Updated to Meteor 1.3 - meteorInstall is not defined

Hi guys,
Having some trouble with this package, having updated to Meteor 1.3. Getting the following error:
/Users/Andrew/.meteor/packages/meteor-tool/.1.3.1.co8qz4++os.osx.x86_64+web.browser+web.cordova/mt-os.osx.x86_64/dev_bundle/server-lib/node_modules/fibers/future.js:267

ReferenceError: meteorInstall is not defined
at packages/useraccounts_flow-routing.js:15:15
at packages/useraccounts_flow-routing.js:323:3
at /Users/Andrew/App/.meteor/local/build/programs/server/boot.js:283:10

I checked out the file, and meteorInstall isn't actually defined.
Any help would be really appreciated. Had to downgrade back to 1.2 in the meantime :(
Thanks - - Andrew

Ran into the same problem after installing this package in my Meteor 1.3.1 app. Had to drop the package altogether.

Fixed, I just opened a PR

Sweet, thanks @gwendall

I'm facing the same issue as well! Has anyone managed to find a fix?

I'm having this issue with Meteor 1.4, is it supported?

im having this issue with Meteor 1.4.2, is it supported?

im having this issue with Meteor <IP_ADDRESS>, is it supported?

Why is this closed? I'm having the same issue on Meteor <IP_ADDRESS>

I have same in 1.8.1


[Bug]: test:assertTrue throws error

Description
@test:Config {}
isolated function testGetMessages2() returns error? {
    Message message = check gmailClient->/users/me/messages/["xxxxxxxx"](format = "raw");
    io:println(message);
    test:assertTrue(message.'from !is (), msg = "/users/[userId]/messages/[id]");
}

'from is an optional field and was not set.
Here the assert is throwing error ,
error {ballerina/test:0}TestError ("/users/[userId]/messages/[id]")
            callableName: createBallerinaError moduleName: ballerina.test.0 fileName: assert.bal lineNumber: 41
            callableName: assertTrue moduleName: ballerina.test.0 fileName: assert.bal lineNumber: 61
            callableName: testGetMessages2 moduleName: ballerinax.googleapis.gmail$test.4.tests.test fileName: tests/test.bal lineNumber: 61
            callableName: testGetMessages2$lambda2$ moduleName: ballerinax.googleapis.gmail$test.4.tests.test_execute-generated_1 fileName: tests/test_execute-generated_1.bal lineNumber: 6

Steps to Reproduce
No response
Affected Version(s)
No response
OS, DB, other environment details and versions
No response
Related area
-> Test Framework
Related issue(s) (optional)
No response
Suggested label(s) (optional)
No response
Suggested assignee(s) (optional)
No response

As you are checking message.'from !is () it becomes false if you don't have a field 'from right? In that case this is the assertion failure log for the test. It is expected.

Ah yes, IT was a mistake on my part.


Unable to expose Kibana to  local network

there is no available parameter on the /etc/kibana/kibana.yml to configure Kibana to listen on any different address than localhost - i am currently running OpenDistro on a Centos VM set up on bridged mode on VirtualBox and i can't reach the server from any of my machines on my LAN - i can see other services exposed (Elasticsearch) but not Kibana.
Any help is appreciated, thank you!

Hi @kremlinkid, even if they're not in the file/commented out, you can use all the standard Kibana parameters. In this case, I think setting server.host to your VM's IP should do the trick. Let me know. Thanks!


rendering again with new data

Once the chart is rendered the first time it won't update with new data.  I can force it by removing the child dom nodes but I'm thinking that's a hack and defeats the purpose of using react.
I've created an example to illustrate the issue here.

Thank you for the example, it puzzles me too, I will figure this out today evening

Just looking at the code it looks like maybe ChartCanvas needs a componentWillReceiveProps.

you are right, I just pushed a fix for that into the gh-pages branch for the docs. The example you created works now after I removed the while loop. I will push the changes in now.

Thanks for fixing this so quickly!

Sure thing, I have been working on 0.2 for some time and finding it hard to update the npm package since there are examples updated for that.
Are you using the npm version? I can update the same fix and push a 0.2.0-alpha to npm if you are willing to give it a test drive.

Yes I'm using the npm version and would be willing to try the alpha.

cool, it also updates a few other defects with compare and window resize.
Most of the work is on

removing the dependency on the .css file and using svg attributes
using pure react axis
option to use canvas + svg instead of svg only

I will try to get the alpha out to npm

Ok sounds good.
I just managed to figure out how to add the css using webpack, I guess I could have saved some time there :grin:

I published both 0.1.5 and 0.2.0-alpha-1
the docs using 0.2.0-alpha-1 code are available in
http://rrag.github.io/react-stockcharts-v0.2-alpha/
The documentation is still not updated, but you can check out canvas and svg used there. Working on updated documentation for 0.2

0.1.5 works like a charm, I'll give 0.2.0 a go now.

if you have styling issues, look at the examples in https://github.com/rrag/react-stockcharts/tree/v0.2/docs/lib/charts
v0.2 does not use .css so all 0.1.x examples wont style properly.

I think I'm part way there, I had issues with importing the axis but got that figured out.  I still have an issue with the area chart drawing, I'm not sure if it's a style thing or something else as it doesn't look like I get a path element.

can you create a plunker with http://rrag.github.io/react-stockcharts-v0.2-alpha/dist/react-stockcharts.js
The reason path element is not present is 0.2 uses canvas based drawing as default, svg is still used for cross hair, axis.
https://github.com/rrag/react-stockcharts/blob/v0.2/src/lib/ChartCanvas.jsx#L150&L157
I am finding canvas performing much better for pan actions, hence the change in default. I am open to feedback

I'm not sure what I have wrong on the go, it works perfectly fine on plunker.
my import used to be:
import {AreaChart, ChartCanvas, Chart, XAxis, YAxis, DataSeries, AreaSeries} from 'react-stockcharts';
I changed it to import ReStock from 'react-stockcharts';
along with defining the vars as:            var ChartCanvas = ReStock.ChartCanvas                     , XAxis = ReStock.axes.XAxis                     , YAxis = ReStock.axes.YAxis                     , AreaSeries = ReStock.AreaSeries                     , Chart = ReStock.Chart                     , DataSeries = ReStock.DataSeries;
The axis show up but no area chart, no error in the console.

I will update the docs with 0.2 code and also update a few of the examples on gist to use the 0.2 version. I did change XAxis and YAxis to go inside ReStock.axes. probably should have continued to reference it from ReStock. Let me know if I can help help with anything.

@gschrader were you able to figure it out. I got another version out and have a plunker http://plnkr.co/edit/gist:f7d18bd9a88b97aa069f?p=preview based on that. May be this will help

sorry @rrag, I had other things get in the way.  I'll try to get back to this soon.

I finally got back to this and was able to get 0.2.5 working successfully.  I did have to use the import syntax above for my ES6 code so that might be worth mentioning somewhere in the docs.


Supporting OkHttp Interceptor

Adds changes for enabling okhttp interceptor usage and introducing WaspLoggingInterceptor as an alternative for logging which logs complete headers.

There is conflict, probably with the readme file

And missing unit test.

it is still cannot be merged, can you check again merge with the development?


storage: Names can be silently transformed when creating an object

storage.NewWriter accepts object names as Go strings, which are sequences of arbitrary bytes. It then (transitively) forwards them on to a JSON marshaller, which can represent only sequences of Unicode codepoints (cf. ECMA 404). The documentation for json.Marshal says this:

String values encode as JSON strings coerced to valid UTF-8, replacing invalid bytes with the Unicode replacement rune.

The net result of this is that if I provide an object name like b\xff (a legal Go string) to storage.NewWriter, it causes my GCS bucket to contain an object with the unexpected name b\xef\xbf\xbd (represented in JSON as b\ufffd).
I think two things should be changed:

The documentation for the storage package should precisely specify what the space of legal object names is.

Requests to create an object with an illegal name should fail, and no object should be created.

It doesn't matter to me whether #2 is achieved by adding a check in the upstream generated google.golang.org/api/storage/v1 package or in the wrapper (though the former is preferable), but as a user of the package it's what I should observe.
(This all probably applies to bucket names, too.)

Coming back to this after a long hiatus.
The storage docs now have thorough descriptions for valid naming. I'll add the highlights to this API and link out for the full reference.
@mcgreevy and I had a chat about the feasibility of doing this in the underlying generated APIs more generally. That looks like it will add a lot of complexity, unless we could get fuller support from the encoding/json package (the current coercing behaviour was added in Go 1.2). I think we'll leave the check local to object names unless we here about people running into this situation in other cases.
Fortunately bucket names are required to be a subset of ASCII, so this corruption won't affect the server's validity check.

Thanks, David!


Building on Windows 11 ARM64

Please consider the #323 PR

You'll have to find a way to let it also build on wasm.

how about #325

Merged


Phi-3 cannot be used in VLLM inference

hi,
vllm 0.6.3.post1
here is the testing script:
llm = LLM(model= llm_name, dtype='float16',
            tensor_parallel_size=4, gpu_memory_utilization= 0.96, #seed=None,
            trust_remote_code=True,
            quantization="bitsandbytes", load_format="bitsandbytes", enforce_eager=True,
            enable_lora=False,
            cpu_offload_gb = 48
            #tokenizer_mode= "mistral" if args.llm_name.startswith('mistralai') else 'auto'
        )

when llm_name is microsoft/Phi-3.5-mini-instruct, or microsoft/Phi-3-mini-128k-instruct or other models under the same series, inference causes error [rank0]: KeyError: 'layers.21.mlp.gate_up_proj.weight'

Hi @yananchen1989
The KeyError you're encountering suggests that there's a mismatch between the model layers expected by the script and the actual model layers loaded from the checkpoint. This can happen due to several reasons, such as differences in model architecture or issues with the quantization process.
Here are a few steps you can take to troubleshoot and resolve this issue:
Verify Model Architecture: Ensure that the model architecture defined in your script matches the architecture of the Phi-3.5-mini-instruct model. Check the model's documentation or source code for any discrepancies.
Check Quantization Settings: Since you're using bitsandbytes quantization, make sure that the quantization settings are correctly applied and compatible with the model3. You might need to adjust the quantization parameters or try a different quantization method.
Update Dependencies: Ensure that all your dependencies, including PyTorch, transformers, and any other libraries, are up to date. Sometimes, compatibility issues can arise from outdated packages.
Load Model Weights Manually: If the automatic loading process is causing issues, you can try loading the model weights manually. This involves specifying the exact layers and weights to load from the checkpoint.
Consult GitHub Issues: Check if others have encountered similar issues and if there are any solutions or workarounds suggested in the GitHub repository or related forums. https://github.com/vllm-project/vllm/issues

thanks. i guess it is the quantization="bitsandbytes", load_format="bitsandbytes" causes the error.


it looks like be banned

Expected Behavior
[2016-07-25 14:40:59] [x] PokemonGO Bot v1.0
[2016-07-25 14:40:59] [x] Configuration initialized
[2016-07-25 14:40:59] [x] Coordinates found in passed in location, not geocoding.
[2016-07-25 14:40:59]
[2016-07-25 14:40:59] [x] Address found: 37.806881,-122.417908
[2016-07-25 14:40:59] [x] Position in-game set as: (37.806881, -122.417908, 0.0)
[2016-07-25 14:40:59]
2016-07-25 14:41:18,786 [    pgoapi] [ERROR] Login failed - unexpected server response!
[2016-07-25 14:41:18] Login Error, server busy
Actual Behavior
and i also cant login by my phone
Steps to Reproduce
Other Information
OS: EC2
Git Commit: (run 'git log -n 1 --pretty=format:"%H"' and paste it here)

Did you even check to see if the servers were working?
http://ispokemongodownornot.com/
I'm guessing you're using a PTC account?

yes,i change my another google account can login by my iphone
so,mayde not so bad,thanks


New donation form is unintuitive with regard to dropoff_location or other donation types

When entering a new donation, there is not an option to enter a dropoff location. Once the donation information is complete and it is added to the "all donations" page, the dropoff location field is there. I would like to be able to add drop off location when entering a new donation. Thank you!
Updated: I just did a new donation (from our pickup location) and see that that's where the dropoff field becomes an option to populate on the new donation form. Would it be possible to have that field on all donation forms? I'd like to be able to see how many donors are dropping off to our warehouse vs. somewhere else.

