Crash when running tensorflow.python.ops.gen_nn_ops.fused_batch_norm_v3

Click to expand!
Issue Type
Bug
Have you reproduced the bug with TF nightly?
No
Source
binary
Tensorflow Version
2.11.0
Custom Code
Yes
OS Platform and Distribution
Ubuntu 22.04
Mobile device
No response
Python version
3.9
Bazel version
No response
GCC/Compiler version
No response
CUDA/cuDNN version
No response
GPU model and memory
No response
Current Behaviour?
A bug happened!

Standalone code to reproduce the issue
import tensorflow as tf
import numpy as np
from tensorflow.python.ops import gen_nn_ops
try:
  try:
    with tf.device('/CPU'):
      arg_0_tensor = tf.random.uniform([4, 10, 10, 2], dtype=tf.float32)
      arg_0 = tf.identity(arg_0_tensor)
      arg_1_tensor = tf.random.uniform([2], dtype=tf.float32)
      arg_1 = tf.identity(arg_1_tensor)
      arg_2_tensor = tf.random.uniform([2], dtype=tf.float32)
      arg_2 = tf.identity(arg_2_tensor)
      arg_3_tensor = tf.random.uniform([2], dtype=tf.float32)
      arg_3 = tf.identity(arg_3_tensor)
      arg_4_tensor = tf.random.uniform([2], dtype=tf.float32)
      arg_4 = tf.identity(arg_4_tensor)
      epsilon = -7.999
      exponential_avg_factor = 1.0
      data_format = "NHWC"
      is_training = False
      out = gen_nn_ops.fused_batch_norm_v3(arg_0,arg_1,arg_2,arg_3,arg_4,epsilon=epsilon,exponential_avg_factor=exponential_avg_factor,data_format=data_format,is_training=is_training,)
  except Exception as e:
    print("Error:"+str(e))
  try:
    with tf.device('/GPU:0'):
      arg_0 = tf.identity(arg_0_tensor)
      arg_0 = tf.cast(arg_0, tf.float32)
      arg_1 = tf.identity(arg_1_tensor)
      arg_1 = tf.cast(arg_1, tf.float32)
      arg_2 = tf.identity(arg_2_tensor)
      arg_2 = tf.cast(arg_2, tf.float32)
      arg_3 = tf.identity(arg_3_tensor)
      arg_3 = tf.cast(arg_3, tf.float32)
      arg_4 = tf.identity(arg_4_tensor)
      arg_4 = tf.cast(arg_4, tf.float32)
      gen_nn_ops.fused_batch_norm_v3(arg_0,arg_1,arg_2,arg_3,arg_4,epsilon=epsilon,exponential_avg_factor=exponential_avg_factor,data_format=data_format,is_training=is_training,)
  except Exception as e:
    print("Error:"+str(e))
except Exception as e:
  print("Error:"+str(e))

### Relevant log output

```shell
2023-01-22 10:50:17.946297: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-01-22 10:50:18.459583: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/lib/:/home/nimashiri/anaconda3/envs/cuda11.2/lib/:/home/nimashiri/anaconda3/envs/cuda11.2/lib/
2023-01-22 10:50:18.459739: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/lib/:/home/nimashiri/anaconda3/envs/cuda11.2/lib/:/home/nimashiri/anaconda3/envs/cuda11.2/lib/
2023-01-22 10:50:18.459746: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
2023-01-22 10:50:18.926143: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-01-22 10:50:18.930560: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-01-22 10:50:18.930665: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-01-22 10:50:18.930955: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-01-22 10:50:18.931633: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-01-22 10:50:18.931734: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-01-22 10:50:18.931822: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-01-22 10:50:19.289017: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-01-22 10:50:19.289150: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-01-22 10:50:19.289240: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-01-22 10:50:19.289322: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 4275 MB memory:  -> device: 0, name: NVIDIA GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5
2023-01-22 10:50:19.844187: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:428] Loaded cuDNN version 8100
2023-01-22 10:50:19.846342: E tensorflow/compiler/xla/stream_executor/dnn.cc:887] CUDNN_STATUS_BAD_PARAM
in tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc(5790): 'cudnnBatchNormalizationForwardInference( cudnn.handle(), mode, &one, &zero, x_descriptor.handle(), x.opaque(), x_descriptor.handle(), y->opaque(), scale_offset_descriptor.handle(), scale.opaque(), offset.opaque(), estimated_mean.opaque(), maybe_inv_var, epsilon)'
Error:{{function_node __wrapped__FusedBatchNormV3_device_/job:localhost/replica:0/task:0/device:GPU:0}} cuDNN launch failure : input shape ([4,10,10,2]) [Op:FusedBatchNormV3]

</details>

@nimashiri I was able to run the code without the crash, could you please have a look at this gist.
Thank you!

@synandi vulnerabilities occur when invalid arguments are being sent to APIs. So your reply here is wrong.
@nimashiri this is not a vulnerability, you're receiving a user error. Please consult SECURITY.md before sending new reports as very few of them are really actionable (and they should have been submitted via private channels)


Populate_db fixes

Commit one fixes a bug where specifying too large a number of messages to generate, the code distributing the pub_dates didn't work as intended, and set them all to timezone_now().
Commit makes it send all the messages in one do_send_messages call, which offer a performance improvement. Some measurements here:
(zulip-py3-venv) vagrant@32de60bf2049:/srv/zulip$ ./measure.sh
Trying command: ./manage.py populate_db -n100
With patch:
time:6.87
Without patch:
time:7.73
Trying command: ./manage.py populate_db -n1000
With patch:
time:22.24
Without patch:
time:28.09
Trying command: ./manage.py populate_db -n10000
With patch:
time:219.82
Without patch:
time:238.34
Trying command: ./manage.py populate_db -n10000 --extra-users=120
With patch:
time:248.82
Without patch:
time:330.19
Trying command: ./manage.py populate_db -n100000 --extra-users=120
With patch:
time:2744.65
Without patch:
time:3302.74

Another run, without the  longest one (100k messages one):
$ ./measure.sh
Trying command: ./manage.py populate_db -n100
With patch:
time:7.58
Without patch:
time:7.44
Trying command: ./manage.py populate_db -n1000
With patch:
time:23.12
Without patch:
time:29.79
Trying command: ./manage.py populate_db -n10000
With patch:
time:203.44
Without patch:
time:258.67

Now, this can of course eat a lot of memory, so for those who need to generate more messages than their machine can handle at once, an option to split the process into smaller chunks is needed - commit 3 takes care of that.

Generally, this looks great.  But a few thoughts:

I would have done the first commit as at least two commits: The first moving the code + creating a function, and the second changing the behavior.  Would have taken like 4x less time to read+review the behavior changes that way.
Do you get similar results with a batch_size of 1000 as with 10000?   If so, we should do 1000, since that should be just as fast and involve less memory issues.
It might be cleaner to make the block at the bottom of send_messages into a function; then we could write:

    if num_messages % message_batch_size:
        actually_send(messages)
        messages = []

if len(messages) > 0:
    actually_send(messages)
return tot_messages

(If we did that, we'd probably want to name the functions generate_and_send_messages and send_messages, not send_messages and actually_send).

My test suggests a smaller batch_size is actually faster:
(zulip-py3-venv) tabbott@coset:~/zulip$ time ./manage.py populate_db -n10000 --batch-size=1000
2019-07-20 23:07:32.303 INFO [] Clearing memcached cache after migrations
Successfully populated test database.

real	1m52.183s
user	1m22.008s
sys	0m6.120s
(zulip-py3-venv) tabbott@coset:~/zulip$ time ./manage.py populate_db -n10000 --batch-size=10000
2019-07-20 23:09:42.847 INFO [] Clearing memcached cache after migrations
Successfully populated test database.

real	1m55.928s
user	1m24.314s
sys	0m6.659s

@timabbott I get confusing, widely varying results when I just do populate_db runs one after another - when I was doing the initial testing I figured out that I need to run tools/do-rebuild-destroy-database to get reliable measurements, so that's what my method is (run rebuild-destroy and then time a populate_db run).
And I did a bunch of testing and there seems to very little difference (with -n10000) between batch sized from 10 to 10000. Size 10 is slower by a few seconds, but everything falls in 2m50s to 3m run time.  Size 1 of course is slower, by a full minute.
So I suppose that indeed default 1000 might be best, as it's just as fast as 10000 in my tests, but will eat less memory.

Rebased - changed the default size in the batching commit to 1000, and added a commit factoring out the "send messages" part.

Merged, after converting the last line to use len(messages) > 0.  As a general rule, we avoid writing if foo where if foo > 0 would be correct; it makes the code more readable (since the reader doesn't need to consider whether foo might be False or None`).
Thanks @mateuszmandera!
If you're excited to optimize this further, I'd look at using line_profiler to see where the time is going creating the messages (see #learning or #discussions on chat.zulip.org, I don't remember which, for notes on using it).  I don't think we need to make it ultra fast, but it could be worth checking if there's any low-hanging fruit taking advantage of the fact we know there's no connected clients.


Update links to arithmetization.md

Some links were broken, others were slightly redundant.
One link was to a specific section of arithmetization.md.
Let's think of other places where such direct links make sense.

Merged elsewhere.


weird tag in minimal book

I created a rather minimal book, here it is in its entirety:
= the book title

== chapter 1

this is chapter 1

== chapter 2

and this, chapter 2

== chapter 3

and this one, muchacho, is chapter 3

when I run asciidoctor-epub3 -D _book book.adoc, and open the resulting book in the Books app, this is what I see (notice the weird green tag at the very end) :

aside: I haven’t looked into the actual epub archive to see what’s going on; I haven’t been able to figure out how to do that yet

That tag is intentional. It marks the end of the chapter visually.

the end of the last chapter? (— as we only see it at the end of chapter 3)
so do I understand correctly that if I made a book with AsciiDoctor-epub, it will have to have this green tag in it?

Say :doctype: book at document header and this thing will be gone

What you actually made with this example is not a book.

I see . I added :doctype: book and the weird tag went away . thank you

Yep, it should only be for an article. It's not a weird tag. It's a typical
end of article mark that you see in a lot of journal publications. But I do
recognize that it's not suitable for all users and perhaps should be
something that's configurable.
On Wed, Jun 19, 2024, 20:29 Raphael Martin Schindler <
@.***> wrote:

I see . I added :doctype: book and the weird tag went away . thank you
—
Reply to this email directly, view it on GitHub
https://github.com/asciidoctor/asciidoctor-epub3/issues/482#issuecomment-2179699319,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/AAATL56XAM7HKW54SJFXJ5DZII5ALAVCNFSM6AAAAABJSWCAYGVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDCNZZGY4TSMZRHE
.
You are receiving this because you commented.Message ID:
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chore(opa): add 0.67.1

See: https://github.com/open-policy-agent/opa/releases/tag/v0.67.1
### Dependencies
- [ ] Vector product update

Should we now remove 0.61.0 since it was deprecated in the last release?

