Form not rendering at all

Hey, have been trying to have a look through your examples and whenever i try to include any of the forms, it renders an empty element with nothing inside.
// Copied from demo - whole file on http://redux-form.com/4.2.0/#/examples/simple?_k=efd5lv
import React, {Component, PropTypes} from 'react';
import {reduxForm} from 'redux-form';
export const fields = ['firstName', 'lastName', 'email', 'sex', 'favoriteColor', 'employed', 'notes'];

class SimpleForm extends Component {
  static propTypes = {
    fields: PropTypes.object.isRequired,
    handleSubmit: PropTypes.func.isRequired,
    resetForm: PropTypes.func.isRequired,
    submitting: PropTypes.bool.isRequired
  };

  render() {
    const {
      fields: {firstName, lastName, email, sex, favoriteColor, employed, notes},
      handleSubmit,
      resetForm,
      submitting
      } = this.props;
    return (<form onSubmit={handleSubmit}>

          First Name

            <input type="text" placeholder="First Name" {...firstName}/>

          Last Name

            <input type="text" placeholder="Last Name" {...lastName}/>

          Email

            <input type="email" placeholder="Email" {...email}/>

          Sex

              <input type="radio" {...sex} value="male" checked={sex.value === 'male'}/> Male

              <input type="radio" {...sex} value="female" checked={sex.value === 'female'}/> Female

          Favorite Color

            <select
              {...favoriteColor}
              value={favoriteColor.value || ''}  // required syntax for reset form to work
                                                 // undefined will not change value to first empty option
                                                 // when resetting
              >
              <option></option>
              <option value="ff0000">Red</option>
              <option value="00ff00">Green</option>
              <option value="0000ff">Blue</option>
            </select>

            <input type="checkbox" {...employed}/> Employed

          Notes

            <textarea
              {...notes}
              value={notes.value || ''} // required for reset form to work (only on textarea's)
                                        // see: https://github.com/facebook/react/issues/2533
            />

          <button type="submit" disabled={submitting}>
            {submitting ?  : } Submit
          </button>
          <button type="button" disabled={submitting} onClick={resetForm}>
            Clear Values
          </button>

      </form>
    );
  }
}

export default reduxForm({
  form: 'simple',
  fields
})(SimpleForm);

It's rendering this
<rfbusinessform data-reactid=".1qxeljdgmps.<IP_ADDRESS>.0.0.0"></rfbusinessform>

Please use StackOverflow for usage questions. This is the only way to ensure the same question doesn’t get asked many times in a row, and that folks who have the same question in the future will be able to find it. (Github search is pretty much useless.)
Sorry we couldn’t answer your question here. Please feel free to post a link to the StackOverflow question, and I’ll be happy to try to answer it.


Titlefix

#99

@Lakshya2610 whenever you get the chance can you update the backend on aws. Thanks


Update Scroll Alpha to Scroll Sepolia

Scroll Alpha (on Goerli) is deprecated in favor of our long-term support testnet, Scroll Sepolia (on Sepolia ;) )
We had a community member asking about access on revoke.cash, and it'd be great to see Scroll Sepolia included in the testnets.
Scroll Sepolia Network Info
Scroll Sepolia on Chainlist.org

Hey, thanks for the request. We've just replaced Scroll Alpha with Scroll Sepolia 🫡


[Soft Crash-1.12] Issue when you try to feed a parrot with wheat seed

When i try to feed a parrot (which have 5/6 health) VanillaFix drops me this soft crash (tell me if need to provide more info, i think all is in the crash report)
Crash Report: https://pastebin.com/6ceGEVK5

many thanks


Support Claude3 haiku and others using litellm

Claude 3 haiku is cheaper than gpt3.5 and better, adding support for many other models and platforms can easily be completed by using litellm

If u want list me the models you would like to have

@TashaSkyUp it seems like a good addition!
We can support LiteLLM adding a new wrapper inside the models module.
ChatLiteLLM and ChatLiteLLMRouter

Closed here 0ab7272, now we can pass directly the llm instance in the graph_config and therefore can be integrated with litellm.
An example using Azure is the following:


Feat: Improper UI in quick links sections.

Describe the feature
In the quick links sections , the home,blogs,etc links are written like normal text. Buttons should be added to it so that it will create an proper UI
Add ScreenShots

Record

[X] I agree to follow this project's Code of Conduct
[X] I'm a GSSOC'24 contributor
[X] I want to work on this issue

It is already assigned


Can I use this in kotlin

I tried this:
@Parcel(implementations = arrayOf(test::class),
    value = Parcel.Serialization.BEAN,
    analyze = arrayOf(test::class))
open class test{
   open val name=""
}

But it does not work :frowning:

I have to admit, I havent spent much time on Kotlin, but I saw this post on Stackoverflow that looks similar, maybe you can model  your class after it:
http://stackoverflow.com/a/33892435/654187

Sorry but it not work to

Try asking your question on Stackoverflow as it will reach people with expertise in Kotlin: http://stackoverflow.com/questions/ask?tags=parceler+kotlin .  Feel free to link your question here if and when you post.

I have the same issue. I found only one post on stackoverflow: http://stackoverflow.com/questions/38910246/kotlin-realm-and-parcel

@dawidhyzy it looks like the issue you've referenced has to do with accessing the class statically at compile time.  Can you give me a concrete example that I can help you with?

It was my mistake. I should use kapt instead off apt.
I've created simple test if anyone have same issue: https://github.com/dawidhyzy/kotlin-sample/tree/parceler_realm

Ah, easy mistake.  If you're interested I'd like to add a Kotlin example to the project examples, would you care to expand the simple test program you have and pull it in?

Sorry for the long response but I was on vacation. Yes, of course, I will
do that.
wt., 4.10.2016 o 18:56 użytkownik John Ericksen<EMAIL_ADDRESS>napisał:

Ah, easy mistake. If you're interested I'd like to add a Kotlin example to
the project examples, would you care to expand the simple test program you
have and pull it in?
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/johncarl81/parceler/issues/144#issuecomment-251412977,
or mute the thread
https://github.com/notifications/unsubscribe-auth/ACXqyHXfNANmTr4-JH6u4anbASUTg7h7ks5qwmkhgaJpZM4G0K7f
.

https://github.com/grandstaish/paperparcel


ImportError: No module named 'voc'

How to reproduce
import tensorflow as tf
import tensornets as nets

inputs = tf.placeholder(tf.float32, [None, 416, 416, 3])
model = nets.YOLOv2(inputs, nets.Darknet19)

img = nets.utils.load_img('cat.png')

with tf.Session() as sess:
    sess.run(model.pretrained())
    preds = sess.run(model, {inputs: model.preprocess(img)})
    boxes = model.get_boxes(preds, img.shape[1:3])

from tensornets.datasets import voc
print("%s: %s" % (voc.classnames[7], boxes[7][0]))  # 7 is cat

Error trace
Traceback (most recent call last):
  File "main.py", line 14, in <module>
    from tensornets.datasets import voc
  File "/usr/local/lib/python3.5/dist-packages/tensornets/datasets/__init__.py", line 1, in <module>
    import voc
ImportError: No module named 'voc'

Possible fix
Commenting out the first (and only) line in /usr/local/lib/python3.5/dist-packages/tensornets/datasets/__init__.py fixes the issue. A real fix would probably deal with absolute and relative imports though.
Environment details
python3.5
tensorflow (1.6.0)
tensorflow-gpu (1.6.0)
tensorflow-tensorboard (0.4.0rc3)
tensornets (0.3.1)
darkflow (1.0.0)

your fix worked for me

The issue has been resolved by this patch. If you want to use the latest version, you can try pip install -U --no-deps git+https://github.com/taehoonlee/tensornets. Thank you for the comments, @KhaledSharif!


getNameWithExtension() returning a number

Uploading the image is working fine, however it is uploading an image that should be called

buttons.png

as

57a479252173e.png

Is there a way I can use getNameWithExtension to upload the correct name?

It seems like uniqid(). if you see at the README of project exists some optionally definition of file name using the uniqid function, maybe you accidentally copy that and now your code is change file name.

Thanks, this was the problem


Don't override nn.Module.parameters

We should come up with a different name that doesn't interfere with the nn.Module.parameters method

transform_parameters?

Sure, that works for me


PLT-37: Extend ECR job to make immutable/mutable repositories

[ ] Allow users to make a newly-created ECR repository immutable by image tags.

Your development orb has been published. It will expire in 30 days.
You can preview what this will look like on the CircleCI Orb Registry at the following link:
https://circleci.com/developer/orbs/orb/premiscale/general?version=dev:426637bfc43d41af648cda54c07a52fcbf66ef6b


[BUG] project method for find query doesn't work

Describe the bug
project method for find query doesn't work. It only return the whole Document instead and is same with projection_model parameter in find method.
To Reproduce
code below returns whole Document.

class UpdatedAtResponse(BaseModel):
    id: Annotated[PydanticObjectId, Field(validation_alias=AliasChoices('id', "_id"))]
    updatedAt: datetime

await Article.find().project(UpdatedAtResponse).to_list()

It works only if i get this through with Settings, projection config like below code as written in beanie official document
class ProductView(BaseModel):
    name: str
    category: str

    class Settings:
        projection = {"name": 1, "category": "$category.name"}

Expected behavior
I hope project method works properly.

Hi, thank you for the report.
I'm unable to reproduce this. Please provided a minimum reproducible example that is fully runnable.
ProductView is not a proper View class with Beanie required fields: source, pipeline.


Retry request with new Location on 307 response

Nest API requires that all 307s are followed. The new location should
become the new base URL for all further requests.

Two things:

It doesn't seem to be saving the new URL as the base for all further requests.
Getting the new base URL might be a bit hacky, but I was having issues trying a regex, and this gives me exactly what I was looking for.

When does Firebase send back the temporary redirects? I've only ever seen them redirect with SSE

It's mentioned in the streaming section:
https://www.firebase.com/docs/rest/api/#section-streaming
I've run into it when trying to use the same Fb connection and Set.
It accepts the 307 response but never does the PUT so you need to re-try.
Nest API:
non-streaming:
https://developer.nest.com/documentation/cloud/rest-guide
specifically
As you read through the Firebase REST references, keep in mind these differences in Nest's implementation:
.JSON suffix is optional for Nest URL endpoints
Your REST products will need to handle redirects with status code 307
Data rate limiting may be applied to your products

streaming:
https://developer.nest.com/documentation/cloud/rest-streaming-guide

Interesting. So from what I'm gathering - you're using firego to also interact with the Nest APIs?

It doesn't seem to be saving the new URL as the base for all further requests.

I would probably say that that is the behavior that is wanted but I'm not too sure as to why that's actually happening.

Nest's API's are based on firebase with a few modifications. They even have you use the firebase.js for non-REST usage. I liked the streaming setup this provided. Honestly I'd rather deal with a websocket API, but for the spike I'm doing I don't really want to reverse engineer the firebase.js to figure out how they're doing the socket stuff.

My one concern with this PR is L#187 where we are storing the URL that's given back on the redirect as the future base URL. I know that for streaming we need to but I don't believe any other of the Firebase APIs require this. My fear is that this could bring up some side-effects when consuming the Firebase API.
cc/ @MrGossett

I'm basing my changes off of Nest's API. I ended up "forking" this repo and converting to a Nest API that works for only what I need. Nest doesn't allow patch or put, and I also am returning the watched events as JSON. I'll eventually return them as a struct, since I know exactly what Nest will be sending. No need for the map[string]interface{}
https://github.com/jgeiger/nestapi
Let me know if there's anything I need to change in the licensing or readme.
Thanks.

It looks great @jgeiger! I'm glad you were able to make use of this code base for you project.
I'm going to close this PR and move any of my comment over to https://github.com/jgeiger/nestapi. Thanks again for your contributions.


