Mojo::UserAgent can't open https://www.justhappy.fr

This seems to be a problem with our empty HTTP response detection code. It works if we remove the expect_close attribute. But that of course results in a few existing tests breaking and doesn't seem exactly compatible with the HTTP spec. This RFC section seems related.
diff --git a/lib/Mojo/Content.pm b/lib/Mojo/Content.pm
index 0a5788b91..a2b79629b 100644
--- a/lib/Mojo/Content.pm
+++ b/lib/Mojo/Content.pm
@@ -6,7 +6,7 @@ use Compress::Raw::Zlib qw(WANT_GZIP Z_STREAM_END);
 use Mojo::Headers;
 use Scalar::Util 'looks_like_number';

-has [qw(auto_decompress auto_relax expect_close relaxed skip_body)];
+has [qw(auto_decompress auto_relax relaxed skip_body)];
 has headers           => sub { Mojo::Headers->new };
 has max_buffer_size   => sub { $ENV{MOJO_MAX_BUFFER_SIZE} || 262144 };
 has max_leftover_size => sub { $ENV{MOJO_MAX_LEFTOVER_SIZE} || 262144 };
@@ -107,8 +107,7 @@ sub parse {
   my $len     = $headers->content_length // '';
   if ($self->auto_relax && !length $len) {
     my $connection = lc($headers->connection // '');
-    $self->relaxed(1)
-      if $connection eq 'close' || (!$connection && $self->expect_close);
+    $self->relaxed(1) if $connection eq 'close' || !$connection;
   }

   # Chunked or relaxed content
@@ -365,13 +364,6 @@ Decompress content automatically if L</"is_compressed"> is true.

 Try to detect when relaxed parsing is necessary.

-=head2 expect_close
-
-  my $bool = $content->expect_close;
-  $content = $content->expect_close($bool);
-
-Expect a response that is terminated with a connection close.
-
 =head2 headers

   my $headers = $content->headers;
diff --git a/lib/Mojo/Message/Response.pm b/lib/Mojo/Message/Response.pm
index 8cc9479bb..0998ce28f 100644
--- a/lib/Mojo/Message/Response.pm
+++ b/lib/Mojo/Message/Response.pm
@@ -101,7 +101,6 @@ sub extract_start_line {
   my $content = $self->content;
   $content->skip_body(1) if $self->code($2)->is_empty;
   defined $content->$_ or $content->$_(1) for qw(auto_decompress auto_relax);
-  $content->expect_close(1) if $1 eq '1.0';
   return !!$self->version($1)->message($3);
 }

It's very easy to see what's going on with MOJO_CLIENT_DEBUG=1 perl -Ilib script/mojo get https://www.justhappy.fr.

Reading the specs (snipped here: https://gist.github.com/jberger/5d9855d71c0fa46ef0167de1128d49f7) I think that the logical action is that in Mojo::Content::parse if no Content-Length header was received and not in one of the listed special cases, then we set relaxed parsing and possibly somehow taint the connect to prevent its reuse. That tainting might be unnecessary because it practice it doesn't appear that there is any way that the connection would attempt to be reused because nothing will cause the parsing phase to end until the connection is otherwise closed or timed out (and thus closed).
With relaxed manually set the body is indeed processed as seen by running
perl -Mojo -E 'my $ua = app->ua; my $tx = $ua->build_tx(GET => "https://www.justhappy.fr"); $tx->res->content->relaxed(1); $ua->start($tx); print $tx->res->body'

The following script could be the base for the corresponding test. It doesn't pass because there is not Content-Length header in the server answer.
use Mojo::Base -strict;
BEGIN { $ENV{MOJO_REACTOR} = 'Mojo::Reactor::Poll'; }
use Test::More;
use Mojo::IOLoop;
use Mojo::UserAgent;

# based on RFC7230 Server response example (after deleting Content-Length header)
my $ans = <<'FIN';
HTTP/1.1 200 OK
Date: Mon, 27 Jul 2009 12:28:53 GMT
Server: Apache
Last-Modified: Wed, 22 Jul 2009 19:15:56 GMT
ETag: "34aa387-d-1568eb00"
Accept-Ranges: bytes
Vary: Accept-Encoding
Content-Type: text/plain

Hello World! My payload includes a trailing CRLF.

FIN

my $buffer = '';
my $id     = Mojo::IOLoop->server(
  {address => '<IP_ADDRESS>'} => sub {
    my ($loop, $stream) = @_;
    $stream->on(
      read => sub {
        my ($stream, $chunk) = @_;
        $buffer .= $chunk;
        return unless $buffer =~ /^\s*$/m;
        $stream->write($ans => sub { shift->close });
      }
    );
  }
);
my $port = Mojo::IOLoop->acceptor($id)->port;
my $ua   = Mojo::UserAgent->new;
my $tx;
$ua->get_p("http://<IP_ADDRESS>:$port/")->then(sub {
  $tx = shift;
  Mojo::IOLoop->stop;
});
Mojo::IOLoop->start;
ok $tx->success, "get succeded";
is $tx->result->body, "Hello World! My payload includes a trailing CRLF.\n\n",
  "gets payload";
done_testing;

The test script will pass whith the current version of Mojo::UserAgent if you manually insert the rigth header:
Content-Length: 51

among the other headers in the $ans variable, as it is in the example taken from RFC 7230 section 2.1.

Fyi it should be pointed out, that, when using curl, the alproxy sends the Content-Length Header.
So, Imho, there's some client Agent (or anything else) detection going on. It's a mass hosters environment with an own proxy solution. Didn't find anything out about alproxy.
curl -v https://www.justhappy.fr 2>&1

Request:
> GET / HTTP/1.1
> Host: www.justhappy.fr
> User-Agent: curl/7.61.0
> Accept: */*

Answer snippet:
{ [5 bytes data]
< HTTP/1.1 200 OK
< Content-Type: text/html; charset=utf-8
< Content-Length: 104807
< Vary: Accept-Encoding
< ETag: W/"f82d9172eb1954092555394d2709a09c"
< Cache-Control: max-age=0, private, must-revalidate
< Set-Cookie: < X-Request-Id: 73f47387-cbce-4050-9658-d14995093525
< Via: 1.1 alproxy
< Date: Wed, 08 Aug 2018 05:27:58 GMT
<


chore: Remove background jobs

What's the rationale for removing background jobs? Will we be adding in a replacement for things that require long-running execution?

This might be me (see discussions here) for more discussion.
The leading question in my mind is: where would background jobs run in the context of distributed execution? Certainly the "run this job on an interval" is not something that (I think) would be reasonable to maintain in GlareDB itself. Even without distributed execution, if you ended up with the same catalog loaded into two instances at once you could end up with multiple executions of the same job, which causes problems if any data is written (internally or externally) as a result of a background job. So the utility is pretty limited.
Having the ability to run a single job in the background that could continue to execute until completion/cancelation (or instance termination,) is something that I think we could have (and this current implementation could have/use)? This would be good for things like "start table optimize" or a longer running COPY TO query. [you could also imagine caching cursors to shared storage, and building a resumable-cursor]. We could use an external service to coordinate interval jobs (e.g. cloud; though the distributed questions there remain, there's just slightly less complexity.)
But back to this code:

giving users a way to "unhook" the current job from the session/its lifecycle is great, but this code doesn't expose that, I think.
as glaredb engineers, background jobs are very difficult to use correctly, because they basically cannot have side-effects (and/or we have to accept more-than-once execution) and the use case for that seems very narrow anyway.
we could keep this table and most of this code but without the interval, as a placeholder for the eventual development of the "single-shot/unhooked background jobs" but I think implementing that might require more design, and might not be compatible with this code anyway.

Certainly the "run this job on an interval" is not something that (I think) would be reasonable to maintain in GlareDB itself.

I think this is the thing I agree with the most. Having systems external to glaredb responsible for running jobs (queries) on an interval seems to be a bit cleaner. I do think there may be something at some point that will make us revisit this (like long running ALTER TABLE statements that we might want to just kick to the background). But I think the "simple" cases can be better served outside glaredb.

Even without distributed execution, if you ended up with the same catalog loaded into two instances at once you could end up with multiple executions of the same job, which causes problems if any data is written (internally or externally) as a result of a background job.

In my mind (if we stuck with background jobs), their state/status wouldn't be stored in the catalog, but in a persistent table on disk (object store). Then each instance would periodically read that table and grab jobs as necessary.

In my mind (if we stuck with background jobs), their state/status wouldn't be stored in the catalog, but in a persistent table on disk (object store). Then each instance would periodically read that table and grab jobs as necessary.

100% agree with this. We might want to have some way of doing this that isn't parquet files or columnar at all, but that's extremely a later problem.

Having systems external to glaredb responsible for running jobs (queries) on an interval seems to be a bit cleaner.

For a lot of cases (triggers, materialized views, etc.) this is def cleaner and quicker

I do think there may be something at some point that will make us revisit this (like long running ALTER TABLE statements that we might want to just kick to the background). But I think the "simple" cases can be better served outside glaredb.

simple case, definitely better for starters
I think the "persistent cursor," feature (e.g. start an operation, let it run, have a UUID that can be used to check the status of the operation and then access the result set) would cover things like this.
glaredb could be made to offload some of those kinds of operations (altertables, optomize, etc.) to be run by the system user (e.g. cloud).


Bump GAMS version in nightly CI worklow

Similar to https://github.com/iiasa/message-ix-models/pull/250#pullrequestreview-2450593364, I'm hoping today's nightly CI test failure also just arises from an incompatibility of using an older GAMS version but a newer GAMS license for this kind of run. This PR thus bumps the GAMS version in the nightly workflow and temporarily enables them to make sure the fix works.
How to review

Read the diff and note that the CI checks all pass.

PR checklist

[ ] Continuous integration checks all ✅
[x] Update tests; coverage checks both ✅
~[ ] Add, expand, or update documentation. ~ Just CI.
~[ ] Update release notes. ~ Just CI.

I've been looking at events that trigger CI runs and I've stumbled upon pull_request_target. If I'm not mistaken, this can close #603 since the nightly tests will now automatically run on every PR that changes any GAMS file (.gms, .gpr, and .gdx for now).


[pending issue 61] Add input data quality control functionality

See related issue #42
Implement checks on TSV file Excel input file column names, data types, and (if possible) data completeness.
More details to be added before work begins. Cross-ref with cheat sheet development: we keep running into things in the dataset that are mandatory, and it's very non-intuitive why these items are required while other items are not.
PENDING merging of PR #110

Detailed To-Implement List
Cross-checks (involving 2 or more datasets)

[ ] The same set of technology name(s) must appear in: designs, indices, parameters, results
[ ] The Lifetime-Index values in designs must equal the Capital-Index values in indices
[ ] The Category-Tranche combinations in investments must be a subset of the Category-Tranche combinations in tranches
[ ] The Technology-Scenario values in parameters must be exactly the set of Technology-Scenario values in designs

designs

[ ] Variable column contains exactly these values: Input, Input efficiency, Input price, Lifetime, Output efficiency, Output price, Scale (case sensitive)
[ ] Every Technology/Scenario combination must have all mandatory Variables

functions

All unique entries under Model must:

[ ] exist as a .py file in tyche/src/technology
[ ] contain the methods defined in Capital, Fixed, Production, and Metrics columns
[ ] Bonus: check the methods for mandatory input parameters
[ ] Bonus: check the methods for np.stack() return values

indices

[ ] Type column contains exactly these values: Capital, Input, Output, Metric
[ ] Offset values within each type must be sequential integers beginning at zero

investments
parameters

[ ] Every Parameter Offset must be the same across all Technology-Scenario combinations
[ ] Within a Technology-Scenario, the Offset values must be sequential integers beginning at zero

results

[ ] Every Technology value must have a Cost Variable and an Index Variable.
[ ] Variable values apart from Cost may be Output or Metric and are not mandatory

tranches

[ ] Within a Category, the Amounts for each Tranche must be unique.

