Plaintext authentication disabled

Hi:
I can send the email, but i can't receive!
Log:
mailserver | Sep 2 09:24:06 mail dovecot: imap-login: Disconnected (no auth attempts in 0 secs): user=<>, rip=<IP_ADDRESS>, lip=<IP_ADDRESS>, session=
mailserver | Sep 2 09:24:06 mail dovecot: imap-login: Disconnected (no auth attempts in 0 secs): user=<>, rip=<IP_ADDRESS>, lip=<IP_ADDRESS>, session=<5cdb5II7RQB1URdy>
mailserver | Sep 2 09:24:06 mail dovecot: imap-login: Login failed: Plaintext authentication disabled: user=<>, rip=<IP_ADDRESS>, lip=<IP_ADDRESS>, session=

Is your mail client configured correctly? Dovecot gets no user user=<>. A successful login should look like this log example:
Sep  2 17:42:13 mail dovecot: imap-login: Login<EMAIL_ADDRESS>method=PLAIN, rip=<IP_ADDRESS>, lip=<IP_ADDRESS>, mpid=13989, TLS, session=</k7D2Yk7hAAC8jMO>

To enable plaintext authentication (no recommended) generate a configuration file named dovecot.cf in config/dovecot.cf (not tested)
disable_plaintext_auth=no

and then
docker-compose restart

@Josef-Friedrich I tested this morning, but the original error happened.
This is my email client:

This is my log:
root@splendidtel:/docker# docker-compose up mailserver
Creating mailserver
Attaching to mailserver
mailserver    | Checking file line endings
mailserver    | Regenerating postfix 'vmailbox' and 'virtual' for given users
mailserver    | user 'harry.zhang' for domain 'mail.umbrellary.com' with password '********'
mailserver    | ==> Warning: 'config/postfix-virtual.cf' is not provided. No mail alias/forward created.
mailserver    | No DKIM key provided. Check the documentation to find how to get your keys.
mailserver    | Postfix configurations
mailserver    | Adding docker network in my networks
mailserver    | bash: /etc/opendkim/TrustedHosts: No such file or directory
mailserver    | No extra postfix settings loaded because optional '/tmp/docker-mailserver/postfix-main.cf' not provided.
mailserver    | ==> Warning: 'SASL_PASSWD' is not provided. /etc/postfix/sasl_passwd not created.
mailserver    | Permissions in /var/mail look OK
mailserver    | Creating /etc/mailname
mailserver    | Configuring Spamassassin
mailserver    | Starting daemons
mailserver    |  * Starting enhanced syslogd rsyslogd
mailserver    |    ...done.
mailserver    |  * Starting dovecot services
mailserver    | Starting POP3 services
mailserver    | Starting SpamAssassin Mail Filter Daemon: spamd.
mailserver    |  * Starting ClamAV daemon clamd
mailserver    |    ...fail!
mailserver    | Starting amavisd: amavisd-new.
mailserver    | Starting OpenDKIM: opendkim: /etc/opendkim.conf: refile:/etc/opendkim/TrustedHosts: dkimf_db_open(): No such file or directory
mailserver    | opendkim.
mailserver    | Starting OpenDMARC: opendmarc.
mailserver    |  * Starting Postfix Mail Transport Agent postfix
mailserver    |    ...done.
mailserver    | Listing users
mailserver    |<EMAIL_ADDRESS>mailserver    | Starting...
mailserver    | Sep  3 01:34:42 mail amavis[470]: No decoder for       .tnef
mailserver    | Sep  3 01:34:42 mail amavis[470]: Internal decoder for .tnef
mailserver    | Sep  3 01:34:42 mail amavis[470]: Found decoder for    .exe  at /usr/bin/arj
mailserver    | Sep  3 01:34:42 mail amavis[470]: Using primary internal av scanner code for ClamAV-clamd
mailserver    | Sep  3 01:34:42 mail amavis[470]: Found secondary av scanner ClamAV-clamscan at /usr/bin/clamscan
mailserver    | Sep  3 01:34:42 mail amavis[470]: Deleting db files  in /var/lib/amavis/db
mailserver    | Sep  3 01:34:42 mail amavis[470]: Creating db in /var/lib/amavis/db/; BerkeleyDB 0.54, libdb 5.3
mailserver    | Sep  3 01:34:42 mail opendmarc[481]: OpenDMARC Filter v1.2.0 starting (args: -c /etc/opendmarc.conf -u opendmarc -P /var/run/opendmarc/opendmarc.pid -p inet:54321@localhost)
mailserver    | Sep  3 01:34:42 mail opendmarc[481]: trusted authentication services: mail.umbrellary.com
mailserver    | Sep  3 01:34:45 mail postfix/master[1289]: daemon started -- version 2.11.0, configuration /etc/postfix
mailserver    | Sep  3 01:35:03 mail dovecot: imap-login: Disconnected (no auth attempts in 0 secs): user=<>, rip=<IP_ADDRESS>, lip=<IP_ADDRESS>, session=<2VO9dJA7lwB1UZwN>
mailserver    | Sep  3 01:35:03 mail dovecot: imap-login: Disconnected (no auth attempts in 0 secs): user=<>, rip=<IP_ADDRESS>, lip=<IP_ADDRESS>, session=<jA2+dJA7mAB1UZwN>
mailserver    | Sep  3 01:35:03 mail dovecot: imap-login: Login failed: Plaintext authentication disabled: user=<>, rip=<IP_ADDRESS>, lip=<IP_ADDRESS>, session=<3D2/dJA7mQB1UZwN>

I have solved the problem!
just turn off the ssl!

Adding the dovecot.cf config didn't do anything for me, still getting this:
imap-login: Login failed: Plaintext authentication disabled: user=<>, rip=<IP_ADDRESS>, lip=<IP_ADDRESS>, session=<YmzHnlJyTIEK/wCn>
And I'm not using SSL in my mailclient

@GiganteX I'm experiencing the same issue.


⚠ Beulah Revival Network has degraded performance

In 785d0d4, Beulah Revival Network (https://beulah.clicki.digital) experienced degraded performance:

HTTP code: 200
Response time: 13180 ms

Resolved: Beulah Revival Network performance has improved in d1c2372.


增加故障切换功能

⚠ Please verify that this feature request has NOT been suggested before.

[X] I checked and didn't find similar feature request

🏷 Feature Request Type
API
🔖 Feature description
增加故障切换功能，比如ping超时后dnspod切换解析！
✔ Solution
ping超时后dnspod切换解析！
❓ Alternatives
无
📝 Additional Context
无

I think uptime-kuma should only focus on monitoring, and making DNS updates on third party services is outside of the scope.
You could, however, use the webhook to create a custom notification, and have another service to update the DNS.


