WhereIsAI/github-issue-similarity huggingface dataset:
Issue 1:
https://pantheon.corp.google.com/storage/browser/kubernetes-jenkins/pr-logs/pull/24138/kubernetes-pull-test-unit-integration/21794

Updated external link: https://console.cloud.google.com/storage/browser/kubernetes-jenkins/pr-logs/pull/24138/kubernetes-pull-test-unit-integration/21794

FAIL k8s.io/kubernetes/pkg/storage/etcd3 2.999s

=== RUN TestWatch
--- FAIL: TestWatch (0.34s)
watcher_test.go:204: #1: ResultChan should have been closed

Hit this in #24138

@kubernetes/sig-api-machinery

Issue 2:
It failed in https://console.cloud.google.com/storage/browser/kubernetes-jenkins/pr-logs/pull/24291/kubernetes-pull-test-unit-integration/22014/

--- FAIL: TestWatch (0.81s)
watcher_test.go:204: #1: ResultChan should have been closed

cc @wojtek-t @xiang90
Issue 1:
System Info

Each run of the trainer two tfevent files are being generated, it looks like this:

/runs

--Feb27_09-46-42_...

----events.out.tfevents....0

----/1677491207.0429652

------events.out.tfevents....1

When reading these files with TensorBoard I don't get any output from the .1 file, how can I get rid of it (because these clutter my TensorBoard) or get actual data from it?

Thanks in advance

@sgugger

Who can help?

No response

Information

The official example scripts

My own modified scripts

Tasks

An officially supported task in the examples folder (such as GLUE/SQuAD, ...)

My own task or dataset (give details below)

Reproduction

Simply starting any training.

Expected behavior

One tfevents file or a valid output from the .1 file.

Issue 2:
Hello,

I wanted to use Roberta for Sentence classification on protein sequences which I have converted into sentences. So first I train a tokenizer for my custom vocabulary.

tokenizer.train("bert_vocab.txt",vocab_size=8000,special_tokens=[
"[CLS]",
"[SEP]",
"[UNK]",
"[MASK]",
])

tokenizer.save_model("EsperBERTo")

from tokenizers.implementations import CharBPETokenizer
from tokenizers.processors import BertProcessing

tokenizer._tokenizer.post_processor = BertProcessing(("[SEP]", tokenizer.token_to_id("[SEP]")), ("[CLS]", tokenizer.token_to_id("[CLS]")),)
print(tokenizer.encode("AAA ATA AKA"))
print(tokenizer.encode("AAA ATA AKA").tokens)

tokenizer.enable_truncation(max_length=512)

import torch
torch.cuda.is_available()

from transformers import RobertaConfig

config = RobertaConfig(
vocab_size=8000,
max_position_embeddings=514,
num_attention_heads=12,
num_hidden_layers=6,
type_vocab_size=1,
)

from transformers import RobertaTokenizerFast

tokenizer = RobertaTokenizerFast.from_pretrained("./EsperBERTo", max_len=512)

When i try to train the model, i get the error ValueError: Expected input batch_size (64) to match target batch_size (8192).

model = RobertaForSequenceClassification(config=config)

model.num_parameters()

from transformers import DataCollatorForLanguageModeling

from transformers import LineByLineTextDataset

dataset = LineByLineTextDataset(
tokenizer=tokenizer,
file_path="bert_data.txt",
block_size=64,
)

data_collator = DataCollatorForLanguageModeling(
tokenizer=tokenizer)

from transformers import Trainer, TrainingArguments

training_args = TrainingArguments(
output_dir="./EsperBERTo",
overwrite_output_dir=True,
num_train_epochs=100,
per_device_train_batch_size=64,
save_steps=10_000,
save_total_limit=2,
)

trainer = Trainer(
model=model,
args=training_args,
data_collator=data_collator,
train_dataset=dataset,
prediction_loss_only=True,
)
trainer.train()

trainer.save_model("./EsperBERTo")

