installing tflite-model-maker encounters errors in regards to tf-nightly==2.4.0.dev20200810

System information

OS Platform and Distribution (e.g., windows):
TensorFlow installed from both source and binary: tried on both
TensorFlow version:  source 2.2.0 , binary 2.0.0
Python version: 3.7.4
Installed using virtualenv? pip? conda?: pip
Bazel version (if compiling from source): 3.2.0
GCC/Compiler version (if compiling from source):
CUDA/cuDNN version:
GPU model and memory: N/A

Describe the problem
I am trying to pip install tflite-model-maker, (tried from the source too) but I keep getting this error:
ERROR: No matching distribution found for tf-nightly==2.4.0.dev2.4.0.dev20201012
It seems that there is no version of tf-nightly 2.4.0.dev2.4.0.dev20201012
I tried to install another version of tf-nightly but that also encounters other error
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "c:\users\chingool\anaconda3\lib\site-packages\pip_internal\cli\base_command.py", line 228, in _main
status = self.run(options, args)
File "c:\users\chingool\anaconda3\lib\site-packages\pip_internal\cli\req_command.py", line 182, in wrapper
return func(self, options, args)
File "c:\users\chingool\anaconda3\lib\site-packages\pip_internal\commands\install.py", line 324, in run
reqs, check_supported_wheels=not options.target_dir
File "c:\users\chingool\anaconda3\lib\site-packages\pip_internal\resolution\legacy\resolver.py", line 183, in resolve
discovered_reqs.extend(self._resolve_one(requirement_set, req))
File "c:\users\chingool\anaconda3\lib\site-packages\pip_internal\resolution\legacy\resolver.py", line 388, in _resolve_one
abstract_dist = self._get_abstract_dist_for(req_to_install)
File "c:\users\chingool\anaconda3\lib\site-packages\pip_internal\resolution\legacy\resolver.py", line 340, in _get_abstract_dist_for
abstract_dist = self.preparer.prepare_linked_requirement(req)
File "c:\users\chingool\anaconda3\lib\site-packages\pip_internal\operations\prepare.py", line 469, in prepare_linked_requirement
hashes=self._get_linked_req_hashes(req)
File "c:\users\chingool\anaconda3\lib\site-packages\pip_internal\operations\prepare.py", line 259, in unpack_url
hashes=hashes,
File "c:\users\chingool\anaconda3\lib\site-packages\pip_internal\operations\prepare.py", line 130, in get_http_url
link, downloader, temp_dir.path, hashes
File "c:\users\chingool\anaconda3\lib\site-packages\pip_internal\operations\prepare.py", line 282, in _download_http_url
for chunk in download.chunks:
File "c:\users\chingool\anaconda3\lib\site-packages\pip_internal\cli\progress_bars.py", line 168, in iter
for x in it:
File "c:\users\chingool\anaconda3\lib\site-packages\pip_internal\network\utils.py", line 88, in response_chunks
decode_content=False,
File "c:\users\chingool\anaconda3\lib\site-packages\pip_vendor\urllib3\response.py", line 576, in stream
data = self.read(amt=amt, decode_content=decode_content)
File "c:\users\chingool\anaconda3\lib\site-packages\pip_vendor\urllib3\response.py", line 541, in read
raise IncompleteRead(self._fp_bytes_read, self.length_remaining)
File "c:\users\chingool\anaconda3\lib\contextlib.py", line 130, in exit
self.gen.throw(type, value, traceback)
File "c:\users\chingool\anaconda3\lib\site-packages\pip_vendor\urllib3\response.py", line 442, in _error_catcher
raise ReadTimeoutError(self._pool, None, "Read timed out.")
pip._vendor.urllib3.exceptions.ReadTimeoutError: HTTPSConnectionPool(host='files.pythonhosted.org', port=443): Read timed out.
I feel like I will never be able to install tflite-model-maker, though it looks to be very useful.
Is there any other alternative to that?

Nightly versions change everyday and all the nightly versions are not saved in PyPi only the latest ones.
tf-nightly==2.4.0.dev20200810 tries to install nightly package built on 08/10/2020 which is not available.
Can you try;
pip install tflite-model-maker-nightly

This worked for me.
For installing latest TF try,
pip install tf-nightly

I tried the nightly build too. Didn't work.
However later I realized that tf-nightly 2.4.0.dev2.4.0.dev20201012 version has no wheel for python3.7, upgrading to 3.8 and running it again solved the problem.
However now I get another error:
AttributeError: module 'tensorflow_lite_support.metadata.metadata_schema_py_generated' has no attribute 'ModelMetadataT'

Yes nightly wheels get updated daily and are not saved in Pypi.
How are you hitting?
AttributeError: module 'tensorflow_lite_support.metadata.metadata_schema_py_generated' has no attribute 'ModelMetadataT'

Do you have a repro code to share?

yeah I didn't know that. I am quite new to Python. Things were different in Java world.

Do you have a repro code to share?
Yes, it just happens at the last line of example, which is exporting. As a matter of fact I just raised an issue here:
https://github.com/tensorflow/tensorflow/issues/44062

I will close this issue since the original topic is solved. We can continue the discussion on the other thread you created. Thanks!


Enhancement of Sign up page

I want to add placeholder , icons and hover effect on submit button etc. Please assign this issue to me under JWOC.

@codervivek5 I would like to work on this issue please assign it to me..

@codervivek5 can you assign it to me , I want to work on this issue...

@codervivek5 can you suggest me some other issues....

@RahulRana701 if you really want to work  with me then kindly go to VigyBag repo in my ID. use readme file for your reference and check out that .....after that if you have any issue then drop msg me on instagram or LinkedIn or whenever you want


DALL-E API integration

https://venturebeat.com/ai/openai-shares-dall-e-api-use-case-in-advance-of-autumn-release/

Hi, thanks for the awesome plugin. I am wondering if we can use DALL-E 2 API in future. I guess we are currently using DALL-E.

Yup! We are actually already are using the dall-e 2 API!


Organize everything in index.html

New UI 😊.

Woah nice! Thanks @ramlmn

Noice!!
Warmest regards,
Kevin Ancil Weekes
Mobile 1.868.342.1078
Skype kevin.weekes
On Sat, May 20, 2017 at 11:46 AM, Surma<EMAIL_ADDRESS>wrote:

Merged #85 https://github.com/GoogleChrome/ui-element-samples/pull/85.
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chore: configure renovate

This re-configures renovate with some desirable settings.

Codecov Report
Patch coverage: 100.00% and project coverage change: +0.01 :tada:

Comparison is base (bffc423) 91.09% compared to head (d41dae8) 91.10%.

Additional details and impacted files
@@            Coverage Diff             @@
##             main      #10      +/-   ##
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+ Coverage   91.09%   91.10%   +0.01%
==========================================
  Files          25       25
  Lines         865      866       +1
  Branches      234      234
==========================================
+ Hits          788      789       +1
  Misses         70       70
  Partials        7        7

Impacted Files
Coverage Δ

packages/ynap-parsers/src/bank2ynab/bank2ynab.ts
68.96% <ø> (ø)

...ackages/ynap-parsers/src/de/comdirect/comdirect.ts
96.42% <100.00%> (ø)

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Hololens2 Sample code snippets

Code snippets should match the script from https://github.com/MicrosoftEdge/WebView2Samples/blob/main/GettingStartedGuides/HoloLens2_GettingStarted/HoloLens2GetStartedApp/Assets/WebViewBrowser.cs
Right now, it seems that those screenshots are from a previous version.

Document Details
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ID: 76a8da9c-3c3c-bc28-5c70-7e5e5c7a9409
Version Independent ID: 76a8da9c-3c3c-bc28-5c70-7e5e5c7a9409
Content: Get started with WebView2 in HoloLens 2 Unity apps (Preview) - Microsoft Edge Developer documentation
Content Source: microsoft-edge/webview2/get-started/hololens2.md
Service: microsoft-edge
Sub-service: webview
GitHub Login: @MSEdgeTeam
Microsoft Alias: msedgedevrel

Thanks for filing. I'll make sure our WebView2 team sees and prioritizes this.


Add: changelog job and bump pkg version

Also fixes intra-doc links which were explicit

Codecov Report

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crates/archlinux/src/lib.rs
81.6% <ø> (+1.1%)
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Minor fixes: health checks, httpd golr conf, qos

qos
health checks
httpd golr conf

Looks good.


oracle view table indexes report  an error: ORA-01417

System information:

Operating system (distribution) and version : windows 10
DBeaver version : 21.1.1
Additional extensions

Connection specification:

Database name and version :oracle 11gR2
Driver name:ojdbc8
Do you use tunnels or proxies (SSH, SOCKS, etc)? : no

Describe the problem you're observing:
An error is reported when the table indexesis viewed. When I tried the earlier version (21.0.5), everything worked fine
Steps to reproduce, if exist:
Open an Oracle connection and select a table, right-click on the menu and select view table,Select the indexes item in the right panel.
Include any warning/errors/backtraces from the logs
ORA-01417: a table may be outer joined to at most one other table
SQL Error [1417] [72000]: ORA-01417: a table may be outer joined to at most one other table
Error : 1417, Position : 397, Sql = SELECT  i.OWNER,i.INDEX_NAME,i.INDEX_TYPE,i.TABLE_OWNER,i.TABLE_NAME,i.UNIQUENESS,i.TABLESPACE_NAME,i.STATUS,i.NUM_ROWS,i.SAMPLE_SIZE,
ic.COLUMN_NAME,ic.COLUMN_POSITION,ic.COLUMN_LENGTH,ic.DESCEND,iex.COLUMN_EXPRESSION
FROM ALL_INDEXES i
, ALL_IND_COLUMNS ic, ALL_IND_EXPRESSIONS iex WHERE ic.INDEX_OWNER=i.OWNER AND ic.INDEX_NAME=i.INDEX_NAME
AND iex.INDEX_OWNER(+)=i.OWNER AND iex.INDEX_NAME(+)=i.INDEX_NAME AND iex.COLUMN_POSITION(+)=ic.COLUMN_POSITION
AND i.TABLE_OWNER=:1  AND i.TABLE_NAME=:2
ORDER BY i.INDEX_NAME,ic.COLUMN_POSITION, OriginalSql = SELECT  i.OWNER,i.INDEX_NAME,i.INDEX_TYPE,i.TABLE_OWNER,i.TABLE_NAME,i.UNIQUENESS,i.TABLESPACE_NAME,i.STATUS,i.NUM_ROWS,i.SAMPLE_SIZE,
ic.COLUMN_NAME,ic.COLUMN_POSITION,ic.COLUMN_LENGTH,ic.DESCEND,iex.COLUMN_EXPRESSION
FROM ALL_INDEXES i
, ALL_IND_COLUMNS ic, ALL_IND_EXPRESSIONS iex WHERE ic.INDEX_OWNER=i.OWNER AND ic.INDEX_NAME=i.INDEX_NAME
AND iex.INDEX_OWNER(+)=i.OWNER AND iex.INDEX_NAME(+)=i.INDEX_NAME AND iex.COLUMN_POSITION(+)=ic.COLUMN_POSITION
AND i.TABLE_OWNER=? AND i.TABLE_NAME=?
ORDER BY i.INDEX_NAME,ic.COLUMN_POSITION, Error Msg = ORA-01417: a table may be outer joined to at most one other table
ORA-01417: a table may be outer joined to at most one other table

