shortcuts without modifier keys prevent writing that key in inputs

<DockMonitor toggleVisibilityKey='s'>
  <LogMonitor/>
</DockMonitor>

Hi,
setting a shortcut like that (without a modifier key) prevents me from typing that letter in inputs (because  handleKeyDown() calls event.preventDefault())
one solution for this would be to add this line
if (e.target !== document.body) return
to handleKeyDown() because if an input is focused the target of the keydown event is that input, if no input is focused the target of the event is the body
another solution would be to return if the tag name of the target of the event is input, textarea, etc.
would you be open to doing one of these things, or some other way of not triggering the shortcuts while an input is focused?
Thanks,
Nuno
(also, thanks a lot for all you've done with redux,etc it plays a big part in how i write apps now :smiley: )

Thanks for kind words.
Why not just set a shortcut with modifier key?

that was just because this app i'm working on has other shortcuts like that (without modifier key) and it was just to be consistent. (the shortcut library https://craig.is/killing/mice i use for the other shortcuts does this thing of not triggering them when focused on an input)

I'm happy to add a check like this. Want to send a PR?

done :)

Fixed by #30.


VirtualService with empty header prefix match casues envoy validation error

Bug Description
When a VirtualService tries to use prefix: "" for header matching (eg. does the header exist), it validates OK, but on configuration push from istio to envoy it will fail envoy configuration validation.
Given the following example, where it tries to match for a prefix: "" on the header appkey it cases the following error message.
apiVersion: networking.istio.io/v1beta1
kind: VirtualService
metadata:
  name: nexus
spec:
  gateways:
  - istio-system/devns
  hosts:
  - example
  http:
  - match:
    - headers:
        appkey:
          prefix: ""
    route:
    - destination:
        host: nexus.nexus.svc.cluster.local
        port:
          number: 8080

gRPC config for type.googleapis.com/envoy.config.route.v3.RouteConfiguration rejected: Proto constraint validation failed (RouteConfigurationValidationError.VirtualHosts[3]: embedded message failed validation | caused by VirtualHostValidationError.Routes[0]: embedded message failed validation | caused by RouteValidationError.Match: embedded message failed validation | caused by RouteMatchValidationError.Headers[0]: embedded message failed validation | caused by HeaderMatcherValidationError.StringMatch: embedded message failed validation | caused by StringMatcherValidationError.Prefix: value length must be at least 1 characters): name: "https.443.https.devns.istio-system"
...
virtual_hosts {
  name: "x:443"
  domains: "x"
  routes {
    match {
      prefix: "/"
      case_sensitive {
        value: true
      }
      headers {
        name: "appkey"
        string_match {
          prefix: ""
        }
      }
    }
    route {
      cluster: "outbound|8080||nexus.nexus.svc.cluster.local"
      timeout {
      }
...

Based on the feedback I have gathered, this was implemented partly due to the VirtualService spec documentation, which I think leads in this direction. Could there be something missing when testing for present headers?:

The header keys must be lowercase and use hyphen as the separator, e.g. x-request-id.
Header values are case-sensitive and formatted as follows:

exact: "value" for exact string match
prefix: "value" for prefix-based match
regex: "value" for RE2 style regex-based match (https://github.com/google/re2/wiki/Syntax).

If the value is empty and only the name of header is specified, presence of the header is checked. Note: The keys uri, scheme, method, and authority will be ignored.

This got a lot bigger of an issue for us, since a ingress gateway scaled up and started without configuration, which cased a whole lot of 404's/service outage.
This is not the only issue we have seen where the admission webhook allows virtualservice configuration that cases a broken envoy configuration. A good example of this (which we now validate on our own in our ci pipeline) is where a regex match is present that is not valid re2.
Version
$ istioctl version
client version: 1.17.1
control plane version: 1.17.2
data plane version: 1.17.2 (13 proxies)

$ kubectl version --short
Client Version: v1.24.7
Kustomize Version: v4.5.4
Server Version: v1.25.6-eks-48e63af

Additional Information
No response
Affected product area

[ ] Ambient
[X] Docs
[ ] Installation
[X] Networking
[ ] Performance and Scalability
[ ] Extensions and Telemetry
[ ] Security
[ ] Test and Release
[ ] User Experience
[ ] Developer Infrastructure
[ ] Upgrade
[ ] Multi Cluster
[ ] Virtual Machine
[ ] Control Plane Revisions

The way to check for presence is like this:
  - match:
    - headers:
        appkey:
          prefix: {}

This should be clarified in the docs.


It's just not working on windows

Hello,
I have installed it successfully on Windows following the guide
https://github.com/andygock/magic-animate/blob/main/INSTALL-Windows.md
I launced and provided the inputs but no progress, it is stuck at 0%

Console
C:\stable_diffusion\magic-animate\magicanimate\pipelines\pipeline_animation.py:43: FutureWarning: Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.
  from diffusers.pipeline_utils import DiffusionPipeline
Initializing MagicAnimate Pipeline...
loaded temporal unet's pretrained weights from pretrained_models/stable-diffusion-v1-5\unet ...
### missing keys: 560;
### unexpected keys: 0;
### Temporal Module Parameters: 417.1376 M
The config attributes {'addition_embed_type': None, 'addition_embed_type_num_heads': 64, 'addition_time_embed_dim': None, 'conditioning_channels': 3, 'encoder_hid_dim': None, 'encoder_hid_dim_type': None, 'global_pool_conditions': False, 'num_attention_heads': None, 'transformer_layers_per_block': 1} were passed to ControlNetModel, but are not expected and will be ignored. Please verify your config.json configuration file.
It is recommended to provide `attention_head_dim` when calling `get_down_block`. Defaulting `attention_head_dim` to 8.
It is recommended to provide `attention_head_dim` when calling `get_down_block`. Defaulting `attention_head_dim` to 8.
It is recommended to provide `attention_head_dim` when calling `get_down_block`. Defaulting `attention_head_dim` to 8.
It is recommended to provide `attention_head_dim` when calling `get_down_block`. Defaulting `attention_head_dim` to 8.
A matching Triton is not available, some optimizations will not be enabled.
Error caught was: No module named 'triton'
C:\stable_diffusion\magic-animate\magicanimate\pipelines\pipeline_animation.py:103: FutureWarning: The configuration file of this scheduler: DDIMScheduler {
  "_class_name": "DDIMScheduler",
  "_diffusers_version": "0.21.4",
  "beta_end": 0.012,
  "beta_schedule": "linear",
  "beta_start": 0.00085,
  "clip_sample": true,
  "clip_sample_range": 1.0,
  "dynamic_thresholding_ratio": 0.995,
  "num_train_timesteps": 1000,
  "prediction_type": "epsilon",
  "rescale_betas_zero_snr": false,
  "sample_max_value": 1.0,
  "set_alpha_to_one": true,
  "steps_offset": 0,
  "thresholding": false,
  "timestep_spacing": "leading",
  "trained_betas": null
}
 is outdated. `steps_offset` should be set to 1 instead of 0. Please make sure to update the config accordingly as leaving `steps_offset` might led to incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json` file
  deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
C:\stable_diffusion\magic-animate\magicanimate\pipelines\pipeline_animation.py:116: FutureWarning: The configuration file of this scheduler: DDIMScheduler {
  "_class_name": "DDIMScheduler",
  "_diffusers_version": "0.21.4",
  "beta_end": 0.012,
  "beta_schedule": "linear",
  "beta_start": 0.00085,
  "clip_sample": true,
  "clip_sample_range": 1.0,
  "dynamic_thresholding_ratio": 0.995,
  "num_train_timesteps": 1000,
  "prediction_type": "epsilon",
  "rescale_betas_zero_snr": false,
  "sample_max_value": 1.0,
  "set_alpha_to_one": true,
  "steps_offset": 1,
  "thresholding": false,
  "timestep_spacing": "leading",
  "trained_betas": null
}
 has not set the configuration `clip_sample`. `clip_sample` should be set to False in the configuration file. Please make sure to update the config accordingly as not setting `clip_sample` in the config might lead to incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json` file
  deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
Initialization Done!
Running on local URL:  http://<IP_ADDRESS>:7860

This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)
Exception in callback _ProactorBasePipeTransport._call_connection_lost(None)
handle: <Handle _ProactorBasePipeTransport._call_connection_lost(None)>
Traceback (most recent call last):
  File "C:\Program Files\Python310\lib\asyncio\events.py", line 80, in _run
    self._context.run(self._callback, *self._args)
  File "C:\Program Files\Python310\lib\asyncio\proactor_events.py", line 162, in _call_connection_lost
    self._sock.shutdown(socket.SHUT_RDWR)
ConnectionResetError: [WinError 10054] An existing connection was forcibly closed by the remote host
C:\stable_diffusion\magic-animate\magicanimate\pipelines\pipeline_animation.py:624: FutureWarning: Accessing config attribute `in_channels` directly via 'UNet3DConditionModel' object attribute is deprecated. Please access 'in_channels' over 'UNet3DConditionModel's config object instead, e.g. 'unet.config.in_channels'.
  num_channels_latents = self.unet.in_channels
  0%|                                                                                | 0/25 [00:00<?, ?it/s]

It finally worked

Hi, I encountered the same problem, the progress bar always stayed at 0%. How did you solve it?

@Mo01iHt
I refered this
https://youtu.be/O-MTqV7lapg


panic with left shift

n = 1
while True:
 n = n << 10 ** 100

expected result:

OverflowError: too many digits in integer

current result:

thread 'main' panicked at 'Failed converting<PHONE_NUMBER>0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000 to rust usize', vm/src/obj/objint.rs:250:13

Hello @andrew-ld , thank you for filing this issue. This is a very good confined issue, ideal for people starting to contribute to this project.

Related #276

#564  has solved the problem, I close.


Example are not working

The examples are not working.
File "alexnet_darknet_test.py", line 4, in
from acuitylib.interface.importer import DarknetLoader
ModuleNotFoundError: No module named 'acuitylib.interface'
Do you change the API? By the way there is no 6.27.0 from PIP.
Eric Hsiao

The examples are not working.
File "alexnet_darknet_test.py", line 4, in from acuitylib.interface.importer import DarknetLoader ModuleNotFoundError: No module named 'acuitylib.interface'
Do you change the API? By the way there is no 6.27.0 from PIP.
Eric Hsiao

New API 'acuitylib.interface' used since Acuitylite 6.21.0, please use a higher Acuitylite version.
There is Acuitylite 6.27.0 on PIP, please try again.

Hi.
The docker file that I used is ubuntu20.04+python3.8. I believe you do not update acuitylite package for python3.8. Please add it for python3.8 or remove the docker file that you provide.
builder@5a89bc5735eb:/acuitylite$ pip3 install acuitylite --no-deps
Collecting acuitylite
Using cached acuitylite-6.20.0-cp38-cp38-manylinux2010_x86_64.whl (178.2 MB)
Installing collected packages: acuitylite
Successfully installed acuitylite-6.20.0
builder@5a89bc5735eb:/acuitylite$

Eric Hsiao

please use the corresponding docker what Acuitylite 6.27.0 (or other version) required.


