distutils/dist.py", line 975, in run_commands
self.run_command(cmd)
File "/.pythonbrew/build/Python-2.6.5/Lib/distutils/dist.py", line 995, in run_command     cmd_obj.run()   File "/.pythonbrew/build/Python-2.6.5/Lib/distutils/command/build.py", line 134, in run
self.run_command(cmd_name)
File "/.pythonbrew/build/Python-2.6.5/Lib/distutils/cmd.py", line 333, in run_command     self.distribution.run_command(command)   File "/.pythonbrew/build/Python-2.6.5/Lib/distutils/dist.py", line 995, in run_command
cmd_obj.run()
File "`/.pythonbrew/build/Python-2.6.5/Lib/distutils/command/build_ext.py", line 340, in run
self.build_extensions()
File "./setup.py", line 103, in build_extensions
missing = self.detect_modules()
File "./setup.py", line 337, in detect_modules
self.add_multiarch_paths()
File "./setup.py", line 314, in add_multiarch_paths
if not find_executable('dpkg-architecture'):
NameError: global name 'find_executable' is not defined
make: *** [sharedmods] Error 1
I think that the problem is in the patch because the manual installation works

It looks that installing python require the package included "dpkg-architecture".
So please try installing the package of "dpkg-dev".
"dpkg-dev" might be including "dpkg-architecture".

dpkg-architecture is provided by dpkg-dev, but that isn't what is causing this error. I'm facing this same issue. After having spent an hour trying to figure out what was going on, I've finally discovered the cause.
When pythonbrew installs a python distribution, it will do the following:

Download the tarball
Extract the tarball
Apply patches
Configure
Make
... etc

This error doesn't manifest until after everything is done and the setup.py is run, but it comes back to when the file is patched in step 3.
The patch that is being applied to 2.6.5 is all/common/patch-setup.py.diff. If you examine this patch, you will note that although it makes use of "find_executable" it does not explicitly import the function.
If you examine another patch, all/python25/patch-setup.py.diff, you will note that "from distutils.spawn import find_executable" is provided at the top. It is this import that is not being patched for 2.6.5 that is causing this error with setup.


[org-node] Bug: Failed to send update refs request to client: receive failed

While testing rad subscribe, noticing error when attempting to send req requests to client, exiting with a receive failed.
Also appears maybe the monorepo project I have could be corrupted? e.g. panics on reference target must exist.
Possibly due to changing location of org-node socket.
Nov 19 07:07:39.717 DEBUG org-node: Signed refs for rad:git:hnrkbtw9t1of4ykjy6er4qqwxtc54k9943eto updated: heads={OneLevel("master"): Oid(6841688e4f5b5f449ed99f8a0a4e33ff34535eb1)} at=2503fa2014ae506ec778966d884a773aedee260e
Nov 19 07:07:39.719 DEBUG org-node: Setting ref "refs/namespaces/hnrkbtw9t1of4ykjy6er4qqwxtc54k9943eto/HEAD" -> "refs/namespaces/hnrkbtw9t1of4ykjy6er4qqwxtc54k9943eto/refs/heads/master"
thread 'tokio-runtime-worker' panicked at 'reference target must exist', org-node/src/client.rs:590:46
note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace
Nov 19 07:07:39.720 ERROR org-node: Failed to send update refs request to client: receive failed
Nov 19 07:07:39.720  INFO org-node: Exiting..

@cloudhead any chance this would be due to using an org with the address of 0x0000000000000000000000000000000000000001; I know we did that to avoid tracking other projects, but not sure if that would cause an issue here.
Other thoughts, maybe the state of the repo is stale on my end?
Let's sync next week when you're available. Will make some more progress on rad subscribe and ssh signing implementations and try to debug this in the process.

Pretty sure this is due to a divergent branch between upstream and local repo, but we probably need to handle this error a little more gracefully than calling .expect()

Pretty sure this is due to a divergent branch between upstream and local repo, but we probably need to handle this error a little more gracefully than calling .expect()

Hmm, actually I am not sure this is the issue. It might be, but it seems there may be another issue at play. @cloudhead if you have time tomorrow let's try to sync and walk through it for sanity check purposes.
Also, this is occurring in the UpdateRefs handler, and not the set head method.
The bizarre thing is that when I look at the mono repo git tree, the ref appears to be there, but when attempting to set the symbolic reference, it errors, suggesting the target does not exist. Possibly corrupted mono-repo?

As far as I can tell, this is due to a replication bug: https://github.com/radicle-dev/radicle-link/issues/782
I don't think it should happen in normal scenarios, ie. if things are working correctly, though I guess it's possible if the server shuts down abruptly in the middle of replication, so we need a way to handle that.