Same as above essentially.
System information:
Operating system (distribution) and version : ubuntu 20.04
DBeaver version : <IP_ADDRESS>106210824
Additional extensions
Connection specification:
Database name and version :oracle 11gR2
Driver name: ojdbc8 release <IP_ADDRESS>
Do you use tunnels or proxies (SSH, SOCKS, etc)? : SSH
Describe the problem you're observing:
The tables node shows some but not all (2 out of 100s in my case) tables.
Sometimes an error (see below for log) is reported when the tables node is opened.
Steps to reproduce, if exist:
Open an Oracle connection, right-click select the Tables node in the object tree and choose View Tables.
Include any warning/errors/backtraces from the logs
ORA-01417: a table may be outer joined to at most one other table
SQL Error [1417] [72000]: ORA-01417: a table may be outer joined to at most one other table
!STACK 0
Error : 1417, Position : 397, Sql = SELECT  i.OWNER,i.INDEX_NAME,i.INDEX_TYPE,i.TABLE_OWNER,i.TABLE_NAME,i.UNIQUENESS,i.TABLESPACE_NAME,i.STATUS,i.NUM_ROWS,i.SAMPLE_SIZE,
ic.COLUMN_NAME,ic.COLUMN_POSITION,ic.COLUMN_LENGTH,ic.DESCEND,iex.COLUMN_EXPRESSION
FROM ALL_INDEXES i
, ALL_IND_COLUMNS ic, ALL_IND_EXPRESSIONS iex WHERE ic.INDEX_OWNER=i.OWNER AND ic.INDEX_NAME=i.INDEX_NAME
AND iex.INDEX_OWNER(+)=i.OWNER AND iex.INDEX_NAME(+)=i.INDEX_NAME AND iex.COLUMN_POSITION(+)=ic.COLUMN_POSITION
AND i.OWNER=:1
ORDER BY i.INDEX_NAME,ic.COLUMN_POSITION, OriginalSql = SELECT  i.OWNER,i.INDEX_NAME,i.INDEX_TYPE,i.TABLE_OWNER,i.TABLE_NAME,i.UNIQUENESS,i.TABLESPACE_NAME,i.STATUS,i.NUM_ROWS,i.SAMPLE_SIZE,
ic.COLUMN_NAME,ic.COLUMN_POSITION,ic.COLUMN_LENGTH,ic.DESCEND,iex.COLUMN_EXPRESSION
FROM ALL_INDEXES i
, ALL_IND_COLUMNS ic, ALL_IND_EXPRESSIONS iex WHERE ic.INDEX_OWNER=i.OWNER AND ic.INDEX_NAME=i.INDEX_NAME
AND iex.INDEX_OWNER(+)=i.OWNER AND iex.INDEX_NAME(+)=i.INDEX_NAME AND iex.COLUMN_POSITION(+)=ic.COLUMN_POSITION
AND i.OWNER=?
ORDER BY i.INDEX_NAME,ic.COLUMN_POSITION, Error Msg = ORA-01417: a table may be outer joined to at most one other table
at oracle.jdbc.driver.T4CTTIoer11.processError(T4CTTIoer11.java:498)
at oracle.jdbc.driver.T4CTTIoer11.processError(T4CTTIoer11.java:446)
at oracle.jdbc.driver.T4C8Oall.processError(T4C8Oall.java:1054)
at oracle.jdbc.driver.T4CTTIfun.receive(T4CTTIfun.java:623)
at oracle.jdbc.driver.T4CTTIfun.doRPC(T4CTTIfun.java:252)
at oracle.jdbc.driver.T4C8Oall.doOALL(T4C8Oall.java:612)
at oracle.jdbc.driver.T4CPreparedStatement.doOall8(T4CPreparedStatement.java:226)
at oracle.jdbc.driver.T4CPreparedStatement.doOall8(T4CPreparedStatement.java:59)
at oracle.jdbc.driver.T4CPreparedStatement.executeForDescribe(T4CPreparedStatement.java:747)
at oracle.jdbc.driver.OracleStatement.executeMaybeDescribe(OracleStatement.java:904)
at oracle.jdbc.driver.OracleStatement.doExecuteWithTimeout(OracleStatement.java:1082)
at oracle.jdbc.driver.OraclePreparedStatement.executeInternal(OraclePreparedStatement.java:3780)
at oracle.jdbc.driver.T4CPreparedStatement.executeInternal(T4CPreparedStatement.java:1343)
at oracle.jdbc.driver.OraclePreparedStatement.execute(OraclePreparedStatement.java:3887)
at oracle.jdbc.driver.OraclePreparedStatementWrapper.execute(OraclePreparedStatementWrapper.java:1079)
at org.jkiss.dbeaver.model.impl.jdbc.exec.JDBCPreparedStatementImpl.execute(JDBCPreparedStatementImpl.java:261)
at org.jkiss.dbeaver.model.impl.jdbc.exec.JDBCPreparedStatementImpl.executeStatement(JDBCPreparedStatementImpl.java:205)
at org.jkiss.dbeaver.model.impl.jdbc.cache.JDBCCompositeCache.loadObjects(JDBCCompositeCache.java:291)
at org.jkiss.dbeaver.model.impl.jdbc.cache.JDBCCompositeCache.getObjects(JDBCCompositeCache.java:120)
at org.jkiss.dbeaver.ext.oracle.model.OracleSchema.cacheStructure(OracleSchema.java:411)
at org.jkiss.dbeaver.erd.model.ERDUtils.lambda$0(ERDUtils.java:141)
at org.jkiss.dbeaver.model.exec.DBExecUtils.tryExecuteRecover(DBExecUtils.java:171)
at org.jkiss.dbeaver.erd.model.ERDUtils.collectDatabaseTables(ERDUtils.java:139)
at org.jkiss.dbeaver.erd.ui.editor.ERDEditorEmbedded.loadFromDatabase(ERDEditorEmbedded.java:210)
at org.jkiss.dbeaver.erd.ui.editor.ERDEditorEmbedded.access$1(ERDEditorEmbedded.java:193)
at org.jkiss.dbeaver.erd.ui.editor.ERDEditorEmbedded$1.evaluate(ERDEditorEmbedded.java:168)
at org.jkiss.dbeaver.erd.ui.editor.ERDEditorEmbedded$1.evaluate(ERDEditorEmbedded.java:1)
at org.jkiss.dbeaver.ui.LoadingJob.run(LoadingJob.java:88)
at org.jkiss.dbeaver.ui.LoadingJob.run(LoadingJob.java:72)
at org.jkiss.dbeaver.model.runtime.AbstractJob.run(AbstractJob.java:105)
at org.eclipse.core.internal.jobs.Worker.run(Worker.java:63)

Same behaviour with <IP_ADDRESS>105160657

Error is with Oracle < 12c
It has been fixed : https://docs.oracle.com/database/121/NEWFT/chapter12101.htm#NEWFT138
Transforming Oracle native joins with ansi joins is ok in 11g
SELECT
	i.OWNER,
	i.INDEX_NAME,
	i.INDEX_TYPE,
	i.TABLE_OWNER,
	i.TABLE_NAME,
	i.UNIQUENESS,
	i.TABLESPACE_NAME,
	i.STATUS,
	i.NUM_ROWS,
	i.SAMPLE_SIZE,
	ic.COLUMN_NAME,
	ic.COLUMN_POSITION,
	ic.COLUMN_LENGTH,
	ic.DESCEND,
	iex.COLUMN_EXPRESSION
FROM
	ALL_INDEXES i
JOIN ALL_IND_COLUMNS ic ON
	i.owner = ic.index_owner
	AND i.index_name = ic.index_name
LEFT JOIN ALL_IND_EXPRESSIONS iex ON
	iex.index_owner = i.owner
	AND iex.INDEX_NAME = i.INDEX_NAME
	AND iex.COLUMN_POSITION = ic.COLUMN_POSITION
WHERE
	i.TABLE_OWNER = ?
	AND i.TABLE_NAME =?
ORDER BY
	i.INDEX_NAME,
	ic.COLUMN_POSITION

thanks for the report

the fix can be checked in EA version

I am still having the issue on the EA Version <IP_ADDRESS>107060947 (Linux DEB)

@ugurbor cannot reproduce it against 11g. which oracle version do you use?

From the SELECT * FROM v$version
Oracle Database 11g Enterprise Edition Release <IP_ADDRESS>.0 - 64bit Production
These are the libraries from the connection properties. Don't really know if it matters: https://i.gyazo.com/fc1f53ed13c31db9c9c63257c39712cb.png

Same here when opening the Indexes node .
flyway <IP_ADDRESS>107060947

Me too. Oracle version <IP_ADDRESS>.0 - 64bit Production
DBeaver version: <IP_ADDRESS>107060947

Me too.
Oracle version:
Oracle Database 10g Enterprise Edition Release <IP_ADDRESS>.0 - Prod
AND
Oracle Database 11g Enterprise Edition Release <IP_ADDRESS>.0 - 64bit Production
All the same problem.
DBeaver version: <IP_ADDRESS>107060947

seems something going wrong with previous EA. Should be fixed in current EA version (Version <IP_ADDRESS>107081153)

seems something going wrong with previous EA. Should be fixed in current EA version (Version <IP_ADDRESS>107081153)

I tested it and solved it. I didn't report any more errors here, thank you!

I can confirm Version <IP_ADDRESS>107081153 fixes the issue for me as well. Thanks.

It's ok for me in Oracle Database 11g Release <IP_ADDRESS>.0 - 64bit Production. Thanks.


Linking to FAST URIs when there's $0

Has there been any consideration to in the spec to reconcile 6XXs with FAST URIs? It could be relatively easy with the right second indicator 2, $7, and $0 combination that could easily map to URIs. E.g. https://newcatalog.library.cornell.edu/catalog/1710524/librarian_view.
Or is this outside of the scope of LC's marc2bibframe2 spec?

Some things to try:
If you convert the $0 to a uri it should work.
Otherwise you have this structure which you want to lookup.
       <bf:subject>
          <bf:Topic rdf:about="http://bibframe.example.org/622168#Topic650-18">
            <bf:identifiedBy>
              <bf:Identifier>
                <rdf:value>fst00978933</rdf:value>
                <bf:source>
                  <bf:Source>
                    <rdfs:label>OCoLC</rdfs:label>
                  </bf:Source>
                </bf:source>
              </bf:Identifier>
            </bf:identifiedBy>
            <rdfs:label>Irish Americans.</rdfs:label>
            <madsrdf:authoritativeLabel>Irish Americans.</madsrdf:authoritativeLabel>
            <bf:source>
              <bf:Source>
                <bf:code>fast</bf:code>
              </bf:Source>
            </bf:source>
          </bf:Topic>
        </bf:subject>

In your yaz config, you would want something that takes the identifier and adds it to http://id.worldcat.org/fast/.
    <rdf-lookup timeout="3" debug="1" >
        <namespace prefix="bf" href="http://id.loc.gov/ontologies/bibframe/"/>
        <namespace prefix="bflc" href="http://id.loc.gov/ontologies/bflc/"/>
        <namespace prefix="rdf" href="http://www.w3.org/1999/02/22-rdf-syntax-ns#"/>
        <namespace prefix="madsrdf" href="http://www.loc.gov/mads/rdf/v1#"/>
        <namespace prefix="rdfs" href="http://www.w3.org/2000/01/rdf-schema#"/>
        <lookup xpath="//bf:Work/bf:subject/*[string(bf:source/bf:Source/bf:code)='fast']/bf:identifiedBy/bf:Identifier">
            <key field="rdf:value"/>
            <server url="http://id.worldcat.org/fast/%s" method="HEAD"/>
        </lookup>
    </rdf-lookup>

Even though that url returns a location (Location: http://id.worldcat.org/fast/984188/) it doesn't work in my test. Wayne what do you think?

Hi, @sfolsom. I'm guessing what you're proposing is an enhancement to the 6XX spec such that, if ind2 = 7 and $2 = "fast", the IRI for the generated bf:Topic RDF subject is http://id.worldcat.org/fast/{id extracted from $0}? This extra processing would eliminate the need to do the extract lookup step that @kirkhess refers to above.
My instinct is that this would be out of scope, more aligned with local cataloging practice and conventions than with the MARC or BIBFRAME standards (though that line is of course not very hard and fast). I also wonder what should happen if there is more than one $0 in the 6XX data field (the $0 is repeatable). @jodiw01 -- any comments?
I will do more testing on the possibility of using YAZ for post-processing -- that does seem like it should work with the right configuration.