@SCDB -- between issue #225 (renaming Dropoff Location to Donation Site) and issue #240 (create a Diaper Drive Participant as part of the existing workflow) -- I think we're addressing all the issues you brought up in this one. Would you agree? I can close it if there isn't anything else that still needs to be handled.

Good morning @armahillo --I agree this one can be closed. Those other two issues seem to be covering everything perfectly. Thank you!


aptible config:get

Retrieves a single environment variable, and prints it out (shell-escaped) with no other output

This issue is currently low in priority. Closing this out.


BUILDKITE_PLUGIN_BUILDPIPE_BUILD_PROJECTS="*" not honored

Hello,
I'm currently working out an issue where BUILDKITE_PLUGIN_BUILDPIPE_BUILD_PROJECTS="*" is ignored in the environmental variables section of the Buildkite UI's "manual" build feature and when injected into a trigger from a different pipeline. In both cases only the last changed project is built. This behavior does not change when manually specifying comma separated project names vs. the wildcard. I did confirm the variable is being passed into the pipeline build. Please let me know what relevant debug or logging information I can provide.
Thanks,
-- Jim

That did it. Greatly appreciated!


Test custom assembly

Requirement

[ ] #13
[ ] #5

Tasks

[ ] Write tests
[ ] Run smoke tests
[ ] Run artifacts tests

Useful links

Running integration, system, and package tests
Writing integration and system tests

Job Parameters:

It expects a parameter named PKG_SUITE which is a choice parameter with options specified by the variable pkgTestsuites.

Source Control Management (SCM):

It uses Git as the source control management system.
It specifies the Git repository URL and branch to pull the source code from based on the gitBranch variable.

Steps:

It executes a series of shell commands as part of the build process.
It seems to be preparing the environment by setting up environment variables related to Hadoop.
It downloads repository files and GPG keys based on the target operating system and label.
It then runs a Docker container with specific settings and executes Maven commands inside it. These Maven commands likely involve building and testing packages.
The Maven command parameters seem to include the repository file URL, log level, debug mode, and test inclusion based on the chosen package suite.

Publishers:

It archives JUnit test reports generated during the build process.
It retains long standard output to capture any relevant information.

Axes:

It defines axes for the build, likely for matrix-based testing.
It specifies text axes for TARGET_OS and slaves based on the variables targetOS and dockerLabel respectively.


1.0?

@jonschlinkert is this module ready to be 1.0?

yeah, I think so. I've been trying to get them all updated to 1.0. thanks for pointing them out. I'll do this asap


Meeting a lot of problem  when testing data.

Hi,everyone, I'm new in Multi-Object tracking and  I want test one of MOT17 data(such as MOT17-01-DPM in test),I meet a lot of problem.

I just use  000001.jpg, 000002.jpg,..... in MOT17-01-DPM/img1, is this ok? Do I still need to do something else with the dataset?
Where I set the test data file path ，I can't find it.
python main.py configs/conv3d_sep2 "{"task":"forward_tracking","dataset":"MOTS_segtrack_feed","load_epoch_no":5,"batch_size":5,"export_detections":true,"do_tracking":false,"video_tags_to_load":["MOT17-01-DPM"]}", Is there any problem in this command? And what does "video_tags_to_load" use for?
I have been confused for many days, I sincerely hope that someone can answer my question.
Thanks very much.

hi, i am done mot this working perfect.

000001.jpg, 000002.jpg,..... in MOT17-01-DPM/img1 this format is working perfect no need to change.

TrackR-CNN/datasets/MOT/segtrack/MOTS_segtrack_feed.py  change this file
and set DEFAULT_PATH as your dataset test path
import glob
from datasets.Loader import register_dataset
from datasets.KITTI.segtrack.KITTI_segtrack_feed import KittiSegtrackLikeFeedDataset

NAME = "MOTS_segtrack_feed"
DEFAULT_PATH =  "mot17/test"

SEQ_IDS_TRAIN = []
SEQ_IDS_VAL = ["%04d" % idx for idx in [2, 5, 9, 11]]

@register_dataset(NAME)
class MOTSSegtrackFeedDataset(KittiSegtrackLikeFeedDataset):
  def __init__(self, config, subset):
    super().__init__(config, subset, "MOTS_segtrack", DEFAULT_PATH, SEQ_IDS_TRAIN, SEQ_IDS_VAL, False)
    self.time_starts_at_1 = True

  def get_filenames_for_video_idx(self, idx):
    return sorted(glob.glob(self.data_dir + self._video_tags[idx] +"/img1"+ "/*.jpg"))

python main.py configs/conv3d_sep2 "{"task":"forward_tracking","dataset":"MOTS_segtrack_feed","load_epoch_no":5,"batch_size":5,"export_detections":true,"do_tracking":false,"video_tags_to_load":["MOT17-01-DPM"]}" here no need to change ..
@LiXm1002

hi, i am done mot this working perfect.

000001.jpg, 000002.jpg,..... in MOT17-01-DPM/img1 this format is working perfect no need to change.

TrackR-CNN/datasets/MOT/segtrack/MOTS_segtrack_feed.py change this file
and set DEFAULT_PATH as your dataset test path
import glob
from datasets.Loader import register_dataset
from datasets.KITTI.segtrack.KITTI_segtrack_feed import KittiSegtrackLikeFeedDataset

NAME = "MOTS_segtrack_feed"
DEFAULT_PATH =  "mot17/test"

SEQ_IDS_TRAIN = []
SEQ_IDS_VAL = ["%04d" % idx for idx in [2, 5, 9, 11]]

@register_dataset(NAME)
class MOTSSegtrackFeedDataset(KittiSegtrackLikeFeedDataset):
  def __init__(self, config, subset):
    super().__init__(config, subset, "MOTS_segtrack", DEFAULT_PATH, SEQ_IDS_TRAIN, SEQ_IDS_VAL, False)
    self.time_starts_at_1 = True

  def get_filenames_for_video_idx(self, idx):
    return sorted(glob.glob(self.data_dir + self._video_tags[idx] +"/img1"+ "/*.jpg"))

python main.py configs/conv3d_sep2 "{"task":"forward_tracking","dataset":"MOTS_segtrack_feed","load_epoch_no":5,"batch_size":5,"export_detections":true,"do_tracking":false,"video_tags_to_load":["MOT17-01-DPM"]}" here no need to change ..
@LiXm1002

Hi,
Thank you for sharing! I repeated your steps but the detection results txt file was still empty. Did you modify the config file? Where did you put MOT datasets and did you use the relative path of the dataset directory?
Thank you so much.

my dataset path like
mot17/
->train
->test
->MOT17-01-DPM
->img1
dataset folder mot17 is in TrackR-CNN so not require whole path so i am give only mot17/test in DEFAULT_PATH in TrackR-CNN/datasets/MOT/segtrack/MOTS_segtrack_feed.py

my dataset path like
mot17/
->train
->test
->MOT17-01-DPM
->img1
dataset folder mot17 is in TrackR-CNN so not require whole path so i am give only mot17/test in DEFAULT_PATH in TrackR-CNN/datasets/MOT/segtrack/MOTS_segtrack_feed.py
@kliao4243

Thank you so much. Do you mean that the path is "TrackR-CNN/test/MOT17-01-DPM/img1"? Mine still does not work...

if this "TrackR-CNN/test/MOT17-01-DPM/img1" is your dataset path then give DEFAULT_PATH =TrackR-CNN/test/ and "video_tags_to_load":["MOT17-01-DPM"]
@kliao4243

sorry @kliao4243   it's my mistake  in config file "MOTS_segtrack_data_dir": "TrackR-CNN/test/"

@kishan2110 Thank you for your help, I can already test it.

hi, i am done mot this working perfect.

000001.jpg, 000002.jpg,..... in MOT17-01-DPM/img1 this format is working perfect no need to change.

TrackR-CNN/datasets/MOT/segtrack/MOTS_segtrack_feed.py change this file
and set DEFAULT_PATH as your dataset test path
import glob
from datasets.Loader import register_dataset
from datasets.KITTI.segtrack.KITTI_segtrack_feed import KittiSegtrackLikeFeedDataset

NAME = "MOTS_segtrack_feed"
DEFAULT_PATH =  "mot17/test"

SEQ_IDS_TRAIN = []
SEQ_IDS_VAL = ["%04d" % idx for idx in [2, 5, 9, 11]]

@register_dataset(NAME)
class MOTSSegtrackFeedDataset(KittiSegtrackLikeFeedDataset):
  def __init__(self, config, subset):
    super().__init__(config, subset, "MOTS_segtrack", DEFAULT_PATH, SEQ_IDS_TRAIN, SEQ_IDS_VAL, False)
    self.time_starts_at_1 = True

  def get_filenames_for_video_idx(self, idx):
    return sorted(glob.glob(self.data_dir + self._video_tags[idx] +"/img1"+ "/*.jpg"))

change python main.py configs/conv3d_sep2 "{"task":"forward_tracking","dataset":"MOTS_segtrack_feed","load_epoch_no":5,"batch_size":5,"export_detections":true,"do_tracking":false,"MOTS_segtrack_data_dir": "path/to/mot/test","video_tags_to_load":["MOT17-01-DPM"]}"
@LiXm1002

python scripts/eval/segtrack_tune_experiment.py /path/to/detections/ /path/to/groundtruth/ /path/to/precomputed_optical_flow/ /path/to/output_file /path/to/tmp_folder/ /path/to/mots_eval/ association_type num_iterations
hello, i want to ask you for help. can you tell me that what your paths "/path/to/detections/ /path/to/groundtruth/ /path/to/precomputed_optical_flow/ /path/to/output_file /path/to/tmp_folder/ /path/to/mots_eval/ association_type num_iterations" are?
Thank you so much!

Hey, thanks for your discussion! Unfortunately, it is very hard to track who is getting which errors in this thread, so I'll close this for now. Please open a new issue with your exact problem description, if something still doesnt work.

