BUG with chatbot-ui: model does not exist

llama: model does not exist gpt: model does not exist gpt2: model does not exist stableLM: model does not exist
The UI interface cannot be used, and the page cannot select a local model. I am using 'ggml gpt4all j' locally

I think it is due to the following configuration not being added in the image.
https://github.com/mckaywrigley/chatbot-ui/blob/main/types/openai.ts
Here：
export const fallbackModelID = OpenAIModelID.GPT_3_5;

Maybe it's another problem

This is the file I referenced and build in MacOS:
https://github.com/go-skynet/LocalAI/tree/master/examples/chatbot-ui

@chenbt-hz  can you share your chatbot-ui docker file , you are one step ahead of me

After I pulled the latest version and modified the configuration as follows, the error prompt on the web no longer appears, but new errors still appear。
examples/chatbot-ui/docker-compose.yaml:
`version: '3.6'
services:
api:
image: quay.io/go-skynet/local-ai:latest
build:
context: ../../
dockerfile: Dockerfile.dev
ports:
- 8080:8080
environment:
- DEBUG=true
- MODELS_PATH=/models
volumes:
- ./models:/models:cached
command: ["/usr/bin/local-ai" ]
chatgpt:
image: ghcr.io/mckaywrigley/chatbot-ui:main
ports:
- 3000:3000
environment:
- 'OPENAI_API_KEY=sk-XXXXXXXXXXXXXXXXXXXX'
- 'OPENAI_API_HOST=http://:8080'`
examples/chatbot-ui/models/gpt-3.5-turbo.yaml:
`name: gpt-3.5-turbo
parameters:
model: ggml-gpt4all-j
top_k: 80
temperature: 0.2
top_p: 0.7
context_size: 1024
threads: 4
backend: gptj
stopwords:

"HUMAN:"
"GPT:"
"### Response:"
roles:
user: "HUMAN:"
system: "GPT:"
template:
completion: completion
chat: ggml-gpt4all-j`

I tried to start the model in Docker, but got the problem that computation crashing when asked. Eventually, I used a local build and successfully started the service
`./local-ai --models-path ./models/ --debug
Starting LocalAI using 4 threads, with models path: ./models/
┌───────────────────────────────────────────────────┐
│                   Fiber v2.44.0                   │
│               http://<IP_ADDRESS>:8080               │
│       (bound on host <IP_ADDRESS> and port 8080)       │
│                                                   │
│ Handlers ............ 12  Processes ........... 1 │
│ Prefork ....... Disabled  PID ............. 72719 │
└───────────────────────────────────────────────────┘
11:35AM DBG Request received: {"model":"","prompt":null,"instruction":"","input":"","stop":null,"messages":null,"stream":false,"echo":false,"top_p":0,"top_k":0,"temperature":0,"max_tokens":0,"n":0,"batch":0,"f16":false,"ignore_eos":false,"repeat_penalty":0,"n_keep":0,"seed":0}
11:35AM DBG No model specified, using: ggml-gpt4all-j
11:35AM DBG Parameter Config: &{OpenAIRequest:{Model:ggml-gpt4all-j Prompt: Instruction: Input: Stop: Messages:[] Stream:false Echo:false TopP:0.7 TopK:80 Temperature:0.9 Maxtokens:512 N:0 Batch:0 F16:false IgnoreEOS:false RepeatPenalty:0 Keep:0 Seed:0} Name: StopWords:[] Cutstrings:[] TrimSpace:[] ContextSize:512 F16:false Threads:4 Debug:true Roles:map[] Backend: TemplateConfig:{Completion: Chat: Edit:}}
11:35AM DBG Template found, input modified to: The prompt below is a question to answer, a task to complete, or a conversation to respond to; decide which and write an appropriate response.
Prompt:
Response:
`


"Demo Source" links on examples page goes to 404 page

go to this url
https://akveo.github.io/ng2-smart-table/#/examples/custom-editors-viewers
try to click on any of the "demo source" links
gets 404

Hi @hope4555, I believe you can get source code here


Add support for H5P content in converse.js

Use the h5p standalone renderer to implement an H5P renderer for converse.js. Intercept xAPI "completed" events and post a message back into conversaion with conversation participant's score.

This requires ofchat 0.9.3 Release 8

Pade is used to upload h5p files. The webmeet plugin for converse.js is used to render H5P in converse.js conversations. The Experience Learning events (xAPI) from content interaction are intecepted and injected back into the conversation.
I am now thinking an XEP might be needed to formalise how to publish/subscribe xAPI events for viewing in a dashboard like the xAPI dashboard from ADL

In practice, interactive content is authored in Tiki and then uploaded to Openfire to be rendered in converse.js. The xAPI experience events are published to Openfire with XMPP pubsub and can be consumed by any other XMPP endpoint for monitoring, reporting, visualization, etc
@marclaporte, are you interested in co-authoring an XEP for the publishing/subscribing of H5P experience events (xAPI) using XMPP?

Yes! Great idea!

Hi!
I am the founder of H5P. I think this is a great idea. Count me in!
Svein-Tore

Sorry, it has taken a while to get back to this. We actually do not need to publish a new XEP as the following existing XEPs achieve what we need

XEP-0163: Personal Eventing Protocol
XEP-0335: JSON Containers

xAPI statements (JSON) will be wrapped in XMPP  using XEP-0335 and sent to the PEP node of the user who published the H5P interactive content.

All right!  XMPP is amazing!


Agreeing list of 'features'

OK now with the list of features?
It might be too long, but I struggle to shorten it by merging/removing redundancy - some features are similar/related, but still I find valuably distinct, and don't want to loose that.
I like that several features are software-specific, i.e. it's not just a list of features of great communities in general.
Only a few of us contributed so far - perhaps if we move now to start collecting specific examples/anecdotes/evidence from specific projects, to support these features as important, more people can more easily contribute?
Let me know which of these you think's the best way to move forward:

discuss (by mail? here in github's issues?) and agree together changes to current set of features
move on now to collecting examples from specific OSBC's we've been involved in, and reaching out/inviting others to contribute

I'm voting for both and here in public :)

I agree with Björn. Both the options should be open for some people might find it more comfortable writing to you than directly here.

I agree, we need more people to contribute. The good idea would be to post about this on Biostars, & repost it on Twitter. In terms of discussion, mail and github sound absolutely fine. Another option is Google docs but I understand that some people may not prefer it.

Alrighty (in the words of Ace Ventura) - thanks for your comments. In that case, let's:

post this on biostars and re-tweet it - given you suggested it, Fatima, I encourage you to do that
if you've specific examples to add to illustrate the points already listed, go to it and add them
if there are some of the points you think it'd be good to merge/adapt/etc. , let us know here and/or pull request away
if there's anyone you think would like to get involved, mail 'em to encourage them to do so

Have tried clustering features into broad categories. Inevitably, some (most?) features could be placed in multiple categories. Please go ahead and try out your own clustering if you can see ways to improve it!

Just posted to the BioStar forum section encouraging people to send us their contributions to this https://www.biostars.org/p/153770/

One could easily cross reference the features and sub features with the Software Sustainability Institute's criteria based software evaluation guide http://www.software.ac.uk/software-evaluation-guide

Thanks  a lot for that, Michael. Lots of overlap with the points in that document; when I've a 'mo, I'll have a go at integrating (and referencing) these points into the list here. And/or, of course, if someone else wants to do that (Michael...? :) ) more than happy for you to edit listOfFeaturesOfGreatOSBCs.md and send a pull request!

A Doodle poll to find time to meet online for several hours to try and agree together a potentially-finalish conciser-than-currently list of features http://doodle.com/h78f4adxbgpt2csm

We agreed to meet 14.00 on Thursday 10th September 2015, German time (CEST http://www.timeanddate.com/time/zones/cest), which is 13.00 UK time (BST http://www.timeanddate.com/time/zones/bst)/. This time works for everyone who's filled in the poll http://doodle.com/h78f4adxbgpt2csm

Sorry for the delay in getting back to this - getting my contract renewed kept me kinda distracted, off sick, the normal RL stuff :)
Just now checked out the googledoc that was the result of the previous GoogleHangout Sep 10th 2015, thinking about what to do next.
To move on from here I think we need to do the following things next:
(A) and (B) can be done in parallel.

(A) move the text from the googledoc into markdown (putting the comments in using HTML comment tags) to replace the current listOfFeaturesOfGreatOSBCs.md

(A.1) once it's in Markdown, divide up the task of summarising and standardising the text associated with each of the different features

(B) devising a list of three questions to contact members of communities we're interested in including in the manuscript with, to get their anecdotes on how they've dealt with building their OS scientific communities (B.1) and (B.2) can be done in parallel

(B.1) testing these questions ourselves by answering them for communities we're involved in
(B.2) choosing a list of priority communities for us to contact for feedback

Once we've got that done, we can reach out to these communities and work out how to build their content/answers to these questions into the manuscript.
Would any of you like to start going with any of that? I'll start on it myself, but would be quicker, more in the spirit of what we're trying to do, and I expect much better, if some of you were up for doing some of these things.
I'll mail all the contributors so far now, to let you know about this post, so hopefully we can start moving with this again.

For A1 - I’ll get that done in a moment…
I’ve used this Google script before for converting google docs into markdown, and it worked quite well at the time.
I’ll try it out and submit a PR in a moment - hopefully it should be good enough for this…

Thanks for that @IsmailM - this gave me a chance to train my merge-conflict resolution skills, as I did something similar ('manually' rather than using a script) while in the air. Happily resolved now :)


Update addon-framework to v0.9.3

updates to some function calls as older ones have been removed
unit test updates needed as the underlying functions have slightly different implementations - one in particular required an external clustermanagementaddon controller to update status (this controller is part of OCM/ACM, see here:  https://github.com/open-cluster-management-io/ocm/blob/main/pkg/addon/controllers/cmainstallprogression/controller.go#L66).  Needed to fake out the status update for the unit tests to work properly as this controller is external to the ones started by the addon-framework (i.e. not part of volsync-addon-controller) and therefore not running during the unit tests.

/cc @birsanv


The thumbnail macro doesn't work in Redmine 3.3

While the attachment functionality has been fixed by the recent version, the thumbnail macro {{thumbnail(image.png)}} doesn't seem to work yet.
Redmine config:
Environment:
  Redmine version                3.3.0.stable
  Ruby version                   2.0.0-p645 (2015-04-13) [x86_64-linux]
  Rails version                  4.2.6
  Environment                    production
  Database adapter               Mysql2
SCM:
  Subversion                     1.8.13
  Git                            1.9.5
  Filesystem
Redmine plugins:
  advanced_roadmap_v2            2.4.2
  redmine_agile                  1.4.1
  redmine_codebutton             0.1.0
  redmine_graphs                 0.1.0
  redmine_issue_completion       0.0.2
  redmine_issue_history          1.0.0
  redmine_issue_templates        0.1.2
  redmine_lightbox2              0.3.0

Where do you use the thumbnail macro? On issue description, or on issue notes or on a wiki page?

