feat: generalized selector for configuration betwen V1 and V2 price range based on pool ID

Context
As part of lowering our min spot price to 10^-30 (V2), we will continue to support the existing pools with 10^-12 (V1) to avoid migrations.
As a result, we need to implement a generalized configuration selector for deciding between V1 and V2 from pool ID.
It should be as simple as taking the pool ID as a parameter and outputting the minimum spot price.
A/C

Design implemented
Unit tested

Superceeded by SF position migration:
https://hackmd.io/FrJNXgqySVyCxorFmNzgAA


update narrative object/report

From<EMAIL_ADDRESS>on March 04, 2009 12:08:24
Hi,
During the OBI workshop in Vancouver the OBI group identified some terms to
be added/updated in IAO. If there is no objection this term will be updated
in the IAO file on Tuesday March 10th.
report # would replace narrative object
subclass of: information content entity
definition: A report is an information content entity assembled by an
author for the purpose of providing information for the audience.
A report is the output of a documenting process and has the objective to be
consumed by a specific audience. Topic of the report is on something that
has completed. A report is not a single figure. Examples of reports are
journal article, patent application, grant progress report, case report
(not patience record)
editor note: would replace narrative object
definition source :GROUP:OBI" with an editor note "2009-xx-xx: work on this
term has been finalized during the OBI workshop winter 2009"
Melanie
Original issue: http://code.google.com/p/information-artifact-ontology/issues/detail?id=11

From<EMAIL_ADDRESS>on March 05, 2009 21:49:14
report does not replace narrative object. It is a subclass of narrative object. Other types of narrative objects
include fiction. Report as subclass of narrative object, with definition as give makes sense.

From<EMAIL_ADDRESS>on March 06, 2009 17:34:46
Regarding comment #1 "Other types of narrative objects include fiction."
This may be, but regarding fiction, narrative object is a subclass of information content entity, which is
defined as follows
"an information content entity is an entity that is generically dependent on some artifact and stands in relation
of aboutness to some entity"
where "aboutness" is defined by
"a representation is_about anything exactly when some part of the information_artifact denotes the entity"
Many works of fiction are not about any (real) entity. I thought fiction was out of scope for IAO for this reason?
Can you give another example of narrative object (which would necessarily be about some entity) that is not a
report?

From<EMAIL_ADDRESS>on March 07, 2009 00:10:36
The working definition of aboutness is that a part denotes. Works of fiction don't spring out of thin air. But for an
easy example, consider a novel about what might happen if President Obama decided to take a vacation on the
moon. 'obama' denotes, as does 'moon', even if 'Obama on the moon' does not.

From<EMAIL_ADDRESS>on March 11, 2009 15:27:19
Proposed resolution following discussion Alan/Melanie Wednesday 11th March
report
subclass of: narrative object
definition: A report is a narrative object assembled by an author for the purpose of
providing information for the audience. A report is the output of a documenting
process and has the objective to be consumed by a specific audience. Topic of the
report is on something that has completed. A report is not a single figure. Examples
of reports are journal article, patent application, grant progress report, case
report (not patient record)
definition source: GROUP:OBI
editor note "2009-xx-xx: work has been done on this term during during the OBI
workshop winter 2009 and the current definition was considered acceptable for use in
OBI. If there is a need to modify this definition please notify OBI"
Note: narrative object include fiction (e.g. novel) and reports: report is a subclass
of narrative object, but doesn't replace it.

From<EMAIL_ADDRESS>on March 16, 2009 12:48:30
done. added editor note wrt Darren's comment.
Status: Done


Add plugin's settings page in Project Settings

I'll add something similar to what i did for AzSpeech:

In this page, i'll add some configurations to enhance working with some upcoming changes. :)


Updates for continued support

Hello! I have been working with the repository to get it running for myself, and found a few areas when using Codespaces that could use refreshing:

Fewer packages have to be installed when using Dev Container features.
Features in general provide a lot more ease when working with the components.
Prettier caught a number of syntax issues.
Tools, both in CI and runtime, had newer versions.
.NET 8 can be used.

I have a working install in Azure using this setup and thought the upstream could benefit.

@geekzter any thoughts?


updating the zoom function inside maptsx

tested on docker and the code is working

tested on docker and the code is working


Fix for MODCLUSTER-680.

See http://tomcat.apache.org/tomcat-9.0-doc/config/http.html#Standard_Implementation looks for SSLEnabled.
Jira
https://issues.jboss.org/browse/MODCLUSTER-680

@Karm @jfclere Do you think we need a backport to 1.x?

Yes please.

1.4 PR - https://github.com/modcluster/mod_cluster/pull/369
Do we need this for older branches? I wouldn't think so. Let me know.

We don't need the older branches.

👍


fix: redirect issues

What issues does this PR close?
### Checklist
- [ ] The PR title is short and expressive.
- [ ] I have updated the CHANGELOG.md. See [changelog_update.md](https://github.com/sovity/authority-portal/tree/main/docs/dev/changelog_updates.md) for more information.
- [ ] I have updated the Deployment Migration Notes Section in the CHANGELOG.md for any configuration / external API changes.
- [ ] I have performed a **self-review**

@SebastianOpriel I closed this PR because the new solution is much better and smaller >> #382


How to move .bak format type data in azure blob storage dataset service in ADF v2?

Not able to move database backup files (.bak & .log) stored in on-premises SQL server to azure blob storage because in format tab of azure blob storage there is no option of .bak format or .log format.

Document Details

ID: df7b56de-6c44-a6a7-7cb1-2d77025f17f5
Version Independent ID: fa99fc00-8d68-a1de-558e-0ef651548f34
Content: Copy and transform data in Azure Blob storage - Azure Data Factory
Content Source: articles/data-factory/connector-azure-blob-storage.md
Service: data-factory
GitHub Login: @linda33wj
Microsoft Alias: jingwang

Thanks for the question! We are taking a look into this and will get back to you soon.

@Harshitjain188 - Please use binary format that can move files without looking at the content of it and this might be helpful in this scenario. Please refer this document for more details on this.
Please let us know how it goes.


added Comment and detailed FCR info

Did you commit the init.py? I can only see examples.py, README.md and pyproject.toml.
Also, please fix Flake8 issues (x3)reported.

Did you commit the init.py? I can only see examples.py, README.md and pyproject.toml. Also, please fix Flake8 issues (x3)reported.

Sorry, I have done changes there but forgot to upload, good catch :)

More to come so not needed to approve now

@faanskit ops I merged it now. I thought I only updated and sent the changes to complete the PR


A session ended very soon after starting. Check that command in profile "Default" is correct.

Describe the bug | Bug描述
按照提示在macOS上生成了Xcode項目並完成了編譯。但是無法打開編輯器應用程式文件。
Screenshots/Logs | 截图/日志

Desktop | 桌面环境

OS: macOS Monterey
Version 12.4 BETA(21F5048e)
CPU Apple Silicon(M1)
GPU Apple Silicon(M1)
Memory 16GB
Compiler Apple Clang 13.1.6(clang-13<IP_ADDRESS>)
Shell iTerm2(zsh4humans)

可以生成一个Debug版本，给出程序崩溃的callstack吗？

Details
Could not launch “PilotEditor”
Domain: IDELaunchErrorDomain
Code: 20
Recovery Suggestion: The LaunchServices launcher has returned an error. Please check the system logs for the underlying cause of the error.
User Info: {
DVTErrorCreationDateKey = "2022-04-15 11:33:57 +0000";
DVTRadarComponentKey = 968756;
IDERunOperationFailingWorker = IDELaunchServicesLauncher;
}
The operation couldn’t be completed. Launch failed.
Domain: RBSRequestErrorDomain
Code: 5
Failure Reason: Launch failed.
Launchd job spawn failed
Domain: NSPOSIXErrorDomain
Code: 153
Analytics Event: com.apple.dt.IDERunOperationWorkerFinished : {
"device_model" = "MacBookAir10,1";
"device_osBuild" = "12.4 (21F5048e)";
"device_platform" = "com.apple.platform.macosx";
"launchSession_schemeCommand" = Run;
"launchSession_state" = 1;
"launchSession_targetArch" = arm64;
"operation_duration_ms" = 3935;
"operation_errorCode" = 20;
"operation_errorDomain" = IDELaunchErrorDomain;
"operation_errorWorker" = IDELaunchServicesLauncher;
"operation_name" = IDERunOperationWorkerGroup;
"param_consoleMode" = 0;
"param_debugger_attachToExtensions" = 0;
"param_debugger_attachToXPC" = 1;
"param_debugger_type" = 3;
"param_destination_isProxy" = 0;
"param_destination_platform" = "com.apple.platform.macosx";
"param_diag_MainThreadChecker_stopOnIssue" = 0;
"param_diag_MallocStackLogging_enableDuringAttach" = 0;
"param_diag_MallocStackLogging_enableForXPC" = 1;
"param_diag_allowLocationSimulation" = 1;
"param_diag_gpu_frameCapture_enable" = 0;
"param_diag_gpu_shaderValidation_enable" = 0;
"param_diag_gpu_validation_enable" = 0;
"param_diag_memoryGraphOnResourceException" = 0;
"param_diag_queueDebugging_enable" = 1;
"param_diag_runtimeProfile_generate" = 0;
"param_diag_sanitizer_asan_enable" = 0;
"param_diag_sanitizer_tsan_enable" = 0;
"param_diag_sanitizer_tsan_stopOnIssue" = 0;
"param_diag_sanitizer_ubsan_stopOnIssue" = 0;
"param_diag_showNonLocalizedStrings" = 0;
"param_diag_viewDebugging_enabled" = 1;
"param_diag_viewDebugging_insertDylibOnLaunch" = 1;
"param_install_style" = 0;
"param_launcher_UID" = 2;
"param_launcher_allowDeviceSensorReplayData" = 0;
"param_launcher_kind" = 0;
"param_launcher_style" = 99;
"param_launcher_substyle" = 8192;
"param_runnable_appExtensionHostRunMode" = 0;
"param_runnable_swiftVersion" = "5.6";
"param_runnable_type" = 1;
"param_testing_launchedForTesting" = 0;
"param_testing_suppressSimulatorApp" = 0;
"param_testing_usingCLI" = 0;
"sdk_canonicalName" = "macosx12.3";
"sdk_osVersion" = "12.3";
"sdk_variant" = macos;
}
System Information
macOS Version 12.4 (Build 21F5048e)
Xcode 13.3 (20102) (Build 13E113)
Timestamp: 2022-04-15T19:33:57+08:00


ci: merge staging to master

This is an automatic PR generated by the pipeline CI/CD. This will be automatically fast-forward merged if successful.