I'm encountering the same issue in my local setup:
Nov 25 20:56:21.982  INFO org-node: Updating refs
Nov 25 20:56:21.983  INFO org-node: updating refs for urn: Urn { id: Oid(02c1f6dbdbadd0d269dd91ab9dd38080bb08989d), path: None }
thread 'tokio-runtime-worker' panicked at 'reference target must exist', org-node/src/client.rs:604:46
stack backtrace:
   0: rust_begin_unwind
             at /rustc/a85f584aebd9b08314bf30b9adc17b4a752143e5/library/std/src/panicking.rs:515:5
   1: core::panicking::panic_fmt
             at /rustc/a85f584aebd9b08314bf30b9adc17b4a752143e5/library/core/src/panicking.rs:92:14
   2: core::option::expect_failed
             at /rustc/a85f584aebd9b08314bf30b9adc17b4a752143e5/library/core/src/option.rs:1243:5
   3: core::option::Option<T>::expect
             at /rustc/a85f584aebd9b08314bf30b9adc17b4a752143e5/library/core/src/option.rs:351:21
   4: radicle_org_node::client::Client::handle_request::{{closure}}
             at ./org-node/src/client.rs:604:27
   5: <core::future::from_generator::GenFuture<T> as core::future::future::Future>::poll
             at /rustc/a85f584aebd9b08314bf30b9adc17b4a752143e5/library/core/src/future/mod.rs:80:19
   6: radicle_org_node::client::Client::run::{{closure}}::{{closure}}
             at ./org-node/src/client.rs:289:47
   7: <core::future::from_generator::GenFuture<T> as core::future::future::Future>::poll
             at /rustc/a85f584aebd9b08314bf30b9adc17b4a752143e5/library/core/src/future/mod.rs:80:19
   8: tokio::runtime::task::core::CoreStage<T>::poll::{{closure}}
             at /home/user/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.12.0/src/runtime/task/core.rs:161:17
   9: tokio::loom::std::unsafe_cell::UnsafeCell<T>::with_mut
             at /home/user/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.12.0/src/loom/std/unsafe_cell.rs:14:9
  10: tokio::runtime::task::core::CoreStage<T>::poll
             at /home/user/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.12.0/src/runtime/task/core.rs:151:13
  11: tokio::runtime::task::harness::poll_future::{{closure}}
             at /home/user/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.12.0/src/runtime/task/harness.rs:461:19
  12: <std::panic::AssertUnwindSafe<F> as core::ops::function::FnOnce<()>>::call_once
             at /rustc/a85f584aebd9b08314bf30b9adc17b4a752143e5/library/std/src/panic.rs:347:9
  13: std::panicking::try::do_call
             at /rustc/a85f584aebd9b08314bf30b9adc17b4a752143e5/library/std/src/panicking.rs:401:40
  14: __rust_try
  15: std::panicking::try
             at /rustc/a85f584aebd9b08314bf30b9adc17b4a752143e5/library/std/src/panicking.rs:365:19
  16: std::panic::catch_unwind
             at /rustc/a85f584aebd9b08314bf30b9adc17b4a752143e5/library/std/src/panic.rs:434:14
  17: tokio::runtime::task::harness::poll_future
             at /home/user/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.12.0/src/runtime/task/harness.rs:449:18
  18: tokio::runtime::task::harness::Harness<T,S>::poll_inner
             at /home/user/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.12.0/src/runtime/task/harness.rs:98:27
  19: tokio::runtime::task::harness::Harness<T,S>::poll
             at /home/user/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.12.0/src/runtime/task/harness.rs:53:15
  20: tokio::runtime::task::raw::poll
             at /home/user/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.12.0/src/runtime/task/raw.rs:113:5
  21: tokio::runtime::task::raw::RawTask::poll
             at /home/user/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.12.0/src/runtime/task/raw.rs:70:18
  22: tokio::runtime::task::LocalNotified<S>::run
             at /home/user/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.12.0/src/runtime/task/mod.rs:343:9
  23: tokio::runtime::thread_pool::worker::Context::run_task::{{closure}}
             at /home/user/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.12.0/src/runtime/thread_pool/worker.rs:420:13
  24: tokio::coop::with_budget::{{closure}}
             at /home/user/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.12.0/src/coop.rs:106:9
  25: std::thread::local::LocalKey<T>::try_with
             at /rustc/a85f584aebd9b08314bf30b9adc17b4a752143e5/library/std/src/thread/local.rs:400:16
  26: std::thread::local::LocalKey<T>::with
             at /rustc/a85f584aebd9b08314bf30b9adc17b4a752143e5/library/std/src/thread/local.rs:376:9
  27: tokio::coop::with_budget
             at /home/user/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.12.0/src/coop.rs:99:5
  28: tokio::coop::budget
             at /home/user/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.12.0/src/coop.rs:76:5
  29: tokio::runtime::thread_pool::worker::Context::run_task
             at /home/user/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.12.0/src/runtime/thread_pool/worker.rs:419:9
  30: tokio::runtime::thread_pool::worker::Context::run
             at /home/user/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.12.0/src/runtime/thread_pool/worker.rs:386:24
  31: tokio::runtime::thread_pool::worker::run::{{closure}}
             at /home/user/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.12.0/src/runtime/thread_pool/worker.rs:371:17
  32: tokio::macros::scoped_tls::ScopedKey<T>::set
             at /home/user/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.12.0/src/macros/scoped_tls.rs:61:9
  33: tokio::runtime::thread_pool::worker::run
             at /home/user/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.12.0/src/runtime/thread_pool/worker.rs:368:5
  34: tokio::runtime::thread_pool::worker::Launch::launch::{{closure}}
             at /home/user/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.12.0/src/runtime/thread_pool/worker.rs:347:45
  35: <tokio::runtime::blocking::task::BlockingTask<T> as core::future::future::Future>::poll
             at /home/user/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.12.0/src/runtime/blocking/task.rs:42:21
  36: tokio::runtime::task::core::CoreStage<T>::poll::{{closure}}
             at /home/user/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.12.0/src/runtime/task/core.rs:161:17
  37: tokio::loom::std::unsafe_cell::UnsafeCell<T>::with_mut
             at /home/user/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.12.0/src/loom/std/unsafe_cell.rs:14:9
  38: tokio::runtime::task::core::CoreStage<T>::poll
             at /home/user/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.12.0/src/runtime/task/core.rs:151:13
  39: tokio::runtime::task::harness::poll_future::{{closure}}
             at /home/user/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.12.0/src/runtime/task/harness.rs:461:19
  40: <std::panic::AssertUnwindSafe<F> as core::ops::function::FnOnce<()>>::call_once
             at /rustc/a85f584aebd9b08314bf30b9adc17b4a752143e5/library/std/src/panic.rs:347:9
  41: std::panicking::try::do_call
             at /rustc/a85f584aebd9b08314bf30b9adc17b4a752143e5/library/std/src/panicking.rs:401:40
  42: __rust_try
  43: std::panicking::try
             at /rustc/a85f584aebd9b08314bf30b9adc17b4a752143e5/library/std/src/panicking.rs:365:19
  44: std::panic::catch_unwind
             at /rustc/a85f584aebd9b08314bf30b9adc17b4a752143e5/library/std/src/panic.rs:434:14
  45: tokio::runtime::task::harness::poll_future
             at /home/user/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.12.0/src/runtime/task/harness.rs:449:18
  46: tokio::runtime::task::harness::Harness<T,S>::poll_inner
             at /home/user/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.12.0/src/runtime/task/harness.rs:98:27
  47: tokio::runtime::task::harness::Harness<T,S>::poll
             at /home/user/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.12.0/src/runtime/task/harness.rs:53:15
  48: tokio::runtime::task::raw::poll
             at /home/user/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.12.0/src/runtime/task/raw.rs:113:5
  49: tokio::runtime::task::raw::RawTask::poll
             at /home/user/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.12.0/src/runtime/task/raw.rs:70:18
  50: tokio::runtime::task::UnownedTask<S>::run
             at /home/user/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.12.0/src/runtime/task/mod.rs:379:9
  51: tokio::runtime::blocking::pool::Inner::run
             at /home/user/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.12.0/src/runtime/blocking/pool.rs:265:17
  52: tokio::runtime::blocking::pool::Spawner::spawn_thread::{{closure}}
             at /home/user/.cargo/registry/src/github.com-1ecc6299db9ec823/tokio-1.12.0/src/runtime/blocking/pool.rs:245:17
note: Some details are omitted, run with `RUST_BACKTRACE=full` for a verbose backtrace.
Nov 25 20:56:26.037 ERROR org-node: Failed to send update refs request to client: receive failed