I ran ./upload_new_opa_version.sh 0.67.1 ...


[2313] Add missing conditionals in build workflow

Context
Pipeline is broken: https://github.com/DFE-Digital/teacher-training-api/runs/3180094482?check_suite_focus=true
Changes proposed in this pull request
Add missing conditionals
Guidance to review
Checklist

[ ] Make sure all information from the Trello card is in here
[ ] Attach to Trello card
[ ] Rebased master
[ ] Cleaned commit history
[ ] Tested by running locally

 Cache docker build in pull requests


Audit requests to API server when we could use a lister instead

Background: when the controller job starts (at installation or upgrade, after a crash, rescheduling to another node, etc.), a SharedInformerFactory fetches all TaskRuns/PipelineRuns/Pods etc into a cache in memory, indexes them if necessary, and watches for updates to notify reconcilers for updates. In addition to this startup global resync, we also configure a global resync every 10 hours, in case we missed some update.
We should consult this cache whenever possible, even though it may be slightly out-of-date with the latest state, to prevent overloading the k8s API server with requests, particularly when globally resyncing many many objects. Consulting the cache is also much faster than hitting the API server.
In practice this looks like using a pipelineRunLister or taskRunLister or podLister, instead of a kubernetes.Clientset (or generated Tekton typed clients), if some staleness is allowable. Even when staleness is not preferred, we can fail and retry a reconciliation later until the cache is up-to-date.
I've identified some places where we are calling the API server where we might be able to get away with using a Lister:
https://github.com/tektoncd/pipeline/blob/v0.25.0/pkg/reconciler/taskrun/taskrun.go#L392
https://github.com/tektoncd/pipeline/blob/v0.25.0/pkg/reconciler/taskrun/taskrun.go#L407
In both of these cases, we seem to get or list Pods on every pass through the TaskRun reconcile loop.
If we can eliminate these, we should also try to take some steps to make new instances harder to creep back in, or at least add comments indicating that their use should be carefully scrutinized.
/kind cleanup

Thinking about this some more: If we replace the slow pods.get with a faster podlister cache lookup, we'll need to be careful to handle pod-not-found errors correctly, since with the podlister we could be getting a pod-not-found error due to cache inconsistency. When we call directly to the API server, we know a not-found means we have to create the Pod.
If we use a podlister, we might need to change the logic to something like (pseudocode):
var pod *corev1.Pod
if tr.Status.PodName != "" {
  // tr.status says its pod is "foo", find it.
  pod, err = c.podLister.Pods(tr.Namespace).Get(tr.Status.PodName)
  if k8serrors.IsNotFound(err) {
    // if tr.age > 30s -> set tr.Status=Failed and give up; podlister cache should have the pod by now
    // else, maybe podlister's cache is out of date, retry
  }
} else {
  // tr.status doesn't have a pod yet, but maybe that's because
  // the controller died after creating the pod but before
  // updating the TaskRun, or TaskRun update failed.
  // List pods labelled for the TaskRun, find the last non-failed
  // one (because retries 🙄) and use that pod.
  pods, err := c.podLister.Pods(tr.Namespace).List("tekton.dev/taskrun="+tr.Name))
  for _, p := range pods {
    if metav1.IsControlledBy(&p, tr) && !podconvert.DidTaskRunFail(&p) {
      pod = &p
      // TODO: break? 🤔
    }
  }
  if pod == nil {
    // if tr.age > 30s -> set tr.Status=Failed and give up; podlister cache should have the pod by now
    // else, maybe podlister's cache is out of date, retry
  }
}


login page weird layout shift

https://github.com/user-attachments/assets/d09c8b4c-95db-4d7e-ad09-a58874c72658

running locally and even testing again on safari incognito I don't see it happening


Segmentation fault: 11

The last PR breaks the app.
Throws a segmentation fault: 11
It is on CourseCatalogDetailViewController on the function listen
private func listen() {
        self.courseStream.listen(self,
            success: {[weak self] (course, enrolled) in
                self?.aboutView.applyCourse(course: course)
                if enrolled {
                    self?.aboutView.actionText = Strings.CourseDetail.viewCourse
                    self?.aboutView.action = {completion in
                        self?.showCourseScreen()
                        completion()
                    }
                }
                else {
                    self?.aboutView.actionText = Strings.CourseDetail.enrollNow
                    self?.aboutView.action = {[weak self] completion in
                        self?.enrollInCourse(completion: completion)
                    } as? ((() -> Void) -> Void)
                }
            }, failure: {[weak self] error in
                self?.loadController.state = LoadState.failed(error: error)
            }
        )
        self.aboutView.loaded.listen(self) {[weak self] _ in
            self?.loadController.state = .Loaded
        }
    }

The problem seems to be on
self?.aboutView.action = {[weak self] completion in
    self?.enrollInCourse(completion: completion)
} as? ((() -> Void) -> Void)

If I comment that entire code then the app builds correctly but then the users wouldn't be able to enroll
@saeedbashir @salman2013

@jagonzalr It's a weird xCode issue. Some times xCode start throwing this error while compliing swift files. Which xCode you are using? Can you try by deleting xcdata and cleaning your project.

@saeedbashir I'm still using Xcode 8.2.1

Did you tried by deleting DrivedData and cleaning project?

@saeedbashir I'm gonna try that. And also I got Xcode 8.3.2 so I'm also gonna try in that version.

@saeedbashir it seems to build in Xcode 8.3.2


proposal: third party modules

Like we did with logspout, we'll support 3rd party modules in registrator. More details to come, but this is a placeholder issue.

Is there any progress or information to share?

+1 I'd like to propose a cloud KV store as a backend (Azure Table Storage, AWS SimpleDB, Google Cloud Datastore etc), but conscious this may be out of scope for the core Registrator project?


Enable Password Policy by default for Links

Value

Security by default

Steps to reproduce

Click on the quickaction "Copy Link"
No password is required for the link, anyone who has the link, can access

Expected behavior

A password should be required by default (for links anyone can access)

Actual behavior

No password is required for the link

@ScharfViktor Can you take care of that?
I can change the default config, but then we need to see how the tests are behaving.

yes, sure. I'll take care. do you want set OCIS_SHARING_PUBLIC_SHARE_MUST_HAVE_PASSWORD by default?
I can do it and change test if needed

we also want the password policy to be enabled by default.

I question if a password makes it more secure. What is gained when both are send in the same email?! Same with a strong password policy ... we should think through the complete user story ... what will the user do with the link and the PW?

we also want the password policy to be enabled by default.

I'm changing api test now. @micbar @tbsbdr which envs do you want to have as default?  I can enable them in my PR

I would like to
FRONTEND_PASSWORD_POLICY_MIN_CHARACTERS=8
FRONTEND_PASSWORD_POLICY_MIN_LOWERCASE_CHARACTERS=1
FRONTEND_PASSWORD_POLICY_MIN_UPPERCASE_CHARACTERS=1
FRONTEND_PASSWORD_POLICY_MIN_DIGITS=1
FRONTEND_PASSWORD_POLICY_MIN_SPECIAL_CHARACTERS=1

but i want them as a default in the code.

Please also deploy a minimal ‘banned passwords list’ file.

Example: https://github.com/owncloud/ocis/blob/master/tests/config/drone/banned-password-list.txt

I created this list only for api and e2e tests https://github.com/owncloud/ocis/blob/master/tests/acceptance/features/apiGraph/enforcePasswordPublicLink.feature#L286
how written in docs: https://doc.owncloud.com/ocis/next/deployment/services/s-list/frontend.html#the-password-policy The admin can define the path of the banned passwords list file
I'm not sure that it is default config and don't know where is default location for this file, so I would delegate the resolution of this issue to the developers.

I would like to
FRONTEND_PASSWORD_POLICY_MIN_CHARACTERS=8
FRONTEND_PASSWORD_POLICY_MIN_LOWERCASE_CHARACTERS=1
FRONTEND_PASSWORD_POLICY_MIN_UPPERCASE_CHARACTERS=1
FRONTEND_PASSWORD_POLICY_MIN_DIGITS=1
FRONTEND_PASSWORD_POLICY_MIN_SPECIAL_CHARACTERS=1

but i want them as a default in the code.

@micbar it's done and all tests were changed. Should we close issue or should do something with banned passwords list file?

@ScharfViktor thanks!
banned passwords needs a follow up. We can close this ticket here.


UI tests

UI tests for view and edit functionalities for

PV
PVS

I don't know if we have any kind of automated test framework for this. We may just want to define a manual test plan.
@ChristopherFry @ganchandrasekaran @chander12shekhar

Most likely manual tests for R1


Drop backwards compatibility for old-style dask tasks

see: https://github.com/dask-contrib/dask-awkward/pull/556
TLDR: we can likely remove many if statements by dropping compatibility with old dask versions (below 2024.12.0)

This may be on hold for a bit.


All Button for Material Dialogs are set Dismissable Dialog by Default.

Is there a way to make those Buttons (Positive, Negative and Neutral Buttons) do not dismiss/closes itself automatically?
.setNeutralButton("Capture", new DialogInterface.OnClickListener() {
                    @Override
                    public void onClick(DialogInterface dialog, int which) {

                    }
                })

This line of code closes automatically the dialog itself even without action inside the Listener.

just download the library and comment the dialog.cancel()
    protected final void attemptCloseDialog() {         switch (buttonType) {             case AlertDialog.BUTTON_NEGATIVE:                 dialog.cancel();                 break;             case AlertDialog.BUTTON_NEUTRAL:                 dialog.cancel();                 break;             case AlertDialog.BUTTON_POSITIVE:                 dialog.dismiss();                 break;             default:                 break;         }     }

The piece of code @devilzlayer is talking about can be found in the class AbstractListenerWrapper. Removing the dismiss() method call would indeed prevent the dialog from being closed. However, this requires to modify the library's source code, which is probably inconvenient for you. Furthermore, this is not a change, that I will accept upstream, as the current behavior is useful in most cases.
A better solution for you would probably be to use the validation API, the library provides. You can add a DialogValidator to your dialog using the addValidator-method:
MaterialDialog.Builder builder = new MaterialDialog.Builder(context);
// configure builder...
builder.addValidator(new DialogValidator() {

    @Override
    public boolean validate(@NonNull ValidateableDialog dialog) {
        return false;
    }

});
builder.show();

As long as at least one of the added validators returns false, the dialog will not be dismissed on button clicks. However, if you invoke dismiss() or cancel() on the dialog manually, it should be closed anyway.
I don't know what behavior you actually want to achieve. Please tell, if this solution meets your requirements.

I think this will do what i am seeking, thanks for the advice you shared!

Glad to hear this. Feel free to reopen this issue, if any problems occur.


