Cannot set console baud rate in sdkconfig.defaults (IDFGH-5885)

Environment

Development Kit:      TinyPICO
Module or chip used:  ESP32-PICO-D4
IDF version (run git describe --tags to find it): v4.4-dev-3042-g220590d599 | v4.3.1
Build System:        idf.py
Compiler version (run xtensa-esp32-elf-gcc --version to find it):  xtensa-esp32-elf-gcc (crosstool-NG esp-2021r1) 8.4.0
Operating System:     macOS
Using an IDE?: No
Power Supply:    USB

Problem Description
I cannot set the console baud rate in the sdkconfig.defaults file.
Using the console example application (standalone version can be found at https://github.com/davidzuhn/esp-idf-console-bug-example), I'm trying to set the console speed to 230400 in the sdkconfig.defaults file.   Using ESP-IDF v4.2, this example results in an sdkconfig file with an entry 'CONFIG_ESP_CONSOLE_UART_BAUDRATE=230400'.   Using ESP-IDF v4.3.1 or v4.4 (master), the sdkconfig file generated contains 'CONFIG_ESP_CONSOLE_UART_BAUDRATE=115200'.
In the sdkconfig.defaults file:
CONFIG_ESPTOOLPY_MONITOR_BAUD_230400B=y
CONFIG_ESPTOOLPY_MONITOR_BAUD=230400
CONFIG_ESP_CONSOLE_UART_BAUDRATE=230400

The generated binary uses the value that's in the sdkconfig file.
Expected Behavior
$ grep CONSOLE_UART_BAUDRATE sdkconfig
CONFIG_ESP_CONSOLE_UART_BAUDRATE=230400
CONFIG_CONSOLE_UART_BAUDRATE=230400

Actual Behavior
$ grep CONSOLE_UART_BAUDRATE sdkconfig
CONFIG_ESP_CONSOLE_UART_BAUDRATE=115200
CONFIG_CONSOLE_UART_BAUDRATE=115200

Steps to reproduce

git clone https://github.com/davidzuhn/esp-idf-console-bug-example
cd esp-idf-console-bug-example
idf.py build

// If possible, attach a picture of your setup/wiring here.
Code to reproduce this issue
https://github.com/davidzuhn/esp-idf-console-bug-example
Debug Logs
The error is not seen at runtime, except that the baud rate is not the expected value.
Other items if possible

I think the issue is that CONFIG_ESP_CONSOLE_UART_BAUDRATE option only gets enabled when CONFIG_ESP_CONSOLE_UART_CUSTOM is:
https://github.com/espressif/esp-idf/blob/d5f58ab13551cd883e8d8478ba367b6e4543ffec/components/esp_system/Kconfig#L310-L312
So in your sdkconfig.defaults file you need an additional line:
CONFIG_ESP_CONSOLE_UART_CUSTOM=y
CONFIG_ESP_CONSOLE_UART_BAUDRATE=230400

After testing, I see that this works.  It is, however, a change in behavior that's not documented in the Programming Guide, and the description for CONFIG_ESP_CONSOLE_UART_BAUDRATE mentions ranges based on PM_ENABLE, but doesn't indicate that the _CUSTOM setting applies to this as well.

Yeah, @davidzuhn this depends on information is indeed not included in the kconfig  docs. I've added a ticket for this.
Closing this one, as this issue is not really related to just this UART option and the orginal question has been answered.


about OCE

Hi, sir.
Thanks for your great job, I have some confuse about OCBE ：
MFF aligns multi-scale features from teacher E and OCE condenses the obtained rich feature to a compact bottleneck code.
But, the MMF part can did all things above. Why still use OCE module? Table 5 shows ablation study on Pre, Pre+OCE, Pre+OCE+MFF.
did you do the ablation study on pre+MFF?
OCBE module condenses the multi-scale patterns into an extreme low-dimensional space for downstream normal representation reconstruction.  Then, the abnormal representations generated by the teacher model are likely to be abandoned by OCBE.  why？？？？
I am looking forward to your reply!
Thanks.

Hello,
Pre+OCE means that we re-train the bottleneck block, while only pre is that we only use the frozen bottleneck block. We train the bottleneck block on one-class samples so that it refers to one-class embedding (OCE). The pre+MFF is meaningless, because the pre-trained bottleneck block (like 4th layer in ResNet) received features from the last layer rather than multi-scale feature when it was trained on ImageNet. If we want to fuse multi-scale features, we should train the bottleneck block to adapt to multi-scale features.
This is the same as why auto-encoder can achieve anomaly detection. During training, we compress the normal images to a low-dimensional codes, and then restore the normal images. Although code contains less information, the normal images are generated still well as it is trained. The anomalous case will generate a larger error as some anomalous informations are discarded. A toy case is that if normal case is [0,0,0,0,0,0,0] and anomalous case is [0,0,1,1,1,0,0]. If we compress it to a code with length of 5, the anomalous code maybe [0,1,1,1,0]. If we compress it to a code with length of 3, the anomalous code maybe [0,1,0]. The normal case is still restored as [0,0,0,0,0,0,0], but the anomalous case will be restored as [0,0,1,1,1,0,0] and [0,0,0,1,0,0,0] respectively. This case is very extreme and just for understanding it. Actually, previous studies have shown that the more compact latent code lead to larger anomaly error. When it comes back to OCBE, the default compression is like [(16,16,1024)] -> [(8,8,2048)] and MFF should be [(64,64,256),(32,32,512),(16,16,1024)] -> [(8,8,2048)]. Although we give the same target space, but there is much more information input. It relatively compresses information.

Hello,
Pre+OCE means that we re-train the bottleneck block, while only pre is that we only use the frozen bottleneck block. We train the bottleneck block on one-class samples so that it refers to one-class embedding (OCE). The pre+MFF is meaningless, because the pre-trained bottleneck block (like 4th layer in ResNet) received features from the last layer rather than multi-scale feature when it was trained on ImageNet. If we want to fuse multi-scale features, we should train the bottleneck block to adapt to multi-scale features.
This is the same as why auto-encoder can achieve anomaly detection. During training, we compress the normal images to a low-dimensional codes, and then restore the normal images. Although code contains less information, the normal images are generated still well as it is trained. The anomalous case will generate a larger error as some anomalous informations are discarded. A toy case is that if normal case is [0,0,0,0,0,0,0] and anomalous case is [0,0,1,1,1,0,0]. If we compress it to a code with length of 5, the anomalous code maybe [0,1,1,1,0]. If we compress it to a code with length of 3, the anomalous code maybe [0,1,0]. The normal case is still restored as [0,0,0,0,0,0,0], but the anomalous case will be restored as [0,0,1,1,1,0,0] and [0,0,0,1,0,0,0] respectively. This case is very extreme and just for understanding it. Actually, previous studies have shown that the more compact latent code lead to larger anomaly error. When it comes back to OCBE, the default compression is like [(16,16,1024)] -> [(8,8,2048)] and MFF should be [(64,64,256),(32,32,512),(16,16,1024)] -> [(8,8,2048)]. Although we give the same target space, but there is much more information input. It relatively compresses information.

Thank you very much for the reply  and I'm sorry to bother you again.

I still can't understand why pre+MFF is meaningless.
You said that the pre-trained bottleneck block (like 4th layer in ResNet) received features from the last layer rather than
multi-scale feature when it was trained on ImageNet.  I actually didn't really understand that.
pre+MFF get the multi-scale feature  from the pre-trained encoder and use MFF align them. Then put it to decoder.  There
only use the output of diferent bottleneck block that were pre-trained on ImageNet and no OCE module to use.

