[CLI]: Multi-node training with Horovod fails to start

Describe the bug

While a single-node, multi-gpu training works as expected when wandb is used within a PyTorch training code with Horovod, training fails to start when I use > 1 node.

from __future__ import print_function
# below two lines are for fixing hanging issue for wandb
#import os
#os.environ['IBV_FORK_SAFE']=''
# -------------------------------------------------------
import argparse
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
import torch.utils.data.distributed

# Horovod: import horovod
import horovod.torch as hvd

import time

# Training settings
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument('--batch_size', type=int, default=64, metavar='N',
                    help='input batch size for training (default: 64)')
parser.add_argument('--test-batch-size', type=int, default=64, metavar='N',
                    help='input batch size for testing (default: 64)')
parser.add_argument('--epochs', type=int, default=32000, metavar='N',
                    help='number of epochs to train (default: 10)')
parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
                    help='learning rate (default: 0.01)')
parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
                    help='SGD momentum (default: 0.5)')
parser.add_argument('--seed', type=int, default=42, metavar='S',
                    help='random seed (default: 42)')
parser.add_argument('--log-interval', type=int, default=100, metavar='N',
                    help='how many batches to wait before logging training status')
parser.add_argument('--fp16-allreduce', action='store_true', default=False,
                    help='use fp16 compression during allreduce')
parser.add_argument('--device', default='cpu',
                    help='Wheter this is running on cpu or gpu')
parser.add_argument('--wandb', action='store_true',
                    help='whether to use wandb to log data')
parser.add_argument('--num_threads', default=0, help='set number of threads per worker', type=int)
parser.add_argument('--project', default="sdl-pytorch-mnist", type=str)
parser.add_argument('--num_workers', default=1, help='set number of io workers', type=int)

args = parser.parse_args()

args.cuda = args.device.find("gpu")!=-1
# Horovod: initialize library.
hvd.init()

if args.wandb and hvd.rank()==0:
    try:
        import wandb
        #wandb.require("service")
        wandb.init(project=args.project)
    except:
        args.wandb = False
    config = wandb.config          # Initialize config
    config.batch_size = args.batch_size         # input batch size for training (default: 64)
    config.test_batch_size = args.test_batch_size    # input batch size for testing (default: 1000)
    config.epochs = args.epochs            # number of epochs to train (default: 10)
    config.lr = args.lr              # learning rate (default: 0.01)
    config.momentum = args.momentum         # SGD momentum (default: 0.5)
    config.device = args.device        # disables CUDA training
    config.seed = args.seed               # random seed (default: 42)
    config.log_interval = args.log_interval     # how many batches to wait before logging training status
    config.num_workers = hvd.size()

torch.manual_seed(args.seed)
print("Horovod: I am worker %s of %s." %(hvd.rank(), hvd.size()))
if args.device.find("gpu")!=-1:
    # Horovod: pin GPU to local rank.
    torch.cuda.set_device(hvd.local_rank())
    torch.cuda.manual_seed(args.seed)
if (args.num_threads!=0):
    torch.set_num_threads(args.num_threads)

if hvd.rank()==0:
    print("Torch Thread setup: ")
    print(" Number of threads: ", torch.get_num_threads())

kwargs = {'num_workers': args.num_workers, 'pin_memory': False} if args.device.find("gpu")!=-1 else {}
train_dataset = \
    datasets.MNIST('datasets/', train=True, download=True,
                   transform=transforms.Compose([
                       transforms.ToTensor(),
                       transforms.Normalize((0.1307,), (0.3081  ,))
                   ]))
# Horovod: use DistributedSampler to partition the training data.
train_sampler = torch.utils.data.distributed.DistributedSampler(
    train_dataset, num_replicas=hvd.size(), rank=hvd.rank())
train_loader = torch.utils.data.DataLoader(
    train_dataset, batch_size=args.batch_size, sampler=train_sampler, **kwargs)

test_dataset = \
    datasets.MNIST('datasets', train=False, transform=transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.1307,), (0.3081,))
    ]))
# Horovod: use DistributedSampler to partition the test data.
test_sampler = torch.utils.data.distributed.DistributedSampler(
    test_dataset, num_replicas=hvd.size(), rank=hvd.rank())
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=args.test_batch_size,
                                          sampler=test_sampler, **kwargs)

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x)

model = Net()
if args.wandb and hvd.rank()==0:
    wandb.watch(model)
if args.device.find("gpu")!=-1:
    # Move model to GPU.
    model.cuda()

# Horovod: scale learning rate by the number of GPUs.
optimizer = optim.SGD(model.parameters(), lr=args.lr * hvd.size(),
                      momentum=args.momentum)

# Horovod: broadcast parameters & optimizer state.

hvd.broadcast_parameters(model.state_dict(), root_rank=0)
hvd.broadcast_optimizer_state(optimizer, root_rank=0)

# Horovod: (optional) compression algorithm.
compression = hvd.Compression.fp16 if args.fp16_allreduce else hvd.Compression.none
# Horovod: wrap optimizer with DistributedOptimizer.
optimizer = hvd.DistributedOptimizer(optimizer,
                                     named_parameters=model.named_parameters(),
                                     compression=compression)

def train(epoch):
    model.train()
    running_loss = 0.0
    training_acc = 0.0
    # Horovod: set epoch to sampler for shuffling.
    train_sampler.set_epoch(epoch)
    for batch_idx, (data, target) in enumerate(train_loader):
        if args.cuda:
            data, target = data.cuda(), target.cuda()
        optimizer.zero_grad()
        output = model(data)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        pred = output.data.max(1, keepdim=True)[1]
        training_acc += pred.eq(target.data.view_as(pred)).cpu().float().sum()
        running_loss += loss.item()

        if batch_idx % args.log_interval == 0:
            # Horovod: use train_sampler to determine the number of examples in
            # this worker's partition.
            print('[{}] Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(hvd.rank(),
                epoch, batch_idx * len(data), len(train_sampler), 100. * batch_idx / len(train_loader), loss.item()/args.batch_size))
    running_loss = running_loss / len(train_sampler)
    training_acc = training_acc / len(train_sampler)
    loss_avg = metric_average(running_loss, 'running_loss')
    training_acc = metric_average(training_acc, 'training_acc')
    if hvd.rank()==0: print("Training set: Average loss: {:.4f}, Accuracy: {:.2f}%".format(loss_avg, training_acc*100))
    return loss_avg, training_acc

def metric_average(val, name):
    tensor = torch.tensor(val)
    avg_tensor = hvd.allreduce(tensor, name=name)
    return avg_tensor.item()

def test():
    model.eval()
    test_loss = 0.
    test_accuracy = 0.
    n = 0
    for data, target in test_loader:
        if args.cuda:
            data, target = data.cuda(), target.cuda()
        output = model(data)
        # sum up batch loss
        #test_loss += F.nll_loss(output, target, size_average=False).item()
        test_loss += F.nll_loss(output, target).item()
        # get the index of the max log-probability
        pred = output.data.max(1, keepdim=True)[1]
        test_accuracy += pred.eq(target.data.view_as(pred)).cpu().float().sum()
        n=n+1

    # Horovod: use test_sampler to determine the number of examples in
    # this worker's partition.
    test_loss /= len(test_sampler)
    test_accuracy /= len(test_sampler)

    # Horovod: average metric values across workers.
    test_loss = metric_average(test_loss, 'avg_loss')
    test_accuracy = metric_average(test_accuracy, 'avg_accuracy')

    # Horovod: print output only on first rank.
    if hvd.rank() == 0:
        print('Test set: Average loss: {:.4f}, Accuracy: {:.2f}%\n'.format(
            test_loss, 100. * test_accuracy))
    return test_loss, test_accuracy

t0 = time.time()
for epoch in range(1, args.epochs + 1):
    tt0=time.time()
    train_loss, train_acc = train(epoch)
    test_loss, test_acc = test()
    tt1 = time.time()
    if (hvd.rank()==0):
        print("[Epoch - %d] Time per epoch: %10.6f." %(epoch, tt1 - tt0))
    if (hvd.rank()==0) and args.wandb:
        wandb.log({'time_per_epoch':tt1 - tt0,
            "training_loss": train_loss, "training_acc": train_acc,
            "test_loss": test_loss, "test_acc":test_acc}, step=epoch)
t1 = time.time()
if hvd.rank()==0:
    print("Total training time: %s seconds" %(t1 - t0))

wandb: Tracking run with wandb version 0.13.3
wandb: Run data is saved locally in /lus/grand/projects/RNAtoImage/src/test_horovod/sdl_workshop/distributedDeepLearning/Horovod/wandb/run-20230408_071302-2cm2rxkm
wandb: Run `wandb offline` to turn off syncing.
wandb: Syncing run dandy-shadow-10
wandb: ⭐ View project at https://wandb.ai/tnnandi/mnist_hvd
wandb: 🚀 View run at https://wandb.ai/tnnandi/mnist_hvd/runs/2cm2rxkm
(hangs here for multi-node jobs)

Additional Files
No response
Environment
WandB version: 0.13.3
OS: Linux
Python version: 3.8.13
Versions of relevant libraries: horovod 0.25.0,
Additional Context
No response

Hi @tnnandi thanks for reporting this issue for a multi-node system. I can confirm that Horovod/wandb should work for a single-node multi-gpu. May I please ask in which infrastructure are you running this code, is it on a cloud provider?
Could you please provide us with the debug.log and debug-internal.log files from this run that hangs? These should be in your local disk where you've executed the script under /wandb/run--/logs directory. Also, I would suggest that you upgrade to our most recent client/SDK version v0.14.2 and try there again.

Hi @tnnandi just checking in here to see if the issue still persists for you, and if you could provide us with any additional information requested in the previous message to help us debug further. Thanks!

Hi @thanos-wandb , thank you very much for the response and I apologize for not getting back to you earlier. I saw your response only yesterday and is planning to give it a try today. I'll get back to you with my observations shortly.

Hi @thanos-wandb , I just tested with wandb 0.14.2 and had the exact same issue. Please find attached the log files you requested for.
debug-internal.log
debug.log

Hi @tnnandi thanks for sending these debug files, but there isn't enough information on these to further debug. It seems to hang somewhere for multi-node training. I have attached another example with Sweeps and Horovod, could you please give this a go (you will need to change the entity variable in horovod-keras-wandb.py script)?  I will then try to reproduce on my end too.
HorovodExample.zip

Hi @thanos-wandb , thank you very much for the response. I use something like "aprun -n 8 -N 4  python pytorch_mnist.py --device gpu --num_workers=0 --wandb --project mnist_hvd >& pytorch_mnist.out.8" to run the scripts. I'll try your example shortly.

Hi @tnnandi thanks for the command, it seems you're using aprun to launch jobs in a Cray computing system, and are you specifying somewhere a horovodrun command?
I was wondering if your code works without wandb or if it's multi-node horovod issue, such as this issue here. You could easily test this by disabling wandb either by the CLI command: wandb disabled or using the env variable: WANDB_MODE='disabled'. This will then treat any wandb call as no-op. Please let us know if that worked for you.