FYI @TJTapajyoti and @clhughes , I'm working on adding some data validation functionality to IO.py in branch issue-56.  I'd appreciate you reviewing my list of checks to implement (see comment above) and adding to it if you can think of anything else to verify. Right now the things I'm checking for would either break the code downstream or produce nonsensical results. We can also add in "warning" checks for things that wouldn't break the code but could be user errors (...can't think of any examples at the moment, but I'm sure there are some).
I'm expecting to get this issue almost complete before the end of the week, so if you have any checks to add please put them in before October 31 and I'll implement & merge them when I get back.

Additional checks following first review on PR #118
As a side note, strictly speaking the results dataset can be more loosely defined than we're enforcing here. Some Metrics or Outputs can be missing from the results dataset, and the analysis code will run fine although not all the Metrics will be present in the results. However, one obvious outcome is an incompletely specified results dataset that makes the code look like it's broken (not computing everything it should be). At this point I'd rather enforce a complete results dataset than leave that up to the user.

[ ] Cross-check: The Index values for Output Type in the indices dataset must match the Index values for Output Variable in results and Output efficiency Variable in designs.
[ ] Cross-check: Metric Index values match in results and indices


Switching to rocblas_gemm_ex for MFMA-enabled architectures

@ekuznetsov139 @deven-amd , can you help cherry-pick this PR to the following list of release branches after merged?
r1.15-rocm-enhanced
r2.3-rocm-enhanced
r2.4-rocm-enhanced


Env.runLayer ignores pending effects introduced by layers

Failing test:
            "should provide all layers" in {
                val effect: Int < Env[String & Int & Boolean & Char] =
                    Env.get[String] *> Env.get[Int] *> Env.get[Boolean] *> Env.get[Char].as(23)
                val layerChar   = Layer(IO('c'))
                val layerString = Layer("value")
                val layerInt    = Layer(1)
                val layerBool   = Layer(false)
                val handled =
                    Env.runLayer(
                        layerChar,
                        layerString,
                        layerInt,
                        layerBool
                    )(effect)
                assert(Memo.run(handled).eval == 23)
            }

The above should fail with a compilation error. handled should have type Int < (IO & Memo) because layerChar has an IO effect input. Instead, Env.runLayer casts it to Int < Memo. As a result, it compiles but then fails at runtime with kyo.bug$KyoBugException: Unexpected pending effect

I think I have a fix. Here is what Env.runLayer should look like:
    transparent inline def runLayer[A, S, V](inline layers: Layer[?, ?]*)(value: A < (Env[V] & S)): A < (S & Memo & Nothing) =
        inline Layer.init[V](layers*) match
            case layer: Layer[V, s] =>
                layer.run.map { env =>
                    runTypeMap(env)(value)
                }

Have a PR: #604 -- I'm seeing if I can get a provideSome method working too...

nice! that was quick :)

Fixed by #604


Helm v3 - Linting multiple charts merges the error messages together

Output of helm version:
$ helm3 version
version.BuildInfo{Version:"v3.0.0-alpha.2", GitCommit:"97e7461e41455e58d89b4d7d192fed5352001d44", GitTreeState:"clean", GoVersion:"go1.12.7"}

Output of kubectl version:
N/A
Cloud Provider/Platform (AKS, GKE, Minikube etc.):
N/A
When linting multiple charts in helm3 all error messages are merged together making it hard to work out which chart caused a failure.
$ mkdir -p /tmp/broken_chart_{1,2}
$ touch /tmp/broken_chart_1/Chart.yaml
$ helm3 lint /tmp/broken_chart*
Error: 1 chart(s) linted, 2 chart(s) failed
        %!s(<nil>)
        no chart found for linting (missing Chart.yaml)
        [ERROR] Chart.yaml: name is required
        [ERROR] Chart.yaml: directory name (broken_chart_1) and chart name () must be the same
        [ERROR] Chart.yaml: apiVersion is required. The value must be either "v1" or "v2"
        [ERROR] Chart.yaml: version is required
        [INFO] Chart.yaml: icon is recommended
        [INFO] values.yaml: file does not exist
        [WARNING] templates/: directory not found

I'm not sure if this is the expected behavior of the lint action or not but this change makes it difficult to work out what failed compared to the same command in helm2.
$ helm lint /tmp/broken_chart*
==> Linting /tmp/broken_chart_1
[ERROR] Chart.yaml: name is required
[ERROR] Chart.yaml: directory name (broken_chart_1) and chart name () must be the same
[ERROR] Chart.yaml: version is required
[INFO] Chart.yaml: icon is recommended
[INFO] values.yaml: file does not exist
[WARNING] templates/: directory not found

==> Skipping /tmp/broken_chart_2
No chart found for linting (missing Chart.yaml)

Error: 1 chart(s) linted, 2 chart(s) failed

I think this is a UX bug. PRs welcome to fix.

Have done some initial work on this in https://github.com/helm/helm/pull/6296, will continue to work on this.

@mattfarina I've taken a shot at this and opened two PRs #6296 and #6310.

Closing this as fixed by #6310 and #6296.


[GenericPoseGraph] Add 'group_id' field to NodeId.

@wally-the-cartographer merge

Merge requested by authorized user cschuet. Merge queue now has a length of 1.


Datetime picker starts on Sunday

How to set it to start on monday?

Hi @jrcacd . I'm a beginner to Android App development and I would like to work on this issue. Can I proceed working on it?


gc: tail 100 lines of pod logs before collections

useful for debugging. we may want to have the pod logs print on the debug level?
\cc @hongchaodeng

We shouldn't print pods' logs in operator log.
pods' logs should be managed by cluster level, e.g. using kubectl, ElasticSearch, etc.


Unable to Select Realm

There is no realm selection for the US region.  All other regions have a realm list. The US list is blank.  Even when typing out a realm selection.  There is nothing to select.

should be resolved now, thank you for reporting it.


constant velocity model

Thanks for your wonderful work! Do you have the code for the constant velocity model on Nuscenes dataset? I tried to implement the constant velocity model but cannot reproduce the results. Is there any preprocessing of the velocity or time stamp? Thank you very much!

@ifzhang We haven't summarized the code for the constant velocity model yet, but you can check out our dev branch for an "uncleaned" implementation. In brief, you only need to change the motion_model field in the configuration file to velo, as in this example of Waymo Open Dataset.

I will check the dev branch. Thank you very much!


Enable running without a config file

Currently, the .aiida-testing-config.yml config file is committed to the repository. In general (and for plugins), this kind of defeats the purpose of the config file: It contains the parts that are different on different systems, and can not be hardcoded in the tests.
A partial solution is to just recommend not committing .aiida-testing-config.yml, however it would be good to make all fixtures run (if all caches are present) also without a config file, for the purposes of CI. Having to create a special "CI config file" instead would be quite cumbersome.

Indeed - ideally I would prefer not having a config file at all, but I don't really see a viable way of avoiding it.
I would propose the following approach for aiida plugins using aiida-testing:

don't commit the .aiida-testing-config.yml
ideally, running the tests for the first time would create somehow a template configuration file that one just needs to copy into .aiida-testing-config.yml. Otherwise one needs to make sure to document the shape of the config file for every plugin (also possible)
definitely it needs to be possible to run the tests without a config file being present (as it's not actually needed unless one needs to regenerate test data)

For CI, I don't quite get the issue. I think it's fine to assume that, on CI, users will be using the test data.

Yeah, CI was just my use case for "being able to run without config file".

* ideally, running the tests for the first time would create somehow a template configuration file that one just needs to copy into `.aiida-testing-config.yml`. Otherwise one needs to make sure to document the shape of the config file for every plugin with the needed code labels (also possible)

Hmm, for the mock codes maybe when trying to run a code that has no entry it just populates the config file with <label>: null.
It's not quite clear to me how to find the place where the config file should be put, though. Maybe the "automatic empty config file" should be behind a pytest flag? If we just do it automatically, all of the considerations w.r.t. overwriting existing config files, backup, etc. comes into play again.

It's not quite clear to me how to find the place where the config file should be put, though.

I guess this question of the config file location applies just as well to  config loading (https://github.com/aiidateam/aiida-testing/issues/12 ).
If we provide a way to specify the search path, it should apply to both.
Otherwise, I would say the CWD is also fine for the moment.

Maybe the "automatic empty config file" should be behind a pytest flag? If we just do it automatically, all of the considerations w.r.t. overwriting existing config files, backup, etc. comes into play again.

I agree that overwriting an existing config file automatically is not a good idea.
If it's easy enough to add a flag --generate-config (analogous to jupyter ) to pytest, that would be great.
There was still some more uncertainty hidden in the "somehow" of my comment - how is the creation of the template going to work?
Does the mock_code_factory (with the flag enabled) add new labels to the config file?

I guess this question of the config file location applies just as well to config loading (#12 ).
If we provide a way to specify the search path, it should apply to both.
Otherwise, I would say the CWD is also fine for the moment.

It's not quite the same though, because with an existing config you can upwards-search from CWD, making sure to always hit it as long as you're in the repo (if you put it at the root level).
But if we make --generate-config a flag, I think CWD is fine -- move it afterwards if really needed.

I agree that overwriting an existing config file automatically is not a good idea.
If it's easy enough to add a flag --generate-config (analogous to jupyter ) to pytest, that would be great.

Yeah, I agree that this is the way to go -- since pytest flags is a limited namespace the flag name probably needs to be something like --generate-aiida-testing-config, though.

There was still some more uncertainty hidden in the "somehow" of my comment - how is the creation of the template going to work?
Does the mock_code_factory (with the flag enabled) add new labels to the config file?

Yes, I think this would be the way to go - obviously it will only create the it for codes that are actually used in the test run, but there doesn't appear to be a way around that.
A point to consider is that the configuration generation should work for the different parts of aiida-testing, not only mock codes -- so we need a common (internal) API for updating the config file, which is then used by the different parts.

since pytest flags is a limited namespace the flag name probably needs to be something like --generate-aiida-testing-config, though.

Sounds good!
All the rest seems more or less agreed. Now we just need to implement it ;-)


add flatArray() shortcut

Hello,
sometimes I need to flatMap an array, that can possibly be null.
This scenario is not so common, but it is encountered.
A trivial (but meaningful) example is when working with Files, because File.listFiles() states:

If this abstract pathname does not denote a directory, then this method returns null. Otherwise an array of File objects is returned, one for each file or directory in the directory.

So, to flat file structure (single level) you have to:
// specific approach: null-check is based on doc statement
.flatMap(x -> x.isDirectory() ? Arrays.stream(x.listFiles()) : null)

OR
// specific approach: same as before, but using only method references
.filter(File::isDirectory)
.map(File::listFiles)
.flatMap(Arrays::stream)

OR
// generic approach: preemptive null pruning (sub-optimal time consuming?)
.map(File::listFiles)
.nonNull()
.flatMap(Arrays::stream)

Because of this, I suggest to add the AbstractStreamEx.flatArray just like the  AbstractStreamEx.flatCollection:
public <R> StreamEx<R> flatArray(Function<? super T, ? extends R[]> mapper) {
	return flatMap(t -> {
		R[] a = mapper.apply(t);
		return a == null ? null : Arrays.stream(a);
	});
}

Thank you!

Thank you for your suggestion. I don't remember why I did not added it before. I though that there was some problem with generics or something. Nevertheless seems that it works.
Also used StreamEx.of(Arrays.spliterator(a)) and StreamEx.of(c.spliterator()). This is actually more efficient for many short streams than standard JDK stream.

Thank you! I owe you a couple of beers ;)


Release Google.Cloud.WebRisk.V1Beta1 version 3.0.0-beta03

Changes in this release:
No API surface changes; just dependency updates.

