Koma Analytics SDK for Flutter — event tracking, user identification, and user properties with a durable offline queue. Behavior parity with the Koma React SDK. Changelog excerpt: First stable release. Feature and behavior parity with the Koma React SDK. ### Added - Public API: `Koma`singleton (`init`, `track`, `identify`, `setUserProperty`), `KomaConfig`, `ConnectionError`/ `ConnectionErrorCode`, and the opt-in `KomaNavigatorObserver`/ `KomaScreenEvent`. - Event tracking, user identification, and user properties over Socket.IO, matching the server wire contract (with `insertId`deduplication). - Durable FIFO event queue (`shared_preferences`+ file): no event loss across[...]

I'm trying to download a lot of datasets from different WFS URLs using ogr2ogr (and to an extent, owslib) in Python. My problem arises when I try to connect to a second WFS. In my test code, I have first tried connecting to one WFS, then downloading the layer. This went fine. Afterwards, I replaced the URL with a second WFS URL. this time I got the error: ConnectionError: ('Connection aborted.', ConnectionResetError(10054 An existing connection was forcibly closed by the remote host)). In the first part of the code I try to extract the version number from the WFS: import subprocess from owslib.wfs import WebFeatureService as wfsmod #connecting to second WFS using owslib url = 'http://wfs2-miljoegis.mim.dk/raastofpaahavet/ows? version=1.1.0&OUTPUTFORMAT=GML2' wfs = wfsmod(url) version = (wfs.identification.version) typenam ="rashavtilladelser20auktion" The connection error happens at line "wfs = wfsmod(url)". I also try to download a specific layer from this url using ogr2ogr/subprocesses: if str(version) == "2.0.0": #layername is the specific title of the layer on the WFS server. layername = "&TYPENAMES="+layername else: layername = "&TYPENAME="+layername cmd = [r"C:\OSGeo4W\bin\ogr2ogr.exe", "-f", "ESRI Shapefile", r"C:\havplan\havplan_data\wfs_downloads\havbrugszoner.shp", "WFS:"+url+"version="+str(version) +"&request=GetFeature"+layername] subprocess.check_call(cmd) as far as I understand, the error happens because I can only connect to one WFS at a time in Python. Is there some way to close the connection to a WFS in owslib and ogr2ogr using a command?

This blog post will guide you through various methods to resolve the "connection error could not log out of Snapchat" problem.

This blog post will guide you through various methods to resolve the "connection error could not log out of Snapchat" problem.

Signals issues related to network connectivity.

Those are i'm talking about: impl From<diesel::ConnectionError> for BoxedAppError { fn from(err: diesel::ConnectionError) -> BoxedAppError { Box::new(err) } } impl From<DieselError> for BoxedAppError { fn from(err: DieselError) -> BoxedAppError { match err { DieselError::NotFound => not_found(), e if is_read_only_error(&e) => { let detail = "crates.io is currently in read-only mode. Please check https://status.crates.io/ for details and try again later."; custom(StatusCode::SERVICE_UNAVAILABLE, detail) } DieselError::DatabaseError(DatabaseErrorKind::ClosedConnection, _) => { service_unavailable() } _ => Box::new(err), } } } They send some db errors. I guess like duplicate violation, foreign key etc etc. And here they just `Box::new(e)` them without any converting and/or formatting