How to store data locally by phaser?

If i want to make a dynamic map, which need to store the new generated map data into local files, then which api should i use? I looked through the whole phaser code, but didn't get it.Could you please add some apis like these?

Use local storage.
On Mar 16, 2016 11:13 AM, "zhanll"<EMAIL_ADDRESS>wrote:

If i want to make a dynamic map, which need to store the new generated map
data into local files, then which api should i use? I looked through the
whole phaser code, but didn't get it.Could you please add some apis like
these?
—
You are receiving this because you are subscribed to this thread.
Reply to this email directly or view it on GitHub
https://github.com/photonstorm/phaser/issues/2386

I agree, use LocalStorage. There's no Phaser interface to it because it's so easy to use on its own:
https://developer.mozilla.org/en-US/docs/Web/API/Storage


integrate honeybadger error reporting

Currently I have Slack setup as my notifications when errors occur. This works for very small projects for as they get bigger, this is not something that works well for me to manage bugs and fixing them.
Therefore, an error reporting tool would be very helpful.
I like honeybadger as I like the values they have as a bootstrapped small business.
https://www.honeybadger.io/for/node/

Done


Adding Helper HTML to the factory

Hello!
I've been using this Helper inside my project and I want to add it inside your factory!

Hi @renatomefidf this is a good enhancement!!


openebs provisioner cannot contact maya-apiserver

Is this a BUG REPORT or FEATURE REQUEST?
BUG
What happened:
openebs-provisioner seems to be stuck in CrashLoopBackOff
Name:           openebs-provisioner-74f87ccd6-rzzfr
Namespace:      default
Node:           tom/<IP_ADDRESS>
Start Time:     Mon, 12 Mar 2018 23:13:28 -0500
Labels:         name=openebs-provisioner
                pod-template-hash=309437782
Annotations:    <none>
Status:         Running
IP:             <IP_ADDRESS>
Controlled By:  ReplicaSet/openebs-provisioner-74f87ccd6
Containers:
  openebs-provisioner:
    Container ID:   docker://2d1ebe03c53584c4068f5c893e4ce47035d4b28500e62eca938ed9b265e096e0
    Image:          openebs/openebs-k8s-provisioner:0.5.1
    Image ID:       docker-pullable://openebs/openebs-k8s-provisioner@sha256:94e3e3b522a8a969a8321e98479967668ddd41cee81f9f4f5cab932228c716f1
    Port:           <none>
    State:          Waiting
      Reason:       CrashLoopBackOff
    Last State:     Terminated
      Reason:       Error
      Exit Code:    1
      Started:      Mon, 12 Mar 2018 23:36:02 -0500
      Finished:     Mon, 12 Mar 2018 23:36:08 -0500
    Ready:          False
    Restart Count:  9
    Environment:
      NODE_NAME:                (v1:spec.nodeName)
      OPENEBS_MONITOR_URL:     http://<IP_ADDRESS>:32515/dashboard/db/openebs-volume-stats?orgId=1
      OPENEBS_MONITOR_VOLKEY:  &var-OpenEBS
      MAYA_PORTAL_URL:         https://mayaonline.io/
    Mounts:
      /var/run/secrets/kubernetes.io/serviceaccount from openebs-maya-operator-token-9cb2p (ro)
Conditions:
  Type           Status
  Initialized    True
  Ready          False
  PodScheduled   True
Volumes:
  openebs-maya-operator-token-9cb2p:
    Type:        Secret (a volume populated by a Secret)
    SecretName:  openebs-maya-operator-token-9cb2p
    Optional:    false
QoS Class:       BestEffort
Node-Selectors:  <none>
Tolerations:     node.kubernetes.io/not-ready:NoExecute for 300s
                 node.kubernetes.io/unreachable:NoExecute for 300s
Events:
  Type     Reason                 Age                 From               Message
  ----     ------                 ----                ----               -------
  Normal   Scheduled              26m                 default-scheduler  Successfully assigned openebs-provisioner-74f87ccd6-rzzfr to tom
  Normal   SuccessfulMountVolume  26m                 kubelet, tom       MountVolume.SetUp succeeded for volume "openebs-maya-operator-token-9cb2p"
  Normal   Created                25m (x4 over 26m)   kubelet, tom       Created container
  Normal   Started                25m (x4 over 26m)   kubelet, tom       Started container
  Normal   Pulling                24m (x5 over 26m)   kubelet, tom       pulling image "openebs/openebs-k8s-provisioner:0.5.1"
  Normal   Pulled                 24m (x5 over 26m)   kubelet, tom       Successfully pulled image "openebs/openebs-k8s-provisioner:0.5.1"
  Warning  BackOff                1m (x104 over 25m)  kubelet, tom       Back-off restarting failed container

logs from the openebs-provisioner container:
E0313 04:30:55.195192       1 openebs-provisioner.go:243] Error getting server version: Get https://<IP_ADDRESS>:443/version: dial tcp <IP_ADDRESS>:443: getsockopt: no route to host
I0313 04:30:55.195902       1 maya_api.go:63] OpenEBS volume provisioner namespace default
E0313 04:30:58.201529       1 maya_api.go:67] Error getting maya-apiserver IP Address: Get https://<IP_ADDRESS>:443/api/v1/namespaces/default/services/maya-apiserver-service: dial tcp <IP_ADDRESS>:443: getsockopt: no route to host
E0313 04:30:58.201568       1 openebs-provisioner.go:69] Error getting maya-apiserver IP Address: Get https://<IP_ADDRESS>:443/api/v1/namespaces/default/services/maya-apiserver-service: dial tcp <IP_ADDRESS>:443: getsockopt: no route to host

What you expected to happen:
openebs to be up and running
How to reproduce it (as minimally and precisely as possible):
Spin up a cluster using kubeadm in a multimaster setup  then
git clone <https://github.com/openebs/openebs.git>
cd openebs/k8s
kubectl apply -f openebs-operator
kubectl apply -f openebs-storageclasses.yaml

or fully scripted built by packer, and provisioned into KVM:
https://github.com/kubash/kubash
kubash build;kubash provision;kubash init;kubash openebs
Anything else we need to know?:
Environment:

kubectl get nodes

NAME      STATUS    ROLES     AGE       VERSION
bob       Ready     master    7m        v1.9.3
charles   Ready     master    5m        v1.9.3
joe       Ready     node      5m        v1.9.3
joy       Ready     node      5m        v1.9.3
sue       Ready     master    5m        v1.9.3
tom       Ready     node      5m        v1.9.3

kubectl get pods --all-namespaces

NAMESPACE     NAME                                      READY     STATUS             RESTARTS   AGE
default       maya-apiserver-5b48756968-vrcsv           1/1       Running            0          1m
default       openebs-provisioner-7b59878f76-sm45d      0/1       CrashLoopBackOff   2          1m
kube-system   calico-etcd-5j447                         1/1       Running            0          5m
kube-system   calico-etcd-6z5tc                         1/1       Running            0          5m
kube-system   calico-etcd-wxmn7                         1/1       Running            0          5m
kube-system   calico-kube-controllers-d554689d5-66256   1/1       Running            0          5m
kube-system   calico-node-5blbm                         2/2       Running            1          5m
kube-system   calico-node-c925b                         1/2       Running            2          5m
kube-system   calico-node-cp6zk                         2/2       Running            0          5m
kube-system   calico-node-g2ktc                         2/2       Running            1          5m
kube-system   calico-node-pjsrh                         2/2       Running            0          5m
kube-system   calico-node-pszhp                         2/2       Running            0          5m
kube-system   kube-apiserver-bob                        1/1       Running            0          6m
kube-system   kube-apiserver-charles                    1/1       Running            0          5m
kube-system   kube-apiserver-sue                        1/1       Running            0          5m
kube-system   kube-controller-manager-bob               1/1       Running            0          5m
kube-system   kube-controller-manager-charles           1/1       Running            0          5m
kube-system   kube-controller-manager-sue               1/1       Running            0          5m
kube-system   kube-dns-6f4fd4bdf-2zkf7                  2/3       Running            1          6m
kube-system   kube-proxy-5cq9r                          1/1       Running            0          6m
kube-system   kube-proxy-6v7nn                          1/1       Running            0          5m
kube-system   kube-proxy-cd24j                          1/1       Running            0          5m
kube-system   kube-proxy-fkt5g                          1/1       Running            0          5m
kube-system   kube-proxy-fqjzw                          1/1       Running            0          5m
kube-system   kube-proxy-tkqxx                          1/1       Running            0          5m
kube-system   kube-scheduler-bob                        1/1       Running            0          6m
kube-system   kube-scheduler-charles                    1/1       Running            0          5m
kube-system   kube-scheduler-sue                        1/1       Running            0          5m

kubectl get services

NAME                     TYPE        CLUSTER-IP     EXTERNAL-IP   PORT(S)    AGE
kubernetes               ClusterIP   <IP_ADDRESS>      <none>        443/TCP    6m
maya-apiserver-service   ClusterIP   <IP_ADDRESS>   <none>        5656/TCP   38s

kubectl get sc

NAME                 PROVISIONER                    AGE
openebs-cassandra    openebs.io/provisioner-iscsi   36m
openebs-es-data-sc   openebs.io/provisioner-iscsi   36m
openebs-jupyter      openebs.io/provisioner-iscsi   36m
openebs-kafka        openebs.io/provisioner-iscsi   36m
openebs-mongodb      openebs.io/provisioner-iscsi   36m
openebs-percona      openebs.io/provisioner-iscsi   36m
openebs-redis        openebs.io/provisioner-iscsi   36m
openebs-standalone   openebs.io/provisioner-iscsi   36m
openebs-standard     openebs.io/provisioner-iscsi   36m
openebs-zk           openebs.io/provisioner-iscsi   36m

kubectl get pv

No resources found.

kubectl get pvc

No resources found.

OS (e.g. from /etc/os-release):

cat /etc/os-release
NAME="CentOS Linux"
VERSION="7 (Core)"
ID="centos"
ID_LIKE="rhel fedora"
VERSION_ID="7"
PRETTY_NAME="CentOS Linux 7 (Core)"
ANSI_COLOR="0;31"
CPE_NAME="cpe:/o:centos:centos:7"
HOME_URL="https://www.centos.org/"
BUG_REPORT_URL="https://bugs.centos.org/"

CENTOS_MANTISBT_PROJECT="CentOS-7"
CENTOS_MANTISBT_PROJECT_VERSION="7"
REDHAT_SUPPORT_PRODUCT="centos"
REDHAT_SUPPORT_PRODUCT_VERSION="7"

Kernel (e.g. uname -a):
Linux tom 3.10.0-693.17.1.el7.x86_64 #1 SMP Thu Jan 25 20:13:58 UTC 2018 x86_64 x86_64 x86_64 GNU/Linux

Install tools:
https://github.com/kubash/kubash

@joshuacox - The provisioner pod uses the incluster-config to connect to the kubernetes-apiserver. Could there be any security restrictions in allowing the pod to connect with kubernetes at 443 port?
E0313 04:30:55.195192       1 openebs-provisioner.go:243] Error getting server version: Get https://<IP_ADDRESS>:443/version: dial tcp <IP_ADDRESS>:443: getsockopt: no route to host

its a fresh 1.9.3 cluster built with kubeadm, I can tear one down completely and reprovision and initialize in under ten minutes from a fresh rebase from the qcows that are built using packer.  So it's fairly repeatable from my perspective at least.

hi @joshuacox , we are attempting to bring up the cluster using kubash.  And, we observe from your pods output that one container in the pod  kube-dns-6f4fd4bdf-2zkf7 is not running.  Can you please check and describe.

yes indeed the kube-dns pod is having issues:
  Warning  Unhealthy              1m (x7 over 2m)  kubelet, tom       Readiness probe failed: Get http://<IP_ADDRESS>:8081/readiness: dial tcp <IP_ADDRESS>:8081: connect: invalid argument
  Warning  Unhealthy              1m               kubelet, tom       Liveness probe failed: Get http://<IP_ADDRESS>:10054/metrics: dial tcp <IP_ADDRESS>:10054: connect: invalid argument

closing this issue, I'll reopen if indeed it's something on openEBS's end, but I'm certain I probably broke kube-dns somehow, let me investigate.

as an addendum it is calico that I appear to be having issues with, after switching to flannel things seem to be working again, and all is well with openebs too!