Pipeline Attempt on<PHONE_NUMBER> for 03531ffefa34bd4892aeecfe91fb59308da2bc69
https://gitlab.com/MatrixAI/open-source/js-ws/-/pipelines/1015270494

Pipeline Succeeded on<PHONE_NUMBER> for 03531ffefa34bd4892aeecfe91fb59308da2bc69
https://gitlab.com/MatrixAI/open-source/js-ws/-/pipelines/1015270494


Document when the SQLite database needs to be configured for copying to the output directory

I'm trying to implement the code based on console application sample but in WPF and I get the following error:

I've added the migration and updated the database. This is how my DbContext looks like:
    public class SettingsDbContext : DbContext
    {
        public DbSet<UserSettings> UserSettings { get; set; }

        protected override void OnConfiguring(DbContextOptionsBuilder options)
            => options.UseSqlite("Data Source=settings.db");

        protected override void OnModelCreating(ModelBuilder modelBuilder)
        {
            modelBuilder.Entity<Client1Credentials>(c =>
            {
                c.HasNoKey();
                c.Property(p => p.Username);
                c.Property(p => p.Password);
            });

            modelBuilder.Entity<Client2Credentials>(c =>
            {
                c.HasNoKey();
                c.Property(p => p.Username);
                c.Property(p => p.Password);
            });
        }
    }

Here is how my Model looks like:
    public class UserSettings
    {
        public int Id { get; set; }

        public bool RememberCredentials { get; set; }

        [NotMapped]
        public Client1Credentials Client1Credentials { get; set; }

        [NotMapped]
        public Client2Credentials Client2Credentials { get; set; }
    }

Below you can see that the migration and database were created:

Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: 7ac57b24-45aa-e679-6ff9-5a25c369c1ff
Version Independent ID: cc576199-b14c-a135-2291-cc48bf301b4e
Content: Getting Started - EF Core
Content Source: entity-framework/core/get-started/index.md
Product: entity-framework
Technology: entity-framework-core
GitHub Login: @Rick-Anderson
Microsoft Alias: avickers

@dgroh It looks like the current directory is different at runtime and hence the app isn't finding the SQLite database file. Try deploying to the current directory of the app at runtime, or being more explicit about the path to the file in the connection string.

Hi @ajcvickers thanks. I figured it out. The trick was to make the .db file to Copy if newer or Copy always in the File properties -> Copy to output directory . I could not see this in the documentation.

Note from team discussion: this step isn't needed for ASP.NET Core, but is for other targets such as WPF.

See also https://github.com/dotnet/efcore/issues/18624


Emit on ancestors

Fixes (the rest of) #612.

From #628

Since the WebSockets protocol is draft and will change entirely because it is insecure, this probably should be where CSS should stop putting extra effort into it. We emit all the events we can, sometimes even more than strictly needed, but that should be all good.
We can implement rigorously when there is a rigorous protocol.

Obsoleted by #628.


Add CeilometerProxy image

The ceilometer proxy image (which is used for TLS termination) is missing.
rh-jira: https://issues.redhat.com/browse/OSPRH-6400

I want to check the CI and confirm, that the image is being set correctly before "unholding".

After the following PR is merged, it'll be worth to give the CI another try. The image should appear: https://github.com/openstack-k8s-operators/openstack-operator/pull/771

recheck
Let's see if it appears

/retest

/unhold

The new image is there and it's being used in the telemetry CR created by the openstack-operator. CI is passing, this is ready for reviews.

/retest
Swift isn't ready. Swift-proxy pod events:
  Normal   Started         20m (x2 over 20m)    kubelet            Started container proxy-httpd
  Normal   Pulled          20m (x2 over 20m)    kubelet            Container image "quay.io/podified-antelope-centos9/openstack-swift-proxy-server@sha256:d28bdd9e1119fd420576c07f00ab44618f16b68117d51c3c9c3a42472e007a00" already present on machine
  Warning  BackOff         20m (x5 over 20m)    kubelet            Back-off restarting failed container proxy-server in pod swift-proxy-5f666c98d7-2sxz6_openstack(4ad23809-7cdb-4a74-a394-822415dc2175)
  Warning  Unhealthy       20m (x3 over 20m)    kubelet            Liveness probe failed: HTTP probe failed with statuscode: 503
  Warning  Unhealthy       20m (x4 over 20m)    kubelet            Readiness probe failed: HTTP probe failed with statuscode: 503
  Normal   Killing         20m                  kubelet            Container proxy-httpd failed liveness probe, will be restarted
  Warning  BackOff         46s (x102 over 20m)  kubelet            Back-off restarting failed container proxy-httpd in pod swift-proxy-5f666c98d7-2sxz6_openstack(4ad23809-7cdb-4a74-a394-822415dc2175)


Broadcasting dimensions in _match_nans

Currently, the _match_nans function in https://github.com/raybellwaves/xskillscore/blob/master/xskillscore/core/np_deterministic.py requires a and b have identical dimensions, while most operations on xarray.Dataset objects broadcast across non-common dimensions. This means that all dependent skill scores (e.g. _mae) are inconsistent between skipna=True and skipna=False options.
def _match_nans(a, b, weights):
    """
    Considers missing values pairwise. If a value is missing
    in a, the corresponding value in b is turned to nan, and
    vice versa.
    Returns
    -------
    a, b, weights : ndarray
        a, b, and weights (if not None) with nans placed at
        pairwise locations.
    """
    if np.isnan(a).any() or np.isnan(b).any():
        # Find pairwise indices in a and b that have nans.
        idx = np.logical_or(np.isnan(a), np.isnan(b))
        a[idx], b[idx] = np.nan, np.nan
        if weights is not None:
            weights[idx] = np.nan
    return a, b, weights

I think the following modifications could work to address this:
def _match_nans(a, b, weights):
    """
    Considers missing values pairwise. If a value is missing
    in a, the corresponding value in b is turned to nan, and
    vice versa.
    Returns
    -------
    a, b, weights : ndarray
        a, b, and weights (if not None) with nans placed at
        pairwise locations.
    """
    if np.isnan(a).any() or np.isnan(b).any():
        a, b = xr.broadcast(a, b)
        # Find pairwise indices in a and b that have nans.
        idx = np.logical_or(np.isnan(a), np.isnan(b))
        a[idx], b[idx] = np.nan, np.nan
        if weights is not None:
            weights[idx] = np.nan
    return a, b, weights

I'll try to test this myself and put in a Pull Request sometime soon but just wanted to open this issue first.

Doesn't the apply_ufunc handle the broadcasting already? Also the current proposed solution wouldn't work (using xr.broadcast()) since these functions need to remain purely numpy in nature.

It seems like apply_ufunc is working at the np.logical_or() step but not the indexing step a[idx], b[idx]`?
Here's the error I'm currently getting:
---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-39-8185ab1e4b41> in <module>
      3 for key, ens_masked in tqdm(ens_masked_dict.items()):
      4     #_, obs_masked2 = xr.broadcast(ens_masked, obs_masked)
----> 5     mae_masked_dict[key] = xs.mae(obs_masked, ens_masked, ['lat', 'lon'], weights=area, skipna=True).compute()

/srv/conda/envs/cmip6hack-multigen/lib/python3.7/site-packages/xarray/core/dataset.py in compute(self, **kwargs)
    803         """
    804         new = self.copy(deep=False)
--> 805         return new.load(**kwargs)
    806
    807     def _persist_inplace(self, **kwargs) -> "Dataset":

/srv/conda/envs/cmip6hack-multigen/lib/python3.7/site-packages/xarray/core/dataset.py in load(self, **kwargs)
    647
    648             # evaluate all the dask arrays simultaneously
--> 649             evaluated_data = da.compute(*lazy_data.values(), **kwargs)
    650
    651             for k, data in zip(lazy_data, evaluated_data):

/srv/conda/envs/cmip6hack-multigen/lib/python3.7/site-packages/dask/base.py in compute(*args, **kwargs)
    434     keys = [x.__dask_keys__() for x in collections]
    435     postcomputes = [x.__dask_postcompute__() for x in collections]
--> 436     results = schedule(dsk, keys, **kwargs)
    437     return repack([f(r, *a) for r, (f, a) in zip(results, postcomputes)])
    438

/srv/conda/envs/cmip6hack-multigen/lib/python3.7/site-packages/dask/threaded.py in get(dsk, result, cache, num_workers, pool, **kwargs)
     79         get_id=_thread_get_id,
     80         pack_exception=pack_exception,
---> 81         **kwargs
     82     )
     83

/srv/conda/envs/cmip6hack-multigen/lib/python3.7/site-packages/dask/local.py in get_async(apply_async, num_workers, dsk, result, cache, get_id, rerun_exceptions_locally, pack_exception, raise_exception, callbacks, dumps, loads, **kwargs)
    484                         _execute_task(task, data)  # Re-execute locally
    485                     else:
--> 486                         raise_exception(exc, tb)
    487                 res, worker_id = loads(res_info)
    488                 state["cache"][key] = res

/srv/conda/envs/cmip6hack-multigen/lib/python3.7/site-packages/dask/local.py in reraise(exc, tb)
    314     if exc.__traceback__ is not tb:
    315         raise exc.with_traceback(tb)
--> 316     raise exc
    317
    318

/srv/conda/envs/cmip6hack-multigen/lib/python3.7/site-packages/dask/local.py in execute_task(key, task_info, dumps, loads, get_id, pack_exception)
    220     try:
    221         task, data = loads(task_info)
--> 222         result = _execute_task(task, data)
    223         id = get_id()
    224         result = dumps((result, id))

/srv/conda/envs/cmip6hack-multigen/lib/python3.7/site-packages/dask/core.py in _execute_task(arg, cache, dsk)
    117         func, args = arg[0], arg[1:]
    118         args2 = [_execute_task(a, cache) for a in args]
--> 119         return func(*args2)
    120     elif not ishashable(arg):
    121         return arg

/srv/conda/envs/cmip6hack-multigen/lib/python3.7/site-packages/dask/optimization.py in __call__(self, *args)
    980         if not len(args) == len(self.inkeys):
    981             raise ValueError("Expected %d args, got %d" % (len(self.inkeys), len(args)))
--> 982         return core.get(self.dsk, self.outkey, dict(zip(self.inkeys, args)))
    983
    984     def __reduce__(self):

/srv/conda/envs/cmip6hack-multigen/lib/python3.7/site-packages/dask/core.py in get(dsk, out, cache)
    147     for key in toposort(dsk):
    148         task = dsk[key]
--> 149         result = _execute_task(task, cache)
    150         cache[key] = result
    151     result = _execute_task(out, cache)

/srv/conda/envs/cmip6hack-multigen/lib/python3.7/site-packages/dask/core.py in _execute_task(arg, cache, dsk)
    117         func, args = arg[0], arg[1:]
    118         args2 = [_execute_task(a, cache) for a in args]
--> 119         return func(*args2)
    120     elif not ishashable(arg):
    121         return arg

/srv/conda/envs/cmip6hack-multigen/lib/python3.7/site-packages/xskillscore/core/np_deterministic.py in _mae(a, b, weights, axis, skipna)
    365     sumfunc, meanfunc = _get_numpy_funcs(skipna)
    366     if skipna:
--> 367         a, b, weights = _match_nans(a, b, weights)
    368     weights = _check_weights(weights)
    369

/srv/conda/envs/cmip6hack-multigen/lib/python3.7/site-packages/xskillscore/core/np_deterministic.py in _match_nans(a, b, weights)
     33         # Find pairwise indices in a and b that have nans.
     34         idx = np.logical_or(np.isnan(a), np.isnan(b))
---> 35         a[idx], b[idx] = np.nan, np.nan
     36         if weights is not None:
     37             weights[idx] = np.nan

IndexError: too many indices for array

I find it very difficult to figure out what the numpy funcs are actually receiving from appy_ufunc(). So it's hard to debug.
Does this still happen with the new 0.0.11 version release? I imagine so given that just changed up the weights paths.

