Error with rules_python-0.13.0

Upgrading to rules_python-0.13.0 results in
external/mypy_integration/mypy/BUILD:3:10: no such package '@mypy_integration_pip_deps_mypy//': The repository '@mypy_integration_pip_deps_mypy' could not be resolved: Repository '@mypy_integration_pip_deps_mypy' is not defined and referenced by '@mypy_integration//mypy:mypy'

which is likely due to https://github.com/bazelbuild/rules_python/pull/807 which suggest to change to pip_parse instead of pip_install.

you can add this after loading the repo:
pip_parse(
    name = "mypy_integration_pip_deps",
    python_interpreter_target = python_interpreter,
    requirements_lock = "//path/to/local/lock_file/mypy:requirements3.lock.txt",
)

load("@mypy_integration_pip_deps//:requirements.bzl", install_deps_mypy="install_deps")

install_deps_mypy()

Works great, thanks! Only additional step that was missing was adding additional dependencies of mypy, e.g.,
tomli==2.0.1
typing-extensions==4.4.0
mypy-extensions==0.4.3

to the requirements lock file (I was only using mypy==0.981 before - unsure how they were pulled in automatically before?)

We've got a couple open PRs now that are upgrading the Python rules, so I'm going to close this in favor of those:

#61
#82


Use JsonPatchDocument without Newtonsoft.Json

Describe the bug
The JsonPatchDocument does not specify a converter for the new System.Text.Json serializer and so any request to an action using JsonPatchDocument<T> returns a 400 model valutation error.
To Reproduce
Steps to reproduce the behavior:

Using this version of ASP.NET Core '3.0.0-preview7'
Use the System.Text.Json serializer, not Json.Net.
Make an action with parameter JsonPatchDocument<SomeModel>
Call the action with a simple patch, e.g. [{"op":"replace","path":"/number","value":115}].
See error 400 being returned

Expected behavior
Excpect the parameter to be correctly bound to the patch and a status 200.
Additional context
Include the output of dotnet --info
.NET Core SDK (reflecting any global.json):
 Version:   3.0.100-preview7-012821
 Commit:    6348f1068a

Runtime Environment:
 OS Name:     Windows
 OS Version:  10.0.18362
 OS Platform: Windows
 RID:         win10-x86
 Base Path:   C:\Program Files (x86)\dotnet\sdk\3.0.100-preview7-012821\

Host (useful for support):
  Version: 3.0.0-preview7-27912-14
  Commit:  4da6ee6450

.NET Core SDKs installed:
  3.0.100-preview7-012821 [C:\Program Files (x86)\dotnet\sdk]

.NET Core runtimes installed:
  Microsoft.AspNetCore.App 3.0.0-preview7.19365.7 [C:\Program Files (x86)\dotnet\shared\Microsoft.AspNetCore.App]
  Microsoft.NETCore.App 3.0.0-preview7-27912-14 [C:\Program Files (x86)\dotnet\shared\Microsoft.NETCore.App]
  Microsoft.WindowsDesktop.App 3.0.0-preview7-27912-14 [C:\Program Files (x86)\dotnet\shared\Microsoft.WindowsDesktop.App]

The following hack seems to work:
In Startup.cs:
public void ConfigureServices(IServiceCollection services)
{
            var fakeServiceProvider = new ServiceCollection();
            fakeServiceProvider
                .AddLogging()
                .AddMvc()
                .AddNewtonsoftJson();

            var fakeMvcOptions = fakeServiceProvider.BuildServiceProvider().GetRequiredService<IOptions<MvcOptions>>().Value;

            services.AddMvc(options =>
                {
                    var jsonPatchInputFormatter = fakeMvcOptions.InputFormatters.OfType<Microsoft.AspNetCore.Mvc.Formatters.NewtonsoftJsonPatchInputFormatter>().Single();
                    options.InputFormatters.Insert(0, jsonPatchInputFormatter);
                });
}

Is there any more elegang way to accomplish JsonPatch support?

Thanks for contacting us, @rmja.
We don't support JSONPatch without Newtonsoft.Json.
You can simply call .AddNewtonsoftJson() on the service collection instance.

@mkArtakMSFT year, but that replaces the System.Text.Json formatter for all json which is really unfortunate. How do I use Json.Net only for application/json-patch+json requests in a more clean way than my example above?

A PATCH request to WebAPI always returns an UnsupportedMediaType Response.  Taking a dependence on Newtonsoft.Json (and calling AddNetwtonsoftJson()) does not change that.  I have tried decorating the method with a Consumes attribute, writing my own custom InputFormatter (which appear to be broken in general), even changing the ContentType Header to a media type that should be supported (IE: application\json), nothing seems to work.

@rmja your original code is actually the way to go in that case.
@dhhunter your concerns are unrelated to this topic. Please file a separate issue if you want, and we will look into that separately.


Unable to build app beacuse of top level await

Describe the bug
I'am using top level await in js file, then i export result to vue/other files. If i log imported result on development everyting work but when i try to build this error shows:

Reproduction

export await result from js file export const data = await fetch("/filtered_data.json")   .then((res) => res.json())
import it
yarn build

System Info
OS: Windows 10 10.0.18363
Node: 12.18.3 - D:\Node\node.EXE
Yarn: 1.22.10 - ~\AppData\Roaming\npm\yarn.CMD
npm: 6.14.6 - D:\Node\npm.CMD
@vitejs/plugin-vue: ^1.2.3
vite: ^2.3.8

Used package manager: npm | yarn

Before submitting the issue, please make sure you do the following

[x] Read the Contributing Guidelines.
[x] Read the docs.
[x] Check that there isn't already an issue that reports the same bug to avoid creating a duplicate.
[x] Provide a description in this issue that describes the bug.
[x] Make sure this is a Vite issue and not a framework-specific issue. For example, if it's a Vue SFC related bug, it should likely be reported to https://github.com/vuejs/vue-next instead.
[x] Check that this is a concrete bug. For Q&A open a GitHub Discussion or join our Discord Chat Server.

The error message is quite clear IMO.
Top-level await is only supported in these new browser versions.
You can change the browser target following the documentation: https://vitejs.dev/guide/build.html#browser-compatibility


Data Process and Storage

The Data process and storage section has a repeated sentence, one with hyperlinks and one without. Suggest to delete the sentence without the hyperlinks
https://docs.microsoft.com/en-us/microsoft-365/managed-desktop/service-description/privacy-personal-data?view=o365-worldwide#microsoft-managed-desktop-data-process-and-storage

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

ID: 70c7d29a-8fc7-6895-530d-2d29ff775a01
Version Independent ID: fd4facf5-3e8e-151c-adc4-3aae0af57d9e
Content: Privacy and personal data - Microsoft Managed Desktop
Content Source: microsoft-365/managed-desktop/service-description/privacy-personal-data.md
Service: m365-md
GitHub Login: @jaimeo
Microsoft Alias: jaimeo

@oscargsune Thank you for your feedback. We have made changes to the document via PR#https://github.com/MicrosoftDocs/microsoft-365-docs/pull/6346
Thanks
Sri

Awesome! Glad I could help :)
Oscar Gonzalez
Technical Specialist- Security and Compliance
0430 845 111

From: Sriraman M S @.>
Sent: Wednesday, September 15, 2021 9:03:03 AM
To: MicrosoftDocs/microsoft-365-docs @.>
Cc: oscargsune @.>; Mention @.>
Subject: Re: [MicrosoftDocs/microsoft-365-docs] Data Process and Storage (#6336)
@oscargsunehttps://github.com/oscargsune Thank you for your feedback. We have made changes to the document via PR##6346https://github.com/MicrosoftDocs/microsoft-365-docs/pull/6346
Thanks
Sri
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHubhttps://github.com/MicrosoftDocs/microsoft-365-docs/issues/6336#issuecomment-919570695, or unsubscribehttps://github.com/notifications/unsubscribe-auth/ASUPVJLZT2LZ6VUOSX42QTTUB7ICPANCNFSM5D7AKKCQ.
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@msbemba Thank you very much for the contribution and updating the documentation with PR. @oscargsune Hope this update is helpful for you. Thanks for taking out some time to open the issue. Appreciate and encourage you to do the same in future also.


Haskell examples

Lagrange interpolation and spline approximation (quadratic and cubic).
This code was original designed to write data to files for plotting, so I've gutted it a little to make for better examples.

Thanks 👍🏼


Don't render component on the server

Thanks for this fix @ndarilek . I've not really done anything with server-side rendering so this is appreciated!

No problem! Any word on when it will be published?

Hi @ndarilek I'll try and get it done tonight.

HI @ndarilek
This should now be available via npm as v2.2.0
Cheers

Thanks!

You've partially reverted my PR and have undone my fix.
<script>
   var Dropzone = require('dropzone')

   export default {

My PR specifically did not do this because it is not universal.
Dropzone cannot be required on the server as it attempts to use window
which is not present. With those lines in place, this issue still
persists, and that's what's present in your released version.

The only place where you touch dropzone as required is in mounted.
It isn't enough to simply return if you're on the server. You have to
move all of the initialization code for Dropzone into this function
because Dropzone does something after being required that expects
window to be available, which it isn't on the server. The rest should
be handled by callbacks/Vue methods which aren't run in the context of
SSR, so are unaffected.
I've been using my PR in an app, and it seemed to work just fine, both
rendering what was possible on the server while working fully on the
client. Please merge the PR as submitted, or let me know what in it was
unworkable while still preserving the fact that dropzone has to be
required specifically after the SSR guard ensures that it isn't running
on the server. Thanks for your time and attention.

Hey @ndarilek
Im sorry for butchering your pull request, not quite sure what I was thinking. As mentioned previously I don't have any experience with SSR. I'm very intrigued how this module works in SSR if you're not requiring dropzone, that's an investigation for another day :)
I've republished to npm as version 2.2.1
Cheers
Rowan

The idea is that it doesn't work in SSR, but I'm using it in a
codebase that mostly does, so I need it to silently do nothing while
everything around it runs. So if I have 9 other modules, and they all
work in SSR, the presence of this one module breaks everything on the
server and introduces rendering errors even if the other 9 modules work
fine.
Thanks, I'll give the new version a shot.


add patent category

Add category of patent, including TIPO, EPO and USPTO.

[O] Your submissions are formatted according to the guidelines in the contributing guide.
[O ] Your changes are made in the README file, not the auto-generated JSON.
[O] Your additions are ordered alphabetically.
[O] Your submission has a useful description.
[O] Each table column should be padded with one space on either side.
[O] You have searched the repository for any relevant issues or PRs.
[O] Any category you are creating has the minimum requirement of 3 items.

Hi @tigercosmos,
The build is failing as one of the Auth columns in your addition is "Yes" which is not an accepted value. Please could it be updated to one of the valid, accepted, values please 😄
https://github.com/toddmotto/public-apis/blob/master/.github/CONTRIBUTING.md#formatting

@mikestreety  Thanks for help. It is fixed. :)

Thanks!