PermifyProvider in the npm registry package is missing { children }

Hi all.
VSCode reports this problem with PermifyProvider:

Type '{ children: never[]; }' has no properties in common with type 'IntrinsicAttributes'.ts(2559)

Indeed, the PermifyProvider.d.ts in the package downloaded via npm or yarn has this definition:
declare const PermifyProvider: React.FunctionComponent;

I've tried cleaning the cache, and tried on different machines (Lubuntu 20.04 with yarn and Windows 10 with npm),
To check if there were problems with webpack, I built react-role locally, and the definition was right:
declare const PermifyProvider: ({ children }: PropsWithChildren) => JSX.Element;

Finally, I linked the local library via npm link to my project, and the problem was gone, but I cannot really use this workaround :sweat_smile:
Let me know how I can help :smiley:

For now, I've upcasted the PermifyProvider object to any like this:
import { PermifyProvider as PProv } from '@permify/react-role';
const PermifyProvider = PProv as any;

Should be fixed by #9  when merged


Issues with keras 2.3.0

When training two pysster Model objects with the same seed, model weights are not equal anymore with keras 2.3.0. Respective tests have been disabled for now. Might not need a fix, since we want to switch to tf.keras anyway.

Well, there seem to be more issues, like a decrease in predictive performance for my test models. I am blacklisting this keras version.

No changes with keras 2.3.1.


a played white card has to delete in the users white cards and replaced, if still possible

Has been implemented already


highlights only the first character entered in the search box

Hi,
when I use mark.js only the first character that I type in the search box is highlighted. When i type in the second character all highlighting disappears. If I type a space between each character, the highlighting works as I would expect it (as your example in https://jsfiddle.net/julmot/vpav6tL1/ shows - but when I use your example, I don't have to type the space between each character entered).
I have used the debug option in both your example (the jsfiddle) and my test so you can see what's being generated (see attached images). I used the same 3 characters in the example and in my  test (I used the word "new"). As you can see from the images, the "searching with expression" strings are very different.
Steps to reproduce
This problem only occurs on my Windows 10 laptop (I've tried Chrome and IE11 and they both have the same issue).
markjs-error.docx
NOTE: The my exact same test code works correctly on my Mac (running El Capitain) - works on Chrome and Safari.
I've also tried copying and pasting your example code into html, js and css files. The same problem occurs.
Let me know if you need more information. I've been looking at this and trying various options for hours. I'm thinking there must be something about my Windows laptop that's different, but I don't know what. I've checked the region settings and they look correct.
Describe how to reproduce the issue. Provide a fiddle if possible.
Note: Please do not provide a full website as example!
Environment

Windows 10
Browser name and version

Chrome Version 52.0.2743.116 m (64-bit)
IE11

mark.js version: mark.js v8.0.1
markjs-error.docx

Hi @tonyfarr,
Thanks for submitting.
First of thank you for this detailed information :+1: I see such reports rarely (unfortunately).
I'm aware of a bug in IE11, as you can see in my comments starting from here. But after I've reported, it was marked as fixed. I'm also not aware of an issue in Chrome.
Could you reproduce the error in a specific HTML construct or generally?
Anyway, at least in IE11 the cause is that splitted text nodes aren't merged when using the DOM's normalize() method used in .unmark(). As mark.js searches only within one text node by default it won't find further matches after running .unmark(). But luckily mark.js also offers an option acrossElements that will indeed search across splitted text nodes. So you'll probably have sucess using it while a fix for this browser bug is released. Am I right?
I'm going to dig into this as soon as I've the time.

Hi Julian,
Thanks for getting back so quickly. That's also rare these days!
I've tried a couple of test HTML/JS approaches. One of them was to copy the code from the jsfiddle in the examples on your site. Same outcome. Doesn't work on my Windows laptop, but works on my Mac.
The fact that the problem occurs both in IE11 and Chrome on my Windows machine, led me to believe that this issue was bigger than the browser. I thought there may have been a Windows issue/setting (or there could be some setting on my Windows laptop that I'm not aware of - I did check region and language settings).
The code works fine on my Mac in Chrome and Safari.
I'll do a bit more testing later today. I thought I'd check with you in case there was something really obvious that I was missing.
Regards.Tony.
Tony Farrugiamob: +61 411 484<EMAIL_ADDRESS>Date: Sat, 10 Sep 2016 13:52:12 -0700
From<EMAIL_ADDRESS>To<EMAIL_ADDRESS>CC<EMAIL_ADDRESS><EMAIL_ADDRESS>Subject: Re: [julmot/mark.js] highlights only the first character entered in the search box (#73)
Hi @tonyfarr,
Thanks for submitting.
First of thank you for this detailed information 👍 I see such reports rarely (unfortunately).
I'm aware of a bug in IE11, as you can see in my comments starting from here. But after I've reported, it was marked as fixed. I'm also not aware of an issue in Chrome.
Could you reproduce the error in a specific HTML construct or generally?
Anyway, at least in IE11 the cause is that splitted text nodes aren't merged when using the DOM's normalize() method used in .unmark(). As mark.js searches only within one text node by default it won't find further matches after running .unmark(). But luckily mark.js also offers an option acrossElements that will indeed search across splitted text nodes. So you'll probably have sucess using it while a fix for this browser bug is released. Am I right?
I'm going to dig into this as soon as I've the time.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub, or mute the thread.

Hi Julian,
I was able to get around this issue by putting the test page up on my website. So now it works for IE11 and Chrome.
The testing I was doing up until now was on my laptop. I don't know why it should behave differently if I'm running the html locally versus on the web site. If you know why this is the case, I'm very interested to know.
I did try using the acrossElements option before moving the test page to the website. It didn't work.  I have left this option in (just in case there's a problem without it).
Regards.Tony.
Tony Farrugiamob: +61 411 484<EMAIL_ADDRESS>Date: Sat, 10 Sep 2016 13:52:12 -0700
From<EMAIL_ADDRESS>To<EMAIL_ADDRESS>CC<EMAIL_ADDRESS><EMAIL_ADDRESS>Subject: Re: [julmot/mark.js] highlights only the first character entered in the search box (#73)
Hi @tonyfarr,
Thanks for submitting.
First of thank you for this detailed information 👍 I see such reports rarely (unfortunately).
I'm aware of a bug in IE11, as you can see in my comments starting from here. But after I've reported, it was marked as fixed. I'm also not aware of an issue in Chrome.
Could you reproduce the error in a specific HTML construct or generally?
Anyway, at least in IE11 the cause is that splitted text nodes aren't merged when using the DOM's normalize() method used in .unmark(). As mark.js searches only within one text node by default it won't find further matches after running .unmark(). But luckily mark.js also offers an option acrossElements that will indeed search across splitted text nodes. So you'll probably have sucess using it while a fix for this browser bug is released. Am I right?
I'm going to dig into this as soon as I've the time.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub, or mute the thread.

