UGUI Slider doesn't work with Mouse Input (MouseDeviceManager)

Overview
UGUI Sliders do not move when using the default MouseDeviceManager as an InputDataProvider.
All other UGUI elements seem to work fine.
Expected behavior
Clicking and dragging should move slider as it does with normal mouse/UGUI interaction without MRTK.
Actual behavior
Slider registers that it is being clicked (handle changes color) but the handle does not move when moving the mouse back and forth.
Steps to reproduce

Create Profile that uses Mouse input as described in "MousePointer" section of https://microsoft.github.io/MixedRealityToolkit-Unity/Documentation/Input/Pointers.html
(Why isn't there a Mouse Input Profile enabled by default? This is important for cross-platform applications)
Open HandInteractionExamples scene
Configure MRTK script in scene to use Mouse profile
Hit Play
Attempt to interact with the Slider on the "Unity UI Examples" window (note that other UI elements work fine)

Unity editor version
2019.2.19f1
Mixed Reality Toolkit release version
2.4

I am having the same issue.

Disabled Input Simulation Service from Input Data Providers , it worked.

We appreciate your feedback and thank you for reporting this issue.
Microsoft Mixed Reality Toolkit version 2 (MRTK2) is currently in limited support. This means that Microsoft is only fixing high priority security issues. Unfortunately, this issue does not meet the necessary priority and will be closed. If you strongly feel that this issue deserves more attention, please open a new issue and explain why it is important.
Microsoft recommends that all new HoloLens 2 Unity applications use MRTK3 instead of MRTK2.
Please note that MRTK3 was released in August 2023. It features an all-new architecture for developing rich mixed reality experiences and has a minimum requirement of Unity 2021.3 LTS. For more information about MRTK3, please visit https://www.mixedrealitytoolkit.org.
Thank you for your continued support of the Mixed Reality Toolkit!


Check how functorch interacts with autocast

I have a feeling it doesn't

Vmap seems working with autocast
# Creates some tensors in default dtype (here assumed to be float32)
a_float32 = torch.rand(4, 2, 3, device="cuda")
b_float32 = torch.rand(4, 3, 2, device="cuda")
c_float32 = torch.rand(4, 2, 2, device="cuda")
d_float32 = torch.rand(4, 3, 2, device="cuda")

def func(x, y, z, w):
    with autocast():
        e_float16 = torch.matmul(x, y)
        assert e_float16.dtype == torch.float16, e_float16.dtype
        f_float16 = torch.matmul(z, e_float16)
        assert f_float16.dtype == torch.float16, f_float16.dtype
    return torch.matmul(w, f_float16.float())

expected = func(a_float32, b_float32, c_float32, d_float32)
print(expected.shape)

out = vmap(func, in_dims=0)(a_float32, b_float32, c_float32, d_float32)
print(out.shape)

assert expected.allclose(out)

@autocast()
def func(x, y, z, w):
    e_float16 = torch.matmul(x, y)
    assert e_float16.dtype == torch.float16, e_float16.dtype
    f_float16 = torch.matmul(z, e_float16)
    assert f_float16.dtype == torch.float16, f_float16.dtype
    return torch.matmul(w, f_float16)

expected = func(a_float32, b_float32, c_float32, d_float32)
print(expected.shape)

out = vmap(func, in_dims=0)(a_float32, b_float32, c_float32, d_float32)
print(out.shape)

assert expected.allclose(out)

@zou3519 can you detail in which case you think it wont work ?

Hmm, so I have three questions:
What do we expect to be the behavior of the following?
Does the position of the context manager change anything?
def bar(x):
  with autocast():
    ...

# case 1: context manager inside function
vmap(bar)(x)

# case 2: context manager outside
with autocast():
  vmap(foo)(x)

Do we know when autocast runs?
We would want all of the functorch transforms to run before autocast. That is, if we do vmap(grad(func))(x, y) where func has autocast, then:

x and y get wrapped in TensorWrapper(BatchedTensor(..))
We should dispatch to the autograd implementation of e.g. matmul
This will redispatch to the batching rule for matmul
Finally, this will redispatch, and now, autocast should probably happen.

Is autocast fully composable with our transforms?
Autocast in PyTorch happens before autograd: https://github.com/pytorch/pytorch/blob/f3983f9c478c19c2f9054976d285c4a57a6e9ccb/c10/core/DispatchKey.h#L254-L257 . This might mean that it implement support for backward operators like convolution_backward because it doesn't need to!
If autocast indeeds run after functorch transforms, then grad(F.conv2d) (where func is the function you defined above) will cause convolution_forward and convolution_backward (NB: these not the actual names of these operators)  to be emitted, at which point autocast might not be able to handle convolution_backward.

Thanks for details !
I think the answer to the question 1 is that autocast works (at least on torch.matmul) as expected inside/outside vmap. I updated my previous message and put testing code snippet. Do you think we should add them to test_vmap.py ?
I'll check in depth other questions.

I think the answer to the question 1 is that autocast works (at least with torch.matmul) as expected inside/outside vmap. I updated my previous message and put testing code snippet. Do you think we should add them to test_vmap.py ?

Thanks for checking. Test cases are always great!

Some last years investigations results:
amp_dtype = torch.float16
device_type = "cuda"

def func(x, y):
    with autocast(dtype=amp_dtype, device_type=device_type):
        res = torch.mm(x, y)
        assert res.dtype == amp_dtype, res.dtype
    # return res
    return res.sum()

def func_no_autocast(x, y):
    res = torch.mm(x, y)
    return res.sum()

a = torch.ones(4, 12, 5, device=device_type)
b = 1.2 * torch.ones(4, 5, 12, device=device_type, dtype=torch.float)

expected = vmap(grad(func_no_autocast), in_dims=0)(a, b)
# -- expected: torch.Size([4, 12, 5]) tensor([[14.4000, 14.4000, 14.4000, 14.4000, 14.4000], ...
out = vmap(grad(func), in_dims=0)(a, b)
# -- out: torch.Size([4, 12, 5]) tensor([[14.4062, 14.4062, 14.4062, 14.4062, 14.4062],

AOT Autograd currently doesn't support autocast, and we confirm this through torch->xla lowering.
For example a simple conv2d:
PyTorch module
model = torch.nn.Conv2d(16, 33, 3, stride=2)
compiled_module = aot_module(model, print_mhlo_to_file('conv2d_forward'), print_mhlo_to_file('conv2d_backward'))
data = torch.randn(20, 16, 50, 100)

with torch.autocast('cuda'):
    output = model(data)

Forward FX graph
def forward(self, primals_1, primals_2, primals_3):
    convolution = torch.ops.aten.convolution(primals_3, primals_1, primals_2, [2, 2], [0, 0], [1, 1], False, [0, 0], 1);  primals_2 = None
    return [convolution, primals_1, primals_3]

Backward FX graph
def forward(self, primals_1, primals_3, tangents_1):
    convolution_backward = torch.ops.aten.convolution_backward(tangents_1, primals_3, primals_1, [33], [2, 2], [0, 0], [1, 1], False, [0, 0], 1, [False, True, True]);  tangents_1 = primals_3 = primals_1 = None
    getitem_1 = convolution_backward[1]
    getitem_2 = convolution_backward[2];  convolution_backward = None
    return [getitem_1, getitem_2]

Forward MHLO
autocast is correctly handled by mhlo.convert.
module  {
  func @main(%arg0: tensor<33x16x3x3xf32>, %arg1: tensor<33xf32>, %arg2: tensor<20x16x50x100xf32>) -> tuple<tensor<20x33x24x49xf16>, tensor<33x16x3x3xf32>, tensor<20x16x50x100xf32>> {
    %0 = "mhlo.convert"(%arg2) : (tensor<20x16x50x100xf32>) -> tensor<20x16x50x100xf16>
    %1 = "mhlo.convert"(%arg0) : (tensor<33x16x3x3xf32>) -> tensor<33x16x3x3xf16>
    %2 = "mhlo.convert"(%arg1) : (tensor<33xf32>) -> tensor<33xf16>
    %3 = call @aten.convolution_overrideable.7(%0, %1, %2) : (tensor<20x16x50x100xf16>, tensor<33x16x3x3xf16>, tensor<33xf16>) -> tensor<20x33x24x49xf16>
    %4 = "mhlo.tuple"(%3, %arg0, %arg2) : (tensor<20x33x24x49xf16>, tensor<33x16x3x3xf32>, tensor<20x16x50x100xf32>) -> tuple<tensor<20x33x24x49xf16>, tensor<33x16x3x3xf32>, tensor<20x16x50x100xf32>>
    return %4 : tuple<tensor<20x33x24x49xf16>, tensor<33x16x3x3xf32>, tensor<20x16x50x100xf32>>
  }
  func private @aten.convolution_overrideable.7(%arg0: tensor<20x16x50x100xf16>, %arg1: tensor<33x16x3x3xf16>, %arg2: tensor<33xf16>) -> tensor<20x33x24x49xf16> {
    %0 = mhlo.convolution(%arg0, %arg1) dim_numbers = [b, f, 0, 1]x[o, i, 0, 1]->[b, f, 0, 1], window = {stride = [2, 2], pad = [[0, 0], [0, 0]], lhs_dilate = [1, 1], rhs_dilate = [1, 1]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64, precision_config = ["DEFAULT", "DEFAULT"]} : (tensor<20x16x50x100xf16>, tensor<33x16x3x3xf16>) -> tensor<20x33x24x49xf16>
    %1 = "mhlo.broadcast_in_dim"(%arg2) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<33xf16>) -> tensor<20x24x49x33xf16>
    %2 = "mhlo.transpose"(%1) {minor_to_major = dense<[1, 3, 2, 0]> : tensor<4xindex>, permutation = dense<[0, 3, 1, 2]> : tensor<4xi64>} : (tensor<20x24x49x33xf16>) -> tensor<20x33x24x49xf16>
    %3 = mhlo.add %0, %2 : tensor<20x33x24x49xf16>
    return %3 : tensor<20x33x24x49xf16>
  }
}