I'll see if we can be more graceful about this.

I'll see if we can be more graceful about this.

Let me know a good time to get a pair programming session together. It's going to need to be early morning my time, somewhere around 4-5am PST. Just send me a msg on discord.

I see some works on #77, let me know if there is anything I can assist with.

Closing as we haven't this pop up in a while.


[SPARK-48881][SQL] Some dynamic partitions can be compensated to specific partition values

What changes were proposed in this pull request?

When writing dynamic partitions, some dynamic partitions in InsertIntoHadoopFsRelationCommand can be compensated to specific partition values.
Why are the changes needed?

When executing the following SQL ：
INSERT OVERWRITE TABLE A PARTITION(event_day, event_type) SELECT id, event_day, event_type from B where event_day = '20240712'
before this pr staticPartitions would be empty in InsertIntoHadoopFsRelationCommand

after this pr staticPartitions would be specific partition values

The benefit is that when executing

in InsertIntoHadoopFsRelationCommand, it can greatly reduce the pressure on hive and improve the efficiency of task execution.
Does this PR introduce any user-facing change?

No
How was this patch tested?

manually test
Was this patch authored or co-authored using generative AI tooling?

No

I haven't reviewed the code changes in the pr yet, but:

The PR title should reflect the work done in the current PR as much as possible, the current title looks more like a Jira title
Please ensure the completeness of the PR description, and the following parts are also required to be filled in:

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

### Was this patch authored or co-authored using generative AI tooling?

In the PR description, it mentions in InsertIntoHadoopFsRelationCommand, it can greatly reduce the pressure on hive and improve the efficiency of task execution. Is this quantifiable?

@fusheng-rd Could you add some new test cases for this?

I haven't reviewed the code changes in the pr yet, but:

The PR title should reflect the work done in the current PR as much as possible, the current title looks more like a Jira title
Please ensure the completeness of the PR description, and the following parts are also required to be filled in:

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

### Was this patch authored or co-authored using generative AI tooling?

In the PR description, it mentions in InsertIntoHadoopFsRelationCommand, it can greatly reduce the pressure on hive and improve the efficiency of task execution. Is this quantifiable?

When a table named A with millions of partitions executes the following sql：
INSERT OVERWRITE TABLE A PARTITION(event_day, event_type) SELECT id, event_day, event_type from B where event_day = '20240712'

It will appear in hive that all partitions of table A are fetched at once, which is very likely to cause slow queries in hive metastore and even drag down the overall performance of Hive's metadata queries. At the same time, the job execution is very slow.
After this PR, only the specified partition and its sub-partitions are fetched, which takes milliseconds to seconds.

@fusheng-rd hmm... It seems that the way you merge the code is not quite right.

@fusheng-rd hmm... It seems that the way you merge the code is not quite right.
Yes, I am fixing that

@fusheng-rd Could you add some new test cases for this?

ok，done~

The idea LGTM. I think it's cleaner if the implementation is an optimizer rule that rewrites InsertIntoHadoopFsRelation command. We can look at the constraints of the input query, if we can infer an equality predicate against a partition column, we can fill the static partitions of the InsertIntoHadoopFsRelation command.

The idea LGTM. I think it's cleaner if the implementation is an optimizer rule that rewrites InsertIntoHadoopFsRelation command. We can look at the constraints of the input query, if we can infer an equality predicate against a partition column, we can fill the static partitions of the InsertIntoHadoopFsRelation command.

ok，I will implement it in the optimization rule

Please help review it when you have free time, thanks!  @ulysses-you
cc @cloud-fan


feat: Twilio Tool to send SMS

Adding a TwilioTool to send SMS messages.
Fixes #1445

@justindra going to close due to merge conflicts. If you'd still like to get this merged in, please open off of an up-to-date main branch, and I'll review. Thank you!


Adds more error information when either analysis or turbo-carto is not well formed

cc @rochoa

it needs that turbo-carto exposes the context as an object

I think we are ready to merge, don't you?


Added missing likely locales

Checklist

[x] At least one test case is included for this feature or bug fix.
[x] ReleaseNotes has added or is not needed.
[x] This PR has passed all of the test cases on Nodejs and Chrome Browser.
[ ] This PR has passed all of the test cases on QT/QML.
[ ] This is an API breaking change.
[ ] Requires a major version change.