Hi @tonyfarr,
Thanks for fast your reply.
We can exclude the browser bug of IE11 I've mentioned as a cause of your issue as it would work with acrossElements in this case.
Do I understand it right that you just have this problem locally?
I'm now at my laptop too and I see something I've missed yesterday on my smartphone (was on a journey). The regular expression that is logged to the console in the second screenshot of your .docx file is wrong. This probably appears because of a wrong file encoding. Please try to add a charset attribute to your <script> tag which includes mark.js, as explained in the documentation (chapter "Integration").
Let me know if this helps. Otherwise I need the following information:

Open the fiddle and type in "Lor". Is it highlighted?
Download debug.zip (zip because GitHub doesn't allow uploading HTML), extract it and open the containing "debug.html" locally in the specified browsers. Type in "Lor". Is it highlighted?
Open "debug2.html" from the extracted zip and type in "Lor". Is it highlighted?
Upload "debug.html" to any server and type in "Lor". Is it highlighted?

If it won't work in all of above named steps, please enable the option debug for each of them and reply the log here.
Thank you for your help!

Hi Julian,
Yay! adding the charset to the

Hi @tonyfarr,
Basically there are two different encodings:

Encoding from your local files or files on your server
Encoding in which the server – or your browser when the files are available locally – serves files

As you'd probably expect when you save a file in UTF-8 it might still be possible that your server or browser doesn't serve files in UTF-8. In your case your serves does, which is why it works. Your local browser doesn't (at least the Windows one).
I've never heard of a browser configuration that serves files in ASCII and I've also never made this experience. Anyway, you can workaround that with the charset attribute.
In future releases I'll use diacritics mapping information from the currently stuck in development diacritics project, when we've finalized the spec. We'll encode diacritics to unicode numbers in production to avoid such problems. It'll then work even when serving files in ASCII.
I hope this answers your questions. Feel free to reopen if not or open further issues when you have more questions or problem.
Cheers :beers:

Hi Julian,
Yes, this answers my question very well.
Thanks for your help with this. It was very frustrating!
Regards.Tony.
Tony Farrugiamob: +61 411 484<EMAIL_ADDRESS>Date: Mon, 12 Sep 2016 11:13:54 -0700
From<EMAIL_ADDRESS>To<EMAIL_ADDRESS>CC<EMAIL_ADDRESS><EMAIL_ADDRESS>Subject: Re: [julmot/mark.js] highlights only the first character entered in the search box (#73)
Hi @tonyfarr,
Basically there are two different encodings:
Encoding from your local files or files on your server
Encoding in which the server – or your browser when the files are available locally – serves files
As you'd probably expect when you save a file in UTF-8 it might still be possible that your server or browser doesn't serve files in UTF-8. In your case your serves does, which is why it works. Your local browser doesn't (at least the Windows one).
I've never heard of a browser configuration that serves files in ASCII and I've also never made this experience. Anyway, you can workaround that with the charset attribute.
In future releases I'll use diacritics mapping information from the currently stuck in development diacritics project, when we've finalized the spec. We'll encode diacritics to unicode numbers in production to avoid such problems. It'll then work even when serving files in ASCII.
I hope this answers your questions. Feel free to reopen if not or open further issues when you have more questions or problem.
Cheers 🍻
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub, or mute the thread.


Consider migrating away from the lexical_core crate

Hi! I am the maintainer of pydantic and pydantic-core in Fedora Linux that now require this crate. jitter depends on lexical_core to parse floats. lexical has multiple soundness/safety issues and is no longer actively maintained. We would thus prefer not to package it in Fedora. Would it be possible for jitter to use a different crate for this purpose?
See also: the list of alternative libraries in RUSTSEC-2023-0055

Thanks @gotmax23 for letting us know.
Discussion is continuing on #53.

I think you would avoid pulling in the components that have unsafe / unsound code in them by just depending on lexical-parse-float directly instead of using the re-exported functionality in lexical-core. That is also what ruff does. Though of course there's still the tradeoff between the negative performance impact and the fact that the lexical-* crates all look unmaintained.


Settings portal binding no contrast key?

Looking a bit at the source code of the Settings interface I don't see anywhere support for the contrast key
https://flatpak.github.io/xdg-desktop-portal/docs/doc-org.freedesktop.portal.Settings.html
Isn't implemented or is in another place?

That portal is new, contributions are welcome 🤗

That portal API is new, contributions are welcome 🤗

Didn't know. EDIT: found it here https://github.com/flatpak/xdg-desktop-portal/discussions/1151
Got it, thank you!


Do a check for old versions of jquery templates

A small thing but I spent an hour trying to figure out why JSRender was not working for me.  It was because I was still loading Jquery Templates in the page.  Might want to spit out a console error if JSRender is called when JQuery templates also exists.

I'll into that idea. Thanks...

I spent a similar hour so +1 vote

It's on its way, with the next major commit...


Issue 167

Implements 3rd-Party OAuth
Resolves #167

@bayssmekanique It won't let me re-request a review on GitHub, but I have push changes that you mentioned you would like to see to this PR whenever you get a chance to take a look.

@JohnathanHill Fix failed test and update to latest API changes.


Fallback to libx264 encoder if libopenh264 isn't available

Motivation and context

On my local system I have libx264 but not libopenh264. When I run tests, some of them are failed.
>>> c = av.codec.Codec("libopenh264", "w")
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "av/codec/codec.pyx", line 184, in av.codec.codec.Codec.__cinit__
  File "av/codec/codec.pyx", line 193, in av.codec.codec.Codec._init
av.codec.codec.UnknownCodecError: libopenh264

>>> c = av.codec.Codec("libx264", "w")
>>> c.name
'libx264'

How has this been tested?

Manually (libx264 works as expected know).
Checklist

[x] I submit my changes into the develop branch
[ ] I have added description of my changes into CHANGELOG file
[ ] I have updated the documentation accordingly
[ ] I have added tests to cover my changes
[ ] I have linked related issues (read github docs)
[ ] I have increased versions of npm packages if it is necessary (cvat-canvas,
cvat-core, cvat-data and cvat-ui)

License

[x] I submit my code changes under the same MIT License that covers the project.
Feel free to contact the maintainers if that's a concern.
[x] I have updated the license header for each file (see an example below)

# Copyright (C) 2021 Intel Corporation
#
# SPDX-License-Identifier: MIT

@nmanovic please fix tests

Coverage increased (+0.01%) to 72.231% when pulling 862a8f5a27ae9a55bd568402f8b579ae09ee9ea8 on nm/libx264 into 86eef84717fc901a1910a0c0290758b774835164 on develop.


Catalog apps naming messed up.

Rancher versions:
rancher/rancher:2.1.0
Infrastructure Stack versions:
kubernetes: 1.10 + 1.11
Steps to Reproduce:
Install an app from catalog e.g. MariaDB to a custom namespace e.g. testnamespace
Enter some answers (content doesn't matter) and click install.
Results:
The app is installed but named testnamespace/mariadb-mariadb-0 I expected it to be named testnamespace/mariadb like I entered during the catalog app configuration.

it is the catalog app name that you have defined,  the mariadb-mariadb-0 is generated by the mariadb yaml file, this is regular naming standard in helm and if you want to keep your own name you can folk and edit the file accordingly.