Original Entity

https://github.com/PlayFab/API_Specs/blob/master/Legacy%2FPlayStreamEventDefns%2FPlayStreamEventModels.json#L1-L34

I have not authorized the change from this, I am the only one to authorize any changes for me. If I want someone added rather it be a child or whomever I will setup an extension account for that. I am in total control of my money and affairs. Repeat I am in TOTAL CONTROL OF MY OWN PERSONAL AND BUSINESS AFFAIRS! ANY OTHER VARIANCE FROM THIS WILL BE IN VIOLATION AND PEOPLE WILL BE SUBJECT TO PROSECUTION AND BEING SUED IN LEGAL COURTS NOT SMALL CLAIMS! THIS IS MY FINAL WARNING!


Recurring subscriptions possible?

Thanks for creating this tutorial!
Couldn't seem to find the answer to this online - do you know if it is possible to create recurring subscriptions as well? It seems to be only for one-off orders at this point.

This is my question too

You could definitely implement that by keeping a short registry of users' subscription (and expiration) dates in a data base and then repeatedly, when it comes to renewing that subscription, let the bot send a message, e.g. including Renew and Cancel buttons.


Create synchronized regional hub resources without status

Signed-off-by: myan<EMAIL_ADDRESS>Fixed multicluster-global-hub-agent issue:
{"level":"error","ts":1663554121.5829706,"logger":"generic-bundle-syncer","msg":"failed to update object","name":"default","namespace":"default","kind":"ManagedClusterSetBinding","error":"failed to update object - failed to create typed patch object (default/default; cluster.open-cluster-management.io/v1beta1, Kind=ManagedClusterSetBinding): .status: field not declared in schema","stacktrace":"github.com/stolostron/multicluster-global-hub/agent/pkg/spec/controller/workers.(*Worker).start.func1\n\t/workspace/agent/pkg/spec/controller/workers/worker.go:45"}

/retest


runtime type issues

Commit(s) to merge
unknown
Target
unknown
Prepared changelist for beta/stable
unknown
Issue Description
"The runtime type always matches the static type": Almost always.. Due to type parameters being covariant by default.  this is the most annoying design choice it has.
What is the fix
unknown
Why cherry-pick
"The runtime type always matches the static type": Almost always.. Due to type parameters being covariant by default.  this is the most annoying design choice it has.
Risk
unknown
Issue link(s)
unknown
Extra Info
NO

See https://github.com/dart-lang/language/labels/variance for the efforts around improving it.


[get_csrf_tokens failed] 2023.04.06

requests.get("https://chat.openai.com/api/auth/csrf"), return status_code with 403!!! right now, 04.06 morning...
How to fix it? Thanks!!

Any suggestions?!


Clarify how to stop hourly copy.

Specificy govuk_sync_mirror as the cron job that needs to be stopped
in order to stop the hourly copy from the mirrorer machine overwriting the
copy.
Update last_reviewed_on

@KyleMacPherson can this be merged?

@tijmenb No probably not, opened this PR 3 months ago so probably worth someone having a look at it with a fresh pair of eyes as I do not know how much has changed since then. Will close this PR now.


Add Warning when delete/update package that have dependent packages

Now a package can be deleted without any warnings, when other packages depends on this package

This is a duplicate of an old one (#58) - closing this one in favor of the older (to say "we should have done this a long time ago")


remove for loop from uniform sampler

Was just reading through the code to get a better understanding of FastSHAP (in prep for the long email I sent you @iancovert!) and came across this todo which seemed like a quick fix.
The idea is to first generate this matrix, which is (num_players + 1) x (num_players), representing each of the possible mask densities:
[
    [0, 0, 0, ... , 0],
    [1, 0, 0, ... , 0],
    [1, 1, 0, ... , 0],
    [1, 1, 1, ... , 0],
      .   .   .   .    .
    [1, 1, 1, ... , 1],

Then, we create a new matrix in which each member of the batch randomly selects one of the mask density rows, resulting in a batch_size x num_players matrix.
Then, we generate a permutation of indices 0,1,..,num_players-1 for each row.  Finally we use those permutation indices to re-arrange each row to get our final mask permutations for each batch element.
Not a super significant PR, but it was simple and quick so I figured I"d try to knock it out!

This looks reasonable! Just to be sure, were you able to test that it runs successfully (at least for a couple epochs)?

Not yet! Been away from my GPU. I just did a/b testing with the outputs of the original Module and the updated Module.
For testing, I figured I will run the census notebook with the original and the new version, then compare runtimes as well as results?
I can also put together a little benchmark test for the Module in isolation to see if the runtime gains are really worth it. Obviously at larger batch sizes it will be, but at low batch sizes but large number of players it possibly might not be more efficient (though I expect it still will be).
I will let you know after I run a full test.

I ran tests in isolation, gridding over the number of players in the sampler and the number of samples to take.  For each pair of n_players and n_samples I sampled 100 times, taking the average of the run times to make the following plot:

Definitely seems worth it!  Obviously lots of time is spent doing other things in the surrogate training loop, so I ran some benchmarks there as well:

Looks like you were on to this approach here:
https://github.com/iancovert/fastshap/blob/f5fbc52bc046bd3501463e2c8f46ab9a44a6fab5/fastshap/utils.py#L161-L195
It probably makes sense to implement a similar approach for the ShapleySampler with the necessary adaptations to handle the complementary sampling, since obviously much more time is spent in the FasthSHAP training loop.  Maybe that can be a separate PR though.

I pushed a change which adapts the ShaplyeSampler to avoid for loops while also still allowing paired sampling.
I ran the whole notebook.
The new version of the FastSHAP training loop took 140s for 56 epochs, 2.6 s/epoch, stopping at a best loss of 0.058353.  Results comparison with kernelShap below:

The old version of the FastShap training loop took 1,057s for 57 epochs, 18.6 s/epoch, stopping at a best loss of 0.059535.

Seems like a pretty productive performance gain!

Aha! I originally wanted to do a random-vs-threshold comparator to achieve the permutations, but couldn't figure out how to get the correct distribution over the different sizes of S... the trick is to use 1 for the second dimension of the thresholder, not the num_players... clever! Eg we equally choose 20% threshold, 30% threshold, 40% threshold etc for each row - cool!
That approach should be much more performant than mine since it doesn't rely on argsort like mine does. I had to rely on argsort because randperm doesn't have an axis/dim arg. This means that my approach has complexity that goes with the complexity of argsort, which in turn goes with the num_players. This is why you can see in my isolated performance test graphic, the new version will start to catch up to the old version 1 the old version had no time penalty for the num_players (or at least very minimal) but the new version does.
Switching to the random/threshold comparator approach should resolve this.
This also primarily explains the performance differences you were observing.

in my isolated uniform sampler performance plot, you can clearly see the number of players causing a penalty for the new version relative to the old version (at a given batch size, the new version runtimes start to spread out with the number of players while the old version stays identical)
in my tests for the uniform sampler within the surrogate training loop context, the number of players is quite small compared to your test with 196. For me test as the batch size increased we saw expected performance increases because num_players stayed the same; if we used a different dataset with more features, we would expect to see smaller performance gains.
In the ShapleySampler, the python interpreter penalty is worse than in the Uniform sampler since there is both the for loop and the nested if/else block.
I also suspect the ratio of time spent sampling vs backpropping is higher in the FastSHAP training loop than in the surrogate, but could be wrong there.
my version of the ShapleySampler also suffers from the same complexity penalty with the num_players due to also using argsort, so previous statements which explain why you saw a smaller gain hold.

So in summary, before we merge this, I will try out the random threshold comparator approach, which we expected should be even better than the approach in the PR currently.
In re: converting samplers to nn.Modules - yeah I was thinking about that as well. These aren't very large operations so I don't know how beneficial it will be... maybe I will just add a hard coded .to(device) in for testing purposes to see if GPU operations help a significant amount.
I'll probably be able to do this stuff sometime over the weekend or Monday.

UniformSampler performance in Feature Removal Surrogate training loop table, updated (still using census, so num_features = 12).  As you can see, the random/thresh tester performs significantly better than the tril/gather approach, because it does not have to do any argsort.

Batch Size
Sampler Version
Epochs
Time (s)
Final Loss
s/epoch
Performance Factor

32
Old
83
352
0.1247
4.241
n/a

32
New (Tril/Gather)
70
207
0.1251
2.957
30.3%

32
New (Thresh)
89
236
0.1230
2.618
38.3%

64
Old
63
197
0.1258
3.127
n/a

64
New (Tril/Gather)
89
175
0.1244
1.966
37.2%

64
New (Thresh)
52
94
0.1276
1.808
42.2%

128
Old
70
178
0.1256
2.542
n/a

128
New (Tril/Gather)
70
97
0.1312
1.386
44.5%

128
New (Thresh)
76
98
0.1299
1.289
50.7%

To further confirm this, and to confirm the behavior you saw, I ran a new test where I simply duplicated the columns of the census data to get up to 192 features and ran the training loop again, only at a batch size of 128.  As you can see, compared to the num_features=12 results above, the old version had almost identical performance compared to num_features=12 as expected (no argsort/feature size complexity penalty, only batch-size complexity penalty).  The tril/gather approach suffered a significant penalty compared to its num_features=12 test due to the increased sorting complexity, while the random/thresh approach only had a very minor change.

Batch Size
Sampler Version
Epochs
Time (s)
Final Loss
s/epoch
Performance Factor

128
Old
63
160
-
2.540
n/a

128
New (Tril/Gather)
78
163
-
2.090
17.8%

128
New (Thresh)
52
75
-
1.442
43.3%

This definitely suggests a major advantage for the random/thresh approach (as expected), so I committed it for the UniformSampler.
The ShapleySampler will take a little extra thinking to make sure it only generates rows with 1,,,.num_features-1 ones, but I'll come up with something.

Well this looks great for the UniformSampler, seems like we know which version to go with! And very nice to see expected behavior wrt the number of players, mainly the larger penalty when we require $O(n \log n)$ sorting.
Re: the other bits:

It's less clear to me how we would use a similar thresholding trick to achieve the correct Shapley distribution. The math underlying the uniform version is related to the Beta Binomial distribution (the cardinality distribution under our trick is distributed with alpha = beta = 1), and by letting the threshold be arbitrary Beta you can achieve some but not all distributions over a fixed range. I haven't thought about it much, but happy to punt on this for now and go with your torch.gather improvement.

Putting the samplers on device is different enough that we can probably make that a separate PR. And sounds like we're both unsure how much it would help, especially in the more important applications with larger networks (which become even more of a bottleneck).

So what do you think, is this good to merge? Or is there anything else to do first? Thanks again for the attention you put into this!

