TensorFlow Not Detecting GPU on Compute Node

Issue type
Build/Install
Have you reproduced the bug with TensorFlow Nightly?
Yes
Source
binary
TensorFlow version
v1.12.1-99044-gc6ecfeac886 2.15.0-dev20230825
Custom code
No
OS platform and distribution
CentOS Linux release 7.9.2009 (Core)
Mobile device
No response
Python version
3.9.13
Bazel version
No response
GCC/compiler version
No response
CUDA/cuDNN version
11.8.0 / <IP_ADDRESS>
GPU model and memory
No response
Current behavior?
Behavior summary 🖥
I'm trying to set up TensorFlow on a compute node within our scientific computing cluster and am running into a problem. After setting up a new environment and installing TensorFlow, running TensorFlow's tf.config.list_physical_devices('GPU') method returns an empty list, indicating no GPU devices are detected. However, nvidia-smi shows a Quadro RTX 6000 GPU present on the system. The TensorFlow CPU validation works without issues, but the GPU validation does not.
Possible Areas of Concern 🚩

Modules vs. Conda/Pip Installations: I've loaded specific versions of CUDA and cuDNN using the cluster's module system. However, I also used conda and pip to install these within my environment. Could this mixed approach cause any conflicts for TensorFlow? I'm unsure which version TensorFlow might prioritize or if it would create any confusion.
CUDA Version Mismatch?: When I run nvidia-smi, it indicates a CUDA version of 12.2. Yet, I've loaded and installed a CUDA version of 11.8 using both modules and conda. I'm wondering if this difference could lead to any issues. Does TensorFlow need a strict match with the CUDA version?
Different cuDNN Version: The TensorFlow installation guide mentioned cuDNN version <IP_ADDRESS>, but I installed <IP_ADDRESS> for what I thought might be better consistency with the modules I loaded. Could this version difference be problematic? Is TensorFlow particular about the cuDNN version it works with?

I'm looking for insights from anyone who might have navigated similar issues or can provide clarity on these points. Also of course if anyone knows how I might go about further troubleshooting/diagnosing this that'd be great. Thanks for the help!
Standalone code to reproduce the issue
# Setting up a new conda environment with python 3.9
conda create --name myenv python=3.9
conda activate myenv

# Running NVIDIA's System Management Interface to check GPU
nvidia-smi

# Loading necessary modules for CUDA/cuDNN
module load cuda-11.8.0-gcc-8.2.0-rqftjjg
module load cudnn-<IP_ADDRESS>-11.8-gcc-8.2.0-qibz3ue

# Installing CUDA Toolkit and cuDNN using Conda and Pip
conda install -c conda-forge cudatoolkit=11.8.0
pip install nvidia-cudnn-cu11==<IP_ADDRESS>

# Configuring system paths
mkdir -p $CONDA_PREFIX/etc/conda/activate.d
echo 'CUDNN_PATH=$(dirname $(python -c "import nvidia.cudnn;print(nvidia.cudnn.__file__)"))' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
echo 'export LD_LIBRARY_PATH=$CUDNN_PATH/lib:$CONDA_PREFIX/lib/:$LD_LIBRARY_PATH' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh

# Upgrading pip and installing TensorFlow
pip install --upgrade pip
pip install tensorflow==2.13.*

# Running CPU validation
python3 -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"

# Running GPU validation
python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"

Relevant log output
# CPU validation
2023-08-25 14:11:07.056941: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2023-08-25 14:11:07.105836: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-25 14:11:08.620525: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2023-08-25 14:11:10.459520: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:268] failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error
2023-08-25 14:11:10.459591: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:168] retrieving CUDA diagnostic information for host: scu-node018.scu
2023-08-25 14:11:10.459608: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:175] hostname: scu-node018.scu
2023-08-25 14:11:10.459712: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:199] libcuda reported version is: 535.104.5
2023-08-25 14:11:10.459783: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:203] kernel reported version is: 535.104.5
2023-08-25 14:11:10.459811: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:309] kernel version seems to match DSO: 535.104.5
tf.Tensor(-964.9978, shape=(), dtype=float32)

# GPU validation
2023-08-25 14:11:35.537281: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2023-08-25 14:11:35.582391: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-25 14:11:36.286683: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2023-08-25 14:11:36.956393: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:268] failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error
2023-08-25 14:11:36.956458: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:168] retrieving CUDA diagnostic information for host: scu-node018.scu
2023-08-25 14:11:36.956495: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:175] hostname: scu-node018.scu
2023-08-25 14:11:36.956591: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:199] libcuda reported version is: 535.104.5
2023-08-25 14:11:36.956655: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:203] kernel reported version is: 535.104.5
2023-08-25 14:11:36.956683: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:309] kernel version seems to match DSO: 535.104.5
[]

# nvidia-smi Outpu
Fri Aug 25 13:59:33 2023
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   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  Quadro RTX 6000                Off | 00000000:AF:00.0 Off |                    0 |
| N/A   31C    P0              54W / 250W |      0MiB / 23040MiB |      5%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+

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

Update on Issue with TensorFlow GPU Detection
After my initial post, I took the following steps to further investigate the issue:

** Replicated the problem with Different cuDNN Version:** Even after pip installing the cuDNN version <IP_ADDRESS> recommended in TensorFlow's documentation, the problem persisted. TensorFlow still could not detect the GPU.
Module Deactivation Check: To determine if there was a conflict between the cluster's module system and my conda/pip installations, I deactivated the modules and relied solely on the versions installed via conda and pip.
Custom Environment Variables for Modules: After confirming the issue persisted without the modules, I then tried setting up the environment using the modules exclusively configuring the paths using those of the module system rather than those provided by the installation instructions:

For CUDA: /home/software/spack/opt/spack/linux-centos7-x86_64/gcc-8.2.0/cuda-11.8.0-rqftjjg3pwtogsetgcrrytjcqutxgtaj/bin
For cuDNN: /home/software/spack/opt/spack/linux-centos7-x86_64/gcc-8.2.0/cudnn-<IP_ADDRESS>-11.8-qibz3uecpmz5hiosbohqaniedyu6m6r5/lib

I adjusted the PATH and LD_LIBRARY_PATH environment variables to prioritize the module system's CUDA and cuDNN installations whenever my specific Conda environment was activated.
Despite these troubleshooting steps, TensorFlow still does not recognize the GPU on the system. I'm continuing to seek insights or suggestions to resolve this issue.

I am new here, but as per my checks class PhysicalDevice is empty, so list_physical_devices('GPU') is failing( tensorflow.config.-physical-device.pbtxt)
https://github.com/tensorflow/tensorflow/blob/ece328dabf48f09e73ad62e950e1e04f096c3762/tensorflow/python/eager/context.py#L346-L367

I am new here, as per my initial checks class PhysicalDevice is empty i.e list_physical_devices is failing (tensorflow.config.-physical-device.pbtxt). Check it out:
https://github.com/tensorflow/tensorflow/blob/ece328dabf48f09e73ad62e950e1e04f096c3762/tensorflow/python/eager/context.py#L346-L367

To my understanding tensorflow-gpu has been deprecated since 2022.

Hi @masonhargrave ,
I think you are missing these 2 steps as mentioned in documentation instructions for GPU setup. Please refer the attached source and follow the instructions sequentially.
CUDNN_PATH=$(dirname $(python -c "import nvidia.cudnn;print(nvidia.cudnn.__file__)"))
export LD_LIBRARY_PATH=$CUDNN_PATH/lib:$CONDA_PREFIX/lib/:$LD_LIBRARY_PATH

I have followed the same instructions and able to detect GPU.Please refer attached logs.
(tf2.13) suryanarayanay@surya-ubuntu20:~$ python3 -c "import tensorflow as tf; print(tf.__version__)"
2023-08-31 06:52:35.206843: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9498] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2023-08-31 06:52:35.206913: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2023-08-31 06:52:35.211084: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2023-08-31 06:52:35.524830: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-31 06:52:37.148708: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
WARNING:tensorflow:From /home/suryanarayanay/miniconda3/envs/tf2.13/lib/python3.11/site-packages/tensorflow/python/ops/distributions/distribution.py:259: ReparameterizationType.__init__ (from tensorflow.python.ops.distributions.distribution) is deprecated and will be removed after 2019-01-01.
Instructions for updating:
The TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.
WARNING:tensorflow:From /home/suryanarayanay/miniconda3/envs/tf2.13/lib/python3.11/site-packages/tensorflow/python/ops/distributions/bernoulli.py:165: RegisterKL.__init__ (from tensorflow.python.ops.distributions.kullback_leibler) is deprecated and will be removed after 2019-01-01.
Instructions for updating:
The TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.
2.15.0-dev20230813

(tf2.13) suryanarayanay@surya-ubuntu20:~$ nvidia-smi
Thu Aug 31 06:56:15 2023
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 525.85.12    Driver Version: 525.85.12    CUDA Version: 12.0     |
|-------------------------------+----------------------+----------------------+
| 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  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |
| N/A   71C    P0    31W /  70W |      2MiB / 15360MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
|   1  Tesla T4            Off  | 00000000:00:05.0 Off |                    0 |
| N/A   67C    P0    28W /  70W |      2MiB / 15360MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
|   2  Tesla T4            Off  | 00000000:00:06.0 Off |                    0 |
| N/A   69C    P0    30W /  70W |      2MiB / 15360MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
|   3  Tesla T4            Off  | 00000000:00:07.0 Off |                    0 |
| N/A   72C    P0    31W /  70W |      2MiB / 15360MiB |      7%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+
(tf2.13) suryanarayanay@surya-ubuntu20:~$