-bash: watchify: command not found

sudo npm install watchify
completed successfully, then tried to run
watchify main.js -d -o bundle.js -v
getting a command not found error.
<EMAIL_ADDRESS>

Needs to be globaly installed?
sudo npm install -g watchify

Thanks, that fixed it! Might wanna make a note in the readme to install it globally.


PHOENIX-7174 Rebuild HBase in connectors github action CI script

:confetti_ball: +1 overall

Vote
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reexec
5m 47s
Docker mode activated.

_ Prechecks _

+1 :green_heart:
dupname
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No case conflicting files found.

+1 :green_heart:
@author
0m  0s
The patch does not contain any @author tags.

_ master Compile Tests _

_ Patch Compile Tests _

+1 :green_heart:
whitespace
0m  0s
The patch has no whitespace issues.

+1 :green_heart:
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There were no new yamllint issues.

_ Other Tests _

+1 :green_heart:
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The patch does not generate ASF License warnings.

6m 27s

Subsystem
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Docker
ClientAPI=1.43 ServerAPI=1.43 base: https://ci-hadoop.apache.org/job/Phoenix/job/Phoenix-Connectors-PreCommit-GitHub-PR/job/PR-127/1/artifact/yetus-general-check/output/Dockerfile

GITHUB PR
https://github.com/apache/phoenix-connectors/pull/127

Optional Tests
dupname asflicense yamllint

uname
Linux e94ef5021334 5.15.0-88-generic #98-Ubuntu SMP Mon Oct 2 15:18:56 UTC 2023 x86_64 x86_64 x86_64 GNU/Linux

Build tool
maven

Personality
dev/phoenix-connectors-personality.sh

git revision
master / 80abdb4

Max. process+thread count
47 (vs. ulimit of 30000)

modules
C: . U: .

Console output
https://ci-hadoop.apache.org/job/Phoenix/job/Phoenix-Connectors-PreCommit-GitHub-PR/job/PR-127/1/console

versions
git=2.7.4 maven=3.3.9 yamllint=1.20.0

Powered by
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This message was automatically generated.

:confetti_ball: +1 overall

Vote
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Comment

+0 :ok:
reexec
0m 29s
Docker mode activated.

_ Prechecks _

+1 :green_heart:
dupname
0m  0s
No case conflicting files found.

+1 :green_heart:
@author
0m  0s
The patch does not contain any @author tags.

_ master Compile Tests _

_ Patch Compile Tests _

+1 :green_heart:
whitespace
0m  0s
The patch has no whitespace issues.

+1 :green_heart:
yamllint
0m  2s
There were no new yamllint issues.

_ Other Tests _

+1 :green_heart:
asflicense
0m 21s
The patch does not generate ASF License warnings.

1m 10s

Subsystem
Report/Notes

Docker
ClientAPI=1.43 ServerAPI=1.43 base: https://ci-hadoop.apache.org/job/Phoenix/job/Phoenix-Connectors-PreCommit-GitHub-PR/job/PR-127/2/artifact/yetus-general-check/output/Dockerfile

GITHUB PR
https://github.com/apache/phoenix-connectors/pull/127

Optional Tests
dupname asflicense yamllint

uname
Linux 73ad0d92e636 5.15.0-88-generic #98-Ubuntu SMP Mon Oct 2 15:18:56 UTC 2023 x86_64 x86_64 x86_64 GNU/Linux

Build tool
maven

Personality
dev/phoenix-connectors-personality.sh

git revision
master / 80abdb4

Max. process+thread count
52 (vs. ulimit of 30000)

modules
C: . U: .

Console output
https://ci-hadoop.apache.org/job/Phoenix/job/Phoenix-Connectors-PreCommit-GitHub-PR/job/PR-127/2/console

versions
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Thanks.
You are right.
I did make the effort because I am generally not happy with the maven setup, and may do major changes which would require re-writing the detection script.

merged manually.

merged manually.


nodebuilder: make proxy constructors private in all modules

Overview
Closes #1375
Made all the proxy constructors private except for the Key constructor of the p2p package cause it is also used in swamp

Checklist

[ ] New and updated code has appropriate documentation
[ ] New and updated code has new and/or updated testing
[ ] Required CI checks are passing
[ ] Visual proof for any user facing features like CLI or documentation updates
[ ] Linked issues closed with keywords

Codecov Report

Merging #1643 (c7b1a5c) into main (f3b334f) will decrease coverage by 0.01%.
The diff coverage is 82.22%.

@@            Coverage Diff             @@
##             main    #1643      +/-   ##
==========================================
- Coverage   54.91%   54.91%   -0.01%
==========================================
  Files         231      231
  Lines       14995    14999       +4
==========================================
+ Hits         8235     8236       +1
- Misses       5880     5882       +2
- Partials      880      881       +1

Impacted Files
Coverage Δ

nodebuilder/core/constructors.go
0.00% <0.00%> (ø)

nodebuilder/core/module.go
80.48% <ø> (ø)

nodebuilder/das/constructors.go
75.00% <ø> (ø)

nodebuilder/gateway/constructors.go
0.00% <0.00%> (ø)

nodebuilder/gateway/module.go
15.78% <0.00%> (ø)

nodebuilder/header/module.go
97.80% <ø> (ø)

nodebuilder/header/service.go
57.14% <ø> (ø)

nodebuilder/state/core.go
100.00% <ø> (ø)

nodebuilder/state/keyring.go
0.00% <0.00%> (ø)

nodebuilder/das/module.go
95.65% <100.00%> (ø)

... and 16 more

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Port production projects to project.json

This PR will port all "production" projects to project.json, side by side with the old .csprojs, compiling for net45 and dnx451 :sparkles:
Next step is to get the demo- and test projects over, then port everything to dotnet5.2. Once everything is ported and runs, we can finally kill all the .csprojs, and hopefully most of the ruby stuff as well.
The PR builds on top of #2154.

LGTM

Only looked at the last commit. :+1: for that.
Also agree that the step you wrote in the PR description are a good path to get to core.

Also quite like the restructuring. Especially getting the samples into their own folder.

Looks good to me. I just hope prior to V2 release we can ditch the csproj files.

I can't bother keeping this up to date if it's being held off until 3.0 anyway...


Sometime Documents is staying into queue for permanently

Sometimes status is not updating and not updating the processing file ahead and stays in queue only
It's not a very big file, its like a 1.40MB file with 1500rows and 5 columns of data

Hi @ravikhunt
Try these troubleshooting steps and let us know what you find.
https://github.com/microsoft/PubSec-Info-Assistant/blob/main/docs/deployment/troubleshooting.md

@ryonsteele  here is the detail
{     "id": "xxxxx",     "file_path": "upload/testing mock data.csv",     "file_name": "testing mock data.csv",     "state": "Queued",     "start_timestamp": "2024-02-07 09:33:51",     "state_description": "",     "state_timestamp": "2024-02-07 09:49:42",     "status_updates": [         {             "status": "File uploaded from browser to Azure Blob Storage",             "status_timestamp": "2024-02-07 09:33:51",             "status_classification": "Info"         },         {             "status": "Pipeline triggered by Blob Upload",             "status_timestamp": "2024-02-07 09:33:58",             "status_classification": "Info"         },         {             "status": "FileUploadedFunc - FileUploadedFunc function started",             "status_timestamp": "2024-02-07 09:33:58",             "status_classification": "Debug"         },         {             "status": "FileUploadedFunc - csv file sent to submit queue. Visible in 257 seconds",             "status_timestamp": "2024-02-07 09:33:58",             "status_classification": "Debug"         },         {             "status": "FileLayoutParsingOther - Starting to parse the non-PDF file",             "status_timestamp": "2024-02-07 09:38:29",             "status_classification": "Info"         },         {             "status": "FileLayoutParsingOther - Message received from non-pdf submit queue",             "status_timestamp": "2024-02-07 09:38:29",             "status_classification": "Debug"         },         {             "status": "FileLayoutParsingOther - SAS token generated to access the file",             "status_timestamp": "2024-02-07 09:38:29",             "status_classification": "Debug"         },         {             "status": "FileLayoutParsingOther - partitioning complete",             "status_timestamp": "2024-02-07 09:38:45",             "status_classification": "Debug"         },         {             "status": "FileLayoutParsingOther - chunking complete. 3045 chunks created",             "status_timestamp": "2024-02-07 09:38:47",             "status_classification": "Debug"         },         {             "status": "FileLayoutParsingOther - chunking stored.",             "status_timestamp": "2024-02-07 09:49:42",             "status_classification": "Debug"         },         {             "status": "FileLayoutParsingOther - message sent to enrichment queue",             "status_timestamp": "2024-02-07 09:49:42",             "status_classification": "Debug"         }     ],     "_rid": "XHUAAPn2+-IjAAAAAAAAAA==",     "_self": "dbs/XHUAAA==/colls/XHUAAPn2+-I=/docs/XHUAAPn2+-IjAAAAAAAAAA==/",     "_etag": "\"02006397-0000-0d00-0000-65c352360000\"",     "_attachments": "attachments/",     "_ts":<PHONE_NUMBER> }

Hi ravikhunt
From the detail you posted it seems chunking completed successfully and the file is stuck in enrichment queue/ embedding process. Can you please verify if your enrichment app is up and running?

