text:
Inlet and outlet not called using a pipeline as a base_model to create custom model

Hello! When I create a model by going to Workspaces -> Model -> Create a Model and selecting the model ID of a custom pipeline as the base model, the inlet and outlet functions don't seem to be triggered.
Any insights on why this might be happening? Thank you!

Can you post the code of your custom pipeline?

Due to company policies, I had to remove some code related to system prompt construction.
Currently, the main issue is that when using the pipeline directly, the self.file_contents variable updates correctly. However, if I wrap the pipeline to create a custom model from the UI, the inlet (and the oultet) is not called, and the self.file_contents list doesn’t update across different chats.
Here a code example
"""
title: pipeline
author: user
version: 1.0
license: MIT
description: A pipeline
requirements:  langfuse, openai
"""

from typing import List, Optional, Union, Generator, Iterator
import os
import uuid
from pydantic import BaseModel
import openai
import re
from langfuse import Langfuse
from langfuse.api.resources.commons.errors.unauthorized_error import UnauthorizedError
import os
from utils.pipelines.main import get_last_assistant_message

# print Setup
...

# OIFile Class to manage openwebui file data
class OIFile:
    def __init__(self, file_id: str, filename: str, content: str):
        self.id = file_id
        self.filename = filename
        self.content = content

    def __repr__(self):
        return f"File(id={self.id}, filename={self.filename}, content={len(self.content)} bytes)"

class Pipeline:
    class Valves(BaseModel):
        LLM_BASE_URL: str
        LLM_API_KEY: str
        LLM_MODEL_NAME: str
        LLM_TEMPERATURE: float
        LLM_MAX_TOKENS: int

    def __init__(self):
        self.name = "pipeline"
        self.valves = self._initialize_valves()
        self.file_contents = [OIFile]

    def _initialize_valves(self) -> Valves:
        return self.Valves(
            LLM_BASE_URL=os.getenv("LLM_BASE_URL", "url"),
            LLM_API_KEY=os.getenv("LLM_API_KEY", "empty"),
            LLM_MODEL_NAME=os.getenv(
                "LLM_MODEL_NAME", "meta-llama/Llama-3.2-3B-Instruct"
            ),
            LLM_TEMPERATURE=float(os.getenv("LLM_TEMPERATURE", 0)),
            LLM_MAX_TOKENS=int(os.getenv("LLM_MAX_TOKENS", 10000)),
        )

    async def on_startup(self):
        print.info(f"Server {self.name} is starting.")

    async def on_shutdown(self):
        print.info(f"Server {self.name} is shutting down.")

    async def on_valves_updated(self):
        print.info("Valves updated.")

    async def inlet(self, body: dict, user: dict) -> dict:
        print.info("Processing inlet request")

        # Extract file info for all files in the body
        self.file_contents = self._extract_file_info(body)

        # Log the extracted file information
        for file in self.file_contents:
            print.info(
                f"File info extracted: ID={file.id}, Filename={file.filename}, Content size={len(file.content)} bytes"
            )
        return body

    def _extract_file_info(self, body: dict) -> list:
        """Extracts the file info from the request body for all files."""
        files = []
        for file_data in body.get("files", []):
            file = file_data["file"]
            file_id = file["id"]
            filename = file["filename"]
            file_content = file["data"]["content"]

            # Create a OIFile object and append it to the list
            files.append(OIFile(file_id, filename, file_content))

        return files

    def pipe(
        self, body: dict, user_message: str, model_id: str, messages: List[dict]
    ) -> Union[str, Generator, Iterator]:

        print.info("Starting PIPE process")

        # Extract parameters from body with default fallbacks
        stream = body.get("stream", True)
        max_tokens = body.get("max_tokens", self.valves.LLM_MAX_TOKENS)
        temperature = body.get("temperature", self.valves.LLM_TEMPERATURE)

        system_prompt = self._extract_system_prompt(messages)

        # Generate and update the system prompt if required
        if self.file_contents:
            # build or retrieve a prompt
            # adding file contents

        # Call the LLM API
        return self._call_openai_api(messages, max_tokens, temperature, stream)

    def _call_openai_api(
        self,
        messages: List[dict],
        max_tokens: int,
        temperature: float,
        stream: bool,
    ) -> Union[str, Generator, Iterator]:

        client = openai.Client(
            api_key=self.valves.LLM_API_KEY,
            base_url=self.valves.LLM_BASE_URL,
        )

        try:
            # Call OpenAI API with the prepared parameters
            response = client.chat.completions.create(
                model=self.valves.LLM_MODEL_NAME,
                messages=messages,
                max_tokens=max_tokens,
                temperature=temperature,
                stream=stream,
                stream_options={"include_usage": True},
            )

            return response
        except Exception as e:
            print.error(f"Error during OpenAI API call: {e}")
            return f"Error: {e}"

    async def outlet(self, body: dict, user: Optional[dict] = None) -> dict:
        print(f"outlet:{__name__}")
        print(f"Received body: {body}")
        self.file_contents=[]
        return body

I need to see what the "wrapping" code looks like.

Sorry, I use wrap but I should use another term.
When I create a model by going to Workspaces -> Model -> Create a Model and selecting the model ID of a custom pipeline as the base model, the inlet and outlet functions don't seem to be triggered.
The pipeline used is the one showed above (plus some function to extract the system prompt and adding the file contents and other details).
When I create the model as described at the beginning of this comment, the function inlet and outlet are not triggered

Moreover, if I load a pipeline and go to Workspaces -> Model -> the pipeline
and I change the name, the pipeline works but the outlet is not called correctly

@explorigin any update?

Moreover, i have seen this behaviour:
In this custom pipeline setup, the workflow is as follows:
Documents are loaded and fed into OpenWebUI.
OpenWebUI encodes the documents, splits them into chunks, and stores them.
The /inlet endpoint on the pipeline is called.
OpenWebUI then performs the RAG operation.
The pipeline’s /pipe endpoint is triggered, displaying the response on OpenWebUI.
Finally, the client calls the /outlet endpoint to retrieve the result.

Question on Implementation:
Why do the /inlet, /pipe, and /outlet endpoints each have different request bodies?

any updates?

repo: open-webui/pipelines


text:
Support Version 2.0.0 changes for /v1/list-operation-servers-at-application

Fixes #212

Reviewer is @DanaSunal

source: gharchive/pull-request

repo: openBackhaul/ApplicationLayerTopology

license_type: permissive

license_source: github-api


text:
Upgrading layerProtocolName in ForwardingDomain and ForwardingConstruct too

LayerProtocol class contains the layerProtocolName attribute.
During TSP datatype of the layerProtocolName attribute has been changed from LayerProtocolNameAndQualifier to layerProtocolNameType datatype.
layerProtocolNameType (enumeration) gets loaded with a layer specific value for being able to implement a conditioned augmentation of the LayerProtocol class.
Same change should be made in the layerProtocolName attribute of the ForwardingDomain and the ForwardingConstruct classes.
Proposed Solution:
ForwardingDomain::layerProtocolName : LayerProtocolNameAndQualifier => ForwardingDomain::layerProtocolName : layerProtocolNameType
and
ForwardingConstruct ::layerProtocolName : LayerProtocolNameAndQualifier => ForwardingConstruct ::layerProtocolName : layerProtocolNameType

All layer-protocol-name attributes are of type "layer-protocol-name-type" in revision "2019-07-09".
demx8as6@devmartin:~/workspace/onf/5G-xHaul/models/tools$ grep -anr -A 1 "leaf layer-protocol-name" ./distribution/core-model.yang
957:    leaf layer-protocol-name {
958-      type layer-protocol-name-type;
--
1390:    leaf layer-protocol-name {
1391-      type layer-protocol-name-type;
--
1576:    leaf layer-protocol-name {
1577-      type layer-protocol-name-type;
demx8as6@devmartin:~/workspace/onf/5G-xHaul/models/tools$

@openBackhaul: Please close after successful test.

source: gharchive/issue

repo: openBackhaul/core


text:
🛑 Server is down

In d1f7b34, Server ($SERVER_SITE_GET_ENDPOINT) was down:

HTTP code: 0
Response time: 0 ms

Resolved: Server is back up in 6bfd7cb.

repo: openCONTRABASS/upptime-server

license: MIT


text:
modify the resolution manually

I have a 25601660 portable screen, but it can only output 19201080, can it be modified manually? the image used is rock5b-openfyde-r108.img.xz

I have a story to tell about this.
My screen resolution is also 2560x1660p.
However last week for some reason It was only detecting 1920x1080p.
No on OpenFyde I couldnt do nothing. But on the screen settings I noticed I had HDR turned on and there was some other option that couldnt the screen trigger the 120hz something HCP.
In Short I have a 2k screen that can do 120hz however thats not the case if connected via hdmi only via type c.
So it does 2k@60hz but there was an option it that was preventing openFyde from pulling its max resolution.
My suggestions... try playing with it HDR etc

Via HDMI or Type-C?

Via HDMI or Type-C?

My case Hdmi as type C is connected to an HUB.

On mine if I turn off freesync and then reboot sometimes puts my screen locked to 1080p.
The way to fix it is to turn it on. This is an issue that only happened to me once.

my screen only has HDMI, no HDR no sync, i tried many times, only 1080p can be detected

my screen only has HDMI, no HDR no sync, i tried many times, only 1080p can be detected

OK. May I know your monitor model?

my screen only has HDMI, no HDR no sync, i tried many times, only 1080p can be detected

OK. May I know your monitor model?

VFV16in, display driver board is rtd2555T or rtd2556T? panel is NE160QDM-NY3
I‘ve tried armbian and another rk3399 board, it worked well

authors: Damenly DarkevilPT GuoqiangW


text:
support kill queries

> kill query 1000001

I will support it.

repo: openGemini/openGemini


text:
logstore的bloomfiter索引增加预取操作

Is your feature request related to a problem? Please describe.（如果该特性或者功能和已知某个问题相关，请描述问题是什么）
logstore的bloomfiter索引增加预取操作，提升bf查询效率
Describe the solution you'd like（问题的解决办法是什么）
logstore的bloomfiter索引增加预取操作，提升bf查询效率
Describe alternatives you've considered（描述您考虑过的替代方案）
No response
Additional context（其他补充内容）
No response

已合入


text:
No visitor for

Add support for glossdiv tag.
glossdiv — A division in a Glossary.
http://tdg.docbook.org/tdg/5.0/glossdiv.html

Needs testcase

authors: jcayouette kkaempf

repo: openSUSE/docbookrx


text:
France

http://adresse.data.gouv.fr

Looks like a clickthrough ODbL license. I’m downloading the file.

Translation says “Cette version de la BAN ne contient pas de données ODbL, et peut être utilisée gratuitement, sous réserve de repartage des contributions.” (“This version of the NAO does not contain ODbL data, and can be used free of charge, subject to redivision of contributions.”), so perhaps not ODbL.