Issue Resolved / Feature Added
Resolution
Links

@gouniLee view this PR with the "Hide whitespace changes" turned on or else it will be much larger than it should be. (I changed the testlocalematch.json from dos to unix line endings).
Also, add in any missing locales you find from your investigations and add unit tests for them.

@ehoogerbeets  I've added more locales to 'likelyLocalesAdditional.json` and unit test to verify them. Please review them.

The other branch for the getLikelyLocaleMinimal should go first, as there are conflicts with this branch and there are updates to the genlikelylocales that need to be merged in and the localematch.json regenerated with them.

@gouniLee please re-review the changes from fixing the merge conflicts.


unable to get windows 1709

Hello,
I am struggling to create a kubernetes hybrid cluster on windows server 2016 data center edition in AWS. Can you please help me with more elaborated steps of how to create the 1709 VM and then deploy kubernetes? Immediate response would be appreciated. Thanks.

Hi @sravs1993
I'm trying to do same thing, last weekend I have been working on another project from @ptylenda and forked it to: https://github.com/pablodav/kubernetes-for-windows/tree/feature/testing-win1709
Just didn't create pull request here yet because I haven't finished the deployment as I had to work on some other things last days.

Thanks @pablodav can you just help me with some documentation as how you could do it?

Hi @sravs1993
Sorry to respond late, but I was working on some other priorities at work.
What other documentation do you need? I think the readme is quiet well explained https://github.com/pablodav/kubernetes-for-windows#ansible-usage
I'm just trying it and will try to improve/fix anything once I have some findings and results.

Hi @sravs1993 , I have just created the first pull request with my changes #5 , it has big modifications and new instructions in the Readme.
Probably this could help you:
https://github.com/pablodav/kubernetes-for-windows#ansible-usage

Hi @sravs1993
I have something ready for a quickstart here:
https://github.com/pablodav/kubernetes-for-windows-quickstart
More comments here:
https://github.com/kubernetes-incubator/kubespray/issues/2889
could you test it?
Note, all this was updated to windows 1803.

I think it is ready to be closed


Add the ability to include templates in other templates

This creates the ability of re-using particular sections of documents across a whole set of documents.

Added in c801f70


auth: update auth

pre cursor to setting up tests for https://github.com/ollama/ollama/pull/5441
moved initialize keypair to auth
use initialize keypair implicitly when reading pubkey

Do we still need this if we can find away to avoid using auth for local file copies in https://github.com/ollama/ollama/pull/5441?


Verify user can make proposal

Is your feature request related to a problem? Please describe.
Our UI allows a user to specify any DAO address and make a proposal TX to it, which will fail. SputnikDAO contracts have a permissioned model for who can do what on the dao, specified in the DAO policy. We also don't check existence of a multicall instance deployed in the target multicall address.
Describe the solution you'd like

Fetch policy of the target DAO, find the current user's role and verify his role actually allows him to create a proposal
verify existence of a multicall instance deployed for that DAO

Describe alternatives you've considered
Using the Astro API for fetching user data is also an option, but for the moment it has few issues with being up-to-date, it also introduces trusted intermediaries

#18 (pr: #23) implements the following checks already:

(Dao address is valid)
Address refers to a sputnik dao
The dao has a multicall instance on the correspondent address
User has required proposal rights (FunctionCall:AddProposal, more info)


Remove explicit width from leading element in ListTile

This is (another) followup to ensure consistency between leading and trailing elements in ListTiles.
See related previous PRs:
https://github.com/flutter/flutter/pull/12221
https://github.com/flutter/flutter/pull/11858
Testing note: I think I am testing this well, but could also add more asserts that depend on the actual rendered width of the labels, instead of the (more definitively known) 16.0 width of the padding. I think it's probably preferable to depend only on the precisely set widths if possible to avoid breakages from e.g. text rendering changes.

Sorry, my relative inexperience w/ GitHub PRs is showing; only the last commit on this branch is relevant. I can restructure this so its on a branch with only commits related to this PR if needed.

You can rely on the text widths not changing, we run the tests with a special font where the ASCII characters all have exactly the same width as the font height.

I don't think we want to remove the leading width, it's intentionally there so that whether you have a leading widget or not, the labels are all aligned.
We could add a way to configure that, though I'm not sure what a good name for it would be...

I don't think that's what happens right now; the width there is on a container which is added to the ListTile only if the leading element was specified, so if there's no leading element passed then you won't get any extra spacing on the left.
Here's a screenshot that shows this in a Flutter app:

The ability to have no leading element is also needed to create a layout like this one from the Material spec:

Ah, right, I see.
Why would we remove it then? Wouldn't that lead to each widget with a differently-sized leading widget having a different left margin for the text?

@zzzev Can you elaborate on your desire to remove the explicit width on the leading widget?

@Hixie sorry for the delay. Removing the explicit width will allow the user to set a leading widget that's either narrower (probably not common) or wider (possibly more common, including e.g. in the test for this widget, which had text that overflowed the leading element's width) than 40dps.
I also realized when thinking about this that as is this will actually cause a regression for existing ListTiles which contain only an icon, which currently pick up some additional whitespace via this explicit width.
I think the ideal thing would be to add the padding for Icons to match the spec (which specifies that specifically icons and avatars should have the spacing that exists at head), and allow other Widgets to flow freely however they'd like, but I'm not sure that "looking into the leading" like that is necessarily a good idea.
Since this all was in the name of consistency between trailing and leading elements, and not a concrete use case that we need, I'm ok with just dropping this PR if you think the current behavior is good. If you think that users should be able to have leading Widgets with a non-40dp-width, I can update this to do the is-leading-an-Icon check I mention above, let me know what you'd prefer.

