WhereIsAI/github-issue-similarity huggingface dataset:
Issue 1:
System information

Have I written custom code (as opposed to using a stock example script provided in TensorFlow): Custom code included below

OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Ubuntu 14.04

TensorFlow installed from (source or binary): From pip

TensorFlow version (use command below): 1.3.0 and 1.4.0

Python version: 2.7

Bazel version (if compiling from source):

GCC/Compiler version (if compiling from source):

CUDA/cuDNN version: cuda-8.0 cudnn-6.0

GPU model and memory: GTX 1080 8GB

Exact command to reproduce: python <example_script.py>

Describe the problem

Bug. TensorFlow runs out of GPU memory (ResourceExhaustedError) when using version 1.4.0 when running code that runs fine on version 1.3.0. Please see the following script to reproduce.

Source code / logs

import tensorflow as tf
import tensorflow.contrib.slim.nets as nets
import tensorflow.contrib.slim as slim
from tensorflow.contrib.slim.nets import resnet_v2

kBatchSize = 4
kCropSize = 500
kNumClasses = 10

with tf.device('/gpu:0'):
images = tf.random_normal([kBatchSize, kCropSize, kCropSize, 3])
labels = tf.constant(0, dtype=tf.int32, shape=[kBatchSize, kCropSize, kCropSize])

with slim.arg_scope(resnet_v2.resnet_arg_scope()):
backbone, end_points = resnet_v2.resnet_v2_101(
images, None, is_training=True, global_pool=False,
output_stride=8)

final_conv = tf.layers.conv2d(backbone, kNumClasses, [1, 1], name='final_conv')
logits = tf.image.resize_bilinear(final_conv, tf.slice(tf.shape(images), [1], [2]))

loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=labels, logits=logits)

optimizer = tf.train.GradientDescentOptimizer(learning_rate=.001)
train_op = slim.learning.create_train_op(loss, optimizer)
slim.learning.train(train_op, '/tmp/resnet')

Issue 2:
System information

Have I written custom code (as opposed to using a stock example script provided in TensorFlow): https://github.com/tkuanlun350/Tensorflow-SegNet

OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Windows 7 x64

TensorFlow installed from (source or binary): https://pypi.python.org/pypi/tensorflow-gpu/1.4.0rc1

TensorFlow version (use command below): 1.4.0

Python version: 3.5

CUDA/cuDNN version: Cuda release 8.0, V8.0.60. cuDNN 6.

GPU model and memory: NVIDIA P4

Exact command to reproduce: c:\python35\python3 main.py --log_dir=./logs --image_dir={image dir} --val_dir= {validation dir} --batch_size=15 --training=True

Describe the problem

Under 1.3.0 I was able to use a batch size of {15, put your max batch size here} for training. Under 1.4.0 I get Resource Exhausted errors for that batch size. So use of GPU resources is going up. Not the right direction.

For me here are the performance effects:

TensorFlow GPU 1.3.0: 9.8 images/sec for batch size: 15

TensorFlow GPU 1.4.0: Can't do batch size: 15. 7.8 images/sec for batch size: 12

Source code / logs

tf_bug2.txt
Issue 1:
Along with the formatting constants time.RFC3339 and time.RFC3339Nano, it would be very convenient to have time.RFC3339Milli and time.RFC3339Micro with the indicated second precisions.

Issue 2:
by konrad.meyer:

> Before filing a bug, please check whether it has been fixed since
> the latest release: run "hg pull -u" and retry what you did to
> reproduce the problem. Thanks.

Did this; problem still exists.

What steps will reproduce the problem?
1. Compile and run the attached go program (starts a bunch of goroutines
that try to open a bunch of TCP connections).
2.
3.

What is the expected output? What do you see instead?
There should be no output. Instead, I see the results in the attached .txt
file (somewhat truncated, but it's 11M unzipped).

What is your $GOOS? $GOARCH?
Linux, amd64

Which revision are you using? (hg identify)
88d305c3f261 tip

Please provide any additional information below.
Feel free to contact me if you need more information. I think the provided
.go source is a fairly minimal test (I *think* it only occurs from
goroutines, and only with a fairly large number of attempts to bogus,
non-localhost ports). I could be wrong, though.

Attachments:

test.go (293 bytes)

a.txt.gz (179450 bytes)
Issue 1:
I'm trying to customize a build of Bootstrap however everytime I download one, no matter what options I choose (even leaving all defaults) I get a css error.

A less error occured trying to build your bundle. You've likely entered an invalid input into the less variable field. Check your syntax and try again!

thanks!