Apparent column meanings:

nom_commune - city name
code_post - postcode
lat, lon - WGS84
numero - street number
nom_voie - street name, lowercase
nom_afnor  - street name, all-caps, possible AFNOR relation?
libelle_acheminement - city name, all-caps, possible routing use?
code_insee - possible INSEE relation?

authors: MiniCodeMonkey migurski

repo: openaddresses/openaddresses


text:
Add addresses in Spartanburg, SC

Status:

[ ] Research sources and propose best source (and it's data license)
[ ] Review proposed source
[ ] Approve source based on review comments
[ ] Add to OpenAddresses

NOTE: Proposed source notes should include basic column mapping to OpenAddress data model.

there is data here:http://www.spartanburgcounty.org/185/Geographic-Information-Systems
but I think is in tiles or some fancy locked up version of an interactive rest service I haven't seen

@justinelliotmeyers Can you contact someone there asking for the raw data, please?

sent email:rbrown@spartanburgcounty.org
04/11/2017

County Parcels:https://services7.arcgis.com/deoj2Y8l1tBr7P5X/ArcGIS/rest/services/LauderdaleService/FeatureServer/4

I found Spartansburg parcels here last week:  http://arcgis4.roktech.net/arcgis/rest/services/Spartanburg/ROKMAPS/MapServer/10
Fixed in #2823

Do you think the county layer would be better than the resource you found @trescube ? should we change the name of the issue to Lauderdale County ? Thanks!

Isn't Lauderdale County in Mississippi?

I'm not sure how the issue changed from Spartanburg, SC to Lauderdale County, MS.

oops - I was working on https://github.com/openaddresses/openaddresses/issues/2023#event-1034640936

K, I'm going to close this issue then!

thanks! sorry!

authors: justinelliotmeyers nvkelso trescube


text:
NZ New Plymouth District Council

I couldn't find any licensing details online, but I've sent an email to clarify.
http://data-npdc.opendata.arcgis.com/datasets/9841c547880d403eaf7129cb5901a477_2

Link is now 404 and I can't find on their open data portal https://geohub.npdc.govt.nz/search?type=feature layer


text:
Add units to US/MT

The majority of the changes are for fixing number and street for the sources that have the same format (OwnerAddre -> AddressLin).
unit has been aded where found

Thanks for making these large-scale edits!

authors: migurski trescube


text:
Montgomery County, MD

Was able to get the County Planning office to put up a shapefile.  That's ~350,000 addresses!!

Very nice! Thanks for contacting them.

authors: geobrando iandees


text:
Influence of batch size of training convergence

According to your paper you use a large batch size of ~32k samples which means that the raw untrained network initially has a chance of ~1/32k of predicting the correct pair.
I am wondering, how the convergence/learning process would differ, if instead a binary classification problem was formulated and the network would be presented with matching text/image pairs and non-matching pairs alternatingly and be tasked with predicting whether those samples are actually in agreement or not.
In other words, what does the softmax over ~32k entries prior to cross entropy calculation bring to the table which cannot be achieved more conveniently by using sigmoid and binary cross entropy to predict matching/non-matching pairs. As a side effect this would also abolish the dependence on the batch size which seems to be rather crucial?

Not the authors (and curious about their response), but CLIP is trained with contrastive loss (vs say a binary prediction problem suggested). As far as I've seen contrastive learning is really great for learning representations of features whereas a small-batch binary problems does well for learning how to classify. As they are different objectives, the metrics optimize over different things.

Not the authors (and curious about their response), but CLIP is trained with contrastive loss (vs say a binary prediction problem suggested). As far as I've seen contrastive learning is really great for learning representations of features whereas a small-batch binary problems does well for learning how to classify. As they are different objectives, the metrics optimize over different things.

Great question, we looked into this and related objectives a bit during preliminary research before settling on the training objective described in the paper.
We have to at least run encoders to calculate feature embeddings for the matching text / image pairs. This step is the vast majority of compute cost for training. The pairwise combinations of all image-text feature interactions are then cheap to compute on top of this, because they only involve calculating a single additional inner product of relatively low-dimensional vectors per pair.
We found that convergence was an issue if we trained with a binary cross entropy, instead of categorical cross entropy objective. We had to modify BCE (manual reweighting to prevent negative pairs from dominating the loss) or "warm up" from a small amount of negatives similar to what you propose. We felt these tweaks were kind of hacky and settled on using the cross entropy loss over all pairwise combinations because softmax cross entropy is so common in image classification models.
After we settled on our training objective, however, recent work has questioned the choice of CCE for image classification. For instance, see discussion in Are we done with ImageNet?

Great discussion! I am a bit confused regarding this part from the paper:
The calculation of embedding similarities was also sharded with individual GPUs computing
only the subset of the pairwise similarities necessary for their local batch of embeddings

Does this mean that pairwise logit calculations were only restricted to the batch for a given GPU? So, if we distribute training to have a batch size of 128 per GPU then it means the loss will be calculated from the pairwise comparison of 128 samples only. For example, AFAIK in SimCLR they use a large batch to improve contrastive learning:
To keep it simple, we do not train the model with a memory
bank (Wu et al., 2018; He et al., 2019). Instead, we vary
the training batch size N from 256 to 8192. A batch size
of 8192 gives us 16382 negative examples per positive pair
from both augmentation views.

or MoCo uses Queues to increase the number of negative samples per loss calculation.
Is there any such mechanism in CLIP to increase the number of effective samples during loss calculation or is it regular distributed training with losses from 128 samples per GPU?
Thanks for this amazing work once again!

@KeremTurgutlu I found the same question when I want to train CLIP with 8 GPUs. I found that if my total batch size is 128, then pytorch will split it to 832, then i will have eight 3232 pair. But if i use one GPU ansd set batch size to 128, i will have one 128128 pair.  As we know, eight 3232 is not the same with one 128*128, it lost many negative pairs. And I don't know which one is suitable for CLIP training.
What's more, my loss didn't decrease during training(may be some wrong in my code)，it's too hard to do training.

@KeremTurgutlu I found the same question when I want to train CLIP with 8 GPUs. I found that if my total batch size is 128, then pytorch will split it to 8x32, then i will have eight 32x32 pair. But if i use one GPU and set batch size to 128, i will have one 128x128 pair. As we know, eight 32x32 is not the same with one 128x128, it lost many negative pairs. And I don't know which one is suitable for CLIP training.
What's more, my loss didn't decrease during training(may be some wrong in my code)，it's too hard to do training.

If you have 8 gpus you can surely fit 128 samples on each of them independently, if 128 already fits to a single gpu in your experiments. Maybe your current code splits like that.
My question was rather about increasing the number of effective negatives during loss calculation beyond what can be fed to a single gpu. One option is to gather all text and image embeddings from all the gpus for each gpu, this is what original CLIP training does as far as I understand from other issues. This is also probably how SimCLR is trained using the same InfoNCE loss.
Unfortunately, not everyone has access to multi-node gpu machines, even getting an 8 gpu machine is very expensive but let's assume you have access to such machine for this kind of training. Still, you would be capped with maximum of 128*8 = 1024 samples. MoCo addresses this exact issue by using queues, you can set the number of samples in the queue to a much more higher number than 1024. My question was more about whether using a pair of query and key encoder for both images and texts would make sense? Like, 1 image query encoder, 1 image key encoder, 1 text query encoder, and 1 text image encoder. Then similarities can be calculated between image query embedding - text key embedding and also text query embedding - image key embedding.

@KeremTurgutlu
It's no doubt that using queues is a good way if whose GPUs are not very good.
I know the way contrastive  learning(such as MoCo) do.
If train CLIP in it's paper's way, it's a task of n_class(batch_size)-classification
If train CLIP in contrastive's way, it's a task of Binary-classification(related or interrelated)
This question is really interesting.
I spent some time thinking about this problem, I think Binary-classification may not work well as n_class-classification do. Because in contrastive learning, they are all images(in same domain), so it's easy to judgment whether matching or not, but in CLIP, one is image, the other is text, it's not easy to directly judgment whether matching or not, it need some negative samples to make a relative distinction. In this way CLIP make the problem simple.
This is just my personal idea, everyone is welcome to discuss.

@KeremTurgutlu I found the same question when I want to train CLIP with 8 GPUs. I found that if my total batch size is 128, then pytorch will split it to 8x32, then i will have eight 32x32 pair. But if i use one GPU and set batch size to 128, i will have one 128x128 pair. As we know, eight 32x32 is not the same with one 128x128, it lost many negative pairs. And I don't know which one is suitable for CLIP training.
What's more, my loss didn't decrease during training(may be some wrong in my code)，it's too hard to do training.

I also encountered this issue of loss getting stuck while trying to train CLIP on a single 8-gpu machine. By sacrificing some image resolution (resize down to 128) I was able to start training with a batch size of 1024 and after ~500 steps the loss started to improve. I tried many different configurations of layers and widths for batches between 32-256 and none of them could learn.
I am curious if you or Kerem have successfully tried any of the tricks mentioned above to increase the effective size of the contrastive pairs.

I am curious if you or Kerem have successfully tried any of the tricks mentioned above to increase the effective size of the contrastive pairs.

So far, I tried with both 128 bs 224 res and 256 bs 224 res (with grad checkpointing) on a single GPU. They are both trained for 1 epoch with a dataset of 4.4 million image-text pairs, ~40k steps. Models seem to converge. I am now going to try my distributed InfoNCE loss implementation and with 256*4 effective batch size to see if it improves.

I am curious if you or Kerem have successfully tried any of the tricks mentioned above to increase the effective size of the contrastive pairs.

So far, I tried with both 128 bs 224 res and 256 bs 224 res (with grad checkpointing) on a single GPU. They are both trained for 1 epoch with a dataset of 4.4 million image-text pairs, ~40k steps. Models seem to converge. I am now going to try my distributed InfoNCE loss implementation and with 256*4 effective batch size to see if it improves.

Hi, KeremTurgutlu,
I noticed you have implemented your training code and even get startted to train on own machine, which is really cool!! I get stuck in how to implement the training code by myself. Could you kindly share it with me ? I can not wait to reproduce this amazaing work on your training code! thank you. here is my email addr: liangqu.long@gmail.com

@KeremTurgutlu I found the same question when I want to train CLIP with 8 GPUs. I found that if my total batch size is 128, then pytorch will split it to 8x32, then i will have eight 32x32 pair. But if i use one GPU and set batch size to 128, i will have one 128x128 pair. As we know, eight 32x32 is not the same with one 128x128, it lost many negative pairs. And I don't know which one is suitable for CLIP training. What's more, my loss didn't decrease during training(may be some wrong in my code)，it's too hard to do training.

I chose to just collect all embeddings from 8 GPUs, then make the similarity matrix and compute loss in CPU with a huge memory. I mean it's able to get a 128x128 similarity matrix in multiple GPUs. But it is really hard to run a 32k batch size, and in my tests, it seems that the small batch size does reduce the performance.