Thanks @joshuacox

I'm inviting you both to kubash, I'd love to have either of you review or just point out where it blew up on you if you tried kubash

maybe you need to check the setting of you calico. In my case, the default value of property CALICO_IPV4POOL_CIDR happens to be the subnet of my host network.


[zh-cn] resync workload-resources/replication-controller-v1.md

Listed in #34733.
805 content/en/docs/reference/kubernetes-api/workload-resources/replication-controller-v1.md

Need to wait for #34319 due to [zh-cn] REF_NOT_FOUND: Ref "../workload-resources/pod-template-v1"
Preview: https://deploy-preview-34891--kubernetes-io-main-staging.netlify.app/zh-cn/docs/reference/kubernetes-api/workload-resources/replication-controller-v1/

/approve

Please rebase this onto 'main' branch where 'pod-template-v1.md' has been translated.

/lgtm


statusline is glitched with non-printable character and highlight

Steps to reproduce

Run vim --clean
Source the following file:

set laststatus=2
let &statusline = "\x08\u258e%#TabLine#"
redraw!

statusline is glitched:

Also, if the Unicode character \u258e is changed to multiple ASCII characters:
set laststatus=2
let &statusline = "\x08abc%#TabLine#"
redraw!

Highlight is off by one:

Expected behaviour
statusline looks normal, for exmaple, changing the \x08 to literal ^H above results in

Version of Vim
9.0.1046
Environment
Operating system: Arch Linux
Terminal: kitty
Value of $TERM: xterm-kitty
Shell: fish
Logs and stack traces
No response

start in hltab and tabtab should need to be adjusted after this transstr():
    // Make all characters printable.
    p = transstr(buf);
    if (p != NULL)
    {
	vim_strncpy(buf, p, sizeof(buf) - 1);
	vim_free(p);
    }

Not sure how to do this.

start in hltab and tabtab should need to be adjusted after this
transstr():
    // Make all characters printable.
    p = transstr(buf);
    if (p != NULL)
    {
	vim_strncpy(buf, p, sizeof(buf) - 1);
	vim_free(p);
    }

Not sure how to do this.

I do not think this is possible.  The call to transstr() is only there
just in case there is something wrong and unprintable characters would
mess up the screen.
The result of 'statusline' should not contain unprintable characters.
When it does, Vim won't crash but you won't get the status line you
wanted either.
I do not see a good reason why 'statusline' would produce unprintable
characters.  Also, the conversion will change the length, thus any item
in 'statusline' depending on the length would get confused.  The
highlighting example is just one of them.
So, unless you can give a good reason why 'statusline' should contain
unprintable characters, I don't think there is anything to change.
--
A disclaimer for the disclaimer:
"and before I get a huge amount of complaints , I have no control over the
disclaimer at the end of this mail :-)" (Timothy Aldrich)
/// Bram Moolenaar -- @.*** -- http://www.Moolenaar.net   \
///                                                                      \
\\        sponsor Vim, vote for features -- http://www.Vim.org/sponsor/ ///
\\            help me help AIDS victims -- http://ICCF-Holland.org    ///

There isn't really a good reason


Randomly Column Order Changes

I have a very simple app and I noticed when I hit "tab" too many times the order of the columns as they are rendered occasionally changes.
I have a repo with the code at fault.  https://github.com/csg33k/bubbles/tree/main/02_preview but for context to capture it in the issue.
func NewModel() *rootModel {
	srv := NewSurvey()
	modelsMap := make(map[focusIndex]tea.Model)
	mainModel := NewGreetingModel("Welcome to the Greeter Builder\n")
	previewModel := NewPreviewModel(&srv)
	modelsMap[Main] = mainModel
	modelsMap[Preview] = previewModel
	m := flexbox.NewHorizontal(0, 0)
	columns := []*flexbox.Column{
		m.NewColumn().AddCells(
			flexbox.NewCell(1, 1).SetContent(mainModel.View()),
		),
		m.NewColumn().AddCells(
			flexbox.NewCell(1, 1).SetContent(previewModel.View()),
		),
	}
	m.AddColumns(columns)

	return &rootModel{
		flexBox:      m,
		paneSelected: Main,
		answers:      &srv,
		modelsMap:    modelsMap,
	}
}

func (r *rootModel) Update(msg tea.Msg) (tea.Model, tea.Cmd) {
	var (
		cmd  tea.Cmd
		cmds []tea.Cmd
	)

	switch msg := msg.(type) {
	case tea.WindowSizeMsg:
		r.flexBox.SetWidth(msg.Width)
		r.flexBox.SetHeight(msg.Height)
	case tea.KeyMsg:
		key := msg.String()
		switch key {
		case "j":
			r.answers.Age = 42
		case "tab":
			r.paneSelected = (r.paneSelected + 1) % 2
		case "ctrl+c", "esc":
			return r, tea.Quit
		}
	}

	ndx := 0
	//Update all models
	for _, val := range r.modelsMap {
		_, cmd = val.Update(msg)
		if ndx == int(r.paneSelected) {
			content := activeStyle.Render(val.View())
			r.flexBox.GetColumn(ndx).GetCell(0).SetContent(content)
		} else {
			r.flexBox.GetColumn(ndx).GetCell(0).SetContent(val.View())
		}
		cmds = append(cmds, cmd)
		ndx++
	}
	return r, tea.Batch(cmds...)
}

Is there anything I'm doing wrong that would cause this issue?
I also attached an image of the behavior I'm seeing.  The only thing I'm doing is running the app and hitting tab till I see the behavior.

Doh sorry about that.  reading over that code again it makes perfect sense.  thank you for your response.

Doh sorry about that. reading over that code again it makes perfect sense. thank you for your response.

Don't worry. I only realized this because the same happened to me, lol.


Question, have there been any behind the scenes changes for NOT annotations?

I spotted this:

I haven't previously noticed any  problems with NOT annotations, so I wonder if the issue is specific for NOT annotations curated in Canto?

(this one is)

I'm not sure the data the curations were made so I don't know if it is something which has been fixed post release...

grep NOT pombase-build-2016-06-25-v1\ (3).gaf  |grep ssl3
gives nothing, but other NOTs are there so we should explore the Canto  problem idea first....

I labelled as a bug but it isn't super urgent right now....ideally before the build to create the next update....

The gene is ssl3
and there is no NOT in PomBase 2 so I will keep this open for now:
http://pombase2.aska.gen.nz/gene/SPAC1687.18c

Looks like a loading bug as there is no NOT for that annotation in Chado.

The annotation seems to be there now:
http://pombase2.aska.gen.nz/gene/SPAC1687.18c
And it's in the GAF file.
I don't know why it disappeared and then came back.

The screenshot above is the live site. I see now its there its just not rendering
(PBHQ:002)


Reducing RAM usage for large models.

Llama 2 70B is using more ram than it needs to right now. It’s actually doing a pretty good job, but it’s not quite fitting. On a v3-64 we’re using about 23GiB/device, which ends up being about 21.5 bytes/parameter (this includes data and activations and what not). We need to get to under 15.8GiB or so.
(See bottom for detailed ram breakdown)

12.5GiB/device is for parameters, optimizer states, and data which is 12.44 bytes/parameter (12 bytes/parameter is bare minimum so pretty good)
4.6GiB is “fragmentation” (which is annoying and hard to reason about)
2.5GiB is for storing intermediate activations or gradients or something for the transformer blocks (shape is [Layers, Batch, Seq, Embed]). I don’t think JAX should be allocating this, but I don’t know how to make it go away without changing to a fancier scan operation.
2.2 GiB is (I think) for storing bf16 copies of the various parameters.  These have shape [Layers, Something] and are like the qkv projections. etc.

This is with per_device_parallelism=1, no grad accum.
Typically making usage goes away makes memory used by “fragmentation” go down.  so I think optimistically we could make this fit, but it would require the intermediate activations thing go away and probably also we eliminate the bf16 copies of the parameters. On the other hand, this is not necessarily the only point in the compute where things go OOM, so fixing this might not be enough.

For the 2.5GB of unnecessary activations, we could probably do a much fancier checkpointed scan, possibly by rolling it ourselves. There’s something in Equinox (checkpointed_while) and another thing in Flax (remat_scan) that could help.
For the bf16 copies, I think we just need to be a bit smarter with how we do it. Currently in Levanter, we do something like this:

def loss(m, x):
    m = convert(m, bf16)  # produces a sharded bf16 model
    for layer in m.layers:
       x = layer(x)  # implicit all-gather of layer

Unfortunately JAX isn’t smart enough to just-in-time/fuse the conversion with the all-gather, so we store a full bf16 copy of all the parameters on each device. Fixing this could reduce memory usage by ~2.2GB.
Total hbm usage >= 21.91G:
    reserved        530.00M
    program           8.88G
    arguments        12.51G

Output size 12.51G; shares 12.51G with arguments.

Program hbm requirement 8.88G:
    global           321.0K
    scoped            3.66M
    HLO temp          8.87G (100.0% utilization: Unpadded (4.61G) Padded (4.61G), 48.1% fragmentation (4.26G))

  Largest program allocations in hbm:

  1. Size: 2.50G
     Operator: op_name="jit(train_step)/jit(main)/jvp(LlamaTransformer)/broadcast_in_dim[shape=(80, 64, 2048, 8192) broadcast_dimensions=()]" source_file="/home/dlwh/venv310/lib/python3.10/site-packages/haliax/hof.py" source_line=88
     Shape: bf16[80,1,2048,8192]{3,2,1,0:T(8,128)(2,1)}
     Unpadded size: 2.50G
     XLA label: broadcast.659 = broadcast(constant.983), dimensions={}
     Allocation type: HLO temp
     ==========================

  2. Size: 560.00M
     Operator: op_name="jit(train_step)/jit(main)/convert_element_type[new_dtype=bfloat16 weak_type=False]" source_file="/home/dlwh/venv310/lib/python3.10/site-packages/jmp/_src/policy.py" source_line=31
     Shape: bf16[80,128,28672]{2,1,0:T(8,128)(2,1)}
     Unpadded size: 560.00M
     XLA label: convert.150 = convert(param.14)
     Allocation type: HLO temp
     ==========================

  3. Size: 560.00M
     Operator: op_name="jit(train_step)/jit(main)/convert_element_type[new_dtype=bfloat16 weak_type=False]" source_file="/home/dlwh/venv310/lib/python3.10/site-packages/jmp/_src/policy.py" source_line=31
     Shape: bf16[80,128,28672]{2,1,0:T(8,128)(2,1)}
     Unpadded size: 560.00M
     XLA label: convert.151 = convert(param.15)
     Allocation type: HLO temp
     ==========================

  4. Size: 560.00M
     Operator: op_name="jit(train_step)/jit(main)/convert_element_type[new_dtype=bfloat16 weak_type=False]" source_file="/home/dlwh/venv310/lib/python3.10/site-packages/jmp/_src/policy.py" source_line=31
     Shape: bf16[80,28672,128]{2,1,0:T(8,128)(2,1)}
     Unpadded size: 560.00M
     XLA label: convert.152 = convert(param.16)
     Allocation type: HLO temp
     ==========================