Yeah, I agree it's not obvious to me how to debug it.
The following hack - broadcasting the NaNs before calling the metric works fine as a workaround for my purposes:
a2, b2 = xr.broadcast(a, b)
xs.mae(a2, b2, ['lat', 'lon'], weights=area, skipna=True)

Can you reproduce this with a small self-contained example? I.e. just make two very small DataArrays that replicate the issue. That might help us figure out what's going on.

@hdrake are you able to provide some simple code to reproduce?

I think this same error message came up with Yohei Takano. Where are we at on this?

I think this is addressed in #166.


Clicking in custom instrument based on Oscillator Bank

Hi,
I'm working on an instrument based on the Oscillator Bank.
For the Oscillator Bank, when a note amp gets to almost 0.0 it is flagged for removal:
    if (stage == stageRelease && amp < 0.00001) {         clear();         remove();      }
   void clear() {             stage = stageOff;             amp = 0;         }
    // linked list management
    void remove() {
        if (prev) prev->next = next;
        else kernel->playingNotes = next;

        if (next) next->prev = prev;

        //prev = next = nullptr; Had to remove due to a click, potentially bad

        --kernel->playingNotesCount;

        sp_osc_destroy(&osc);
        sp_adsr_destroy(&adsr);
    }`

My instrument is very similar, but it uses two adsr objects, a moog ladder filter and three oscillators. The problem is that when the note is removed there is an audible click. The notes seem to take a couple of seconds to fully decay, so if you play three or four note on/offs you'll hear 4 clicks in roughly the same pattern about 3 seconds after the notes have been played.
sp_moogladder_destroy(&moogladder);
sp_blsquare_destroy(&blsquare);
sp_blsaw_destroy(&blsaw1);
sp_blsaw_destroy(&blsaw2);
sp_adsr_destroy(&freq_adsr);
sp_adsr_destroy(&adsr);
Any idea why there might be clicking in this way. I've checked all the bank instruments in AudioKit and they don't seem to have this problem.

It turns out the two ADSRs were not working well together. If I give them the same release time then the clicking goes away.


Is there any paper or docs describe the algorithm in detail?

RT
I want to learn the LightGBM Algorithm detailedly, but the wiki is somewhat simple, it just introduce the LightGBM. Is there any paper or docs describe the algorithm in detail?
THX a lot!

refer to #310


content of matching messages in alert e-mail body

Issue Description
Hello. I've just installed sentinl and would like to thank you for the work you put into its development.
An issue I can't seem to solve or find in the documentation is that one of my managers would like to receive the content of the one or multiple messages that match an alert. I noticed the payload variable that can be used for the occurences but can't seem to find the documentation on how to retrieve the content of certain fields of the messages themselves.

Is this a possibility with Sentinl and if yes could someone point me in the right direction please? Thanks.

@Evertvz
Thank you for the kind words.
Elasticsearch documents that you receive after a watcher execution are objects inside payload.hits.hits array. You can parse the array and retrieve any data you want from these documents.
For example, you want to list all compnies names which you can find in the received docs. Then you can write a small JS script in Transfrom tab which parses the docs and puts company names into some new property, payload.hits.my_companies in my example.
{
  "script": {
    "script": "payload.hits.my_companies=[];for(var i=0;i<payload.hits.hits.length;i++)payload.hits.my_companies.push({name:payload.hits.hits[i]._source.label});"
  }
}

Then you create email html action under Actions tab and render the compnies list using mustache syntax and HTML:

Companies

{{#payload.hits.my_companies}}

{{name}}

{{/payload.hits.my_companies}}

For more details, please refer to our wiki here on GitHub https://github.com/sirensolutions/sentinl/wiki


Fixes #8036 : yii\log\Logger unable to export session id

Fixes #8036 : yii\log\Logger unable to export session id

Looks OK.

:+1:

Hi,
I have the same problem as tlanyan in yii2 version 2.0.7.
I resolved the bug by modify the file Logger.php, init() function, line no 120 like this :
$this->flush(true);
Is it a good solution ?

@gregos22 please open a new issue for this as it should be fixed since 2.0.4 already. also provide more details on how to reproduce the issue.


Injection is disabled if ge url is not set

Should the documentation as well be updated ?

Good point! Updated the readme.


12 - As a player, after the recreation phase ends, I am shown a visual overview of all the recreated pictures so that I can submit guesses

[x] The initial pictures coordinate grid is shown
[x] The recreations of the other players are shown in a second grid
[x] A player can submit each guess in a field below the recreated picture
[x] A player can click on a button "Submit Guess" to finalize his guesses
[x] After all players click on "Submit Guess", they are redirected to the Scoring GUI
- [ ] (A countdown is shown such that the players make their guess in appropriate time, if the player doesn't take a guess after the countdown is over, an arbitrary guess will be set on the scoring sheet)

8dafcbb5780921dbb5b0d0eaca00e0e00598d467


New KServe Docs in Kubeflow website for KServe v0.7

I described this issue in previous release team and kubeflow meetings
KServe has its own website: https://kserve.github.io/website/0.7/
Kubeflow has its own website: https://www.kubeflow.org/
In order for users to navigate  from the kubeflow website to KServe website, Links or Cards should be added in kubeflow documentation
cc @animeshsingh @yuzisun @shannonbradshaw

/close


Simple Agent Output Json Schema Error

the following json schema output format was responsible for the error. Issue parsing into a base model.
type: object
properties:
 stock_prices:
   type: array
   items:
     type: object
     properties:
       Symbol:
         type: string
         description: The symbol or ticker representing a stock or asset.
       Price:
         type: number
         description: The price of the asset at the given time.
       Time:
         type: string
         format: date-time
         description: The time of the price in ISO 8601 format.
       Time zone:
         type: string
         description: The time zone for the recorded time.
       Delta:
         type: number
         description: The change in price since the last recorded time.
     required:
       - Symbol
       - Price
       - Time
       - Time zone
       - Delta
     additionalProperties: false
required:
 - stock_prices
additionalProperties: false

Looks like this was just a yaml formatting issue. The following agent has an action with a working json schema representation object:
apiVersion: server.eidolonai.com/v1alpha1
kind: Agent
metadata:
  name: hello-world

spec:
  implementation: SimpleAgent
  actions:
    - name: converse
      output_schema:
        type: object
        properties:
          stock_prices:
            type: array
            items:
              type: object
              properties:
                Symbol:
                  type: string
                  description: The symbol or ticker representing a stock or asset.
                Price:
                  type: number
                  description: The price of the asset at the given time.
                Time:
                  type: string
                  format: date-time
                  description: The time of the price in ISO 8601 format.
                Time zone:
                  type: string
                  description: The time zone for the recorded time.
                Delta:
                  type: number
                  description: The change in price since the last recorded time.
              required:
                - Symbol
                - Price
                - Time
                - Time zone
                - Delta
              additionalProperties: false
        required:
        - stock_prices
        additionalProperties: false
  description: "This is an example of a agent using the 'SimpleAgent' template."

This did however point out that the errors we report for invalid json schema are insufficient, so I will make a pr to clear that up


Upscale all raster layers in GeoNode to 1km resolution.

Use the align function to resample all rasters using the grid of the population 1 km layer. The layers that need to be resampled are:

[ ] Population

Is already done on the platform, will be closing this


[EMB-119] Add feedback button Ember component

Purpose
Add feedback button to Quick Files page

Example JIRA issue: https://openscience.atlassian.net/browse/FEED-9
This replaces #67
Summary of Changes

Add feedback-button component

Side Effects / Testing Notes

Requires the following configuration (for staging and prod):

       microfeedback: {
            url: 'https://microfeedback-osf.herokuapp.com/13209/3',
            pageParams: {
                QuickFiles: {
                    componentID: '13836',
                    priorityID: '10100',
                },
            },
        },

Ticket
https://openscience.atlassian.net/browse/EMB-119
Reviewer Checklist

[ ] meets requirements
[ ] easy to understand
[ ] DRY
[ ] testable and includes test(s)
[ ] changes described in CHANGELOG.md

close #67
close #71

Coverage remained the same at 4.673% when pulling b940f447a1013e96e715b5fae0484ebabd370657 on feature/feedback-button-component into 4bb18908b0bfebf900c473dfcdf055f8bf1928a9 on develop.

@binoculars I've removed the JIRA IDs from the config.
I'm not sure there's a whole lot of value in a standalone ember addon. It would have to know how dialogs are displayed--for example, this PR is tied to the fact that we are using ember-bootstrap. So really it would be ember-bootstrap-microfeedback-addon--awfully specific, yeah?
I'm not entirely opposed to having a standalone addon, but can be a future improvement. Even if we did that, we would still want to have the feedback-component in ember-osf-web that had OSF-specific stuff, e.g., the "Contact me for further opportunities to improve the OSF" checkbox, etc.

Discussed with @binoculars : JIRA-specific config is now removed. The ember config will contain the page-specific query params. This allows different backends to be used (not just microfeedback-jira).

With the way this PR works, this is the necessary configuration:
        microfeedback: {
            url: 'https://microfeedback-osf.herokuapp.com/13209/3',
            pageParams: {
                QuickFiles: {
                    componentID: '13836',
                    priorityID: '10100',
                }
            },
        },

This can be the same for all deployments, incl. production. @binoculars

@binoculars Is this good enough for the 0.2.0 release?

I don't see why not. Should be a quick QA once merged and the configs are updated.

Cool. Can you do an Approval review to keep it all official?


Update React peerDependency to V 15.3.0

Please update the peerDependencies for react to V 15.3.0. Just started a new project and it uses the latest version.
Thanks

The version in peerDependencies is ^15.0.0 which matches any 15.x.x version. There's no need to change it.

Yep.


fs.writeFileSync vs Webpack chunks?

I'm admittedly not a Webpack expert (especially for chunks, which I rarely use), but instead of using fs.writeFileSync and have webpack synchronously write out files, wouldn't be better to just use Webpack's emitFile method for this? You can see it in action in the file-loader which emits the file to disk and returns the path.
By emitting a file, I'm pretty sure it gets refed to webpack and extra processing can happen. At that point, you can use SplitChunksPlugin, specifically the .name property to bundle the tests, or not (it becomes up to the user). It'd probably require similar options to file-loader tbh, which would get around the hardcoded __tests__ path.
Perhaps it would just be better to use file-loader?