@kishan2110  Hello! Thanks for sharing ! I followed your steps,but I got the error:

Traceback (most recent call last):
File "/home/echo/a_project/TrackR-CNN/main.py", line 39, in
tf.app.run(main)
File "/home/echo/anaconda3/envs/MOTS/lib/python3.6/site-packages/tensorflow/python/platform/app.py", line 125, in run
_sys.exit(main(argv))
File "/home/echo/a_project/TrackR-CNN/main.py", line 22, in main
assert os.path.exists(config_path), config_path
AssertionError: {"task":"forward_tracking","dataset":"MOTS_segtrack_feed","load_epoch_no":5,"batch_size":5,"export_detections":true,"do_tracking":false,MOTS_segtrack_data_dir: /home/echo/a_project/TrackR-CNN/kitti_mots/mot/mot17/test/,"video_tags_to_load":[MOT17-12-SDP]}

DO you know how to solve this problem?

Please do not ask new questions in closed issues. Open a new issue instead if needed.
 string as the only argument to main.py. You also need to give a config file as argument.)


Source IP for Services with Type=ClusterIP - does not work

Not sure if this is the doc issue or the feature issue. I have already opened a feature issue here - https://github.com/kubernetes/kubernetes/issues/122954
FYI

/language en

related page : https://kubernetes.io/docs/tutorials/services/source-ip/#source-ip-for-services-with-type-clusterip

Kubernetes Services allows external connection to the internal cluster Pods and also manages internal communication among the Pods within the cluster via different Services type in defined in the ServiceSpec.
These service types are

ClusterIP
NodePort
LoadBalancer

Please refer for ClusterIP Type https://kubernetes.io/docs/concepts/services-networking/cluster-ip-allocation/

I have found the root cause - linkerd proxy. Described it here - https://github.com/kubernetes/kubernetes/issues/122954#issuecomment-1910482077
It would be helpful though to have a comment in the documentation warning that the presence of a proxy sidecar, like linkerd is likely to change the outcome.


Constraint layout and compose constraint layout conflict

Checklist

[x] I tried to reproduce the bug in my fork of the Kotlin Libraries Playground, or in one of the samples in this repository
[ ] I have run with $ ./gradlew --scan refreshVersions to provide a Build Scan URL

Describe the bug

When using the jetpack compose constraint layout library with the legacy library, the versions conflict and must be manually defined.
To Reproduce
Build scan URL: https://scans.gradle.com/s/xxxxxxxxxx

Add constraint layout (legacy) to build.gradle (v2.0.4)
Add constraint layout (compose) to build.gradle (v1.0.0-alpha03)
Try to sync, notice it syncs fine
Replace versions with underscores
Notice that versions.properties fills out with version.androidx.constraintlayout=2.0.4

Expected behavior

The constraint layout compose library should get its own separate version entry so that I do not need to define it manually in my gradle file.
Additional context

https://maven.google.com/web/index.html#androidx.constraintlayout:constraintlayout-compose:1.0.0-alpha03
https://maven.google.com/web/index.html#androidx.constraintlayout:constraintlayout:2.0.4

Hello, I just wanted to tell you I acknowledged the issue, and I'll provide a solution in the next release.

Version 0.10.0 is out with the fix!


RGB image

How to use the raw RGB instead of the grayscaled image ?
I have some troubles with the neural networks shape which doesn't match the observation shape (84,84,3) ?

Why dont you just convert them into grayscale.

because some objects are very similar and only their color can be used to "recognize" them

I would also like to know this, I tried using 3D Convolutions since the shape is gonna be 5 dimensions, but I haven't worked on it any farther.

@coreylynch Any help would be appreciated thanks.


Proxy not working in version 1.0.x ?

I updated from 0.9.5 to 1.0.5 and it says that:
"Proxy error: Could not proxy request /ui_host/api/v1/account/context from localhost:3000 to http://[::1]:5000/."
Any ideas?

Can you downgrade to 1.0.4 and try?

Normally, this error would mean that your server is not running on http://[::1]:5000/. Are you sure your server is running?

Im 100% sure that server works and is running properly. Im not sure what this [::1] means?

What does your proxy config look like?

[::1] is the IPv6 equivalent of localhost.
@tomjal can you try changing your proxy from localhost to <IP_ADDRESS>?
What host are you binding to with your application running on 5000?
If your application on 5000 is binding to <IP_ADDRESS> and you're specifying localhost in your proxy, this is expected to not work.

@Timer you were right, it works with <IP_ADDRESS>. The server is .NET Core Kestrel and for some reason it does not work with IPv6. Thanks

I'm going to reopen this because I think we can make this smarter.
We resolve localhost to IPv6 because normally that's what it is, but that is obviously painful for servers that don't support it.
I think we should act more in line with the underlying network and try both, or only perform this resolution when you're offline.

What version of .NET core are you on? This announcement seems relevant: https://github.com/aspnet/Announcements/issues/185.

For what it's worth, I had exactly the same problem with a Google App Engine backend. I fixed it by changing the proxy from localhost to <IP_ADDRESS>, as already suggested.
There might be other strange and wonderful servers that people are using that can be affected by this.

While inspecting these individual cases, it's amazing how deep into the rabbit hole you can go.
In @uvtzxpm's case, dev_appserver.py connects to localhost but Python 2 by default doesn't enable IPv6 (you have to configure it with --enable-ipv6; Python 3 has it on by default afaik).

I went ahead and did some testing on Windows.
So it seems that localhost is resolvable as long as you're on a network, but not necessarily online.
Since this is the case, we don't really want to resolve localhost to something else if we're on a network.
If we're not on a network, we are going to be super safe and assume <IP_ADDRESS>.
See #2332.

@uvtzxpm can you manually patch your files with that PR and make sure it works when you switch back to localhost?
@tomjal would appreciate if you could try too.

This should be fixed in 1.0.6.
https://github.com/facebookincubator/create-react-app/releases/tag/v1.0.6

@Timer I can confirm that using localhost works for me using 1.0.6, thanks.


Updated validations.py python script.

Updated validations.py python script.
Fixed the behavior of validate_user function in validations.py.
Fixes #<issue_number_goes_here>

It's a good idea to open an issue first for discussion.

[x] Tests pass
[x] Appropriate changes to README are included in PR

I've learned a lot, thank you!

@googlebot I signed it!


Sync Method with Drive

Hi
On source : D:\Test\A\1.pdf
First Time : I launch rclone with sync method => TEST\A\1.pdf was created on drive
Second Time : Deletion on source of the subfolder A containing the file 1.PDF
Third Time : I Launch rclone with Sync Method and nothing changes on Drive
The file was deleted but the subfolder stills present on Drive
Regards

This is a known bug I'm afraid - see http://rclone.org/bugs/
I'm planning on fixing it eventually though - see #100 for progress.
Thanks for the report
Nick


add support for lmcut pyperplan heuristic

realized that pyperplan facts are actually strings, not tuples. this wasn't an issue for hmax/hadd/hff but led to an error for lmcut.

here are all of them: {blind,hadd,hff,hmax,hsa,landmark,lmcut}
so the only other one we could add is hsa. I will do that I guess, can't hurt.


Fixed Issue #100

Pywal was using sequences that iterm2 recommends against.  See https://iterm2.com/documentation-escape-codes.html

Have you tested this in iTerm2? I don't have a macOS machine so I can't verify this myself.
LGTM :+1:

Pushed up the syntax change.  I am running iTerm2 on Sierra


Update checks list

Updated checks list for recent FOIA request I made

can you link to the request? did you make it on MR because I can't find it on there and its not tagged with the proper tag.

Nah I didn't use Muckrock


Fullscreen Performance Degredation

Hello!
I've come across an issue where Vulkan framerate seems to drop off when an application enters fullscreen mode.  Before I get into the details, I'm using an M1 Max MacBook Pro 16 inch, no external monitors are connected, and I've got the latest version of MacOS (14.5) and the latest Vulkan SDK for Mac from LunarG.
I’m working on a tech demo game engine that’s rendering part of a 3D scene with Vulkan and GLFW, and I also have a basic FPS counter implemented to track performance.  Since MacOS usually imposes a system-wide Vsync, it runs at a consistent 120 FPS in windowed mode.  As soon as I go to fullscreen mode, though, it drops down to about 80-90 FPS and gets stuck there.
At first I thought this might be related to fullscreen using more resolution, but if I turn off Vsync by using the MacOS Quartz debug tools, the FPS performance hovers between 140-180 FPS, regardless of if it’s in windowed or fullscreen mode. So from a hardware perspective, the system should be able to output more than enough frames to keep pace with the 120 Hz refresh rate.
I’m wondering if it could be related to MacOS’s Vsync and how it interacts with the variable refresh rate in the ProMotion retina screen, so I tried setting the MacBook screen to a fixed 60 Hz resolution, and doing that I got a smooth 60 Hz in windows and fullscreen mode.
I was also able to reproduce the same behavior in the default Vulkan cube demo app that comes with the SDK, by viewing the framerate with the Metal HUD, so I don't think it's related to anything specific in my code itself.
I've never posted an issue here before, so apologies if I didn't format this correctly or if more information is needed.
Thanks for your time!
John

Thanks for posting this, and thanks for the diligent research you did exploring different scenarios and possible causes! It's great that you explored this with the Cube demo, because it should allow us to easily replicate the issue.

I've never posted an issue here before, so apologies if I didn't format this correctly

No need to apologize. You provided great content!

Thanks, I'm glad the information I shared was helpful!  Hopefully it can be reproduced pretty easily with the cube, but if there's anything else I can share, or test I can do, I'd be happy to!

Hi @Muketsu,
I gave it a try using the Cube demo example and I'm getting a stable 120 fps, windowed or fullscreen (well there's a frame drop sometimes, but nothing similar to your issue).
I've a 14" MacBook Pro with M1 Pro, running macOS 14.6.1. Using an external display also gives a stable frame rate, according to Metal HUD.
Not saying that your issue doesn't exist, but it looks like that in order to reproduce it, there're more conditions than just running the cube demo on macOS.
Given that disabling vsync removes the issue, I assume that if you instead set _useDisplayLink = NO; in DemoViewController.m, then you also don't run into the issue?


[Question]

❔ Question
Is it normal that when installing Paladin Cloud, the installer creates 7 m4.large instances?
🔦 Context
I successfully installed Paladin Cloud, then I checked my EC2 instances and then I saw 7 new instances. Those are very expensive instances and I couldn't find any details about it in the Installation notes

Can you share the Instance names?  Normal operations is 2 steady state instances + transient instances during batch processing. I think those m4.large are transient to support batch scanning.
I will make sure the installation docs get updated so everyone has a clear understanding of compute footprint.