Backward MHLO:
There is no autocast, causing a failure calling mistyped f16/f32 convolution in the backend.
module  {
  func @main(%arg0: tensor<33x16x3x3xf32>, %arg1: tensor<20x16x50x100xf32>, %arg2: tensor<20x33x24x49xf16>) -> tuple<tensor<33x16x3x3xf32>, tensor<33xf16>> {
    %0 = call @aten.convolution_backward_overrideable.8(%arg2, %arg1, %arg0) : (tensor<20x33x24x49xf16>, tensor<20x16x50x100xf32>, tensor<33x16x3x3xf32>) -> tuple<tensor<20x16x50x100xf32>, tensor<33x16x3x3xf32>, tensor<33xf16>>
    %1 = "mhlo.get_tuple_element"(%0) {index = 0 : i32} : (tuple<tensor<20x16x50x100xf32>, tensor<33x16x3x3xf32>, tensor<33xf16>>) -> tensor<20x16x50x100xf32>
    %2 = "mhlo.get_tuple_element"(%0) {index = 1 : i32, minor_to_major = dense<[0, 1, 3, 2]> : tensor<4xindex>} : (tuple<tensor<20x16x50x100xf32>, tensor<33x16x3x3xf32>, tensor<33xf16>>) -> tensor<33x16x3x3xf32>
    %3 = "mhlo.get_tuple_element"(%0) {index = 2 : i32} : (tuple<tensor<20x16x50x100xf32>, tensor<33x16x3x3xf32>, tensor<33xf16>>) -> tensor<33xf16>
    %4 = "mhlo.tuple"(%2, %3) : (tensor<33x16x3x3xf32>, tensor<33xf16>) -> tuple<tensor<33x16x3x3xf32>, tensor<33xf16>>
    return %4 : tuple<tensor<33x16x3x3xf32>, tensor<33xf16>>
  }
  func private @aten.convolution_backward_overrideable.8(%arg0: tensor<20x33x24x49xf16>, %arg1: tensor<20x16x50x100xf32>, %arg2: tensor<33x16x3x3xf32>) -> tuple<tensor<20x16x50x100xf32>, tensor<33x16x3x3xf32>, tensor<33xf16>> {
    %0 = "mhlo.transpose"(%arg2) {minor_to_major = dense<[1, 0, 2, 3]> : tensor<4xindex>, permutation = dense<[2, 3, 1, 0]> : tensor<4xi64>} : (tensor<33x16x3x3xf32>) -> tensor<3x3x16x33xf32>
    %1 = "mhlo.reverse"(%0) {dimensions = dense<[0, 1]> : tensor<2xi64>, minor_to_major = dense<[1, 0, 2, 3]> : tensor<4xindex>} : (tensor<3x3x16x33xf32>) -> tensor<3x3x16x33xf32>
    %2 = mhlo.convolution(%arg0, %1) dim_numbers = [b, f, 0, 1]x[0, 1, o, i]->[b, f, 0, 1], window = {stride = [1, 1], pad = [[2, 3], [2, 3]], lhs_dilate = [2, 2], rhs_dilate = [1, 1]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64, precision_config = ["DEFAULT", "DEFAULT"]} : (tensor<20x33x24x49xf16>, tensor<3x3x16x33xf32>) -> tensor<20x16x50x100xf32>
    %3 = mhlo.convolution(%arg1, %arg0) dim_numbers = [f, b, 0, 1]x[i, o, 0, 1]->[0, 1, b, f], window = {stride = [1, 1], pad = [[0, -1], [0, -1]], lhs_dilate = [1, 1], rhs_dilate = [2, 2]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64, precision_config = ["DEFAULT", "DEFAULT"]} : (tensor<20x16x50x100xf32>, tensor<20x33x24x49xf16>) -> tensor<3x3x16x33xf32>
    %4 = "mhlo.transpose"(%3) {minor_to_major = dense<[0, 1, 3, 2]> : tensor<4xindex>, permutation = dense<[3, 2, 0, 1]> : tensor<4xi64>} : (tensor<3x3x16x33xf32>) -> tensor<33x16x3x3xf32>
    %5 = mhlo.constant dense<0.000000e+00> : tensor<f16>
    %6 = "mhlo.reduce"(%arg0, %5) ( {
    ^bb0(%arg3: tensor<f16>, %arg4: tensor<f16>):  // no predecessors
      %8 = mhlo.add %arg3, %arg4 : tensor<f16>
      "mhlo.return"(%8) : (tensor<f16>) -> ()
    }) {dimensions = dense<[0, 2, 3]> : tensor<3xi64>} : (tensor<20x33x24x49xf16>, tensor<f16>) -> tensor<33xf16>
    %7 = "mhlo.tuple"(%2, %4, %6) : (tensor<20x16x50x100xf32>, tensor<33x16x3x3xf32>, tensor<33xf16>) -> tuple<tensor<20x16x50x100xf32>, tensor<33x16x3x3xf32>, tensor<33xf16>>
    return %7 : tuple<tensor<20x16x50x100xf32>, tensor<33x16x3x3xf32>, tensor<33xf16>>
  }
}

IMHO there is 2 possible fixes:

return weights with casted dtypes in forward, and feed them directly to backward
fix autocast for backward ops in core, possibly having unnecessary casts in backward

This was checked already, but I want to take a closer look at it one more time

Problem: Autocast doesn't work with functorch's grad transform
autocast rules are implemented for forward-passes only (think convolution) and not backward operators (e.g. convolution_backward). This leads to some interesting situations:
(1) grad over function that uses autocast
def f(x, w):
  with torch.autocast('cuda'):
    return F.conv2d(x, w).sum()

x = torch.randn(2, 3, 4, 4).cuda()
w = torch.randn(3, 3, 2, 2).cuda()

gm = make_fx(grad(f))(x, w)

print(gm.code)

the functorch transforms run first
grad(f) produces some code that does convolution and convolution_backward
Then, autocast imposes and then transforms the convolution to be in fp16, but not convolution_backward.
Ideally grad(f) would have the same behavior as performing y = f(x); torch.autograd.grad(y, x). The non-functorch version does the right thing (convolution and convolution backward are run in fp16) but the functorch version

(2) autocast over grad(f)
def f(x, w):
  return F.conv2d(x, w).sum()

x = torch.randn(2, 3, 4, 4).cuda()
w = torch.randn(3, 3, 2, 2).cuda()

with torch.autocast('cuda'):
  gm = make_fx(grad(f))(x, w)

print(gm.code)

the functorch transforms run first
grad(f) produces some code that does convolution and convolution_backward
Then, autocast imposes and then transforms the convolution to be in fp16, but not convolution_backward.
This case is weird but perhaps not a problem -- autocast doesn't support directly transforming backward ops, and grad(f) creates backward ops.

Potential solutions
Solution 1: have autocast participate in functorch's stack-based dispatching
If with torch.autocast('cuda') pushed a mode onto functorch's stack, then we would get the correct behavior.

In case 1, at the time of invoking conv2d, the functorch stack would look like [grad, autocast] <- top, and autocast would get to run first!

The downside is that this adds some additional dispatching overhead for people who do not use functorch. But maybe we can optimize this away: only push onto the functorch stack if a functorch transform is already active.
Solution 2: Special-case autocast in functorch's grad transform
functorch's grad transform will enable only the autograd keys and then do a dispatch through them. The problem with this in case 1 is that we want autocast to run first. To fix it, we can have functorch's grad transform enable the autocast key as well as the autograd keys.
This feels super hacky to me; autocast is logically something separate from autograd.
Discussion
Thoughts? @samdow @Chillee @ezyang

Why don't you just put functorch keys after autocast? It directly solves (1); and I agree that in (2) it should be "as if" you ran autocast on a program that explicitly had written out its forwards and backwards; weird but acceptable.

Why don't you just put functorch keys after autocast? It directly solves (1); and I agree that in (2) it should be "as if" you ran autocast on a program that explicitly had written out its forwards and backwards; weird but acceptable.

I think this breaks case 2. The expected behavior for case 2 is if we ran autocast on a program that had explicitly had written out its forwards and backward. In that case, convolution would be run in fp16 and convolution_backward would be run in fp32.
Walking through case 2, under the assumption that we put the functorch keys after autocast:
def f(x, w):
  return F.conv2d(x, w).sum()

x = torch.randn(2, 3, 4, 4).cuda()
w = torch.randn(3, 3, 2, 2).cuda()

with torch.autocast('cuda'):
  gm = make_fx(grad(f))(x, w)

print(gm.code)

On the call to conv2d, autocast goes first. So it'll do F.conv2d(x.half(), w.half())
When the grad key gets to conv2d, autograd saves the fp16 version of x and w.
convolution_backward will get run in fp16 instead of fp32!

This is one of those "the ordering of the dispatch keys matters" things that functorch tries to solve with the stack-based dispatching

But... isn't that the point?
import torch
from torch.fx.experimental.proxy_tensor import make_fx
import torch.nn.functional as F

def f(x, w):
  return F.conv2d(x, w).sum()

x = torch.randn(2, 3, 4, 4, device='cuda', requires_grad=True)
w = torch.randn(3, 3, 2, 2, device='cuda', requires_grad=True)

with torch.autocast('cuda'):
  gm = make_fx(lambda x, w: torch.autograd.grad(f(x, w), (x, w)))(x, w)

print(gm)

gives
def forward(self, x_1, w_1):
    _to_copy = torch.ops.aten._to_copy.default(w_1, dtype = torch.float16);  w_1 = None
    _to_copy_3 = torch.ops.aten._to_copy.default(x_1, dtype = torch.float16);  x_1 = None
    convolution = torch.ops.aten.convolution.default(_to_copy_3, _to_copy, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1)
    sum_5 = torch.ops.aten.sum.default(convolution, dtype = torch.float32);  convolution = None
    ones_like = torch.ops.aten.ones_like.default(sum_5, dtype = torch.float32, layout = torch.strided, device = device(type='cuda', index=0), pin_memory = False, memory_format = torch.preserve_format);  sum_5 = None
    expand = torch.ops.aten.expand.default(ones_like, [2, 3, 3, 3]);  ones_like = None
    _to_copy_8 = torch.ops.aten._to_copy.default(expand, dtype = torch.float16);  expand = None
    convolution_backward = torch.ops.aten.convolution_backward.default(_to_copy_8, _to_copy_3, _to_copy, [0], [1, 1], [0, 0], [1, 1], False, [0, 0], 1, [True, True, False]);  _to_copy_8 = _to_copy_3 = _to_copy = None
    getitem = convolution_backward[0]
    getitem_11 = convolution_backward[1]
    getitem_12 = convolution_backward[2];  convolution_backward = None
    _to_copy_13 = torch.ops.aten._to_copy.default(getitem, dtype = torch.float32, layout = torch.strided, device = device(type='cuda', index=0));  getitem = None
    _to_copy_14 = torch.ops.aten._to_copy.default(getitem_11, dtype = torch.float32, layout = torch.strided, device = device(type='cuda', index=0));  getitem_11 = None
    return (_to_copy_13, _to_copy_14)

Er, to clarify, my claim is that (A) should behave the same as (B) which is supposed to be different from (C):
Snippet A:
import torch
from torch.fx.experimental.proxy_tensor import make_fx
import torch.nn.functional as F
from functorch import grad

def f(x, w):
  return F.conv2d(x, w).sum()

x = torch.randn(2, 3, 4, 4).cuda()
w = torch.randn(3, 3, 2, 2).cuda()

# A
with torch.autocast('cuda'):
  gm = make_fx(grad(f))(x, w)

print(gm.code)

Snippet B:
import torch
from torch.fx.experimental.proxy_tensor import make_fx
import torch.nn.functional as F
from functorch import grad

def f(x, w):
  return F.conv2d(x, w).sum()

x = torch.randn(2, 3, 4, 4).cuda()
w = torch.randn(3, 3, 2, 2).cuda()

# B
explicit_forward_backward = make_fx(grad(f))(x, w)
with torch.autocast('cuda'):
  gm = make_fx(explicit_forward_backward)(x, w)

print(gm.code)

Snippet C:
import torch
from torch.fx.experimental.proxy_tensor import make_fx
import torch.nn.functional as F

def f(x, w):
  return F.conv2d(x, w).sum()

x = torch.randn(2, 3, 4, 4, device='cuda', requires_grad=True)
w = torch.randn(3, 3, 2, 2, device='cuda', requires_grad=True)

# C
with torch.autocast('cuda'):
  gm = make_fx(lambda x, w: torch.autograd.grad(f(x, w), x))(x, w)

print(gm)

Are you saying the expected behavior should be that A behaves the same way as C?
If we put the autocast key before the functorch key, then A will behave the same way as C.
NB: looks like on PyTorch master, A behaves the same way as C, so under my claim above this is a bug

Yes, I think I am fitting my intuition to what PyTorch master does today. What is muddying the waters is the autocast context manager, as compared to a hypothetical autocast functorch transform.
The claim I'll make is that in conventional PyTorch, when you use autograd and autocast together, autocast ALWAYS gets applied first, no matter where the autocast context manager shows up in the code. This is of course what the point of dispatch key ordering is: it fixes an order transforms apply--go use functorch if you want to reorder. So of course an autocast transform would work differently, since you'd expect the order transforms are applied to be respected.
Does that help?


client.mutate does not work due to the presence of _

why do you have _ on this line : https://github.com/kadirahq/lokka/blob/master/index.js#L126
with that we dont get invalid query response ! once that _ is removed we get the response
Can the method be updated !
Thanks !