After experimenting it looks like I can in fact just use file-loader to load the tests like this module does, but with better integration into Webpack.
I also had to use the latest documentation for vue-loader.vuejs.org as the example for adding a loader to vue-loader in the README wasn't working for me
extend(config) {
  config.externals = config.externals || {};
  config.externals.ava = 'ava';

  config.module.rules.push({
    resourceQuery: /blockType=test/,
    loader: 'file-loader',
    options: {
      name: '[name].spec.js',
      outputPath: 'componentTests'
    }
  });
}

After experimenting it looks like I can in fact just use file-loader to load the tests like this module does, but with better integration into Webpack.
I also had to use the latest documentation for vue-loader.vuejs.org as the example for adding a loader to vue-loader in the README wasn't working for me
extend(config) {
  config.externals = config.externals || {};
  config.externals.ava = 'ava';

  config.module.rules.push({
    resourceQuery: /blockType=test/,
    loader: 'file-loader',
    options: {
      name: '[name].spec.js',
      outputPath: 'componentTests'
    }
  });
}


Fixed VM Snapshot Extension test failure - Expected <10> to be less than <10>, but was not

Test VmSnapsotLinuxBVTExtension.verify_vmsnapshot_extension was failing due AssertionError: [Restore point creation failed.] Expected <10> to be less than <10>, but was not.
For this, have removed redundant count increment from the func _verify_vmsnapshot_extension and now it is passing

@arisettisanjana please fix the CI check error


unable to reproduce AAS server JSON serialization

Hi,
I am using the official docker image of AAS server (eclipsebasyx/aas-server:1.3.0). However, the JSON response of AAS server is different than basyx-python-sdk JSON serialization result (Version: 0.2.2)
For example, when kind of submodel is instance there is no "kind":"Instance" in the output. In the image below you can see the structure is not consistent and this is reflected in the code too, however I don't know this is according to specification or just implementaiton difference.

And here you can see there are asset and assetRef on the left produced by AAS server. python sdk asset is equivalent to assetRef and asset is completely missing.

Is there any way to reproduce exact result of AAS server in python? or which java library should be used, basyx-java-sdk or eclipse-aas4j/aas4j or something else?
Please let me know if you require further information.

Which version of the asset administration shell model does this server use? Version 0.2.2 uses Version 2.0.1. of the AAS model (https://www.plattform-i40.de/IP/Redaktion/DE/Downloads/Publikation/Details_of_the_Asset_Administration_Shell_Part1_V2.pdf?__blob=publicationFile&v=6)

The output without "kind":"Instance" is still schema conform in V2.0.1, as the Kind=Instance per default, if nothing is given in JSON.

There is no assetRef in JSON Schema for the version V2.0.1 of the AAS. I guess all the content which is in "assetRef" shoud actually be in "asset".


Date properties are strings

OneLogin custom objects' date properties should be output as a datetime object, rather than a string. Also, this should convert UTC to local time and vice-versa.

fixed in commit #67eb778b9fe3d743af303e48cd90beab65fbec78


[TOPIC: DTS] dts: edtlib: Improve how we get the compatible string from bindings

Use Galak's idea from
https://github.com/zephyrproject-rtos/zephyr/pull/18313 to read the
'properties: compatible: constraint: "foo"' string from bindings in a
more robust way.
First, check if any of the compatible strings are in the file (needed as
an optimization). If any of them are, do a more careful check for the
'properties: compatible: constraint: ...' value matching a compatible,
to filter out false positives from comments and the like.
This commit a no-op in itself besides making things a bit more robust,
but it'll make later work easier (supporting multiple compatibles for a
binding, in a dt-schema-like way).

Piggybacking moving dts/binding/binding-template.yaml to dts/binding-template.yaml, from https://github.com/zephyrproject-rtos/zephyr/pull/18313.

Hmm... just thought of a case where this gets a bit icky. _load_binding() will run _check_binding(), but we're not sure that what we're loading is a full binding after the hacky string grepping thingy.
Will restructure a bit.

Hmm... just thought of a case where this gets a bit icky. _load_binding() will run _check_binding(), but we're not sure that what we're loading is a full binding after the hacky string grepping thingy.
Will restructure a bit.

Fixed now. Moved the !include handling and binding sanity checking later.


Change input type to number

Change input type to number for better UX.

Thank you for your pr, but this example I hope will show that when the value is illegal, there are corresponding errors.
So I won't merge it, thank you again for your kindness.

No problem, didn't realise that was the purpose, nevermind then 😉


Floating label get cutted

react-native, react and native-base version
react-native-cli: 2.0.1
react-native: 0.52.1
native-base: 2.3.7
react: 16.2.0
Windows: 10
Android: 8.0
I'm getting the same bug of #1006
The floating label get cutted when is floating
Expected behaviour

Actual behaviour

Steps to reproduce (code snippet or screenshot)
        <Container>
             ...
            <Content>
                <Form>
                    <Item floatingLabel>
                        Username
                        <Input />
                    </Item>
                    <Item floatingLabel last>
                        Password
                        <Input />
                    </Item>
                </Form>
            </Content>
        </Container>

Screenshot of emulator/device

Even without the header component

#1532
Fixed with 2.3.8

I am facing  the same issue with 2.3.10


fix: Stop pairs of tags from getting put into paragraphs

[x] I have read freeCodeCamp's contribution guidelines.
[x] My pull request has a descriptive title (not a vague title like Update index.md)
[x] My pull request targets the master branch of freeCodeCamp.
[x] None of my changes are plagiarized from another source without proper attribution.
[x] All the files I changed are in the same world language (for example: only English changes, or only Chinese changes, etc.)
[x] My changes do not use shortened URLs or affiliate links.

Closes #35727, which is a specific case of a problem that appears throughout the challenges.  As @RandellDawson pointed out in https://github.com/freeCodeCamp/freeCodeCamp/issues/35727#issuecomment-478433879, if you have a space between any two tags, the second tag will appear in a new paragraph.
With this PR if the parser encounters a line which is just whitespace, it will leave it alone and not try to wrap it into a paragraph.  As far as I can see, this should only appear when some text is nestled between two tags.  In that case we don't want it to get its own paragraph.

@moT01 Thanks for the review!  So, we could definitely fix that challenge by removing the tags or by wrapping the space.  Unfortunately this problem crops up all over the place.  For example, here after "not", here after "Bird's" and here after "&&".
I'll compile a list of them, just in case we do want to fix the challenges rather than the parser.  However, I think it would be better if we could write whatever tags are needed and not have to worry about spaces.  Having said all that, I entirely agree that it needs reviewing by a core member!

Well, I suppose since you already did the work here, and there is more than just the one challenge with the issue... maybe we should just get this looked at, and if it gets merged we don't need to worry about the issue popping back up. I'm not sure a list is necessary at the moment, I would wait on it until it's decided we want to go that route - but go ahead if you want.

@moT01 Yeah, you're right, I'll put that on hold for now.  There are rather a lot of them!

This looks like it's probably the way to go then.


Links with anchor do not lead to correct section

for example https://cap.cloud.sap/docs/guides/databases#native-db-queries does not lead me to the native-db-queries section, but to a section which is below the desired one. Tested on the newest Chrome Version as well as in Safari.

We know about a little issue with anchor links, but this offset is quite huge.
@swaldmann Could pls check this? Thanks a lot!

Reported here: cap/issues#13650 → subscribe there


[Bug]: Authorizations are broken after zitadel upgradations

Preflight Checklist

[X] I could not find a solution in the documentation, the existing issues or discussions
[ ] I have joined the ZITADEL chat

Environment
Self-hosted
Version
2.48.1
Database
PostgreSQL
Database Version
postgres 16
Describe the problem caused by this bug
After Zitadel Version upgrade(2.48.1), the Authorisations list is empty. when we revert back to previous version(2.48.0) the Authorisations list is showing all users.
To reproduce
Go to zitadel Admin Dashboard
Check "Authorisation" - the list is empty
Screenshots
No response
Expected behavior
No response
Operating System
No response
Relevant Configuration
No response
Additional Context
No response

@AjayKuruvath Did you enable --init-projections when upgrading?
This will ensure that all projections are uptodate when updating.
Otherwise it may take a moment till all projections are computed

@hifabienne  Yes we have already enabled it.

@hifabienne Yes we have already enabled it.

Where did you set that? Why is it called Compose projections and not init projections?
I am not aware that we do call it like this somewhere.

@hifabienne  We have added --init-projections  and it worked correctly. Thank you very much

Thanks @AjayKuruvath for the feedback.
In that case I will close the issue.


Could you please provide the metadata.jsonl or any other .json file used for training?

Hi,
We have uploaded the metadata.jsonl which we used for training here


add dry-run

Feature-request
Description
For better testing of sakura-files a dry-run feature is necessary, which makes only checks without executing the scripts. This should check for:

syntax-errors
subtree-links
ressource-, file- and template-references
blossom-input and output requirements

add new --dry-run-flag


Add implementation of production and consumption stats RPCs

Still WIP.
Depends on #86 and #87.
Will eventually resolve #82.

@pulsastrix I think this PR should now be a ready for an initial review. As discussed yesterday, it is now possible to associate one producer and multiple consumers with the NodeInstance service, the consumers can be differentiated via the edgeName. The way the production stats are collected probably needs some more discussion, looking forward to your opinion here.
To achieve a cleaner diff and focus on the actual changes associated with this PR, #86 and #87 should get merged first before conducting an actual review, though.

@JKRhb I will take a look at it as soon as #87 is merged.

As discussed yesterday, it is now possible to associate one producer and multiple consumers with the NodeInstance service, the consumers can be differentiated via the edgeName.

A single node instance might have multiple outgoing edges with differing data (e.g. one output for video frames, one for audio frames, ...), in which case we might need more than one producer for a single instance.

A single node instance might have multiple outgoing edges with differing data (e.g. one output for video frames, one for audio frames, ...), in which case we might need more than one producer for a single instance.

Hmm, yeah, good point, I will revert this aspect of the PR.


Add TaggedMetricName, Timer/Meter extensions that support metric-leve…

…l TimeUnits, additional configuration options for reporter
TODO:

Timer/Histogram extensions that support clearing
String -> TaggedMetricName cache in reporter

The latest commit depends on https://github.com/wavefrontHQ/wavefront-sdk-java/tree/han/update-wfhist-snapshot

Not necessary anymore


Add copy() and query() methods to Vector and add test_geovector.py file

First off, no tests existed for geovector.py!
I created some very simple tests, but these should be improved in the future.
I have added two methods to Vector:

copy() just returns a copy of itself. Since Vector is only a wrapper for a gpd.GeoDataFrame right now, this was dead simple.
query() allows for text-based queries to the dataframe. In xdem, this is used to query individual glacier outlines from a larger outlines vector. It would be nice to have this method directly instead of a hacky copy approach done right now.

