WhereIsAI/github-issue-similarity huggingface dataset:
Issue 1:
Environment

Windows build number: Microsoft Windows [Version 10.0.18362.295]
Windows Terminal version (if applicable): Windows Terminal (Preview) Version: 0.5.2681.0

Steps to reproduce

Install the latest (0.5.2681.0) windows terminal on windows 10 (1903), launch it and it works.

RDP (using window RDP tool) your system from another windows computer

Try to launch the windows terminal, it opens and crashed instantly

Expected behavior

Terminal should work while accessing it through RDP session

Actual behavior

Terminal crashed with below given windows event log entry:

Faulting application name: WindowsTerminal.exe, version: 1.0.1909.25001, time stamp: 0x5d8b1a61

Faulting module name: TerminalControl.dll, version: 1.0.1909.25001, time stamp: 0x5d8b194b

Exception code: 0xc0000409

Fault offset: 0x0000000000052691

Faulting process id: 0x1d3c

Faulting application start time: 0x01d579d25edff7c0

Faulting application path: C:\Program Files\WindowsApps\Microsoft.WindowsTerminal_0.5.2681.0_x64__8wekyb3d8bbwe\WindowsTerminal.exe

Faulting module path: C:\Program Files\WindowsApps\Microsoft.WindowsTerminal_0.5.2681.0_x64__8wekyb3d8bbwe\TerminalControl.dll

Report Id: 98b3041c-8f29-4d12-a639-8deebd0f828f

Faulting package full name: Microsoft.WindowsTerminal_0.5.2681.0_x64__8wekyb3d8bbwe

Faulting package-relative application ID: App

Issue 2:
Environment

Windows build number: Windows 10 Insiders 18995 (until the next update)
Windows Terminal version: 0.5.2762.0

Any other software?
PowerShell: any version with PSReadLine
PSReadLine, 2.0.0 beta 2 through 5

Steps to reproduce

See also: PowerShell/PSReadLine#1081

Copy some script with multiple lines from somewhere, an editor like VS Code for instance, or even from the code section of this issue.

Use PowerShell with PSReadLine installed

Right-click paste in to the Windows Terminal session of PowerShell.

The issue will be amplified if the current cursor/prompt is already at the bottom of the window, as is shown below.

Expected behavior

A paste to occur just as in Conhost.

Actual behavior

Without PSReadLine loaded, this behavior will not be noticed, but this is just because PSReadLine uses CTRL-ENTER to insert lines past the cursor's position.

CTRL-ENTER is getting pasted along with regular ENTER, causing PSReadLine to attempt to advance past the end of the buffer, causing an exception in SetCursorPosition, in addition to blank unnecessary lines.

What I pasted was:

filter val_2_base33 ([int]$digits = 1) {
# convert value to base 33 (VIN) string representation
[int]$value = $_
[string]$result = ''
[int]$digs = 0
while (++$digs -le $digits -or $value -gt 0) {
$result = '0123456789ABCDEFGHJKLMNPRSTUVWXYZ'[$value % 33] + $result
$value = [math]::Truncate($value / 33)
}
$result
}

Problem doesn't seem to happen if using CTRL-V.

I think the issue is that CTRL-ENTER aka CTRL-J aka LineFeed is contained in the clipboard copy of the data, and its not being filtered out when Right-Click paste. Is there a reason for that, or is this an accident?
Issue 1:
There's no single source of reference for configuration options which makes configuring Elasticsearch awkward. https://www.elastic.co/guide/en/elasticsearch/reference/current/setup-configuration.html is a good start but I believe more is omitted than included (path.repo for example).

Elasticsearch needs a single page that lists every possible configuration option with an explanation of what the setting does, Django has a good example https://docs.djangoproject.com/en/1.8/ref/settings/

Issue 2:
Provide a comprehensive complete list of all settings for each es version like https://gist.github.com/jprante/7dccffa7f0c0c7576fa9 does for 1.2
Issue 1:
Summary.

Expected Result

That the library would automatically change the name of one of the headers in order to store it in response.headers, or have some alternative way to get response headers that allows for duplicate header names.

Actual Result

Only one of the headers is shown, due to Python not allowing duplicat keys.