Alright, confirmed! Will be fixed with 0.3.1 in a couple of minutes...

it works. Many thanks!

Great, you're welcome!

We see this issue still in 0.3.1 with Redmine 3.3.2. Clicking on a thumbnail in a wiki page generated by the {{thumbnail(filename, size=x, Title=Foo)}} macro will return a 404 response. For some reason, version 0.3.1 of this plugin is using the attachment title set in the thumbnail macro instead of the file name itself:

Started GET "/attachments/download/4163/Bottom%20of%20rack%20-%20rear%20view" for <IP_ADDRESS> at 2017-01-26 10:35:53 -0400
Processing by AttachmentsController#download as HTML
Parameters: {"id"=>"4163", "filename"=>"Bottom of rack - rear view"}
Current user: test_user (id=9001)
Rendered common/error.html.erb within layouts/base (0.2ms)
Rendered plugins/redmine_code_review/app/views/code_review/_html_header.html.erb (0.1ms)
Rendered plugins/redmine_code_review/app/views/code_review/_body_bottom.html.erb (0.1ms)
Filter chain halted as :find_attachment rendered or redirected
Completed 404 Not Found in 44ms (Views: 25.7ms | ActiveRecord: 12.7ms)

This is limited to the lightbox2 plugin, as removing the plugin and clicking on the attachment link will display the image. If this is fixed in master, could we please get a new version of the package tagged?

We see this issue still in 0.3.1 with Redmine 3.3.2.

Please see #33 for further discussion!

Will this one also be fixed for redmine 3.2? In tag 0.2.8 e.g.?
I currently have redmine installed via ubuntu 16.04 package manager.

@robertfausk see #36

Note: This fix has been backported without testing to the redmine-3.0-3.2 branch for Redmine 3.0-3.2 by request of @robertfausk.  Please see #36 for further discussion.


⚠ Thumbnails API Endpoint has degraded performance

In dcaca75, Thumbnails API Endpoint (https://thumbnails.roblox.com/v1/assets?assetIds=82738847&format=Png&isCircular=false&size=30x30) experienced degraded performance:

HTTP code: 200
Response time: 1086 ms

Resolved: Thumbnails API Endpoint performance has improved in 20825a4 after 9 minutes.


Request explain worm program

I want use this worm program for another botnet. Can I have a explanation so that I can know how does each worm program work

I strictly follow to your license

I want use this worm program for another botnet. Can I have a explanation so that I can know how does each worm program work

This project was supposed to be an "art project". As it is stated in the README page, it was never meant to work out of box. You have to have some expertise to add attack vectors like up-to-date combo list or Log4j exploit.
As per documentation, I have other things in life that caught up with me. Unless I'm getting paid to do this, it has been really difficult to make time for this project.  Look up Zeus. It should be a good start. I don't have time for wanna-be hackers like you. Believe me, you're not the first.

ok thankyou!
But have you write it from scratch with C? Awesome!


DOC-2831 Auto-upgrade and expiration policy

Harness Developer Pull Request
Thanks for helping us make the Developer Hub better. The PR will be looked at
by the maintainers.
What Type of PR is This?

[X] Issue
[ ] Feature
[ ] Maintenance/Chore

If tied to an Issue, list the Issue(s) here:

DOC-2831

House Keeping
Some items to keep track of. Screen shots of changes are optional but would help the maintainers review quicker.

[X] Tested Locally
[ ] Optional Screen Shoot.

Closing without merging. The entire delegate doc set was refactored, which created several conflicts in this file. It's easier to create a new PR than to fix the conflicts. This content will be in a new PR.


Allow a custom display string as part of the remote jupyter server provider

Extend the IJupyterServerUri to include a custom display string.

validated


TheFace-Ari

I decided to make a face (+arms) with different parts that can move around. While it is visually simple, the face can still show multiple expressions with the random variation it has.
While I do have experience programming, I've usually done more non-visual things (for example involving networks) and want to get more into design-based projects.
Overall this took me a few hours to get right. To optimize drawing and abuse the symmetry in my work, I made a function that lets me rotate differently based on a given direction which lets me draw both sides equally with minimal changes.

Thank you for the submission. I like where you are going with this but think we could level up the piece some more. You mention working with networks. How about making a network geometric looking face?
Something like this?

Thanks for the suggestion. I rewrote my code and created something similar (and more expressive than before).
The way I did made it is I created a base set of points where the points are then transformed within a certain range on both axes to create variation in the face. The right side is generated based on flipping the left-side points x-coordinates across the midpoint line and adjusting the connection ids.
To keep it closer to a face structure, the transformation range is set to 3 (that mean a -3 to +3 change in position on each axis) but can be adjusted at the top of the file.

Thanks for your patience. It seems to have lost some of the face qualities. How could you make the features more recognizable?
I've been working on incorporating feedback into the updated toolkit. It is described in the editor toolkit on the editor page.
https://blot.hackclub.com/editor
Would love your feedback after using both. Let me know if you have any questions!
I'm going to close this for housekeeping but please resubmit! You've got a good concept.


Prepull Void Eruption

Now that we can easily build insanity prepull with PW:S we need to test if it is worth it to prepull void eruption, or if the damage increase is negligible (likely just with LotV/S2M).

Uldir Composite

Actor
DPS
Increase

Prepull_VoiEr
16851
0.50%

Base
16767
0.00%

Single Target

Actor
DPS
Increase

Prepull_VoiEr
16102
0.49%

Base
16023
0.00%


Response model doesn't allow models with allow_arbitrary_types

First check

[x] I added a very descriptive title to this issue.
[x] I used the GitHub search to find a similar issue and didn't find it.
[x] I searched the FastAPI documentation, with the integrated search.
[x] I already searched in Google "How to X in FastAPI" and didn't find any information.
[x] I already read and followed all the tutorial in the docs and didn't find an answer.
[x] I already checked if it is not related to FastAPI but to Pydantic.
[x] I already checked if it is not related to FastAPI but to Swagger UI.
[x] I already checked if it is not related to FastAPI but to ReDoc.

Example - not specifying response_model
Creating a Pydantic model that has a field with an arbitrary class will raise RuntimeError stating that there is no validator for class ArbitraryClass:
from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class ArbitraryClass:

    def __init__(self, attr: str) -> None:
        self.attr = attr

class SomeModel(BaseModel):

    name: str = 'SomeModel'
    arbitrary_field: ArbitraryClass

    class Config:
        arbitrary_types_allowed = True

@app.get('/')
async def return_model_with_arbitrary_type():
    model = SomeModel(arbitrary_field=ArbitraryClass('attr_value'))
    return model

Results of running example without response_model specified
This works fine, since Pydantic allows creation of models where fields have arbitrary types when we use arbitrary_types_allowed in Config. The returned JSON is also nicely formatted, asattribute: value pair:
{
  "name": "SomeModel",
  "arbitrary_field": {
    "attr": "attr_value"
  }
}

Example - specifying response_model
When we try to specify that the response model for this endpoint is SomeModel:
@app.get('/', response_model=SomeModel)
async def return_model_with_arbitrary_type():
    model = SomeModel(arbitrary_field=ArbitraryClass('attr_value'))
    return model

We get an error:
Traceback (most recent call last):
  File "/Users/fisheye36/Projects/fastapi-issue/.env/lib/python3.8/site-packages/fastapi/utils.py", line 65, in create_response_field
    return response_field(field_info=field_info)
  File "pydantic/fields.py", line 284, in pydantic.fields.ModelField.__init__
  File "pydantic/fields.py", line 362, in pydantic.fields.ModelField.prepare
  File "pydantic/fields.py", line 538, in pydantic.fields.ModelField.populate_validators
  File "pydantic/validators.py", line 629, in find_validators
RuntimeError: no validator found for <class 'backend.main.ArbitraryClass'>, see `arbitrary_types_allowed` in Config

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.8/lib/python3.8/runpy.py", line 193, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.8/lib/python3.8/runpy.py", line 86, in _run_code
    exec(code, run_globals)
  File "/Users/fisheye36/Projects/fastapi-issue/src/backend/__main__.py", line 1, in <module>
    from backend.main import run_server
  File "/Users/fisheye36/Projects/fastapi-issue/src/backend/main.py", line 37, in <module>
    async def return_model_with_arbitrary_type():
  File "/Users/fisheye36/Projects/fastapi-issue/.env/lib/python3.8/site-packages/fastapi/routing.py", line 494, in decorator
    self.add_api_route(
  File "/Users/fisheye36/Projects/fastapi-issue/.env/lib/python3.8/site-packages/fastapi/routing.py", line 439, in add_api_route
    route = route_class(
  File "/Users/fisheye36/Projects/fastapi-issue/.env/lib/python3.8/site-packages/fastapi/routing.py", line 315, in __init__
    ] = create_cloned_field(self.response_field)
  File "/Users/fisheye36/Projects/fastapi-issue/.env/lib/python3.8/site-packages/fastapi/utils.py", line 91, in create_cloned_field
    use_type.__fields__[f.name] = create_cloned_field(
  File "/Users/fisheye36/Projects/fastapi-issue/.env/lib/python3.8/site-packages/fastapi/utils.py", line 94, in create_cloned_field
    new_field = create_response_field(name=field.name, type_=use_type)
  File "/Users/fisheye36/Projects/fastapi-issue/.env/lib/python3.8/site-packages/fastapi/utils.py", line 67, in create_response_field
    raise fastapi.exceptions.FastAPIError(
fastapi.exceptions.FastAPIError: Invalid args for response field! Hint: check that <class 'backend.main.ArbitraryClass'> is a valid pydantic field type

Question
Pydantic allows models like that. FastAPI also allows returning models like that and handles them well. The only problem here is something wrong happens when that kind of model is specified as a response model.
I know I could create validators for ArbitraryClass, but what if the class is coming from some other library and I can't edit its source?
Environment

OS: macOS Catalina 10.15.7
FastAPI Version: 0.61.2
Python version: 3.8.2

I am running into this as well. This was also previously reported here but the issue was closed before resolved.

Strange, I was searching for similar issues but couldn't find any.
The comment here seems interesting: https://github.com/tiangolo/fastapi/issues/1186#issuecomment-667780031
@tiangolo could you please take a look at the issue I reported? It seems that @bcb's comment was overlooked. Also, my explanation here is more understandable (and easily reproducible) than in #1186.

Has this issue been resolved? Any solutions?

Has this issue been resolved? Any solutions?

I am facing this issue too.

I am facing this issue too.

Are there any workarounds (aside from changing my classes to known types)?

Same problem here. Has anybody found a solution or workaround?

@ylassoued I'm using strings as my output now, turning this:
class SomeModel(BaseModel):

    name: str = 'SomeModel'
    arbitrary_field: ArbitraryClass

    class Config:
        arbitrary_types_allowed = True

into this:
class SomeModel(BaseModel):

    name: str = 'SomeModel'
    arbitrary_field: str

Which in my case does not have any drawbacks, but that is not always the case.