Cannot Manage Targets/instances With Anything Other Than "Via Target"

Operating System
Ubuntu 24.04
AMP Version and Build Date
2.6
AMP Release Stream
Mainline
I confirm that

[X] I have searched for an existing bug report for this issue.
[X] I am using the latest available version of AMP.
[X] my operating system is up-to-date.

Intended Action
N/A
Expected Behaviour
N/A
Actual Behaviour
N/A
Reproduction
Attempting to manage a target or instance on a target using any mode other than "Via Target" results in no response, or an error stating the token is for a different instance.

This is fixed in the release coming tomorrow.


Issue #6 child: revise sync code

https://github.com/ctomkow/verify/blob/b733eab0d168f7b06c882e67eca9224c0a872a90/Verify.py#L116

Cleaned up sync code by adding a sync test method. Added a timeout for the sync testing.
https://github.com/ctomkow/verify/commit/3d07a8c99b29faa61c5b815d767ce1c52d4e7b3c


Cameras Not Displaying and Warning after 0.110 Upgrade

After upgrading HA to 0.110 I am getting errors regarding Kuna cameras and the video is not displaying in Lovelace.  I have uninstalled and reinstalled via HACS and still get the same errors.

Here is the data from the logs.
2020-05-22 09:35:24 WARNING (MainThread) [homeassistant.helpers.translation] kuna: the '.translations' directory has been moved, the new name is 'translations', starting with Home Assistant 0.111 your translations will no longer load if you do not move/rename this
2020-05-22 09:35:40 WARNING (MainThread) [homeassistant.components.binary_sensor] BinarySensorDevice is deprecated, modify KunaBinarySensor to extend BinarySensorEntity
2020-05-22 09:35:40 WARNING (MainThread) [homeassistant.components.switch] SwitchDevice is deprecated, modify KunaSwitch to extend SwitchEntity
2020-05-22 09:35:41 WARNING (MainThread) [homeassistant.helpers.translation] kuna: the '.translations' directory has been moved, the new name is 'translations', starting with Home Assistant 0.111 your translations will no longer load if you do not move/rename this
2020-05-22 09:35:41 WARNING (MainThread) [homeassistant.helpers.translation] kuna: the '.translations' directory has been moved, the new name is 'translations', starting with Home Assistant 0.111 your translations will no longer load if you do not move/rename this
2020-05-22 09:35:41 WARNING (MainThread) [homeassistant.helpers.translation] kuna: the '.translations' directory has been moved, the new name is 'translations', starting with Home Assistant 0.111 your translations will no longer load if you do not move/rename this
2020-05-22 09:35:41 WARNING (MainThread) [homeassistant.helpers.translation] kuna: the '.translations' directory has been moved, the new name is 'translations', starting with Home Assistant 0.111 your translations will no longer load if you do not move/rename this
2020-05-22 09:35:41 WARNING (MainThread) [homeassistant.helpers.translation] kuna: the '.translations' directory has been moved, the new name is 'translations', starting with Home Assistant 0.111 your translations will no longer load if you do not move/rename this

According to this thread https://community.home-assistant.io/t/kuna-surveillance-camera-support/15391/162 we are waiting for a fix.


Support new Wazuh version 4.10.2 in wazuh-dashboard repository

Description
This issue attempts to support a new version 4.10.2 in the wazuh-dashboard repository.
Tasks

[x] Bump 4.10.2 branch to 4.10.2 version on wazuh-dashboard repository, including revision and changelog.

https://github.com/wazuh/wazuh-dashboard/pull/342

LGTM :heavy_check_mark:


New Pattern: aws-wafv2-apigateway

This is on our radar, we hope to introduce WAF constructs for several different types of endpoints this year.


Remove dimensions.

Get rid of the biggest lag maker. It makes everyone around you invisible, you don't know who is where and when you change a dimension the game freezes for minimum 1 second. Try for a week/month without them and see the difference.

But mta cant handle 40+ vehicles in the same place. Low fps guaranteed,  that's the reason why dimensions was made

I didn't had any freezes at all btw

When changing dim, it's hell...cars popin' up from nowhere, game has to load around 10 cars at once which makes lag :D

Look at fps and general slowdown when changing https://www.dropbox.com/s/sdw0lnka5gp03jp/fudims.mp4?dl=0

I think dims need to be 1 mode for laggers blockers and high pingers and another for everyone else


[android] Build failing

Hello!
I'm trying to build for android but the build is failing with this error.
> Task :azzapp_react-native-skia-video:configureCMakeDebug[arm64-v8a] FAILED
C/C++: CMake Error at CMakeLists.txt:17 (add_library):
C/C++:   Target "react-native-skia-video" links to target
C/C++:   "react-native-reanimated::worklets" but the target was not found.  Perhaps
C/C++:   a find_package() call is missing for an IMPORTED target, or an ALIAS target
C/C++:   is missing?

My setup

React native - 0.74.5
Expo - 51

Updating reanimated to  "react-native-reanimated": "3.15.0", seems to fix the problem , but I still can't build.
I have a new error
Task :azzapp_react-native-skia-video:buildCMakeDebug[arm64-v8a] FAILED

FAILURE: Build failed with an exception.

* What went wrong:
Execution failed for task ':azzapp_react-native-skia-video:buildCMakeDebug[arm64-v8a]'.

....

  [6/13] Building CXX object CMakeFiles/react-native-skia-video.dir/cpp/VideoFrame.cpp.o
  FAILED: CMakeFiles/react-native-skia-video.dir/cpp/VideoFrame.cpp.o

            auto string = static_ref_cast<jni::JString>(error);
                 ^
  1 warning generated.
  ninja: build stopped: subcommand failed.

That's not error just warning.
I guess the android minSdkVersion required >= 26
The example in this repo has minSdkVersion target 28
you can set version in expo app.json with expo-build-properties
or
    // project/android/build.gradle
    minSdkVersion = Integer.parseInt(findProperty('android.minSdkVersion') ?: '23')  // change 26

Should be fixed in 0.8


enable ssh extension on vscode for building object and execute the program

is that a way to build and debug remotely with ssh extension which usually i do on terminal.

What development tools are you using for building and debugging?


fix: set `AuthAltTypes="auth/jwt" for jwt authentication

Description
This enables using slurmrestd to comunicate with slurmdbd.
How was the code tested?
Locally on an Ubuntu Noble development machine.
Checklist

[x] I am the author of these changes, or I have the rights to submit them.
[x] I have added the relevant changes to the README and/or documentation.
[x] I have self reviewed my own code.
[ ] All requested changes and/or review comments have been resolved.

@jedel1043 does this need to be against experimental or main?

Main. This is also a bug on the main branch, but we probably haven't caught it since we aren't testing http requests against slurmrestd


infra sections to categories

🚀 Deployed on https://6706bda1faf98a18c8c375a4--cozy-fox-0defec.netlify.app


Latest NVIDIA Container Runtime Support not working anymore with K3S

Environmental Info:

K3s Version: v1.27.4+k3s1
Node(s) CPU architecture, OS, and Version:

Server - Linux pop-os 6.4.6-76060406-generic #202307241739~1690928105~22.04~d567a38 SMP PREEMPT_DYNAMIC Tue A x86_64 x86_64 x86_64 GNU/Linux
Agent - Linux gpu1 5.15.0-79-generic Ubuntu SMP Mon Jul 10 16:07:21 UTC 2023 x86_64 x86_64 x86_64 GNU/Linux with NVIDIA RTX 4090

Cluster Configuration:

1 Server, 1 agent
Describe the bug:

Nvidia device plugin pod with crashloopbackoff, unable to detect GPU.
The documentation to enable GPU workload doesn't work anymore when using latest nvidia drivers (535) and Nvidia runtime toolkit (1.13.5) here https://docs.k3s.io/advanced?_highlight=nvidia#nvidia-container-runtime-support
Steps To Reproduce:

Installed K3s on server :  curl -sfL https://get.k3s.io | sh -
Installed K3s on agent: curl -sfL https://get.k3s.io | K3S_URL=https://myserver:6443 K3S_TOKEN=mynodetoken sh -

Install latest Nvidia drivers on agent with GPU RTX 4090 per Nvidia-Cuda documentation https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#common-installation-instructions-for-ubuntu

Note: I installed both with and without base because I wasn't sure how to proceed regarding CDI support in K3S
nvidia-smi
Sat Aug 26 07:42:01 2023
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.86.10              Driver Version: 535.86.10    CUDA Version: 12.2     |
|-----------------------------------------+----------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |
|                                         |                      |               MIG M. |
|=========================================+======================+======================|
|   0  NVIDIA GeForce RTX 4090        On  | 00000000:04:00.0 Off |                  Off |
|  0%   39C    P8              37W / 450W |      3MiB / 24564MiB |      0%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+

+---------------------------------------------------------------------------------------+
| Processes:                                                                            |
|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
|        ID   ID                                                             Usage      |
|=======================================================================================|
|  No running processes found                                                           |
+---------------------------------------------------------------------------------------+

Install Nvidia container toolkit per Nvidia documentation https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#step-1-install-nvidia-container-toolkit

nvidia-ctk --version
NVIDIA Container Toolkit CLI version 1.13.5
commit: 6b8589dcb4dead72ab64f14a5912886e6165c079

k3s is catching up Nvidia container runtime automatically

Note: I have restarted k3s-agent just in case
sudo cat /var/lib/rancher/k3s/agent/etc/containerd/config.toml

# File generated by k3s. DO NOT EDIT. Use config.toml.tmpl instead.
version = 2

[plugins."io.containerd.internal.v1.opt"]
  path = "/var/lib/rancher/k3s/agent/containerd"
[plugins."io.containerd.grpc.v1.cri"]
  stream_server_address = "<IP_ADDRESS>"
  stream_server_port = "10010"
  enable_selinux = false
  enable_unprivileged_ports = true
  enable_unprivileged_icmp = true
  sandbox_image = "rancher/mirrored-pause:3.6"

[plugins."io.containerd.grpc.v1.cri".containerd]
  snapshotter = "overlayfs"
  disable_snapshot_annotations = true

[plugins."io.containerd.grpc.v1.cri".cni]
  bin_dir = "/var/lib/rancher/k3s/data/dc43f496a0a9ac19d3b2444d390db38e0cfb38e672721f838b075422b8734994/bin"
  conf_dir = "/var/lib/rancher/k3s/agent/etc/cni/net.d"

[plugins."io.containerd.grpc.v1.cri".containerd.runtimes.runc]
  runtime_type = "io.containerd.runc.v2"

[plugins."io.containerd.grpc.v1.cri".containerd.runtimes.runc.options]
  SystemdCgroup = true

[plugins."io.containerd.grpc.v1.cri".containerd.runtimes."nvidia"]
  runtime_type = "io.containerd.runc.v2"
[plugins."io.containerd.grpc.v1.cri".containerd.runtimes."nvidia".options]
  BinaryName = "/usr/bin/nvidia-container-runtime"