(tf2.13) suryanarayanay@surya-ubuntu20:~$ python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
2023-08-31 06:54:14.005166: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9498] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2023-08-31 06:54:14.005226: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2023-08-31 06:54:14.005283: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2023-08-31 06:54:14.014421: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-31 06:54:14.824009: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
WARNING:tensorflow:From /home/suryanarayanay/miniconda3/envs/tf2.13/lib/python3.11/site-packages/tensorflow/python/ops/distributions/distribution.py:259: ReparameterizationType.__init__ (from tensorflow.python.ops.distributions.distribution) is deprecated and will be removed after 2019-01-01.
Instructions for updating:
The TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.
WARNING:tensorflow:From /home/suryanarayanay/miniconda3/envs/tf2.13/lib/python3.11/site-packages/tensorflow/python/ops/distributions/bernoulli.py:165: RegisterKL.__init__ (from tensorflow.python.ops.distributions.kullback_leibler) is deprecated and will be removed after 2019-01-01.
Instructions for updating:
The TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU'), PhysicalDevice(name='/physical_device:GPU:1', device_type='GPU'), PhysicalDevice(name='/physical_device:GPU:2', device_type='GPU'), PhysicalDevice(name='/physical_device:GPU:3', device_type='GPU')]

Hi @SuryanarayanaY, thank you for your reply!
As you can see in the Configuring System Path section of the Standalone code to reproduce the issue I do in fact run those commands from the instructions, except in the more automated way suggested below the commands you are referencing.
# Configuring system paths
mkdir -p $CONDA_PREFIX/etc/conda/activate.d
echo 'CUDNN_PATH=$(dirname $(python -c "import nvidia.cudnn;print(nvidia.cudnn.__file__)"))' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
echo 'export LD_LIBRARY_PATH=$CUDNN_PATH/lib:$CONDA_PREFIX/lib/:$LD_LIBRARY_PATH' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh

I reran my code using the non-automated commands you suggested with no change to the problem. This is expected as the commands appear to be redundant and no different than the commands included in my minimal example.
(tf-test) [mah4021@scu-node018 mah4021]$ python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
2023-09-02 21:53:49.520842: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2023-09-02 21:53:49.566393: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-09-02 21:53:50.515064: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2023-09-02 21:53:51.443662: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:268] failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error
2023-09-02 21:53:51.443727: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:168] retrieving CUDA diagnostic information for host: scu-node018.scu
2023-09-02 21:53:51.443767: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:175] hostname: scu-node018.scu
2023-09-02 21:53:51.443861: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:199] libcuda reported version is: 535.104.5
2023-09-02 21:53:51.443921: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:203] kernel reported version is: 535.104.5
2023-09-02 21:53:51.443946: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:309] kernel version seems to match DSO: 535.104.5
[]

Any other thoughts here?

Hi @masonhargrave ,
Could you please confirm the command you used for tensorflow installation?
Please note that from Tf2.14V onwards GPU package available as tensorflow[and-cuda]. Please try using the command
pip install tensorflow[and-cuda] . You can refer the documentation here.

If anyone like me has reached this thread for ideas about how to use your own CUDA and cuDNN, instead of installing through pip or conda:
export LD_LIBRARY_PATH=/path/to/cuda-12.2/lib:/path/to/cudnn-linux-x86_64-<IP_ADDRESS>_cuda12-archive/lib
python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"

It took me a while to realize that you also have to provide cuDNN libraries for tensorflow to discover your GPUs. That is why the second term in LD_LIBRARY_PATH.
By the way, when you actually train a mode, you will also need:
export XLA_FLAGS=--xla_gpu_cuda_data_dir=/path/to/cuda-12.2/

python script.py


Add API definition for Snow provider

Issue #, if available:
#1113
Description of changes:

Add initial EKS-A Snow API definition for beta

Testing (if applicable):

make unit-test

By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.

/lgtm


Silverstripe security advisories

Hello @stof ,
thanks for having a look at this. I've open a pull request to fix this.
Spek

https://github.com/FriendsOfPHP/security-advisories/pull/120


A multiplayer dice game

Describe the project
A multiplayer Ludo game where one can play with his friends. Player can roll a dice, and then decide to hold the score or continue. If he continues and get '1', scores are reset
Issue Type:

[ ] Bug Report
[ ] Feature Request
[ ] Documentation
[X] New Project

Additional context

Okay, so no games right? only frontend projects?

Okay, so no games right? only frontend projects?

Yes


Q: Installation

Hello,
I failed to install this repository on my Apple MacBook Pro (Chip: Apple M1 Pro) following the ReadMe. Upon investigation, I noticed that the transformer library included in this repository is rather old (3.1.0 from 2020) requiring me to compile some dependencies for my machine with an older Rust Compiler. I could not figure it which version I needed.
Q: Can you provide guidance on how to install this library on the above machine? Or can you provide more information on the dependencies? Is it possible to upgrade the transformer version? Which Rust Compiler version do I need?
Thank you in advance!

Hi, as we discussed in the email, refer to this colab notebook can help set up: https://colab.research.google.com/drive/1qzB-sIuqNIarQjVVg41BrNyCfAd1LdsZ?usp=sharing


[docs] web3Modal.adapter is undefined when trying to disconnect

Summary
Doc Referrence
https://docs.reown.com/appkit/javascript/core/actions#disconnect
Description
I am using @reown/appkit and encountered an issue where web3Modal.adapter is undefined when attempting to call disconnect(). The wallet connection works fine, and the WalletConnect modal successfully displays the connected account. However, clicking the "Disconnect" button logs undefined for the adapter, making it impossible to disconnect the wallet.
The code is based on the official documentation.
import { createAppKit } from "@reown/appkit";
import { EthersAdapter } from "@reown/appkit-adapter-ethers";
import { bsc, mainnet } from "@reown/appkit/networks";

const projectId = "xxx";

const metadata = {
  name: "xxx",
  description: "xx",
  url: "https://xx.io",
  icons: ["https://xxxx.io/favicon.ico"],
};

const web3Modal = createAppKit({
  adapters: [new EthersAdapter()],
  networks: [mainnet, bsc],
  allowUnsupportedChain: true,
  metadata,
  projectId,
  features: {
    analytics: true, // Optional - defaults to your Cloud configuration
    socials: false,
    email: false,
  },
});

document.getElementById("connect").addEventListener("click", () => {
  web3Modal.open();
});

document.getElementById("disconnect").addEventListener("click", () => {
  console.log(web3Modal.adapter);
  web3Modal.adapter?.connectionControllerClient?.disconnect();
});

Actual Result
web3Modal.adapter is undefined after clicking the "Connect" button and attempting to disconnect.
Expected Behavior
After connecting the wallet, web3Modal.adapter should be properly initialized. Callingweb3Modal.adapter?.connectionControllerClient?.disconnect() should successfully disconnect the wallet.
Actual Behavior
Calling web3Modal.adapter?.connectionControllerClient?.disconnect() fails because web3Modal.adapter is undefined.
Environment
  "dependencies": {
    "@reown/appkit": "^1.4.1",
    "@reown/appkit-adapter-ethers": "1.4.1",
    "ethers": "^6.13.4",
    "vite": "^5.4.11"
  }

Node.js version: 20.18.0
Link to the related docs page
https://docs.reown.com/appkit/javascript/core/actions#disconnect

Could you try using await modal.disconnect() , we will be updating the docs


Create item class and its associations

In this Pull request we:

Implement methods in item class and add it relationship with other classes

Thank you @ibr5500 for the code review


Version 4's lack of strong naming is a hardship

Please continue strong-naming. For companies that need some assemblies to be strong named, this leaves us no option but to create modified SharpDX DLLs. Using SharpDX in our build process is no longer as simple as a package reference.
It's unusual for a top library on NuGet to not be signed. People that don't need signing won't care either way, but life just got a little worse for those of us who do.
Thanks for considering! ❤

Well, this is a bug when switching to appveyor.yml  while previously the config was private on appveyor. The snk file was written from the config, but it seems that I forgot to port it to appveyor.yml
I always thought that people would try the pre-release version of SharpDX in order to report this problem... should have maybe insist on the twitter feed to encourage users to do this...

@xoofx I'm glad to hear it wasn't intentional! Twitter is not a bad idea, would have worked for me. Another idea for this particular scenario is a build-time check which fails the build if there are any unsigned assemblies about to be packed or published.

strange, while checking the log, we are actually signing assemblies... not sure what is going on... will have to investigate a bit more...

Whatever got uploaded to NuGet in the net45 folders was not signed. The ones I checked are SharpDX, SharpDX.DXGI, SharpDX.Direct2D1 and SharpDX.Direct3D11.

I have pushed 4.0.1. This should hopefully fix this

Thanks for the fast resolution!


[feat] Allow .local access

The primary use case of hosting the RoboSats client at the moment is to use any browser locally while enjoying the benefit of the torification. By only allowing .onion users of the StartOS app still need Tor Browser plus their requests take double the time (Tor circuit to their node + tor circuit to the coordinator(s).
In preliminary tests @kn0wmad found that the chat was not working when using .local it was no further investigated. I see no clear issue why the chat would not work, so I think this should be an easy fix if the issue can be replicated.

I've taken on the robosats-startos package for Start9 and will look into this.