It's running only and run all the time

We are also having an issue with csv and xlsx files (the 'products' file in the example Ice Cream data set) getting stuck in embedding process, but it's a max "requeue limit" issue, which I assume can be corrected with a change to the 'max_requeue_count', but we haven't tested that yet.
{
"id": "dXBsb2FkL0ljZSBDcmVhbS9wcm9kdWN0cy54bHN4",
"file_path": "upload/Ice Cream/products.xlsx",
"file_name": "products.xlsx",
"state": "Error",
"start_timestamp": "2024-02-09 18:28:53",
"state_description": "",
"state_timestamp": "2024-02-09 19:05:46",
"status_updates": [
{
"status": "File uploaded from browser to Azure Blob Storage",
"status_timestamp": "2024-02-09 18:28:53",
"status_classification": "Info"
},
{
"status": "Pipeline triggered by Blob Upload",
"status_timestamp": "2024-02-09 18:29:00",
"status_classification": "Info"
},
{
"status": "FileUploadedFunc - FileUploadedFunc function started",
"status_timestamp": "2024-02-09 18:29:00",
"status_classification": "Debug"
},
{
"status": "FileUploadedFunc - xlsx file sent to submit queue. Visible in 59 seconds",
"status_timestamp": "2024-02-09 18:29:00",
"status_classification": "Debug"
},
{
"status": "FileLayoutParsingOther - Starting to parse the non-PDF file",
"status_timestamp": "2024-02-09 18:30:23",
"status_classification": "Info"
},
{
"status": "FileLayoutParsingOther - Message received from non-pdf submit queue",
"status_timestamp": "2024-02-09 18:30:23",
"status_classification": "Debug"
},
{
"status": "FileLayoutParsingOther - SAS token generated to access the file",
"status_timestamp": "2024-02-09 18:30:23",
"status_classification": "Debug"
},
{
"status": "FileLayoutParsingOther - partitioning complete",
"status_timestamp": "2024-02-09 18:30:41",
"status_classification": "Debug"
},
{
"status": "FileLayoutParsingOther - chunking complete. 173 chunks created",
"status_timestamp": "2024-02-09 18:30:41",
"status_classification": "Debug"
},
{
"status": "FileLayoutParsingOther - chunking stored.",
"status_timestamp": "2024-02-09 18:30:55",
"status_classification": "Debug"
},
{
"status": "FileLayoutParsingOther - message sent to enrichment queue",
"status_timestamp": "2024-02-09 18:30:56",
"status_classification": "Debug"
},
{
"status": "TextEnrichment - Received message from text-enrichment-queue ",
"status_timestamp": "2024-02-09 18:31:13",
"status_classification": "Debug"
},
{
"status": "TextEnrichment - detected language of text is en.",
"status_timestamp": "2024-02-09 18:31:14",
"status_classification": "Debug"
},
{
"status": "TextEnrichment - Text enrichment is complete",
"status_timestamp": "2024-02-09 18:33:49",
"status_classification": "Debug"
},
{
"status": "Embeddings process started with model azure-openai_text-embedding-ada-002",
"status_timestamp": "2024-02-09 18:34:07",
"status_classification": "Info"
},
{
"status": "Message requed to embeddings queue, attempt 1. Visible in 60 seconds. Error: .",
"status_timestamp": "2024-02-09 18:34:18",
"status_classification": "Error",
"stack_trace": "Traceback (most recent call last):\n  File "/opt/python/3.10.12/lib/python3.10/threading.py", line 973, in _bootstrap\n    self._bootstrap_inner()\n  File "/opt/python/3.10.12/lib/python3.10/threading.py", line 1016, in _bootstrap_inner\n    self.run()\n  File "/opt/python/3.10.12/lib/python3.10/threading.py", line 953, in run\n    self._target(*self._args, **self._kwargs)\n  File "/tmp/8dc225b4bec7dc3/app.py", line 289, in poll_queue_thread\n    poll_queue()\n  File "/tmp/8dc225b4bec7dc3/app.py", line 420, in poll_queue\n    statusLog.upsert_document(blob_path, f'Message requed to embeddings queue, attempt {str(requeue_count)}. Visible in {str(backoff)} seconds. Error: {str(error)}.',\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/tenacity/init.py", line 382, in call\n    result = fn(*args, **kwargs)\n  File "/tmp/8dc225b4bec7dc3/app.py", line 87, in encode\n    response = openai.Embedding.create(\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/openai/api_resources/embedding.py", line 33, in create\n    response = super().create(*args, **kwargs)\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/openai/api_resources/abstract/engine_api_resource.py", line 153, in create\n    response, _, api_key = requestor.request(\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/openai/api_requestor.py", line 226, in request\n    resp, got_stream = self._interpret_response(result, stream)\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/openai/api_requestor.py", line 619, in _interpret_response\n    self._interpret_response_line(\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/openai/api_requestor.py", line 679, in _interpret_response_line\n    raise self.handle_error_response(\nopenai.error.InvalidRequestError: This model's maximum context length is 8191 tokens, however you requested 28524 tokens (28524 in your prompt; 0 for the completion). Please reduce your prompt; or completion length.\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File "/tmp/8dc225b4bec7dc3/app.py", line 230, in embed_texts\n    embeddings = model_obj.encode(texts)\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/tenacity/init.py", line 289, in wrapped_f\n    return self(f, *args, **kw)\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/tenacity/init.py", line 379, in call\n    do = self.iter(retry_state=retry_state)\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/tenacity/init.py", line 326, in iter\n    raise retry_exc from fut.exception()\ntenacity.RetryError: RetryError[<Future at 0x7675e37f2a40 state=finished raised InvalidRequestError>]\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File "/tmp/8dc225b4bec7dc3/app.py", line 363, in poll_queue\n    embedding = embed_texts(target_embeddings_model, [text])\n  File "/tmp/8dc225b4bec7dc3/app.py", line 244, in embed_texts\n    raise HTTPException(status_code=500, detail=f"Failed to embed: {str(error)}") from error\nfastapi.exceptions.HTTPException\n"
},
{
"status": "Embeddings process started with model azure-openai_text-embedding-ada-002",
"status_timestamp": "2024-02-09 18:36:07",
"status_classification": "Info"
},
{
"status": "Message requed to embeddings queue, attempt 2. Visible in 193 seconds. Error: .",
"status_timestamp": "2024-02-09 18:36:15",
"status_classification": "Error",
"stack_trace": "Traceback (most recent call last):\n  File "/opt/python/3.10.12/lib/python3.10/threading.py", line 973, in _bootstrap\n    self._bootstrap_inner()\n  File "/opt/python/3.10.12/lib/python3.10/threading.py", line 1016, in _bootstrap_inner\n    self.run()\n  File "/opt/python/3.10.12/lib/python3.10/threading.py", line 953, in run\n    self._target(*self._args, **self._kwargs)\n  File "/tmp/8dc225b4bec7dc3/app.py", line 289, in poll_queue_thread\n    poll_queue()\n  File "/tmp/8dc225b4bec7dc3/app.py", line 420, in poll_queue\n    statusLog.upsert_document(blob_path, f'Message requed to embeddings queue, attempt {str(requeue_count)}. Visible in {str(backoff)} seconds. Error: {str(error)}.',\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/tenacity/init.py", line 382, in call\n    result = fn(*args, **kwargs)\n  File "/tmp/8dc225b4bec7dc3/app.py", line 87, in encode\n    response = openai.Embedding.create(\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/openai/api_resources/embedding.py", line 33, in create\n    response = super().create(*args, **kwargs)\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/openai/api_resources/abstract/engine_api_resource.py", line 153, in create\n    response, _, api_key = requestor.request(\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/openai/api_requestor.py", line 226, in request\n    resp, got_stream = self._interpret_response(result, stream)\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/openai/api_requestor.py", line 619, in _interpret_response\n    self._interpret_response_line(\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/openai/api_requestor.py", line 679, in _interpret_response_line\n    raise self.handle_error_response(\nopenai.error.InvalidRequestError: This model's maximum context length is 8191 tokens, however you requested 28524 tokens (28524 in your prompt; 0 for the completion). Please reduce your prompt; or completion length.\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File "/tmp/8dc225b4bec7dc3/app.py", line 230, in embed_texts\n    embeddings = model_obj.encode(texts)\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/tenacity/init.py", line 289, in wrapped_f\n    return self(f, *args, **kw)\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/tenacity/init.py", line 379, in call\n    do = self.iter(retry_state=retry_state)\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/tenacity/init.py", line 326, in iter\n    raise retry_exc from fut.exception()\ntenacity.RetryError: RetryError[<Future at 0x7675e85cb940 state=finished raised InvalidRequestError>]\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File "/tmp/8dc225b4bec7dc3/app.py", line 363, in poll_queue\n    embedding = embed_texts(target_embeddings_model, [text])\n  File "/tmp/8dc225b4bec7dc3/app.py", line 244, in embed_texts\n    raise HTTPException(status_code=500, detail=f"Failed to embed: {str(error)}") from error\nfastapi.exceptions.HTTPException\n"
},
{
"status": "Embeddings process started with model azure-openai_text-embedding-ada-002",
"status_timestamp": "2024-02-09 18:40:18",
"status_classification": "Info"
},
{
"status": "Message requed to embeddings queue, attempt 3. Visible in 279 seconds. Error: .",
"status_timestamp": "2024-02-09 18:40:28",
"status_classification": "Error",
"stack_trace": "Traceback (most recent call last):\n  File "/opt/python/3.10.12/lib/python3.10/threading.py", line 973, in _bootstrap\n    self._bootstrap_inner()\n  File "/opt/python/3.10.12/lib/python3.10/threading.py", line 1016, in _bootstrap_inner\n    self.run()\n  File "/opt/python/3.10.12/lib/python3.10/threading.py", line 953, in run\n    self._target(*self._args, **self._kwargs)\n  File "/tmp/8dc225b4bec7dc3/app.py", line 289, in poll_queue_thread\n    poll_queue()\n  File "/tmp/8dc225b4bec7dc3/app.py", line 420, in poll_queue\n    statusLog.upsert_document(blob_path, f'Message requed to embeddings queue, attempt {str(requeue_count)}. Visible in {str(backoff)} seconds. Error: {str(error)}.',\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/tenacity/init.py", line 382, in call\n    result = fn(*args, **kwargs)\n  File "/tmp/8dc225b4bec7dc3/app.py", line 87, in encode\n    response = openai.Embedding.create(\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/openai/api_resources/embedding.py", line 33, in create\n    response = super().create(*args, **kwargs)\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/openai/api_resources/abstract/engine_api_resource.py", line 153, in create\n    response, _, api_key = requestor.request(\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/openai/api_requestor.py", line 226, in request\n    resp, got_stream = self._interpret_response(result, stream)\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/openai/api_requestor.py", line 619, in _interpret_response\n    self._interpret_response_line(\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/openai/api_requestor.py", line 679, in _interpret_response_line\n    raise self.handle_error_response(\nopenai.error.InvalidRequestError: This model's maximum context length is 8191 tokens, however you requested 28524 tokens (28524 in your prompt; 0 for the completion). Please reduce your prompt; or completion length.\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File "/tmp/8dc225b4bec7dc3/app.py", line 230, in embed_texts\n    embeddings = model_obj.encode(texts)\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/tenacity/init.py", line 289, in wrapped_f\n    return self(f, *args, **kw)\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/tenacity/init.py", line 379, in call\n    do = self.iter(retry_state=retry_state)\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/tenacity/init.py", line 326, in iter\n    raise retry_exc from fut.exception()\ntenacity.RetryError: RetryError[<Future at 0x7675e8467d30 state=finished raised InvalidRequestError>]\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File "/tmp/8dc225b4bec7dc3/app.py", line 363, in poll_queue\n    embedding = embed_texts(target_embeddings_model, [text])\n  File "/tmp/8dc225b4bec7dc3/app.py", line 244, in embed_texts\n    raise HTTPException(status_code=500, detail=f"Failed to embed: {str(error)}") from error\nfastapi.exceptions.HTTPException\n"
},
{
"status": "Embeddings process started with model azure-openai_text-embedding-ada-002",
"status_timestamp": "2024-02-09 18:46:44",
"status_classification": "Info"
},
{
"status": "Message requed to embeddings queue, attempt 4. Visible in 292 seconds. Error: .",
"status_timestamp": "2024-02-09 18:46:54",
"status_classification": "Error",
"stack_trace": "Traceback (most recent call last):\n  File "/opt/python/3.10.12/lib/python3.10/threading.py", line 973, in _bootstrap\n    self._bootstrap_inner()\n  File "/opt/python/3.10.12/lib/python3.10/threading.py", line 1016, in _bootstrap_inner\n    self.run()\n  File "/opt/python/3.10.12/lib/python3.10/threading.py", line 953, in run\n    self._target(*self._args, **self._kwargs)\n  File "/tmp/8dc225b4bec7dc3/app.py", line 289, in poll_queue_thread\n    poll_queue()\n  File "/tmp/8dc225b4bec7dc3/app.py", line 420, in poll_queue\n    statusLog.upsert_document(blob_path, f'Message requed to embeddings queue, attempt {str(requeue_count)}. Visible in {str(backoff)} seconds. Error: {str(error)}.',\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/tenacity/init.py", line 382, in call\n    result = fn(*args, **kwargs)\n  File "/tmp/8dc225b4bec7dc3/app.py", line 87, in encode\n    response = openai.Embedding.create(\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/openai/api_resources/embedding.py", line 33, in create\n    response = super().create(*args, **kwargs)\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/openai/api_resources/abstract/engine_api_resource.py", line 153, in create\n    response, _, api_key = requestor.request(\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/openai/api_requestor.py", line 226, in request\n    resp, got_stream = self._interpret_response(result, stream)\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/openai/api_requestor.py", line 619, in _interpret_response\n    self._interpret_response_line(\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/openai/api_requestor.py", line 679, in _interpret_response_line\n    raise self.handle_error_response(\nopenai.error.InvalidRequestError: This model's maximum context length is 8191 tokens, however you requested 28524 tokens (28524 in your prompt; 0 for the completion). Please reduce your prompt; or completion length.\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File "/tmp/8dc225b4bec7dc3/app.py", line 230, in embed_texts\n    embeddings = model_obj.encode(texts)\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/tenacity/init.py", line 289, in wrapped_f\n    return self(f, *args, **kw)\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/tenacity/init.py", line 379, in call\n    do = self.iter(retry_state=retry_state)\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/tenacity/init.py", line 326, in iter\n    raise retry_exc from fut.exception()\ntenacity.RetryError: RetryError[<Future at 0x7675e8680430 state=finished raised InvalidRequestError>]\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File "/tmp/8dc225b4bec7dc3/app.py", line 363, in poll_queue\n    embedding = embed_texts(target_embeddings_model, [text])\n  File "/tmp/8dc225b4bec7dc3/app.py", line 244, in embed_texts\n    raise HTTPException(status_code=500, detail=f"Failed to embed: {str(error)}") from error\nfastapi.exceptions.HTTPException\n"
},
{
"status": "Embeddings process started with model azure-openai_text-embedding-ada-002",
"status_timestamp": "2024-02-09 18:53:01",
"status_classification": "Info"
},
{
"status": "Message requed to embeddings queue, attempt 5. Visible in 730 seconds. Error: .",
"status_timestamp": "2024-02-09 18:53:13",
"status_classification": "Error",
"stack_trace": "Traceback (most recent call last):\n  File "/opt/python/3.10.12/lib/python3.10/threading.py", line 973, in _bootstrap\n    self._bootstrap_inner()\n  File "/opt/python/3.10.12/lib/python3.10/threading.py", line 1016, in _bootstrap_inner\n    self.run()\n  File "/opt/python/3.10.12/lib/python3.10/threading.py", line 953, in run\n    self._target(*self._args, **self._kwargs)\n  File "/tmp/8dc225b4bec7dc3/app.py", line 289, in poll_queue_thread\n    poll_queue()\n  File "/tmp/8dc225b4bec7dc3/app.py", line 420, in poll_queue\n    statusLog.upsert_document(blob_path, f'Message requed to embeddings queue, attempt {str(requeue_count)}. Visible in {str(backoff)} seconds. Error: {str(error)}.',\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/tenacity/init.py", line 382, in call\n    result = fn(*args, **kwargs)\n  File "/tmp/8dc225b4bec7dc3/app.py", line 87, in encode\n    response = openai.Embedding.create(\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/openai/api_resources/embedding.py", line 33, in create\n    response = super().create(*args, **kwargs)\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/openai/api_resources/abstract/engine_api_resource.py", line 153, in create\n    response, _, api_key = requestor.request(\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/openai/api_requestor.py", line 226, in request\n    resp, got_stream = self._interpret_response(result, stream)\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/openai/api_requestor.py", line 619, in _interpret_response\n    self._interpret_response_line(\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/openai/api_requestor.py", line 679, in _interpret_response_line\n    raise self.handle_error_response(\nopenai.error.InvalidRequestError: This model's maximum context length is 8191 tokens, however you requested 28524 tokens (28524 in your prompt; 0 for the completion). Please reduce your prompt; or completion length.\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File "/tmp/8dc225b4bec7dc3/app.py", line 230, in embed_texts\n    embeddings = model_obj.encode(texts)\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/tenacity/init.py", line 289, in wrapped_f\n    return self(f, *args, **kw)\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/tenacity/init.py", line 379, in call\n    do = self.iter(retry_state=retry_state)\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/tenacity/init.py", line 326, in iter\n    raise retry_exc from fut.exception()\ntenacity.RetryError: RetryError[<Future at 0x7675e84d37f0 state=finished raised InvalidRequestError>]\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File "/tmp/8dc225b4bec7dc3/app.py", line 363, in poll_queue\n    embedding = embed_texts(target_embeddings_model, [text])\n  File "/tmp/8dc225b4bec7dc3/app.py", line 244, in embed_texts\n    raise HTTPException(status_code=500, detail=f"Failed to embed: {str(error)}") from error\nfastapi.exceptions.HTTPException\n"
},
{
"status": "Embeddings process started with model azure-openai_text-embedding-ada-002",
"status_timestamp": "2024-02-09 19:05:35",
"status_classification": "Info"
},
{
"status": "An error occurred, max requeue limit was reached. Error description: ",
"status_timestamp": "2024-02-09 19:05:46",
"status_classification": "Error",
"stack_trace": "Traceback (most recent call last):\n  File "/opt/python/3.10.12/lib/python3.10/threading.py", line 973, in _bootstrap\n    self._bootstrap_inner()\n  File "/opt/python/3.10.12/lib/python3.10/threading.py", line 1016, in _bootstrap_inner\n    self.run()\n  File "/opt/python/3.10.12/lib/python3.10/threading.py", line 953, in run\n    self._target(*self._args, **self._kwargs)\n  File "/tmp/8dc225b4bec7dc3/app.py", line 289, in poll_queue_thread\n    poll_queue()\n  File "/tmp/8dc225b4bec7dc3/app.py", line 425, in poll_queue\n    statusLog.upsert_document(\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/tenacity/init.py", line 382, in call\n    result = fn(*args, **kwargs)\n  File "/tmp/8dc225b4bec7dc3/app.py", line 87, in encode\n    response = openai.Embedding.create(\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/openai/api_resources/embedding.py", line 33, in create\n    response = super().create(*args, **kwargs)\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/openai/api_resources/abstract/engine_api_resource.py", line 153, in create\n    response, _, api_key = requestor.request(\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/openai/api_requestor.py", line 226, in request\n    resp, got_stream = self._interpret_response(result, stream)\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/openai/api_requestor.py", line 619, in _interpret_response\n    self._interpret_response_line(\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/openai/api_requestor.py", line 679, in _interpret_response_line\n    raise self.handle_error_response(\nopenai.error.InvalidRequestError: This model's maximum context length is 8191 tokens, however you requested 28524 tokens (28524 in your prompt; 0 for the completion). Please reduce your prompt; or completion length.\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File "/tmp/8dc225b4bec7dc3/app.py", line 230, in embed_texts\n    embeddings = model_obj.encode(texts)\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/tenacity/init.py", line 289, in wrapped_f\n    return self(f, *args, **kw)\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/tenacity/init.py", line 379, in call\n    do = self.iter(retry_state=retry_state)\n  File "/tmp/8dc225b4bec7dc3/antenv/lib/python3.10/site-packages/tenacity/init.py", line 326, in iter\n    raise retry_exc from fut.exception()\ntenacity.RetryError: RetryError[<Future at 0x7675e8573b20 state=finished raised InvalidRequestError>]\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File "/tmp/8dc225b4bec7dc3/app.py", line 363, in poll_queue\n    embedding = embed_texts(target_embeddings_model, [text])\n  File "/tmp/8dc225b4bec7dc3/app.py", line 244, in embed_texts\n    raise HTTPException(status_code=500, detail=f"Failed to embed: {str(error)}") from error\nfastapi.exceptions.HTTPException\n"
}
],
"_rid": "jtwnAL-HDLWcAAAAAAAAAA==",
"_self": "dbs/jtwnAA==/colls/jtwnAL-HDLU=/docs/jtwnAL-HDLWcAAAAAAAAAA==/",
"_etag": ""9701b912-0000-0100-0000-65c6778a0000"",
"_attachments": "attachments/",
"_ts":<PHONE_NUMBER>
}