Reproduction Steps

import requests

# fetch some website that sends a response with multiple headers of the same name
response = requests.get("some url")
# {"set-token": "foo", "set-token": "baz"}

# only shows one of the headers
response.headers
# {"set-token": "foo"}

System Information

{
"chardet": {
"version": null
},
"charset_normalizer": {
"version": "2.0.4"
},
"cryptography": {
"version": ""
},
"idna": {
"version": "3.2"
},
"implementation": {
"name": "CPython",
"version": "3.8.7"
},
"platform": {
"release": "10",
"system": "Windows"
},
"pyOpenSSL": {
"openssl_version": "",
"version": null
},
"requests": {
"version": "2.26.0"
},
"system_ssl": {
"version": "1010107f"
},
"urllib3": {
"version": "1.26.6"
},
"using_charset_normalizer": true,
"using_pyopenssl": false
}

Issue 2:
When you send a request which replaces an existing cookie value with a new one and also returns a redirect response, when the new request is made, its request header from r.request.headers['Cookies'] shows both the old and the new cookie value. However, the actual request header only contains the new cookie value.

Expected Result

What would be expected is that r.request.headers['Cookies'] would only have the new replaced cookie value rather than the old

Actual Result

Its request header from r.request.headers['Cookies'] shows both the old and the new cookie value

Reproduction Steps

python
import requests

r = requests.get('http://httpbin.org/cookies/set?FOO=BAZ', cookies={'FOO': 'BAR'})

print(r.json()) #{'cookies': {'FOO': 'BAZ'}}

print(r.request.headers['Cookie']) # FOO=BAR; FOO=BAZ

From what I understand, the latter should be just FOO=BAZ as I believe it should only reflect the request headers from the final request. I think what is going on is that the cookies from the previous requests are merged into this.

System Information

$ python -m requests.help

{
"chardet": {
"version": "3.0.4"
},
"cryptography": {
"version": ""
},
"idna": {
"version": "2.9"
},
"implementation": {
"name": "CPython",
"version": "3.6.9"
},
"platform": {
"release": "5.3.0-59-generic",
"system": "Linux"
},
"pyOpenSSL": {
"openssl_version": "",
"version": null
},
"requests": {
"version": "2.23.0"
},
"system_ssl": {
"version": "1010100f"
},
"urllib3": {
"version": "1.25.9"
},
"using_pyopenssl": false
}
Issue 1:
This happens if second parameter is not passed to $this->container->get('form.factory')->create().

I tried to create a test case for it in Symfony\Component\Form\Tests\FormFactoryTest, but I get Symfony\Component\Form\Exception\FormException: Could not load type "text". Looks like there is no such type of tests, so probably some setup need to be done first.

I think this is critical bug in forms, and it exists for a while now.

Issue 2:
Since the recent refactoring of the way default options are merged for a Form, setting a default value according to the value of another option does not work anymore for the hierarchy as the parent type will apply this default with its own default value even if the child type changed the default value. This is because the way the defaults are applied changed so the child type is applied only later.

A good way to see this is to define a type setting a default value for the data_class property. The empty_data default value set by the FieldType won't be set properly in the form (we would expect getting a closure crating an instance of the class).

This is a BC break and makes the type inheritance less powerful IMO.

/cc @bschussek
Issue 1:
What / Why

Your package manager couldn't get any packages. (404 not found)

When

Now

Where

npm public registry

How

n/a

Current Behavior

404 not found

Issue 2:
open https://www.npmjs.com/package/@2gis/tiny-map got 503 {"error":"Service Unavailable"} or sometimes

ESOCKETTIMEDOUT

id: 22e8c953-b92e-4626-96c3-66857626c11b

also 404's sometimes on packages install

upd see current status on https://status.npmjs.org/
Issue 1:
The tooltips on the documentation page all appear at the top, ignoring the data-placement attribute:

http://twitter.github.com/bootstrap/javascript.html#tooltips

Verified on Chrome, Firefox, Safari, IE.

Issue 2:
congrats on releasing 2.3.0!

I was checking out some of the docs and saw that tooltips data-placement is not working correctly. I think its because of the one pull request that makes options set in javascript override the options that were set in the html but I'm not sure..
Issue 1:
Expected Behavior

1 error component is rendered

Current Behavior

2 error components are rendered below eachother. 1 through SSR and 1 through client side rendering.