Hi @tnnandi just checking in here to see if you have tried to run your code without wandb, so as to help us root cause this. Thank you!

Hi @tnnandi since we haven't heard back from you on this, I will go ahead and close this ticket for now. If you're still experiencing any issues, please feel free to reopen this and we will be happy to keep investigating!


[dask] Result shape from DaskLGBMClassifier.predict(pred_contrib=True) for CSC matrices is inconsistent with LGBMClassifier

See the discussion in https://github.com/microsoft/LightGBM/pull/3866#discussion_r566106219 for full details.
lightgbm.dask.DaskLGBMClassifier tries to stay as close as possible to the API of lightgbm.sklearn.LGBMClassifier. This feature describes one known inconsistency.
In lightgbm.sklearn.LGBMClassifier, for multiclass classification tasks, if you call .predict(X, pred_contrib=True) and X is a scipy.sparse.csc_matrix, the result will be a list of CSC matrices, 1 per class.
In lightgbm.dask.DaskLGBMClassifier, for multiclass classification taks, if you call .predict(X, pred_contrib=True) and X is a Dask Array whose partitions are each a scipy.sparse.csc_matrix, the result will be a Dask Array that, once .compute()'d, returns a scipy.sparse.coo_matrix.
To complete this feature, try to make Dask's behavior match the behavior from lightgbm.sklearn.LGBMClassifier, or document why that can't / shouldn't be done.

Added this to #2302, where we store feature requests for this project. Anyone is welcome to contribute this feature. Leave a comment below if you'd like to pick it up and the issue can be re-opened.

Added this to #2302, where we store feature requests for this project. Anyone is welcome to contribute this feature. Leave a comment below if you'd like to pick it up and the issue can be re-opened.

One addition:

In lightgbm.sklearn.LGBMClassifier, for multiclass classification tasks, if you call .predict(X, pred_contrib=True) and X is a scipy.sparse.csc_matrix, the result will be a list of CSC matrices, 1 per class.

The same is true for CSR matrix as well.
See the following core Python API test to better understand what is expected:
https://github.com/microsoft/LightGBM/blob/217642ca716df8cefb592630907a4e658cd3439a/tests/python_package_test/test_engine.py#L1058-L1100

One addition:

In lightgbm.sklearn.LGBMClassifier, for multiclass classification tasks, if you call .predict(X, pred_contrib=True) and X is a scipy.sparse.csc_matrix, the result will be a list of CSC matrices, 1 per class.

The same is true for CSR matrix as well.
See the following core Python API test to better understand what is expected:
https://github.com/microsoft/LightGBM/blob/217642ca716df8cefb592630907a4e658cd3439a/tests/python_package_test/test_engine.py#L1058-L1100

re-opening this to note that I am currently working on a fix for this, to try to unblock #4351


fix: escape keywords in code generation for idl service method arguments

Motivation
To solve the issue when a argument name in an IDL service method conflicts with a Rust syntax keyword, which results in a compilation error in the generated code.
service HelloService {
    HelloResponse Hello1 (1: HelloRequest type)
    HelloResponse Hello2 (1: HelloRequest self)
}

Solution
In the above example, type is a keyword in rust, so we will generate this field name as r#type in code. And when you use volo cli tool to initialize your project, this field name will be generated as _type in the server code template, so you should rename it as r#type when using it. In the two scenes, keyword like self will be generated as _self.

#495


Fix typo in README.md

line 151 in README.md reads require("path.to.concord").aorlds instead of  require("path.to.concord").assemblages

The master branch is already kinda deprecated, and work has continued on the dev branch, I'm fixing the docs right now, including the README, to accommodate all the changes, and Assemblages will be gone in the next version so that's the reason why I haven't merged this nor #24.
Thanks for your PR! Hope you can send one if you find any other mistakes once the next version is properly released

Since dev was merged into master this change doesn't apply anymore
I thank you a lot for this PR, if you find any other issues all PRs are welcomed

Since dev was merged into master this change doesn't apply anymore
I thank you a lot for this PR, if you find any other issues all PRs are welcomed


Synchronous Vertx as dependency for engine api

PR description
Make synchronous vertex EngineJsonRpcMethods a dependency instead of creating a new one. Key reasons for this change:

allow other methods that require synchronization with engine_ to share same vertx instance. (e.g rollup_ extension method in #4146)
improve testability, before some tests failed with too many files open because vext inside EngineJsonRpcMethods was not being closed on test's afterEach hook;

Fixed Issue(s)

Documentation

[x] I thought about documentation and added the doc-change-required label to this PR if
updates are required.

Changelog

[x] I thought about the changelog and included a changelog update if required.

I think it's fine, feels no worse than what is there already - would appreciate a second opinion.


egctl support metrics

Since easegress and etcd both use prometheus to report metrics. Our metrics through /apis/v2/metrics already contains metrics in etcd.  (#526 )

Codecov Report
All modified and coverable lines are covered by tests :white_check_mark:

Comparison is base (4a57baf) 81.23% compared to head (934aa75) 81.21%.

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Additional details and impacted files
@@            Coverage Diff             @@
##             main    #1137      +/-   ##
==========================================
- Coverage   81.23%   81.21%   -0.03%
==========================================
  Files         148      148
  Lines       16623    16623
==========================================
- Hits        13504    13500       -4
- Misses       2480     2484       +4
  Partials      639      639

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Style + Util: Add subtle border and shadow to objects

This adds a bit of a performance hit, but it looks very pretty :)
Lu edit: Closes #108

(this is a test)
@all-contributors please add @jessiejs for design and code


FR: Anonymous settings

Currently there is an "anonymous" setting which stops all personal information from getting sent to Sentry.
However, for something like a membership site, some of this information can be very useful. But for GDPR reasons we don't want to send anything that can be considered personally identifiable information.
It would be useful to be able to choose what sensitive information is sent. For example a User ID is ok to send, but a Username or Email is not.
Is it possible to specify which PII gets sent?

Good idea @Joobs, thanks :) I've read the documentation about the send_default_pii parameter and I'm thinking of disabling it in favor of manually set the user in the Sentry scope.
A good solution can be to set the anonymous parameter to an array of key => boolean like this. Sounds good ?
'enabled' => true,
'anonymous' => [
    'email' => false,
    'id' => true,
    'ip_address' => false,
    'username' => false,
],
'dsn' => App::env('SENTRY_DSN'),
'release' => App::env('SENTRY_RELEASE'),
'environment' => App::env('SENTRY_ENVIRONMENT'),
'levels' => ['error', 'warning'],
'options' => [],
'exceptCodes' => [403, 404],
'exceptPatterns' => [],

Hi @Joobs! This was added in the latest release using the new userPrivacy setting.
https://github.com/diginov/craft-sentry-logger/blob/main/README.md#userprivacy


fix zkMonitor bug

What is the purpose of the pull request
This pull request is for fix bug in MonitorService

Codecov Report

:exclamation: No coverage uploaded for pull request base (dev@2106374). Click here to learn what that means.
The diff coverage is 0%.

@@          Coverage Diff           @@
##             dev    #1645   +/-   ##
======================================
  Coverage       ?   13.41%
======================================
  Files          ?      282
  Lines          ?    13906
  Branches       ?     2249
======================================
  Hits           ?     1866
  Misses         ?    11834
  Partials       ?      206

Impacted Files
Coverage Δ

...e/dolphinscheduler/api/service/MonitorService.java
22.72% <0%> (ø)

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Fix dotty runtime error

reproduce
in http4s-finagle
sbt "++ 0.24.0-RC1 test"

will get runtime exception
May 14, 2020 1:21:20 PM com.twitter.util.RootMonitor$$anonfun$2 applyOrElse
SEVERE: Fatal exception propagated to the root monitor!
java.lang.AbstractMethodError: Method org/http4s/dsl/io$.org$http4s$dsl$impl$Methods$_setter_$GET_$eq(Lorg/http4s/Method$Semantics$Safe;)V is abstract
	at org.http4s.dsl.io$.org$http4s$dsl$impl$Methods$_setter_$GET_$eq(io.scala)
	at org.http4s.dsl.impl.Methods.$init$(Methods.scala:12)
	at org.http4s.dsl.io$.<clinit>(io.scala:11)

fix
publishLocal and rerun sbt "++ 0.24.0-RC1 test" in http4s-finagle
dotty runtime is not binary compat to initialize PermitsBody because it extends an abstract class
well I found it is unnecessary to extends though because all methods are new Method extended these two trait, kind of duplicated

i don't understand, CI complain
[error] There are files without headers!
[error]   /home/runner/work/http4s/http4s/async-http-client/src/main/scala/org/http4s/client/asynchttpclient/NoOpCookieStore.scala

but I didn't change anything there... :man_shrugging:

We opened a PR that added a file before sbt-headers, and merged it after.  CI never ran on the merge until it was already on master.  That's what broke your build.  I pushed a fix in 1f009aad74.

The IsPermitsBody seems like it gets us back to about the same place as we were, and nothing I see is extending it.  I'm confused why that test still passes.

IsPermitsBody is replacing the old PermitsBody for the unit test
dotty is fine if nothing extends IsPermitsBody

My point is that nothing extends that IsPermitBody trait, so the case is never true.  The test passes if we remove that case, so we don't need to clutter Method with the new trait.

oh, now it is weird when you point out... :thinking: should I really delete the test case?

I looked closer at it while attempting to simplify the whole method-properties-by-subtyping mess, and that test is marked pendingUntilFixed.  This dates back to before the body was fs2, and we could examine the algebra to determine if there were any effects.  When we lost that, we lost some functionality in this middleware.
I don't think we should introduce a new trait to core to support a test that doesn't pass.  This says what we need in the test, and keeps core clean:
case (POST | PUT) if body.nonEmpty =>

are we waiting for another review to merge?

We usually look for two reviews on code, one on docs. I like to make sure people have a chance to weigh in, but I also don't like good work to be held up, and reviews come in bursts on a volunteer project.  Let's merge it, and if anyone sees something we missed, there's always the next PR.


Add D2V support (back)

This PR fixes the lost d2v support, as not everyone uses DGIndexNV (e.g. those with AMD GPUs).

Adds IndexExists.PATH_IS_D2V and IndexExists.D2V_EXISTS, mirroring the behavior for the dgi types
Adds checking for d2v
Replaces the nonexistent core.dgdecode.DGSource with core.dgdecode.MPEG2SOURCE

The first argument of this must be the d2v, but the remaining args and kwargs due to the differences to dgdecodenv

Replaces check_output with check_call since the output of the command is irrelevant. Unless nvidia-smi sets a non-zero exit code when called without arguments. This needs testing on another system

Marked it as a draft due to the differences between dgdecode and dgdecodenv, will fix it later on

Ideas we talked about:

Reverse the percentage check (d2v's mark the % VIDEO, not FILM. To resolve this, we can simply do abs(PERCENTAGE - 100), I think? Need to check whether it's consistent at the same place, and whether any values change given different types of files.
Different d2v indexer than dgindex? Currently it has trouble indexing certain file types (like mkv), which isn't ideal when you explicitly need it (like for remuxes, for example). Not sure I wanna add explicit support, or just tell users to call the functions directly themselves
d2vsource as source filter for d2v files instead of dgdecode
dgdecode with d2v.ApplyRff


Add an author whitelist for secure mode

I use hundreds of modules that I wrote that don't have many stars.
It'd be really handy to also be able to allow automatic installation of my own modules via a white-list.