BCE is now popularized in https://arxiv.org/pdf/2303.15343.pdf using pretty much exactly what was tried by the CLIP team before it seems:

At initialization, the heavy imbalance coming from the many negatives dominates the loss, leading to large initial optimization
steps attempting to correct this bias. To alleviate this, we
introduce an additional learnable bias term b similar to the
temperature t. We initialize t0 and b to log 10 and −10 respectively. This makes sure the training starts roughly close
to the prior and does not require massive over-correction.
Algorithm 1 presents a pseudocode implementation of the
proposed sigmoid loss for language image pre-training.

I'm trying to fit the HF implementation of 336px (so 440M Params), the with LoRA (4mill parameters are trained), I'm training using lighting fabric as it can be adapter easier to the OpenCLIP repo, but I cannot make the batch size greater than 8 per device, so 8 V100 x 8 = 64 of effective batch size, is this normal/expected? Or I should be able to fit a larger batch size ? Any help would be greatly appreciated :)

authors: CDitzel KAndHisC KeremTurgutlu LOOKCC Newmu dragen1860 miguelalba96 patrickvonplaten raijinspecial thoppe

repo: openai/CLIP

license: mit

license_source: bigquery


text:
json_schema structured output type not supported in gpt-4o assistants

Confirm this is an issue with the Python library and not an underlying OpenAI API

[X] This is an issue with the Python library

Describe the bug
Not sure if this is a bug in the docs or the code. The API reference for the response_format parameter of assistants.create has:

Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.
Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema.

But I am seeing this error message:

"Invalid parameter: 'response_format' of type 'json_schema' is not supported with model version gpt-4o."

>>> from pydantic import BaseModel
>>>
>>> class Project_Spending(BaseModel):
...     Authority_Name: str
...     Project_Count: int
...     Total_spending: int
...
>>> client.beta.assistants.create(
...     name = "prjdata",
...     tools = [{'type':'file_search'}],
...     model = "gpt-4o",
...     description = "find the required data from the attachment",
...     response_format={
...         'type': 'json_schema',
...         'json_schema':
...         {
...             "name": "Project_Spending",
...             "schema": Project_Spending.model_json_schema()
...         }
...     }
... )
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/opt/homebrew/anaconda3/envs/apt-research/lib/python3.12/site-packages/openai/resources/beta/assistants.py", line 146, in create
    return self._post(
           ^^^^^^^^^^^
  File "/opt/homebrew/anaconda3/envs/apt-research/lib/python3.12/site-packages/openai/_base_client.py", line 1277, in post
    return cast(ResponseT, self.request(cast_to, opts, stream=stream, stream_cls=stream_cls))
                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/homebrew/anaconda3/envs/apt-research/lib/python3.12/site-packages/openai/_base_client.py", line 954, in request
    return self._request(
           ^^^^^^^^^^^^^^
  File "/opt/homebrew/anaconda3/envs/apt-research/lib/python3.12/site-packages/openai/_base_client.py", line 1058, in _request
    raise self._make_status_error_from_response(err.response) from None
openai.BadRequestError: Error code: 400 - {'error': {'message': "Invalid parameter: 'response_format' of type 'json_schema' is not supported with model version `gpt-4o`.", 'type': 'invalid_request_error', 'param': 'response_format', 'code': None}}
>>>

To Reproduce
Call OpenAI().beta.assistants.create() with a response_format parameter
Code snippets
No response
OS
macOS
Python version
Python 3.12.7
Library version
openai v1.52.2

After some more investigation, I find I get the same error from gpt-3.5-turbo-1106.
I also find that although the models documentation says that gpt-4o points to gpt-4o-2024-08-06, if I point the assistant in the above code explicitly to gpt-4o-2024-08-06 (i.e. model="gpt-4o-2024-08-06"), I get a different error:
BadRequestError: Error code: 400 - {'error': {'message': 'Invalid tools: all tools must be of type `function` when `response_format` is of type `json_schema`.', ...

Is it possible that in the python library, gpt-4o does not actually point to gpt-4o-2024-08-06?

repo: openai/openai-python


text:
Using Azure OpenAI with AAD example broken link in OpenAI v1 Release Notes Discussion

https://github.com/openai/openai-python/discussions/631 links to https://github.com/openai/openai-python/blob/v1/examples/azure_ad.py
Fix: Should be https://github.com/openai/openai-python/blob/main/examples/azure_ad.py

Thanks for the report! I've updated the link in the discussion.

authors: RobertCraigie achandmsft


text:
tiktoken not found even though its installed.

I'm using tiktoken in a streamlit app that connects to openai.
Unfortunately, I get this error in the frontend.
Could not import tiktoken python package. This is needed in order to for OpenAIEmbeddings. Please install it with pip install tiktoken.
However, tiktoken is installed in the python env. See below.
$ pip list
Package                   Version

....
streamlit                 1.29.0
tenacity                  8.2.3
tiktoken                  0.5.2
toml                      0.10.2
toolz                     0.12.0
tornado                   6.4
tqdm                      4.66.1
typing_extensions         4.9.0
typing-inspect            0.9.0
tzdata                    2023.4
tzlocal                   5.2
urllib3                   2.1.0
validators                0.22.0
watchdog                  3.0.0
wikipedia                 1.4.0
yarl                      1.9.4
youtube-transcript-api    0.6.2
zipp                      3.17.0
I also cannot import the module into a file, because I get a runtime error that the module couldn't be found.
This same functionality works fine when not using the streamlit app.
Does anyone know how to solve this issue?

Are you sure pip corresponds to the right environment? In general, if you run python script.py use python -m pip list to specifically check the environment corresponding to that Python.
Closing, since this is exceedingly unlikely to be an issue in tiktoken

repo: openai/tiktoken


text:
[AMD] turn on reduce tests with argmax/argmin

The latest main branch seems working fine for all cases in test_reduce, so turn on them for amd backend.

Looks like the tests are still failing? Are you sure it was passing?

Looks like the tests are still failing? Are you sure it was passing?

I tested locally sever times, did not see any failing. If failed, could you please revert it?

Looks like the tests are still failing? Are you sure it was passing?

I tested locally sever times, did not see any failing. If failed, could you please revert it?

can you send a revert PR and I'll merge it?

BTW I see it failing on the mainline bot:
https://github.com/openai/triton/actions/runs/8227536191/job/22495596954

Actually looking at the error it might be a problem with the numpy version on the CI bot. @zhanglx13 might know how to fix it

Actually looking at the error it might be a problem with the numpy version on the CI bot. @zhanglx13 might know how to fix it

Should we revert this PR to avoid CI failure?

Actually looking at the error it might be a problem with the numpy version on the CI bot. @zhanglx13 might know how to fix it

Should we revert this PR to avoid CI failure?

I would still suggest reverting it to avoid the noise and make sure there aren't more failures piling on top of this. Someone should look at what is causing the problem on the CI bot

FYI: https://numpy.org/doc/stable/reference/generated/numpy.argmin.html
looks like we need to update the bot to numpy version 1.22.0.

authors: ThomasRaoux scxiao

repo: openai/triton


text:
Error in docs for OIDC with Auth0

The Auth0 specific parts of the OIDC docs state that the login / post-login flow action should be:
exports.onExecutePostLogin = async (event, api) => {
  api.idToken.setCustomClaim("https://shinyproxy.io/shinyproxy_roles", user.app_metadata.shinyproxy_roles);
};

However, user is part of the event object, thus the correct code is:
exports.onExecutePostLogin = async (event, api) => {
  api.idToken.setCustomClaim("https://shinyproxy.io/shinyproxy_roles", event.user.app_metadata.shinyproxy_roles);
};

I tested this modification using both the the Auth0 API and ShinyProxy 3.0.2 and it works.

This finding by @psolymos helped me a lot!
Recently I started getting messages kind of 'Proxy failed to start'. The above fix, as well as the removal of permission to download docker images, brought the system back to life.
proxy:
  docker:
    image-pull-policy: Never

Thanks a lot for reporting this issue, I just fixed this on our website.

authors: DzimitryM LEDfan psolymos

repo: openanalytics/shinyproxy


text:
int64 or int32 format not identified by openapi4j tool

Version
1.0.2
Describe the bug
int64 or int32 format not identified by openapi4j tool
        "properties": {
          "request_depth": {
            "maximum": 9007199254740992,
            "default": 9007199254740992,
            "minimum": -1,
            "type": "integer",
            "format": "int64"
          }

I get the below error when openapi4j is used for validation
paths./model_to_review.put.requestBody.content.application/json.schema.properties.model_to_review.properties.instances.properties.data.properties.request_depth.default: Value '9007199254740992' is incompatible with schema type 'integer' (code: 138)
From: paths./model_to_review.put.requestBody.content.application/json.schema.properties.model_to_review.properties.instances.properties.data.properties.request_depth.default
components.schemas.HTTPInspectInstanceObject.properties.data.properties.request_depth.default: Value '9007199254740992' is incompatible with schema type 'integer' (code: 138)
From: components.schemas.HTTPInspectInstanceObject.properties.data.properties.request_depth.default
Sample Code:
OpenApi3 openAPI = new OpenApi3Parser().parse(new File(openAPISpecPath), false);
   // Explicit validation of the API spec
    ValidationResults results = OpenApi3Validator.instance().validate(openAPI);

Expected behavior
The above schema error should not be given.
Additional context
Add any other context about the problem here.
To Reproduce
Steps to reproduce the behavior:

...
...
...

Reproducer
Please fill a reproducer for debugging purpose.

Hi and welcome,
The issue is located in parser module.
The default value isn't checked with 'format' keyword, so 'integer' type is always checked... against Integer.
As a workaround, you can remove or lower your default value for now.