  5. Size: 160.00M
     Shape: bf16[80,128,8,8,128]{1,4,3,2,0:T(8,128)(2,1)}
     Unpadded size: 160.00M
     XLA label: copy.221 = copy(param.8), sharding={devices=[1,64,1,1,1]<=[64]}
     Allocation type: HLO temp
     ==========================

  6. Size: 160.00M
     Operator: op_name="jit(train_step)/jit(main)/convert_element_type[new_dtype=bfloat16 weak_type=False]" source_file="/home/dlwh/venv310/lib/python3.10/site-packages/jmp/_src/policy.py" source_line=31
     Shape: bf16[80,64,128,128]{3,2,1,0:T(8,128)(2,1)}
     Unpadded size: 160.00M
     XLA label: convert.147 = convert(param.11)
     Allocation type: HLO temp
     ==========================

  7. Size: 40.00M
     Shape: bf16[80,2048,128]{1,2,0:T(8,128)(2,1)}
     Unpadded size: 40.00M
     XLA label: copy.224 = copy(param.12), sharding={replicated}
     Allocation type: HLO temp
     ==========================

  8. Size: 40.00M
     Shape: bf16[80,2048,128]{1,2,0:T(8,128)(2,1)}
     Unpadded size: 40.00M
     XLA label: copy.225 = copy(param.13), sharding={replicated}
     Allocation type: HLO temp
     ==========================

  9. Size: 32.00M
     Operator: op_name="jit(train_step)/jit(main)/jvp(LlamaEmbedding.embed)/jit(_take)/select_n" source_file="/home/dlwh/venv310/lib/python3.10/site-packages/haliax/core.py" source_line=691
     Shape: bf16[1,2048,8192]{2,1,0:T(8,128)(2,1)}
     Unpadded size: 32.00M
     XLA label: fusion.1515 = fusion(fusion.1549, fusion), kind=kLoop, calls=fused_computation.1437
     Allocation type: HLO temp
     ==========================

  10. Size: 20.00M
     Shape: bf16[80,128,8,128]{1,3,2,0:T(8,128)(2,1)}
     Unpadded size: 20.00M
     XLA label: copy.222 = copy(param.9), sharding={devices=[1,64,1,1]<=[64]}
     Allocation type: HLO temp
     ==========================

  11. Size: 20.00M
     Shape: bf16[80,128,8,128]{1,3,2,0:T(8,128)(2,1)}
     Unpadded size: 20.00M
     XLA label: copy.223 = copy(param.10), sharding={devices=[1,64,1,1]<=[64]}
     Allocation type: HLO temp
     ==========================

XLA compiler output: xla_out_70b.txt

I did a whole bunch of work to make the first allocation (in the ckpt_scan haliax branch) and it reduced ram usage by...500MB, which is a lot less than the 2.5GB.
  1. Size: 560.00M
     Operator: op_name="jit(train_step)/jit(main)/transpose(jvp(LlamaTransformer))/Stacked.fold/ckpt_scan(layers)/remat/broadcast_in_dim[shape=(80, 28672, 8192) broadcast_dimensions=()]" source_file="/home/dlwh/venv310/lib/python3.10/site-packages/haliax/jax_utils.py" source_line=208
     Shape: bf16[80,28672,128]{2,1,0:T(8,128)(2,1)}
     Unpadded size: 560.00M
     XLA label: broadcast.1162 = broadcast(constant.1711), dimensions={}
     Allocation type: HLO temp
     ==========================

  2. Size: 560.00M
     Operator: op_name="jit(train_step)/jit(main)/transpose(jvp(LlamaTransformer))/Stacked.fold/ckpt_scan(layers)/remat/broadcast_in_dim[shape=(80, 8192, 28672) broadcast_dimensions=()]" source_file="/home/dlwh/venv310/lib/python3.10/site-packages/haliax/jax_utils.py" source_line=208
     Shape: bf16[80,128,28672]{2,1,0:T(8,128)(2,1)}
     Unpadded size: 560.00M
     XLA label: broadcast.1161.clone = broadcast(constant.1711), dimensions={}
     Allocation type: HLO temp
     ==========================

  3. Size: 560.00M
     Operator: op_name="jit(train_step)/jit(main)/convert_element_type[new_dtype=bfloat16 weak_type=False]" source_file="/home/dlwh/venv310/lib/python3.10/site-packages/jmp/_src/policy.py" source_line=31
     Shape: bf16[80,128,28672]{2,1,0:T(8,128)(2,1)}
     Unpadded size: 560.00M
     XLA label: convert.273 = convert(param.24)
     Allocation type: HLO temp
     ==========================

  4. Size: 560.00M
     Operator: op_name="jit(train_step)/jit(main)/convert_element_type[new_dtype=bfloat16 weak_type=False]" source_file="/home/dlwh/venv310/lib/python3.10/site-packages/jmp/_src/policy.py" source_line=31
     Shape: bf16[80,128,28672]{2,1,0:T(8,128)(2,1)}
     Unpadded size: 560.00M
     XLA label: convert.274 = convert(param.25)
     Allocation type: HLO temp
     ==========================

  5. Size: 560.00M
     Operator: op_name="jit(train_step)/jit(main)/convert_element_type[new_dtype=bfloat16 weak_type=False]" source_file="/home/dlwh/venv310/lib/python3.10/site-packages/jmp/_src/policy.py" source_line=31
     Shape: bf16[80,28672,128]{2,1,0:T(8,128)(2,1)}
     Unpadded size: 560.00M
     XLA label: convert.275 = convert(param.26)
     Allocation type: HLO temp
     ==========================

  6. Size: 560.00M
     Operator: op_name="jit(train_step)/jit(main)/transpose(jvp(LlamaTransformer))/Stacked.fold/ckpt_scan(layers)/remat/broadcast_in_dim[shape=(80, 8192, 28672) broadcast_dimensions=()]" source_file="/home/dlwh/venv310/lib/python3.10/site-packages/haliax/jax_utils.py" source_line=208
     Shape: bf16[80,128,28672]{2,1,0:T(8,128)(2,1)}
     Unpadded size: 560.00M
     XLA label: broadcast.1161 = broadcast(constant.1711), dimensions={}
     Allocation type: HLO temp
     ==========================

  7. Size: 448.00M
     Operator: op_name="jit(train_step)/jit(main)/transpose(jvp(LlamaTransformer))/Stacked.fold/ckpt_scan(layers)/remat/while/body/jit(core_closed_call)/while/body/remat/remat/LlamaDecoderLayer/LlamaMlp/Linear/0 1 2 , 2 3 -> 0 1 3/dot_general[dimension_numbers=(((2,), (0,)), ((), ())) precision=None preferred_element_type=bfloat16]" source_file="/home/dlwh/venv310/lib/python3.10/site-packages/haliax/core.py" source_line=1024
     Shape: bf16[1,28672,8192]{2,1,0:T(8,128)(2,1)}
     Unpadded size: 448.00M
     XLA label: all-gather.101 = all-gather(fusion.2870), channel_id=105, replica_groups={{0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63...
     Allocation type: HLO temp
     ==========================

  8. Size: 160.00M
     Operator: op_name="jit(train_step)/jit(main)/transpose(jvp(LlamaTransformer))/Stacked.fold/ckpt_scan(layers)/remat/broadcast_in_dim[shape=(80, 8192, 8, 8, 128) broadcast_dimensions=()]" source_file="/home/dlwh/venv310/lib/python3.10/site-packages/haliax/jax_utils.py" source_line=208
     Shape: bf16[80,128,8,8,128]{4,1,3,2,0:T(8,128)(2,1)}
     Unpadded size: 160.00M
     XLA label: broadcast.1158 = broadcast(constant.1711), dimensions={}
     Allocation type: HLO temp
     ==========================

  9. Size: 160.00M
     Shape: bf16[80,128,8,8,128]{1,4,3,2,0:T(8,128)(2,1)}
     Unpadded size: 160.00M
     XLA label: copy.309 = copy(param.18), sharding={devices=[1,64,1,1,1]<=[64]}
     Allocation type: HLO temp
     ==========================

  10. Size: 160.00M
     Operator: op_name="jit(train_step)/jit(main)/convert_element_type[new_dtype=bfloat16 weak_type=False]" source_file="/home/dlwh/venv310/lib/python3.10/site-packages/jmp/_src/policy.py" source_line=31
     Shape: bf16[80,64,128,128]{3,2,1,0:T(8,128)(2,1)}
     Unpadded size: 160.00M
     XLA label: convert.270 = convert(param.21)
     Allocation type: HLO temp
     ==========================

  11. Size: 160.00M
     Operator: op_name="jit(train_step)/jit(main)/transpose(jvp(LlamaTransformer))/Stacked.fold/ckpt_scan(layers)/remat/broadcast_in_dim[shape=(80, 64, 128, 8192) broadcast_dimensions=()]" source_file="/home/dlwh/venv310/lib/python3.10/site-packages/haliax/jax_utils.py" source_line=208
     Shape: bf16[80,64,128,128]{3,2,1,0:T(8,128)(2,1)}
     Unpadded size: 160.00M
     XLA label: broadcast.1160 = broadcast(constant.1711), dimensions={}
     Allocation type: HLO temp
     ==========================

  12. Size: 125.00M
     Operator: op_name="jit(train_step)/jit(main)/jvp(Linear)/0 1 2 , 3 2 -> 0 1 3/dot_general[dimension_numbers=(((2,), (1,)), ((), ())) precision=None preferred_element_type=bfloat16]" source_file="/home/dlwh/venv310/lib/python3.10/site-packages/haliax/core.py" source_line=1024
     Shape: bf16[2048,32000]{1,0:T(8,128)(2,1)}
     Unpadded size: 125.00M
     XLA label: fusion.2719 = fusion(all-gather.45.remat3.1, get-tuple-element.4618, fusion.2625, all-reduce.28), kind=kOutput, calls=fused_computation.2505
     Allocation type: HLO temp
     ==========================

  13. Size: 112.00M
     Operator: op_name="jit(train_step)/jit(main)/transpose(jvp(LlamaTransformer))/Stacked.fold/ckpt_scan(layers)/remat/while/body/jit(core_closed_call)/while/body/remat/remat/LlamaDecoderLayer/LlamaMlp/Linear/0 1 2 , 2 3 -> 0 1 3/dot_general[dimension_numbers=(((2,), (0,)), ((), ())) precision=None preferred_element_type=bfloat16]" source_file="/home/dlwh/venv310/lib/python3.10/site-packages/haliax/core.py" source_line=1024
     Shape: bf16[2048,28672]{1,0:T(8,128)(2,1)}
     Unpadded size: 112.00M
     XLA label: fusion.2869 = fusion(all-gather.81.remat.1.remat, fusion.2866, get-tuple-element.5593, fusion.2863, ...(+1)), kind=kOutput, calls=fused_computation.1885.clone
     Allocation type: HLO temp
     ==========================

  14. Size: 40.00M
     Shape: bf16[80,2048,128]{1,2,0:T(8,128)(2,1)}
     Unpadded size: 40.00M
     XLA label: copy.312 = copy(param.22), sharding={replicated}
     Allocation type: HLO temp
     ==========================

  15. Size: 40.00M
     Shape: bf16[80,2048,128]{1,2,0:T(8,128)(2,1)}
     Unpadded size: 40.00M
     XLA label: copy.313 = copy(param.23), sharding={replicated}
     Allocation type: HLO temp
     ==========================