Closing and opening to unstuck some checks.

Created release for Google.Cloud.WebRisk.V1Beta1-3.0.0-beta03


Feature Request: Add command line option to pass time zone or UTC offset

Issue:
I host an html5 wrapper for FreeRDP that allows users to connect via web browser. The FreeRDP software is executed on a server in a time zone that is different from some of the users. Because of this, the timezone always matches the server time and not the local time for the user.
Request:
Please provide the ability to pass the time zone or UTC offset as an option to the FreeRDP executable via command line. Specifically we use the wfreerdp.exe build because we host on a Windows server.

Pull requests are welcome, but we currently will not implement this ourselves.

https://github.com/FreeRDP/FreeRDP/pull/10391


Armored jetpack seems to have no armor

Please use the search functionality before reporting an issue. Also take a look at the closed issues!
Issue description:
Armored jetpack shows no armor in the tooltip nor does it so any armor above the toolbar. See the attached screenshot.
Steps to reproduce:

None, tooltip said no armor in JEI before I even made it.

Version (make sure you are on the latest version before reporting):
Forge: <IP_ADDRESS>6
Mekanism: <IP_ADDRESS>1-fix
Other relevant version: Minecraft 1.11.2
If a (crash)log is relevant for this issue, link it here: (It's almost always relevant)
No crash logs but I can add just a regular log if that's helpful at all.

Can't reproduce this on my end, there's a good chance a mod is interfering with Forge's armor handling. I bet that the armor still functions as intended.

Same issue exist in All the Mods 3 modpack.

Yep, this exists in ATM3.

Please report to your pack maker if it does not absorb damage or to whatever mod adds the info to the tooltip. The jetpack does not use the extra 2 armor values provided by forge, just the vanilla damage absorption.


fix(npc): create ipsets with only valid xml characters

Why:

xml output of ipset is invalid because ipset itself doesn't appear to
escape invalid chars

fixes #2958

For the record, the only reason CircleCI showed up as "failed" is because of a lint error which has been fixed in #2962.


Cherry Pick PR 1286 - Fix #1206 by passing instance name as anchor

Chery picking PR #1286 as requested.
(cherry picked from commit 88106cbe72b6a444a9b335ee53089d8eac503bd7)

Unfortunately there is no nightly GHC release so that CI always fails for this branch :(
Wether the test suite runs through can only be seen once we bump the haddock submodule in GHC.

Unfortunately there is no nightly GHC release so that CI always fails for this branch :(
Wether the test suite runs through can only be seen once we bump the haddock submodule in GHC.


Obfuscar with .net core 2.1

Hi,
Is it possible to use Obfuscar to obfuscate a DLL file created using .net core 2.1?
I had an existing DLL file which was obfuscating as expected while it was compiled using .net framework 4.6.1 however, it stopped obfuscating after we migrated our source code to .net core 2.1. Am I missing anything? How to make this work?
Thanks,
Aashish Singh.

Did you check #132 from FAQ?

Hi Lex,
I just had a look at #132 as suggested by you. However, my problem statement is a bit different. My problem statement is not related to the availability of all DLL files in a particular folder. In my case, obfuscation commands run successfully ; -
1>Note that Rollbar API is enabled by default to collect crashes. If you want to opt out, please run with -s switch
1>Loading project obfuscar.xml...Processing assembly: Runtime.Security, Version=<IP_ADDRESS>, Culture=neutral, PublicKeyToken=null
1>Loading assemblies...Extra framework folders: Done.
1>hiding strings...
1>Renaming:  fields...parameters...properties...events...methods...types...Done.
1>Saving assemblies...Done.
1>Writing log file...Done.
1>Completed, 1.58 secs.
In my obfuscator.xml file, I have also mentioned suppressing Ildasm and hiding strings like : -

Still, when I try to open my obfuscated DLL file into ildasm through elevated Visual Studio 2017 developer command prompt, it opens the DLL file in ildasm and also shows sensitive information in plain text. Am I missing anything? Kindly suggest.
Aashish Singh

ILDASM attribute is practically a useless feature. It hides nothing at all. Obfuscar includes it to match other obfuscation tools, but you can safely forget about it.

I have updated the documentation to reflect such, http://docs.obfuscar.com/getting-started/configuration.html#control-string-hiding


C++ Ubuntu version make error on jetson xavier nx

Hi,
I encounter make error on  jetson xavier nx
ros-yolov3-sort/SORT/cpp_ubuntu/build$ cmake .. && make
......
[ 80%] Building CXX object CMakeFiles/sort_cpp_node.dir/src/main.cpp.o
/home/nxm/ros-yolov3-sort/SORT/cpp_ubuntu/src/main.cpp: In function ‘void TestSORT(std::__cxx11::string, bool)’:
/home/nxm/ros-yolov3-sort/SORT/cpp_ubuntu/src/main.cpp:389:41: error: ‘CV_WINDOW_NORMAL’ was not declared in this scope
cv::namedWindow("SORT-C++", CV_WINDOW_NORMAL);
^~~~~~~~~~~~~~~~
CMakeFiles/sort_cpp_node.dir/build.make:62: recipe for target 'CMakeFiles/sort_cpp_node.dir/src/main.cpp.o' failed
make[2]: *** [CMakeFiles/sort_cpp_node.dir/src/main.cpp.o] Error 1
CMakeFiles/Makefile2:104: recipe for target 'CMakeFiles/sort_cpp_node.dir/all' failed
make[1]: *** [CMakeFiles/sort_cpp_node.dir/all] Error 2
Makefile:83: recipe for target 'all' failed
make: *** [all] Error 2
It seems CV_WINDOW_NORMAL not deifned, where should I add the define?

compile OK after modified CV_WINDOW_NORMAL as WINDOW_NORMAL

compile OK after modified CV_WINDOW_NORMAL as WINDOW_NORMAL

Good to hear that you successfully made it!
Thanks a lot man!


WIP:: Graph review and other things

Try to fix  https://github.com/siconos/siconos/issues/184

Bass guitar example implementation.

Along with @vacary,  I recommend squashing this locally to one commit and using git log -p to select the parts that go semantically together. (e.g. separate commits or PRs for changes to the kernel, changes to music stuff, etc.)

Do you think that we will be able to merge this branch soon ?


Defining Interval for the detector

Hi,
Is there any way to define an interval for the detector model, And leave the tracker to do the hard work in those intervals?
similar to this issue but with a different tracker.

@jahdkaran,
Ultimately it is possible. However, it will take quite a bit of effort to modify BYTETracker to deal with no new location input, just to predict the next position. At the moment we don't have time to change the code.


Fixed Cannot read property 'sub' of undefined.

Fix for bug introduced at https://github.com/coinbase/gdax-node/commit/d85184e2c844fdbba5fc7a5182fce9294bd6197c
this just recovers old logic before new logic throws an error (Cannot read property 'sub' of undefined).
:gift_heart:

well, fixes comes in pairs, the second is because https://github.com/coinbase/gdax-node/issues/24


Envoy now rejecting header values with trailing whitespace

After upgrading nghttp2 to 1.49.0 (#23014), Envoy is now rejecting header values with trailing whitespace
Description:
nghttp2 v1.49.0 rejects trailing whitespaces in header values to follow validation introduced in RFC9113. This is newly introduced behavior which can break existing deployments of Envoy using HTTP2.
At Lyft, the impact of this change has been significantly increased HTTP2 connection churn (upstream_cx_destroy_remote) leading to widespread UC and 503's between our Envoy front proxy and Envoy service mesh sidecars.
Repro steps:
Deploy any version of Envoy that uses nghttp2 v1.49.0 and send requests with whitespace-padded header values. These requests should fail but will succeed if Envoy uses v1.48.0.

Thanks for debugging. Cross reference https://github.com/nghttp2/nghttp2/pull/1792
cc @alyssawilk @RyanTheOptimist @yanavlasov I think we need to default to the previous behavior?


DenseToDenseSetOperation: Could not satisfy explicit device specification '/device:GPU:0' because no supported kernel for GPU devices is available?

System information

Have I written custom code (as opposed to using a stock example script provided in TensorFlow):
OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Ubuntu 18.04
TensorFlow installed from (source or binary): conda -c anaconda
TensorFlow version (use command below): 1.14
Python version: 3.6
Bazel version (if compiling from source): None
GCC/Compiler version (if compiling from source): 7.4
CUDA/cuDNN version: cuda10.1/cudnn7.6.4
GPU model and memory: Quadro P5000

Describe the current behavior
tf.data.Dataset throws a error since I pass to tf114 from tf113. My following code can reproduce this error.
Describe the expected behavior
I explain my code here. The Dataset takes a list of .h5 file names in string then shuffle this list.  A wrapper written with tf.py_func() will load the .h5 file (I know it's deprecated in TF2, but since it works well in my previous code). It's also my question. Is this bug exists only in TF1 or it does in TF2? Should I move to TF2?
Code to reproduce the issue
I wrote a small Seg-Net network to to reproduce the same bug. Everything goes well until I change the output and input of the NN by tf.data.
import tensorflow as tf
import numpy as np
from tqdm import tqdm
from tensorflow.python.tools.optimize_for_inference_lib import optimize_for_inference
from tensorflow.python.framework import dtypes
import os

def up_2by2_ind(input_layer, ind, name=''):
    with tf.name_scope(name):
        in_shape = input_layer.get_shape().as_list()
        out_shape = [tf.cast(tf.shape(input_layer), dtype=tf.int64)[0], in_shape[1] * 2, in_shape[2] * 2, in_shape[3]]

        # prepare
        _pool = tf.reshape(input_layer, [-1])
        _range = tf.reshape(tf.range(out_shape[0], dtype=ind.dtype), [out_shape[0], 1, 1, 1])
        tmp = tf.ones_like(ind) * _range
        tmp = tf.reshape(tmp, [-1, 1])
        _ind = tf.reshape(ind, [-1, 1])
        _ind = tf.concat([tmp, _ind], 1)

        # scatter
        unpool = tf.scatter_nd(_ind, _pool, [out_shape[0], out_shape[1] * out_shape[2] * out_shape[3]])

        # reshape
        unpool = tf.reshape(unpool, out_shape)
        return unpool

###################### input pipeline
def wrapper(a, b):
    return tf.py_func(
        wrawrapper,
        [a, b],
        [tf.float32, tf.float32],
    )

def wrawrapper(a, b):
    return np.ones((1, 50, 50, 1), dtype=np.float32), np.ones((1, 50, 50, 1), dtype=np.float32)

a_ph = tf.placeholder(tf.string, shape=[None], name='a_ph')
b_ph = tf.placeholder(tf.int32, shape=[None], name='b_ph')

batch = tf.data.Dataset.from_tensor_slices((a_ph, b_ph))
batch = batch.shuffle(tf.cast(tf.shape(a_ph)[0], tf.int64))
batch = batch.map(wrapper).prefetch(10).repeat()
it = tf.data.Iterator.from_structure(batch.output_types, batch.output_shapes)
iter_init_op = it.make_initializer(batch, name='iter_init_op')
X_it, y_it = it.get_next()

dropout = tf.placeholder(tf.float32, [], name='dropout')
BN_phase = tf.placeholder(tf.bool, [], name='BN_phase')
save_summary_step = 20
save_model_step = 100