{"type":"Parse","message":"missing opening `{`","filename":"bootstrap.css","index":360070,"line":6031,"column":0,"extract":["</html>","",null]}

Issue 2:
when the user try to download a custom version only without the responsive 1200, the navbar, and the hero unit, the download get an error >

"""

A less error occured trying to build your bundle. You've likely entered an invalid input into the less variable field. Check your syntax and try again!

thanks!

{}

"""
Issue 1:
If you know how to fix the issue, make a pull request instead.

I tried using the @types/styled-components package and had problems.

I tried using the latest stable version of tsc. https://www.npmjs.com/package/typescript

I have a question that is inappropriate for StackOverflow. (Please ask any appropriate questions there).

Authors: @eps1lon @Igorbek @Jessidhia

The problem is that styled components types makes my typescript intellisense work real slow. It takes sometimes up to 100% CPU. I found out that this particular line

export type ThemedStyledProps<P, T> = P & ThemeProps<T>;

is causing troubles. Once you remove P & it works again as normal.

Issue 2:
Adding the latest version of @types/styled-components to an existing project causes build times to go up by about 1-2 minutes, by using the latest tsc@3.4.1.

By using this seed repo, I've tested the following versions:

w/o 2.3s
4.0.0 5.6s
4.1.0 8.0s
4.1.5 8.0s
4.1.10 10.1s
4.1.11 90.1s
4.1.12 97.1s

My machine is a Linux 4.18.0 (Ubuntu 18.10) laptop, with a i7-6700HQ CPU @ 2.60GHz

It seems clear that the release 4.1.11 is what's causing this performance issue. The PR for this release is #33061. As of why, I really don't know - I tried to dig into the internals of the compiler to see where it was getting stuck at, but I couldn't understand it.

I tried using the @types/xxxx package and had problems.

I tried using the latest stable version of tsc. https://www.npmjs.com/package/typescript

I have a question that is inappropriate for StackOverflow. (Please ask any appropriate questions there).

Mention the authors (see Definitions by: in index.d.ts) so they can respond.

Authors: @eps1lon @Igorbek @Jessidhia
Issue 1:
I followed the with-dotenv example to structure my environment variables. When I do development (aka npm run dev), everything works as expected. The app is pulling the environmental variable from the .env file.

But when I write a docker file, build the image, upload it to Docker Hub, and then host my cluster on Docker Cloud, for some reason the environment variable isn't being included in the app.js that is served to the browser.

For reference, here is my Dockerfile:

FROM node:alpine

# Create app directory
RUN mkdir -p /usr/src/app
WORKDIR /usr/src/app

# Install app dependencies
COPY package.json /usr/src/app/
RUN npm install

# Bundle app source
COPY . /usr/src/app

RUN npm run build
EXPOSE 3014

CMD [ "npm", "start" ]

The build and start scripts correspond to next build and next start respectively.

From my understanding, the environment variables should be included at runtime (from next start). So I even logged in to the specific container and wrote echo $MY_ENV_VAR and confirmed that it was the same variable that I set on the Docker Cloud GUI. The problem is, it is undefined when served to the client.

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

Expected Behavior

The app served by next start to the client should include the environment variables that I set on Docker Cloud for that particular container.

Current Behavior

The value of that variable in the browser seems to be undefined. Everything works in development, and I have also proved that the environment variable exists inside the container by typing echo $MY_ENV_VAR inside the specific container.

Your Environment

Tech
Version

next
3.2.2

node
8.4.0

Issue 2:
Hey guys, I'm trying to build a portion of my app in Next.js (only the portion that needs to be SSR).

I've used CRA to build most of my project and now I'm trying to migrate just one page to Next. I'm importing my redux files from a directory that's outside of the next.js dir, so for example I have

root/server/pages is where my next.js page lives, and I'm importing from root/src/redux.

This is causing this issue here:

> Failed to build
{ Error: (client) ../src/redux/auth.js
Module parse failed: Unexpected token (100:10)
You may need an appropriate loader to handle this file type.
| return dispatch({
| type: AuthConstants.UPDATE_USER,
| ...params,
| })
| })

It looks like Webpack doesn't like the spread operator, but I have all of the necessary plugins for the spread operator to build in my .babelrc:

{
"presets": ["es2015", "react", "stage-0"],
"plugins": [
"transform-decorators-legacy",
"transform-object-rest-spread",
"transform-es2015-destructuring",
],
"env": {
"debug": true
}
}

Any clue what's up?