Testing succesfully containerd with nvidia runtime directly on agent after ssh

sudo ctr image pull docker.io/nvidia/cuda:12.1.1-base-ubuntu22.04
sudo ctr run --rm -t     --runc-binary=/usr/bin/nvidia-container-runtime     --env NVIDIA_VISIBLE_DEVICES=all     docker.io/nvidia/cuda:11.6.2-base-ubuntu20.04     cuda-11.6.2-base-ubuntu20.04 nvidia-smi

Sat Aug 26 08:12:47 2023
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.86.10              Driver Version: 535.86.10    CUDA Version: 12.2     |
|-----------------------------------------+----------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |
|                                         |                      |               MIG M. |
|=========================================+======================+======================|
|   0  NVIDIA GeForce RTX 4090        On  | 00000000:04:00.0 Off |                  Off |
|  0%   39C    P8              36W / 450W |      3MiB / 24564MiB |      0%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+

+---------------------------------------------------------------------------------------+
| Processes:                                                                            |
|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
|        ID   ID                                                             Usage      |
|=======================================================================================|
|  No running processes found                                                           |
+---------------------------------------------------------------------------------------+

Back to server (control plane) - Installing NVIDIA device plugin (v0.14) via helm per instruction in https://github.com/NVIDIA/k8s-device-plugin

Note: there are additional containerd instructions required here which I didn't follow https://github.com/NVIDIA/k8s-device-plugin#configure-containerd
helm repo add nvdp https://nvidia.github.io/k8s-device-plugin
helm repo update
helm upgrade -i nvdp nvdp/nvidia-device-plugin \
  --namespace nvidia-device-plugin \
  --create-namespace \
  --version 0.14.1

Adding label and taint to restrict daemonset to agent with gpu installed only

kubectl label nodes gpu1 gpu=installed
kubectl taint nodes pop-os gpu:NoSchedule

Pods keep crashing, logs showing device not detected

kubectl get pods -n nvidia-device-plugin -o wide
NAME                              READY   STATUS             RESTARTS        AGE   IP          NODE   NOMINATED NODE   READINESS GATES
nvdp-nvidia-device-plugin-zfkj7   0/1     CrashLoopBackOff   18 (3m7s ago)   70m   <IP_ADDRESS>   gpu1   <none>           <none>

kubectl logs -n nvidia-device-plugin nvdp-nvidia-device-plugin-zfkj7
I0826 08:14:48.251257       1 main.go:154] Starting FS watcher.
I0826 08:14:48.251291       1 main.go:161] Starting OS watcher.
I0826 08:14:48.251529       1 main.go:176] Starting Plugins.
I0826 08:14:48.251544       1 main.go:234] Loading configuration.
I0826 08:14:48.251682       1 main.go:242] Updating config with default resource matching patterns.
I0826 08:14:48.251934       1 main.go:253]
Running with config:
{
  "version": "v1",
  "flags": {
    "migStrategy": "none",
    "failOnInitError": true,
    "nvidiaDriverRoot": "/",
    "gdsEnabled": false,
    "mofedEnabled": false,
    "plugin": {
      "passDeviceSpecs": false,
      "deviceListStrategy": [
        "envvar"
      ],
      "deviceIDStrategy": "uuid",
      "cdiAnnotationPrefix": "cdi.k8s.io/",
      "nvidiaCTKPath": "/usr/bin/nvidia-ctk",
      "containerDriverRoot": "/driver-root"
    }
  },
  "resources": {
    "gpus": [
      {
        "pattern": "*",
        "name": "nvidia.com/gpu"
      }
    ]
  },
  "sharing": {
    "timeSlicing": {}
  }
}
I0826 08:14:48.251947       1 main.go:256] Retreiving plugins.
W0826 08:14:48.252203       1 factory.go:31] No valid resources detected, creating a null CDI handler
I0826 08:14:48.252244       1 factory.go:107] Detected non-NVML platform: could not load NVML library: libnvidia-ml.so.1: cannot open shared object file: No such file or directory
I0826 08:14:48.252266       1 factory.go:107] Detected non-Tegra platform: /sys/devices/soc0/family file not found
E0826 08:14:48.252276       1 factory.go:115] Incompatible platform detected
E0826 08:14:48.252281       1 factory.go:116] If this is a GPU node, did you configure the NVIDIA Container Toolkit?
E0826 08:14:48.252287       1 factory.go:117] You can check the prerequisites at: https://github.com/NVIDIA/k8s-device-plugin#prerequisites
E0826 08:14:48.252292       1 factory.go:118] You can learn how to set the runtime at: https://github.com/NVIDIA/k8s-device-plugin#quick-start
E0826 08:14:48.252297       1 factory.go:119] If this is not a GPU node, you should set up a toleration or nodeSelector to only deploy this plugin on GPU nodes
E0826 08:14:48.258856       1 main.go:123] error starting plugins: error creating plugin manager: unable to create plugin manager: platform detection failed

Expected behavior:

Expecting kubectl describe node gpu1 detecting GPU specification and adding annotation
Actual behavior:

the node gpu1 not showing any GPU related component. I didn't run the nbody-gpu-benchmark pod to test, given the limit resource specification nbody-gpu-benchmark
Additional context / logs:

The K3S documentation for Nvidia runtime https://docs.k3s.io/advanced?_highlight=nvidia#nvidia-container-runtime-support describes a working solution using driver 515.
I used this approach successfully until now (with k3s. v1.24, NFD v.013 and gpu-feature-discovery) but I have recently upgraded my GPU and installed newer driver version 535 for compatibility. Also reinstalled k3s v1.27.4+k3s1 in the process
Ideas for resolution

it could be a regression issue by using latest nvidia driver 535 but haven't tested out yet, given how long it would take to downgrade and test out.

There are additional instruction for containerd configuration with runtime described in Nvidia device plugin which I didn't follow.  https://github.com/NVIDIA/k8s-device-plugin#configure-containerd
Shall I define them in config.toml.tmpl ?

There is now CDI https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#step-2-generate-a-cdi-specification but no instruction for containerd, even less for k3s.

Not sure if this is on K3S or Nvidia side, looking forward to hearing your feedback
Thank you in advance
Jean-Paul

I had this same issue and I was able to fix it by applying the changes from https://github.com/NVIDIA/k8s-device-plugin#configure-containerd in a config.toml.tmpl based on the format here: https://github.com/k3s-io/k3s/blob/master/pkg/agent/templates/templates_linux.go. Here's the diff:
`root@magoon:/var/lib/rancher/k3s/agent/etc/containerd# diff config.toml.default config.toml.nvidia
27a28

default_runtime_name = "nvidia"
74c75,78
< [plugins."io.containerd.grpc.v1.cri".containerd.runtimes.runc]

[plugins."io.containerd.grpc.v1.cri".containerd.runtimes.nvidia]
privileged_without_host_devices = false
runtime_engine = ""
runtime_root = ""
75a80,81
[plugins."io.containerd.grpc.v1.cri".containerd.runtimes.nvidia.options]
BinaryName = "/usr/bin/nvidia-container-runtime"
76a83,84
[plugins."io.containerd.grpc.v1.cri".containerd.runtimes.runc]
runtime_type = "io.containerd.runc.v2"
112,117d119
< {{range $k, $v := .ExtraRuntimes}}
< [plugins."io.containerd.grpc.v1.cri".containerd.runtimes."{{$k}}"]
<   runtime_type = "{{$v.RuntimeType}}"
< [plugins."io.containerd.grpc.v1.cri".containerd.runtimes."{{$k}}".options]
<   BinaryName = "{{$v.BinaryName}}"
< {{end}}`

I restarted k3s and I also had to delete the nvidia-device-plugin-daemonset pod:
kubectl delete pod nvidia-device-plugin-daemonset-b6lqm -n kube-system
After that it stopped showing:
I0830 23:04:19.417692       1 factory.go:107] Detected non-NVML platform: could not load NVML library: libnvidia-ml.so.1: cannot open shared object file: No such file or directory
And logged:
I0830 23:07:20.795657       1 main.go:256] Retreiving plugins. I0830 23:07:20.796097       1 factory.go:107] Detected NVML platform: found NVML library I0830 23:07:20.796128       1 factory.go:107] Detected non-Tegra platform: /sys/devices/soc0/family file not found I0830 23:07:21.658884       1 server.go:165] Starting GRPC server for 'nvidia.com/gpu' I0830 23:07:21.659483       1 server.go:117] Starting to serve 'nvidia.com/gpu' on /var/lib/kubelet/device-plugins/nvidia-gpu.sock I0830 23:07:21.661373       1 server.go:125] Registered device plugin for 'nvidia.com/gpu' with Kubelet
One thing to be aware of that I'm still checking on is that after a reboot, all of my kube-system pods started to fail with CrashLoopBackoff. I found that other people had an issue linked with the Cgroup line in #5454. I confirmed that removing the nvidia config from the config.toml.tmpl file stops the CrashLoopBackoff condition but I'm still not entirely sure why.

It sounds like the main difference here is just that we need to set SystemdCgroup in the nvidia runtime options?
Do you know which release of the nvidia container runtime started requiring this?

Relevant issue: https://github.com/NVIDIA/k8s-device-plugin/issues/406

After trying out all the suggestions from here and other issues, I got it working by following this blog https://medium.com/sparque-labs/serving-ai-models-on-the-edge-using-nvidia-gpu-with-k3s-on-aws-part-4-dd48f8699116

After trying out all the suggestions from here and other issues, I got it working by following this blog https://medium.com/sparque-labs/serving-ai-models-on-the-edge-using-nvidia-gpu-with-k3s-on-aws-part-4-dd48f8699116

That link gives me HTTP 404
However, I have solved the could not load NVML library: libnvidia-ml.so.1 issue by adding runtimeClassName: nvidia to the K8s device plugin from here https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/v0.14.1/nvidia-device-plugin.yml (or modify the Helm template, either works).
The reason is that the k3s detects the nvidia container runtime, but it does not make it the default one. The Helm chart, or the nvidia-device-plugin.yml, expect that the default runtime is nvidia, which is not. Explicitly adding that in, or using a correct k3s config template to make it the default runtime, will solve the issue.

After trying out all the suggestions from here and other issues, I got it working by following this blog https://medium.com/sparque-labs/serving-ai-models-on-the-edge-using-nvidia-gpu-with-k3s-on-aws-part-4-dd48f8699116

That link gives me HTTP 404
However, I have solved the could not load NVML library: libnvidia-ml.so.1 issue by adding runtimeClassName: nvidia to the K8s device plugin from here https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/v0.14.1/nvidia-device-plugin.yml (or modify the Helm template, either works).
The reason is that the k3s detects the nvidia container runtime, but it does not make it the default one. The Helm chart, or the nvidia-device-plugin.yml, expect that the default runtime is nvidia, which is not. Explicitly adding that in, or using a correct k3s config template to make it the default runtime, will solve the issue.

not work,
Error creating: pods "nvidia-device-plugin-daemonset-" is forbidden: pod rejected: RuntimeClass "nvidia" not found

not work,
Error creating: pods "nvidia-device-plugin-daemonset-" is forbidden: pod rejected: RuntimeClass "nvidia" not found

@xinmans Try applying this manifest:
apiVersion: node.k8s.io/v1
kind: RuntimeClass
metadata:
  name: nvidia
handler: nvidia

And re-create the nvidia plugin.