##################### train graph on gpu1
with tf.device('/device:GPU:0'):
    with tf.name_scope('model'):
        with tf.name_scope('conv'):
            with tf.variable_scope('conv', reuse=False):
                w1 = tf.get_variable('w', shape=[3, 3, 1, 1], initializer=tf.initializers.glorot_normal())
            out1 = tf.nn.conv2d(X_it, w1, strides=[1, 1, 1, 1], padding='SAME', name='conv')
            with tf.variable_scope('conv', reuse=False):
                out1 = tf.layers.batch_normalization(out1, training=BN_phase, name='batch_norm')
            out1 = tf.nn.relu(out1, 'relu')
            out1, ind1 = tf.nn.max_pool_with_argmax(out1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name='maxpool')
        with tf.name_scope('dnn'):
            flat = tf.reshape(out1, [1, 625])
            with tf.variable_scope('dnn2', reuse=False):
                w2 = tf.get_variable('w2', shape=[625, 625], initializer=tf.initializers.glorot_normal())
            dnn_out = tf.matmul(flat, w2)
            dnn_out = tf.nn.dropout(dnn_out, keep_prob=dropout, name='do')
            dnn_out = tf.nn.relu(dnn_out, name='relu')
            dnn_out = tf.reshape(dnn_out, shape=[1, 25, 25, 1], name='dnn')

        with tf.name_scope('deconv'):
            out1 = up_2by2_ind(dnn_out, ind1, 'up1')
            with tf.variable_scope('deconv', reuse=False):
                w3 = tf.get_variable('w', shape=[3, 3, 1, 1], initializer=tf.initializers.glorot_normal())
            out1 = tf.nn.conv2d(out1, w3, strides=[1, 1, 1, 1], padding='SAME', name='deconv')
            with tf.variable_scope('deconv', reuse=False):
                out1 = tf.layers.batch_normalization(out1, training=BN_phase, name='batch_norm')
            logits = tf.nn.relu(out1, 'logits')

        #todo: here with tabulation with tf.name_scope('operation'):
    with tf.name_scope('operation'):
        update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
        with tf.control_dependencies(update_ops):
            mse = tf.losses.mean_squared_error(labels=y_it, predictions=logits)
        opt = tf.train.AdamOptimizer(learning_rate=0.0001, name='Adam')

        grads = opt.compute_gradients(mse)
        train_op = opt.apply_gradients(grads, name='apply_grad')

with tf.name_scope('train_metrics'):
    acc_val_op, acc_update_op = tf.metrics.accuracy(labels=y_it, predictions=logits)
    summ_acc = tf.summary.merge([tf.summary.scalar('accuracy', acc_val_op)])
    grad_sum = tf.summary.merge([tf.summary.histogram('{}/grad'.format(g[1].name), g[0]) for g in grads])

with tf.name_scope('train_summary'):
    merged = tf.summary.merge([summ_acc, grad_sum, tf.summary.histogram("weights", w1)])

###################### test graph on gpu2
with tf.device('/device:GPU:1'):
    with tf.name_scope('model'):
        with tf.name_scope('conv'):
            with tf.variable_scope('conv', reuse=True):
                w1 = tf.get_variable('w', shape=[3, 3, 1, 1], initializer=tf.initializers.glorot_normal())
            out1 = tf.nn.conv2d(X_it, w1, strides=[1, 1, 1, 1], padding='SAME', name='conv')
            with tf.variable_scope('conv', reuse=True):
                out1 = tf.layers.batch_normalization(out1, training=BN_phase, name='batch_norm')
            out1 = tf.nn.relu(out1, 'relu')
            out1, ind1 = tf.nn.max_pool_with_argmax(out1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name='maxpool')
        with tf.name_scope('dnn'):
            flat = tf.reshape(out1, [1, 625])
            with tf.variable_scope('dnn2', reuse=True):
                w2 = tf.get_variable('w2', shape=[625, 625], initializer=tf.initializers.glorot_normal())
            dnn_out = tf.matmul(flat, w2)
            dnn_out = tf.nn.dropout(dnn_out, keep_prob=dropout, name='do')
            dnn_out = tf.nn.relu(dnn_out, name='relu')
            dnn_out = tf.reshape(dnn_out, shape=[1, 25, 25, 1], name='dnn')

        with tf.name_scope('deconv'):
            out1 = up_2by2_ind(dnn_out, ind1, 'up1')
            with tf.variable_scope('deconv', reuse=True):
                w3 = tf.get_variable('w', shape=[3, 3, 1, 1], initializer=tf.initializers.glorot_normal())
            out1 = tf.nn.conv2d(out1, w3, strides=[1, 1, 1, 1], padding='SAME', name='deconv')
            with tf.variable_scope('conv', reuse=True):
                out1 = tf.layers.batch_normalization(out1, training=BN_phase, name='batch_norm')
            logits = tf.nn.relu(out1, 'logits')

with tf.name_scope('test_metrics'):
    acc_val_op2, acc_update_op2 = tf.metrics.accuracy(labels=y_it, predictions=logits)
    summ_acc2 = tf.summary.merge([tf.summary.scalar('accuracy', acc_val_op2)])

with tf.name_scope('test_summary'):
    merged2 = tf.summary.merge([summ_acc2, tf.summary.histogram("weights", w1), tf.summary.histogram("weights", w2)])

##############################################
with tf.Session() as sess:
    sess.run([tf.global_variables_initializer(), tf.local_variables_initializer(), iter_init_op],
             feed_dict={a_ph: ['a'], b_ph: [10]})
    model_saver = tf.train.Saver(max_to_keep=100000)
    train_writer = tf.summary.FileWriter('./dummy/gpus/train/', sess.graph)
    test_writer = tf.summary.FileWriter('./dummy/gpus/test/', sess.graph)
    for i in tqdm(range(1000)):
        if i % save_summary_step == 0:
            _, rlt, summary, _ = sess.run([train_op, out1, merged, acc_update_op], feed_dict={
                dropout: 1,
                BN_phase: True,
            }
                                          )
            # print('train:', rlt)
            train_writer.add_summary(summary, global_step=i)
        else:
            _, rlt = sess.run([train_op, out1], feed_dict={dropout: 1, BN_phase: True})
            # print('train:', rlt)
        if i % save_model_step == 0:
            model_saver.save(sess, './dummy/ckpt/step{}'.format(i))
            if i != 0:
                for j in tqdm(range(5)):
                    rlt, summary = sess.run([logits, merged2], feed_dict={
                        dropout: 1,
                        BN_phase: False,
                    }
                                            )
                    # print('test:', rlt)
                    test_writer.add_summary(summary, global_step=j)

Other info / logs
Traceback (most recent call last):
  File "/home/anaconda3/envs/tf114/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1356, in _do_call
    return fn(*args)
  File "/home/anaconda3/envs/tf114/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1339, in _run_fn
    self._extend_graph()
  File "/home/anaconda3/envs/tf114/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1374, in _extend_graph
    tf_session.ExtendSession(self._session)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Cannot assign a device for operation operation/mean_squared_error/assert_broadcastable/is_valid_shape/has_valid_nonscalar_shape/has_invalid_dims/DenseToDenseSetOperation: Could not satisfy explicit device specification '/device:GPU:0' because no supported kernel for GPU devices is available.
Colocation Debug Info:
Colocation group had the following types and supported devices:
Root Member(assigned_device_name_index_=-1 requested_device_name_='/device:GPU:0' assigned_device_name_='' resource_device_name_='' supported_device_types_=[CPU] possible_devices_=[]
DenseToDenseSetOperation: CPU

Colocation members, user-requested devices, and framework assigned devices, if any:
  operation/mean_squared_error/assert_broadcastable/is_valid_shape/has_valid_nonscalar_shape/has_invalid_dims/DenseToDenseSetOperation (DenseToDenseSetOperation) /device:GPU:0

Op: DenseToDenseSetOperation
Node attrs: T=DT_INT32, validate_indices=true, set_operation="a-b"
Registered kernels:
  device='CPU'; T in [DT_STRING]
  device='CPU'; T in [DT_UINT16]
  device='CPU'; T in [DT_UINT8]
  device='CPU'; T in [DT_INT64]
  device='CPU'; T in [DT_INT32]
  device='CPU'; T in [DT_INT16]
  device='CPU'; T in [DT_INT8]

	 [[{{node operation/mean_squared_error/assert_broadcastable/is_valid_shape/has_valid_nonscalar_shape/has_invalid_dims/DenseToDenseSetOperation}}]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/media/snippet.py", line 1930, in <module>
    feed_dict={a_ph: ['a'], b_ph: [10]})
  File "/home/anaconda3/envs/tf114/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 950, in run
    run_metadata_ptr)
  File "/home/anaconda3/envs/tf114/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1173, in _run
    feed_dict_tensor, options, run_metadata)
  File "/home/anaconda3/envs/tf114/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1350, in _do_run
    run_metadata)
  File "/home/anaconda3/envs/tf114/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1370, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Cannot assign a device for operation operation/mean_squared_error/assert_broadcastable/is_valid_shape/has_valid_nonscalar_shape/has_invalid_dims/DenseToDenseSetOperation: Could not satisfy explicit device specification '/device:GPU:0' because no supported kernel for GPU devices is available.
Colocation Debug Info:
Colocation group had the following types and supported devices:
Root Member(assigned_device_name_index_=-1 requested_device_name_='/device:GPU:0' assigned_device_name_='' resource_device_name_='' supported_device_types_=[CPU] possible_devices_=[]
DenseToDenseSetOperation: CPU

Colocation members, user-requested devices, and framework assigned devices, if any:
  operation/mean_squared_error/assert_broadcastable/is_valid_shape/has_valid_nonscalar_shape/has_invalid_dims/DenseToDenseSetOperation (DenseToDenseSetOperation) /device:GPU:0

Op: DenseToDenseSetOperation
Node attrs: T=DT_INT32, validate_indices=true, set_operation="a-b"
Registered kernels:
  device='CPU'; T in [DT_STRING]
  device='CPU'; T in [DT_UINT16]
  device='CPU'; T in [DT_UINT8]
  device='CPU'; T in [DT_INT64]
  device='CPU'; T in [DT_INT32]
  device='CPU'; T in [DT_INT16]
  device='CPU'; T in [DT_INT8]

	 [[node operation/mean_squared_error/assert_broadcastable/is_valid_shape/has_valid_nonscalar_shape/has_invalid_dims/DenseToDenseSetOperation (defined at media/snippet.py:1875) ]]