I believe this should be the correct configuration:
  <rdf-lookup timeout="3" debug="1">
    <namespace prefix="bf" href="http://id.loc.gov/ontologies/bibframe/"/>
    <namespace prefix="rdf" href="http://www.w3.org/1999/02/22-rdf-syntax-ns#"/>
    <lookup xpath="//bf:Work/bf:subject/*[contains(@rdf:about,'example.org') and bf:source/bf:Source/bf:code='fast']">
      <key field="bf:identifiedBy/bf:Identifier/rdf:value"/>
      <server url="http://id.worldcat.org/fast/%s" method="HEAD"/>
    </lookup>
  </rdf-lookup>

However, id.worldcat.org/fast returns a 303 status code with a Location: header. The YAZ retrieval rdf-lookup conversion does not know what to do with a 303 (see https://software.indexdata.com/yaz/doc/tools.retrieval.html for documentation). The rdf-lookup conversion is tuned specifically to LC's label service, which returns a 302 for a string match with an X-URI header. Changing that behavior would require a change to YAZ.

The reason for the 303 is you are supposed to include a format or a content-type.
Generic Document http://id.worldcat.org/fast/12345/
MARC-21 (UTF-8 raw data) http://id.worldcat.org/fast/12345.mrc
MARC-21 xml raw data http://id.worldcat.org/fast/12345.mrc.xml
RDF/XML raw data http://id.worldcat.org/fast/12345.rdf.xml
Either way, since it has nothing in the response headers you could use it isn't going to work.
I suppose we would have to wrap this api with something that worked like our id.loc.gov searches.
We also could use QuestioningAuthority - it does not support HEAD requests like this either, but I'm pretty sure Loretta could just add them to the term searches.
https://lookup.ld4l.org/authorities/show/linked_data/oclcfast_direct/12345

Getting back to this....
@sfolsom does this capture what you were hoping for?

Hi, @sfolsom. I'm guessing what you're proposing is an enhancement to the 6XX spec such that, if ind2 = 7 and $2 = "fast", the IRI for the generated bf:Topic RDF subject is http://id.worldcat.org/fast/{id extracted from $0}?

FAST URIs are created now: https://id.loc.gov/tools/bibframe/compare-lccn/full-rdf?find=2021285925
bf:subject
<bf:Topic rdf:about="http://id.worldcat.org/fast/00899294">
<rdf:type rdf:resource="http://www.loc.gov/mads/rdf/v1#Topic"/>
rdfs:labelDucks</rdfs:label>
madsrdf:authoritativeLabelDucks</madsrdf:authoritativeLabel>
<bf:source rdf:resource="http://id.loc.gov/vocabulary/subjectSchemes/fast"/>
</bf:Topic>
</bf:subject>


Deploy cesiumjs.org branch to cesiumjs.org

Pushing to cesiumjs.org branch will deploy to cesiumjs.org/Cesium.

@ggetz, thanks for the pull request! Maintainers, we have a signed CLA from @ggetz, so you can review this at any time.
:warning: I noticed that CHANGES.md has not been updated. If this change updates the public API in any way, fixes a bug, or makes any non-trivial update, please add a bullet point to CHANGES.md and comment on this pull request so we know it was updated. For more info, see the Pull Request Guidelines.

I am a bot who helps you make Cesium awesome! Contributions to my configuration are welcome.
:earth_africa: :earth_americas: :earth_asia:

@mramato Updated to use the right version number.


Collapse doesn't work if id contains dot

Hello
Collapse doesn't work if id contains dot.
<!doctype html>

    <meta charset="utf-8">
    <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
    <link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.1.0/css/bootstrap.min.css">
    <title>Hello, world!</title>

                <button class="btn btn-link" type="button" data-toggle="collapse" data-target="#collapse.One">
                    Collapsible Group Item #1
                </button>

                xxx

                <button class="btn btn-link collapsed" type="button" data-toggle="collapse" data-target="#collapse.Two">
                    Collapsible Group Item #2
                </button>

                yyy

Please fix the bug.
Thank you.
--
Mykola

@lystor  I've been working with CSS for some time now, I haven't ever came across a point where I'd use a . in my naming conventions for IDs. I'd recommend simply changing from a . to a -, this should work after then. Is there a specific reason why we're using a dot in your ID?

@sts-ryan-holton

Is there a specific reason why we're using a dot in your ID?

Yes.
I use product versions as id. Url contains them too. Required target is opened automatically on page load.
Example:
http://example.com/products/xxx#5.11.3 will open tab with id=5.11.3
JQuery supports meta-characters in id:
https://api.jquery.com/category/selectors/

To use any of the meta-characters ( such as  !"#$%&'()*+,./:;<=>?@[]^`{|}~ ) as a literal part of a name, it must be escaped with with two backslashes: \. For example, an element with id="foo.bar", can use the selector $("#foo\.bar"). The W3C CSS specification contains the complete set of rules regarding valid CSS selectors. Also useful is the blog entry by Mathias Bynens on CSS character escape sequences for identifiers.

I have been using JQuery UI many years and it works with dots in id out of the box.
Now I can't migrate to Bootstrap from JQuery UI due to this issue :(

You have to escape your selector that's what jQuery said by :

can use the selector $("#foo.bar")

and it'll be fine 😉
Everything passed in our data-target attribute is passed to jQuery as a selector

Hi, @ Johann-S
It works, thank you.
May be it is better to improve Bootstrap to escape meta-characters in data-target automatically?
So collapse will work without any workarounds out of the box like in jQuery UI.
Thank you

We made that in a previous PR but finally we reverted that because it's too hard to detect correctly special selectors.
See:

#25390
#25273

We added a quick note in our documentation see: https://getbootstrap.com/docs/4.1/getting-started/javascript/#escaping-selectors


scrolling in profile component

Please create a new PR which still uses the header component from this one, but also uses the technique demonstrated here: https://github.com/peapods/peapods.com/pull/330


Enable kernel config required for DFS

Is your feature request related to a problem? Please describe.
I'm unable to mount a CIFS DFS share because the kernel doesn't have support and DRVFS is too slow to be useful. WSL2 is being sold as the easy way to interop between Linux and windows yet I can't mount one of the most basic enterprise shares. It's so incredibly frustrating I can't even being to explain it.
Describe the solution you'd like
I'd the kernel to be DFS enabled.
Describe alternatives you've considered
DrvFS - too slow
Compiling my own kernel - Ew
Mounting the servers directly without DFS - also Ew
Additional context
Please enable the following kernel configs:
CONFIG_CIFS_UPCALL=y
CONFIG_CIFS_DFS_UPCALL=y
CONFIG_CIFS_SWN_UPCALL=y

I'm not alone! https://github.com/microsoft/WSL/issues/5534

IMO, your best option is Ew, compile your own kernel. Adding all sorts of storage protocols ( LIO was requested in https://github.com/microsoft/WSL/issues/9511 few days ago) makes the kernel blob larger. Soon we'll need 8GB of RAM to load the kernel only 🤷♂

I'm doing that now but considering dfs is a first class MSFT product offering I'd assume it would be supported in wsl

This is blocked by: https://github.com/microsoft/WSL/issues/9540

@elsaco For virtualization development 8GB of start-up RAM is pretty reasonable. For example, that's the amount needed to start windows server on Hyper-V.
Would it be feasible to have multiple kernel builds then? 1 for default and another for "extended-io-features" or even "full-upstream-parity"?

@jabbera
Is this still blocked? Looks like #9540 got closed, but I don't see a PR.

@fpf3 yes. This is blocked by https://github.com/microsoft/azurelinux/issues/8810 as well as the kernel config.


"Password Hashing" doc updates

[ ] fix link to "see also the source code for ASP.NET Identity’s PasswordHasher type for a real-world use case - actual link is https://github.com/aspnet/Identity/blob/dev/src/Microsoft.AspNetCore.Identity/PasswordHasher.cs
[ ] edit review - linenos
[ ] update to latest

will fix in next update


Disable root cert loading

https://github.com/grpc/grpc/issues/11632

setDefaultRootsPem() is allocating a memory buffer large enough to contain the roots pem. The memory buffer is never freed. Calling setDefaultRootsPem() multiple times will allocate the buffer again, and leak more memory:

We think this is the cause of a bunch of problems we've seen with the gRPC client in production of late. We don't use credentialed connections to linkerd/with gRPC right now, so I'm removing the call to setDefaultRootsPem until the above is fixed.
I've also made the client error out if anything but ChannelCredentials::createInsecure (null) is passed.
Needs testing.

🙇

OK, world's most primitive memory leak testing because it's late and I just want to get this done.. I setup a single FPM worker, and monitored the RSS assigned to the process and watched it climb without the patch:
root@bcapp:~# service php7.0-fpm restart
root@bcapp:~# while true; do grep RssAnon /proc/`pidof 'php-fpm: pool bigcommerce_app'`/status; sleep 10; done
RssAnon:	    7864 kB
RssAnon:	   18084 kB
RssAnon:	   19280 kB
RssAnon:	   21136 kB
RssAnon:	   22444 kB
RssAnon:	   24300 kB
RssAnon:	   25628 kB
RssAnon:	   27484 kB
RssAnon:	   28768 kB
RssAnon:	   30060 kB
RssAnon:	   31932 kB
RssAnon:	   33196 kB
RssAnon:	   33028 kB

With the patch applied:
root@bcapp:~# service php7.0-fpm restart
root@bcapp:~# while true; do grep RssAnon /proc/`pidof 'php-fpm: pool bigcommerce_app'`/status; sleep 10; done
RssAnon:	    7864 kB
RssAnon:	   17472 kB
RssAnon:	   17472 kB
RssAnon:	   17472 kB
RssAnon:	   17472 kB
RssAnon:	   17472 kB
RssAnon:	   17472 kB
RssAnon:	   17472 kB
RssAnon:	   17472 kB
RssAnon:	   17472 kB

I'm going to bring this down and update grphp.


Create a new file

I like potatoes
What the program do ?
Tell you whether something is a potato
In what programming language it is written?
Python

This pull request do not contains files in specific folders. Please fix it and reopen


Edit Order form should be prepopulated

When a user wants to edit their hashpower order, the form is blank. This is problematic, because a user may just want to verify that they entered their information correctly. I would expect to see the information that the user entered, likely with an edit button so they can make an informed decision about which fields they would like to edit.

Jira Ticket created

Edit form is now prepopulated with values from contract.


GraphDefinition for the retrieval of all resources needed for 1 Referral prescription

see: https://be-ehealth-standards.atlassian.net/browse/ESI-301
see: https://be-ehealth-standards.atlassian.net/browse/ESI-272

This issue is not openly documented. This is against the HL7 criteria and processes. Please remove references and paste the relevant content.


l11_create_web_app_basic_portal.png

Image was not translated

Hello contributor,
Thank you for your contribution! We are processing your suggestions and we will update you shortly.
Kind regards,
Microsoft Worldwide Learning Team


Support for Dynamic Real Features in Multivariate Grouper

Description
While trying to apply some GluonTS models in forecasting examples, I have encountered that for using models like GPVar and LSTNet we have to use the MultivariateGrouper to group time-series data into a single target for the models to work. This has already been discussed in multiple issues (#713, #1005 to name a few).
My dataset consists of additional features that I want to retain while building and training models. I've noticed that the MultivariateGrouper does not return the feat_dynamic_real feature of the input dataset even if you pass it. This results in potential loss of data and any accuracy that our models may be able to achieve.
Real-Life Example for a use-case:
In the example of Stock Market forecasting, say if we want to forecast the Open price for a stock for the next business day (prediction length = 1B) and we have at our disposal Open, High, Low, Close and Volume (OHLCV) information of past (Context length), we should be able to set 'Open' as our target while High, Low, Close & Volume could be part of feat_dynamic_real.
One may claim that OHLC data is highly correlated and which is mathematically true for the most part, but much of the technical analysis of stock-trading talks about the way OHLC data is arranged in the past to predict/forecast future trends. So having room to use this data with models like GPVar and LSTNet would be immensely useful.

@theharshalpatil you should know that the MultivariateGrouper is just a helper to take existing univariate time series data and turn them into multivariate datasets. For your usecase you could just prepare a multivariate dataset from the start and add the features to it as you see fit.