Yeah I got it close to working for the Shapley sampler, but it's as you mentioned not generating quite the correct distribution over number of features selected, since the approach I took essentially sets the thresholds for n-2 of the samples according to the selected number of features for that row; forces the remaining two features to be 0,1 to avoid the grand coalition / null coalition from being generated, and then does a random rotation. It gets the shape mostly correct up to the few bins on either edge. As you said, it's an issue with the Bernoulli distributions overlapping.
I also added much more thorough benchmarks in a notebook for the Shapley sampler, including GPU tests, in the "benchmark" folder which I uploaded (along with the summary graphics).
I was going to do a little write up tomorrow.
The gist is that:

GPU is better for large batch sizes/num players, but at low values, cpu can perform better without the overhead.
ArgSort approach on CPU eventually gets worse than python looping when the num players gets large enough it seems
Slightly different approach for generating the permutations indices using multinomial sampler instead of argsort is more performant on cpu than argsort but less performant on gpu.

I just spent some time thinking about the thresholding idea for the ShapleySampler, specifically how to derive the correct PMF from a BetaBinomial distribution. It's impossible to make it work with a single threshold, because we can't prevent sampling either 0 or d players. I thought about a rejection sampling approach, where we ignore rows with 0 or d players, and I don't think that will work either (at least with a Beta-distributed threshold). I also thought about whether we could use the thresholding trick over d - 2 players, so that we could concatenate a 0 and 1 to ensure we avoid the grand/null coalitions, but this didn't seem possible either (plus it might defeat the purpose, since we'll need to shuffle in the 0/1). I see you got close by setting the threshold based on the num_included sample, but yeah weirdly it's off at the edges. Anyway, this is tricky enough that we should maybe set it aside for now. Your notebook is quite helpful though.
Let me know when you're happy with your writeup, I see you mentioned that in your last message.
And I guess the last thing is, I see you switched to numpy for the permutation in ShapleySampler rather than using torch.gather? I see in your benchmarking plots that it's faster (NumpyPermuteCPU vs ArgSortCPU). I wonder if we could get the same speed in pure PyTorch by replacing the indices = torch.argsort(torch.rand_like(S), dim=-1) line with torch.randperm - argsorting shouldn't be necessary to generate a random ordering.

Yeah, at the end of the day, a lot of our problems/challenges with this are because PyTorch doesn't have great support for random sampling/shuffling/rotating etc with independent along across an axis.  As I've been diving into this over the past week or two, it turns out there are lots of threads online/feature requests for more parity with numpy.random.choice/shuffle etc.

I also thought about whether we could use the thresholding trick over d - 2 players, so that we could concatenate a 0 and 1 to ensure we avoid the grand/null coalitions, but this didn't seem possible either (plus it might defeat the purpose, since we'll need to shuffle in the 0/1).

Yeah, the way I handled this was... interesting, not sure if you saw.  The idea was to make the first two columns 0/1, the rest of the columns controlled by the threshold determined per row based off the desired number of players for that row, and then instead of shuffling each row, we do a simpler rotation independently for each row - essentially by generating range(n) for each row, adding a random int from 0...n-1 separately to each range(n), and then taking the modulus to generate rotation indices, then using gather to execute the rotations.

I see you got close by setting the threshold based on the num_included sample, but yeah weirdly it's off at the edges. Anyway, this is tricky enough that we should maybe set it aside for now. Your notebook is quite helpful though.

Yeah, I was wondering if it would be possible to update the weights for the original Categorical sample distribution to get this to work... essentially just redistributing the extra mass in 1, n-1 categories to the other categories to bring them back up to where they are supposed to be... but it seemed like it would require pretty careful thought for those new weights, which I wasn't up for lol...

And I guess the last thing is, I see you switched to numpy for the permutation in ShapleySampler rather than using torch.gather? I see in your benchmarking plots that it's faster (NumpyPermuteCPU vs ArgSortCPU). I wonder if we could get the same speed in pure PyTorch by replacing the indices = torch.argsort(torch.rand_like(S), dim=-1) line with torch.randperm - argsorting shouldn't be necessary to generate a random ordering.

The problem with randperm is that it doesn't have the ability to generate multiple permutations simultaneously - it only accepts an integer argument.  The examples in my notebook with the MultiNomial sampling approach are the closest you can get in PyTorch to generating permutation indices per row directly without an argsort, at least that I've come across.
I was trying to think if there was some sort of approach where you do something like this:
num_ixs = batch_size * num_players
shuffled_ixs = torch.randperm(num_ixs)
shuffled_ixs = shuffled_ixs.reshape(batch_size, num_players)

This returns a matrix like this...
[
  [ 3,  2,  7],
  [12,  9,  8],
  [ 6, 14, 13],
  [ 5, 10,  1],
  [ 0, 11,  4]
]

The ends up reducing to the same problem as the argsort approach with random numbers - i.e. we need to figure out which how [3,2,7] becomes [1,0,2] or how [12,9,8] becomes [2,1,0].
It took a bit of work, but I did end up figuring out a method for converting this into permutation matrices / [0...n-1] row index permutations using just pytorch ops / no for loops / no argsort by leveraging the integer nature, but it actually ended up being way slower smh...
At the end of the day, I think the numpy approach is the best.

I did some tests with the numpy method using both the census notebook and the cifar notebook.
For the census notebook, there are the expected large performance improvements compared to the original, since the number of players is so low AND since the network is so small, the time spent in forward/backward is small relative to the time spent generating samples.
In the cifar notebook, there were almost no difference between using the original, argsort gpu, and numpy mode, presumably because the time spent in forward and backward is much much greater relative to the time spent sampling.  I tried with a few different selections for the num_samples and batch_size and this seemed to hold.

At the end of the day, the lesson here is that when the ratio of time spent in forward/backward to the time spent sampling is low, this can yields large performance improvements (i.e. sampling is a bottleneck), but when it's high, the effects are negligible.
I'm going to delete the benchmarking folder so it doesn't clutter the main repo and just move it to another branch on my fork.  Then I would say this is ready to merge!

Yeah the ShapleySampler distribution is tricky, I wonder if it's even possible to use an efficient thresholding trick... But that's a problem for another day.
I see what you mean about the lack of PyTorch/numpy parity for shuffling. I just looked through some PyTorch discussion board posts about independently shuffling rows of a tensor - I remember looking into this before and I guess there's still no great solution. As a last-ditch effort to stick with pure PyTorch, do you think we could match the numpy version's speed by generating separate permutations with something like indices = torch.stack([torch.randperm(d) for _ in range(n)])? We might pay a price for having to generate the permutations sequentially, if so I'm good to merge in the current version.

I'm pretty sure that approach would be the same as the old one essentially since it is just trading a for loop for a lost comprehension.
Having said that, it's worth testing, and, as one last thing that might be worth trying if we want to try to stick with pure (kinda) torch...
Using TorchScript might get us back to what we want, maybe better if we are lucky, and in a fairly readable way too, eg:
@torch.jit.script
def permute_rows(S, num_players, batch_size):
  for i in range(batch_size):
    ixs = torch.randperm(num_players, dtype=torch.int16)
    S[i] = S[i, ixs]
  return S

This is essentially identical to the original approach, but since the function is now jit'd it shouldn't incur the python interpreter penalty for the for loop. It may still not be quite as performant as the numpy version but worth testing.

The one potential speedup is using torch.gather to apply the reorderings in parallel, vs my original approach which applies them sequentially. Using numpy isn't so bad though, we could also just go with that.


java.lang.NoClassDefFoundError: androidx.media3.exoplayer.ExoPlayer$Builder$$ExternalSyntheticLambda11

Media3 Version
Media3 1.0.2
Devices that reproduce the issue
50% :  Oppo
22% : Huawei
22% : Samsung
6% : Lenovo Mobile
Devices that do not reproduce the issue
other devices
Reproducible in the demo app?
No
Reproduction steps
i got the error in firebase dashboard Sorry
Expected result
no ANR
Actual result
the app crashes
Media
self hosted songs
Bug Report

[ ] You will email the zip file produced by adb bugreport to<EMAIL_ADDRESS>after filing this issue.

Have a look through previous issues, there are various suggested fixes depending on the exact root cause you're experiencing: https://github.com/search?q=repo%3Agoogle%2FExoPlayer+noclassdeffounderror&type=issues
Specifically, try depending on android-specific Guava if you currently depend on the JRE variant: https://github.com/google/ExoPlayer/issues/9704#issuecomment-975390727


Include iRT standards in counts on final wizard dialog

The test failures suggest that the change works as expected. Can you update the expected counts in the tests and at the same time verify that the status bar shows the same numbers as the updated expected counts?

The test failures suggest that the change works as expected. Can you update the expected counts in the tests and at the same time verify that the status bar shows the same numbers as the updated expected counts?

I did some quick tests of it - can you confirm that it works for the case you were seeing as well? I'll merge after that.

Yes, status bar numbers are consistent with counts dialog with CiRT.

Ack, wait, the CiRT auto-add doesn't work now. I get the The calculator DIA-PASEF-tutorial requires all 15 of its standard peptides to be in the targets list in order to determine a regression. error again.

You should be able to change one of the old (QE/TTOF) tests to CiRT to repro as before.

You should be able to change one of the old (QE/TTOF) tests to CiRT to repro as before.

Can you share a diff? I'm not able to reproduce it with either CiRT or automatic on the DiaTtofTutorial.

OK so it happens with the small dataset but not the big one (importing only target_protein_sequences.fasta vs napedro_3mixed_human_yeast_ecoli_20140403_iRT_reverse.fasta . So I guess the CiRT peptides that get picked for the small dataset are not in target_protein_sequences.fasta, but they are in the full FASTA. So the iRT group gets added in the former case but not the latter. But for some reason later on in the import process it thinks the CiRT peptides are NOT in the document. :/ Does this make sense to you?
I can't give you a repro yet because I'm still uploading the DIA-PASEF tutorial zip, but I would expect it to also happen on the DiaTtofFullSearchTutorial (and not on DiaTtofTutorial).

OK yes it does repro on DiaTtofFullSearchTutorial (after setting IrtStandard to CIRT_SHORT).

Yeah, I can reproduce it with the other dataset, looking into it now.

It turned out that if we add the iRT standards before showing the dialog, some of the refinement stuff the dialog does can end up removing them (and of course if we add them after, the counts shown on the dialog don't include them). So we have to be a bit careful about the order things happen, should be fixed now.

Note that TeamCity only runs the Ttof version. The other 3 versions of that test are going to need updating too. I'm running them now.

It is a big time commitment, but when making changes that could impact tutorial log files, you really need to run everything under Tutorials and all test in Perf that end with "Tutorial" in 3 languages (English, Chinese, Japanese).

So far this change doesn't affect audit logs; other than localization issues, wouldn't only an audit-log-matching failure in one language indicate the need to run and update all the other languages? It occurs to me now that perhaps that's not right in this case. Should adding iRT peptides independent of the FASTA result in an audit log entry? I can see it either way (because it's part of the wizard so as long as the wizard execution is audited the iRT adding can be considered implied).