Hi there, with the 070 release (+ patch (Thanks @KoalaSat)) things seem to be working, and even most things are working on the .local, EXCEPT when a trade has been initiated and the messaging needs to happen, then we're seeing this (only on .local, .onion works perfectly):
Uncaught (in promise) DOMException: The operation is insecure.
    _connect https://ourreallylong.local/static/frontend/main.js:205
    promise callback*14262/e.prototype._connect https://ourreallylong.local/static/frontend/main.js:205
    e https://ourreallylong.local/static/frontend/main.js:205
    e https://ourreallylong.local/static/frontend/main.js:205
    e https://ourreallylong.local/static/frontend/main.js:205
    e https://ourreallylong.local/static/frontend/main.js:205
    f https://ourreallylong.local/static/frontend/main.js:205
    M https://ourreallylong.local/static/frontend/main.js:205
    Z https://ourreallylong.local/static/frontend/main.js:205
    r https://ourreallylong.local/static/frontend/main.js:205
    s https://ourreallylong.local/static/frontend/main.js:205
    o https://ourreallylong.local/static/frontend/main.js:205
    o https://ourreallylong.local/static/frontend/main.js:205
    e https://ourreallylong.local/static/frontend/main.js:205
    Ke https://ourreallylong.local/static/frontend/main.js:205
    P https://ourreallylong.local/static/frontend/main.js:205
    tl https://ourreallylong.local/static/frontend/main.js:205
    wc https://ourreallylong.local/static/frontend/main.js:205
    ac https://ourreallylong.local/static/frontend/main.js:205
    Ho https://ourreallylong.local/static/frontend/main.js:205
    bc https://ourreallylong.local/static/frontend/main.js:205
    bc https://ourreallylong.local/static/frontend/main.js:205
    nc https://ourreallylong.local/static/frontend/main.js:205
    E https://ourreallylong.local/static/frontend/main.js:205
    z https://ourreallylong.local/static/frontend/main.js:205
main.js:205

I opened the file but I don't even see many newline chars.  Any ideas?  Thanks!

Yes looks like that's initially why we turned off .local access:
Here was the issue (on the frontend at least, no console log from then) in 0.5.3:

I seems related to a limitation on the description library we use for chat messages. I'll  need to take a look but I'm not sure if I can fix it.

Some extra info: https://github.com/openpgpjs/openpgpjs/issues/320

Just FYI with .local, our users trust their server's CA system wide and instruct Firefox specifically to trust system-trusted certs:
So in theory it should know it's in a secure context.

For the record: I tested to redirect my hosts file to http://test.local and visiting on my machine the chat works. It doesn´t seem to be related to the address


Allow client svc nodeport to be specified

For access via bastion, allow service nodeport to be defined in TPR.

Closed in #60


Ephemeral volume claims not taken into account when scheduling?

Version

Karpenter Version: v0.16.3

Kubernetes Version: v1.21.14
Expected Behavior
Karpenter should take into account ephemeral volumeClaimTemplates when scheduling pods to nodes
Actual Behavior
Karpenter does not appear to take ephemeral storage into account when deciding whether it can schedule a pod to a node, and thus does not spin up new nodes to meet new additional capacity
Steps to Reproduce the Problem
I have a number of pods with the following spec:
  resources:
    limits:
      cpu: 6
      memory: 45G
    requests:
      cpu: 5
      memory: 45G
...
  volumes:
   - ephemeral:
        volumeClaimTemplate:
...
          spec:
            accessModes: ["ReadWriteOnce"]
            storageClassName: localnvme
            resources:
              requests:
                storage: 1850G

I've got topolvm provisioning the localnvme storage on nodes based on the local NVM drives in the machines
My situation is that I run 4 of these pods and specify the i4i family in my provisioner spec - Karpenter spins up an i4i.8xlarge to run them on, which fills up as so:

20 / 32 CPU in-use
180G / 256G Memory in-use
7400G / 7500G local NVMe in-use by ephemeral volume claim templates

Now if I scale my deployment up to 5 pods Karpenter decides that that pod can schedule to the existing node - the CPU and memory "fits" but the storage does not, so k8s refuses to schedule the pod there and it sits in "Pending" status with the following Events:
Events:
  Type     Reason            Age                    From               Message
  ----     ------            ----                   ----               -------
  Normal   Nominate          5m13s (x58 over 120m)  karpenter          Pod should schedule on <node with 4 pods already running>
  Warning  FailedScheduling  12s (x125 over 116m)   default-scheduler  0/20 nodes are available: 1 node(s) did not have enough free storage, 1 node(s) had taint {tainted: }, that the pod didn't tolerate, 10 node(s) didn't match Pod's node affinity/selector, 4 Insufficient
memory, 8 Insufficient cpu.

Note that in this case I can just increase the cpu/memory requests of my pods to make those cause karpenter to make the correct scheduling decision, but this fall apart as you go up to bigger node sizes and different instance families with different specs
Resource Specs and Logs
See steps to reproduce
Community Note

Please vote on this issue by adding a 👍 reaction to the original issue to help the community and maintainers prioritize this request
Please do not leave "+1" or "me too" comments, they generate extra noise for issue followers and do not help prioritize the request
If you are interested in working on this issue or have submitted a pull request, please leave a comment

We don't currently support this, but it would be very interesting to explore this. Future readers, remember to +1 if this is interesting to you.

We sometimes encounter another issue that I believe should also be solved by supporting this.
When pods have ephemeral volumes backed by EBS they sometimes get stuck in the CreatingContainer state, seemingly because Karpenter is not taking the ephemeral volume into account when scheduling the pod in the proper zone as it does with the Persistent Volume Topology capability, and it's trying to schedule it in a zone different to where the EBS volume was created.
Some logs:
Error:

  Warning  FailedAttachVolume  3m36s (x31 over 109m)  attachdetach-controller  (combined from similar events): AttachVolume.Attach failed for volume "pvc-*************" : rpc error: code = Internal desc = Could not attach volume "vol-*************" to node "i-*************": could not attach volume "vol-*************" to node "i-*************": operation error EC2: AttachVolume, https response error StatusCode: 400, RequestID: *************, api error InvalidVolume.ZoneMismatch: The volume 'vol-*************' is not in the same availability zone as instance 'i-*************'

could be similar to https://github.com/aws/karpenter-provider-aws/issues/2394


Adding format_control support

Update OutputWriter to support passing through --no-binary and --only-binary parameters

TIL about format control. Ideally we could just pass through these flags in a way that would not require pip-tools to specifically add support for it, but I don't know a better way for now.

Thanks!


Option for larger font size when playing tour

When devs are presenting code or scrolling through new codebases, the tour itself may blend into the code and devs have a hard time finding their place again. Customers have requested to be able to customize font size or font for CodeTours.
Stage 1: Option for larger font size when playing tour
Stage 2: Option for author specify specific font size or family when recording tour

Currently, the only way to increase the tour step font size, is for the end-user to zoom VS Code in general (e.g. by pressing cmd+). Since the tour steps are defined as markdown and rendered by VS Code, we don’t have control over styling, outside of the things that are allowed by markdown itself (e.g. headings, links, lists, etc.). So if zooming in wasn’t sufficient, then we’d need to think about how else to  address this.

@gsmile What’s your thoughts on the next steps? I’m not aware of any good options here based on current VS Code support. And so we may need to spec out what we want the experience to be and then see if we need to make any asks on the VS Code team.

Great question! I believe that to enable a better presentation experience, if we could adjust font size of the steps independently of the rest of the screen through the click of an icon (like a magnifying glass) that would be great!
Currently, here's an example of what zooming in would look like if I'm presenting code:

From a prioritization perspective, I could imagine that we consider presentation scenarios as P2 (for now), while we focus on training/tutorials/general-purpose code onboarding scenarios. What do you think? If so, we could make an ask on VS Code to allow more UI customization support, and then revisit this as part of a broader focus on improving presentations in the near future.

That sounds like a good plan! I'll change the prioritization. :)

Closing this as resolved!

@lostintangent Is this really resolved? There's a link from this issue to #104, which closes out with a link back to this issue - #111.
Yes, it looks like the only resolution to this issue is that it's a P2 label instead of P1. But what does that mean?


Ability to have personalized dashboards

Details
Describe the solution you'd like:
Each user should be able to have their own dashboard with apps they have access to. Authentik offers a very rudimentary version of this, but so does flame and that's prettier and similar to Hajimari as they're both based on SUI. You (myself, lol) may consider shifting to one of these or potentially looking at a more 'radical' shift to something like Homer or Forecastle
Anything else you would like to add:
Low maintenance preferred
Additional Information:
Don't jar users plz

