The repository countersignature's timestamp found a chain building issue: UntrustedRoot: self signed certificate in certificate chain

I get this error doing dotnet build on Blazor sample (most other samples also get this error):

error NU3028: Package 'Microsoft.Extensions.Logging.Console 5.0.0' from source 'https://api.nuget.org/v3/index.json': The author primary signature's timestamp found a chain building issue: UntrustedRoot: self signed certificate in certificate chain

dotnet --info:
.NET SDK (reflecting any global.json):
 Version:   5.0.201
 Commit:    a09bd5c86c

Runtime Environment:
 OS Name:     ubuntu
 OS Version:  20.10
 OS Platform: Linux
 RID:         ubuntu.20.10-x64
 Base Path:   /usr/share/dotnet/sdk/5.0.201/

Host (useful for support):
  Version: 5.0.4
  Commit:  f27d337295

.NET SDKs installed:
  3.1.407 [/usr/share/dotnet/sdk]
  5.0.201 [/usr/share/dotnet/sdk]

.NET runtimes installed:
  Microsoft.AspNetCore.App 3.1.13 [/usr/share/dotnet/shared/Microsoft.AspNetCore.App]
  Microsoft.AspNetCore.App 5.0.4 [/usr/share/dotnet/shared/Microsoft.AspNetCore.App]
  Microsoft.NETCore.App 3.1.13 [/usr/share/dotnet/shared/Microsoft.NETCore.App]
  Microsoft.NETCore.App 5.0.4 [/usr/share/dotnet/shared/Microsoft.NETCore.App]

That is quite strange. This is the only report we've seen of this. The problem appear to be with a Microsoft package "Microsoft.Extensions.Logging.Console 5.0.0", not one of our packages. Perhaps try re-installing the .NET 5 SDK? It may be how it was installed. We've see issues trying to install it from a package manager. Our Quick Start Guide mentions this under dependencies:
On Linux this cannot be installed through the Snap package manager and must be installed using the scripted install method described here.

That is quite strange. This is the only report we've seen of this. The problem appear to be with a Microsoft package "Microsoft.Extensions.Logging.Console 5.0.0", not one of our packages. Perhaps try re-installing the .NET 5 SDK? It may be how it was installed. We've see issues trying to install it from a package manager. Our Quick Start Guide mentions this under dependencies:
On Linux this cannot be installed through the Snap package manager and must be installed using the scripted install method described here.

Thanks, I will try reinstalling .Net 5 SDK.

Cleaning my system of all traces of .NET Core and installing v5.0 did not work. After quite a struggle since some of my other projects depend on v3.1, I reinstalled SDK 3.1 and 5.0 but the above error still persisted when trying to dotnet run the Photino.Blazor example. Then I found this... https://github.com/NuGet/Home/issues/10491#issuecomment-778841003 that did the trick. So apparently some certs in nuget.org are expired or what not. I am now able to dotnet run the Photino.Blazor example although it gives an error (File "app://app/" could not be found.) that is mentioned in another issue I will look at.


System-wide nvchad for 2.5

In previous 2.0 I could install nvchad systemwide. to benefit of shared initial configs, mappings... for many users.   But I am having troubles with 2.5. Maybe I am lazy, but do you have any idea about how can I get it working again.
I get:
Error detected while processing /etc/xdg/nvim/sysinit.vim[2]../etc/xdg/nvim/NvChad_starter/init.lua:
Failed to run `config` for NvChad

/etc/xdg/nvim/NvChad_starter/init.lua:24: module 'options' not found:
^Ino field package.preload['options']
cache_loader: module options not found
cache_loader_lib: module options not found
^Ino file './options.lua'
^Ino file '/usr/share/luajit-2.1.0-beta3/options.lua'
^Ino file '/usr/local/share/lua/5.1/options.lua'
^Ino file '/usr/local/share/lua/5.1/options/init.lua'
^Ino file '/usr/share/lua/5.1/options.lua'
^Ino file '/usr/share/lua/5.1/options/init.lua'
^Ino file './options.so'
^Ino file '/usr/local/lib/lua/5.1/options.so'
^Ino file '/usr/lib/x86_64-linux-gnu/lua/5.1/options.so'
^Ino file '/usr/local/lib/lua/5.1/loadall.so'

the tree of /etc/xdg/nvim is:
.
├── NvChad_starter
│   ├── LICENSE
│   ├── init.lua
│   └── lua
│       ├── README.md
│       ├── chadrc.lua
│       ├── configs
│       │   ├── conform.lua
│       │   ├── lazy.lua
│       │   ├── lspconfig.lua
│       │   ├── null-ls.lua
│       │   └── overrides.lua
│       ├── highlights.lua
│       ├── mappings.lua
│       ├── myinit.lua
│       ├── options.lua
│       └── plugins
│           ├── init.lua
│           └── myplugins.lua
├── README.md
└── sysinit.vim

sysinit.vim is:
set runtimepath+=/etc/xdg/nvim/NvChad_starter
source /etc/xdg/nvim/NvChad_starter/init.lua

Adding in lua/configs/lazy.lua:
  performance = {
    rtp = {
+      reset = false,
      disabled_plugins = {

is a workaround, but I see
Re-sourcing your config is not supported with lazy.nvim
Error detected while processing /etc/xdg/nvim/sysinit.vim[2]../etc/xdg/nvim/NvChad_starter/init.lua:
Invalid spec module: `plugins`
Expected a `table` of specs, but a `boolean` was returned instead

But it seems it works

no idea about this,  do other nvim configs work?

Yes, By now everything works but some part(s?) of the theme, e.g. the toggle:
Error detected while processing function TbToggle_theme:
line    1:
E5108: Error executing lua cannot open /root/.config/nvim/lua/chadrc.lua: No such file or directory
stack traceback:
        [C]: in function 'dofile'
        /root/.local/share/nvim/lazy/base46/lua/base46/init.lua:184: in function 'toggle_theme'
        [string ":lua"]:1: in main chunk
Press ENTER or type command to continue

chadrc is a must for using nvchad

Yes,yes, I know it, and in fact is used /etc/xdg/nvim/NvChad_starter/lua/chadrc.lua , but it seems that settings are only read just the first time, as it lets me set the theme for example.
But afterward, /etc/xdg/nvim/NvChad_starter/lua/chadrc.lua is not used anymore. So I cannot change the theme, or use theme_toggle.

Just to note, that adding /root/.config/nvim/lua/chadrc.lua makes it working as it should. But it will be great to let define it system-wide and read the settings from   /etc/xdg/nvim/NvChad_starter/lua/chadrc.lua . Maybe too adapted to my use case...

/etc/xdg/nvim/lua/chadrc.lua
that should be the real path, the starter repo is basically ~/.config/nvim i.e a nvim  config

/etc/xdg/nvim/lua/chadrc.lua
that should be the real path, the starter repo is basically ~/.config/nvim i.e a nvim config

Despite this in /etc/xdg/nvim/sysinit.vim :
set runtimepath+=/etc/xdg/nvim/NvChad_starter
source /etc/xdg/nvim/NvChad_starter/init.lua

i honestly have no idea about this xdg stuff, you should ask in our discord server, someone might help!

It seems that some other people are having issues with lazy and global config:https://github.com/folke/lazy.nvim/issues/636 . My last major issue was the python provider (I need this for plugin vdebug) that only runs ok the first time.  So I think that the issue comes from lazy, and so is not in your field.
Anyway I've switched to nvim-dap and everything runs fine.
Thank's !

for python provider check https://github.com/NvChad/NvChad/issues/1417

for python provider check NvChad/NvChad#1417

These lines are there, and checkhealth whows everything fine. In fact, as said before, it works fine the first time that nvim is run (when no ~/.config/nvim ~/.cache/nvim or ~/.local/share/nvim exists yet).


Garbage byte when decompressing files

Tested versions
Reproducible in: 4.1.2, 4.1.4, 4.2.2, 4.3rc1, master
System information
Any
Issue description
If the size of the original data for compression is a multiple of 4096, then when reading the data, an extra random byte appears at the end. The decompression code does not take into account that the last block may be of size zero.
Steps to reproduce
	var file := FileAccess.open_compressed("test.bin", FileAccess.WRITE)
	var data := PackedByteArray()
	var size := 4 * 1024
	data.resize(size)
	file.store_buffer(data)
	file.close()

	file = FileAccess.open_compressed("test.bin", FileAccess.READ)
	data = file.get_buffer(size * 2)
	assert(data.size() == size)

Minimal reproduction project (MRP)
N/A

Note that this does not happen if you try to read the actual number of bytes with data = file.get_buffer(size), only when you try to read more than the data in the file


Update branch r0.11-flink1.13

Problem description
Need to cherry-pick some of changes in #610 into branch r0.11-flink1.13.
Problem location
Suggestions for an improvement

Closed via #612


Add ethereum call from

Gives a mapping sender of the EthereumCall it is triggering on.

@leodasvacas This is a breaking change due to the memory organization of structs in AssemblyScript, right? So we'll have to version the EthereumCall struct, similar to how we do it here:
https://github.com/graphprotocol/graph-node/blob/9c4cd1d6e5ab5f52df6a3eff1c3d19a43cf7d8fb/runtime/wasm/src/module/mod.rs#L245-L274

@Jannis ahh, that makes sense and is unfortunate

Yep, that will be necessary.


chore(Topology/Order): move&generalize 4 lemmas

Generalize lemmas from CompleteLinearOrder to
ConditionallyCompleteLattice or ConditionallyCompleteLinearOrder.

It looks like I've changed implicitness of some args. I'm going to fix it later today.

Thanks!
bors d+

bors merge


Adds PURL copy button.

closes #1034
Why was this change made?
Per ticket.
How was this change tested?
Local
Which documentation and/or configurations were updated?
NA


Enable multiple gcs config

I want to use multiple gcs configs, but now only one.
This PR enable to inherit this class and pass parameter when we want to use multiple gcs configs.
Thanks!


loading workflow_ui.json

When trying to load the architecture in comfyui facing this error:

you need to install custom_node.json in default comfyui.


We lost you for a second. Try reloading the page or go

Hi,
When i join a room i sometimes get this error message for no reason with my local network.
I always test in the same place. I got this message this morning from 10:40 am to 10:45 am.
No change source code or something else.
Cordialy

Thank you for the information, we checked our monitoring and did not see any interruptions. Do you still have any troubles using our services?

No problems today.
The problem appear only 3 days ago


Leetcode Question Number 1758

Leetcode Question Number 1758 solution needs to be added in the repo .
The link for the question is this.
Accepted Languages C++ / JAVA .
You Only need to add the  class Solution in the file.
Make Sure you have read the contributions guidelines before creating an PR.

I want to work on this issue.


if centos7  can do this?