@theharshalpatil you should know that the MultivariateGrouper is just a helper to take existing univariate time series data and turn them into a multivariate dataset. For your usecase you could just prepare a multivariate dataset from the start and add the features to it as you see fit.

Understood! However, it'd still be helpful if MultivariateGrouper does retain any additional features in the dataset instead of ignoring them.


Suspend cause lose of wifi

On long terms idk how to log it

Can confirm I have the same issue unfortunately (running 5.5.4-1 on a surface pro 3 and Manjaro)
The WIFI connection is dropped and cannot be recovered (no WIFI entries appear on scan) and the only solution is a restart, or to run nmcli radio wifi off before closing the laptop, and then running nmcli radio wifi on once I open it again (btw, this has to be done manually, every attempt and automating this e.g. via a systemd service doesn't work, despite service running)


Refactor unit tests

Codecov Report

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 The original DDP multi-gpu training was fine with Flux Lora training, but as long as you installed DeepSpeed and enabled in your training script by adding --deepspeed --zero_stage=2, it would throw an error "RuntimeError: mat1 and mat2 must have the same dtype, but got Float and BFloat16". Even though you configured mixed_precision for both accelerate and scripts with bf16, the error still existed. Maybe @BootsofLagrangian would like to take a look. Thanks.

The error was not caused by DDP multi-gpu training, but by DeepSpeed... The original DDP multi-gpu training was fine with Flux Lora training, but as long as you installed DeepSpeed and enabled in your training script by adding --deepspeed --zero_stage=2, it would throw an error "RuntimeError: mat1 and mat2 must have the same dtype, but got Float and BFloat16". Even though you configured mixed_precision for both accelerate and scripts with bf16, the error still existed. Maybe @BootsofLagrangian would like to take a look. Thanks.

Thank you for noticing. I'll check it out and add a comment.

The error was not caused by DDP multi-gpu training, but by DeepSpeed... The original DDP multi-gpu training was fine with Flux Lora training, but as long as you installed DeepSpeed and enabled in your training script by adding --deepspeed --zero_stage=2, it would throw an error "RuntimeError: mat1 and mat2 must have the same dtype, but got Float and BFloat16". Even though you configured mixed_precision for both accelerate and scripts with bf16, the error still existed. Maybe @BootsofLagrangian would like to take a look. Thanks.

Thank you for noticing. I'll check it out and add a comment.
would you have idea to solve this problem?

Updated the sd3 branch. Multi-GPU training should now work. Please report again if the issue remains.

I have four A100-40G,Is it feasible to train flux model with multiple graphics cards?I've been having problems with OOM, but when  I add this command like --deepspeed --zero_stage=2 --offload_optimizer_device="cpu"  , it will report the same errors like "RuntimeError: mat1 and mat2 must have the same dtype, but got Float and BFloat16"

Updated the sd3 branch. Multi-GPU training should now work. Please report again if the issue remains.

 Multi-GPU training should now work. Please report again if the issue remains.

I have four A100-40G,Is it feasible to train flux model with multiple graphics cards?I've been having problems with OOM, but when I add this command like --deepspeed --zero_stage=2 --offload_optimizer_device="cpu"  , it will report the same errors like "RuntimeError: mat1 and mat2 must have the same dtype, but got Float and BFloat16"

In sd-scripts, autocast is enabled. But for some reason I don't know, autocasting for deepspeed model is not work. I think it takes times. But, other implemantion of ZeRO, FSDP, not implemented in sd-script, works.

Updated the sd3 branch. Multi-GPU training should now work. Please report again if the issue remains.

I have four A100-40G,Is it feasible to train flux model with multiple graphics cards?I've been having problems with OOM, but when I add this command like --deepspeed --zero_stage=2 --offload_optimizer_device="cpu"  , it will report the same errors like "RuntimeError: mat1 and mat2 must have the same dtype, but got Float and BFloat16"

In sd-scripts, autocast is enabled. But for some reason I don't know, autocasting for deepspeed model is not work. I think it takes times. But, other implemantion of ZeRO, FSDP, not implemented in sd-script, works.

how to modify my command to run the flux_train.py?

Updated the sd3 branch. Multi-GPU training should now work. Please report again if the issue remains.

I have four A100-40G,Is it feasible to train flux model with multiple graphics cards?I've been having problems with OOM, but when I add this command like --deepspeed --zero_stage=2 --offload_optimizer_device="cpu"  , it will report the same errors like "RuntimeError: mat1 and mat2 must have the same dtype, but got Float and BFloat16"

this my complete command
accelerate launch  --gpu_ids 0,1,2 --mixed_precision bf16 --num_cpu_threads_per_process 3 flux_train.py \
    --pretrained_model_name_or_path ${flux_model_path} --clip_l ${clip_l_path} --t5xxl ${t5xxl_path} --ae ${ae_path} --save_model_as safetensors \
    --sdpa --persistent_data_loader_workers --max_data_loader_n_workers 2 --seed 42 --gradient_checkpointing --mixed_precision bf16 --save_precision bf16 \
    --output_dir ${output_path} --output_name flux_dev  --highvram --cache_text_encoder_outputs_to_disk --cache_latents_to_disk --save_every_n_epochs 1  \
    --learning_rate 5e-5 --max_train_epochs 10 \
    --optimizer_type adamw8bit  \
    --timestep_sampling sigmoid --model_prediction_type raw --guidance_scale 1.0 \
    --cpu_offload_checkpointing \
    --resolution="1024,1024" --bucket_reso_steps=64 --bucket_no_upscale --min_bucket_reso=256 --max_bucket_reso=2048 --enable_bucket \
    --train_data_dir=${input_path} --caption_extension=".txt"  \
    --deepspeed --zero_stage=2 --full_bf16  --gradient_accumulation_steps=1 --cache_latents --offload_optimizer_device="cpu"

Regarding flux_train.py, even remove --deepspeed --zero_stage=2, and just use the original DDP multi-gpu training of the script, you will still see the OOM error, no matter how you optimize your configurations as kohya mentioned in notes of flux_train.py, which is becaused some configurations only work for 1 GPU condition.

Updated the sd3 branch. Multi-GPU training should now work. Please report again if the issue remains.

I have four A100-40G,Is it feasible to train flux model with multiple graphics cards?I've been having problems with OOM, but when I add this command like --deepspeed --zero_stage=2 --offload_optimizer_device="cpu"  , it will report the same errors like "RuntimeError: mat1 and mat2 must have the same dtype, but got Float and BFloat16"

this my complete command
accelerate launch  --gpu_ids 0,1,2 --mixed_precision bf16 --num_cpu_threads_per_process 3 flux_train.py \
    --pretrained_model_name_or_path ${flux_model_path} --clip_l ${clip_l_path} --t5xxl ${t5xxl_path} --ae ${ae_path} --save_model_as safetensors \
    --sdpa --persistent_data_loader_workers --max_data_loader_n_workers 2 --seed 42 --gradient_checkpointing --mixed_precision bf16 --save_precision bf16 \
    --output_dir ${output_path} --output_name flux_dev  --highvram --cache_text_encoder_outputs_to_disk --cache_latents_to_disk --save_every_n_epochs 1  \
    --learning_rate 5e-5 --max_train_epochs 10 \
    --optimizer_type adamw8bit  \
    --timestep_sampling sigmoid --model_prediction_type raw --guidance_scale 1.0 \
    --cpu_offload_checkpointing \
    --resolution="1024,1024" --bucket_reso_steps=64 --bucket_no_upscale --min_bucket_reso=256 --max_bucket_reso=2048 --enable_bucket \
    --train_data_dir=${input_path} --caption_extension=".txt"  \
    --deepspeed --zero_stage=2 --full_bf16  --gradient_accumulation_steps=1 --cache_latents --offload_optimizer_device="cpu"

Regarding flux_train.py, even remove --deepspeed --zero_stage=2, and just use the original DDP multi-gpu training of the script, you will still see the OOM error, no matter how you optimize your configurations as kohya mentioned in notes of flux_train.py, which is becaused some configurations only work for 1 GPU condition.

i also meet the problem, but when i used the same command to sdxl_train.py, it is ok, so i think the flux has some problem with deepspeed

The error was not caused by DDP multi-gpu training, but by DeepSpeed... The original DDP multi-gpu training was fine with Flux Lora training, but as long as you installed DeepSpeed and enabled in your training script by adding --deepspeed --zero_stage=2, it would throw an error "RuntimeError: mat1 and mat2 must have the same dtype, but got Float and BFloat16". Even though you configured mixed_precision for both accelerate and scripts with bf16, the error still existed. Maybe @BootsofLagrangian would like to take a look. Thanks.

i also meet the problem, but when i used the same command to sdxl_train.py, it is ok, so i think the flux has some problem with deepspeed

Updated the sd3 branch. Multi-GPU training should now work. Please report again if the issue remains.

I have four A100-40G,Is it feasible to train flux model with multiple graphics cards?I've been having problems with OOM, but when I add this command like --deepspeed --zero_stage=2 --offload_optimizer_device="cpu"  , it will report the same errors like "RuntimeError: mat1 and mat2 must have the same dtype, but got Float and BFloat16"

In sd-scripts, autocast is enabled. But for some reason I don't know, autocasting for deepspeed model is not work. I think it takes times. But, other implemantion of ZeRO, FSDP, not implemented in sd-script, works.

how to modify my command to run the flux_train.py?

DDP or Deepspeed? If you wanna try to run flux_train.py with DDP, you have to fix some codes like this. In case of Deepspeed, still in working.

DDP seems to consume a lot of memory. I guess it's because the number of parameters is so large that the synchronization overhead is large, but I don't know why, so if anyone knows, please let me know.

Regarding flux_train.py, even remove --deepspeed --zero_stage=2, and just use the original DDP multi-gpu training of the script, you will still see the OOM error, no matter how you optimize your configurations as kohya mentioned in notes of flux_train.py, which is becaused some configurations only work for 1 GPU condition.

Could you try reducing the resolution to about 512x512?

I tried the resolution 512 with A100 80G, but still OOM

The following options might work:  --sdpa --optimizer_type adafactor --optimizer_args relative_step=False scale_parameter=False warmup_init=False --full_bf16 --ddp_gradient_as_bucket --ddp_static_graph --cpu_offload_checkpointing --fused_backward_pass

Thank you very much, but i want to know when can use deepseed to finetune flux, using DDP can only with small batch and resulution

I'm not familiar with DeepSpeed so it will probably take a while.
thank you, i found when train using 1 gpu,the vram only used 40g,but using the same config, two gpu training, the vram used 80g, why?

Updated the sd3 branch. Multi-GPU training should now work. Please report again if the issue remains.

I have four A100-40G,Is it feasible to train flux model with multiple graphics cards?I've been having problems with OOM, but when I add this command like --deepspeed --zero_stage=2 --offload_optimizer_device="cpu"  , it will report the same errors like "RuntimeError: mat1 and mat2 must have the same dtype, but got Float and BFloat16"

In sd-scripts, autocast is enabled. But for some reason I don't know, autocasting for deepspeed model is not work. I think it takes times. But, other implemantion of ZeRO, FSDP, not implemented in sd-script, works.

I trained sdxl with deepspeed is ok by sd-scripts, but flux is not, so i think the flux code has some bugs?

@BootsofLagrangian Hi there, hope that i can reach out to you. I also get this dtype error when training flux lora with deepspeed multigpu. Do you have any updates maybe no what it might be? Thank you for your time!
RuntimeError: mat1 and mat2 must have the same dtype, but got Float and BFloat16

@BootsofLagrangian Hi there, hope that i can reach out to you. I also get this dtype error when training flux lora with deepspeed multigpu. Do you have any updates maybe no what it might be? Thank you for your time!
RuntimeError: mat1 and mat2 must have the same dtype, but got Float and BFloat16

This is quite tricky problem. It might be cause by input of models (probably cached token, Float), but autocast must handle this in context manager. Sorry for this

@terrificdm  With the resolution=1024 condition, the multi-gpu Flux finetuning is OOM, have you ever occur the same problem? Thanks a lot！My config script is :

@terrificdm With the RTX3090(24GB), image resolution=1024 condition, the multi-gpu Flux finetuning is OOM, have you ever occur the same problem? Thanks a lot！My config script is :

I have the same issue, I think this is related to GPU size?


skipping any erroneous items from selectionArrayWillChange

When creating a new record, and persisting the record, server side validation can force the record into an erroneous state.  When this comes back, the obj can be an empty "isError" record, which causes _selectize.removeItem to choke.