Hi! So I was experimenting with Actix and Diesel and realized that I actually need the Diesel query to be an async call. To do so I picked up the diesel-async package: diesel_async - Rust After some reading I came up with this factory function here to help be build a new Pool manager: use diesel::{ConnectionError, sqlite::SqliteConnection}; use diesel_async::{ AsyncConnection, pooled_connection::{ AsyncDieselConnectionManager, deadpool::{BuildError, Pool}, }, sync_connection_wrapper::SyncConnectionWrapper, }; use dotenvy::dotenv; use std::env; pub type DatabaseConnection = SyncConnectionWrapper<SqliteConnection>; pub type DatabaseConnectionError = ConnectionError; pub type DatabaseConnectionPool = Pool<SyncConnectionWrapper<SqliteConnection>>; pub type DatabaseConnectionPoolError = BuildError; pub async fn build_db_conn_pool() -> Result<DatabaseConnectionPool, DatabaseConnectionPoolError> { dotenv().ok(); let db_url = env::var("DATABASE_URL").expect("DATABASE_URL must be set"); let manager = AsyncDieselConnectionManager::<DatabaseConnection>::new(db_url); DatabaseConnectionPool::builder(manager).build() } So far so good. Now I updated the main function to inject it the `web::Data` object: use actix_web::{App, HttpResponse, HttpServer, Responder, get, web::Data}; use maud::{Markup, html}; use todo_mash_v2::controllers::{database::build_db_conn_pool, pages::home}; #[actix_web::main] async fn main() -> std::io::Result<()> { let db_pool = build_db_conn_pool() .await .expect("Failed to create database pool"); // Start Actix server HttpServer::new(move || { App::new() .app_data(Data::new(db_pool.clone())) .service(hello) .service(home) //.route("/", web::get().to(hello)) }) .bind("0.0.0.0:8080")? .run() .await } So far so good. Then I proceed to update the controller function signature: use actix_web::{HttpResponse, Responder, get, web::Data}; use maud::{Markup, html}; use super::database::DatabaseConnectionPool; #[get("/")] async fn home(db_pool: Data<DatabaseConnectionPool>) -> impl Responder { let mut obj_conn = db_pool.get().await.unwrap(); // Fetching Data /// ... // Rendering Data let content: Markup = html! { h1 { "Todo App" } }; HttpResponse::Ok().body(content.into_string()) } Here is where I got stuck. As far as I can tell I should be able Fetch the data with: let todo_lists: Vec<TodoList> = todo_list::table() .select(TodoList::as_select()) .load::<TodoList>(&mut obj_conn) .await?.unwrap(); But the compiler is throwing: the trait bound `SyncConnectionWrapper<SqliteConnection>: LoadConnection` is not satisfied the trait `LoadConnection` is implemented for `SqliteConnection` required for `SelectStatement<FromClause<table>, SelectClause<SelectBy<..., ...>>>` to implement `diesel::query_dsl::LoadQuery<'_, SyncConnectionWrapper<SqliteConnection>, TodoList>` the full name for the type has been written to '/home/debian-user/Projects/personal/todo-mash-v2/target/debug/deps/todo_mash_v2-af117dbb16e6a223.long-type-2082051071945310070.txt' consider using `--verbose` to print the full type name to the console All the examples that I've read seems to assume that the result from the `pool.get` will return a struct that can used as a connection struct. But clearly that's not the case. Questions: 1. What am I missing? 2. Should I swap Pool manager? 3. Should I drop the diesel-async crate in favor of using "web::Block" feature? Thank you for reading

Hmm… It seems working… Firewall or something issue? import os os.environ["HF_DEBUG"] = "1" from datasets import load_dataset dataset = load_dataset("wanglab/CT_DeepLesion-MedSAM2") Flower Discuss – 25 Jun 25 ### Hugging Face dataset cannot be accessed General datasets My IP cannot directly access huggingface.co, and even when using a proxy IP, I still encounter the following error during debugging:‌ raise ConnectionError(f"Couldn’t reach ‘{path}’ on the Hub ({e.class.name})") from e ConnectionError: Couldn’t...

I’m relatively new, I have 3 spaces for generating images from text promps, the models used are Flux Dev, FluxSchnell and Stable Diffusion Large. All 3 spaces stopped working yesterday afternoon., Ichecked the logs but don’t really understand them. Below is an extract from 1 of the spaces: ConnectionError(e, request=request) requests.exceptions.ConnectionError: HTTPSConnectionPool(host=‘api-inference.huggingface.co’, port=443): Max retries exceeded with url: /models/stabilityai/stable-diffusion-3.5-large-turbo (Caused by NameResolutionError(“<urllib3.connection.HTTPSConnection object at 0x7f5840efbf40>: Failed to resolve ‘api-inference.huggingface.co’ ([Errno -2] Name or service not known)”)) I HAVE ALL THE CORRECT TOKENS, AND HAVE ALS REFRESHED EACH TOKEN. STILL NO JOY. I’ve browsed similar spaces and all seem to be having the same issue. My spaces run on CPU so I’ve only checked other spaces on CPU. Can anyone help please?

Thank you very much, will watch over that issue

@John6666 Dear John and other community members, Do you know why and how to solve this error? I have reset my token several times and it seems doesn’t help the connections at all. Best regards, Sean