  16. Size: 32.00M
     Operator: op_name="jit(train_step)/jit(main)/transpose(jvp(LlamaTransformer))/Stacked.fold/ckpt_scan(layers)/remat/while/body/jit(core_closed_call)/while/body/remat/remat/LlamaDecoderLayer/LlamaAttention/Linear/0 1 2 3 , 1 3 4 -> 0 2 4/dot_general[dimension_numbers=(((1, 3), (0, 1)), ((), ())) precision=None preferred_element_type=bfloat16]" source_file="/home/dlwh/venv310/lib/python3.10/site-packages/haliax/core.py" source_line=1024
     Shape: bf16[1,2048,8192]{2,1,0:T(8,128)(2,1)}
     Unpadded size: 32.00M
     XLA label: fusion.2860 = fusion(get-tuple-element.6265, bitcast.963, all-gather.100), kind=kOutput, calls=fused_computation.1886.clone
     Allocation type: HLO temp
     ==========================

  17. Size: 32.00M
     Operator: op_name="jit(train_step)/jit(main)/transpose(jvp(LlamaTransformer))/Stacked.fold/ckpt_scan(layers)/remat/while/body/jit(core_closed_call)/while/body/remat/remat/LlamaDecoderLayer/LlamaMlp/Linear/0 1 2 , 2 3 -> 0 1 3/dot_general[dimension_numbers=(((2,), (0,)), ((), ())) precision=None preferred_element_type=bfloat16]" source_file="/home/dlwh/venv310/lib/python3.10/site-packages/haliax/core.py" source_line=1024
     Shape: bf16[1,2048,8192]{2,1,0:T(8,128)(2,1)}
     Unpadded size: 32.00M
     XLA label: fusion.2873 = fusion(get-tuple-element.5593, fusion.2869, all-gather.101), kind=kOutput, calls=fused_computation.1892.clone
     Allocation type: HLO temp
     ==========================

  18. Size: 32.00M
     Operator: op_name="jit(train_step)/jit(main)/transpose(jvp(LlamaTransformer))/add_any" source_file="/home/dlwh/venv310/lib/python3.10/site-packages/haliax/wrap.py" source_line=14
     Shape: bf16[1,2048,8192]{2,1,0:T(8,128)(2,1)}
     Unpadded size: 32.00M
     XLA label: fusion.2583 = fusion(get-tuple-element.4618, get-tuple-element.5369, fusion.2639, fusion.2625, ...(+1)), kind=kLoop, calls=fused_computation.2432
     Allocation type: HLO temp
     ==========================

  19. Size: 20.00M
     Shape: bf16[80,128,8,128]{1,3,2,0:T(8,128)(2,1)}
     Unpadded size: 20.00M
     XLA label: copy.310 = copy(param.19), sharding={devices=[1,64,1,1]<=[64]}
     Allocation type: HLO temp
     ==========================

  20. Size: 20.00M
     Shape: bf16[80,128,8,128]{1,3,2,0:T(8,128)(2,1)}
     Unpadded size: 20.00M
     XLA label: copy.311 = copy(param.20), sharding={devices=[1,64,1,1]<=[64]}
     Allocation type: HLO temp
     ==========================

(1), (2), (6), (7), (8) are gradient buffers. (2.4 GiB) and are mostly "new" in the allocation (they would have been there anyway, but XLA gave up before). These can't be removed probably?
(3), (4), (5), (10) are definitely wasteful bf16 conversions (1.8 GiB)

This accounts for 4.2 of the 4.6 "unwasted" space. We can make that 1.8 mostly go away probably but that won't be enough unless fragmentation drops off massively. (This isn't impossible but increasingly seems unlikely).
I think we probably can't make it fit without drastic rethink, but I still think it should be find on v3-128.
I still don't know why memory usage gets worse with TP turned on. We should investigate


Refactor to clean filenames and remove old files. Fixes #89

This may conflict with changes to the aloe tests, but, in that case, just discard these changes.


Issue apearing while trying to serialize, deserialize object which contains lists.

This issue is appearing only in the latest version of okaeri-configs (3.0.0) in 2.7.24 this is working great.
Stacktrace:
https://paste.gg/p/anonymous/1acfaa8469e84d388a1be7488fb8351b

New safety check was incorrect due to a one letter typo. Fixed in https://github.com/OkaeriPoland/okaeri-configs/commit/5a4935494869799e72df9c87a84b01bf0b852755


[Arges] Mica component of Bascov village is not found in Arges county - Fail

Precondition
SOLR: http://zimbor.go.ro/solr/#/romania/query
browser: Chrome
Steps to Reproduce:

Step
Action
Expected
Status

1
open SOLR in browser
SOLR UI is loaded without any errors
Fail

2
type Mica in the q field
you are able to type Mica in the q field
Fail

3
type judet: Arges in the fq field
you are able to type judet: Arges in the fq field
Fail

4
click on Execute Query button
"judet:Arges" and "localitate: Mica", "comuna: Bascov " are displayed
Fail

Actual Results:
Mica component of Bascov village is not found in Arges county

https://orase.testquality.com defect D48.

Este comuna Bascov cu satul Mica.


fix: upgraded k8s deps

upgraded k8s deps after https://github.com/kubeflow/spark-operator/pull/1968
fixed docs generation

after recent change in master upgraded depdendencies to fix CI pipeline also it decreases binary size

@yuchaoran2011 after recent change in master upgraded depdendencies to fix CI pipeline also it decreases binary size


Turkish Translation of README.md

As I wish to make any FOSS accessable for my country (Türkiye), I wanted to translate README to Turkish. I wish you like it :)

nice initiative mate but I have a few points to make:

a PR like this might influence other people to start creating PRs translating the readme to different languages which will be bad for the overall QoL of the repository.

from what I perceive, at present, the application is exclusively available in English, lacking support for additional languages, so despite any modifications to the readme, the language of the actual product remains unchanged.

utilizing tools such as the google translate extension can also let users access the same content in their preferred languages too, without the need of having separate readme's.

If this pull request is approved, kindly relocate the readme to a separate directory designated for readme files in various languages aswell, it looks cleaner :)

I agree with @Assassin-1234.
Although I appreciate the effort of translating everything, this will be become tedious when I will have to modify the original README.md as it will require me to also translate the other files, which can make a mess.


Add example for pango text attributes

cc @GuillaumeGomez

Please add explanations about what the code is doing (it's examples after all). :)

Updated

Not much comments, but considering the "complexity" of the example, I think just the description at the beginning of the file is enough.

Updated

Thanks!


SA1313 Allow primary constructor parameters to start with underscore.

It would be nice to have a perhabs configurable way to prevent primary constructors cause SA1313 warnings when their parameters start with underscore.
This would actually support the naming convention of private fields to start with underscore.

Duplicate of #3752


Too many receivers, total of 1000

In the context of my app I need to retrieve the peripheral in order to get the identifier.
The problem is that, on Android, it registers a broadcast receiver every time and I quickly reach the limit of 1000 receivers which is prohibited by the OS.
I open this issue to discuss about ways to solve the problem in order to contribute by a pull request soon.
I suggest to share one broadcast receiver across all peripherals.
As a possible workaround, I will try to cancel the attached coroutine scope in order to unregister the receiver.

Thanks for the report!

I suggest to share one broadcast receiver across all peripherals.

I agree, this is likely the best approach.

I open this issue to discuss about ways to solve the problem in order to contribute by a pull request soon.

My initial thought is that we'll want to have the single broadcast receiver you mentioned as an internal object and it will "broadcast" the BLE state to all peripherals by providing the state via a MutableStateFlow. Or, perhaps a MutableSharedFlow that only spins up the broadcast receiver when there is at least 1 subscriber.

I haven't had a chance to test it yet, but the following SNAPSHOT build may fix this issue:
repositories {
    maven("https://oss.sonatype.org/content/repositories/snapshots")
}

dependencies {
    implementation("com.juul.kable:core:0.18.0-issue-359-1-SNAPSHOT")
}

Awesome ! I'm on vacation, I can check this starting august 22th. 😉

Thanks so much for following up! I'll get it merged (and a new release out) soon; hopefully in the coming days.


[TBDGen] Set tbd files to two-level namespace and explicitly set dylib compatibility version to 1

Fixes rdar://43151135

@swift-ci please smoke test

@ributzka Good to merge?


Implementing Iterators for iterating containers.

Implementing Basic iterators for iterating containers and adding more iterator methods
My basic idea is to create a iterator struct inside the vector struct so that each vector will have a iterator associated with it.
The iterator struct will have defined methods like the C++ iterators have See so that we can user (.) operator to call the iterator associated with it.
And making each iterator struct's object/variable as a stand alone datatype which can be used to iterate vector for now only, but the issue with this is whether to choose if the iterator will have a type, like in C++ there are iterator of all datatype so how to implement something similar in this scenario.
Feel free to share any ideas.

I think instead of first implementing the containers, you should implement templates / generics. This will enable you to write the containers for all datatypes easily.

I think instead of first implementing the containers, you should implement templates / generics. This will enable you to write the containers for all datatypes easily.

I am trying to work my way around types by representing the types in enums. And on every operation the type of the container/iterator will be checked and depending on the type the data will be type casted accordingly.


Update alpine, remove glibc and reduce image size.

Image size reduced from 41.25mb to 27.82mb

👍


/read does not work with images, only /add

Issue
Claude could not see an image with /read but could see it when /add was used.
Version and model info
Aider 0.60.1

Out of curiosity, are you using caching?  Does caching support images?

The fix is available in the main branch. You can get it by installing the latest version from github:
aider --install-main-branch

# or...

python -m pip install --upgrade --upgrade-strategy only-if-needed git+https://github.com/Aider-AI/aider.git

If you have a chance to try it, let me know if it works better for you.

I'm going to close this issue for now, but feel free to add a comment here and I will re-open. Or feel free to file a new issue any time.


Checklist for adding a new strategy

Here is a simple checklist to look when reviewing a PR for a new strategy:
Overview

The strategy must be unique.
If the strategy does only a single call with an address as input, it's preferable to use the strategy "contract-call" instead of creating a new one.

Code

There should be a maximum of 5 requests, a request can use "fetch" a "subgraphRequest" or "multicall".
The strategy should not send a request for each voters, this doesn't scale.
The strategy PR should not add any dependency in Snapshot.js.
The score returned by the strategy should use the same casing for address than on the input, or should return checksummed addresses.

Example

Example must include at least 1 address with a positive score.
Example must use a snapshot block number in the past.

Test

The strategy should take less than 10sec to resolve.
The strategy should work with 500 addresses. Here is a list of addresses.

Recommended

Add a README.md file that describe the strategy and provide an example of parameters.
Use string ABI instead of object.

Test

The strategy should take less than 10sec to resolve.
The strategy should work with 500 addresses. Here is a list of addresses.

@bonustrack less than 10 sec to resolve 500 address? or 1 address? is there any time limit for 500 addresses?

Less than 10sec for 500 addresses

qqq

We now have these checks here https://github.com/snapshot-labs/snapshot-strategies#code


Add documentation for autoscaling in Welkin

Describe the improvement
Add documentation for Autoscaling. The document explains Cluster autoscaling and pod autoscaling in general and then dives into welkin specifics such as,

How can a reader determine if cluster autoscaling is available in welkin
How can pod autoscaling be used in welkin
A running example of user-demo, showing the commands used to trigger an increased load, launching a new pod and a new node.

Screenshots
If applicable, add screenshots to help explain your problem.
Definition of done
There is now documentation for autoscaling in the navbar after CI/CD

Could this be merged with this issue: https://github.com/elastisys/welkin/issues/799 ?
I think they have some overlap.

Could this be merged with this issue: #799 ? I think they have some overlap.

Yes, I'll close this issue in favor of #799 and update the other one.