OCE was adopt the 4th residule block of ResNet. You said pre+OCE means that we re-train the bottleneck block. So, OCE
is part of teacher network (4th residule block of teacher network) or is OCE just a new residule block like 4th residule block
of Teacher?

As we use the 1st, 2nd, 3rd layers of a resnet as the teacher encoder, so the dimension of the features from the 3rd layer is fitting for the 4th layer of the resnet, so it's naturally we use the 4th layer of the encoder as the bottleneck layer. Pre refers to the pertained but frozen 4th layer which is the same as 1st, 2nd, 3rd in teacher encoder. The whole encoder is a pre-trained model on ImageNet. As the 4th layer receive the feature from the 3rd layer and trained on ImageNet, not from MFF when pre-training, we should re-train it on MVTec when we want to add MFF features.
OCE is part pf resnet used in teacher network. But sometimes we should train the OCE (4th layer) but freeze the teacher encoder (1st, 2nd, 3rd layers). Or modifying it as MFF+OCE.

As we use the 1st, 2nd, 3rd layers of a resnet as the teacher encoder, so the dimension of the features from the 3rd layer is fitting for the 4th layer of the resnet, so it's naturally we use the 4th layer of the encoder as the bottleneck layer. Pre refers to the pertained but frozen 4th layer which is the same as 1st, 2nd, 3rd in teacher encoder. The whole encoder is a pre-trained model on ImageNet. As the 4th layer receive the feature from the 3rd layer and trained on ImageNet, not from MFF when pre-training, we should re-train it on MVTec when we want to add MFF features. OCE is part pf resnet used in teacher network. But sometimes we should train the OCE (4th layer) but freeze the teacher encoder (1st, 2nd, 3rd layers). Or modifying it as MFF+OCE.

Thanks a lot.


Sorting on the basis of Index of String

let's say I have a table 'test' which contains a column 'col1' which i have created with ngram analyzer.
assuming it has data such as -
65123
81234
12304
87541

and i query for '123'
such as
select col1 from test where col1 = '123';

It's giving me results with arbitrary sorting.
how can I achieve sorting as per the index of the matched phrase such as
 12304
 81234
 65123

There Is a Function or a reserved value "POSITION" present In the system which I assumed resembled the position function in postgresql but It doesn't works the similar way and I couldn't find any Implementation for It in the documentation either.
additionally, i found a sort parameter " _score " which scores every match and we can sort on the basis of this match. However, _score doesn't account for the index of the string in the matching score. What that means is it doesn't matter on which index 123 appears in the result set it won't affect the matching score. Is there anything i can add as a column which additionally helps me sort this better ? @mfussenegger  can you help me gain clarity if this is possible currently or is there a way to achieve it ?

I think currently there is no good way to achieve what you want, but I didn't have a closer look yet, it's on my todo list.

Hello @ekanshdubey,
Unfortunately it is not possible to score on the index position of the query string. However you can try to following approach as a workaround:
Index your data with an edge_ngram analyzer:
create analyzer edge_ngram_analyzer (TOKENIZER my_ngram with (type = edge_ngram, min_gram = 3, max_gram =5));

create table test (values text INDEX using fulltext with (analyzer = 'edge_ngram_analyzer'));

insert into test(values) values('65123') , ('81234') , ('12304'), ('87541');

Use a fuzzy query and sort by score:
select values, _score from test where match(values,'123') using best_fields with (fuzziness='AUTO') order by _score desc;

+--------+------------+
| values |     _score |
+--------+------------+
| 12304  | 0.26152915 |
| 81234  | 0.17435277 |
| 65123  | 0.04358819 |
+--------+------------+

This seems to be the behaviour you are looking for, but the scoring is not based on the index position
of your query term. It is based on the number of matches of your query term '123' with the edge ngrams from your data using
https://en.wikipedia.org/wiki/Okapi_BM25 scoring. The fuzzy query will allow expansion/replacements of up to 2 character of the matching string, so this will only work for reasonable small strings. This method also adds storage overhead to the table and processing overhead to the query, so you want to be careful to use this with large data sets.


use publicly accessible link to docker build in README

 Thank you for your submission! We really appreciate it. Like many open source projects, we ask that you sign our Contributor License Agreement before we can accept your contribution.You have signed the CLA already but the status is still pending? Let us recheck it.


add "& T" to some places where it's missing in RecordInstance

Not sure why it was missing from updateIn<U> or withMutations arguments.
Fix #1465

@leebyron it would be really good to have this, it's causing a bit of an issue for us. We have to put // $FlowFixMe above all our updateIn's which has caused some very real bugs for us.

Makes sense, thanks for the fix

Thank you so much for merging this!


Align handling of hyphenated words with LIAAD/yake

This comes up in the google-text test, since it contains the word "competition-centric", but this is most easily demonstrated using a short example text:
"I am a competition-centric person! I really like competitions. Every competition is a hoot!"
Extracting 10 keywords with yake-rust (my branch, but this problem is not handled in any branch) returns (this is a set, not ordered - does not matter for this example):
{'person', 'competition', 'hoot', 'centric'}

whereas LIAAD/yake returns
{'person', 'competition', 'hoot', 'competition-centric'}

In other words, yake-rust treats "competition" and "centric" as two words, whereas LIAAD/yake keeps "competition-centric" as one word.
I tried removing - from the punctuation symbols, but that does not fix it. I tried to figure out if it had anything to do with the unicode segmentation, but that investigation is still ongoing.

Based on my playing with split_word_bounds in the rust playground, it does not seem like unicode-segmentation splits "competition-centric".

Tokenization approaches differ, yeah. I bet segtok handles English texts better than unicode-segmentation. Not much of a choice, though. https://github.com/huggingface/tokenizers is a relatively novel crate, haven't tried it yet.
As for Yake, it should accept the tokenized input as argument.

So you would have to tokenize everything yourself? Wouldn't that make the crate a lot less useful? Or are you suggesting that as an option?

Tokenize yourself or use the predefined fallback from yake.
It makes sense, as language specific tokenizers show better performance. Check out Razdel for ru or sentok for en, de.

Ok, as long as there is still a default fallback, I agree.
I am all for configurability, I just want to make sure it is an easy drop-in replacement for LIAAD/yake in most cases.

Could you please share the list of tokens which python impl produces for google text? [[word, ..], ..]

Assuming that the list of tokens requested is the thing generated in this line:
 self.sentences_str = [ [w for w in split_contractions(web_tokenizer(s)) if not (w.startswith("'") and len(w) > 1) and len(w) > 0] for s in list(split_multi(text)) if len(s.strip()) > 0]

Then you can find them in this attached file:
sentences_str.json

From there it creates a list of "unique terms". This involves e.g. the plural-normalization. You can find those here:
terms.json

This is the word tokenizer used in LIAAD/yake: https://github.com/fnl/segtok/blob/master/segtok/tokenizer.py#L301

Interestingly, syntok (https://github.com/fnl/syntok) is apparently the successor to segtok and it does split "competition-centric" into two words like our current code does, unlike LIAAD/yake.