Original stack trace for 'operation/mean_squared_error/assert_broadcastable/is_valid_shape/has_valid_nonscalar_shape/has_invalid_dims/DenseToDenseSetOperation':
  File "media/snippet.py", line 1875, in <module>
    mse = tf.losses.mean_squared_error(labels=y_it, predictions=logits)
  File "home/anaconda3/envs/tf114/lib/python3.6/site-packages/tensorflow/python/ops/losses/losses_impl.py", line 646, in mean_squared_error
    losses, weights, scope, loss_collection, reduction=reduction)
  File "home/anaconda3/envs/tf114/lib/python3.6/site-packages/tensorflow/python/ops/losses/losses_impl.py", line 181, in compute_weighted_loss
    weights_broadcast_ops.assert_broadcastable(weights, losses),)):
  File "home/anaconda3/envs/tf114/lib/python3.6/site-packages/tensorflow/python/ops/weights_broadcast_ops.py", line 132, in assert_broadcastable
    name="is_valid_shape")
  File "home/anaconda3/envs/tf114/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py", line 507, in new_func
    return func(*args, **kwargs)
  File "home/anaconda3/envs/tf114/lib/python3.6/site-packages/tensorflow/python/ops/control_flow_ops.py", line 1988, in cond
    orig_res_f, res_f = context_f.BuildCondBranch(false_fn)
  File "home/anaconda3/envs/tf114/lib/python3.6/site-packages/tensorflow/python/ops/control_flow_ops.py", line 1814, in BuildCondBranch
    original_result = fn()
  File "home/anaconda3/envs/tf114/lib/python3.6/site-packages/tensorflow/python/ops/weights_broadcast_ops.py", line 131, in <lambda>
    weights_rank, weights_shape, values_rank, values_shape),
  File "home/anaconda3/envs/tf114/lib/python3.6/site-packages/tensorflow/python/ops/weights_broadcast_ops.py", line 57, in _has_valid_nonscalar_shape
    name=scope)
  File "home/anaconda3/envs/tf114/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py", line 507, in new_func
    return func(*args, **kwargs)
  File "home/anaconda3/envs/tf114/lib/python3.6/site-packages/tensorflow/python/ops/control_flow_ops.py", line 1977, in cond
    orig_res_t, res_t = context_t.BuildCondBranch(true_fn)
  File "home/anaconda3/envs/tf114/lib/python3.6/site-packages/tensorflow/python/ops/control_flow_ops.py", line 1814, in BuildCondBranch
    original_result = fn()
  File "home/anaconda3/envs/tf114/lib/python3.6/site-packages/tensorflow/python/ops/weights_broadcast_ops.py", line 55, in <lambda>
    lambda: _has_valid_dims(weights_shape, values_shape),
  File "home/anaconda3/envs/tf114/lib/python3.6/site-packages/tensorflow/python/ops/weights_broadcast_ops.py", line 40, in _has_valid_dims
    invalid_dims = sets.set_difference(weights_shape_2d, valid_dims)
  File "home/anaconda3/envs/tf114/lib/python3.6/site-packages/tensorflow/python/ops/sets_impl.py", line 273, in set_difference
    return _set_operation(a, b, "a-b" if aminusb else "b-a", validate_indices)
  File "home/anaconda3/envs/tf114/lib/python3.6/site-packages/tensorflow/python/ops/sets_impl.py", line 132, in _set_operation
    a, b, set_operation, validate_indices)
  File "home/anaconda3/envs/tf114/lib/python3.6/site-packages/tensorflow/python/ops/gen_set_ops.py", line 98, in dense_to_dense_set_operation
    name=name)
  File "home/anaconda3/envs/tf114/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 788, in _apply_op_helper
    op_def=op_def)
  File "home/anaconda3/envs/tf114/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py", line 507, in new_func
    return func(*args, **kwargs)
  File "home/anaconda3/envs/tf114/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 3616, in create_op
    op_def=op_def)
  File "home/anaconda3/envs/tf114/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 2005, in __init__
    self._traceback = tf_stack.extract_stack()

Found the solution, I will close the issue. One shouldn't put tf.losses.MSE(); tf.summary.merge() inside GPU tf.device('/device:GPU:X') since means, update ops, or variables are in the RAM and operated by CPU. In contrast, gradient, loss and model are in GPU.


Time zone not right, always rotate the out_file at 08:00AM  in the morning, not at 00:10AM

Check CONTRIBUTING guideline first and here is the list to help us investigate the problem.

'fluentd' version '1.2.2'
CentOS release 6.10 (Final)
My configuration

<source>
  @type forward
  port 60000
</source>
<match lcmcn_login_pay.**>
    @type copy
    <store>
      @type file
      path /data/from_infra/lcmcn/from_log/%Y%m%d/lcmcn-login-pay_%Y%m%d
      append true
      compress gzip
      <format>
	@type json
      </format>
      <buffer>
        timekey 1d
        timekey_wait 10m
      </buffer>
    </store>
    <store>
	@type kafka2
	brokers <IP_ADDRESS>:19092,<IP_ADDRESS>:19092,<IP_ADDRESS>:19092
	default_topic new_lcm_realkpi
	<format>
	  @type json
	</format>
	<buffer topic>
	  flush_interval 1s
	</buffer>
    </store>
</match>

==============================================

My Problem:
The out_file always rotate at 08:00AM not 0:00,no matter I add

timekey 1d
timekey_wait 10m

or just delete buffer section(keep the buffer as out_file default setting)

Could you please help? Thanks in advance

Upgrade Fluentd to v1.2.4 or later.
See https://github.com/fluent/fluentd/blob/master/CHANGELOG.md#release-v124---20180801 for more details.

If this problem happens with latest version, please reopen the issue.

Hi  Masahiro San & okkez,
I checked this morning, after upgrading to v1.2.6, the problem is solved.
Thanks a lot ~~~


Aluminum: principled scenario exploration through minimality

http://dl.acm.org/citation.cfm?id=2486820

No empirical data


File upload field

The current version of the file upload is inapplicable IMO. You can use it only when updating the data object (e.g. user) because it doesn't work when on submit.
There are 2 problems. 1. the form doesn't have "enctype=multipart/form-data"  and 2. when you submit the form the data gets serialized. At least I am very certain it does. Didn't investigate this too much.
I have created my own hard-coded/hacked version of this file upload but it is not ready to be packed into the ng-admin...
Is there any interest in adding this field or is this too advance and is OK that programmer must code this manually?

Currently the file is uploaded once selected, and then you retrieve its name from the upload API. IMO, it is a wrong behavior, and we should upload it only when the form is submitted. It is especially wrong if you have heavy treatment on uploaded files, like video encoding.
But then, JSON API may not work with a multipart/form-data request.
Not sure about the implications. @karantan, have you successfully dealt with it?

@jpetitcolas Yes I have solved this by creating a simple directive. But it works only when object (e.g. user) gets updated... atm this is not priority and will come back later when other things are working...
Here is what I did:
directive:
...
controller: function($scope) {
  $scope.onFileSelect = function($files){
    $scope.files = $files;
  };
  $scope.upload = function(){
    var formData = new FormData();
    formData.append("gift_id", $scope.entry.values['id']);
    for (var i = 0; i < $scope.files.length; i++) {
      var file = $scope.files[i];
      formData.append('images', file);
    }
    $.ajax({
       url: '/gift/images',
       type : 'POST',
       data : formData,
       processData: false,  // tell jQuery not to process the data
       contentType: false,  // tell jQuery not to set contentType
       success : function(data) {
         console.log(data);
         humane.log(data);
       }
    });
  };
},
...

and directive template:

<button
    class="btn btn-default"
    ngf-select
    ng-model="entry.values['images']"
    ngf-change="onFileSelect($files)"
    ngf-multiple="true"
>Upload</button>

    <button
      type="button"
      class="btn btn-success btn-s"
      ng-click="upload()">
         Upload all
    </button>

So when I select files onFileSelect function remembers them and when I click the upload button the upload function starts which uploads the files...
I know its not a nice solution but it works :)

@hmendes00 I haven't been following ng-admin for some time now so I can't tell you if there is a better solution now.
The above fix will definitely work.


Multiple Maps Causing Errors

I am attempting to use multiple maps for different purposes on the same page.
This appears to be causing issues with the let map= ...
The error is as follows: SyntaxError: Can't create duplicate variable: 'url'
Any recommendations on how to address this? The url should only need to be declared a single time per page.
Is there a way to prevent it from attempting to create a duplicate variable.

Hi @ryangurn,
in the the pr is the code to fix this issue. I will be making a release shortly

Great thanks!


Basic items price check is crashing sidekick

When I try to price check (ctrl+d) on scarabs and other fragment types, it always causes an error screen and sidekick is unresponsive afterwards until I exit and restart it.
https://gyazo.com/c8747dc8fbe6d68597626d3d08c7bba0
Link of example ^^. I'm not sure what the issue is, maybe I need to reinstall it in a new directory? I don't see this issue listed in the recently closed ones or currently open, yet it's a pretty fundamental problem.

Thanks for the report, this is being fixed right now. We should have a patch ready in the next few hours.


Collection sorting on the communities page

In DSpace versions 7.4, 7.3 and 7.2, when accessing the community page, sorting sub-communities and collections in ascending and descending order is not working (test done in google chrome).
Simulate the scenario: Enter dspace, go to communities and collections, open a community.
Exemple:

This is a duplicate of #2160


Issue no #928 : Scraping data from geeksforgeeks

Description

I have created a new folder name geeksforgeeks which contain 2 files.
geeksforgeeks.py file has a function get_profile() which scrape the data of provided username.

Resolves: #928
Checklist

Before submitting this pull request, kindly verify that the ensuing checkpoints have been reached.

[x] Have you adhered to the repository's defined coding convention rules?
[ ] Have you updated the 'documentation.md' file with the method/function documentation?
[x] Have you sent a message along with the result or response?
[x] Have you used the try-catch technique?
[x] Has the method/class been added to the documentation (md file)?

Screenshots

Uploading a screenshot illustrating the approach you developed for validation is required.

Additional Notes/Comments

Any additional remarks or suggestions concerning this pull request are welcome.

I certify that I have carried out the relevant checks and provided the requisite screenshot for validation by submitting this pull request.
I appreciate your contribution.

@nikhil25803 added the module in  dev-documentation


Difference in coverage results between Windows and Linux

I'm getting different coverage results between Windows and Linux.
In a .net solution with references: Test Project -> ProjectA -> Project B, if tests in Test Project don't exercise anything from Project B, then on Linux Project B is not included in coverage results, but it is included (with 0 coverage as expected) when run on Windows.
I've checked this with .Net 7 and Microsoft.NET.Test.Sdk 17.5.0, as well as with .Net 6 and Microsoft.NET.Test.Sdk 17.6.3.
I'm attaching a tiny solution (projects: Tests -> Console -> Library) that shows the problem Project.zip. For this solution I get (note missing library.dll in Linux results):
on Linux:

on Windows:

In both cases the coverage was obtained with: dotnet test --collect "Code Coverage"

I've fixed issue. It will be available in 17.8.0 release. If you want you can use preview package: https://dev.azure.com/dnceng/public/_artifacts/feed/test-tools/NuGet/Microsoft.NET.Test.Sdk/overview/17.8.0-preview-23363-04


Snippets need to be updated to the code in the Sample projects.

There are some snippets such as:

That show code that is not actually in the sample project.

I think this has been removed in an PR, although you don't specify where exactly it was.


[BUG] No module named 'minihack.version' when importing

When installing via pip, you get an bug after importing: "ModuleNotFoundError: No module named 'minihack.version'". This can be resolved by adding a file "version.py" with the correct info to the install directory. (version = '0.1.3+4c398d4'
git_version = '4c398d480eac26883104e867280d1d3ddbcb9a20' ).

I also just tried it again, and it worked this time. I don't know how this happened, as I didn't do anything strange. I am happy to leave this as closed, hopefully it will help if someone else manages to somehow get this issue.


silent shutter on a6000

Hi there,
Firstly - love your work!
Just wondering if theres any possibility of enabling silent shutter on the a6000?
Cheers!

Yes, that would be really great for timelapses! (even if the quality is reduced).

+1

I have dumped the bin files for silent shooting on/off here #397 but have been unable thus far to figure out which setting(s) need to be changed to get this working on other cameras. I can see references to silent shooting settings in the dumped firmware all the way down to the a5000 so i think it should be possible.


Fail on schema problems with context

There are currently a few places where an error causes schematic to exit
with an error with no context to resolve the problem:
schematic: unknown type <nil>

This updates the places where we exit on schema problems to also print
the schema that was being processed.
Now these cases will fail with better context:
> schematic tmp/schema.json
Error processing schema element:
    {
      "description": "the pipeline which owns this test-run",
      "properties": {
        "id": {
          "description": "unique identifier of pipeline",
          "readOnly": true,
          "example": "01234567-89ab-cdef-0123-456789abcdef",
          "format": "uuid",
          "type": [
            "string"
          ]
        }
      }
    }

Failed with: unknown type <nil>

It does not add this context for Link, which is the other case where we panic on an error, because attempting to marshal the Link with a missing HRef introduces an infinite loop.

nice!


inputting dataset to development

Hi astorfi,
I'm trying to train my own data set on your model. Is there also an update for the development files to feed dataset from input_features.py? It looks like train_softmax.py still takes in an hdf5 file.
Thanks,
Lucas

Yes, please ignore that and feed your specific input customized for your dataset. HDF5 is not required. You only need to feed the data using feed_dict.