I understand i do not specify the output labels anywhere in the code but I am yet to find any example which I could follow to figure this.
Issue 1:
Not sure how much room there is to improve this, but the error message for the following user error doesn't provide much information about where the error occurs:

julia> struct Inner{T}
x::T
end

julia> struct Outer{T}
y1::Inner{T}
y2::Inner{T}
y3:Inner{T} # This single colon is where the user error occurs
end
ERROR: UndefVarError: T not defined
Stacktrace:
[1] top-level scope at REPL[2]:1

This occurs for me on:

Julia Version 1.4.1
Commit 381693d3df (2020-04-14 17:20 UTC)
Platform Info:
OS: macOS (x86_64-apple-darwin19.3.0)
CPU: Intel(R) Core(TM) i9-8950HK CPU @ 2.90GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-8.0.1 (ORCJIT, skylake)

Issue 2:
As @StefanKarpinski noted here, it would be nice to have (very basic) logging as part of Base, which logs messages to output (STDOUT or other). This could then be extended by packages to add more complicated logging features (syslog, etc.).

Some ideas:

Use/add standard log level functions (info, warn, error, debug, critical) to Base, perhaps wrapped in a module (Logging.info, Logging.debug, etc.)

Define a new interface with a single log function (Logging.log(DEBUG, "some debug message"); Logging.log(INFO, "Here's some info"), etc.)

Thoughts? If we go with the first idea above, I would propose to move Logging.jl to the JuliaLang organization as a start.

For additional reference and ideas, current logging packages include my Logging.jl and Lumberjack.jl.
Issue 1:
Apache Airflow version

2.1.4

Operating System

Amazon Linux 2 AMI

Versions of Apache Airflow Providers

No response

Deployment

Other

Deployment details

Cloudformation template based on https://github.com/villasv/aws-airflow-stack/blob/v2/aws/cloud-formation-template.yml

What happened

DAG can have multiple active runs despite of max_active_runs set to 1 when it is triggered externally from TriggerDagRunOperator

What you expected to happen

If DAG has max_active_runs = 1 it should never be started more than once

How to reproduce

DAG #1

with DAG('dag_1',
default_args=default_args,
max_active_runs=1,
start_date=datetime(2021, 11, 1),
schedule_interval="* * * * *",
dagrun_timeout=timedelta(minutes=1),
catchup=False) as dag:

TriggerDagRunOperator(task_id='trigger_dag_2',
trigger_dag_id='dag_2')

DAG #2

from airflow.operators.bash import BashOperator

with DAG('dag_2',
default_args=default_args,
max_active_runs=1,
start_date=datetime(2021, 11, 1),
schedule_interval=None,
dagrun_timeout=timedelta(minutes=10),
catchup=False) as dag:

BashOperator(
task_id="dag_keeper",
bash_command="sleep 5m"
)

Anything else

No response

Are you willing to submit PR?

Yes I am willing to submit a PR!

Code of Conduct

I agree to follow this project's Code of Conduct

Issue 2:
Apache Airflow version

2.1.4 (latest released)

Operating System

Ubuntu 20.04

Versions of Apache Airflow Providers

No response

Deployment

Virtualenv installation

Deployment details

No response

What happened

max_active_runs isn't respected when dag is triggered externally by TriggerDagRunOperator

What you expected to happen

Limit number of concurrent runs specified by max_active_runs ,subsequent dag runs should be allocated on new queue state

How to reproduce

Modify example_dags\example_trigger_target_dag.py to limit max_active_runs = 2 and add time.sleep for test convenience :

from airflow import DAG
from airflow.operators.bash import BashOperator
from airflow.operators.python import PythonOperator
from airflow.utils.dates import days_ago

def run_this_func(**context):
"""
Print the payload "message" passed to the DagRun conf attribute.