May return to this at some point, but the need for this has disappeared


Runtime error

Launching pyppeteer
Trying to log in
Logged in
Uploading audio file
Waiting for upload to finish
Traceback (most recent call last):
  File "/home/thinkpad/.local/bin/tube-cast", line 8, in <module>
    sys.exit(cli())
  File "/home/thinkpad/.local/lib/python3.10/site-packages/click/core.py", line 1130, in __call__
    return self.main(*args, **kwargs)
  File "/home/thinkpad/.local/lib/python3.10/site-packages/click/core.py", line 1055, in main
    rv = self.invoke(ctx)
  File "/home/thinkpad/.local/lib/python3.10/site-packages/click/core.py", line 1657, in invoke
    return _process_result(sub_ctx.command.invoke(sub_ctx))
  File "/home/thinkpad/.local/lib/python3.10/site-packages/click/core.py", line 1404, in invoke
    return ctx.invoke(self.callback, **ctx.params)
  File "/home/thinkpad/.local/lib/python3.10/site-packages/click/core.py", line 760, in invoke
    return __callback(*args, **kwargs)
  File "/home/thinkpad/.local/lib/python3.10/site-packages/click/decorators.py", line 26, in new_func
    return f(get_current_context(), *args, **kwargs)
  File "/home/thinkpad/.local/lib/python3.10/site-packages/src/__main__.py", line 65, in youtube_id_from_file
    convert_youtube_to_podcast(ids, **ctx.obj)
  File "/home/thinkpad/.local/lib/python3.10/site-packages/src/youtube_to_anchorFM.py", line 38, in convert_youtube_to_podcast
    if _convert_youtube_to_podcast(id, **parameters):
  File "/home/thinkpad/.local/lib/python3.10/site-packages/src/youtube_to_anchorFM.py", line 176, in _convert_youtube_to_podcast
    asyncio.run(Lauch())
  File "/usr/lib64/python3.10/asyncio/runners.py", line 44, in run
    return loop.run_until_complete(main)
  File "/usr/lib64/python3.10/asyncio/base_events.py", line 646, in run_until_complete
    return future.result()
  File "/home/thinkpad/.local/lib/python3.10/site-packages/src/youtube_to_anchorFM.py", line 124, in Lauch
    await page.waitForXPath(
  File "/home/thinkpad/.local/lib/python3.10/site-packages/pyppeteer/frame_manager.py", line 855, in __await__
    raise result
pyppeteer.errors.TimeoutError: Waiting for XPath "//div[contains(text(),"Save")]/parent::button[not(boolean(@disabled))]" failed: timeout 300000ms exceeds.
Exception ignored in atexit callback: <function Launcher.launch.<locals>._close_process at 0x7fe3b9723130>
Traceback (most recent call last):
  File "/home/thinkpad/.local/lib/python3.10/site-packages/pyppeteer/launcher.py", line 153, in _close_process
    self._loop.run_until_complete(self.killChrome())
  File "/usr/lib64/python3.10/asyncio/base_events.py", line 621, in run_until_complete
    self._check_closed()
  File "/usr/lib64/python3.10/asyncio/base_events.py", line 515, in _check_closed
    raise RuntimeError('Event loop is closed')
RuntimeError: Event loop is closed
sys:1: RuntimeWarning: coroutine 'Launcher.killChrome' was never awaited
RuntimeWarning: Enable tracemalloc to get the object allocation traceback

It would be a good idea if the tool would catch the RuntimeError and retry for that video


Last half of 'github search for "libera.chat"' changes.

Removed duplicate RTL-SDR entry
Ran Freenode->Libera list through sort.

@crabbedhaloablution did you automate the sort somehow?


Assembly Guide Prototype 3

Do you have a video or a guide to assembly prototype 3? The videos on your channel show one of the older ones, and I printed the parts for the 3.1 and have no idea how to assembly it. Thanks in advance

I am going to be posting a video tutorial on youtube soon.

i need help with wiring, so I see that on the wiring demonstration it shows how to wire to the AO A1 and so on but the other 2 pins on the petameter I don't get how to wire those. btw im wiring it without a breadboard.

@CameronKlinge The other two pins correspond to GND and 5V. It needs to be split from the Arduino so that each potentiometer can get GND and 5V. (Or 3V3 if using an ESP32)
This guide may help:
https://www.arduino.cc/en/tutorial/potentiometer

@lucas-vrtech Ok that makes more sense thank you so much!

Going to close this now that the tutorial is out, but please do feel free to re-open if you need more clarification.

Thank you @danwillm , Awesome work


Fix output of freqasc, freqdesc, distinct_sort_num and distinct_sort_…

…num_desc operations.
Put delimiter after each element for freqasc, freqdesc,
distinct_sort_num and distinct_sort_num_desc operations
of bedtools groupby and bedtools map.
This fixes https://github.com/arq5x/bedtools2/issues/490:
groupby -o freqdesc does not put delimiters between value:freq pairs

AWESOME! Thank you for this fix!


Update stable version in repo from 2.1.0 to 2.1.1

Think about update stable version to 2.1.1
Version: stable=2.1.0 (74 days old) => latest=2.1.1 (15 days old)
Installs: stable=638 (53.79%), latest=155 (13.07%), total=1186
Click to use developer portal
Click to edit
Do not close this issue manually as a new issue will be created if condition for update still exists.
Please drop a comment if any reason exists which blocks updating to version 2.1.1 at this time.
Note: This is an automatically generated message and not personally authored by bluefox!
@mcm1957 for evidence

This issue seems to be outdated.
This issue suggests to update the stable version of this adapter to 2.1.1 but the current stable version is already 2.1.1.
So this issue should be closed.
@mcm1957 for evidence


Fill in asset ids for P-chain allocation transaction outputs

During block parsing we lookup assets using GetAssetDescription API if the asset id does not match AVAX asset id for the network. Asset ids for the mainnet genesis allocation transaction outputs were not properly set which results in incorrect lookups and request failures. This PR sets them.
Testing
Parsed Mainnet genesis successfully and ran testnet data validation.

Merged into https://github.com/ava-labs/avalanche-rosetta/pull/164, can be closed

Closing down as  https://github.com/ava-labs/avalanche-rosetta/pull/164 has been merged


Is there documentation on what the recommended methods are removed from 0.11?

I'm studying bevy_roguelike implementation of bevy and ascii terminal and I see it uses 0.11.
I see the latest version of bevy_ascii_terminal is 0.14.
Some methods in 0,11 appear not to be implemented in 0.14. draw_progress_bar for example, among many other changes.
Is there a new preferred way to handle these in 0.14 or is the recommendation to only use 0.11?
For the project, I don't think I need any 0.14 features necessarily, but prefer to use the latest if it makes any notable improvements over previous versions. Are there release notes anywhere for the various versions?
Thank you for this otherwise excellent plug-in. It has been a lot of fun to work with on this project.

There's no equivalent for the progress-bar functionality in the newer versions. I felt the "fancy" ui stuff like progress bars and buttons didn't quite fit the "simplicity" of the crate and would be better served in a separate crate at some point which I of course never got around to. With that being said it was all pretty simple so probably not too hard to just copy paste it if you decide to upgrade.
I don't think there's much of a reason for you to upgrade unless you need some newer bevy functionality. Newer versions of bevy_ascii_terminal have mostly just been straight ports to new bevy versions with some minor api changes and a lot of cutting stuff out to try and fit this "simple and easy to use" vision I have in my head.
I had a detailed changelog for a short time but I gave up on since I make so many breaking changes between minor version. I try to detail changes a bit more with some of my bigger git commits but even that is a bit spotty, sorry!

Thank you!


"ServiceContext in OperationContext does not match !" Intermittent Error in log

Description:
Getting the following error intermittently in the error log,
[2020-08-22 10:18:12,448] ERROR - NativeWorkerPool Uncaught exception
org.apache.axis2.engine.AxisError: ServiceContext in OperationContext does not match !
	at org.apache.axis2.context.MessageContext.setOperationContext(MessageContext.java:1404) ~[axis2_1.6.1.wso2v41.jar:?]
	at org.wso2.carbon.core.multitenancy.MultitenantMessageReceiver.processResponse(MultitenantMessageReceiver.java:156) ~[org.wso2.carbon.core_4.6.0.jar:?]
	at org.wso2.carbon.core.multitenancy.MultitenantMessageReceiver.receive(MultitenantMessageReceiver.java:91) ~[org.wso2.carbon.core_4.6.0.jar:?]
	at org.apache.synapse.transport.passthru.TargetErrorHandler$1.run(TargetErrorHandler.java:157) ~[synapse-nhttp-transport_2.1.7.wso2v183.jar:?]
	at org.apache.axis2.transport.base.threads.NativeWorkerPool$1.run(NativeWorkerPool.java:172) [axis2_1.6.1.wso2v41.jar:?]
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) [?:1.8.0_231]
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) [?:1.8.0_231]
	at java.lang.Thread.run(Thread.java:748) [?:1.8.0_231]

Steps to reproduce:
Tried in a multi-tenant setup, API-M 3.2 RC
Environment details (with versions):

OS: Linux 19.04, Java 1.8

Similar behavior was observed with the latest APIM 3.2.0 (U2 level 221) pack with 10KB of load. A simple load test with 2 concurrent users fails with the same error log for every request.


Rails - global configuration doesn't work

I try to use it with Rails.
Created config/initializers/biz.rb
Biz.configure do |config|
  config.hours = {
    mon: {'10:00' => '19:00'},
    tue: {'10:00' => '19:00'},
    wed: {'10:00' => '19:00'},
    thu: {'10:00' => '19:00'},
    sat: {'10:00' => '19:00'}
  }

  config.holidays =  [Date.new(2016, 1, 1), Date.new(2017, 1, 1)]

  config.time_zone = 'Europe/Kiev'

end

Biz::Schedule.new do |config|
  config.hours = {
    mon: {'10:00' => '19:00'},
    tue: {'10:00' => '19:00'},
    wed: {'10:00' => '19:00'},
    thu: {'10:00' => '19:00'},
    sat: {'10:00' => '19:00'}
  }

  config.holidays =  [Date.new(2016, 1, 1), Date.new(2017, 1, 1)]

  config.time_zone = 'Europe/Kiev'
end

In config/application.rb added
require 'biz'
require 'biz/core_ext'

But when try to use Biz in view I got exception: ActionView::Template::Error (Biz has not been configured)
Works only  if configure Biz in Controller method.
How to configure it to use with Rails?

Hey, @oslyak!
When configuring the top-level schedule, biz creates one schedule per thread.
Are you using a threaded server such as puma? If so, you will need to configure biz on a per-thread basis. If you want to continue with centralizing the configuration, check out on_worker_boot. Otherwise, it sounds like configuring it in the controller or a middleware is your best bet.
I'm working on a project called biz-rails to make it easier to work with biz in a Rails environment out of the box. You might want to keep an eye out for that.

@oslyak, I don't see anything actionable here on our part, so I'm going to close this for now. Please let me know if you continue to run into any issues with biz that you can't resolve.