@KoryNunn Added in https://github.com/siddharthkp/auto-install/pull/41
Usage: auto-install --secure --trust-author KoryNunn
Does not support multiple authors right now. According to me that would be cli parameters overkill. I am thinking of moving these --flags to package.json. What do you think?
Example:
"auto-install": {
    "secure": "true",
    "trusted-authors": ["KoryNunn", "siddharthkp"]
},

Looks good.


Update devicelab ios phones to 14.4.2

Some of the phones have been force upgraded to ios 14.4.2 to avoid having inconsistent versions and/or lose capacity we will update all the devicelab phones to use 14.4.2.

\cc @xster @jmagman

\cc @keyonghan please close when the phones are fully recovered.

This is now complete.


Docker backend doesn't support customresponseheaders with ":" or ","

Do you want to request a feature or report a bug?
BUG
What did you do?
As a workaround to https://github.com/containous/traefik/issues/2493, I attempted to use customresponseheaders to set the XFO header:
"traefik.frontend.headers.customresponseheaders" = "X-Frame-Options:ALLOW-FROM https://domain.tld"
What did you expect to see?
The XFO header being set on all responses.
What did you see instead?
The header is not set.
Output of traefik version: (What version of Traefik are you using?)

If applicable, please paste the log output in debug mode (--debug switch)
time="2017-12-04T06:34:04Z" level=error msg="Could not load "traefik.frontend.headers.customresponseheaders""

/cc @dtomcej

Closed by #2509


Integration Issues with new and old ContentModule/Filter implementations

This issue is a Discussion
Related issue(s) and/or pull request(s)
None
Expected Behaviour
N/A
Observed behaviour
Sony team is trying to integrate URMS with Readium but the design/implementation of ContentModule/Filters has changed and there are questions about the proper integration approach.
Sony/URMS team wrote:
We have some questions after checking “feature/lcp” branch which seems to contain a lot of changes.

Our solution does not use ContentModule approach, just one ContentFilter which is decrypting appropriate resources. Is this way still valid/supported or do we need to implement new ContentModule for this purpose ? The code snippet below contains some comments about non-encrypted loading(RED comment below) when no ContentModule is available.  However it seems to be working with encrypted resources when suitable ContentFilter is registered. using commit [24e004a] in this branch.
ContainerPtr Container::OpenContainer(const string &path) {
     ContainerPtr container = ContentModuleManager::Instance()->LoadContentAtPath(path);

     // 1: everything went well, we've got an encrypted EPUB handled by a ContentModule
     if (bool(container)) {
     return std::move(container);
 }

 // 2: no ContentModule was suitable, let's try non-encrypted loading // <— HERE
 container = OpenContainerForContentModule(path);
 if (bool(container)) {
     return std::move(container);
 } else {
     return nullptr;
 }

 // 3: there's always the option of a raised exception, which the caller captures to
 degrade gracefully

}

Some of our customers will probably try to use more DRM systems in one Application.
For     example, would be possible to make working solution with LCP and standalone Marlin
ContentFilter or is needed to implement one more ContentModule for Marlin and register both
in ContentModuleManager? Basically what is suggested way to implement multi-DRM support using one Readium-SDK instance.

Test file(s)
N/A
Product
SDK
Additional information
None yet

At this point in time, the feature/lcp branch of the readium-sdk GitHub repository ( https://github.com/readium/readium-sdk/tree/feature/lcp ) contains many changes that do not directly relate to LCP / DRM functionality, but that were nonetheless seen as important / necessary in order to efficiently debug the LCP "client lib" implementation, and to work around some compiler toolchain limitations. For example (just from the top of my head), we removed dead code / unused functionality, we disabled thread usage, and we eliminated the levels of indirections that managed smart pointer allocations (templated C++ code).
I plan to break down the code diff into separate manageable pieces, to narrow down the DRM scope (essentially: "Content Filter" and "Content Module"). I appreciate that a Pull Request could not realistically be created based on the existing bulk code changes.
The key API "entry-points" are RegisterFilter():
https://github.com/readium/readium-sdk/blob/feature/lcp/ePub3/ePub/filter_manager_impl.cpp#L37
...and RegisterContentModule() (which in turn invokes RegisterFilter()):
https://github.com/readium/readium-sdk/blob/feature/lcp/ePub3/ePub/content_module_manager.cpp#L63
Although it is recommended to use the "Content Module" facility to bootstrap multiple DRM schemes, it is technically possible to directly use the lower-level "Content Filter" construct. However, an implementor would pretty much have to write from scratch the equivalent of Readium's "Content Module" in order to manage several "Content Filters".
The ContentModuleManager::LoadContentAtPath() function "dispatches" the actual resource decryption requests to actual compatible Content Module / Filters:
https://github.com/readium/readium-sdk/blob/feature/lcp/ePub3/ePub/content_module_manager.cpp#L141
    ContainerPtr
    ContentModuleManager::LoadContentAtPath(const string& path)
    {
        if (_known_modules.empty())
        {
            return nullptr;
        }

        for (auto& item : _known_modules)
        {
            std::shared_ptr<ContentModule> modulePtr = item.second;

            ContainerPtr container = modulePtr->ProcessFile(path);

            if (bool(container)) {

                // Singleton FilterManagerImpl::RegisterFilter() uses .emplace() with unique keys,
                // so ContentFilters with the same name => only the first inserted one is preserved (subsequent insertions ignored).
                modulePtr->RegisterContentFilters();

                // Problem: container was loaded okay, but if Navigation Document was encrypted,
                // then because the ContentFilters were not registered yet:
                // the NavDoc XHTML silently failed to load (populate the internal ReadiumSDK data structures)
                // as the byte buffers were scrambled. So, we need to either reload the entire EPUB, or reload only the navdoc.
                std::shared_ptr<Package> package = container->DefaultPackage();
                package->Unpack_Finally(true);

                return std::move(container);
            }
        }

        return nullptr;
    }

Each concrete (registered) Content Module's ProcessFile() function call determines whether a particular DRM scheme handles a given EPUB publication. If no DRM module is capable of handling the loading EPUB, or if the EPUB is not encrypted, or if no Content Modules are registered at all, then OpenContainerForContentModule() is invoked to completely bypass the DRM pipeline, as shown in Container::OpenContainer():
https://github.com/readium/readium-sdk/blob/feature/lcp/ePub3/ePub/container.cpp#L169
    ContainerPtr Container::OpenContainer(const string &path) {

        ContainerPtr container = ContentModuleManager::Instance()->LoadContentAtPath(path);

        // 1: everything went well, we've got an encrypted EPUB handled by a ContentModule
        if (bool(container)) {
            return std::move(container);
        }

        // 2: no ContentModule was suitable, let's try non-encrypted loading
        container = OpenContainerForContentModule(path);
        if (bool(container)) {
            return std::move(container);
        } else {
            return nullptr;
        }

        // 3: there's always the option of a raised exception, which the caller captures to degrade gracefully
    }

Now documented here.  Closing


Test for nonce reuse

Resolves https://github.com/frostsnap/frost-esp32/issues/14
Builds on and based on testing approach in https://github.com/frostsnap/frost-esp32/pull/66

Hey can we put all the tests in malicious.rs that are to do with a malicious device or coordinator. What's the difference between this new malicious poly test and the old one?

Now creates a new sign request and that has the same nonces as the previous signing session


fix: build changes related to scw changes

ideally when we're making a new release something is to be merged to master from development. prior to that release branch would have been already merged into development


Set Target Framerate

Hi.
It would be great if ToyBox could have the BagOfTricks "Set target FrameRate" feature. Indeed, many players can't play on laptop because the game consumes too much resources. Lowering the fps was the ideal solution on Kingmaker: personally, I could hardly run the game with the default framerate, but by lowering it to 20 fps, my laptop didn't even heat up. I'd like to know if it's possible to integrate this feature. Thanks in advance :)

That bugged me too. Unfortunately Application.targetFrameRate = X; doesn't work. Or it is changed back by the game.

It's weird that it works with Kingmaker but not WOTR... Are you sure there is no other approach to change this value ?

@Truinto It's weird that it works with Kingmaker but not WOTR... Are you sure there is no other approach to change this value?


installing sqitch from master on macOS fails

Issue description
installing 'perl-App-Sqitch-0.9996'
error: while evaluating the attribute 'propagatedBuildInputs' of the derivation 'perl-App-Sqitch-0.9996' at /nix/store/p0ww35zx7jacxpsly5dxrz8r547krxsk-latest/latest/pkgs/stdenv/generic/make-derivation.nix:148:11:
while evaluating the attribute 'propagatedBuildInputs' of the derivation 'perl-IO-Pager-0.39' at /nix/store/p0ww35zx7jacxpsly5dxrz8r547krxsk-latest/latest/pkgs/stdenv/generic/make-derivation.nix:148:11:
while evaluating the attribute 'handled' at /nix/store/p0ww35zx7jacxpsly5dxrz8r547krxsk-latest/latest/pkgs/stdenv/generic/check-meta.nix:203:3:
while evaluating 'handleEvalIssue' at /nix/store/p0ww35zx7jacxpsly5dxrz8r547krxsk-latest/latest/pkgs/stdenv/generic/check-meta.nix:126:21, called from /nix/store/p0ww35zx7jacxpsly5dxrz8r547krxsk-latest/latest/pkgs/stdenv/generic/check-meta.nix:204:10:
Package ‘util-linux-2.31.1’ in /nix/store/p0ww35zx7jacxpsly5dxrz8r547krxsk-latest/latest/pkgs/os-specific/linux/util-linux/default.nix:71 is not supported on ‘x86_64-darwin’, refusing to evaluate.

Steps to reproduce

add latest as channel:

$ nix-channel --add https://github.com/NixOS/nixpkgs/archive/master.tar.gz latest
$ nix-channel --update latest

run nix-env -iA latest.perlPackages.AppSqitch --show-trace

Technical details
I think this is from this commit: https://github.com/NixOS/nixpkgs/pull/30798/commits/d8ef4d510bc80cb073aedf45767777ca8ed6d416#diff-7125c606898e1eb839ffc277a5eca467R7163
That propagated build input should not be activated for macOS.
 - system: `"x86_64-darwin"`
 - host os: `Darwin 17.4.0, macOS 10.13.3`
 - multi-user?: `yes`
 - sandbox: `no`
 - version: `nix-env (Nix) 2.0`
 - channels(root): `"nixpkgs-18.03pre129212.098c7f3d945"`
 - channels(me): `"latest"`
 - nixpkgs: `/nix/var/nix/profiles/per-user/root/channels/nixpkgs`

Would updating that line to propagatedBuildInputs = stdenv.lib.optional stdenv.isLinux pkgs.utillinux.bin; fix this? I am unable to test this because of another odd bug when running nix-build -A perlPackages.AppSqitch from a clone of this repo.