:param context: The execution context
:type context: dict
"""
import time
time.sleep(10)
print(f"Remotely received value of {context['dag_run'].conf['message']} for key=message")

with DAG(
dag_id="example_trigger_target_dag",
default_args={"owner": "airflow"},
start_date=days_ago(2),
schedule_interval=None,
max_active_runs=2,
tags=['example'],
) as dag:

run_this = PythonOperator(task_id="run_this", python_callable=run_this_func)

bash_task = BashOperator(
task_id="bash_task",
bash_command='echo "Here is the message: $message"',
env={'message': '{{ dag_run.conf["message"] if dag_run else "" }}'},
)

And manually trigger multiple runs of example_trigger_controller_dag :

Anything else

Same behavior if triggered with cli:

for i in {1..10}; do airflow dags trigger example_trigger_target_dag -c '{"message": "Hello World"}'; done

Are you willing to submit PR?

Yes I am willing to submit a PR!

Code of Conduct

I agree to follow this project's Code of Conduct
Issue 1:
The recompose dependency doesn't have the createHelper.js file.

acdlite/recompose#380

Issue 2:
I have searched the issues of this repository and believe that this is not a duplicate.

Expected Behavior

I would like to be able to set some custom alignment for the data within my tables and I'm wondering if it is possible. Currently, all my data aligns to the left, which is not the ideal experience when dealing with numbers.

Current Behavior

Tech
Version

Material-UI
v.0.19.4

React

browser

etc
Issue 1:
Hello,

while running the program at https://github.com/raintank/raintank-metric/tree/master/metric_tank

I'm seeing mark-and-scan times of 15s cpu time, 20002500 ms clock time. (8 core system) for a heap of about 6.5GB

(STW pauses are fine and ~1ms)

I used https://circleci.com/gh/raintank/raintank-metric/507 to obtain the data below.

$ metric_tank --version
metrics_tank (built with go1.6, git hash 8897ef4f8f8f1a2585ee88ecadee501bfc1a4139)
$ go version
go version go1.6 linux/amd64
$ uname -a #on the host where the app runs
Linux metric-tank-3-qa 3.19.0-43-generic #49~14.04.1-Ubuntu SMP Thu Dec 31 15:44:49 UTC 2015 x86_64 x86_64 x86_64 GNU/Linux

I know the app is currently not optimized for GC workload: while i've gotten allocations down in various parts of the program there are currently probably about a million or more live pointers referencing pieces of data. I was going to work on optimizing this when Dave Cheney suggested there's a problem with the runtime and I should file a bug (https://groups.google.com/forum/#!topic/golang-nuts/Q0rXKYjy1cg)

Here's the log with gctrace and schedtrace enabled: https://gist.githubusercontent.com/Dieterbe/18453451c5af0cdececa/raw/9c4f2abd85bb7a815c6cda5c1828334d3d29817d/log.txt

at http://dieter.plaetinck.be/files/go/mt3-qa-gc-vs-no-gc.zip you'll find a zip containing this log, the binary, a cpu profile taken during gc run 1482, and a cpu and heap profile in between run 1482 and 1483

I also have these two dashboards that seem useful. (they both end just after the spike induced by GC run 1482)

https://snapshot.raintank.io/dashboard/snapshot/MtLqvc4F6015zbs4iMQSPzfizvG7OQjC

shows memory usage, GC runs and STW pause times. it also shows that incoming load (requests) of the app is constant so this conveys to me that any extra load is caused by GC, not by changing workload

https://snapshot.raintank.io/dashboard/snapshot/c2zwTZCF7BmfyzEuGF6cHN9GX9aM1V99

this shows the system stats. note the cpu spikes corresponding to the GC workload.

let me know if there's anything else I can provide,

thanks,

Dieter.

Issue 2:
by porjo38@yahoo.com.au:

Anchor link history for Golang API web pages is broken when browsing with Firefox.

For example:

1. browse to http://golang.org/pkg/net/
2. click on any of the items in the index. You will be auto-scrolled down to the item
heading
4. Click back button...nothing happens

Back button should take you back to the index.

By trial and error, I've discovered that removing the stylesheet from the following line
fixes the issue:

<div id="mainframe" style="position: fixed; bottom: 0; top:0;
overflow: auto; width: 100%;">

I'm not sure why this stylesheet is a problem for Firefox. Posting here so that others
can provide some insight.