My system is centos7.  And I had installed and meet this error.
<EMAIL_ADDRESS>load mod_audio_stream
+OK Reloading XML
-ERR [module load file routine returned an error]
2023-08-09 16:32:26.795988 98.90% [INFO] switch_stun.c:896 External ip address detected using STUN: <IP_ADDRESS>
2023-08-09 16:32:27.039581 98.90% [INFO] switch_stun.c:896 External ip address detected using STUN: <IP_ADDRESS>
2023-08-09 16:32:27.218248 98.90% [INFO] mod_enum.c:884 ENUM Reloaded
2023-08-09 16:32:27.218248 98.90% [CRIT] switch_loadable_module.c:1754 Error Loading module /usr/local/freeswitch/lib/freeswitch/mod/mod_audio_stream.so
/usr/local/freeswitch/lib/freeswitch/mod/mod_audio_stream.so: undefined symbol: is_valid_utf8
2023-08-09 16:32:27.218248 98.90% [INFO] switch_time.c:1433 Timezone reloaded 1750 definitions
<EMAIL_ADDRESS>I don't know how to solve. Any suggestion is useful .Thank you.

Is your FS built and installed from source? If so, please take a look at this
No sure what exactly caused the issue, but the path from loading module looks weird
Error Loading module /usr/local/freeswitch/lib/freeswitch/mod/mod_audio_stream.so
If FreeSWITCH is at custom location like /usr/local/freeswitch modules should be in /usr/local/freeswitch/mod directory. Please check what pkg-config --libs freeswitch returns. If not in path, export PKG_CONFIG_PATH and rebuild the module (make sure to init the submodule after cloning the repo). I will update the main readme file for users with FS at custom location.

My installation method is as follows
Add mod name in /usr/local/src/freeswitch/modules.conf
Add mod folder in /usr/local/src/freeswitch/src/mod
cd /usr/local/src/freeswitch  execute make make install
sorry it's been a long time to reply. I followed the steps above to installed another mod.

Following this method enabled me to successfully install.Thank u.
https://github.com/amigniter/mod_audio_stream/issues/2
I meet a new error and change a little code to successfully install.
audio_streamer_glue.cpp strncpy error
extern "C" {
int validate_ws_uri(const char* url, char wsUri) {
// Regular expression to match a WebSocket URL
std::regex regex("(wss?|ws)://[a-zA-Z0-9]+([\-\.]{1}[a-zA-Z0-9]+)(:[0-9]+)?(/.*)?");
    if(std::regex_match(url, regex)) {
        strncpy(wsUri, url, MAX_WS_URI);
        return 1;
    }
    return 0;
}


Fix openfile doc

Add some doc in user guide for JMX MBean Monitor.

please re-submit the PR to the master branch


feat(router): add external authentication flow

Type of Change

[ ] Bugfix
[x] New feature
[ ] Enhancement
[ ] Refactoring
[ ] Dependency updates
[ ] Documentation
[ ] CI/CD

Description
add external authentication flow
For a quick understanding of 3DS, please read this blog:
Refer this blog for 3D Secure explained in detail.
Hyperswitch currently uses the "Processor driven approach" for 3DS and is now looking to move towards "Third Party 3DS provider approach" - Using a specialized 3DS provider for checking card enrollment and 3DS authentication
Eg: Cardinal, Netcetra
The 3DS authentication can be done once and the authentication response that we get can be sent to any processor to authorize the payment.
There are few benefits to it:

The authentication failure reasons and authorization failures can be tracked separately.
Authorization with any processor adds the orchestration capabilities at the authorization layer, giving more granular control to the merchant. Better UX handling of failures can be done.
Cost benefits

Flow:

In payments create, merchant sends request_external_three_ds_authentication as true to indicate the payment has to go through external authentication
In payments confirm, we check if payment is requested for external_three_ds_authentication, then we check if payment connector selected allows external authenticaton. If these are true, then we do pre-auth (versioning) call with authentication_connector to check if card is enrolled for 3ds2. If everything is true, then we proceed with external authentication, else we fallback to connector authentication.
If it's external authentication, next_action would have three_ds_invoke with relevant three_ds_method_data required for 3ds data collection
SDK does three_ds_data_submission using next_action block
SDK does authentication call to backend, to initiate authentication step where we call authentication_connector to trigger the authentication.
Authentication response can be challenge / frictionless. If it's challenge, we return back challenge params with status as the response. Else we return status alone
Based, on authentication response SDK constructs the challenge. Challenge is submitted by the user, Then it redirects to payments_authorize_url where we do the post-auth (retrieve) call with authentication_connector to get status and authentication details
Using authentication details, we do the authorization with payment connector and redirect to merchant's return_url with payment status

Flow Diagram

API Contract and Schema changes - https://docs.google.com/document/d/1SOG1KxUZjFYJ1zWUShD9wi1ZhiS_lZulh1dLHvCe1zg/edit?usp=sharing
Additional Changes

[x] This PR modifies the API contract
[x] This PR modifies the database schema
[ ] This PR modifies application configuration/environment variables

How did you test it?

Test case sheet - https://docs.google.com/spreadsheets/d/1Z8GPfQQZuCwEbwzgXwjBY1rUSrzV8iIY4LdhBucAl5E/edit?usp=sharing
Checklist

[x] I formatted the code cargo +nightly fmt --all
[x] I addressed lints thrown by cargo clippy
[x] I reviewed the submitted code
[ ] I added unit tests for my changes where possible
[ ] I added a CHANGELOG entry if applicable

Closing in favour of https://github.com/juspay/hyperswitch/pull/4015


Catch and report bad request change inputs

It is possible for malformed change requests to come in, so we will catch those situations and report them as errors instead of allowing the node to crash.

Does this address https://github.com/open-rmf/rmf_ros2/issues/99?


[Request] New Plot Type

First of all, thank you for the web toolkit. This is very helpful!
May I ask to add an additional plot type selection?
It is a mixture of the plot types "Preset 3" and "Preset 1".
Particularly, I would like to have the percentile value of preset 3 and the spider matrix of preset 1.
If this is not possible in a short
Thank you very much!

Hi @sraedler ,
We are happy if it helps, and thank you for the feedback/suggestion!
This is certainly a good suggestion, and we are planning to provide more freedom to the user in the single-study dashboard (e.g. choosing which combination of visualizations the user wants to use, so you could decide yourself). But as I am currently in the writing phase of my PhD thesis and @jan-behrends just started a new job, a new release will probably be worked on toward the end of this year.
In the meantime, as a workaround: Maybe you can export the chart in both versions and then overlay them with Paint or another photo editor?
Hope that helps!

This feature request would be covered through #12


Saksham LittleFS library upgrade

The new version of LittleFS contains updated code about superblocks.
I have also made the changes to the includes from #include "LittleFS/lfs.h" to #include "lfs.h" since we are using the quotation marks, and the files requesting these libraries are in the same folder as the libraries.

Looks good to me. Can you please update the PR title to include the LittleFS release version number and git commit hash that these came from, so that they're in the squashed commit?

I updated the original comment and the PR Title, not sure if I want to include all the git hashes from the release to the title.

Just the latest hash in the title is perfect! The hash points to a specific state. The changelog is also valuable, but probably doesn't need to be tracked in here.
Feel free to squash and merge!


scRNA_parse: main.c:242: parse_options: Assertion `n1==n2 && n1>0' failed.

I  process the raw data and meet this problem

Multiple fastq.gz are separated by comma, example A1_oligo_R1.gz,A2_oligo_R1.gz. There cannot be a space after a comma.

Multiple fastq.gz are separated by comma, example A1_oligo_R1.gz,A2_oligo_R1.gz. There cannot be a space after a comma.

I process the raw data and meet this problem


Model and data downloading

Hi I am trying this guide https://github.com/microsoft/DeepSpeedExamples/tree/master/applications/DeepSpeed-Chat.
I have set up the environment but I didn't find in the document what models and datasets I need to download and where to store them. The connection is slow and storage is limited so I wanna prepare everything in advance. So, how can I make the script to load models from local path?????
I have prepared opt-1.3b and opt-350m and Dahoas dataset, and they are under the project directory.
DeepSpeedExamples/applications/DeepSpeed-Chat# ls Dahoas facebook
Dahoas:
rm-static

facebook:
opt-1.3b  opt-350m

but when I ran python train.py --actor-model facebook/opt-1.3b --reward-model facebook/opt-350m --deployment-type single_gpu --step 2 3 the script is still trying to download it .. and returned connection error...
[2023-05-26 10:47:55,167] [WARNING] [runner.py:191:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only.
[2023-05-26 10:47:55,197] [INFO] [runner.py:541:main] cmd = /opt/conda/bin/python -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMF19 --master_addr=<IP_ADDRESS> --master_port=29500 --enable_each_rank_log=None main.py --model_name_or_path facebook/opt-350m --num_padding_at_beginning 1 --weight_decay 0.1 --disable_dropout --gradient_accumulation_steps 4 --zero_stage 0 --deepspeed --output_dir /workspace/DeepSpeedExamples/applications/DeepSpeed-Chat/output/reward-models/350m
[2023-05-26 10:47:56,698] [INFO] [launch.py:222:main] 0 NV_LIBNCCL_DEV_PACKAGE=libnccl-dev=2.9.9-1+cuda11.3
[2023-05-26 10:47:56,698] [INFO] [launch.py:222:main] 0 NCCL_VERSION=2.9.9-1
[2023-05-26 10:47:56,698] [INFO] [launch.py:222:main] 0 NV_LIBNCCL_PACKAGE_VERSION=2.9.9-1
[2023-05-26 10:47:56,698] [INFO] [launch.py:222:main] 0 NV_LIBNCCL_PACKAGE=libnccl2=2.9.9-1+cuda11.3
[2023-05-26 10:47:56,698] [INFO] [launch.py:222:main] 0 NV_LIBNCCL_DEV_PACKAGE_NAME=libnccl-dev
[2023-05-26 10:47:56,698] [INFO] [launch.py:222:main] 0 NV_LIBNCCL_PACKAGE_NAME=libnccl2
[2023-05-26 10:47:56,698] [INFO] [launch.py:222:main] 0 NV_LIBNCCL_DEV_PACKAGE_VERSION=2.9.9-1
[2023-05-26 10:47:56,698] [INFO] [launch.py:229:main] WORLD INFO DICT: {'localhost': [0]}
[2023-05-26 10:47:56,698] [INFO] [launch.py:235:main] nnodes=1, num_local_procs=1, node_rank=0
[2023-05-26 10:47:56,698] [INFO] [launch.py:246:main] global_rank_mapping=defaultdict(<class 'list'>, {'localhost': [0]})
[2023-05-26 10:47:56,698] [INFO] [launch.py:247:main] dist_world_size=1
[2023-05-26 10:47:56,698] [INFO] [launch.py:249:main] Setting CUDA_VISIBLE_DEVICES=0
[2023-05-26 10:47:58,770] [INFO] [comm.py:622:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl
Traceback (most recent call last):
  File "/opt/conda/lib/python3.7/site-packages/huggingface_hub/utils/_errors.py", line 259, in hf_raise_for_status
    response.raise_for_status()
  File "/opt/conda/lib/python3.7/site-packages/requests/models.py", line 960, in raise_for_status
    raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 401 Client Error: Unauthorized for url: https://huggingface.co/opt-350m/resolve/main/config.json