Monitor Roku for closed session and close debugger

Certain situations cause the Roku app to close, and the debugger is not always aware of that, such as a home button press from a physical remote, or the app encounters a exit command.
Roku provides an external control api which would allow us to monitor the running app, and whenever a running app quits, we could kill any active debug session. This would only be activated on debug start, and would be terminated on debug stop.
https://sdkdocs.roku.com/display/sdkdoc/External+Control+API#ExternalControlAPI-ExternalControlServiceCommands
query/active-app
I envision this being some type of polling (every 10 seconds, "is my sideloaded app running?"), and if the app is not running, stop the debug session.
This will also need to be configurable through a setting because some developers will not want to stop their debug session. A boolean "enabled/disabled" flag should be sufficient.

I'll probs end up doing this.. it drives me nuts

@georgejecook does #150 resolve this as well?


Add Link Preloading

Simplest to use is probably https://instant.page which is also running on cloudflare workers.
To implement, let's put this right before the closing </body>
<script src="//instant.page/5.1.1" type="module" integrity="sha384-MWfCL6g1OTGsbSwfuMHc8+8J2u71/LA8dzlIN3ycajckxuZZmF+DNjdm7O6H3PSq"></script>

External links aren’t preloaded by default (and we want them to, so all the links across our domains will load instantly then), to allow them all add a data-instant-allow-external-links attribute to the <body>.

added the script and the attribute

Added a while ago. Complete


Make sure to correctly record errors

Using the command || errors=... syntax makes errexit and pipefail options moot, and any command run in this manner will continue running after failure (which could result in a 0 return code).
Make sure errors are recorded properly.
Resolves #610
Signed-off-by: Mike Kolesnik<EMAIL_ADDRESS>

If errors stop the process immediately, is there any point in counting them?

If errors stop the process immediately, is there any point in counting them?

It helps to know as much as possible in this case, since this workflow is not transactional - eg you can't (simply) roll back all your changes.
Also some errors can be recoverable manually, eg bumping the shipyard/admiral/etc versions, so in these cases it makes sense to let the process proceed.
The easiest of course is to do nothing and let it trip up the entire workflow, but I don't think it has any benefit to us in this case.

If errors stop the process immediately, is there any point in counting them?

It helps to know as much as possible in this case, since this workflow is not transactional - eg you can't (simply) roll back all your changes.
Also some errors can be recoverable manually, eg bumping the shipyard/admiral/etc versions, so in these cases it makes sense to let the process proceed.
The easiest of course is to do nothing and let it trip up the entire workflow, but I don't think it has any benefit to us in this case.

But isn’t that what this PR does? do-release.sh will exit the first time it encounters an error.

But isn’t that what this PR does? do-release.sh will exit the first time it encounters an error.

Ah right that's a serious facepalm moment right there :facepalm:
Hmm I'll have to find another way to implement this, unless you have a suggestion?

But isn’t that what this PR does? do-release.sh will exit the first time it encounters an error.

Ah right that's a serious facepalm moment right there facepalm
Hmm I'll have to find another way to implement this, unless you have a suggestion?

Perhaps a targeted fix would be appropriate: extract the for project … update_dependencies loop, and make it abort if it introduces errors (after completing, so we see all the dependency update errors for a given release).


Readiness endpoint

Problem statement
When restarting the indexer it takes some time to load all the resources and to check all SG's. during this loading period the loadbalancer is sending traffic to the pod and those queries fail increasing the error rate.
I'd like to propose to create a /ready readiness endpoint which starts to respond with 200 http code only after everything was loaded and checked. This will allow the loadbalancer to know when to start sending traffic to the backend.
We could configure a delay for the traffic to be sent to the backends but that's a static configuration and sometimes it takes less / more time than usual so better is to have an endpoint that we could configure the loadbalancer to query to know when to send traffic
Expectation proposal
To have an endoint exposed by the indexer, like /readiness, which I can hit and get the readiness of the indexer to receive traffic so we can have the loadbalancer managing these backends without responding to the user with an error.

Hi I was looking at this issue.
What is the condition to be considered ready? How can we know that "everything was loaded and checked"?

thans @PanGan21 , ready should be considered when all required services / backends / databases connections are created and working. these required services should be revisited and make sure to mark as required only the needed and not all of them because there might be a dependency that's not critical for the indexer-service to serve traffic


Add contributors thanks message

#14
Edit readme.md to add a short thanks message for contributors and add a link to CONTRIBUTORS.MD

Great Work!! Thankyou


Add Ramona-Saintandre's file for Gitlab tutorial

added a new file to my branch, and added to the file
Add Ramona-Saintandre's file

deleted my branch heading to the next lesson


Text exercise (assessment): when using "Next Assessment" the complete next assessment is marked

Describe the bug

When assessing the next submission by using the "Next Assessment" button, the students submission is marked as one sentence. Even going back to the Tutor Course Dashboard and continuing the assessment does not help.
To Reproduce

Go to the tutor's exercise dashboard for a text exercise (with at least 2 submissions)
Click on Start new assessment and assess the first submission
Click on Next Assessment
The new submission will be recognized as one sentence

Expected behavior

It should be possible to assess each sentence on its own.
Screenshots

Environment

- OS: Windows 10 Pro
- Browser: Google Chrome
- Version 81.0.4044.138

As some tutors pointed out this usually happens with German submissions.
Sometimes it happens with English ones as well but tbh sometimes English ones are marked as German.
Therefore it might be a good start to check if it has something to do with submissions marked as German.
Besides: a workaround is to simply reload the page. Then the selection of the text is back to normal.


Calling residual! instead of jacobian!

Hi @JordiManyer, I was doing some code profiling (an issue to be reported/addressed in Gridap) and I found this possible error:
https://github.com/gridap/GridapSolvers.jl/blob/26aa6f995e156034de102f40e4c69be510399bb3/src/BlockSolvers/BlockFEOperators.jl#L64
I think here it should be calling jacobian! instead of residual!, right?

Hi @oriolcg , The BlockFEOperators are halfway-implemented. I need to re-define the API and add functionalities that I want to have. I am currently tied into other things, I'll probably leave this for version 0.4.


Rehaul legacy generation

Each legacy should have its own chance, and that should be changeable within the config.

Now implemented.

Now implemented.


Making comparison more user friendly

eq now compares the hexdigests as opposed to the hashes themselves. This way if one of them is a string already then it will still return True.

Thanks for the pull request! Changes look good at a glance, so I'll have a play this weekend and merge then.

Merged - thanks once more for the pull request!


Dactyl is using old std version

When running a basic dactyl application there's errors:
  Type 'URL' is not assignable to type 'string'.
  return new URL(url).pathname
                 ~~~
    at https://deno.land/std@0.51.0/path/win32.ts:911:18

TS2345 [ERROR]: Argument of type 'string | URL' is not assignable to parameter of type 'string'.
  Type 'URL' is not assignable to type 'string'.
  return new URL(url).pathname;
                 ~~~
    at https://deno.land/std@0.51.0/path/posix.ts:433:18

Found 2 errors.

Versions
deno 1.2.0
v8 8.5.216
typescript 3.9.2

<EMAIL_ADDRESS>is not compatible with 1.2.0 deno version.

same problem, please update std dependancies

Same problem.
Found std not compatible version with deno v1.2.* at https://github.com/liamtan28/dactyl/blob/master/deps.ts#L11 line 11 to 13. Need review in PR https://github.com/liamtan28/dactyl/pull/15.
Thank you

Why did you close this issue? It is not fixed


New language pack: Nias

Hello,
I have created the language pack for my mother tongue spoken mainly on Nias Island, Indonesia following your recent instruction. Everything is ready: the dictionary, the icons, the keyboard layout etc. (see the attached screenshot of the keyboard layout with the next word prediction active). After testing it for a while now I think it is ready to be proposed.
I would be grateful if you could accept this new language pack in ASK.
Thank you very much.