OK but why did it work as expected before Rancher 2.0.8

You can deploy the MariaDB app with version v3.0.3, this should be the one you are originally using, and it has no dependency on the rancher version.

https://github.com/rancher/charts/blob/master/charts/mariadb/latest/templates/_helpers.tpl#L18
https://github.com/rancher/charts/blob/master/charts/mariadb/v3.0.3/templates/_helpers.tpl#L14

Got you. Thanks


[BUG] Setting Dask Cluster through UCX not working

I tried setting the LocalCUDACluster using ucx but get this ModuleNotFoundError: No module named 'ucp' I'm running this in NVTabular merlin-pytorch-training:0.5.3 NGC container. I thought that container had everything we need for UCX.
Here's my code:
protocol = "ucx"
# Select GPUs to place workers. Here 1st and 2nd GPU
# If you want the first 4 GPUs it would be 0,1,2,3 and so on
visible_devices = "0,1"
# Get the IP Address
cmd = "hostname --all-ip-addresses"
process = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE)
output, error = process.communicate()
IPADDR = str(output.decode()).split()[0]
cluster = LocalCUDACluster(
    ip=IPADDR,
    protocol=protocol,
    enable_nvlink=True,
    CUDA_VISIBLE_DEVICES=visible_devices,
    local_directory=dask_workdir,
    device_memory_limit=0.8 # This can be changed depending on your workflow
)
# Create the distributed client
client = Client(cluster)
client

Here's the error traceback:
/opt/conda/lib/python3.8/site-packages/dask_cuda/local_cuda_cluster.py:219: UserWarning: When using NVLink we recommend setting a `rmm_pool_size`. Please see: https://dask-cuda.readthedocs.io/en/latest/ucx.html#important-notes for more details
  warnings.warn(
---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
<ipython-input-17-f505f8fa4f24> in <module>
     14 IPADDR = str(output.decode()).split()[0]
     15
---> 16 cluster = LocalCUDACluster(
     17     ip=IPADDR,
     18     protocol=protocol,
/opt/conda/lib/python3.8/site-packages/dask_cuda/local_cuda_cluster.py in __init__(self, n_workers, threads_per_worker, processes, memory_limit, device_memory_limit, CUDA_VISIBLE_DEVICES, data, local_directory, protocol, enable_tcp_over_ucx, enable_infiniband, enable_nvlink, enable_rdmacm, ucx_net_devices, rmm_pool_size, rmm_managed_memory, rmm_log_directory, jit_unspill, log_spilling, **kwargs)
    284         )
    285
--> 286         super().__init__(
    287             n_workers=0,
    288             threads_per_worker=threads_per_worker,
/opt/conda/lib/python3.8/site-packages/distributed/deploy/local.py in __init__(self, name, n_workers, threads_per_worker, processes, loop, start, host, ip, scheduler_port, silence_logs, dashboard_address, worker_dashboard_address, diagnostics_port, services, worker_services, service_kwargs, asynchronous, security, protocol, blocked_handlers, interface, worker_class, scheduler_kwargs, **worker_kwargs)
    229         workers = {i: worker for i in range(n_workers)}
    230
--> 231         super().__init__(
    232             name=name,
    233             scheduler=scheduler,
/opt/conda/lib/python3.8/site-packages/distributed/deploy/spec.py in __init__(self, workers, scheduler, worker, asynchronous, loop, security, silence_logs, name, shutdown_on_close)
    280         if not self.asynchronous:
    281             self._loop_runner.start()
--> 282             self.sync(self._start)
    283             self.sync(self._correct_state)
    284
/opt/conda/lib/python3.8/site-packages/distributed/deploy/cluster.py in sync(self, func, asynchronous, callback_timeout, *args, **kwargs)
    186             return future
    187         else:
--> 188             return sync(self.loop, func, *args, **kwargs)
    189
    190     def _log(self, log):
/opt/conda/lib/python3.8/site-packages/distributed/utils.py in sync(loop, func, callback_timeout, *args, **kwargs)
    351     if error[0]:
    352         typ, exc, tb = error[0]
--> 353         raise exc.with_traceback(tb)
    354     else:
    355         return result[0]
/opt/conda/lib/python3.8/site-packages/distributed/utils.py in f()
    334             if callback_timeout is not None:
    335                 future = asyncio.wait_for(future, callback_timeout)
--> 336             result[0] = yield future
    337         except Exception as exc:
    338             error[0] = sys.exc_info()
/opt/conda/lib/python3.8/site-packages/tornado/gen.py in run(self)
    760
    761                     try:
--> 762                         value = future.result()
    763                     except Exception:
    764                         exc_info = sys.exc_info()
/opt/conda/lib/python3.8/site-packages/distributed/deploy/spec.py in _start(self)
    309                 cls = import_term(cls)
    310             self.scheduler = cls(**self.scheduler_spec.get("options", {}))
--> 311             self.scheduler = await self.scheduler
    312         self.scheduler_comm = rpc(
    313             getattr(self.scheduler, "external_address", None) or self.scheduler.address,
/opt/conda/lib/python3.8/site-packages/distributed/core.py in _()
    283                         )
    284                 else:
--> 285                     await self.start()
    286                     self.status = Status.running
    287             return self
/opt/conda/lib/python3.8/site-packages/distributed/scheduler.py in start(self)
   3462
   3463         for addr in self._start_address:
-> 3464             await self.listen(
   3465                 addr,
   3466                 allow_offload=False,
/opt/conda/lib/python3.8/site-packages/distributed/core.py in listen(self, port_or_addr, allow_offload, **kwargs)
    398             addr = port_or_addr
    399             assert isinstance(addr, str)
--> 400         listener = await listen(
    401             addr,
    402             self.handle_comm,
/opt/conda/lib/python3.8/site-packages/distributed/comm/core.py in _()
    206     def __await__(self):
    207         async def _():
--> 208             await self.start()
    209             return self
    210
/opt/conda/lib/python3.8/site-packages/distributed/comm/ucx.py in start(self)
    389                 await self.comm_handler(ucx)
    390
--> 391         init_once()
    392         self.ucp_server = ucx_create_listener(serve_forever, port=self._input_port)
    393
/opt/conda/lib/python3.8/site-packages/distributed/comm/ucx.py in init_once()
     54         return
     55
---> 56     import ucp as _ucp
     57
     58     ucp = _ucp
ModuleNotFoundError: No module named 'ucp'

This is on all our staging containers -

On our staging containers - will be in the 0.6 release


BUG:in dark mode text not display

Is there an existing issue for this?