I guess maybe not. This change in ordering was intended to fix what the UI says the document will contain. What actually happened to the document did not change. I probably just have audit logs on the brain from doing the translation updates. Carry on.

Looks like only the non-full-search tutorials changed counts.
For TestDiaQeDiaUmpireTutorial:
TargetCounts = new[] { 14, 185, 215, 1289 },
FinalTargetCounts = new[] { 10, 185, 215, 1289 },
For TestDiaTtofDiaUmpireTutorial:
TargetCounts = new[] { 14, 213, 277, 1661 },
FinalTargetCounts = new[] { 10, 213, 277, 1661 },

Thanks Matt, I updated the tutorials with those numbers. Shall I go ahead and merge this then?

I just force-ran the perf tests. If all tests pass, it's ready.


Fix of issue #10479

Fixes issue #10479

Can one of the admins verify this patch?

I signed it

@smksyj any luck with this?

Nagging Awaiting Response: It has been 14 days with no activityand the awaiting response label was assigned. Is this still an issue?

Nagging Awaiting Response: It has been 14 days with no activityand the awaiting response label was assigned. Is this still an issue?

Nagging Awaiting Response: It has been 14 days with no activityand the awaiting response label was assigned. Is this still an issue?

Please reopen when you've had a chance to finish this PR.


rename .release into .removecycle

Rename .release into .removecycle, the .release command will exists as an alias for the new goal.

used version.clearcycle instead as name.


No change to generated code when required is set to true or false for formData parameter of type file

Problem statement
When I set the required property to false (which i believe should be default behavior)  for a  formData parameter with type set to file and name set to attachment1 as shown below,  the generated code within the BindRequest func makes attaching a file mandatory. An http.ErrMissingFile is returned from the r.FormFile func and a 400 response is returned to the client.  In short setting the required property to false in a formData parameter with type set to file is not working.
Swagger specification
post:
  tags: ["emails"]
  summary: Received parsed emails from sendgrid
  description: |
     Received parsed emails from sendgrid via sendgrid inbound parse api
  consumes:
    - multipart/form-data
  parameters:
    - in: formData
      name: to
      type: string
      description: The recipient of the parsed email
    - in: formData
      name: from
      type: string
      description: The sender of the parsed email
    - in: formData
      name: subject
      type: string
      description: The subject of the parsed email
    - in: formData
      name: html
      type: string
      description: The html body of the parsed email
    - in: formData
      name: attachments
      type: string
      description: Attachments included in the inbound email parse
    - in: formData
      name : attachment-info
      type: string
      description: The attachment file information including number of files, content types and file names
    - in: formData
      name: attachment1
      required: false
      type: file
      description: The first file attached to the email
    - in: formData
      name: attachment2
      required: false
      type: file
      description: The second file attached to the email
    - in: formData
      name: attachment3
      required: false
      type: file
      description: The third file attached to the email
  responses:
    200:
      description: Emails request received
    500:
      description: Internal server error

Generated Code
------ Generated params struct snippet:
type PostSendgridEmailsParams struct {

	// HTTP Request Object
	HTTPRequest *http.Request

	/*The attachment file information including number of files, content types and file names
	  In: formData
	*/
	AttachmentInfo *string
	/*The first file attached to the email
	  In: formData
	*/
	Attachment1 runtime.File ....

-------Generated BindRequest func snippet:
// BindRequest both binds and validates a request, it assumes that complex things implement a Validatable(strfmt.Registry) error interface
// for simple values it will use straight method calls
func (o *PostSendgridEmailsParams) BindRequest(r *http.Request, route *middleware.MatchedRoute) error {
	var res []error
	o.HTTPRequest = r

	if err := r.ParseMultipartForm(32 << 20); err != nil {
		if err != http.ErrNotMultipart {
			return err
		} else if err := r.ParseForm(); err != nil {
			return err
		}
	}
	fds := runtime.Values(r.Form)

	fdAttachmentInfo, fdhkAttachmentInfo, _ := fds.GetOK("attachment-info")
	if err := o.bindAttachmentInfo(fdAttachmentInfo, fdhkAttachmentInfo, route.Formats); err != nil {
		res = append(res, err)
	}

	attachment1, attachment1Header, err := r.FormFile("attachment1")
	if err != nil {
		Log.Error("failed to get attachment 1 in generated code", "error", err)
		res = append(res, errors.New(400, "reading file %q failed: %v", "attachment1", err))
	} else {
		o.Attachment1 = runtime.File{Data: attachment1, Header: attachment1Header}
	}

Environment
swagger version: 0.11.0
go version: 1.8.1
OS:   Sierra

I'm seeing the same thing.

Current work around:
patched the code in the BindRequest func that is generated from the formdata parameters provided in spec. The  BindRequest func validates the payload, i believe in the middleware, bc my handler is never hit if the payload is malformed. Also had to add some logic in my handler to deal with pointer derefs if not all three files were included in the payload, and updated swagger generate build script to keep patch in place during spec generations. I attached a file containing the patch.
bindRequest.txt

merged the fix for this


Make config a bit more formal

What do you like/dislike about this as a shape?

We could summarize the points we want to fix/improve and then come up with something that addresses them, so like:

Better user experience if any config value is missing.
Type validation with callbacks or something like that.
Accept .yaml/.json config files.
Sensible defaults whenever possible.
In general, I like your idea of having named tuples as our model for handling the config values.

As for the configuration requirement goes, only the session config is strictly required (host, port, username and password), as everything else can be set as part of the class constructor or being set through instance attributes, so we can make those a static check/pre-requisite. Some stuff like SSH/WinRM credentials are only needed if you want to run those commands, so we probably want a dynamic check for those (I'm doing sort of a similar thing in the code of those functions, but that could be abstracted out to be a config responsibility). So something like this would be neat:
from vcdriver.config import requires

def foo():
    with requires('vm_ssh_username', 'vm_ssh_password'):  # Check for env vars, .yaml and .json and throw some nice exception if missing
        print('foo')

We could also use the same singleton-in-a-module pattern I used to handle the session, just brainstorming here a bit :slightly_smiling_face:

My feeling on that is that having a default config is good, but the library shouldn't limit you to talking to one vsphere at a time, which enforcing that it is a singleton would do.

I've been batted away from this, I'm intending to come back to the deploying stuff at some point, but I don't know exactly when and don't want to leave this hanging.

The config is something that definitely needs to be improved and I appreciate your interest and the second pair of eyes on it. I have been thinking about it a bit more than the brainshitstorming of my previous comments, and I think the two main problems we should address are:

Better errors if config is missing
The fact that importing the config is not lazy, atm you have to set the environment before the imports

Furthermore, I would like to allow configuration injection at different levels:

Let the user pass parameters to the functions, if you don't mind about having more lines of code and don't want to fiddle with env vars or config files.
Look at the environment as a fallback for those missing parameters.
Optionally, let the user load config from some sort of .json, .yml or .ini config file.

If I can find a time gap before you do I'll try to move this forward!


Allow for returning results unwrapped

Both server and embedded modes

Call unwrapped in your query chain.

To clarify: What I meant in this issue was for us to expose an unwrapped option in #query

I'm currently working on the ROM (ruby object mapper) adapter for Neo4J. neo4j-core underlies this adapter. I've encountered confusing behavior:
Neo4j::Session.query("MATCH (n) RETURN n")  # query 1
Neo4j::Session.query.match('(n)').return(:n).to_enum  # query 2

These two queries return different types of objects. Both give enumerables of Hashes where the keys are symbol corresponding to returned items from the query. So far so good. But the values in the query 1 Hashes are Neo4j::Server::CypherNode objects, whereas those from query 2 are Hashes holding the REST response from the server. This is really counterintuitive to me-- why should Session#query give me a different kind of result depending on if I use a plain string or the Cypher DSL? After all, I assume that Query objects are rendered to Cypher anyway before being submitted to the server.
It would be good to be able to specify exactly how you want your results wrapped.

Interesting, that definitely sounds buggy to me.  I don't think I'm seeing quite what you are, though.  Here's what I get:
2.2.3 :012 >   Neo4j::Session.query("MATCH (n) RETURN n").first
 CYPHER 1247ms MATCH (n) RETURN n
 => #<struct n=#<GraphGist uuid: "dbc52679-2bfe-452c-ada8-4aa9b5a737d5", asciidoc: "= A small social networking website +\n\nThis database is a small example of a networking site where u", created_at: Thu, 29 Oct 2015 08:17:12 +0000, featured: nil, html: "
\n
\n
\n
This database is a small ex", legacy_id: "d0e4b90e9d6854183793e958706d24ed", legacy_neo_id: 415167, legacy_poster_image: nil, legacy_rated: "6.3", private: false, raw_url: "https://gist.githubusercontent.com/RaulEstrada/8389170/raw/3eda0620bbfb5993700bef6c28d61fa0bbce559a/", status: "live", summary: "This database is a small example of a networking site where users can watch movies, subscribe to TV ", title: "A small social networking website
", updated_at: Wed, 04 Nov 2015 06:06:17 +0000, url: nil>>
2.2.3 :013 > Neo4j::Session.query.match('(n)').return(:n).to_enum.first
 CYPHER 289ms
  MATCH (n)
  RETURN n
 => #<struct n=#<GraphGist uuid: "dbc52679-2bfe-452c-ada8-4aa9b5a737d5", asciidoc: "= A small social networking website +\n\nThis database is a small example of a networking site where u", created_at: Thu, 29 Oct 2015 08:17:12 +0000, featured: nil, html: "
\n
\n
\n
This database is a small ex", legacy_id: "d0e4b90e9d6854183793e958706d24ed", legacy_neo_id: 415167, legacy_poster_image: nil, legacy_rated: "6.3", private: false, raw_url: "https://gist.githubusercontent.com/RaulEstrada/8389170/raw/3eda0620bbfb5993700bef6c28d61fa0bbce559a/", status: "live", summary: "This database is a small example of a networking site where users can watch movies, subscribe to TV ", title: "A small social networking website
", updated_at: Wed, 04 Nov 2015 06:06:17 +0000, url: nil>>

I notice in the gemspec for rom-neo4j that the version is tied specifically to 5.1.3.  Could you try making that ~> 5.1.11  so that it includes the latest fixes and so that people can update easily to the latest in the 5.1 series?  We try our best to follow the http://semver.org/ conventions so it should be safe to do so.
Also be aware that we're about to release 6.0.0.  You can try that out by specifying ~> 6.0.0.rc.
Lastly, you should also be aware that we've been working on a new Session API which will replace the current one.  That's available off to the side in 6.0.0 but it's a bit beta right now.  Hopefully we'll be able to release it as part of an upcoming major version (with deprecation warnings) and hopefully it will help us fix any inconsistencies like what you're seeing.
Let us know if upgrading your version doesn't help.