Set Alibaba Namespace to be /Attributes

Originally it was /Attribute, it is supposed to be /Attributes. See: https://www.alibabacloud.com/help/doc-detail/113735.html?spm=0.10695662.1996646101.searchclickresult.661c312cwqirsk#title-9u5-i5k-h37 step 'c'

@xom-pschiess : Thanks for your contribution! The author(s) have been notified to review your proposed change.

@chetansriv  and @v-nagta @v-viinde Can we please make this change in the article?

@xom-pschiess Thanks for pointing this out. We have released a feature recently which auto populates the attributes so we don't need this attribute section at all. We'll update the tutorial accordingly.
@v-pugoe @v-viinde Could you please update the metadata and article by removing the attribute section

Resolved the merge conflict, which resolves the issue stated in the pull request. We'll close the PR. Thanks.


Bug: unable to use the AI assistant

Getting the below error even after configuring current openai key
Sorry! An error occoured while accessing the LLM. See server logs for details.

Just installed OpenOps on Docker and I am seeing the same error.


Fix  #2828: Renaming project in RTVS breaks it.

Not sure if we should rename *.rproj file as well

👍

Probably yes. I think C#/C++ do that?

I don't know if there is anything like that in C#/C++. We don't really use *.rproj except for creating *.rxproj in a first place. On the other hand, if user still wants to open project in RStudio, old path in recent projects won't work after renaming. Project folders aren't renamed as well. So I'll leave it as it is for now.


Adding stories with translations for chromatic and storybook

Closes

Added some chromatic and storybook with translations to improve coverage with different locales and fonts.
Added translations to new stories in Arabic, Hebrew, Japanese, Korean, Chinese simplified, and Chinese traditional. Used Google Translate. Some of the translations are not good. Will we need real translations? This is meant to test fonts, but we don't want anything wrong and in some cases I saw English words being interpreted by Google Translate as proper names in a language other than English.
The stories were added as separate Chromatic files in the components they relate to, with within Chromatic are part of the languages grouping.

[X] Run chromatic - https://www.chromatic.com/build?appId=5f0dd5ad2b5fc10022a2e320&number=562
✅ Pull Request Checklist:

[ ] Included link to corresponding React Spectrum GitHub Issue.
[ ] Added/updated unit tests and storybook for this change (for new code or code which already has tests).
[x] Filled out test instructions.
[ ] Updated documentation (if it already exists for this component).
[ ] Looked at the Accessibility Practices for this feature - Aria Practices

📝 Test Instructions:
Added a new story called "with translations" to DialogTrigger, Forms, ListBox, and MenuTrigger. The stories shouldn't crash and should exist for the 6 languages plus English.
There are new stories in Chromatic to review.
🧢 Your Project:
RSP

Note for the "Adobe" in the Arabic translations. Google translate turned the company name into gibberish so I left it Adobe. I checked adobe.com and it looks like we don't ever change the company name from "Adobe". I could choose a different string that doesn't include the company name or change all the translations to have the English string "Adobe".


typo: seperate -> separate

:warning: Validation status: warnings

File
Status
Preview URL
Details

articles/libraries/algorithms.md
:warning:Warning

Details

articles/libraries/algorithms.md

Line 123: [Warning] Invalid file link:(~/media/draper.png).
Line 137: [Warning] Invalid file link:(~/media/beau.png).

For more details, please refer to the build report.
Note: If you changed an existing file name or deleted a file, broken links in other files to the deleted or renamed file are listed only in the full build report.

@anpaz-msft I've rebase the branch

:warning: Validation status: warnings

File
Status
Preview URL
Details

articles/libraries/standard/algorithms.md
:warning:Warning

Details

articles/libraries/standard/algorithms.md

Line 118: [Warning] Invalid file link:(~/media/draper.png).
Line 132: [Warning] Invalid file link:(~/media/beau.png).

For more details, please refer to the build report.
Note: If you changed an existing file name or deleted a file, broken links in other files to the deleted or renamed file are listed only in the full build report.


Exercise 1 Revision 1

This PR revises the Pioneer Exercise 1 for the Friends & Family Shelley Testnet.
Changes:

Added section on providing feedback via GitHub.

The command to run a node,  cardano-node run --config …, says you should check the IOHK documentation to find the complete command. The current build.sh file only tells you how to run a node on mainnet through a script. It'd be good if we gave a proper example of running the node in build.sh.


Permissions to display relations form

User needs to be set as Administrator to see form for adding relations

The problem also exists in other projects

ajax requests for loading relation forms are not fired (probably not even added?) when user is not admin.

still working fine in vanilla instances

for rdf we might want to remove the checking of model level permissions. We might want to distinguish between users who are allowed to edit and those who are only allowed to view items.

this has been resolved in recent versions of apis_core


request.user.is_superuser and request.user.is_staff are False

My DRF settings are:
REST_FRAMEWORK = {
    'DEFAULT_AUTHENTICATION_CLASSES': (
        'rest_framework_simplejwt.authentication.JWTStatelessUserAuthentication',
    ),
    ...
}

I have an admin user, if I view the information in the admin panel, the user is really superuser and staff, but if I look for the user's information in the request object, is_superuser and is_staff are False. I really need the superuser information taken from the request, cause I'm filtering data by user:
Django filter set:
class CustomFilterSet(filters.FilterSet):
    user = filters.CharFilter(field_name='user__username')

    class Meta:
        model = models.MyModel
        fields = '__all__'

    def __init__(self, data=None, queryset=None, *, request=None, prefix=None):
        super().__init__(data, queryset, request=request, prefix=prefix)
        user = self.request.user
        if user and not user.is_superuser:
            self.queryset = self.queryset.filter(user=user.id)

I'm using the version 5.3.1 of simplejwt.

The solution that I found, was getting the user's pk from request and then get the user from the User model:
user = User.objects.get(id=request.user.pk)

And with that user I can implement what I want.
But I don't know if that is efficient because I think that the library is getting the request.user info from database and then I consult to the db again.

JWTStatelessUserAuthentication returns a TokenUser instead of the actual User from the database.
You can customize the token claims to add is_superuser and is_staff, which will then be present on the TokenUser instance.

@confuzeus It worked! Thank you.

@confuzeus It worked! Thank you.


[MSL] Error parsing chunked response, video does not play

I'm submitting a ...

[X] bug report, maybe?
[ ] feature request
[ ] support request

Prerequisites

[X] Are you running the latest version?

Installation

[ ] I installed the plugin via zip from the Releases page
[X] I´m using the Netflix Repo
[ ] I´m using a different source (Please tell which)

Operating System

[ ] Linux (x86/x64)
[ ] OSX (x86/x64)
[X] Windows (x86/x64)
[ ] Linux (ARM)
[ ] Android

Not sure if this is on my end, since it seems the system refuse to resolve msftncsi.com
Disabled my firewall, tested with a "working" VPN. Same error.
Netflix works on Chrome though.
11:03:08.258 T:18244  NOTICE: Running the application...
11:03:08.259 T:17792  NOTICE: ES: Starting UDP Event server on port 9777
11:03:08.259 T:17792  NOTICE: UDP: Listening on port 9777 (ipv6 : false)
11:03:09.052 T:1824   ERROR: CCurlFile::Exists - Failed: Couldn't resolve host name(6) for http://www.msftncsi.com/ncsi.txt
11:03:11.436 T:10396   ERROR: XFILE::CDirectory::GetDirectory - Error getting
11:03:25.183 T:19640   ERROR: Previous line repeats 6 times.
11:03:25.183 T:19640 WARNING: Invalid media type ""
11:03:26.904 T:15532 WARNING: Previous line repeats 5 times.
11:03:26.904 T:15532   ERROR: CCurlFile::Exists - Failed: Couldn't resolve host name(6) for http://www.msftncsi.com/ncsi.txt
11:03:30.266 T:16092 WARNING: Invalid media type ""
11:03:38.978 T:18244  NOTICE: VideoPlayer::OpenFile: http://localhost:16919/manifest?id=70221567
11:03:38.978 T:14772  NOTICE: Creating InputStream
11:03:41.430 T:3880   ERROR: Traceback (most recent call last):
11:03:41.430 T:3880   ERROR:   File "C:\Program Files (x86)\Kodi\system\python\Lib\SocketServer.py", line 290, in _handle_request_noblock
11:03:41.430 T:3880   ERROR:     self.process_request(request, client_address)
11:03:41.430 T:3880   ERROR:   File "C:\Program Files (x86)\Kodi\system\python\Lib\SocketServer.py", line 318, in process_request
11:03:41.430 T:3880   ERROR:     self.finish_request(request, client_address)
11:03:41.430 T:3880   ERROR:   File "C:\Program Files (x86)\Kodi\system\python\Lib\SocketServer.py", line 331, in finish_request
11:03:41.431 T:3880   ERROR:     self.RequestHandlerClass(request, client_address, self)
11:03:41.431 T:3880   ERROR:   File "C:\Program Files (x86)\Kodi\system\python\Lib\SocketServer.py", line 652, in __init__
11:03:41.431 T:3880   ERROR:     self.handle()
11:03:41.431 T:3880   ERROR:   File "C:\Program Files (x86)\Kodi\system\python\Lib\BaseHTTPServer.py", line 340, in handle
11:03:41.431 T:3880   ERROR:     self.handle_one_request()
11:03:41.431 T:3880   ERROR:   File "C:\Program Files (x86)\Kodi\system\python\Lib\BaseHTTPServer.py", line 328, in handle_one_request
11:03:41.431 T:3880   ERROR:     method()
11:03:41.431 T:3880   ERROR:   File "C:\Users\XXXXXX\AppData\Roaming\Kodi\addons\plugin.video.netflix\resources\lib\MSLHttpRequestHandler.py", line 59, in do_GET
11:03:41.431 T:3880   ERROR:     data = MSL.load_manifest(int(params['id'][0]))
11:03:41.431 T:3880   ERROR:   File "C:\Users\XXXXXX\AppData\Roaming\Kodi\addons\plugin.video.netflix\resources\lib\MSL.py", line 234, in load_manifest
11:03:41.431 T:3880   ERROR:     data = self.__decrypt_payload_chunk(resp['payloads'][0])
11:03:41.431 T:3880   ERROR:   File "C:\Users\XXXXXX\AppData\Roaming\Kodi\addons\plugin.video.netflix\resources\lib\MSL.py", line 312, in __decrypt_payload_chunk
11:03:41.431 T:3880   ERROR:     data = json.JSONDecoder().decode(data)[1]['payload']['data']
11:03:41.431 T:3880   ERROR:   File "C:\Program Files (x86)\Kodi\system\python\Lib\json\decoder.py", line 364, in decode
11:03:41.431 T:3880   ERROR:     obj, end = self.raw_decode(s, idx=_w(s, 0).end())
11:03:41.431 T:3880   ERROR:   File "C:\Program Files (x86)\Kodi\system\python\Lib\json\decoder.py", line 380, in raw_decode
11:03:41.431 T:3880   ERROR:     obj, end = self.scan_once(s, idx)
11:03:41.431 T:3880   ERROR: ValueError: Unterminated string starting at: line 1 column 78 (char 77)
11:03:41.432 T:14772   ERROR: CCurlFile::FillBuffer - Failed: Server returned nothing (no headers, no data)(52)
11:03:41.432 T:14772   ERROR: CCurlFile::Open failed with code 0 for http://localhost:16919/manifest?id=70221567|acceptencoding=gzip&seekable=0&user-agent=Mozilla%2f5.0%20(Windows%20NT%206.1%3b%20WOW64)%20AppleWebKit%2f537.36%20(KHTML%2c%20like%20Gecko)%20Chrome%2f59.0.3071.115%20Safari%2f537.36
11:03:41.432 T:14772   ERROR: AddOnLog: InputStream Adaptive: Could not open / parse mpdURL (http://localhost:16919/manifest?id=70221567)
11:03:41.432 T:14772   ERROR: CVideoPlayer::OpenInputStream - error opening [http://localhost:16919/manifest?id=70221567]
11:03:41.432 T:14772  NOTICE: CVideoPlayer::OnExit()
11:03:44.987 T:18244 WARNING: CGUIWindowManager - CGUIWindowManager::HandleAction - ignoring action 107, because topmost modal dialog closing animation is running

I rephrased the title, as this is not the real problem.
The real problem ist, that Netflix seems to have changed something in the way they deliver the chunked responses for the manifest (for some titles), in a way that the plugin is not able to cope with it.
I hope that this might be an temporary issue, but if not, I would definitely hope that @trummerjo can take a look, as he´s the one who originally wrote the service...