Happy to help, thanks for installing.  I mis-spoke above, apologies, the 2 steady state are for testing purposes, not PaladinCloud execution.  We use Fargate for containers and Lambda so no EC2 required.  We also use Batch which dynamically creates and destroys EC2 as needed, the quantity and size depends on the number and size of the jobs which depends on the size of your cloud,  In our demo environment running PaladinCloud and some sample resources, Batch launches 4, m4.xLarge.  The instances launch/terminate on Paladin's default 6 hour interval.  Does this help?
I will update installation documents to make this more clear.
AWS Batch = https://aws.amazon.com/batch/

wiki Installation  has been updated to call out the dynamic allocation of EC2 while using Batch.  Thanks for pointing out this issues @estemendoza

How does Paladin do the calculation for how many EC2 instances and type is going to create?  We can only recommend a count and type to the Batch service.  our recommendation is 4, m4.Large.  The service then adjusts count/type as it learns more about execution.

are instances executed for 6 hours straight, is that the max time for being on or it depends on what they have to process?  Yes, it does depend on what is being processed which loosely translates to how many assets you have in your cloud.  That being said, this does not sound right.  Would you like to vid conf to discuss and triage?

Thanks for your reply, I think we are good for now, we will give it a try for a few days and will check how many resources are created


Added support for a custom date format in StringInitializable

Previously, if your date wasn't in one of those 3 formats, it wouldn't parse. This lets you add a custom format if your API doesn't use a "normal" format, and is more scalable than updating the entire framework every time someone has a new format.

Loving this. I actually needed this but forgot to fix it in Serializable or at least make an issue. Thanks, Chris!


Fixed some imports

Some modules were removed from the leptos prelude, so I fixed the path.

Thanks!


[Request] Add support for model YLYTD-0003

https://www.amazon.com/dp/B09GB33N7C

@jahirfiquitiva
I don't have YLYTD-0003, so could you tell me the Model information that you can check with the Yeelight app?
Information can be found on the screen below.
Yeelight app > Your device > Setting > Device Info > Model

@kyuuri10010 it shows the exact same model

@jahirfiquitiva
If the same model is shown, I think that "YLYTD-0003" can also be used with the currently published version.

@kyuuri10010 it shows "Not supported model" as error 😕

@jahirfiquitiva
The model information that can be obtained with the API may be different.
I have published "1.0.3-beta.0" that outputs the model obtained by the API to the log.
Can you check the model information with this?

@kyuuri10010 how can i install such version? i don't see that option in homebridge

@jahirfiquitiva
You can install by specifying the version.
npm i -g<EMAIL_ADDRESS>If you are using "Homebridge Config UI X", you can install it by specifying the version from the list on the Plugins page.

thanks @kyuuri10010 it is now working properly 😄


Doesn't compare two genes

The signature on the method calling for the comparison of genes is not sending the current geneSet (regression from the update to have multiple references)

This is fixed with the latest release.


Movement sprite state activates on pressing walk button

Description
Title describes the issue.
Reproduction
Press walk button while standing still. Or if you use toggle walk setting just press walk button once and movement animation will play.
Screenshots

Isn't this kind of unique and cool though? You can pretend to tap dance! We just got mothroach animations too...


[Admin/Chronology block] Created contexts in the list are duplicated.

Environment: Windows 11,
Google Chrome Version 111.0.5563.147 (64-bit)
Reproducible: Always
Build found: Commit 1b79c16
Type: Functional
Priority: High
Severity: Critical
Preconditions

The "StreetCode page" is opened.

Logged as admin.

Steps to reproduce

Scroll down to the "Chronology" block.

Сlick on the '+' button.

Сlick on the context field.

Select some context from the list.

Actual result
Highlights two duplicated context names from the list.
Expected result
Highlights only one selected context name from the list.
User story and test case links
User story #124
Test case #398
https://github.com/ita-social-projects/StreetCode/assets/135108144/21b29573-233c-4b12-974a-5e70a0df4d06

It's not a bug anymore, we just need to clean the DB and the problem will disappear


Rotate /var/log/messages

I think that /var/log/messages should/could be rotate (maybe between each reboot?)
By the way, a logrotate service could be a must ;)
EDIT: it's the same for docker.log and system-docker.log

Definitely on the todo list, PR's welcome :)
Logrotate is already packaged in RancherOS, we just have yet to create a container for it.  If your adventurous you can try to create a logrotate service at https://github.com/rancherio/os/tree/master/scripts/dockerimages and then add it to config/default.go

I have to finish to set up my server before. But why not after if it hasn't been already done ^^

... this still isn't added?

@Nicofuma its in https://github.com/rancher/os/releases/tag/v1.1.0-rc2
implemented in https://github.com/rancher/os/pull/1966


Alert users to new product changes

Why?
As SimpleReport continues to scale and add new features, the SimpleReport team needs a way to notify users of changes that require their attention.
Use cases

There are new features that are interesting to know (i.e. nice to knows)
There are alerts to changes we made that changes how they use the app (i.e. this requires your action)

Covered by the tickets around "What's new page"


OpenSearch Domain capacity now configurable

Description

Made the OpenSearch Domain used for SPI data configurable via the command-line.
By default, the user gets a minimal Capture Node ASG and OpenSearch Domain.  If they specify non-default values, they get a cluster that should be capable of servicing their indicated traffic load.  If they re-deploy after setting non-default values, the previously configured values are re-used unless they provide new values.
The user can change the individual capacity options and the code will gracefully pull the other, unspecified values if they exist.

Issues

https://github.com/arkime/aws-aio/issues/56

Testing

Added Unit Tests
Tested the behavior manually using the CLI.  Confirmed it correctly followed the constraints we imposed with our capacity plan for a few scenarios as a supplement to the extensive unit testing.  Pasted below is the CLI output and cluster capacity configuration after deployment.

(.venv) chelma@3c22fba4e266 aws-aio % ./manage_arkime.py create-cluster --name MyCluster3 --expected-traffic 0.01 --spi-days 30 --replicas 1
2023-06-01 16:55:46 - Debug-level logs save to file: /Users/chelma/workspace/Arkime/aws-aio/manage_arkime.log
2023-06-01 16:55:46 - Using AWS Credential Profile: default
2023-06-01 16:55:46 - Using AWS Region: default from AWS Config settings
2023-06-01 16:55:47 - Executing command: deploy MyCluster3-CaptureBucket MyCluster3-CaptureNodes MyCluster3-CaptureVPC MyCluster3-OSDomain MyCluster3-ViewerNodes
2023-06-01 16:55:47 - NOTE: This operation can take a while.  You can 'tail -f' the logfile to track the status.
2023-06-01 17:15:28 - Deployment succeeded

-----------------------------------------------------------

{"busArn":"arn:aws:events:us-east-2:968674222892:event-bus/MyCluster3CaptureNodesClusterBus224EB36F","busName":"MyCluster3CaptureNodesClusterBus224EB36F","clusterName":"MyCluster3","vpceServiceId":"vpce-svc-0d6b05027bae05312","capacityPlan":{"captureNodes":{"instanceType":"m5.xlarge","desiredCount":1,"maxCount":2,"minCount":1},"captureVpc":{"numAzs":2},"ecsResources":{"cpu":3584,"memory":15360},"osDomain":{"dataNodes":{"count":2,"instanceType":"t3.small.search","volumeSize":100},"masterNodes":{"count":3,"instanceType":"m5.large.search"}}},"userConfig":{"expectedTraffic":0.01,"spiDays":30,"replicas":1}}

(.venv) chelma@3c22fba4e266 aws-aio % ./manage_arkime.py create-cluster --name MyCluster3 --expected-traffic 0.1
2023-06-01 17:16:35 - Debug-level logs save to file: /Users/chelma/workspace/Arkime/aws-aio/manage_arkime.log
2023-06-01 17:16:35 - Using AWS Credential Profile: default
2023-06-01 17:16:35 - Using AWS Region: default from AWS Config settings
2023-06-01 17:16:36 - Executing command: deploy MyCluster3-CaptureBucket MyCluster3-CaptureNodes MyCluster3-CaptureVPC MyCluster3-OSDomain MyCluster3-ViewerNodes
2023-06-01 17:16:36 - NOTE: This operation can take a while.  You can 'tail -f' the logfile to track the status.
2023-06-01 17:45:43 - Deployment succeeded
-----------------------------------------------------------

{"busArn":"arn:aws:events:us-east-2:968674222892:event-bus/MyCluster3CaptureNodesClusterBus224EB36F","busName":"MyCluster3CaptureNodesClusterBus224EB36F","clusterName":"MyCluster3","vpceServiceId":"vpce-svc-0d6b05027bae05312","capacityPlan":{"captureNodes":{"instanceType":"m5.xlarge","desiredCount":1,"maxCount":2,"minCount":1},"captureVpc":{"numAzs":2},"ecsResources":{"cpu":3584,"memory":15360},"osDomain":{"dataNodes":{"count":2,"instanceType":"r6g.large.search","volumeSize":1024},"masterNodes":{"count":3,"instanceType":"m6g.large.search"}}},"userConfig":{"expectedTraffic":0.1,"spiDays":30,"replicas":1}}

(.venv) chelma@3c22fba4e266 aws-aio % ./manage_arkime.py create-cluster --name MyCluster3
2023-06-01 17:46:55 - Debug-level logs save to file: /Users/chelma/workspace/Arkime/aws-aio/manage_arkime.log
2023-06-01 17:46:55 - Using AWS Credential Profile: default
2023-06-01 17:46:55 - Using AWS Region: default from AWS Config settings
2023-06-01 17:46:56 - Executing command: deploy MyCluster3-CaptureBucket MyCluster3-CaptureNodes MyCluster3-CaptureVPC MyCluster3-OSDomain MyCluster3-ViewerNodes
2023-06-01 17:46:56 - NOTE: This operation can take a while.  You can 'tail -f' the logfile to track the status.
2023-06-01 17:47:24 - Deployment succeeded

-----------------------------------------------------------

{"busArn":"arn:aws:events:us-east-2:968674222892:event-bus/MyCluster3CaptureNodesClusterBus224EB36F","busName":"MyCluster3CaptureNodesClusterBus224EB36F","clusterName":"MyCluster3","vpceServiceId":"vpce-svc-0d6b05027bae05312","capacityPlan":{"captureNodes":{"instanceType":"m5.xlarge","desiredCount":1,"maxCount":2,"minCount":1},"captureVpc":{"numAzs":2},"ecsResources":{"cpu":3584,"memory":15360},"osDomain":{"dataNodes":{"count":2,"instanceType":"r6g.large.search","volumeSize":1024},"masterNodes":{"count":3,"instanceType":"m6g.large.search"}}},"userConfig":{"expectedTraffic":0.1,"spiDays":30,"replicas":1}}

License
I confirm that this contribution is made under an Apache 2.0 license and that I have the authority necessary to make this contribution on behalf of its copyright owner.