If this isn't a problem you are currently running into, I say we close it and wait for to solve it when we have a real problem so we can study it more carefully.
I have no objection to making the leading widget's size more customisable. There's various ways we could do this (e.g. ListTileTheme, an argument, etc).
Regarding the spacing not matching the material spec, I didn't quite understand what you meant but if you wouldn't mind filing a bug explaining the difference between what Flutter does and what the spec says we can definitely look into fixing this.

To clarify: the spacing for ListTiles at head does match the spec for leading elements that are Icon or CircleAvatar widgets, but the code in this PR would cause a regression for Icons (CircleAvatars need no extra padding, so they'd be the look the same with this code). The spec doesn't have any examples of leading elements that aren't Icons or CircleAvatars, so I think it's an area of somewhat undefined expectations.
I'll go ahead and close this PR for now and I'll revisit this if needed.


Fixing [$injector:unpr] Unknown provider: kbnAggTableDirectiveProvider error

After #14 code changes, I realized that Kibana angular decorators should run only in Kibana app scope (same error as #1, #2 and #3), so I wrote this quick fix (following the db7aba3 changes example).

Hey guys, any updates on merging this PR?

Hi.
Sry for the time to answer, my boy was born 2 weaks ago. i had no time to connect :p

@tcharlot-datasweet No problem! And congratulations on your newest family member! 🙂


Don't use mutable default argument

What spurred this?

Was just reading the code. Mutable default arguments are a common gotcha. The next time that function is called without a value for s3_params, it will have whatever the last value given to s3_params was. Perhaps that was intended? Maybe a bit too clever IMO, if so.
https://pythonconquerstheuniverse.wordpress.com/category/python-gotchas/

In general that's a good rule of thumb, but it doesn't apply here. The dictionary default argument is never mutated itself, it's just immediately splatted into the kwargs of a further function. In the example in that post, the function mutates the list. I'm not saying this isn't a good fix as it might catch future bugs if the code changes. Ping @mrocklin for a final opinion, as this is his code.

Yeah, that's true. Still struck me as potentially problematic down the road and maybe best to avoid before people start writing code against it.

Perhaps instead of changing the signature of the function, just do:
def read_block_from_s3(filename, offset, length, s3_params=None, delimiter=None,
                       compression=None):
     s3 = S3FileSystem(**(s3_params or {}))

Simple, still separates kwargs for S3FileSystem from others, and no mutable default.

That's a fairly standard idiom for this case.

Yeah, that would have been my first choice, but then I looked at the rest of the signatures.

Those are user facing, rather than just tasks in the graph. Using kwargs in functions in the graph results in (apply, func, (arg1, arg2, ...), {'key1': val1, ...}), rather than just (func, arg1, arg2, ...). Both work fine, but the second option is slightly simpler which is why many task functions don't take keyword arguments like that (s3_open in that same file, for example). Again, not a big deal, but there is a mild reason for things being the way they are.

Fair. Feel free to close the issue.

No, it's a valid point, sorry for being so contrarian here. I was just arguing for the simple solution posted above instead of a change in the signatures. If you don't want to update the PR here, I can take care of it separately.

Made your suggested change. Makes sense.
Still some context for me to understand, and a bit that's non-obvious (to me). Is there something to differentiate between the user-facing API and tasks in the graph here?

Thanks.

Still some context for me to understand, and a bit that's non-obvious (to me). Is there something to differentiate between the user-facing API and tasks in the graph here?

That's a fair point. In this case, the public functions are those here: https://github.com/jseabold/dask/blob/4292a35986ed57ac526cf78d6aa7d1a3c83416f3/dask/bytes/core.py. This file defines the implementations of those needed for the s3 protocol backend (so none of the functions in the s3 file are directly user facing). It appears this isn't documented anywhere though, will fix.


Build fails due to missing header

Hi, I'm trying to build fulgor on Fedora 36 and getting an issue related to a missing include for <memory> in the sshash submodule. This seems to be because sshash is pulling an old version of https://github.com/jermp/essentials which is missing the include statement in the essentials.hpp file. The newest version of the essentials submodule seems to have fixed this. I can work around this by adding the include manually but just letting you know.
Log from build
temaklin@xps13 build]$ make
[ 14%] Building CXX object CMakeFiles/sshash_static.dir/external/sshash/include/dictionary.cpp.o
In file included from /home/temaklin/software/fulgor/external/sshash/include/../external/pthash/include/encoders/compact_vector.hpp:6,
                 from /home/temaklin/software/fulgor/external/sshash/include/../external/pthash/include/encoders/encoders.hpp:3,
                 from /home/temaklin/software/fulgor/external/sshash/include/../external/pthash/include/pthash.hpp:3,
                 from /home/temaklin/software/fulgor/external/sshash/include/hash_util.hpp:3,
                 from /home/temaklin/software/fulgor/external/sshash/include/util.hpp:8,
                 from /home/temaklin/software/fulgor/external/sshash/include/dictionary.hpp:3,
                 from /home/temaklin/software/fulgor/external/sshash/include/dictionary.cpp:1:
/home/temaklin/software/fulgor/external/sshash/include/../external/pthash/include/encoders/../../external/essentials/include/essentials.hpp: In function ‘std::string essentials::demangle(const char*)’:
/home/temaklin/software/fulgor/external/sshash/include/../external/pthash/include/encoders/../../external/essentials/include/essentials.hpp:371:10: error: ‘unique_ptr’ is not a member of ‘std’
  371 |     std::unique_ptr<char, decltype(&std::free)> ptr(
      |          ^~~~~~~~~~
/home/temaklin/software/fulgor/external/sshash/include/../external/pthash/include/encoders/../../external/essentials/include/essentials.hpp:22:1: note: ‘std::unique_ptr’ is defined in header ‘<memory>’; did you forget to ‘#include <memory>’?
   21 | #include <cxxabi.h>  // for name demangling
  +++ |+#include <memory>
   22 | #endif
/home/temaklin/software/fulgor/external/sshash/include/../external/pthash/include/encoders/../../external/essentials/include/essentials.hpp:371:21: error: expected primary-expression before ‘char’
  371 |     std::unique_ptr<char, decltype(&std::free)> ptr(
      |                     ^~~~
/home/temaklin/software/fulgor/external/sshash/include/../external/pthash/include/encoders/../../external/essentials/include/essentials.hpp:373:12: error: ‘ptr’ was not declared in this scope
  373 |     return ptr.get();
      |            ^~~
/home/temaklin/software/fulgor/external/sshash/include/../external/pthash/include/encoders/../../external/essentials/include/essentials.hpp:369:12: warning: unused variable ‘len’ [-Wunused-variable]
  369 |     size_t len = 0;
      |            ^~~
/home/temaklin/software/fulgor/external/sshash/include/../external/pthash/include/encoders/../../external/essentials/include/essentials.hpp:370:9: warning: unused variable ‘status’ [-Wunused-variable]
  370 |     int status = 0;
      |         ^~~~~~
/home/temaklin/software/fulgor/external/sshash/include/../external/pthash/include/encoders/../../external/essentials/include/essentials.hpp:368:58: warning: unused parameter ‘mangled_name’ [-Wunused-parameter]
  368 | [[maybe_unused]] static std::string demangle(char const* mangled_name) {
      |                                              ~~~~~~~~~~~~^~~~~~~~~~~~
At global scope:
cc1plus: note: unrecognized command-line option ‘-Wno-unknown-attributes’ may have been intended to silence earlier diagnostics
make[2]: *** [CMakeFiles/sshash_static.dir/build.make:90: CMakeFiles/sshash_static.dir/external/sshash/include/dictionary.cpp.o] Error 1
make[1]: *** [CMakeFiles/Makefile2:112: CMakeFiles/sshash_static.dir/all] Error 2
make: *** [Makefile:91: all] Error 2