Steps to Reproduce (for bugs)

import Link from 'next/link'

export default class extends React.Component {
static async getInitialProps() {
// Just throw an error in getInitialProps
throw new Error('test');
}
render() {
return (
<div>
<h1>Hello world</h1>
</div>
)
}
}

Context

Issue can be seen here: https://create-next-example-app-olyuogqcxe.now.sh/ (deployment with the above code). Scroll down cause the component itself is screen filling 😅

@harshmaur surfaced this.

Your Environment

Tech
Version

next
2.x+ (so beta too)

mode
production

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

Expected Behavior

I copied the initApollo.js and withData.js files in the /lib folder of the with-apollo example and used them as in the example.

When changing view (client-side routing) I expected to see a data fetch call client-side, over the network, to update data.

Current Behavior

It seems that the only data fetching happens on the server, while data isn't updated on view changes (componentDidMount of Pages). Is it by design?

I'd like instead to have updated content loaded from the client on change view.

Thank you,

Matteo
Issue 1:
I installed numpy 1.16 for Python3.6 by upgrading it with pip3 --upgrade. And numpy fails at import on this line

numpy/numpy/core/_methods.py

Line 158
in
971e2e8

_NDARRAY_ARRAY_FUNCTION = mu.ndarray.__array_function__

Reproducing code example:

python3

import numpy as np

additionally, please find sys.path output here:

['', '/usr/lib/python36.zip', '/usr/lib/python3.6', '/usr/lib/python3.6/lib-dynload', '/home/user/.local/lib/python3.6/site-packages', '/usr/local/lib/python3.6/dist-packages', '/usr/lib/pytho
n3/dist-packages']

Error message:

Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/home/user/.local/lib/python3.6/site-packages/numpy/__init__.py", line 142, in <module>
from . import core
File "/home/user/.local/lib/python3.6/site-packages/numpy/core/__init__.py", line 59, in <module>
from . import numeric
File "/home/user/.local/lib/python3.6/site-packages/numpy/core/numeric.py", line 3093, in <module>
from . import fromnumeric
File "/home/user/.local/lib/python3.6/site-packages/numpy/core/fromnumeric.py", line 17, in <module>
from . import _methods
File "/home/user/.local/lib/python3.6/site-packages/numpy/core/_methods.py", line 158, in <module>
_NDARRAY_ARRAY_FUNCTION = mu.ndarray.__array_function__
AttributeError: type object 'numpy.ndarray' has no attribute '__array_function__'

Numpy/Python version information:

Numpy: numpy-1.16.0

Python: Python 3.6.7 (default, Oct 22 2018, 11:32:17) [GCC 8.2.0] on linux

Issue 2:
I pip installed the latest numpy and get the below error. The error goes away if I roll back to v1.15.4.

The most closely related issue I could find is #12028.

Reproducing code example:

import numpy as np

Error message:

Traceback (most recent call last):
File "/home/trey/script.py", line 6, in <module>
import numpy as np
File "/home/trey/.local/lib/python3.6/site-packages/numpy/__init__.py", line 142, in <module>
from . import core
File "/home/trey/.local/lib/python3.6/site-packages/numpy/core/__init__.py", line 59, in <module>
from . import numeric
File "/home/trey/.local/lib/python3.6/site-packages/numpy/core/numeric.py", line 3093, in <module>
from . import fromnumeric
File "/home/trey/.local/lib/python3.6/site-packages/numpy/core/fromnumeric.py", line 17, in <module>
from . import _methods
File "/home/trey/.local/lib/python3.6/site-packages/numpy/core/_methods.py", line 158, in <module>
_NDARRAY_ARRAY_FUNCTION = mu.ndarray.__array_function__
AttributeError: type object 'numpy.ndarray' has no attribute '__array_function__'

Numpy/Python version information:

1.16.0

3.6.7 (default, Oct 22 2018, 11:32:17)