To make the improvement apparent, this is what had to be done previously in my case:
glacier_specific_outline = gu.Vector(all_outlines.ds.copy())
glacier_specific_outline.ds = glacier_specific_outline.ds.query(query_string)

but with this addition:
glacier_specific_outline = all_outlines.query(query_string)


jupyter notebook kernel dies when running a UNet with tensorflow=2.1.0

System information

OS - Linux RedHat 8
conda install tensorflow-gpu - tensorflow version = 2.1.0
Python version: 3.7.4
Jupyter Notebook
CUDA/cuDNN version: - 10.2
CPU: AMD Threadripper 3960x
GPU: 2x RTX Titans
Memory: 128 GB Corsair 3200MHz

I recently upgraded to tensorflow=2.1.0 from tensorflow=2.0.0 and now my code will not run. Before the first epoch ends I get a message saying 'The kernel appears to have died. It will restart automatically.'. The code is using MirroredStrategy() to distribute the UNet to the GPUs. I don't get this error when I remove MirroredStrategy() and run on a single-gpu.
Originally I was running my code with tensorflow=2.0.0 using jupyter notebook and it would worked fine except that when I would try to load my model and make a prediction I get an error at model.predict() saying AttributeError: 'Model' object has no attribute 'loss'. I called model.summary() after loading the model and it showed the model was empty. So I upgraded to tensorflow=2.1.0 and called model.summary() this time it showed a readout. However I still get the same error AttributeError: 'Model' object has no attribute 'loss'
Is there currently an incompatibility with MirroredStrategy() and tensorflow=2.1.0?
def get_model(optimizer, loss_metric, metrics, lr=1e-4):
    with tf.device('/job:localhost/replica:0/task:0/device:GPU:0'):
        inputs = Input((sample_width, sample_height, sample_depth, 1))
        conv1 = Conv3D(32, (3, 3, 3), activation='relu', padding='same')(inputs)
        conv1 = Conv3D(32, (3, 3, 3), activation='relu', padding='same')(conv1)
        pool1 = MaxPooling3D(pool_size=(2, 2, 2))(conv1)
        drop1 = Dropout(0.5)(pool1)
        conv2 = Conv3D(64, (3, 3, 3), activation='relu', padding='same')(drop1)
        conv2 = Conv3D(64, (3, 3, 3), activation='relu', padding='same')(conv2)
        pool2 = MaxPooling3D(pool_size=(2, 2, 2))(conv2)
        drop2 = Dropout(0.5)(pool2)
        conv3 = Conv3D(128, (3, 3, 3), activation='relu', padding='same')(drop2)
        conv3 = Conv3D(128, (3, 3, 3), activation='relu', padding='same')(conv3)
        pool3 = MaxPooling3D(pool_size=(2, 2, 2))(conv3)
        drop3 = Dropout(0.3)(pool3)
        conv4 = Conv3D(256, (3, 3, 3), activation='relu', padding='same')(drop3)
        conv4 = Conv3D(256, (3, 3, 3), activation='relu', padding='same')(conv4)
        pool4 = MaxPooling3D(pool_size=(2, 2, 2))(conv4)
        drop4 = Dropout(0.3)(pool4)
        conv5 = Conv3D(512, (3, 3, 3), activation='relu', padding='same')(drop4)
        conv5 = Conv3D(512, (3, 3, 3), activation='relu', padding='same')(conv5)
    with tf.device('/job:localhost/replica:0/task:0/device:GPU:1'):
        up6 = concatenate([Conv3DTranspose(256, (2, 2, 2), strides=(2, 2, 2), padding='same')(conv5), conv4], axis=4)
        conv6 = Conv3D(256, (3, 3, 3), activation='relu', padding='same')(up6)
        conv6 = Conv3D(256, (3, 3, 3), activation='relu', padding='same')(conv6)
        up7 = concatenate([Conv3DTranspose(128, (2, 2, 2), strides=(2, 2, 2), padding='same')(conv6), conv3], axis=4)
        conv7 = Conv3D(128, (3, 3, 3), activation='relu', padding='same')(up7)
        conv7 = Conv3D(128, (3, 3, 3), activation='relu', padding='same')(conv7)
        up8 = concatenate([Conv3DTranspose(64, (2, 2, 2), strides=(2, 2, 2), padding='same')(conv7), conv2], axis=4)
        conv8 = Conv3D(64, (3, 3, 3), activation='relu', padding='same')(up8)
        conv8 = Conv3D(64, (3, 3, 3), activation='relu', padding='same')(conv8)
        up9 = concatenate([Conv3DTranspose(32, (2, 2, 2), strides=(2, 2, 2), padding='same')(conv8), conv1], axis=4)
        conv9 = Conv3D(32, (3, 3, 3), activation='relu', padding='same')(up9)
        conv9 = Conv3D(32, (3, 3, 3), activation='relu', padding='same')(conv9)
        conv10 = Conv3D(1, (1, 1, 1), activation='sigmoid')(conv9)
        model = Model(inputs=[inputs], outputs=[conv10])
        model.compile(optimizer=optimizer(lr=lr), loss=loss_metric, metrics=metrics)
        return model

smooth = 1.
def dice_coef(y_true, y_pred):
    y_true_f = K.flatten(y_true)
    y_pred_f = K.flatten(y_pred)
    intersection = K.sum(y_true_f * y_pred_f)
    return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)
def dice_coef_loss(y_true, y_pred):
    return -dice_coef(y_true, y_pred)

mirrored_strategy = tf.distribute.MirroredStrategy()
with mirrored_strategy.scope():
    model = get_model(optimizer=Adam, loss_metric=dice_coef_loss, metrics=[dice_coef], lr=1e-4)
observe_var = 'dice_coef'
strategy = 'max' # greater dice_coef is better
model_checkpoint = ModelCheckpoint('{epoch:04}model', monitor=observe_var, save_best_only=True)
model.fit(train_x, train_y, batch_size = 2, epochs= 1000, verbose=1, shuffle=True, validation_split=.2, callbacks=[model_checkpoint])
model.save( 'finalmodel')

model.summary():
Model: "model"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
input_1 (InputLayer)         multiple                  0
_________________________________________________________________
conv3d (Conv3D)              multiple                  896
_________________________________________________________________
conv3d_1 (Conv3D)            multiple                  27680
_________________________________________________________________
max_pooling3d (MaxPooling3D) multiple                  0
_________________________________________________________________
dropout (Dropout)            multiple                  0
_________________________________________________________________
conv3d_2 (Conv3D)            multiple                  55360
_________________________________________________________________
conv3d_3 (Conv3D)            multiple                  110656
_________________________________________________________________
max_pooling3d_1 (MaxPooling3 multiple                  0
_________________________________________________________________
dropout_1 (Dropout)          multiple                  0
_________________________________________________________________
conv3d_4 (Conv3D)            multiple                  221312
_________________________________________________________________
conv3d_5 (Conv3D)            multiple                  442496
_________________________________________________________________
max_pooling3d_2 (MaxPooling3 multiple                  0
_________________________________________________________________
dropout_2 (Dropout)          multiple                  0
_________________________________________________________________
conv3d_6 (Conv3D)            multiple                  884992
_________________________________________________________________
conv3d_7 (Conv3D)            multiple                  1769728
_________________________________________________________________
max_pooling3d_3 (MaxPooling3 multiple                  0
_________________________________________________________________
dropout_3 (Dropout)          multiple                  0
_________________________________________________________________
conv3d_8 (Conv3D)            multiple                  3539456
_________________________________________________________________
conv3d_9 (Conv3D)            multiple                  7078400
_________________________________________________________________
conv3d_transpose (Conv3DTran multiple                  1048832
_________________________________________________________________
concatenate (Concatenate)    multiple                  0
_________________________________________________________________
conv3d_10 (Conv3D)           multiple                  3539200
_________________________________________________________________
conv3d_11 (Conv3D)           multiple                  1769728
_________________________________________________________________
conv3d_transpose_1 (Conv3DTr multiple                  262272
_________________________________________________________________
concatenate_1 (Concatenate)  multiple                  0
_________________________________________________________________
conv3d_12 (Conv3D)           multiple                  884864
_________________________________________________________________
conv3d_13 (Conv3D)           multiple                  442496
_________________________________________________________________
conv3d_transpose_2 (Conv3DTr multiple                  65600
_________________________________________________________________
concatenate_2 (Concatenate)  multiple                  0
_________________________________________________________________
conv3d_14 (Conv3D)           multiple                  221248
_________________________________________________________________
conv3d_15 (Conv3D)           multiple                  110656
_________________________________________________________________
conv3d_transpose_3 (Conv3DTr multiple                  16416
_________________________________________________________________
concatenate_3 (Concatenate)  multiple                  0
_________________________________________________________________
conv3d_16 (Conv3D)           multiple                  55328
_________________________________________________________________
conv3d_17 (Conv3D)           multiple                  27680
_________________________________________________________________
conv3d_18 (Conv3D)           multiple                  33
=================================================================
Total params: 22,575,329
Trainable params: 22,575,329
Non-trainable params: 0

Prediction Code:
norm_images='imagedata\'
model = load_model('finalmodel',compile = False)
print(model.summary())
prediction = model.predict(norm_images, verbose = 1)

Attribute Error:
AttributeError                            Traceback (most recent call last)
<ipython-input-2-06140bdcec2b> in <module>
     75
     76 print('Predicting the labels')
---> 77 prediction = model.predict(norm_images, verbose = 1)
     78 # prediction = tf.keras.Model.predict(norm_images)
     79

~\.conda\envs\gputest\lib\site-packages\tensorflow_core\python\keras\engine\training.py in predict(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)
   1011         max_queue_size=max_queue_size,
   1012         workers=workers,
-> 1013         use_multiprocessing=use_multiprocessing)
   1014
   1015   def reset_metrics(self):

~\.conda\envs\gputest\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in predict(self, model, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing, **kwargs)
    496         model, ModeKeys.PREDICT, x=x, batch_size=batch_size, verbose=verbose,
    497         steps=steps, callbacks=callbacks, max_queue_size=max_queue_size,
--> 498         workers=workers, use_multiprocessing=use_multiprocessing, **kwargs)
    499
    500

~\.conda\envs\gputest\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in _model_iteration(self, model, mode, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing, **kwargs)
    424           max_queue_size=max_queue_size,
    425           workers=workers,
--> 426           use_multiprocessing=use_multiprocessing)
    427       total_samples = _get_total_number_of_samples(adapter)
    428       use_sample = total_samples is not None

~\.conda\envs\gputest\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in _process_inputs(model, mode, x, y, batch_size, epochs, sample_weights, class_weights, shuffle, steps, distribution_strategy, max_queue_size, workers, use_multiprocessing)
    644     standardize_function = None
    645     x, y, sample_weights = standardize(
--> 646         x, y, sample_weight=sample_weights)
    647   elif adapter_cls is data_adapter.ListsOfScalarsDataAdapter:
    648     standardize_function = standardize

~\.conda\envs\gputest\lib\site-packages\tensorflow_core\python\keras\engine\training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, batch_size, check_steps, steps_name, steps, validation_split, shuffle, extract_tensors_from_dataset)
   2358     is_compile_called = False
   2359     if not self._is_compiled and self.optimizer:
-> 2360       self._compile_from_inputs(all_inputs, y_input, x, y)
   2361       is_compile_called = True
   2362

~\.conda\envs\gputest\lib\site-packages\tensorflow_core\python\keras\engine\training.py in _compile_from_inputs(self, all_inputs, target, orig_inputs, orig_target)
   2609     self.compile(
   2610         optimizer=self.optimizer,
-> 2611         loss=self.loss,
   2612         metrics=self._compile_metrics,
   2613         weighted_metrics=self._compile_weighted_metrics,
**Other info / logs** Include any logs or source code that would be helpful to
diagnose the problem. If including tracebacks, please include the full
traceback. Large logs and files should be attached.

Can you also clarify the issues you had with the respective TF versions?
In TF 2.0 - you had issues with calling model.predict?
In TF 2.1 - you had a problem with the kernel dying? Where were you running this?

