Required role

Please identify the required roles to do this work in the Prerequisites section
Document Details
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ID: f8e131bf-99d7-a850-a597-ac02b9f6c3a9
Version Independent ID: a27625d3-cf35-ffaf-5734-d1a2bbcdd60c
Content: Tutorial: Azure Active Directory single sign-on (SSO) integration with AWS Single Sign-on
Content Source: articles/active-directory/saas-apps/aws-single-sign-on-tutorial.md
Service: active-directory
Sub-service: saas-app-tutorial
GitHub Login: @jeevansd
Microsoft Alias: jeedes

@ssivas Thank you for your feedback . We will investigate and update the thread.

You must have either of Azure AD role to perform Enterprise application creation in AAD for AWS SSO integration. Hope this helps.
Azure AD roles:
Global Administrator
Application Administrator
Cloud Application Administrator


[BEAM-7864] fix Spark reshuffle translation with Python SDK

The previous implementation of reshuffle on the portable Spark runner made assumptions its inputs that proved false when running some Python pipelines. This new translation is made more general to fix that.

Run Java Spark PortableValidatesRunner Batch

Run Java Spark PortableValidatesRunner Batch

Run Python Spark ValidatesRunner

R: @iemejia Any particular reason we need to separate the keys and values in the current reshuffle translation? https://github.com/apache/beam/blob/c5d45331796693ad48c0cceaf1e4d9903c1d98fb/runners/spark/src/main/java/org/apache/beam/runners/spark/translation/GroupCombineFunctions.java#L191-L197

Still LGTM -- as I mentioned, I'm not entirely sure why the original implementation is done as it is!
It seems that there could be some useful discussion around Reshuffle and it's contract -- if it weren't deprecated!
As far as I can tell, the intention of Reshuffle.of is to ensure that the "upstream" is materialized, and if upstream is ever rematerialized for any reason, the resulting partitions are deterministic.  We don't care whether partition sizes are skewed.
The reference implementation implies that records with the same key are in the same partition, but the spark replacement has never done this.  It also rebalances the partitions (deterministically) whether we care or not.
The intention of Reshuffle.viaRandomKey is to add balancing the partitions.  We don't care whether the results are deterministic.
The spark implementation rebalances the partitions, but it would have done that anyway if the entire .viaRandomKey were replaced with an .of.  The entire viaRandomKey() translation is extra unnecessary cruft in Spark unless random repartitioning is a requirement.  Is it?
I want materialization of partitions of approximately the same size  does not mean I need the data to be randomly assigned to partitions.
Anyway sorry for the sidetrack!  I just feel like I might be missing a piece here and would welcome clarity  :D  In my opinion, all of the current usages of Reshuffle.of() and viaRandomKey() are valid with this re-implementation, except for the Deduplicate in GDF which isn't relevant.

Thanks Kyle.
@RyanSkraba raises some valid points. There is something weird in our current translation and the fact that we ignore keys in particular for the Reshuffle.viaRandomKey() case.
We should maybe fill a JIRA to track this + discuss in the mailing list. (some previous discussion on Reshuffle here).
I was also wondering to what extent in our current implementation (and in particular for the random key case) we could do a repartition with more partitions (based on available CPUs). Of course this has the risk of eating more resources than defined by the job but on the other hand it could be a way to optimize such shuffles downstream. [but well this is a different subject just thinking]


Automated cherry pick of #9589: fix(cloudevent,devtool): missing metadata table

Cherry pick of #9589 on release/3.6.
#9589: fix(cloudevent,devtool): missing metadata table

Codecov Report

Merging #9590 (5d298e7) into release/3.6 (b9d30c0) will decrease coverage by 1.89%.
The diff coverage is 3.40%.

@@               Coverage Diff               @@
##           release/3.6   #9590       +/-   ##
===============================================
- Coverage         5.86%   3.96%    -1.90%
===============================================
  Files              828     170      -658
  Lines           129133   25499   -103634
===============================================
- Hits              7577    1012     -6565
+ Misses          120876   24373    -96503
+ Partials           680     114      -566

Flag
Coverage Δ

aFlag
3.96% <3.40%> (-1.90%)
:arrow_down:

Flags with carried forward coverage won't be shown. Click here to find out more.