After trying out all the suggestions from here and other issues, I got it working by following this blog https://medium.com/sparque-labs/serving-ai-models-on-the-edge-using-nvidia-gpu-with-k3s-on-aws-part-4-dd48f8699116

There's a dot at the end of the URL for some reason, that needs to be removed. In any case, the mentioned article uses the GPU operator which in turn uses the operator framework which automates this whole process. It did immediately work for me, ymmv.
https://github.com/NVIDIA/gpu-operator
Using helm:
$: helm repo add nvidia https://helm.ngc.nvidia.com/nvidia \
   && helm repo update

$: helm install --wait nvidiagpu \
     -n gpu-operator --create-namespace \
    --set toolkit.env[0].name=CONTAINERD_CONFIG \
    --set toolkit.env[0].value=/var/lib/rancher/k3s/agent/etc/containerd/config.toml \
    --set toolkit.env[1].name=CONTAINERD_SOCKET \
    --set toolkit.env[1].value=/run/k3s/containerd/containerd.sock \
    --set toolkit.env[2].name=CONTAINERD_RUNTIME_CLASS \
    --set toolkit.env[2].value=nvidia \
    --set toolkit.env[3].name=CONTAINERD_SET_AS_DEFAULT \
    --set-string toolkit.env[3].value=true \
     nvidia/gpu-operator

NAME: nvidiagpu
LAST DEPLOYED: Tue Aug  8 00:54:41 2023
NAMESPACE: gpu-operator
STATUS: deployed
REVISION: 1
TEST SUITE: None

@henryford my bad, I updated the medium article link. Good to see that you got it working.

I cannot get K38s to recognize my GPU.  I have followed the official docs, and my config.toml lists the nvidia entries:
[plugins."io.containerd.grpc.v1.cri".containerd.runtimes."nvidia"]
  runtime_type = "io.containerd.runc.v2"
[plugins."io.containerd.grpc.v1.cri".containerd.runtimes."nvidia".options]
  BinaryName = "/usr/bin/nvidia-container-runtime"
  SystemdCgroup = true

nvidia-smi works as expected, even when testing with docker directly (which uses the nvidia-container toolkit, though I think my K38s is using the default containerd mode instead of docker mode):
docker run --rm -ti --gpus all nvidia/cuda:12.3.1-runtime-ubuntu22.04 nvidia-smi

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

CUDA Version 12.3.1

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

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

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

Wed Jan 10 19:19:23 2024
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 545.29.06              Driver Version: 545.29.06    CUDA Version: 12.3     |
|-----------------------------------------+----------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |
|                                         |                      |               MIG M. |
|=========================================+======================+======================|
|   0  NVIDIA GeForce RTX 2080        Off | 00000000:0A:00.0 Off |                  N/A |
| 27%   34C    P8               3W / 225W |     15MiB /  8192MiB |      0%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+

+---------------------------------------------------------------------------------------+
| Processes:                                                                            |
|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
|        ID   ID                                                             Usage      |
|=======================================================================================|
+---------------------------------------------------------------------------------------+

But checking for GPU availability on my node I get:
kubectl get nodes -o=custom-columns=NAME:.metadata.name,GPUs:.status.capacity.'nvidia\.com/gpu'
NAME     GPUs
thelio   <none>

and any pod intialized with GPU remains in Init:CrashLoopBackOff (i.e. the pods created in the  gpu-operator helm chart shown above), or in Pending, waiting for gpu resource.  What have I missed?
Notes/additional questions:

The docs mention that e.g. cuda-drivers-fabricmanager-515 nvidia-headless-515-server must be installed in addition.  As you see in the nvidia-smi output, I'm on 545, but there is no 545 version of these packages available in the repos.  Is that most likely my problem? How can that be resolved?
Some sources suggest that nvidia-runtime should also be the default runtime in containerd/config.toml.  Is that really accurate?  It looks to me like the official K3S docs configuration has this an opt-in via runtimeClassName: nvidia, which makes sense (no need to use it on pods that don't need GPU) and I'm using that test spec, so I don't think that's the issue?

I'm going to convert this to a discussion, as it seems like a K8s/NVIDIA related issue, rather than a k3s bug


Restored model objective does not match objective reported during optimisation

Hi there,
I have a really strange bug which I would really appreciate some help with!
System information:

Python version: Python 3.6.4
TensorFlow installed from (source or binary): binary (I believe)
TensorFlow version (use command below): b'v1.13.1-0-g6612da8951' 1.13.1
GPflow installed from (source or binary): binary
GPflow version: 1.3.0

... Describe the current behavior
When running the new-style multi-output GP, the optimisation runs fine, and the objective reaches a low value. When saved, performance is terrible on reload, and the objective gives a correspondingly high value.
... Describe the expected behavior
Objective reported at end of optimisation should match that of restored model.
... Code to reproduce the issue Provide a reproducible test case that is the bare minimum necessary to generate the problem.
I first encountered this on my own data, but I've made an example with fake data below, which might be easier to debug.
import os
import argparse
from sklearn.cluster import KMeans
import gpflow as gpf
import numpy as np
from scipy.stats import norm
import gpflow.multioutput.kernels as mk
import gpflow.multioutput.features as mf

parser = argparse.ArgumentParser()
parser.add_argument('--M', required=True, type=int)
parser.add_argument('--L', required=True, type=int)
parser.add_argument('--train-inducing-points', required=False,
                    action='store_true')
parser.add_argument('--output-dir', required=True)
parser.add_argument('--n-data', required=False, type=int, default=1200)
parser.add_argument('--n-covariates', required=False, type=int, default=8)
parser.add_argument('--n-outputs', required=False, type=int, default=371)
args = parser.parse_args()

if not os.path.isdir(args.output_dir):
    os.makedirs(args.output_dir)

M = args.M
L = args.L
train_inducing_points = args.train_inducing_points

# Set seed for reproducibility
np.random.seed(2)

# Generate some data
X = np.random.normal(size=(args.n_data, args.n_covariates))

bias = np.random.uniform(-3, 0, size=args.n_outputs)

true_lin_weights = np.random.normal(size=(args.n_covariates, args.n_outputs))
output = X @ true_lin_weights + bias

y = (norm.cdf(output) > np.random.uniform(size=output.shape)).astype(int)

D = X.shape[1]
P = y.shape[1]
train_inducing_points = False
maxiter = int(1E6)

Z = KMeans(M).fit(X).cluster_centers_

data_to_save = {
    'X': X,
    'y': y,
    'Z': Z,
    'M': M,
    'L': L
}

np.savez(os.path.join(args.output_dir, 'data'), **data_to_save)

gpf.reset_default_graph_and_session()

# Values at the inducing points.
# M inducing points; L different outputs.
q_mu = np.zeros((M, L))
q_sqrt = np.repeat(np.eye(M)[None, ...], L, axis=0) * 1.0

kern_list = [gpf.kernels.RBF(D, ARD=True) for _ in range(L - 1)]
kern_list += [gpf.kernels.Bias(D)]

W_init = np.random.randn(P, L)
kernel = mk.SeparateMixedMok(kern_list, W_init)

feature = mf.MixedKernelSharedMof(gpf.features.InducingPoints(Z))

m = gpf.models.SVGP(X, y, kernel, gpf.likelihoods.Bernoulli(),
                    feat=feature, q_mu=q_mu, q_sqrt=q_sqrt)

# Try without training the location of the inducing points
m.feature.set_trainable(train_inducing_points)

opt = gpf.train.ScipyOptimizer(options={'maxfun': maxiter})
opt.minimize(m, disp=True, maxiter=maxiter)

# Store the model itself
saver = gpf.saver.Saver()
saver.save(os.path.join(args.output_dir, 'problematic_model'), m)

Run with M=100, L=12, final objective value is reported as:
 F =   92721.086740390674
However, when I run the result in a script:
import sys
import gpflow

gpflow.reset_default_graph_and_session()
m = gpflow.saver.Saver().load(sys.argv[1])

sess = m.enquire_session()
print(sess.run(m.objective))

The objective reported is:
507238.6801604379
Which, needless to say, is much worse (and in fact extremely poor).
I've attached the saved model. Unfortunately I didn't save the log, but it converged with
CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH
After 20,770 iterations.
Strangely, things seem to work fine for L<=10, and only goes wrong for higher values than that, as far as I can tell.
data_and_model.zip

I've been able to work out that the problem occurs only when L>10. As soon as L=11, the restore doesn't work properly.

I think I've found the problem. Tensorflow threw some useful Object was never used errors that pointed me in the right direction.
The problematic line is:
https://github.com/GPflow/GPflow/blob/master/gpflow/saver/coders.py#L262
For L=11, the value of keys before sorting is:
dict_keys(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10'])
Which is fine. But after sorting, it becomes:
['0', '1', '10', '2', '3', '4', '5', '6', '7', '8', '9']
Which scrambles everything, unsurprisingly.
I guess this could be addressed either by:

Using a key which converts things to int before sorting or
Relying on the dict order being correct.

I don't know the codebase well enough to know which one would work here.

The GPflow saver will go away after GPflow 2.0. Standard TF2.0 checkpointing and saver will work with gpflow model. Check awav/gpflow-2.0 branch.

@awav I think this is something we still ought to fix. GPflow 1 is going to be around for a while longer- as much as you wish otherwise - and this is clearly a bug.
@martiningram thanks for already figuring out why and where it's going wrong! now we just have to figure out how to fix it (and if you have an idea/solution, please do open a pull request for it)!
@awav Why does the decoder sort the keys? Why does the encoder not sort the keys?