To be honest, I’m not sure, but I think your case is similar to the second link? Connection Error when Accessing Dataset URL on Hugging Face 🤗Datasets > I am currently experiencing an issue while trying to access my private dataset in my repo for example: (https://huggingface.co/dataset/Mustafa21/dataset) The error message I encountered is as follows: ionError(f"Couldn't reach {url} ({repr(head_error)})") ConnectionError: Couldn't reach https://huggingface.co/datasets/Mustafa21/dataset/resolve/main/dataset_infos.json (ConnectionError('Unauthorized for URL https://huggingface.co/datasets/Mustafa21/dataset/resolve/main/dataset_infos.json. Pleas… github.com/huggingface/datasets #### Connection error of the HuggingFace's dataset Hub due to SSLError with proxy opened 06:56AM - 07 Nov 22 UTC leemgs ### Describe the bug It's weird. I could not normally connect the dataset H…ub of HuggingFace due to a SSLError in my office. Even when I try to connect using my company's proxy address (e.g., http_proxy and https_proxy), I'm getting the SSLError issue. What should I do to download the datanet stored in HuggingFace normally? I welcome any comments. I think those comments will be helpful to me. * Dataset address - https://huggingface.co/datasets/moyix/debian_csrc/viewer/moyix--debian_csrc * Log message ``` ............ OMISSION .............. Traceback (most recent call last): File "/data/home/geunsik-lim/qtlab/./transformers/examples/pytorch/language-modeling/run_clm.py", line 587, in <module> main() File "/data/home/geunsik-lim/qtlab/./transformers/examples/pytorch/language-modeling/run_clm.py", line 278, in main raw_datasets = load_dataset( File "/home/geunsik-lim/anaconda3/envs/deepspeed/lib/python3.10/site-packages/datasets/load.py", line 1719, in load_dataset builder_instance = load_dataset_builder( File "/home/geunsik-lim/anaconda3/envs/deepspeed/lib/python3.10/site-packages/datasets/load.py", line 1497, in load_dataset_builder dataset_module = dataset_module_factory( File "/home/geunsik-lim/anaconda3/envs/deepspeed/lib/python3.10/site-packages/datasets/load.py", line 1222, in dataset_module_factory raise e1 from None File "/home/geunsik-lim/anaconda3/envs/deepspeed/lib/python3.10/site-packages/datasets/load.py", line 1179, in dataset_module_factory raise ConnectionError(f"Couldn't reach '{path}' on the Hub ({type(e).__name__})") ConnectionError: Couldn't reach 'moyix/debian_csrc' on the Hub (SSLError) [2022-11-07 15:23:38,476] [INFO] [launch.py:318:sigkill_handler] Killing subprocess 6760 [2022-11-07 15:23:38,476] [ERROR] [launch.py:324:sigkill_handler] ['/home/geunsik-lim/anaconda3/envs/deepspeed/bin/python', '-u', './transformers/examples/pytorch/language-modeling/run_clm.py', '--local_rank=0', '--model_name_or_path=Salesforce/codegen-350M-multi', '--per_device_train_batch_size=1', '--learning_rate', '2e-5', '--num_train_epochs', '1', '--output_dir=./codegen-350M-finetuned', '--overwrite_output_dir', '--dataset_name', 'moyix/debian_csrc', '--cache_dir', '/data/home/geunsik-lim/.cache', '--tokenizer_name', 'Salesforce/codegen-350M-multi', '--block_size', '2048', '--gradient_accumulation_steps', '32', '--do_train', '--fp16', '--deepspeed', 'ds_config_zero2.json'] exits with return code = 1 real 0m7.742s user 0m4.930s ``` ### Steps to reproduce the bug Steps to reproduce this behavior. ``` (deepspeed) geunsik-lim@ai02:~/qtlab$ ./test_debian_csrc_dataset.py Traceback (most recent call last): File "/data/home/geunsik-lim/qtlab/./test_debian_csrc_dataset.py", line 6, in <module> dataset = load_dataset("moyix/debian_csrc") File "/home/geunsik-lim/anaconda3/envs/deepspeed/lib/python3.10/site-packages/datasets/load.py", line 1719, in load_dataset builder_instance = load_dataset_builder( File "/home/geunsik-lim/anaconda3/envs/deepspeed/lib/python3.10/site-packages/datasets/load.py", line 1497, in load_dataset_builder dataset_module = dataset_module_factory( File "/home/geunsik-lim/anaconda3/envs/deepspeed/lib/python3.10/site-packages/datasets/load.py", line 1222, in dataset_module_factory raise e1 from None File "/home/geunsik-lim/anaconda3/envs/deepspeed/lib/python3.10/site-packages/datasets/load.py", line 1179, in dataset_module_factory raise ConnectionError(f"Couldn't reach '{path}' on the Hub ({type(e).__name__})") ConnectionError: Couldn't reach 'moyix/debian_csrc' on the Hub (SSLError) (deepspeed) geunsik-lim@ai02:~/qtlab$ (deepspeed) geunsik-lim@ai02:~/qtlab$ (deepspeed) geunsik-lim@ai02:~/qtlab$ (deepspeed) geunsik-lim@ai02:~/qtlab$ cat ./test_debian_csrc_dataset.py #!/usr/bin/env python from datasets import load_dataset dataset = load_dataset("moyix/debian_csrc") ``` 1. Adde proxy address of a company in /etc/profile 2. Download dataset with load_dataset() function of datasets package that is provided by HuggingFace. 3. In this case, the address would be "moyix--debian_csrc". 4. I get the "`ConnectionError: Couldn't reach 'moyix/debian_csrc' on the Hub (SSLError`)" error message. ### Expected behavior * error message: ConnectionError: Couldn't reach 'moyix/debian_csrc' on the Hub (SSLError) ### Environment info * software version information: ``` (deepspeed) geunsik-lim@ai02:~$ (deepspeed) geunsik-lim@ai02:~$ conda list -f pytorch # packages in environment at /home/geunsik-lim/anaconda3/envs/deepspeed: # # Name Version Build Channel pytorch 1.13.0 py3.10_cuda11.7_cudnn8.5.0_0 pytorch (deepspeed) geunsik-lim@ai02:~$ conda list -f python # packages in environment at /home/geunsik-lim/anaconda3/envs/deepspeed: # # Name Version Build Channel python 3.10.6 haa1d7c7_1 (deepspeed) geunsik-lim@ai02:~$ conda list -f datasets # packages in environment at /home/geunsik-lim/anaconda3/envs/deepspeed: # # Name Version Build Channel datasets 2.6.1 py_0 huggingface (deepspeed) geunsik-lim@ai02:~$ uname -a Linux ai02 5.4.0-131-generic #147-Ubuntu SMP Fri Oct 14 17:07:22 UTC 2022 x86_64 x86_64 x86_64 GNU/Linux (deepspeed) geunsik-lim@ai02:~$ cat /etc/lsb-release DISTRIB_ID=Ubuntu DISTRIB_RELEASE=20.04 DISTRIB_CODENAME=focal DISTRIB_DESCRIPTION="Ubuntu 20.04.5 LTS" ``` github.com/huggingface/transformers #### SSLError: HTTPSConnectionPool(host='huggingface.co', port=443) opened 03:46PM - 08 Jun 22 UTC closed 03:02PM - 15 Aug 22 UTC alexsomoza I'm trying in python: from sentence_transformers import SentenceTransformer …sbert_model = SentenceTransformer('all-MiniLM-L6-v2') and I get this error: SSLError: HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /api/models/sentence-transformers/all-MiniLM-L6-v2 (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self signed certificate in certificate chain (_ssl.c:1091)'))) I have no proxy, just getting direct to internet !!!