I came here for the same reason. Maybe the best option for the moment is to make a fork and edit on your own.

yep, i did the same 👍


Maxwell: skip streams without routing

Fixes #1578

User reports this is fixing the problem in the issue. Since I don't the format super well let me know if we should ping Sam instead to review. But this works for me.


Fix "Invalid date" of pending transactions, and refactoring

When I tested this PR, I encountered an error in the FF extension but did not come across this error in a Chrome extension or web wallet.
I send a tip on Youtube via the Youtube button, transactions in my wallet are marked as pending:

While I see the same transaction in Chrome extension as sent:
FF extension - pending:

Chrome extension - sent:

When I send tip I got this error in the web wallet:

and in FF extension:

@Liubov-crypto this PR was merged, so please move it to a new issue to don't lose it.

@Liubov-crypto this PR was merged, so please move it to a new issue to don't lose it.

done, moved to #695


False negative with \n

This evaluates as valid JSON, but it's invalid: "\n".
Just try JSON.parse('"\n"').

This repo doesn't seem very active.


Set material stepper to horizontal or vertical based on attribute

#### Bug, feature request, or proposal:
proposal
#### What is the expected behavior?
Have a single component for both Horizontal and Vertical stepper, and have a bindable attribute to define if it should display vertically or horizontally
#### What is the current behavior?
There are two separate components: matHorizontalStepper and matVerticalStepper
#### What are the steps to reproduce?
N/A
#### What is the use-case or motivation for changing an existing behavior?
Horizontal steppers are great, and show the steps on a very clear way, but are not good for small screens (specially when the number fo steps grow).
Vertical steppers fits small screens very well, but do not provide a very clear view of all steps and the current selected one (specially if there is much content on each or some steps).
For providing a great experience on both small and large screens, a combination of both is required. This means needing to have both components on the template and choosing between one of them, and also duplicating content or encapsulating it using other strategies (which makes code less clearer).
Being able to use a single component on template and choose its type based on a property makes it much easier to use and allow for smaller and clearer code.
Which versions of Angular, Material, OS, TypeScript, browsers are affected?
N/A
Is there anything else we should know?

See https://github.com/angular/material2/pull/9173.

Thanks @crisbeto! Couldn't find that thread, so I've opened this one. I will keep track from there.


1d regression example

I was trying to run the 1d regression notebook straight out of the box as provided. Ran into this issue of size mismatch. Any help would be appreciated. Thank you
ValueError                                Traceback (most recent call last)
 in ()
32     optim.zero_grad()
33     y_pred, kl, loss, mse_loss, y_std = model(context_x, context_y, target_x,
---> 34                                               target_y)
35     loss.backward()
36     optim.step()
ValueError: not enough values to unpack (expected 5, got 3)

What branch/commit are you on? You will need to make sure the repo is on the same commit that you got the notebook from.
If you've checked all that, it might be that I changed the output of the model and haven't updated the script. It should be fairly easy for you to update that line.. It's been a while since I've worked on this, I would need more info to be able to help.


Parquet timestamp columns not showing expected dates

I am trying to view parquet files that contain timestamp columns, using your plugin. The columns are reported as type = bytes, and do not show the expected date/time, but some garbled bytes.
When I read the parquet file in my application and print the column contents, the output shows the correct dates:
    val org = sparkSession.read.parquet(s"$rootDir/ORG")
    org.select("ACT_DTS", "REL_ID", "LGL_STC_TP_GGM_CODE").show()

    +-------------------+----------------+-------------------+
    |            ACT_DTS|          REL_ID|LGL_STC_TP_GGM_CODE|
    +-------------------+----------------+-------------------+
    |2021-01-01 00:00:00|ybZorkicohGtqiuD|                 CV|
    |2021-01-01 00:00:00|pfbiboglqpUaidcm|                VOF|
    |2021-01-01 00:00:00|dgafOmyrnFVrhcap|                 BV|
    +-------------------+----------------+-------------------+

Is this the expected behavior?

The timestamp column ACT_DTS is created in my code using a cast to go from the original string column:
      df.withColumn("ACT_DTA", unix_timestamp(df("ACT_DTS")).cast(TimestampType))

The timestamp column ACT_DTS is created in my code using a cast to go from the original string column:
      df.withColumn("ACT_DTA", unix_timestamp(df("ACT_DTS")).cast(TimestampType))

I'm afraid so - Parquet's relationship with timestamps is inconsistent at best. In this case the timestamps will be stored as INT96, which is no longer supported by Parquet. To avoid the entire file read failing I simply print the bytes of INT96 columns .
You will be able to get dates to display correctly if you change how the data is written (see here), but I appreciate that this isn't always possible:
spark.conf.set("spark.sql.parquet.outputTimestampType", "TIMESTAMP_MICROS")

I'm afraid so - Parquet's relationship with timestamps is inconsistent at best. In this case the timestamps will be stored as INT96, which is no longer supported by Parquet. To avoid the entire file read failing I simply print the bytes of INT96 columns .
You will be able to get dates to display correctly if you change how the data is written (see here), but I appreciate that this isn't always possible:
spark.conf.set("spark.sql.parquet.outputTimestampType", "TIMESTAMP_MICROS")

Thanks for the info. Using your suggested setting works for me.

Thanks for the info. Using your suggested setting works for me.

Hello, INT96 columns now display correctly.


Stale LOADED_SYMBOLS if function is recreated in another session

Similar to the issue for transaction semantics around CREATE OR REPLACE FUNCTION for concurrent transactions, there's an issue with staleness in LOADED_SYMBOLS.
https://github.com/tcdi/plrust/blob/main/src/plrust.rs#L26-L28
If a session A executes a PL/Rust function foo, it loads/caches the symbol into LOADED_SYMBOLS.
However if another session B were to run CREATE OR REPLACE foo we never unload the symbols from session A meaning it'll execute with a stale function definition for the remainder of session A.
Initial thoughts are to check the TopTransactionXid and store it alongside LOADED_SYMBOLS? That way if the TopTransactionXid changes, it'll always LOAD the symbol again and prevent staleness to a certain extent

Not sure if PGX has api to assess TopTransactionId. But one way that i can think of to address this is to track the mtime of the source code / .so file in the in-memory map. On function execution, compare the in memory mtime of the function with the actual mtime of the source code / .so file. If the user function has been modified, the it should reload the function from the .so file.

PGX can add the relevant header if necessary.

I feel apprehensive about CREATE OR REPLACE FUNCTION in general, honestly. In the concurrent function creation case, with two sessions connected to Postgres, isn't this always a form of race condition?
We can create a coherent model for when to build, load, rebuild, and discard symbols, and use various tricks like symbol generations to make sure we're always using the correct symbol... but I don't think "two sessions open a transaction and try to commit creating the same function, and the second one will clobber the first, because even though they both think they are creating the same function, one will actually be OR REPLACE" is a situation where working around that with more and more epicycles is going to get us anywhere useful, as we then have to account for ROLLBACK transactions as well. Postgres avoids trying to unload symbols for similar reasons.

I think the issue does not necessary applies to two racing CREATE OR REPLACE. It could be some situation where one session performs a CREATE OR REPLACE and the function is up-to-date on this session. But on the other sessions the function is now stale.
# On Session1
postgres=> SELECT foo(1);
 foo
-----
   1
(1 row)

# On Session2
postgres=> SELECT foo(1);
 foo
-----
   1
(1 row)

# Update the function on Session1, the function is up-to-date
postgres=> CREATE OR REPLACE FUNCTION foo(i int) RETURNS INT AS
postgres-> $$
postgres$> Some(i.unwrap() + 1)
postgres$> $$ LANGUAGE plrust;
NOTICE:  INFO plrust::plrust: unloaded function
    at src/plrust.rs:40

CREATE FUNCTION
postgres=> SELECT foo (1);
 foo
-----
   2
(1 row)

# However in Session2, the function is stale
postgres=> SELECT foo(1);
 foo
-----
   1
(1 row)

For plpgsql, this is handled properly
# Session1
postgres=# SELECT test_plpgsql(1);
 test_plpgsql
--------------
            1
(1 row)

# Session2
postgres=# SELECT test_plpgsql(1);
 test_plpgsql
--------------
            1
(1 row)

# Update the function on Session1
postgres=# CREATE OR REPLACE FUNCTION test_plpgsql(i int)
  RETURNS int AS
$$
BEGIN
  RETURN i + 1;
END
$$ LANGUAGE plpgsql;
CREATE FUNCTION
postgres=# SELECT test_plpgsql(1);
 test_plpgsql
--------------
            2
(1 row)

# On Session2
postgres=# SELECT test_plpgsql(1);
 test_plpgsql
--------------
            2
(1 row)

I know that part, I'm just annoyed because of the other implications.
e.g. whatever schema we use to approach this has to do something to avoid rebuilding and replacing the function and opening new sharedlibs with new symbols constantly or else it becomes distressingly plausible to just repeatedly issue CREATE OR REPLACE FUNCTION until the machine runs out of hard drive space and memory. But this becomes comparatively easier if we always know whether we're creating instance "generation 0" for a function or advancing the generation by 1.

This has been fixed.  We now track the xmin of the row from pg_catalog.pg_proc.  And if that's different than what we previously loaded we then (attempt to) unload the function and reload the new version.


Scrolling-Width is wrong

I have the following problem:

The scrollbar width is not enough, please help.

What triggers it? Can you give me a minimal HTML page that reproduces the issue?

Yes, I uploaded it to http://cr4xy.square7.ch
Thanks for your fast response!

Any idea?

I suspect this to be related to the bootstrap css you've included - when deleted, I don't see the issue. May be related to border-box, etc.

I'm not seeing anything useful at the URL you linked anymore. If you narrow this down to a specific piece of external CSS, feel free to comment, and I'll investigate further. For now, I'm closing the issue.


updated testimonials cards

GSSoC 24 has been completed Finally'
It was a great experience working with you all
Thanks💗 for your valuable contributions!
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Suppor for relative paths?

How can I include a file using relative paths using Edge?
Example:
@include('./includes.user-card')

Does not work. Then I have to do:
@include('pages.user.includes.user-card')

Thanks a lot if you can help.

Nope, Edge doesn't support relative paths. There is no technical reasons to it but a matter of preference.
When you have relative paths, it's harder to move views into different directories, since you have to change the relative references too.
Whereas with current structure, it doesn't matter where in the folder structure your views are, they will always reference other views with absolute paths.