Tested using Firefox 30.0 on Fedora 20 x86_64. Also confirmed this issue with others on
#go-nuts irc channel.
Issue 1:
I wanted to use the features of canvas (group, chord, chain ...) to manage my workflow.

If we take the graph just behind, I didn't succeed to find a way to execute it without having double nodes for the dependencies.

Does someone know how to execute it this graph using canvas? How could I handle that?

Issue 2:
Consider the following (where A, B, C, D, E are tasks):

sub_A = A.s()
chord_D = chord([sub_A, B.s()], D.s())
chord_E = chord([sub_A, C.s()], E.s())
res = group(chord_D, chord_E)()

This creates two separate invocations for task A. For many cases, we would want the subtask for A to be invoked just once, and the work shared.

It seems that manually giving the subtask a uuid leads to the desired outcome, but it would be nice to have support for this in the API.
Issue 1:
Helllo, I have a problem with installation angular2 beta 15

npm install angular2 -d

npm info it worked if it ends with ok

npm info using npm@3.8.7

npm info using node@v5.9.1

npm info attempt registry request try #1 at 10:22:16 AM

npm http request GET https://registry.npmjs.org/angular2

npm http 304 https://registry.npmjs.org/angular2

npm ERR! cb() never called!

This problem is only with the beta15, If I try to install previous versions, then all is well

node version: v5.9.1

npm version: 3.8.7

Issue 2:
Running npm i --save angular2 or npm i --save angular2@2.0.0-beta.15 is returning the following error:

npm ERR! cb() never called!

$ npm -v
3.8.6

$ node -v
v5.9.0

I'm on Windows 10, if that helps.
Issue 1:
The first time I built it successfully (build_windows.bat), but it didn't seem to find CUDA or cuDNN. I deleted the build directory and tried

set USE_CUDA=ON

It seemed to find the GPU locations successfully. However this error eventually popped up:

"C:\Users\pazzu\Documents\pytorch\build\ALL_BUILD.vcxproj" (default target) (1) ->

"C:\Users\pazzu\Documents\pytorch\build\caffe2\caffe2_gpu.vcxproj" (default target) (26) ->

(CustomBuild target) ->

Error : Internal Compiler error (codegen): "there was an error in verifying the lgenfe output!" [C:\Users\pazz

u\Documents\pytorch\build\caffe2\caffe2_gpu.vcxproj]

37821 Warning(s)
1 Error(s)

Time Elapsed 01:50:03.60

"Caffe2 building failed"

I'm using VS 15 2017 Win64 for the compiler.

Issue 2:
Can we have unique to return unique rows and cols as well, similar to numpy.unique? Example usage to give an idea:

>>> a = torch.tensor([[1, 0, 0], [1, 0, 0], [2, 3, 3]])
>>> torch.unique(a)
tensor([0, 1, 2, 3])
>>> torch.unique(a, dim=0)
tensor([[1, 0, 0], [2, 3, 3]])
>>> torch.unique(a, dim=1)
tensor([[1, 0], [1, 0], [2, 3]])
Issue 1:
https://k8s-gubernator.appspot.com/build/kubernetes-jenkins/logs/kubernetes-e2e-gke-test/13272/

Failed: BeforeSuite {Kubernetes e2e suite}

/go/src/k8s.io/kubernetes/_output/dockerized/go/src/k8s.io/kubernetes/test/e2e/e2e.go:159
Jul 5 02:20:03.844: Error waiting for all pods to be running and ready: Not all pods in namespace 'kube-system' running and ready within 10m0s

Previous issues for this test: #26135 #26236

Issue 2:
https://k8s-gubernator.appspot.com/build/kubernetes-jenkins/logs/kubernetes-e2e-gce-scalability/8770/

Failed: BeforeSuite {Kubernetes e2e suite}

/go/src/k8s.io/kubernetes/_output/dockerized/go/src/k8s.io/kubernetes/test/e2e/e2e.go:159
Jun 22 19:31:23.362: Error waiting for all pods to be running and ready: Not all pods in namespace "kube-system" are successful within 10m0s

Previous issues for this test: #26135 #26236
Issue 1:
System info

Playwright Version: [v1.35.0]

Operating System: [Windows 10, possibly All]

Browser: [Edge, possibly All]

Other info: node v20.2.0, npm 9.6.6

Source code

No source is needed. It is clear after using "npm install"

[x ] I provided exact source code that allows reproducing the issue locally.