At some point we should put delete protection on OS and S3

Good idea; made a new issue for that: https://github.com/arkime/aws-aio/issues/59


AttributeError: 'OneflowGraph' object has no attribute '_input_op_names'

Describe the bug
A clear and concise description of what the bug is.
Onediff was working well after fixing issue #805 according to the reply resolution.
However, it broke again without any operation after a while.
The errors are different.
Your environment
OS
Ubuntu 22.04.1 LTS
OneDiff git commit id
b9b7921
OneFlow version info
libibverbs not available, ibv_fork_init skipped
path: ['/home/ubuntu/.local/lib/python3.10/site-packages/oneflow']
version: 0.9.1.dev20240410+cu122
git_commit: b7cd9a4
cmake_build_type: Release
rdma: True
mlir: True
enterprise: False
How To Reproduce
Steps to reproduce the behavior(code or script):
The attachment issue #805:
https://github.com/siliconflow/onediff/files/14944052/workflow.json
The complete error message
ERROR:root:!!! Exception during processing !!!
ERROR:root:Traceback (most recent call last):
  File "/home/ubuntu/ComfyUI/execution.py", line 152, in recursive_execute
    output_data, output_ui = get_output_data(obj, input_data_all)
  File "/home/ubuntu/ComfyUI/execution.py", line 82, in get_output_data
    return_values = map_node_over_list(obj, input_data_all, obj.FUNCTION, allow_interrupt=True)
  File "/home/ubuntu/ComfyUI/execution.py", line 75, in map_node_over_list
    results.append(getattr(obj, func)(**slice_dict(input_data_all, i)))
  File "/home/ubuntu/ComfyUI/nodes.py", line 1368, in sample
    return common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise)
  File "/home/ubuntu/ComfyUI/nodes.py", line 1338, in common_ksampler
    samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
  File "/home/ubuntu/ComfyUI/custom_nodes/ComfyUI-Impact-Pack/modules/impact/sample_error_enhancer.py", line 9, in informative_sample
    return original_sample(*args, **kwargs)  # This code helps interpret error messages that occur within exceptions but does not have any impact on other operations.
  File "/home/ubuntu/ComfyUI/comfy/sample.py", line 100, in sample
    samples = sampler.sample(noise, positive_copy, negative_copy, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed)
  File "/home/ubuntu/ComfyUI/comfy/samplers.py", line 713, in sample
    return sample(self.model, noise, positive, negative, cfg, self.device, sampler, sigmas, self.model_options, latent_image=latent_image, denoise_mask=denoise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
  File "/home/ubuntu/ComfyUI/comfy/samplers.py", line 618, in sample
    samples = sampler.sample(model_wrap, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar)
  File "/home/ubuntu/ComfyUI/comfy/samplers.py", line 557, in sample
    samples = self.sampler_function(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **self.extra_options)
  File "/home/ubuntu/.local/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "/home/ubuntu/ComfyUI/comfy/k_diffusion/sampling.py", line 137, in sample_euler
    denoised = model(x, sigma_hat * s_in, **extra_args)
  File "/home/ubuntu/.local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/home/ubuntu/.local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/ubuntu/ComfyUI/comfy/samplers.py", line 281, in forward
    out = self.inner_model(x, sigma, cond=cond, uncond=uncond, cond_scale=cond_scale, model_options=model_options, seed=seed)
  File "/home/ubuntu/.local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/home/ubuntu/.local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/ubuntu/ComfyUI/comfy/samplers.py", line 271, in forward
    return self.apply_model(*args, **kwargs)
  File "/home/ubuntu/ComfyUI/comfy/samplers.py", line 268, in apply_model
    out = sampling_function(self.inner_model, x, timestep, uncond, cond, cond_scale, model_options=model_options, seed=seed)
  File "/home/ubuntu/ComfyUI/comfy/samplers.py", line 248, in sampling_function
    cond_pred, uncond_pred = calc_cond_uncond_batch(model, cond, uncond_, x, timestep, model_options)
  File "/home/ubuntu/ComfyUI/custom_nodes/onediff_comfy_nodes/modules/sd_hijack_utils.py", line 41, in <lambda>
    lambda *args, **kwargs: self(*args, **kwargs),
  File "/home/ubuntu/ComfyUI/custom_nodes/onediff_comfy_nodes/modules/sd_hijack_utils.py", line 57, in __call__
    return self.__sub_func(self.__orig_func, *args, **kwargs)
  File "/home/ubuntu/ComfyUI/custom_nodes/onediff_comfy_nodes/modules/hijack_samplers.py", line 126, in new_calc_cond_uncond_batch
    output = model.apply_model(input_x, timestep_, **c).chunk(batch_chunks)
  File "/home/ubuntu/ComfyUI/comfy/model_base.py", line 91, in apply_model
    model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds).float()
  File "/home/ubuntu/.local/lib/python3.10/site-packages/onediff/infer_compiler/utils/online_quantization_utils.py", line 48, in wrapper
    output = func(self, *args, **kwargs)
  File "/home/ubuntu/.local/lib/python3.10/site-packages/onediff/infer_compiler/utils/args_tree_util.py", line 42, in wrapper
    count = len(self._deployable_module_dpl_graph._input_op_names)
  File "/home/ubuntu/.local/lib/python3.10/site-packages/oneflow/nn/graph/graph.py", line 2155, in __getattr__
    raise AttributeError(
AttributeError: 'OneflowGraph' object has no attribute '_input_op_names'

Additional context
Add any other context about the problem here.

这个错误是由于 使用 vae speedup 触发 AssertionError: runtime_state_dict_to is only supported in the enterprise edition.
从而导致的 生成 graph 对象不正常 AttributeError: 'OneflowGraph' object has no attribute '_input_op_names'。

 全部报错信息
==> function onediff.infer_compiler.utils.graph_management_utils.generate_graph_file_name  try to run...
Convert <class 'comfy.ldm.modules.diffusionmodules.openaimodel.UNetModel'> ...
Convert <class 'comfy.ldm.modules.diffusionmodules.openaimodel.UNetModel'> done!
generate_graph_file_name run time 3.6249234676361084 seconds
<== function generate_graph_file_name finish run.

Building a graph for <class 'register_comfy.openaimodel.UNetModel'> ...
==> function onediff.infer_compiler.oneflow.graph.load_graph  try to run...
load_graph cuda mem before 8858.0 MB
load_graph host mem before 1763.0 MB
  0%|                                                                     | 0/20 [00:04<?, ?it/s]
!!! Exception during processing !!!
Traceback (most recent call last):
  File "/data/home/fengwen/ComfyUI/execution.py", line 151, in recursive_execute
    output_data, output_ui = get_output_data(obj, input_data_all)
  File "/data/home/fengwen/ComfyUI/execution.py", line 81, in get_output_data
    return_values = map_node_over_list(obj, input_data_all, obj.FUNCTION, allow_interrupt=True)
  File "/data/home/fengwen/ComfyUI/execution.py", line 74, in map_node_over_list
    results.append(getattr(obj, func)(**slice_dict(input_data_all, i)))
  File "/data/home/fengwen/ComfyUI/nodes.py", line 1344, in sample
    return common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise)
  File "/data/home/fengwen/ComfyUI/nodes.py", line 1314, in common_ksampler
    samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
  File "/data/home/fengwen/ComfyUI/custom_nodes/ComfyUI-AnimateDiff-Evolved/animatediff/sampling.py", line 267, in motion_sample
    return orig_comfy_sample(model, noise, *args, **kwargs)
  File "/data/home/fengwen/ComfyUI/comfy/sample.py", line 37, in sample
    samples = sampler.sample(noise, positive, negative, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed)
  File "/data/home/fengwen/ComfyUI/comfy/samplers.py", line 755, in sample
    return sample(self.model, noise, positive, negative, cfg, self.device, sampler, sigmas, self.model_options, latent_image=latent_image, denoise_mask=denoise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
  File "/data/home/fengwen/ComfyUI/comfy/samplers.py", line 657, in sample
    return cfg_guider.sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
  File "/data/home/fengwen/ComfyUI/comfy/samplers.py", line 644, in sample
    output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
  File "/data/home/fengwen/ComfyUI/comfy/samplers.py", line 623, in inner_sample
    samples = sampler.sample(self, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar)
  File "/data/home/fengwen/ComfyUI/comfy/samplers.py", line 534, in sample
    samples = self.sampler_function(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **self.extra_options)
  File "/data/home/fengwen/miniconda3/envs/py310/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "/data/home/fengwen/ComfyUI/comfy/k_diffusion/sampling.py", line 137, in sample_euler
    denoised = model(x, sigma_hat * s_in, **extra_args)
  File "/data/home/fengwen/ComfyUI/comfy/samplers.py", line 272, in __call__
    out = self.inner_model(x, sigma, model_options=model_options, seed=seed)
  File "/data/home/fengwen/ComfyUI/comfy/samplers.py", line 610, in __call__
    return self.predict_noise(*args, **kwargs)
  File "/data/home/fengwen/ComfyUI/comfy/samplers.py", line 613, in predict_noise
    return sampling_function(self.inner_model, x, timestep, self.conds.get("negative", None), self.conds.get("positive", None), self.cfg, model_options=model_options, seed=seed)
  File "/data/home/fengwen/ComfyUI/comfy/samplers.py", line 258, in sampling_function
    out = calc_cond_batch(model, conds, x, timestep, model_options)
  File "/data/home/fengwen/ComfyUI/comfy/samplers.py", line 218, in calc_cond_batch
    output = model.apply_model(input_x, timestep_, **c).chunk(batch_chunks)
  File "/data/home/fengwen/ComfyUI/comfy/model_base.py", line 97, in apply_model
    model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds).float()
  File "/data/home/fengwen/miniconda3/envs/py310/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/data/home/fengwen/miniconda3/envs/py310/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1561, in _call_impl
    result = forward_call(*args, **kwargs)
  File "/home/fengwen/worksplace/onediff/src/onediff/infer_compiler/utils/online_quantization_utils.py", line 48, in wrapper
    output = func(self, *args, **kwargs)
  File "/home/fengwen/worksplace/onediff/src/onediff/infer_compiler/utils/args_tree_util.py", line 50, in wrapper
    output = func(self, *mapped_args, **mapped_kwargs)
  File "/home/fengwen/worksplace/onediff/src/onediff/infer_compiler/oneflow/utils.py", line 18, in wrapper
    return func(self, *args, **kwargs)
  File "/home/fengwen/worksplace/onediff/src/onediff/infer_compiler/utils/graph_management_utils.py", line 87, in wrapper
    handle_graph_loading()
  File "/home/fengwen/worksplace/onediff/src/onediff/infer_compiler/utils/graph_management_utils.py", line 67, in handle_graph_loading
    self.load_graph(graph_file, torch2oflow(graph_device))
  File "/home/fengwen/worksplace/onediff/src/onediff/infer_compiler/oneflow/deployable_module.py", line 149, in load_graph
    self.get_graph().load_graph(file_path, device, run_warmup)
  File "/home/fengwen/worksplace/onediff/src/onediff/infer_compiler/utils/cost_util.py", line 62, in clocked
    out = func(*args, **kwargs)
  File "/home/fengwen/worksplace/onediff/src/onediff/infer_compiler/oneflow/graph.py", line 26, in load_graph
    state_dict = flow.nn.Graph.runtime_state_dict_to(state_dict, device)
  File "/data/home/fengwen/miniconda3/envs/py310/lib/python3.10/site-packages/oneflow/nn/graph/graph.py", line 1375, in runtime_state_dict_to
    assert (
AssertionError: runtime_state_dict_to is only supported in the enterprise edition.