Fixed with d947c59

I still see error with 1.0.5 version. The sample schema file contents is given below.
"max_resp_len": {
"minimum": 0,
"type": "integer",
"format": "int64",
"description": "maximum FTP response accepted by client",
"maximum": 4294967295,
"default": 4294967295
}
Error
Validation error(s) :
paths./f_client.put.requestBody.content.application/json.schema.properties.instances.properties.data.properties.max_resp_len.default: Value '4294967295' is incompatible with schema type 'integer' (code: 138)
From: paths./f_client.put.requestBody.content.application/json.schema.properties.instances.properties.data.properties.max_resp_len.default
components.schemas.f_client.max_resp_len.default: Value '4294967295' is incompatible with schema type 'integer' (code: 138)
From: components.schemas.f_client_snort.max_resp_len.default
at org.openapi4j.parser.validation.v3.OpenApi3Validator.validate(OpenApi3Validator.java:28)
at com.cisco.api.external.rest.docs.OpenAPI4jValidationPOCEntireContents.main(OpenAPI4jValidationPOCEntireContents.java:45)

I still see error with 1.0.5 version. The sample schema file contents is given below.
"max_resp_len": {
"minimum": 0,
"type": "integer",
"format": "int64",
"description": "maximum FTP response accepted by client",
"maximum": 4294967295,
"default": 4294967295
}
Error
Validation error(s) :
paths./f_client.put.requestBody.content.application/json.schema.properties.instances.properties.data.properties.max_resp_len.default: Value '4294967295' is incompatible with schema type 'integer' (code: 138)
From: paths./f_client.put.requestBody.content.application/json.schema.properties.instances.properties.data.properties.max_resp_len.default
components.schemas.f_client.max_resp_len.default: Value '4294967295' is incompatible with schema type 'integer' (code: 138)
From: components.schemas.f_client_snort.max_resp_len.default
at org.openapi4j.parser.validation.v3.OpenApi3Validator.validate(OpenApi3Validator.java:28)
at com.cisco.api.external.rest.docs.OpenAPI4jValidationPOCEntireContents.main(OpenAPI4jValidationPOCEntireContents.java:45)


text:
Correct indentation and use of eventual BG

I'm just starting out, but the indentation and use of "BG" vs. "eventual BG" vs. "projected BG" doesn't look right. I may not understand the algorithm correctly, so I could be completely off-base here. Any input appreciated. Thanks!

Closing pull request so I can resubmit against the dev branch.

repo: openaps/docs


text:
Don't use xdrip/glucose.json when old or empty

This moves the xdrip logic out of cron into its own shell script, and adds logic (identical to what we already have in get-ns-bg) to only copy the xdrip/glucose.json file over if it actually contains recent data.

This has not yet been tested, as I don't have xdrip.  Will need someone who does to do the following (preferably on a dev rig):
First, kill BT tethering and confirm with tail -f ~/myopenaps/monitor/glucose.json that you're seeing the glucose.json file get truncated (and not re-populated until the next ns-loop run).
cd ~/src/oref0
git pull
git checkout fix-xdrip
npm run global-install
~/myopenaps/oref0-runagain.sh

answer Yes to deleting the old crontab
Check if everything still works.  If so, kill BT tethering again and re-run tail -f ~/myopenaps/monitor/glucose.json that you're no longer seeing the glucose.json file get truncated.

@kcrcgm tested this, and it fixed her issue.  Does anyone else want to test it before I merge it to dev?


text:
improve some language in failure-detection.md

Continued to read some docs, found some more things that could be worded better :)
By the way, I assume that orchestrator is still fine with handling MySQL 8.0 standard async replication? There doesn't seem to be any info in the docs and around the web about this, which to me would mean that since 8.0 is acting the same as e.g. 5.7, orchestrator doesn't care and works as expected?

Orchestrator should be fine with 8.0 async replication.

authors: seeekr shlomi-noach

repo: openark/orchestrator


text:
[REQUEST] Add Recaptcha v2 Entreprise in solve captcha solutions

Is your feature request related to a problem? Please describe.
Yes, some captcha solving services have an option for solving recaptcha v2 entreprise, unfortunetly that's not available on openbullet 2.
Describe the solution you'd like
Like a checkbox on recaptcha v2 block to specify that it's recaptcha v2 entreprise.
Describe alternatives you've considered
Don't know honestly.
Additional context
My english is really bad, i hope you understood what i've been trying to explain tho. Thanks for all you did with openbullet 1 & 2.

OB2 uses library for it you must open issue in library github https://github.com/openbullet/CaptchaSharp

I'll close this since the other one is open on the CaptchaSharp repo

authors: JeanMenage mastercho openbullet


text:
[BUG]Custom Hits isn't displayed in Hits DB

I created a config and add a "Key Check" block through the Stack-View.
For "Type" I choose "Custom".
For "Custom Type" I can only choose "CUSTOM" this is the only entry in the pull-down menu.

So I changed to LOLISCRIPT-view and changed the line to:
KEYCHAIN Custom "CUSTOMizedby Myself" OR

At the Runner I can see that there are CUSTOM-Hits, the is a count at the bottom and entries in the table above.
After stopping the runner I these Hits aren't displayed at Hits DB, I can only choose between the TYPES "SUCCESS","NONE" and "CUSTOM". But my Hits are saved as Type "CUSTOMizedby Myself".
I can foun them in the "OpenBullet.db"-file
That's all.
I wrote this tu Ruris Shoutbox and he asked me to make an Issue.

Hello, you can easily define your custom statuses (that will appear in the dropdown box of the keycheck block) through the Environment.ini file.
Check the guide here https://openbullet.github.io/usage.html#environment
Let me know if you solved your problem so I can close this, thanks

Yes, this solved my problem, you can close this. Thanks!

authors: Ei-Dotter openbullet

repo: openbullet/openbullet


text:
dj backend csfr error

I try to use the new django. I  got it installed, but when i try to conect i got a CSRF verification error from Django.. any hints for me

You should add the origin in settings.py file
On Fri, Dec 8, 2023, 17:54 JuergenCornely @.***> wrote:

I try to use the new django. I got it installed, but when i try to conect
i got a CSRF verification error from Django.. any hints for me
—
Reply to this email directly, view it on GitHub
https://github.com/openchatai/OpenChat/issues/212, or unsubscribe
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.
You are receiving this because you are subscribed to this thread.Message
ID: @.***>

If allowed hosts is not defined in .env, it should be listening on
0.0.0.0:8000 by default
On Fri, Dec 8, 2023, 18:13 JuergenCornely @.***> wrote:

By the way it is listening on port 8001?
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or unsubscribe
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.
You are receiving this because you commented.Message ID:
@.***>

Please update .env with the following and restart your containers
ALLOWED_HOSTS=localhost,0.0.0.0
APP_URL=http://0.0.0.0:8000

then navigate to http://0.0.0.0:8001/login
Default admin user
user - admin
password - admin_password

Same problem, i get the login Page. After pressing the login button i got the CSFR verification failed

in my envoirment the makefile at the force_migrate section goes to the docker Container web. But the installtion makes a container oc_web.. hope this can help

Temporarelly you can bypass in settings.py
'django.middleware.csrf.CsrfViewMiddleware',

Can you check now please? Get the latest release.

It works,  thanks a lot...

authors: JuergenCornely codebanesr lvalics

repo: openchatai/OpenChat


text:
Replace ppn hyperlink on the apply_confirm page

Replace the hyperlink to Cook County site with local equivalent.

I'm commenting the lines out -- I don't think it matters for our version, since we only care about the delinquent taxes for the owners property and not the lot they are applying for.

Is this working? I feel like it is...

Just tested - still not working. The HTML is commented out so the user dues not see the link, but it's still sent to the end user. And it's currently pointing to the Cook Count website so we need to point it to the Cuyahoga County website.
This link is working on the Apply page (see issue 27) so we just need to plug in similar code.

Example of a working link to the Cuyahoga County website: http://clevelandlots.org/apply/#/browse?parcel=106-01-016

Additionally, the broken hyperlink is due to a similar issue of linking back to a Cook County site.

The GIS of Cuyahoga might have actual photos of the parcels, http://gis.cuyahogacounty.us/en-US/GIS-Data.aspx, but getting it might be more difficult as I couldn't find their images publicly available (seem to be only by request).

Fixes in 45cb7626dbfcac8e176605f7ab760df177894cd0

@arielkoiman please review

Any updates on this?

authors: arielkoiman cwang912 eamonjohnson girricane pkoepke

repo: opencleveland/large-lots


text:
Development v0.2.5

Pull Request
Description

update colour scheme #157
Hide map feature #173
fix hot keys range
https://github.com/openclimatefix/nowcasting/issues/159
https://github.com/openclimatefix/nowcasting/issues/160
https://github.com/openclimatefix/nowcasting/issues/161
https://github.com/openclimatefix/nowcasting/issues/169
TODO bump version
#172
#196
#203
#202
#210

UAT
Auth

[ ] Can I log on with Auth?
[ ] Can i log out?

National

[ ] Does PV live estimate show on the plot? (black dashed)
[ ] Does PV live updated show on the plot? (black solid)
[ ] Does OCF forecast show up on plot? (yellow)
[ ] For OCF forecast, is the future a dashed line?
[ ] Can I hover on the plot to show the values on the plot?
[ ] Does the data look like solar profiles?

Time

[ ] Is time now in Europe London time? It should be 30minute, but rounded up.
[ ] Is the hover time in Europe London time?
[ ] Are the X axis in Europe London time?
[ ] Does the data show yesterday aswell?

Header

[ ] Is this the title clear it is National data?
[ ] Does it show the current Forecasted value?

GSP

[ ] Does PV live estimate show on the plot? (dashed black)
[ ] Does PV live updated show on the plot? (solid black)
[ ] Does OCF forecast show up on plot? (yellow)
[ ] For OCF forecast, is the future a dashed line?
[ ] Can I hover on the plot to show the values on the plot?
[ ] Does the data look like solar profiles?

Time

[ ] Is time now in Europe London time?
[ ] Is the hover time in Europe London time?
[ ] Are the X axis in Europe London time?

Title

[ ] Does the GSP name show up?
[ ] Is the Load factor visible?
[ ] Is the current MW visible?
[ ] Is the installed capacity visible?

Map

[ ] Does a map of the UK show up?
[ ] Are the GSP displayed?
[ ] Can I click on '%' and 'MW'
[ ] on 'MW' view, is there very little power in Scotland?

General

[ ] is the version visible?
[ ] has the version been bumped?
[ ] Clicking on a GSP makes the GSP plot show up?
[ ] Clicking on a GSP when already selected, makes the GSP plot go away?
[ ] Can I click on a time in the national plot which then updates the map?
[ ] Can I click on a time in the GSP plot which then updates the map?
[ ] Are values higher on the map in the middle of the day, compare to early morning?
[ ] Does the feedback button work?

Refresh

[ ] After 10 mins, does the forecast update?
[ ] After 10 mins, does the forecast creation time update?

Thanks @braddf for updating this

repo: openclimatefix/nowcasting


text:
Add Fake data generation

Pull Request
Description
The fake data will be generated off the configuration to replace the loading of the test data. This should mean that the fake data has the same shape as the test data, so covers all the UK, etc. vs nowcasting_dataset's one that is the batch data.  But can also have both, to test the numpy batch processors vs the xarray processors.
Fixes #2
How Has This Been Tested?
Please describe the tests that you ran to verify your changes. Provide instructions so we can reproduce.
Please also list any relevant details for your test configuration

[ ] Yes

If your changes affect data processing, have you plotted any changes? i.e. have you done a quick sanity check?

[ ] Yes

Checklist:

[ ] My code follows OCF's coding style guidelines
[ ] I have performed a self-review of my own code
[ ] I have made corresponding changes to the documentation
[ ] I have added tests that prove my fix is effective or that my feature works
[ ] I have checked my code and corrected any misspellings

Codecov Report

Merging #48 (20ccdbd) into main (977db73) will decrease coverage by 4.15%.
The diff coverage is 0.00%.