Steps

I am new to both versions and have no idea how they differ (except the former won't need me to install any browser, latter download all major ones upon installation)

I stumbled upon the issue because I was trying to convert some python code to javascript and it imports "playwright", now "@playwright/test".

But using "npm init playwright" initializes with a base test suite using "@playwright/test", not "playwright".

With a naive approach, I did "npm install playwright" to continue my conversion. But it seems these two packages, however both the latest, are not playing along fine about which one will use the name.

Both packages add "playwright-core" scripts into the binary folder, and both insist on using the same "playwright" script name for themselves; one points to $basedir/../@playwright/test/cli.js and the other points to $basedir/../playwright/cli.js"

The main issue here also has many other occurrences associated with this message: Please install @playwright/test package before running. This happens because installing "playwright" overrides the scripts for "@playwright/test".

They both work fine if they are used only as imports since both packages have their own unique directories. the problem arises when one tries to use scripts from the command line as npx playwright ...

Expected

Both packages are at "1.35.00" version. In this case, I would expect "@playwright/test" to use another script name, or both packages to use a unified script to suit them both.

Actual

Installation order determines which one will use the "playwright" name for the scripts to run from terminal.

Also causes this problem: Please install @playwright/test package before running

Issue 2:
System info

Playwright Version: [v1.XX]

Operating System: [All, Windows 11, Ubuntu 20, macOS 13.2, etc.]

Browser: [Chromium]

Other info:

Source code

I provided exact source code that allows reproducing the issue locally.

Link to the GitHub repository with the repro

[https://github.com/your_profile/playwright_issue_title]

browser = playwright.chromium().launch

(new BrowserType.LaunchOptions().setArgs(Arrays.asList("--disable-notifications")).setHeadless(false));

Actual: Location notification still displayed in headless(false) mode but in headless(true) mode
Issue 1:
Bug report

What is the current behavior?

With 5.0.0-beta.14 the generated code for https://github.com/bvaughn/react-virtualized/blob/1a85f588420378aee32216e1c7f51d61155eb2b6/source/CellMeasurer/index.js

is:

/* harmony import */ var _CellMeasurer__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ./CellMeasurer */ "./node_modules/react-virtualized/dist/es/CellMeasurer/CellMeasurer.js");
/* harmony import */ var _CellMeasurerCache__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ./CellMeasurerCache */ "./node_modules/react-virtualized/dist/es/CellMeasurer/CellMeasurerCache.js");

/* unused harmony default export */ var _unused_webpack_default_export = (CellMeasurer);

This crashes with error ReferenceError: CellMeasurer is not defined

If the current behavior is a bug, please provide the steps to reproduce.

I have not tried to create a minimal reproduction but we just import react-virtualized with:

import {
AutoSizer,
Column,
Table,
TableCellProps,
TableHeaderProps,
} from "react-virtualized"

What is the expected behavior?

That it would generate a bundle without reference errors :) It worked fine in 5.0.0-beta.13

I assume the unused default export would either have to be removed completely or rewritten to export _CellMeasurer__WEBPACK_IMPORTED_MODULE_0__

Other relevant information:

webpack version: 5.0.0-beta.14

Node.js version: 12.16.1

Operating System: Ubuntu 20.04

react-virtualized: 9.21.2

Issue 2:
Bug report

What is the current behavior?

Under some circumstances, the tree shaking process for an unused default export would only remove the variable definition while still preserves the variable reference in an expression like var _unused_webpack_default_export = (style), causing a ReferenceError in browsers.

Since webpack 5.0.0-beta.14 turned optimization.usedExports on by default, this bug became quite noticeable.