I a having the same exact problem with this, as there is no get_model.
It is jupyter notebook and TF 2.1.
def build_model2():
model = Sequential([
Dense(n, input_dim=n, kernel_initializer='normal', activation='relu'),
Dense(17, kernel_initializer='normal', activation='relu'),
Dense(4, kernel_initializer='normal', activation='relu'),
Dense(1, kernel_initializer='normal')])
model.compile(loss='mean_absolute_error', optimizer='adam', metrics=['mae', 'mse'])
return model
strategy = tf.distribute.MirroredStrategy(devices=["/gpu:2"])
with strategy.scope():
parallel_model = build_model2()
parallel_model.compile(loss='mean_absolute_error', optimizer='adam')
--> Kernel is dying at this cell and I am not able to run the below cells.
parallel_model.summary()
parallel_model.fit(x, y, epochs=20, batch_size=256)

I solved my problem by downgrading to tensorflow-gpu=2.0.0 and had to add the .h5 extension when saving my final model and when saving my models at my checkpoint.

I a having the same exact problem with this, as there is no get_model.
It is jupyter notebook and TF 2.1.
def build_model2():
model = Sequential([
Dense(n, input_dim=n, kernel_initializer='normal', activation='relu'),
Dense(17, kernel_initializer='normal', activation='relu'),
Dense(4, kernel_initializer='normal', activation='relu'),
Dense(1, kernel_initializer='normal')])
model.compile(loss='mean_absolute_error', optimizer='adam', metrics=['mae', 'mse'])
return model
strategy = tf.distribute.MirroredStrategy(devices=["/gpu:2"])
with strategy.scope():
parallel_model = build_model2()
parallel_model.compile(loss='mean_absolute_error', optimizer='adam')
--> Kernel is dying at this cell and I am not able to run the below cells.
parallel_model.summary()
parallel_model.fit(x, y, epochs=20, batch_size=256)

@BanuSelinTosun I had the same issue and I was solve it by moving 'with strategy.scope():' line into the function, just like how @rzaratx did, but I am not sure why it doesn't work in their case.
Best,
Emre


terragrunt s3 bucket creation fails when using root credentials

When using the root credentials from a newly created AWS account, the S3 bucket is created but the root user doesn't have permissions to the newly created bucket.
The issue is probably that we don't add a policy that grants the root-user access per the [troubleshooting guide].(https://docs.aws.amazon.com/IAM/latest/UserGuide/troubleshoot_iam-s3.html)
Running the command as an IAM User with AdministratorAccess yields the expected result.
$ terragrunt init
[terragrunt] [/Users/matt/code/src/github.com/mcalhoun/infrastructure/live/root/_global/organization] 2019/07/01 17:03:59 Running command: terraform --version
[terragrunt] 2019/07/01 17:03:59 Reading Terragrunt config file at /Users/matt/code/src/github.com/mcalhoun/infrastructure/live/root/_global/organization/terragrunt.hcl
[terragrunt] 2019/07/01 17:03:59 WARNING: no double-slash (//) found in source URL /Users/matt/code/src/github.com/mcalhoun/infrastructure/modules/organization. Relative paths in downloaded Terraform code may not work.
[terragrunt] 2019/07/01 17:03:59 Downloading Terraform configurations from file:///Users/matt/code/src/github.com/mcalhoun/infrastructure/modules/organization into /Users/matt/code/src/github.com/mcalhoun/infrastructure/live/root/_global/organization/.terragrunt-cache/WnMUGlp2BJCICBcDh_ddHo4u-xQ/Q0y8bTwcIJYLO1ORZJW6dyJl7OE
[terragrunt] 2019/07/01 17:03:59 Copying files from /Users/matt/code/src/github.com/mcalhoun/infrastructure/live/root/_global/organization into /Users/matt/code/src/github.com/mcalhoun/infrastructure/live/root/_global/organization/.terragrunt-cache/WnMUGlp2BJCICBcDh_ddHo4u-xQ/Q0y8bTwcIJYLO1ORZJW6dyJl7OE
[terragrunt] 2019/07/01 17:03:59 Setting working directory to /Users/matt/code/src/github.com/mcalhoun/infrastructure/live/root/_global/organization/.terragrunt-cache/WnMUGlp2BJCICBcDh_ddHo4u-xQ/Q0y8bTwcIJYLO1ORZJW6dyJl7OE
[terragrunt] 2019/07/01 17:03:59 Initializing remote state for the s3 backend
[terragrunt] 2019/07/01 17:03:59 [terragrunt]  Remote state S3 bucket matt-calhoun-root-state does not exist or you don't have permissions to access it.  This is required for access logging.
[terragrunt] 2019/07/01 17:04:03 Waiting for ACL bucket matt-calhoun-root-state to have the updated ACL for access logging.
[terragrunt] 2019/07/01 17:04:03 Bucket matt-calhoun-root-state still does not have the ACL permissions for access logging. Will sleep for 5s and check again.
[terragrunt] 2019/07/01 17:04:08 Bucket matt-calhoun-root-state now has the proper ACL permissions for access logging!
[terragrunt] 2019/07/01 17:04:08 Enabling bucket-wide Access Logging on AWS S3 bucket "matt-calhoun-root-state" - using as TargetBucket "matt-calhoun-root-state"
[terragrunt] 2019/07/01 17:04:08 Lock table terraform-state-locks does not exist in DynamoDB. Will need to create it just this first time.
[terragrunt] 2019/07/01 17:04:08 Creating table terraform-state-locks in DynamoDB
[terragrunt] 2019/07/01 17:04:08 Table terraform-state-locks is not yet in active state. Will check again after 10s.
[terragrunt] 2019/07/01 17:04:18 Success! Table terraform-state-locks is now in active state.
[terragrunt] 2019/07/01 17:04:18 No tags for lock table given.
[terragrunt] 2019/07/01 17:04:18 Running command: terraform init -backend-config=dynamodb_table=terraform-state-locks -backend-config=encrypt=true -backend-config=key=_global/organization/terraform.tfstate -backend-config=region=us-east-1 -backend-config=bucket=matt-calhoun-root-state

Initializing the backend...

Successfully configured the backend "s3"! Terraform will automatically
use this backend unless the backend configuration changes.

Error: Failed to get existing workspaces: AccessDenied: Access Denied
	status code: 403, request id: 149E20E34744EA3A, host id: 39ECU1efjpQEfG+s/GrHm2hrrk3BU+y4q3LaqExrSYUcM5Bz/aMGvDqkoZUR1hCAuZZf

i'm not sure this should be enabled by default, as it looks like it would overwrite existing bucket policies that people may have in place. Also, the aws document specifically says that for the root user to be denied, that there would be a bucket policy in place on the bucket that's not allowing it. Up until now, terragrunt has not been adding bucket policies to buckets it has created in my accounts.

nevermind, i see this only happens on bucket creation, so wouldn't overwrite

I agree and not making this the default behavior as working with root credentials is very very very bad practice and should not be supported in a default configuration. https://docs.aws.amazon.com/IAM/latest/UserGuide/best-practices.html#lock-away-credentials


[INLONG-4719][All] Remove commons-lang 2.6 dependency for all modules

Fixes #4719

@ningpp there workflows failed, please fix it and change the license file at the same time.

Please refer the license and notice guide: https://inlong.apache.org/community/how-to-maintain-3rd-party-dependencies

@healchow  what should i do?

@healchow what should i do?

@ningpp Hi, I did not find your email or other contact info. Can you send an email to me?
My email address is<EMAIL_ADDRESS>

already send a email to you @healchow

already send a email to you @healchow

OK, let's talk in IM.

other library denpend commons-lang 2.6,
apache-inlong-manager-web-xxx-bin.tar.gz will contain commons-lang 2.6
do i need delete license in licenses folder

@ningpp that's ok to me. thanks.


sys: util: fix WAIT_FOR() timeout calculation

Use k_us_to_cyc_ceil64() for calculating the number of hardware cycles for the given timeout value instead of using a custom calculation.
Fixes: #50032

Could we instead change WAIT_FOR to do cycle_count = MAX(<...>, 1), so that it sleeps at lest one cycle?

Agreed, this looks like a bug in WAIT_FOR() to me. FWIW there's a whole library of overflow-resistant and optimized time conversion utilities in <zephyr/sys/time_units.h>. Looks like this should have just been a k_us_to_cyc_ceil64().

Thank you for the hint, I was not aware of those utility functions. I have changed WAIT_FOR() to use that and verified on my usual set of development boards (which includes a mimxrt1024_evk on which I could reproduce the initially reported issue).
Please re-review.

Rebased to switch to 32-bit utility function variant.


DaoRegistryAdapterContract add to the Dao Manager list of adapters/extensions

The DaoRegistryAdapterContract contract allows members to updateDelegateKey

Already done


Add module for parsing Shipyard.json file

Target PR's into json-files if it's still open.

Changes are merged into #3


Post Season Picker

Should only display once all games are picked.

Idea: use columns to display each playoff round. Sort of like this:
| AFC Wild Card | AFC Divisional | AFC Championship | SuperBowl | NFC Championship | NFC Divisional | NFC Wild Card |
Create a slightly different game button set? Be sure to take seeding into account. Auto-update if the user goes back to change season predictions?

Done, closing this and opening #43


Add minimal isIntersecting polyfill for Edge 15

Fixes #211.

Hm, I tried to fix the IPR issue but I'm getting a "forbidden" error from labs.w3.org when I try to revalidate. :confused:

Hmmm, I'm not sure why. @marcoscaceres any ideas?

@nolanlawson it seems to think that you didn't join the WICG? Can you confirmed that you joined? If not, you can join here: https://www.w3.org/community/wicg/

My bad; I hadn't joined the WICG yet. Just sent a request for membership.

np. Let me know once that's gone through so I can pull the right levers on this end.

OK, I'm in the WICG now. :)

Thanks!

Published version 0.4.0 with this change.

Hi everyone, thanks for the work on this.
I'm confused about this change, it seems inconsistent with how isIntersecting is documented at least on MDN:

You can tell which direction the visibility changed in (that is, whether the element became more visible or less visible) by checking the value of the isIntersecting property on the IntersectionObserverEntry passed into the callback function at the time of the visibility change. If isIntersecting is true, the target element has become at least as visible as the threshold that was passed. If it's false, the target is no longer as visible as the given threshold.

The isIntersecting polyfill introduced in this PR doesn't match this behavior, which is problematic. Is it MDN's docs that are incorrect, or is this isIntersect polyfill too simple?

The MDN document is still correct. isIntersecting describes the direction that the visibility changed. The issue is that Edge 15 implements an older version of the spec, and this polyfill brings it as close as possible to the current version.
Have you tested Edge 15 and seen any issues with this behavior? In my own testing, it's working pretty well – isIntersecting correctly flips from true to false or vice versa when the boundary is passed.

@nolanlawson I do think it will work well enough for most use cases; if you're only interested in things like knowing whether something came into view (started intersecting) or not. However if you want to do things like react to when something starts going out of view, with this implementation it's not possible to do using the isIntersecting value..


Add Async++ package

I had to fork original Async++ repository to create tarball release and change Async++Config.cmake install path according to https://github.com/forexample/package-example
There is one problem. Async++ links with threading library using
set(THREADS_PREFER_PTHREAD_FLAG ON)
find_package(Threads REQUIRED)
target_link_libraries(Async++ PUBLIC Threads::Threads)

and attempt to link with it using
target_link_libraries(${PROJECT_NAME} Async++::Async++)

lead to error
CMake Error at src/CMakeLists.txt:5 (add_executable):
  Target "foo" links to target "Threads::Threads" but the target
  was not found.  Perhaps a find_package() call is missing for an IMPORTED
  target, or an ALIAS target is missing?