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/opt/conda/lib/python3.7/site-packages/transformers/utils/hub.py", line 429, in cached_file
    local_files_only=local_files_only,
  File "/opt/conda/lib/python3.7/site-packages/huggingface_hub/utils/_validators.py", line 120, in _inner_fn
    return fn(*args, **kwargs)
  File "/opt/conda/lib/python3.7/site-packages/huggingface_hub/file_download.py", line 1199, in hf_hub_download
    timeout=etag_timeout,
  File "/opt/conda/lib/python3.7/site-packages/huggingface_hub/utils/_validators.py", line 120, in _inner_fn
    return fn(*args, **kwargs)
  File "/opt/conda/lib/python3.7/site-packages/huggingface_hub/file_download.py", line 1541, in get_hf_file_metadata
    hf_raise_for_status(r)
  File "/opt/conda/lib/python3.7/site-packages/huggingface_hub/utils/_errors.py", line 291, in hf_raise_for_status
    raise RepositoryNotFoundError(message, response) from e
huggingface_hub.utils._errors.RepositoryNotFoundError: 401 Client Error. (Request ID: Root=1-64708e60-3fe7220d286716304dafaa08)

Repository Not Found for url: https://huggingface.co/opt-350m/resolve/main/config.json.
Please make sure you specified the correct `repo_id` and `repo_type`.
If you are trying to access a private or gated repo, make sure you are authenticated.
Invalid username or password.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "main.py", line 352, in <module>
    main()
  File "main.py", line 204, in main
    tokenizer = load_hf_tokenizer(args.model_name_or_path, fast_tokenizer=True)
  File "/workspace/DeepSpeedExamples/applications/DeepSpeed-Chat/training/utils/utils.py", line 53, in load_hf_tokenizer
    fast_tokenizer=True)
  File "/opt/conda/lib/python3.7/site-packages/transformers/models/auto/tokenization_auto.py", line 659, in from_pretrained
    pretrained_model_name_or_path, trust_remote_code=trust_remote_code, **kwargs
  File "/opt/conda/lib/python3.7/site-packages/transformers/models/auto/configuration_auto.py", line 928, in from_pretrained
    config_dict, unused_kwargs = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
  File "/opt/conda/lib/python3.7/site-packages/transformers/configuration_utils.py", line 574, in get_config_dict
    config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs)
  File "/opt/conda/lib/python3.7/site-packages/transformers/configuration_utils.py", line 641, in _get_config_dict
    _commit_hash=commit_hash,
  File "/opt/conda/lib/python3.7/site-packages/transformers/utils/hub.py", line 434, in cached_file
    f"{path_or_repo_id} is not a local folder and is not a valid model identifier "
OSError: opt-350m is not a local folder and is not a valid model identifier listed on 'https://huggingface.co/models'
If this is a private repository, make sure to pass a token having permission to this repo with `use_auth_token` or log in with `huggingface-cli login` and pass `use_auth_token=True`.
[2023-05-26 10:48:01,710] [INFO] [launch.py:428:sigkill_handler] Killing subprocess 334
[2023-05-26 10:48:01,711] [ERROR] [launch.py:434:sigkill_handler] ['/opt/conda/bin/python', '-u', 'main.py', '--local_rank=0', '--model_name_or_path', 'facebook/opt-350m', '--num_padding_at_beginning', '1', '--weight_decay', '0.1', '--disable_dropout', '--gradient_accumulation_steps', '4', '--zero_stage', '0', '--deepspeed', '--output_dir', '/workspace/DeepSpeedExamples/applications/DeepSpeed-Chat/output/reward-models/350m'] exits with return code = 1

It also happened when I ran step 1 earlier... but later it somehow became fine and succesfully proceeded... but now with step 2 and 3 the same issue happened again...
May I know how to get around with it?

Maybe you can try the scripts on google Colab , because its a little  troublesome to download models from huggingface .
Besides, you can use .model.save_pretraiend('your_local_path') to save a model from huggingface.
example get the model from hf
checkpoint = "bigcode/starcoder"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint)
save locally
model.save_pretrained("your_local_path")
#reload the saved model
re_model = AutoModelForCausalLM.from_pretrained("your_local_path")
reference:
https://stackoverflow.com/questions/72108945/saving-finetuned-model-locally
https://huggingface.co/docs/transformers/main_classes/model

这个网址是无效的：https://huggingface.co/opt-350m/resolve/main/config.json.
要将facebook/opt-350m/config.json文件做如下修改：
"_name_or_path": "opt-350m",
改为
"_name_or_path": "facebook/opt-350m"
因为现在模型id不完整


add font-awesome and links to twitter and meetup with icons to footer

Implements https://github.com/M0nica/React-Ladies/issues/5

Thank you so much for this contribution @ankkukku!


tpiu: bitfield ACPR, verify set_swo_baud_rate args and ops

Draft improvement that closes #388.
At this point we might as well implement a TPIU::configure akin to {DWT,ITM}::configure. Other register functions will have to be reviewed to see if any other errors can be caught. We should also check swo_supports to see if a non-zero prescaler is supported at all.

r? @thalesfragoso
(rust-highfive has picked a reviewer for you, use r? to override)


CUDA out of memory during training with A100 GPUs

I am trying to finetune AF-multimer and I start with a mock dataset of just 3 proteins. I get the following error:
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1.95 GiB. GPU 0 has a total capacty of 39.39 GiB of which 1.90 GiB is free. Including non-PyTorch memory, this process has 37.48 GiB memory in use. Of the allocated memory 35.28 GiB is allocated by PyTorch, and 1.44 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
Here is my full error::
[rank: 0] Seed set to 77843
WARNING:root:load from versionmodel_1_multimer_v3
GPU available: True (cuda), used: True
TPU available: False, using: 0 TPU cores
HPU available: False, using: 0 HPUs
You are using a CUDA device ('NVIDIA A100-PCIE-40GB') that has Tensor Cores. To properly utilize them, you should set torch.set_float32_matmul_precision('medium' | 'high') which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision
[rank: 0] Seed set to 77843
initializing deepspeed distributed: GLOBAL_RANK: 0, MEMBER: 1/1
/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/callbacks/model_checkpoint.py:652: Checkpoint directory /work/10110/abhinav22/ls6/openfold_test/training/output_dir/checkpoints exists and is not empty.
Enabling DeepSpeed BF16. Model parameters and inputs will be cast to bfloat16.
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2]
| Name  | Type          | Params | Mode
0 | model | AlphaFold     | 93.2 M | train
1 | loss  | AlphaFoldLoss | 0      | train
93.2 M    Trainable params
0         Non-trainable params
93.2 M    Total params
372.895   Total estimated model params size (MB)
SLURM auto-requeueing enabled. Setting signal handlers.
/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/core/module.py:518: You called self.log('train/distogram', ..., logger=True) but have no logger configured. You can enable one by doing Trainer(logger=ALogger(...))
/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/core/module.py:518: You called self.log('train/distogram_epoch', ..., logger=True) but have no logger configured. You can enable one by doing Trainer(logger=ALogger(...))
/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/core/module.py:518: You called self.log('train/experimentally_resolved', ..., logger=True) but have no logger configured. You can enable one by doing Trainer(logger=ALogger(...))
/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/core/module.py:518: You called self.log('train/experimentally_resolved_epoch', ..., logger=True) but have no logger configured. You can enable one by doing Trainer(logger=ALogger(...))
/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/core/module.py:518: You called self.log('train/fape', ..., logger=True) but have no logger configured. You can enable one by doing Trainer(logger=ALogger(...))
/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/core/module.py:518: You called self.log('train/fape_epoch', ..., logger=True) but have no logger configured. You can enable one by doing Trainer(logger=ALogger(...))
/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/core/module.py:518: You called self.log('train/plddt_loss', ..., logger=True) but have no logger configured. You can enable one by doing Trainer(logger=ALogger(...))
/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/core/module.py:518: You called self.log('train/plddt_loss_epoch', ..., logger=True) but have no logger configured. You can enable one by doing Trainer(logger=ALogger(...))
/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/core/module.py:518: You called self.log('train/masked_msa', ..., logger=True) but have no logger configured. You can enable one by doing Trainer(logger=ALogger(...))
/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/core/module.py:518: You called self.log('train/masked_msa_epoch', ..., logger=True) but have no logger configured. You can enable one by doing Trainer(logger=ALogger(...))
/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/core/module.py:518: You called self.log('train/supervised_chi', ..., logger=True) but have no logger configured. You can enable one by doing Trainer(logger=ALogger(...))
/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/core/module.py:518: You called self.log('train/supervised_chi_epoch', ..., logger=True) but have no logger configured. You can enable one by doing Trainer(logger=ALogger(...))
/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/core/module.py:518: You called self.log('train/violation', ..., logger=True) but have no logger configured. You can enable one by doing Trainer(logger=ALogger(...))
/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/core/module.py:518: You called self.log('train/violation_epoch', ..., logger=True) but have no logger configured. You can enable one by doing Trainer(logger=ALogger(...))
/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/core/module.py:518: You called self.log('train/tm', ..., logger=True) but have no logger configured. You can enable one by doing Trainer(logger=ALogger(...))
/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/core/module.py:518: You called self.log('train/tm_epoch', ..., logger=True) but have no logger configured. You can enable one by doing Trainer(logger=ALogger(...))
/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/core/module.py:518: You called self.log('train/chain_center_of_mass', ..., logger=True) but have no logger configured. You can enable one by doing Trainer(logger=ALogger(...))
/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/core/module.py:518: You called self.log('train/chain_center_of_mass_epoch', ..., logger=True) but have no logger configured. You can enable one by doing Trainer(logger=ALogger(...))
/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/core/module.py:518: You called self.log('train/unscaled_loss', ..., logger=True) but have no logger configured. You can enable one by doing Trainer(logger=ALogger(...))
/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/core/module.py:518: You called self.log('train/unscaled_loss_epoch', ..., logger=True) but have no logger configured. You can enable one by doing Trainer(logger=ALogger(...))
/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/core/module.py:518: You called self.log('train/loss', ..., logger=True) but have no logger configured. You can enable one by doing Trainer(logger=ALogger(...))
/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/core/module.py:518: You called self.log('train/loss_epoch', ..., logger=True) but have no logger configured. You can enable one by doing Trainer(logger=ALogger(...))
/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/core/module.py:518: You called self.log('train/lddt_ca', ..., logger=True) but have no logger configured. You can enable one by doing Trainer(logger=ALogger(...))
/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/core/module.py:518: You called self.log('train/drmsd_ca', ..., logger=True) but have no logger configured. You can enable one by doing Trainer(logger=ALogger(...))
Traceback (most recent call last):
File "/work/10110/abhinav22/ls6/openfold/train_openfold.py", line 706, in
main(args)
File "/work/10110/abhinav22/ls6/openfold/train_openfold.py", line 455, in main
trainer.fit(
File "/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 543, in fit
call._call_and_handle_interrupt(
File "/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 43, in _call_and_handle_interrupt
return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
File "/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 105, in launch
return function(*args, **kwargs)
File "/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 579, in _fit_impl
self._run(model, ckpt_path=ckpt_path)
File "/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 986, in _run
results = self._run_stage()
File "/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1030, in _run_stage
self.fit_loop.run()
File "/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 205, in run
self.advance()
File "/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 363, in advance
self.epoch_loop.run(self._data_fetcher)
File "/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 140, in run
self.advance(data_fetcher)
File "/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 250, in advance
batch_output = self.automatic_optimization.run(trainer.optimizers[0], batch_idx, kwargs)
File "/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 190, in run
self._optimizer_step(batch_idx, closure)
File "/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 268, in _optimizer_step
call._call_lightning_module_hook(
File "/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 159, in _call_lightning_module_hook
output = fn(*args, **kwargs)
File "/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/core/module.py", line 1308, in optimizer_step
optimizer.step(closure=optimizer_closure)
File "/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/core/optimizer.py", line 153, in step
step_output = self._strategy.optimizer_step(self._optimizer, closure, **kwargs)
File "/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/strategies/ddp.py", line 270, in optimizer_step
optimizer_output = super().optimizer_step(optimizer, closure, model, **kwargs)
File "/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/strategies/strategy.py", line 238, in optimizer_step
return self.precision_plugin.optimizer_step(optimizer, model=model, closure=closure, **kwargs)
File "/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/deepspeed.py", line 129, in optimizer_step
closure_result = closure()
File "/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 144, in call
self._result = self.closure(*args, **kwargs)
File "/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 138, in closure
self._backward_fn(step_output.closure_loss)
File "/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 239, in backward_fn
call._call_strategy_hook(self.trainer, "backward", loss, optimizer)
File "/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 311, in _call_strategy_hook
output = fn(*args, **kwargs)
File "/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/strategies/strategy.py", line 212, in backward
self.precision_plugin.backward(closure_loss, self.lightning_module, optimizer, *args, **kwargs)
File "/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/deepspeed.py", line 117, in backward
deepspeed_engine.backward(tensor, *args, **kwargs)
File "/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/deepspeed/utils/nvtx.py", line 15, in wrapped_fn
ret_val = func(*args, **kwargs)
File "/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/deepspeed/runtime/engine.py", line 1944, in backward
self.optimizer.backward(loss, retain_graph=retain_graph)
File "/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/deepspeed/runtime/zero/stage_1_and_2.py", line 2019, in backward
self.loss_scaler.backward(loss.float(), retain_graph=retain_graph)
File "/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/deepspeed/runtime/fp16/loss_scaler.py", line 63, in backward
scaled_loss.backward(retain_graph=retain_graph)
File "/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/torch/_tensor.py", line 492, in backward
torch.autograd.backward(
File "/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/torch/autograd/init.py", line 251, in backward
Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
File "/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/torch/autograd/function.py", line 288, in apply
return user_fn(self, *args)
File "/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/deepspeed/runtime/activation_checkpointing/checkpointing.py", line 691, in backward
torch.autograd.backward(output_tensors, grad_tensors)
File "/work/10110/abhinav22/ls6/src/miniforge/envs/openfold_cuda12/lib/python3.10/site-packages/torch/autograd/init.py", line 251, in backward
Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1.95 GiB. GPU 0 has a total capacty of 39.39 GiB of which 1.90 GiB is free. Including non-PyTorch memory, this process has 37.48 GiB memory in use. Of the allocated memory 35.28 GiB is allocated by PyTorch, and 1.44 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
The following are my input parameters:
export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:256
DATA_DIR=/work/10110/abhinav22/ls6/openfold_test/training
TEMPLATE_MMCIF_DIR=/work/10110/abhinav22/ls6/database_2024_07_06/pdb_mmcif
CHECKPOINT_PATH=openfold/resources/params/params_model_1_multimer_v3.npz
CACHE_DIR=$DATA_DIR/cache_dir
python3 train_openfold.py $DATA_DIR/mmcif_dir/train $DATA_DIR/alignments_dir/train $TEMPLATE_MMCIF_DIR/mmcif_files $DATA_DIR/output_dir 2018-04-30
--config_preset model_1_multimer_v3
--template_release_dates_cache_path $CACHE_DIR/mmcif_cache.json
--seed 77843
--obsolete_pdbs_file_path $TEMPLATE_MMCIF_DIR/obsolete.dat
--num_nodes 1
--resume_from_jax_params $CHECKPOINT_PATH
--resume_model_weights_only False
--train_mmcif_data_cache_path $CACHE_DIR/train_mmcif_data_cache.json
--val_mmcif_data_cache_path $CACHE_DIR/val_mmcif_data_cache.json
--val_data_dir $DATA_DIR/mmcif_dir/val
--val_alignment_dir $DATA_DIR/alignments_dir/val
--gpus 3
--train_epoch_len 5
--max_epochs 1
--checkpoint_every_epoch
--precision bf16-mixed
--deepspeed_config_path ./deepspeed_config.json
However, this training runs on H100 GPUs, but only a problem with 40GB A100s. Is there a workaround for this?