Thanks @cheerfulstoic, I thought I was using the latest version but I must not have been. Using 5.1.11, I no longer see the REST hash response. Both queries now give me the same CypherNode objects. I don't see the structs that you show in your response, but I assume those must be part of 6.0.0.
In any case, it would still be good to be able to specify the return format. For rom-neo4j, I am going to need to convert nodes to hash form anyway, so it will just be extra overhead to have them automatically wrapped.
If wrapping the REST responses brings s

Agreed about controlling the response format!
What are you seeing instead of structs?

So as it turns out there was a monkey patch to rom-neo4j that was causing the issue. It was coercing the results of Query#each to hashes but not those from the Enumerator you get if you call Session::query with a raw Cypher string.

Oh gosh! ;)  Glad that you got that figured out, thanks!


Resuming the training from a checkpoint isn't possible if FP16Optimizer is used.

A similar problem was fixed in #424. The fixed optimizer is now called MemoryEfficientFP16Optimizer, and FP16Optimizer now doesn't work, since it's similar to the one before it was fixed in #424. MemoryEfficientFP16Optimizer is slow, so I wonder if you can modify the current FP16Optimizer to accommodate the resuming option without making it slower? If you don't have a time to do it any time soon, could you give me a brief advice on how to fix it?

Can you provide more details? Resuming models trained with --fp16 should work as expected. The --memory-efficient-fp16 optimizer is somewhat experimental, but should it also be possible to resume models trained with it.


Fixed typographical error, changed accross to across in README.

@cousine, I've corrected a typographical error in the documentation of the mebla project. You should be able to merge this pull request automatically. However, if this was intentional or if you enjoy living in linguistic squalor, please let me know and create an issue on my home repository.

Thanks @orthographic-pedant :)


Adds createComponentAtom util to use Ember.Component as atoms

This rebases #59.
Adds:

addAtom action on yield from {{ember-mobiledoc-editor}}
exports createComponentAtom (similar to createComponentCard) from 'ember-mobiledoc/utils/create-component-atom'

Includes some cleanup to the integration tests for clarity.

@rlivsey Thanks for your help on this! Will be released soon

released in v0.4.2


GAE: Removed usage of /etc/ca-certificates.crt

Removed a StreamHandler config line that caused my appengine to log a notice every time i made an api call. ("Unsupported SSL context options are set. The following options are present, but have been ignored: allow_self_signed, cafile")
The reason for this is that guzzle sets the cafile and allow_self_signed options when you use the verify-option and both are not supported by GAE.
Removed the config line and everything works fine now. Maybe it was needed in a past version of the GAE?

Signed it.

Ok, seems like this didn't fix anything at all. Don't know why but these notices are back even it worked like an hour ago.
Will close this pull request now and maybe open a second one if i will ever fix this issue.


add cli option to download all manifests

Add all-manifests option to both ctr content fetch and ctr images pull. By default it is false.
TheAppendDistributionSourceLabel in RemoteContext will only works when this option is set.

Fixes #3126
Signed-off-by: Yu Yi<EMAIL_ADDRESS>

If we agree to change default behavior, then this test would also have to be modified:
--- FAIL: TestImagePullSomePlatforms (0.31s)
    client_test.go:285: content digest sha256:c84b0a3a07b628bc4d62e5047d0f8dff80f7c00979e1e28a821a033ecda8fe53: not found

@estesp Thanks for the reminder and this is my first PR so that I was not aware some tests would not run without root. Sorry about that!
I've reverted the behavior in:https://github.com/containerd/containerd/commit/506b8154838c0561ec51c497c34e7f1c611bb356#diff-51565ad50d7426213c2f33579f6f0e71 and the test passes now.

Had an offline discussion with @alculquicondor and it seems we need two flags here:

all-manifests: for ctr content fetch and ctr images pull, containerd downloads all manifests/configs, but not layers.
store-distribution-source: containerd downloads all manifests/configs + the target platform's layers.

@dmcgowan Do you think my understanding is right?

carried


Remove unused function

SetPinnedStatusTimeStampByIndex is not called, all calls are to SetPinnedStatusTimeStamp with a quest id instead of a journal id

Thank you for your suggestion.
Yes, there are some unused methods.
However, we would like to keep them as we may use these methods somewhere in the future.
Since you made a pull request, let me explain a little more about the design intent for this section.
The vanilla Quest Journal screen and standard Quest Tracker provided by ESOUI do not use quest-specific IDs, but instead use a Lua Index called Journal Index. In other words, ESOUI intentionally does not provide an API for add-ons to retrieve QuestId.
The unused method you propose to remove was prepared to bypass the standard UI's Quest Journal, etc., and might be used, for example, to add keybindings to the Quest Journal screen to pin specific quests.
We have not yet tried to implement such a feature to avoid conflicts with CQuestTracker and other quest journal add-ons, but it may be offered in the future as a separate design to make quests more manageable. Stay tuned.

Thanks for the context! Do you mind issues/PRs in this repo? If you'd prefer not, I'll avoid doing so.

Thanks for your interest in this repository. As you have probably noticed, I am currently having difficulty devoting time to add-ons. So, while it would be great if I could manage the GitHub project better, realistically it would be difficult for me to respond to any new Issues you write here.
Pull requests for translation improvements remain open. For feature improvements and ideas, I'd appreciate it if you post them in my add-on Feedback.
I would also appreciate it if you could post about bugs in the feedback. If you can provide me with a way to reproduce them, I will do my best to address them. ;-)


[6.x] Serialize not stored model

Clone of #33655, but for 6.x

This PR provides a failing test if a to-be-serialized model is not stored to the database.
See this example code:
class ProcessPodcast implements ShouldQueue
{
    use Dispatchable, InteractsWithQueue, Queueable, SerializesModels;

    // at serialization time, this is a fresh model instance, but it's not stored to database. So obviously it has no Key yet.
    // trying to get the key for serialization will fail.
    protected Podcast $podcast;

    public function __construct(Podcast $podcast)
    {
        $this->podcast = new Podcast();
    }

    public function handle(AudioProcessor $processor)
    {
        $this->podcast->save();
    }
}

It might be edge-case, but in our usecase we use temporary model instances, so instances that are never actually stored to the database.
I made the PR for the failing test first to provide a platform for a short discussion, as i did not yet get a response to my comment in #33637. I'm willing to realize the fix, but I'm not sure how to do it.

Suggested Fix
I've got two possible solutions:

we check getQueueableEntity() for null here
we expand the interface for a isQueuable() method (breaking change)

I think the getQueueableEntity() for null makes sense but will the model still be restored that way or will it be null upon unserialization?

Hi
I made a mistake in my suggestions. I wanted to propose to additionally check for !is_null($value->getQueueableId()), so if the ID is null, it's obviously not stored. I proofed this to work in our environment, so I think it should be safe to implement for 6.x/7.x (backwards compatible fix).
But a team member did give the input that a key != null is still not a sure indication if the model is stored, which is now obvious to me. It's technically possible to set the key manually, especially likely if not-autoincrementing keys are used.
This leads me now to the suggestion, that to expand the interface for a new method might be the best approach. So something like:

isIdentifiable() (close to the ModelIdentifier-class)
isPersisted() (closer to the Model-domain)

This would be 8.x-branch (backwards compatible fix from above would still make sense in this PR, it's better than no check)

Thanks for your pull request to Laravel!
Unfortunately, I'm going to delay merging this code for now. To preserve our ability to adequately maintain the framework, we need to be very careful regarding the amount of code we include.
If possible, please consider releasing your code as a package so that the community can still take advantage of your contributions!
If you feel absolutely certain that this code corrects a bug in the framework, please "@" mention me in a follow-up comment with further explanation so that GitHub will send me a notification of your response.

@kohlerdominik I guess for now we won't be supporting non-existing models. Can you maybe PR that to the docs?


[xchain-terra] Add getDefaultFees helper

Similar to other xchain-* packages

Resolved by https://github.com/xchainjs/xchainjs-lib/commit/63c63aa16b0c4d36b27a532d9096fe59d42e841e


Update request: cura 4.13.1 → 5.8.1

Package name: cura
Latest released version: 5.8.1

Current version on the unstable channel: 4.13.1
Current version on the stable/release channel: 4.13.1

[x] Checked the nixpkgs pull requests

Notify maintainers

@abbradar
@gebner

Note for maintainers: Please tag this issue in your PR.

Add a :+1: reaction to issues you find important.

Dupe of #186570. Unfortunately working with Conda is akin to self-flagellation and no one has quite figured out how to make it work just yet without repackaging the upstream AppImage


CUDA runtime error

NOTE: if this is not a bug report, please use the GitHub Discussions for support questions (How do I do X?), feature requests, ideas, showcasing new applications, etc.
Bug description
Please enter a clear and concise description of what the bug is.
RuntimeError: CUDA error: CUBLAS_STATUS_INTERNAL_ERROR when calling cublasCreate(handle)
environment: Pytorch 1.9.0 (py3.7_cuda10.2_cudnn7.6.5_0)
This error did not occur in the previous ESM version, the version without ESM-1v
Reproduction steps
Enter steps to reproduce the behavior.
Expected behavior
Give a clear and concise description of what you expected to happen.
Logs
Please paste the command line output:
Traceback (most recent call last):
File "main.py", line 209, in
main()
File "main.py", line 204, in main
explorer.run(landscape, verbose=False)
File "/home/hury/local/bio/fasthit/fasthit/explorer.py", line 171, in run
encodings = self._encoder.encode(training_data["sequence"].to_list())
File "/home/hury/local/bio/fasthit/fasthit/encoders/esm.py", line 99, in encode
extracted_embeddings[i] = self._embed(temp_seqs, combo_batch)
File "/home/hury/local/bio/fasthit/fasthit/encoders/esm.py", line 130, in _embed
toks, repr_layers=[self._pretrained_model.num_layers], return_contacts=False
File "/home/hury/.local/lib/python3.7/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/hury/local/bio/fasthit/fasthit/encoders/esm/esm/model.py", line 160, in forward
x, self_attn_padding_mask=padding_mask, need_head_weights=need_head_weights
File "/home/hury/.local/lib/python3.7/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/hury/local/bio/fasthit/fasthit/encoders/esm/esm/modules.py", line 130, in forward
attn_mask=self_attn_mask,
File "/home/hury/.local/lib/python3.7/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/hury/local/bio/fasthit/fasthit/encoders/esm/esm/multihead_attention.py", line 223, in forward
v_proj_weight=self.v_proj.weight,
File "/home/hury/.local/lib/python3.7/site-packages/torch/nn/functional.py", line 4702, in multi_head_attention_forward
q = linear(query, q_proj_weight_non_opt, in_proj_bias[0:embed_dim])
File "/home/hury/.local/lib/python3.7/site-packages/torch/nn/functional.py", line 1753, in linear
return torch._C._nn.linear(input, weight, bias)
RuntimeError: CUDA error: CUBLAS_STATUS_INTERNAL_ERROR when calling cublasCreate(handle)
Output goes here

Additional context
Add any other context about the problem here. (like proxy settings, network setup, overall goals, etc.)