Ok, thanks.


reporduce training on esc-50 has an error

My CUDA version is 11.1, Pytorch version is 1.9.0, Pytorch-lighting version is 1.6.0.
When I reproduce your HTS-Transformer on esc-50, I get this error,  could you help me to solve this error？
Details

Traceback (most recent call last):
File "main.py", line 432, in
main()
File "main.py", line 428, in main
train()
File "main.py", line 398, in train
trainer.fit(model, audioset_data)
File "/data/chenyuanjian/anaconda3/envs/htstrans/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 771, in fit
self._call_and_handle_interrupt(
File "/data/chenyuanjian/anaconda3/envs/htstrans/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 724, in _call_and_handle_interrupt
return trainer_fn(*args, **kwargs)
File "/data/chenyuanjian/anaconda3/envs/htstrans/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 812, in _fit_impl
results = self._run(model, ckpt_path=self.ckpt_path)
File "/data/chenyuanjian/anaconda3/envs/htstrans/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1237, in _run
results = self._run_stage()
File "/data/chenyuanjian/anaconda3/envs/htstrans/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1324, in _run_stage
return self._run_train()
File "/data/chenyuanjian/anaconda3/envs/htstrans/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1354, in _run_train
self.fit_loop.run()
File "/data/chenyuanjian/anaconda3/envs/htstrans/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 204, in run
self.advance(*args, **kwargs)
File "/data/chenyuanjian/anaconda3/envs/htstrans/lib/python3.8/site-packages/pytorch_lightning/loops/fit_loop.py", line 269, in advance
self._outputs = self.epoch_loop.run(self._data_fetcher)
File "/data/chenyuanjian/anaconda3/envs/htstrans/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 204, in run
self.advance(*args, **kwargs)
File "/data/chenyuanjian/anaconda3/envs/htstrans/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 208, in advance
batch_output = self.batch_loop.run(batch, batch_idx)
File "/data/chenyuanjian/anaconda3/envs/htstrans/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 204, in run
self.advance(*args, **kwargs)
File "/data/chenyuanjian/anaconda3/envs/htstrans/lib/python3.8/site-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 88, in advance
outputs = self.optimizer_loop.run(split_batch, optimizers, batch_idx)
File "/data/chenyuanjian/anaconda3/envs/htstrans/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 204, in run
self.advance(*args, **kwargs)
File "/data/chenyuanjian/anaconda3/envs/htstrans/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 203, in advance
result = self._run_optimization(
File "/data/chenyuanjian/anaconda3/envs/htstrans/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 256, in _run_optimization
self._optimizer_step(optimizer, opt_idx, batch_idx, closure)
File "/data/chenyuanjian/anaconda3/envs/htstrans/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 369, in _optimizer_step
self.trainer._call_lightning_module_hook(
File "/data/chenyuanjian/anaconda3/envs/htstrans/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1596, in _call_lightning_module_hook
output = fn(*args, **kwargs)
File "/data/chenyuanjian/anaconda3/envs/htstrans/lib/python3.8/site-packages/pytorch_lightning/core/lightning.py", line 1625, in optimizer_step
optimizer.step(closure=optimizer_closure)
File "/data/chenyuanjian/anaconda3/envs/htstrans/lib/python3.8/site-packages/pytorch_lightning/core/optimizer.py", line 168, in step
step_output = self._strategy.optimizer_step(self._optimizer, self._optimizer_idx, closure, **kwargs)
File "/data/chenyuanjian/anaconda3/envs/htstrans/lib/python3.8/site-packages/pytorch_lightning/strategies/strategy.py", line 193, in optimizer_step
return self.precision_plugin.optimizer_step(model, optimizer, opt_idx, closure, **kwargs)
File "/data/chenyuanjian/anaconda3/envs/htstrans/lib/python3.8/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py", line 155, in optimizer_step
return optimizer.step(closure=closure, **kwargs)
File "/data/chenyuanjian/anaconda3/envs/htstrans/lib/python3.8/site-packages/torch/optim/lr_scheduler.py", line 65, in wrapper
return wrapped(*args, **kwargs)
File "/data/chenyuanjian/anaconda3/envs/htstrans/lib/python3.8/site-packages/torch/optim/optimizer.py", line 88, in wrapper
return func(*args, **kwargs)
File "/data/chenyuanjian/anaconda3/envs/htstrans/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 28, in decorate_context
return func(*args, **kwargs)
File "/data/chenyuanjian/anaconda3/envs/htstrans/lib/python3.8/site-packages/torch/optim/adamw.py", line 65, in step
loss = closure()
File "/data/chenyuanjian/anaconda3/envs/htstrans/lib/python3.8/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py", line 140, in _wrap_closure
closure_result = closure()
File "/data/chenyuanjian/anaconda3/envs/htstrans/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 148, in call
self._result = self.closure(*args, **kwargs)
File "/data/chenyuanjian/anaconda3/envs/htstrans/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 143, in closure
self._backward_fn(step_output.closure_loss)
File "/data/chenyuanjian/anaconda3/envs/htstrans/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 311, in backward_fn
self.trainer._call_strategy_hook("backward", loss, optimizer, opt_idx)
File "/data/chenyuanjian/anaconda3/envs/htstrans/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1766, in _call_strategy_hook
output = fn(*args, **kwargs)
File "/data/chenyuanjian/anaconda3/envs/htstrans/lib/python3.8/site-packages/pytorch_lightning/strategies/strategy.py", line 168, in backward
self.precision_plugin.backward(self.lightning_module, closure_loss, *args, **kwargs)
File "/data/chenyuanjian/anaconda3/envs/htstrans/lib/python3.8/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py", line 80, in backward
model.backward(closure_loss, optimizer, *args, **kwargs)
File "/data/chenyuanjian/anaconda3/envs/htstrans/lib/python3.8/site-packages/pytorch_lightning/core/lightning.py", line 1370, in backward
loss.backward(*args, **kwargs)
File "/data/chenyuanjian/anaconda3/envs/htstrans/lib/python3.8/site-packages/torch/_tensor.py", line 255, in backward
torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs)
File "/data/chenyuanjian/anaconda3/envs/htstrans/lib/python3.8/site-packages/torch/autograd/init.py", line 147, in backward
Variable._execution_engine.run_backward(
RuntimeError: upsample_bicubic2d_backward_out_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True)'. You can turn off determinism just for this operation if that's acceptable for your application. You can also file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation.

I have the same question. Have you solved it?

Sure, please refer issue 13.

Thanks for your reply, but I got a new problem "corrupted size vs. prev_size
已放弃 (核心已转储)"。Have you encountered this problem？


Can't install on Mac when directory contains spaces

When trying to install better-sqlite on a mac machine, and my directory path contains a space, I get this error

My understanding is that the copyfile command is failing

This is documented here https://github.com/WiseLibs/better-sqlite3/blob/master/docs/troubleshooting.md#no-special-characters-in-your-project-path
It has nothing to do with better-sqlite3 and is a limitation of node-gyp outside of our control.


[Bug] When we select a font-family that's not on our computer, the import statement doesn't import true bolds and italics, and there's no way to configure it otherwise

Describe the bug
When we select a font-family that's not installed on our computer, a google api important statement is inserted. However, that import statement only imports the one style and one face, resulting in faux bold and faux italics used throughout the page. Copying and pasting the import statement from Google Fonts site doesn't work, cause that supports css2 and Stylebot probably doesn't.
To Reproduce
Steps to reproduce the behavior:

https://hbr.org/2019/05/recruiting
Click on style bot, and then the text div on the article
Change font-family to "Merriweather" or any other serif font that's not installed, so observe the faux variations clearly.

Expected behavior
Either the import statement should already import all the variations (styles/weights) based on what's used on the page, or it should include them based on if I select that variation while configuring. Or it should allow the user to import them.
Screenshots

Thanks for reporting @saadrehman. I think you found a couple of issues:

pasting the import statement from Google Fonts site doesn't work, cause that supports css2 and Stylebot probably doesn't.

If you remove the quotes from the @import statement, it would work:
@import url(https://fonts.googleapis.com/css2?family=Merriweather:wght@300;400;500&display=swap)

I will fix the code so that it is able to parse @import url('https://fonts.googleapis.com/css2?family=Merriweather:wght@300;400&display=swap') correctly.

Either the import statement should already import all the variations (styles/weights) based on what's used on the page or it should include them based on if I select that variation while configuring.

This is a little non-trivial since I will have to look at the computed styles and the custom style for the selected elements, but I think it is feasible. I will attempt a fix for this too.
Thanks!

@saadrehman this should now be fixed in the latest update.


pretty overrides doom's default and sets org-highlight-latex-and-related to (native script entities)

What did you expect to happen?
I was expecting low CPU use and no latency when typing and deleting characters on large files in org-mode, thanks in part to doom's default setting of org-highlight-latex-and-related to nil, as described in #2998.
What actually happened?

Typing and deleting characters had high latency and CPU use was high on large files in org-mode
org-highlight-latex-and-related was set to (native script entities)

Describe your attempts to resolve the issue

After running profiler, I was able to attribute most of the CPU use to org-do-latex-and-related.
I found that org-highlight-latex-and-related was set to (native script entities), instead of nil, which is doom's default`.
I verified that the CPU use went back to normal after adding (after! org (setq org-highlight-latex-and-related nil)) to config.el.
I verified that my config.el was not to blame by running Doom Emacs with my config commented out.
I verified that the default was being overridden by pretty by commenting out +pretty in init.el and checking that org-highlight-latex-and-related was back to nil.

Steps to reproduce

Open large file in org-mode
Type and delete characters

System Information
https://pastebin.com/g5Pq3Vz8

Out of curiosity, did this occur when you were editing a very long line, or just large files overall?
I've had issues editing really long lines, but splitting the lines up helped

@Infinidoge it seemed to only depend on the size of the file, as the latency/CPU use went up even when starting new lines.


Error Install Cartoview Dockers

root@ide:/home/geo/geogra# make run
bring up the services
docker-compose up -d
Starting geogra_elasticsearch_1 ... done
Starting geogra_nginx_1         ... done
Starting geogra_rabbitmq_1      ... done
Starting geogra_postgis_1       ... done
Starting geogra_geoserver_1     ... done
Starting geogra_cartoview_1     ... done
sleep 5
docker-compose exec cartoview python manage.py makemigrations app_manager
Makefile:16: recipe for target 'prepare_manager' failed
make: *** [prepare_manager] Error 137

Please fill the following information to avoid closing issue
Describe your issue in detail

Cartoview Version : (x.xx.x)
Geonode Version : (x.xx.x)
log (optional):

please
put
your
log
here

@dpadron sorry for being late,

fixed in the new docker image try the following :

docker-compose pull cartoview
docker-compose stop cartoview
docker-compose up -d


[Bug] Sending image from gradio interface to 4bit model

Checklist

[ ] 1. I have searched related issues but cannot get the expected help.
[ ] 2. The bug has not been fixed in the latest version.