Can you elaborate, please?
You're saying that your use case involves persisting objects which are on content with ember-data.
Then those objects may become to an erroneous state, and you don't want them to be possible to select.
Is this right?
Can you describe a concrete scenario where this applies?

Checking for an isError property, seems like a very specific way to check for errors.
I doubt this generalizes well for others.


Triage jdk8u222-b10_openj9-0.15.1

x64Mac_XL:

sanity.openjdk : 7 failures => https://ci.adoptopenjdk.net/job/Test_openjdk8_j9_sanity.openjdk_x86-64_mac_xl/7/testReport/
java/net/Inet6Address/B6206527.java.B6206527
java/net/Inet6Address/B6558853.java.B6558853
java/net/InetAddress/CheckJNI.java.CheckJNI
java/net/Socket/LinkLocal.java.LinkLocal
java/net/ipv6tests/B6521014.java.B6521014
java/net/ipv6tests/TcpTest.java.TcpTest
java/net/ipv6tests/UdpTest.java.UdpTest
These are most likely a known issue with testing on Mac10.14: https://github.com/eclipse/openj9/issues/6035
Same set (and more) fail with Hotspot : https://hyc-runtimes-jenkins.swg-devops.com/view/Test_grinder/job/Grinder_Advanced/330/testReport/

Win64:

sanity.openjdk : 1 failure => https://ci.adoptopenjdk.net/job/Test_openjdk8_j9_sanity.openjdk_x86-64_windows/6/
java/util/Locale/LocaleProviders.sh.LocaleProviders
Passes locally, likely node issue : https://hyc-runtimes-jenkins.swg-devops.com/view/Test_grinder/job/Grinder_Advanced/334/console
sanity.system : Pass
extended.system : Pass

zLinux:

sanity.openjdk : 1 failure => https://ci.adoptopenjdk.net/job/Test_openjdk8_j9_sanity.openjdk_s390x_linux/7/testReport/
java/nio/channels/Selector/LotsOfChannels.java.LotsOfChannels
sanity.system : 8 failures => https://ci.adoptopenjdk.net/job/Test_openjdk8_j9_sanity.system_s390x_linux/7/tapResults/
Pass at ci.eclipse
extended.system : Pass

AIX:

sanity.openjdk : JOB failed (out of memory) ==> https://ci.adoptopenjdk.net/job/Test_openjdk8_j9_sanity.openjdk_ppc64_aix/2/
sanity.system : JOB failed (out of memory) ==> https://ci.adoptopenjdk.net/job/Test_openjdk8_j9_sanity.system_ppc64_aix/1/
extended.system : JOB failed (out of memory) ==> https://ci.adoptopenjdk.net/job/Test_openjdk8_j9_extended.system_ppc64_aix/1/

hi @andrew-m-leonard - if this triage effort is done (with relevant issues raised), can we close this?

yes closing


Requested operation is not valid: Setting different DAC user or group on OVMF_VARS-1024x768.fd which is already in use

This occurs after shutting down the system. When trying to start the VM up again:
Requested operation is not valid: Setting different DAC user or group on OVMF_VARS-1024x768.fd which is already in use
I'm having the same issue with SELinux, its the exact same error except replace DAC with "SELinux Attribute". I attempted to rectify the issue by setting remember_owner = 0 in /etc/libvirt/qemu.conf as specified in https://bugzilla.redhat.com/show_bug.cgi?id=1774373
I also found a chinese-language forum post talking about a similar issue, and their fix was again to set the remember_owner = 0 .
see https://bbs.huaweicloud.com/forum/thread-69587-1-1.html
I attempted to use this, and the machine was unbootable, instead going into a UEFI shell, but the error disappeared and the machine at least started.

see also https://github.com/foxlet/macOS-Simple-KVM/issues/409

@gwillgues It the VM still works fine, I would ignore it

@gwillgues or just try chown the file

machine does not boot, at least when using virt manager
Heres my SELinux specific error, although libvirt, virtmanager, qemu etc all have the permissions they need to change SELinux labels and run the machine:
libvirt.libvirtError: Requested operation is not valid: Setting different SELinux label on /opt/dir/OneClick-macOS-Simple-KVM/firmware/OVMF_VARS-1024x768.fd which is already in use

setting remember_owner=0 in /etc/libvirt/qemu.conf has resolved the issue, for whatever reason the first time around I was getting the QEMU uefi shell instead of bootloader


Possible false "outstanding PCIE DMA request" error

Running the test eth_fw_data_check causes luwen to think that DMA keeps going so the chip hangs with the following error -
It is not currently safe to communicate with ARC because, there is an outstanding PCIE DMA request
Did some digging and looks like the register ARC_CSM.ARC_PCIE_DMA_REQUEST.trigger has a non 0 value, while if that same register is read using the old backend I see its value being 0

Reading from spi or running test-pcie-dma produces the same error. This was observed on 2 different WH systems, in both cases the chip was still accessible afterwards and passes test-pcie-dma.

I committed a fix in v0.4.5 that fixed the issue on the system that I managed to repro on. Please test it and let me know if you see any improvements.


Unable to build v2.4.2 on Macbook

Ran the following commands:
cd chef-bcpc
git checkout v2.4.2
./tests/automated_install.sh

+ cp ../Vagrantfile .
+ [[ -f ../Vagrantfile.local.rb ]]
+ cp ../Vagrantfile.local.rb .
+ [[ ! -f insecure_private_key ]]
+ vagrant status
Current machine states:

bootstrap                 not created (virtualbox)

The environment has not yet been created. Run `vagrant up` to
create the environment. If a machine is not created, only the
default provider will be shown. So if a provider is not listed,
then the machine is not created for that environment.
+ cp /Users/smayani/.vagrant.d/insecure_private_key .
+ vagrant up --provision
Bringing machine 'bootstrap' up with 'virtualbox' provider...
There are errors in the configuration of this machine. Please fix
the following errors and try again:

vm:
* The host path of the shared folder is missing: /usr/local/share/ca-certificates

+ popd
~/Downloads/bbg/chef-bcpc
+ [[ -d ../cluster ]]
+ tar -C .. -cf - cluster
+ vagrant ssh -c 'cd ~; tar -xvf -'
VM must be created before running this command. Run `vagrant up` first.```

@saumilmayani Try the multi-cluster #788 branch. I pushed fee1..fe58 to it which should rectify this for you.

Saumil now gets the following; looking to understand which version of bash OS X uses:
cd chef-bcpc

./tests/automated_install.sh
./tests/automated_install.sh: line 31: typeset: -l: invalid option
typeset: usage: typeset [-afFirtx] [-p] name[=value] ...```

To attempt using Homebrew bash and /usr/bin/env bash for "bash portability" I have created cbaenziger/chef-bach branch osx_success; @saumilmayani let me know how it goes.

Thanks Clay, I got passed typeset error. Now hitting.
./vm-to-cluster.sh: line 25: bundle: command not found
./vm-to-cluster.sh: line 26: bundle: command not found
./vm-to-cluster.sh: line 27: bundle: command not found
./vm-to-cluster.sh: line 28: bundle: command not found
+ echo '############## VBOX CREATE CLUSTER VMs RETURNED 127 ##############'
############## VBOX CREATE CLUSTER VMs RETURNED 127 ##############
+ exit 1```

Please ensure your ChefDK is up-to-date and try setting your PATH to include /opt/chefdk/embedded/bin and please let me know if the path is different than that on OS X.

Closing due to inactivity. @saumilmayani please let me know if you want to pursue further.


Exception not raised outside with block

Publishing to a non-existing exchange does not raise exception if the channel is not used as a context manager:

conn = rabbitpy.Connection('amqp://LenelRabbit:Secur1ty%23@ms7:5672/LenelHost')
ch = conn.channel()
msg = rabbitpy.Message(ch, body_value='hello')
msg.publish(exchange='invalid')

If the channel is inside a with block the exception does come up:

conn = rabbitpy.Connection('amqp://myuser:mypass@ms7:5672/myhost')
with conn.channel() as ch:
...     msg = rabbitpy.Message(ch, body_value='hello')
...     msg.publish(exchange='invalid')
...
Traceback (most recent call last):
File "", line 3, in
File "D:\Programming\mqclient\env36x64\lib\site-packages\rabbitpy\channel.py", line 101, in exit
self.close()
File "D:\Programming\mqclient\env36x64\lib\site-packages\rabbitpy\channel.py", line 147, in close
super(Channel, self).close()
File "D:\Programming\mqclient\env36x64\lib\site-packages\rabbitpy\base.py", line 204, in close
self._check_for_pending_frames()
File "D:\Programming\mqclient\env36x64\lib\site-packages\rabbitpy\base.py", line 318, in check_for_pending
frames
self._check_for_rpc_request(value)
File "D:\Programming\mqclient\env36x64\lib\site-packages\rabbitpy\channel.py", line 271, in _check_for_rpc_r
equest
super(Channel, self)._check_for_rpc_request(value)
File "D:\Programming\mqclient\env36x64\lib\site-packages\rabbitpy\base.py", line 328, in _check_for_rpc_requ
est
self._on_remote_close(value)
File "D:\Programming\mqclient\env36x64\lib\site-packages\rabbitpy\base.py", line 380, in _on_remote_close
raise exceptions.AMQPvalue.reply_code
rabbitpy.exceptions.AMQPNotFound: <pamqp.specification.Channel.Close object at 0x333e438>

The reason why this is happening here is because without publisher confirmations, Message.publish is fire and forget at the protocol level. The exception is raised when RabbitMQ closes the channel remotely. It's an awkward side-effect of the protocol.
If you enable publisher confirmations and perform mandatory publishing, it raises where you would expect it. The following test passes:
class Issue119TestCase(unittest.TestCase):

    def test_exception_is_raised(self):
        conn = rabbitpy.Connection(os.environ['RABBITMQ_URL'])
        channel = conn.channel()
        channel.enable_publisher_confirms()
        msg = rabbitpy.Message(channel, body_value='hello')
        with self.assertRaises(exceptions.AMQPNotFound):
            msg.publish(exchange='invalid', mandatory=True)

If I purposefully pause and wait for a potential channel close after publishing, non mandatory publishing without publisher confirmations will suffer a pretty big performance degradation.

Unfortunately I can't think of a good way to force the behavior you are expecting with negatively impacting performance. It's not a bug in the library as much as it is a behavior in the protocol.
Using publisher confirmations will result in the behavior you expect, but will come with a performance tradeoff.


Update Dockerfile and Readme

What this PR does / why we need it:
Update Dockerfile for HuggingFace and Readme update

/lgtm

/approve


security.user.* properties with Spring Boot 2 / Finchley do not work anymore

Hello,
From Spring Boot 2, when setting credentials in bootstrap.properties, the properties referenced in the documentation, security.user.password && security.user.name do not work anymore. (ie : an application with spring-cloud-starter-config can't connect the the Configuration server, credentials are rejected, if they are encrypted)
spring-boot-properties-migrator warns us to replace those by spring.security.user.name&& spring.security.user.password, which works.
Documentation should be updated to reflect this change. I can submit a PR for it, but is there a particular documentation that targets Spring Boot 2 && Finchley ?
The actual documentation section to update is here
Example of a bootstrap.properties at Configuration Server level :
spring.security.user.name=user
spring.security.user.password={cipher}12c545c7e569d579c0b205bfebeef99dcbf07c0d079b4035ce2980ef6ef2301e

This is the file you want to update in the master branch https://github.com/spring-cloud/spring-cloud-config/blob/master/docs/src/main/asciidoc/spring-cloud-config.adoc

Thanks for feedback. PR here https://github.com/spring-cloud/spring-cloud-config/pull/900


[docs] Visual Studio publishing and

This is a documentation/readme suggestion based on https://github.com/AvaloniaUI/Avalonia/issues/1665#issuecomment-408824373
The SkiaSharp NuGet package offers runtime versions such as win7-x64 and win10-x64, but by default Visual Studio 2017's Publish options only list fallback runtime identifiers such as win-x64. The result is a missing libSkiaSharp.dll and the Unable to load DLL exception.
The solution is simple but not necessarily obvious: add a <RuntimeIdentifiers> node to the csproj, which expands the available RIDs in the VS Publish dialog.
I'm suggesting this and the specific available runtime versions should be documented, especially given that the readme mentions the same exception but attributes it to a different problem, which could be misleading and confusing.