[X] I have searched the existing issues

What happened?
in dark mode theme search input text not display

Record

[X] I agree to follow this project's Code of Conduct
[X] I'm a GSSOC'24 contributor
[X] I want to work on this issue

@PatilHarshh ,assign this issue

@PatilHarshh ,assign me

Hello @Varsani2520  I saw your previous work related to frontend. I am developing a Project related to GATE aspirants . looking for teammates who are best in frontend. if you are interested or you  can suggest me such person. !!
Benifits 👍

This Project can be allowed to showcase in your Resume.
Collaboration with devs

Yes I am Intersted @PatilHarshh


Add code to have version number in the name of the taco

Add code to packager to make sure that plugin-version is defined in the connectors files.
Add the version of the taco in the name of the taco.
Things To Remember:

Be sure to merge into the right branch. The choices are:

master if your PR runs on the current version of Tableau.
dev-2021.2

You should update at least one of the following:

The changelog of the project you modified.
The root README.md if the PR is a release of the SDK, Connector Packager, or TDVT.
=====

Hi, @jainam1995! Thanks for opening this PR!

In the file connector-packager/connector_packager/xml_parser.py:

Line 75:1: W293 blank line contains whitespace
Line 38:35: E261 at least two spaces before inline comment

For reference: Error Code Reference, PEP8, Python code style

There is already a pull request to do add the version to the taco name: https://github.com/tableau/connector-plugin-sdk/pull/912/files
I haven't merged that one because right now, when two connectors in the same folder conflict, we chose the one that comes first alphabetically. If we tag on the version number on the end, that means that the oldest version will be loaded first in this scenario, since the taco-v1 comes alphabetically before taco-v2.
We have a story on the backlog to revise the loading logic to load the newest version of the connector we find, and we shouldn't add logic to put the version name into the packager until then.
Also, adding a check for plugin-version is redundant, as the XSD already enforces that that attribute is present: https://github.com/tableau/connector-plugin-sdk/blob/master/validation/connector_plugin_manifest_latest.xsd#L156

There is already a pull request to do add the version to the taco name: https://github.com/tableau/connector-plugin-sdk/pull/912/files
I haven't merged that one because right now, when two connectors in the same folder conflict, we chose the one that comes first alphabetically. If we tag on the version number on the end, that means that the oldest version will be loaded first in this scenario, since the taco-v1 comes alphabetically before taco-v2.
We have a story on the backlog to revise the loading logic to load the newest version of the connector we find, and we shouldn't add logic to put the version name into the packager until then.
Also, adding a check for plugin-version is redundant, as the XSD already enforces that that attribute is present: https://github.com/tableau/connector-plugin-sdk/blob/master/validation/connector_plugin_manifest_latest.xsd#L156

Working on updating the connector load logic in monolith


[Bug] Can't create account on selfhosted offline instance

Is there an existing issue for this?

[X] Yes, I have searched the existing issues

Product Variant
Self-Hosted
Current Behavior
It's all in the title, I was expecting the account creation to work on a self-hosted instance and not need internet connection.
The app starts as expected without an issue
Expected Behavior
The account creation should work offline
Steps To Reproduce
restrict the app access to offline mode and try to create an account
What browsers are you seeing the problem on?
No response
Anything else?
No response

Never mind, it was a configuration issue on my side. I'm closing this.

What was the config change you made to make it work?

if I remember correctly, it was a configuration issue with the database or the client, just make sure you can connect to all of them with the provided url you set up


initializer update

Fixes the errors that comes with the PR https://github.com/theislab/moscot/pull/748

Thanks @selmanozleyen , once we found a final solution in https://github.com/theislab/moscot/pull/748, we can merge.

I am closing this as the final solution won't require any updates on notebooks side


Package: go-neb: it's been discontinued

Not sure if there is some policy around this topic (haven't found it or an template) so going a bit free style.

Package name: go-neb
Archived: 2023-06-23

The go-neb project was archived/discontinued by the developer last year. Currently it seems to work still but with it beeing abandoned sofware i think a removal/marking as insecure or whatever should be considered at some point.
It doesn't look like there are viable forks to switch to. Or i haven't quickly found them.
On a side node there is at least one prominent deployment.
Notify maintainers
@mweinelt @maralorn

Note for maintainers: Please tag this issue in your PR.

Add a :+1: reaction to issues you find important.

I am not neccessarily against a removal but I am currently using this very successfully for alertmanager notifications and would appreciate finding an alternative before we drop it. 😆

Yes, im also still looking into alternatives exactly for those notifcations and will have to migrate one deployment. :sweat_smile:
There is matrix-hook (nixpkgs) as seen in nix-community/infra. It has fewer dependencies but it also doen't look too actively developed (the dependencies are around a year old (the last go-neb bump was ~two years ago so it's newer)).
Then there is the in the discontinuation announcement mentioned matrix-hookshot (nixpkgs). It covers more of what go-neb does/did and apparently also has some support for alertmanager.
Don't know if there are other alternatives.

Matrix-alertmanager exists specifically for alertmanager based notifications. Hookshot is the "official" replacement for go-neb for things outside of alertmanager notifications.


kie-issues#1203: Overriding apache mime4j dependency version

Closes https://github.com/apache/incubator-kie-issues/issues/1203

@tiagobento  Only this module is reported. However, there's something weird, I'm still investigating (I shared my doubts in this ticket)

Added sonataflow as well - just to be sure :)
@porcelli @tiagobento


💄 Using Issue Forms Instead Of Markdown

https://docs.github.com/communities/using-templates-to-encourage-useful-issues-and-pull-requests/configuring-issue-templates-for-your-repository#creating-issue-forms

fixed in #349


Avoid mounting check confd volume if there is no config

What does this PR do?
Avoid mounting check confd volume if there is no config
Related PR: https://github.com/DataDog/integrations-core/pull/8719
Motivation
This change is needed for snmp test requiring /etc/datadog-agent/conf.d/snmp.d/auto_conf.yaml to be present.
Currently, check confd (e.g. /etc/datadog-agent/conf.d/snmp.d) is always mounted and will make auto_conf.yaml unavailable.
Right now, ddev will mount confd even if there is no config.

why is auto_conf.yaml unavailable if conf.d is mounted? does it overwrite it?

@pducolin

why is auto_conf.yaml unavailable if conf.d is mounted? does it overwrite it?

Yes, the whole folder is overwritten by the mounted folder.


bug

Parse error: syntax error, unexpected 'SettingsContainerInterface' (T_STRING), expecting function (T_FUNCTION) or const (T_CONST) in C:\laragon\www\testeqrcode_php\vendor\chillerlan\php-qrcode\src\QRCode.php on line 153

It's not a bug - you're likely running not on PHP 7.4 - it is caused by a typed property which is not supported by older PHP versions (composer should have thrown an error while trying to install on an invalid PHP version).
You can instead use the 3.2 branch, which is for PHP 7.2+, or an older one which fits your PHP version.


Add new keys and adapt UsageMetrics to include mapzen services

HereIsolinesUsageMetrics has been renamed to a more generic IsolinesUsageMetrics, and the code that uses it has been updated for the change. See occurrences here - HereIsolinesUsageMetrics and here - here_isolines_usage_metrics.