Similar issue happening to me. Was working the days before.
raspberry pi 3: LibreELEC (Milhouse): devel-20171010210712-#1010-g87d8234 [Build #1010] 9.0, kernel: Linux ARM 32-bit version 4.13.5
19:51:42.943 T:1942861808  NOTICE: m_playbackStarting
19:51:42.943 T:1942861808  NOTICE: StereoscopicsManager::IsPlaying
19:51:43.170 T:1672242064   ERROR: Traceback (most recent call last):
19:51:43.170 T:1672242064   ERROR:   File "/usr/lib/python2.7/SocketServer.py", line 290, in _handle_request_noblock
19:51:43.170 T:1672242064   ERROR:   File "/usr/lib/python2.7/SocketServer.py", line 318, in process_request
19:51:43.170 T:1672242064   ERROR:   File "/usr/lib/python2.7/SocketServer.py", line 331, in finish_request
19:51:43.170 T:1672242064   ERROR:   File "/usr/lib/python2.7/SocketServer.py", line 652, in __init__
19:51:43.170 T:1672242064   ERROR:   File "/usr/lib/python2.7/BaseHTTPServer.py", line 340, in handle
19:51:43.170 T:1672242064   ERROR:   File "/usr/lib/python2.7/BaseHTTPServer.py", line 328, in handle_one_request
19:51:43.171 T:1672242064   ERROR:   File "/storage/.kodi/addons/plugin.video.netflix/resources/lib/MSLHttpRequestHandler.py", line 49, in do_GET
19:51:43.173 T:1672242064   ERROR:     data = msl.load_manifest(int(params['id'][0]))
19:51:43.173 T:1672242064   ERROR:   File "/storage/.kodi/addons/plugin.video.netflix/resources/lib/MSL.py", line 226, in load_manifest
19:51:43.178 T:1672242064   ERROR:     data = self.__decrypt_payload_chunk(resp['payloads'][0])
19:51:43.178 T:1672242064   ERROR:   File "/storage/.kodi/addons/plugin.video.netflix/resources/lib/MSL.py", line 297, in __decrypt_payload_chunk
19:51:43.178 T:1672242064   ERROR:     data = json.JSONDecoder().decode(data)[1]['payload']['data']
19:51:43.178 T:1672242064   ERROR:   File "/usr/lib/python2.7/json/decoder.py", line 364, in decode
19:51:43.178 T:1672242064   ERROR:   File "/usr/lib/python2.7/json/decoder.py", line 380, in raw_decode
19:51:43.179 T:1672242064   ERROR: ValueError: Unterminated string starting at: line 1 column 78 (char 77)
19:51:43.179 T:1125880720   ERROR: CCurlFile::FillBuffer - Failed: Server returned nothing (no headers, no data)(52)
19:51:43.179 T:1125880720   ERROR: CCurlFile::Open failed with code 0 for http://localhost:39047/manifest?id=80025322|acceptencoding=gzip&seekable=0&user-agent=Mozilla%2f5.0%20(X11%3b%20CrOS%20armv7l%207647.78.0)%20AppleWebKit%2f537.36%20(KHTML%2c%20like%20Gecko)%20Chrome%2f59.0.3071.115%20Safari%2f537.36
19:51:43.180 T:1125880720   ERROR: AddOnLog: InputStream Adaptive: Could not open / parse mpdURL (http://localhost:39047/manifest?id=80025322)
19:51:43.182 T:1125880720   ERROR: CVideoPlayer::OpenInputStream - error opening [http://localhost:39047/manifest?id=80025322]
19:51:43.182 T:1125880720  NOTICE: CVideoPlayer::OnExit()

I can report exactly same erross like @fkpp
Raspberry Pi 2: LibreELEC-RPi2.arm-9.0-Milhouse-20171017210247-#1017-gdc954af

Same problem here:
LibreELEC (community): Leia-testing-Gendo-01-10-2017 (S905.arm)
23:01:07.552 T:3596088208   ERROR: Traceback (most recent call last): 23:01:07.553 T:3596088208   ERROR:   File "/usr/lib/python2.7/SocketServer.py", line 290, in _handle_request_noblock 23:01:07.553 T:3596088208   ERROR:   File "/usr/lib/python2.7/SocketServer.py", line 318, in process_request 23:01:07.553 T:3596088208   ERROR:   File "/usr/lib/python2.7/SocketServer.py", line 331, in finish_request 23:01:07.554 T:3596088208   ERROR:   File "/usr/lib/python2.7/SocketServer.py", line 652, in __init__ 23:01:07.554 T:3596088208   ERROR:   File "/usr/lib/python2.7/BaseHTTPServer.py", line 340, in handle 23:01:07.555 T:3596088208   ERROR:   File "/usr/lib/python2.7/BaseHTTPServer.py", line 328, in handle_one_request 23:01:07.555 T:3596088208   ERROR:   File "/storage/.kodi/addons/plugin.video.netflix/resources/lib/MSLHttpRequestHandler.py", line 49, in do_GET 23:01:07.555 T:3596088208   ERROR:     data = msl.load_manifest(int(params['id'][0])) 23:01:07.555 T:3596088208   ERROR:   File "/storage/.kodi/addons/plugin.video.netflix/resources/lib/MSL.py", line 226, in load_manifest 23:01:07.556 T:3596088208   ERROR:     data = self.__decrypt_payload_chunk(resp['payloads'][0]) 23:01:07.556 T:3596088208   ERROR:   File "/storage/.kodi/addons/plugin.video.netflix/resources/lib/MSL.py", line 297, in __decrypt_payload_chunk 23:01:07.556 T:3596088208   ERROR:     data = json.JSONDecoder().decode(data)[1]['payload']['data'] 23:01:07.556 T:3596088208   ERROR:   File "/usr/lib/python2.7/json/decoder.py", line 364, in decode 23:01:07.557 T:3596088208   ERROR:   File "/usr/lib/python2.7/json/decoder.py", line 380, in raw_decode 23:01:07.557 T:3596088208   ERROR:   File "/usr/lib/python2.7/json/decoder.py", line 36, in errmsg 23:01:07.557 T:3596088208   ERROR: TypeError: 'NoneType' object is not callable 23:01:07.557 T:3410514832   ERROR: CCurlFile::FillBuffer - Failed: Server returned nothing (no headers, no data)(52) 23:01:07.557 T:3410514832   ERROR: CCurlFile::Open failed with code 0 for http://localhost:33223/manifest?id=80098734|acceptencoding=gzip&seekable=0&user-agent=Mozilla%2f5.0%20(X11%3b%20CrOS%20armv7l%207647.78.0)%20AppleWebKit%2f537.36%20(KHTML%2c%20like%20Gecko)%20Chrome%2f59.0.3071.115%20Safari%2f537.36 23:01:07.558 T:3410514832   ERROR: AddOnLog: InputStream Adaptive: Could not open / parse mpdURL (http://localhost:33223/manifest?id=80098734) 23:01:07.560 T:3410514832   ERROR: CVideoPlayer::OpenInputStream - error opening [http://localhost:33223/manifest?id=80098734] 23:01:07.560 T:3410514832  NOTICE: CVideoPlayer::OnExit()

Netflix has changed something in the request system. Can you fix it?

@dreamhd2015 You´ve seen what I´ve written in this exact issue 18 hours ago? https://github.com/asciidisco/plugin.video.netflix/issues/197#issuecomment-337631939

@asciidisco If so, ask if it is believed that it could be repaired
Thanks for answering

I think have fixed this issue in my fork: https://github.com/truedread/plugin.video.netflix
Please let me know if it is OK to make a pull request @asciidisco

@truedread That was my thought as well, but haven’t had the time to dig in further.
Please PR it. Thank you very much :)

@truedread Confirmed your fix works for me. Thanks a lot!

Released as 0.12.2.
Thank you again for delivering this fix 👍


Return a future from APSDE-DATA.request

I took a stab at this. I am a complete beginner with Rust but hoping to learn and any criticism is very welcome.

Great! Thanks!


Record download speeds

This will log the download speed from GCS similar to the upload speeds
we record.
Change-Id: Icdaf313b6312844cfcc39be69022d94e88c00ebc

Looks like we grabbed some other commits off, they tested clean so I'm going to submit with them to keep everything in sync.


Fix cuTensorNet shim layer

Fix two bugs:

The cuTensorNet handle was not reused
The memory_limit was too large (4**31 bytes =<PHONE_NUMBER> GB)

In cuQuantum Python, the semantics of memory_limit is different from that of {numpy,cupy}.einsum(). In the latter it is the size of the largest intermediate tensor, but in the former it is used

as a constraint for max device memory to guide the contraction path optimizer
additionally, as a hard limit to determine if the actual workspace size is too large

Since there's no perfect way to map the two memory_limits, I just disable it entirely and let cuQuantum Python's default kick in.

/test mini


Remove log enricher, rely on agent

I removed the winston log enricher as this will be automatically done in agent now.  We were also missing local decorating env var. Until the new agent version is released to use this you'll have to link the agent
npm link # in main of agent
npm link newrelic # in this app
node log-generator.js # see logs getting fowarded with proper context info

Should this PR wait until the new agent release? Otherwise LGTM.


Use of dask-gateway leads to prometheus server memory blowing up

Node count
Prometheus memory use

In this time span of a few hours with 200 or so nodes, the prometheus memory increased from a stable 13.9Gi to a stable 16.8Gi. This surely is related to huge amounts of data collected when dask-gateway cluster is doing heavy work, in this case when 200 worker nodes was started last week.
Prometheus blowing up like this has caused us a lot of trouble historically, and is absolutely unsustainable.
An idea to handle this would be to investigate what data scraped that likeley made the memory use blow up, and if we need that data to be scraped, possibly also dropping such historically collected data if possible.

I think the issue was that prometheus got too much scraping targets with too much data inside them. This was remidiated by using larger dask gateway workers so that the same amount of work could be done with fewer nodes.
Its a key reason to not let dask-gateway be used with small workers still.


Hello, where can I download code generation 2024.08~2024.11 data? Beacause I find the datasets last update by Oct 7 in Huggingface.

Hello, where can I download code generation 2024.08~2024.11 data? Beacause I find the datasets last update by Oct 7 in Huggingface.

Hi, currently v4 has been updated till October 7 only. The leaderboard shows problems till 1st November -- there are no problems between October 7 and the end of November.
That said, we will add new problems and update the leaderboards in the next two weeks!

Hi, currently livecodebench-v4 has been updated till October 7 only. The leaderboard displays problems till 1st November -- however there are no problems between October 7 and the end of November.
That said, we will add new problems and update the leaderboards in the next two weeks!

thanks a lot.


Text does not wrap around image

https://mrjules.gitlab.io/hugo2/about/
It's only when I add float: right to #content img and remove display: flex from .section does my text wrap around the image, but doing that breaks the alignment for my other pages.
Do I have to add two classes; one for the image and the content of my about page in order to use flexbox properties correctly? Cause I've done it locally and can't seem to get my text to wrap around my image, besides using the above method. Or maybe I'm not using the right properties?