hi
what is your config["evoformer_stack"]['blocks_per_ckpt'] ?, should be 1

Yes, it is one. I have not changed the config file. Here is what it says:
if train:
    c.globals.blocks_per_ckpt = 1

"evoformer_stack": {
"c_m": c_m,
"c_z": c_z,
"c_hidden_msa_att": 32,
"c_hidden_opm": 32,
"c_hidden_mul": 128,
"c_hidden_pair_att": 32,
"c_s": c_s,
"no_heads_msa": 8,
"no_heads_pair": 4,
"no_blocks": 48,
"transition_n": 4,
"msa_dropout": 0.15,
"pair_dropout": 0.25,
"no_column_attention": False,
"opm_first": False,
"fuse_projection_weights": False,
"blocks_per_ckpt": blocks_per_ckpt,
"clear_cache_between_blocks": False,
"tune_chunk_size": tune_chunk_size,
"inf": 1e9,
"eps": eps,  # 1e-10,

I was able to get it running in A100s. I realised that the num of recycles are set to 20 for multimer training and has to be corrected with an experiment_config_file with the following. I also changed the train_crop_size to 384, just like the one in multimer_v2.2 training. I have also tried clearing cache between blocks in extra msa stack and evoformer. While clearing cache in evoformer increases training time significantly, the extra msa stack works fine with negligible increase in time required.  Now it runs (haven't formally trained anything but can run about 20 epochs without crashing):
{
"data.common.max_recycling_iters": 3,
"data.train.crop_size": 384,
"model.recycle_early_stop_tolerance":-1
}


Schools lose students' work due to Chromebook policies

Expected Behavior
It should be easier to save/export projects to a shared location when a school's policy dictates that user data is cleared when they log off. In general it might be better to give people a way to configure how projects are saved.
Actual Behavior
ScratchJr saves the project data in the app database. When schools have shared chromebooks, they are often set up to clear the student's data, including all apps, when the student logs off. In ScratchJr this means they lose all projects they've saved. In the current app, they would need to share the project by email, then save it to a place that is not purged, e.g., their Google Drive. Due to the Apple/Google requirements, the share by email is behind a parental gate (math problems), so it makes it harder for children in school to use.
We have also heard that some people have problems opening .sjr files that are saved on drive. The chromebook doesn't recognize the extension as being associated with ScratchJr, so it's not possible to open. See https://github.com/LLK/scratchjr/issues/258 for a way to work around that.
Steps to Reproduce
Example report in FreshDesk - sorry only ScratchTeam will be able to see.
Device and Operating System
ChromeOS

Make an epic and leave it for now.

JR-252


[API] Activity API

Hi,
Is there an API for activity/dashboard view?
I also noticed that there is private variable used in api/v1/user/repos API for private/public access type. In java it is reserved keyword and cannot be used. Is there a way around it?. I need it for showing lock icon.
Thanks

@mmarif4u Looks like this issue is for a long time. Have you resolve the problem yourself? I'm sorry I have no android phone so I cannot test the great APP.

@lunny unfortunately no, we are still waiting for an API to utilize it.

And think it more, since it's a restful API, the android has to check it every internal. It will waste much electricity. Maybe a websocket implementation or a blocked HTTP request is better.

Well that depends, we don't call an API unless you are in the active view that way we reduce the calls. User has the option to refresh it through pull.

@Zettat123 thanks for the work on this.

Well that depends, we don't call an API unless you are in the active view that way we reduce the calls. User has the option to refresh it through pull.

OK. So I think #23494 may fix this issue.


Ability to run additional programs

Make it possible to select the launch of the main program.
And for example, upon successful launch, launch an additional one.
If it's rude like a system of triggers.
For example, the Conan Exiles server works fine for me through your software, but when I restart, I first need to run the server / mod update check, and when the check program is closed (steamcmd.exe), start the server itself.
And after starting the server, run my exe that throws a webhook into the discord.

try bat/cmd file(Windows) or sh file(Unix)

This is of course yes, but I just thought it might be convenient for many, for example, to prescribe what trigger to expect in the console output to launch the subprogram.


Add current URL to error report

Adding the current page URL to the error report along with the existing captured image ("canopy-image(iVBORw...)" would add more value to logged information.
needs to be added to this line: consoleReporter.fail ex id ss

I will add Url, Time information (how long a test took), and stacktrace to the HTML reporter.

How does this look to you?

And because I realized that I am not sure if you are talking about the console out put or the html out put, here it is in the console

Thank you. Are you going to support the new Selenium release 2.46.0 ?

BTW, I don't see the changes published to NuGet or GITHUB  :^)

I didn't make the changes yet, I was making sure this was addressing your problem =).
And yes I can update to latest Selenium too.

Yes, this is what I was looking for. Thanks or your prompt actions.

Released now, enjoy!

I broke the reporter link with 0.9.27 and I am fixing it now.  I have removed that listing from nuget and am working on a fix now.

Fixed in 0.9.28 and published.  Sorry!


Library needs to be updated for multiDrag

I'm hoping to use the SortableJS multiDrag feature that's in the next-version branch. When I use that version of SortableJS with this adapter, I get an error saying "Uncaught ReferenceError: Sortable is not defined: at MultiDrag.drop". Am I right in my suspicion that this library just needs a small update to add this support? Or is this an issue with SortableJS?

Did you end up getting MultiDrag to work or are we still waiting on this? Was hoping to use MultiDrag in my current project

I'm not sure. Does it needs to be mounted using Sortable.mount([plugins])?. I'm doing the rewrite for this repo in Typescript and it's nearing completion, so once that is done I can look into this.