Well, I would still recommend to look at TF2.0 and GPflow 2.0, as it has much more appropriate solution to the problem. Changes are little in the API.
@st-- , it requires debugging to answer that question (which I will be able to do after 24 May), but I'm not convinced that (un)sorted indices are the real issue, as a list restored by created empty list and putting values in place by stored indices (sorted or unsorted - doesn't matter).
@martiningram, check GPflow 2.0 modified example:
# conda create -n tf2 python=3.6
# pip install tf-nightly-2.0-preview tfp-nightly

import numpy as np
import tensorflow as tf
from sklearn.cluster import KMeans
from tensorflow_probability import distributions as tfd

import gpflow
from gpflow.features import InducingPoints, MixedKernelSharedMof
from gpflow.kernels import RBF, Bias, SeparateMixedMok

np.random.seed(999)
tf.random.set_seed(999)

output_dir = "/tmp/tf-checkpoint"
M = 10
L = 2
train_inducing_points = 100
num_data = 1000
num_covariates = 3
num_outputs = 2

dtype = gpflow.default_float()

# Generate some data
X = tf.random.normal((num_data, num_covariates), dtype=dtype)
bias = tf.random.uniform((num_outputs, ), minval=-3, maxval=0, dtype=dtype)

true_lin_weights = tf.random.normal((num_covariates, num_outputs), dtype=dtype)
output = X @ true_lin_weights + bias

norm = tfd.Normal(loc=tf.cast(0., dtype), scale=tf.cast(1., dtype))
y_uncasted = (norm.cdf(output) > tf.random.uniform(output.shape, dtype=dtype))
y = tf.cast(y_uncasted, tf.int32)

D = X.shape[1]
P = y.shape[1]
train_inducing_points = False
maxiter = int(1e3)

Z = KMeans(M).fit(X.numpy()).cluster_centers_
Z = Z + tf.random.normal(Z.shape, dtype=dtype) * 0.001

W_init = tf.random.normal((P, L), dtype=dtype)

# Values at the inducing points.
# M inducing points; L different outputs.
kern_list = [*[RBF(ard=True) for _ in range(L - 1)], Bias()]
kernel = SeparateMixedMok(kern_list, W_init)
feature = MixedKernelSharedMof(InducingPoints(Z))

model = gpflow.models.SVGP(kernel=kernel, likelihood=gpflow.likelihoods.Bernoulli(), feature=feature, num_latent=L)

# Try without training the location of the inducing points
# model.feature.trainable = False

def loss_closure():
    return model.elbo(X, y)

gpflow.optimizers.Scipy().minimize(loss_closure, variables=model.trainable_variables, options={'maxiter': maxiter})

# Store model
ckpt = tf.train.Checkpoint(model=model)
mngr = tf.train.CheckpointManager(ckpt, output_dir, max_to_keep=3)
mngr.save()

trained_q_mu = model.q_mu.numpy()
model.q_mu.assign(tf.zeros(model.q_mu.shape))
assigned_q_mu = model.q_mu.numpy()

# Restore
ckpt.restore(mngr.latest_checkpoint)

# Test restoring
assert not np.allclose(model.q_mu.numpy(), assigned_q_mu)
np.testing.assert_array_equal(model.q_mu.numpy(), trained_q_mu)

@awav @st-- thanks for the comments!
@awav Thanks, the new-style code seems to be working for me. Am I right in thinking though that since the line ckpt = tf.train.Checkpoint(model=model) requires a model argument, we basically need to make sure to build the model before we're able to restore it? If so, while workable, that seems like a step back from the old way, where we could just load the entire model with m = gpflow.saver.Saver().load(saved_model_file), and it would restore the kernel settings and so on. If we need to build it first, there's always a possibility of getting the model build code out of sync with the saved model, which could be painful. What do you think?
Re sorting / not sorting: Removing the line in question does fix the restore for me in this particular case, but of course I can't say whether it fixes it in general.

@martiningram , I don't think it is a step back. The GPflow saver doesn't protect you from the code changes, moreover it is not safe to use it at all, as it only gives you false impression that you use the same model. The structure might be the same, but internal implementation might still change. The GPflow saver doesn't keep information about sources, therefore there is not guarantee that your trained model and loaded model use the same code - you still have to keep SHA which you used somehow.
In version 2.0 you have two choices: either checkpointing or tensor saving approach (tf.saved_model). I showed only checkpointing, check TF2.0 documentation for details.

I see, thanks. I might take a look at the saved model approach!

@awav what should we do with this bug ?

@martiningram is your issue resolved? If not please re-open (though I hope this will be less of an issue with gpflow 2.0 now!)

@st-- thanks for asking! I definitely found workarounds, so happy to close this. I look forward to trying out GPFlow 2.0!


When init AndroidIDEInfo, parse the manifest to get the java pacakge name if it is null from the proto.

Checklist

[x] I have filed an issue about this change and discussed potential changes with the maintainers.
[ ] I have received the approval from the maintainers to make this change.
[x] This is not a stylistic, refactoring, or cleanup change.

Please note that the maintainers will not be reviewing this change until all checkboxes are ticked. See
the Contributions section in the README for more
details.
Discussion thread for this change
Issue number: The resource linking is broken in IDE when custom_package is not set
Description of this change
As described in the issue: If the  custom_package = "com.myPackage", is not set when defining a kt_android_library, and if the BUILD file is under the root of your project dir, the rules_kotlin or rules_android will pass a null value to init the IntelljiInfo.  This will cause the resource link/preview not working in the IDE.
This change parse the packageName defined in the AndroidManifest.xml when the passed over javaPackage name is null. And use it as the resource package.
After the fix the IDE resource preview worked as expected. And when clicking on the resource from Kotlin code it will link to the resource xml definition, instead of the generated Int.

@mai93 do you have a chance to take a look this change. Let me know if you have any questions.

Thanks @ivyspirit! The change looks good to me, we are currently trying to move the code for the AS plugin outside of this repository and we cannot accept changes to it at the moment. I will try to import this change once the other repository is ready.


Calculate fraction of BigInteger division

Fix #844

Thanks for the PR!
While this is definitely an improvement in certain cases, it causes a regression in the following case:
x = int('2'*400+'7')
assert x / (x+1) == 1.0

Got it. Add the logic to round to the nearest FPU.


The workflow hanging after the GTDBtk step

Dear authors,
Thanks for the great tool. I'm currently running the following command on Computerome:
maginator -v <...>
-r <...>
-c <...>
-o <...>
-g "/home/databases/gtdbtk/release207_v2"
--cluster qsub --cluster_info "-l nodes=1:ppn={cores},mem={mem_gb}gb,walltime={runtime}"
--max_cores=10 --max_mem=150
The command itself was also run through qsub system.
The GTDBtk step seem to be completed succesfully, but then nothing more hapens. The main job is still running, and all the gtdbtk spawned job are finished. The output folder now looks like that:
total 98
drwxr-xr-x    5 svekut ku_00200  4096 Aug 19 15:54 .
drwxrwxr-x   43 svekut ku_00200 16384 Aug 19 16:05 ..
drwxrwxr-x 1328 svekut svekut   65536 Aug 19 15:51 clusters
drwxrwxr-x    2 svekut svekut   16384 Aug 19 15:58 gtdbtk
drwxr-xr-x    4 svekut ku_00200  4096 Aug 19 14:08 logs
-rw-r--r--    1 svekut ku_00200   979 Aug 19 15:38 parameters.tab

The latest update in the folder was yesterday, and now the main job is running for more than 24 hours. It looks like it is hanging, but  I see no errors.
Is it something you could help with?

It is difficult to know what went wrong. Can you try to run the GTDB-snake "manually" to see if any og the clusters seems to not have completed successfully?
snakemake --use-conda --latency-wait 150 -s MAGinator/maginator/workflow/gtdbtk.Snakefile --rerun-triggers mtime --config wd= reads= contigs= vamb= params=


error prompt

Applied patches:
StringFixer: Failed (Init error: Could not find decrypter method)
ResourceResolver: Failed (Init error: Could not find resolver type)
AssemblyResolver: Failed (StringFixer is required!)

Your assembly is not obfuscated by EazFuscator.

I use "Exeinfo PE" software,
The contents are as follows:
image is 32bit executable:
Eazfuscator.NET v3.3 - 5.0 [ v3.x ] - (C) 2008-2015 Gapotchenko  - http://www.gapotchenko.com/eazfuscator.net - EP Token : 06000253
Lamer Info - Help Hint -Unpack info:
.NET obf/license protector - for ver. < 5.0 - Unpack with : de4dot v3.1  2011-2014<EMAIL_ADDRESS>- http://github.com/0xd4d/de4dot

i use Eazfuscator.NET v3.3 obfuscate my assembly
then use eazfixer deobfuscated it, The result is the same as above.

That is an older version of EazFuscator. Use de4dot. If that doesn't work, try making your own version of this tool.

thank u
I'm not obfuscate project, I'm obfuscate assembly. the eazfuscator don`t deobfuscate assembly.
i use de4dot , the string deobfuscate results  as follow GForm GClass.


Position classes are blocking page content

Hey,
The design of position classes is blocking page content.. e.g. toast-bottom-left is blocking whole page.. some others are blocking the full width when they are cornered.. this needs to be fixed please..
Even in your plunker you can see this issue.. I just changed container position to be toast-bottom-left and you are not able to use the buttons cause its blocking whole page..
http://plnkr.co/edit/ZVcqJIu3GGe4aQzpOGxE?p=preview
<toaster-container toaster-options="{'toaster-id': 3, 'time-out': 25000, 'close-button':true, 'animation-class': 'toast-top-left'}"></toaster-container>
<toaster-container toaster-options="{'toaster-id': 6, 'time-out': 5000, 'close-button':true, 'animation-class': 'toast-bottom-left'}"></toaster-container>
Please make it the way it is not blocking the page content (if shown or not)..
Thanks, Michal

You are changing animation-class, which is incorrect.  You should be changing position-class.

true, may bad.. I just copy paste from your plunker.. sorry for not rtfm, thanks

No problem!


No config option for Bluetooth Controller not available

I recently came across a bug, if the bluetooth controller is not available in the device there is no config option for that
i have a hyprland arch usb, that i use between my pc and laptop, my pc does not have a bluetooth controller and laptop has, when i am using my laptop the waybar shows the bluetooth config correctly, but as my pc does not have bluetooth, it would not show text inside the disabled field, and there is not other option for controller not available
this is my bluetooth config for waybar
"bluetooth": {
        "format": "\udb80\udcaf  {status}",
        "format-disabled": "",
        "format-connected": " {device_alias}",
        "format-connected-battery": " {device_alias} {device_battery_percentage}%",
        "tooltip-format": "{controller_alias}\t{controller_address}\n\n{num_connections} connected",
        "tooltip-format-connected": "{controller_alias}\t{controller_address}\n\n{num_connections} connected\n\n{device_enumerate}",
        "tooltip-format-enumerate-connected": "{device_alias}\t{device_address}",
        "tooltip-format-enumerate-connected-battery": "{device_alias}\t{device_address}\t{device_battery_percentage}%"
    }

when on pc, this is the error i get (ignore CoreTime error and pixmap as it was caused by me)

the blank space is the bluetooth slot
as per the image
[error] findCurController() failed: no bluetooth controller found is the error
there is no config option that can be used when controller is not available or fromat-disabled being used when controller not found

Fixed in 0.9.23


Return an error when passing unsupported mount options

Relate: https://github.com/containers/youki/issues/2307
https://github.com/opencontainers/runtime-spec/blob/main/config.md#linux-mount-options

Codecov Report

Merging #2308 (6d1d36b) into main (8d3be67) will decrease coverage by 0.08%.
The diff coverage is 87.01%.

Additional details and impacted files
@@            Coverage Diff             @@
##             main    #2308      +/-   ##
==========================================
- Coverage   64.88%   64.81%   -0.08%
==========================================
  Files         129      129
  Lines       15179    15171       -8
==========================================
- Hits         9849     9833      -16
- Misses       5330     5338       +8


add info_replication_valkey_compatible=true to dragonfly flags

info_replication_valkey_compatible changes how some of the replication related output is presented in the INFO command.
it's just few minor changes but they potentially might break the operator, see https://github.com/search?q=repo%3Adragonflydb%2Fdragonfly info_replication_valkey_compatible&type=code for details.
In preparation to flipping this flag to true in dragonfly, please make sure the operator runs dragonfly with info_replication_valkey_compatible=true as true, and make sure it parses master/replica states correctly.