Since CSS variables are used across the theme where you may not want wrapping to occur, I'd suggest achieving your goal using HTML in your about.md page. You can write HTML in the Markdown file.


Feature/add revocation get applications

Issue https://github.com/Scottish-Natural-Heritage/Licensing/issues/1377.
Updates the API to return part of revoked / cancelled records to allow them to be displayed on the details page, and also to return the revocation object for the search page.

Looks all good


Please add errorCallback to example

After using the example to build login in my own code. I found the following to be very helpful for debuggin in callback\index.js then added to the CallbackComponent´s errorCallback={this.errorCallback}.
errorCallback = (e) => {     console.error('errorCallback',e)   }

Sorry for the late reply - I've added the suggested line, Thank you!


auto bind NICs to EC2s

Turns out this is not actually necessary, the AWS api will automagically assign a NIC


MM-T2503 Boolean value check

Summary
This PR checks that true/false values returned by the demo-plugin's /dialog form are indeed boolean.
Steps

the form is completed the first time with all values checked as true
once the form is submitted, the true values are asserted to be true (not "true")
the above is then repeated for false values

Links
vercel
TM4J

Hello @MikeworX,
Thanks for your pull request! A Core Committer will review your pull request soon. For code contributions, you can learn more about the review process here.

Hi @srkgupta - thank-you for reviewing. Are you referring to the 2 assertion steps or the form setup?

Hi @srkgupta - thank-you for reviewing. Are you referring to the 2 assertion steps or the form setup?

I mean from the steps from line 37 to 75 are almost the same except for the condition true or false. Hence you can just have one function and accept one parameter (true or false) and accordingly set the form values and then validate the tests, instead of duplicating these steps

I agree with Rohitesh. Ping me whenever ready for review.


Null-aware operation not needed to get WidgetsBinding.instance

Flutter 3 fix

Hello @MaximilianFlechtner !
Thank you for this contribution !


add ClientBuilder and custom TLS certs

Reopening #97 with edits. Specifically, the tests have been pulled out, since we can assume for now that the TLS library does what it says it does. Also updated the ClientBuilder to match the pattern in #108.
Closes #89

Thanks @little-dude! This is a big win!


Fix startup time on Wayland in some cases

Are you aware of why swapping buffers causes issues here? And is there any problem with 'garbage' data filling the space Alacritty occupies before startup?

Are you aware of why swapping buffers causes issues here? And is there any problem with 'garbage' data filling the space Alacritty occupies before startup?

I've just found that removing this swap_buffers actually resolve the startup issue at least on my system, so I should go through Wayland logs, but the issue is in waiting on swap_buffers for a long. However we're not even waiting for 200ms seconds here, but in fn draw, the most interesting thing that removing this call actually affects the result and resolves issue on my system. So I want to gather some feedback whether it actually do something for other folks.
Oh, and the window isn't presented until you actually draw something into it, so the call is useless on Wayland tbh.

I've just found that removing this swap_buffers actually resolve the startup issue at least on my system, so I should go through Wayland logs, but the issue is in waiting on swap_buffers for a long.

Does winit do anything special when this is called? Like query monitor information or something like that?

During swap_buffers? Nope it just calls to EGL as I can see, maybe it's doing something, but it's not the first call of swap_buffers when it actually blocks for a while. It's like 2-3d one.

To my understanding, eglSwapBuffers() on wayland blocks the thread until the compositor requests a redraw, if vsync is on:
https://github.com/jwilm/alacritty/blob/474032742b3bf325e655eb6cddb19632cb989c1a/alacritty/src/window.rs#L124
I think the right way to do it on wayland is to disable vsync, then render on the frame callback yourself manually, which winit exposes as RedrawRequested.

To my understanding, eglSwapBuffers() on wayland blocks the thread until the compositor requests a redraw, if vsync is on:

First of all, the issue is strange, we were doing the same things before, but now if you call swap_buffers 3 times in a row it'll block for ~200ms without any reason for it. So it's just a happened that things are working with lower number of swap_buffer calls. So to solve an issue we can pass sway -Dtxn-timeout=20 (the default is 200ms). Alacritty doesn't start like 200ms, so I don't understand how to we even reach it now. So this patch is more a workaround then a real solution, but lets just go with it for now, since I can't repro the issue on a normal use anyway.

I think the right way to do it on wayland is to disable vsync, then render on the frame callback yourself manually, which winit exposes as RedrawRequested.

Yes, but we can't do it in alacritty now, since we're not a Wayland only terminal and it'll add a maintenance burden.

Great addition for the 0.4.1 release, thanks!


Continuous covariate for batch in scVI

In the original publication, s is supposed to be any type of random variable even though it is introduced with only batch effects. Unfortunately, the codebase only works for discrete values of s. There would be actually quite some changes involved as the batch id is transformed far inside the module into a one-hot
https://github.com/YosefLab/scVI/blob/master/scvi/models/modules.py#L57
Being able to condition on a continuous, multi-dimensional measurement (which includes the discrete case if adding the onehot transformation) would be really useful for people in the YosefLab and the greater comp bio community.

Should this not also be handled in #374 by adding another tensor in the data loader of continuous covariates?

We are finally close to being able to implement one version of this based on our dataset refactoring. Essentially we will allow arbitrary numbers of categorical or continuous covariates, though only one "special" covariate will be available for correction in differential expression. z will be enforced to be invariant to all covariates.
@davemcg @vals do you have any datasets in mind that would be good to test and demonstrate this on?

For Smart-seq data a reasonable covariate would be the number of detected genes. In many cases for Smart-seq the cDNA libraries are experimentally normalized (take a fixed mass of cDNA from each well), so you will lose the correlation between number of sequenced reads and number of detected genes.
I'm not sure of UMI droplet data with a priori known continuous covariates, you could imagine a high resolution drug dilution or a time series though.
One idea could be spermatogenesis datasets. They have probably the most clear trajectories of any datasets I have seen. For example this https://www.nature.com/articles/s41422-018-0099-2  or this https://www.nature.com/articles/s41598-018-24725-0 ,  or this https://www.nature.com/articles/s41467-019-09182-1 . You could identify some kind of pseudotime with a first analysis. Then redo the analysis and regress that pseudotime out and see that you get discrete clusters of the biological replicates.

Thanks @vals, we will take a look at this.


[DataFactory] GitHub bring your own app swagger update for ADF/Synapse ARM only

MSFT employees can try out our new experience at OpenAPI Hub  - one location for using our validation tools and finding your workflow.

Changelog
Please ensure to add changelog with this PR by answering the following questions.

What's the purpose of the update?

[ ] new service onboarding
[ ] new API version
[X] update existing version for new feature
[ ] update existing version to fix swagger quality issue in s360
[ ] Other, please clarify

When you are targeting to deploy new service/feature to public regions? Please provide date, or month to public if date is not available yet.
June 7th, 2021
When you expect to publish swagger? Please provide date, or month to public if date is not available yet.
June 2021
If it's an update to existing version,  please select SDKs of specific language and CLIs that require refresh after swagger is published.

[ ] SDK of .NET (need service team to ensure code readiness)
[ ] SDK of Python
[ ] SDK of Java
[ ] SDK of Js
[ ] SDK of Go
[ ] PowerShell
[ ] CLI
[ ] Terraform
[ ] No, no need to refresh for updates in this PR

Contribution checklist:

[ ] I commit to follow the Breaking Change Policy of "no breaking changes"
[ ] I have reviewed the documentation for the workflow.
[ ] Validation tools were run on swagger spec(s) and errors have all been fixed in this PR. How to fix?

If any further question about AME onboarding or validation tools, please view the FAQ.
ARM API Review Checklist

[ ] Ensure to check this box if one of the following scenarios meet updates in the PR, so that label “WaitForARMFeedback” will be added automatically to involve ARM API Review. Failure to comply may result in delays for manifest application. Note this does not apply to data plane APIs, all “removals” and “adding a new property” no more require ARM API review.

Adding new API(s)
Adding a new API version
[ ] Ensure to copy the existing version into new directory structure for first commit (including refactoring) and then push new changes including version updates in separate commits. This is required to review the changes efficiently.
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[ ] Please ensure you've reviewed following guidelines including ARM resource provider contract and REST guidelines. Estimated time (4 hours). This is required before you can request review from ARM API Review board.

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Breaking Change Review Checklist
If there are following updates in the PR, ensure to request an approval from Breaking Change Review Board as defined in the Breaking Change Policy.

[ ] Removing API(s) in stable version
[ ] Removing properties in stable version
[ ] Removing API version(s) in stable version
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[ ] Updating API(s) in public preview over 1 year (refer to Retirement of Previews)

Action: to initiate an evaluation of the breaking change, create a new intake using the template for breaking changes. Addition details on the process and office hours are on the Breaking change Wiki.
Please follow the link to find more details on PR review process.

Data-plane PR can be found here: https://github.com/Azure/azure-rest-api-specs/pull/14689
Broke it into two PR's at request of ARM review team.

Pending breaking change approval.

/azp run

/azp run

/azp run

@jmcallister-msft  the lint or breaking change exception are weird , seems there is something wrong when checking out the repo in the pipeline, it's may be a bug of git,  I think  you can try  merging 4 commits into 1 commit to see if it can be fixed

/azp run

@jianyexi @ruowan Commit above should fix build failure.

/azp run

@jianyexi @ruowan any update on this PR review/can we please rerun the build pipeline? Build failure should be fixed and the dependent change https://github.com/Azure/azure-rest-api-specs/pull/14689 has been checked in


Omit rule for the next line

cfn-lint version: 0.44.4
Description of issue.
Please provide as much information as possible:

Feature request:

I have included some Macros that expand the CloudFormation capabilities of my template for example the Explode Function
When I write my file, I do something like:

AWSTemplateFormatVersion: '2010-09-09'
Description: AWS SAM template with a simple API definition
Transform:
  - Explode
  - AWS::Serverless-2016-10-31

Mappings:

  EnvironmentsMap:
    Dev:
      Environment: Test
      Prefix: "-test"
    Staging:
      Environment: Staging
      Prefix: "-staging"
    Prod:
      Environment: Production
      Prefix: ""

Resources:

  ApiGateway:
    ExplodeMap: EnvironmentsMap
    Type: AWS::CloudFormation::Stack
    Properties:
      TemplateURL: apigateway.yaml
      Parameters:
         StageName: "!Explode Environment"

Which causes a linting error on the ExplodeMap (Rule E3001) and EnvironmentsMap (W7001) key definition I don't want to disable the entire check using:
   ....
    Metadata:
      cfn-lint:
        config:
          ignore_checks:
          - E3001
          - W7001
   ...

I just want to omit it for a specific line. As a feature can we add a line comment to disable the rule? Something like:
AWSTemplateFormatVersion: '2010-09-09'
Description: AWS SAM template with a simple API definition
Transform:
  - Explode
  - AWS::Serverless-2016-10-31

Mappings:

  # cfn-lint: ignore-check W007
  EnvironmentsMap:
    Dev:
      Environment: Test
      Prefix: "-test"
    Staging:
      Environment: Staging
      Prefix: "-staging"
    Prod:
      Environment: Production
      Prefix: ""

Resources:

  ApiGateway:
    # cfn-lint: ignore-check E001
    ExplodeMap: EnvironmentsMap
    Type: AWS::CloudFormation::Stack
    Properties:
      TemplateURL: apigateway.yaml
      Parameters:
         StageName: "!Explode Environment"

Where if you would like to disable multiple values you can do something like:
  # cfn-lint: ignore-check W007, E001
Cfn-lint uses the CloudFormation Resource Specifications as the base to do validation. These files are included as part of the application version. Please update to the latest version of cfn-lint or update the spec files manually (cfn-lint -u)

tracking in https://github.com/aws-cloudformation/cfn-python-lint/issues/113

tracking in https://github.com/aws-cloudformation/cfn-python-lint/issues/113