Any update on this issue?

@Nemesis7, I set the arbitrary_types_allowed option globally as follows, which worked for me. I haven't tried @pmsoltani's approach though.
# Assuming this is the module where you declare your data model...
from pydantic import BaseConfig

BaseConfig.arbitrary_types_allowed = True

But eventually, I decided to implement the whole Rest API's data model using ModelBase. So when I generate the responses, I convert ArbitraryClass instances (returned by the underlying Python core APIs) into SomeModelBaseClass instances supported by pydantic. I believe this is cleanest approach: separate the core data model from the Rest data model, and convert from one to another.

Setting arbitrary_types_allowed in the BaseConfig works, thanks @ylassoued !

Glad to be of help @Nemesis7! Good luck!


Background timer

Make timer that runs in background, when know tabs are open to the extension. This way, the chrome notification will be sent even if the extension tab is closed.

Timer alarm runs in background


[BUG] Tested the 2662 PR, It fails for GPTJ 6B and few others

Describe the bug
We have tested PR again a few models  https://github.com/microsoft/DeepSpeed/pull/2662

OPT 1.3B, 2 tp degree, fp16
OPT 13B, 4 tp degree, [fp16, int8]
OPT 30B, 8 tp degree [fp16, int8]
GPT NeoX 20B [fp16, int8]
GPTJ 6B

Our test involves 2 steps.

Load the model to the device and then generate the partitions and save them in a local directory.

While generating the partitions, the model can be loaded with or without meta tensor. (This affects the partition generation)
Meta tensors, have the same dimensions of the real tensor, but it contains no data.

Load back the generated partition files to run inference.

We used your test suite to test your models.
System info (please complete the following information):

1 machine with 8 GPUS. NVIDIA A10G, 24GB memory per GPU
Ubuntu

Check the tables below to know the results we got upon testing.

OPT 1.3B
2
float 16
Successfully generates presharded checkpoint files
Successfully load back the presharded checkpoints and run inference and generate outputs.

GPTJ 6B
4
float 16
Successfully generates presharded checkpoint files
But loading back them returns the error. NotImplementedError: Cannot copy out of meta tensor; no data!Traceback (most recent call last): File "inference-test.py", line 57, in <module> pipe.model = deepspeed.init_inference(pipe.model, File "/usr/local/lib/python3.8/dist-packages/deepspeed/__init__.py", line 311, in init_inference engine = InferenceEngine(model, config=ds_inference_config) File "/usr/local/lib/python3.8/dist-packages/deepspeed/inference/engine.py", line 129, in __init__ self.module.to(device) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 927, in to return self._apply(convert) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 579, in _apply module._apply(fn) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 602, in _apply param_applied = fn(param) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 925, in convert return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)NotImplementedError: Cannot copy out of meta tensor; no data!

GPT NeoX 20B
8
float 16
Successfully generates presharded checkpoint files
Successfully load back the presharded checkpoints and run inference and generate outputs.

OPT 13B
4
int 8
Successfully generates presharded checkpoint files
Loaded the pre-sharded checkpoints, throws error while generating outputs.Traceback (most recent call last):File "inference-test.py", line 88, in <module>outputs = pipe(inputs,File "/tmp/ws/models/utils.py", line 69, in __call__outputs = self.generate_outputs(input_list, num_tokens=num_tokens, do_sample=do_sample)File "/tmp/ws/models/utils.py", line 113, in generate_outputsoutputs = self.model.generate(**input_tokens, **generate_kwargs)File "/usr/local/lib/python3.8/dist-packages/deepspeed/inference/engine.py", line 537, in _generatereturn self.module.generate(*inputs, **kwargs)File "/usr/local/lib/python3.8/dist-packages/torch/autograd/grad_mode.py", line 27, in decorate_contextreturn func(*args, **kwargs)File "/usr/local/lib/python3.8/dist-packages/transformers/generation_utils.py", line 1422, in generatereturn self.sample(File "/usr/local/lib/python3.8/dist-packages/transformers/generation_utils.py", line 2049, in samplenext_token_scores = logits_warper(input_ids, next_token_scores)File "/usr/local/lib/python3.8/dist-packages/transformers/generation_logits_process.py", line 92, in __call__scores = processor(input_ids, scores)File "/usr/local/lib/python3.8/dist-packages/transformers/generation_logits_process.py", line 233, in __call__indices_to_remove = scores < torch.topk(scores, top_k)[0][..., -1, None]RuntimeError: CUDA error: an illegal memory access was encounteredCUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.For debugging consider passing CUDA_LAUNCH_BLOCKING=1.

OPT 30B
8
int 8
Successfully generates presharded checkpoint files
Traceback (most recent call last):File "inference-test.py", line 88, in <module>outputs = pipe(inputs,File "/tmp/ws/models/utils.py", line 69, in __call__outputs = self.generate_outputs(input_list, num_tokens=num_tokens, do_sample=do_sample)File "/tmp/ws/models/utils.py", line 113, in generate_outputsoutputs = self.model.generate(**input_tokens, **generate_kwargs)File "/usr/local/lib/python3.8/dist-packages/deepspeed/inference/engine.py", line 537, in _generatereturn self.module.generate(*inputs, **kwargs)File "/usr/local/lib/python3.8/dist-packages/torch/autograd/grad_mode.py", line 27, in decorate_contextreturn func(*args, **kwargs)File "/usr/local/lib/python3.8/dist-packages/transformers/generation_utils.py", line 1422, in generatereturn self.sample(File "/usr/local/lib/python3.8/dist-packages/transformers/generation_utils.py", line 2049, in samplenext_token_scores = logits_warper(input_ids, next_token_scores)File "/usr/local/lib/python3.8/dist-packages/transformers/generation_logits_process.py", line 92, in __call__scores = processor(input_ids, scores)File "/usr/local/lib/python3.8/dist-packages/transformers/generation_logits_process.py", line 233, in __call__indices_to_remove = scores < torch.topk(scores, top_k)[0][..., -1, None]RuntimeError: CUDA error: an illegal memory access was encounteredCUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.For debugging consider passing CUDA_LAUNCH_BLOCKING=1.

GPT NeoX 20B
8
int8
Successfully generates presharded checkpoint files
Loading them back throws the error. Traceback (most recent call last):File "inference-test.py", line 57, in <module>pipe.model = deepspeed.init_inference(pipe.model,File "/usr/local/lib/python3.8/dist-packages/deepspeed/__init__.py", line 311, in init_inferenceengine = InferenceEngine(model, config=ds_inference_config)File "/usr/local/lib/python3.8/dist-packages/deepspeed/inference/engine.py", line 126, in __init__self._apply_injection_policy(config)File "/usr/local/lib/python3.8/dist-packages/deepspeed/inference/engine.py", line 339, in _apply_injection_policyreplace_transformer_layer(client_module,File "/usr/local/lib/python3.8/dist-packages/deepspeed/module_inject/replace_module.py", line 850, in replace_transformer_layerload_model_with_checkpoint(replaced_module,File "/usr/local/lib/python3.8/dist-packages/deepspeed/module_inject/load_checkpoint.py", line 252, in load_model_with_checkpointload_module_recursive(r_module)File "/usr/local/lib/python3.8/dist-packages/deepspeed/module_inject/load_checkpoint.py", line 246, in load_module_recursiveload_module_recursive(File "/usr/local/lib/python3.8/dist-packages/deepspeed/module_inject/load_checkpoint.py", line 246, in load_module_recursiveload_module_recursive(File "/usr/local/lib/python3.8/dist-packages/deepspeed/module_inject/load_checkpoint.py", line 244, in load_module_recursivelayer_policies[child.__class__](child, prefix + name + '.')File "/usr/local/lib/python3.8/dist-packages/deepspeed/module_inject/load_checkpoint.py", line 176, in load_transformer_layerload_parameters(child, prefix + n + '.')File "/usr/local/lib/python3.8/dist-packages/deepspeed/module_inject/load_checkpoint.py", line 85, in load_parametersassert tmp_data.dtype != torch.int8, \AssertionError: Merging of the checkpoints are not supported when using INT8 checkpoint! Please use a as many GPUs as TP-size for the checkpoint

Load with Meta Tensor, save to DS sharded and load back

Model
Partitions
Dtype
Result : Generate DS presharded checkpoints
Result: Loaded back DS presharded and run inference.

OPT 1.3B
2
float 16
Could not even generate presharded checpoints for the model. It generates the following error during init_inference API call. Traceback (most recent call last):File "inference-test.py", line 57, in <module>pipe.model = deepspeed.init_inference(pipe.model,File "/usr/local/lib/python3.8/dist-packages/deepspeed/__init__.py", line 311, in init_inferenceengine = InferenceEngine(model, config=ds_inference_config)File "/usr/local/lib/python3.8/dist-packages/deepspeed/inference/engine.py", line 126, in __init__self._apply_injection_policy(config)File "/usr/local/lib/python3.8/dist-packages/deepspeed/inference/engine.py", line 339, in _apply_injection_policyreplace_transformer_layer(client_module,File "/usr/local/lib/python3.8/dist-packages/deepspeed/module_inject/replace_module.py", line 820, in replace_transformer_layerload_model_with_checkpoint(replaced_module,File "/usr/local/lib/python3.8/dist-packages/deepspeed/module_inject/load_checkpoint.py", line 252, in load_model_with_checkpointload_module_recursive(r_module)File "/usr/local/lib/python3.8/dist-packages/deepspeed/module_inject/load_checkpoint.py", line 246, in load_module_recursiveload_module_recursive(File "/usr/local/lib/python3.8/dist-packages/deepspeed/module_inject/load_checkpoint.py", line 246, in load_module_recursiveload_module_recursive(File "/usr/local/lib/python3.8/dist-packages/deepspeed/module_inject/load_checkpoint.py", line 244, in load_module_recursivelayer_policies[child.__class__](child, prefix + name + '.')File "/usr/local/lib/python3.8/dist-packages/deepspeed/module_inject/load_checkpoint.py", line 30, in loadmodule.weight = mp_replace.copy(module.weight.data, sd[0][prefix + 'weight'])KeyError: 'decoder.embed_tokens.weight'
-

OPT 13B
4
float 16
Successfully generates presharded checkpoint files
Successfully load back the presharded checkpoints and run inference and generate outputs.

OPT 30B
8
float 16
Successfully generates presharded checkpoint files
Successfully load back the presharded checkpoints and run inference and generate outputs.