Getting rid of linux specific dependencies like utillinux or glibc is usually a good first step.

Has this been fixed? The more thing is definitely not an issue anymore.

Seems to be fixed for me. I'll note that it used precompiled sources so am not fully sure.


config option for RBD object size

The RBD driver currently uses the default 4MB object size when creating new RBDs. Add a config option to specify difference object sizes.

Hi, I just started to use libStorage with Ceph and for me some others options will be very intesting. (like --cluster, -m, --keyring, ...)
Do you think we could transform this tickets to support various options, and maybe the simplest way is to support a extra_options where the user can setup everything he wants instead of mapping the rdb command field by field?
I'm new to go and libStorage but I can try to help on this ;)

@richard-julien That's a really interesting idea, the extra_options proposal. I'll think about it. The first thing that came to mind was wondering if you could use CEPH_ARGS today and accomplish some of what you need. That's an env var that the Ceph CLI tools look for already, but it's been a while since I've used it.
Second thought was wondering how to de-conflict if we do end up adding first-class support for any of these options (say rbd.cluster) and you also specify --cluster in the extra_options field.
Anyways, it's good to know someone is looking for this functionality. That helps us prioritize what to work on!

Hi @codenrhoden thanks for your answer.
My idea is to be able to configure a ceph mount without any specific configuration file on the host. (everything through the volume-opt). So the CEPH_ARGS for me is not really an option ;)
For the extra_options and the de-conflict maybe the simplest solution for the ceph driver is to only provide a free text zone. I dont think libStorage need to ensure the consistency of the command, and helping the user with 2 or 3 options is not really necessary. I will be happy to provide every options i need in the command line and have directly the ceph error message if i forget to provide the basic options.

Issue moved to codedellemc/rexray #957 via ZenHub


Hitbox is off when manually placing Monika

When manually placing Monika, the hitbox doesn't completely follow her. It places itself the bottom of it is at her eyes.
Red line is where she starts fading. Blue is where she reach maximum fade.

Fixed in newest release!


Fix emcee old_chain for white-light curve fit bug

Quick fix of issue #621 so that the emcee run can also continue from a white-light emcee chain run.
I used the same nomenclature (channel_tag) as in the save_fit function.

Thanks for finding and patching this!


Bug: deleted entries not removed from sitemap

Issue Summary
When I delete entries (or make posts static) these URL's cease to work (as expected) but are still included in the sitemap.
Steps to Reproduce

Create a page

View sitemap

Delete the page

View sitemap - page index is still visible

Attempt to click on link and visit page results in 404

Create port

View sitemap

Convert post to static page

View sitemap - static page still visible as a post

Attempt to click on legacy post results in a 404

The sitemap should not include objects know to result in a 404.
Technical details

Ghost Version: 0.7.0
Client OS: Ubuntu 15.04
Server OS: Ubunti 15.04
Node Version: v0.10.25
Browser: Chrome
Database: Sqlite3

@oddjobz Thanks for the bug report, this is expected to work & seems like a regression to me.
A bug fix, including a broken test case, would be great here.

@ErisDS While looking into this I found that you by accident disabled all but one test for the post model.
https://github.com/TryGhost/Ghost/commit/0462607296f2a1a5b3ac3a3d98eb254e5fdfd605#diff-64e60e2e79e27d0a8c66873904f62de2
When I re-enable the test it is failing one tests. I hope to raise a PR soon to fix both issues.

derp :flushed: thanks!


Remove unwanted tr after search result

Hello, @kevincobain2000
Remove unwanted tr (It generate extra space) after search result

Cool thanks. Will merge it soon and push the tag. Sorry playing Fortnite right now.


[FLINK-20822][hive] Don't check whether a function is generic in hive…

… catalog

What is the purpose of the change
Don't check whether a function is generic in HiveCatalog
Brief change log

Stop calling CatalogFunction::isGeneric in HiveCatalog.
Add test case to verify

Verifying this change
New test case
Does this pull request potentially affect one of the following parts:

Dependencies (does it add or upgrade a dependency): no
The public API, i.e., is any changed class annotated with @Public(Evolving): no
The serializers: no
The runtime per-record code paths (performance sensitive): no
Anything that affects deployment or recovery: JobManager (and its components), Checkpointing, Kubernetes/Yarn/Mesos, ZooKeeper: no
The S3 file system connector: no

Documentation

Does this pull request introduce a new feature? no
If yes, how is the feature documented? NA

hello, i want to ask when i execute JobManagerProcessUtils.generateJvmParametersStr(),then it throw an exception:NosuchFieldError:JVM_DIRECT_MEMORY_LIMIT_ENABLED，i use flink-1.11.0

hello, i want to ask when i execute JobManagerProcessUtils.generateJvmParametersStr(),then it throw an exception:NosuchFieldError:JVM_DIRECT_MEMORY_LIMIT_ENABLED，i use flink-1.11.0

Hi @bitao666 , please ask questions in user or user-zh mailing list.
Very few people can see your question if asking in PR.

Hi @bitao666 , please ask questions in user or user-zh mailing list.
Very few people can see your question if asking in PR.


Why use Hadamard product in Matrix Factorization router?

What is the significance of using Hadamard product between model embedding and query embedding in the Matrix Factorization router? Why not directly compute the score using the dot product?

Thanks for the great question!
The reason is that the Hadamard product preserves more information about the interaction between the two vectors. Initially, we explored applying additional non-linear operations to the fusion of these vectors. However, we find that a linear classification on top provided the best performance. In this case, the Hadamard product is not strictly necessary because we can collapse all linear transformations into one as discussed in https://github.com/lm-sys/RouteLLM/issues/9.
Despite this, we decided to keep the original code for simplicity and potential future modifications.


Analyzer thinks that private methods from other library is overridable

Analyzer thinks that private methods from other library is overridable, but according to the spec they are not.
See section 10.9.1 Inheritance and Overriding in the Dart Language spec:
C inherits all accessible instance members of A that have not been overridden by a declaration in C.
"accessible" is defined in section 6.2 Privacy:
A declaration m is accessible to library L if m is declared in L or if m is public.
Now consider this code:
file1.dart
library someLib;

import 'file2.dart';

main() {
  var b = new B();
  var c = new C();

  b.publicMethod();
  b._privateMethod();

  c.publicMethod();
  c._privateMethod();
}

class A {
  publicMethod() {print('A.publicMethod');}
  _privateMethod() {print('A._privateMethod');}
}

class B extends A {
  publicMethod() {print('B.publicMethod');}
  _privateMethod() {print('B._privateMethod');}
}

file2.dart
library otherLib;

import 'file1.dart';

class C extends A {
  publicMethod() {print('C.publicMethod');}
  _privateMethod() {print('C._privateMethod');}
}

Output is:
B.publicMethod
B._privateMethod
C.publicMethod
A._privateMethod

Output shows that B._privateMethod() overrides A._privateMethod - DAS thinks the same, so far so good.
But also output shows that C._privateMethod() doesn't override A._privateMethod. But all DAS methods that deal with hierarchy say that it does. I mean:

AnalysisServer.search_getTypeHierarchy()
AnalysisServerListener.computedOverrides() notification
AnalysisServerListener.computedImplemented() notification

May be some other DAS methods use incorrect assumption as well, not sure.

CL for analysis.override https://codereview.chromium.org/1610773003

CL for analysis.implemented https://codereview.chromium.org/1615023002

CL for type hierarchy https://codereview.chromium.org/1615093002

Fixed.

It seems that this bug has resurfaced

import 'package:flutter/material.dart';

class _PercentText extends MultiChildLayoutDelegate {

  performLa

  @override
  bool shouldRelayout(MultiChildLayoutDelegate oldDelegate) => false;
}

[√] Flutter (Channel stable, v1.0.0, on Microsoft Windows [Version 10.0.17134.407], locale en-BE)
    • Flutter version 1.0.0 at C:\tools\flutter
    • Framework revision 5391447fae (7 days ago), 2018-11-29 19:41:26 -0800
    • Engine revision 7375a0f414
    • Dart version 2.1.0 (build 2.1.0-dev.9.4 f9ebf21297)

https://dart-review.googlesource.com/c/sdk/+/86480


deps: remove jupyter notebook dependencies

Checklist

[x] Used a personal fork of the feedstock to propose changes
[x] Bumped the build number (if the version is unchanged)
[x] Reset the build number to 0 (if the version changed)
[x] Re-rendered with the latest conda-smithy (Use the phrase @conda-forge-admin, please rerender in a comment in this PR for automated rerendering)
[x] Ensured the license file is being packaged.

Feels free to remove it !

@conda-forge-admin, please rerender


Quantization Support?

Would like to quantize the model to 8 or 4-bit for running on GPU with 6GB of memory and for possible speed boost. Is this possible?

There will likely be impact on quality... you'll probably need to quantise the first stage and also swap out the multiband diffusion model with vocos. This should take down model weights from ~10-12GB to ~2.7GB.
Keep us updated, and do open a PR with any progress, and we'll try to help you along!
quantisation
you can try the code at https://github.com/pytorch-labs/gpt-fast/blob/main/quantize.py#L528 and https://github.com/pytorch-labs/gpt-fast/blob/main/generate.py#L219 ... we've borrowed this code for the faster inference, so it should fit in nicely...
swapping out mbd (multiband diffusion) + dfn (deepfilternet)
I think currently the models weights take up:

first stage (~5GB)
second stage (~60MB)
speaker encoder (~20MB)
multiband diffusion + deep filter net (~5GB)

So replacing multiband diffusion with vocos (https://huggingface.co/charactr/vocos-encodec-24khz) should reduce the memory usage by another ~5GB

I'd be interested in contributing this in the future (few weeks from now) if nobody else does.
@gururise would you be interested in working on this?

@danablend that would be amazing !

hey folks, int4/int8 weight only quantisation has been added here: https://github.com/metavoiceio/metavoice-src/pull/95 ... we're seeing 2x speedups for the first stage model with some degradation in quality.


[Package Request]: Connect Fonts

How can we help?
I would like someone else to build the manifest.
Please read and ensure the following

[X] The installer meets the above requirements

Please provide the following information
Download Page Url: https://www.extensis.com/support/connect-fonts
Publisher: Extensis
Package Name: Connect Fonts
Description: Software used for installing fonts
Package Version: 25.1.1
Installer URL: https://links.extensis.com/connect_fonts/cf_latest?language=en&platform=win

This package has the ability to install Microsoft.VCRedist.2015+.x64 and Microsoft.VCRedist.2015+.x86 by itself, but user authentication is required for this process.
Also the package seems requires x64 and x86 version at the same time, but we can't add them into a single manifest.