I would love to!
Please, open a PR with the changes.
I can also publish that to Google Play Store if you wish.


pcre support requested

error !!! no internal routing support, rebuild with pcre support !!! on uwsgi start

Closed via https://github.com/italia/Satosa-Saml2Spid/pull/12


Avoid commit of package*.json changes

NPM install is changing the package.json and package-lock.json files, which may cause the master-dist build to fail

:tada: This PR is included in version 3.26.72 :tada:
The release is available on:

npm package (@latest dist-tag)
GitHub release

Your semantic-release bot :package::rocket:


unittests/map.cpp compile error under VS2013/VS2015

VS apparently does not define stream operators for std::pair. Compiling map.cpp results in:
map.cpp
1>d:\boost\boost_1_61_0_b1\boost\test\tools\detail\print_helper.hpp(47): error C2338: Type has to implement operator<< to be printable
1>  d:\boost\boost_1_61_0_b1\boost\test\tools\detail\print_helper.hpp(178): note: see reference to class template instantiation 'boost::test_tools::tt_detail::print_log_value<T>' being compiled
1>          with
1>          [
1>              T=std::pair<const std::string,int>
1>          ]
1>  d:\boost\boost_1_61_0_b1\boost\test\utils\wrap_stringstream.hpp(66): note: see reference to function template instantiation 'std::ostream &boost::test_tools::tt_detail::operator <<<std::pair<const _Kty,_Ty>>(std::ostream &,const boost::test_tools::tt_detail::print_helper_t<std::pair<const _Kty,_Ty>> &)' being compiled
1>          with
1>          [
1>              _Kty=std::string,
1>              _Ty=int
1>          ]
1>  d:\boost\boost_1_61_0_b1\boost\test\tools\old\impl.hpp(213): note: see reference to function template instantiation 'boost::basic_wrap_stringstream<char> &boost::operator <<<char,boost::test_tools::tt_detail::print_helper_t<std::pair<const _Kty,_Ty>>>(boost::basic_wrap_stringstream<char> &,const T &)' being compiled
1>          with
1>          [
1>              _Kty=std::string,
1>              _Ty=int,
1>              T=boost::test_tools::tt_detail::print_helper_t<std::pair<const std::string,int>>
1>          ]

One way to eliminate this problem is to add the following to the top of map.cpp:
typedef  std::pair<const std::string, int>       str_int_map_type;
BOOST_TEST_DONT_PRINT_LOG_VALUE(StructBase)
BOOST_TEST_DONT_PRINT_LOG_VALUE(str_int_map_type)

See this [link  ](http://www.boost.org/doc/libs/1_60_0/libs/test/doc/html/boost_test/test_output/testing_tool_output_disable.html)for documentation.

Made a typo on the solution. Add the following to map.cpp:
typedef  std::pair<const std::string, int>       str_int_map_type;
BOOST_TEST_DONT_PRINT_LOG_VALUE(StructBase)
BOOST_TEST_DONT_PRINT_LOG_VALUE(str_int_map_type)

I see my typo correction didn't actually correct anything. The typedef should be:
typedef std::pair<const std::string,int> str_int_map_type;

I can't reproduce this with 2013 and boost 1.55 but I feel like I've run into this before (and fixed it before). I think it's related to using a later version of boost - will install 1.6 or later and check again.

As you can see from the listing above, I was using Boost 1.61.0 beta 1.

Were you encountering this under the latest release/master or the develop branch? I think this was fixed on develop a while back (see comment on #295).

You appear to be correct. This is fixed in develop branch version as of June 7, 2016 (Commit: 8401da04af3c38678bb2173a5798c44cbf719aa3 [8401da0]) - may have been fixed before this.


conda activate fails in Visual Studio Code terminal

Issue Type: Bug
Cross-posting from the conda issue tracker.
When opening a new bash shell in the Visual Studio Code terminal, and then activating a conda environment, the environment's packages are not available despite the environment appearing to have been activated. This occurs both when using source activate and conda activate. Issue does not reproduce in the Apple Terminal app, or in iTerm.
A workaround is to call conda deactivate or source deactivate before the activate call.
Steps to reproduce:
$ source activate gi
(gi) $ python test.py
Traceback (most recent call last):
  File "test.py", line 5, in <module>
    import pandas as pd
ImportError: No module named pandas

VS Code version: Code 1.22.2 (3aeede733d9a3098f7b4bdc1f66b63b0f48c1ef9, 2018-04-12T16:32:53.389Z)
OS version: Darwin x64 17.5.0

System Info

Item
Value

CPUs
Intel(R) Core(TM) i5-7360U CPU @ 2.30GHz (4 x 2300)

Load (avg)
3, 2, 2

Memory (System)
8.00GB (0.02GB free)

Process Argv
/Users/markedwards/Applications/Visual Studio Code.app/Contents/MacOS/Electron

Screen Reader
no

VM
0%

Extensions (15)

Extension
Author (truncated)
Version

gc-excelviewer
Gra
2.0.20

scala
dal
0.0.5

jupyter
don
1.1.4

vscode-sort
hen
0.2.5

octave
lea
0.1.1

rainbow-csv
mec
0.2.1

anaconda-extension-pack
ms-
1.0.0

python
ms-
2018.3.1

java
red
0.23.0

vscode-yaml
red
0.0.11

vscode-java-debug
vsc
0.8.0

vscode-java-pack
vsc
0.3.0

vscode-java-test
vsc
0.5.0

vscode-maven
vsc
0.7.0

markdown-all-in-one
yzh
1.2.0

Thanks @Tyriar.  This has something to do with how conda is being initialised.  I'll follow it up on the conda issue tracker.


EarlyStopping callback won't restore best weights unless training stops early

The EarlyStopping callback will, if the restore_best_weights option is True, restore the best weights if and only if it requests the stopping itself, not if stopping is requested by another callback or the training loop has simply run for a given number of epochs. The call to Model.set_weights is inside the on_epoch_end method guarded by a comparison of wait and patience[1].
If I read the code correctly, then in a scenario where the best weights happened fewer than patience epochs before the last epoch, the model will keep the weights from the last epoch.
If this is intentional, I think it should be documented. If not, I think it would be an easy fix to move the weight restoring logic to the on_train_end method.
[1] https://github.com/keras-team/keras/blob/f0eb8d538c82798944346b4b2df917a06bf5e9d4/keras/callbacks.py#L823-L830

I can confirm this issue on keras 2.2.4.

hope this can be resolved

Stumbled upon this issue. I think ModelCheckpoint is the way to go if you wish to restore best weights if EarlyStopping does not trigger. However, then there is no usecase I can think of for restoring best weights option in EarlyStopping as ModelCheckpoint will always save the best weights and one can restore those after model.fit. Please correct me if I'm wrong or missing anything.

ModelCheckpoint strangely does not have the option to automatically restore the best weights like EarlyStopping does. It is not always easy to pick out the best weights saved by ModelCheckpoint in a programmatic manner: For example, if I tell ModelCheckpoint to give unique names to all the weights files it saves, then I can personally pick the best weight file by inspection, but it takes extra lines of code to automatically interpret the weight file names and pick out the best one.
So right now we can automatically restore the best model weights with EarlyStopping, but only if the training is actually stopped early. Or we can save the best weights in any case with ModelCheckpoint, but it does not have an option to automatically restore the best weights. I would love if there was one standard callback that could do both: save the best weights in any training run, and automatically restore them at the end. Presumably this would best be implemented in ModelCheckpoint.