@romange, I tested the operator with the flag, scaled up, scaled down, did fail over. Everything looks normal.

In that case, I am flipping the default value to true in dragonfly. Will change starting from 1.20.0


Remove console.log

It looks like the v1.3.0 release on npm includes an accidental console.log.
compass-vertical-rhythm/dist/index.js (line 107)
console.log('baseLineheight unit', unit(options.baseLineHeight));

Oops! I removed the console.log and then committed but didn't rebuild. Thanks for the notice! Just published 1.3.1

Thanks!


none of the deployment works with okd 3.11

latest deployment for okd makes packageservice restarts
0.7.4 deployment make operator crashloopbackoff
catalogs don't work, and issuing CSV alone to openshift-operators fails, in cluster console failed staus and none deployments are created
any ideas how to install OLM with okd 3.11?

@gacopl could you try using the manifests from the 0.8.1 release and remove all container arguments from 0000_50_olm_06-olm-operator.deployment.yaml before you apply them to the cluster?

I would have expected CI to catch this type of bug. Is that not the case?

Our CI is geared towards OpenShift 4.0 and we do not re-test older releases.
On Sat, Feb 16, 2019 at 1:02 PM ron1<EMAIL_ADDRESS>wrote:

I would have expected CI to catch this type of bug. Is that not the case?
—
You are receiving this because you commented.
Reply to this email directly, view it on GitHub
https://github.com/operator-framework/operator-lifecycle-manager/issues/715#issuecomment-464367915,
or mute the thread
https://github.com/notifications/unsubscribe-auth/AD-4LpZUmKEQHeaY_MwVOmcAp0rSqDgyks5vOEe8gaJpZM4a-sc9
.

--
Nick Hale

@njhale ok that seemed to work at least olm is not dying now i need to figure out what's with requirementsnotmet when installing CSV

@gacopl The requirement status section of the CSV's status should tell you exactly what's missing on the cluster for the CSV to run. You can either create the missing resources manually, or if you have an OLM CatalogSource that contains your CSV, you can create a Susbcription which will attempt to resolve and apply them for you.
All resource generation besides APIService and Deployment are now handled by the catalog-operator and requires a Susbcription.

Thanks @njhale i'm trying to wrap my head around catalogs, after installatio only pacageserver catalog is present i want to try out community operators, specificaly couchbase from certified operators, how can i add catalogs? I see the packageserver is served through grpc from some pod. In Enterprise OCP 3.11 there were special configmaps but that is more than half year old

I understand that CSV is parsed and the installplan is created but this happens only when you subscribe from something from catalog, how can i add more stuff or new catalog to test out patches i made for CSV

Our CI is geared towards OpenShift 4.0 and we do not re-test older releases.
…
On Sat, Feb 16, 2019 at 1:02 PM ron1 @.***> wrote: I would have expected CI to catch this type of bug. Is that not the case? — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#715 (comment)>, or mute the thread https://github.com/notifications/unsubscribe-auth/AD-4LpZUmKEQHeaY_MwVOmcAp0rSqDgyks5vOEe8gaJpZM4a-sc9 .
-- Nick Hale

@njhale Thanks for the feedback. Given that OLM is in Tech Preview for OCP 3.11, is there intention to keep the latest version of OLM working on OCP 3.11 or are all efforts focused exclusively on deployments to OKD/OCP 4.0 pre-releases?

@ron1 We are really just restricted in whether we depend on any backwards incompatible kubernetes changes. So we can't guarantee the latest OLM image and manifests will work with OpenShift installations based on older kubernetes versions. Our previous release manifests are tied to specific OLM image digests, so if a version of these manifests works on 3.11 it should continue to work, unless the manifests were changed somewhere along the way (need to double check this hasn't already happened).

Thanks @njhale i'm trying to wrap my head around catalogs, after installatio only pacageserver catalog is present i want to try out community operators, specificaly couchbase from certified operators, how can i add catalogs? I see the packageserver is served through grpc from some pod. In Enterprise OCP 3.11 there were special configmaps but that is more than half year old

@gacopl If you just want to try out community-operators with a newer version of OLM 0.8.1 you can generate the following CatalogSource in the namespace you have OLM running in:
apiVersion: operators.coreos.com/v1alpha1
kind: CatalogSource
metadata:
  name: community-operators
spec:
  displayName: Community Operators
  image: quay.io/njhale/community-operators@sha256:37f1dd6ab4f1082af9d8f9ef028a2be4fb2837c5a75ba59bd127ebc723bfee8d
  publisher: community-operators
  sourceType: grpc

When you create a new subscription to the operator you want, be sure to specify the correct sourceNamespace field.

@njhale Does it make sense that 0000_50_olm_14-operatorstatus.yaml fails to apply on OCP 3.11? I see that this file exists for OCP but not OKD. Am I correct this file exclusively targets OCP 4.0?

@ron1 your first point is correct. 0000_50_olm_14-operatorstatus.yaml is a CustomResource for reporting second level operator (SLO) status to OCP 4.0s cluster-version-operator (no relation to OLM's CSVs), which is not present in any version < 4.0, OKD, or upstream.
You are also correct in that https://github.com/operator-framework/operator-registry/blob/master/upstream.Dockerfile is the "basis" for how I packaged community-operators as an OLM catalog - it provides an example of how to build a an OLM operator-registry image, which is OLM's preferred way to package operator catalog content. We have a PR in-flight that should be merging soon to update the docs in that repo to better reflect this.
As for the 3rd issue - it seems like the version of OLM being used is older than what's currently in master. From the provided panic log, registry.go:166 is supposed to be accessing a nil pointer, but in master this seems to be a log call. We also have a check early in this function to bail out early if the RegistryStatus is nil.

@gacopl I just tested the 0.7.4 OKD manifests against a kube 1.11 (which is the version used in OKD 3.11) cluster and everything worked fine. I even generated a subscription to etcd, which resolved correctly and installed etcd:
apiVersion: operators.coreos.com/v1alpha1
kind: Subscription
metadata:
  name: etcd
  namespace: openshift-operator-lifecycle-manager
spec:
  source: rh-operators
  sourceNamespace: openshift-operator-lifecycle-manager
  name: etcd
  channel: alpha

How did you install OKD? At this point, I'm reasonably sure OLM's manifests and images are good for 0.7.4.

basic oc cluster up of 3.11, the 0,8x branch worked for me after doing the args fix

@njhale Does OLM 0.7.4 support the latest CSV schemas used by operators currently in the community-operators repository? Also, does it support the operator-registry-based CatalogSources with sourceType grpc you described in your prior comment?

@ron1

CSVs are somewhat forward compatible - newer fields like InstallModes won't be respected.
0.7.4 does not support operator-registry based CatalogSources

@njhale Given that OLM has changed significantly between 0.7.4 and 0.8.1+ including grpc CatalogSources, OperatorGroups, InstallModes, etc., and given that Operators currently in the community-operators repo are likely being tested only against OLM 0.8.1+, would you expect OLM 0.7.4 to reliably manage the current set of Operators in the community-operators repo? If so, what is the best way to assemble/deploy community-operators/upstream-community-operators into a ConfigMap-based CatalogSource for use by OLM 0.7.4?

Current status for 3.11:

There are not currently plans to backport newer OLM releases to the openshift-ansible installer for 3.11
We have some groups successfully using the latest upstream release on 3.11 when needed, but that is not an "officially" supported path for openshift 3.11.

If you need to play with OLM and are okay with those caveats on 3.11, you might try the upstream installation instructions: https://github.com/operator-framework/operator-lifecycle-manager/releases/tag/0.10.0


microcodeIntel: 20200520 -> 20200609

Motivation for this change
Microcode updates for lots of generations.
https://github.com/intel/Intel-Linux-Processor-Microcode-Data-Files/releases/tag/microcode-20200609
Things done

[x] Tested using sandboxing (nix.useSandbox on NixOS, or option sandbox in nix.conf on non-NixOS linux)
Built on platform(s)

[x] NixOS
[ ] macOS
[ ] other Linux distributions

[ ] Tested via one or more NixOS test(s) if existing and applicable for the change (look inside nixos/tests)
[ ] Tested compilation of all pkgs that depend on this change using nix-shell -p nixpkgs-review --run "nixpkgs-review wip"
[ ] Tested execution of all binary files (usually in ./result/bin/)
[ ] Determined the impact on package closure size (by running nix path-info -S before and after)
[ ] Ensured that relevant documentation is up to date
[x] Fits CONTRIBUTING.md.

@andir

Looks like the hash needs updating.


Button text are not visible due to small height of button

Description

Button Height are too small that why the button text are not visible .
https://meshery.io/community/meshmate/members
Screenshots

Enviroment:

OS: Mac Linux Windows
Browser: Chrome Safari Firefox
Version:
Device: Desktop Mobile

Contributor Guides and Handbook
The meshery.io website uses Jekyll and GitHub Pages. Site content is found under the master branch.

📚 See the Contributing to Meshery.io Website section of the readme.md and other contributing instructions, too.
🎨 Wireframes and designs for Meshery UI in Figma (open invite)
🙋🏾🙋🏼 Questions: Discussion Forum and Community Slack

@Chadha93 @vishalvivekm /assign so i can fix this issue.

Hi @Cvr421, can I take up this issue. I also wanted to start contributing to this repo and this issue seems fine.
If its okay with you, I can fix it ?

Hi @Cvr421 can I take up the issue?

@Chadha93 and @leecalcote sir i created PR for this issue please review it.
https://github.com/meshery/meshery.io/pull/1414


WEB-2510: Pass headers through in render API

Summary:
We're going to ask that the render environment handle all things header-related (including tracking header access) so that we don't have to try and manage this API across process boundaries.
Issue: WEB-2510
Test plan:
yarn test

@somewhatabstract As far as I can tell all the other changes will be happening inside the code in Webapp, the changes here are pretty minimal!

@somewhatabstract As far as I can tell all the other changes will be happening inside the code in Webapp, the changes here are pretty minimal!

Awesome.


docs: add docs for different PyPI mirror

Why?
Conda-forge is apparently very slow from China. A local mirror for PyPI exists, so we might as well point users to it.

All looks good!


go-sqlite when go build with windows gui program error

cmd error log:
D:\Go\pkg\tool\windows_amd64\link.exe: running gcc failed: exit status 1
D:/mingw-w64/mingw64/bin/../lib/gcc/x86_64-w64-mingw32/8.1.0/../../../../x86_64-w64-mingw32/bin/ld.exe: i386 architecture of input file `C:\Users\HUDENG~1\AppData\Local\Temp\go-link-643890423\000000.o' is incompatible with i386:x86-64 output
collect2.exe: error: ld returned 1 exit status
The issue is same, how to solve?

Probably, you are using 64bit go and 32bit C compiler.