[GCC 8.2.0]
Issue 1:
======================================================================
ERROR: test_improvement (test_quadpack.TestCtypesQuad)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/lib64/python3.4/site-packages/numpy/testing/decorators.py", line 146, in skipper_func
return f(*args, **kwargs)
File "/var/tmp/portage/sci-libs/scipy-0.16.0/work/scipy-0.16.0-python3_4/build/test/lib/scipy/integrate/tests/test_quadpack.py", line 42, in setUp
self.lib = ctypes.CDLL(file)
File "/usr/lib64/python3.4/ctypes/__init__.py", line 351, in __init__
self._handle = _dlopen(self._name, mode)
OSError: /usr/lib64/libm.so: invalid ELF header

======================================================================
ERROR: test_typical (test_quadpack.TestCtypesQuad)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/lib64/python3.4/site-packages/numpy/testing/decorators.py", line 146, in skipper_func
return f(*args, **kwargs)
File "/var/tmp/portage/sci-libs/scipy-0.16.0/work/scipy-0.16.0-python3_4/build/test/lib/scipy/integrate/tests/test_quadpack.py", line 42, in setUp
self.lib = ctypes.CDLL(file)
File "/usr/lib64/python3.4/ctypes/__init__.py", line 351, in __init__
self._handle = _dlopen(self._name, mode)
OSError: /usr/lib64/libm.so: invalid ELF header

This is with glibc-2.22 on Gentoo Linux

/* GNU ld script
*/
OUTPUT_FORMAT(elf64-x86-64)
GROUP ( /lib64/libm.so.6 AS_NEEDED ( /lib64/libmvec.so.1 ) )

Issue 2:
On Fedora Rawhide (F24):

ERROR: test_typical (test_quadpack.TestCtypesQuad)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/lib64/python2.7/site-packages/numpy/testing/decorators.py", line 146, in skipper_func
return f(*args, **kwargs)
File "/builddir/build/BUILDROOT/scipy-0.16.0-1.fc24.x86_64/usr/lib64/python2.7/site-packages/scipy/integrate/tests/test_quadpack.py", line 42, in setUp
self.lib = ctypes.CDLL(file)
File "/usr/lib64/python2.7/ctypes/__init__.py", line 360, in __init__
self._handle = _dlopen(self._name, mode)
OSError: /lib64/libm.so: invalid ELF header

This is because libm.so is a GNU ld script:

# cat /usr/lib64/libm.so
/* GNU ld script
*/
OUTPUT_FORMAT(elf64-x86-64)
GROUP ( /lib64/libm.so.6 AS_NEEDED ( /lib64/libmvec.so.1 ) )
Issue 1:
System information

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

Yes

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

Linux Ubuntu 16.04

TensorFlow installed from (source or binary):

pip install

TensorFlow version (use command below):

v1.4.0-rc1-11-g130a514 1.4.0

Python version:

3.5.2

Bazel version (if compiling from source):

NA

GCC/Compiler version (if compiling from source):

NA

CUDA/cuDNN version:

8.0.61

GPU model and memory:

GeForce GTX 1080 Ti, 11GB

Exact command to reproduce:

Describe the problem

When using tf.layers, I notice that an extra op is generated when not specifying the name.

For example, here is an example using tf.layers.dense. "dense_1" is an extra op. Similar problem has been observed for tf.layers.conv2d also.

x = tf.placeholder(tf.float32, [None, 16], name='x')
hidden = tf.layers.dense(x, 32)
hidden = tf.layers.dense(hidden, 32)
y = tf.layers.dense(hidden, 1, name='y')

The graph in tensorboard is as follows.

If I add the name,

x = tf.placeholder(tf.float32, [None, 16], name='x')
hidden = tf.layers.dense(x, 32, name='h1')
hidden = tf.layers.dense(hidden, 32, name='h2')
y = tf.layers.dense(hidden, 1, name='y')

then the graph looks as expected,

Issue 2:
System information

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

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

TensorFlow installed from (source or binary): binary

TensorFlow version (use command below): 1.3.0

Python version: 3.6

Bazel version (if compiling from source): N/A

CUDA/cuDNN version: N/A

GPU model and memory: N/A

Exact command to reproduce: N/A

Describe the problem

This is a tiny code that creates layers and connects them in series.

import tensorflow as tf
from tensorflow.python.layers.base import Layer

class A(Layer):
def build(self, input_shape):
self.v = self.add_variable('v', (), tf.float32)
self.built = True

def call(self, inputs):
return self.v * inputs

# Case 1
with tf.Graph().as_default() as graph:
x = tf.placeholder(tf.float32, (), 'x')

out = x
out = A()(out)
out = A()(out)
out = A()(out)

tf.summary.FileWriter('/tmp/tensorboard/1', graph=graph).close()