Describe the bug
Hello, I have created the space on HF and trying to send the image from gradio input to quanitized model. However, getting an error. Is it possible to read an image from local machine or the only way, how can I send the image to VLLM offline is through url?
Reproduction

Environment
sys.platform: linux
Python: 3.10.14 (main, Jul 12 2024, 13:17:12) [GCC 11.4.0]
CUDA available: True
MUSA available: False
numpy_random_seed:<PHONE_NUMBER>
GPU 0: Tesla T4
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 12.3, V12.3.107
GCC: gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
PyTorch: 2.2.2+cu121
PyTorch compiling details: PyTorch built with:
  - GCC 9.3
  - C++ Version: 201703
  - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v3.3.2 (Git Hash 2dc95a2ad0841e29db8b22fbccaf3e5da7992b01)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX512
  - CUDA Runtime 12.1
  - NVCC architecture flags: -gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90
  - CuDNN 8.9.2
  - Magma 2.6.1
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=8.9.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.2.2, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF,

TorchVision: 0.17.2+cu121
LMDeploy: 0.5.1+7263e03
transformers: 4.42.4
gradio: 4.36.1
fastapi: 0.111.0
pydantic: 2.8.2
triton: 2.2.0
NVIDIA Topology:
        GPU0    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      0-3     0               N/A

Legend:

  X    = Self
  SYS  = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
  NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
  PHB  = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
  PXB  = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
  PIX  = Connection traversing at most a single PCIe bridge
  NV#  = Connection traversing a bonded set of # NVLinks

Error traceback
=== Application stopped (exit code: 1) at 2024-07-18 11:20:18.299319328 UTC ===
Set max length to 16384
Dummy Resized

Convert to turbomind format:   0%|          | 0/32 [00:00<?, ?it/s]
Convert to turbomind format:   3%|▎         | 1/32 [00:01<00:39,  1.26s/it]
Convert to turbomind format:  28%|██▊       | 9/32 [00:04<00:11,  1.93it/s]
Convert to turbomind format:  72%|███████▏  | 23/32 [00:06<00:02,  4.37it/s]
[WARNING] gemm_config.in is not found; using default GEMM algo
                                                                            INFO:httpx:HTTP Request: GET https://checkip.amazonaws.com/ "HTTP/1.1 200 "
Running on local URL:  http://<IP_ADDRESS>:7860
INFO:httpx:HTTP Request: GET http://localhost:7860/startup-events "HTTP/1.1 200 OK"
INFO:httpx:HTTP Request: HEAD http://localhost:7860/ "HTTP/1.1 200 OK"

To create a public link, set `share=True` in `launch()`.
INFO:httpx:HTTP Request: GET https://api.gradio.app/pkg-version "HTTP/1.1 200 OK"
INFO:matplotlib.font_manager:generated new fontManager
INFO:__main__:Received a new message.
WARNING:__main__:Expected history to be a list of tuples, but got string: You are a friendly Chatbot.
ERROR:__main__:Error during chat completion: '>' not supported between instances of 'Image' and 'int'

Code:
import gradio as gr
from PIL import Image
import io
import logging
import base64
from lmdeploy import pipeline, TurbomindEngineConfig

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Model setup
model_id = "PATH_TO_PRIVATE_QUANITIZED_HF_MODEL"
backend_config = TurbomindEngineConfig(cache_max_entry_count=0.2, tp=1)
pipe = pipeline(model_id, backend_config=backend_config)

def respond(
    message,
    image,
    history,
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    logger.info("Received a new message.")
    messages = [{"role": "system", "content": str(system_message)}]  # Ensure system_message is a string

    # Ensure history is a list of tuples
    if isinstance(history, str):
        logger.warning(f"Expected history to be a list of tuples, but got string: {history}")
        history = []
    elif isinstance(history, list):
        for val in history:
            if isinstance(val, list) and len(val) == 2:
                messages.append({"role": "user", "content": val[0]})
                messages.append({"role": "assistant", "content": val[1]})
            else:
                logger.warning(f"Unexpected format in history: {val}")
    else:
        logger.warning(f"Unexpected type for history: {type(history)}")

    messages.append({"role": "user", "content": message})

    image_prompt = None
    if isinstance(image, Image.Image):
        try:
            buffered = io.BytesIO()
            image.save(buffered, format="PNG")
            img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
            image_prompt = {'type': 'image_url', 'image_url': {'url': 'data:image/png;base64,' + img_str}}
        except Exception as e:
            logger.error(f"Error processing image: {e}")

    prompts = [
        {
            'role': 'user',
            'content': [{'type': 'text', 'text': message}]
        }
    ]

    if image_prompt:
        prompts[0]['content'].append(image_prompt)

    response = ""

    try:
        responses = pipe(prompts, max_tokens=max_tokens, temperature=temperature, top_p=top_p)
        response = responses[0]['content']
        yield response
    except Exception as e:
        logger.error(f"Error during chat completion: {e}")
        yield "An error occurred during the chat completion process."

demo = gr.ChatInterface(
    fn=respond,
    additional_inputs=[
        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
        gr.Image(type="pil", label="Input Image (optional)")
    ],
)

if __name__ == "__main__":
    demo.launch()


Removed warnings when compiling with Swift 2.2.

We're using Swift 2.2 and Armchair in our project, which causes warnings when we build. This gets rid of those warnings when building with Swift 2.2.

I just realized this is basically the same changes that @codytwinton proposed in #44. :-)


How to Promote package that was just pushed to the feed?

Hi, our use case is to have these steps in the Build Pipeline:

Restore
Pack
Push

Once these steps done we would like to Promote freshly pushed package to the @Prerelease view. Extension does not allow to use variables to specify version. Is any other way I could achieve this?

Does a linked variabele work ? So you can set iT on pipeline level?
Verstuurd vanaf mijn iPhone
Op 19 dec. 2018 om 11:57 heeft Algis <notifications<EMAIL_ADDRESS>het volgende geschreven:
Hi, our use case is to have these steps in the Build Pipeline:

Restore
Pack
Push

Once these steps done we would like to Promote freshly pushed package to the @prereleasehttps://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fprerelease&data=02|01||84ea366c186a4206071b08d665a0ce9b|84df9e7fe9f640afb435aaaaaaaaaaaa|1|0|636808138661576899&sdata=De4rEVZsUmY2DYIcIay%2BowEBQZ1qsc3wRo%2BSvokPdfo%3D&reserved=0 view. Extension does not allow to use variables to specify version. Is any other way I could achieve this?
—
You are receiving this because you are subscribed to this thread.
Reply to this email directly, view it on GitHubhttps://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Frenevanosnabrugge%2Fvsts-promotepackage-task%2Fissues%2F11&data=02|01||84ea366c186a4206071b08d665a0ce9b|84df9e7fe9f640afb435aaaaaaaaaaaa|1|0|636808138661576899&sdata=qvVvtRDOpRjXmu4GpCJ7GnY23kll3gcFmBe6RvATAAY%3D&reserved=0, or mute the threadhttps://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fnotifications%2Funsubscribe-auth%2FAFx9WqrNG4OK0AfPBFSk7jq3hJBrEbJSks5u6hungaJpZM4ZaAEo&data=02|01||84ea366c186a4206071b08d665a0ce9b|84df9e7fe9f640afb435aaaaaaaaaaaa|1|0|636808138661576899&sdata=%2FCqdDHocvb4GarlT7HpjVUPaIqpN4F3pOEM5EFfcanc%3D&reserved=0.

Does a linked variabele work ? So you can set iT on pipeline level?

Thanks for the hint. It looks really promising, but I'm still getting error: This setting is invalid. Select a valid option from the dropdown.
Here is what I have done:

Created Pipeline Variable "Parameters.BuildNo" and set value to "$(Build.BuildNumber)"
Added "Promote package to PreRelease View" Task, selected Feed, Package and View from the dropdowns;
In Link Settings Selected "Package Version" and in "The pipeline parameter to link to this setting" field typed "Parameters.BuildNo"
YAML of the Promote Package task looks like this:

variables:
  Parameters.BuildNo: '$(Build.BuildNumber)'
steps:
- task: rvo.vsts-promotepackage-task.vsts-promotepackage-task.rvo-vsts-promotepackage-task@1
  displayName: 'Promote package to PreRelease View'
  inputs:
    feed: 'GUID...'
    definition: 'GUID...'
    version: '$(Parameters.BuildNo)'
    releaseView: 'GUID...'

At this stage Save of the configuration is disabled and below Pipeline there is error: "Some settings need attention"
Clicking on that I see Package version defaulted to the Last Build and Error message I have mentioned earlier: "This setting is invalid. Select a valid option from the dropdown."

Added a pull request for restoring the option to manually specify a variable in the version field.
#12


chore: release 1.2.85

:robot: I have created a release *beep* *boop*
1.2.85 (2022-04-08)
Features

stapel-to-buildah: basic implementation of dependencies* stages (9ead236)

Bug Fixes

buildah: use crun instead of runc (fbae777)
server-dry-run: possible fix for 'unable to recognize ...: no matches for kind ... in version ...' (5b13270)

This PR was generated with Release Please. See documentation.

:robot: Release is at https://github.com/werf/werf/releases/tag/v1.2.85 :sunflower:


TextBox uses is_printable to filter text input which is unrelieable.

The is_printable fn on KeyCode is not very useful, because it alone does not tell you whether a key event will have printable text.
As an example: ctrl + e on macOS uses the 'printable' KeyE keycode, but sends the unprintable ascii value 0x5. similarly with ctrl + u, ctrl + k, and others.

I think this is now a non-issue, since I cannot find reference to is_printable in the codebase, and it is most likely a function that was removed in some part of the event rework.

Alright, let's close this for now.


sys_platform = "== 'win32'" no longer works

Issue description
We are building our project for different platforms, but we have modules that should only be installed on certain platforms. So far we have used the following notation:
sys_platform = "== 'win32'"
This worked fine with release 2018.11.26, but not with release 2020.5.28 anymore.
Expected result
A module with the limitation for a certain platform should not be installed on all other platforms.
Actual result
The limitation for a certain platform is ignored.
Steps to replicate
Write a module with the restriction for a specific platform in the notation sys_platform = "== 'win32'" into a pipfile and install pipfile.

Please fill out the entire bug report including your Pipfile where you are having this issue. I cannot reproduce this issue locally.
To confirm, this is not specified in the report, but this is about using this syntax in a Pipfile, correct?