Add mapzen_isolines key as valid for isolines

Add geocoder_mapzen key as valid for geocoder
Add RoutingUsageMetrics class

Add routing_mapzen key as valid for routing

Just finished testing manually, specially the new Routing class and the renamed Isolines. It's looking good!

Hey @ethervoid, I added the new service/providers as we discussed before. Could you please take a look?

Looks good to me 👍


feat(storage): support max version pinning duration

I hereby agree to the terms of the RisingWave Labs, Inc. Contributor License Agreement.
What's changed and what's your intention?
This PR adds an upper limiter for hummock version pinning duration, to avoid blocking storage GC infinitely.
A long hummock pinning duration can be caused by

slow hummock iteration.
bug that doesn't unpin version as expected.

The new config max_version_pinning_duration_sec is 3 hours by default. A version pinned by compute node will be unpinned after max_version_pinning_duration_sec. After that, there is no guarantee of success for hummock reading upon this version, because SSTs referenced by it may have been GCed.
Checklist

[ ] I have written necessary rustdoc comments
[ ] I have added necessary unit tests and integration tests
[ ] I have added fuzzing tests or opened an issue to track them. (Optional, recommended for new SQL features #7934).
[ ] My PR contains breaking changes. (If it deprecates some features, please create a tracking issue to remove them in the future).
[ ] All checks passed in ./risedev check (or alias, ./risedev c)
[ ] My PR changes performance-critical code. (Please run macro/micro-benchmarks and show the results.)

[ ] My PR contains critical fixes that are necessary to be merged into the latest release. (Please check out the details)

Documentation

[ ] My PR needs documentation updates. (Please use the Release note section below to summarize the impact on users)

Release note
If this PR includes changes that directly affect users or other significant modifications relevant to the community, kindly draft a release note to provide a concise summary of these changes. Please prioritize highlighting the impact these changes will have on users.

Not related to this PR. Do you think we need to add a timeout for RowSeqExecutor and storage call including get and iter as well?


Is it possible to have multiple Graphql APIs ?

Which Category is your question related to?
AWS CLI
Amplify CLI Version
4.18.0
What AWS Services are you utilizing?
Amplify
Provide additional details e.g. code snippets
I'm trying to add a new API to my project, but it seems I'm limited to one, is this really the case ?
When running amplify add api I have this error You already have an AppSync API in your project. Use the "amplify update api" command to update your existing AppSync API., I don't want to update the existing one, I want a new API.

why would you want 2 GraphQL Apis in one project? If you have a need for a 2nd one, I'd just create a new project and just use the 2nd one there.

Why would I create a new API related to the same project in another one ?
I have 2 business cases for the same project and I want to separate them in two different api's

GraphQL's paradigm kind of implies everything should be in an all in one setting. I think Amplify projects have limits on this to enforce this. Maybe with proper logic abstraction, one API could work? If not I don't see how you can make a 2nd GraphQL API in one project.

The CLI currently does not support more than one GraphQL API per environment. What you can do is create a GraphQL API in a different environment. This would allow you to have a different API in your dev environment as opposed to your prod environment.
Docs on Environments: https://docs.amplify.aws/cli/teams/overview#setting-up-master-and-dev-environments

I considered multiple APIs as a way to save high volume daily data locally and then summarize it for saving in the cloud. I was hoping that I could use one API locally only with DataStore, and the other Amplify API with DataStore and AppSync. But Amplify isn't meant to be used this way. Is there another way of accomplishing this? If I could force my Amplify app into offline (DataStore only) mode, that would do it for me, but that also doesn't seem to be a feature (yet) either.


Cannot access field properties 'is_hw_readable' and 'is_hw_writable'

When accessing field properties is_hw_readable and is_hw_writable I get the following error (doc):
Traceback (most recent call last):
  File "run.py", line 17, in <module>
    is_hw_writable: {descendant.is_hw_writable}""")
AttributeError: 'FieldNode' object has no attribute 'is_hw_readable'

// test.rdl
addrmap test {
    reg {
        field {} a;
        field {} b;
    } status;
};

# run.py
from systemrdl import RDLCompiler
from systemrdl.node import FieldNode

rdlc = RDLCompiler()

rdlc.compile_file('test.rdl')
root = rdlc.elaborate()

for descendant in root.top.descendants():
    if isinstance(descendant, FieldNode):
        print(f"""{descendant.get_path_segment()}:
    is_sw_readable: {descendant.is_sw_readable}
    is_sw_writable: {descendant.is_sw_writable}
    is_virtual: {descendant.is_virtual}
    is_volatile: {descendant.is_volatile}
    is_hw_readable: {descendant.is_hw_readable}
    is_hw_writable: {descendant.is_hw_writable}""")

After investigating I can see it is committed on master #84 but not released yet. So the documentation is ahead of the latest release  :-)

Yup. Completely forgot that i had this unreleased! Thanks for the reminder


Add a variable "type" to the input

It allows us to add the "type" directly on the component.
By default, the value is "text" (As before).
It can be useful if we want to add the type "tel" for example (On mobile, the keyboard allows us to type letters (A-Z) and specials characters which is not valid for a phone number).

@nikhiln Can you accept this pull request.


Dockerize product microservice

As an Architect, I want to dockerize product microservice, for ease of management with other microservices.
Requirements

Create a specific docker file, that also separates the libs from the main Jar for docker layer caching and reusability.
Image base Java jdk is openjdk:14-slim-buster.
Use a multi-stage docker file to unzip the files for better image layering and faster startup.

Done


 val_loss is not available in training after some epochs

Please go to TF Forum for help and support:
https://discuss.tensorflow.org/tag/keras
If you open a GitHub issue, here is our policy:
It must be a bug, a feature request, or a significant problem with the documentation (for small docs fixes please send a PR instead).
The form below must be filled out.
Here's why we have that policy:.
Keras developers respond to issues. We want to focus on work that benefits the whole community, e.g., fixing bugs and adding features. Support only helps individuals. GitHub also notifies thousands of people when issues are filed. We want them to see you communicating an interesting problem, rather than being redirected to Stack Overflow.
System information.