Thanks for pointing this out. I updated the RID and should be in the next major release.


Publish public docker container

It would be great to have a published docker container to be used as an init container to keycloak kubernetes/helm installations, along with instructions to integrate that.

@bentatham thanks for the suggestion, I think it's a good idea.
@pb82 We use this in an init container also, right? Is there anything that you could link here that shows how we do it, as an initial step?

@bentatham @grdryn Yes, we use an init container in the keycloak operator to install plugins (at the moment only the Metrics SPI).
The way it works is that we bundle the SPI together with a small Python script in a container named sso plugins init
This container is then injected into the (upstream) sso template in the keycloak operator here.
We do this only because we don't want to modify and maintain our own sso template, so we inject our changes during runtime. If you have control over the template to deploy keycloak then you can just add the init container there.
When the init container runs it checks an env var named SSO_PLUGINS (see here) which has the comma separated list of plugins. It then just copies the requested plugins into a volume mounted to the directory where the server expects extensions.
We have not formally documented this anywhere but i agree that it would make a good example use case.

I'm trying to through this and understand.  Not 100% clear to me wit the details you gave.
We are deploying keycloak with helm charts (now at) codecentric/keycloak.
I built my own container with the keycloak-metrics-spi jar in it (FROM alpine)
I then used these instructions to mount the providers into /opt/jboss/keycloak/providers, which I can see.  However, adding metrics-listener to the config, I get this message:
20:08:23,589 ERROR [org.keycloak.services] (default task-1) KC-SERVICES0083: Event listener 'metrics-listener' registered, but provider not found

I suspect it is a permissions problem:  if I exec onto the pod, I can see:
[jboss@keycloak-id-dev-0 keycloak]$ ls -l /opt/jboss/keycloak/providers
total 88
-rw------- 1 root jboss 89622 Jun 25 19:56 keycloak-metrics-spi-1.0.1.jar
[jboss@keycloak-id-dev-0 keycloak]$ ls -l /opt/jboss/keycloak/
total 528
drwxrwx--x 3 jboss root    4096 Apr 23 14:09 bin
drwxrwxr-x 7 jboss root      92 Apr 23 14:09 docs
drwxrwx--x 5 jboss root      50 Apr 17 06:24 domain
-rw-rw-r-- 1 jboss root  488551 Apr 17 06:24 jboss-modules.jar
-rw-rw-r-- 1 jboss root   10637 Apr 17 06:24 License.html
-rw-rw-r-- 1 jboss root   26530 Apr 17 06:24 LICENSE.txt
drwxrwxr-x 3 jboss root      39 Apr 23 14:09 modules
drwxrwsrwx 2 root  jboss     44 Jun 25 19:56 providers
drwxrwx--x 1 jboss root      61 Apr 23 14:09 standalone
drwxrwxr-x 5 jboss root      76 Apr 23 14:08 themes
-rw-rw-r-- 1 jboss root      25 Mar  4 07:16 version.txt
drwxrwxr-x 2 jboss root      42 Apr 23 14:09 welcome-content
[jboss@keycloak-id-dev-0 keycloak]$

Any suggestions on how to fix that, of if permissions is indeed the issue?

I prefer to release pre build artifacts.
https://docs.travis-ci.com/user/deployment/releases/#draft-releases-with-draft-true
It's more flexible, since users just can run curl from github directly inside the init container, entrypoint script or just getting the aritfact inside the docker build if they build an own keycloak image.

Hello, @bentatham I have the same issue. I mount the jar into my keycloak docker container at /opt/jboss/keycloak/providers and it returns the same error message as you. Did you fix the issue ? if yes, how ? thanks

@cleman95 looks like someone provided an answer to this here: https://github.com/helm/charts/issues/4633#issuecomment-509195839
@bentatham Did you manage to get this working? Can you share the image you created?

This is the Dockerfile I'm using and it works (versions probably outdated at this point):
FROM jboss/keycloak:9.0.0

# https://github.com/aerogear/keycloak-metrics-spi
ADD https://github.com/aerogear/keycloak-metrics-spi/releases/download/1.0.4/keycloak-metrics-spi-1.0.4.jar \
    /opt/jboss/keycloak/providers/keycloak-metrics-spi-1.0.4.jar

USER root
RUN chown -R jboss:root /opt/jboss/keycloak/providers/ &&\
    chmod +x /opt/jboss/keycloak/providers/keycloak-metrics-spi-1.0.4.jar
USER jboss

This is the Dockerfile I'm using and it works:
FROM jboss/keycloak:10.0.2

# https://github.com/aerogear/keycloak-metrics-spi
ADD https://github.com/aerogear/keycloak-metrics-spi/releases/download/2.0.1/keycloak-metrics-spi-2.0.1.jar \
    /opt/jboss/keycloak/providers/keycloak-metrics-spi-2.0.1.jar

USER root
RUN chown -R jboss:root /opt/jboss/keycloak/providers/ &&\
    chmod +x /opt/jboss/keycloak/providers/keycloak-metrics-spi-2.0.1.jar
USER jboss

This didn't work for me. I had to add it to the "/opt/jboss/keycloak/standalone/deployments" and according to the latest documentation it should be there. Working solution for me is:
ARG KEYCLOAK_VERSION
FROM jboss/keycloak:$KEYCLOAK_VERSION
ADD https://github.com/aerogear/keycloak-metrics-spi/releases/download/2.0.1/keycloak-metrics-spi-2.0.1.jar
/opt/jboss/keycloak/standalone/deployments/keycloak-metrics-spi-2.0.1.jar
USER root
RUN chown -R jboss:root /opt/jboss/keycloak/standalone/deployments/ &&
chmod +x /opt/jboss/keycloak/standalone/deployments/keycloak-metrics-spi-2.0.1.jar &&
touch /opt/jboss/keycloak/standalone/deployments/keycloak-metrics-spi-2.0.2.jar.dodeploy
USER jboss

If you're using the codecentric helm chart, you don't even need an init container or to build anything. Presume this would work fine for themes too
#values.yaml
extraVolumeMounts: |
  - name: metrics-plugin
     mountPath: /opt/keycloak/providers/
extraVolumes: |
  - name: metrics-plugin
     secret:
     secretName: keycloak-metrics-plugin # this might need changing, I have 'keycloak' set as my full name override
secrets:
  metric-plugin:
     type: opaque
     data:
       keycloak-metrics-spi.jar: |
          <base64 data>

Then by running
cat keycloak-metrics-spi.jar | base64 >> values.yaml
I can put a base64'd dump of the file into the secret. Its a large base64 dump, but allows me to mount the jar file directly in at runtime, and store the versioned jar file in my GitOps yaml files.

Then by running
cat keycloak-metrics-spi.jar | base64 >> values.yaml
I can put a base64'd dump of the file into the secret. Its a large base64 dump, but allows me to mount the jar file directly in at runtime, and store the versioned jar file in my GitOps yaml files.

I'm amazed this doesn't exceed the maximum size for a secret.
The current release (3.0.0) is 151KB, which I guess is indeed smaller than the maximum size for a kubernetes secret.
Though I don't imagine its a great long-term solution.


Deploy review apps for dependabot PRs

Context
To support automatic merging of dependabot PRs we need to deploy review apps for these PRs.
Changes proposed in this pull request
This change deploys review apps for PRs with the dependencies label.  The required secrets have been added separately to the Dependabot context.
Guidance to review
As dependabot PRs are created from the main branch it won't be possible to test this change until after it's been merged to main.
Link to Trello card
https://trello.com/c/k1Ub6gI1
Things to check

[x] If the code removes any existing feature flags, a data migration has also been added to delete the entry from the database
[x] This code does not rely on migrations in the same Pull Request
[x] If this code includes a migration adding or changing columns, it also backfills existing records for consistency
[x] API release notes have been updated if necessary
[x] Required environment variables have been updated added to the Azure KeyVault

 Apply: Deploy dependabot PRs to review apps


I can't install android app

Description
There is a bug that does not install the app when I build android after the project is first created.
yarn run v1.22.17
$ react-native run-android
info Running jetifier to migrate libraries to AndroidX. You can disable it using "--no-jetifier" flag.
Jetifier found 863 file(s) to forward-jetify. Using 10 workers...
info JS server already running.
info Installing the app...

Task :app:processDebugMainManifest FAILED
11 actionable tasks: 2 executed, 9 up-to-date

FAILURE: Build failed with an exception.

What went wrong:
Execution failed for task ':app:processDebugMainManifest'.

Unable to make field private final java.lang.String java.io.File.path accessible: module java.base does not "opens java.io" to unnamed module @3ddaca54

Try:
Run with --stacktrace option to get the stack trace. Run with --info or --debug option to get more log output. Run with --scan to get full insights.

Get more help at https://help.gradle.org

BUILD FAILED in 2s
error Failed to install the app. Make sure you have the Android development environment set up: https://reactnative.dev/docs/environment-setup.
Error: Command failed: ./gradlew app:installDebug -PreactNativeDevServerPort=8081
FAILURE: Build failed with an exception.

What went wrong:
Execution failed for task ':app:processDebugMainManifest'.

Unable to make field private final java.lang.String java.io.File.path accessible: module java.base does not "opens java.io" to unnamed module @3ddaca54

Try:
Run with --stacktrace option to get the stack trace. Run with --info or --debug option to get more log output. Run with --scan to get full insights.

Get more help at https://help.gradle.org

BUILD FAILED in 2s
at makeError (/Users/junochoi/Documents/ReactNative/TodoApp/node_modules/execa/index.js:174:9)
at /Users/junochoi/Documents/ReactNative/TodoApp/node_modules/execa/index.js:278:16
at processTicksAndRejections (node:internal/process/task_queues:96:5)
at async runOnAllDevices (/Users/junochoi/Documents/ReactNative/TodoApp/node_modules/@react-native-community/cli-platform-android/build/commands/runAndroid/runOnAllDevices.js:109:5)
at async Command.handleAction (/Users/junochoi/Documents/ReactNative/TodoApp/node_modules/@react-native-community/cli/build/index.js:192:9)

info Run CLI with --verbose flag for more details.
error Command failed with exit code 1.
info Visit https://yarnpkg.com/en/docs/cli/run for documentation about this command.
Version
0.67.2
Output of npx react-native info
System:
OS: macOS 12.2
CPU: (10) arm64 Apple M1 Pro
Memory: 97.39 MB / 16.00 GB
Shell: 5.8 - /bin/zsh
Binaries:
Node: 16.14.0 - ~/.nvm/versions/node/v16.14.0/bin/node
Yarn: 1.22.17 - ~/.nvm/versions/node/v16.14.0/bin/yarn
npm: 8.3.1 - ~/.nvm/versions/node/v16.14.0/bin/npm
Watchman: 2<IP_ADDRESS> - /opt/homebrew/bin/watchman
Managers:
CocoaPods: 1.11.2 - /usr/local/bin/pod
SDKs:
iOS SDK:
Platforms: DriverKit 21.2, iOS 15.2, macOS 12.1, tvOS 15.2, watchOS 8.3
Android SDK:
API Levels: 29, 30, 31, 32
Build Tools: 29.0.2, 30.0.2, 30.0.3, 31.0.0
System Images: android-29 | Intel x86 Atom, android-29 | Intel x86 Atom_64, android-29 | Google APIs ARM 64 v8a, android-29 | Google APIs Intel x86 Atom, android-29 | Google APIs Intel x86 Atom_64, android-29 | Google Play ARM 64 v8a, android-29 | Google Play Intel x86 Atom, android-29 | Google Play Intel x86 Atom_64, android-30 | Intel x86 Atom_64, android-30 | Google APIs Intel x86 Atom, android-30 | Google APIs Intel x86 Atom_64, android-30 | Google Play ARM 64 v8a, android-31 | Intel x86 Atom_64, android-31 | Google APIs ARM 64 v8a, android-31 | Google APIs Intel x86 Atom_64, android-32 | Google Play ARM 64 v8a
Android NDK: Not Found
IDEs:
Android Studio: 2021.1 AI-211.7628.21.2111.8139111
Xcode: 13.2.1/13C100 - /usr/bin/xcodebuild
Languages:
Java: 17.0.2 - /usr/bin/javac
npmPackages:
@react-native-community/cli: Not Found
react: 17.0.2 => 17.0.2
react-native: 0.67.2 => 0.67.2
react-native-macos: Not Found
npmGlobalPackages:
react-native: Not Found
Steps to reproduce
In my case, it does not work immediately after project creation.
Snack, code example, screenshot, or link to a repository
No response

Try using the lowest version of Java and then work your way up to the highest compatible version. If I remember right this is a JAVA16+ problem.
Either by point  JAVA_HOME to a compatible 8.0 or 11 version or setup  the config to buildGradle (android/app) and maybe also the root project buildGradle (there are two) if you cant actually rollback your java this is the safer option.
 allprojects {
    sourceCompatibility = 1.8
    targetCompatibility = 1.8
}.