@oslyak, one more suggestion: If you want global schedule shared across threads, you can always assign it to a global variable:
$biz = Biz::Schedule.new do |config|
  # ...
end

Thank You.
2015-03-27 21:35 GMT+02:00 Craig Little<EMAIL_ADDRESS>
@oslyak https://github.com/oslyak, one more suggestion: If you want
global schedule shared across threads, you can always assign it to a global
variable:
$biz = Biz::Schedule.new do |config|
...end
—
Reply to this email directly or view it on GitHub
https://github.com/zendesk/biz/issues/18#issuecomment-87067295.

--
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Осляк Антон. +38 050 562-87-78
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Sieve Registry load strange

Everytime i start a custom modpack. I get a different supply on sieveable things. Sometimes all THe things that schould e available are there sometime just dust is sievable and so on. I changed the config a bit. but im sure that i did it correct. Just strange that its sometimes correct and sometimes not

What things are inconsistent when registering? If its just the ores it's possible load order is affecting it. Ex Nihilo attempts to use the ore dict  to know if things like copper exist and that it should add the pieces/chunks.
Could you put your configs and a list of mods which give different sieve lists in a pastebin?

 Sometimes i can sieve verything sometimes just sand and gravel, sometimes just dust.

Are you sure you are not confusing the different mesh types? On some you
can only sieve specific stuff
Rams59<EMAIL_ADDRESS>schrieb am Do., 2.  Sometimes i can sieve verything sometimes just sand and gravel,
sometimes just dust.
—
You are receiving this because you are subscribed to this thread.
Reply to this email directly, view it on GitHub
https://github.com/BloodWorkXGaming/ExNihiloCreatio/issues/101#issuecomment-409963391,
or mute the thread
https://github.com/notifications/unsubscribe-auth/AHwpZmkmyIFCpHhVwO1GYMNyfGfpz78zks5uMxgrgaJpZM4Vctir
.

yeah im very sure. I have autosieves running with 2 differents meshes. THey run perfecty first, when i reload the pack suddendly they dont wokr because gravel doesn't even show up as sievable at all. Its about whats sievable not what i get out of the sieves

Okey, so check your json with this tool for errors: https://jsonlint.com/
The one you posted has one and that is most likely the reason why it isn't loading correctly when you reload the game.


Update snowflake-profile.md to add info for GCP and Azure-based Snowflake accounts

Adds info for GCP and Azure-based Snowflake accounts.
Description & motivation

To-do before merge

Pre-release docs
Is this change related to an unreleased version of dbt?

[ ] Yes: please

update the base branch to next
add Changelog components: <Changelog>[New/Changed] in v0.x.0</Changelog>
add links to the "New and changed documentation" section of the latest Migration Guide

[ ] No: please ensure the base branch is current
[ ] Unsure: we'll let you know!

Checklist
If you added new pages (delete if not applicable):

[ ] The page has been added to website/sidebars.js
[ ] The new page has a unique filename

If you removed existing pages (delete if not applicable):

[ ] The page has been removed from website/sidebars.js
[ ] An entry has been added to _redirects

@clausherther Could you include AWS? I assume there are folks also on an AWS hosted Snowflake instance.

@clausherther Could you include AWS? I assume there are folks also on an AWS hosted Snowflake instance.

Done!

@amychen1776 — sounds like you know a bit more about this than me! Are you good to review this one?

@clrcrl I'll let you decide where to best put this. Just wanted to get this going since I ran into the GCP issue.

Closing in favor fo #473


Allow fine-tuning of healthcheck settings (picking up #184)

This is a continuation of #184 since it seems abandoned
what

Allow Healthchecks fine-tuning of EBS health check settings. See https://docs.aws.amazon.com/elasticbeanstalk/latest/dg/command-options-general.html#command-options-general-environmentprocess
Works with classic application and network load balancers

why

Some applications return custom codes or require more aggressive healthcheck configuration (E.g. check every second for a 302 on a proxy app endpoint)
This adds 5 healthcheck related settings : healthcheck_interval healthcheck_timeout healthcheck_healthy_threshold_count
healthcheck_unhealthy_threshold_count healthcheck_httpcodes_to_match

references

https://docs.aws.amazon.com/elasticbeanstalk/latest/dg/command-options-general.html#command-options-general-environmentprocess

Hmm, I added cloudpossebot as an outside collaborator in my fork, but I don't know how to get the auto-formatter called again.


Add C type time_t

Add C type time_t.

time_t is not a primitive type. It is a typedef in ucrt\corecrt.h. It is also a typedef to __time64_t (which is a typedef to __int64) in both the 32 and 64bit UCRT, and I have no intention of supporting a 32-bit time_t (what MinGW does here is irrelevant as I don't support MinGW specific stuff).

ucrt/corecrt.h:
Line519-Line525: https://github.com/LuoZijun/WindowsSDK/blob/master/10.0.16299.0/ucrt/corecrt.h#L519-L525
um/IPTypes.h:
Line30: https://github.com/LuoZijun/WindowsSDK/blob/master/10.0.16299.0/um/IPTypes.h#L30
Line99: https://github.com/LuoZijun/WindowsSDK/blob/master/10.0.16299.0/um/IPTypes.h#L99
you can see, in file um/IPTypes.h , they include <time.h> , not um/IPTypes.h .
so in my case, how do i translate um/IPTypes.h:99 ?

Just because they include time.h to get the time_t definition, does not actually mean time_t was defined there. time.h includes other headers and somewhere along the way ucrt\corecrt.h gets included so it can define time_t.

okay, i have write like this:
pub type __time32_t = long;
pub type __time64_t = __int64;

#[cfg(target_pointer_width = "32")]
pub type time_t = __time32_t;
#[cfg(target_pointer_width = "64")]
pub type time_t = __time64_t;

But time_t is always 64-bit, even on 32bit Windows (unless you define _USE_32BIT_TIME_T but winapi intentionally does not support that).

Oh, got it .
i will do this on um/iptypes.rs.
type __time64_t = __int64;
type time_t = __time64_t;

thank you very much.


Hotstrings / canned responses / pasting predefined text snippets

Description of the new feature / enhancement
Option to enter shortcuts and replacements for that, eg
Shortcut:
br#
Result:
best regards
Alex
So whenever you enter "br#" in any application, it automatically replaces the text with the result.
Also great would be an option to not just enter text, but also rich text (like with formatting, links, etc).
Also an idea to send keystrokes in this process, like
Shortcut:
hap#
Result:
I am happy to meet you in front of the appartment.{Left 27}+{Right 11}
This in Autohotkey would eg highlight the part "in front of" so you can easily replace it just by continuing to type:

Scenario when this would be used?
This can be a daily timesaver. You can define eg shortcuts for

the current date (needs dynamic content)
the current time (needs dynamic content)
personal data you need to write a lot, like your

email, eg shortcut @@@ gets replaced with<EMAIL_ADDRESS>address block for message forms on the internet
bank details

canned responses outside your email program, like chats, crm software, etc
templates, like you want to always enter the same data (and modify it) in commit messages, online request forms eg default text in ebay, etc...
code templates without needing the IDE to support it, like wrapping something in a try/catch block,...

Supporting information
Autohotkey already has a feature like, that, but you script everything by hand, which makes it of course more versatile, but also more complex.
Ac'tivaid also has a module called HotStrings, which handles those replacements.

Duplicate of #5074

Issue is a duplicate
Thank you for your issue! We've linked your report against another issue to centralize the thread.
Thanks for helping us to make PowerToys a better piece of software.


Xcelium compile failure

After commit 23ad2e95333bad5f41476e89c557552003728d39 I am seeing an issue with the include_file macro during Xcelium (19.03.001) compile.
  `include_file(`RISCV_CORE_SETTING)
                                   |
xmvlog: *E,COFILX (./src/riscv_instr_pkg.sv,651|35): cannot open include file '``f``'.
(`define macro: include_file [./src/riscv_instr_pkg.sv line 25], file: ./src/riscv_instr_pkg.sv line 651)

The syntax in this include_file macro does not seem to be LRM compliant. I believe it should be:
  `define include_file(f) `include `"f`"

You could also do something like this, instead of the include_file macro:
 `define quoted(f) `"f`"
 `include `quoted(`RISCV_CORE_SETTING)

Thanks for reporting, fixed


ARROW-3490: [R] streaming of arrow objects to streams

This makes write_record_batch and write_table generic with dispatch on the stream type.
write_record_batch <- function(x, stream, ...){
  UseMethod("write_record_batch", stream)
}
write_table <- function(x, stream, ...) {
  UseMethod("write_table", stream)
}

The stream argument can be various things for different use cases:

an arrow::pic::RecordBatchWriter created either with record_batch_stream_writer() or record_batch_file_writer(). This is the lowest level and that calls its $WriteBatch() or $WriteTable() method depending on what is being streamed

an arrow::io::OutputStream : this first creates an arrow::ipc::RecordBatchStreamWriter and streams into it.
W  checking Rd \usage sections ...
   Undocumented arguments in documentation object 'record_batch_stream_writer'
     ‘schema’

   Functions with \usage entries need to have the appropriate \alias
   entries, and all their arguments documented.
   The \usage entries must correspond to syntactically valid R code.
   See chapter ‘Writing R documentation files’ in the ‘Writing R
   Extensions’ manual.
   S3 methods shown with full name in documentation object 'stream.arrow::RecordBatch':
     ‘stream.arrow::RecordBatch’

   S3 methods shown with full name in documentation object 'stream.arrow::Table':
     ‘stream.arrow::Table’

   The \usage entries for S3 methods should use the \method markup and not
   their full name.
   See chapter ‘Writing R documentation files’ in the ‘Writing R
   Extensions’ manual.

Not quite sure what the workaround is.