Support for multiple dependencies has been added.


PR proposal

Hi maintainers,
would you be open to a PR to use jsoneditor in the demo instead of a plain text field? I already had it for my own usage but I thought it would be nice to contribute back to this nice repo.

please let me know.

Thanks Nilegfx. But I think jsoneditor is too heavy, it will make users feel more complicated


FEATURE: add custom localization support

Signed-off-by: Billaids<EMAIL_ADDRESS>

works fine for me

Thanks for contributing @Billaids! I will review this PR this weekend.

Hey, I'm using 2.5.0-5.1.pre it's flutter beta
Get Outlook for Androidhttps://aka.ms/AAb9ysg

From: May Lau @.>
Sent: Sunday, August 22, 2021 9:51:08 AM
To: MayLau-CbL/flutter_horizontal_center_date_picker @.>
Cc: Billaids @.>; Mention @.>
Subject: Re: [MayLau-CbL/flutter_horizontal_center_date_picker] FEATURE: add custom localization support (#2)
@MayLau-CbL commented on this pull request.

In lib/date_item_widget.darthttps://github.com/MayLau-CbL/flutter_horizontal_center_date_picker/pull/2#discussion_r693460288:

           return Text(

               DateFormat.E(Locale(this.locale).toString())

                   .format(this.dateTime),

Which flutter version are you using? I am using 2.2.3 and the it keeps warning me The argument type 'String?' can't be assigned to the parameter type 'String'.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHubhttps://github.com/MayLau-CbL/flutter_horizontal_center_date_picker/pull/2#pullrequestreview-735549942, or unsubscribehttps://github.com/notifications/unsubscribe-auth/AIVEJPUTSK3DXGJELMRP5PDT6CT6ZANCNFSM5CJIWFQQ.
Triage notifications on the go with GitHub Mobile for iOShttps://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675 or Androidhttps://play.google.com/store/apps/details?id=com.github.android&utm_campaign=notification-email.

what's the return from Intl.systemLocale? The return value should be like here in this list
https://api.flutter.dev/flutter/date_symbol_data_http_request/availableLocalesForDateFormatting.html

what's the return from Intl.systemLocale? The return value should be like here in this list
https://api.flutter.dev/flutter/date_symbol_data_http_request/availableLocalesForDateFormatting.html

normally should be en_US, depends on the system setting at that time. Or for more secure, we can set it as locale: widget.locale ?? 'en_US'; directly as it is only a default setting for this widget

Yes, this is a good idea!

Could you merge it? :D

sorry for the later reply, i dunt feel so well recently.🤦♀ do you mind applying those comments to the branch first if you have no question on them? if ur branch is not convenient for merging those changes, i can fork yours and apply them later.


Build Failure because of jacoco-maven-plugin

In the pom.xml file, there is the following code:

This plugin appears to have issues when run. on Docker.  The suggested fix in the class comments of adding ./mvnw clean verify before ./mvnw package does not prevent the following build failure:
[INFO] --- jacoco-maven-plugin:0.8.5:report (report) @ spring-petclinic ---
[INFO] Loading execution data file /var/jenkins_home/workspace/spring-petclinic/target/jacoco.exec
[INFO] ------------------------------------------------------------------------
[INFO] BUILD FAILURE
[INFO] ------------------------------------------------------------------------
[INFO] Total time:  01:30 min
[INFO] Finished at: 2022-04-30T01:34:55Z
[INFO] ------------------------------------------------------------------------
[ERROR] Failed to execute goal org.jacoco:jacoco-maven-plugin:0.8.5:report (report) on project spring-petclinic: An error has occurred in JaCoCo report generation.: Error while creating report: null: EOFException -> [Help 1]
[ERROR]
[ERROR] To see the full stack trace of the errors, re-run Maven with the -e switch.
[ERROR] Re-run Maven using the -X switch to enable full debug logging.
[ERROR]
[ERROR] For more information about the errors and possible solutions, please read the following articles:
[ERROR] [Help 1] http://cwiki.apache.org/confluence/display/MAVEN/MojoExecutionException
Build step 'Execute shell' marked build as failure
Finished: FAILURE

I'm having the same issue, anyone has a solution? Googling says it's due to the Jacoco exec file truncated while copied from the host to the Docker container VM, which seems common on Mac OS. However, I'm not seeing a working solution for it.


Loosen validation and mypy type requirements for date/time types, start increased testing

The Records Spec uses hints to describe how dates, times, datetime, and datetimetz types are formatted in CSVs.
It does this by describing a set of allowed values. The format of the values were originally cribbed from Redshift's COPY statement.
The current set of actually allowed values for those types (dateformat/timeonlyformat/datetimeformattz/datetimeformat) are fairly arbitrary, incomplete, inconsistent with each other, and don't hold up as terribly consistent under scrutiny. They've also been a source of bugs over time.
To make those complete even insomuch as the patterns they currently try to support would involve the start of some combinatorial explosion:

Hour can be specified as HH, HH24 or HH12 (note also that HH and HH24 mean the same thing, but we documented it both ways in at least one case.)
SS can either be specified or not.
OF can be specified for timezone offset or not
Dates can be in MDY, DMY or YMD format
Years can be in YYYY or YY format
Dates can be segmented by - or /
...

I think this does not bode well for maintaining an allowlist of formats - instead, I think we're going to need to (like Redshift) support a general formatting language in the spec, and do translations as needed to the formatting languages of the different databases/tools that Records Mover integrates with.
In the face of that, I'd like to expand the allowed values in a future PR. The motivation there is to ensure we allow likely DMY-flavored formats, to more generally support more things users may need in the future.
This PR is step one of that. It adds one additional test as a beginning of comprehensive testing of the existing supported values, and makes it easy to add more testable values in the future.
To deal with the combinatorial explosion problem for the time being, I've loosened the internal constraints by changing the Literal types to strings, and fixed up the various things based on what the testing found.
This PR does not yet loosen up the spec in any way.
The rest of the tests can be found in #140, which is probably too big to review right now--this is the first PR chipped off of that boulder.

Looks like I have a test failure, which is annoying.  I’ll open up another PR related to that and probably merge master back into this branch afterwards.  If you could review the PR in the meantime ignoring the test failure for the moment, I’ll hold off merging until that fixes the passing tests.


use sbt-release to manage release versions and commits

This PR switches to sbt-release to manage release versions. Right now, it only helps set release version, commit, add release tag, and then bump release version, and commit again.
Same as https://github.com/databricks/tensorframes/pull/150 but for this project.

Codecov Report

Merging #139 into master will not change coverage.
The diff coverage is n/a.

@@           Coverage Diff           @@
##           master     #139   +/-   ##
=======================================
  Coverage   85.37%   85.37%
=======================================
  Files          33       33
  Lines        1921     1921
  Branches       44       44
=======================================
  Hits         1640     1640
  Misses        281      281

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Is there anyway to setup up the mad catz strike 7 keyboard with aurora?

I dont know much about aurora, but i know in the faq it says it will support anything that has an sdk, well i have the sdk for the mad catz strike 7 and i was wondering if there is a way to configure it or do they just mean they will be able to add it one day if the product has an sdk? any help is greatly appreciated

From what I was able to find it seems that the SDK for that keyboard is only for the touchscreen that it has. Which I don't think would be much use for integration with Aurora.


refactor: typing contents of MessageLoadOracleInputs

Please read contributing guidelines and remove this line.

[!WARNING]
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Current dependencies on/for this PR:

master

PR #4335

PR #4351   👈

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Benchmark results
Metrics with a significant change:

note_history_trial_decrypting_time_in_ms (5): 178 (+118%)
note_trial_decrypting_time_in_ms (32): 129 (+205%)

Detailed results
All benchmarks are run on txs on the Benchmarking contract on the repository. Each tx consists of a batch call  to create_note and increment_balance, which guarantees that each tx has a private call, a nested private call, a public call, and a nested public call, as well as an emitted private note, an unencrypted log, and public storage read and write.
This benchmark source data is available in JSON format on S3 here.
Values are compared against data from master at commit 83686598 and shown if the difference exceeds 1%.
L2 block published to L1
Each column represents the number of txs on an L2 block published to L1.

Metric
8 txs
32 txs
128 txs

l1_rollup_calldata_size_in_bytes
45,412
179,524
716,068

l1_rollup_calldata_gas
221,488
866,512
3,448,360

l1_rollup_execution_gas
313,975
984,252
3,668,017

l2_block_processing_time_in_ms
1,154 (-1%)
4,369
17,225

note_successful_decrypting_time_in_ms
306 (-9%)
985 (-4%)
3,684 (-3%)

note_trial_decrypting_time_in_ms
16.4 (-62%)
:warning: 129 (+205%)
144

l2_block_building_time_in_ms
18,228 (-1%)
73,377 (+2%)
293,009 (+1%)

l2_block_rollup_simulation_time_in_ms
13,079
52,668 (+2%)
210,866 (+1%)

l2_block_public_tx_process_time_in_ms
5,119 (-2%)
20,575 (+1%)
81,890

L2 chain processing
Each column represents the number of blocks on the L2 chain where each block has 16 txs.

Metric
5 blocks
10 blocks

node_history_sync_time_in_ms
13,732
26,150 (-2%)

note_history_successful_decrypting_time_in_ms
2,314 (-1%)
4,702 (+1%)

note_history_trial_decrypting_time_in_ms
:warning: 178 (+118%)
200 (+15%)

node_database_size_in_bytes
17,342,544
33,144,912

pxe_database_size_in_bytes
29,923
59,478

Circuits stats
Stats on running time and I/O sizes collected for every circuit run across all benchmarks.

Circuit
circuit_simulation_time_in_ms
circuit_input_size_in_bytes
circuit_output_size_in_bytes

private-kernel-init
310 (-3%)
44,481 (-1%)
26,193

private-kernel-ordering
194
44,513
14,929

base-rollup
1,416 (+1%)
128,970
881

root-rollup
82.2
4,088
677

private-kernel-inner
413 (-3%)
71,640 (-1%)
26,193

public-kernel-private-input
244
32,615
26,193

public-kernel-non-first-iteration
243
32,657
26,193

merge-rollup
7.62 (-6%)
2,608
881

Tree insertion stats
The duration to insert a fixed batch of leaves into each tree type.

Metric
0 deployed contracts
1 deployed contracts

tx_size_in_bytes
15,575
37,972

Transaction processing duration by data writes.

Metric
0 new commitments
1 new commitments

tx_pxe_processing_time_ms
574 (-2%)
1,375

Metric
1 public data writes

tx_sequencer_processing_time_ms
631


Add ability to change the theme of BookFace.

solves #133

Codecov Report

Merging #167 (60fcd88) into master (4f3d9ff) will decrease coverage by 0.27%.
The diff coverage is 0.00%.