GPT J 6B
4
float 16
Successfully generates presharded checkpoint files
Traceback (most recent call last):File "inference-test.py", line 57, in pipe.model = deepspeed.init_inference(pipe.model,File "/usr/local/lib/python3.8/dist-packages/deepspeed/init.py", line 311, in init_inferenceengine = InferenceEngine(model, config=ds_inference_config)File "/usr/local/lib/python3.8/dist-packages/deepspeed/inference/engine.py", line 126, in __init__self._apply_injection_policy(config)File "/usr/local/lib/python3.8/dist-packages/deepspeed/inference/engine.py", line 339, in _apply_injection_policyreplace_transformer_layer(client_module,File "/usr/local/lib/python3.8/dist-packages/deepspeed/module_inject/replace_module.py", line 820, in replace_transformer_layerload_model_with_checkpoint(replaced_module,File "/usr/local/lib/python3.8/dist-packages/deepspeed/module_inject/load_checkpoint.py", line 252, in load_model_with_checkpointload_module_recursive(r_module)File "/usr/local/lib/python3.8/dist-packages/deepspeed/module_inject/load_checkpoint.py", line 246, in load_module_recursiveload_module_recursive(File "/usr/local/lib/python3.8/dist-packages/deepspeed/module_inject/load_checkpoint.py", line 246, in load_module_recursiveload_module_recursive(File "/usr/local/lib/python3.8/dist-packages/deepspeed/module_inject/load_checkpoint.py", line 244, in load_module_recursivelayer_policies[child.class](child, prefix + name + '.')File "/usr/local/lib/python3.8/dist-packages/deepspeed/module_inject/load_checkpoint.py", line 30, in loadmodule.weight = mp_replace.copy(module.weight.data, sd[0][prefix + 'weight'])KeyError: 'decoder.embed_tokens.weight'

GPT NeoX 20B
8
float 16
Could not even generate presharded checpoints for the model. It generates the following error during init_inference API call. Traceback (most recent call last):File "inference-test.py", line 57, in <module>pipe.model = deepspeed.init_inference(pipe.model,File "/usr/local/lib/python3.8/dist-packages/deepspeed/__init__.py", line 311, in init_inferenceengine = InferenceEngine(model, config=ds_inference_config)File "/usr/local/lib/python3.8/dist-packages/deepspeed/inference/engine.py", line 126, in __init__self._apply_injection_policy(config)File "/usr/local/lib/python3.8/dist-packages/deepspeed/inference/engine.py", line 339, in _apply_injection_policyreplace_transformer_layer(client_module,File "/usr/local/lib/python3.8/dist-packages/deepspeed/module_inject/replace_module.py", line 820, in replace_transformer_layerload_model_with_checkpoint(replaced_module,File "/usr/local/lib/python3.8/dist-packages/deepspeed/module_inject/load_checkpoint.py", line 252, in load_model_with_checkpointload_module_recursive(r_module)File "/usr/local/lib/python3.8/dist-packages/deepspeed/module_inject/load_checkpoint.py", line 246, in load_module_recursiveload_module_recursive(File "/usr/local/lib/python3.8/dist-packages/deepspeed/module_inject/load_checkpoint.py", line 246, in load_module_recursiveload_module_recursive(File "/usr/local/lib/python3.8/dist-packages/deepspeed/module_inject/load_checkpoint.py", line 244, in load_module_recursivelayer_policies[child.__class__](child, prefix + name + '.')File "/usr/local/lib/python3.8/dist-packages/deepspeed/module_inject/load_checkpoint.py", line 178, in load_transformer_layerreplace_policy.load_params(module,File "/usr/local/lib/python3.8/dist-packages/deepspeed/module_inject/replace_policy.py", line 864, in load_paramsmaybe_copy(module.attention,File "/usr/local/lib/python3.8/dist-packages/deepspeed/module_inject/replace_policy.py", line 250, in maybe_copydst = mp_replace.copy(dst, weight_quantizer.quantize(tmp if weight_quantizer.q_int8 else \File "/usr/local/lib/python3.8/dist-packages/deepspeed/module_inject/replace_module.py", line 116, in copydst = dst.reshape(-1).data.copy_(weight_split.reshape(-1)).reshape(
-

OPT 13B
4
int 8
Successfully generates presharded checkpoint files
```Traceback (most recent call last):File "inference-test.py", line 88, in outputs = pipe(inputs,File "/tmp/ws/models/utils.py", line 69, in __call__outputs = self.generate_outputs(input_list, num_tokens=num_tokens, do_sample=do_sample)File "/tmp/ws/models/utils.py", line 113, in generate_outputsoutputs = self.model.generate(**input_tokens, **generate_kwargs)File "/usr/local/lib/python3.8/dist-packages/deepspeed/inference/engine.py", line 537, in _generatereturn self.module.generate(*inputs, **kwargs)File "/usr/local/lib/python3.8/dist-packages/torch/autograd/grad_mode.py", line 27, in decorate_contextreturn func(*args, **kwargs)File "/usr/local/lib/python3.8/dist-packages/transformers/generation_utils.py", line 1422, in generatereturn self.sample(File "/usr/local/lib/python3.8/dist-packages/transformers/generation_utils.py", line 2035, in sampleoutputs = self(File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1130, in _call_implreturn forward_call(*input, **kwargs)File "/usr/local/lib/python3.8/dist-packages/transformers/models/opt/modeling_opt.py", line 935, in forwardoutputs = self.model.decoder(File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1130, in _call_implreturn forward_call(*input, **kwargs)File "/usr/local/lib/python3.8/dist-packages/transformers/models/opt/modeling_opt.py", line 699, in forwardlayer_outputs = decoder_layer(File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1130, in _call_implreturn forward_call(*input, **kwargs)File "/usr/local/lib/python3.8/dist-packages/deepspeed/model_implementations/transformers/ds_transformer.py", line 153, in forwardself.attention(input,File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1130, in _call_implreturn forward_call(*input, **kwargs)File "/usr/local/lib/python3.8/dist-packages/deepspeed/ops/transformer/inference/ds_attention.py", line 147, in forwarddist.all_reduce(output, group=self.mp_group)File "/usr/local/lib/python3.8/dist-packages/deepspeed/comm/comm.py", line 127, in log_wrapperreturn func(*args, **kwargs)File "/usr/local/lib/python3.8/dist-packages/deepspeed/comm/comm.py", line 535, in all_reducereturn cdb.all_reduce(tensor, op, group, async_op)File "/usr/local/lib/python3.8/dist-packages/deepspeed/comm/torch.py", line 45, in all_reducereturn torch.distributed.all_reduce(tensor=tensor,File "/usr/local/lib/python3.8/dist-packages/torch/distributed/distributed_c10d.py", line 1322, in all_reducework = group.allreduce([tensor], opts)RuntimeError: NCCL error in: ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1191, unhandled cuda error, NCCL version 2.10.3

OPT 30B
8
int 8
Successfully generates presharded checkpoint files
```Traceback (most recent call last):File "inference-test.py", line 88, in outputs = pipe(inputs,File "/tmp/ws/models/utils.py", line 69, in __call__outputs = self.generate_outputs(input_list, num_tokens=num_tokens, do_sample=do_sample)File "/tmp/ws/models/utils.py", line 113, in generate_outputsoutputs = self.model.generate(**input_tokens, **generate_kwargs)File "/usr/local/lib/python3.8/dist-packages/deepspeed/inference/engine.py", line 537, in _generatereturn self.module.generate(*inputs, **kwargs)File "/usr/local/lib/python3.8/dist-packages/torch/autograd/grad_mode.py", line 27, in decorate_contextreturn func(*args, **kwargs)File "/usr/local/lib/python3.8/dist-packages/transformers/generation_utils.py", line 1422, in generatereturn self.sample(File "/usr/local/lib/python3.8/dist-packages/transformers/generation_utils.py", line 2035, in sampleoutputs = self(File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1130, in _call_implreturn forward_call(*input, **kwargs)File "/usr/local/lib/python3.8/dist-packages/transformers/models/opt/modeling_opt.py", line 935, in forwardoutputs = self.model.decoder(File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1130, in _call_implreturn forward_call(*input, **kwargs)File "/usr/local/lib/python3.8/dist-packages/transformers/models/opt/modeling_opt.py", line 699, in forwardlayer_outputs = decoder_layer(File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1130, in _call_implreturn forward_call(*input, **kwargs)File "/usr/local/lib/python3.8/dist-packages/deepspeed/model_implementations/transformers/ds_transformer.py", line 153, in forwardself.attention(input,File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1130, in _call_implreturn forward_call(*input, **kwargs)File "/usr/local/lib/python3.8/dist-packages/deepspeed/ops/transformer/inference/ds_attention.py", line 147, in forwarddist.all_reduce(output, group=self.mp_group)File "/usr/local/lib/python3.8/dist-packages/deepspeed/comm/comm.py", line 127, in log_wrapperreturn func(*args, **kwargs)File "/usr/local/lib/python3.8/dist-packages/deepspeed/comm/comm.py", line 535, in all_reducereturn cdb.all_reduce(tensor, op, group, async_op)File "/usr/local/lib/python3.8/dist-packages/deepspeed/comm/torch.py", line 45, in all_reducereturn torch.distributed.all_reduce(tensor=tensor,File "/usr/local/lib/python3.8/dist-packages/torch/distributed/distributed_c10d.py", line 1322, in all_reducework = group.allreduce([tensor], opts)RuntimeError: CUDA error: an illegal memory access was encounteredCUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.

GPT NeoX 20B
8
int 8
Successfully generates checkpoint files.
Error occurs when we load back the presharded checkpoint files. Traceback (most recent call last):File "inference-test.py", line 57, in <module>pipe.model = deepspeed.init_inference(pipe.model,File "/usr/local/lib/python3.8/dist-packages/deepspeed/__init__.py", line 311, in init_inferenceengine = InferenceEngine(model, config=ds_inference_config)File "/usr/local/lib/python3.8/dist-packages/deepspeed/inference/engine.py", line 126, in __init__self._apply_injection_policy(config)File "/usr/local/lib/python3.8/dist-packages/deepspeed/inference/engine.py", line 339, in _apply_injection_policyreplace_transformer_layer(client_module,File "/usr/local/lib/python3.8/dist-packages/deepspeed/module_inject/replace_module.py", line 850, in replace_transformer_layerload_model_with_checkpoint(replaced_module,File "/usr/local/lib/python3.8/dist-packages/deepspeed/module_inject/load_checkpoint.py", line 252, in load_model_with_checkpointload_module_recursive(r_module)File "/usr/local/lib/python3.8/dist-packages/deepspeed/module_inject/load_checkpoint.py", line 246, in load_module_recursiveload_module_recursive(File "/usr/local/lib/python3.8/dist-packages/deepspeed/module_inject/load_checkpoint.py", line 246, in load_module_recursiveload_module_recursive(File "/usr/local/lib/python3.8/dist-packages/deepspeed/module_inject/load_checkpoint.py", line 244, in load_module_recursivelayer_policies[child.__class__](child, prefix + name + '.')File "/usr/local/lib/python3.8/dist-packages/deepspeed/module_inject/load_checkpoint.py", line 176, in load_transformer_layerload_parameters(child, prefix + n + '.')File "/usr/local/lib/python3.8/dist-packages/deepspeed/module_inject/load_checkpoint.py", line 85, in load_parametersassert tmp_data.dtype != torch.int8, \AssertionError: Merging of the checkpoints are not supported when using INT8 checkpoint! Please use a as many GPUs as TP-size for the checkpoint

A quick summary in words.