I believe this may be due to chunking of unstructured.io. In the version we have the library doesn't chunk by size, just creates a single chunk, which will crash later steps that can only cope with a chunk of a particular size. We have a ticket on the board to address this as part of the 1.1 releae

PR #558 was applied to main to address these issues. Pull latest from main and re-run make deploy.

resolved and closed due to inactivity


Examples in documentation don't show up anymore

When you go to https://uber.github.io/react-vis/examples/showcases/plots, it will just show a blank page as shown below.

The error message in Chrome DevTools' console is:
Failed to load resource: the server responded with a status of 404 ()

Should be resolved now, sorry about that


add CI via github actions, fixes #16

hah, the pep8 checker got me! :-)

ok, all the pytests worked, so i'll require the linter now again. the linter will be happy after #18 is merged.


Request - Idling

Like in VSCode, could we get idle status, and maybe aswell showing time having the application running not a certain file/project etc.

Implemented in this commit


Gabriella/update developer guide

Codecov Report

Merging #201 into master will decrease coverage by 9.98%.
The diff coverage is 7.00%.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update 9823f5f...3dacf2d. Read the comment docs.


the live demo has some strange,when hovered the bottom icon.

i know that now, i will fix it.

thanks！


"urlString" doesn't contain "id" param

Hi, I think I found a bug. I had tried to sign Transaction in example project, but alert with signed data didn't appear. I think the problem in parseURL(urlString) function:
    function parseURL(urlString) {
        const url = new whatwg_url_1.URL(urlString);
        const id = url.searchParams.get('id') || '';
        let result = url.searchParams.get('result') || '';
        result = result.replace(/ /g, '+');
        return {
            'id': id,
            'result': buffer_1.Buffer.from(result, 'base64').toString('hex')
        };
    }

"urlString" in response looks like this:
trust-rn-example://sign-transaction?result=+HkFhDuaygCCUgiU5HSUN5wdSO5zRUwl…X19fGHQozQt2DqKcCciQcPQUSdbxG/oAWNtubEbQFsWWBesQyl278qdo1tt0Lk3IhYkWK9oYvl

It doesn't contain "id" param, but this code tries to parse this from response:
const id = url.searchParams.get('id') || '';

Ok, I understand. Will be waiting for new commits. Our project in development too and we would like to use your app for transaction signing. Wish you a success.