@@             Coverage Diff              @@
##             master     #167      +/-   ##
============================================
- Coverage     54.50%   54.22%   -0.28%
  Complexity      485      485
============================================
  Files           114      115       +1
  Lines          1976     1986      +10
  Branches        202      202
============================================
  Hits           1077     1077
- Misses          824      834      +10
  Partials         75       75

Impacted Files
Coverage Δ

...ain/java/bookface/logic/commands/ThemeCommand.java
0.00% <0.00%> (ø)

src/main/java/bookface/ui/MainWindow.java
0.00% <0.00%> (ø)

Help us with your feedback. Take ten seconds to tell us how you rate us. Have a feature suggestion? Share it here.

approved


Use ui-router instead ngRoute

Thanks for your form-builder.
Can i use ui-router instead ngRoute?
It seems to me i need change only $routeParams on $stateParams view.js in
FormService.form($stateParams.id).then(function(form) {
$scope.form = form;
}); ?

This is a simple demonstraion of how you can create a form builder using AngularJS. You can definitely fork and use ui-router if that fits in your project.


Ci cd workflows

Add build script that is invoked whenever there is a pull request to master branch, to ensure all microservices build without errors

lgtm!


Changes to refinement procedure (NQH flips, no real-space refinement!)

Main discussion on N/Q/H flips in the commit message for 8f15653 .

For future reference: it's not a wise idea to enable real-space refinement.
From Gydo:

That procedure to my knowledge performs a rotamer scan for each residue, plus the optimization does not take into account multiple conformers in the data-driven part of the energy function, so it typically collapses residues, as it doesnt do a density substraction of one conformer from the other, if that makes sense. Neither of these options seem particularly attractive for the qFit routine.


Identify target for set_multivalues() call from GLOBAL_LOOKUP_REF.

Use jquery to find other fields to be updated based on element names.

https://issues.apache.org/jira/browse/OFBIZ-11274 has been created for that, still pending, closing here


[WIP] Brownfield land facts for National dataset page

Calculate figures for facts section (see image).

Still needs some design but the figures should be accurate

Code that analyses the dataset needs to be made more generic but going to merge for now.


Renaming a file loses its Git history

Is there a way to avoid losing the git history when renaming a file?

So this is actually a GitHub problem, and probably not worth worrying too much about.


Server week 3

Pull Request for step 1, servlets

PR for step 2

Oh, one more thing to add, since this is branched from the Luna-Session, make sure you merge the Luna-Session PR onto master before merging this CL. If you merge it now these changes will show up in Luna-Session which could confuse the review their.


resolve saveToEs saves case classes fields with NULL values #998

This PR fix the Scala Writer when it processes case classes with null fields.
Correction about issue #998

[x] I have signed the [Contributor License Agreement (CLA)][]

Can this be configurable? If I want nulls to show - then this PR removes it.

It is configurable with the ES_SPARK_DATAFRAME_WRITE_NULL_VALUES parameter (
es.spark.dataframe.write.null).
The default is false then it has to be set true for showing null values
Le jeu. 21 mai 2020 à 19:43, rohitgcs<EMAIL_ADDRESS>a écrit :

Can this be configurable? If I want nulls to show - then this PR removes
it.
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Hi @lucebert. Sorry for the very long delay. Thanks for the PR, and especially the tests. If you are still around I would like to get this merged in. It looks pretty good to me but now has merge conflicts that need to be cleared up before it can be merged.

@lucebert do you want to merge this? I think you'll have permissions now. If not, I can merge it.

@masseyke Thanks for the review. I don't seem to be able to merge, so you can do it 👍


console told me to report issue so I am, I received an exception

Environment data

Steps to reproduce or exception report

This is fixed in 2.0.0-beta1.


Automated Package Upgrade

:tada: This PR is included in version 5.0.23 :tada:
The release is available on:

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Feature request:  Minidump support

It's a rare day I actually need to dump the entire system memory.  But process dumps via procdump or similar I use all the time...any chance we could get support for a minidump file?  Thank you.

Hi @DigiAngel,
I shall certainly give the idea strong consideration, as I often rely on minidumps myself. There appears to be a library for processing minidump files under python (https://github.com/skelsec/minidump/tree/master/minidump), so hopefully it would be fairly trivial to implement.
In the meantime, if you have Rekall installed, it is possible to dump processes/modules/drivers on a live system without the need for a full memory dump...
$ rekall --live Memory
[1] Live (Memory) 00:00:00> run -i pe_tree_rekall.py

From there, you can dump the PE's you are interested in (right click -> dump) and select "Rebuild imports" to build a new IAT...

Thanks,
Tom

One other approach that may work? If you have IDAPro you can open the minidump file that way and use the PE Tree IDA plugin to view/dump processes and modules.

Thank you.  My hope has been to run and dump on a machine without IDA, then copy the procdump's over and analyze them further.  I'll try the above approach as well..thank you!


refac: Add all distinct qualities for each quarter

Description
Wholesale should not determine final quality as it depends on the B2B communication protocol (CIM vs eBIX vs other).

References

#199

Not needed until epic 3 (eBIX)


Parent last rssi

Added thread api call to get the last rssi for the parent

Codecov Report

Merging #1558 into master will decrease coverage by 11.87%.
The diff coverage is 0%.

@@             Coverage Diff             @@
##           master    #1558       +/-   ##
===========================================
- Coverage   69.14%   57.27%   -11.88%
===========================================
  Files         156       69       -87
  Lines       20117     5638    -14479
  Branches     2480      590     -1890
===========================================
- Hits        13910     3229    -10681
+ Misses       5304     2202     -3102
+ Partials      903      207      -696

Impacted Files
Coverage Δ

src/core/api/thread_api.cpp
37.71% <0%> (+8.01%)
:arrow_up:

src/core/thread/lowpan.hpp
0% <0%> (-100%)
:arrow_down:

src/core/thread/announce_begin_server.cpp
8.88% <0%> (-86.77%)
:arrow_down:

src/core/thread/energy_scan_server.cpp
15.47% <0%> (-78.71%)
:arrow_down:

src/core/thread/thread_tlvs.hpp
25% <0%> (-75%)
:arrow_down:

src/core/meshcop/tlvs.hpp
26.2% <0%> (-62.64%)
:arrow_down:

include/openthread/types.h
50% <0%> (-50%)
:arrow_down:

src/core/api/commissioner_api.cpp
0% <0%> (-46.67%)
:arrow_down:

src/core/thread/network_data.cpp
40.82% <0%> (-46.07%)
:arrow_down:

src/core/thread/network_data_tlvs.hpp
55.17% <0%> (-44.83%)
:arrow_down:

... and 124 more

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Allow conditionally extending nodes/links

When extending nodes (i.e. nodeThreeObjectExtend is true) it should be possible to conditionally return either a three object, or undefined from nodeThreeObject. The latter would be nice to have to be able to get the default node, and conditionally extend it.

Not sure I'm following. I think this is how it already works. If a falsy value is returned from nodeThreeObject, it falls back to the default node, regardless of whether nodeThreeObjectExtend is set or not.

Sorry for the super confusing description. I'm going to close this, I thought about this the wrong way.


[A11y] screen reader cannot announce and select items of ListView() on destop app for Windows

On Windows app, screen reader cannot focus ListView().
So user cannot select items from that.
[√] Flutter (Channel beta, 2.10.0-0.1.pre, on Microsoft Windows [Version 10.0.22000.434], locale ja-JP)
    • Flutter version 2.10.0-0.1.pre at C:\Android\flutter
    • Upstream repository https://github.com/flutter/flutter.git
    • Framework revision 628f0e3f3a (2 days ago), 2022-01-11 19:50:05 -0600
    • Engine revision 63ca99584a
    • Dart version 2.16.0 (build 2.16.0-134.1.beta)
    • DevTools version 2.9.2

[!] Android toolchain - develop for Android devices (Android SDK version 30.0.3)
    • Android SDK at C:\Android\sdk
    X cmdline-tools component is missing
      Run `path/to/sdkmanager --install "cmdline-tools;latest"`
      See https://developer.android.com/studio/command-line for more details.
    X Android license status unknown.
      Run `flutter doctor --android-licenses` to accept the SDK licenses.
      See https://flutter.dev/docs/get-started/install/windows#android-setup for more details.

[√] Chrome - develop for the web
    • Chrome at C:\Program Files (x86)\Google\Chrome\Application\chrome.exe

[X] Visual Studio - develop for Windows
    X Visual Studio not installed; this is necessary for Windows development.
      Download at https://visualstudio.microsoft.com/downloads/.
      Please install the "Desktop development with C++" workload, including all of its default components

[√] Android Studio (version 2020.3)
    • Android Studio at C:\Program Files\Android\Android Studio
    • Flutter plugin can be installed from:
       https://plugins.jetbrains.com/plugin/9212-flutter
    • Dart plugin can be installed from:
       https://plugins.jetbrains.com/plugin/6351-dart
    • Java version OpenJDK Runtime Environment (build 11.0.10+0-b96-7249189)

[√] VS Code (version 1.63.2)
    • VS Code at C:\Users\nyama\AppData\Local\Programs\Microsoft VS Code
    • Flutter extension version 3.26.0

[√] Connected device (4 available)
    • S1 (mobile)       • MS3GLP6711409145 • android-arm64  • Android 9 (API 28)
    • Windows (desktop) • windows          • windows-x64    • Microsoft Windows [Version 10.0.22000.434]
    • Chrome (web)      • chrome           • web-javascript • Google Chrome 96.0.4664.110
    • Edge (web)        • edge             • web-javascript • Microsoft Edge 96.0.1054.62

[√] HTTP Host Availability
    • All required HTTP hosts are available

! Doctor found issues in 2 categories.

@yama3nomori
Check this issue comment. According to it, can you switch to latest master and try your use case once again and see if it works as expected or still the behavior persists ?


Use event aggregation to provide animation events

Same as for issue https://github.com/aurelia/animator-css/issues/13 for the CssAnimator, we should call the mentioned DOM-Events. In case of JS based events, the timeout events won't be necessary. I guess same applies for staggering animations
animation:enter:begin
animation:enter:active
animation:enter:done
animation:enter:timeout

animation:leave:begin
animation:leave:active
animation:leave:done
animation:leave:timeout

animation:stagger:next

animation:remove-class:begin
animation:remove-class:done
animation:remove-class:timeout

animation:add-class:begin
animation:add-class:done
animation:add-class:timeout

these have been implemented now :)


[Bug]: Name of the VSS Spec is hardcoded to vspec.json for K3D configmap

Severity
Critical
What release version, tag or commit-hash did you use?
v2.2.4
Current Behavior
If one has a custom vspec with a different name than vspec.json, the databroker in the K3D runtime fails to start due a hardcoded value in the helm-charts for the databroker.
Steps to Reproduce

create a vss.json in the app-directory
adapt appmanifest
regenerate model
start k3d runtime up
databroker fails to start due to missing configmap

Expected Behavior
details are taken from the appmanifest and are not hardcoded
Possible Solution
No response
Additional Information
No response
Code of Conduct

[X] I agree to follow this project's "Code of Conduct".