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

Expected Behavior

Build runs correctly

Current Behavior

Build doesn't run

Your Environment

Tech
Version

next
5.0.0

node
v8.1.2

OS
MacOS
Issue 1:
Migrated issue, originally created by Michael Bayer (@zzzeek)

Issue 2:
Migrated issue, originally created by Anonymous

In version 0.5.0beta3, if a module such as datetime or decimal is imported and used in a constructor as a default for a parameter an error like

NameError: name 'datetime' is not defined

is returned. The error is returned for both datetime and decimal. The workaround is to refer to the module in the body of the init function rather than in the parameters.

Some code that triggers the error:

import datetime
import sqlalchemy.ext.declarative
from sqlalchemy import *

Base = sqlalchemy.ext.declarative.declarative_base()

class Foo(Base):
__tablename__ = 'foo'
id = Column(Integer, primary_key=True)
bar = Column(Date)

def __init__(self, bar=datetime.date.today()):
self.bar = bar

Engine = sqlalchemy.create_engine('sqlite:///:memory:')
Base.metadata.create_all(Engine)

Hope this helps.

Thanks.
Issue 1:
Bug summary

When saving the output as an .eps file, the saved file removes the right y-axis and parts of the x-axes. This behavior occurs only when used with rc options.

Code for reproduction

import matplotlib
import matplotlib.pyplot as plt
from matplotlib import rc

rc('text', usetex=True) # removing this line results in expected output

# Generic plot
plt.plot([1, 2], [2, 1])
plt.tight_layout() # removing line doesn't change things
plt.savefig("pub_figures/test.eps",
format='eps')
plt.show()

Actual outcome

Outcome when outputting as .eps with rc('text', usetex=True) (this is a screenshot of the .eps file, since GitHub doesn't support .eps files being uploaded). Changing the backend to PS, did not solve the issue.

Expected outcome

Saving as .png works as expected

Additional information

This seems to be related to rc. Once this line is removed in the minimal examples above, the .eps file displays as expected.

Operating system

Ubuntu 20.04.5

Matplotlib Version

3.6.2

Matplotlib Backend

TkAgg

Python version

3.9.5

Jupyter version

No response

Installation

pip

Issue 2:
Bug summary

When saving a wide eps with text.usetex=True, the spine is clipped, but no other artists.

Code for reproduction

import matplotlib.pyplot as plt
plt.rcParams['text.usetex']=True
plt.figure(figsize=(20, 4))
plt.plot([0, 1], [0, 1], 'b')
plt.savefig('clippedspine.eps')

Actual outcome

Expected outcome

Additional information

Expected output not using usetex.

Saving as ps stores using letter paper, so the whole paper is clipped.

The line is just there to illustrate that lines are not clipped as such (nor are texts).

It seems like the spine covers the same absolute width when changing figure width.

Operating system

CentOS

Matplotlib Version

3.6.0.dev3474+g8179d12.d20220830

Matplotlib Backend

No response

Python version

3.8.13

Jupyter version

No response

Installation

git checkout
Issue 1:
In SciPy 1.3.0, it appears that selecting a value from a CSR sparse matrix (unsure if other sparse matrices are affected) now returns a 0-D array; whereas, in SciPy 1.2 (and before) it returned a scalar. Below is an example of this behavior.

Reproducing code example:

In [1]: import numpy as np

In [2]: import scipy.sparse as sp

In [3]: x = np.array([1, 2, 3])

In [4]: spx = sp.csr_matrix(x)

In [5]: spx[0, 0] # previously returned a scalar
Out[5]: array(1)

Scipy/Numpy/Python version information:

SciPy 1.3.0

NumPy 1.16.4

cc @stuartarchibald

Issue 2:
Indexing csr or csc sparce matrix return an array of one element with scipy 1.3, With previous version it returned a scalar. I believe it is an oversight since lil_matrix indexing still return a scalar.

import scipy
import numpy as np
print(type(scipy.sparse.csr_matrix(np.eye(3))[0,0]))
print(type(scipy.sparse.csc_matrix(np.eye(3))[0,0]))
print(type(scipy.sparse.lil_matrix(np.eye(3))[0,0]))

<class 'numpy.ndarray'>
<class 'numpy.ndarray'>
<class 'numpy.float64'>

Scipy/Numpy/Python version information:

scipy 1.3.0

numpy 1.16.3

python 3.7.0
Issue 1:
Bug report

Bug summary

I use a custom tick formatter in my plot. It worked great with an older version of matplotlib but I just upgraded to 2.0 and I see overlapping tickmark labels (the default labels are still there):