Great to hear that, could you please tell us something about what project you're building? you can contact us (@vikmeup or @hewig) on Telegram.

@hewigovens merged that PR, can you update the lib on this side?

@hewigovens done: https://github.com/TrustWallet/trust-wallet-ios/commit/9f15c8f8aa05c9eee9828f5e2639795ac5d9c739

@ezykov this issue should be fixed

@hewigovens Yes, it's working correctly now 👍


Fix userbar transition on Firefox 52

Fixes #3443. Make the transition animation happen slightly after visibility has been set, so that it doesn't revert to the original invisible style after the transition has completed.
Really hoping that someone can find a less hacky solution than this, because this is horrible :-( The only way I could find to prevent the list items from jumping back to the original visibility: hidden style was to trigger the animation a few milliseconds after the visibility: visibility style is applied. 50ms seems to be enough to consistently work on my machine - 10ms isn't enough.

Aha! While trying to narrow the bug down further to report it to Firefox (only to find it's already fixed in the latest nightly), I found it only occurs when at least one of the animated properties is declared as !important... which seems to be redundant here. A better PR coming right up...


Admin UI drop down lists empty

When using the admin UI, attempting to change a 'block' type the control is a drop down list, however that list has no options to select.
The only error I had in the console log was related to favicon.
I'm using;
Chromium	45.0.2454.101 (Developer Build) Built on Ubuntu 14.04, running on LinuxMint 17.1 (64-bit)
It worked fine with Firefox.

I cannot reproduce this locally on either Chrome or Chromium, @davidoae, so it could have been related to a particular build (I'm on Chromium 49.0.2623.108 Built on Ubuntu 14.04, running on LinuxMint 17.1 (64-bit)). Are you still experiencing the problem?

Going to resolve this as "Cannot reproduce". @davidoae : Please re-open this ticket with additional information if you can reproduce this.


Final evaluation notebook

[x] Specify the model names (e.g. llama2_100k, llama2_200k,... or list with different checkpoints)
[x] Load next logprobs data (for each of the model names)
→ for now we just load a slice of the dataset (to develop faster)
[x] Specify the tokens we are interested (a sort of dynamic category)
→ using Rai’s GUI (need to be tested when integrate, just try to use the selected_ids as a dynamic variable and see how it turns out for now)
[x] Compute the statistics from the plot (copy form end2end.ipynb , currently commented out)
→ these calculate the percentiles for a group of tokens (which should be arbitrary)
[x] Plot: using function in delphi.eval.vis_per_token_model
[x] Specify 2 models for comparison.
[x] Get prompt examples (Rai’s notebook model_diff.ipynb)

The notebook will be called eval_notebook.ipynb for now. Working on the first 2 bullet points today.

we have a PR and a trello card, we don't need this issue


Duplicated Arrows on re-init

I check the code and fix it, I'm not sure if this is the best way to do it;
http://prntscr.com/cljvaq
check it out!

And only 8 years later .. issue has been fixed in version 2.0.1 😁


Jguer/ /prepare release

Add automatic changelogs
Limit threads for scanning ( avoid heavy request rate)

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LCSC Part import fails

I had the same issue as #4 and tried to run the master. I might have done something wrong when creating the package, as I never did that before. Nevertheless, when I try to run the master I get this error:
$ inventree-part-import C1525 --verbose

Traceback (most recent call last):
  File "<frozen runpy>", line 198, in _run_module_as_main
  File "<frozen runpy>", line 88, in _run_code
  File "C:\Users\tigo\scoop\apps\python\current\Scripts\inventree-part-import.exe\__main__.py", line 4, in <module>
  File "C:\Users\tigo\scoop\apps\python\current\Lib\site-packages\inventree_part_import\__init__.py", line 1, in <module>
    from .config import setup_inventree_api
  File "C:\Users\tigo\scoop\apps\python\current\Lib\site-packages\inventree_part_import\config\__init__.py", line 23, in <module>
    from ..error_helper import *
ModuleNotFoundError: No module named 'inventree_part_import.error_helper'

Found my issue, forgot to update the submodules. For anyone having the same problem in the future, the following command does the trick:
git submodule update --init --recursive

You can also just directly install the current version from master via pipx install git+https://github.com/30350n/inventree_part_import.git


Add support for higher-taxon match

The plan is to record that, for a particular phylogeny, a certain node should be considered as including a particular taxonomic unit. We can model this in CDAO by assigning multiple taxonomic units to the same node. This PR will add user interface elements to Klados to allow the curator the change which taxonomic units as assigned to which (labeled) node.
WIP

By the time I stopped worked on this PR in August 2020, it was a very early version of #228 that allowed users to directly modify the JSON object associated with each labeled node on a phylogeny, which was always intended to be replaced by a better UI in the future. I'm developing that in PR https://github.com/phyloref/klados/pull/307 now. I've copied over the code for accessing the additionalNodeProperties to PR https://github.com/phyloref/klados/pull/307 in case that turns out to be useful; otherwise, I think it makes sense to close this PR.


Use the term 'elbow boost' instead of 'corner boost'.

Done. But corner-boost is treated as a special instance of an elbow-boost.


Ionic 3 error, exec proxy not found for :: DBMeter :: start

I follow the steps to install and work with PayPal-Cordova-Plugin from here
and everything goes ok !
But when i run the command : ionic cordova run browser
i got this error :
Error: exec proxy not found for :: PayPalMobile :: init
( Missing Command Error cdv-plugin-paypal-mobile-sdk.js:41 )
and even when i try it on my android device i get the same problem :frowning:
can any one help me to understand what happen and what wrong i have done !?
thank you :slight_smile:

I solve it
all what i do is : run this command after plugged in my device
ionic cordova run android --device
(ionic cordova run browser is not working with me)
and i make it production mode


Impl Server List Protocol (client & server).

As seen in #40.
I renamed ping module to slp, makes more sense now, it's not just ping after all.

Build should fail only because of tests not working.
A vanilla server is required and JSON decoding is closely related to #37.

Repushing to fix std_misc feature warning.

Just remove the #[test] for now, I think. Or add an #[ignore].

This looks like a great start to me, by the way.

@atheriel IRC guys at #rust told me #[cfg()] let's us do this... I'm currently searching how to run cargo test to trigger that.

Rebased onto master, should get green by Travis.

Currently broken because of serde dependencies.

I rebased onto master and got rid of serde as dependency, using plain old rustc-serialize as ChatJson does.

Final repush to get rid of all warnings related to slp module.


Android sample gltf-viewer crashed when running in single thread mode

What I have changed is:

and the crash log is:

I've tried to fix this, but only got a black screen without crashing anymore. It seems Android gltf-viewer doesn't support single thread mode ...

We only support this mode for we go.


Compile PyTorch with CuPBoP.

Hi,  @drcut @hyesoon Excellent Work! Can I Compile PyTorch with CuPBoP?

Thanks for reaching out.
I think it is possible to do so, but there are some challenges which is indepdent with CuPBoP. Please correct me if I'm wrong.
First, some source code in PyTorch are automatically generated, thus, before building the PyTorch, these code is not existing, and we can only find them after the build process.
Second, also related with CUDA code, is that some kernels are generated at runtime (JIT), thus, we have to also integrate CuPBoP to the JIT process.

First, some source code in PyTorch are automatically generated, thus, before building the PyTorch, these code is not existing, and we can only find them after the build process. Second, also related with CUDA code, is that some kernels are generated at runtime (JIT), thus, we have to also integrate CuPBoP to the JIT process.

Right, we should generate these files previously by executing the codegen.py manually, then use the CuPBoP to compile them. Thx for your information.


docs: Update model support with Triton's FIL backend

Motivation
Triton introduced support for more model frameworks last year.
Modifications

Update the supported models doc
Doc how to deploy the out-of-the-box example models using Triton which depends on https://github.com/kserve/modelmesh-minio-examples/pull/7.

Result
Closes #185

#485 replaces this PR.


Issues with picker redirecting to App and back to browser on mobile

I have the picker (v7.2) working nicely from desktop browsers. However, when using it on from an android phone where the app is installed I see the following happen :

A popup opens as normal
The popup redirects to the app
The app refuses to open the link and displays an error offering to redirect to a browser
Opening in the browser will then show the picker. However, the "success" callback is then broken, and is not called when a file is selected. (even cancelling just leaves the picker hanging there)

App error message :

Options for the picker :
       clientId: oneDriveInit.clientId,         action: "download",         multiSelect: false,         openInNewWindow: true,         advanced: {             redirectUri: oneDriveInit.uri,         },

I have a similar issue with the File Picker (v7.2) in my web app running on an Android device, and that is, the javascript version of the File Picker doesn't work on an Android device.
The issue I'm experiencing is the following (on an Android phone with OneDrive native app installed):

Click on the button on a web app that opens the javascript version of the File Picker
The pop-up appears letting the user authenticates with OneDrive normally
OneDrive app opens and there is no way to communicate back to the web app

I can go to "Default Apps" settings on the Android device to change the App Links setting for "Go to supported URLs" in OneDrive to "Always Ask" so that I can choose to open File Picker on the browser from my web app, but that's not desirable.  I've also looked through the settings and advanced options in OneDrive javascript api to see if there is a way to always open the picker on a browser but can't find anything.
Is there a way to always open the picker on a browser on an Android device?
Thanks!
PS.: iOS doesn't have this issue.

Is there a way to always open the picker on a browser on an Android device?

This is due to how Android intercepts URIs redirecting to the application. Unfortunately there is not a workaround from the library POV. The user would need to modify their settings as @ooboobay mentioned above.
The lookup is based on the URL used and since we are using OneDrive URLs the OS redirects to the application which says in its manifest file that it understands those URLs.

Is it possible to have an alternative URL other than the defaults that basically just proxies to the main ones? -------- Original Message --------Subject: Re: [OneDrive/onedrive-api-docs] Issues with picker redirecting to App and back to browser on mobile (#836)From: Kevin Coughlin<EMAIL_ADDRESS>OneDrive/onedrive-api-docs<EMAIL_ADDRESS>Peekee a.riddell@daxtra.com,Author<EMAIL_ADDRESS>Is there a way to always open the picker on a browser on an Android device?
This is due to how Android intercepts URIs redirecting to the application. Unfortunately there is not a workaround from the library POV. The user would need to modify their settings as @ooboobay mentioned above.
The lookup is based on the URL used and since we are using OneDrive URLs the OS redirects to the application which says in its manifest file that it understands those URLs.
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As part of a repository clean up effort we are closing older issues. If this issue remains, please: open a new issue, reference this issue, and provide any additional details that may help in resolution. Thank you for your understanding as we work to improve our responsiveness.