:exclamation: Current head 20ccdbd differs from pull request most recent head f296ba1. Consider uploading reports for the commit f296ba1 to get more accurate results

@@            Coverage Diff             @@
##             main      #48      +/-   ##
==========================================
- Coverage   81.67%   77.52%   -4.16%
==========================================
  Files          65       68       +3
  Lines        2276     2398     +122
==========================================
  Hits         1859     1859
- Misses        417      539     +122

Impacted Files
Coverage Δ

ocf_datapipes/fake/coordinates.py
0.00% <0.00%> (ø)

ocf_datapipes/fake/satellite.py
0.00% <0.00%> (ø)

ocf_datapipes/fake/utils.py
0.00% <0.00%> (ø)

:mega: We’re building smart automated test selection to slash your CI/CD build times. Learn more

authors: codecov-commenter jacobbieker


text:
Display Dashboard

Display the data from backend to frontend and show all the fields.
For any doubts ask on discord.
Go to localhost:3000/dashboard to see your changes.

claim

Assigned to @GammaMicrowave

repo: opencodeiiita/SaveMyForm-Frontend


text:
fix for isHost using Members table instead of Stripe

Fixes https://github.com/opencollective/opencollective/issues/736#issuecomment-349751651

Coverage remained the same at 75.718% when pulling acf3b61bf8653345014c7b8a96283e0f83d156d1 on fix736 into 86231b45b33e6372daf69a994df0b92ac58d2ae3 on master.

authors: coveralls xdamman

repo: opencollective/opencollective-api


text:
Open Collective Roadmap FY21Q3S2

Project board: https://github.com/opencollective/opencollective/projects/54
Enforce minimum number of admins
Owner:: @BenJam
Design: @aminakazi
Tracking issue: https://github.com/opencollective/opencollective/issues/3593
Status: 💚
Goals/KPIs: Enforce host policy in code, reduce amount of support time dedicated to enforcing.
Retro: got a good design for the direct to apply flow on host pages and the generic flow for creating a collective. Covered the edge cases in which an admin is removed and there aren't enough admins left.
Two to sign/multiple expense approvals
Owner:: @benjam
Design: ?
Tracking issue: https://github.com/opencollective/opencollective/issues/3081
Status: 💛
Goals/KPIs: Greater accountability for collectives, lower support time.
Retro: Some confusion around the process here. Need to step back to the original pain points and iterate. Make sure that we have a proper kick-off conversation next it's because it wasn't clear up-front. Create an issue for the design discussion.
Next Steps: review designs.
Collect legal names/addresses for entities contributing >5k per host per year
Owner:: @BenJam
Design: @Memo-Es
Tracking issue: https://github.com/opencollective/opencollective/issues/4692
Status: 💚
Goals/KPIs: Compliance. Reduce time spent hunting for details when accounts need to be filed.
Retro: Got the process designed for picking up folks when the hit 5k contributed per host. Need to implement that.
Next Steps: do the thing.
New Open Collective Homepage
Owner:: @Memo-Es
Design: @Memo-Es
Tracking issue: https://github.com/opencollective/opencollective/issues/4689
Status: 💚
Goals: Decouple the identity of Open Collective from hosts. Direct users to correct funnel (apple to a host, become a host, create a collective etc). Talk more about the work Open Collective does distinct from hosts.
KPIS: Increase conversion for host applications, host creation etc. decrease support time spent guiding folks to correct flow.
Retro: Goal was to scope this sprint. Led to some interesting conversations about marketing strategy etc. with caroline and Alanna.
Next Steps: scope a project including the narrative - and how we land that narrative.
Differentiate legal/display name
Owner:: @Betree
Developer: @SudharakaP
Design: @Memo-Es
Tracking issue: https://github.com/opencollective/opencollective/issues/3689
Status: ✅
Pending: Add it to the contribution flow.
Goals: Increase user safety on the platform.
KPIS: Honestly, we don't care. If one person is safer as a result of this change it is worth it.
Retro Still need to add this to the contribution flow. Spec's/design were not ready at the start of sprint. Design is now ready to go.  No field in he 'classic' sign in flow or for organisation signups, needs to be added there.
Improve CSV Export
Owner:: @znarf
Developer: @znarf
Tracking issue: https://github.com/opencollective/opencollective/issues/4590
Status: ✅
Goals: Enable hosts and admins to export what they see to import into accounting software etc.
KPIS: Reduce the amount of time/complexity to work with our accountants.
Retro Completed. Last part is to follow up with accountants for feedback etc. and to create a follow up issue for design.
Implement Captcha for contribution flow
Owner:: @kewitz
Developer: @SudharakaP
Tracking issue: https://github.com/opencollective/opencollective/issues/4329
Status: ✅
Pending: Merge, re-enable and monitor logs (after some failure we deactivated it)
Goals: Reduce CC fraud on the site.
KPIS: Lower incidence of CC fraud.
Retro: Completed, deployed, it worked. Some people are unable to complete the captcha. Some logging was added. Some support tickets that people were just failing the captcha. Seems like it is having an impact on the CC fraud — i.e. giving 50c donations to collectives. 3331/2900 solved, 1700 verified since launch.
Add GDPR consents and notices to contribution flow
Owner:: @benjam
Designer: ?
Developer: ?
Tracking issue: https://github.com/opencollective/opencollective/issues/4537
Status: 💛
Goals: Compliance — ensure it is clear that data is shared between users and collectives. Add opt out process to policy.
Status: Design done
Pending: Comms that incognito information will be hidden. Comms as an update.
Also pending implementation.
Retro: Will have to make an announcement that incognito updates are now incognito i.e. not sharing email addresses. This will impact some collectives like meet.coop, we may have to offer an opt-out for collectives that rely on that data for services.
Fix PayPal transaction synchronization issues
Owner: @Betree
Developer:
Tracking issue: https://github.com/opencollective/opencollective/issues/4368
Status: ✅
Goals: Check capture status when confirming transaction. Add webhooks for payment refund events.
KPIS: Lower incidence of bad records in ledger (incomplete orders/refunds)
Retro: small project, was mostly completed when we started the sprint. Nothing much to talk about.
Host dashboard and settings update
Owner: @Betree
Developer: @kewitz @SudharakaP
Tracking issue(s): https://github.com/opencollective/opencollective/issues/2896 https://github.com/opencollective/opencollective/issues/4300 https://github.com/opencollective/opencollective/issues/3880
Status: 💚
Goals: Enable hosts to better understand their financial position and success. Simplify host settings and reports sections. Improve navigation.
KPIS:  TBD.
Retro: Settings merged. Still a little buggy. Follow-ups will need to be scheduled/prioritised for next sprint.
Host reports
Owner: @Betree
Developer: @SudharakaP
Tracking issue(s): https://github.com/opencollective/opencollective/issues/4300
Status: 💛
Goals: Provide hosts with a clearer picture of their finances.
Retro: Needs some more testing, nearly there, some time series problems. Was a little difficult to implement the entire design with everything else that we had in place for the sprint. Good synchronisation. Kind of anticipated that it would be a push, didn't take the opportunity to clearly define what was going to be implemented this sprint and what could be done later.
Status Key
💔  Problem
💛  Needs attention
💚  On track
✅. Completed

The saga continues https://github.com/opencollective/opencollective/issues/4783

repo: opencollective/opencollective


text:
Allow to add invoices from external vendors (without bothering them)

The entire process of paying the expense of a vendor is really problematic.
When you receive an invoice, the vendor has other things to do than register on open collective.
As a collective admin, I want to be able to add the invoice of any vendor.
I shouldn't have to invite them to the platform. It's an unnecessary step.
In the meantime, can you please mark this expense as verified so that we can pay it asap?
https://opencollective.com/regen-village/expenses/211138
Thank you.

[x] mark expense #211138 as verified
[x] mark expense #211135 as verified
[x] mark expense #211665 as verified
[ ] allow host admins to pay expenses that are approved even if they have not been verified

Hey Xavier,
We have a deployed solution for this: Vendors: https://docs.opencollective.com/help/fiscal-hosts/fiscal-host-dashboard/vendors
If you want collective admins to be able to utilize vendors, you will need to ensure this is turned on in your policies section.
We are yet to build the functionality for collective admins to add vendors. For now this process is done by a Fiscal Host admin by request of a collective.
Please let me know if you have any questions about this feature.

Ah, I saw vendors but only when adding funds. Didn't know I had to activate it (I'm an admin of the fiscal host so it looks like a bug might be hidden there).
Update: I've activated it but I still can't create a vendor when submitting an invoice:

I tried to log out and log in again, same issue. What am I missing?
Any chance the expenses listed above could be marked as ready to be paid? Otherwise we have to reject them all (which will notifiy the vendor) and submit again.
Thanks.

Please read the documentation on how to create vendors: https://docs.opencollective.com/help/fiscal-hosts/fiscal-host-dashboard/vendors
You can just delete the expenses and resubmit them once you have the vendors set up, this won't reinvite the users and they'll be payable as soon as they have been approved

Got it. I created the vendors and edited the expenses. Thank for the support.
There is still an UX issue: people should be able to add a vendor in the context of selecting "who is going to be paid for this expense", in the same way that they can invite someone new right from there.

There are some potential complications we need to sort out on the fiscal host side (reviewing expenses and managing vendors) in order to make it possible to create new vendors within the expense submission flow. Once we have a plan of action we will try to prioritize this.

Not sure I follow this reasoning. If it is already allowed to add vendors somewhere else, then surely it should be allowed to add them from that other place (or at the very list, add a note with a link: "to add a vendor, go here").
Anyway, I'm reopening this because I'm hitting another bug.
As you can see, I've created a lu.ma vendor and I was able to add funds on their behalf (ticket sales).

But when I try to upload the subscription for their premium plan, I can't find it in the list.
(Yes, I'm an admin of the collective and of the fiscal host)

Any clue?

For a vendor to function with expenses a payout method need to be added to their vendor profile. Does the Luma Vendor have a payout method attached?

Just wanted to say thank you for helping with this 🙏

authors: iamronen shannondwray xdamman


text:
Added status No content as accepted response for deleting Account (fix for Issue #18)

Fix for Issue #18
Signed-off-by: Haligowska, Emilia emilia.haligowska@intel.com

+1

This PR was approved at the OPENRMC-DM meeting.

authors: MaciejMis ehaligow


text:
image-layout: organize for anchors

Signed-off-by: Vincent Batts vbatts@hashbangbash.com

lgtm

@opencontainers/image-spec-maintainers PTAL

LGTM

sorry, rebase time

updated. PTAL

LGTM

lgtm

interesting. @stevvooe's LGTM didn't work? @caniszczyk ?