Prompt executed in 24.41 seconds
  0%|                                                                     | 0/20 [00:00<?, ?it/s]
!!! Exception during processing !!!
Traceback (most recent call last):
  File "/data/home/fengwen/ComfyUI/execution.py", line 151, in recursive_execute
    output_data, output_ui = get_output_data(obj, input_data_all)
  File "/data/home/fengwen/ComfyUI/execution.py", line 81, in get_output_data
    return_values = map_node_over_list(obj, input_data_all, obj.FUNCTION, allow_interrupt=True)
  File "/data/home/fengwen/ComfyUI/execution.py", line 74, in map_node_over_list
    results.append(getattr(obj, func)(**slice_dict(input_data_all, i)))
  File "/data/home/fengwen/ComfyUI/nodes.py", line 1344, in sample
    return common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise)
  File "/data/home/fengwen/ComfyUI/nodes.py", line 1314, in common_ksampler
    samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
  File "/data/home/fengwen/ComfyUI/custom_nodes/ComfyUI-AnimateDiff-Evolved/animatediff/sampling.py", line 267, in motion_sample
    return orig_comfy_sample(model, noise, *args, **kwargs)
  File "/data/home/fengwen/ComfyUI/comfy/sample.py", line 37, in sample
    samples = sampler.sample(noise, positive, negative, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed)
  File "/data/home/fengwen/ComfyUI/comfy/samplers.py", line 755, in sample
    return sample(self.model, noise, positive, negative, cfg, self.device, sampler, sigmas, self.model_options, latent_image=latent_image, denoise_mask=denoise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
  File "/data/home/fengwen/ComfyUI/comfy/samplers.py", line 657, in sample
    return cfg_guider.sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
  File "/data/home/fengwen/ComfyUI/comfy/samplers.py", line 644, in sample
    output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
  File "/data/home/fengwen/ComfyUI/comfy/samplers.py", line 623, in inner_sample
    samples = sampler.sample(self, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar)
  File "/data/home/fengwen/ComfyUI/comfy/samplers.py", line 534, in sample
    samples = self.sampler_function(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **self.extra_options)
  File "/data/home/fengwen/miniconda3/envs/py310/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "/data/home/fengwen/ComfyUI/comfy/k_diffusion/sampling.py", line 137, in sample_euler
    denoised = model(x, sigma_hat * s_in, **extra_args)
  File "/data/home/fengwen/ComfyUI/comfy/samplers.py", line 272, in __call__
    out = self.inner_model(x, sigma, model_options=model_options, seed=seed)
  File "/data/home/fengwen/ComfyUI/comfy/samplers.py", line 610, in __call__
    return self.predict_noise(*args, **kwargs)
  File "/data/home/fengwen/ComfyUI/comfy/samplers.py", line 613, in predict_noise
    return sampling_function(self.inner_model, x, timestep, self.conds.get("negative", None), self.conds.get("positive", None), self.cfg, model_options=model_options, seed=seed)
  File "/data/home/fengwen/ComfyUI/comfy/samplers.py", line 258, in sampling_function
    out = calc_cond_batch(model, conds, x, timestep, model_options)
  File "/data/home/fengwen/ComfyUI/comfy/samplers.py", line 218, in calc_cond_batch
    output = model.apply_model(input_x, timestep_, **c).chunk(batch_chunks)
  File "/data/home/fengwen/ComfyUI/comfy/model_base.py", line 97, in apply_model
    model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds).float()
  File "/data/home/fengwen/miniconda3/envs/py310/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/data/home/fengwen/miniconda3/envs/py310/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1561, in _call_impl
    result = forward_call(*args, **kwargs)
  File "/home/fengwen/worksplace/onediff/src/onediff/infer_compiler/utils/online_quantization_utils.py", line 48, in wrapper
    output = func(self, *args, **kwargs)
  File "/home/fengwen/worksplace/onediff/src/onediff/infer_compiler/utils/args_tree_util.py", line 42, in wrapper
    count = len(self._deployable_module_dpl_graph._input_op_names)
  File "/data/home/fengwen/miniconda3/envs/py310/lib/python3.10/site-packages/oneflow/nn/graph/graph.py", line 2155, in __getattr__
    raise AttributeError(
AttributeError: 'OneflowGraph' object has no attribute '_input_op_names'

Prompt executed in 0.03 seconds

工作流

相关环境：
python 3.10
python -m oneflow --doctor
path: ['/data/home/fengwen/miniconda3/envs/py310/lib/python3.10/site-packages/oneflow']
version: 0.9.1.dev20240411+cu121
git_commit: b7cd9a4
cmake_build_type: Release
rdma: True
mlir: True
enterprise: False

已修复，请参考如下指令重新安装： @uxyz-yang
# install oneflow
pip uninstall oneflow -y && python3 -m pip install -U --pre oneflow -f https://oneflow-pro.oss-cn-beijing.aliyuncs.com/branch/community/cu121

# onediff
cd onediff && git pull && pip install -e .

# install onediff_comfy_nodes
cp -r onediff/onediff_comfy_nodes ComfyUI/custom_nodes/

测试工作流：

测试环境：
Python 3.10.13

$ python -m oneflow --doctor

path: ['/data/home/fengwen/miniconda3/envs/py310/lib/python3.10/site-packages/oneflow']
version: 0.9.1.dev20240411+cu121
git_commit: 931ca52
cmake_build_type: Release
rdma: True
mlir: True
enterprise: False

onediff:
    commit e4d56a046024194597068c73ef8c6e160e2cd371 (HEAD -> main, origin/main, origin/HEAD)
    Date:   Thu Apr 11 16:29:19 2024 +0800

comfyui:
    commit fd7c63668080f8c2eb56edffeef028aa3932e0fe (HEAD -> master)
    Date:   Wed Apr 10 20:29:35 2024 -0400


remove logic that was preventing opus from live streaming

Related: https://github.com/livekit/server-sdk-go/issues/192#issue-1624390660

 Thank you for your submission! We really appreciate it. Like many open source projects, we ask that you sign our Contributor License Agreement before we can accept your contribution.You have signed the CLA already but the status is still pending? Let us recheck it.

https://github.com/livekit/server-sdk-go/pull/210


[Issue 12314][Go Functions] Remove extra call to gi.stats.incrTotalProcessedSuccessfully()

This call will be made by gi.processResult if

there is a result
there were no system exceptions

Fixes #12314

Verifying this change

[ ] Make sure that the change passes the CI checks.

This change is a trivial rework / code cleanup without any test coverage.
NOTES

due to the way the pulsar-function-go SDK is structured, adding regression tests to the Go code itself would be impossible without spinning up a complete environment; the call has been removed from the core loop of startFunction(), which depends on a complete environment with successfully-initialized client, producer, consumer, log producer and metrics server, as well as instance communication.
This means that the best way to test this would be in the Java example tests or E2E testing, but I have been unable to locate any Java tests that exercise the metrics, so if there is a place I could add this easily, I would be most grateful for guidance (and even more so for a patch or PR :D )
for these reasons, the change must be verified in situ for now

Does this pull request potentially affect one of the following parts:
If yes was chosen, please highlight the changes

Dependencies (does it add or upgrade a dependency): no
The public API: no
The schema: no
The default values of configurations: no
The wire protocol: no
The rest endpoints: no
The admin cli options: no
Anything that affects deployment: no

Documentation
Check the box below and label this PR (if you have committer privilege).

[x] no-need-doc
As far as I can tell, this change restores the ProcessedSuccessfully metric to what it should be:

<= ReceivedTotal

Only incremented on an error-free function handler that produces a result.

@flowchartsman this might be helpful https://github.com/apache/pulsar/blob/branch-2.9/pulsar-functions/instance/src/main/java/org/apache/pulsar/functions/instance/JavaInstanceRunnable.java#L342-L361

@flowchartsman can you please update this PR to the master branch? thanks.

@freeznet done!

@nlu90 please help to review this PR when you have time, thanks.


RISC-V implementation files have inaccurate copyright date or missing copyright header.

The RISC-V implementation files in 'sim/' have  inaccurate copyright date (should be 2017-2018, Zhengyang Gu (author)) and several are missing the copyright header / block.
(cc: @Z-Gu)

Should be fixed in #132.


Are the heading values of tracks in waymo motion dataset already normalized?

When using the waymo motion dataset, i noticed that some tracks have heading values even greater than 2pi. In the string docs  it has mentioned heading should be normalized to [-pi, pi].
Below is the code snippet i used to read the data. I would greatly appreciate it if you could help me identify where the issue might be.
        scenario = scenario_pb2.Scenario()
        scenario.ParseFromString(bytearray(data.numpy()))

        scenario_id = scenario.scenario_id
        ego_id = scenario.sdc_track_index

        ego_track = scenario.tracks[ego_id]

        for step in range(len(scenario.timestamps_seconds)):
            ego_state = ego_track.states[step]
            if not ego_state.valid:
                continue

            ego_position = [ego_state.center_x, ego_state.center_y, ego_state.center_z]
            ego_heading = ego_state.heading
            ego_velocity = [ego_state.velocity_x, ego_state.velocity_y, 0]

            for track in scenario.tracks:
                if track.id == ego_track.id:
                    continue

                object_state = track.states[step]
                heading = object_state.heading

Thanks for bringing this up. The comment is incorrect in the code. The headings are not necessarily normalized. We will fix the comment.


Add +optional to optional fields

Expected Behavior
The k8s API conventions explain that optional fields should have both the json omitempty directive and should have the +optional comment.
Actual Behavior
We are currently using only "omitempty" to indicate which fields are optional, e.g. a Pipeline's Task source bindings: https://github.com/knative/build-pipeline/blob/ef113e1d513c67b692eb1096904789be0e9616b6/pkg/apis/pipeline/v1beta1/pipeline_types.go#L55
Additional Info

k8s API conventions

/assign @pivotal-nader-ziada


Can I use sample layer separately ?

In the examples folder, fastertransformer shows how to use a whole model. My model structure has undergone some complex modifications, and it's hard for me to directly use these models.
Can I build a .so separately so that the sample layer can be used separately ?

You can refer the example with this flag https://github.com/NVIDIA/FasterTransformer/blob/main/examples/pytorch/gpt/multi_gpu_gpt_example.py#L138, which only encapsulate the gpt transformer layers.

I have seen this sample code. The use_gpt_decoder_ops parameter will cause the model to use the ParallelGPT class instead of the Gpt class, but both models use torch.classes.FasterTransformer.DynamicDecodeOp during inference, and the layers of gpt cannot be separated separately in this module.
Am I missing some information?

It can use custom sampling kernels, but you need to handle the cache update correctly. To be convenient, we use DynamicDecodeOp directly.

We are using a non-standard GPT module, so we cannot use DynamicDecodeOp directly. Is there a way to use the sample kernels alone ?

As I describe, you can use custom sampling kernels, but you need to handle the cache update. It would be not too hard under sampling case.


What I can do to make the slave receive the second datapackage using the ESP_NOW? (AEGHB-832)

Checklist

[X] Checked the issue tracker for similar issues to ensure this is not a duplicate.
[X] Provided a clear description of your suggestion.
[X] Included any relevant context or examples.