Any progress on this? My team need this, really looking forward to it.

Any progress on this?

Any estimation for when will this be done?
Pretty please with cherry on top

Any progress? I really need this for a project. Thanks.

@SunHuawei  @LucianHij @RyanEdwardHall @ronanmorris  Here we are: https://github.com/SortableJS/react-sortablejs/issues/83
I'm not sure what implications multidrag has on the DOM, but atleast we can load it in as a prop and check.

Hey! If this is done, could you please share a small coding sample with how it is used, if i do not ask for too much? Thanks!


Getting error: TypeError: HD.addons.register is not a function

Angular project running 17.1.3. Storybook 8.0.9 and storybook-addon-angular-router 1.10.0
A simple component which requires RouterLink injection. The component story works but I get this non blocking error in the console.

+1

"@angular/core": "^15.2.9"
"@storybook/angular": "^8.1.4"
"storybook-addon-angular-router": "^1.10.0"

I'm having the exact same error, "HD.addons.register is not a function".

Having the same issue with angular 18.0.4 and storybook 8.1.11


clickhouse-client (1.1.54370) running in screen hangs on pretty output at Debian8

Debian GNU/Linux 8 (jessie)
$ screen -r
$ clickhouse-client -h chdw1-1.sde316.mycmdb.net
ClickHouse client version 1.1.54370.
Connecting to chdw1-1.sde316.mycmdb.net:9000.
Connected to ClickHouse server version 1.1.54371.
:) select 1;
SELECT 1
��1�
^D^D[screen is terminating]
No problem without screen, or Ubuntu 16.04 with and without screen.
Do you have any ideas what debian package need to update?

No screen:
echo "select 1 as col FORMAT Pretty"|clickhouse-client
┏━━━━━┓
┃ col ┃
┡━━━━━┩
│   1 │
└─────┘

With screen
echo "select 1 as col FORMAT Pretty"|clickhouse-client
�������
� col �
�������
�   1 �
�������

Seems it's a known screen's issue.


Question: ... Is it possible put archivebox in background and close terminal ?

im tring to launch  archivebox in background on my server usinge this commands :
sudo -u archivebox archivebox server <IP_ADDRESS>:8000 &
disown {{job_id}}
still see output of web server in console like "GET /admin/core/snapshot/ HTTP/1.1" 200 105763"
And if i try to close terminal webserver close as well.

You should use an init system like systemd or supervisord to start it. Another alternative is to use a terminal session manager like tmux or screen. As a last resort, if you really must background it (which I don't recommend as it's generally harder to manage long-term), you can use nohup archivebox ... &.

thx, already solved problem with screen.

I ran into 1 more problem, the app will consume all available RAM (6 GB) and slow down to 1-2 links per hour. basically all the memory is occupied by chromium instances. Is it possible to solve this somehow ?

That issue should be solved soon, plz follow this or for updates https://github.com/ArchiveBox/ArchiveBox/pull/1219

add this service to systemd service, and
sudo sytemctl enable archivebox-server.service

archivebox-server.service:
[Unit]
Description=Archivebox
#After=

[Service]
WorkingDirectory=/**path to where archivebox is init-ed**
ExecStart=/**path to archive box binary**/archivebox server
User=<**username**>

WatchdogSec=4000s
WatchdogSignal=SIGKILL

Restart=always
RestartSec=5

NoNewPrivileges=yes

PrivateDevices=yes

ProtectControlGroups=yes

ProtectKernelModules=yes

ProtectKernelTunables=yes

ProtectHostname=yes

RestrictRealtime=yes

ProtectKernelLogs=yes

ProtectClock=yes

SystemCallArchitectures=native

LockPersonality=yes

[Install]
WantedBy=multi-user.target


Lack of java compatibility with M1 / Rosetta 2

Detected macOS version later than 10.15 Catalina, calling java in a hacky way (might not work)
2023-10-12 16:08:02.814 java[14029:2022719] JavaNativeFoundation: GetGlobalVM: Failed to locate @rpath/libjvm.dylib for JNI_GetCreatedJavaVMs(). A JVM must be loaded before calling this function.
2023-10-12 16:08:02.814 java[14029:2022719] Apple AWT: +[ThreadUtilities getJNIEnvUncached] attempting to attach current thread after JNFObtainEnv() failed
Cultris II 1.4c Community Patch 04/12/2019, Network 29
./run.sh: line 12: 14029 Segmentation fault: 11  /Library/Java/JavaVirtualMachines/1.6.0.jdk/Contents/Home/bin/java -Djava.library.path="$DIR/libs/" -jar "$DIR/cultris2.jar"

:(


Upgrade Angular version in pdx-admin-ui

As a developer, I want to have an angular version up to date, that allows me to make use of improved functionalities and that it's easier to maintain.
The current versions for Angular and Angular CLI in the project are:
Angular CLI: 8.3.29
Angular: 8.2.14
And the latest versions available are:
Angular CLI: 11.0.1
Angular: 11.0.0
This information can be retrieved with the command:
ng --version

still blocked, moving to the next sprint

@zperova done

thanks, well done @mauroz77


#660 add single dose function

Closes #660

Hey @thomas-neitmann and @kabis-ops,
The function currently only works for Days and Weeks in a certain format. I looked into making it more robust but there were lots of possible formats for EXDOSFRQ (like "10 DAYS PER MONTH" or "THRICE"), so I thought it best to keep it simpler for now.

Please review @kabis-ops and @konstantinakoukR.

How large a lookup table do we want to use for this, and where are lookup tables generally stored in the package architecture?
The lookup table I could make from the list i found at https://evs.nci.nih.gov/ftp1/CDISC/SDTM/CDASH Terminology.pdf is relatively short, such that even the case of the example in the issue thread is not covered.

@amsmith214 , I think you opened the wrong file. It should be https://evs.nci.nih.gov/ftp1/CDISC/SDTM/SDTM Terminology.pdf. If you search for "C71113", you'll find all frequencies.
If you create the look-up table in open code instead of within a function, it is added to the package workspace when loading the package. See tte_source.R for and example.

For frequencies like "6 TIMES EVERY MONTH", shall I just assume for the default lookup that doses occur at the closest approximation to "evenly spaced over the given interval" as I can get? E.g., assuming a month is 30 days, doses would happen every 5 days.

@amsmith214 , yes, I think that makes sense. I just wonder which length of a month we should use. At Roche a month has 30.4375 days and a year has 365.25 days. What is used at GSK?

@amsmith214 , yes, I think that makes sense. I just wonder which length of a month we should use. At Roche a month has 30.4375 days and a year has 365.25 days. What is used at GSK?

I actually don't know. I will code using those numbers currently and try to find out what's used here. In previous CRO work I know we used 365.25 for a year, which would indeed imply months have 30.4375 days for these purposes.

i still have to add ADEX vignette language around this function but i've got it passing everything but 3.5 (seems like period objects were not supported?)


remove ': () :' from input prompts

Pull Request Test Coverage Report for Build<PHONE_NUMBER>

0 of 4 (0.0%)  changed or added relevant lines in 1 file are covered.
No unchanged relevant lines lost coverage.
Overall coverage remained the same at 19.195%

Changes Missing Coverage
Covered Lines
Changed/Added Lines
%

pkg/app/templates_apply.go
0
4
0.0%

Totals

Change from base Build<PHONE_NUMBER>:
0.0%

Covered Lines:
615

Relevant Lines:
3204

💛  - Coveralls


log information about SSLFactory renewal

Add logs to changes made in https://github.com/apache/pinot/pull/12277 and https://github.com/apache/pinot/pull/12325 to make it easy to track operations and debug problems

Codecov Report
All modified and coverable lines are covered by tests :white_check_mark:

Comparison is base (ab525ee) 61.72% compared to head (303fd5c) 34.85%.

Additional details and impacted files
@@              Coverage Diff              @@
##             master   #12357       +/-   ##
=============================================
- Coverage     61.72%   34.85%   -26.87%
+ Complexity      207        6      -201
=============================================
  Files          2426     2350       -76
  Lines        132667   128926     -3741
  Branches      20499    19941      -558
=============================================
- Hits          81888    44940    -36948
- Misses        44774    80770    +35996
+ Partials       6005     3216     -2789

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java-11
0.00% <0.00%> (-61.67%)
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java-21
34.85% <100.00%> (-26.73%)
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skip-bytebuffers-false
34.83% <100.00%> (-26.88%)
:arrow_down:

skip-bytebuffers-true
34.83% <100.00%> (-26.71%)
:arrow_down:

temurin
34.85% <100.00%> (-26.87%)
:arrow_down:

unittests
46.73% <100.00%> (-14.99%)
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optimized spatialization

This refactors the spatialization code so that it is as vectorized as I could make it. I also added some indexing that reduced zero computations in the matrix mults.
To run the tests which currently compare end-to-end audio equality, do: pytest tests
you can listen to them here: tests/output/generation
Changes:

spatialize function

improve vectorization, interpretability, and reduce array copies
move to spatialize.py
encapsulate scipy.signal.convolve for single IRs
accept irs=(n_ch, n_irs, n_ir_samples) instead of irs=(n_ir_samples, n_ch, n_irs)
change argument name s -> snr
outputs identical to outputs from before changes

core.py

change spatialize import to spatialize.py
add progress bar to synthesize_events_and_labels
single IR handling removed and moved to spatialize

utils.py

added set_seed for test reproducibility
spatialize can be deleted **

tests (I considered making this a separate PR since this is just general regression testing)

test_generate.py: basic end-to-end generation tests. compares generated audio with trusted reference outputs
data/*: reference wav and csv files using original spatialize function

tests/misc: test utils

profile_generation.py: basically example_generation.py but with profiling
profile_spatialize.py: specifically profile spatialize over different input parameters
compare_audio.py: diff spectrograms of two audio files

Before merging, we can remove the following functions from utils.py:

spatialize
_spatialize
stft_ham
ctf_ltv_direct

Since there are test audio files in the commit history, make sure to squash the commits when merging.
Benchmarking
Here's timings when generating 15 60s soundscapes with moving sources and a fixed random seed.
script: tests/misc/profile_spatialize.py (to use the old spatialization, comment out from .spatialize import spatialize from core.py)
Improvement: 610s (40.6s per audio file) -> 134s (8.9s per audio file).
Before:

After:

I isolated the spatialize function and systematically tested it using all random noise:
sr = 24000, ir_samples = 7200

This is awesome @beasteers! Implementation wise things look in great shape to me. Do you need help performing validation? I believe generating test sets and bench-marking with a pre-trained SELDnet would be a good sanity check. I see you also added some functional tests.