For OPT 1.3B and GPT NeoX 20B with dtype float 16, We COULD NOT EVEN GENERATE the partition files when we load the model with meta tensor.
For GPTJ 6B with dtype float16, we are able to generate the pre-sharded checkpoint files with/without meta tensor. But loading them back generates the error.
For int8 dtype,

For all OPT 13B, OPT 30B models, We could generate the pre-sharded checkpoint files when the model is loaded without meta tensor, i.e the traditional way, but generating the output after loading the model throws error.
For GPT NeoX 20B model, we could generate the pre-sharded checkpoint files in both with and without meta tensor. But loading them back throws the error.

The major problem here are the following:

GPTJ presharded checpoint files generated does not work.
Presharded checkpoints generation with int8 quantization does not work as well.

Hello @sindhuvahinis @lanking520, with the merge of https://github.com/microsoft/DeepSpeed/pull/2725, the major part of this issue should have been resolved. I tested the models you listed with the master branch of DeepSpeed with meta tensor and int8 checkpoint loading. These models run smoothly. The GPTJ 6B model gives a different result. I believe it is another issue and I am actively investigating it. Could you do a quick check on your side to see if you still have this issue with the current master branch of DeepSpeed? Thank you!

@HeyangQin we did some tests on 2725 as well and still observing the major issues with INT8. Will share more details and setup

@HeyangQin we did some tests on 2725 as well and still observing the major issues with INT8. Will share more details and setup

@lanking520 Thank you for the update! If possible, could you share the command line you use to reproduce this issue?

Thanks for the update @HeyangQin As Qing said, we also tested #2725. You can check the comments in the PR the error we faced.
we tested in multiple GPU size with tp_size more than 1.

We used the same test suite as yours. For example, for GPT-NeoX.

Maybe we could close this issue since Meta tensor and checkpoint loading for other precision type is mostly working (FP16/32). And open one for INT8 specifically. @HeyangQin what do you think?

Hi @lanking520 @sindhuvahinis, Thank you for the information. Previously I only tested checkpoint loading with int8. Now when I test checkpoint saving with int8, I see the same error as you reported. After some initial investigation, I think there are multiple underlying issues that caused these errors:

DeepSpeedExample tries to load checkpoint even if they don't exist. I fixed it by https://github.com/microsoft/DeepSpeedExamples/commit/efacebb3ddbea86bb20c3af30fd060be0fa41ac8
load_params() should reside in policy. I fixed it by https://github.com/microsoft/DeepSpeed/pull/2875. I will merge this once it is reviewed.
Kernel issues. I am working on this.

I agree with @lanking520 that opening a new issue would make it more organized as this issue is about a PR that has been merged.


Failed to retrieve buckets: a bytes-like object is required, not 'str'

Hello,
I am trying to use minio as aws s3 alternative, but  am unable to make it work. I have the following configuration in the hue.ini file.
    [aws]
    [[aws_accounts]]
    [[[default]]]
    access_key_id=ACCESS-KEY
    secret_access_key=SECRET-KEY
    proxy_address=<IP_ADDRESS>
    proxy_port=9000
    is_secure: true

PS: I am using this in kubernetes. I have used helm chart to deploy hue. The minio is not in kubernetes cluster but in same network.

It would help more to share the full stack trace?

Here is the log
Traceback (most recent call last):
  File "/usr/share/hue/apps/filebrowser/src/filebrowser/views.py", line 248, in view
    return listdir_paged(request, path)
  File "/usr/share/hue/apps/filebrowser/src/filebrowser/views.py", line 504, in listdir_paged
    all_stats = request.fs.do_as_user(do_as, request.fs.listdir_stats, path)
  File "/usr/share/hue/desktop/core/src/desktop/lib/fs/proxyfs.py", line 131, in do_as_user
    return fn(*args, **kwargs)
  File "/usr/share/hue/desktop/core/src/desktop/lib/fs/proxyfs.py", line 153, in listdir_stats
    return self._get_fs(path).listdir_stats(path, **kwargs)
  File "/usr/share/hue/desktop/libs/aws/src/aws/s3/__init__.py", line 54, in wrapped
    return fn(*args, **kwargs)
  File "/usr/share/hue/desktop/libs/aws/src/aws/s3/s3fs.py", line 309, in listdir_stats
    raise S3FileSystemException(_('Failed to retrieve buckets: %s') % e)
aws.s3.s3fs.S3FileSystemException: Failed to retrieve buckets: a bytes-like object is required, not 'str'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/share/hue/build/env/lib/python3.6/site-packages/django/core/handlers/base.py", line 181, in _get_response
    response = wrapped_callback(request, *callback_args, **callback_kwargs)
  File "/usr/lib/python3.6/contextlib.py", line 52, in inner
    return func(*args, **kwds)
  File "/usr/share/hue/apps/filebrowser/src/filebrowser/views.py", line 259, in view
    raise PopupException(msg, detail=e)
desktop.lib.exceptions_renderable.PopupException: S3 filesystem exception.
[24/Mar/2021 07:22:25 -0700] exceptions_renderable ERROR    Potential trace: [<FrameSummary file /usr/share/hue/apps/filebrowser/src/filebrowser/views.py, line 248 in view>, <FrameSummary file /usr/share/hue/apps/filebrowser/src/filebrowser/views.py, line 504 in listdir_paged>, <FrameSummary file /usr/share/hue/desktop/core/src/desktop/lib/fs/proxyfs.py, line 131 in do_as_user>, <FrameSummary file /usr/share/hue/desktop/core/src/desktop/lib/fs/proxyfs.py, line 153 in listdir_stats>, <FrameSummary file /usr/share/hue/desktop/libs/aws/src/aws/s3/__init__.py, line 54 in wrapped>, <FrameSummary file /usr/share/hue/desktop/libs/aws/src/aws/s3/s3fs.py, line 309 in listdir_stats>]
[24/Mar/2021 07:22:25 -0700] exceptions_renderable ERROR    Potential detail: Failed to retrieve buckets: a bytes-like object is required, not 'str'
[24/Mar/2021 07:22:25 -0700] connection   DEBUG    Proxy connection: CONNECT s3.amazonaws.com:443 HTTP/1.0
[24/Mar/2021 07:22:25 -0700] connection   DEBUG    establishing HTTPS connection: host=s3.amazonaws.com, kwargs={'timeout': 60, 'port': 443}
[24/Mar/2021 07:22:25 -0700] connection   DEBUG    Params: {}
[24/Mar/2021 07:22:25 -0700] connection   DEBUG    Port: 443
[24/Mar/2021 07:22:25 -0700] connection   DEBUG    Host: s3.amazonaws.com:443
[24/Mar/2021 07:22:25 -0700] connection   DEBUG    Headers: {}
[24/Mar/2021 07:22:25 -0700] connection   DEBUG    Data:
[24/Mar/2021 07:22:25 -0700] connection   DEBUG    Path: https://s3.amazonaws.com:443/
[24/Mar/2021 07:22:25 -0700] connection   DEBUG    Method: GET
[24/Mar/2021 07:22:25 -0700] connection   DEBUG    auth_path=/
[24/Mar/2021 07:22:25 -0700] connection   DEBUG    path=/
[24/Mar/2021 07:22:25 -0700] provider     DEBUG    Using secret key provided by client.
[24/Mar/2021 07:22:25 -0700] provider     DEBUG    Using access key provided by client.
[24/Mar/2021 07:22:25 -0700] access       INFO     <IP_ADDRESS> admin - "GET /desktop/api2/contex

There is probably some Python3 releated issues. To check if path or other params need to be converted to bytes.
https://github.com/cloudera/hue/blob/master/desktop/libs/aws/src/aws/s3/s3fs.py#L294


'Bad exit', ['flocker-changestate' ...

Replaced by https://clusterhq.atlassian.net/browse/FLOC-951
Hi,

add application node, run flocker-deploy
remove application from that node, run flocker-deploy
add that application back on that node, run flocker-deploy ->ends with this error. If I manually delete ZFS volume before calling 3) , there is no problem, but, obviously, no data persistence...
What am I doing wrong? Or is it some bug?
I am testing flocker-deploy on my personal computer (debian), node is on same computer.
Files:
application.yml

"version": 1
"applications":
  "test":
    "image": "test"
    "ports":
    - "internal": 80
      "external": 8080
    "volume":
      "mountpoint": "/var/www/static/data"

deployment.yml in 1)
"version": 1
"nodes":
  "<IP_ADDRESS>": ['test']

deployment.yml in 2)
"version": 1
"nodes":
  "<IP_ADDRESS>": []

deployment.yml in 3)
"version": 1
"nodes":
  "<IP_ADDRESS>": ['test']

Whole error:
Unhandled Error
Traceback (most recent call last):
Failure: flocker.volume.filesystems.zfs.CommandFailed:
main function encountered error
Traceback (most recent call last):
Failure: twisted.internet.defer.FirstError: FirstError[#0, [Failure instance: Traceback (failure with no frames): <class 'flocker.volume.filesystems.zfs.CommandFailed'>:
]]
Unhandled error in Deferred:
Unhandled Error
Traceback (most recent call last):
  File "/usr/lib/python2.7/threading.py", line 783, in __bootstrap
    self.__bootstrap_inner()
  File "/usr/lib/python2.7/threading.py", line 810, in __bootstrap_inner
    self.run()
  File "/usr/lib/python2.7/threading.py", line 763, in run
    self.__target(*self.__args, **self.__kwargs)
--- <exception caught here> ---
  File "/home/samuel/docker/flocker-tutorial/local/lib/python2.7/site-packages/twisted/python/threadpool.py", line 196, in _worker
    result = context.call(ctx, function, *args, **kwargs)
  File "/home/samuel/docker/flocker-tutorial/local/lib/python2.7/site-packages/twisted/python/context.py", line 118, in callWithContext
    return self.currentContext().callWithContext(ctx, func, *args, **kw)
  File "/home/samuel/docker/flocker-tutorial/local/lib/python2.7/site-packages/twisted/python/context.py", line 81, in callWithContext
    return func(*args,**kw)
  File "/home/samuel/docker/flocker-tutorial/local/lib/python2.7/site-packages/flocker/common/_ipc.py", line 90, in get_output
    raise IOError("Bad exit", remote_command, e.returncode, e.output)
exceptions.IOError: ('Bad exit', ['flocker-changestate', '"version": 1\n"nodes":\n  "<IP_ADDRESS>": [\'test\']', '"version": 1\n"applications":\n  "test":\n    "image": "test"\n    "ports":\n    - "internal": 80\n      "external": 8080\n    "volume":\n      "mountpoint": "/var/www/static/data"', '<IP_ADDRESS>:\n  applications: {}\n  used_ports: [22, 25, 53, 80, 139, 445, 631, 9001, 9033, 33805, 33806, 33807, 33808,\n    33810, 33813, 33814, 33815, 38348, 38349, 44587, 44837, 45320, 45321, 45325, 45330,\n    45331, 47654, 48216, 48782, 50516, 51732, 51733, 51734, 51736, 51737, 51738, 51742,\n    51743, 51744, 51882, 52206, 53337, 54548, 55456, 56677, 58805, 59216, 59266, 59687,\n    59787]\n  version: 1\n', '<IP_ADDRESS>'], 1, '')

We are moving our development planning to JIRA. This is issue is now being tracked at https://clusterhq.atlassian.net/browse/FLOC-940. You are welcome to file additional issues in GitHub if that's easier for you.