If the current behavior is a bug, please provide the steps to reproduce.

git clone https://github.com/sodatea/webpack-used-exports-bug.git
cd webpack-used-exports-bug
yarn && yarn start

Then visit http://localhost:5000/example/index.html to see the error message.

What is the expected behavior?

Edit the webpack.config.js to turn off optimization.usedExports

rerun yarn start

visit http://localhost:5000/example/index.html and the error disappears

Other relevant information:

webpack version: v5.0.0-beta.14

Node.js version: v12.16.1

Operating System: macOS Catalina 10.15.3

Additional tools: None
Issue 1:
🚀 Feature

Support for Same and valid convolutions in the Conv2d and related operations

Motivation

I just started using pytorch (was using tensorflow before) and really like the pythonic API, only problem I encounter is when creating a model , I have recalculate the parameters for all the layers.

Pitch

adding padding='same' and padding='valid' in the Conv2d constructor,and to be used in tandem with a torch.compile(model=model,input_shape=(3,224,224)). and throw an error if a forward pass is attempted if the padding=='same' and torch.compile() is not run.

Alternatives

recalculating padding for every forward pass ,but that wouldn't be very efficient

Issue 2:
The implementation would be easy, but could help many people suffered from the headache of calculating how many padding they need.

cc @ezyang @gchanan @zou3519 @bdhirsh @jbschlosser @albanD @mruberry @walterddr
Issue 1:
Summary of the new feature/enhancement

Virtual Desktops have been a feature of windows for a long time. However, there is no way to configure how you interact with the feature, and especially, there is no keybinding to send the current window to a virtual desktop. Adding a menu in PowerToys that allows you to change these keybindings (and set a keybinding to send to the next dekstop, i.e. Windows+ Ctrl + Shift + Left or Right) would complement FancyZones for users with limited screen space.

Proposed technical implementation details (optional)

Add a menu in the left hand bar that allows you to change and interact with the keybindings for Virtual Desktops.

Issue 2:
One of the thing I've found lacking ever since I started using the virtual desktops is the ability to move a window with you while moving to another virtual desktop.

Currently, we can move to another desktop using the shortcut ctrl + win + left/right, I feel like a shortcut along the lines of ctrl + win + alt + left/right could be used to make the current active window move with you to the next desktop

I have no idea if this fits as a powertoy, please tell me if it doesn't !
Issue 1:
Context:

Playwright Version: ^1.16.0-next-alpha-oct-7-2021

Operating System: Linux

Node.js version: v12.20.0

Browser: Firefox

const options = {
acceptDownloads: true,
headless : false,
}
async function start(){
const browser = await firefox.launchPersistentContext("/tmp/firefox", options);
const page = browser.pages()[0]
page.on('download', download => {
download.path().then("DOWNLOAD DONE", console.log) //never shows up
});
}
start().catch((e)=>{
console.log(e)
})

The bug:

I'm using a simple example with headless:false and trying to download a file in a random website, but the function "download.path()" never resolves the promise, which should return the file path. The code is working with "launch" function.

Issue 2:
Context:

Playwright Version: 1.2.1

Operating System: Mac

Node version: 14.5

Browser: Chromium

Code Snippet

const { chromium } = require("playwright");

(async () => {
const context = await chromium.launchPersistentContext("./udd", {
headless: false,
acceptDownloads: true,
downloadsPath: "./",
});
context.on("page", (page) => {
page.on("download", (download) => {
console.log("download", download);
});
});

const newPage = await context.newPage();
await newPage.goto(
"https://support.spatialkey.com/spatialkey-sample-csv-data"
);
newPage.on("download", (download) => download.path().then(console.log));
})();

Describe the bug

When the page loads, click on any of the download links

Expectation:

See the path of downloaded file as well as complete download object

Current behavior:

Most of the times I don't see any logs

At times I would see logs for context based listener

I never see the download path's promise being resolved. I think it might be because the download size is too small but I would expect it to keep working properly even for small files.

Is there something wrong with my machine? I tried quitting existing chrome installation and reinstalling all packages to no avail. Any suggestion would be helpful, thanks!