Issue or Suggestion Description
I Have an issue that drives me crazy .....I try to operate an agility light project with 4 pieces ...one master and 3 slaves .... that if your get you hand close to the sensor then interruption will occurs and the ws2812b on the other .....without much details there are 2 data packets the first data packets (packetsettings) is sent sucessfuly but the second datapacket(datapacketalone) didnot sent even the call back function activated in the slave piece (as I serial.print the recieving mac addreess ) but the data sent didnt copied in the address directed to even the data length are the same
I tried using several techniques .....I thought first it a hardware issue I put capacitors ,logic converter,....changed the power source
but then after learning and make logging I found it is software and I donnot know what it is ....I searced alot and tried several solutions like
1.using delay
2.using ack system
3.change wifi mode
4.wifi.setsleep (false)
5.change wifi channel
nothing solved I thought I should change the communication protocol but I found the best one is esp-now
so What I should do if the receiver didnot not receive the second data package even the code is fine
Master Code

uint8_t receiverAddress1[] = { 0x40,0x91,0x51,0x4E,0x10,0x31 };
  // /*replaceValueHere*/ uint8_t receiverAddress1[] = { 0xF4, 0xCF, 0xA2, 0x5D, 0x75, 0x28 };    //  this ECU MAC address ,only for example purposes
  /*replaceValueHere*/ uint8_t receiverAddress2[] = {
0xAC,0x0B,0xFB,0xDA,0xE0,0x11};
uint8_t receiverECU_Address[] = { 0x00, 0x00, 0x00, 0x00, 0x00, 0x00 };  //Placeholder for the receiver address

uint8_t receiverArray[MAXAVAILABLEECU][MACADDRESSSIZE];

#define MAXAVAILABLEECU 10

dataPacketSettings packetSettings = { 0 };

struct __attribute__((packed)) dataPacketSettings {
  uint8_t training_NrOfEcus;
  uint8_t training_trainingType;
  uint8_t training_nrOfColors;
  uint8_t training_counterValStop;
  uint16_t training_stopTimeDuration;
  uint8_t training_partnerMode_P1Color;
  uint8_t training_partnerMode_P2Color;
  uint32_t training_maxIntervalTime;
  uint32_t training_minIntervalTime;
  uint8_t winnerPartner;
};

struct __attribute__((packed)) dataPacketAlone {
  uint8_t LED_Token;  // Token for activating ECUs
  uint8_t counterExerciseData;
};
dataPacketAlone packetAlone = { 1, 0 };

void initReceiverAddress(void) {

  // memcpy(&receiverArray[0], NOECU, 6); //no ECU is allowed to be on 0 position
  // memcpy(&receiverArray[1], receiverAddress1, 6);  //This is my ECU position doesn't need to be filed.
  switch (training_SelectNrOfECUs) {
case 1:
memcpy(&receiverArray[2], receiverAddress2, 6);
esp_now_add_peer(receiverAddress2, RECEIVER_ROLE, WIFI_CHANNEL, NULL, 0);
break;

case 2:
memcpy(&receiverArray[2], receiverAddress2, 6);
memcpy(&receiverArray[3], receiverAddress3, 6);
esp_now_add_peer(receiverAddress2, RECEIVER_ROLE, WIFI_CHANNEL, NULL, 0);
esp_now_add_peer(receiverAddress3, RECEIVER_ROLE, WIFI_CHANNEL, NULL, 0);
break;

case 3:
memcpy(&receiverArray[2], receiverAddress2, 6);
memcpy(&receiverArray[3], receiverAddress3, 6);
memcpy(&receiverArray[4], receiverAddress4, 6);
esp_now_add_peer(receiverAddress2, RECEIVER_ROLE, WIFI_CHANNEL, NULL, 0);
esp_now_add_peer(receiverAddress3, RECEIVER_ROLE, WIFI_CHANNEL, NULL, 0);
esp_now_add_peer(receiverAddress4, RECEIVER_ROLE, WIFI_CHANNEL, NULL, 0);
break;

case 4:
memcpy(&receiverArray[2], receiverAddress2, 6);
memcpy(&receiverArray[3], receiverAddress3, 6);
memcpy(&receiverArray[4], receiverAddress4, 6);
//to add
esp_now_add_peer(receiverAddress2, RECEIVER_ROLE, WIFI_CHANNEL, NULL, 0);
esp_now_add_peer(receiverAddress3, RECEIVER_ROLE, WIFI_CHANNEL, NULL, 0);
esp_now_add_peer(receiverAddress4, RECEIVER_ROLE, WIFI_CHANNEL, NULL, 0);
//to add
break;
  }
  //.......
  //and so on until MAXAVAILABLEECU
}

void initESPNOWcomm(void) {
  WiFi.mode(WIFI_STA);
  WiFi.disconnect();  // we do not want to connect to a WiFi network

  if (esp_now_init() != 0) {
Serial.println("ESP-NOW initialization failed");
return;
  }

  Serial.print("ESP Board MAC Address:  ");
  Serial.println(WiFi.macAddress());

  esp_now_set_self_role(MY_ROLE);
  esp_now_register_send_cb(transmissionComplete);  // this function will get called once all data is sent
  esp_now_register_recv_cb(dataReceived);          // this function will get called whenever we receive data

  /*replaceValueHere*/                                                       //add peers here or modify the reciverAddress to the right ECUS
  esp_now_add_peer(receiverAddress1, RECEIVER_ROLE, WIFI_CHANNEL, NULL, 0);  // this is the master and we need to add it before everyone else because the commands come from it.
  memcpy(&receiverArray[1], receiverAddress1, 6);
Serial.println("initESPNOWcomm");
}

uint8_t randomECUselect(void) {

  randomSeed(millis());
  uint8_t returnValue = 0;
  uint8_t randomNumber = 0;
  while (returnValue == 0) {
randomNumber = random(0, training_NrOfEcus + 2);  //we have +2 because 1 is master and the function is exclusive

if ((randomNumber != MY_ECU) && (randomNumber != NO_ECU)) {
returnValue = randomNumber;
}
  }
Serial.println("randomECUselect");
delay(500);
  return returnValue;
}
void selectECU_number(uint8_t ECU) {
  memcpy(&receiverECU_Address, receiverArray[ECU], MACADDRESSSIZE);
  packetAlone.LED_Token = ECU;
  TransmisionStatus = SENDDATA_en;
  Serial.print("selectECU_number");

delay(500);
}
randomECUSelection = randomECUselect();

selectECU_number(randomECUSelection);

esp_now_send(receiverECU_Address, (uint8_t *)&packetAlone, sizeof(packetAlone));

Slave Code

uint8_t receiverAddress1[] = { 0x40,0x91,0x51,0x4E,0x10,0x31 };
  // /*replaceValueHere*/ uint8_t receiverAddress1[] = { 0xF4, 0xCF, 0xA2, 0x5D, 0x75, 0x28 };    //  this ECU MAC address ,only for example purposes
  /*replaceValueHere*/ uint8_t receiverAddress2[] = {
0xAC,0x0B,0xFB,0xDA,0xE0,0x11};
uint8_t receiverECU_Address[] = { 0x00, 0x00, 0x00, 0x00, 0x00, 0x00 };  //Placeholder for the receiver address

uint8_t receiverArray[MAXAVAILABLEECU][MACADDRESSSIZE];

#define MAXAVAILABLEECU 10
  struct __attribute__((packed)) dataPacketAlone {
  uint8_t LED_Token;  // Token for activating ECUs
  uint8_t counterExerciseData;
};
dataPacketAlone packetAlone = { 1, 0 };

dataPacketSettings packetSettings = { 0 };

struct __attribute__((packed)) dataPacketSettings {
  uint8_t training_NrOfEcus;
  uint8_t training_trainingType;
  uint8_t training_nrOfColors;
  uint8_t training_counterValStop;
  uint16_t training_stopTimeDuration;
  uint8_t training_partnerMode_P1Color;
  uint8_t training_partnerMode_P2Color;
  uint32_t training_maxIntervalTime;
  uint32_t training_minIntervalTime;
  uint8_t winnerPartner;
};

void initReceiverAddress(void) {

  switch (packetSettings.training_NrOfEcus) {

    case 2:

      memcpy(&receiverArray[1], receiverAddress1, 6);
      esp_now_add_peer(receiverAddress1, RECEIVER_ROLE, WIFI_CHANNEL, NULL, 0);
      break;

    case 3:

      memcpy(&receiverArray[3], receiverAddress3, 6);
      memcpy(&receiverArray[1], receiverAddress1, 6);
      esp_now_add_peer(receiverAddress3, RECEIVER_ROLE, WIFI_CHANNEL, NULL, 0);
      esp_now_add_peer(receiverAddress1, RECEIVER_ROLE, WIFI_CHANNEL, NULL, 0);
      break;

    case 4:

      memcpy(&receiverArray[1], receiverAddress1, 6);

      memcpy(&receiverArray[3], receiverAddress3, 6);
      memcpy(&receiverArray[4], receiverAddress4, 6);
      //to add 5
      esp_now_add_peer(receiverAddress3, RECEIVER_ROLE, WIFI_CHANNEL, NULL, 0);
      esp_now_add_peer(receiverAddress4, RECEIVER_ROLE, WIFI_CHANNEL, NULL, 0);
     esp_now_add_peer(receiverAddress1, RECEIVER_ROLE, WIFI_CHANNEL, NULL, 0);
      //to add 5
      break;
  }
  //and so on until MAXAVAILABLEECU
}

void initESPNOWcomm(void) {
  WiFi.mode(WIFI_STA);
  WiFi.disconnect();  // we do not want to connect to a WiFi network

  if (esp_now_init() != 0) {
    Serial.println("ESP-NOW initialization failed");
    return;
  }

  Serial.print("ESP Board MAC Address:  ");
  Serial.println(WiFi.macAddress());

  esp_now_set_self_role(MY_ROLE);
  esp_now_register_send_cb(transmissionComplete);  // this function will get called once all data is sent
  esp_now_register_recv_cb(dataReceived);          // this function will get called whenever we receive data

  /*replaceValueHere*/                                                       //add peers here or modify the reciverAddress to the right ECUS
  esp_now_add_peer(receiverAddress1, RECEIVER_ROLE, WIFI_CHANNEL, NULL, 0);
  esp_now_add_peer(receiverAddress2, RECEIVER_ROLE, WIFI_CHANNEL, NULL, 0);
  esp_now_add_peer(receiverAddress4, RECEIVER_ROLE, WIFI_CHANNEL, NULL, 0); // this is the master and we need to add it before everyone else because the commands come from it.
  memcpy(&receiverArray[1], receiverAddress1, 6);
    memcpy(&receiverArray[2], receiverAddress2, 6);
  memcpy(&receiverArray[4], receiverAddress4, 6);

}

void dataReceived(uint8_t *senderMac, uint8_t *data, uint8_t dataLength) {
  char macStr[18];
  snprintf(macStr, sizeof(macStr), "%02x:%02x:%02x:%02x:%02x:%02x", senderMac[0], senderMac[1], senderMac[2], senderMac[3], senderMac[4], senderMac[5]);

  Serial.println();
  Serial.print("Received data from: ");
  Serial.println(macStr);
  switch (dataLength) {
    case 2:
      Serial.println(" case 2 ");
      memcpy(&packetAlone, data, sizeof(packetAlone));
      if(packetSettings.training_trainingType==TRAINING_TIMERMODE && packetAlone.LED_Token==MY_ECU) {
        timer1_write(randomTimerInterval());
      }
      Serial.print(packetAlone.counterExerciseData);
            Serial.print(packetAlone.LED_Token);
      break;

    case 3:
      Serial.println("local");
      memcpy(&partnerLocal, data, sizeof(partnerLocal));

      break;

    case 17:
      Serial.println(" case 8");
      memcpy(&packetSettings, data, sizeof(packetSettings));
      settingsReceivedFlag = false;
      break;
  }
  TransmisionStatus = DATARECEIVED_en;
}

that is the main code if you want to have alook Ecu1 is the master and Ecu2,3,4 are the slaves
https://github.com/projectswithalex/Reaction-Lights-Training-Module

@TheprofessionalKhaled Please reference OTA or provisioning example.