Independently of this, @javierluraschi I'd like your input on stream, as a replacement to $to_stream()

I'd have used serialize like in 🐍  but there's already base::serialize

Hm, I'm concerned the term "stream" might cause confusion with the other kinds of streams. If serialize is unavailable we could use some synonym

Maybe we can make serialize generic ?

Codecov Report

Merging #2749 into master will decrease coverage by 0.01%.
The diff coverage is n/a.

@@            Coverage Diff             @@
##           master    #2749      +/-   ##
==========================================
- Coverage   87.57%   87.56%   -0.02%
==========================================
  Files         403      403
  Lines       61483    61483
==========================================
- Hits        53845    53835      -10
- Misses       7568     7574       +6
- Partials       70       74       +4

Impacted Files
Coverage Δ

go/arrow/math/int64_avx2_amd64.go
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go/arrow/memory/memory_avx2_amd64.go
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go/arrow/math/uint64_avx2_amd64.go
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go/arrow/memory/memory_amd64.go
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... and 3 more

Continue to review full report at Codecov.

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Still interested in input about the functionality, and the choices of what underlying stream is used for each case.
And I guess we need a name for both ways, maybe repurpose the read_arrow / write_arrow couple we initially had.

The replacement of $to_stream() looks great; looks like I would use stream(df, raw()) to send to Spark and then df <- read_table(raw_stream) to get the data frame back from Spark. I'll give it a shot and close my PR once this is merged.
Regarding naming, I like stream() but not sure I understand which other streams Arrow provides that are not of RecordBatch type, but assuming there are other ones, we could consider renaming this to record_batch_stream().

@romainfrancois one more comment... related to https://github.com/apache/arrow/pull/2714/files#r224517253, and specifically:

Reading a single record batch alone given a known schema

I just found out a relevant use case to optimize in R/Spark, I still have work a few days implementing another piece of sparklyr, but this feature is definitely useful, I'll spare you the details unless you want them. Let me know if I misread the PR and is in fact already implemented here, otherwise, looking forward to this.
The way I would expect this to work, not sure if python is the same, would be something like:
schema <- read_schema(raw_schema)
df1 <- read_table(raw_batch_1, schema)
df2 <- read_table(raw_batch_2, schema)
df3 <- read_table(raw_batch_3, schema)

Maybe i’ll just roll back to write_table and write_record_batch and have them be s3 generics, i.e drop having a single double dispatch generic.

@javierluraschi you’re one step ahead again. I’ll do reading from various streams in the next pr.

I ⏪ to :

write_record_batch to stream arrow::RecordBatch
write_table to stream arrow::Table

The previous stream is now write_arrow which dispatch on the first argument with methods for:

arrow::RecordBatch -> write_record_batch
arrow::Table -> write_table
data.frame -> ~ write_record_batch(record_batch(.), ...)

Ready to be merged as far as I'm concerned

I have a few more commits, for implementing changes to read_table and read_record_batch
@wesm I can either merge them here or create another PR once this one is squashed ?

Plan to review this tomorrow (I'm on US/Eastern time, so feel free to add more commits if you are working earlier in the day)

Alright I folded in the other branches I had.

type promotion (e.g. promote an arrow::Array. of int16 to an R integer vector, ... There is no support for casting arrays yet, so this lacks testing.

read_record_batch and read_table from various streams

Similar dispatch rules for read_table, except that the lowest level methods use :

arrow::ipc::RecordBatchFileReader calls the Table__from_RecordBatchFileReader function, so that the loop to retrieved all the record batches is done on the C++ side.

arrow::ipc::RecordBatchStreamReader calls the Table__from_RecordBatchStreamReader function for the same reason.

The test covers various ways to read a record batch (same thing for table):
test_that("read_record_batch handles various streams (ARROW-3450, ARROW-3505)", {
  tbl <- tibble::tibble(
    int = 1:10, dbl = as.numeric(1:10),
    lgl = sample(c(TRUE, FALSE, NA), 10, replace = TRUE),
    chr = letters[1:10]
  )
  batch <- record_batch(tbl)
  tf <- local_tempfile()
  write_record_batch(batch, tf)

  bytes <- write_record_batch(batch, raw())
  buf_reader <- buffer_reader(bytes)

  batch1 <- read_record_batch(tf)
  batch2 <- read_record_batch(fs::path_abs(tf))

  readable_file <- close_on_exit(file_open(tf))
  batch3 <- read_record_batch(readable_file)

  mmap_file <- close_on_exit(mmap_open(tf))
  batch4 <- read_record_batch(mmap_file)

  batch5 <- read_record_batch(bytes)
  batch6 <- read_record_batch(buf_reader)

  stream_reader <- record_batch_stream_reader(bytes)
  batch7 <- read_record_batch(stream_reader)

  file_reader <- record_batch_file_reader(tf)
  batch8 <- read_record_batch(file_reader)

  expect_equal(batch, batch1)
  expect_equal(batch, batch2)
  expect_equal(batch, batch3)
  expect_equal(batch, batch4)
  expect_equal(batch, batch5)
  expect_equal(batch, batch6)
  expect_equal(batch, batch7)
  expect_equal(batch, batch8)
})

@wesm could we merge this one or are we missing anything? From my side, just moved the sparklyr PR (https://github.com/rstudio/sparklyr/pull/1611) to use this new interface.

I'm pretty waterlogged but reviewing this now


Using a delay vs botocore retrying

I noticed this library uses a sleep to avoid AWS throttling requests - is there a reason the built in exponential retry handler that ships with botocore didn't work?
Also, I was curious why you have to sign your own request to put the integration method in: https://github.com/Miserlou/Zappa/blob/master/zappa/zappa.py#L509

I may have discovered why it has the sleep - is there a timing issue between publishing and it actually appearing on the gateway?

Botocore retrying seems like a much, much better solution than sleep(). That was just a quick hack to fix this for a user: https://github.com/Miserlou/django-zappa/issues/4

And I believe that the integration had to be done "manually" because boto didn't support multiple content mapping templates, but don't quote me on that!

+1 on removing the delay -- the current setup adds 1 second for every endpoint, for every verb, which ends up adding 400+ seconds to a clean deploy in django-zappa.

I just got my custom API Gateway exporter/importer functioning and was able to export and import the full API deployment of one of my django apps:
$ time apigpen --export 'ms-test' > ms32.yaml
apigpen --export 'ms-test' > ms32.yaml  0.54s user 0.03s system 16% cpu 3.443 total

$ time apigpen --import 'test2' < ms32.yaml
zkkrdf3kw9
apigpen --import 'test2' < ms32.yaml  1.46s user 0.08s system 3% cpu 46.677 total

Importing at 46 seconds is not exactly blazing fast, but I didn't have to do any sleeping and didn't seem to need to sign any requests by hand (I just used the stock boto3 stuff). It should be possible to set up Zappa to create the API resources in a similar fashion. I'll take a look in the next few days!

Well, 46 seconds is an ~order of magnitude better than 400, so certainly an improvement there! And always nice to simplify code.
Looking forward to your analysis!

Hey @collingreen, I've got a branch set up to remove the sleeps and the manual request signing. You can try it out by installing the two branches (in this order):
pip install -U git+git://github.com/mathom/django-zappa.git@nosleep
pip install -U git+git://github.com/mathom/zappa.git@nosleep

Without the sleeping it takes about 40-50 seconds to setup the API. I've also added a progress bar but tqdm is kind of a pain to use the way I had to so I may drop that commit.

@mathom hey, nice work! I'll check this out tomorrow. Your approach is much better than the naive retrying I was shoehorning in.

Ooo, that looks nice.. looking forward to your merge, @mathom!

@mathom just ran through the whole thing and it worked like a charm - great work. I hope you can keep the progress bar -- that was on my list and, again, your implementation is much better than what I had started on.
:+1:

Awesome, if both of you guys are happy with the changes I'll push em to master. Let me know if you discover any issues with it!


Load the netcoreapp3.1 compiled task when running in Core MSBuild

Fixes https://github.com/aspnet/AspNetCore/issues/17308

Description:
The tasks assembly in the RazorSDK are cross-compiled between desktop and netcoreapp. The SDK is meant to load the appropriate task dll based on the flavor of MSBuild that's executing a build. However due to a bug, the 3.1 version of the Razor SDK always ends up loading the tasks dll compiled for net46. We haven't had any reports of this resulting in failures, but don't fully understand the ways in this may interact or fail with user's build systems.
Customer impact
None. However this does not preclude MSBuild failures depending on user's build setup.
Regression?
Yes. This is a regression introduced in the 3.1 SDK.
Risk
Low. Users do not directly interact with the tasks dll.

These were approved for 3.1.2, so updating the milestone accordingly /cc @jamshedd

Moved to Mar as per Tactics. Will need to be explicitly approved for Feb, if required.


Add instances of Eq[Error] and Show[Error] in Error companion object.

Current coverage is 78.70% (diff: 66.66%)

Merging #690 into master will decrease coverage by 0.05%

@@             master       #690   diff @@
==========================================
  Files            30         30
  Lines           631        634     +3
  Methods         608        613     +5
  Messages          0          0
  Branches         23         21     -2
==========================================
+ Hits            497        499     +2
- Misses          134        135     +1
  Partials          0          0

Powered by Codecov. Last update 25ac5e1...6f64e50

Hey @haribageski! This still has some commits from master. Could you rebase that and override you branch? Presumably, an easiest way to do that would be just drop the branch and start a new one given that the diff is just a couple of lines of code.

Let's try one more time.
Where shell I add the corresponding test? There is no more ErrorSpec.

Let's omit the test for now. It's fine.

Done.

Thanks @haribageski! I just merged that in. Sorry it took me so long.