[vault-secrets-webhook] pod mutation with multiple imagePullSecrets

Describe the bug:
We have a pod with two containers, each uses an image from a private registry that has two "organizations" (e.g. our-registry.com/org1/container-image1 & our-registry.com/org2/container-image2) each of these registry organizations have unique credentials, the Pod in question lists two secrets under imagePullSecrets (one per each "org").
K8s is able to pull images for both containers using the provided pull secrets.
However, vault-secrets-webhook picks the first listed imagePullSecret, for org1, where the image being pulled is from org2.
Expected behaviour:
vault-secrets-webhook should attempt all listed imagePullSecrets to find the one that works.
Steps to reproduce the bug:

create a pod with 2 containers
each container image should be stored in a separate private registry (container1 in org1, container2 in org2)
each private registry should have unique credentials
pod should have both credentials listed under imagePullSecrets, in the following order: org1, org2
mutation should be defined for container2 (with image in org2)
vault-secrets-webhook will attempt to fetch the image from org1 using credentials for org1 and fail

Additional context:
N/A
Environment details:

Kubernetes version: v1.17.13-gke.2600
Cloud-provider/provisioner (e.g. AKS, GKE, EKS, PKE etc): GKE
bank-vaults version (e.g. 0.4.17): 1.9.0
Install method (e.g. helm or static manifests): helm
Logs from the misbehaving component (and any other relevant logs):

[vault-secrets-webhook-764c6dcb49-7gmpf] time="2021-01-13T21:02:45Z" level=info msg="found credentials for registry container-registry.our-domain.com in pod imagePullSecret: platform-docs/registry-org1"
[vault-secrets-webhook-764c6dcb49-7gmpf] time="2021-01-13T21:02:45Z" level=info msg="I'm using registry https://container-registry.our-domain.com"
[vault-secrets-webhook-764c6dcb49-7gmpf] time="2021-01-13T21:02:45Z" level=error msg="admission webhook error: cannot fetch image descriptor: GET https://container-registry.our-domain.com/v2/external/oauth2-proxy/manifests/v6.1.1: UNAUTHORIZED: unauthorized to access repository: external/oauth2-proxy, action: pull: unauthorized to access repository: external/oauth2-proxy, action: pull" app=vault-secrets-webhook

for above image, the correct pull secret is the 2nd one listed under pod's imagePullSecrets: registry-org2, however vault-secrets-webhook uses the first one listed registry-org1 and fails.

Resource definition (possibly in YAML format) that caused the issue, without sensitive data:

I suppose the only relevant bit here is the imagePullSecrets definition:
    imagePullSecrets:
    - name: registry-org1
    - name: registry-org2

/kind bug

Accepted, this is going to be fixed with the already planned: https://github.com/google/go-containerregistry/blob/master/pkg/authn/k8schain/k8schain.go

Accepted, this is going to be fixed with the already planned: https://github.com/google/go-containerregistry/blob/master/pkg/authn/k8schain/k8schain.go

Thanks @bonifaido !

Thanks @bonifaido !

Hi @bonifaido, I'm cross-posting this from #1269 which seem to be a similar issue, however... it seems that the fix in #1236 didn't quite solve the problem and we're still reproducing the issue described here :-(
as a reminder, we have multiple imagePullSecrets defined that indeed exist and are correct (the reason is because same Pod has containers coming from different internal repos that have different secrets).
Now in this scenario, VSW is only able to pull images for the registry whose secret is the first in the imagePullSecrets list, if the image is in the registry whose secret is 2nd - it fails to pull the image and mutation is not performed.


add styli.sh to the list

please add styli.sh - a text formatter and renderer for the shell.
check out the markdown usage docs on the github repository, which are also generated by styli.sh (!).
💜
disclaimer: i created it

We dont use this list for self promotion. Feel free to resubmit after gaining some endorsement.


loss drops to 0

Hello, I noticed a strange thing, when training stationary scenes (such as waiting for a traffic light), the loss will quickly drop to 0 and lose vitality

Thanks for the insight! Although this sounds like a reasonable thing to do at first (e.g., when the world and the ego-agent is stationary, you cannot infer anything about the environment other than what has been seen in the past), maybe this is sub-optimal for other cases when the ego-agent or the environment is dynamic.

Thanks for the insight! Although this sounds like a reasonable thing to do at first (e.g., when the world and the ego-agent is stationary, you cannot infer anything about the environment other than what has been seen in the past), maybe this is sub-optimal for other cases when the ego-agent or the environment is dynamic.

No, I didn't try out the code yet. I will leave a message here if I try something new out.
Coud you help me figure out another question? I think I may miss something in the paper. It's about the result comparision in Table 1. The performance of the S2Net is quite different with the original paper. In the original paper, the L1 is 0.545 for 1s, 0.560 for 3s on nuScenes, but the Table 1 reports 3.49 for 1s and 4.78 for 3s. What is the different in the evaluation settings? Or could you point me out where I can find the information?

Good catch! The evaluation of both S2Net and SPFNet are quite different from the benchmarking protocol we propose.

Both the methods output a confidence score for each predicted future point. Upon discussion with the authors, we find that this confidence score is used to weigh the per-point metrics (L1 or L2 or Chamfer distance etc.) before taking their average. We proposed a modified version of this evaluation, where there is no confidence weighting (note how you could predict n points with 0 confidence and your metrics could reduce to 0).
Additionally our metrics, excluding vanilla chamfer distance, are computed in the near-field bounded volume.
Specifically for S2Net, the numbers in their paper are Top-5 numbers.

Those are all the differences.

Thanks for your reply, now I have divided into a lot of sports scenarios and started training


when scrcpy mirror screen is minimized, Not running for OBS

Hi,
When scrcpy mirror screen is minimized, Not running anymore so that we can capture for OBS
Is there a way to solve this?

On Linux, you could use v4l2, which exposes the video stream directly as a webcam (this avoids to render on a window then capture a window): https://github.com/Genymobile/scrcpy#v4l2loopback
On other systems, I don't know.

In Windows, I figured it out that if the scrcpy is added to OBS via "Game Capture", that would work even when scrcpy screen mirror window is minimized! and that solved the problem!
Thanks a million for creating and sharing this great tool! 🙏


Space before first tick on y-axis

Hi,
Is it possible to add some space before the first tick mark on the y axis? Basically I don't want the first ticks on the x and y axis to be touching
For example:

In this screenshot I basically want 2.52 to move up about 20 pixels so that it doesn't look mashed up against 05-01
Thank you

There has to be a way to do this, has anyone found a way to add a bit of padding to the y axis starting point?

Set the padding for the x axis to move it down a bit:
chart.axis.x.padding.top = 10

as Heliodor stated u need to add padding
in general we encourage users to ask questions via Google groups
si im closing this thread


messageCid cursor & reverse index kept around during delete

There can be a scenario where a user attempts to use a messageCid as a cursor for a message after it's been deleted.
In order to remedy this, we keep the reverse index lookup for the items when issuing a delete. Then when we have a query with a cursor, we can look up the indexes, and use the item as a cursor point for the in-memory pagination.
I added a purge method that truly purges all indexes + reverse, although we never use it in practice (yet).  I think I should probably remove it and we can always add it back later as it's simple.

Discussed with @thehenrytsai
Will open issue to address this immediately after the other large merge instead of this band-aid.
The proposed solution would be to use the sortProperty + sortPropertyValue + dataCid as a cursor that could be cross implementation and not require storing additional data that's not necessary.
This will also move us towards removing the need for EventLog and use MessageStore as the necessary source of truth, reducing indexing needs.


an Interesting idea

as simplenote gives you unlimited storage for notes if we can convert any file to text and paste that text in simple note we can recover the original file, I suggest you to try the possibilities.

Nice Idea


quark_se_c1000_devboard: Enable testing for the platform

This enables sanitycheck builds for the board, i.e. all tests with
usb_device supported.
Signed-off-by: Andrei Emeltchenko<EMAIL_ADDRESS>

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  Coverage   52.36%   52.36%
=======================================
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  Branches     5185     5185
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@nashif we have usb samples where whitelist was removed and now sanitycheck never build them. They have following line:
     depends_on: ieee802154 usb_device

Closing old PR


Importing initial CI scripts and config

Implementation of a basic CI workflow via github actions.

Every routine in magma has a tester and "make test" will generate all of them. The framework to run them is run_tests.py but this includes exhaustive runs that take hours. There are provisions only certain things to be tested, e.g.,
./run_tests.py -s testing_sgemm > sgemm.log
testing_sgemm -l -NN -c                           ok
testing_sgemm -l -NC -c                           ok
testing_sgemm -l -CN -c                           ok
testing_sgemm -l -CC -c                           ok
testing_sgemm -l -NT -c                           ok
testing_sgemm -l -TN -c                           ok
testing_sgemm -l -TT -c                           ok

summary

all 609 tests in 7 commands passed!
Typically, if we want to just test an installation I would test sgemm, e.g.,
make testing_sgemm
./testing/testing_sgemm -n 300
% MAGMA 2.6.1 svn 32-bit magma_int_t, 64-bit pointer.
% Compiled with CUDA support for 3.0
% CUDA runtime 7000, driver 7050. MAGMA not compiled with OpenMP.
% device 0: GeForce GT 750M, 925.5 MHz clock, 2047.6 MiB memory, capability 3.0
% Fri Apr  7 14:30:01 2023
% Usage: ./testing/testing_sgemm [options] [-h|--help]
% If running lapack (option --lapack), MAGMA and cuBLAS error are both computed
% relative to CPU BLAS result. Else, MAGMA error is computed relative to cuBLAS result.
% transA = No transpose, transB = No transpose
%   M     N     K   MAGMA Gflop/s (ms)  cuBLAS Gflop/s (ms)   CPU Gflop/s (ms)  MAGMA error  cuBLAS error
%========================================================================================================
300   300   300     35.76 (   1.51)      78.83 (   0.68)     ---   (  ---  )    0.00e+00        ---    ok
where the ok at the end indicates test is successful. If it fails we print "failed".

I think the issue I"m facing is that when using cmake with out-of-source build, the run_tests.py is not put into place, and the "make test" or "make check" is not defined.

OK, I have also implemented Stan's suggested minimal tests (sgemm), and the CI scripts look good to merge.  If there aren't any objections, I will merge this.


docs: fix aggregate_functions link.

Thanks @huskar-t , please follow the Details link in DCO check to pass it.

Thanks @huskar-t , please follow the Details link in DCO check to pass it.

done


Alert

Hi ,
i want to create the alert for metricbeat-* with the filter of specific host-name Because I have 150 nodes hosted on same project so if created an alert i am not able to find which went down for example less CPU usage kind of that
Please help me with this it's little emergency


Add more test to the test suite

Adds more database tests for methods in the user and author repos.
Resolves #180

I would agree with @Haruyzal on this one. I would probably do a merge with main and change the parts that give errors after that. And if anything gives errors then make a new branch and try to fix those from main after the merge

This branch has now been update to catch up with main


When using the "Build your own Trainer" example for Fabric, memory randomly doubles with HF transformers setup.