@TheprofessionalKhaled Closing this issue since there has been no update on this. Please feel free to reopen if required.


Offline installation fails when a remote user is defined

SUMMARY
The offline installation is falling when the playbook is trying to check the local download archive path with an exception related to an incorrect password. The local user used to execute the playbook is different to the remote user to connect to the remote servers.
Full stack trace:
on 🎩 ❯ ansible-playbook -i inventories/my-local-vm playbooks/amq_broker_ha.yml
BECOME password:

PLAY [Ansible Playbook to install a high-availability shared storage AMQ Broker (live and backup)] ****************

TASK [Gathering Facts] ********************************************************************************************
ok: [f38mw02]
ok: [f38mw01]

TASK [middleware_automation.amq.activemq : Validating arguments against arg spec 'main'] **************************
ok: [f38mw01]
ok: [f38mw02]

TASK [middleware_automation.amq.activemq : Check prerequisites] ***************************************************
included: /home/rmarting/.ansible/collections/ansible_collections/middleware_automation/amq/roles/activemq/tasks/prereqs.yml for f38mw01, f38mw02

TASK [middleware_automation.amq.activemq : Clear internal templating variables] ***********************************
ok: [f38mw01]
ok: [f38mw02]

TASK [middleware_automation.amq.activemq : Validate credentials] **************************************************
ok: [f38mw01]
ok: [f38mw02]

TASK [middleware_automation.amq.activemq : Validate TLS config] ***************************************************
skipping: [f38mw01]
skipping: [f38mw02]

TASK [middleware_automation.amq.activemq : Validate TLS mutual auth config] ***************************************
skipping: [f38mw01]
skipping: [f38mw02]

TASK [middleware_automation.amq.activemq : Check local download archive path] *************************************
fatal: [f38mw01 -> localhost]: FAILED! => {"changed": false, "module_stderr": "\nSorry, try again.\n[sudo via ansible, key=wmpdbqmdxqqzqbpajbwutgxmveljwwli] password:\nsudo: timed out reading password\nsudo: 1 incorrect password attempt\n", "module_stdout": "", "msg": "MODULE FAILURE\nSee stdout/stderr for the exact error", "rc": 1}

NO MORE HOSTS LEFT ************************************************************************************************

PLAY RECAP ********************************************************************************************************
f38mw01                    : ok=5    changed=0    unreachable=0    failed=1    skipped=2    rescued=0    ignored=0
f38mw02                    : ok=5    changed=0    unreachable=0    failed=0    skipped=2    rescued=0    ignored=0

ISSUE TYPE

Bug Report

ANSIBLE VERSION

ansible [core 2.16.6]
  config file = /etc/ansible/ansible.cfg
  configured module search path = ['/home/rmarting/.ansible/plugins/modules', '/usr/share/ansible/plugins/modules']
  ansible python module location = /usr/lib/python3.12/site-packages/ansible
  ansible collection location = /home/rmarting/.ansible/collections:/usr/share/ansible/collections
  executable location = /usr/bin/ansible
  python version = 3.12.3 (main, Apr 17 2024, 00:00:00) [GCC 14.0.1 20240411 (Red Hat 14.0.1-0)] (/usr/bin/python3)
  jinja version = 3.1.4
  libyaml = True

COLLECTION VERSION

on 🎩 ❯ ansible-galaxy collection list

# /home/rmarting/.ansible/collections/ansible_collections
Collection                               Version
---------------------------------------- -------
ansible.posix                            1.5.4
community.hashi_vault                    6.0.0
fedora.linux_system_roles                1.78.2
kubernetes.core                          3.0.0
middleware_automation.amq                2.0.1
middleware_automation.amq_streams        0.0.6
middleware_automation.common             1.2.1
redhat.amq_streams                       1.0.0
redhat.runtimes_common                   1.1.3

STEPS TO REPRODUCE

The playbook is executed from a laptop to install into 2 virtual machines running the amq broker binaries. Before running the playbook, a copy of the binaries was copied into the /tmp folder of the local server, it was also copied into the /tmp folder of each VM. In all the cases the issue was the same:
The user to connect to the remote VM is different from the user connected into the local server where the playbook is executed.

---
- name: "Ansible Playbook to install a high-availability shared storage AMQ Broker (live and backup)"
  hosts: all
  remote_user: rhmw
  gather_facts: yes
  vars:
    # Offline Installation
    activemq_offline_install: true
    activemq_local_archive_repository: "/tmp"
  collections:
    - middleware_automation.amq
  roles:
    - activemq

EXPECTED RESULTS

Offline intallation using the remote_user to connect to the remote servers and coping the local binary from the bastion without any issue.
ACTUAL RESULTS

Installation failing with an error in the Check local download archive path:
TASK [middleware_automation.amq.activemq : Check local download archive path] *************************************
fatal: [f38mw01 -> localhost]: FAILED! => {"changed": false, "module_stderr": "\nSorry, try again.\n[sudo via ansible, key=wmpdbqmdxqqzqbpajbwutgxmveljwwli] password:\nsudo: timed out reading password\nsudo: 1 incorrect password attempt\n", "module_stdout": "", "msg": "MODULE FAILURE\nSee stdout/stderr for the exact error", "rc": 1}

The local configuration of ansible is (ansible.cfg file):
[defaults]
host_key_checking = False
retry_files_enabled = False
nocows = 1

[inventory]
# fail more helpfully when the inventory file does not parse (Ansible 2.4+)
unparsed_is_failed = true

[galaxy]
server_list = automation_hub,galaxy

[galaxy_server.galaxy]
url = https://galaxy.ansible.com/

[galaxy_server.automation_hub]
url = https://cloud.redhat.com/api/automation-hub/
auth_url = https://sso.redhat.com/auth/realms/redhat-external/protocol/openid-connect/token
token = ***************************************************

[privilege_escalation]
become = true
become_method = sudo
become_ask_pass = true

you problem is on the controller (localhost) not the target host (ie. it's trying to escalate to read from /tmp/). One workaround is to run on the controller with a no password sudoer. Also wierd that it timeout waiting for the sudo password, did you get the prompt at all?

I am using my own laptop (with my own user), as I usually do with other collections (e.g.: amq_streams), just because I did not get any issue. It seems that the offline installation between amq_streams and activemq collections are different.
I am not getting any prompt, so after sometime I got the error.


Move all mypy configuration to configuration file

Remove unused # type: ignores
Make mypy complain about unused # type: ignores
Move all mypy configuration to configuration file (a command line flag was used in scripts/lint.sh)

Ah, different Python versions require different # type: ignores it seems. We can either:

Close this PR
Make Travis only run mypy with one Python version (I suggest the lowest supported version).

@tiangolo :+1: done

Perfect, thank you! :rocket: :wrench:


Create new React project

Create new React project

Create new React project and create project folder structure.

[x] Create and initialize New React Project and install dependencies.
[x] Clean Up React template.
[ ] Setup basic folder structure.

[ ] Created and initialized New React Project and installed dependencies.
[ ]  Cleaned Up React template.


[Story]: Improve the toolbar on the pipeline details page

Goal
Implements the new toolbar on the pipeline details page
Dependency issue
No dependencies
Itemized goals

Add a new toggle that will open the dropdown menu to view the table of the versions in the pipeline
Add a new dropdown item to support uploading pipeline versions

Aspects continued elsewhere
Create the pipeline versions table in the dropdown menu - Ticket TBD
Mocks
https://www.sketch.com/s/6c72b6b2-2c32-45ff-b07c-381d9d8c8267/a/w5xbELE

Closed by https://issues.redhat.com/browse/RHOAIENG-265


Initial slides on top of the real slides

Hi,
I am presenting videos with react-slick and auto-play videos when they are in the center. I am showing 3 slides each time (slidesToShow: 3) and I am using beforeChange and afterChange to play/pause.
I am able to play and pause using a ref I added a ref={'preview' + index} for every video so I can play it when it gets into focus and pause it when it goes out of focus.
So this all works, videos play and pause well.  Except the 3 first slides for which I just hear audio. After investigating it turned out that the Slider holds 3 extra divs, with data-index -1, -2 and -3 for the first 3 slides with exactly the same content of my first three slides. When I play one of the videos on these 3 slides it works, but I can't see the video because there another PreviewPlayer on top of the one that is playing.
I hope I was clear.
The important bit in my code is this:
itemWillMove(index) {
    var playerRef = 'preview' + index;
    console.log("pausing " + playerRef);
    this.refs[playerRef].pause();
},
itemMoved(index) {
    var playerRef = 'preview' + index;
    console.log("playing " + playerRef);
    this.refs[playerRef].play();
},
render() {
    var previewPlayers = [];
    this.state.videos.forEach(function(video, index) {
        console.log("pushing video index - " + index);
        previewPlayers.push(
<PreviewPlayer video={video} ref={'preview' + index} />
);
    }.bind(this));

    var settings = {
        autoplay: false,
        arrows: true,
        dots: true,
        slidesToShow: 3,
        slidesToScroll: 1,
        centerMode: true,
        beforeChange: this.itemWillMove,
        afterChange: this.itemMoved
    };

    return(

                <Slider {...settings} >
                    {previewPlayers}
                </Slider>

    );
}

I have basically the same setup too. But instead of playing video, I have slides that the User can delete, so when the User starts deleting 1 by 1. Until the last 1 remains, there are still 3 slides, 1 original & 2 extra slides(copies of the original 1). Any help ?

Still facing the issue and I couldn't figure out the issue. @drorsun @surajhell88  do you guys have found any solution?
Here is the sandbox with the issue to play around. Please check and provide the possible solution around
https://codesandbox.io/s/slick-slider-react-slick-bug-with-video-forked-967b5?file=/src/index.js


feat(AlgebraicTopology/Nerve): nerve is 2-coskeletal

The nerve functor is 2-coskeletal. This is a major lemma in the construction of the nerve adjunction.
Co-Authored-By: Emily Riehl<EMAIL_ADDRESS>and Pietro Monticone<EMAIL_ADDRESS>

[x] depends on: #16781

Prerequisite for #16784.

Could you split this PR? I would suggest starting with a PR with only the definition of Path, Path.interval, and strict Segal simplicial sets and their basic API. As the file SimplicialSet.Basic is already long, I would suggest put Path, Path.interval in a new file SimplicialSet.Path, and the basic definitions of spine and strict Segal simplicial sets in a file SimplicialSet.StrictSegal.

@joelriou I just moved the remainder of this to #19057. I don't believe @digama0 and I will need anything else in here anymore.

Subsumed by #19057