I think I am also having this issue with sys_platform = "== 'Linux'".
Here is a Pipfile which works on pipenv==2018.11.26 but fails with pipenv==2020.6.2:
[[source]]
name = "pypi"
url = "https://pypi.org/simple"
verify_ssl = true

[packages]
google-python-cloud-debugger = {version = "*",sys_platform = "== 'Linux'"}

[dev-packages]

[requires]
python_version = "3.7"

Output:
pipenv lock
Locking [dev-packages] dependencies…
Locking [packages] dependencies…
Building requirements...
Resolving dependencies...
✘ Locking Failed!
Ignoring error when setting locale: unsupported locale setting
Ignoring error when setting locale: unsupported locale setting
Traceback (most recent call last):
  File "/Users/dhendry/.pyenv/versions/3.7.4/lib/python3.7/site-packages/pipenv/resolver.py", line 807, in <module>
    main()
  File "/Users/dhendry/.pyenv/versions/3.7.4/lib/python3.7/site-packages/pipenv/resolver.py", line 803, in main
    parsed.requirements_dir, parsed.packages, parse_only=parsed.parse_only)
  File "/Users/dhendry/.pyenv/versions/3.7.4/lib/python3.7/site-packages/pipenv/resolver.py", line 785, in _main
    resolve_packages(pre, clear, verbose, system, write, requirements_dir, packages)
  File "/Users/dhendry/.pyenv/versions/3.7.4/lib/python3.7/site-packages/pipenv/resolver.py", line 753, in resolve_packages
    requirements_dir=requirements_dir,
  File "/Users/dhendry/.pyenv/versions/3.7.4/lib/python3.7/site-packages/pipenv/resolver.py", line 736, in resolve
    req_dir=requirements_dir
  File "/Users/dhendry/.pyenv/versions/3.7.4/lib/python3.7/site-packages/pipenv/utils.py", line 1386, in resolve_deps
    req_dir=req_dir,
  File "/Users/dhendry/.pyenv/versions/3.7.4/lib/python3.7/site-packages/pipenv/utils.py", line 1091, in actually_resolve_deps
    deps, index_lookup, markers_lookup, project, sources, req_dir, clear, pre
  File "/Users/dhendry/.pyenv/versions/3.7.4/lib/python3.7/site-packages/pipenv/utils.py", line 649, in create
    pre=pre, clear=clear
  File "/Users/dhendry/.pyenv/versions/3.7.4/lib/python3.7/site-packages/pipenv/utils.py", line 463, in get_metadata
    req, resolver=transient_resolver
  File "/Users/dhendry/.pyenv/versions/3.7.4/lib/python3.7/site-packages/pipenv/utils.py", line 610, in get_deps_from_req
    best_match = pypi.find_best_match(req.ireq) if pypi else None
  File "/Users/dhendry/.pyenv/versions/3.7.4/lib/python3.7/site-packages/pipenv/patched/piptools/repositories/pypi.py", line 202, in find_best_match
    raise NoCandidateFound(ireq, all_candidates, self.finder)
pipenv.patched.piptools.exceptions.NoCandidateFound: Could not find a version that matches google-python-cloud-debugger
No versions found
Was https://pypi.org/simple reachable?

Misc other notes:
uname -sr
Darwin 19.5.0

Just chiming in to say I'm seeing this issue too

@dhendry the issues don't look the same. Did you access pypi behind a proxy? Was https://pypi.org/simple reachable?

@frostming No proxy - pypi.org is reachable:

Any update on this? Its pretty annoying
@frostming you earlier commented that the issues do not look the same. Do you want me to open a new issue?
@techalchemy This issue still has a Status: Needs More Information label. I believe I have included a simple example to reproduce the issue. Is there any additional information I could provide to help you out?

any updates?

Sorry, I do not have an update at this time.  Possibly related:  https://github.com/pypa/pipenv/issues/5239

I would ask, could this be rechecked with pipenv==2022.8.19 and if possible provide a reproduction example?


Uncaught TypeError: Cannot read property 'prototype' of undefined

I installed superagent using npm install superagent it successfully installed after that browserify my project which contain 1 js file, then i tried to execute, but am getting the following error
Uncaught TypeError: Cannot read property 'prototype' of undefined
at Object.84../test (index.js:16330)
at s (index.js:1)
at index.js:1
at Object.83../lib/agent (index.js:16287)
at s (index.js:1)
at index.js:1
at Object.88.chai (index.js:16798)
at s (index.js:1)
at e (index.js:1)
at index.js:1
Error Line:
module.exports.agent = require('./lib/agent'); -< 16287
TestAgent.prototype.proto = Agent.prototype; -<16330
Kindly help me out from this, thanks in advance

Looks like TestAgent doesn't have a prototype. That's not a type used in Superagent.


fix: clear nuget cache in Docker image before publishing

https://github.com/microsoft/playwright-dotnet/issues/2694#issuecomment-1712741094
This saves us 600 MB.

Sounds like doing both is the best:
root@e3386b9a4a0b:/# dotnet nuget locals all --list
http-cache: /root/.local/share/NuGet/http-cache
global-packages: /root/.nuget/packages/
temp: /tmp/NuGetScratchroot
plugins-cache: /root/.local/share/NuGet/plugin-cache
root@e3386b9a4a0b:/#

then we delete the whole  /root/.nuget folder as well.


Unable to decode into TxLegacy due to LeadingZero error

The below test hits the LeadingZero error from here
Various online tools like this and this seem happy to decode, but assuming that this LeadingZero error is properly enforcing the spec, would there be a way to opt-in to bypass that enforcement?
use alloy::hex;
use alloy::primitives::private::alloy_rlp::Decodable;
use alloy_consensus::TxLegacy;

#[test]
fn test_unsigned_trx() {
    let raw = hex::decode("f78212aa8575a1c379a28307a120947282835cf78a5e88a52fc701f09d1614635be4b8900000000000000000000000000000000080808080").unwrap();

    let tx = TxLegacy::decode(&mut raw.as_slice());
    if tx.is_err() {
        println!("Failed to decode unsigned transaction: {:?}", tx.err());
        assert!(false, "Failed to decode unsigned transaction");
    }
}

Where is the raw rlp from? this looks like invalid rlp, as the value is entirely zeros

For example this is the tx "decoded":
list header: f7
82
nonce: 12aa
85
gas_price: 75a1c379a2
83
gas_limit: 07a120
94
to: 7282835cf78a5e88a52fc701f09d1614635be4b8
90
value:<PHONE_NUMBER>0000000000000000000000
input: 80
v: 80
r: 80
s: 80

Our EVM is implemented as C++ >> WASM, and our RLP implementation is adapted from bloq/rlpvalue, we allow interoperability between the WASM network and the EVM which is why there is no signature (it was signed/authorized on the WASM network).  This RLP implementation accepted the trx, similarly the tools listed above decode it to the same values that we currently have in our explorer which seems to differ after value:
["0x12aa","0x75a1c379a2","0x07a120","0x7282835cf78a5e88a52fc701f09d1614635be4b8","0x00000000000000000000000000000000","0x","0x","0x","0x"]
Currently we are building our new RPC version that uses alloy/reth and so the goal is to decode into the TxLegacy type from this RLP format that is in our block history.

how are you encoding the value field?
this doesn't look like an integer

Our EVM is implemented as C++ >> WASM smart contract, and our RLP implementation is adapted from bloq/rlpvalue, we allow interoperability between the WASM network and the EVM which is why there is no signature (it was signed/authorized on the WASM network). This RLP implementation accepted the trx, similarly the tools listed above decode it to the same values that we currently have in our explorer which seems to differ after value:
["0x12aa","0x75a1c379a2","0x07a120","0x7282835cf78a5e88a52fc701f09d1614635be4b8","0x00000000000000000000000000000000","0x","0x","0x","0x"]
Currently we are building our new RPC version that uses alloy/reth and so the goal is to decode into the TxLegacy type from this RLP format that is in our block history.

yeah, the rlp implementation you are using is incorrect, and should output 0x80 for zero value. the online decoders should note that it's not valid tx rlp

For this specific transaction, I'm 99% sure it came from here via this implementation.
And yes, I assumed this was wrong... totally fine with closing this issue and implementing a workaround on our side.

this looks wrong
https://github.com/AscensionWx/Eosio-smart-contract/blob/08ac7a642f1bad1f45b118601a0e57eb210e3d97/src/helpers.cpp#L168-L169
this uses a padded u8 vec for the amount when encoding the tx body, which would explain this issue
https://github.com/AscensionWx/Eosio-smart-contract/blob/08ac7a642f1bad1f45b118601a0e57eb210e3d97/src/helpers.cpp#L188


Add queue_name to all ems_operations queue roles

In order to allow providers to bring their own OperationsWorkers, we need to support a queue_name for all ems_operations type work.
Example: https://github.com/ManageIQ/manageiq/pull/19479, https://github.com/ManageIQ/manageiq/pull/19544
Parent issue: https://github.com/ManageIQ/manageiq-providers-vmware/issues/484

Probably shoulda put this list here, but for now: https://github.com/ManageIQ/manageiq-providers-vmware/issues/484#issuecomment-561223801


Hi @shorebin, I'm your friendly neighborhood Azure Pull Request Bot (You can call me AZPRBOT). Thanks for your contribution!

This seems like a small (but important) contribution, so no contribution license agreement is required at this point. Real humans will now evaluate your PR.

TTYL, AZPRBOT;

Closing - intent of change is not clear.


Tray is blurry: font is not the same as the system

ClearType enabled?

Same result with and without ClearType.

To which part are you actually referring? the top with Segoe or the log with the fixed width Serif font?
It is a compressed image and scaled and it all looks blurry now... not like on my machine

Please check your DPI settings

https://www.ghacks.net/2020/01/02/the-perils-of-running-windows-10-on-a-4k-monitor/
http://crsouza.com/2015/04/13/how-to-fix-blurry-windows-forms-windows-in-high-dpi-settings/

    static class Program
    {
        [STAThread]
        static void Main()
        {
            if (Environment.OSVersion.Version.Major >= 6)
                SetProcessDPIAware();

            Application.EnableVisualStyles();
            Application.SetCompatibleTextRenderingDefault(false);
            Application.Run(new MainForm());
        }

        [System.Runtime.InteropServices.DllImport("user32.dll")]
        private static extern bool SetProcessDPIAware();

Tried the fix mentioned in http://crsouza.com/2015/04/13/how-to-fix-blurry-windows-forms-windows-in-high-dpi-settings/ and the fonts now do look sharper/crispier, not sure this is recognizable in the screenshots below, but definitely looks better than before..

please verify the fix in this PR https://github.com/code-ready/tray-windows/pull/77 :)


[KED-1474] SparkSession initialized when using parallel runner throws random errors

Description
I'm trying to use Kedro's parallel runner to parallelize a few independent pipelines in my set of pipelines which have only sparkDatasets as input. Unfortunately  unable to do so since parallel runner with a pre established sparkSession. or spark-submit.
Context
How has this bug affected you? What were you trying to accomplish?
Trying to achieve parallelism when possible in my set of pipelines.
Set of pipelines I have run as expected when I use sequentialRunner. I have used both kedro cli and spark-submit and both these ways work just fine.
but when I use parallel runner, in spite of all intermediate results being persisted - execution stops soon after loading my first dataset returning me inconsistent/random errors.
This happens specifically if sparkSession is initialized in run.py as mentioned in the documentation.
Steps to Reproduce

setup at least 2 SparkDatasets in catalog
Define a simple node that reads and writes(also a sparkDataset) for a pipeline that can run in parallel for 2 datasets
Initialize sparkSession in run.py
Execute the pipeline with parallel runner