[feature request] Tags

Michael Kohler asked me to start a thread about this.
Would be nice if we can assign 'tags' at a device, control and sensor level.  A tag would just be an open ended way for the plugin writer to communicate special attributes that the brain can then act on.
An example everyone knows about right now is the 'brightness' slider for a LIGHT type.  If we had the ability to tag the slider as a 'brightness' slider - then the name of the slider wouldn't matter.  Likewise we could do that with the various hue, saturation, etc sliders (with an appropriate tag).
At the device level, you've already (kinda) use tags with the device capabilities ("alwaysOn", etc).
Beyond behavioral tags, you could also use tags to communicate UI needs.  Plugin writer would be able to specify which widget's to use via a tag ("transport", etc) and then use tags on the buttons/switches to identify how to link them up ("play","pause",etc) rather than deduce the widgets from what specially named buttons are included.  Tags make it explicit what is needed rather than guessing what is needed (and hoping things are named correctly).
Lastly - looking beyond NEEO at potential integration, tags allow the plugin writer to communicate how to expose items to integrations (homekit, etc).  If I put together an ecobee plugin, I could tag a sensor as "CURRENTTEMPERATURE" and then homekit knows that's the current temperature item.
Upside to tags - it's open ended and easy to use
Downside to tags - it's open ended (ie needs good docs) and tags are single independent attributes - once you get more complex - you need another approach.  But they are a good starting point, are simple (both in concept and usage) and cover ALOT of cases..
Bottom line
Basically anything that would require special identification or special handling could be communicated between the plugin/brain through an open ended system using tags.

Thanks for the write up!
As you mentioned this is already done for devices through capabilities.
We're looking into implementing a role field for most components but essentially it's similar to the tags what you described above. We need to know the purpose of these components and how they are to be integrated on the bigger picture and it should solve the name problem for sliders.

note: Alexa also uses capabilities as a name for this.


Running individual tests will fail due to wrong path for common.sh

Running individual tests will fail due to wrong path for common.sh

Can one of the admins verify this patch?

The reason the sourcing is tests/common.sh is because they are expected to be run from the ci scripts in the root of the ripsaw tree (i.e. from the parent directory). If you need to run them separately please run them from the parent directory.

you mean
 # pwd
/root/ripsaw
root@ip-172-31-59-125: ~/ripsaw # ./tests/test_fiod.sh
+ source common.sh
./tests/test_fiod.sh: line 5: common.sh: No such file or directory

The test scripts are kicked off through the test.sh and run_test.sh scripts in the ripsaw directory. Those then reference the test via https://github.com/ekuric/benchmark-operator/blob/9ea64aa5c9a4053de5e0a0c6d9bab45539df5034/run_test.sh#L24.
If you need to run the test script individually you can invoke it like the run_test script, via /bin/bash. It will then correctly reference the common.sh file
i.e.
[dry@rzaleski ripsaw]$ /bin/bash tests/test_fiod.sh
+ source tests/common.sh
++ ERRORED=false
+ trap error ERR
+ trap finish EXIT
+ functional_test_fio
++ basename tests/test_fiod.sh
+ figlet test_fiod.sh
 _            _        __ _           _       _
| |_ ___  ___| |_     / _(_) ___   __| |  ___| |__
| __/ _ \/ __| __|   | |_| |/ _ \ / _` | / __| '_ \
| ||  __/\__ \ |_    |  _| | (_) | (_| |_\__ \ | | |
 \__\___||___/\__|___|_| |_|\___/ \__,_(_)___/_| |_|
                |_____|                            ```

I see , thanks @dry923 , closing this one.


too much whitespace around graph in small panel.

Dear all,
First of all, thanks for this flexible plugin! One of the few options left to create flexible XY plots in grafana without Angular.
However, it does not take all space in my small panel.
My histogram type plot looks fine in edit mode:

But not in dashboard mode:

When I enlarge the graph it gets visible, but still has much whitespace around it.

My layout looks like:
font:
  family: Inter, Helvetica, Arial, sans-serif
  color: rgb(25,27,31)
paper_bgcolor: white
plot_bgcolor: white
hoverlabel:
  bgcolor: white
xaxis:
  type: value
  autorange: true
  automargin: true
yaxis:
  type: log
  automargin: true
  autorange: true

Is there a way to use more of the panel height and width such that the graph is more visible?
I tried with automargin:false, but that does not seem to have any effect.
Kind regards,
Dennis

After some fiddling I found the right layout parameters, guess they could be added to the examples...
margin:
  b: 0
  t: 0
  l: 0
  r: 0
legend:
  orientation: h

Right, the reason that space exists is actually for the X and Y axis titles, you can set margin to 0 for everything, as you've found, and it goes away!

White space can be reclaimed for graph using the layout margin parameters after diving into plotjs


Add c99 to the CFLAGS

Not including this results in a failure to build with the following
error (gcc 4.8.4).

==> scenic_driver_glfw mkdir -p priv/prod
cc -O3 -fPIC -o priv/prod/scenic_driver_glfw c_src/main.c c_src/comms.c
c_src/nanovg/nanovg.c c_src/utils.c c_src/render_script.c c_src/tx.c
pkg-config --static --libs glfw3 glew -lGL -lm -lrt
c_src/render_script.c: In function ‘delete_all’:
c_src/render_script.c:118:3: error: ‘for’ loop initial declarations are
only allowed in C99 mode for (GLuint i = 0; i < p_data->num_scripts; i++
) { ^
c_src/render_script.c:118:3: note: use option -std=c99 or -std=gnu99 to
compile your code
c_src/render_script.c: In function ‘arc’:
c_src/render_script.c:576:5: error: ‘for’ loop initial declarations are
only allowed in C99 mode
 for (int i = 0; i <= segment_count; ++i) { ^

c_src/render_script.c: In function ‘sector’:
c_src/render_script.c:614:5: error: ‘for’ loop initial declarations are
only allowed in C99 mode
 for (int i = 0; i <= segment_count; ++i) { ^ make: ***
 [priv/prod/scenic_driver_glfw] Error 1 could not compile dependency
 :scenic_driver_glfw, "mix compile" failed. You can recompile this
 dependency with "mix deps.compile scenic_driver_glfw", update it
 with "mix deps.update scenic_driver_glfw" or clean it with "mix
 deps.clean scenic_driver_glfw"

I'll do my best to get to this tomorrow. just a crazy few days for me right now...

This creates some build warnings. Will need to dig in later...


re-freeze the model and the accurate was decreased by tf-nightly 1.13

Hi, victor
I was using the function: export_inference_graph.py, from newest version of tensorflow object detection API, to generate the frozen_inference_graph.pb. (tensorflow version: tf-nightly 1.13..., Ubuntu 18.04)
But the accuracy was so bad both in ssd_mobile_v1 and ssdlite_mobile_v2.
The confidences were inaccurate and the bounding boxes were also not in where they should be.
(A few boxes were in the right place, but the sizes were not accurate, and some confidences of the incorrect boxes were higher than the real hand boxes.)
The results were worse than what the frozen_inference_graph.pb in folder hand_inference_graph made.
Can you please help me to analyse the possible problems?
A wired phenomenon is that when running export_inference_graph.py to generate the frozen pb file, a group of files were generated at the same time, including pipeline.config, model.ckpt.data-00000-of 00001, and its index and meta files, which are different from the original .config and .ckpt files.

Ha!
I haven’t tried exporting  new nightly!
Is it possible that what you have installed is TensorFlow 2.0 api ? I know there are changes to the news api that pretty much break things for tf 1.x. I’d begin with searching for side effects for the object detection api when used with 1.13 nightly .
Sent from my iPhone

On Mar 7, 2019, at 07:39, sammymx<EMAIL_ADDRESS>wrote:
Hi, victor
I was using the function: export_inference_graph.py, from newest version of tensorflow object detection API, to generate the frozen_inference_graph.pb. (tensorflow version: tf-nightly 1.13..., Ubuntu 18.04)
But the accuracy was so bad both in ssd_mobile_v1 and ssdlite_mobile_v2.
The confidences were inaccurate and the bounding boxes were also not in where they should be.
(A few boxes were in the right place, but the sizes were not accurate, and some confidences of the incorrect boxes were higher than the real hand boxes.)
Can you please help me to analyse the possible problems?
A wired phenomenon is that when running export_inference_graph.py to generate the frozen pb file, a group of files were generated at the same time, including pipeline.config, model.ckpt.data-00000-of 00001, and its index and meta files, which are different from the original .config and .ckpt files.
—
You are receiving this because you are subscribed to this thread.
Reply to this email directly, view it on GitHub, or mute the thread.

Thank you very much.
Very pleasure if you can confirm that the frozen.pb in folder hand_inference_graph was generated from ./model-checkpoint/ssdmobilenetv1?
I found two facts:

ssdlitemobilenetv2 is worse than ssdmobilenetv1. The reason might be that the num_steps of  ssdlitemobilenetv2 was just 20000, far less than 200000 of ssdmobilenetv1.
The format of the .config files of them are different. The ssdlitemobilenetv2's format is same with the model downloaded from tf model zoo. And also, I have got the same results by both using the frozen.pb in  ssdlitemobilenetv2 and that I generated by the tf function.

Anyway, I just want to clear that the difference between tf versions could create the difference of the accuracy during the conversion of the models.

Ha! I haven’t tried exporting new nightly! Is it possible that what you have installed is TensorFlow 2.0 api ? I know there are changes to the news api that pretty much break things for tf 1.x. I’d begin with searching for side effects for the object detection api when used with 1.13 nightly .
…
Sent from my iPhone
On Mar 7, 2019, at 07:39, sammymx @.***> wrote: Hi, victor I was using the function: export_inference_graph.py, from newest version of tensorflow object detection API, to generate the frozen_inference_graph.pb. (tensorflow version: tf-nightly 1.13..., Ubuntu 18.04) But the accuracy was so bad both in ssd_mobile_v1 and ssdlite_mobile_v2. The confidences were inaccurate and the bounding boxes were also not in where they should be. (A few boxes were in the right place, but the sizes were not accurate, and some confidences of the incorrect boxes were higher than the real hand boxes.) Can you please help me to analyse the possible problems? A wired phenomenon is that when running export_inference_graph.py to generate the frozen pb file, a group of files were generated at the same time, including pipeline.config, model.ckpt.data-00000-of 00001, and its index and meta files, which are different from the original .config and .ckpt files. — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub, or mute the thread.

Hi @sammymx
Yes, frozen model was generated from model-checkpoint v1.
I tested v1 more carefully (a while ago though) and I'd say you focus more on this version.  v2 checkpoints was done more recently using the recent version of the object detection api.
Now as to accuracy, I have less training steps mainly because an observation on MAP/loss showed there was not much improvement after this. While I have not exported the v2 model to a frozen graph and tested in python, I have converted it to the tensorflow.js webmodel format and I confirm it works as expected. See demo here
Perhaps try installing an older version of tensorflow e.g. 1.10, export the graph and try again?

Closing ..
feel free to reopen if still having issues.


Add capability to change the stream's resolution from application side

After some comment by @sabitertan in #5 I wanted to improve the component such that it allows the application to control the stream's resolution nearly live.
I was considering to call applyConstraints on the track, yet it didn't give a smooth transition either. So I sticked to a complete restart in order to avoid potential side effects induced by sudden stream resolution changes.

Ideally we shouldn't create side effects in JSInterop. And I am not sure restarting stream is ideal for library too.

Ideally we shouldn't create side effects in JSInterop.

What side effect are you referring to?

And I am not sure restarting stream is ideal for library too.

Well at least it is of the same effect as if the user hit the start/stop buttons. What crossed my mind though was, whether it's worth to recreate the BarcodeReader in JS every time we start the stream to "reset" the whole thing. On the other hand, decodeFromConstraints in zxing-js' BrowserCodeReader calls through some hops decodeFromStream which in turn resets calls this.reset() (which stops camera, etc.) and then this.attachStreamToVideo.
So I'm not very concerned about that approach.