Thanks, @tmaklin!  @jermp, can we bump the essentials submodule upstream in sshash?

@tmaklin and @rob-p, done as of latest commit in main.
Thank you for reporting.


[ListItem] Cannot wrap IconButton into other component

Problem description
If I pass an instance of IconButton as the rightIconButton prop of ListItem, everything goes well
const InnerTaskList = ({ tasks = [], removeTask }) => {
  return <List>
    {
      tasks.map(task => <ListItem
        key={task._id}
        primaryText={task.content}
        rightIconButton={<IconButton>
          <ActionDelete color={red500}/>
        </IconButton>}
      />)
    }
  </List>
};

but if I wrap the IconButton into a component, say
const InnerTaskList = ({ tasks = [], removeTask }) => {
  return <List>
    {
      tasks.map(task => <ListItem
        key={task._id}
        primaryText={task.content}
        rightIconButton={<RemoveTaskButton/>}
      />)
    }
  </List>
};

const RemoveTaskButton = ({ task, removeTask }) => {
  return <IconButton>
    <ActionDelete color={red500}/>
  </IconButton>
};

then the style corrupts

Link to minimally-working code that reproduces the issue
https://github.com/zhaoyao91/civeno-next/tree/but-list-item-right-icon-button
Versions

Material-UI:  0.16.5
React: 15.4.1
Browser: chrome  55.0.2883.87 m (64-bit)

@zhaoyao91: can you please try passing down all the props to RemoveTaskButton?
Something like:
const RemoveTaskButton = ({ task, removeTask, ...props }) => {
  return (
    <IconButton {...props}>
      <ActionDelete color={red500} />
    </IconButton>
  );
};

@lucasbento Thanks, it works!
Then I feel this a little tricky, so should it be fixed or the docs be updated?

Your component is intercepting props that are used to style the IconButton, so not so easy to fix given the current styling approach. Things will get better here with next. Closing for now


Style changes don't appear in the app

After recompiling the app with all the XML changes, it looks exactly the same on the device as in the previous step. I even included a test change to the text in XML to verify that XML changes get to the device - they indeed do, but aside from different text, app colors don't change:

Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: 46228c67-1dc9-99e0-d279-ff6e722bfd5e
Version Independent ID: 41994570-80f8-55ae-9a31-9696610b2353
Content: Style a cross-platform Xamarin.Forms application - Xamarin
Content Source: docs/get-started/quickstarts/styling.md
Product: xamarin
Technology: xamarin-forms
GitHub Login: @davidbritch
Microsoft Alias: dabritch

OK, my bad.


vendor locking it seems

[Enter feedback here]
All examples require using active directory. OAuth2 implementation should be agnostic.
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ID: fa82acc4-680a-1431-f4fc-4848e8efe2ce
Version Independent ID: fbf31137-c536-2d90-3a76-bca7cd88cf53
Content: Authorize developer accounts using OAuth 2.0 in API Management - Azure API Management
Content Source: articles/api-management/api-management-howto-oauth2.md
Service: api-management
GitHub Login: @dlepow
Microsoft Alias: danlep

@josersleal Just following up here... Hope my previous comment clears things up.


Support fullscreen mode

Added fullscreen property triggering fullscreen player. (true — show, false — dismiss)
Also, added callbacks regarding it's visibility.
onFullscreenPlayerWillPresent: PropTypes.func,
onFullscreenPlayerDidPresent: PropTypes.func,
onFullscreenPlayerWillDismiss: PropTypes.func,
onFullscreenPlayerDidDismiss: PropTypes.func,

@sjchmiela just only work on iOS?

Unfortunately yes.

@sjchmiela - looks good, thanks!


Calendar details view improvement suggestions

[ ] Ideally, it should be dismissable by clicking anywhere outside of the details box
[ ] It should also be dismissable with Esc
[ ] It should be centered on the page or near the click, so that one can quickly look at many events in sequence.