# Case 2
with tf.Graph().as_default() as graph:
x = tf.placeholder(tf.float32, (), 'x')

out = x
out = A(name='a')(out)
out = A(name='a_1')(out)
out = A(name='a_2')(out)

tf.summary.FileWriter('/tmp/tensorboard/2', graph=graph).close()

# Case 3
with tf.Graph().as_default() as graph:
x = tf.placeholder(tf.float32, (), 'x')

out = x
out = A(name='a_1')(out)
out = A(name='a_2')(out)
out = A(name='a_3')(out)

tf.summary.FileWriter('/tmp/tensorboard/3', graph=graph).close()

Results

Other than case 3, an unexpected graph is generated.

Is this a bug?

Case 1

Case 2

Case 3
Issue 1:
🐛 Order dependent min and max

The functions torch.min and torch.max depend on the order of their arguments when one of the arguments is nan. This is unexpected behaviour.

To Reproduce

Here is a minimal working example

import torch
import numpy as np
a, b = torch.tensor(0.), torch.tensor(np.nan)
print('min(0,nan): ', torch.min(a, b) )
print('min(nan, 0): ', torch.min(b, a))

Expected behavior

These function should not depend on order.

Environment

Collecting environment information...
PyTorch version: 1.2.0
Is debug build: No
CUDA used to build PyTorch: 10.0.130

OS: Ubuntu 18.04.3 LTS
GCC version: (Ubuntu 7.4.0-1ubuntu1~18.04.1) 7.4.0
CMake version: version 3.10.2

Python version: 3.6
Is CUDA available: Yes
CUDA runtime version: Could not collect
GPU models and configuration:
GPU 0: GeForce RTX 2080 Ti
GPU 1: GeForce RTX 2080 Ti
GPU 2: GeForce RTX 2080 Ti
GPU 3: GeForce RTX 2080 Ti

Nvidia driver version: 440.33.01
cuDNN version: /usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.5

Versions of relevant libraries:
[pip3] numpy==1.14.3
[pip3] numpydoc==0.8.0
[pip3] torch==1.2.0
[pip3] torchfile==0.1.0
[pip3] torchnet==0.0.4
[pip3] torchvision==0.4.0a0+6b959ee
[conda] blas 1.0 mkl
[conda] mkl 2018.0.2 1
[conda] mkl-service 1.1.2 py36h17a0993_4
[conda] mkl_fft 1.0.1 py36h3010b51_0
[conda] mkl_random 1.0.1 py36h629b387_0
[conda] pytorch 1.2.0 py3.6_cuda10.0.130_cudnn7.6.2_0 pytorch
[conda] torchfile 0.1.0 pypi_0 pypi
[conda] torchnet 0.0.4 pypi_0 pypi
[conda] torchvision 0.4.0 py36_cu100 pytorch

Issue 2:
🐛 Bug

When using torch.max() between two tensors where one tensor contains nans, the result varies depending upon the order of input into torch.max().

To Reproduce

import torch
x = torch.ones(3)
y = torch.ones(3) * torch.tensor(float('nan'))

print(f'torch.max(x, y) : {torch.max(x, y)}')
print(f'torch.max(y, x) : {torch.max(y, x)}')

Returns:

torch.max(x, y) : tensor([nan, nan, nan])
torch.max(y, x) : tensor([1., 1., 1.])

Expected behavior

I'd expect the output to be consistent in both cases.

i.e. torch.max(x, y) = torch.max(y, x) in all cases.

My personal preference would be as in the above case of torch.max(y,x) = [1,1,1], but the most important part is that it should be consistent.
Issue 1:
I am using a containerized env on CentOS with Anaconda, and a RTX 2080 Ti GPU card with 540 Tensor Core Processing Units.

According to the docs: https://www.tensorflow.org/install/gpu

it may not work due to lack of versioning support.

Also - how do I leverage the NVIDIA TPUs/is there support for that?

Issue 2:
Edited: 17-DEC-2018 Nightly builds are CUDA 10

Edited: 27-NOV-2018 Added 1.12 FINAL builds.