Expected Result
Execution be parallel and should complete without any issues. eg: 2 output files be created
Actual Result
A random set of errors are thrown from the execution.
There errors are in no order.
Same execution gives random errors in no order
##############################################################################################################
return SparkSession.builder.getOrCreate()
File "/usr/hdp/current/spark2-client/python/lib/pyspark.zip/pyspark/sql/session.py", line 183, in getOrCreate
session._jsparkSession.sessionState().conf().setConfString(key, value)
AttributeError: 'NoneType' object has no attribute 'conf'
###############################################################################################################
WARN FileStreamSink: Error while looking for metadata directory.
concurrent.futures.process._RemoteTraceback:
"""
Traceback (most recent call last):
File "/home/user.name/.local/lib/python3.6/site-packages/kedro/io/core.py", line 194, in load
return self._load()
File "/home/user.name/.local/lib/python3.6/site-packages/kedro/contrib/io/pyspark/spark_data_set.py", line 246, in _load
load_path, self._file_format, **self._load_args
File "/home/user.name/.local/lib/python3.6/site-packages/pyspark/sql/readwriter.py", line 166, in load
return self._df(self._jreader.load(path))
AttributeError: 'NoneType' object has no attribute 'load'
##################################################################################################################
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/usr/local/lib/python3.6/concurrent/futures/process.py", line 175, in _process_worker
r = call_item.fn(*call_item.args, **call_item.kwargs)
File "/home/user.name/.local/lib/python3.6/site-packages/kedro/runner/runner.py", line 180, in run_node
inputs = {name: catalog.load(name) for name in node.inputs}
File "/home/user.name/.local/lib/python3.6/site-packages/kedro/runner/runner.py", line 180, in
inputs = {name: catalog.load(name) for name in node.inputs}
File "/home/user.name/.local/lib/python3.6/site-packages/kedro/io/data_catalog.py", line 318, in load
result = func()
File "/home/user.name/.local/lib/python3.6/site-packages/kedro/io/core.py", line 560, in load
return super().load()
File "/home/user.name/.local/lib/python3.6/site-packages/kedro/io/core.py", line 203, in load
raise DataSetError(message) from exc
kedro.io.core.DataSetError: Failed while loading data from data set SparkDataSet(file_format=parquet, filepath=s3a://abc/file/).
'NoneType' object has no attribute 'load'
######################################################################################################################
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/Users/sriram_ravichandran/.virtualenvs/test/bin/kedro", line 8, in
sys.exit(main())
File "/Users/sriram_ravichandran/.virtualenvs/test/lib/python3.7/site-packages/kedro/cli/cli.py", line 638, in main
("Project specific commands", project_groups),
File "/Users/sriram_ravichandran/.virtualenvs/test/lib/python3.7/site-packages/click/core.py", line 764, in call
return self.main(*args, **kwargs)
File "/Users/sriram_ravichandran/.virtualenvs/test/lib/python3.7/site-packages/click/core.py", line 717, in main
rv = self.invoke(ctx)
File "/Users/sriram_ravichandran/.virtualenvs/test/lib/python3.7/site-packages/click/core.py", line 1137, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File "/Users/sriram_ravichandran/.virtualenvs/test/lib/python3.7/site-packages/click/core.py", line 956, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "/Users/sriram_ravichandran/.virtualenvs/test/lib/python3.7/site-packages/click/core.py", line 555, in invoke
return callback(*args, **kwargs)
File "/Users/sriram_ravichandran/dev/odysseyDemo/kedro_cli.py", line 274, in run
pipeline_name=pipeline,
File "/Users/sriram_ravichandran/.virtualenvs/test/lib/python3.7/site-packages/kedro/context/context.py", line 479, in run
return runner.run(filtered_pipeline, catalog)
File "/Users/sriram_ravichandran/.virtualenvs/test/lib/python3.7/site-packages/kedro/runner/runner.py", line 82, in run
self._run(pipeline, catalog)
File "/Users/sriram_ravichandran/.virtualenvs/test/lib/python3.7/site-packages/kedro/runner/parallel_runner.py", line 259, in _run
node = future.result()
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/concurrent/futures/_base.py", line 428, in result
return self.__get_result()
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/concurrent/futures/_base.py", line 384, in __get_result
raise self._exception
KeyError: 'c'
Your Environment
Include as many relevant details about the environment in which you experienced the bug:
env1

Kedro version used (pip show kedro or kedro -V): 0.15.5
Python version used (python -V): 3.6.3
Operating system and version: RHEL7
Spark : 2.3.2

env2

Kedro version used (pip show kedro or kedro -V): 0.15.5
Python version used (python -V): 3.7.5
Operating system and version: Mac Catalina
Spark : 2.4.5

Thanks for reporting this error! @adengappa, I've put it on our backlog to look into.

ThreadRunner was added to run Spark dataset concurrently.

Implemented in https://github.com/quantumblacklabs/kedro/commit/c79c21a50924b2f79f1207de167b27d7c7e8bef0


Update README.md

If you are submitting a change to index.md, you can go ahead and delete this template.
If you are submitting your badge completing file in !BADGE-SUBMISSIONS -- first, congratulations on completing your badge!
Make sure that you have met all the requirements before you submit:

[ ] Link to a Home Towns Pull Request
[ ] Link to main portfolio page

[ ] At least one heading
[ ] Some text
[ ] Links to other portfolio pages
[ ] At least one image

[ ] Link to a portfolio page that has a map generated with Python (this can be the main page again)
[ ] Link to climate change portfolio page

[ ] Instructions have been removed or replaced
[ ] Analysis of a different place (not Boulder)
[ ] Descriptive header
[ ] Site description
[ ] Analysis overview
[ ] Final figure
[ ] Headline for final figure

[ ] Link to vegetation portfolio page

[ ] Instructions have been removed or replaced
[ ] Analysis of a different event (not Cameron Peak fire)
[ ] Descriptive header
[ ] Site description
[ ] Analysis overview
[ ] Final figure
[ ] Headline for final figure

Hi @bradeckhardt - Here you would want to edit the index.md file rather than the README.md.
See if you can add your information to the index.md file here:
https://github.com/cu-esiil-edu/2024-data-short-course/blob/main/index.md
Try adding your information to the bottom of the Participants section using the model below
| Name      | Organization | GitHub Username |  GitHub Portfolio Link    |
Here, each entry sits between vertical bars (aka pipes) and can be found on the button above return on most keyboards.
I'm going to close this PR (pull request). You can submit another PR after you edit the index.md with your information. Thanks Brad! -NQ

Hi Nathan I believe we were able to add onto the participants list and
create the pull request. Please let me know if I need to do anything
different.
On Sat, May 18, 2024 at 9:27 AM Nathan Quarderer @.***>
wrote:

Hi @bradeckhardt https://github.com/bradeckhardt - Here you would want
to edit the index.md file rather than the README.md.
See if you can add your information to the index.md file here:
https://github.com/cu-esiil-edu/2024-data-short-course/blob/main/index.md
Try adding your information to the bottom of the Participants section
using the model below
| Name | Organization | GitHub Username | GitHub Portfolio Link |
Here, each entry sits between vertical bars (aka pipes) and can be found
on the button above return on most keyboards.
I'm going to close this PR (pull request). You can submit another PR after
you edit the index.md with your information. Thanks Brad! -NQ
—
Reply to this email directly, view it on GitHub
https://github.com/cu-esiil-edu/2024-data-short-course/pull/103#issuecomment-2118857591,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/A26RZ3ONYHEBGJM6MDTEGDLZC5XOFAVCNFSM6AAAAABHXAROGCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDCMJYHA2TONJZGE
.
You are receiving this because you were mentioned.Message ID:
@.***>

Got it @bradeckhardt and thank you! -NQ


fix complete_tf_315 test

@swift-ci please clean test tensorflow

The test failures are a subset of the expected test failures (from comment https://github.com/apple/swift/pull/24597#issuecomment-490324073), so I'm merging!


[Multiplayer] Sync headset, controllers and player action over network.

Sync headset, controllers and player action over network.

Is it possible with the currently provided unity server tools ?
How to view another player and his movements ( think camera mode in job simulator, or altspaceVR )

I've not actually used the Unity server tools, I watched a tutorial on it once, seems pretty easy, you just say which variables you want to sync.
It would be possible to view another player's hand and head movements by transmitting the transform of the objects (position/rotation).
It would suffer a latency issue for the view on the other end though so wouldn't be real time movements (I don't know what the lag would be like).
AltspaceVR seems to look decent enough when people move. I've seen people play Rock, Paper, Scissors using the virtual hands (provided by the LeapMotion) so the lag can't be that bad.

I tried to make ot work and I can get the server ui to show up ingame. But I have no clue how to show the headset and controllers to another player. And then there is a slight problem of not being able to test unless I get a second Vive :/

To make the headset and controllers show up, you'd need to have models in the game that would position based on the server variables.
I expect you'd need custom models as the ones in the camera rig wouldn't work (i dont think)

This isn't really a candidate for the toolkit. It can be possible to do with using Unity Networking or Photon PUN.
Basically you'd just need an object that was a child of the headset and controllers and is only enabled when the player is not the local player. Have this object transform synced over the network and this should do what you need.


Numpy large tensor test batch 4

This is the 4th batch of the numpy large tensors tests
@josephevans @access2rohit

@access2rohit  can you review?


Update ugdg and ppp

Coverage decreased (-0.07%) to 88.84% when pulling 3edb6f240b7fb7a08ba755fe8d0a24f24b4a1cf5 on zhengwei143:update-ugdg-and-ppp into c29faf7594cb6d5c3be937c8cecb9e793b2939f9 on CS2103-AY1819S1-T12-4:milestone-v1.4.


Gesture Handler does not work on Chrome based web views

Gesture handler cannot proceed after an error is thrown when it tries to modify target property of Touch events.
Got this error on  Android 6.0.1, Chrome 56.0.2924.87
Uncaught TypeError: Cannot assign to read only property 'target' of object '#<Event>'
    at GestureHandler$$module$node_modules$erste$src$lib$base$gesture_handler.onTouchend

Hm, I'm pretty sure this is something new... I remember reading about this.

Actually, in one of the last commits I had moved on to using target from another custom property. Apparently this was why we were using a custom property before.


Add missing setup.py packages entry

Addresses ModuleNotFoundError: No module named 'cubvh' error from git install: https://github.com/ashawkey/cubvh/issues/1#issuecomment-1382705618

@kulits Thanks for the contribution!


Update bottom margin to accommodate iPhone X home indicator

[x] I've read and understood the Contributing guidelines and have done my best effort to follow them.
[x] I've read and agree to the Code of Conduct.
[x] I've been mindful about doing atomic commits, adding documentation to my changes, not refactoring too much.
[x] I've a descriptive title and added any useful information for the reviewer. Where appropriate, I've attached a screenshot and/or screencast (gif preferrably).
[ ] I've written tests to cover the new code and functionality included in this PR.
[x] I've read, agree to, and signed the Contributor License Agreement (CLA).

PR Summary
This PR updates the bottom margin to accommodate the home indicator on iPhone X.
Related Issues
Fixes (at least the biggest issue with) #618

Guys any ETA when this pull request will get merged. I need to make my App ready for iPhone X as soon as possible.

@hipwelljo This updates the UI so the UITextView appears on the right place, but it doesn't react to touch on iOS 11 (no matter the iPhone version).
It seems that Slack is not maintaining this library anymore?

Bump

This commit didn't solve it for me.

This fixes the issue for me. Any chance this will get merged?

For me this fix isn't completely working, and I also can't understand how it should.
What happens if you scroll upwards in the conversation? For me the text appears underneath the input bar, under the home button of the iPhone X, because this PR literally adds a margin between the bottom of the screen and the input bar. So in my eyes this isn't a complete solution, or I'm completely missing something here. 🙂

@Qonstrukt I'm not seeing what you're seeing. When scrolling up, the bar still covers the text. If you set the background color of the input bar, the entire bar is colored, this should just add padding to the bottom of the bar.