CC @melissawm @psobolewskiPhD
Just implementing the easiest one of the above would be enough for a first pass. Right now, there's a lot of mousing back and forth between the calendar and the X to close the event!

The first 2 are priorities, IMO.
For me the 3rd is take it or leave it. I actually rather like the current less in the way implementation on a larger monitor--I can see the calendar and the event. On a small screen it's basically centered anyways.
Super minor thing:
the dates (e.g. 17) and days of the week "light up" on hover, like they should do something when you click, but they don't.
BTW: for me it shows all times America New York is that for everyone? I think if it could be aware of time zones it would be amazing.

It is - that should be your time zone. Is it not? Maybe that's a birwser thing again?

Nono, sorry I wasn't clear -- it was showing me my timezone! I wasn't sure if it wasn't sure if it was showing the same times for everyone (with the message re: timezone) or actually adapting. Looks like it adapts, which is killer!
❤

The event popover looks awful on mobile because I hardcoded it to be 20% of the screen  - needs to be made responsive 😅

Amazing work as always @melissawm! 😍 Man I am so pumped to see this live on the stable site!


Use runners from vars if available

This was removed in #801 to bring the file in line with downstream. However, it is better to rely on the vars environment when invoking the main workflow from dispatch, while offering an override when invoking the workflow as a sub-workflow.

@compnerd We should make a decision on #811 first or this is going to break downstream.


bug in yolact when run train.py

Hello, I'm trying to execute the yolact code :
https://github.com/dbolya/yolact
but I have this bug when I run train.py:

runfile('C:/Users/Sylvain ARD/Dropbox/Documents Sylvain/travail avec Boubacar/yolact/yolact-master/yolact-master/train.py', wdir='C:/Users/Sylvain ARD/Dropbox/Documents Sylvain/travail avec Boubacar/yolact/yolact-master/yolact-master')
Scaling parameters by 0.12 to account for a batch size of 1.
Per-GPU batch size is less than the recommended limit for batch norm. Disabling batch norm.
loading annotations into memory...
Done (t=0.03s)
creating index...
index created!
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
Initializing weights...
Begin training!

Traceback (most recent call last):

  File "C:\Users\Sylvain ARD\Dropbox\Documents Sylvain\travail avec Boubacar\yolact\yolact-master\yolact-master\train.py", line 505, in <module>
    train()

  File "C:\Users\Sylvain ARD\Dropbox\Documents Sylvain\travail avec Boubacar\yolact\yolact-master\yolact-master\train.py", line 271, in train
    for datum in data_loader:

  File "C:\Users\Sylvain ARD\.conda\envs\yolact\lib\site-packages\torch\utils\data\dataloader.py", line 819, in __next__
    return self._process_data(data)

  File "C:\Users\Sylvain ARD\.conda\envs\yolact\lib\site-packages\torch\utils\data\dataloader.py", line 846, in _process_data
    data.reraise()

  File "C:\Users\Sylvain ARD\.conda\envs\yolact\lib\site-packages\torch\_utils.py", line 369, in reraise
    raise self.exc_type(msg)

TypeError: __init__() missing 4 required positional arguments: 'casting', 'from_', 'to', and 'i'

please help me
thank you
best regards

It's look like there is a problem with you're  annotations

yes it is, I fixed my annotations and it fixes the bug
Sylvain Ard
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Le mer. 3 févr. 2021 à 14:24, sdimantsd<EMAIL_ADDRESS>a écrit :

It's look like there is a problem with you're annotations
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You are receiving this because you authored the thread.
Reply to this email directly, view it on GitHub
https://github.com/dbolya/yolact/issues/601#issuecomment-772503258, or
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Hi,
I came across the same bug. However, I think my annotations are fine. Can you explain where this bug comes from and what you fixed in your annotations to solve the bug? Kind regards

add
boxes=boxes.astype("float64")####Ajouté par Sylvain Ard
in the line 168 of utils/augmentation.py
best regards

before
boxes[:, [0, 2]] *= (width  / img_w)
boxes[:, [1, 3]] *= (height / img_h)

I forgot to precise that I did that to fix the bug

The conversion to float64 worked. Thank you very much!

Hi, i have the same error, but not from box :
_    raise self.exc_type(msg)
TypeError: init() missing 1 required positional argument: 'dtype'_
I don't find yet a solution, can you help me?


chore!: Update to latest Polars

Currently, the latest polars version is 1.7.1. Our project https://github.com/stefanDeveloper/heiDGAF relies on this library, so it would be great to update it.

Sure! I am on vacation for the next two weeks without access to my computer and I'll merge your PR once I am back. Can you aim your PR at the dev branch instead of main? I'll create a release by merging it into main afterwards. Thanks!

I realized I can change the target branch from GitHub mobile. However, the poetry.lock file is not up to date with the pyproject.toml which is causing the tests to fail. You'll need to run a poetry lock and push the updated poetry.lock

Thanks for your quick reply! I updated the poetry.lock.

Thanks! I tried running the GitHub actions, but it seems I forgot to merge main back into dev after the last release. I'll need to check this on a proper terminal, not just on my phone 😅. I've added a reminder to fix this and merge your PR by the 30th of September. I also need to add a contributing guide, as the commits must follow the conventional commits specification to correctly generate the changelog.md

Changes were merged in https://github.com/jordandelbar/feature-engineering-polars/pull/38
Thanks again for the help!


all tutorial - copying from sp24

@JoyMitra Need to check which version of node to recommend to students for installation.

Under types in Typescript, there is another type called literal type. This example is present under Boolean type "let e: true = true" but this actually acts as a constant, and I believe this is a literal type. A few other examples are let count: 10 = 10; // Can only be 10, let status: "pass" = "pass"; // Can only be "pass"
Under Loops, we can add a "Tip" saying for and while are entry level loops and do while is exit level loop (and will get executed atleast once no matter what).
In Functions as Contructors ": void" is not required and "this: any" can be specified in the parameters of the functions, it will reduce the number of errors coming. Or else you will get an error like - 'this' implicitly has type 'any' because it does not have a type annotation.