Thanks @vkostyukov!
If you want me to write tests for the above let me know.


Fix minor typo

Many thanks for submitting your Pull Request :heart:!
Please specify parts this PR updates:

[x] Specification
[ ] Schema
[ ] Examples
[ ] Extensions
[ ] Roadmap
[ ] Use Cases
[ ] Community
[ ] TCK
[ ] Other

What this PR does / why we need it:
I think our reviewer's name was added in specification. :)
Special notes for reviewers:
Additional information (if needed):

Hello, I have a few PRs failed on DCO check. After running "git commit --amend --no-edit --signoff" suggested by DCO details, still didn't sign commit c137dcaa102b3409a0ee897034562a055587a412. :( .. Should I close this PR and start a new fresh one?

its fine dont worry about it now. maybe we can try again for future ones.
On Wed, May 26, 2021 at 4:36 PM yuri @.***> wrote:

Hello, I have a few PRs failed on DCO check. After running "git commit
--amend --no-edit --signoff" suggested by DCO details, still didn't sign
commit c137dca
https://github.com/serverlessworkflow/specification/commit/c137dcaa102b3409a0ee897034562a055587a412.
:( .. Should I close this PR and start a new fresh one?
—
You are receiving this because your review was requested.
Reply to this email directly, view it on GitHub
https://github.com/serverlessworkflow/specification/pull/392#issuecomment-849100168,
or unsubscribe
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.

…

@tsurdilo thx :)


[Bug] Prefix Config Option Broken [Details Provided] [Sponge 7.x]

Forge: forge-1.12.2-<IP_ADDRESS>38-universal
Sponge: spongeforge-1.12.2-2838-7.2.1
LuckPerms: LuckPerms-Sponge-5.1.17
LuckPerms config: https://gist.github.com/pinkpig3777/96ab67db14d3e2dbbb3822e84fdb3495
latest.log: https://gist.github.com/pinkpig3777/62fb57dff93e24f845b1d3e0e30e1d52 (Server crashed shortly after but that is unrelated to the problem, although if you do see why please LMK)
debug.log: https://gist.github.com/pinkpig3777/21c450fdd1b713f4bf445f852b1708c6
Issue:

The prefix the user has is not consistent with their actual rank.
I believe this is due to this section of the config being broken (probably suffixes too, I didn't test):
meta-formatting {
prefix {
format = [
"highest_on_track_builders"
"highest_on_track_staff"
"highest_on_track_donors"
]
Specifically the "highest_on_track_" feature (others may be too, not sure).

The Bug is best explained in an example:

Let's say my Staff track consists of "Mod", "Admin" and "Owner". And I am set to Mod rank. The LuckPerms config is set to show the "highest_on_track_staff". Thus I should have the [Mod] prefix because that is the highest rank I am added to on that track. However, what it actually does is show [Owner].

In the following picture you can see a similar situation, but the track consists of:
....., Admin, ....., Owner ranks. In the picture I am always Admin rank. The only thing that changes is at first it uses "lowest_on_track_staff" then I change it to "highest_on_track_staff" and reload the config.
https://gyazo.com/818d50e4fa55258c75ad62526d57acf0

Well, figured out the issue. My inheritance was messed up.


New Account + New Wallets Locked due to never setting an encrypt password.

Describe the bug
New accounts and wallets are locked out due to never setting an encrypt password, but the app demands an encrypt password. I never set one. I can't retrieve my 12 word recovery and have never seen it because I don't have the encrypt password. Request encrypt password is enabled by default, attempting to turn it off asks for the password.
To Reproduce
Steps to reproduce the behavior:

Sign up for a new account.
Attempt to send coins or make a payment.
The app will ask for an encrypt password
Due to not having an encrypt password, you are locked out of sending or making payments.

Expected behavior
Being able to set up an encrypt password or make a payment without one.
Screenshots

Smartphone (please complete the following information):

Device: Samsung Galaxy S20FE
OS: Android 12
App Version 12.11.7

Additional context
To make sure I never set one up, I signed up for a new account and set up wallets. I never set up an encrypt password. Even on the new account, the encrypt password was enabled by default.
I've seen this issue on google and github many times in similar fasion. It is usally someone who used the app prior to an update that required the encrypt password. This seems to be effecting all new accounts I am trying to make.
I'm scared to delete the app, because I sent about $400 USD worth of Eth to the wallet to buy something, then was unable to when it asked for the encrypt password. I can't obtain the recovery phrase and never have been due to the encrypt password - which I only became aware of when attempting to make a purchase.

Same thing happened to me but thanks to Blackhathacker111 who helped me message him on telegram his telegram username is Blackhathacker111

I would advise you if you not sure of you're doing is right don't do anything before you get help and study all protocols.......
friend


Support for app skeleton generator

Look into noe

@solnic @timriley can this be closed now?

Yeah, I'll close this now @AMHOL. What we have is certainly not perfect, but we can file other issues about improving it in particular ways.


"Can I run Windows Server containers on AKS?" FAQ is incorrect

Hi,
The FAQ item "Can I run Windows Server containers on AKS?" is incorrect.
With GA, it's possible to run mixed-mode clusters.
Can this documentation be corrected?
Thanks!

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

ID: d501ff67-9208-8bcb-e8df-f09e9b16a802
Version Independent ID: e3fc3851-ddc4-d26c-251d-6ae53d714081
Content: Frequently asked questions for Azure Kubernetes Service
Content Source: articles/aks/faq.md
Service: container-service
GitHub Login: @iainfoulds
Microsoft Alias: iainfou

Thanks for the feedback! I have assigned the issue to the content author to investigate further and update the document as appropriate.

Thanks for reaching out, @thelious1974. Sorry if there's some confusion running Windows containers within AKS. The FAQ entry is valid:

You cannot currently run mix-mode nodes within an AKS cluster. You can, however, run Windows containers using Virtual Kubelet to schedule them on Azure Container Instances - https://docs.microsoft.com/en-us/azure/aks/virtual-kubelet

Upcoming support for Windows containers in AKS without Virtual Kubelet / ACI is planned. This was announced at the Build conference in May. You can register for a private preview of Windows containers natively in AKS here - https://aka.ms/aks-windows-preview

I'll update the FAQ entry to point to the Virtual Kubelet doc as a way of being able to run Windows containers in AKS, which hopefully makes this clearer.
#in-progress

I think the confusion is around mixed-os clusters and mixed-os nodes
Windows Server nodes are definitely available on AKS - I created a cluster with both Windows and Linux nodes today!

@thelious1974 Can you confirm what you're running here - AKS, ACS with Kubernetes, or acs-engine?
The FAQ originally linked to this issue was for AKS. As noted previously, you can register for a private preview of Windows support here https://aka.ms/aks-windows-preview.
ACS with Kubernetes does support creating Windows nodes, as shown here https://docs.microsoft.com/en-us/azure/container-service/kubernetes/container-service-kubernetes-windows-walkthrough
acs-engine with Kubernetes for Windows nodes is also outlined here https://github.com/Azure/acs-engine/blob/master/docs/kubernetes/windows.md
As such, I'm not sure how you're running mixed Windows and Linux nodes in the same AKS cluster?

@iainfoulds The cluster was definitely managed kubernetes (aks) cluster. It was created was using an ARM template defining the resource of type Microsoft.ContainerService/managedClusters  (as documented https://docs.microsoft.com/en-us/azure/templates/microsoft.containerservice/managedclusters)
The ARM template defined two agent pools profiles - the first of type Linux, the second of type Windows.
After it was provisioned, I was able to use az aks get-credentials to get the credentials for the cluster and use kubectl describe nodes to get the node information.
In this,  one agent pool had an OS image of Windows Server Datacentre,  the  other had an OS image of Ubuntu 16.04.4 LTS.
Disclaimer: one of my former colleagues may have entered into the private preview!

@thelious1974 The template reference is actually incorrect and is being updated now, sorry. As AKS and ACS share some common definitions, it shows Windows as being a valid osType. While technically valid and deploys without error, it doesn't provide a functional deployment. The Windows nodes are created within the AKS cluster, but you likely cannot correctly schedule pods on them and network services together. Engineering work is ongoing for private preview of this in AKS.

@iainfoulds Thanks for the update,  that really does explain a lot!
Thanks for all your help and patience with this documentation issue!

@iainfoulds Any idea when we can expect the availability of Windows containers without Virtual Kubelet / ACI on AKS?

@CptQuint Windows host support is in progress, but I don't have a timeframe for full support in AKS. Can upvote the feature request at https://feedback.azure.com/forums/914020-azure-kubernetes-service-aks/suggestions/34973563-add-support-for-windows-nodes.
You can also register interest in a private preview of Windows host support in AKS.

@iainfoulds I did actually register for the private preview several months a go but never heard anything back.

@ai2admin The private preview form was designed to gauge interest and build a pipeline of interested customers. We will reach out once it is ready for you to try. In the interim, you can get started with Kubernetes on Windows today using acs-engine. This will allow you to validate your Windows containers on a K8s cluster running in Azure and make it straightforward to migrate them to AKS once the managed service supports it.
For the record, EKS does not support Windows agent nodes yet either.

Last I tried, I have not been able to even get started with ACS - it keeps failing the deployment for me every time I attempt to create one. It's an outdated option that can hardly justify additional time investment.
Surely EKS doesn't support Windows containers (though ECS does) - I was comparing two services with the same functionality.

To be clear, I am talking about acs-engine (the open source project), not the ACS service (I realize the naming is confusing). We are actively investing in the acs-engine project as it forms the basis for how we provision Kubernetes clusters in AKS. If you have issues deploying Windows-based agents with acs-engine, I'm sure @PatrickLang would love to hear about them.