Have I written custom code (as opposed to using a stock example script provided in Keras): Yes
OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Windows 10 - 21H2
TensorFlow installed from (source or binary): pip
TensorFlow version (use command below): v2.8.0-rc1-32-g3f878cff5b6 2.8.0
Python version: 3.8.12 (default, Oct 12 2021, 03:01:40) [MSC v.1916 64 bit (AMD64)]
Bazel version (if compiling from source): N/A
GPU model and memory: GTX 1080 - 8 GB
Exact command to reproduce: N/A

You can collect some of this information using our environment capture script:
https://github.com/tensorflow/tensorflow/tree/master/tools/tf_env_collect.sh
You can obtain the TensorFlow version with:
python -c "import tensorflow as tf; print(tf.version.GIT_VERSION, tf.version.VERSION)"
Describe the problem.
This issue first appeared here as issue 55060.
I train a multi-scale deep convolutional autoencoder. After some epochs val_loss cannot be identified by tensorflow callbacks any more:
WARNING:tensorflow:Learning rate reduction is conditioned on metric val_loss which is not available. Available metrics are: loss,accuracy,lr
WARNING:tensorflow:Early stopping conditioned on metric val_loss which is not available. Available metrics are: loss,accuracy,lr
My callbacks are:
    callbacks = [tf.keras.callbacks.ReduceLROnPlateau(monitor="val_loss",
                                                      factor=0.5, patience=8, min_lr=0.00001),
                 tf.keras.callbacks.ModelCheckpoint("models/ms-model1-1", monitor="val_loss"),
                 tf.keras.callbacks.EarlyStopping(monitor="val_loss", patience=25),
                 tf.keras.callbacks.TensorBoard(log_dir="train/ms-log1-1/", histogram_freq=1),
                 tf.keras.callbacks.ModelCheckpoint('models/ms-models-epochs/model{epoch:08d}', period=5)]

The last ModelCheckpoint callback I added to work around this bug. This sadly means that I have to put more work into it, in order for it to work as I have to reduce the LR manually.
In tensorboard it looks like this:

You can see that the val_loss is not even calculated any more. This happens after epoch 4, so after the ModelCheckpoint was first reached.
Describe the expected behavior.
The expected behaviour would be that val_loss is further calculated and can therefore also be used in the ModelCheckpoints, EarlyStoppings and other callbacks I use.
I am not entirely sure if it is me using the library wrong or whether it really fails due to a bug (I am by no means an expert in TF). The code for the network generation is also included in the PR, the data sets are too big though and are not online available. The used data are Copernicus 4 channel 256x256 images. This is why I did not include the dataloading code as it seems irrelevant to me (I can include it though if you need it).
Contributing.

Do you want to contribute a PR? (yes/no): no
If yes, please read this page for instructions
Briefly describe your candidate solution(if contributing): n/a

Standalone code to reproduce the issue.
Provide a reproducible test case that is the bare minimum necessary to generate
the problem. If possible, please share a link to Colab/Jupyter/any notebook.
Source code / logs.
Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached. Try to provide a reproducible test case that is the bare minimum necessary to generate the problem.
def create_normalized_convolution(filters: int,
                                  activation=None,
                                  strides=1,
                                  kernel_size=3,
                                  padding='SAME',
                                  seq_name=None,
                                  **kwargs) -> tf.keras.Sequential:
    if activation is None:
        activation = LeakyReLU()
    return tf.keras.Sequential([
        Conv2D(filters=filters, strides=strides, kernel_size=kernel_size, padding=padding, **kwargs),
        BatchNormalization(),
        activation
    ], seq_name)

def create_normalized_convolution_transpose(filters: int,
                                            activation=None,
                                            strides=1,
                                            kernel_size=3,
                                            padding='SAME',
                                            seq_name=None,
                                            **kwargs) -> tf.keras.Sequential:
    if activation is None:
        activation = LeakyReLU()
    return tf.keras.Sequential([
        Conv2DTranspose(filters=filters, strides=strides, kernel_size=kernel_size, padding=padding, **kwargs),
        BatchNormalization(),
        activation
    ], name=seq_name)

class BaseAutoencoder(tf.keras.Model):
    """
    Base class for all used AEs.
    The advantage of a base class is that it only requires you to specify the network structure in the constructor and
    removes almost all boilerplate code.
    """

    def __init__(self, latent_dim: int, image_size=256, channels=4):
        super(BaseAutoencoder, self).__init__()

        self._latent_dim = latent_dim
        self._image_size = image_size
        self._channels = channels
        self._encoder: tf.keras.Sequential = None
        self._decoder: tf.keras.Sequential = None

    def get_latent_dim(self) -> int:
        return self._latent_dim

    def get_image_size(self) -> int:
        return self._image_size

    def get_channels(self) -> int:
        return self._channels

    def call(self, inputs):
        bottleneck = self._encoder(inputs)
        return self._decoder(bottleneck)

    def encoder(self) -> tf.keras.Sequential:
        return self._encoder

    def decoder(self) -> tf.keras.Sequential:
        return self._decoder

class MultiscaleAutoencoder(BaseAutoencoder):
    """
    A custom multiscale implementation of an AutoEncoder.
    """

    def __init__(self, latent_dim: int, image_size=256, channels=4, base_filter_count=32):
        super(MultiscaleAutoencoder, self).__init__(latent_dim, image_size, channels)
        self._base_filter_count = base_filter_count

        raw_inp = Input(shape=(image_size, image_size, channels), dtype=tf.float32)
        enc_inp = InputLayer(name='Input-ENCODER_0')(raw_inp)

        high_res_enc = tf.keras.Sequential([
            create_normalized_convolution(filters=base_filter_count,
                                          strides=2,
                                          seq_name="NormConv-LeakyReLU-HR_ENCODER_1"),
            create_normalized_convolution(filters=base_filter_count * 2,
                                          strides=2,
                                          seq_name="NormConv-LeakyReLU-HR_ENCODER_2"),
            create_normalized_convolution(filters=base_filter_count * 4,
                                          strides=2,
                                          seq_name="NormConv-LeakyReLU-HR_ENCODER_3"),
            create_normalized_convolution(filters=base_filter_count * 8,
                                          strides=2,
                                          seq_name="NormConv-LeakyReLU-HR_ENCODER_4"),
        ], name="HighResolution_ENCODER")(enc_inp)

        med_res = MaxPooling2D(padding='same', name="MaxPoolingMR_ENCODER")(enc_inp),

        medium_res_enc = tf.keras.Sequential([
            create_normalized_convolution(filters=base_filter_count * 2,
                                          strides=2,
                                          seq_name="NormConv-LeakyReLU-MR_ENCODER_1",
                                          input_shape=(int(image_size / 2), int(image_size / 2), channels)),
            create_normalized_convolution(filters=base_filter_count * 4,
                                          strides=2,
                                          seq_name="NormConv-LeakyReLU-MR_ENCODER_2"),
            create_normalized_convolution(filters=base_filter_count * 8,
                                          strides=2,
                                          seq_name="NormConv-LeakyReLU-MR_ENCODER_3"),
        ], name="MediumResolution_ENCODER")(med_res)

        low_res_enc = tf.keras.Sequential([
            MaxPooling2D(padding='same', name="MaxPoolingLR_ENCODER",
                         input_shape=(int(image_size / 2), int(image_size / 2), channels)),
            create_normalized_convolution(filters=base_filter_count * 4,
                                          strides=2,
                                          seq_name="NormConv-LeakyReLU-LR_ENCODER_1"),
            create_normalized_convolution(filters=base_filter_count * 8,
                                          strides=2,
                                          seq_name="NormConv-LeakyReLU-LR_ENCODER_2"),
        ], name="LowResolution_ENCODER")(med_res)