Changes looks good to me no additional changes from my end


Multiple GPU training stuck on Loading extension module utils / Tijme to load utils op XXX seconds

When starting the training using multiple GPU, it gets stuck indefinitely (I let it stuck for 10 minutes then I closed it) on the following log. Runpod.io tells me that the GPU have 34% VRAM used and 100% GPU Usage :
With 2 gpu:
Loading extension module utils...
Time to load utils op: 11.247538805007935 seconds
Loading extension module utils...
Time to load utils op: 11.123851776123047 seconds

With 4 it looks like this:
Loading extension module utils...
Time to load utils op: 11.247538805007935 seconds
Loading extension module utils...
Time to load utils op: 11.123851776123047 seconds
Loading extension module utils...
Time to load utils op: 11.247538805007935 seconds
Loading extension module utils...
Time to load utils op: 11.123851776123047 seconds

Here is the stack trace :
root@492f6f0b65c4:/workspace/Lit-Parrot# ./start_training.sh
[2023-06-19 15:41:32,009] [INFO] [real_accelerator.py:110:get_accelerator] Setting ds_accelerator to cuda (auto detect)
{'eval_interval': 8, 'save_interval': 20, 'eval_iters': 100, 'log_interval': 1, 'devices': 2, 'learning_rate': 0.0003, 'batch_size': 128, 'micro_batch_size': 2, 'gradient_accumulation_iters': 64, 'num_epochs': 60, 'max_iters': 31320, 'weight_decay': 0.01, 'lora_r': 8, 'lora_alpha': 16, 'lora_dropout': 0.05, 'warmup_iters': 100}
initializing deepspeed distributed: GLOBAL_RANK: 0, MEMBER: 1/2
[2023-06-19 15:41:32,209] [WARNING] [comm.py:152:init_deepspeed_backend] NCCL backend in DeepSpeed not yet implemented
[2023-06-19 15:41:35,207] [INFO] [real_accelerator.py:110:get_accelerator] Setting ds_accelerator to cuda (auto detect)
{'eval_interval': 8, 'save_interval': 20, 'eval_iters': 100, 'log_interval': 1, 'devices': 2, 'learning_rate': 0.0003, 'batch_size': 128, 'micro_batch_size': 2, 'gradient_accumulation_iters': 64, 'num_epochs': 60, 'max_iters': 31320, 'weight_decay': 0.01, 'lora_r': 8, 'lora_alpha': 16, 'lora_dropout': 0.05, 'warmup_iters': 100}
initializing deepspeed distributed: GLOBAL_RANK: 1, MEMBER: 2/2
[2023-06-19 15:41:35,376] [WARNING] [comm.py:152:init_deepspeed_backend] NCCL backend in DeepSpeed not yet implemented
Enabling DeepSpeed BF16.
[rank: 1] Global seed set to 1338
/usr/local/lib/python3.10/dist-packages/lightning/fabric/loggers/csv_logs.py:186: UserWarning: Experiment logs directory trained-model/falcon-7b-dsul_1/version_0 exists and is not empty. Previous log files in this directory will be deleted when the new ones are saved!
  rank_zero_warn(
[rank: 0] Global seed set to 1337
Loading model 'checkpoints/tiiuae/falcon-7b/lit_model.pth' with {'block_size': 2048, 'vocab_size': 50254, 'padding_multiple': 512, 'padded_vocab_size': 65024, 'n_layer': 32, 'n_head': 71, 'n_embd': 4544, 'rotary_percentage': 1.0, 'parallel_residual': True, 'bias': False, 'n_query_groups': 1, 'shared_attention_norm': True}
^[[CNumber of trainable parameters: 3506176
Using /root/.cache/torch_extensions/py310_cu118 as PyTorch extensions root...
Creating extension directory /root/.cache/torch_extensions/py310_cu118/utils...
Emitting ninja build file /root/.cache/torch_extensions/py310_cu118/utils/build.ninja...
Building extension module utils...
Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)
Using /root/.cache/torch_extensions/py310_cu118 as PyTorch extensions root...
[1/2] c++ -MMD -MF flatten_unflatten.o.d -DTORCH_EXTENSION_NAME=utils -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -isystem /usr/local/lib/python3.10/dist-packages/torch/include -isystem /usr/local/lib/python3.10/dist-packages/torch/include/torch/csrc/api/include -isystem /usr/local/lib/python3.10/dist-packages/torch/include/TH -isystem /usr/local/lib/python3.10/dist-packages/torch/include/THC -isystem /usr/include/python3.10 -D_GLIBCXX_USE_CXX11_ABI=0 -fPIC -std=c++17 -c /usr/local/lib/python3.10/dist-packages/deepspeed/ops/csrc/utils/flatten_unflatten.cpp -o flatten_unflatten.o
[2/2] c++ flatten_unflatten.o -shared -L/usr/local/lib/python3.10/dist-packages/torch/lib -lc10 -ltorch_cpu -ltorch -ltorch_python -o utils.so
Loading extension module utils...
Time to load utils op: 11.247538805007935 seconds
Loading extension module utils...
Time to load utils op: 11.123851776123047 seconds

I don't have this error using only one GPU.

This seems to be a DeepSpeed issue, so it might disappear with #118.
Which script are you running?
I would suggest trying out a DeepSpeed example on your setup to make sure it's an issue with this repository. You can find DeepSpeed examples here: https://github.com/microsoft/DeepSpeedExamples/tree/master/training/cifar

I had a similar hanging issue and all it took was deleting the cache directory that should look something like
Using /home/griffina/.cache/torch_extensions/py311_cu118 as PyTorch extensions root...