Guys, this is super important. It happened today to us for the first time and we just found it because while the model had been evaluated with a good loss (compared to other experiments), the final results were terrible. The model reached the maximum number of epochs and thus the callback did not restored the best weights from an earlier epoch, as it was meant to do.
In my opinion this issue should be tagged as a bug, as it is still out there in the final version and master branch. It needs to be resolved as suggested by @Stigjb by moving the restoration of the weights in on_train_end, otherwise many people may run and report wrong number or stop believing in their fresh ideas!

If EarlyStopping doesn't trigger, doesn't that mean that you don't have enough epochs to begin with?

If EarlyStopping doesn't trigger, doesn't that mean that you don't have enough epochs to begin with?

Hi @meanmikeyk , it definitely means that the current set of hyper-parameters needed more epochs than you possibly expected. But this does not mean that the model shall not use the best parameters after training.
In my case, the total number of epochs was 50 and patience was 10. The val loss was improving until the 41st epoch and then it overfitted very quickly in the next 9 (most probably it would never change direction), but the patience was not enough, so the final model had terrible parameters.  Should I set max epochs to 200 or 1000, or maybe I should also set lower patience?
There is not really great answer, but in any means I should have the right to evaluate my model given the best parameters when I chose to use early stoping  with restore_best_weights=True, which did not happen because the callback is not written correctly to prevent these unfortunate situations.

@meanmikeyk If EarlyStopping doesn't trigger it simply means that the training didn't proceed for patience epochs after the best scoring epoch.
There are many reasons why this could happen: Perhaps you are training the network for the first time and don't know how many epochs it really needs; perhaps some random aspect of the training causes the best epoch to arrive later than expected; or perhaps you simply have a finite amount of patience and/or computing resources and cannot train for more than some fixed time.
The fact that EarlyStopping does not save the best parameters in any case, or offer any option to do so, catches many people by surprise. This ruins long and potentially expensive training runs.

As I already said @jjbuchanan in worst case scenario, people are not reviewing the training process and just evaluate and log results. There is a great change that people will report wrong and misleading results, which may mislead some people or a big community and even worse good ideas may get ruined because they had terrible performance (based on the last terrible parameters) and get withdrawn....

I think the best solution to this is to also add a restore_best_weights option to Model.fit that you can have this behavior on early termination and regular termination.
As pointed out in https://github.com/tensorflow/tensorflow/issues/35634#issuecomment-612644409, the user may want to continue training after normal termination, so EarlyStoppingCallback always overriding your weights would present a challenge. That's why I think Model.fit also needs this option.

I don't believe it belongs in Model.fit, for the same reasons the EarlyStopping class exists in the first place. The Keras team could have added a patience option to Model.fit but chose not to in order to avoid further muddying that method.  Let's avoid adding a 'restore_best_weights` option for the same reason.
I'm strongly of the opinion that EarlyStopping should restore the best weights if restore_best_weights is True, regardless of why training stopped.  Failing to restore the weights is not the behavior of least surprise, which by some definitions makes it a bug. At best, it is ill-advised.
So what about supporting the ability to resume training?  I say we add another flag, maybe weights_restored_without_early_stop, that defaults to True but if explicitly set to False will result in the current behavior.

So what about supporting the ability to resume training? I say we add another flag, maybe weights_restored_without_early_stop, that defaults to True but if explicitly set to False will result in the current behavior.

This would indeed be a very nice addition. I was a little confused about why the weights weren't being restored until I read this thread. As a workaround, I've been doing this:

Sorry to come back to this, but I see this issue was closed without a milestone. Is this fixed? If so, in which version? Thanks.

Is there any fix yet?

any news on this?

Sadly, today I found this bug today after wasting hours for the training finishes. Of course this "feature" should be tagged as a bug.

This issue still seems to be present.


Pre formatted descriptions

Desceiptions of applications often have a format which would be helpful if maintained (\n an such). I think that adding <pre> in description would improve the readability of some applications

We would need to mark the field as safe. It's just django striping html from the text to avoid any injection

https://docs.djangoproject.com/en/1.10/ref/templates/builtins/ -> safe

Added on #130


update api-server-proxy

How to categorize this PR?

/area networking
/kind enhancement
What this PR does / why we need it:
Update api-server-proxy to v.0.3.0.
Which issue(s) this PR fixes:
Fixes #
Special notes for your reviewer:
Release note:

Update api-server-proxy to `v0.3.0`.

/kind enhancement


feature/UAR-323-update-you-have-signed-out-page

JIRA link
Change description
Work checklist

[ ] Tests added where applicable
[ ] UI changes meet accessibility criteria

Merge instructions
We are committed to keeping commit history clean, consistent and linear.  A BREAKING CHANGE can be part of commits of any type,
types other than fix: and feat: are allowed, for example build:, chore:, ci:, docs:, style:, refactor:, perf:, test:, and others,
footers other than BREAKING CHANGE: <description> may be provided.

I guess you want to redirect to the sign in page. The authentication.middleware.ts redirects to "/signin". It also allows you to return to the journey start after sign in but maybe we should be 'returning' to the "your-filings" page after sign in?


ARROW-5787: [Release][Rust] Use local modules to verify RC

Because we don't publish modules to crates.io yet.

+1
We should remove this change and use { version = "...", path = "..." } style after we release 0.14.0.


Calendar format

Hello, and thanks for this fantastic product.
It is possible to show the calendar in other formats other than English?
In Spanish for example, the format is DD-MM-YYYY.
Tanks!

Hello @JuanSinmiedo466
We have used Flat-picker for date , so you can change the date format and can get it to desired language.
Please follow the given article - https://flatpickr.js.org/localization/.
Thanks

I need to localize flatpickr to Japanese language.
Where should I put the ja.ts file?

Hi @michael-arif
You don't need put this file any where as flatpicker already consist this language file, you just need to change locale after including this file.
Just read the given article.

I am closing this issue, I hope the above reply solved your issue


Output all

Addresses the output-all command and the spin-* command renaming from https://github.com/gruntwork-io/terragrunt/issues/118 (though not the apply-all-interactive yet).

Renames spin-up and tear-down to apply-all and destroy-all
Adds a deprecation warning printed to stdout when spin-up and tear-down are called
Adds an output-all command to run terraform output in all dirs in a stack
Renames the spin module to configstack (the name stack was already used in a lot of places throughout the codebase)
Renames various other functions to match new terminology

I might be seeing a race condition in the s3 remote state bucket creation.  Investigating...

This is ready to be looked at again.  Updated with the integration test as described.  I had to mess with a lot of the app to be able to capture the stdout of the terraform commands in the tests... there's probably a better way to do that that I'm not seeing :)
As it stands, attached is the full output from running AWS_PROFILE=terragrunt-test go test -v -parallel 128 $(glide novendor) 2>&1 | tee test-results.txt.
test-results.txt

Good call on avoiding nils.  After those changes, ouput of:
AWS_PROFILE=terragrunt-test go test -v -parallel 128 $(glide novendor) 2>&1 | tee test-results-$(git rev-parse HEAD).txt
Is attached:
test-results-41fecb5def8963b1e10c91dac4553943a45a0583.txt

Fantastic, thank you!

The new release is here: https://github.com/gruntwork-io/terragrunt/releases/tag/v0.11.0. If the build passes the binaries should show up there in a few minutes.