Impacted Files
Coverage Δ

pkg/cloudcommon/db/apply_manager_method.go
0.00% <0.00%> (ø)

pkg/cloudcommon/db/archs.go
0.00% <0.00%> (ø)

pkg/cloudcommon/db/db_dispatcher.go
0.00% <0.00%> (ø)

pkg/cloudcommon/db/models.go
0.00% <0.00%> (ø)

pkg/cloudcommon/db/scoperesource.go
0.00% <0.00%> (ø)

pkg/cloudcommon/db/sharablebase.go
6.01% <0.00%> (-0.06%)
:arrow_down:

pkg/cloudcommon/db/virtualjointbase.go
0.00% <0.00%> (ø)

pkg/cloudcommon/db/virtualresource.go
0.00% <0.00%> (ø)

pkg/cloudcommon/policy/policy.go
1.20% <0.00%> (-0.01%)
:arrow_down:

pkg/cloudprovider/instance.go
0.00% <ø> (ø)

... and 608 more

Continue to review full report at Codecov.

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report a metric that shows spot vs on-demand usage

Tell us about your request
Report a metric that shows spot vs on-demand usage. It might be helpful to have a few metrics for spot vs on-demand CPU and spot vs on-demand node count.
Tell us about the problem you're trying to solve. What are you trying to do, and why is it hard?
I want to monitor the amount of spot vs on-demand capacity in my cluster.
Are you currently working around this issue?
More complex prometheus queries.
Additional Context
No response
Attachments
No response
Community Note

Please vote on this issue by adding a 👍 reaction to the original issue to help the community and maintainers prioritize this request
Please do not leave "+1" or "me too" comments, they generate extra noise for issue followers and do not help prioritize the request
If you are interested in working on this issue or have submitted a pull request, please leave a comment

I also was looking for a way to do this recently using Prometheus/Grafana.
I ended up exposing node labels through kube-state-metrics and leveraging the labels to identify how many of each type worker I had. There were lots of useful labels available: cpu, instance family, memory, etc.
Extra arg on kube-state-metrics
--metric-labels-allowlist=nodes=[*]
Then in prometheus/grafana, just:
sum(kube_node_labels{label_karpenter_sh_capacity_type=~".+"}) by (label_karpenter_sh_capacity_type)

This is a good use-case that we should capture as part of our upcoming metrics audit 👍


Centering content in browser

I think it seems a little weird that the content is floated to the left, it would probably be better if it would be centered.
Are you new to Zurb Foundation?
I'm guessing that in main.js, instead of:

      {this.props.children}

you meant something like:

      {this.props.children}

I don't know react.js, but I think it supposed to look like that. You can read more here.

Ah yes! That has been on my list. Thanks for pointing out how to fix it! Embarrassingly I'm not that new to foundation but I haven't invested in mastering it.
Thanks again! Also PRs are always welcome.

Fixed. Thanks again. I don't remember how I got it in my head that you could make the same div be a row and columns, but it was clearly wrong.


Cleanup map importing pipeline

A backlog of related things to simplify OSM import.

From Mateusz: is_road should list known highway values and skip everything else
Things like bus stop matching are split between convert_osm and map_model/src/make in unclear ways. Consolidate topics when possible.
Current structure of the pipeline here isn't clear when reading the code. Something better is possible.

is_road, get_lane_types, and get_rank are all coupled together in the assumptions they make. It's confusing. I'm considering a consolidated, but possibly much more complicated version that takes Tags and, if the thing should count as a road, returns the lane types in each direction and the rank (used for deciding stop sign priority and for some color schemes). Slight complication is that parking inference happens later in the importer (and it has to stay that way, because you can't decide the closest line to snap to without knowing all the relevant lines).


Only for SQL API in Cosmos DB

[Enter feedback here]
I think we should make it clear that uber jar mentioned at below link can be used only for SQL API in cosmosdb.
https://docs.microsoft.com/en-us/azure/databricks/data/data-sources/azure/cosmosdb-connector

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

ID: 7197a1f8-bc64-1ac0-bf54-4a76381acc87
Version Independent ID: 3c5a8e1d-bb1e-0bcc-571a-7cee8edad2ba
Content: Azure Cosmos DB - Azure Databricks
Content Source: databricks/data/data-sources/azure/cosmosdb-connector.md
Service: azure-databricks
GitHub Login: @mamccrea
Microsoft Alias: mamccrea

Hi @prashanthmadi, Thanks for the feedback! We have assigned the issue to the content author to further review this and update the document as appropriate.

#reassign:mssaperla

Created ticket DOC-3001 in the Databricks docs team backlog to update this article with the suggested information. In the meantime, I think this ticket can be closed.
#please-close

Only Microsoft employees can close issues or pull requests via commenting in this repo.