Feature Request - additional content-type support?

love the viewer - any possiblity of adding support (maybe by configuration) for other content-types?  i.e. I'm looking for application/atom+xml to view OData feeds.

Is there a way to force content to view in XV? I want to use it to view .log files.

It’s possible only if content page is served with xml content-type

I was actually looking in the code to see if I could modify for other content-types to see OData feeds. Button idea would be a nice workaround. If you have some pointers as to how to accomplish that I'd be happy to contribute.

When viewing a site which produces "Content-Type:text/xml;charset=UTF-8" the extension does not kick-in when using Chrome. RFC3023 has standardised:

text/xml,
application/xml,
text/xml-external-parsed-entity,
application/xml-external-parsed-entity, and
application/xml-dtd
as all being valid. I do note the comment you made on Mar 19, but is there a way in which these limitations can be addressed? Can we submit tickets against Chrome, or up-vote a ticket you have raised?

I


test issue worklfow project board

What happened?
test
What are the steps to reproduce the bug?
NA
Version
NA
Platform (OS and architecture)
NA
Relevant log output
No response
Accompanying data
No response
Organisation
No response

Can this be closed?


doc: added web link of Huggingface to be downloaded

This commit is to append the webste address of Hugingface in the README.md.
Signed-off-by: Geunsik Lim<EMAIL_ADDRESS>

After this PR:

Looks good, thanks!


[C++] Add RE2 version of regex-redux benchmark

[x] Rewrite in RE2 (install dependency with sudo apt install libre2-9 libre2-dev)
[ ] Rewrite in CTRE (Blocked: Does not implement Replace function)

Let's just make this patch RE2 and have another one later for CTRE if you get that working.
I'm assuming you are compiling this fine because you have RE2 installed locally, could you make a small FetchContent cmake file so it works in isolation?

We can just have something like this in the Makefile:

compile:
	cmake . -B build -DCMAKE_BUILD_TYPE=Release
run:
	./build/regexredux_run 0 < ../../regexredux-input5000000.txt
<...>

Add a .gitignore for the build directory.

Here is a CMake file I was just working on that has a FetchContent and LTO example: https://github.com/plasma-umass/cwhy/blob/main/mutations/CMakeLists.txt

Also, I believe you want to use something like this for OpenMP:
find_package(OpenMP REQUIRED)
target_link_libraries(<target> PRIVATE OpenMP::OpenMP_CXX)

Nevermind, I just saw you can install the dependency through APT. This is fine.


"material_theme_accent_yellow" and "material_theme_appbar_yellow" look orange not yellow

The colors for the following look more orange than yellow:
"material_theme_accent_yellow": true,
"material_theme_appbar_yellow": true,
The sidebar and appbar themes do not look same as yellow text in preferences, they look orange.  I suspect color values are different or possibly render different if color values are same.

Your appbar looks a bit weird. Make sure to restart Sublime Text. For the color I've use the 800 from material design yellow palette. This make a bit more contrast with the white labels. Orange accent (Deep Orange) is a lot darker.
Maybe i will rename yellow to orange and orange to deep orange.

Thanks for exposing me to material design colors and palette.
I'd like to suggest & request Yellow #500 be used as it is "yellow" and when you add feature to allow light and dark ( white & black ) text on appbar, then dark ( black text ) will be very visible when appbar color is yellow or other bright colors.
See Yellow 500 below from material design below.  Yellow 500 looks good and black text is visible.

The first yellow accent was 500. But there were many problems with the lighter theme. So i switched to the 800 one.

First I want to say I'm just glad and grateful to have access to your theme and have learned about sublime.
Ultimately it's your decision...
I just think, yellow should be yellow and if person  chooses light theme, then it's undestood they may have to selet dark accent.  There are plenty of dark accents to choose from.
Same goes for appbar...
If person choose white theme, then they should get upset if they couldn't also use white text.
For consistence, when selectin yellow should not get another color.
If it was originally 500, shouldn't it stay 500 and add additional accents or appbar colors if necessary.
Again, truly appreciate everything you have and are doing.  Whatever you decide I'm ok with it.
It's privileged to even have any themes...

At the moment i've no time to work on it. I will wait for a PR.

No problem.  There is no rush.


Remove control-state-mixin visual tests

control-state-mixin functionality should be tested in control-state-mixin, not here.
Also, these tests are broken for IE11:

I tested that manually and everything works fine, so most probably a gemini issue.

This change is

Reviewed 10 of 10 files at r1.
Review status: all files reviewed at latest revision, all discussions resolved.

Comments from Reviewable


Add X Button

What kind of change does this PR introduce?
Feature

Issue Number:
Fixes #34
Did you add tests for your changes?
No

Thank you for you contribution Siddhesh. I'm actually looking for something fancier. Something animated to be honest. You've probably seen the examples there. Like those that have transitions.

@lnxpy  Man you dont have to  close the PR directly I could've refractored it   😒

You can still work on it. :)


🐞Can not build on XCode 14.0.1

Description
The error message:
~/Library/Developer/Xcode/DerivedData/CodeEdit-dhhvsfoxrbcqstdirhnbftqgzakd/SourcePackages/checkouts/GRDB.swift/GRDB/Core/WALSnapshot.swift:32:48: error build: Cannot find type 'sqlite3_snapshot' in scope
To Reproduce

Click build

Expected behavior
Build successfully
Version information
CodeEdit: 1.0
Hash: 538e4274e0b51cf51983a597065122a7587a1b16
MacOS: 12.6.0
Xcode: 14.0.1
Additional context
No response

Did you tried a clean build?

Sorry to reopen this, but I just tried building the project and received the same errors. I tried a clean build and received the same errors. Rather than opening a new issue, I figured it might be better to reopen this one.
Commit Hash: 92d4bf7f3ea8f1aa861dd6ad1c106f9e38ddd59a
macOS: 12.6
Xcode: 14.0.1

Had the same issue. GRDB reports v6.0.0 as the minimum for Swift 5.7
Sure enough, the project builds fine when updating the Package definition of CodeEditModules.


Confusing order-dependency in command-line arguments

$ docker-machine -v
docker-machine version 0.1.0

Here's an example:
$ docker-machine create --driver vmwarefusion --vmwarefusion-memory-size 2048 vmware
INFO[0000] Creating SSH key...
INFO[0000] Creating VM...
INFO[0003] Waiting for VM to come online...
INFO[0042] "vmware" has been created and is now the active machine.
INFO[0042] To point your Docker client at it, run this in your shell: $(docker-machine env vmware)
$ docker-machine inspect vmware
{
    ...
        "Memory": 2048,
    ...
}
$ docker-machine rm vmware

Everything here worked as expected.
Now, if I move the --vmwarefusion-memory-size option to the end:
$ docker-machine create --driver vmwarefusion vmware --vmwarefusion-memory-size 2048
INFO[0000] Creating SSH key...
INFO[0000] Creating VM...
INFO[0002] Waiting for VM to come online...
INFO[0040] "vmware" has been created and is now the active machine.
INFO[0040] To point your Docker client at it, run this in your shell: $(docker-machine env vmware)
$ docker-machine inspect vmware
{
    ...
        "Memory": 1024,
    ...
}
$ docker-machine rm vmware

:open_mouth:
I was super confused at first, since this was the first invocation I actually tried.
I am sure there is some subtlety here, but can we make the options somehow position-independent?
Alternately, can we warn the user clearly if a specified option is being ignored?

Yeah unfortunately this is a limitation of how we are using an upstream cli lib.   We could probably check for args after the machine name.
/cc @sthulb @nathanleclaire

I'm writing my own CLI lib, perhaps we can use it a bit later.

Sorry to hear it was confusing @nchammas.  Perhaps there is a way we can update the documentation to make the syntax / argument order more clear?  I'd be -1 on parsing flags after arguments are specified - that's not a common pattern.  For instance, docker run -it ubuntu bash -v /tmp/foo:/foo won't work and IMO shouldn't.
Interestingly, I tried this on the console out of curiosity (git grep -i foo will do a case-insensitive search for foo in a git repository):
$ git grep foo -i
fatal: bad flag '-i' used after filename

I'm a fan of this approach, though I am not sure how if/how it could work in codegangsta/cli - perhaps we could contribute a patch upstream to enable a similar feature.

Throwing an error sounds like the right thing to do. Silently ignoring an option seems scary, because you'll think an option took when it didn't.
FWIW, the flow that led to my tacking on the option at the end was:

I started off with:
docker-machine create --driver vmwarefusion vmware

I realized I needed more memory so I killed this machine.

I then scrolled up through my shell history to my most recent create command and tacked on the --vmwarefusion-memory-size option.

I wonder if a check for len(c.Args()) would be the most elegant solution here.

Fixed. Duplicate of #1914


Create a CLI tool for launching UWP applications

In order to launch UWP applications, we need to use a win32 native API:
https://github.com/flutter/flutter/pull/79684/files#diff-c87c94e076dd721f425a1061bd7c9d9be35ceb0e24963b559021b717ae27994cR17
This would be the only usage of ffi/win32 in the tool. Since the tool is not really set up to test this usage, we could instead build a C/C++ program in the engine which provides a command line application to launch apps, similar to the libimobile device tools.
This could be more easily tested in the engine, and the tool could depend on it like it does the existing prebuilts.
Example:
uwp_launcher launch SOME-ID ARGS

@cbracken @clarkezone

https://github.com/flutter/flutter/issues/70211

Tool is landed and rolled to the framework. I’m updating @jonahwilliams ’ patch to delegate out to it similar to how we do with adb/imobiledevice

Leaving this open until install/uninstall are added.

Opened https://github.com/flutter/flutter/issues/82399 for install/uninstall. Closing this as fixed since we've got the tool and the flutter tools integration.


Make source code buildable on macOS

This PR updates the package buildable on macOS, to address the SwiftUI preview issue on macOS, see https://github.com/devicekit/DeviceKit/issues/341

@JulesMoorhouse @honghaoz hey! First of all, thanks for taking your time to fix this issue - it's been bugging me for a while and getting SwiftUI canvas back would be awesome ✨ Will you have some time soon to take a look at the failing action? I just added the forked version to my project and seems to be fine 🤞

Hello @Zandor300 Can you please merge this PR?

@Archish27 Hi I have just updated the PR. I hope we can merge this change sooner. 🙇

@Zandor300 Thanks for triggering the build. However, it seems like danger action failed because it couldn't find commit 87f53f07, which is the commit from my forked repo:
bundler: failed to load command: danger (/Users/runner/work/DeviceKit/DeviceKit/vendor/bundle/ruby/2.6.0/bin/danger)
RuntimeError: Commit 87f53f07 doesn't exist. Are you running `danger local/pr` against the correct repository? Also this usually happens when you rebase/reset and force-pushed.