As workaround I have to add to project CMakeLists.txt
find_package(Threads REQUIRED)

Is it library error? Can I fix it?
By the way, thanks for great project. I have to come back to С++ after couple of years of Android development and was missing how easy it was using 3rdparty libraries.

I had to fork original Async++ repository to create tarball release

This place can be used for such tasks: https://github.com/hunter-packages
I've made a fork: https://github.com/hunter-packages/asyncplusplus
You have permissions to make a releases there.

Is it library error? Can I fix it?

You can use find_dependency macro in *Config.cmake file.
By the way you can add example for testing,

to directory: https://github.com/ruslo/hunter/tree/master/examples
instructions: https://github.com/ruslo/hunter/wiki/dev.hunter.root#jenkinspy

One more note: Threads::Threads available since CMake 3.1, there is no such target in CMake 3.0. Please add cmake_minimum_required(VERSION 3.1) to the *Config.cmake file.

By the way, thanks for great project

Thanks for contribution, enjoy :)

Could you grant me push access to repository https://github.com/hunter-packages/asyncplusplus or should I fork it and send pull request?

Could you grant me push access to repository

Oops, I forget to add a repository to the team :) Sorry, try again.

Thanks, I pushed new packages version and fixed pull request.

New wiki page: https://github.com/ruslo/hunter/wiki/pkg.async--

I've got runtime error on Linux with GCC:
terminate called after throwing an instance of 'std::system_error'
  what():  Enable multithreading to use std::thread: Operation not permitted
Aborted (core dumped)

This can be fixed by setting THREADS_PREFER_PTHREAD_FLAG to TRUE (-pthread argument to linker, see this OS answer), add either to CMakeLists.txt of example before find_package or to *Config.cmake file before find_dependency.

Added THREADS_PREFER_PTHREAD_FLAG to *Config.cmake files and bumped package version

Released: https://github.com/ruslo/hunter/releases/tag/v0.12.28

Just for your information I've disabled next tests that failed (commit):

libstdcxx https://travis-ci.org/ruslo/hunter/jobs/92904105#L557

In file included from /Users/travis/build/ruslo/hunter/examples/Async++/asyncpp.cpp:2:
/Users/travis/build/ruslo/hunter/_testing/Hunter/_Base/d557a43/55402bc/da32312/Install/include/async++.h:25:10: fatal error: 'atomic' file not found
#include <atomic>
         ^
1 error generated.

I guess this one can be ignored because of lack of c++11 support in current GCC

ios https://travis-ci.org/ruslo/hunter/jobs/92904108#L494

/Users/travis/build/ruslo/hunter/_testing/Hunter/_Base/ad711bf/55402bc/097785c/Build/Async++/Source/src/scheduler.cpp:68:8: error: thread-local storage is unsupported for the current target
static THREAD_LOCAL wait_handler thread_wait_handler = generic_wait_handler;
       ^

Seems library can't be used on iOS platform.

analyzer https://travis-ci.org/ruslo/hunter/jobs/92904109#L514

/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/../include/c++/v1/algorithm:2840:52: warning: The result of the '>>' expression is undefined
    __mask0_ = __w0_ > 0 ? _Engine_result_type(~0) >> (_EDt - __w0_) :
                           ~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~
1 warning generated.

Probably a real error (?) Need to investigate.
Let me know if you plan to report/fix this. I'll revert testing back.

I agree that failing test due to missing atomic header should be ignored
since library explicitly requires C++11 compiler.
As for analyzer warning, I'll look into it.
Sent with AquaMail for Android
http://www.aqua-mail.com
On November 24, 2015 23:28:25 Ruslan Baratov<EMAIL_ADDRESS>wrote:

Just for your information I've disabled next tests that failed
(commit):

libstdcxx https://travis-ci.org/ruslo/hunter/jobs/92904105#L557

In file included from
/Users/travis/build/ruslo/hunter/examples/Async++/asyncpp.cpp:2:
/Users/travis/build/ruslo/hunter/_testing/Hunter/_Base/d557a43/55402bc/da32312/Install/include/async++.h:25:10:
fatal error: 'atomic' file not found
#include <atomic>
         ^
1 error generated.

I guess this one can be ignored because of lack of c++11 support in
current GCC

ios https://travis-ci.org/ruslo/hunter/jobs/92904108#L494

/Users/travis/build/ruslo/hunter/_testing/Hunter/_Base/ad711bf/55402bc/097785c/Build/Async++/Source/src/scheduler.cpp:68:8:
error: thread-local storage is unsupported for the current target
static THREAD_LOCAL wait_handler thread_wait_handler = generic_wait_handler;
       ^

Seems library can't be used on iOS platform.

analyzer https://travis-ci.org/ruslo/hunter/jobs/92904109#L514

/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/../include/c++/v1/algorithm:2840:52:
warning: The result of the '>>' expression is undefined
    __mask0_ = __w0_ > 0 ? _Engine_result_type(~0) >> (_EDt - __w0_) :
                           ~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~
1 warning generated.

Probably a real error (?) Need to investigate.
Let me know if you plan to report/fix this. I'll revert testing back.

Reply to this email directly or view it on GitHub:
https://github.com/ruslo/hunter/pull/268#issuecomment-159395993

Looks like Apple's version of clang doesn't support thread_local from C++11 standard SO answer While it's unfortunate I don't think it's a library problem since it specifically requires C++11-compliant compiler and standard library.

According to this (SO answer)[http://stackoverflow.com/questions/29128320/clang-static-code-analyzer-the-result-of-the-expression-is-undefined] this is false positive from clang-analyzer.
By the way does clang-analyzer have option to ignore warnings from system headers similar to gcc (system headers)[https://gcc.gnu.org/onlinedocs/cpp/System-Headers.html] or this is not possible and bad idea?

According to this SO answer this is false positive from clang-analyzer

I've found this bug, think it relates: https://llvm.org/bugs/show_bug.cgi?id=17634
Especially: The analyzer doesn't handle bit shifts right now

By the way does clang-analyzer have option to ignore warnings from system headers

No from what I see in FAQ. I guess using __clang_analyzer__ is the only way to suppress message explicitly.

or this is not possible and bad idea?

I think yes, in some cases it's a bad idea since same code can be used correctly (don't produce warning) and wrongly (produce warning). It depends on conditions/branches.
I've tried to add assert to the alogorithm header and it fix warning:
_LIBCPP_ASSERT((_Edt - __w0_) < sizeof(_Engine_result_type), "")
(using hint from llvm bugreport).


ImagePullSecrets do not exist in the deployments

Describe the bug:
ImagePullSecrets flag exists in the values.yaml file, but it is not call by the deployments
Expected behaviour:
ImagePullSecrets flag should be called by the deployments.
Steps to reproduce the bug:
Just run the helm chart :)

Thanks for raising this!
We put the image pull secrets into the ServiceAccount used by the various cert-manager pods: https://github.com/cert-manager/cert-manager/blob/4e3c162734cf244bb5c51c9a81b03244d9c00a62/deploy/charts/cert-manager/templates/serviceaccount.yaml#L5-L7
Is there a specific reason you would need them to be specified on the deployments? My instinct is that this should be fine (and indeed I've successfully used image pull secrets to pull cert-manager from a private registry!)

/remove-lifecycle stale
please consider this values.yaml
serviceAccount:
  create: false
  name: user-created-sa
global:
  imagePullSecrets:
  - name: image-pull-secret
image:
  repository: private-repo.jfrog.io/jetstack/cert-manager-controller

the global setting for image pull secrets is misleading. I expect images are pulled using image pull secrets defined on the global level even if service account is not created by the helm chart.

@SgtCoDFish - in case this slipped through the cracks

I guess it makes sense if the SA is not being created to add it to the deployments, yeah. Thank you for that context!
I'll label this as backlog and a good first issue. I can't imagine we'll work on it any time soon but we'd welcome a PR!


Folder structure configuration

Hello, I attempted to use the code but encountered difficulty in determining the appropriate folder structure for training the model.

What file (or path) do I need to modify to commence training?
What is the appropriate folder structure for training the model? Could you specify the one you utilized?

I already have the mimic-cxr-jpg and chexpert downloaded but currently I can't configure this model correctly.
I'm sorry if my question is too elementary. Thank you so much for your help.

Hi, please refer to chexpert_dataset and cxr_dataset.
This code is designed for use of multiple datasets, so you will also have to also edit the datamodule code.
About the folder structure, you can refer to the code.


Upgrade prometheus-operator to v6.20.3 helm chart

Thanks for this, will be live on the site shortly.

Is 0.32.0 still correct for the version of Prometheus Operator? https://github.com/civo/kubernetes-marketplace/blob/master/prometheus-operator/manifest.yaml


Adds runtime v2 support for Windows shim's

Implements the various requirements for the runtime v2 code to abstract
away the unix/linux code into the appropriate platform level
abstractions to use the runtime v2 on Windows as well.
Adds support in the Makefile.windows to actually build the runtime v2
code for Windows by setting a shell environment BUILD_WINDOWS_V2=1
before calling make. (Note this disables the compilation of the Windows
runtime v1)
Signed-off-by: Justin Terry (VM<EMAIL_ADDRESS>

@crosbymichael @jhowardmsft - PTAL

Not sure why ltag was so angry but removed the headers. Applied via the template and pushed updates. Hopefully will pass the build this time.

@crosbymichael  - I think this was due to a core.autocrlf = input issue switching between code in linux/windows. Will verify with this latest push

Codecov Report

Merging #2495 into master will not change coverage.
The diff coverage is n/a.

@@           Coverage Diff           @@
##           master    #2495   +/-   ##
=======================================
  Coverage   44.76%   44.76%
=======================================
  Files          93       93
  Lines        9555     9555
=======================================
  Hits         4277     4277
  Misses       4585     4585
  Partials      693      693

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@jterry75 it's green!

LGTM

Nice! Thanks for the help

And...ignore me; _unix.go is not a special directive for Go; only valid GOOS entries are; so I guess the use of _unix.go elsewhere is slightly idiomatic, but not generally useful as you still need a +build directive

@estesp I think I would still prefer for the consistency of using _unix.go everywhere though :)

LGTM


Common model for human and car keypoint

HI, thanks for sharing!
Can you please elaborate more, in your second demo video we can see you detected human and vehicle key points in each frame? Was it two different models of a single model? If it is a single model please let me know where to get it.
Thank you in advance.