Yeah @adrianSRoman if there are any other tests you'd like to run (e.g. validating with SELDnet), I'd love the help as I'm not familiar with the pipeline atm.
Also, if you could just reproduce the tests (pytest tests) on your end that would be great! soundata threw some zip errors when I was downloading the datasets so I don't fully trust my download.
If the tests fail (because there's something wrong with my datasets), you can try regenerating the test files with the old spatialization code by commenting out from .spatialize import spatialize in core.py and rerunning pytest tests .
Then once the old spatialization files are generated, make those the new reference files
rm -r tests/data/generation
mv tests/output/generation tests/data/generation

and finally, uncomment from .spatialize import spatialize and rerun pytest tests. If they are different, then perhaps you could commit your corrected versions. Though I'm thinking that perhaps the better solution is to not make the tests dependent on dataset files (e.g. using artificial sources/IRs).
The functional test is based off how scaper does their tests, but lmk if you have other ideas.


[Bug] datax doriswriter write failed

Search before asking

[X] I had searched in the issues and found no similar issues.

Version
master
What's Wrong?
2022-06-27 16:06:42.980 [0-0-0-writer] ERROR WriterRunner - Writer Runner Received Exceptions:
com.alibaba.datax.common.exception.DataXException: Code:[DBUtilErrorCode-05], Description:[往您配置的写入表中写入数据时失败.].  - org.apache.http.client.ClientProtocolException
	at org.apache.http.impl.client.InternalHttpClient.doExecute(InternalHttpClient.java:186)
	at org.apache.http.impl.client.CloseableHttpClient.execute(CloseableHttpClient.java:82)
	at org.apache.http.impl.client.CloseableHttpClient.execute(CloseableHttpClient.java:107)
	at com.alibaba.datax.plugin.writer.doriswriter.DorisWriterEmitter.doHttpPut(DorisWriterEmitter.java:210)
	at com.alibaba.datax.plugin.writer.doriswriter.DorisWriterEmitter.doStreamLoad(DorisWriterEmitter.java:105)
	at com.alibaba.datax.plugin.writer.doriswriter.DorisWriter$Task.flush(DorisWriter.java:108)
	at com.alibaba.datax.plugin.writer.doriswriter.DorisWriter$Task.startWrite(DorisWriter.java:101)
	at com.alibaba.datax.core.taskgroup.runner.WriterRunner.run(WriterRunner.java:56)
	at java.lang.Thread.run(Thread.java:748)
Caused by: org.apache.http.ProtocolException: Content-Length header already present
	at org.apache.http.protocol.RequestContent.process(RequestContent.java:96)
	at org.apache.http.protocol.ImmutableHttpProcessor.process(ImmutableHttpProcessor.java:132)
	at org.apache.http.impl.execchain.ProtocolExec.execute(ProtocolExec.java:182)
	at org.apache.http.impl.execchain.RetryExec.execute(RetryExec.java:88)
	at org.apache.http.impl.execchain.RedirectExec.execute(RedirectExec.java:110)
	at org.apache.http.impl.client.InternalHttpClient.doExecute(InternalHttpClient.java:184)
	... 8 more
 - org.apache.http.client.ClientProtocolException
	at org.apache.http.impl.client.InternalHttpClient.doExecute(InternalHttpClient.java:186)
	at org.apache.http.impl.client.CloseableHttpClient.execute(CloseableHttpClient.java:82)
	at org.apache.http.impl.client.CloseableHttpClient.execute(CloseableHttpClient.java:107)
	at com.alibaba.datax.plugin.writer.doriswriter.DorisWriterEmitter.doHttpPut(DorisWriterEmitter.java:210)
	at com.alibaba.datax.plugin.writer.doriswriter.DorisWriterEmitter.doStreamLoad(DorisWriterEmitter.java:105)
	at com.alibaba.datax.plugin.writer.doriswriter.DorisWriter$Task.flush(DorisWriter.java:108)
	at com.alibaba.datax.plugin.writer.doriswriter.DorisWriter$Task.startWrite(DorisWriter.java:101)
	at com.alibaba.datax.core.taskgroup.runner.WriterRunner.run(WriterRunner.java:56)
	at java.lang.Thread.run(Thread.java:748)

What You Expected?
.
How to Reproduce?
No response
Anything Else?
No response
Are you willing to submit PR?

[x] Yes I am willing to submit a PR!

Code of Conduct

[X] I agree to follow this project's Code of Conduct

What is your httpclient version, generally too low will report this error？


swagger-ui.html can't be proxied by another path - js error occurs

Please take the time to search the repository, if your question has already been asked or answered.

Version 2.9.2

Steps to reproduce:

There is a spring boot application -> REST API, e.g. it runs on localhost:1001/api.
Run a proxy, e.g. nginx on localhost:1002 with proxy configuration
/api-docs -> localhost:1001/api/swagger-ui.html
/webjars/springfox-swagger-ui -> localhost:1001/api$request_uri
/swagger-resources -> localhost:1001/api$request_uri
Open localhost:1002/api-docs

Actual result:
All resources are loaded. JS error occurs.
Uncaught (in promise) TypeError: Cannot read property '1' of null
    at e (springfox.js?v=2.9.2:1)
    at springfox.js?v=2.9.2:1
    at x (springfox.js?v=2.9.2:1)
    at Generator._invoke (springfox.js?v=2.9.2:1)
    at Generator.t.(anonymous function) [as next] (http://localhost:9080/webjars/springfox-swagger-ui/springfox.js?v=2.9.2:1:50487)
    at e (springfox.js?v=2.9.2:1)
    at springfox.js?v=2.9.2:1
    at new Promise (<anonymous>)
    at springfox.js?v=2.9.2:1
    at window.onload (springfox.js?v=2.9.2:1)

Expected result:
It should work as expected.
Noted in JS code:
return/(.*)\/swagger-ui.html.*/.exec(window.location.href)[1] - but window.location.href is a proxy href.
Please fix it or help me to find a workaround until you fix it.

No activity from repo owners, please explain why it won't be fixed
Or probably it is fixed in newer versions

I have the exaxt same problem. Please provide a workaround.

as a workaround: you have to make the proxy href end with swagger-ui.html for the code line above to work. Change your nginx configuration to contain something like:
location ^~ /api-docs/swagger-ui.html {       proxy_pass  localhost:1001/api/swagger-ui.html; }

@halungge This should be fixed and would work much better in 3.0.0.
NOTE: the url has changed from /swagger-ui.html to having everything related to swagger ui under /swagger-ui/** and what used to be swagger-ui.html is now index.html under that root

Still having same problem with
 Uncaught (in promise) TypeError: Cannot read properties of null (reading '1')
    at springfox.js:139
    at s (runtime.js:45)

String 139 still contains:
const urlMatches = /(.*)\/swagger-ui(\/index.html.*)*/.exec(window.location.href);

Springfox version: 3.0.0
I've tried my best to solve this problem but after a week I have no more strength to fight with this one :)
Problem shows with this nginx configuration part:
upstream servers {
    server service1:8080 fail_timeout=50s max_fails=5 weight=2;
    server service2:8080 fail_timeout=50s max_fails=5;
    server service2:8080 fail_timeout=50s max_fails=5;
}

server {
    ...
    location /api/ {
        proxy_no_cache 1;
        proxy_pass http://servers/swagger-ui/;
    }
}

And, of course, when i using something really terrible like:
location /api { #/ {
        proxy_no_cache 1;
        rewrite ^/api$ http://localhost:8181/swagger-ui/ redirect;
    }

It works.
So, I have no idea how to solve this problem.


Update issue templates

The existing changed templates are not showing up.  This workflow may show where they need to be added.

This was a test PR, automatically created by the issue template creation workflow on GitHub.  Needed to see how to make the necessary changes in a Gerrit CL.  Abandoning.


feat(isolation): improve canonicalization of host paths

This turns host paths that do not exist yet into absolute paths.


Clarify the enforcement dates

In https://github.com/chromium/ct-policy/pull/20, the main README.md was updated to reference the enforcement dates, but this file was overlooked. Align the two and reword the intro for implementers.

@devonobrien For your review & merging


Get commmitlint to put some respect on the default squash+merge commit string

Problem
Commitlint doesn't like the default commit string for squash+merged PRs.
Impact
Why does this matter? Who does it impact? How much does it impact them? What is the urgency?
Makes CI red.
Proposed Solution
Make a custom exception in the commitlint config to be okay with the default commit string.
Acceptance Criteria
From an end user perspective, the below criteria must be met to consider this done:

[ ] Commitlint doesn't prevent CI from completing for squash+merged PRs

if we dont fix this we cant release things without going back and fixing and it wastes engineering capcity

is this still an issue? I couldn't find the commits which failed at prod. Can you guys link a case here?


ARROW-9516: [Rust][DataFusion] refactor of column names

This PR is respective to ARROW-9516.
It:

simplifies how we construct and handle columns: all columns are now name-based, not index-based. This simplifies our code base significantly.
makes all column naming happen on the logical plan, not physical plan
gives more expressive column names to the end schema of a logical plan, particularly to aggregated expressions (e.g. SUM(a), SUM(b) instead of SUM, SUM.

This is currently a proof of value: all tests pass and stuff, but there is no decision made on whether we should proceed with these changes. More details available at ARROW-9516.

@andygrove fyi

This is looking good overall. I think we need to keep the Alias in the logical plan though.

The test csv_query_group_by_avg_with_projection is now failing (after rebase).
The query is SELECT avg(c12), c1 FROM aggregate_test_100 GROUP BY c1 and the error is that the order of the result is result: it is c1, avg(c12), not c1, avg(c12). I am looking into it.

@andygrove , this is IMO now ready. There were two errors that I fixed in two separate commits:

3503c0d fixed an error caused by a mistake of mine
ff3ade9 fixed an error in one of the tests

The first one was related to the need of adding a projection when the order of SELECT is different from [groups] + [aggregates] in a group by. The current code has this and I had removed that part by mistake in this PR. Also, since this was only being tested in the integration tests, I have added a new test on the library to validate the logical plan.
The second issue is IMO an error in one of the tests: it was parsing SELECT c2, MIN(c12), MAX(c12) FROM aggregate_test_100 GROUP BY c1 as a valid statement and interpreting that statement as c2 representing c1. With this PR, that is identified as an error because c2 is not a valid column name in this context.

@jorgecarleitao Thanks. It looks like you will have to rebase one more time because master was rebased as part of the 1.0.0 release process. I can merge this once that is done.

@andygrove done. Thanks a lot for the effort and help!

@jorgecarleitao The parquet version fix is merged, so one more rebase should get the tests passing.

rebased


Update the SageMaker Model Monitoring module with model bias monitoring.

Describe your changes
Expands the module from #117 & #125 (sagemaker-model-monitoring-module) to include monitoring model bias.
Similar to #117 & #125 this is somewhat difficult to test as you need a SageMaker Pipeline with the proper check steps & also ground truth for the data.
Issue ticket number and link
N/A
Checklist before requesting a review

[x] I updated CHANGELOG.MD with a description of my changes
[x] If the change was to a module, I ran the code validation script (scripts/validate.sh)
[x] If the change was to a module, I have added thorough tests
[x] If the change was to a module, I have added/updated the module's README.md
[x] If a module was added, I added a reference to the module to the repository's README.md
[x] I verified that my code deploys successfully using seedfarmer apply

By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.