Bug description
https://github.com/Lightning-AI/lightning/blob/master/examples/fabric/build_your_own_trainer/trainer.py
I believe there's a memory leak somewhere in the "build your own trainer" example.
I'm having a problem tracking this down. I'm using a transformers model that's been fairly customized, and Fabric to give me more control over the training loop than what HF allows, since they try to enforce strict input datatypes that I need to get away from longterm. I borrowed the example provided and set up my Lightning module and trainer.
Then, I init my transformer model and apply peft's 8-bit and LoRA transformations before constructing the lightning module
training_config = {
    "max_epochs": 100,
    "checkpoint_frequency": 5,
    "validation_frequency": 5,
    "plugins": Precision(), #To disable Lightning's automated precision
    "accelerator": "cpu" if torch.backends.mps.is_built() else "auto"}

lora_config = LoraConfig(**lora_config)
model = prepare_model_for_int8_training(model)
model = PeftModel(model, lora_config)
model = TrainingModule(**module_config)

And this works great! For about 35 batches (full training would require like 60k batches)
At that point, the model parameters randomly double and forces my GPU OOM.
I'm using a 4090 laptop GPU, and the base model takes up about 8.2gb, so doubling the memory just barely exceeds capacity.
print(torch.cuda.memory_summary(device=None, abbreviated=False))
Running cuda memory summary shows that the memory is allocated but not used by Pytorch.

I'm just having trouble locating what's even happening. It SEEMS like the model is getting duplicated in memory, but the only callbacks I see in the basic loop are training start/stop and epoch start/stop. I'm not using gradient accumulation.
There's no reason anything should be happening at a random step like 35.
class TrainingModule(L.LightningModule):
    def __init__(self, model, base_dataclass=None, length=1281167, learning_rate=3e-4, min_lr=1e-6, base_lora_config=None, processor=None):
        #We should probably shard off the optimizer information too at some point, this is clunky
        super().__init__()
        self.num_classes = model.config.num_classes
        self.model = model
        self.loss_fn = torch.nn.CrossEntropyLoss()
        self.base_dataclass = base_dataclass
        self.lr = learning_rate
        self.min_lr = min_lr
        self.length = length
        self.base_lora = base_lora_config
        self.processor = processor
        del model

    def forward(self, x: torch.Tensor):
        with torch.no_grad(): #Don't track preprocessing gradients
            x = x.squeeze(0)
            x = self.processor(x, self.base_dataclass) if self.processor is not None else x
        return self.model(x)

    def training_step(self, batch, batch_idx: int):
        x, y = batch
        dataclass = self(x)
        dataclass.logits = dataclass.logits.unsqueeze(0)
        loss = self.loss_fn(dataclass.logits, y)
        accuracy_train = accuracy(dataclass.logits.argmax(-1), y, num_classes=self.num_classes, task="multiclass", top_k=1)
        del dataclass
        #print(torch.cuda.memory_summary(device=None, abbreviated=False))
        return {"loss": loss, "accuracy": accuracy_train}

    def configure_optimizers(self):
        optim = torch.optim.Adam(self.parameters(), lr=1e-4)
        return optim, {"scheduler": ParabolicLRScheduler(optim, self.min_lr, self.lr, self.length),
                       "monitor": "val_accuracy",
                       "interval": "epoch",
                       "frequency": 1}

    def validation_step(self, *args, **kwargs):
        return self.training_step(*args, **kwargs)

I've made minor edits to the trainer, so maybe that's helpful. I'm going to see if I can't replicate this with a pretrained model so that I can upload an open-source version of the code for testing.
Fabric is EXACTLY what I was looking for. A great stepping stone between Huggingface and a fully proprietary system, just gotta figure out what's going wrong.
What version are you seeing the problem on?
master
How to reproduce the bug
Use a huggingface model, with a LoRA adapter, and the example training script @ https://github.com/Lightning-AI/lightning/blob/master/examples/fabric/build_your_own_trainer/trainer.py

Error messages and logs
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 88.00 MiB. GPU 0 has a total capacty of 15.70 GiB of which 16.25 MiB is free. Including non-PyTorch memory, this process has 15.67 GiB memory in use. Of the allocated memory 13.74 GiB is allocated by PyTorch, and 1.62 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

Environment

Current environment
* CUDA:
	- GPU:
		- NVIDIA GeForce RTX 4090 Laptop GPU
	- available:         True
	- version:           12.1
* Lightning:
	- lightning:         2.0.6
	- lightning-cloud:   0.5.37
	- lightning-utilities: 0.9.0
	- pytorch-lightning: 2.0.6
	- pytorch-triton:    2.1.0+9e3e10c5ed
	- torch:             2.1.0.dev20230721+cu121
	- torchaudio:        2.1.0.dev20230722+cu121
	- torchmetrics:      1.0.1
	- torchvision:       0.16.0.dev20230722+cu121
* Packages:
	- accelerate:        0.21.0
	- aiohttp:           3.8.5
	- aiosignal:         1.3.1
	- annotated-types:   0.5.0
	- anyio:             3.7.1
	- arrow:             1.2.3
	- async-timeout:     4.0.2
	- attrs:             23.1.0
	- backoff:           2.2.1
	- beautifulsoup4:    4.12.2
	- blessed:           1.20.0
	- certifi:           2022.12.7
	- charset-normalizer: 2.1.1
	- click:             8.1.6
	- croniter:          1.4.1
	- datasets:          2.14.0
	- dateutils:         0.6.12
	- deepdiff:          6.3.1
	- dill:              0.3.7
	- fastapi:           0.100.0
	- filelock:          3.9.0
	- frozenlist:        1.4.0
	- fsspec:            2023.4.0
	- h11:               0.14.0
	- huggingface-hub:   0.16.4
	- idna:              3.4
	- inquirer:          3.1.3
	- itsdangerous:      2.1.2
	- jinja2:            3.1.2
	- lightning:         2.0.6
	- lightning-cloud:   0.5.37
	- lightning-utilities: 0.9.0
	- markdown-it-py:    3.0.0
	- markupsafe:        2.1.2
	- mdurl:             0.1.2
	- minlora:           0.1.0
	- mpmath:            1.2.1
	- multidict:         6.0.4
	- multiprocess:      0.70.15
	- networkx:          3.0rc1
	- numpy:             1.24.1
	- ordered-set:       4.1.0
	- packaging:         23.1
	- pandas:            2.0.3
	- peft:              0.4.0
	- pillow:            9.3.0
	- pip:               23.1.2
	- psutil:            5.9.5
	- pyarrow:           12.0.1
	- pydantic:          2.0.3
	- pydantic-core:     2.3.0
	- pygments:          2.15.1
	- pyjwt:             2.8.0
	- python-dateutil:   2.8.2
	- python-editor:     1.0.4
	- python-multipart:  0.0.6
	- pytorch-lightning: 2.0.6
	- pytorch-triton:    2.1.0+9e3e10c5ed
	- pytz:              2023.3
	- pyyaml:            6.0.1
	- readchar:          4.0.5
	- regex:             2023.6.3
	- requests:          2.28.1
	- rich:              13.4.2
	- safetensors:       0.3.1
	- setuptools:        67.8.0
	- six:               1.16.0
	- sniffio:           1.3.0
	- soupsieve:         2.4.1
	- starlette:         0.27.0
	- starsessions:      1.3.0
	- sympy:             1.11.1
	- tokenizers:        0.13.3
	- torch:             2.1.0.dev20230721+cu121
	- torchaudio:        2.1.0.dev20230722+cu121
	- torchmetrics:      1.0.1
	- torchvision:       0.16.0.dev20230722+cu121
	- tqdm:              4.65.0
	- traitlets:         5.9.0
	- transformers:      4.31.0
	- typing-extensions: 4.7.1
	- tzdata:            2023.3
	- urllib3:           1.26.13
	- uvicorn:           0.23.1
	- wcwidth:           0.2.6
	- websocket-client:  1.6.1
	- websockets:        11.0.3
	- wheel:             0.38.4
	- xxhash:            3.2.0
	- yarl:              1.9.2
* System:
	- OS:                Linux
	- architecture:
		- 64bit
		- ELF
	- processor:         x86_64
	- python:            3.11.4
	- release:           6.2.0-26-generic
	- version:           #26-Ubuntu SMP PREEMPT_DYNAMIC Mon Jul 10 23:39:54 UTC 2023

#- Lightning Component: Fabric, LightningModule:
#- PyTorch Lightning Version 2.0.6:
#- PyTorch Version (e.g., 2.1.0):
#- Python version (e.g., 3.11.4):
#- OS (Linux):
#- CUDA/cuDNN version: 12.1
#- GPU models and configuration: RTX 4090 Laptop
#- How you installed Lightning: pip
#- Running environment of LightningApp (e.g. local, cloud): local

More info
I've included everything else in the initial paragraphs. Thanks

Hi @ElleLeonne
Thanks for opening the issue.
I'll help debug this as much as I can. However, with only this description

Use a huggingface model, with a LoRA adapter, and the example training script @ https://github.com/Lightning-AI/lightning/blob/master/examples/fabric/build_your_own_trainer/trainer.py

I think it would take me a bit too long and I might not run into the same problem than you have. It would be perfect if you could share the full code, but fully I understand if it's not possible.
Since you describe a random memory duplication at an arbitrary step, it could be difficult to find what's wrong. This is just a regular training step right? You're not transitioning from training stage to validation or vice versa at this step number?

Fabric is EXACTLY what I was looking for. A great stepping stone between Huggingface and a fully proprietary system, just gotta figure out what's going wrong.

We're very happy to hear this! Glad you found the trainer template useful and I'd be interested to take a look at the modifications you made too.

Yes, it's just a random step, which is why it's so odd. I spent like twelve hours today trying to pinpoint the issue but failed.
Currently trying to overwrite HF's trainer to see if's my model somehow (even though that alone is just a refactor of one that was working before) so I guess I'll report back tomorrow.

Alright, still happening in HF's trainer, which though I understand is still built on top of Pytorch Lightning, probably means I need to pick over the tensor logic before considering a migration. Thanks for hearing me out regardless.


Images don't load when using internal routing Jellyfin (and I guess Emby or Plex too) address

Hello,
Thanks for your nice software!
I'm encountering an edge case with image loading in the web client. My Jellyfin and ErsatzTV instances are set up in a single docker-compose. That means that they can reach them via their respective container's name. So in ErsatzTV I set up my JF instance as jellyfin:8096 and I added the channels in JF with the URL ersatztv:8409.
On the web client on my PC, there's no loaded image cause ErsatzTV tries to load the JF images from http://jellyfin:8096/Items/1cb2c2cf1f44d95e290acdd3467be95c/Images/Primary?tag=f768c90d8159a98fb66ce492110b7adc&fillHeight=440 for instance. And as I am being outside the docker-compose network, I can't load them.
I see 3 possibilities to fix this, 1 being on my end, 2 on your end:

I just use my JF public URL jellyfin.whatever.com to fill in ErsatzTV so that my web client can load the images
ErsatzTV could proxy the images through its software one way or another
When adding my JF instance in ErsatzTV, I could give 2 URL. One being for ErsatzTV to make API calls to JF (the only URL there is now) and an optional "public URL" that would be used to link against images in the web client. If the optional isn't filled, you use the classic URL as it is currently done.