It should mention the maximum java version for the 4.2.2 gradle version or you should update the gradle version used in the lastest react native version.

There is a note about it in the Getting Started page: https://reactnative.dev/docs/environment-setup#installing-dependencies

If you have already installed JDK on your system, make sure it is JDK 11 or newer.

But it doesn't mention what gradle version should be used for that particular JDK version and this is a problem as I had to manually change the default gradle version from 4.2.2 to 7.1.1 for it to work with JDK 11. You guys should either update react native to use version 7.1.1 by default or mention in your getting started pages that a lower JDK version should be used as gradle 4.2.2 doesn't support JDK 11.

a lower JDK version should be used as gradle 4.2.2 doesn't support JDK 11.

That's not correct.
Android Gradle Plugin (not gradle) version 4.2.2 supports JDK >= 8
Android Gradle Plugin (not gradle) version 7.0.x supports JDK >= 11

You guys should either update react native to use version 7.1.1 by default or mention in your getting started pages that a lower

The default template is already updated to use AGP 7.0.x


syncer: Uniform use of global variables set role labels.

What type of PR is this?
/enhancement
Which issue(s) this PR fixes?
Fixes #
What this PR does?
Summary:

Judging whether the node is Leader when scrolling is updated

When initializing the node, the default status of the node is set to CANDIDATE.（In order to maintain a unified）

Special notes for your reviewer?

LGTM


ENH: glob for S3 CSVs

@cpcloud this would be super useful for me. Redshift dumps a (gzipped) CSV per node.
My questions:

priority? I think I took the same as the CSV glob
Better way to list the keys matching the glob than creating a new S3 connection inside resource_s3_csv_glob?
It doesn't actually work yet... is there another step to hook this up to the network?
tests
is there a better path to hook into than directly from chunks(s3(CSV)) -> chunks(DataFrame)?

I've only spent 1-2 hours on this and I'll continue to work on it. I'm just guessing that some of these will be easier for you to answer :)

Ah, I see why it wasn't getting added to the graph. convert expects (destination, source), not (source, destination). A leftover from into I'm guessing.

Indeed
On Fri, Aug 14, 2015 at 12:01 PM, Tom Augspurger<EMAIL_ADDRESS>wrote:

Ah, I see why it wasn't getting added to the graph. convert expects
(destination, source), not (source, destination). A leftover from into
I'm guessing.
—
Reply to this email directly or view it on GitHub
https://github.com/ContinuumIO/odo/pull/282#issuecomment-131210244.

@TomAugspurger what do you think about generalizing a bit here and making a Bucket type?

maybe something like
from odo import Bucket
bucket = resource('s3://path/to/my/bucket/*.csv')  # type(bucket) == Bucket(S3(CSV))
bucket.name == 'bucket'
bucket.path == 'path/to/my/my/bucket'

Better way to list the keys matching the glob than creating a new S3 connection inside resource_s3_csv_glob?

Call get_s3_connection which memoizes the connection keyed by the requisite AWS credentials

priority? I think I took the same as the CSV glob

yours should be higher priority, since you want yours to match before matching a plain CSV file

Hm, I wonder if we could make this work with boto.Bucket objects...

Ok, scratch the boto.Bucket idea for now ... doesn't look like there's a way to make the bucket look as if it has a particular set of files, which we need to do if we wanted to use bucket + globbing.
@TomAugspurger I'm think the chunks approach is nice.

Ok, refactored a bit using your comments. I'm reusing the s3 connection, added / tweaked the S3 docs a bit. Tests are still lacking :/ but it works for now at least.
In [1]: import odo

In [2]: odo.odo('s3://vehcuity/tst*.csv.gz', 'test.csv')
Out[2]: <odo.backends.csv.CSV at 0x105a58358>

In [3]: !cat test.csv
0,1
0.07331551328957626,-0.4154916469412773
-1.562069894747674,-1.4722870727590358
-0.15131201641683129,-0.31657923775839325
-0.666743351952952,-1.0564613439645762

In [4]: r = odo.resource('s3://vehcuity/tst*.csv.gz')

In [5]: r
Out[5]: <odo.chunks.chunks(odo.S3(CSV)) at 0x105ae4748>

In [6]: r.
r.container  r.data

In [6]: r.data
Out[6]:
[<odo.backends.aws.S3(CSV) at 0x105ae4390>,
 <odo.backends.aws.S3(CSV) at 0x105ae42b0>]

@cpcloud inlined those to functions, I had them broken out for testing, since they don't do any IO, but then gave up on testing :) I need to learn how mock works.

Fixing this up and merging. I've added a test


pkgs.formats.ini: don't coerce to list

Description of changes
That makes listToValue easier to implement.

Things done

Built on platform(s)

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

For non-Linux: Is sandboxing enabled in nix.conf? (See Nix manual)

[ ] sandbox = relaxed
[ ] sandbox = true

[ ] Tested, as applicable:

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

[ ] Tested compilation of all packages that depend on this change using nix-shell -p nixpkgs-review --run "nixpkgs-review rev HEAD". Note: all changes have to be committed, also see nixpkgs-review usage
[ ] Tested basic functionality of all binary files (usually in ./result/bin/)
24.11 Release Notes (or backporting 23.11 and 24.05 Release notes)

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

[x] Fits CONTRIBUTING.md.

Add a :+1: reaction to pull requests you find important.

Could you add an explanation to the commit message? It's not at all obvious to me what this intends to do or how it'd work.


initial checkin of Mac ARM scripts

This looks awesome! Just a couple minor inline comments.
How long does it take to build?

Building the C++ core took 96 minutes.  The Python builds took 2 minutes or so.  I did "-j4".  The MacBook Air has 8 cores, but 4 are performance cores and 4 are efficiency.  Ours has 8 gig of RAM.  I think it was slightly thrashy with 4 compiles going.

Something is wrong with this PR, likely not rebased correctly on master? Probably only one-two files changed and not 108. Please create a clean PR.

Yeah, clearly I've made a mess of things.


DistutilsError saying there's a sandbox violation.

I'm on macos.
Ran pip install organize-tool but get this error:
distutils.errors.DistutilsError: Setup script exited with error: SandboxViolation: mkdir('/Users/parsa/Library/Caches/com.apple.python/private/var/folders/xn/96skgj1d0wn4_9hzzm66xwlh0000gn/T/easy_install-iswwqubq', 511) {}

Could install it as root but it's not good to.
How can I resolve that issue ?

Which macOS version are you on? Can you do an pip install -U pip before?

I don't know how But I finally got through it.
Thank you for your help.


Introduce MonoFromOptionalSwitchIfEmpty and OptionalMapMonoJust Refaster rules

I've spotted the following interesting pattern:
Optional.of(someNullableValue())
  .map(Mono::just)
  .orElse(someMono());

The mix between Optional and Mono API is unnecessary.
These rules aim to rewrite these to:
Mono.justOrEmpty(someNullableValue())
  .switchIfEmpty(someMono());

Suggested commit message:
Introduce `MonoFromOptionalSwitchIfEmpty` Refaster rule (#384)

Unable to resolve action<EMAIL_ADDRESS>unable to find version v3.7.0

Build fails due to actions/setup-java#422...

Swapped the order of the names to make 'em sorted 😉.


OV Bloomz with use_cache=True generated output differ from ORT and Pytorch

Hi, I met following issue when I compared output results of bloomz-560m with OV, Pytorch and ORT on CPU.
It seems that Pytorch,ORT and OV without cache generate same outputs, while OV with cache (by default) generate strange outputs: bloomz_with_cache_issue.log
Input text:  DeepSpeed is a machine learning framework.
Pytorch generated text:             DeepSpeed is a machine learning framework. It is a software tool that uses deep learning to automatically learn from large volumes of data.
ORT generated text:                 DeepSpeed is a machine learning framework. It is a software tool that uses deep learning to automatically learn from large volumes of data.
OV without cache generated text:    DeepSpeed is a machine learning framework. It is a software tool that uses deep learning to automatically learn from large volumes of data.
OV with cache generated text:       DeepSpeed is a machine learning framework. Machine... Machine... Machine... Machine... Machine... Machine... Machine... Machine... Machine... Machine...

Here is a code sample to reproduce this issue on latest master:
from transformers import AutoModelForCausalLM, AutoTokenizer
from optimum.intel import OVModelForCausalLM
from optimum.onnxruntime import ORTModelForCausalLM

MODEL_ID = "bigscience/bloomz-560m"
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
pt_model = AutoModelForCausalLM.from_pretrained(MODEL_ID, use_cache=True)
ort_model = ORTModelForCausalLM.from_pretrained(MODEL_ID, export=True, use_cache=True)
ov_model_with_cache = OVModelForCausalLM.from_pretrained(MODEL_ID, export=True, use_cache=True)
ov_model_without_cache = OVModelForCausalLM.from_pretrained(MODEL_ID, export=True, use_cache=False)
input_text = "DeepSpeed is a machine learning framework."
print("Input text: ", input_text)
inputs = tokenizer(input_text, return_tensors="pt")

pt_gen_tokens = pt_model.generate(**inputs, max_new_tokens=20)
pt_gen_text = tokenizer.batch_decode(pt_gen_tokens, skip_special_tokens=True)[0]

ort_gen_tokens = ort_model.generate(**inputs, max_new_tokens=20)
ort_gen_text = tokenizer.batch_decode(ort_gen_tokens, skip_special_tokens=True)[0]

ov_with_cache_gen_tokens = ov_model_with_cache.generate(**inputs, max_new_tokens=20)
ov_with_cache_gen_text = tokenizer.batch_decode(ov_with_cache_gen_tokens, skip_special_tokens=True)[0]

ov_without_cache_gen_tokens = ov_model_without_cache.generate(**inputs, max_new_tokens=20)
ov_without_cache_gen_text = tokenizer.batch_decode(ov_without_cache_gen_tokens, skip_special_tokens=True)[0]

print("Pytorch generated text:            ", pt_gen_text)
print("ORT generated text:                ", ort_gen_text)
print("OV without cache generated text:   ", ov_without_cache_gen_text)
print("OV with cache generated text:      ", ov_with_cache_gen_text)

Here are packages in python environment:
openvino                      2023.0.0.dev20230427
openvino-dev                  2023.0.0.dev20230427
optimum                       1.8.6
optimum-intel                 1.8.1.dev0
transformers                  4.29.2

FYI. two hacks make it work:

there is dynamic branch in BloomModel._prepare_attn_mask which is not hit by onnx export, and the TracerWarning actually reported it:

venv/lib/python3.8/site-packages/transformers/models/bloom/modeling_bloom.py:660: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  if src_length > 1:

hack by replace it with if src_length > 0:

_make_causal_mask also contain a python condition which is not hit by onnx export logic:

    mask[:, past_key_values_length:] = seq_ids[:, None] < seq_ids[None, :]

    if past_key_values_length > 0:
        mask[:, :past_key_values_length] = False

changed to
    mask[:, :] = False
    mask[:, past_key_values_length:] = seq_ids[:, None] < seq_ids[None, :]

    #if past_key_values_length > 0:
    #    mask[:, :past_key_values_length] = False

there is a special logic in BloomForCausalLM.prepare_inputs_for_generation needs to be added into OVModelForCausalLM.prepare_inputs_for_generation, which is input_ids = input_ids[:, -1].unsqueeze(-1) when past_key_values is not None.