Edited: 29-OCT-2018 Added 1.12 RC2 builds.

Edited: 17-OCT-2018 Added 1.12 RC1 builds.

Nightly Builds are now CUDA 10 as of 16-DEC-2018

[DONE] CUDA 10 in the nightly builds mid-DEC 2018 if testing goes well

CUDA 10 in official in mid-JAN-2019 as TF 1.13.

Install instructions for CUDA 10 for a fresh system that should also work on an existing system if using apt-get.

TensorFlow will be upgrading to CUDA 10 as soon as possible. As of TF 1.11 building TensorFlow from source works just fine with CUDA 10 and possibly even before. There is nothing special needed other than all of the CUDA, cuDNN, NCCL (optional), and TensorRT (optional) libraries. If people have some builds feel free to link them here (Keep in mind if you download them to decided what risk you want to take based on the source.) as well as any issues. Also NCCL is now open source again and soon will be back to being automatically downloaded by bazel and included in the binary.

CUDA 10 would likely go into TF 1.13, which is not scheduled. I will update this post as I have more info to share. I hope to flip nightly builds to CUDA 10 in November but the TF 1.13 release will likely push to early Jan.

I and some really cool people below made some binaries (even windows, see comments below) to help people out along with my really bad "instructions". I suspect the instructions linked in the comments below are better.

Libraries used (rough list, similar to what I listed above)

Ubuntu 16.04 LTS on GCE from base Google Cloud Ubuntu image.

Python 2.7 because that is how I roll and someday I will change.

Install 410+ driver using apt-get

CUDA 10.0.130 from .tgz install (do not install the driver)

cuDNN-10.0 v7.3.0.29 rom tgz install

nccl 2.3.4 for CUDA 10 (I am not sure it matters I have mixed them up before)

TensorRT-5.0.0 for CUDA 10 cudnn 7.3 (TensorRT 5 was not stated as supported by Tensorflow when

Compute Capability: 3.7,5.2,6.0,6.1,7.0

TF 1.12.0 FINAL

Python 2.7 (Ubuntu 16.04)

AVX2 + CUDA 10

Python 3.5 (Ubuntu 16.04)

AVX2 + CUDA 10

Dockerfiles

These are partial files and the apt-get commands should work on non-Docker systems as well assuming you have the NVIDIA apt-get repositories which should be the same as listed on tf.org.

Devel Confirmed by building TensorFlow at 1.12RC0.

Runtime
Issue 1:
I have created a small piece of code in http://jsfiddle.net/grr308Lx/12/ which is based on post(#2386)

No matter how I change the renderDepth of the blue and red cube, the red one is always behind the blue one.

Issue 2:
I've spent a couple days working on this issue with no result.

I tried to get a jsFiddle together but couldn't get dat.gui to integrate with it in order to demonstrate changing these values with no effect.

But the simple fact is:

With a WebGLRenderer and PerspectiveCamera, any attempt at altering sorting does nothing.

I even tried hacking three.js at one point to introduce a custom sort, but apparently, it's overridden or ignored.

The sample I used is as follows:

var gui = new dat.GUI();
var folder = gui.addFolder("renderDepth");

var R = new THREE.Mesh( new THREE.BoxGeometry(50, 50, 50), new THREE.MeshPhongMaterial({color:0xFF0000}) );
R.position.set(-10,-10,-10);
R.renderDepth = 3;
folder.add(R,"renderDepth").min(0).max(1000).step(1).name("Red").listen();
scene.add(R);

var G = new THREE.Mesh( new THREE.BoxGeometry(50, 50, 50), new THREE.MeshPhongMaterial({color:0x00FF00}) );
G.renderDepth = 2;
folder.add(G,"renderDepth").min(0).max(1000).step(1).name("Green").listen();
scene.add(G);

var B = new THREE.Mesh( new THREE.BoxGeometry(50, 50, 50), new THREE.MeshPhongMaterial({color:0x0000FF}) );
B.position.set(+10,+10,+10);
B.renderDepth = 1;
folder.add(B,"renderDepth").min(0).max(1000).step(1).name("Blue").listen();
scene.add(B);

folder.open();

I also noticed that very few pertinent questions on StackOverflow were answered and so I'm hoping this is enough of a bug to post this here.