Not relevant any longer since we removed support for Dapr middleware and K3D runtime.
Related issues:
https://github.com/eclipse-velocitas/vehicle-app-python-template/issues/233
https://github.com/eclipse-velocitas/vehicle-app-cpp-template/issues/92
https://github.com/eclipse-velocitas/vehicle-app-template/issues/10
https://github.com/eclipse-velocitas/vehicle-app-python-sdk/issues/121
https://github.com/eclipse-velocitas/vehicle-app-cpp-sdk/issues/85
https://github.com/eclipse-velocitas/devenv-devcontainer-setup/issues/59
https://github.com/eclipse-velocitas/devenv-runtimes/issues/68
https://github.com/eclipse-velocitas/devenv-github-workflows/issues/52


Configure keyboard shortcut

I would like to configure the keyboard shortcut to toggle the todo-show tab as it conflicts with that of another package. Can this option be provided in the package's settings? As of now I've edited the source but this would be overwritten if the package is updated (I presume).

The feature of remapping keyboard shortcuts comes standard with Atom. Go to Settings > Keybindings > and search for todo-show. Use the copy icon and paste it into your keymap file. Then edit it to your needs (you might have the use unset! on the old binding).

Hey, that's really neat! Thanks, I didn't know that!


refactored RTS for jvm into CommonRTS

Refactored out RTS from jvm into a CommonRTS in shared. Made the functions not supported by ScalaJS abstract. The code will throw exception on any sync operation in JS, because we are not able to block. Other things missing in ScalaJS:

No WeakHashmap, so made an abstract method getMap in CommonRTS. Hope this will not make impact on performance. There is an implementation in ScalaNative, might be ported to ScalaJS in the future?
The wait() method on AtomicReference (and probably others) would have made a linking exception. This is probably avoided due to code elimination done by the ScalaJS compiler?

Suggest that improvements on the RTS in JS should be done in a later PR. This PR is mostly about refactoring out common properties.
I am not that familiar with the inner workings on RTS, so might be somethings that I missed. I've barely tested this in a browser, but it did work out :-)
Also, since we are not able to block, I've not found a way to run tests for JS yet.

@scalavision Thank you for your work on this and apologies for the delay! I'll do a comprehensive review shortly.

No worries, take your time :-)
Fixed the merge conflicts.

@scalavision This looks great to me. However, due to the large amount of code being moved around, and the new updates that have slid in, I think this is very much out of date and it will be hard to reconcile.
Do you think there is a way we can do this more incrementally? Maybe one PR to introduce some stubs, and then another one that creates a derived class to override the stubs for Javascript? Then after all that's in, we can reorganize the code like you've done here in a more logical fashion.
I'd really like to get this in, in the next couple of weeks and would be happy to assist you moving this forward.

Closing in favor of #515, which was able to happen fast enough to get merge ready!
Thanks for all your work on this @scalavision. It helped light the way for #515!

Thank you for your kind words!! Sorry I was not able to complete. I hope to get more time approaching March when my current project is finished. Hope to be able to contribute then :-)


See contributions and open PR’s on profile/homepage

First of all, this app is great! Congrats to all the contributors.
I think that it would be really nice to check my open PR’s and contributions on github and also the contributions of a user on his profile page.

Agree! This would be awesome!


Support exception chaining

Fixes #190

Unrelated to the code, but any idea which version of Ruby started offering chaining? I didn't actually realize it was even a feature.

 The ticket actually points it out :)

Thanks! Will take a look.

Awesome. Just pushed up a fix to not check reporting when causes aren't supported.


Alma implementation for courses#bibs

This endpoint provides all bib IDs for a given course; powers a course show page (reserves list)
Related to #931; once that issue is closed this one should be straightforward.
Documentation: https://developers.exlibrisgroup.com/alma/apis/courses/

The reading lists are lists of items on reserve. Within those lists are citations. The citation would return the bib ID.
The full reading lists and courses data is returned in the course object, so it doesn't look like we need to use multiple API calls here (see https://developers.exlibrisgroup.com/alma/apis/docs/xsd/rest_courses.xsd/?tags=GET)

Closing as wontfix; we can reopen if print reserves are reinstated.


Problem loading the data

Hi, I am trying to run the fine-tuning script for segmentation TUM Munich dataset and I have problem loading the data. Using your proposed structure:
dataset/SegMunich/
├── dataset/
│   ├── train.txt
│   ├── val.txt
|
├── train/
│   ├── img
│   |   ├── xxx.tif
│   |   ├── xxx.tif
│   ├── label
│   |   ├── xxx.tif
│   |   ├── xxx.tif
├── val/
│   ├── img
│   |   ├── xxx.tif
│   |   ├── xxx.tif
│   ├── label
│   |   ├── xxx.tif
│   |   ├── xxx.tif
...
and also the following which is introduced by mmsegmentation documentations:
├── data
│   ├── my_dataset
│   │   ├── img_dir
│   │   │   ├── train
│   │   │   │   ├── xxx{img_suffix}
│   │   │   │   ├── yyy{img_suffix}
│   │   │   │   ├── zzz{img_suffix}
│   │   │   ├── val
│   │   ├── ann_dir
│   │   │   ├── train
│   │   │   │   ├── xxx{seg_map_suffix}
│   │   │   │   ├── yyy{seg_map_suffix}
│   │   │   │   ├── zzz{seg_map_suffix}
│   │   │   ├── val
With the first structure, I received the error that means it is not the structure that the dataloader expects. With the second one, I get error:   File "/usr/local/lib/python3.10/dist-packages/mmengine/dataset/base_dataset.py", line 768, in _serialize_data
data_bytes = np.concatenate(data_list)
ValueError: need at least one array to concatenate.
Can you please give me a hint how to load the data and what is the correct file structure? Thanks in advance

Thanks for your interest. We uploaded the preprocessed SegMunich dataset to huggingface. Please try this one.
FYI, we plan to release an improved version of DOFA weights. Please stay tuned :)

Thanks for your response. I downloaded the new preprocessed SegMunich dataset and ran the fine-tuning code (by adapting a few lines of codes of config file and /usr/local/lib/python3.10/dist-packages/mmseg/datasets/isaid.py) for optical png files. However, I've not been yet able to run it for multispectral data, as it seems that it is not capable of loading tif files with more than 3 bands or I do not figure out how. Can you please briefly explain how to solve the issue? Thanks

Hi, I will look into it and get back to you this week.
Btw, our new version of DOFA weights have been uploaded to HF
https://huggingface.co/XShadow/DOFA https://huggingface.co/XShadow/DOFA.
Best,
Zhitong
mostaan66 @.***> 于2024年8月27日周二 17:51写道：

Thanks for your response. I downloaded the new preprocessed SegMunich
dataset and ran the fine-tuning code (by adapting a few lines of codes of
config file and
/usr/local/lib/python3.10/dist-packages/mmseg/datasets/isaid.py) for
optical png files. However, I've not been yet able to run it for
multispectral data, as it seems that it is not capable of loading tif files
with more than 3 bands or I do not figure out how. Can you please briefly
explain how to solve the issue? Thanks
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@.***>

Hi  Zhitong,
Thanks for your response. I’ve already deleted my issue from GitHub because
I found the solution and managed to run the model for fine-tuning. The
issue was related to preprocessing that was specific to RGB data
(dict(type='PhotoMetricDistortion')), so I commented out that part of the
code.
Currently, I'm facing another issue. I fine-tuned my model for a binary
segmentation task, but during inference with the generated checkpoint, it
doesn't produce binary predictions. Could you help clarify why this might
be happening and how to resolve it?
Best,
Mostaan
On Mon, Sep 2, 2024 at 4:31 PM Zhitong @.***> wrote:

Hi, I will look into it and get back to you this week.
Btw, our new version of DOFA weights have been uploaded to HF
https://huggingface.co/XShadow/DOFA https://huggingface.co/XShadow/DOFA.
Best,
Zhitong
mostaan66 @.***> 于2024年8月27日周二 17:51写道：

Thanks for your response. I downloaded the new preprocessed SegMunich
dataset and ran the fine-tuning code (by adapting a few lines of codes
of
config file and
/usr/local/lib/python3.10/dist-packages/mmseg/datasets/isaid.py) for
optical png files. However, I've not been yet able to run it for
multispectral data, as it seems that it is not capable of loading tif
files
with more than 3 bands or I do not figure out how. Can you please
briefly
explain how to solve the issue? Thanks
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Could you please provide more info about your current model and output?

Hi, here is a simple example for model inference:
import torch
import matplotlib.pyplot as plt
from mmengine.model.utils import revert_sync_batchnorm
from mmseg.apis import init_model, inference_model, show_result_pyplot

config_file = "configs/dofa_vit_segmunich.py"
checkpoint_file = "work_dirs/dofa_vit_segmunich/dofa_vit_segmunich.pth"

model = init_model(config_file, checkpoint_file, device='cpu')

# test a single image
img = 'data/SegMunich/img_dir/val/645.tif'
if not torch.cuda.is_available():
    model = revert_sync_batchnorm(model)
result = inference_model(model, img)

The result looks like this:
<SegDataSample(

    META INFORMATION
    img_shape: (256, 256)
    img_path: 'data/SegMunich/img_dir/val/645.tif'
    scale_factor: (2.0, 2.0)
    ori_shape: (128, 128)

    DATA FIELDS
    seg_logits: <PixelData(

            META INFORMATION

            DATA FIELDS
            data: tensor([[[ -6.8653,  -7.1571,  -7.7406,  ...,  -9.8622,  -8.7326,  -8.1678],
                         [ -7.1263,  -7.4104,  -7.9786,  ..., -10.1654,  -9.0342,  -8.4687],
                         [ -7.6483,  -7.9171,  -8.4546,  ..., -10.7719,  -9.6375,  -9.0703],
                         ...,
                         [ -7.4796,  -7.7309,  -8.2336,  ...,  -8.7707,  -7.7690,  -7.2682],
                         [ -6.8476,  -7.1284,  -7.6898,  ...,  -8.1029,  -7.2424,  -6.8122],
                         [ -6.5316,  -6.8271,  -7.4180,  ...,  -7.7690,  -6.9791,  -6.5842]],

                        [[ -1.9556,  -1.8963,  -1.7776,  ...,  -1.8071,  -2.0109,  -2.1128],
                         [ -1.9140,  -1.8806,  -1.8137,  ...,  -1.6550,  -1.8652,  -1.9703],
                         [ -1.8308,  -1.8492,  -1.8860,  ...,  -1.3508,  -1.5738,  -1.6853],
                         ...,
                         [ -1.2921,  -1.2279,  -1.0995,  ..., -11.2941, -10.0507,  -9.4290],
                         [ -1.4592,  -1.3533,  -1.1413,  ..., -10.1313,  -9.0501,  -8.5095],
                         [ -1.5428,  -1.4159,  -1.1622,  ...,  -9.5500,  -8.5498,  -8.0497]],