Sounds like either something got broken in your environment, or most likely the wrapper library put things in an unexpected state.
One guess: is the input not moved to cuda device while the model is, or vice versa?
Can you confirm whether or not you can run pytest tests/test_readme.py ? (which calls extract.py which uses GPU when available)

Thanks a lot!
It is that something got broken in my environment. Exactly!


⚠ Bofrost.lu has degraded performance

In 04402e8, Bofrost.lu (https://www.bofrost.lu) experienced degraded performance:

HTTP code: 200
Response time: 1741 ms

Resolved: Bofrost.lu performance has improved in e84b29e after 20 minutes.


Please guide in rendering the mesh?

I've tried different attempts using https://github.com/andreasplesch/aframe-triangleset-component but I get this from the Mount Fuji 256 data:

Which doesn't look to me like the input image:

Mainly because of the pointy spikes.
I'm using
<a-triangleset projectdir="z" material="roughness: 0.2; metalness: 0.3; color: green" rotation="90"  position="-60 -100 30" scale="1 1 1" :vertices="this.vertices" :triangles="this.triangles"></a-triangleset>
Where this.vertices and this.triangles come from a Vue component that uses the data from tile.getMesh(80) to make these attribute data.
Please advise what am I doing wrong?
Thanks for your guidance.

Here's my result when using getMesh(30).

@andreasplesch maybe this is a question for you? ;-)
I also tried rendering with pythreejs but found similar issues.

Obviously I'm a beginner at 3D!
Thanks in advance for you kind advice.

Please provide a live example, on glitch, jsfiddle,

Thanks @andreasplesch - I should've done from the beginning but I have a backend in Python3.7 that is not compatible with Glitch's older interpreter.
I've implemented a new project doing only this:
https://glitch.com/~mahogany-eggplant
My loose aim is to make a libre ("open source") videogame / interactive-experience about the Amazonian rainforest (where I live). Thank you for your nice components!

This is likely because the vertex data generated by this library is [x, y] pairs, without the z component (assuming the renderer will look these up through a texture). I probably need to add more docs for this, and maybe add an option to add z to the buffers too.

Yes, the z component is expected to be provided with the x and y. It would be not too hard to inject the z value from the data into the returned vertices array with something like:
for (i=0, j=0; i<mesh.vertices.length/2; i++) {
  let  x = this.vertices[i*2], y = this.vertices[i*2+1];
  this.vertices.push(x);
  this.vertices.push(response.data[y*257+x]/100);
  this.vertices.push(y);
}

Typically, some processing has to be done anyways after the meshing, the returned x and y coordinates are just grid coordinates, not spatial coordinates.
[The up component is typically y]
[Also, I am not sure how well the component still works with current three and aframe]

Thanks @mourner and @andreasplesch !

That was the missing insight. I did a little tweaking to your snippet and it worked wonderfully!
Thank you  both for the Library and the Component (it works fine with AFrame 0.9.2)
Here's the code and test:
https://glitch.com/~mahogany-eggplant
https://mahogany-eggplant.glitch.me/
@mourner Yes a little more docs would be great, I loved the demo but couldn't figure it out.
Thanks!
:heart:

@icarito  your glitch test is deleted?

@arpu sorry I just renamed to a more accurate name!
https://glitch.com/~mount-fuji

@icarito  thx for poinzing me to the new glitch
but i get
three.js:11050 THREE.DirectGeometry: Faceless geometries are not supported.
and nothing is shown

The mesh can be seen at the bottom, I didn't center it or anything - look to the bottom left

bottom right I mean, sorry it's not in front of the camera

ahh :> thx, great example!

Let's reopen the issue until we have a proper demo / better docs.

@mourner  testing with aframe master and THREEjs v108 and BufferGeometry
https://codesandbox.io/s/aframe-draco-gltf-model-plus-77rld

some update
https://codesandbox.io/s/aframe-draco-gltf-model-plus-o7zki

@mourner  what to you think on return the indices on getMesh ?
than the BufferGeometry could be set with setIndex()
https://threejs.org/docs/#api/en/core/BufferGeometry.setIndex

@arpu that's what triangles property in the returned object contains.

awsome works! i update the codepen on https://codesandbox.io/s/aframe-draco-gltf-model-plus-o7zki

@mourner one more question how can set the createTile with a BufferGeometry?
the idea is bevore i use Martini i use
  var geometry = new THREE.Geometry().fromBufferGeometry(this.groundGeometry);
    geometry.mergeVertices();
    var modifier = new THREE.SubdivisionModifier(1);
    var smoothGeometry = modifier.modify(geometry);
    if (this.groundGeometry) this.groundGeometry.dispose();
    this.groundGeometry = new THREE.BufferGeometry().fromGeometry(smoothGeometry);

to get a smoother Terrain bevore Martini


Errror running on Linux with Flutter from snap

Checklist

[X] I read the troubleshooting guide before raising this issue
[X] I made sure that the issue I am raising doesn't already exist

Current bug behaviour
First install Flutter from snap (and set up Android Studio or VS Code)
Then add dependencies into pubspec.yaml into arbitrary app:
audioplayers: ^5.1.0
Then   flutter pub get
Then   the app can not start.
With this error:
[ +121 ms] [7/8] Linking CXX executable intermediates_do_not_run/test3
[        ] FAILED: intermediates_do_not_run/test3
..
[        ] clang: error: linker command failed with exit code 1 (use -v to see invocation)
[   +1 ms] ninja: build stopped: subcommand failed.
Always. Easy to reproduce.
Can not start the app because of audioplayer :(
Expected behaviour
App should run with Flutter from snap too. We should use it if we want to publish app into snap store.
Flutter apps that use snap store flutter can not start (can not publish) with audioplayer.
The problem occurred from the published audioplayer 1.0 (version 5.1 too).
But:
audioplayer version 0.20, 0.18 was ok.
Steps to reproduce

Add audioplayers: ^5.1.0 into dependencies in arbitrary apps. Like in flutter demo.
Execute flutter run -d linux
And the app can not start

If you would like to see a good one:

Add audioplayers: ^0.18.0 into dependencies in arbitrary apps. Like in flutter demo.
Execute flutter run -d linux
And the app can start without error
:)

Code sample

Code sample
void main() {
}

dependencies:
 audioplayers: ^5.1.0

Affected platforms
Linux
Platform details
Ubuntu 23.04, not relevant
AudioPlayers Version
all from 1.0
Build mode
debug, release
Audio Files/URLs/Sources
No response
Screenshots
No response
Logs

[  +93 ms] [6/8] Linking CXX shared library
plugins/audioplayers_linux/libaudioplayers_linux_plugin.so
[ +121 ms] [7/8] Linking CXX executable intermediates_do_not_run/test3
[        ] FAILED: intermediates_do_not_run/test3
[   +4 ms] : && /snap/flutter/current/usr/bin/clang++  -g

[        ] clang: error: linker command failed with exit code 1 (use -v to see invocation)
[   +1 ms] ninja: build stopped: subcommand failed.

Related issues / more information
No response
Working on PR
no way

See #1631

For an example to publish to snap store, see also: https://github.com/jyotirmoy-paul/planets/blob/main/snap/snapcraft.yaml


Create enum for z-index values

Pull Request
🤨 Rationale
Fixes #539
👩💻 Implementation

Created a new enum in utilities with a few preset z-index values.
Updated stylesheets for anchored-region, dropdown, and table to use enum values rather than hardcoded values.

🧪 Testing
None
✅ Checklist

[x] I have updated the project documentation to reflect my changes or determined no changes are needed.

@atmgrifter00 could you please buddy this?


Fix glTF capitalization in class reference

Fixes https://github.com/godotengine/godot-docs/issues/9750 in the class reference.
Replaces user-facing mentions of "GLTF" with "glTF". This includes the class reference and the README.md of the glTF module.
Does not replace capitalized class names like GLTFAccessor. Does not affect any comments in .cpp or .h files.
There are some remaining user-facing uses of "GLTF" in error messages, for example here. I feel these are out of scope to fix in this PR.
https://github.com/godotengine/godot/blob/ee363af0ed65f6ca9a906f6065d0bd9c19dcf9e4/modules/gltf/extensions/gltf_light.cpp#L184

Thanks!


Feature/media crud

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MSI Token provider does not work from AppServices -works from VMs.

The current SDK 3.4, does not include ManagedServiceIdentityTokenProvider from the previous implementation. Instead, the SDK has the ManagedIdentityTokenProvider, which only works on VMs.

The name change was just a rename.
Functionality was changed to rely upon Azure Authentiction Library, instead of Service Bus SDK getting MSI tokens from a hardcoded URL. In the process, it looks like full functionality provided by the old way was not provided.

It is an oversight as we didn't test it outside of Azure VMs. But the new Service Bus SDK 'Azure-ServiceBus-Messaging' must be up to date with all authentication mechanisms. Please switch to the new SDK if you can.


fxck: more css3 problems

More of that CSS3 stuff. It tries to load css gradients and obviously fails..
“NetworkError: 400 Bad Request – http://recyplast.cms.local/-moz-linear-gradient(50%%200%”
http:/…0%%200%
“NetworkError: 404 Not Found – http://recyplast.cms.local/191”
191
“NetworkError: 404 Not Found – http://recyplast.cms.local/77”
77
“NetworkError: 404 Not Found – http://recyplast.cms.local/47”
47
“NetworkError: 404 Not Found – http://recyplast.cms.local/166“
On this page http://recyplast.f13.cz/ (you have to refresh a couple of times, sometimes it works, sometimes it doesn’t) and in chrome 15.0.874.106, on the other hand, it happens only on my site, not in your demo, so it’s most likely colliding with the other plugins, or my rather messy code itself.

Solved, will be pushed in next commit

Hey gaya,
Just grabbed the latest commit, but my CSS3 Gradients are still hanging up the loader and getting failures to load resources in Chrome 15.0.874.121.
"Failed to load resource: the server responded with a status of 404 (Not Found)"
When I give the problematic divs (with css gradient bgs) a no preload class, it works fine...


new version 4.72

Hi! This is the friendly automated conda-forge-linting service.
I just wanted to let you know that I linted all conda-recipes in your PR (recipe) and found it was in an excellent condition.


Hugo final 1.7 Documentaiton PR -- please read carefully

This PR fixes problems found in review yesterday.  Review so you understand, then, do the following:

Merge into master branch.
Cherry-pick merge commit into the branch where you want me to release docs from.