Groq Models Integration

Groq Models Integration
Benefits:

Open source models with increased inference speed
Free for low usage espacially small projects

Usage example
$ python run.py --model_name groq/llamaversatile70  \
    --data_path https://github.com/princeton-nlp/SWE-agent/issues/666  \
    --repo_path ./ \
    --config_file config/default_from_url.yaml  \
    --apply_patch_locally

This PR closes Groq Models Issue

I don't think we should merge support for small models for now. Willing to be convinced otherwise though, but I would wanna see results on SBL

@ofirpress Thanks for your comment. However, I would like to point out that groq models are not just basic small open source models groq will provide insane speed to the agent as it will increase inference speed. I am also communicating to them on adding more new opensource models like codestral mamba. Let me know your opinion too.

@ofirpress Thanks for your comment. However, I would like to point out that groq models are not just basic small open source models groq will provide insane speed to the agent as it will increase inference speed. I am also communicating to them on adding more new opensource models like codestral mamba. Let me know your opinion too.

@klieret I will take a look!

I'm not saying we should remove this, but I'm kind of opposed to continuing to add more support for small models, because people will try using swe-agent with these small models, it won't work, and they'll be annoyed and will not try bigger models.

anyways having litellm is the right solution, but also when we do finally put in litellm we should maybe issue a warning when a small model is used.


brew cleanup attempts to remove unremovable files (in my case, it is corporate management software I have no control over) unreleated to Homebrew

brew config output
HOMEBREW_VERSION: 3.2.5
ORIGIN: https://github.com/Homebrew/brew
HEAD: 3e52e8b48ab036af77a582219ea250107c84432f
Last commit: 24 hours ago
Core tap ORIGIN: https://github.com/Homebrew/homebrew-core
Core tap HEAD: a26dbaf836f62819884a8b0eb19e0f6a8de0e1c2
Core tap last commit: 3 hours ago
Core tap branch: master
HOMEBREW_PREFIX: /usr/local
HOMEBREW_CASK_OPTS: []
HOMEBREW_DISPLAY: /private/tmp/com.apple.launchd.evPJPV8wic/org.xquartz:0
HOMEBREW_EDITOR: nvim
HOMEBREW_MAKE_JOBS: 8
Homebrew Ruby: 2.6.3 => /usr/local/Homebrew/Library/Homebrew/vendor/portable-ruby/2.6.3_2/bin/ruby
CPU: octa-core 64-bit kabylake
Clang: 12.0.0 build 1200
Git: 2.31.1 => /usr/local/bin/git
Curl: 7.64.1 => /usr/bin/curl
macOS: 10.15.7-x86_64
CLT: <IP_ADDRESS>.21
Xcode: 12.4

brew doctor output
Please note that these warnings are just used to help the Homebrew maintainers
with debugging if you file an issue. If everything you use Homebrew for is
working fine: please don't worry or file an issue; just ignore this. Thanks!

Warning: Unknown support status

Warning: A newer Command Line Tools release is available.
Update them from Software Update in System Preferences or run:
  softwareupdate --all --install --force

If that doesn't show you any updates, run:
  sudo rm -rf /Library/Developer/CommandLineTools
  sudo xcode-select --install

Alternatively, manually download them from:
  https://developer.apple.com/download/more/.
You should download the Command Line Tools for Xcode 12.4.

Warning: Some installed kegs have no formulae!
This means they were either deleted or installed with `brew diy`.
You should find replacements for the following formulae:
  syncthing-inotify
  python@2

Warning: Some installed formulae are deprecated or disabled.
You should find replacements for the following formulae:
  ccze
  encfs
  ilmbase
<EMAIL_ADDRESS>  multitail
  sshfs

Warning: Unbrewed dylibs were found in /usr/local/lib.
If you didn't put them there on purpose they could cause problems when
building Homebrew formulae, and may need to be deleted.

Unexpected dylibs:
  /usr/local/lib/audin.dylib
  /usr/local/lib/cliprdr.dylib
  /usr/local/lib/disk.dylib
  /usr/local/lib/drdynvc.dylib
  /usr/local/lib/libarena-7b4a09112eff7808.dylib
  /usr/local/lib/libclang_rt.asan_osx_dynamic.dylib
  /usr/local/lib/libclang_rt.tsan_osx_dynamic.dylib
  /usr/local/lib/libfmt_macros-117504a674103e68.dylib
  /usr/local/lib/libgraphviz-bf7938ba81155da9.dylib
  /usr/local/lib/libproc_macro-e7661a0a032b371d.dylib
  /usr/local/lib/librustc-b94f1cb3c36277ce.dylib
  /usr/local/lib/librustc_allocator-db49510725305b91.dylib
  /usr/local/lib/librustc_back-fdad73a2fe301846.dylib
  /usr/local/lib/librustc_borrowck-bc825cdf44c82e3f.dylib
  /usr/local/lib/librustc_const_eval-1fea0cc816604f75.dylib
  /usr/local/lib/librustc_const_math-24d7999ebfa9d2ae.dylib
  /usr/local/lib/librustc_cratesio_shim-49d7641d3d29e26e.dylib
  /usr/local/lib/librustc_data_structures-8166739a338fda96.dylib
  /usr/local/lib/librustc_driver-63fbac3b408d0180.dylib
  /usr/local/lib/librustc_errors-c01fc95bfd891d52.dylib
  /usr/local/lib/librustc_incremental-ae13d65fdf15d489.dylib
  /usr/local/lib/librustc_lint-d52c1482d55df9ef.dylib
  /usr/local/lib/librustc_llvm-951f4f9bda844870.dylib
  /usr/local/lib/librustc_metadata-ba0a41f096526296.dylib
  /usr/local/lib/librustc_mir-f5186c33182c3c49.dylib
  /usr/local/lib/librustc_passes-6bda36e99610e59c.dylib
  /usr/local/lib/librustc_platform_intrinsics-cafb408d31bf08bd.dylib
  /usr/local/lib/librustc_plugin-4f0855a0c0497f6a.dylib
  /usr/local/lib/librustc_privacy-cbf6b1fd026ecffa.dylib
  /usr/local/lib/librustc_resolve-fda260e7ebf8a77a.dylib
  /usr/local/lib/librustc_save_analysis-76e7b31f2e2312ce.dylib
  /usr/local/lib/librustc_trans-76a2177563a4920c.dylib
  /usr/local/lib/librustc_trans_utils-eae7eac5de1d8383.dylib
  /usr/local/lib/librustc_typeck-805a44616dde88c6.dylib
  /usr/local/lib/libserialize-a7d5ca3f9ca1d78b.dylib
  /usr/local/lib/libstd-e2a1b7f1c041b010.dylib
  /usr/local/lib/libsyntax-570223d86cb7948d.dylib
  /usr/local/lib/libsyntax_ext-87d286f1a80558c3.dylib
  /usr/local/lib/libsyntax_pos-0210ff8dbe082151.dylib
  /usr/local/lib/libterm-18625f57bc60e498.dylib
  /usr/local/lib/libtest-24fa6627a68aaf09.dylib
  /usr/local/lib/parallel.dylib
  /usr/local/lib/printer.dylib
  /usr/local/lib/rail.dylib
  /usr/local/lib/rdpdbg.dylib
  /usr/local/lib/rdpdr.dylib
  /usr/local/lib/rdpsnd.dylib
  /usr/local/lib/serial.dylib
  /usr/local/lib/tsmf.dylib

Warning: Unbrewed header files were found in /usr/local/include.
If you didn't put them there on purpose they could cause problems when
building Homebrew formulae, and may need to be deleted.