AWS CodeBuild CI Report

CodeBuild project: AIOpsModuleTestsInfrastruct-doMLXEYsnmxr
Commit ID: 663754038cce2e6ccee9969e9decec4c1a9b1bea
Result: SUCCEEDED
Build Logs (available for 30 days)

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AWS CodeBuild CI Report

CodeBuild project: AIOpsModuleTestsInfrastruct-doMLXEYsnmxr
Commit ID: 43b2ab3cd6165c11648d0ac7ae4bba9534dc4bef
Result: SUCCEEDED
Build Logs (available for 30 days)

Powered by github-codebuild-logs, available on the AWS Serverless Application Repository


Try multiple actions on each step?

I'm trying to use baselines to solve the Halite 2 challenge
At each time step (~200 max per game), you have to order multiple ship to go different planets.
I'd like to be able to receive multiple actions in my environment step() function so I can command instruct multiple ships to move on a single time step.
Is this possible using the Deep Q Learning package? Does it even make sense? Sorry if the question is not appropriate here.

Multiple ships? That means you want to create multiplayer environment (not algorithm)? This repo is about published algorithms. I'd recommend to start with single-player environment if you are learning to do this kind of thing.


[pre-adoption-validation]Check kernel args

The edpm_kernel role checks is the content of the edpm_kernel_args
is already in the /proc/cmdline and if it isn't then it reconfigure grub
and request a reboot. This pre-adoption check therefore checks the same
and fails the adoption early to avoid kernel arg reconfiguration during
the edpm_kernel role run.
The edpm_kernel_hugepages ansible var can be used independently from the
edpm_kernel_args var to pass huge page kernel config. So the kernel args
pre-adoption check is extended to consider this variable as well.
Implements: OSPRH-5714

/hold
Lets have a test run of this in data-plane-adoption...

the data-plane adoption CI run the new check here:
https://logserver.rdoproject.org/21/421/8c69005e4470ad2a98dfea3bee5292f13346fc9f/github-check/data-plane-adoption-osp-17-to-extracted-crc-minimal-no-ceph/1d2652d/controller/ci-framework-data/logs/openstack-k8s-operators-openstack-must-gather/namespaces/openstack/pods/pre-adoption-validation-openstack-pre-adoption-openstack-dsqnz/logs/openstackansibleee.log

/unhold

/lgtm


A proper implementation of String.isSwiftIdentifier

Stolen from the Swift lexer.

Thank you for working on this!
Before we merge this change, you will need to add tests of its functionality. What I would suggest you do is look at each identifier used in Swift's test/Parse/identifiers.swift and determine if the test is checking that it is valid or invalid, then create a Tests/SwiftDriverTests/StringAdditionsTests.swift file with an XCTestCase that makes sure isSwiftIdentifier returns the appropriate result for each of them.
Once you've ported these test cases, think about if there are any others you should add. test/Parse/identifiers.swift actually doesn't seem to include some very basic tests, like identifiers containing numbers. For bonus points, you could backport these additional test cases to test/Parse/identifiers.swift, too!
To allow this test to be written, you may need to make isSwiftIdentifier public. Please don't use @testable import; we want to be able to run tests on release builds, and release builds can't enable testable import.
Thanks!

To allow this test to be written, you may need to make isSwiftIdentifier public.

Do I need to add a prefix to the property, since I'm extending a type I don't own with a declaration that doesn't involve types I own?

I don't think we have an official policy on prefixing extension methods, but I asked around and @aciidb0mb3r at least thinks it's a good idea. Please add an sd_ prefix.
(Thank you for thinking of this—I sure didn't.)

@brentdax I've added some tests. However, I'm not sure whether we want to consider Swift keywords as identifiers here. Right now import, class etc. are "identifiers".

@broadwaylamb You should include tests proving that sd_isSwiftIdentifier accepts keywords. The C++ driver uses Lexer::isIdentifier(), which accepts keywords, so that's the behavior we want in swift-driver too. What it's trying to do is make sure that the module name would be valid in an import statement, and keywords would be valid in an import as long as the user surrounded them with backticks, so accepting them is the right behavior here.

@swift-ci please smoke test

@swift-ci please smoke test

@swift-ci please test

@swift-ci please test


how do you set up the bot? what should config.json look like?

im having an issue seting up the bot and in general, I dont really undestand how to do it, I have my bot token and novel token but idk how to set up the config file

im having an issue seting up the bot and in general, I dont really undestand how to do it, I have my bot token and novel token but idk how to set up the config file

when you run the bot for the first time it will generate a config.json under prod folder you can change the language setting there, the bot & novelAI token should be filled into example.env file and then change the file name to .env.

I ran the code and I dont see the config.json, i do see tsconfig.json tho?

Fixed in the latest commit, sorry for the misunderstanding.


Invalid character(s) found in major number "azure-nextgen\n0"

I got some weird error when updating azure-nextgen:
error: failed to discover plugin requirements: illegal semver returned by language host: azurenextgen@vazure-nextgen
0.2.5: Invalid character(s) found in major number "azure-nextgen\n0"

Why is that?

The version format has changed in the version 0.2.5 of the provider, which causes this issue with older Pulumi CLIs. Please upgrade your pulumi CLI to version 2.13.0+.


migration generates invalid SQL statement

Describe the bug
When trying to generate migration for an entity containing an uuid column, a migration with invalid SQL statement is generated
The entity class
import { Entity, PrimaryKey, Property } from '@mikro-orm/core';

@Entity()
export class Author {
    @PrimaryKey({
        columnType: "uuid"
    })
    id!: string;

    @Property()
    name!: string;
}

The generated statment is
create table `author` (`id` uuid not null, `name` varchar(255) not null, primary key (`id`)) default character set utf8mb4 engine = InnoDB;

Environment
Nestjs
Mysql
Mikro-orm
To Reproduce
Steps to reproduce the behavior:

Create entity with a column of type uuid
Run npx mikro-orm migration:create
Run npx mikro-orm migration:up

Expected behavior
A valid SQL create statement should be generated.
Versions

Dependency
Version

node
v16.15.0

typescript
4.7.4

mikro-orm
5.6.16

@mikro-orm/mysql
5.6.16

@mikro-orm/migration
5.6.16

@mikro-orm/nestjs
5.1.8

You need to put valid column type in there, it is your responsibility. As opposed to just type: uuid which resolves to things based on current driver.


fix(typescript): provide hinting to use the correct jsx option on non…

… tsx files

this fixes https://github.com/skolmer/i18n-tag-schema/issues/49

Coverage remained the same at 95.467% when pulling 1e996977b9d8e510f70d4cdc1f863680ee3d947b on RyverApp:topic-tsx-hints into 6a33beec57a4fc3537b0f856fa22a07d42e53002 on skolmer:master.

@all-contributors please add @mmorton for infrastructure, tests and code


Grid View should have Rounded Corners

Honestly, I think grid view would look better with rounded corners. In the current setup, the video and image thumbnails have straight corners, which don't really match with the design.

This issue should be resolved with #5717.


[WIP]Contribute Flow

Describe the PR
Still thinking.
Relation issue
https://github.com/swaggo/swag/issues/359

Codecov Report

Merging #369 into master will not change coverage.
The diff coverage is n/a.

@@           Coverage Diff           @@
##           master     #369   +/-   ##
=======================================
  Coverage   85.17%   85.17%
=======================================
  Files           7        7
  Lines        1322     1322
=======================================
  Hits         1126     1126
  Misses        113      113
  Partials       83       83

Continue to review full report at Codecov.

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Stray pixels when using diff_to_previous with ILI9341

Raspberry PI 3, Model B v1.2
Linux raspberrypi 5.4.51-v7+ #1333 SMP Mon Aug 10 16:45:19 BST 2020 armv71 GNU/Linux
I have a new 320x240 ILI9341 SPI LCD display.  If I invoke the welcome.py demo with the options
--interface spi -d ili9341 --width 320 --height 240 --framebuffer full_frame
it operates as one would expect (albeit a bit slowly, varying with --spi-bus-speed).
If I leave off the --framebuffer option (using the default diff_to_previous), then

the initial attempt at blanking the display leaves white pixels still present and
any "movement" of drawings can leave a trail of pixels (see attached photo).

In the pi_logo demo, the diff_to_previous option allows dark pixels to momentarily appear around the rotating Pi logo.
Is the full_frame buffer inherently required when using an ili9341-based LCD?

I haven’t observed that with any other device that does diff-to-previous, so makes me wonder if the calculation here https://github.com/rm-hull/luma.lcd/pull/94/files#diff-09cfe7a0be1f525d8b47f131de856bd2R485 is the culprit, ... but unlikely as it is the same as the st7735 device.
I wonder if adding some bounding box margin with this method https://github.com/rm-hull/luma.core/blob/master/luma/core/framebuffer.py#L52 may fix the issue.
To be honest, I haven’t tried this implementation personally as I don’t own one of these displays - it was a contribution by @ric2b .. Ricardo are you able to have a look and reproduce please?

Sorry, I don't currently have access to these screens, it will take me some time to test this.
My use case was focused on static images so I was using the full frame option, it's entirely possible that I missed some problems with partial updates.

I'm still in full-on experiment mode here, and I have the display still connected to the RPi.  I can try out any diff / patch anyone would like to suggest.

@mattblovell:

curious to know if you are running the demo's with either or both of --v_offset or -h_offset ?
If you could checkout the latest commit on master in the luma.lcd repo, and in the ili9341 class, in the display class at line 611, alter it as follows:

        if self.framebuffer.redraw_required(image):
            self.framebuffer.inflate_bbox()   # <--- INSERT THIS LINE
            left, top, right, bottom = self.apply_offsets(self.framebuffer.bounding_box)

Reinstall from source with sudo -H pip3 install -e . from the luma.lcd directory.
Rerun the welcome demo and observe.

One of two things should happen - it will either fix it (🤞) or ... it will partially fix it by leaving a trail of breadcrumbs across the screen ( 😦 )

@rm-hull:

curious to know if you are running the demo's with either or both of --v_offset or -h_offset ?

No.  I briefly gave those options a try, but they seemed to make matters worse.
The slight bounding box change unfortunately did not help matters.  Here's a new photo:

... and a photo of just the white band, discussed below.

I don't know if they help, but here are some other tidbits:

The location of the still-remaining band of white pixels (after the initial clear) can change from run to run.  It's usually in about the same spot.
Sometimes, the welcome.py demo runs for a while without leaving any pixel trails.  A band of white about 8 pixels high still remains, though.
Using the default framebuffer option, welcome.py often hangs, in the sense that the display becomes static.  The script is still running, and no error message appears in the terminal from which it was launched.  I'm not actually sure how significant that is, though, since I've also seen welcome.py seemingly hang on startup with the full_frame option.
After such a (visual) hang, it can take several attempts at running welcome.py to get back to a working state (with full_frame on the command line).  It's almost as if some state persists?
Hangs also occur for welcome.pl using full_frame, but it usually takes longer.