These changes will go along with those proposed in #7 .


MyoConnected event is not fired, when wakeing up a myo from sleep

Can be reproduced by using the ConsoleSample:

Start the Sample
Wake up a Myo -> Myo Connected
Wait until Myo disconnects -> Myo Disconnects
Wake up Myo -> Myo should connect again, but it is not working

Thanks for the report. I'll try to reproduce this after work. In the
meantime, because this is open source, feel free to try to fix it on your
own and I'll gladly integrate the patch.
On 7:18AM, Tue, Jan 13, 2015 neo9903<EMAIL_ADDRESS>wrote:

Can be reproduced by using the ConsoleSample:

Start the Sample
Wake up a Myo -> Myo Connected
Wait until Myo disconnects -> Myo Disconnects
Wake up Myo -> Myo should connect again, but it is not working

—
Reply to this email directly or view it on GitHub
https://github.com/tayfuzun/MyoSharp/issues/3.

I've found the problem.
In the latest version a Myo is added on pairing and removed on disconnect, but it should be removed at unpair.  Many Connects and Disconnects can happen between pair and unpair (Myo Standby,...).
-->
UnhookMyoEvents and removing the Myo should be moved from Myo_Disconnected to a new DeviceListener_Unpaired function.

Real life has been crazy. Sorry. I'll try to get this in tonight. Thanks for diving deeper on it.

I have the fix wired up now. Will commit shortly.

@neo9903 would you care to try out https://github.com/tayfuzun/MyoSharp/commit/2c40d0d764ce001e4c9d7463e3ac198059c54e3d to see if this fixes your issue?

Hi,
Sorry for the late response, i was not able to reply earlier.
The bug is fixed now and works perfectly.
Thank you.

@neo9903 Thanks for all of your feedback! Glad I was able to get that resolved for you.


Generating examples for an OPT doesn't work

If opt, do not try to flatten, and it should be fine!

Fixed


3.5 Format JSON

JSON formatting present throughout the functions in the APILink library, no need for an issue for this.


Simulate styles for extended form elements

<dom-module id="cs-textarea">
  <link href="style.css" rel="import" type="css">
  <script>
    Polymer({
      'is': 'cs-textarea',
      'extends': 'textarea'
    });
  </script>
</dom-module>

Styles can't be applied to textarea in Chromium because it uses native Shadow DOM that can't be modified correctly at the moment (I'm using Polymer.dom = 'shadow'), but nothing prohibits so simulate it like in browsers that doesn't have native Shadow DOM support, for instance, it works nicely in Firefox.
The same story about input, we can't do that directly, but we can simulate it, and simulation works nicely in Firefox.
So basically, there is a need to process styles for extended textarea, input (probably some other elements) regardless native Shadow DOM support like for browsers that lacks native support.

This is something we're considering for the next major version of Polymer. To support a use case like this, an element needs to be able to identify its styling scope (document or a containing shadowRoot) and put styles there.

I've worked on this today, but the main obstacle is that code relies heavily on global Polymer.Settings.useNativeShadow, while in order for this to work we basically need similar per-element property like element._supportsNativeShadowRootCreation to be used instead.
With per-element property we can force needed elements that doesn't support Shadow Root creation on them (https://github.com/w3c/webcomponents/issues/110, https://w3c.github.io/webcomponents/spec/shadow/#methods, but Chromium additionally supports currently <button> at least) to use the same mechanism as browsers without native Shadow DOM support (uses full Shadow DOM polyfill).

Here is the simple test case that should pass when implemented: https://github.com/Polymer/polymer/compare/master...nazar-pc:type-extension-test-case

Since extending native elements is discouraged (see https://www.polymer-project.org/2.0/docs/upgrade#remove-type-extension-elements for a larger explanation), you are now required to wrap the native element. As such, any styling concerns fall naturally into place where you can just style the native element directly. Therefore I am closing this issue for now.


Update branch related docs

Add example for renderNothing
improve example for renderComponent
Clarify optional param for branch

Note: I put this work on a branch because I wanted to make another simultaneous pull request.

Your suggestions from previous PR have been implemented

I like this, thank you. @wuct ?

Looks good to me. Thanks!


Deduplicate SliceOrInt

Summary
After doing some post-merge analysis on the impact of #11842 it turns out the enum being modified in that PR was duplicated in the rust code unnecessarily. The same enum was already defined in the euler_one_qubit_decomposer module for it's custom 1 qubit gate sequence return type. This commit updates the code so it just use the one defined in the circuit data module that has the fix from #11842 applied already.
Details and comments

Pull Request Test Coverage Report for Build<PHONE_NUMBER>
Details

0 of 0   changed or added relevant lines in 0 files are covered.
10 unchanged lines in 2 files lost coverage.
Overall coverage decreased (-0.005%) to 89.296%

Files with Coverage Reduction
New Missed Lines
%

crates/qasm2/src/lex.rs
4
92.7%

crates/qasm2/src/parse.rs
6
97.62%

Totals

Change from base Build<PHONE_NUMBER>:
-0.005%

Covered Lines:
58904

Relevant Lines:
65965

💛  - Coveralls

Shall we move this to shared object to a more central / less "owned" location, if we're going to use it in multiple places? cargo fmt's also complaining.

Done in: https://github.com/Qiskit/qiskit/pull/11844/commits/5c35995b6d92bbafabaf10b3c8e048b5c156d8ef


SampleStack : How search options work

Hi,
Would like to know how search options are written to marklogic in this application. In java applications, I have loaded the search options using xml but using java spring when I try to write the options it gives me error.
But, in samplestack I could not find any search options xml .
Thanks

Excellent question @niranjanMaisnam .
Samplestack has no XML at all (with one exception but you can find that)
MarkLogic accepts search options in JSON and in XML -- the folder called
database/options
contains all the search options in use.
XML and JSON options are interchangeable from MarkLogic and REST perspective.  The Java Client's Options interface however is a little bit picky, as it was originally written with XML in mind.
In Samplestack, the build tool stores the options.  Part of ./gradlew dbconfigure
uses REST to send database/options/questions.json to http://localhost:8006/v1/config/options/questions.  That's a PUT, content-type application/json.  You can see the code that does this in
appserver/java-sprint/buildSrc/src/main/groovy/MarkLogicConfigureTask.groovy

Thanks for the clarification


fatal: files bigger than approx. 1500 bytes get truncated

tested again... unfortunately the error occurs only sometimes...

still happens from time to time, currnetly checking by hand...
planned workaround:

must have last line in code "-- EOF"
check for this and error message if not found, offer to retry

will have a look into the details, maybe due to some timing problems around no-blocking not taken into account :-)

moved away from using github and the raw files function.
not recommended anymore, use apache server instead.


shared: Prescribe atomic push when bumping version.

This way, if the maintainer isn't able to update main, the push doesn't add the shared-VERSION tag either. That avoids ending up with a tag that potentially doesn't get included in the history of the main branch.
The Git docs warn that servers might or might not support this feature, but GitHub does -- indeed they boasted about it when it first came out, in Git 2.4 back in 2015:
https://github.blog/2015-04-30-git-2-4-atomic-pushes-push-to-deploy-and-more/
Self-review checklist

[ ] Self-reviewed the changes for clarity and maintainability
(variable names, code reuse, readability, etc.).

Communicate decisions, questions, and potential concerns.

[ ] Explains differences from previous plans (e.g., issue description).
[ ] Highlights technical choices and bugs encountered.
[ ] Calls out remaining decisions and concerns.
[ ] Automated tests verify logic where appropriate.

Individual commits are ready for review (see commit discipline).

[ ] Each commit is a coherent idea.
[ ] Commit message(s) explain reasoning and motivation for changes.

Completed manual review and testing of the following:

[ ] Visual appearance of the changes.
[ ] Responsiveness and internationalization.
[ ] Strings and tooltips.
[ ] End-to-end functionality of buttons, interactions and flows.
[ ] Corner cases, error conditions, and easily imagined bugs.

Merged, thanks @gnprice!


ISPN-11575 EncryptedPropertiesSecurityRealm

https://issues.redhat.com/browse/ISPN-11575

@ryanemerson I've made the requested changes, aside from one which I commented on above.

@tristantarrant Are these failures related:
org.infinispan.server.persistence.ManagedConnectionOperations.testPutGetRemove
org.infinispan.server.persistence.ManagedConnectionOperations.testTwoCachesSameCacheStore

They appear on all three runs, but not on our latest master build.

@tristantarrant Are these failures related:
org.infinispan.server.persistence.ManagedConnectionOperations.testPutGetRemove
org.infinispan.server.persistence.ManagedConnectionOperations.testTwoCachesSameCacheStore

They appear on all three runs, but no on our latest master builds

I saw the failures on other PRs which answered the question. Thanks @tristantarrant


Does there any pure python inference code for a single mode?

Hi, for a newbie in asr, it's really hard to understand what c++ doing step by step, does there any pure python script can inference lstm transducer model? (by reading a wav file then output text)

Yes, I just added one via https://github.com/k2-fsa/sherpa/pull/134
Please see
https://github.com/k2-fsa/sherpa/blob/master/sherpa/bin/lstm_transducer_stateless/offline_asr.py
You can find the usage in
https://github.com/k2-fsa/sherpa/blob/21f829ce5816ce1ef1b00c7e7f194b69386f1dca/sherpa/bin/lstm_transducer_stateless/offline_asr.py#L17-L75


[#127807161] Add G-Cloud 8 contract variation

This is the hopefully "final" version of the contract variation content for this story: https://www.pivotaltracker.com/story/show/127807161
The inserts and deletes are marked up with HTML here in the content. Not the prettiest, but I can't see a reasonable way round it.
Page content from Andy, clause amendments from Alexa.

👍 💯
Nice. I didn't know about <del> and <ins> tags before.


Avoid duplicating lines when using functions.cpp addStringToFile()

Hi @lbgracioso what this tries to solve? I didn't get exactly. It checks if a file is empty?

@viniciusferrao If a string already exists in a file, it doesn't add again.
If you run Cloyster twice with the same data, it'll duplicate all the data that was added before in configuration files, for example.

Thanks @lbgracioso
It's merged.


Improve the Navbar section

I have noticed that the current Navbar section is quite basic. I would like to raise an issue for this and request it to be assigned to me. Enhancing the Navbar is crucial, as it will significantly improve the overall user experience and navigation on the website.

i can solve this issue

@Mayursoni1518 @dushyantkanav sorry guys someone already assigned for this fee free to contribute in some another functionality!


[ERROR] Module Not Found

OS : Windows 11
Python: 3.13
After installing requirements.txt using install.bat, i ran the run.bat and gave me the error

Modules not found! Please run install.bat and restart the tool.

use python 3.11.1


Support Python3

using anaconda python3
>>> import pyhwschema
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "anaconda3/lib/python3.6/site-packages/pyhwschema/__init__.py", line 1, in <module>
    from .schema import HwSchema
  File "anaconda3/lib/python3.6/site-packages/pyhwschema/schema.py", line 3, in <module>
    from avro.schema import parse
  File "anaconda3/lib/python3.6/site-packages/avro/schema.py", line 340
    except Exception, e:
                    ^
SyntaxError: invalid syntax

Thanks for the response. I’ll look for alternatives.