These are not proper fixes, but it confirms the root cause of this issue.

@usstq, thanks for you detailed root cause investigation.
I confirm that with 3 tricks above, OV with cache generated text is same with OV without cache. But torch generated wrong results because we manually change code in transformers.
Input text:  DeepSpeed is a machine learning framework.
Pytorch generated text:             DeepSpeed is a machine learning framework.  It is a software tool that uses deep learning to automatically learn from large volumes of data.
OV without cache generated text:    DeepSpeed is a machine learning framework. It is a software tool that uses deep learning to automatically learn from large volumes of data.
OV with cache generated text:       DeepSpeed is a machine learning framework. It is a software tool that uses deep learning to automatically learn from large volumes of data.

Besides, it was only workaround for bloom model, I still face issue for LLaMA-7b.
@echarlaix, Could you please kindly check if we can have a proper fix for this issue. Thanks!

Thanks a lot for reporting this issue @sammysun0711 and for the detailed investigation @usstq. Will take a look at it today to see how we can fix it !


Is there a way to support alternate column names?

I have a table that I'm trying to adapt from sqlalchemy in which a column name is a Python keyword (open). In my sqlalchemy table, I use open_ = Column("open", Float) so as to avoid overriding the keyword.
In my orm.Model, I'm unable to find a way to have an equivalent mapping of the mymodel.open_ object.
I tried open_ = orm.Float(name="open") but that isn't a supported kwarg.
open_ = orm.Float(title="open") doesn't work, resulting in asyncpg.exceptions.UndefinedColumnError: column tiingo_daily_historical.open_
Am I overlooking a way to do this?
Many thanks in advance!

Hi,
This project seems stale and from what I saw on encode page it's not a priority right now. Since I was tired with reinventing the wheel and needed something as soon as possible I created ormar package, that was inspired by this one.
Ormar bases its validation on pydantic so it can be used directly with fastapi as response and request models.
This issue was implemented in ormar, so feel free to check it  out: https://github.com/collerek/ormar

With or version 2 this is possible:
class Note(orm.Model):
    tablename = "notes"
    registry = models
    fields = {
        "id": orm.Integer(primary_key=True),
        "text": orm.String(max_length=100),
        "completed": orm.Boolean(default=False),
    }


Mocha ignores timeout on Promise test functions

Prerequisites

[ X] Checked that your issue hasn't already been filed by cross-referencing issues with the faq label
[ X] Checked next-gen ES issues and syntax problems by using the same environment and/or transpiler configuration without Mocha to ensure it isn't just a feature that actually isn't supported in the environment in question or a bug in your code.
[ x] 'Smoke tested' the code to be tested by running it outside the real test suite to get a better sense of whether the problem is in the code under test, your usage of Mocha, or Mocha itself
[x ] Ensured that there is no discrepancy between the locally and globally installed versions of Mocha. You can find them with: node node_modules/.bin/mocha --version(Local) and mocha --version(Global). We recommend avoiding the use of globally installed Mocha.

Description
Mocha does not respect specifying a timeout for tests that return promises.
I want to set a max timeout by invoking mochaTest.timeout(1000) but it is ignored.
Steps to Reproduce
describe( "reproduce promise timeout ignores", function () {

	it( "should timeout", function () {
		this.timeout( 1000 );
		return new Promise( function ( resolve, reject ) {
			setTimeout( function () {
				resolve();
			}, 3000 );
		} );
	} );

	const test = it( "should timeout2", function () {
		return new Promise( function ( resolve, reject ) {
			setTimeout( function () {
				resolve();
			}, 3000 );
		} );
	} );
	test.timeout( 1000 );

} );

Expected behavior: [What you expect to happen]
expect the tests to fail after 1000ms due to timeout.  I can understand why the first "should timeout" test might not work with my desired timeout (setting the timeout inside the async method) but the second "should timeout2" should work as desired.
Actual behavior: [What actually happens]
tests pass after aprox 3000ms
  reproduce promise timeout ignores
    √ should timeout (3006ms)
    √ should timeout2 (3001ms)

  2 passing (6s)

Reproduces how often: [What percentage of the time does it reproduce?]
all the time
Versions
mocha --version
5.2.0

node_modules/.bin/mocha --version
5.2.0

node --version
v8.11.4

os:
win10 x64 v1803 b17134.345

shell:
cmd and/or powershell

browser ver:
N/A

mocha modules:
N/A

transpiled via:
N/A

Additional Information
my full commandline for running the tests:
'C:\Users\jason\AppData\Roaming\npm\mocha.cmd' '--watch' '--colors' '--trace-warnings' '--inspect-brk=45370' './dist/**/mocha.test.js'

my workaround code, using typescript, xlib and bluebird, so you'll need to adjust it slightly based on your promise library of choice, and ditch the function name reflection code I'm using from xlib.
/** hack fix for mocha bug, unable to have a timeout for async tests */
function it2( testFcn: () => Promise<any> ) {
	const testName = xlib.reflection.getTypeName( testFcn );
	return it( testName, async function () {
		const timeoutMs = this.timeout();
		return xlib.promise.bluebird.resolve( testFcn.apply( this ) ).timeout( timeoutMs, new xlib.promise.bluebird.TimeoutError( `operation timed out.  Max of ${ timeoutMs }ms exceeded` ) );
	} );
}

If mochaTest.timeout(1000) were ignored then the default timeout of 2000ms would be activ. Therefore your test should fail with or without timeout of 1000. The problem is hidden somewhere else.

I'm using NodeJS v10.5.0  and Mocha 5.2.0 and your example is working perfectly without any change.

Please fill in the Versions information as originally requested for submitting a new issue.

OS: macOS 10.13.6 (17G65)
Node: v10.3.0
Mocha: 5.2.0
$ /var/tmp/mocha/bin/_mocha nakedeyes.js

  reproduce promise timeout ignores
    1) should timeout
    2) should timeout2

  0 passing (2s)
  2 failing

  1) reproduce promise timeout ignores
       should timeout:
     Error: Timeout of 1000ms exceeded. For async tests and hooks, ensure "done()" is called; if returning a Promise, ensure it resolves. (/private/tmp/nakedeyes.js)
      at Test.Runnable._timeoutError (/private/var/tmp/mocha/lib/runnable.js:440:10)
      at Timeout.<anonymous> (/private/var/tmp/mocha/lib/runnable.js:251:24)

  2) reproduce promise timeout ignores
       should timeout2:
     Error: Timeout of 1000ms exceeded. For async tests and hooks, ensure "done()" is called; if returning a Promise, ensure it resolves. (/private/tmp/nakedeyes.js)
      at Test.Runnable._timeoutError (/private/var/tmp/mocha/lib/runnable.js:440:10)
      at Timeout.<anonymous> (/private/var/tmp/mocha/lib/runnable.js:251:24)

Please fill in the Versions information above as originally requested for submitting a new issue.

Dunno since we have proven on both Window and macOS using Node-10.
You're not telling us something...

How your Typescript solution fixes things, but you don't mention it in 'transpiled via' nor include --require ts-node/register and --watch-extentions ts?
Why would your unit tests be in a "dist" directory?

Are you able to try running with Node-10 so we could compare against Juerg's results?

@plroebuck  regarding your questions:

typescript? No.   Though I use typescript to write code, I am running mocha against the transpiled javascript directly.  The only difference in my actual .js file from the repo code I gave is that the .js file has a "use strict"; line at the top and a //# sourceMappingURL=mocha.test.js.map line at the bottom.    I do not think those lines are relevant.   I do not use ts-node or anything of the sort.

dist dir? at "build time" I emit javascript to the /dist/ folder.   Again, this is just normal javascript, identical to my example code reproducing the error (with the exception of an additional first and last line, as explained above)

Node 10?  I see that the node LTS has been updated to 10.x so I'll give that a try.  will write back later once that's done.

Fair enough (concerning Typescript), but I can't explain how you could run the same version of Mocha against the exact file above (even with a different version of Node) and get different results.

when i update node to 10.x I'll also check if there's something weird getting loaded in from node_modules (purge globals and run in an empty local folder)   will let you know then!

I confirm this error with --inspect. This flag is passed through from Mocha to Node, but I suppose it's not a Node's problem.
Promise.race([
    new Promise(function(resolve) {
        setTimeout(resolve, 3000, "promise1")
    }),
    new Promise(function(resolve) {
        setTimeout(resolve, 1000, "promise2")
    })
])
.then( (msg) => console.log(msg));

Above timeout is working correctly with and without --inspect.

mocha --timeout --inspect is giving the correct result and the tests are failing.
This is strange since --timeout is expecting a parameter ms.
mocha --timeout  as expected results in: error: option -t, --timeout <ms> argument missing
mocha --timeout 0 and mocha --inspect show exactly the same results, tests are passing.
My guess is the value of timeout is lost somewhere when used with --inspect.

I narrowed the problem down. it's somehow related to using the v8 inspector debug protocol. When I run mocha ./dist/mocha.test.js it runs as expected (timeout failures after 1000ms) but when I add --inspect it does not timeout.
So in my example, If I run the following, I see the problem:
mocha --inspect ./dist/mocha.test.js

By design. See here in "/bin/mocha".

mocha --timeout --inspect is giving the correct result and the tests are failing.
This is strange since --timeout is expecting a parameter ms.
mocha --timeout as expected results in: error: option -t, --timeout <ms> argument missing
mocha --timeout 0 and mocha --inspect show exactly the same results, tests are passing.
My guess is the value of timeout is lost somewhere when used with --inspect.

They [--timeout/--no-timeouts] cancelled each other out.

ok, but maybe should add docs for that. https://mochajs.org/ has docs decribing how timeouts are disabled for --debug but nothing is mentioned for --inspect

As requested, now documented in usage


Ideas for additional examples

HTTP servers using an automerge document for peer discovery.

Use a bootstrap server where peers would get the initial document
Use autosurgeon.

how to use fs_store

You'll need to include the tokio feature and then you can use automerge_repo::tokio::FsStorage::open(<path>) to create a file system storage.
If you're not using tokio then you'll need to write an implementation of Storage which uses your runtime of choice. The automerge_repo::FsStore is intended to be wrapped in such an implementation.

Yeah the issue is that it's behind a feature flag, I need to set up some stuff to get docs.rs to generate the docs for it.

Another example that is missing: How to sync a document in a browser with a document in a rust client (using a central ws server in between. Maybe along with local changes happening on either side (say every few seconds) - and also with getting the current state of the doc in the other repo upon connection established.
We try to build it ourselves - and it's probably 'just there' - but as beginner in Rust AND automerge, it's a bit tough to connect all dots correctly - and we'd appreciate to see such an example


Subpath::new_rect() creates cubic segments instead of linear segments

Hi,
The bezier-rs Subpath::new_rect() constructor creates cubic segments, but imho linear segments would be more fitting?
Thanks!

Is this still relevant? Looking for a good issue to start contributing with

@mcargille sure, this would be a quick and easy one to handle and should be pretty quick. After you have a PR open for this, let me know if you'd like help picking out a second beginner issue.

PR #2109  fixes this issue.