@seanmck indeed I confused ACS service and the acs-engine. Thanks for the tip - I will give it a try!


Add support for DictionaryLiteral & DictionaryLiteralConvertible

→ https://twitter.com/rob_rix/status/662279930370260993

is this done?

No, this issue is not yet done.


training pipeline

For the first implementation if the training pipeline, we should just implement simple torchvision-based classification model. The input will be formatted based on specification from #3 , but in general, it will come as a JSON file of list of image metadata (guid, timestamp, total duration, label, subtype label, etc) and a numpy matrix of extracted features from images. The JSON list and the matrix should have 1-to-1 mapping.

Another feature we need for future experiments is to block some of GUIDs (passed as a separate argument to, say, train() calls) from being included in the training set. This will allow us to easily swap training sets from different source material (e.g. https://github.com/clamsproject/app-swt-detection/issues/2#issuecomment-1736319426)

opened a branch with the code for data ingestion - output seems good to me, but I'm new to VGG so I'll wait for a greenlight to run it on the data.


Support Bazel 0.18.0

This PR makes a few updates to support the latest bazel, including a
Tulsi bump.
ATM tulsi isn't working for Bazel 0.18.0 and we'll propose a PR to
update it with our changes.

Before update of Tulsi ref:

XCHammer: ** Completed read_aspects in 8.1192s

After update of Tulsi ref:

XCHammer: ** Completed read_aspects in 21.6097s

Actually those numbers were both somehow really high. Just ran again and got: 2.3928s / 5.2243s.


User's don't get widgets with external group changes.

If I alter a user's group membership outside of OWF (like LDAP), the membership change is detected and made by SecurityFilters. Meaning, if I go into the "Users" widget and look at the user's groups, they are correct, but the user does not get the widgets associated with a newly added group or lose them when a group is removed from the user.
This is because there are no person_widget_definition records created for the user.  If I manually remove any group through the OWF admin UI, and re-add it, the widgets show up.  I compared the person_widget_definition table before and after doing this, and the person_widget_definition records aren't there before, and they get added after I remove and re-add the group. Records seem to be added for all widgets, not just those associated with the group I modified.

It looks like the problem is generally related to the "sync" related changes, seen in the following commit:
https://stash.di2e.net/projects/OWFGOSS/repos/aml-owf/commits/546845b03a246551a0fddf6cde06d78ec93c5293#grails-app/controllers/ozone/owf/grails/controllers/ConfigController.groovy
Specifically, the user's groups are updated on login in SecurityFilters.  In 7.14.2, there used to be a call in ConfigController that caused the person_widget_definition records to be updated for the user, based on their updated group membership.  This functionality was moved.
The problem is that the call in ConfigController was never replaced. So even though SecurityFilters updates a user's group memberships, nothing syncs the person_widget_definition records after login. Manually adding or removing a group for the user causes a sync to happen, which "fixes" the problem.
In the commit linked below, I added a call to "AccountService.sync", in SecurityFilters, to force the person_widget_definition records to be synchronized, only if a user's group memberships have changed. This seems to fix the issue.
https://github.com/qotho/owf-framework/commit/8af7be42a5a7cb45f9f9e62a5f4340ededfe4b2e

This looks good, thanks for doing so much digging! If you want to submit a PR for your changes I'll test it out and merge it in.

I saw the instructions for contributing that you committed.  I don't have privileges to create a branch in this repository.  Do you want me to just do a PR from my fork?

Yeah, that’s fine. I’ll update the contributing guidelines too, I meant to say create a fork.
Stephanie

On Aug 25, 2015, at 2:16 PM, qotho<EMAIL_ADDRESS>wrote:
I saw the instructions for contributing that you committed. I don't have privileges to create a branch in this repository. Do you want me to just do a PR from my fork?
—
Reply to this email directly or view it on GitHub https://github.com/ozoneplatform/owf-framework/issues/57#issuecomment-134691254.


Issue using workbox with Firebase Cloud Messaging

Hi all,
I'm using firebase messaging service which has its service worker, and I use workbox.GenerateSW class to create my workbox service worker.
The problem is these two seems can't work together, as after any refresh my workbox service worker becomes "redundant".
I know this cause firebase sw, because whenever I don't register it workbox works perfect, as expected.
Does anyone have any idea what can I do in such situation?
P.S. I'm new in service workers world :)

Hello! 👋
I think your best bet is not to use GenerateSW but instead use InjectManifest, as this will give you more control over your service worker. You can then include the references to the Firebase Messaging service worker in your importScripts().
I don't have a project of my own that demonstrates this, but I did find, e.g., https://github.com/wreszelewski/firebase-messaging-example/blob/master/public/sw.js
Searching for "firebase messaging workbox" has a few other examples as well.
Hope that helps!

Thank you so much for you help!


1.7.0~rc1 kernel missing 9p patch

See: https://github.com/kata-containers/tests/issues/1561.

Seems like this is the root cause:
https://build.opensuse.org/package/rdiff/home:katacontainers:releases:x86_64:master/linux-container?linkrev=base&rev=34
@jcvenegas is helping to run a new build.

I sent a patch to fix the path to kernel patches. Now they should provide a kernel with patches, @chavafg I suggest wait 1/2 hrs while it is propagated the change in OBS mirrors.


X-Forwarded-Host is not passed onto the proxy

I'm using the latest version of this package node-proxy-middleware, proxing all requests with /api to a backend process. listening on another port. It requires the X-Forwarded-Host header.
Somehow this module is not passing on this header. It's missing on the other side.
Does this module copy all the headers?

never mind, all good and fixed on my side


Toggling comments in 'set' commands sections

🟢 Block comments work correctly, but toggling line comments, both Edit Menu & keyboard Ctrl + /  insert  a couple of  forward slashes; 🚩 for set commands # could be the consistent way for comments.

Thanks for pointing that out.
New version v0.2.1 has been published with the line comment fix.
If you still see any problem, please reopen this issue.

Excellent:
Just tested and works flawlessly
Great contribution!! @codeout


Support Dark theme based on the web browser's theme

Now the project doesn't switch to dark theme when the browser's theme is set to dark, we have to make it compatible for dark theme.
To better test this, use edge browser and switch the theme between light and dark.

@krishnaacharyaa
can you assign this issue to me?
I will use Context Api to toggle between Dark and light mode

Are you planning to use switch ? to toggle between dark and light ? If yes don't , we'll have it as another issue. And if you are using context api, kindly research on the best practice as to where the file should be placed in the existing file structure.

Sir in src I will make a folder context.
There I will make a file Theme.tsx.
Sir can u suggest should I us drop down list which contain light  dark and system for modes or I should use toggle or I should use button.
Plz suggest

Sir we need to update header which contain that component

Please carefully read the instruction and the reply, this story is not for having switch using toggle, this story is to support default dark theme based on the browser theme

sir we can use window.matchMedia it will detect browser theme it light or dark

sir I checked using mediaQuery we can change to dark mode. Based on system them because system and browser have same them.
Should I implement it

Thank you for your inputs and research @SankalpHaritash21, But can we try to do this in tailwind level ?
https://stackoverflow.com/questions/72649576/how-to-force-dark-mode-using-tailwindcss-on-browsers/72698408#72698408, kindly refer this and take a look at it.
So that we can reduce creating file and using the above said approaches

Sir I go through this approach but this approach is used  to manual switching.
This approach is given in Tailwind documentation I have read it properly.
Sir plz let me know which approach we need manual or automatic switching

Oh is it ? Let me do a research and get back to you, if we cannot do it in the tailwind-css level,
We'll do it in one of the best proposed approaches by you.
No @SankalpHaritash21, this issue is for automatic switching based on the browser theme.
I'll get back to you, after some research

@SankalpHaritash21, we just have to add dark: prefix
Refer this for more https://tailwindcss.com/docs/dark-mode
And just FYI I have tested in my machine

ok sir I will do. Plz update figma.

It might take me some time to update the figma file, I feel you can manage even without figma design, it should not take much efforts, some background changes at the top level should do is what i feel, and some button design, other than that it should be mostly simple, you can try and come up with a solution, if it requires major changes, i'll create figma design.

ok sir . I will try my best. Till then I will also try figma

sir I am facing merge conflict error. and sir I by mistake removed 2 branch while correcting it. plz help

I didn't get it, by mistake removed 2 branch meaning ?

Try to resolve the merge conflict, you will face because of the latest changes, when rebasing

@krishnaacharyaa  sir I am facing a issue that when we use declaring color as a variable in this if we don't use dark keword before them then the by default first color applyed is implemented and not able to add opacity also.
Should I Change this approach or stay with it.


Future request - categorise by PSP / PSX by original game ID

At the moment PSP games seem to be categorised by the pspemu prefix.
Categorising by the original game ID would allow for automatic cover matching and separate PSP and PSX categories.
Game title (ID) can be selected as an export option using Adrenaline Bubbles Manager.

Should be addressed in v0.4


(Suggestion) auto melee

make a option to make you automatically melee enemies, without holding aimbot button or having autoshoot on

use your brain and do it yourself.. (it's a few lines)

yeah as if everyone knows how to code, hes making a suggestion for a reason cause he DUZNT know how to do it himself


[9.x] TestResponse new assertion about rendered views

Currently, it's not possible to test if the response includes a specific view partial or not, as assertSee* is not the best fit for this case.
My Solution:

Listen to all composing events (which are dispatched during the view rendering)
Store the view names and count how often they were called

Possible problems:

false positives due to View usage during the response from pdf/mail generation

Alternativ solution:

use a rendering engine (during tests) to wrap blade directives and components with HTML comments and search for them in the actual rendered result

Hmm, not sure I want to go this route atm. 🤔