Update to README.md

Added ssh and https instructions for accessing github.

@natifridman @nocturnalastro
Next round.

What's left with this PR @arjuhe ?  Has it diverged from its original purpose and is it still needed?

Closing this, let's open a fresh PR if this is still needed.


Ability to extract resources in memory

Use Case
I want to store SQL scripts as embedded resources in my application. I want to be able to retrieve the SQL script as a string without first saving to disk.
Proposal
public static string ExtractResource(this Assembly assembly, string resourceName){ ... }

Hi thanks for the addition but I don't see a new package in nuget?

There is sometimes a delay before the package shows up while nuget indexes things.


Show chapter progress at last page of each chapter

Last page of each chapter: Show this information:

How many exercises attempted (within the chapter)
How many points gained (within the chapter).

The element should either be integrated into the end of chapter navigation element ( 'Impressive! You've reached the end of this chapter.') or should be a block that takes considerably less space than the current progress summary.

Eva: "There will be an overview of achieved points and completed exercises at the end of each chapter"

Changes to data transfer logic

Accepting Privacy Policy and Terms and Conditions: Data of all students who accept the Privacy Policy is transferred to Mailchimp with the Subscribed status. (If not checkbox not checked, students cannot proceed and start studying the course)
Giving marketing consent : Students who give marketing consent (“I am ok with receiving updates about upcoming language versions…”) are tagged with marketing allowed in Mailchimp.


CompletableFuture Assertions

Here is my work-in-progress proposal for CompletableFuture assertions. Feedback is very welcome.

CompletableFuture Assertions
A CompletableFuture describes an asynchronous operation result that will eventually be fulfilled.
Methods

isDone() - the future has a result (exception or regular result)
isCompletedExceptionally() - the future as an exceptional result
isCancelled() - the future was cancelled and due to that has a CancellationException result
get() waits for and returns the result, throws a CancellationException or throws the exceptional result wrapped in an ExecutionException

States
The CompletableFuture class can be in four different states:

not completed / "incomplete" (!isDone())
completed normally / "completed" (isDone() && !isCompletedExceptionally())
completed exceptionally  / "failed" (isCompletedExceptionally())
cancelled (isCancelled())

Pseudo code to extract the state as an enum value:
if (!isDone())
    return NOT_COMPLETED;

if (!isCompletedExceptionally())
    return COMPLETED;

if (!isCancelled())
    return FAILED;

return CANCELLED;

Assertions
isDone()
assertThat(future).isDone();

// -> Expecting incomplete future to be done

isNotDone()
assertThat(future).isNotDone();

// -> Expecting completed future not to be done
// -> Expecting failed future not to be done
// -> Expecting cancelled future not to be done

isCompletedExceptionally()
assertThat(future).isCompletedExceptionally();

// -> Expecting incomplete future to have completed exceptionally
// -> Expecting completed future to have completed exceptionally

isNotCompletedExceptionally()
assertThat(future).isNotCompletedExceptionally();

// -> Expecting failed future to not have completed exceptionally
// -> Expecting cancelled future to not have completed exceptionally

isCancelled()
assertThat(future).isCancelled();

// -> Expecting incomplete future to be cancelled
// -> Expecting completed future to be cancelled
// -> Expecting failed future to be cancelled

isNotCancelled()
assertThat(future).isNotCancelled();

// -> Expecting cancelled future not to be cancelled

The following assertions do no map directly to CompletableFuture methods.
isCompleted()
assertThat(future).isCompleted();

// -> Expecting incomplete future to be completed
// -> Expecting failed future to be completed
// -> Expecting cancelled future to be completed

isNotCompleted()
assertThat(future).isNotCompleted();

// -> Expecting completed future to not be completed

hasFailed()
assertThat(future).hasFailed();

// -> Expecting incomplete future to have failed
// -> Expecting completed future to have failed
// -> Expecting cancelled future to have failed

hasNotFailed()
assertThat(future).hasNotFailed();

// -> Expecting failed future to not have failed

hasFailedWith()
assertThat(future)
    .hasFailedWith(IllegalArgumentException.class)
    .hasMessage("something");

// -> Expecting incomplete future to have failed
// -> Expecting completed future to have failed
// -> Expecting cancelled future to have failed
// -> Expecting failed future to have failed with an instance of:
//        <java.lang.IllegalArgumentException>
//    but was:
//        <java.lang.RuntimeException: some random error>

https://github.com/nurkiewicz/async-retry/blob/master/src/test/java/com/nurkiewicz/asyncretry/AsyncRetryJobTest.java#L97-L106 is an example of test code that could be simplified with the above proposal.
		assertThat(future.isCompletedExceptionally()).isTrue();

		try {
			future.get();
			failBecauseExceptionWasNotThrown(ExecutionException.class);
		} catch (ExecutionException e) {
			final Throwable cause = e.getCause();
			assertThat(cause).isInstanceOf(SocketException.class);
			assertThat(cause).hasMessage(DON_T_PANIC);
		}

would be simplified to:
		assertThat(future)
                    .hasFailedWith(SocketException.class)
                    .hasMessage(DON_T_PANIC);

Nice one ! Will review it in the next weeks.

@joel-costigliola can I help in some way to speed up the process?

one thing : I'm planning to improve the javadoc by reducing the space around code examples (see https://github.com/joel-costigliola/assertj-core/issues/438) - it means that code examples should be formatted differently as described in the mentioned issue.
Can you do that for this PR ?

@joel-costigliola I could, but please have a look at my comment at https://github.com/joel-costigliola/assertj-guava/commit/841ee1b60df12a59b5e7528901a07e9d18c0f618 first. I'm not sure if this is such a good idea.

@joel-costigliola done

integrated, thx.


Highlight Published vs Unpublished commits

Need to highlight all commits reachable by a remote branch.

Hi, anyone working on this issue?

Nobody is working on it that I know of.


Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/provider/rds_db_parameter_group/v2.rb to aws/lib/puppet/provider/rds_db_parameter_group/v2.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/provider/rds_db_securitygroup/v2.rb to aws/lib/puppet/provider/rds_db_securitygroup/v2.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/provider/rds_db_subnet_group/v2.rb to aws/lib/puppet/provider/rds_db_subnet_group/v2.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/provider/rds_instance/v2.rb to aws/lib/puppet/provider/rds_instance/v2.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/provider/route53_a_record/v2.rb to aws/lib/puppet/provider/route53_a_record/v2.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/provider/route53_aaaa_record/v2.rb to aws/lib/puppet/provider/route53_aaaa_record/v2.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/provider/route53_cname_record/v2.rb to aws/lib/puppet/provider/route53_cname_record/v2.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/provider/route53_mx_record/v2.rb to aws/lib/puppet/provider/route53_mx_record/v2.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/provider/route53_ns_record/v2.rb to aws/lib/puppet/provider/route53_ns_record/v2.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/provider/route53_ptr_record/v2.rb to aws/lib/puppet/provider/route53_ptr_record/v2.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/provider/route53_record.rb to aws/lib/puppet/provider/route53_record.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/provider/route53_spf_record/v2.rb to aws/lib/puppet/provider/route53_spf_record/v2.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/provider/route53_srv_record/v2.rb to aws/lib/puppet/provider/route53_srv_record/v2.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/provider/route53_txt_record/v2.rb to aws/lib/puppet/provider/route53_txt_record/v2.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/provider/route53_zone/v2.rb to aws/lib/puppet/provider/route53_zone/v2.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/provider/s3_bucket/v2.rb to aws/lib/puppet/provider/s3_bucket/v2.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/provider/sqs_queue/v2.rb to aws/lib/puppet/provider/sqs_queue/v2.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/cloudformation_stack.rb to aws/lib/puppet/type/cloudformation_stack.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/cloudfront_distribution.rb to aws/lib/puppet/type/cloudfront_distribution.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/cloudwatch_alarm.rb to aws/lib/puppet/type/cloudwatch_alarm.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/ec2_autoscalinggroup.rb to aws/lib/puppet/type/ec2_autoscalinggroup.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/ec2_elastic_ip.rb to aws/lib/puppet/type/ec2_elastic_ip.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/ec2_instance.rb to aws/lib/puppet/type/ec2_instance.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/ec2_launchconfiguration.rb to aws/lib/puppet/type/ec2_launchconfiguration.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/ec2_scalingpolicy.rb to aws/lib/puppet/type/ec2_scalingpolicy.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/ec2_securitygroup.rb to aws/lib/puppet/type/ec2_securitygroup.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/ec2_volume.rb to aws/lib/puppet/type/ec2_volume.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/ec2_vpc.rb to aws/lib/puppet/type/ec2_vpc.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/ec2_vpc_customer_gateway.rb to aws/lib/puppet/type/ec2_vpc_customer_gateway.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/ec2_vpc_dhcp_options.rb to aws/lib/puppet/type/ec2_vpc_dhcp_options.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/ec2_vpc_internet_gateway.rb to aws/lib/puppet/type/ec2_vpc_internet_gateway.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/ec2_vpc_routetable.rb to aws/lib/puppet/type/ec2_vpc_routetable.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/ec2_vpc_subnet.rb to aws/lib/puppet/type/ec2_vpc_subnet.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/ec2_vpc_vpn.rb to aws/lib/puppet/type/ec2_vpc_vpn.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/ec2_vpc_vpn_gateway.rb to aws/lib/puppet/type/ec2_vpc_vpn_gateway.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/ecs_cluster.rb to aws/lib/puppet/type/ecs_cluster.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/ecs_service.rb to aws/lib/puppet/type/ecs_service.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/ecs_task_definition.rb to aws/lib/puppet/type/ecs_task_definition.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/elb_loadbalancer.rb to aws/lib/puppet/type/elb_loadbalancer.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/iam_group.rb to aws/lib/puppet/type/iam_group.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/iam_instance_profile.rb to aws/lib/puppet/type/iam_instance_profile.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/iam_policy.rb to aws/lib/puppet/type/iam_policy.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/iam_policy_attachment.rb to aws/lib/puppet/type/iam_policy_attachment.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/iam_role.rb to aws/lib/puppet/type/iam_role.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/iam_user.rb to aws/lib/puppet/type/iam_user.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/kms.rb to aws/lib/puppet/type/kms.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/rds_db_parameter_group.rb to aws/lib/puppet/type/rds_db_parameter_group.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/rds_db_securitygroup.rb to aws/lib/puppet/type/rds_db_securitygroup.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/rds_db_subnet_group.rb to aws/lib/puppet/type/rds_db_subnet_group.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/rds_instance.rb to aws/lib/puppet/type/rds_instance.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/route53_a_record.rb to aws/lib/puppet/type/route53_a_record.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/route53_aaaa_record.rb to aws/lib/puppet/type/route53_aaaa_record.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/route53_cname_record.rb to aws/lib/puppet/type/route53_cname_record.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/route53_mx_record.rb to aws/lib/puppet/type/route53_mx_record.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/route53_ns_record.rb to aws/lib/puppet/type/route53_ns_record.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/route53_ptr_record.rb to aws/lib/puppet/type/route53_ptr_record.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/route53_spf_record.rb to aws/lib/puppet/type/route53_spf_record.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/route53_srv_record.rb to aws/lib/puppet/type/route53_srv_record.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/route53_txt_record.rb to aws/lib/puppet/type/route53_txt_record.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/route53_zone.rb to aws/lib/puppet/type/route53_zone.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/s3_bucket.rb to aws/lib/puppet/type/s3_bucket.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet/type/sqs_queue.rb to aws/lib/puppet/type/sqs_queue.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet_x/puppetlabs/aws.rb to aws/lib/puppet_x/puppetlabs/aws.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet_x/puppetlabs/aws_ingress_rules_parser.rb to aws/lib/puppet_x/puppetlabs/aws_ingress_rules_parser.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet_x/puppetlabs/iam_policy.rb to aws/lib/puppet_x/puppetlabs/iam_policy.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet_x/puppetlabs/property/region.rb to aws/lib/puppet_x/puppetlabs/property/region.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet_x/puppetlabs/property/tag.rb to aws/lib/puppet_x/puppetlabs/property/tag.rb
Packing plugin /root/Bolt/Puppet-Bolt/modules/aws/lib/puppet_x/puppetlabs/route53_record.rb to aws/lib/puppet_x/puppetlabs/route53_record.rb
No plugins found in subpath '/root/Bolt/Puppet-Bolt/modules/hosting/lib' (file / directory does not exist)
No pluginfacts found in subpath '/root/Bolt/Puppet-Bolt/modules/hosting/facts.d' (file / directory does not exist)
Packing plugin /root/Bolt/Puppet-Bolt/modules/hosting/files/assign-ip to hosting/files/assign-ip
Packing plugin /root/Bolt/Puppet-Bolt/modules/hosting/files/custom_backup.sh to hosting/files/custom_backup.sh
Packing plugin /root/Bolt/Puppet-Bolt/modules/hosting/files/healcheck.php to hosting/files/healcheck.php
Packing plugin /root/Bolt/Puppet-Bolt/modules/hosting/files/logstash/aws-filter.conf to hosting/files/logstash/aws-filter.conf
Packing plugin /root/Bolt/Puppet-Bolt/modules/hosting/files/logstash/aws-input.conf to hosting/files/logstash/aws-input.conf
Packing plugin /root/Bolt/Puppet-Bolt/modules/hosting/files/logstash/filebeat-input.conf to hosting/files/logstash/filebeat-input.conf
Packing plugin /root/Bolt/Puppet-Bolt/modules/hosting/files/logstash/input-nginx.conf to hosting/files/logstash/input-nginx.conf
Packing plugin /root/Bolt/Puppet-Bolt/modules/hosting/files/logstash/input-s3.conf to hosting/files/logstash/input-s3.conf
Packing plugin /root/Bolt/Puppet-Bolt/modules/hosting/files/logstash/nginx_access.conf to hosting/files/logstash/nginx_access.conf
Packing plugin /root/Bolt/Puppet-Bolt/modules/hosting/files/logstash/nginx_error.conf to hosting/files/logstash/nginx_error.conf
Packing plugin /root/Bolt/Puppet-Bolt/modules/hosting/files/logstash/output-elasticsearch.conf to hosting/files/logstash/output-elasticsearch.conf
Packing plugin /root/Bolt/Puppet-Bolt/modules/hosting/files/master.sh to hosting/files/master.sh
Packing plugin /root/Bolt/Puppet-Bolt/modules/hosting/files/monitoring/aws-scripts-mon/AwsSignatureV4.pm to hosting/files/monitoring/aws-scripts-mon/AwsSignatureV4.pm
Packing plugin /root/Bolt/Puppet-Bolt/modules/hosting/files/monitoring/aws-scripts-mon/CloudWatchClient.pm to hosting/files/monitoring/aws-scripts-mon/CloudWatchClient.pm
Packing plugin /root/Bolt/Puppet-Bolt/modules/hosting/files/monitoring/aws-scripts-mon/LICENSE.txt to hosting/files/monitoring/aws-scripts-mon/LICENSE.txt
Packing plugin /root/Bolt/Puppet-Bolt/modules/hosting/files/monitoring/aws-scripts-mon/NOTICE.txt to hosting/files/monitoring/aws-scripts-mon/NOTICE.txt
Packing plugin /root/Bolt/Puppet-Bolt/modules/hosting/files/monitoring/aws-scripts-mon/awscreds.template to hosting/files/monitoring/aws-scripts-mon/awscreds.template
Packing plugin /root/Bolt/Puppet-Bolt/modules/hosting/files/monitoring/aws-scripts-mon/mon-get-instance-stats.pl to hosting/files/monitoring/aws-scripts-mon/mon-get-instance-stats.pl
Packing plugin /root/Bolt/Puppet-Bolt/modules/hosting/files/monitoring/aws-scripts-mon/mon-put-instance-data.pl to hosting/files/monitoring/aws-scripts-mon/mon-put-instance-data.pl
Packing plugin /root/Bolt/Puppet-Bolt/modules/hosting/files/monitoring/aws_cloudwatch_mysql_replication_status.sh to hosting/files/monitoring/aws_cloudwatch_mysql_replication_status.sh
Packing plugin /root/Bolt/Puppet-Bolt/modules/hosting/files/monitoring/awscli.conf to hosting/files/monitoring/awscli.conf
Packing plugin /root/Bolt/Puppet-Bolt/modules/hosting/files/monitoring/awslogs-agent-setup.py to hosting/files/monitoring/awslogs-agent-setup.py
Packing plugin /root/Bolt/Puppet-Bolt/modules/hosting/files/mysqld_exporter/LICENSE to hosting/files/mysqld_exporter/LICENSE
Packing plugin /root/Bolt/Puppet-Bolt/modules/hosting/files/mysqld_exporter/NOTICE to hosting/files/mysqld_exporter/NOTICE
Packing plugin /root/Bolt/Puppet-Bolt/modules/hosting/files/mysqld_exporter/cnf to hosting/files/mysqld_exporter/cnf
Packing plugin /root/Bolt/Puppet-Bolt/modules/hosting/files/mysqld_exporter/init.d to hosting/files/mysqld_exporter/init.d
Packing plugin /root/Bolt/Puppet-Bolt/modules/hosting/files/mysqld_exporter/mysqld_exporter to hosting/files/mysqld_exporter/mysqld_exporter
Packing plugin /root/Bolt/Puppet-Bolt/modules/hosting/files/mysqld_exporter/sysconfig to hosting/files/mysqld_exporter/sysconfig
Packing plugin /root/Bolt/Puppet-Bolt/modules/hosting/files/nginx/GeoIP2-City.mmdb to hosting/files/nginx/GeoIP2-City.mmdb
Packing plugin /root/Bolt/Puppet-Bolt/modules/hosting/files/nginx/localhost.conf to hosting/files/nginx/localhost.conf
Packing plugin /root/Bolt/Puppet-Bolt/modules/hosting/files/nginx/security.conf to hosting/files/nginx/security.conf
Packing plugin /root/Bolt/Puppet-Bolt/modules/hosting/files/yumrepo/NODESOURCE-GPG-SIGNING-KEY-EL to hosting/files/yumrepo/NODESOURCE-GPG-SIGNING-KEY-EL
Packing plugin /root/Bolt/Puppet-Bolt/modules/hosting/files/yumrepo/RPM-GPG-KEY-follamigos to hosting/files/yumrepo/RPM-GPG-KEY-follamigos
Packing plugin /root/Bolt/Puppet-Bolt/modules/hosting/files/yumrepo/RPM-GPG-KEY-webtatic-7 to hosting/files/yumrepo/RPM-GPG-KEY-webtatic-7
Packing plugin /root/Bolt/Puppet-Bolt/modules/hosting/files/yumrepo/RPM-GPG-KEY-webtatic-andy to hosting/files/yumrepo/RPM-GPG-KEY-webtatic-andy
Packing plugin /root/Bolt/Puppet-Bolt/modules/hosting/files/yumrepo/RPM-GPG-KEY-webtatic-el7 to hosting/files/yumrepo/RPM-GPG-KEY-webtatic-el7
No pluginfacts found in subpath '/root/Bolt/Puppet-Bolt/modules/systemd/facts.d' (file / directory does not exist)
No files found in subpath '/root/Bolt/Puppet-Bolt/modules/systemd/files' (file / directory does not exist)
Packing plugin /root/Bolt/Puppet-Bolt/modules/systemd/lib/facter/systemd.rb to systemd/lib/facter/systemd.rb
Packed plugins in 8527.877728 ms
Running task run '#<struct Bolt::Task name="apply_helpers::apply_catalog", file=nil, files=[{"name"=>"apply_catalog.rb", "path"=>"/opt/puppetlabs/bolt/lib/ruby/gems/2.5.0/gems/bolt-1.40.0/libexec/apply_catalog.rb"}], metadata={"supports_noop"=>true, "input_method"=>"stdin", "implementations"=>[{"name"=>"apply_catalog.rb"}, {"name"=>"apply_catalog.rb", "remote"=>true}]}>' on <IP_ADDRESS>
Initializing ssh connection to <IP_ADDRESS>
Disabling use_agent in net-ssh: ssh-agent is not available
Opened session
Running '/opt/puppetlabs/bolt/lib/ruby/gems/2.5.0/gems/bolt-1.40.0/libexec/apply_catalog.rb' with {"catalog":"Sensitive [value redacted]","plugins":"Sensitive [value redacted]","_task":"apply_helpers::apply_catalog","_noop":null}
Executing: mkdir -m 700 /tmp/e5e031a5-ad36-468c-9d38-40fcc60fffdf
Command returned successfully
Uploading /opt/puppetlabs/bolt/lib/ruby/gems/2.5.0/gems/bolt-1.40.0/libexec/apply_catalog.rb, to /tmp/e5e031a5-ad36-468c-9d38-40fcc60fffdf/apply_catalog.rb
Executing: chmod u+x /tmp/e5e031a5-ad36-468c-9d38-40fcc60fffdf/apply_catalog.rb
Command returned successfully
Executing: id -g root
stdout: 0
Command returned successfully
Executing: sudo -S -u root -p [sudo]\ Bolt\ needs\ to\ run\ as\ another\ user,\ password:\  chown -R root:0 /tmp/e5e031a5-ad36-468c-9d38-40fcc60fffdf
Command returned successfully
Executing: sudo -S -u root -p [sudo]\ Bolt\ needs\ to\ run\ as\ another\ user,\ password:\  sh -c 'echo ad0ffbf3-3376-485a-bdb2-adef8a1db27f 1>&2; /tmp/e5e031a5-ad36-468c-9d38-40fcc60fffdf/apply_catalog.rb'
stderr: ad0ffbf3-3376-485a-bdb2-adef8a1db27f
Stuck here
on the system i see
9962 ?        Ss     0:00      0   794<PHONE_NUMBER>  0.4  _ sshd: ec2-user [priv]
9992 ?        S      0:01      0   794<PHONE_NUMBER>  0.2      _ sshd: ec2-user@notty
10062 ?        Ss     0:00      0   132<PHONE_NUMBER>  0.3          _ sudo -S -u root -p [sudo] Bolt needs to run as another user, password:  sh -c echo ad0ffbf3-3376-485a-bdb2-adef8a1db27f 1>&2; /tmp/e5e031a5-ad36-468c-9d38-40fcc60fffdf/
10075 ?        S      0:00      0   887<PHONE_NUMBER>  0.1              _ sh -c echo ad0ffbf3-3376-485a-bdb2-adef8a1db27f 1>&2; /tmp/e5e031a5-ad36-468c-9d38-40fcc60fffdf/apply_catalog.rb
10076 ?        Sl     0:01      0     2<PHONE_NUMBER>96  6.0                  _ /opt/puppetlabs/puppet/bin/ruby /tmp/e5e031a5-ad36-468c-9d38-40fcc60fffdf/apply_catalog.rb
Stracing the process shows this
mremap(0x7f4d260e8000, 72773632, 72781824, MREMAP_MAYMOVE) = 0x7f4d260e8000
read(0, 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8192) = 8192
mremap(0x7f4d260e8000, 72781824, 72790016, MREMAP_MAYMOVE) = 0x7f4d260e8000