What do you think of this?
Thanks for reading me, have a nice day! :)

Artwork is already proxied for Jellyfin and Emby in the channel guide, but the assumption was that Jellyfin would be accessible to the client performing administrative tasks in the same way it is accessible to the ETV instance. It should be a quick change to also proxy the artwork in the UI.

Fix is available in v0.0.53-alpha.

Thanks!


Node comparison

Hello,
is this library still under active development? I wanted to know if nodes can be made comparable similar to lxml. My actual problem concerns selecting nodes according to CSS rules and certain selection rules overriding previously found nodes so I need to be able to remove certain nodes from the list of previously selected nodes.
Here a simple example just to showcase what I mean:
from selectolax.parser import HTMLParser

html = """

Welcome to selectolax tutorial

Excepteur sint occaecat cupidatat non proident

Lorem ipsum

Lorem ipsum dolor sit amet, ea quo modus meliore platonem.

"""

selectolax_tree = HTMLParser(html)

p_nodes = selectolax_tree.css('p')
# [<Node p>, <Node p>, <Node p>]

p3_nodes = selectolax_tree.css('p.p3')
# [<Node p>, <Node p>]

[node for node in p_nodes if node not in p3_nodes]
# [<Node p>, <Node p>, <Node p>]

with lxml
from lxml.html import fromstring

lxml_tree = fromstring(html)

p_nodes = lxml_tree.xpath('.//p')
# [<Element p at 0x10d24d590>,
#  <Element p at 0x10d252050>,
#  <Element p at 0x10d25add0>]

p3_nodes = lxml_tree.xpath('.//p[@class="p3"]')
# [<Element p at 0x10d24d590>, <Element p at 0x10d252050>]

[node for node in p_nodes if node not in p3_nodes]
# [<Element p at 0x10d25add0>]

Which version of selectolax do you use?

In [8]: [node for node in p_nodes if node not in p3_nodes]
Out[8]: [<Node p>]```

Related: https://github.com/rushter/selectolax/issues/15

Apparently I had 0.2.1 instead of 0.2.3 installed. That fixes my issue, thank you for the quick reply!


Wrapped Unions Fixes

Fix errors with wrapped unions.
Closes #5061
Related #1630

@OlivierCavadenti Are you planning any additional fixes in the nearby future, or can I cut new version after this is merged?

@OlivierCavadenti Are you planning any additional fixes in the nearby future, or can I cut new version after this is merged?

No, in the pipe, (in this order) : finish async / await all query integration files, finish all my opened PR (cast + override connection), then I will dig in open PR to see what we can finish.

When this fix will be published?

I'll try to publish new version today.

Released in 1.0.5


[BUG] - Input.Password has no onChange Event

Describe the bug

when i enter the password value，the onChange event Will not be called
Your Example Website or App
No response
Steps to Reproduce the Bug or Issue

enter the password value
the onChange event Will not be called

Expected behavior
``
Screenshots or Videos
No response
Operating System Version
windows11
Browser
Chrome

In your screenshot it says "onChang" missing the "e".

In your screenshot it says "onChang" missing the "e".

In fact，there has no onChange props in Input.Password and the handlePasswordChange function  to be not called

Hey @wookaoer, as @KrychuTM said, In your screenshot, it says "onChang" missing the "e", try out as follow:
<Input.Password onChange={handlePasswordChange} value={password} />

got it , sry


Disabling/editing Specific Effects

Our game uses homebrew rules for flanking, so having flanking automatically apply currently means we cannot use your mod.  If I could individually disable (or even edit) the effects it would be great, because I'd love to have flanking just work and use the effect (currently +1 to attack roll instead of advantage) that we use.

Custom effects are on the roadmap, but the implementation will take some time. In the meantime, you could dive into the effect-defiintions.js file to figure out how it works and add your own. Just be aware that any updates to the module will override your changes, so you may want to lock the module from updating if you go that route.

Any update for the custom effects? I'm running Foundry on The Forge which makes it impossible to manually add custom effects in the definitions.js unfortunately.

Thanks for the reply.
I'll add a new issue with the effects. You're right, all of those effects could be rolled separately. I prefer adding them all together though, in case of critical hits and for easy hp adjustment of the targeted npc. One of my players actually uses hunter's mark, sneak attack and colossus slayer all at once, it's much easier to have all damage combines in one big number ;-)

Custom effects are out in v1.9.0. Note that you cannot (currently) create custom effects with the same name as already defined effects

That's great news, I'll definately try it this weekend :-)


Review and update deps.

Just some house cleaning. Review and update deps.

reviewed, not required

Cool. Did you also review the devDeps? I noticed several are behind. For example semver is now at 4.2.0 but we're using ~2.1.0 but I donno if it's because of some compat issue.

Did you plan on updating the lodash version too ? It would also be cool to be able to choose the lodash version since v3 was released.

@jan4dev: The lodash version is already at 3.0.
@jdalton: can this be closed after commit 9d8ad86b77bee1a64fdd7126bbcabc32c9822778?


Bug: positionType Not Defined

When running master branch, getting a positionType error from  the gql query in src/pages/job-listings/index.js since it’s a not a defined type (also breaks routing when running locally). This looks like originated with PR #20.
There are two options for resolving it:

Option 1: Removing or commenting it out positionType in src/pages/job-listings/index.js.
Option 2: Adding a positionType to src/data/jobListings.json.

I can open open up a PR with either option as a fix, wanted to see with the group if there was a preference.

Ah snap, thanks for catching this! Here's what the job listing fields are going to look like:
[
    {
        "logoUrl": "https://draftbit.com/logo.svg",
        "applyUrl": "https://draftbit.com/apply",
        "address": "343 W Erie #310 Chicago, IL 60654",
        "featured": true,
        "position":  "Platform Engineer",
        "company": "Drafbit",
        "datePosted": "2018-12-03T15:18:31.433Z",
        "neighborhood": "River North",
        "technologies": ["Javascript", "Node.js", "React.js", "React Native"],
        "benefits": ["Health Insurance", "401k", "Unlimited Vacation Policy", "Dental Insurance"],
        "description": "Draftbit is hiring"
    }
]


SAK-50645 Lessons misplaced items when added to the page

Jira: https://sakaiproject.atlassian.net/browse/SAK-50645
Refer to videos attached to the jira which demo the bug and the proposed fix.

Okay let's merge this in to trunk


docs(README.md): add scope and status badges

What type of PR is this?
/kind documentation
What this PR does / why we need it:
This PR introduces the scope and status badges. See https://github.com/falcosecurity/evolution/issues/271 for more details.

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Proposal: Remove row and column labels

Description
Anecdotally, arbitrary labelling of rows and columns seems to be more confusing than useful.
I'm talking about when you create an index and frame, and you can optionally specify a column label an row label respectively to be used in queries in place of "rowID" and "columnID". Normally to retrieve a row or column, you'd use Bitmap(rowID=<id>, frame=<frame>) or Bitmap(columnID=<id>, frame=<frame>), but if you specified row and column labels, you use those in place of "rowID" and "columnID".
The only value this provides is making queries read in a more relatable way to the data in Pilosa. On the flipside, it increases code complexity (both client and server side), makes it difficult to understand whether a query is accessing a row or column at a glance, and increases cognitive load in that you have to remember which label was used for each frame and index. It also may fragment the language which different groups use to discuss Pilosa and inhibit understanding between different users.
Therefore, in order to simplify Pilosa, and reduce our API surface area, I propose that we deprecate and eliminate the ability to specify arbitrary labels on rows and columns.
The purpose of this ticket is to gather community feedback to determine the level of disruption which this change would cause, and whether we don't understand any important uses of row and column labels.
Success criteria (What criteria will consider this ticket closeable?)
Row and column labels have been removed from Pilosa, the documentation, and any examples or tools which were using them.

Should we codify a deprecation policy? This would be a good place to start.

I propose we change it to row and col

I'm going to move ahead with this unless there are any objections (including the row/col change). As far as deprecation goes, I think since we're pre-1.0 we just make it clear in the changelog and release notes that this is a breaking change.

If this is not a priority, why don't we just set the label defaults to row/col (or something else) and remove the bit about specifying labels from the docs ?

There's quite a bit of work to remove the labels feature, apart from pilosa internals: docs, getting started, clients, client docs, (and other subprojects) etc, not even mentioning external projects. It would be great if we could give some time for our users (and us) to adapt, I agree with @alanbernstein that this would be the perfect time to adapt a deprecation policy.

Alright that makes sense. How about we strip everything out of the docs, and add some warnings in Pilosa and client libs for anyone using the feature. After the 0.7.0 release goes out (or whichever one is next), we can remove the feature entirely for the 0.8.0 release.

Great!

@jaffee @travisturner Can we keep the defaults columnID and rowID ? So code which already uses the defaults don't get broken ?

Let's still change the defaults but try to do so in a safe way - i.e. deprecate first so that we issue a warning if we see the old label, but still support it as normal, and then remove the support in the next release. As we transition to no labels, let's make sure that Pilosa doesn't break existing meta files - it might be possible for pilosa to read older meta files with the row/col labels and just ignore those fields - the implementer should experiment with this.


Use setjmp + longjmp to handle errors

There are not that many places errors can occur and the library itself is designed to not allocate additional memory (except for file buffers). Handling errors in one place reduces the number of possible branches and therefore may positively impact performance and code size.

I have an implementation of this locally. There's a slight increase in performance and code complexity is reduced too. Likely ends up in the work done for #19.


How do I catch a specific SQLite error

I have a unique constraint on my database that prevents inserting a row with identical _userId AND _event values. SQLite properly throws an exception when I try to do this, but how can I catch it specifically? Right now, I have no way of knowing if my insert fails because of my constraint (which I can easily handle) or for some other SQLite related reason (that means a more serious problem.)
do {
    let insertQuery = logTable.insert(_userId <- event.userId, _event <- event.event)
    try db.run(insertQuery)
} catch {
    print("There's an error \(error)")
}

Thanks!

You can use pattern matching:
do {
    try db.run(users.insert(email <- "alice@mac.com"))
    try db.run(users.insert(email <- "alice@mac.com"))
} catch let Result.error(_, code, _) where code == SQLITE_CONSTRAINT {
    print("constraint failed")
} catch let error {
    print("insertion failed: \(error)")
}

@jberkel Hey!
As I understood SQLITE_CONSTRAINT (19) is a Primary Result Code. However what can I do to get deeper understating of the error?
From what I found https://www.sqlite.org/rescode.html there is also an Extended Result Code which is more precise. In my case I would like to handle this error SQLITE_CONSTRAINT_UNIQUE (2067) and say to the user that object was already saved.
Do I miss something or is it not possible?
Thanks in advance!

Hi @jberkel , do you have answer to @wow-such-amazing 's question? Same question here, I want to handle SQLITE_CONSTRAINT_UNIQUE, but unable to find it