If you didn't put them there on purpose they could cause problems when
building Homebrew formulae, and may need to be deleted.

Unexpected static libraries:
  /usr/local/lib/libadsiid.a
  /usr/local/lib/libdinput.a
  /usr/local/lib/libdxerr8.a
  /usr/local/lib/libdxerr9.a
  /usr/local/lib/libdxguid.a
  /usr/local/lib/libmfuuid.a
  /usr/local/lib/libstrmbase.a
  /usr/local/lib/libstrmiids.a
  /usr/local/lib/libuuid.a
  /usr/local/lib/libwinecrt0.a

[X] I ran brew update and am still able to reproduce my issue.
[X] I have resolved all warnings from brew doctor and that did not fix my problem.

What were you trying to do (and why)?
I was attempting to upgrade all installed packages with brew upgrade. Because it has been a while, this required an automatic brew cleanup. When it encountered non-Homebrew files in the /usr/local/lib, it attempted to remove them for some reason. The directory in question is /usr/local/lib/dgagent, which is part of the corporate management software on my machine. I do not have the ability to remove this folder or kill the process which is using the library file within (nor would I want to, since it is required for my employment). Homebrew's assumption that it can remove anything in /usr/local seems a bit aggressive to say the least.
What happened (include all command output)?
==> Upgrading 76 outdated packages:
rubberband 1.9.1 -> 1.9.2
mosh 1.3.2_15 -> 1.3.2_16
syncthing 1.15.1 -> 1.18.0
plantuml 1.2021.4 -> 1.2021.9
libheif 1.11.0_2 -> 1.12.0
libtiff 4.2.0 -> 4.3.0
libtool 2.4.6_3 -> 2.4.6_4
ffmpeg 4.4 -> 4.4_2
xorgproto 2021.3 -> 2021.4
pandoc 2.13 -> 2.14.1
pssh 2.3.1_3 -> 2.3.1_4
libksba 1.5.1 -> 1.6.0
gpgme 1.15.1 -> 1.16.0
go 1.16.3 -> 1.16.6
ghostscript 9.53.3_1 -> 9.54.0
nettle 3.7.2 -> 3.7.3
cmake 3.20.1 -> 3.21.0
libuv 1.41.0 -> 1.42.0
freetype 2.10.4 -> 2.11.0
boost 1.75.0_2 -> 1.76.0
pango 1.48.4 -> 1.48.7
zimg 3.0.1 -> 3.0.2
libogg 1.3.4 -> 1.3.5
icu4c 68.2 -> 69.1
harfbuzz 2.8.0 -> 2.8.2
duck <IP_ADDRESS>493 -> <IP_ADDRESS>019
glib 2.68.1 -> 2.68.3
zsh-completions 0.32.0 -> 0.33.0_1
netpbm 10.86.21 -> 10.86.23
aom 3.0.0 -> 3.1.2
awscli 2.1.38 -> 2.2.22
srt 1.4.2 -> 1.4.3
gobject-introspection 1.68.0 -> 1.68.0_1
sshfs 2.10_2 -> 3.7.2
bash 5.1.4 -> 5.1.8
openexr 2.5.5 -> 3.0.5_1
libgcrypt 1.9.2 -> 1.9.3_1
wfxr/code-minimap/code-minimap 0.5.1 -> 0.6.0
libxfixes 5.0.3 -> 6.0.0
libmaxminddb 1.5.2 -> 1.6.0
macvim 8.2-171 -> 8.2-171_1
snappy 1.1.8 -> 1.1.9
librsvg 2.50.4 -> 2.50.7
graphviz 2.47.0 -> 2.48.0
dav1d 0.8.2 -> 0.9.0
tmux 3.2 -> 3.2a
libtasn1 4.16.0_1 -> 4.17.0
pcre2 10.36 -> 10.37_1
eslint 7.24.0 -> 7.31.0
notmuch 0.31.4_1 -> 0.32.2
nnn 4.0 -> 4.2
leptonica 1.80.0 -> 1.81.1
pcre 8.44 -> 8.45
x264 r3049 -> r3060
libidn 1.36 -> 1.38
node 15.14.0 -> 16.5.0
libomp 12.0.0 -> 12.0.1
neovim 0.4.4_2 -> 0.5.0
talloc 2.3.2 -> 2.3.3
ripgrep 12.1.1 -> 13.0.0
libass 0.15.0 -> 0.15.1
you-get 0.4.1520 -> 0.4.1536
imagemagick 7.0.11-8 -> 7.1.0-4_1
guile 3.0.5 -> 3.0.7
gnupg 2.2.27 -> 2.3.1_1
jasper 2.0.31 -> 2.0.32
clementtsang/bottom/bottom 0.5.7 -> 0.6.3
p11-kit 0.23.22 -> 0.24.0
libx11 1.7.0 -> 1.7.2
luv 1.41.0-0 -> 1.41.1-0
protobuf 3.15.8 -> 3.17.3
wireshark 3.4.4 -> 3.4.7
automake 1.16.3_1 -> 1.16.4
git 2.31.1 -> 2.32.0_1
<EMAIL_ADDRESS>3.9.4 -> 3.9.6
gnutls 3.6.15 -> 3.6.16
==> Upgrading icu4c
68.2 -> 69.1
==> Downloading https://ghcr.io/v2/homebrew/core/icu4c/manifests/69.1
==> Downloading https://ghcr.io/v2/homebrew/core/icu4c/blobs/sha256:3f75c907dadc6e7e647920506e740a312e56279369f3c9708cac54b018410120
==> Downloading from https://pkg-containers.githubusercontent.com/ghcr1/blobs/sha256:3f75c907dadc6e7e647920506e740a312e56279369f3c9708cac54b018410120?se=2021-07-27T21%3A00%3A00Z&sig=BaoiU80loY6gnPftUNwH1uWE2FIbAgTVmSHK6DpGeIY%3D&sp=r&spr=https&sr=b&sv=2019-12-12
==> Pouring icu4c--69.1.catalina.bottle.tar.gz
🍺  /usr/local/Cellar/icu4c/69.1: 259 files, 72.8MB
==> brew cleanup has not been run in 30 days, running now...
Removing: /usr/local/Cellar/icu4c/68.2... (259 files, 72.5MB)
Error: Operation not permitted @ dir_s_rmdir - /usr/local/lib/dgagent
What did you expect to happen?
I expected Homebrew to clean and upgrade files associated with it and not remove unrelated files necessary to my use of the system.
Step-by-step reproduction instructions (by running brew commands)
`brew update`
`brew upgrade`

Can you show the output of
brew cleanup --verbose --debug

?
As a workaround, you can set HOMEBREW_NO_INSTALL_CLEANUP to skip the cleanup step during install/upgrade. You can still do brew cleanup <formulae> to cleanup things that you did install with brew (e.g. brew cleanup $(brew ls --formula)).