        lower_res_concat = concatenate([
            medium_res_enc,
            low_res_enc
        ], name="LowerResolutionsMerger_ENCODER")

        res_concat = Concatenate(name="Multiscale-ENCODER_2")([
            high_res_enc,
            lower_res_concat,
        ])

        conv_3_enc = create_normalized_convolution(filters=self._latent_dim ** 2 * 3,
                                                   seq_name="NormConv-LeakyReLU_ENCODER_3")(res_concat)
        glob_max_pool_enc = GlobalMaxPool2D(name="GlobalMaxPooling_ENCODER_4")(conv_3_enc)
        out_enc = Dense(self._latent_dim ** 2 * 3, name="Dense-ENCODER_5")(glob_max_pool_enc)

        self._encoder = tf.keras.Model(inputs=raw_inp, outputs=out_enc, name="Encoder")

        raw_dec_inp = Input(shape=(self._latent_dim ** 2 * 3), dtype=tf.float32)
        dec_inp = InputLayer(name='Input-DECODER_0')(raw_dec_inp)

        reshape_dec = Reshape(target_shape=[self._latent_dim, self._latent_dim, 3], name='Reshape-DECODER_1')(dec_inp)
        conv_1_dec = create_normalized_convolution_transpose(filters=self._latent_dim ** 2 * 3,
                                                             seq_name="NormConv-LeakyReLU_ENCODER_2")(reshape_dec)

        hr_dec = tf.keras.Sequential([
            create_normalized_convolution_transpose(filters=base_filter_count * 8,
                                                    strides=2,
                                                    seq_name="NormConv-LeakyReLU-HR_DECODER_1"),
            create_normalized_convolution_transpose(filters=base_filter_count * 4,
                                                    strides=2,
                                                    seq_name="NormConv-LeakyReLU-HR_DECODER_2"),
            create_normalized_convolution_transpose(filters=base_filter_count * 2,
                                                    strides=2,
                                                    seq_name="NormConv-LeakyReLU-HR_DECODER_3"),
            create_normalized_convolution_transpose(filters=base_filter_count,
                                                    strides=2,
                                                    seq_name="NormConv-LeakyReLU-HR_DECODER_4")
        ], name="HighResolution_DECODER")(conv_1_dec)

        mr_dec = tf.keras.Sequential([
            create_normalized_convolution_transpose(filters=base_filter_count * 8,
                                                    strides=2,
                                                    seq_name="NormConv-LeakyReLU-MR_DECODER_1"),
            create_normalized_convolution_transpose(filters=base_filter_count * 4,
                                                    strides=2,
                                                    seq_name="NormConv-LeakyReLU-MR_DECODER_2"),
            create_normalized_convolution_transpose(filters=base_filter_count * 2,
                                                    strides=2,
                                                    seq_name="NormConv-LeakyReLU-MR_DECODER_3")
        ], name="MediumResolution_DECODER")(conv_1_dec)

        lr_dec = tf.keras.Sequential([
            create_normalized_convolution_transpose(filters=base_filter_count * 8,
                                                    strides=2,
                                                    seq_name="NormConv-LeakyReLU-LR_DECODER_1"),
            create_normalized_convolution_transpose(filters=base_filter_count * 4,
                                                    strides=2,
                                                    seq_name="NormConv-LeakyReLU-LR_DECODER_2"),
            UpSampling2D(name="UpSampling2x-LR_DECODER")
        ], name="LowResolution_DECODER")(conv_1_dec)

        concat_lower_res = Concatenate(name="LowerResolutionsMerger_DECODER")([
            mr_dec,
            lr_dec
        ])

        up_sample_mr = UpSampling2D(name="UpSampling2x-MR_DECODER")(concat_lower_res)

        concat_high_res = Concatenate(name="HighResolutionMerger_DECODER")([
            hr_dec,
            up_sample_mr
        ])

        norm_conv_4_dec = create_normalized_convolution_transpose(
            filters=4,
            activation=Activation('sigmoid'),
            seq_name='NormConv-Sigmoid-DECODER_4'
        )(concat_high_res)

        self._decoder = tf.keras.Model(inputs=raw_dec_inp, outputs=norm_conv_4_dec, name='Decoder')

BATCH_SIZE = 24
IMG_SIZE = 256
model = autoencoder.MultiscaleAutoencoder(16)
callbacks = [tf.keras.callbacks.ReduceLROnPlateau(monitor="val_loss",
                                                      factor=0.5, patience=8, min_lr=0.00001),
                 tf.keras.callbacks.ModelCheckpoint("models/ms-model1-1", monitor="val_loss"),
                 tf.keras.callbacks.EarlyStopping(monitor="val_loss", patience=25),
                 tf.keras.callbacks.TensorBoard(log_dir="train/ms-log1-1/", histogram_freq=1),
                 tf.keras.callbacks.ModelCheckpoint('models/ms-models-epochs/model{epoch:08d}', period=5)]
model.build(tf.TensorShape((None, IMG_SIZE, IMG_SIZE, 4)))
loss = MeanAbsoluteError()
model.compile(optimizer=tf.keras.optimizers.Adamax(learning_rate=0.032), loss=loss, metrics=['accuracy'])
model.fit(train_ds_final,
          validation_data=val_ds_final,
          epochs=150,
          callbacks=callbacks,
          shuffle=True)

@cb0s ,
I was facing different error while executing the given code.Please find the gist of it here and provide complete code to reproduce the issue.Thanks!

@tilakrayal This is due to the model structure I had in my original package: I had a trainer module and a model module. The module containing the Autoencoder code was called autoencoder. This is why it didn't work for you.
The problem is that I did in fact use a custom dataset and therefore can provide you with the loader code but it is specific to my project and I cannot give you all my data due to the size.
It is a simple dataset loaded by dataset = tf.keras.utils.image_dataset_from_directory() which was mapped through dataset.map(lambda x, y: (x, x)).
Do you want me to add the loader code anyway or do you want to use MNIST for this purpose ?

I should mention though that I did not get any errors. I only got a warning, this also why I didn't include any stacktrace, as there was none.

@cb0s ,
In order to expedite the trouble-shooting process, could you please provide the complete code.Thanks!

aww I am facing the same problem.


Fix two-step buttons in the action bar.

Fix two-step buttons in the action bar.

This has been fixed.


Where is persistent data saved?

Where do you save mysql persistent data?

By default MySQL data is stored in the container, without persistence.
In the event you would like to persist your database(s), you would create a folder anywhere on your computer and mount it to /var/lib/mysql when running the Docker container.

Yes, the issue is #33

Depending on your use case (ex - Kitematic), the folder you create may need to be somewhere inside your user profile directory.

Got it.


Provide licensing text for Zashi-iOS

Do the thing at https://developer.apple.com/help/app-store-connect/manage-app-information/provide-a-custom-license-agreement with the text written for https://github.com/Electric-Coin-Company/zashi/issues/23.

This text has been added as a custom EULA in AppStore Connect -  I believe we can close this ticket?