LGTM

@vbatts Rebase!

rebased!

lgtm

LGTM

authors: jonboulle philips stevvooe vbatts

repo: opencontainers/image-spec


text:
config-linux: fix format and definitely require value of masked and r…

…eadonly paths
Signed-off-by: Ma Shimiao mashimiao.fnst@cn.fujitsu.com

@wking all mentioned fixed.

25f44dd0e8 looks good to me :).

ping @opencontainers/runtime-spec-maintainers

LGTM

LGTM

authors: Mashimiao hqhq vbatts wking

repo: opencontainers/runtime-spec


text:
Add proposal G

This proposal is very simple, it only defines a new compatibility artifact and recommends creating an index of known media types created by communities. Delegates the creation of the schema and overall json structure to the community. Finally, it provides some implementation recommendations.

@sudo-bmitch

LGTM as a complete proposal. Overall I'm not a fan, and would prefer we went the other direction, having NFD create their own artifact with their own media type. And after a few projects either adopt NFD's format, or create competing artifacts, then we consider consolidation. Or at least it should be NFD coming to OCI with the proposal rather than OCI going to NFD.

I agree with that. That's why I have decided to close the PR as it's not OCI centric which leads me to the thought that this proposal doesn't make sense within OCI.

repo: opencontainers/wg-image-compatibility


text:
CloudCost QueryAthena Issues

Hi Everyone,
I am trying to integrate Cloud Cloud feature for AWS. I used following Cloud integration json file for creating secret. I get following error saying  Start Query error not found, ResolveEnpointV2
cloud-integration.json:
{
    "aws": {
      "athena": [
        {
          "bucket": "s3://aws-athena-query-results-fuji-opencost-2",
          "region": "us-east-1",
          "database": "athenacurcfn_fuji_cost_usage_report_2",
          "table": "fuji_cost_usage_report_2",
          "workgroup": "primary",
          "account": "XXXXXXXX",
          "authorizer": {
            "authorizerType": "AWSAccessKey",
            "id": "AKIASV6E7KM3SKF3F722",
            "secret":"secretkey"
          }
        }
      ]
    }
  }

I am not sure I need to check the permission or config for this. The Secret account which I created for the user attached to the Opencost Role is not been executed from Opencost Pod.
2024-04-05T07:40:47.399878024Z ERR CloudCost[184559817527/s3://aws-athena-query-results-fuji-opencost-2]: ingestor: build failed for window [2024-03-29T00:00:00+0000, 2024-04-05T00:00:00+0000): GetCloudCost: error getting Athena columns: QueryAthenaPaginated: start query error: not found, ResolveEndpointV2
2024-04-05T07:40:47.399928855Z ERR CloudCost[184559817527/s3://aws-athena-query-results-fuji-opencost-2]: ingestor: build failed for window [2024-04-01T00:00:00+0000, 2024-04-08T00:00:00+0000): GetCloudCost: error getting Athena columns: QueryAthenaPaginated: start query error: not found, ResolveEndpointV2
2024-04-05T07:40:47.400005605Z ERR CloudCost[184559817527/s3://aws-athena-query-results-fuji-opencost-2]: ingestor: build failed for window [2024-03-22T00:00:00+0000, 2024-03-29T00:00:00+0000): GetCloudCost: error getting Athena columns: QueryAthenaPaginated: start query error: not found, ResolveEndpointV2
2024-04-05T07:40:47.400105886Z ERR CloudCost[184559817527/s3://aws-athena-query-results-fuji-opencost-2]: ingestor: build failed for window [2024-03-21T00:00:00+0000, 2024-03-22T00:00:00+0000): GetCloudCost: error getting Athena columns: QueryAthenaPaginated: start query error: not found, ResolveEndpointV2

value.yaml

opencost:
  dataRetention:
    dailyResolutionDays: 15

  exporter:
    extraEnv:
      EMIT_KSM_V1_METRICS: "false"
      EMIT_KSM_V1_METRICS_ONLY: "true"
      LOG_LEVEL: warn #error
  prometheus:
    internal:
      enabled: true
      serviceName: otel-stack-thanos-query-frontend
      namespaceName: o11y-dev
      port: 9090

  ui:
    enabled: true
  metrics:
    serviceMonitor:
      enabled: true
      namespace: o11y-dev
      honorLables: true

  cloudIntegrationSecret: cloud-costs
  cloudCost:
    enabled: true

serviceAccount:
  # create: true
  annotations:
    eks.amazonaws.com/role-arn: arn:aws:iam::184559817527:role/fuji-opencost-role-new

It looks like you cloud-costs secret is present, could you retrace the CUR/Athena instructions from Kubecost's docs and see if there's something there?
https://docs.kubecost.com/install-and-configure/install/cloud-integration/aws-cloud-integrations#cost-and-usage-report-integration

Issue  logged on Opencost reflects same issue and thanks @mattray for your reply in Slack and in Issue comment. I am tracking this issue will try once fix is applied

@Veeraraghavans can we close this issue since it's not related to the Helm chart?

Closing this issue as https://github.com/opencost/opencost/issues/2697 logged.

authors: Veeraraghavans mattray

repo: opencost/opencost-helm-chart


text:
Fix keyframe attribute of interpolated shapes

Motivation and context

Fixes #1934
Interpolated shapes were inserted with keyframe = True, so all exported tracks had keyframe=1 everywhere in CVAT for video and other formats.

Fixed keyframe attribute of interpolated shapes
Added tests

How has this been tested?

Unit tests
Checklist

[x] I submit my changes into the develop branch
[x] I have added description of my changes into CHANGELOG file
[ ] I have updated the documentation accordingly
[x] I have added tests to cover my changes
[x] I have linked related issues (read github docs)
[ ] I have increased versions of npm packages if it is necessary (cvat-canvas,
cvat-core, cvat-data and cvat-ui)

License

[x] I submit my code changes under the same MIT License that covers the project.
Feel free to contact the maintainers if that's a concern.
[ ] I have updated the license header for each file (see an example below)

# Copyright (C) 2020 Intel Corporation
#
# SPDX-License-Identifier: MIT

Pull Request Test Coverage Report for Build 6589

1 of 1 (100.0%)  changed or added relevant line in 1 file are covered.
1 unchanged line in 1 file lost coverage.
Overall coverage increased (+0.03%) to 64.972%

Files with Coverage Reduction
New Missed Lines
%

cvat/apps/engine/media_extractors.py
1
77.29%

Totals

Change from base Build 6575:
0.03%

Covered Lines:
11087

Relevant Lines:
16658

💛  - Coveralls

authors: coveralls zhiltsov-max

repo: opencv/cvat


text:
Sync with Upstream

This pull request updates the fork to match the latest changes from the upstream repository.

triggered by ODH/CFO release, closing

authors: codeflare-machine-account sutaakar

repo: opendatahub-io/codeflare-operator


text:
Add ML-Metadata Component

Description
Add Optional ML Metadata Component to depoloyed DPSAs (defaults to not deployed)
Resolves https://github.com/opendatahub-io/data-science-pipelines-operator/issues/152
How Has This Been Tested?

Merge criteria:

[x] The commits are squashed in a cohesive manner and have meaningful messages.
[x] Testing instructions have been added in the PR body (for PRs involving changes that are not immediately obvious).
[x] The developer has manually tested the changes and verified that the changes work

Testing Instructions:

Install DSPO on openshift cluster
Replace DSPO image with dev build: quay.io/gmfrasca/data-science-pipelines-operator:dev-add-mlmd-v0.0.1
Deploy a standard DSPA (use config/samples/dspa_simple.yaml). Make sure to include UI
Go to UI, run a pipeline
Click on a pipeline node and then the ML Metadata tab for that step.
Verify ML Metadata tab does not have any information (by default, mlmd turned off)
Update the DSPA CR to include:

spec:
  ...
  mlmd:
    deploy: true
  ...

Re-run a pipeline.  Again, click on a step/node and click the ML Metadata tab
Verify ML Metadata tab now contains relevent information about artifact tracking

During testing we found that the Envoy proxy was hardcoded to expect a Service named metadata-grpc-service, which is not a great pattern.  Added another feature to template in a dynamic envoy configuration that points to the grpc Service created by DSPO.  New build image is quay.io/gmfrasca/data-science-pipelines-operator:add-mlmd-component-v0.0.7


text:
Long Press does not appear to work on Android 5.x

Steps to reproduce:

Use Android 5.x
Open Tables
"Long press" a table in the list of downloaded tables

Expected behavior: A menu with further options for the table is meant to appear (delete table, rename, etc.)
Observed behavior: Nothing happens

Fixed or not an issue as of RC2 or earlier

repo: opendatakit/opendatakit


text:
An Academic query for the source code

Hi,sir!
I'm sorry to disturb you in your busy schedule.Your paper "A Perspective of Q-value Estimation on Offline-to-Online Reinforcement Learning"  gives me great inspiration. But I have encountered some troubles when trying to implement the algorithm. If possible, can you make the source code public?

Thanks for you interest! We are polishing related code and will release the whole training code in 1-2 weeks.

Thanks for your reply!I will be waitting for your source code!


text:
Index mask for ldap group

Hi!
It's possible to define index mask as ${user_name}-* when creating role.
I want to have single role/role mapping for a number of ldap groups.
Is it possible to create mask for group?

Please check the doc for field masking.
Please feel free to reopen the issue if it doesn't help.

Please check the doc for field masking.
Please feel free to reopen the issue if it doesn't help.

repo: opendistro-for-elasticsearch/security


text:
Enable Google Search Console and/or Google Analytics

Problem: We know little about how Google is indexing our pages, and how users are using our sites. “Search Console” (formerly “Webmaster Tools”) and “Google Analytics” are complementary. They pull information from different sources: for Search Console, we just need to demonstrate our ownership of a site; For analytics, we add JS to our pages, and so the browser is reporting back to Google directly.
For now, I think Search Console will be more than enough, and it is less invasive than Analytics.
From https://search.google.com/search-console/welcome:
Possible fix 1 (preferred):

Someone with access to DNS for opendp.org can demonstrate that we own the domain, and all subdomains (ie, “docs”)

Possible fix 2:

Demonstrate that we own a domain or subdomain by adding a file at a particular URL. Going through a docs redeploy for this would probably be more of a nuisance than just demonstrating DNS ownership.

Also explore analytics other than google that are more privacy focused

authors: mccalluc raprasad

repo: opendp/opendp


text:
Python 3.11

Is there a reason why required python versions are set to strictly less than 3.11?. I can easily work around this by using 3.10.6, but I am wondering whether support for 3.11 is coming soon.
(Just be pleased that no one is asking for support for Windows 3.11.)