Ive pushed a new version working with Python3. The required avro package for Python3 is avro-python3 not avro. :)

I installed avro-python3. Tried to upgrade the latest pyhwschema. It says it's already up to date.

import pyhwschema
Traceback (most recent call last):
File "", line 1, in
File "/remote/vgrnd104/guntaka/anaconda3/lib/python3.6/site-packages/pyhwschema/init.py", line 1, in
from .schema import HwSchema
File "/remote/vgrnd104/guntaka/anaconda3/lib/python3.6/site-packages/pyhwschema/schema.py", line 3, in
from avro.schema import parse
ModuleNotFoundError: No module named 'avro.schema'


engine: replica need shutdown hook to handle signal gracefully

Now kill process inside replica won't be caught in the log.

Added additional message for replica shut down in https://github.com/rancher/longhorn-engine/pull/342


Replace HashMap<Byte, ?> by native array in IsolatableSerializerRegistry

As the collection of serializers is immutable and indexed by an integer starting from 0.
This change-set also makes implementations of IsolatableSerializer symmetrical by moving the responsibility of writing the serializer key/index to the super type.
Reviewing cheatsheet
Before merging the PR, comments starting with

❌ ❓must be fixed
🤔 💅 should be fixed
💭 may be fixed
🎉 celebrate happy things

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Most likely caused by the ongoing GitHub incident https://www.githubstatus.com/incidents/pxg3dz4yg7lp

Thanks for the heads up, @cobexer!


ImportError: cannot import name 'datetime'

Hi All, I'm new to both Atom and Python.  Using Atom to practise Python.  My problem is that I can't successfully import python modules.
I wrote a short piece of code to conflate the contents of three .txt files into one new file (with the new filename being the date/time of when the file was created.  I was advised that rather than create a python list for the original 3 .txt files, I could use the glob module to iterate over the file path and pick out the files based on extension.  The code looks like this:
import glob2
from datetime import datetime
filenames = glob2.glob("*.txt")
with open(datetime.now().strftime("%Y-%m-%d-%H-%M-%S-%f")+".txt", 'w') as file:
for filename in filenames:
with open(filename, "r") as f:
file.write(f.read() + "\n")
The problem is that when I execute it from here:
C:\users\rball\OneDrive - Personal\AtomForPython> py concatglob.py
I get this error:
Traceback (most recent call last):
File "concatglob.py", line 1, in
import glob2
ModuleNotFoundError: No module named 'glob2'
As you can see, Atom is tied to my OneDrive account which is where my files are kept.
Python is installed on C:\  but Atom can definitely see the module paths as shown below:
C:\users\rball\OneDrive - Personal\AtomForPython> py -c "import sys;print(sys.path);"
['', 'C:\Program Files\Python36\python36.zip', 'C:\Program Files\Python36\DLLs', 'C:\Program Files\Python36\lib', 'C:\Program Files\Python36', 'C:\Program Files\Python36\lib\site-packages']
Any ideas?

Solved:
I had named a practise file 'datetime.py' which was causing issues when I attempted to download the datetime module.  Deleting the file solved the problem.


Fix typo in manifest.json

Documenation -> Documentation

Thank you for a pr, but other pr beat you to it. Keep it up!


Protect locale

This PR fixes #587 .

Hahaha sorry I failed to get this done. I swear I've had this open on my desktop ever since I left that comment in #587! Just couldn't get to it =(
Thanks, @dritter 🥇


Multi-GPU training speed test?

I know caffe2 have achieved up to 7.7x speed up using 8 GPUs with batch_size 64 on Resnet-50.  https://developer.nvidia.com/caffe2
But I'm a little confused about the setting of batch_size in the speed test. We should set total batch_size as 64*8  or just 64,  when 8 GPUs are used?

The batch size you specify as argument to the resnet50_trainer program is the total batch size, so 512.
Is this the speed test you're referring to?

yes，I just want to test the speedup ability of multi-gpus on my own machine. thank you @pietern


Enhance upsert metadata handling

Make the following enhancement to the upsert metadata manager:

Add replace segment support

Log error and emit metric (PARTIAL_UPSERT_ROWS_NOT_REPLACED) for segment not fully replaced, which can potentially cause inconsistency between replicas for partial upsert table
Remove the remaining primary keys from the replaced segment immediately so that new consuming segment is not affected

Handle empty segment properly
Enhance the log to log the table name, partition id and primary key count
Clean up the code and move the upsert related logic into the metadata manager, such as creating the record info iterator
In IndexSegment, replace getPrimaryKey() with getValue() which is more general and can be used to read the comparison column as well
Fix the bug of assuming the first primary key column is the partition column when fetching the partition id of the segment
Fix the bug of using byte[] as primary key when the column type is BYTES

Release Notes
Added a metric PARTIAL_UPSERT_ROWS_NOT_REPLACED to track the rows not replaced for partial upsert table

LGTM!

Codecov Report

Merging #9095 (06eb4f2) into master (1211f07) will decrease coverage by 43.63%.
The diff coverage is 0.65%.

@@              Coverage Diff              @@
##             master    #9095       +/-   ##
=============================================
- Coverage     70.00%   26.37%   -43.64%
+ Complexity     4745       42     -4703
=============================================
  Files          1838     1826       -12
  Lines         97229    96934      -295
  Branches      14639    14621       -18
=============================================
- Hits          68069    25570    -42499
- Misses        24434    68872    +44438
+ Partials       4726     2492     -2234

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40.00% <0.00%> (-29.82%)
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0.00% <0.00%> (-30.00%)
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...l/indexsegment/immutable/ImmutableSegmentImpl.java
0.00% <0.00%> (-57.32%)
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0.00% <0.00%> (-72.16%)
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...local/indexsegment/mutable/MutableSegmentImpl.java
0.00% <0.00%> (-53.74%)
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0.00% <0.00%> (-64.78%)
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...t/local/upsert/PartitionUpsertMetadataManager.java
0.00% <0.00%> (-75.24%)
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multi-class multi-label classification fine-tuning

I need to do multi-class multi-label classification fine-tuning on my custom dataset.
For setting up the ground truth labels, would it work well if I set gt_parse to {"class" : [class_name1, class_name2, ...]} where class_names are listed in alphabetically order ?

It works!


Fix support for self-referencing updates to convention

This is a follow-up to #27414, which introduced initial support for conventions.
This fixes an issue with mutable state being shared between explicit and convention values, which are meant to be fully independent from one another.
See question that triggered this change here:

:question: Correct me if I'm wrong, but my understanding of this is "if there is no explicit value, then return a copy of the convention's collector; otherwise return the current value". However, all callsites are in setFrom, which discards the copied paths anyway. This looks suboptimal.

Originally posted by @mlopatkin in https://github.com/gradle/gradle/pull/27531#discussion_r1447744479

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Failed to compile for Alpine/musl target

Godot version
v4.0.alpha.custom_build [9f408aef4]
System information
Alpine linux 3.16, Intel N6005
Issue description
Hi, i was trying to compile Godot 4 (master) on an Alpine machine and it compiles correctly until the last linking phase when it throws this error:
[ 96%] Linking Program bin/godot.linuxbsd.tools.64 ...
[100%] progress_finish(["progress_finish"], [])
[100%] /usr/lib/gcc/x86_64-alpine-linux-musl/11.2.1/../../../../x86_64-alpine-linux-musl/bin/ld: platform/linuxbsd/crash_handler_linuxbsd.linuxbsd.tools.64.o: in function `handle_crash(int)':
/home/*****/godot/platform/linuxbsd/crash_handler_linuxbsd.cpp:56: undefined reference to `backtrace'
/usr/lib/gcc/x86_64-alpine-linux-musl/11.2.1/../../../../x86_64-alpine-linux-musl/bin/ld: /home/*****/godot/platform/linuxbsd/crash_handler_linuxbsd.cpp:81: undefined reference to `backtrace_symbols'
collect2: error: ld returned 1 exit status
scons: *** [bin/godot.linuxbsd.tools.64] Error 1
scons: building terminated because of errors.

On the Godot compiling guide are reported the necessary dependencies to build on Alpine, so i suppose it is a supported system, otherwise i'm sorry for the noise.
Thanks for the dedicated time, have a good day.
Steps to reproduce
Just compile Godot targetting/on a musl machine.
Minimal reproduction project
No response

Duplicate of https://github.com/godotengine/godot/issues/57193.


Refactor Command interfacing to Cobra

Now that we have a more grounded version of ParameterDefinition, it is time to start tackling our overdependence on cobra, and especially CobraCommand, which actually has no reason to exist if we want glazed and the ecosystem to be reasonably agnostic about which interfaces are being used to call command.
Because I need to use escuseme and geppetto declarative commands within the context of a full app in facture (for looking up SKUs in ES and transforming CSVs through GPT3), it's now a good time to start cleaning things up.
Other complications that arise from this is the reliance on global viper flags to acquire credentials and connection information to ES / openai API / SQL server which now needs to be tackled by passing in a client factory.

The other question is how do we register the other flags necessary for say, the glazed layers.


notExist filter does not update on observe as it does on get

Below get query returns the ceremony when there is a new one that has completedAt field missing.
await based.get({
    $id: projectId,
    ongoinCeremony: {
      id: true,
      $find: {
        $traverse: 'ceremonies',
        $filter: {
          $field: 'ceremonyType',
          $operator: '=',
          $value: ceremonyType,
          $and: {
            $field: 'completedAt',
            $operator: 'notExists'
          }
        }
      }
    }
  })

Same query as observe does not update for a new ceremony that has completedAt field missing.
based.observe({
    $id: projectId,
    ongoinCeremony: {
      id: true,
      $find: {
        $traverse: 'ceremonies',
        $filter: {
          $field: 'ceremonyType',
          $operator: '=',
          $value: ceremonyType,
          $and: {
            $field: 'completedAt',
            $operator: 'notExists'
          }
        }
      }
    }
  },
  (project: any) => { // never fires}
)

update: this only happens for one-to-many relationships

Apparently GitHub closed this issue automatically.

update: this also happens for many-to-many relationships, but only when there are multiple observering queries running for the related objects. it is also not special to notExist filter. very elusive to pin it.

I suppose Selva 19.1.2 fixed this.

yes @OlliV thanks a lot


Replacement of attachments for .msg files removes annotations/redactions for all attachments for .msg file

Describe the bug in current situation
In the Docreviewer App, when a page flag (and/or redaction) is applied to an attachment of a .msg file, and then the attachment is subsequently REPLACED in the records tab in foi flow -> the annotations and page flag for the attachment is removed (this is good/working as intended) but the annotations for all other attachments associated with the .msg file are remov0de as well (page flag is retained though). This will require the user to reapply all the annotations again for files they did not want to replace, and additionally cause an error with % of completion/redline package creation (see Abin's related bug ticket in the Dependencies tab)
Link bug to the User Story
https://dev.foirequests.gov.bc.ca/foi/foirequests/15724/ministryrequest/15747
Impact of this bug
High
Chance of Occurring (high/medium/low/very low)
Medium
Pre Conditions: which Env, any pre-requesites or assumptions to execute steps?
dev, test and all marshalls
Steps to Reproduce
Steps to reproduce the behavior:

Go to a foi request (or my linked request) and add a .msg file with multiple attachments. Then select redact record and apply page flags + annotations to all the attachments associated with the .msg.
Go to the records tab in the foi flow app and replace one of the attachments with a new file.
Go back to the documentreviewer app and notice that all page flags and annotations are removed for the replaced attachment (this is good) but also notice that all of the annotations are removed for the other .msg attachments (and that the page flags are still retained). This is an error/bug.

Actual/ observed behaviour/ results
For a .msg file, when one attachment is replaced, the replaced attachments annotations and page flags are removed as well as all the other attachments annotations (page flags are retained though)
Expected behaviour
For a .msg file, when one attachment is replaced, the replaced attachments annotations and page flags are removed and all other attachments associated with the .msg file should retain its annotations and page flags.
Screenshots/ Visual Reference/ Source
If applicable, add screenshots to help explain your problem. You an use screengrab.

working as expected so closing