H,
Thank you for your interest in Keypoint Communities!
The shown video was processed with two separate models, one for the car keypoints and the other for the human keypoints.
I don't have a model trained on cars and human keypoints. However, this project is based on Openpifpaf and it should be fairly straight forward to train a model with two heads/decoders.
You could for instance use this command to train a model:
python3 -m openpifpaf.train --lr=0.0001 --momentum=0.95 --b-scale=3.0 --epochs=150 --lr-decay 130 140 --lr-decay-epochs=10 --batch-size=16 --weight-decay=1e-5 --dataset=wholebody-apollo --dataset-weights 0.5 0.5 --wholebody-upsample=2 --basenet=shufflenetv2k16 --loader-workers=16 --wholebody-train-annotations=<PathToYourMSCOCO>/data-mscoco/annotations/person_keypoints_train2017_wholebody_pifpaf_style.json --wholebody-val-annotations=<PathToYourMSCOCO>/data-mscoco/annotations/person_keypoints_val2017_wholebody_pifpaf_style.json --wholebody-train-image-dir=<COCO_train_image_dir> --wholebody-val-image-dir=<COCO_val_image_dir> --apollo-upsample 2 --apollo-bmin 2 --apollo-square-edge=769

This command trains a single model with two different heads. At inference, the two decoders from both heads will be used and you should get a prediction for both human and keypoints.
You might need to tweak some hyperparameters at training, i.e. --dataset-weights describes the weight between the two datasets/heads.
You can also find more info here: https://openpifpaf.github.io/plugins_wholebody.html
and here: https://openpifpaf.github.io/plugins_apollocar3d.html
Let me know if you try the joint training. I would be curious to hear how it goes :)


Skeleton v2 migration to Svelte 5

Svelte 5 has reached Release Candidate status per today's Svelte Summit:

https://www.youtube.com/watch?v=gkJ09joGBZ4
https://github.com/skeletonlabs/skeleton/discussions/2400

The Plan
As such, we will begin investigating the migration process for Skeleton v2 projects. This will start with a research phase will will start asap.
For Users
If you use Skeleton v2 and are keen to update to the Svelte 5 RC, we encourage you do so on a dedicate feature branch. We match Svelte's stance on this and DO NOT recommend you deploy to Production yet. If you encourage issues while doing this, please report them in the comments section below.
For Contributors
Likewise, please post any findings while testing the migration process here. This will be helpful in generating a dedicated Skeleton + Svelte 5 migration guide asap. Note that we will not be enabling Svelte 5 features in Skeleton v2. These should be restricted to Skeleton v3.

I'm happy to report that migrating from Svelte v4 -> Svelte v5 seems to cause no major breaking changes in project running Skeleton v2.

A fresh Skeleton CLI generated project
Migrating a minimal application (migrated: https://www.skeletonlabs.co/)

Still to test:

Upgrading the Skeleton v2 doc site

Note the latter will not be a change we deploy to production. We will not be using Svelte v5 on production sites until the stable release. I'll generate a follow up post once the v2 doc site update test is complete.

I ran into a couple snags while preparing to update the Skeleton v2 doc site. I'll pick up from here tomorrow and verify all is well. Then the migration guide will start immediately after!

The guide is now available here! Find it pinned in the GitHub Discussions section:
https://github.com/skeletonlabs/skeleton/discussions/2640


Feature Request Speed/ETA on source download

As topic says, speed/eta on source download to provide better information.

Further, we should stop using invoking curl and use Go APIs

Sounds good to me!

Implemented :) In future I may use a slightly better (colourful) progressbar format but for now it certainly achieves the goals.

Merged into master, will be in the next release


Performance improvements

use keep alive agent (node 0.12+)
limit the async queue depth (infinity is never a good idea)
3 retries on networking failures, exponential backoff (things always go wrong)
read cache for shared objects (why ask for the same thing twice?)

@ian29 this reduced the time taken to fetch a certain relation!4237279 from never or next to forever to a little over a minute.

🆒


Change Feed Processor public API name changes

Based on feedback, we'll rename the public APIs for the Change Feed Processor and Estimator to:
public delegate Task ChangesHandler<T>(
    IReadOnlyCollection<T> changes,
    CancellationToken cancellationToken);

public delegate Task ChangesEstimationHandler(
    long estimatedPendingChanges,
    CancellationToken cancellationToken);

public abstract ChangeFeedProcessorBuilder DefineChangeFeedProcessor<T>(
    string processorName,
    ChangesHandler<T> onChangesHandler);

public abstract ChangeFeedProcessorBuilder DefineChangeFeedEstimator(
    string processorName,
    ChangesEstimationHandler estimationHandler,
    TimeSpan? estimationPeriod = null);

Is there a tracking document or post that details what all this stuff is - is it going to be a v4 release? is there a stabilisation period? are we doing final changes circling the drain for a release?

The names are fine, but I don't like the indirection of having delegates - there are not enough parameters in the delegate to warrant this

Is this in addition to the existing APIs ? Is having estimatedPendingChanges as a single long not too distilled? For me, backlogs are nuanced - i.e. one might want to infer the min/max and/or how many ranges are behind?

what is the value of the default for estimationPeriod (in both senses of the word value) ?

Also wondering how checkpointing works with this give there's no longer a context argument ? If the old/deeper API still exists, that's fine I guess, but for me it's critical to have support for being able to, for a given batch, be able to checkpoint up to that point independently of the CFP lib's intrinsic support for doing that.
And an aside - given this API, is there any good way to get a rough size of the response - i.e. how many K it was ? (same question for v2)

The delegates were to replace the Func we were using. What do you mean with "there are not anough parameters?"
Which existing APIs? Do you mean the CFP V2 APIs? V3 APIs are somewhat different, the public surface has changed. In V3, the estimator is a push model, like the processor. In V2, you had the IRemainingWorkEstimator which had a method to obtain this same number. We are now making it easier with a push model.
Good point on the undocumented default value, see https://github.com/Azure/azure-cosmos-dotnet-v3/blob/dceeac4e8a1476807ab95ae80331d1efd2efcfd1/Microsoft.Azure.Cosmos/src/ChangeFeedProcessor/FeedProcessing/FeedEstimatorCore.cs#L15, will add it to the signature.

Checkpointing works the same way it works on CFP V2, the delegate is just a representation of ProcessChangesAsync. So automatic checkpointing happens after a successful execution of the delegate. CFP V2 didn't let you do partial checkpoints, even manual checkpointing was for the entire batch, never for X amount of documents. Manual checkpointing in V3 is not supported at this point, we would like to understand what are the motivations for manual checkpointing on a per scenario basis, so if you'd like to share how/if you are currently using manual checkpoint, we can talk about it :)
As for the size of the response, that is basically tied to the SDK diagnostics/monitoring story, not only for the Change Feed Processor, but the entire SDK V3. The idea is that you should be able to obtain these diagnostics for any operation in the SDK.

Thanks for the detailed response. Apologies for the wall of text I'm about to subject you to in response ;)

by "not enough parameters", I mean that using a delegate to a) name a signature b) name the individual parameters has negligible value when the delegate only has 1-3 args, and they're all of different and/or obvious types
re existing APIs, as you inferred, I do mean "v2 style things". ~Not sure I agree I want a push model for the estimator~ Interesting - a push model does seem to make sense on the surface. However, I'd like to understand better how I go from that API and 5 ranges that split to 6 and one that is not responding due to rate limited to presenting "6 ranges, 3 in sync, one behind by x, the other by y, and one not responding". In other words, for me, the only API that makes sense is GetEstimatedRemainingWorkPerPartitionAsync - the other is worse than useless (how will you represent 2 uncontactable ranges in the response if you're pushing anyway?)
I'd suggest not having a default and making people supply a TimeSpan so there is one less thing to read or document (is 5s useful when I have 100 ranges? (all depends on the answer to Q1 I guess))

Checkpointing: This really doesnt work for my use cases and doesnt align with how similar tech works; are you saying this is the default, or the only way ?
I'm not talking about partial checkpoints - I'm saying let me:

control the ingestion rate by responding or not to the API (delaying if I have enough data) - then I can asynchronously update the progress as I achieve it. In other words, I might want to let 5 batches be read ahead and I'll delay my response when I get to 5.
perform the checkpointing inside my processing as and when I note I've completed a batch (all the time bearing in mind that checkpointing batch 5 implies also having completed 0-4)

The reading N batches ahead and asychronously checkpointing is implemented in https://github.com/jet/propulsion. Consumer code looks like this - in this case I'm reading say up to 5 batches ahead on the changefeed, grouping them by stream, feeding them to Kafka and then drive the calls to per-ChangeFeedProcessor batch CheckpointAsync() calls as all the Kafka ProduceAsync calls required to fulfill the projection complete. If you're telling me there will be no CheckpointAsync, I can't do any of this (I was asking this in the other open issue BTW). If there is something of this nature, I'll spike rebasing the impl on V3 (its definitely on the list as there's a big feature roadmap tied to this)
Regarding response size - I appreciate that there is a need and effort re improvments around this. However this doesnt answer the question - given that function signature (which does not have a context parameter), how can I do this in context - I'd like to immediately emit a log line saying "I got a 2MB batch from the changefeed at this point in time" (I assume it ultimately arrives in the response header, but really I'd prefer not to have to do keyhole surgery to correlate a diagnostics stream with the actual response if it can be avoided)


fix: Bot Response page show wrong content for common lg file

Description
Root cause

The Router catches the url common/*, and doesn't get the correct dialog id.
the baseUrl doesn't been added to the useEffect dependencies, so the show code button will use stale base url and navigate to the wrong page.

fix

remote the common/* router, then the url will be cached by the dialogId/*
add base Url to the useEffect dependencies

Task Item
closes #6029

Screenshots

Coverage remained the same at 53.71% when pulling e41c8d54964e8edb01478733f10b1a265c5867c4 on lei9444:lgPage into 7416cb64a0d2fcca27558b0f8763cb120a9c12d6 on microsoft:main.


Can provide a trained model

Thanks for your interest in ViTGaze! We have released the checkpoints trained on GazeFollow and VideoAttentionTarget datasets.


Pilot Ratings Not Updating (Old 266)

In gitlab by @A-Lawrence on Aug 31, 2015, 01:08
It seems pilot ratings aren't updating properly.
This isn't a backwards change, it's new.

In gitlab by @A-Lawrence on Aug 31, 2015, 01:13
Fixed issue with pilot ratings not DISPLAYING rather than not storing.  I didn't check the database, just assumed it wasn't running.  Resolves issue #488476 (266).
ÔåÆ <>
ALawrence66 - 2015-02-22T21:04:26.658098+00:00

In gitlab by @A-Lawrence on Aug 31, 2015, 01:13
As Anthony said, changes were rolled out last night, which should resolve the issue. 1292191 was updated when I updated the whole membership last night, and currently appears to have his S1 in the RTS system.
NFarrington - 2015-02-22T15:44:54.926158+00:00

In gitlab by @A-Lawrence on Aug 31, 2015, 01:13
Neil rolled out a change to our VatsimXML package which was pulling ints as strings and vice versa which we're hoping has fixed this.
We're currently investigating this user though.
ALawrence66 - 2015-02-22T15:38:43.416523+00:00

In gitlab by @A-Lawrence on Aug 31, 2015, 01:13
Still some controller ratings wrong too. 1292191 at 1333 on 21st Feb, SSO still reporting as an OBS, but he was upgraded him to S1 on the evening of 20th.
jpfox - 2015-02-22T15:28:51.003501+00:00


Added NOTES.md file, with content

This project uses semver, please check the scope of this pr:

[x] #none# - documentation fixes and/or test additions
[ ] #patch# - backwards-compatible bug fix
[ ] #minor# - adding functionality in a backwards-compatible manner
[ ] #major# - incompatible API change

Closes #103
CHANGELOG

Added NOTES.md file, with content

👍