Thanks for the reminder to look at this.  Our GAN synthesizers rely on PyTorch, which doesn't have an official release for Python 3.11 yet.  So we have been waiting for torch support.  However, I just tested, and it's possible to install the nightly PyTorch first:
pip install --pre torch -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html

Then, I installed the synthesizers, and all unit tests pass on Python 3.11.  So, I have updated both PyPi packages to allow 3.11.  The refreshed packages are smartnoise-sql==0.2.9.2 and smartnoise-synth==0.3.6.2.

authors: joshua-oss jpgoldberg

repo: opendp/smartnoise-sdk


text:
RWX support via NFS-provisioner

Is your feature request related to a problem? Please describe.
Is it possible to use mayastor with nfs-provisioner for rwx pvc's?
Describe the solution you'd like
helm install nfs stable/nfs-server-provisioner --namespace=mayastor --set=persistence.enabled=true,persistence.storageClass=mayastor-iscsi,persistence.size=120Gi,storageClass.name=nfs-sc,storageClass.provisionerName=openebs.io/nfs

Currently, it is failing to create a new pvc:
failed to provision volume with StorageClass "mayastor-iscsi": rpc error: code = ResourceExhausted desc = Cannot find suitable storage pool(s) for the volume

Not sure what these helm charts do, but it should work. I'm not sure exactly what is you are after with this issue as they things you pasted are unrelated?

Not sure what these helm charts do, but it should work. I'm not sure exactly what is you are after with this issue as the things you pasted are unrelated?

I need to create PersistentVolumeClaims in ReadWriteMany access mode.
In order to create it, I can use https://docs.openebs.io/docs/next/rwm.html nfs-provisioner and deploy it via helm.
What helm command does:

persistence.storageClass=mayastor-iscsi,persistence.size=120Gi : it creates pvc from my existing-mayastor sc.
storageClass.name=nfs-sc,storageClass.provisionerName=openebs.io/nfs - creates new storageclass which I can use to create pvc in rwx mode.

Okay, this issue
failed to provision volume with StorageClass "mayastor-iscsi": rpc error: code = ResourceExhausted desc = Cannot find suitable storage pool(s) for the volume

was related to storage. I figure out that when running kubectl get -n mayastor msv, I could not see any volumes there in either online/pending state. Thereafter, I ve found in the logs:
2020-09-28T10:42:22.753865136+00:00  INFO mayastor::pool: The pool pool-node1 has been created
2020-09-28T10:42:22.753933264+00:00  INFO mayastor::subsys::config: creating bdev aio:///dev/sdb?uuid=9716af44-1136-4fa6-993d-74dc98513f46
2020-09-28T10:42:22.753985295+00:00  WARN mayastor::subsys::config: failed to create bdev aio:///dev/sdb?uuid=9716af44-1136-4fa6-993d-74dc98513f46 during config load, error=bdev /dev/sdb already exists
2020-09-28T10:42:22.753996975+00:00  WARN mayastor::subsys::config: Not all bdevs(1) were imported successfully

Then checked msp
kubectl -n mayastor get msp
NAME         NODE                STATE   AGE
pool-node1   node1.k           13s
pool-node2   node2.k         13s
pool-node3   node3.k         13s

so pool on the nodes did not have the state confirmed. So I could not figure out what was the case for this but after I reinstalled everything and created pools, I got the same error
2020-09-28T10:42:22.753933264+00:00  INFO mayastor::subsys::config: creating bdev aio:///dev/sdb?uuid=9716af44-1136-4fa6-993d-74dc98513f46
2020-09-28T10:42:22.753985295+00:00  WARN mayastor::subsys::config: failed to create bdev aio:///dev/sdb?uuid=9716af44-1136-4fa6-993d-74dc98513f46 during config load, error=bdev /dev/sdb already exists
2020-09-28T10:42:22.753996975+00:00  WARN mayastor::subsys::config: Not all bdevs(1) were imported successfully
2020-09-28T10:42:22.754120326+00:00  INFO mayastor::grpc::server: gRPC server configured at address 10.0.3.30:10124
2020-09-28T10:42:22.754611879+00:00  INFO mayastor: Mayastor started 🚀 ...
2020-09-28T10:42:23.258442158+00:00  INFO mayastor::nats: Connected to the NATS server nats:4222
2020-09-28T10:42:23.258492493+00:00  INFO mayastor::nats: Registering 'node1.dev.trbl.uk' and grpc server 10.0.3.30:10124 ...
2020-09-28T10:42:23.327970222+00:00  INFO mayastor::lvs::lvs_pool: pool pool-node1 destroyed successfully
2020-09-28T10:42:41.495536961+00:00 ERROR mayastor::lvs::lvs_pool: error=failed to import pool pool-node1
2020-09-28T10:42:43.712696659+00:00  INFO mayastor::lvs::lvs_pool: The pool 'pool-node1' has been created on /dev/sdb

but msp are in online state and I can see new volumes created in healthy state
[root@OKD-dev-LB ~]# kubectl -n mayastor get msp
NAME         NODE                STATE    AGE
pool-node1   node1.k   online   14m
pool-node2   node2.k   online   14m
pool-node3   node3.k   online   14m
[root@OKD-dev-LB ~]# kubectl get -n mayastor msv
NAME                                   NODE                SIZE           STATE     AGE
0b905e05-8df8-45ba-ab1c-dcf524faa55a   node3.k   128849018880   healthy   12m

I think this might be related to #437 or atleast the last part of the issue you are facing. A temporary work around could be is to zero out the disk manually with dd.

repo: openebs/Mayastor


text:
failed to mount volume

Follow the steps in this document https://mayastor.gitbook.io/introduction/quickstart/deploy-mayastor,  Deploy a test pod, the pod is not work,
# kubectl describe pod fio
Name:         fio
Namespace:    default
Priority:     0
Node:         main25/192.168.20.25
Start Time:   Mon, 11 Oct 2021 20:47:32 +0800
Labels:       <none>
Annotations:  <none>
Status:       Pending
IP:
IPs:          <none>
Containers:
  fio:
    Container ID:
    Image:         mayadata/fio
    Image ID:
    Port:          <none>
    Host Port:     <none>
    Args:
      sleep
      1000000
    State:          Waiting
      Reason:       ContainerCreating
    Ready:          False
    Restart Count:  0
    Environment:    <none>
    Mounts:
      /var/run/secrets/kubernetes.io/serviceaccount from kube-api-access-84785 (ro)
      /volume from ms-volume (rw)
Conditions:
  Type              Status
  Initialized       True
  Ready             False
  ContainersReady   False
  PodScheduled      True
Volumes:
  ms-volume:
    Type:       PersistentVolumeClaim (a reference to a PersistentVolumeClaim in the same namespace)
    ClaimName:  ms-volume-claim
    ReadOnly:   false
  kube-api-access-84785:
    Type:                    Projected (a volume that contains injected data from multiple sources)
    TokenExpirationSeconds:  3607
    ConfigMapName:           kube-root-ca.crt
    ConfigMapOptional:       <nil>
    DownwardAPI:             true
QoS Class:                   BestEffort
Node-Selectors:              openebs.io/engine=mayastor
Tolerations:                 node.kubernetes.io/not-ready:NoExecute op=Exists for 300s
                             node.kubernetes.io/unreachable:NoExecute op=Exists for 300s
Events:
  Type     Reason       Age                     From     Message
  ----     ------       ----                    ----     -------
  Warning  FailedMount  17m (x863 over 42h)     kubelet  Unable to attach or mount volumes: unmounted volumes=[ms-volume], unattached volumes=[ms-volume kube-api-access-84785]: timed out waiting for the condition
  Warning  FailedMount  8m43s (x1244 over 42h)  kubelet  MountVolume.MountDevice failed for volume "pvc-3cc04f16-98f2-423d-ab20-a7fbe17e974c" : rpc error: code = Internal desc = Failed to stage volume 3cc04f16-98f2-423d-ab20-a7fbe17e974c: attach failed: connect failed: NVMe connect failed: /dev/nvme-fabrics, No such file or directory (os error 2)
  Warning  FailedMount  3m35s (x247 over 41h)   kubelet  Unable to attach or mount volumes: unmounted volumes=[ms-volume], unattached volumes=[kube-api-access-84785 ms-volume]: timed out waiting for the condition

# kubectl get pv
NAME                                       CAPACITY   ACCESS MODES   RECLAIM POLICY   STATUS   CLAIM                     STORAGECLASS   REASON   AGE
pvc-3cc04f16-98f2-423d-ab20-a7fbe17e974c   1Gi        RWO            Delete           Bound    default/ms-volume-claim   mayastor-1              42h

# kubectl get pvc
NAME              STATUS   VOLUME                                     CAPACITY   ACCESS MODES   STORAGECLASS   AGE
ms-volume-claim   Bound    pvc-3cc04f16-98f2-423d-ab20-a7fbe17e974c   1Gi        RWO            mayastor-1     42h

# kubectl get sc
NAME         PROVISIONER               RECLAIMPOLICY   VOLUMEBINDINGMODE      ALLOWVOLUMEEXPANSION   AGE
mayastor-1   io.openebs.csi-mayastor   Delete          WaitForFirstConsumer   false                  42h

# kubectl -n mayastor get msp
NAME              NODE     STATE    AGE
main25-ssd-1.6t   main25   online   42h

# kubectl -n mayastor describe msp main25-ssd-1.6t
Name:         main25-ssd-1.6t
Namespace:    mayastor
Labels:       <none>
Annotations:  <none>
API Version:  openebs.io/v1alpha1
Kind:         MayastorPool
Metadata:
  Creation Timestamp:  2021-10-11T12:39:43Z
  Finalizers:
    finalizer.mayastor.openebs.io
  Generation:  1
  Managed Fields:
    API Version:  openebs.io/v1alpha1
    Fields Type:  FieldsV1
    fieldsV1:
      f:metadata:
        f:annotations:
          .:
          f:kubectl.kubernetes.io/last-applied-configuration:
      f:spec:
        .:
        f:disks:
        f:node:
    Manager:      kubectl-client-side-apply
    Operation:    Update
    Time:         2021-10-11T12:39:43Z
    API Version:  openebs.io/v1alpha1
    Fields Type:  FieldsV1
    fieldsV1:
      f:status:
        .:
        f:capacity:
        f:disks:
        f:reason:
        f:spec:
          .:
          f:disks:
          f:node:
        f:state:
        f:used:
    Manager:      unknown
    Operation:    Update
    Subresource:  status
    Time:         2021-10-11T12:39:45Z
    API Version:  openebs.io/v1alpha1
    Fields Type:  FieldsV1
    fieldsV1:
      f:metadata:
        f:finalizers:
          .:
          v:"finalizer.mayastor.openebs.io":
    Manager:         unknown
    Operation:       Update
    Time:            2021-10-11T12:47:31Z
  Resource Version:  30452
  UID:               a5d2a16f-1cd4-4e9a-86ce-632090010f4d
Spec:
  Disks:
    /dev/sdb
  Node:  main25
Status:
  Capacity:  1917438656512
  Disks:
    aio:///dev/sdb?uuid=5831cb7b-8d48-4488-b6cb-cb8b857d7a15
  Reason:
  Spec:
    Disks:
      /dev/sdb
    Node:  main25
  State:   online
  Used:    1073741824
Events:    <none>