This seems like an isolated problem, and we aren't able to replicate it. I'm going to close since there's nothing to do, but please let us know if you have other issues!


1816 expired link are not highlighted

🍰 Pullrequest

Transaction link list

removed button and added dropdown menu. for space reasons this is the best variant.
expired links only have a delete event and are somewhat paler than active links.

Issues

fixes #1816
fixed #1815

The elements are not properly hidden, the popup still contains a distance most likely there because of the hidden elements


can you add Straberry perl from perl.org please?

I see python on the list, but not perl. I am thinking of Strawberry perl from perl.org.  Could you add Strawberry perl please? This is a feature request.

I see in the YouTube videos that Chris' version of WinUtils has coloured boxes and they flash when you click on them.  I only have grey colours on my version of WinUtils. The grey buttons turn a bit greyer when I click on the buttons, but not coloured. Is your version a special demonstration version, or it is just that my system does not show the colours? I would like to have the colours appear.  I do not know if this is a bug or not.

PR https://github.com/ChrisTitusTech/winutil/pull/1560 created

I do not know how to add a link to Strawberry perl.


Add old-style footer to site

the current footer is minimal. I like the more detailed old-style casper footer, so try adding that.

Would you please put a screenshot of a footer for getting inspiration?


fix(DIA-354): adds missing token to vimeo player

This PR resolves DIA-354
Description
Videos in the Artwork screen image were not correctly loading, this PR fixes the query params extraction.
Android
https://github.com/artsy/eigen/assets/15792853/0cbf22d4-fa94-4b86-9155-a21f7f602254
PR Checklist

[ ] I have tested my changes on iOS and Android.
[ ] I hid my changes behind a feature flag, or they don't need one.
[ ] I have included screenshots or videos, or I have not changed the UI.
[ ] I have added tests, or my changes don't require any.
[ ] I added an app state migration, or my changes do not require one.
[ ] I have documented any follow-up work that this PR will require, or it does not require any.
[ ] I have added a changelog entry below, or my changes do not require one.

To the reviewers 👀

[ ] I would like at least one of the reviewers to run this PR on the simulator or device.

Changelog updates
Changelog updates

Cross-platform user-facing changes

iOS user-facing changes

Android user-facing changes

Dev changes

Need help with something? Have a look at our docs, or get in touch with us.

Warnings

:warning:

❌ An error occurred while validating your changelog, please make sure you provided a valid changelog.

  Generated by :no_entry_sign: dangerJS against c93f1ac2245ab03f7a6473aaa435fad0a6b765f7


Better !silence UX

Currently when an alarm goes off, one needs to type out !silence <alarm text> to silence the alarm. This is a bit unwieldy, especially if someone is on mobile and the alarm text is long and complicated.
Ideally, the command's syntax would be !silence [<alarm text>], whereas not specifying the text would mean all active alarms are silenced, and text could be specified to silence a specific alarm.

Done in https://github.com/anoadragon453/matrix-reminder-bot/commit/6626e93c4c410cde28da7cc00d9c401f58e93244.


2D Array (Spiral Print and Wave Print) | C++

Hello, I'm a GSSoC'21 participant. I would like to implement Spiral  Print and Wave Print with proper function description in C++. Kindly assign me this task. Thank You.

Hi,
I am GSSOC'21 participant
My code will be in JAVA with proper comments and test cases.
@rudrakshi99 @nakul-19 @plazzy99 @tarun26091999  please assign me this issue

cannot solve two ques in same issue.


Automated Package Upgrade

:tada: This PR is included in version 5.0.494 :tada:
The release is available on:

npm package (@latest dist-tag)
GitHub release

Your semantic-release bot :package::rocket:


Add log to happy path

Reason for Change:
DNC requiring to refresh CNS NC associations is not expected to happen too often, but when it does, we need to log all pertinent details. Added the log in CNS should to show refresh detected and initiated by DNC.
Issue Fixed:

Requirements:

[ ] uses conventional commit messages

[ ] includes documentation
[ ] adds unit tests

Notes:

@bohuini pls read this https://github.com/Azure/azure-container-networking/pull/1921#issuecomment-1530069378

I'm with @rbtr here... I think concise logs are better because they tend to tell fewer lies about what's going on as things change over time. Log just the facts, given what's known at the time the log is generated.  Given what I see, it would likely be better to have a middleware that logs requests (path, method, some request ID), and responses (HTTP response code, end-to-end time, the same ID for correlation). All of those should be structured using zap. Something like:
func logHTTP(next http.Handler) http.Handler {
	return http.HandlerFunc(func(rw http.ResponseWriter, r *http.Request) {
		logger.Info("request", zap.String("uri", r.URL.RequestURI()), zap.String("method", r.Method))
		// need to wrap the ResponseWriter in something that can capture the response code
		wrw := WrappedResponseWriter{rw} // this doesn't exist and needs to be written
		next(wrw, r)
		logger.Info("response", zap.Int("code", wrw.ResponseCode)) // etc.
	})
}

@rbtr Revised the log and rebased branch. Please review again when you get a chance. Thanks!

@timraymond That's a good point and I do agree. But for this time I am just making it simple for integration testing purposes.

LGTM 👍 Awaiting @rbtr to hit the approval button.

/azp run


Make sure it looks ok on mobile

hide the legend on mobile
make the location input field limited in size on mobile

+@pcorpet

This change is

Reviewed 1 of 1 files at r1.
Review status: all files reviewed at latest revision, all discussions resolved.

Comments from Reviewable


How about a few Linux examples?

The only example on this page is for a Windows machine.  If we want to configure this service to support Linux machines, it would be very helpful to include Linux examples

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

ID: 0b1a608e-ed83-547e-cfa4-4ce59bfc5515
Version Independent ID: a5f7df2c-cd86-bc1c-51c8-404a3ae88d35
Content: Bind using Secure LDAP (LDAPS) to an Azure AD Domain Services managed domain
Content Source: articles/active-directory-domain-services/active-directory-ds-ldaps-bind-lockdown.md
Service: active-directory
Sub-service: domain-services
GitHub Login: @iainfoulds
Microsoft Alias: iainfou

@grandparoach Thanks for your feedback! We will investigate and update as appropriate.

Thanks for the feedback, @grandparoach
Are there particular examples for app usage you'd like to see? The current example shows LDP.exe as it's a common management tool from the Windows platform to see the secure connection in action, but there's nothing specific about this being Windows-only. Provided the client certificate was trusted by a Linux machine, any application that uses secure LDAP would work. The overall client certificate management steps may be different across distros, too, so it makes it a little hard to show multiple examples here.
#in-progress

Part of it is because we have not been able to configure a Linux client to successfully work, so it would be helpful to have an example that we could follow to see what we are missing.  (CentOS 7 is what we are using)
The other part is just the message that is being sent.  Microsoft claims that Linux is a 1st class citizen on Azure, and 99.9% of the use cases involving LDAP will be Linux based, yet the only example given is Windows.

Okay, but what specifically are you trying to do and where are you running into problems? This example in the doc simply shows using LDP.exe, but the reality is that you'd probably have a web application that needs the certificate applied and trusted. So trying to show examples for the different scenarios, web servers, application languages, etc. is tough to cover all those common scenarios.
I can add something to our docs backlog to provide this kind of update if there's a specific area you're looking for, or @FrankHu-MSFT can help with specific troubleshooting and support issues for your current need which would provide quicker resolution.

I'm currently looking into this issue, thanks for letting us know about your issue @grandparoach. Connecting offline to determine next steps.

Hey @grandparoach I think you will need to file a support ticket to move this issue forward.
Please send an email to AzCommunity[at]microsoft[dot]com with a reference to this thread and also your Azure Subscription GUID.
I will enable a one-time free support ticket for issues related to this.
That being said I will be closing out this issue now as the git doc issues are meant for issues with the docs.
Thanks!


react-components: Update colours for Autocomplete | Select | NativeSelect components

New colours: https://www.figma.com/design/MJze0qkQ2bw0D1oelqqOUK/pv-design-system?node-id=9829-3464&m=dev

Q: Is it safe for all projects?

Q: Is it safe for all projects?

I'm not 100% sure about this, it was a request from the designer (he was informed that it might affect other projects). There are no critical changes there, but we'll have to make sure everything is ok, maybe do an alpha release


Refactor

Refactored the events handler to make it consistent.

@jorgeluiso we also now have a way to deploy this on heroku https://github.com/wenlock/vinotinto-project/tree/master/messenger-app
When this merges, I'll see if I can redeploy it in the morning.


Add statusline support

vim-lsp can show occurred errors at statusline (issue, PR). Is it possible to implement it in this plugin too?

It may be misleading because the name of the plugin is not appropriate now, but this plugin is a collection of settings for using each language server from Nvim's built-in lsp.
The core logic of Nvim's lsp client is in Nvim itself. So please post an issue on lsp client at neovim/neovim.


Remove deprecated ld_classic linker flag

The linker bug was fixed in Xcode 15.1.
FB13097713 in https://developer.apple.com/documentation/xcode-release-notes/xcode-15_1-release-notes

Reverts #11062

[approve ci autest]

[approve ci autest]

Cherry-picked to v10.0.x