I mention the hanging since I didn't observe any such behavior trying out a SSD1309 OLED panel.
Please let me know if there's anything else you'd like me to try.
(For my application, I am content to use full_frame.  I'm trying to make a front panel display for Kodi, complete with album cover.  luma.lcd has been working nicely for that!)
Thanks,
Matt

I was always a bit reluctant to support higher resolution devices because they would always be slow with an interpreted language. The diff to previous framebuffer was introduced to allow for partial screen redraws to get a speed bump. There is a new PR in progress (see https://github.com/rm-hull/luma.core/pull/194) which may alleviate needing this, but that is some way off at the moment.
Your observations are very useful and interesting, especially around the hangs. This makes me wonder what RPi model are you using, and how many amps does your power supply provide? I ask only because I was recently suffering hangs and dropped network connections on a model B 4 whenever the cpu got busy. Using a decent 3A supply and all the problems vanished.
One other thing to try would be to add --gpio-reset-hold-time and --gpio-reset-release-time flags .. it maybe the reset line isn’t being held active for long enough.
I’ll try and get hold of an ili9341 device anyway, which will be useful for testing the Linux framebuffer driver implementation.

@rm-hull :

I was always a bit reluctant to support higher resolution devices because they would always be slow with an interpreted language.

I'm really glad luma added support for the ili9341, though.  It's been quite fun to experiment with.  (Photo of the front panel display I'm working on below.)

This makes me wonder what RPi model are you using, and how many amps does your power supply provide?

The board is a Raspberry Pi 3 Model B v1.2.  The power supply I have for it claims to provide output of 2.4 A at 5.25 V.
I don't typically have much plugged into the RPi, but I can try unplugging the USB flash drive and USB mouse and see if that helps at all.
I also have an Odroid C4 that is my intended "target" for this project, if I can figure out what's necessary to get luma.core and luma.lcd up and running on an Odroid board.

One other thing to try would be to add --gpio-reset-hold-time and --gpio-reset-release-time flags

I'll give those a try later today.
Thanks for making luma such a flexible and (extremely) well-documented package!
Matt

work-in-progress kodi_panel, on ili9341 using luma:

One other thing to try would be to add --gpio-reset-hold-time and --gpio-reset-release-time flags

That seems to help clean up the display wonderfully!  Welcome.py still hangs, but the display is handled appropriately.  The bounce demo is getting 10.6 to 11.1 fps when invoked with the following command line:
python3 bounce.py --interface spi -d ili9341 --width 320 --height 240 \
  --spi-bus-speed 32000000 --backlight-active high \
  --gpio-reset-hold-time 0.2 --gpio-reset-release-time 0.2

Without the reset timing options, bounce.py left tons of pixel trails, similar to what welcome.py does.  (For reference, invoking bounce with the full_frame option drops the display rate about 8 fps.)
The hang from welcome.py is still unusual.  Sometimes it occurs immediately at startup (i.e., the clears, this time correctly, but no initial text ever appears) and sometimes it now gets through 5 or 6 scroll-offs.  The bounce demo is seemingly happy to run forever; I'm going to leave it up while I go do some chores and check on it later.

The bounce.py demo has run for hours now without issue.
I almost wonder whether there is some font that balks at being rendered for the 320x240 display.  Running welcome.py in Windows, via --display pygame, it too stops!

via --display pygame, it too stops!

Ah that's a very useful test. Does the pygame emulator display exactly the same (issues)?

Does the pygame emulator display exactly the same (issues)?

It's never had the pixel artifacts / trails that the physical panel had (now resolved with the reset timing).  The halting seems similar, though.
I'd been using the pygame emulator for my own development and just thought of trying out welcome.py in it this morning.

Off topic from this bug report, I know, but I hope you'll permit it to stay...

I also have an Odroid C4 that is my intended "target" for this project, if I can figure out what's necessary to get luma.core and luma.lcd up and running on an Odroid board.

I don't know if it's the "official" route of getting luma.core onto non-RPi boards or not, but I was able to install the RPi.GPIO-Odroid package from
https://github.com/awesometic/RPi.GPIO-Odroid
and get luma.oled alive.  This route was successful for an Odroid C4 under both Ubuntu 20.04 and under CoreELEC (one has to make use of entware under CoreELEC, as apt isn't available).
The bounce.py demo gets 80 fps on an SSD1309 128x64 OLED display with the Odroid C4!
The tagline for luma.core mentions that it is for "small displays on the Raspberry Pi and other single board computers".  It's been unclear to me, though, what was necessary for installation on other SBCs.

"small displays on the Raspberry Pi and other single board computers"

Any suggestions for a better tagline? :)

Any suggestions for a better tagline? :)

No, for that part does seem accurate!
Being relatively new to trying out this whole GPIO aspect of the SBCs, I found the plethora of alternatives just for the RPi a cause of confusion.  The following seem roughly equivalent:

RPi.GPIO
WiringPi (deprecated in late 2019 by a frustrated author)
WiringPi2
gpiozero

Separate from the above are efforts like Adafruit_Blinka (and, from what I can understand, several Python variants).
Once I understood what luma.core wanted to load, I could then search around for an Odroid equivalent.
My (naive) hope is that for such underlying infrastructure we could have settled on a particular Python package and then had clear per-platform versions of that for the various SBCs that are available.  Perhaps there's still too many differences across them to have such a unified approach.  It could be that things are simpler than I perceive and I just have yet to find the simplifying key!
Perhaps some hints or pointers on handling non-RPi platforms in the (otherwise excellent) documentation could help?

Back to the original topic of this issue, adding non-zero reset hold and release times appears to resolve the problems.  I don't know if any code change is needed, other than perhaps changing the defaults for ili9341 (or suggesting non-zero reset times).

I wrote https://github.com/rm-hull/OPi.GPIO for orange pi sbc’s some time ago and used that with luma.* to drive displays successfully. Others have also had success with chip and odroid sbcs, but you’re right I think, there are platform divergencies that make it painful, although I think opi.gpio came close to unifying it on different platforms. I haven’t really kept up to date with what the latest is, but vaguely recall gpiod was in the ascendant at some point. Maybe we should look at it again to reassess.
Also it isn’t well documented how to use on non RPi devices 😬

Maybe welcome.py is a bit buggy.. I think it will try and pick some random offscreen area to scroll in from, then another to scroll out to. On smaller screens it probably works ok, but for a bigger screen maybe it’s not getting past this while loop https://github.com/rm-hull/luma.examples/blob/master/examples/welcome.py#L210

Related: https://github.com/rm-hull/luma.examples/issues/100

I think this issue can be closed .. we resolved the issue of the trailing pixels, and commit https://github.com/rm-hull/luma.examples/commit/da48c2e4dc00aa99e5f97791f5e5a09c13077805 should have fixed the welcome.py hang

Giving commit da48c2e a try on the RPi3 with the 2.8" display.  Running nicely for over 10 minutes thus far, much longer than previously.


Managing WorkloadClusters using GitOps

Is your feature request related to a problem? Please describe.
Currently registering a cluster as a workload cluster in KCP requires 2 interconnected actions:

Run the kubectl kcp workload sync command on KCP.
Apply the manifest returned as the output of the first command to the cluster to deploy the syncer.

This makes it difficult to fully delegate the registration to ArgoCD.
Describe the solution you'd like
Ideally we would have the following features:

Access to the manifest of the resource(s) deployed on KCP by workload sync command so it can be deployed by gitops.
A way to pass the cluster information to KCP so it deploy the resources on the cluster so there's no need to keep multiple manifests in sync.

Describe alternatives you've considered
The current alternative is to handle this process imperatively by a pipeline executed when the gitops repository is changed.
Additional context
We're trying to reach a state where the only actions performed imperatively are registering the KCP and our cluster to ArgoCD, and everything else is then handed over to ArgoCD.

The explicit intent of the current flow is to not require that any part of the control plane have the permissions required to deploy and manage the syncer in the physical cluster. Is the current flow hard to execute in a pipeline since the manifests are dynamically generated? Please describe more of your issues in implementing some CD approach.

After chatting with @ncdc it seems like this is about being able to emit the YAML for kcp and the physical cluster both, separately, to be checked into a GitOps flow.


Missing antenna options.

Describe the bug
When using the helium app on Android, when viewing your hotspot you cannot update the antenna settings when pressing the gear.
To Reproduce
Steps to reproduce the behavior:

Go to 'gear button:
Click on '....'
Scroll down to '....'
See error

Expected behavior
Expected to have the normal options to update location, antenna settings, transfer hotspot.
Screenshots
App does not allow screenshot, only pair option is available.
Smartphone (please complete the following information):

Device: [e.g. fold 3
OS: [e.g. android 11
App Version [ 3.11.0

Additional context
Add any other context about the problem here.

@acessford101
Do you have access to your wallet and your earnings? Is the wallet address the same as the linked one in the hotspot?

Hello, yes. I uninstalled and reinstalled the app and the options are now
their and I was able to update my antenna information.
Thank you.
Adam.
On Fri, Apr 1, 2022, 2:26 PM dobrevg @.***> wrote:

@acessford101 https://github.com/acessford101
Do you have access to your wallet and your earnings? Is the wallet address
the same as the linked one in the hotspot?
—
Reply to this email directly, view it on GitHub
https://github.com/helium/hotspot-app/issues/1279#issuecomment-1086208029,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/AYPSN5WSWUDSXW3F4CKUPKDVC45WBANCNFSM5SDTGJ4Q
.
You are receiving this because you were mentioned.Message ID:
@.***>

{invalid,insufficient_balance} I get the error and the antenna is not updating 1 hnt in my wallet


Filter blank completions

This filters out blank lines from the output of fish -c "complete -C...", which prevents errors when trying to access l:tokens[0], which is an empty list in the case of a blank completion.
I also created this PR on the upstream but it appears to be unmaintained.

Looks good to me


Can not reproduce the results of ENMF on ml-lcfn dataset as claimed in README

Hi! You have done a good work! I have been trying your code these days and got some expected results, but I found it hard to reproduce the results on ml-lcfn dataset as claimed in README.
Here are my trials with the provided code:

When using default hyperparameters of provided code, i.e., dropout keep_prob=0.7 and negative weight=0.1, the best results I got were<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>When using the suggested hyperparameters in README, i.e., dropout keep_prob=0.5 and negative weight=0.5, the best results I got were<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>When setting the hyperparameters as dropout keep_prob=0.7 and negative weight=0.5 (which is the best pair I have tried), the best results I got were<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>
Note that none of above meets the promising results in README, i.e<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>Could you help me figure out how to reproduce them?

Hi, thank you for your interest in our work. What is your setting of embedding size? For a fair comparison, we also set the embedding size as 128, which is utilized in the LCFN work.

Thank you, I got it. The results were reproduced.