brew cleanup --verbose --debug
/usr/local/Homebrew/Library/Homebrew/brew.rb (Formulary::TapLoader): loading /usr/local/Homebrew/Library/Taps/homebrew/homebrew-core/Formula/libtermkey.rb
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Error: Operation not permitted @ dir_s_rmdir - /usr/local/lib/dgagent
/usr/local/Homebrew/Library/Homebrew/extend/pathname.rb:522:in `rmdir'
/usr/local/Homebrew/Library/Homebrew/extend/pathname.rb:522:in `rmdir'
/usr/local/Homebrew/Library/Homebrew/extend/pathname.rb:522:in `rmdir'
/usr/local/Homebrew/Library/Homebrew/extend/pathname.rb:255:in `rmdir_if_possible'
/usr/local/Homebrew/Library/Homebrew/cleanup.rb:476:in `block in prune_prefix_symlinks_and_directories'
/usr/local/Homebrew/Library/Homebrew/cleanup.rb:472:in `reverse_each'
/usr/local/Homebrew/Library/Homebrew/cleanup.rb:472:in `prune_prefix_symlinks_and_directories'
/usr/local/Homebrew/Library/Homebrew/cleanup.rb:195:in `clean!'
/usr/local/Homebrew/Library/Homebrew/cmd/cleanup.rb:58:in `cleanup'
/usr/local/Homebrew/Library/Homebrew/brew.rb:131:in `<main>'

ls -la /usr/local/lib/dgagent
total 1240
drwxrwxrwt     3 root     wheel      96 Feb 19 08:56 .
drwxrwxr-x  2130 meuserj  admin   68160 Jul 27 17:41 ..
-rwxrwxrwt     1 root     wheel  634784 Jun 16 09:37 libpreload.dylib

I've modified the permissions of the dgagent folder and attempted to delete it manually, but the dylib is loaded in a running process that I cannot kill. I tried this before realizing that the process is the corporate management software required by my company, so I neither can, nor want, to remove it.

Yes, brew shouldn't be trying to remove that folder. I'm not sure why it's even attempting to, but I'll have a closer look at the backtrace to see what's happening.


Refactor dev_msgs.h to be independent of ARCH_NAME

Per @pgkeller - dev_msgs.h is the contract between the host and the device, and it should not be separated.
However, it is currently a blocker in making our code base agnostic to compile time ARCH_NAME usage.
It requires some refactoring / cleanup to make use of Hal.
https://github.com/tenstorrent/tt-metal/blob/main/tt_metal/hw/inc/dev_msgs.h
This file makes ~14 files ARCH_NAME specific.
tt_metal/impl/program/program.hpp
tt_metal/impl/dispatch/command_queue.cpp
tt_metal/impl/device/device.hpp
tt_metal/impl/device/device.cpp
tt_metal/impl/debug/watcher_device_reader.hpp
tt_metal/impl/debug/watcher_server.cpp
tt_metal/llrt/tt_cluster.hpp
tt_metal/llrt/llrt.cpp
tt_metal/llrt/tt_cluster.cpp
tt_metal/llrt/llrt.hpp
tt_metal/tools/profiler/profiler.cpp
tt_metal/tools/profiler/tt_metal_profiler.cpp
tt_metal/tools/profiler/kernel_profiler.hpp
tt_metal/tt_metal.cpp

@pgkeller @abhullar-tt
I am struggling to untangle core_config.h inside of dev_msgs.h.
https://github.com/tenstorrent/tt-metal/blob/3f12bb9bf54a140a54fe95d240f354bdcd190546/tt_metal/hw/inc/dev_msgs.h#L13
We currently try to bring different versions of the file into the translation unit depending on ARCH_NAME:
https://github.com/tenstorrent/tt-metal/blob/main/tt_metal/hw/inc/grayskull/core_config.h
https://github.com/tenstorrent/tt-metal/blob/main/tt_metal/hw/inc/wormhole/core_config.h
https://github.com/tenstorrent/tt-metal/blob/main/tt_metal/hw/inc/blackhole/core_config.h
The file brings in enum types, constants, and structs that are used at COMPILE_TIME.
For example:
https://github.com/tenstorrent/tt-metal/blob/3f12bb9bf54a140a54fe95d240f354bdcd190546/tt_metal/hw/inc/dev_msgs.h#L97-L98
https://github.com/tenstorrent/tt-metal/blob/3f12bb9bf54a140a54fe95d240f354bdcd190546/tt_metal/hw/inc/dev_msgs.h#L101
https://github.com/tenstorrent/tt-metal/blob/3f12bb9bf54a140a54fe95d240f354bdcd190546/tt_metal/hw/inc/dev_msgs.h#L275
https://github.com/tenstorrent/tt-metal/blob/3f12bb9bf54a140a54fe95d240f354bdcd190546/tt_metal/hw/inc/dev_msgs.h#L287
This is a problem Hal cannot currently solve, as Hal is a runtime concept.
Could you please take a stab at solving this when you have time?
cc: @bkeith-TT @TT-billteng

Yes, there is a lot of work to do.  Device side this is fine.  For host side presently we rely on using the "max" values across all arches and core types.  To unify the build we either need to stick w/ the max values (and preferably enforce w/ asserts) or move these messages behind the hal.  Short term easiest to use max, long term we likely want this all behind the hal.


Fixed #38

Hi, appreciate the PR!
Couple of comments:

Is there any need to switch from bsdiff to xdelta3? Are there benefits to using the latter?
Not too big on the idea of adding in compiled binaries, as they'd only work for the system they're compiled for (I'm assuming macOS x86?), when Inferius supports (most, if not) all Linux/macOS hosts.
Could this PR be split up into the various changes that are being done (switch to xdelta3 for patch format, adding in binaries, and adding in functionality for manually specifying SHSH blobs?)

Hi @m1stadev ✌

bsdiff always failed with segmentation fault with 100MB dmg files even on my new MacBook Pro, as you can see I added more prints to understand it. Take a look at #38. bsdiff is very hungry to RAM, I replaced it with faster alternative, now it generates new bundles within 1-2 minutes and doesn't fails.
Yes, it's not a big deal to drop this commit. Better provide binaries for every platform and choice in Python code which to call but I won't implement this.
I added manually setting BLOBS because futurerestore had fatal issue sending wrong TSS request, I talked to it's main developer and he fixed it with my help: https://github.com/tihmstar/tsschecker/issues/169

Feel free to drop/edit commits or wait until tomorrow when I'll go to office and edit it.
Happy Halloween 🎃🦇

Any update on splitting the PRs?

Sorry I'm busy working at the big project for the next BlackHat USA 🎩
Today would be updated.

Coming back to this PR as I prepare for an update, I looked at xdelta3 as a replacement for bsdiff4, but in my testing the patches don't seem to generate files that match the original files diffed against. Have you had any success utilizing xdelta3 patches?

I don’t believe that xdelta3 algorithm doesn’t work as expected. By the way sorry but I’m out.


Updates 'init' command's cert_file property

Desired Outcome

Added fixes to the 'init' command to omit empty string paths for Ruby CLI compatibility

Implemented Changes

Updated files to remove empty string declarations

Connected Issue/Story
https://ca-il-jira.il.cyber-ark.com:8443/browse/ONYX-24956

Do we have integration tests that check that the cert_file will actually not be in the config file in this case?


Language conversion error in logs

There are many error messages in the logs like:
[error][yii\i18n\PhpMessageSource::loadMessages] The message file for category 'yii' does not exist
It might be because of this language setting in the datepicker. Maybe change it to en_US or just eliminate it?
https://github.com/CorWatts/fasterscale/blob/de5c2fc05900a10070a7068cd232c89d42164ee7/site/views/checkin/view.php#L27
<?= yii\jui\DatePicker::widget([
            'name' => 'attributeName',
            'value' => $utc_date,
            'options' => ['class'=> 'btn btn-default'],
            'language' => 'en',

ccee6c7 did not actually fix this issue. Now it is fixed correctly.