> Who would’ve thought a tutorial would use a gated dataset, What? The model specified in the tutorial was a gated model? If that wasn’t explained in the tutorial, it’s almost a bug! Give me the link. I’ll just report the bug…

It’s a github tutorial that uses huggingface so I don’t know if it counts as huggingface faults… but thanks… Here’s the link, I hope I didn’t shame myself by not reading it properly github.com #### huggingface/notebooks/blob/main/examples/semantic_segmentation.ipynb { "cells": [ { "cell_type": "markdown", "metadata": { "id": "CL089-JvFAXe" }, "source": [ "# Fine-tuning for Semantic Segmentation with 🤗 Transformers\n", "\n", "In this notebook, you'll learn how to fine-tune a pretrained vision model for Semantic Segmentation on a custom dataset in PyTorch. The idea is to add a randomly initialized segmentation head on top of a pre-trained encoder, and fine-tune the model altogether on a labeled dataset. You can find an accompanying blog post here. " ] }, { "cell_type": "markdown", "metadata": { "id": "2UEtIWt0sGkR" }, "source": [ "## Model\n", This file has been truncated. show original

By the way, that model is a gated model, so you can’t use it without permission, but did you get permission? huggingface.co ### Gated models We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Who would’ve thought a tutorial would use a gated dataset, pardon my dumb question. Thanks for helping me out

I raised an issue. github.com/huggingface/notebooks #### A dataset used in the tutorial is a gated problem opened 05:08AM - 25 Nov 24 UTC John6666cat The dataset used in one of the tutorials seems to be a gated model, as the title… suggests. As there is no explanation of this in the tutorial, it seems that it was not gated at the time the tutorial was written. I noticed this after someone pointed it out in the forum. ### Code https://github.com/huggingface/notebooks/blob/0454d7dbc7d0b1acbe78fd38e979a30729947026/examples/semantic_segmentation.ipynb#L150 ### Gated dataset https://huggingface.co/datasets/segments/sidewalk-semantic ### Reference https://discuss.huggingface.co/t/connectionerror-couldnt-reach-https-huggingface-co-datasets-segments-sidewalk-semantic-resolve-main-dataset-infos-json-error-403/127549

> It’s a github tutorial that uses huggingface so I don’t know if it counts as huggingface faults… Thank you. That’s the github account managed by HF, so it’s HF’s fault.

You can configure the port in README.md, but it’s not clear whether it will work just by doing this. huggingface.co ### Spaces Configuration Reference We’re on a journey to advance and democratize artificial intelligence through open source and open science.

"I've got a bug that's baffling me, Margaret," Timothy said, frustrated. "I’m trying to catch a...