# kubectl -n mayastor get msn
NAME     STATE    AGE
main25   online   42h

# kubectl -n mayastor get pod -o wide
NAME                   READY   STATUS    RESTARTS   AGE   IP              NODE     NOMINATED NODE   READINESS GATES
mayastor-csi-f5vsb     2/2     Running   0          42h   192.168.20.12   main12   <none>           <none>
mayastor-csi-nmrlh     2/2     Running   0          42h   192.168.20.37   main37   <none>           <none>
mayastor-csi-p7qxl     2/2     Running   0          42h   192.168.20.25   main25   <none>           <none>
mayastor-csi-tlm72     2/2     Running   0          42h   192.168.20.22   main22   <none>           <none>
mayastor-csi-vv8z5     2/2     Running   0          42h   192.168.20.13   main13   <none>           <none>
mayastor-etcd-0        1/1     Running   0          42h   10.0.7.47       main37   <none>           <none>
mayastor-z5knw         1/1     Running   0          42h   192.168.20.25   main25   <none>           <none>
moac-9589d8cb6-l9z6x   3/3     Running   0          42h   10.0.7.48       main37   <none>           <none>
nats-0                 2/2     Running   0          42h   10.0.6.4        main25   <none>           <none>

# kubectl -n mayastor logs mayastor-z5knw
[2021-10-11T12:39:45.204347620+00:00  INFO mayastor::subsys::config::pool:pool.rs:87] pool configuration saved to /var/local/mayastor/pools.yaml
[2021-10-11T12:47:31.399403058+00:00  INFO mayastor::lvs::lvs_pool:lvs_pool.rs:558] created main25-ssd-1.6t/3cc04f16-98f2-423d-ab20-a7fbe17e974c
[2021-10-11T12:47:35.141886424+00:00  INFO mayastor::bdev::nexus::nexus_bdev:nexus_bdev.rs:507] UUID set to 3cc04f16-98f2-423d-ab20-a7fbe17e974c for nexus nexus-3cc04f16-98f2-423d-ab20-a7fbe17e974c
[2021-10-11T12:47:35.141980995+00:00  INFO mayastor::bdev::nexus::nexus_bdev_children:nexus_bdev_children.rs:455] nexus-3cc04f16-98f2-423d-ab20-a7fbe17e974c: opened child bdev:///3cc04f16-98f2-423d-ab20-a7fbe17e974c?uuid=9d232436-6efe-4fc2-a6a1-68174067ffaf
[2021-10-11T12:47:35.142585017+00:00  INFO mayastor::bdev::nexus::nexus_label:nexus_label.rs:1202] creating new label for child bdev:///3cc04f16-98f2-423d-ab20-a7fbe17e974c?uuid=9d232436-6efe-4fc2-a6a1-68174067ffaf
[2021-10-11T12:47:35.142652909+00:00  INFO mayastor::bdev::nexus::nexus_label:nexus_label.rs:1308] writing label to child bdev:///3cc04f16-98f2-423d-ab20-a7fbe17e974c?uuid=9d232436-6efe-4fc2-a6a1-68174067ffaf
[2021-10-11T12:47:35.313165308+00:00  INFO mayastor::persistent_store:persistent_store.rs:112] Putting key 3cc04f16-98f2-423d-ab20-a7fbe17e974c, value {"children":[{"healthy":true,"uuid":"9d232436-6efe-4fc2-a6a1-68174067ffaf"}],"clean_shutdown":false} in store.
[2021-10-11T12:47:35.315296777+00:00  INFO mayastor::persistent_store:persistent_store.rs:122] Successfully put key 3cc04f16-98f2-423d-ab20-a7fbe17e974c, value {"children":[{"healthy":true,"uuid":"9d232436-6efe-4fc2-a6a1-68174067ffaf"}],"clean_shutdown":false} in store.
[2021-10-11T12:47:35.315371511+00:00  INFO mayastor::grpc::mayastor_grpc:mayastor_grpc.rs:459] Created nexus 3cc04f16-98f2-423d-ab20-a7fbe17e974c
[2021-10-11T12:47:35.340394537+00:00  INFO mayastor::subsys::nvmf::subsystem:subsystem.rs:222] added NS ID 1
[2021-10-11T12:47:35.340627273+00:00  INFO mayastor::subsys::nvmf::subsystem:subsystem.rs:371] started "nqn.2019-05.io.openebs:nexus-3cc04f16-98f2-423d-ab20-a7fbe17e974c"
[2021-10-11T12:47:35.340671220+00:00  INFO mayastor::grpc::mayastor_grpc:mayastor_grpc.rs:611] Published nexus 3cc04f16-98f2-423d-ab20-a7fbe17e974c under nvmf://192.168.20.25:8420/nqn.2019-05.io.openebs:nexus-3cc04f16-98f2-423d-ab20-a7fbe17e974c

# kubectl -n mayastor logs -f mayastor-csi-p7qxl mayastor-csi
[2021-10-13T06:46:54Z DEBUG mayastor_csi::node] Volume 3cc04f16-98f2-423d-ab20-a7fbe17e974c has URI nvmf://192.168.20.25:8420/nqn.2019-05.io.openebs:nexus-3cc04f16-98f2-423d-ab20-a7fbe17e974c
[2021-10-13T06:46:54Z DEBUG mayastor_csi::node] Attaching volume 3cc04f16-98f2-423d-ab20-a7fbe17e974c
[2021-10-13T06:46:54Z ERROR mayastor_csi::node] Failed to stage volume 3cc04f16-98f2-423d-ab20-a7fbe17e974c: attach failed: connect failed: NVMe connect failed: /dev/nvme-fabrics, No such file or directory (os error 2)
[2021-10-13T06:48:56Z DEBUG mayastor_csi::node] NodeGetCapabilities request: [StageUnstageVolume]
[2021-10-13T06:48:56Z DEBUG mayastor_csi::node] NodeGetCapabilities request: [StageUnstageVolume]
[2021-10-13T06:48:56Z DEBUG mayastor_csi::node] Volume 3cc04f16-98f2-423d-ab20-a7fbe17e974c has URI nvmf://192.168.20.25:8420/nqn.2019-05.io.openebs:nexus-3cc04f16-98f2-423d-ab20-a7fbe17e974c
[2021-10-13T06:48:56Z DEBUG mayastor_csi::node] Attaching volume 3cc04f16-98f2-423d-ab20-a7fbe17e974c
[2021-10-13T06:48:56Z ERROR mayastor_csi::node] Failed to stage volume 3cc04f16-98f2-423d-ab20-a7fbe17e974c: attach failed: connect failed: NVMe connect failed: /dev/nvme-fabrics, No such file or directory (os error 2)
[2021-10-13T06:50:58Z DEBUG mayastor_csi::node] NodeGetCapabilities request: [StageUnstageVolume]
[2021-10-13T06:50:58Z DEBUG mayastor_csi::node] NodeGetCapabilities request: [StageUnstageVolume]
[2021-10-13T06:50:58Z DEBUG mayastor_csi::node] Volume 3cc04f16-98f2-423d-ab20-a7fbe17e974c has URI nvmf://192.168.20.25:8420/nqn.2019-05.io.openebs:nexus-3cc04f16-98f2-423d-ab20-a7fbe17e974c
[2021-10-13T06:50:58Z DEBUG mayastor_csi::node] Attaching volume 3cc04f16-98f2-423d-ab20-a7fbe17e974c
[2021-10-13T06:50:58Z ERROR mayastor_csi::node] Failed to stage volume 3cc04f16-98f2-423d-ab20-a7fbe17e974c: attach failed: connect failed: NVMe connect failed: /dev/nvme-fabrics, No such file or directory (os error 2)

Main25:~# fdisk -l

Disk /dev/sdb: 1.76 TiB, 1919313510400 bytes, 3748659200 sectors
Disk model: SMC2208
Units: sectors of 1 * 512 = 512 bytes
Sector size (logical/physical): 512 bytes / 4096 bytes
I/O size (minimum/optimal): 4096 bytes / 4096 bytes

is something wrong deployment?

At a quick glance, it seems the nvme_tcp module is not enabled on the node where fio is running

thanks

repo: openebs/mayastor


text:
[Safari] Clicking behavior on IconButton is not the same as in Chrome

Clicking behavior on IconButton with invertColors prop differs from Chrome browser

Same situation as 2781. Safari does not give focus on the button click
https://developer.mozilla.org/en-US/docs/Web/HTML/Element/button#clicking_and_focus

authors: khudym monteri

repo: openedx/paragon


text:
feat: upgrade to superset v4.1.0

Description
This PR upgrades superset to v4.1.0
Now, we manually install MySQL client, see https://github.com/apache/superset/issues/29727 for more information.
This PR also enables tagging and sets default tags to original and translated assets:

Thanks for the pull request, @Ian2012!
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I think it now comes pre-bundled in the image but I need to verify it

Hey @Ian2012, just checking in to see if you're still planning to continue working on this PR?

Yes, waiting on the official release before proceeding,

There's an issue on Superset right now about the images not being available yet: https://github.com/apache/superset/issues/30919

@bmtcril The decision was to leave this out for sumac, right?

Correct, we won't have time to fully test everything before Sumac release. We're still waiting on Sara's latest changes to release the next version for Sumac, so probably tomorrow. I'll close and reopen this to kick the CLA check, it's been hanging a lot.

Hey @Ian2012 and @bmtcril, is this unblocked now that Sumac has been released?

@itsjeyd yes, thanks! We'll be adding it as part of the breaking changes work 👍

authors: Ian2012 bmtcril itsjeyd openedx-webhooks

repo: openedx/tutor-contrib-aspects


text:
Create Kicad layout V1 design based on schematic

Based on schematic https://github.com/openeew/openeew-sensor/issues/25
Using Arrow components where possible to allow us to make the boards at a discounted price.
Use SMT components that a MC-385V2V PnP can use : 0201 components to 100x150mm components

We need to take care of the signal integrity of REF_CLK in the hardware design! Keep the trace as short as possible. Keep it away from RF devices. Keep it away from inductor elements.
https://docs.espressif.com/projects/esp-idf/en/latest/esp32/api-reference/network/esp_eth.html

repo: openeew/openeew-sensor


text:
Enable an LTI icon to be specified for Blackboard LTI launches

Is your feature request related to a problem? Please describe.
Currently the icon in Blackboard for openEQUELLA LTI links is a default Bb LTI icon.  The icon should be configurable.
Describe the solution you'd like
The LTI launch should return a link to the LTI icon
Additional context
This should be handled in a generic LTI fashion

Unfortunately, the Bb service does not honor the LTI icon return value, so this feature cannot be created.

repo: openequella/openEQUELLA