Yes, I also see that in the first screenshot, but I can't understand how that's possible, as in, in my own layouts it's not working that way.
The vertical constraints are based on the following:
V:|[scrollView(0@750)][typingIndicatorView(0)]-0@999-[textInputbar(0)]|
Then a constrains keyboardHC is extracted between the bottom of the textInputbar and the bottom of container view. And this constraint is then set to the bottom margin which in turn is then set to self.view.safeAreaInsets.bottom in this PR. How is it then possible for the bottom bar to reach the bottom of the screen?
More specifically, what makes you say this works as a padding, an not as a margin? Just trying to understand what I might be doing wrong here. 🙂

@Qonstrukt I see what you mean. Checking out view debugger I see the input bar itself has a bottom margin of 34pt. Yet its background fills to the bottom of the screen. Which in my app and the demo this appears to be working exactly as desired, but perhaps it won't work for all setups as you're seeing.  Also I see hiding the input bar leaves the safe area amount visible. Seems this needs more attention to work properly for all situations.

iPhone X is due out 3rd November, that's 9 working days away! What's the likelihood of this getting fixed in time?

@dzenbot @Qonstrukt did have some concerns, mentioned it's not working as intended for his app, if you have any insight into why that would be or how it can be tweaked, it may need some attention there before being merged in.

I can merge that later tonight I'm sure. I can envision how that's a problem with the view instead of toolbar now. When it is a UIToolbar, I'm still confused why the toolbar itself is moved up yet the background of the bar still stretches to the bottom, as you can see in the view debugger.
I was thinking, a different approach could be to adjust constraints in the SLKTextInputbar instead of SLKTextViewController. I looked into that briefly, but there's a lot of items constrained to the bottom in there. I wasn't sure if that was a good approach anyways.

@dzenbot Ok merged and pushed up :) Your commit for hiding the input bar is working well.

@dzenbot that’s exactly what I’m experiencing yea. I have some time reserved next week to try and fix it, but I’ve translated this to C# for use with Xamarin so I can’t directly push it back, but will share my solution if I’ll fimd one before you guys. Might also try the UIToolbar approach.

Ok, so I found a working solution in this SO post:
https://stackoverflow.com/a/46448751/2782141
I've reverted back to a UIToolbar, and then added this line:
[self layoutSubViews];
In - (void)slk_commonInit, right above [self addSubview:self.editorContentView];
This combined with the solution in this PR makes the text input bar stretch all the way to the bottom with the correct margins.

Cool, merged and pushed :)

The position is initially correct now, but the textfield is now hidden behind the keyboard suggestions:


Create model to capture GA applicant Solicitor details

Before creating a pull request make sure that:

[x] commit messages are meaningful and follow good commit message guidelines
[x] README and other documentation has been updated / added (if needed)
[x] tests have been updated / new tests has been added (if needed)

Please remove this line and everything above and fill the following sections:
JIRA link (if applicable)
https://tools.hmcts.net/jira/browse/CIV-1697
Change description

Capture Civil Service Claimant and Defendant information.

Set GA Applicant and Respondent details and their organization details

Related PR
https://github.com/hmcts/civil-general-applications/pull/162
https://github.com/hmcts/civil-ccd-definition/pull/650/files
https://github.com/hmcts/civil-general-apps-ccd-definition/pull/117
Does this PR introduce a breaking change? (check one with "x")
[x] Yes
[ ] No


Add ClusterInfo as a property in StreamInfo

Signed-off-by: gargnupur<EMAIL_ADDRESS>For an explanation of how to fill out the fields, please see the relevant section
in PULL_REQUESTS.md
Description:Add ClusterName as a property in StreamInfo. This is because for TCP, there is no RouteEntry and thus we are not able to get clustername for TCP in scenarios where upstream cluster is not healthy
Risk Level: Low
Testing: Unit Tests
Docs Changes:
Release Notes:
[Optional Fixes #Issue]
[Optional Deprecated:]

@mattklein123 , @kyessenov : all the tests are  passing, can you please review it again?

@gargnupur please never force push. It makes reviews much more difficult. Thank you!

@gargnupur please never force push. It makes reviews much more difficult. Thank you!

@mattklein123 : Thanks for the feedback.. didn't know that.

@mattklein123 , @lizan : Can you guys please help merge this?

Thanks @mattklein123, @lizan and @kyessenov for the review and helping it merge it.


Attempt to go even faster

Two main things I did:

As all the dimensions you're vectorizing are perfectly matching the lane sizes you can move from chunks() to chunks_exact() this avoids a branching operation allowing the compiler to evict cmp, jmp and generating epilog "in case"

Emit a prefetching (x86_64 only - _mm_prefetech ) operation in the most inner loop doing the dequantization from zero point + scaling. It seems the compiler (or the CPU prefetech) is not catching the linear access over qzeros and generates cache misses. With the prefetch instruction we ask to load the next chunk into L1/L2[/L3] registers

Closing because portable port moved in.


Show panel if needed permission are given

Fixes #1524

So we will just go with a permission for kicking users which the server ignores/does not know. But when we giv it to all mods(the moderation) group that should be fine. Well some other time the server can be changed to respect the new Permission System.

TODO: Use correct string from @Brutus5000 PR https://github.com/FAForever/server/pull/509/files

In sync with server PR

@Brutus5000 I need a review here

The server access token stuff will be placed on top of this.

The server PR got merged

I wanna make the switch to faf comoons in a different PR


document java dependency for running local openwhisk jar

need java 8 to run the jar
https://github.com/apache/openwhisk/blob/master/tools/macos/README.md#prerequisites

Done: https://github.com/AdobeDocs/project-firefly/blob/master/getting_started/technical_requirements.md


Field Validation

i have issue in validation. Suppose if user doesn't fill the phone field then form validation is not working until we interact with the field
          IntlPhoneField(
            initialCountryCode: 'IN',
            decoration: const InputDecoration(
              border: UnderlineInputBorder(),
              labelText: 'Mobile Number',
              labelStyle: TextStyle(color: Colors.black),
            ),
            validator: (value){print("sss${value}");
              if(value == null || value.number== "" ||value.number.toString().isEmpty){
                return "Please Enter Mobile Number";
              }
              return null;
            },
            onChanged: (phone) {
              setState(() {
                fullPhone = phone.completeNumber;
              });
            },
          ),

if i do this :
autovalidateMode: AutovalidateMode.always,
it shows validation before trigger on button so it's not accurate for me
can you gave me solution of this may be i missed something but i want validation on empty filed not on intreraction.

I was facing the same and I figured its an issue with the code. But since the repo is no longer maintained, we have to make our own fork and fix it there.
You can take a look at how I fixed it here
However, I'm pretty sure this would stop your custom validator from working if you're using one. I wasn't and I couldn't be bothered about it so I did this as a quick fix.

Thanks @hani-hj1908619 Your solution worked for me, I just copy this file intl_phone_field.dart and use after the rename file and class name.

all these participants save my day
thank you @hani-hj1908619  @devsideal  @rawatpb65

Pass your validator and set autovalidateMode on it. This solved my problem
IntlPhoneField(
  validator = (v) {
    if (v == null || v.number.isEmpty) {
      return 'Phone number is required';
    }
    return null;
  },
  autovalidateMode = AutovalidateMode.always,
),


Custom Dataset training

Hello,
Thanks for your awesome work!
I have a question to ask the author, is it possible to learn by including attribute values and images that do not exist in the pretrained model in custom data? (ex. Can I put new Food image and Food attribute values in CelebHQ pretrained model?)
Looking forward for your reply, Thank you!

No, it's unlikely to work. You cannot apply food images to the CelebA-HQ model. (maybe)
The generative model needs to have the ability to generate outputs similar to what you want.
However, CLIP has a strong capability for editing out-of-domain information, so it might be worth trying.
If you want to work with food data, consider using a pre-trained ImageNet model.
Check #1
Thank you.

Thank you for answer!
In terms of performance, I think using ImageNet pretrained means that it is excellent,
but does that mean there is no problem with learning itself?

Thank you for answer!
In terms of performance, I think using ImageNet pretrained means that it is excellent, but does that mean there is no problem with learning itself?

I think that you can try to train your own diffusion model first, then you can make good use of this project.

That made perfect sense. thank you :)


Use kubectl create (without specifying namespace) for sock-shop app

Fix error while deploying sock-shop app (#3214)

This change is

Closing due to incorrect email address in commits.


Support container creation when resource quotas are hit

Currently, when kubedock creates pods and you hit a resource quota, the request fails with this error:
{"message":"pods \"kubedock-a4a0dbae1bd2\" is forbidden: exceeded quota: tenant-quota, requested: limits.cpu=2, used: limits.cpu=31650m, limited: limits.cpu=32"}

Context: we run kubedock as a sidecontainer on Tekton to support java testcontainers.
Is it possible to support creating pods in this situation without having the request fail immediately?
Some solutions I considered:

Use a Deployment instead of a Pod to schedule the container
Use a retry mechanism

And maybe there are other options?

In earlier versions kubedock was using deployments, and had the option to use jobs. Deployments were problematic when the orchestrated containers were actually one-off jobs (jobs did solve that).
A retry option might make sense and probably leads to increased succes when orchestrating, but tests might still fail because of time-outs in the actual tests as well.
Something that might work already, is setting lower requests and limits (via labels, global settings, or a global pod template).

Yes, lowering requests and limits relieves the problem temporarily, and it's definitely something we're doing now to work around the problem, but it does not solve the problem permanently - even on lower limits the pod might hit the quota and get denied.


bitcoin: 25.0 -> 25.1

Description of changes

Things done

Built on platform(s)

[ ] x86_64-linux
[ ] aarch64-linux
[ ] x86_64-darwin
[ ] aarch64-darwin

[ ] For non-Linux: Is sandbox = true set in nix.conf? (See Nix manual)
[ ] Tested, as applicable:

NixOS test(s) (look inside nixos/tests)
and/or package tests
or, for functions and "core" functionality, tests in lib/tests or pkgs/test
made sure NixOS tests are linked to the relevant packages

[ ] Tested compilation of all packages that depend on this change using nix-shell -p nixpkgs-review --run "nixpkgs-review rev HEAD". Note: all changes have to be committed, also see nixpkgs-review usage
[ ] Tested basic functionality of all binary files (usually in ./result/bin/)
23.11 Release Notes (or backporting 23.05 Release notes)

[ ] (Package updates) Added a release notes entry if the change is major or breaking
[ ] (Module updates) Added a release notes entry if the change is significant
[ ] (Module addition) Added a release notes entry if adding a new NixOS module

[ ] Fits CONTRIBUTING.md.

cc @prusnak @0xB10C. Would also be great to get this backported to any other branches with 25.0, if possible.

ACK 53793ca0aecf67164c630ca34dbfde552a8ab085. The source hash looks good to me.


Update multi-factor-authentication-manage-users-and-devices.md

@Banani-Rath,
Thanks for your contribution as a Microsoft full-time employee or intern. You do not need to sign a CLA.
Thanks,
Microsoft Pull Request Bot

@Banani-Rath : Thanks for your contribution to the Azure documentation! The author, @kgremban, has been notified to review your proposed change.

@Banani-Rath Can you explain your reason for making these changes? Both the list numbering and the punctuation were correct before your changes.

#please-close