                        [[ -1.4377,  -1.3470,  -1.1656,  ...,  -1.4771,  -1.7191,  -1.8401],
                         [ -1.3096,  -1.2289,  -1.0676,  ...,  -1.3770,  -1.5953,  -1.7044],
                         [ -1.0534,  -0.9928,  -0.8716,  ...,  -1.1768,  -1.3476,  -1.4330],
                         ...,
                         [ -4.4227,  -4.4194,  -4.4128,  ...,  -9.2482,  -8.7694,  -8.5300],
                         [ -4.3288,  -4.2667,  -4.1426,  ...,  -8.8028,  -8.4594,  -8.2878],
                         [ -4.2818,  -4.1904,  -4.0075,  ...,  -8.5800,  -8.3045,  -8.1667]],

                        ...,

                        [[ -2.2751,  -2.1984,  -2.0449,  ...,  -3.3098,  -3.4472,  -3.5159],
                         [ -2.1042,  -2.0341,  -1.8939,  ...,  -3.2709,  -3.3439,  -3.3804],
                         [ -1.7626,  -1.7057,  -1.5920,  ...,  -3.1932,  -3.1373,  -3.1093],
                         ...,
                         [ -1.2615,  -1.2366,  -1.1867,  ...,  -9.2775,  -8.9401,  -8.7714],
                         [ -1.6097,  -1.5516,  -1.4353,  ...,  -8.8873,  -8.5578,  -8.3931],
                         [ -1.7838,  -1.7091,  -1.5596,  ...,  -8.6922,  -8.3667,  -8.2039]],

                        [[ -2.2086,  -2.2202,  -2.2434,  ...,  -3.4590,  -3.4219,  -3.4033],
                         [ -2.1867,  -2.2193,  -2.2843,  ...,  -3.5102,  -3.4289,  -3.3883],
                         [ -2.1430,  -2.2174,  -2.3663,  ...,  -3.6127,  -3.4431,  -3.3583],
                         ...,
                         [ -2.2061,  -2.4063,  -2.8068,  ..., -10.7627, -10.1027,  -9.7727],
                         [ -2.2716,  -2.3901,  -2.6269,  ...,  -9.8571,  -9.3015,  -9.0237],
                         [ -2.3044,  -2.3819,  -2.5370,  ...,  -9.4044,  -8.9009,  -8.6492]],

                        [[ -6.5529,  -6.5778,  -6.6276,  ...,  -8.4227,  -8.1410,  -8.0002],
                         [ -6.6344,  -6.6729,  -6.7499,  ...,  -8.3211,  -8.0194,  -7.8685],
                         [ -6.7974,  -6.8631,  -6.9945,  ...,  -8.1179,  -7.7760,  -7.6051],
                         ...,
                         [ -4.3621,  -4.3266,  -4.2556,  ...,  -9.5519,  -9.1874,  -9.0052],
                         [ -4.4509,  -4.3350,  -4.1034,  ...,  -9.0364,  -8.7521,  -8.6100],
                         [ -4.4952,  -4.3393,  -4.0273,  ...,  -8.7787,  -8.5345,  -8.4124]]])
        ) at 0x7f3d1c4c7ee0>
    pred_sem_seg: <PixelData(

            META INFORMATION

            DATA FIELDS
            data: tensor([[[3, 3, 3,  ..., 3, 3, 3],
                         [3, 3, 3,  ..., 3, 3, 3],
                         [3, 3, 3,  ..., 3, 3, 3],
                         ...,
                         [3, 3, 3,  ..., 4, 4, 4],
                         [3, 3, 3,  ..., 4, 4, 4],
                         [3, 3, 3,  ..., 4, 4, 4]]])
        ) at 0x7f3d1c4c7d90>
) at 0x7f3d1c4c7d00>

I think "pred_sem_seg" is what you want.

Thanks for your help, I successfully completed the inference this way!


fix: [nuxt-3] Gallery Item List and Transfer NFT issues

Thank you for your contribution to the KodaDot - One Stop Shop for Polkadot NFTs.
👇 __ Let's make a quick check before the contribution.
PR Type

[x] Bugfix
[ ] Feature
[ ] Refactoring

Context

Make list and transfer work gallery item

[x] Ref #7403

[x] Closes #7321

Did your issue had any of the "$" label on it?

[x] Fill up your DOT address: Payout

Community participation

[x] Are you at KodaDot Discord?

Screenshot 📸

[ ] My fix has changed something on UI;

transfer

list

Copilot Summary

🤖 Generated by Copilot at 12ceb09
This pull request refactors some components to use Vue 3 and Vue Router 4 features, fixes some minor UI issues, and renames and moves the ListingCartModal component to ItemTransferModal to better reflect its functionality.

🤖 Generated by Copilot at 12ceb09

ItemTransferModal
New name, new features, new home
Autumn of changes

@yangwao psyout thanks


Pipe intermediate_steps out of map_reduce.run()

Feature request
Pipe intermediate_steps out of MR chain:
# Combining documents by mapping a chain over them, then combining results
    combine_documents = MapReduceDocumentsChain(
        # Map chain
        llm_chain=map_llm_chain,
        # Reduce chain
        reduce_documents_chain=reduce_documents_chain,
        # The variable name in the llm_chain to put the documents in
        document_variable_name="questions",
        # Return the results of the map steps in the output
        return_intermediate_steps=True)

    # Define Map=Reduce
    map_reduce = MapReduceChain(
        # Chain to combine documents
        combine_documents_chain=combine_documents,
        # Splitter to use for initial split
        text_splitter=text_splitter)

    return map_reduce.run(input_text=input_doc)

Error:
ValueError: `run` not supported when there is not exactly one output key. Got ['output_text', 'intermediate_steps'].

Motivation
We want to return the intermediate docs
Your contribution
Will work on this

I'm getting this same issue

I can pick this up @rlancemartin . I was anyway working on a custom map reduce chain where an individual user can send his own chunks and get map-reduced outputs on those chunks instead of sending a textsplitter and a whole text corpus.

@bhavishpahwa: We are also trying to use the intermediate steps and are running into the same error as you. Wondering if you are planning on a fix or update


fix should not fold constant when do ordinal group by

Proposed changes
Issue Number: close #xxx

Further comments
If this is a relatively large or complex change, kick off the discussion at<EMAIL_ADDRESS>by explaining why you chose the solution you did and what alternatives you considered, etc...

run buildall

run buildall


Respect CXX and CXXFLAGS variables

Do not override compiler settings provided by the user.
Use CXX instead of Cxx variable. Use CXXFLAGS instead of CFLAGS because
we are using C++ compiler (instead of C compiler).

Makes sense.


Remove auto initialization of Extension::id

Currently this field is auto initialized from class name
val id: String get() = javaClass.simpleName

I suggest to remove this behavior for 2.0, and force users to declare it explicitly.
This will help to avoid issues like #6464

Good idea. Go for it!

fixed in https://github.com/detekt/detekt/pull/6505


[Cleanup] Format getinfo string

This PR cleans up the format of GetInfo and some log string.

Coverage decreased (-0.01%) to 75.768% when pulling c9a3d3c90ec792ee044fb8e27f8f19f24a1db24e on dengyuchenkit:getinfo into 334016edf49e5fee16876691d1f9db81b7628487 on cmu-db:master.

Changes Unknown when pulling 58415c6db3dc69bda6a393d4cda77858ad7b51ca on dengyuchenkit:getinfo into ** on cmu-db:master**.

Coverage decreased (-0.008%) to 75.755% when pulling 58415c6db3dc69bda6a393d4cda77858ad7b51ca on dengyuchenkit:getinfo into 5409ea2f202b9c1fc3a199798dbf308e14083cf3 on cmu-db:master.

Coverage decreased (-0.03%) to 75.737% when pulling d4ffb0464e6896a6a12f72092b33db1e5cd5526d on dengyuchenkit:getinfo into 5409ea2f202b9c1fc3a199798dbf308e14083cf3 on cmu-db:master.


What happened to this library?

The last commit was over a month ago, and there are currently 250+ open issues and 130+ open PR's.
I love this library, but it doesn't hurt to ask - is this even being actively developed anymore? What alternatives are people using?

Active development has moved to v1-dev branch so we are not merging into
master and only making very specific changes related to v1
On Thu, Sep 28, 2017 at 5:46 PM Blake Brown<EMAIL_ADDRESS>wrote:

The last commit was over a month ago, and there are currently 250+ open
issues and 130+ open PR's.
I love this library, but it doesn't hurt to ask - is this even being
actively developed anymore? What alternatives are people using?
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Thanks for the quick response Dogfalo. It's good to know you guys are still working on it. From the outside it didn't seem like that much was happening - please continue to keep us updated on your progress!! :)
Also let me know if there's anything I can do to help with the V1 release. Would love to have a stable version asap ❤

We're finishing up the plugins and will probably start having 1.0.0 beta releases -> release candidates -> final


Error while running make_pipeline_from_data_check_output while applying data checks suggestion

actions_pipeline = make_pipeline_from_data_check_output(problem_type, messages)
data_df, y = actions_pipeline.fit(data_df, target)
#################################################
Error Message:
File /anaconda/envs/azureml_py310_sdkv2/lib/python3.10/site-packages/woodwork/logical_types.py:475, in IntegerNullable.transform(self, series, null_invalid_values)
473 if null_invalid_values:
474     series = _coerce_integer(series)
--> 475 return super().transform(series)
File /anaconda/envs/azureml_py310_sdkv2/lib/python3.10/site-packages/woodwork/logical_types.py:76, in LogicalType.transform(self, series, null_invalid_values)
74         series = series.astype(new_dtype)
75     except (TypeError, ValueError):
---> 76         raise TypeConversionError(series, new_dtype, type(self))
77 return series
TypeConversionError: Error converting datatype for LoanAmount from type float64 to type Int64. Please confirm the underlying data is consistent with logical type IntegerNullable.

@sainiudit would you to provide us with the dataset you used? Or a sample of the dataset? We would like to reproduce this error.


Fix error when name hashing is enabled and mixed with non-enabled

If MRelationsNameHashing is in use, name-hashing is enabled. Looks like a copy-paste error from a similar error message in MRelationsDefaultImpl.

Can one of the admins verify this patch?

LGTM

LGTM.


Fix keyboard focus

Summary of proposed changes
Focused keyboard input did not register when clicking on the video. This makes the container focusable so that it will focus the keyboard input wherever you click on the video.

Pull requests should be to the develop branch as per docs

If you click a focusable element, it automatically gets focus, so much of that code is redundant. Also, as part of the acccesibility issue, tabindex was removed from the container. What’s the actual bug here?

Okay I didn't know you couldn't use tabindex for the container. But here's the issue that I had.
When I click on the video to pause it or play the video. It does not have keyboard focus and my keyboard shortcuts do not work.
My change also sets the focus on the player whenever you have autoplay enabled. So that you can use the shortcuts right away.

The tabindex has been restored in v3.5.0 which should resolve this 👍