Code for reproduction

import numpy as np
import matplotlib
import matplotlib.pyplot as plt

class MyLogFormatter(matplotlib.ticker.LogFormatter):
def __call__(self, x, pos=None):
e = np.floor(np.log10(x))
m = x/(10**e)
if x < 0.01:
return '%de%d' % (round(m), e)
elif x < 1:
return '%.2f' % x
elif x < 10:
return '%.1f' % x
elif x < 1000:
return '%.0f' % x
else:
return '%de%d' % (round(m), e)
@staticmethod
def ticks(xmin, xmax):
ndecades = np.ceil(np.log10(xmax/xmin))
if ndecades > 6:
mantissas = [1]
elif ndecades > 3:
mantissas = [1,3]
else:
mantissas = [1,2,5]
roundnumbers = (10**np.arange(np.floor(np.log10(xmin)), np.ceil(np.log10(xmax)))*np.atleast_2d(mantissas).T).T.flatten()
return np.hstack((xmin, [x for x in roundnumbers if xmin*1.25 < x < 0.8*xmax], xmax))

data = 0
for k in xrange(4):
data += np.random.randint(6, size=100000)

count,edges = np.histogram(data,10)
x = 100 * (1.1 ** edges)

fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(1,1,1,xscale='log')
ax.bar(x[:-1], count, width=x[1:] - x[:-1], color='red',edgecolor='black',align='edge')
xmin = min(x)
xmax = max(x)
LF = MyLogFormatter(labelOnlyBase=False)
ax.set_xticks(LF.ticks(xmin, xmax))
ax.xaxis.set_major_formatter(LF)

Actual outcome

The output produced by the above code, which may be a screenshot, console output, etc.

Expected outcome

previous version was Matplotlib 1.5.1

(can't post right now, I will have to downgrade my anaconda version)

Matplotlib version

Matplotlib 2.0

Python 2.7.12 |Anaconda 4.1.1 (64-bit)| (default, Jun 29 2016, 11:07:13) [MSC v.1500 64 bit (AMD64)] on win32

Issue 2:
Bug report

contourf produces wrongly formatted plot labels for logarithmic plots when there is only one order of magnitude or less of range of data (eg. 10 to 100 or 100 to 1000).

Code for reproduction

import matplotlib.pyplot as plt
from matplotlib.ticker import StrMethodFormatter
import numpy as np
t = np.linspace(0,1000,1001)
p = np.array([10,20,30,40,50,60,70,80,90,100])
data = np.random.random_sample((len(p),len(t)))
pressure,time = np.meshgrid(p,t)

fig, ax = plt.subplots(nrows=2)
ax1, ax2 = tuple(ax)
ax1.contourf(time,pressure,data.T)
ax1.set_yscale('log')
ax1.set_yticks([10,30,100])
ax1.get_yaxis().set_major_formatter(StrMethodFormatter('{x:.0f}'))
ax2.plot(p)
ax2.set_yscale('log')
ax2.set_yticks([10,30,100])
ax2.get_yaxis().set_major_formatter(StrMethodFormatter('{x:.0f}'))
plt.show()

Actual outcome

Using matplotlib 2.0.0 the labelling of the y-axis of the contour plot is messed up compared to the y-axis of the line plot.

Remark: The default color map was used.

Expected outcome

Using matplotlib 1.5.1 the labelling of the y-axis of the contour plot is as beautiful as the labelling of the y-axis of the line plot.

Remark: The default color map was used.

Matplotlib version

matplotlib 2.0.0 (installed using pip)

python 3.6.1 (installed using brew)

macos
Issue 1:
I'm not sure if this belongs in sdk, but while working on flutter/devtools I've noticed some intermittently, the VM on the device will crash when I hit Reload on the browser (which has a ws connection open to the VM on the device).

It's possible to repro without flutter/devtools by just opening Observatory in your browser and hitting reload a few times.

flutter run -v in a console

Wait for An Observatory debugger and profiler on iPhone X is available at: http://127.0.0.1:59279/

Copy and paste this url into your browser to load Observatory

Hit reload on your browser repeatedly (it doesn't need to be particularly fast)

Observe Flutter app vanish from the device and flutter run terminates:

[ ] An Observatory debugger and profiler on iPhone X is available at: http://127.0.0.1:60056/
[ ] For a more detailed help message, press "h". To detach, press "d"; to quit, press "q".
[+14348 ms] Service protocol connection closed.
[ +1 ms] Lost connection to device.

I'm hitting this using macOS on the iOS Simulator running the flutter_gallery example app (using master code from today).