Send me an email with titled Release Kitematic Docs from BRANCHNAME

Unfortunately, while I fixed the majority of problems with your content there are a few remaining ones that  we must address after the release these are:

couple # links are not working; confirmed sed scripts not the problem
couple links to .md files in a repository are not working (.md)

This is awesome! LGTM
Thanks Mary. Merging

Thanks to Kitematic!


Meta refresh broken for non admin users

Non admin users get a null pointer error when trying to do meta data refresh on Exasol database in any menu/F5 option.
It seems to me  that plugins/org.jkiss.dbeaver.ext.exasol/src/org/jkiss/dbeaver/ext/exasol/model/ExasolDataSource.java. the logic to retrieve a user has getUserHasDictionaryAccess return a non authorize error. A normal user cannot access this SYS table. Maybe it is better to do a:
SELECT PRIVILEGE
FROM SYS.EXA_SESSION_PRIVS
WHERE PRIVILEGE = 'SELECT ANY DICTIONARY';
This is a user table and when a result is retrieved it means that the user has DICTIONARY access to the SYS level tables.
Maybe it helps...
System information:

Windows server 2016 datacentre
DBeaver version <IP_ADDRESS>103211229
All the standard one plus devstyle

Connection specification:

Exasol 7.0.3 db driver 7.0.7
Driver com.exasol.jdbc.EXADriver
Do you use tunnels or proxies (SSH, SOCKS, etc)?

Steps to reproduce, if exist:
Default behavior
Include any warning/errors/backtraces from the logs
From meta query log
Error occurred during SQL script execution
Reason:
SQL Error [42500]: insufficient privileges for accessing view EXA_DBA_CONNECTIONS (Session:<PHONE_NUMBER>942083073)

Thanks for the bug report.
We fixed this problem in scope with https://github.com/dbeaver/dbeaver/issues/12379
Fix will be available on 2021-05-31 in DBeaver 21.1.0


Stock opname enhancemenet

Check box
Add keterangan value automatically
Upload 1 csv document

Coverage decreased (-0.8%) to 34.593% when pulling 9c8d4adfae1df3485fb4a076a56538ae881d3057 on uat into ff09193273eb2046d030110f92a05058e9e9c53f on master.


support backport, committing conflicts

Is your feature request related to a problem? Please describe.
backport doesn't always succeed, especially if the source and target have diverged, but it can still be useful to have a backport PR with conflicts committed that someone can then take to completion
Describe the solution you'd like
option to cherry-pick and commit conflicts instead of aborting
Describe alternatives you've considered
none, but would love to hear some

Hi @mvayngrib This is a great idea, which I'm sure my team would love as well. It will reduce the manual effort of opening the pull request and such.
As conflict can exist in any of the commits, I think it would be best to commit and push the conflicting commit and provide instructions in the pull request on how to resolve the conflicts and continue cherry-picking.
I'll keep this issue in mind when planning new feature work. Of course, I'd be happy to receive a contribution for this as well. Either way, thanks for the great suggestion.

@korthout I'll give this a try, if you have additional information, feel free to add them.

Here's what i currently have in mind, after investigation:
Proposition

cherry-pick every commit up until the first conflict encountered.

DO NOT commit the actual commit conflict.

As I see it, this would put additional burden on the user to actually figure out which files contains a conflict and then make sure to correctly remove comparison operators (i.e. <<<<<<<). Some IDE (i.e., VSCode) natively detect said operators as soon as you open a file and propose you to resolve the conflict, but not all of them do that (i.e., Visual Studio).

push the branch

Proceed to create the PR as DRAFT

May add a label too? I'm open to suggestion.
I really want to avoid the user to merge an unresolved branch. My fear is that this workflow allows the user to approve his own backported PR in repos where only 1 reviewer is needed. The original user could commit this unresolved PR without another review if he does not push anything on the backported branch. At some point it might be a good idea to request reviews from the original approvers when creating the PR, but that would require additional work outside of the context of this issue.

Provide a set of instructions in the Draft PR to continue the cherry-pick.

Resolved by https://github.com/korthout/backport-action/pull/417 and released in v3.0.0.


special unicode character not visible

Version: 0.194.0
OS: Ubuntu 14.10
Some special character are not visible while editing like : ✔

Duplicate of #6214, please subscribe there for updates.


add cloud-specific-attributes for UCA

Code Coverage is 80.6%, details at https://s3.amazonaws.com/rs-code-coverage/rsc/2015-07-20_359_Coverage-80.6.txt (sometimes the URL can take a few minutes to be available)


PHIVE GPG Key Issue

Since today we have issues installing the phive package, whilst the key on the keyserver can't be found.
Did you change something regarding to this project?

Q
A

PHP CS Fixer Baseline version
2.0.0

PHP version
8.2.14

Installation Method
PHIVE

Summary
Phive installation with trusted gpg key can't be installed
Current behavior
Phive Package with Signature can't be installed with the following command:
How to reproduce
phive install --trust-gpg-keys 83F9945BC33EC39E9710206C8B4927076BA50A83
Expected behavior
Package with the above command can be installed (or with a different gpg key if updated)

Hi @sfaujour,
Key was changed. Try to install latest version signed by new key 9D0DD6FCB92C84688B777DF59204DEE8CAE9C22C?
FYI: you can use short fingerprint of key as value of argument  --trust-gpg-keys: 9204DEE8CAE9C22C

P.S.
Check my script "phive-install" in composer.json. Maybe this approach to the managing of trusted keys would be useful for you.

@Aeliot-Tm Thank you! We've updated the key and it works.


ProgressBar transition

Describe the feature in detail (code, mocks, or screenshots encouraged)
ProgressRadial supports a transition when value changes by default. I think same behavior should apply for ProgressBar. Currently the workaround to get the same behavior is by setting "transition-all" to ProgressBar meter prop.
What type of pull request would this be?
Enhancement
Provide relevant links or additional information.
No response

@Drelekin I was able to squeeze this in. The next release drops a week from Tuesday so keep an eye out for this. As discussed in Discord, when you update, revert the bg and transition classes you're adding now, as this should be applied by default now. Additionally both the Bar/Radial now include a transition prop if you wish to customize this class further.


Update Azure work item when a Quest-synced Issue is closed

We've often added new labels, or important comments to an issue when the issue is closed. When an issue is closed, update the internal description field with all updated labels and contents.

Associated WorkItem - 67483

closing, as this was already in place.


Make Variants pickle-able

Need an implementation of __getstate__ and __setstate__ to reduce EnsemblRelease objects to their version and then reconstruct that EnsemblRelease using pyensembl.cached_release.
See: https://docs.python.org/2/library/pickle.html#pickle-protocol

I haven't been following this work but why are we using pickle as our serialization protocol?

It's not the only one, I think the VCF serialization is more important.
But pickling is ubiquitous in Python, so we should support that too.
@timodonnell If you feel inspired while I'm gone: https://github.com/hammerlab/varcode/issues/18 (following ANN for effect annotations probably needs to be its own issue)

In general I strongly prefer cross-language serialization protocols to language-specific ones. Avro is particularly nice in Python as it doesn't require any codegen. I haven't really looked at the current use case but if the serialized data is expected to live longer than the program pickle is rarely a good idea.

I don't have any objections to to_avro(), to_json(), or whatever. But
you'll need to write something __getstate__ and __setstate__ for those
anyway.
On Wed, Apr 29, 2015 at 3:52 PM, Jeff Hammerbacher<EMAIL_ADDRESS>
wrote:

In general I strongly prefer cross-language serialization protocols to
language-specific ones. Avro is particularly nice in Python as it doesn't
require any codegen. I haven't really looked at the current use case but if
the serialized data is expected to live longer than the program pickle is
rarely a good idea.
—
Reply to this email directly or view it on GitHub
https://github.com/hammerlab/varcode/issues/77#issuecomment-97560209.


1.20.1 Fabric crash on startup (Modern Industrialization v1.7.0-beta)

Was told it was an issue with your version of Magna.
crash-2023-08-21_04.55.03-client.txt

SAME


basic app rebranding and skeleton

started on https://github.com/encointer/polkawallet-flutter/tree/encointer
[ ] integrate encointer artwork and naming
[x] keep "assets" and "profile", add a tab for "Ceremonies"
[ ] subscribe to CurrentPhase and display phase on "Ceremonies" tab
[ ] dummy button to send register_participant extrinsic without proof of attendance
[ ] endpoint selection: Gesell and Cantillon
Quickstart

install android studio & flutter
set up AVD device (hit Ctrl-Shift-A and type AVD Manager)
in WSL, runcd lib/js_service_kusama/
yarn install
yarn run build
cd ../js_service_encointer
yarn install
yarn run build

set up AVD device
"run"

it's not so easy to supply our custom types. Acala has it's own lib for this:
https://github.com/AcalaNetwork/acala.js
I've tried to provide it in js_service_encointer directly, but ts clashes with js


Update SolarSailNavigator.netkan

Plugin updated to work in KSP 1.0.5

closing to force a rebuild, as it required to update PersistentThrust first


Installation using composer doesn't works

@temp  hi,when I try to make installation using this command: composer update php-poppler/php-poppler , the installation doesn't happens and I encountered following error:
`Your requirements could not be resolved to an installable set of packages.
Problem 1
- The requested package php-poppler/php-poppler could not be found in any ve
rsion, there may be a typo in the package name.
Potential causes:

A typo in the package name
The package is not available in a stable-enough version according to your min
imum-stability setting
see https://groups.google.com/d/topic/composer-dev/_g3ASeIFlrc/discussion f
or more details.

Read https://getcomposer.org/doc/articles/troubleshooting.md for further commo
n problems.` Can u please look into this. Thanks.

Please try "composer require php-poppler/php-poppler" for installation.
After it is installed, you can update it with "composer update php-poppler/php-poppler".
But at first it has to be installed...
Please refer to https://getcomposer.org/doc/03-cli.md#require for further details.


Transition guide: how to check library version

As suggested here: http://code.opencv.org/issues/4346

is this guide contain information how to check library version in cmake? E.g. OpenCV 3.0 has different modules layout and a user should able to know with which modules he should link

:+1:


During extraction, we need to also extract code metadata descriptions and store those

See https://github.com/Medical-Event-Data-Standard/meds_etl/blob/main/src/meds_etl/mimic/init.py#L439C1-L471C1 for example.

Capability added in #50


Improve Tests/Samples

[ ] Don't do Assert.IsNull() for int/double properties, as we know codegen will always provide a default value. Instead check (if it makes sense) Great or GreaterEqual
[x] Make sure that in our samples, we manage the case where no values (entities, key phrases, etc) are returned
[x] Make return types of methods in our samples be explicit (Response<t>) instead of using implicit cast (T)

Missing work for:

Analyse sentiment

Unsure about:

Healthcare
Analyze

It looks like we are all good so we can close this issue