From the checkout action:
Fetching the repository
  /usr/local/bin/git -c protocol.version=2 fetch --no-tags --prune --progress --no-recurse-submodules --depth=1 origin +028963b08f03943779[45](https://github.com/devicekit/DeviceKit/actions/runs/7345636611/job/20014482957#step:2:50)bba4b94f3c8df80a9585:refs/remotes/pull/342/merge
...
Checking out the ref
  /usr/local/bin/git checkout --progress --force refs/remotes/pull/342/merge
  HEAD is now at 028963b Merge 87f53f07e85393672c276411695faf25e40db13a into 43a9f9388636be506fed8b6b86f858d759a21178
/usr/local/bin/git log -1 --format='%H'
'028963b08f0394377945bba4b94f3c8df80a9585'

The head is 028963b, which is a new merge commit from my commit (87f53f07)
Looks like the checkout action only fetched the last commit (028963b) due to --depth=1, but danger action is trying to find the parent commit 87f53f07, which is not fetched.
Maybe we need to adjust the checkout action fetch more history? or maybe the danger action should smartly use the merging commit.

@honghaoz @NoemiRozpara @Archish27
I've made another PR, that doesn't exclude the entire library when compiling. I'm unable to reproduce the actual issue you are having so could you try my PR or provide an example project where it is happening?
https://github.com/devicekit/DeviceKit/pull/376

@Zandor300 I just tried it out, unfortunately, both the master branch and  feature/merge-prs are broken. It seems like you are still actively working on it?
For the current master branch, it won't compile for two issues:

In Package.swift, it should bump up the spm tool version to 5.8 // swift-tools-version:5.8 to support the privacy manifest file
Buch of compiling issues, like:

@Zandor300 Here is a followup PR based on the latest master branch.

Closes this PR in favor of https://github.com/devicekit/DeviceKit/pull/379

I've made another PR, that doesn't exclude the entire library when compiling.
tbh, since the library doesn't support macOS, it seems unnecessary to include the code for macOS. unless we decided to include Macs.

I'm unable to reproduce the actual issue you are having so could you try my PR or provide an example project where it is happening?

@Zandor300 Here is an example project shows the issue: DeviceKit-Mac.zip


Update README.md

add ACCESS-OM2 conditions of use

No problem. Maybe also consider adding a CITATION file to the OM2 repository?


Doorway link contrast and underline

Description: A Doorway stakeholder shared that links in the text are difficult to find due to their color being very similar to the regular text and links not being underlined.
Figma
AC:

All links on the doorway site should be the correct blue
All links on the doorway site should be underlined.

Updated scope after 10/13 convo:
as a part of this ticket:

underline everything that’s a link
update colors on partners and help center page

Open up follow-up ticket for link colors across the site / on homepage to avoid contrast issues

QA Notes:

Check that the "Dahlia" link on the home page search box and the listings page filter modal are underlined
Check that the Bay Area Housing Finance Authority (BAHFA) link on the homepage is underlined
Check that all links within the partners and help center pages are underlined and reflect the Figma's updated colors

lgtm


[0.62.0-rc.3][Android]ReactNative causing Pytorch crash

We are using ReactNative for one of the module in our app.Recently we started using Pytorch for our research.After integration of Pytorch App started crashing with below error
Error:-
java.lang.UnsatisfiedLinkError: couldn't find DSO to load: libpytorch_jni.so caused by: dlopen failed: cannot locate symbol "_ZN8facebook3jni11JByteBuffer5orderENS0_9alias_refINS0_10JByteOrderEEE" referenced by "/data/app/com.****-thbX8es5mHtefnS-RBdYcQ==/lib/arm64/libpytorch_jni.so"...
Steps to reproduce:
1.Download latest version of react native 0.62.0-rc.3
2.Integrate Pytorch in sample app
3.Try to run sample model using pytorch
Actual:App should work without any crash
Expected:App crashes with above mentioned error

There is a more broad subject related to conflicted fbjni lib on pytorch & also on react-native :/ both does not cohabitate :/


bench / process: visual reporting in Hydra

If we get the membench derivation to produce output in a special format, we can get Hydra to draw historic charts for various metrics.
The bench/process component needs to be extended for this.

https://github.com/input-output-hk/cardano-node/pull/3578

Closing this. If this is still relevant please reopen.


Sweep: Request the SimpleMongoReader

Details
In llama index python, I see that there is SimpleMongoReader. In the nodejs version, I don't see that in documentation. Can you add it? Or is there other way to use Mongo data?

Checklist

[X] Modify packages/core/src/readers/SimpleMongoReader.ts ! No changes made
[X] Create packages/core/src/tests/SimpleMongoReader.test.ts ✓ https://github.com/run-llama/LlamaIndexTS/commit/94961e8f908e4b89b04f00fcba7f03849e6a08f9
[X] Check packages/core/src/tests/SimpleMongoReader.test.ts ✗
[X] Modify packages/core/tsconfig.json ✓ https://github.com/run-llama/LlamaIndexTS/commit/59169d6c8bfa068d820fc9b2f864c112e308a09c
[X] Check packages/core/tsconfig.json ✗
[X] Modify .eslintrc.json ! No changes made
[X] Check packages/core/tsconfig.json ✗
[X] Check packages/core/src/tests/SimpleMongoReader.test.ts ✗

When coding, I still do not see the SimpleMongoReader like in Python. How to use it?

@dinhan92 I just finished a PR that exports the SimpleMongoReader. Feel free to test it: https://github.com/run-llama/LlamaIndexTS/pull/204/files

@marcusschiesser was this merged to main branch ? I installed using npm

It was merged
closing due to inactvity and cleaning the repo


License

Is this meant to be open source software, or just source available?
Please add a license file to the repo.

ok


Diff AARs on PRs

Similar to what #73 does, but this time with a real diff preview.
The main issue to tackle is how/where to get the "previous" AAR file.

One simple solution could be to use GitHub Actions cache to save AAR artifacts when built from the main branch.
And inside the PR, we try to fetch the corresponding cache and do the comparison.
WDYT?


Bug: pod for downloading archive datasets does not support arm64 architecture

What happened: After creating a dataset of type ARCHIVE, I see no content in the pod that mounts the dataset. This is because the download pod fails with
exec /bin/sh: exec format error

What you expected to happen: everything should work
How to reproduce it (as minimally and precisely as possible):

Runkubectl apply -n dlf -f https://github.com/AlessandroPomponio/datashim/blob/ap_365_readme_updates/examples/minio/minio.yaml

Runcat <<EOF | kubectl apply -f -
apiVersion: v1
kind: Pod
metadata:
  name: nginx
  labels:
    dataset.0.id: "archive-dataset"
    dataset.0.useas: "mount"
spec:
  containers:
    - name: nginx
      image: nginx
EOF

Runkubectl exec nginx -- ls mnt/datasets/archive-dataset/

Anything else we need to know?:
Environment:

Datashim version:0.4.0
Kubernetes version (use kubectl version):
Kubernetes distribution:
      Client Version: v1.30.0       Kustomize Version: v5.0.4-0.20230601165947-6ce0bf390ce3       Server Version: v1.30.0
Cloud provider or hardware configuration: Macbook M1, using KIND
Install tools: helm

The function here: https://github.com/datashim-io/datashim/blob/da435c8e470cdbfb61a5772efbc9c97cf4586698/src/dataset-operator/controllers/archive_handler.go#L14
references a docker image that is not built with multi-arch support. The Dockerfile for that image is here: https://github.com/datashim-io/datashim/blob/da435c8e470cdbfb61a5772efbc9c97cf4586698/src/cos-uploader/Dockerfile#L2
To fix this issue:

[ ] Update the image building scripts to include the docker image for archive
[ ] Upload the archive docker image to quay.io/datashim-io

Everything seems to have worked https://github.com/datashim-io/datashim/actions/runs/10167107008/job/28122136358
cc @srikumar003

Closing this issue


Reusable lsat

List of changes that need to occur to achieve this
sphinx-relay

create an endpoint to get active lsat
make an update to the existing updateLsat endpoint to accommodate update of lsat when expired.
add a new column to the lsat table called status, to keep track of lsat status
All these have been implemented, here is the PR [https://github.com/stakwork/sphinx-relay/pull/439]

sphinx-bridge

create a getActiveLsat method to get active lsat from sphinx-relay
create the updateLsat method to update lsat in sphinx-relay
This has been merged to sphinx-bridge already, here is the PR: [https://github.com/kevkevinpal/sphinx-bridge/pull/2]

sphinx-mac

create a method that handles the getLsat method passed from sphinx-bridge
create a method that handles the updateLsat method passed from sphinx-bridge
This has been implemented, here is the PR [https://github.com/kevkevinpal/sphinx-mac/pull/2]

sphinx-nav-fiber

get active lsat from sphinx-relay through sphinx-bridge
update lsat when marked as expired from the bolt-wall server.
All this has been implemented, here is the PR [https://github.com/stakwork/sphinx-nav-fiber/pull/41]

@kevkevinpal

Looked over the relay changes and they look good
Opened a new PR to the stakwork/sphinx-bridge with the changes you made here
https://github.com/stakwork/sphinx-bridge/pull/4

also I think you forgot this change
jarvis-boltwall
https://github.com/stakwork/jarvis-boltwall/pull/1/files

also I think you forgot this change
jarvis-boltwall
https://github.com/stakwork/jarvis-boltwall/pull/1/files

Ops!!!......
That's true, thanks....

Users can still use older relay version and not have the reusable lsats feature but if a user is on the older sphinx-mac version the browser will crash if they try and use getLsat and updateLsat
Also the older relay users wont have the status column so when they do upgrade all those lsats will be marked expired

Merged sphinx-relay and sphinx-bridge
waiting on sphinx-mac to be merged and released before merging the sphinx-nav-fiber changes


Make private types under TypesRN and Props public

Resolves https://github.com/reasonml-community/bs-react-native/issues/153
Let me know if you think an interface file is necessary too 🙂

@jamieparkinson awesome. One question, can we change TypesRN to Types?

@wokalski hmmm, do you think doing that might cause some confusion when (as is likely/recommended) people are using open BsReactNative? Types is a generic enough name that I think there's an argument for including RN, perhaps as a prefix rather than a suffix though. Up to you!

I think that we have namespacing precisely in order to have precise names without suffixes and other things. I'm rooting for Types.

Also, since these are just types, only rei is needed IIRC. We don't need .re.

Yeah, point taken re namespacing and good tip re .rei - have updated. (apologies for weird authorship on last commit, am using my old laptop with outdated git/ssh config)


Admin creates other users

User story
As an administrator, I am able to create accounts.
Narrative
As an administrator, I want to create accounts on behalf of other users such that I can set it up (assign roles, etc.) and they receive an email to complete the registration on their own.
Out of scope
Managing user roles or permissions as well as sending the verification email.
Acceptance criteria

#
Given
When
Then

1
I am an administrator
I try to create a new user
The system creates a new user and sends them an email to complete registration

2
I am an administrator
I try to create a user but their identifier already exists
The system prevents me from creating a duplicate account

3
I am not an administrator
I try to create user
The system prevents me from creating users

UI Mockup:

@Alex-Hayeur
i started work on the users can create users issue.
If you look at my fork and branch you should see it.
I got the create user working, my problems are:
A) When the validation fails, i can't seem to get the errors on the frontend.