Issue 2:
I see a consistent crash when a debugger disconnects from a running Flutter app. To repro:

run a flutter app (flutter run)

connect to the indicated url via the observatory (An Observatory debugger and profiler on iPhone X is available at...)

refresh the page

The Flutter runtime will generally crash on the first page refresh (when the debugger client connection goes away). It will occasionally survive the first page refresh, but generally not more than that.

I've tested this against the iOS simulator. I don't see the same crash when run against the android emulator - I can refresh the observatory page ~12 times.

cc @a-siva @cbracken
Issue 1:
Hello @ALL!

I am a avid user of Bootstrap and am very pleased at general.

But one small thing bugs me on the current version of the docs:

The Position of the Main NavBar should be static-top, so its there even when you scroll down, just as it was in 2.x docs.

It would be a small ergonomic improvement.

In the current version, one has to scroll to the top or has to get to the "back to top" link in the side-navigation, to change the category.

Does someone share my desire?

Issue 2:
Please make de docs navbar fixed again. I use it extensively and it was very nice to quickly change sections in the documentation. Now I have to scroll, and scroll and scroll....
Issue 1:
System information

Have I written custom code (as opposed to using a stock example script provided in TensorFlow): No

OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Linux Debian 8 (jessie)

TensorFlow installed from (source or binary): using virtualenv, pip install

TensorFlow version (use command below): 1.1.0

Bazel version (if compiling from source):

CUDA/cuDNN version: CUDA 7.5, cuDNN 4.0

GPU model and memory: GeForce GTX TITAN X 12GB

Exact command to reproduce: Run this https://github.com/tensorflow/tensorflow/blob/r1.1/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py scripts.

Describe the problem

I am following the tutorials in https://www.tensorflow.org/get_started/graph_viz, downloading and running the mnist_with_summaries.py scripts.

All the Tensorboard visualization works, except the Runtime statistics. In the GRAPHS tab, the Session runs shows (0) items available. And it says 'None'.

Issue 2:
System information

Windows 7 Enterprise 64

TF 1.1.0 installed from pip

CUDA 8.0/cuDNN 5.1.5

GeForce GTX TITAN X

command: python mnist_with_summaries.py

Describe the problem

In https://www.tensorflow.org/get_started/graph_viz, mnist_with_summaries is given as an illustration of how to use tensorboard to show node statistics. However, it does not produce node statistics on my computer. Everything else is working correctly (the graph is there, summaries as well, the training goes smoothly).

Source code / logs

https://github.com/tensorflow/tensorflow/blob/r1.1/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py
Issue 1:
Not sure if this is a bug, intended behaviour or just a consequence of the binning logic, but I ran into this with a real dataset today:

# simplified for demo
sns.displot(
[1e-10,2e-10,3e-10,4e-10,5e-10,10]
)

causes

MemoryError: Unable to allocate 271. GiB for an array with shape (36342411858,) and data type float64

with less extreme outliers it doesn't crash but runs for several minutes, presumably allocating memory, until I have to kill the process. Maybe this is just a pathological case for the bin boundary logic.

Issue 2:
Hello! It is my first Github bug report, so please let me know if some additional info is needed.

I'm trying to use sns.pairplot for some pandas DataFrame and in result my computers are running out of memory and losing terminals with all the data.

The problem set of digits (csv) can be downloaded here:

https://www.dropbox.com/s/2dian5cipxx6dzu/temp.csv?dl=0

Interestingly, when I try to use sns.pairplot for another set of parameters with the same of an even greater number of lines, everything works nice.

Seaborn version: 0.11.0

This problem occurred on both my computers (OSx and Win10).
Issue 1:
Ref https://bugs.chromium.org/p/chromium/issues/detail?id=942279

Issue 2:
Output of node_modules/.bin/electron --version: v2.0.7

Operating System (Platform and Version): macOS High Sierra 10.13.6

Expected Behavior

upon opening the same page multiple times, it should only execute .js file once

Actual behavior

let's say i open the same page/windows 5 time back & forth, then it duplicates the same .js file 5 times and it executes the same code 5 times

Additional Information

I have a main index.html file and i delete/insert tags to create pages on my app. But in the background it duplicates same files and executes them at the same time. In addition let's say i have a function name function xyz() {console.log('a')}' on script A.js and function xyz() {console.log('b')}' on script B.js. If i go to page A and B then execute the function on page B, it executes both functions in A and B and prints both 'a' and 'b'

here is my html structure to load scripts

<html>
<body>
.... i manipulate here
<script src='/a.js'>
</body>
</html>