#please-close


Map error

Steps to Reproduce(UI bug)
1.Go to website
2.Click on Map
3.Click on 9 in Iceland
4.Click on Locator(FabLab Vestmannaeyjar)
Expected Result
The drop down should be same as in UI document
Actual Result
The name of the place over the symbol.

marking it as fixed


Allow backing up audit data

Description
By default, any data belonging to an extension cannot be backed up by
pg_dump. An extension can explicitly mark select tables and sequences as
configuration data which makes them dumpable.
See: https://www.postgresql.org/docs/current/extend-extensions.html#EXTEND-EXTENSIONS-CONFIG-TABLES
Notes
I added the scripts I used to test if it works. They don't fit with the other
tests since they naturally can't be pure SQL, so I guess we'll just drop them.
Closes #52

Thanks for the PR and Sorry for the slow review! I've been OOO
Will take a look first thing Monday


Crash during splash screen on some iOS devices

We implemented the sfmc_plugin and it looked like everything was working fine, however, after distributing our app through TestFlight for further testing we found out the app crashes on the splash screen on some devices. It happens randomly on specific devices so it's hard to reproduce.
The crash log seems to suggest the problem is caused by the sfmc_plugin
Incident Identifier: ---
Hardware Model:      iPhone13,1
Process:             Runner [4646]
Path:                /private/var/containers/Bundle/Application/************************************/Runner.app/Runner
Identifier:         *************************************************************
Version:             *****************
AppStoreTools:       15C5500a
AppVariant:          1:iPhone13,1:17
Beta:                YES
Code Type:           ARM-64 (Native)
Role:                Foreground
Parent Process:      launchd [1]
Coalition:           ***************************************************** [908]
Date/Time:           2024-01-16 15:16:58.5795 +0100
Launch Time:         2024-01-16 15:15:39.6044 +0100
OS Version:          iPhone OS 17.2.1 (21C66)
Release Type:        User
Baseband Version:    4.20.05
Report Version:      104
Exception Type:  EXC_CRASH (SIGABRT)
Exception Codes: 0x0000000000000000, 0x0000000000000000
Exception Reason: *** -[__NSPlaceholderDictionary initWithObjects:forKeys:count:]: attempt to insert nil object from objects[1]
Termination Reason: SIGNAL 6 Abort trap: 6
Terminating Process: Runner [4646]
Triggered by Thread:  0
Last Exception Backtrace:
0   CoreFoundation                	0x19127269c __exceptionPreprocess + 164 (NSException.m:249)
1   libobjc.A.dylib               	0x18952fc80 objc_exception_throw + 60 (objc-exception.mm:356)
2   CoreFoundation                	0x1911dc23c -[__NSPlaceholderDictionary initWithObjects:forKeys:count:] + 728 (NSPlaceholderDictionary.m:0)
3   CoreFoundation                	0x1911dbb60 +[NSDictionary dictionaryWithObjects:forKeys:count:] + 52 (NSDictionary.m:614)
4   SFMCSDK                       	0x1017f125c +[SFMCKeychainItemWrapper updateKeychainAccessibleAttribute] + 1108 (SFMCKeychainItemWrapper.m:229)
5   SFMCSDK                       	0x1017f0de8 +[SFMCKeychainItemWrapper setAccessibleAttribute:] + 168 (SFMCKeychainItemWrapper.m:186)
6   sfmc_plugin                   	0x101dcfad4 specialized SwiftSfmcPlugin.setupSFMC**(appId:accessToken:mid:sfmcURL:delayRegistration:analytics:onDone:) + 676 (SwiftSfmcPlugin.swift:181)
7   sfmc_plugin                   	0x101dd2574 specialized SwiftSfmcPlugin.handle(:result:) + 1756 (SwiftSfmcPlugin.swift:39)
8   sfmc_plugin                   	0x101dcf800 specialized SwiftSfmcPlugin.handle(:result:) + 16 (:0)
9   sfmc_plugin                   	0x101dcf800 @objc SwiftSfmcPlugin.handle(_:result:) + 84
10  Flutter                       	0x104781e28 __45-[FlutterMethodChannel setMethodCallHandler:]_block_invoke + 172 (FlutterChannels.mm:268)
11  Flutter                       	0x1041ef5a4  + -1 (platform_message_handler_ios.mm:73)
12  libdispatch.dylib             	0x1991776a8 _dispatch_call_block_and_release + 32 (init.c:1530)
13  libdispatch.dylib             	0x199179300 _dispatch_client_callout + 20 (object.m:561)
14  libdispatch.dylib             	0x199187998 _dispatch_main_queue_drain + 984 (queue.c:7813)
15  libdispatch.dylib             	0x1991875b0 _dispatch_main_queue_callback_4CF + 44 (queue.c:7973)
16  CoreFoundation                	0x1911bd01c CFRUNLOOP_IS_SERVICING_THE_MAIN_DISPATCH_QUEUE + 16 (CFRunLoop.c:1780)
17  CoreFoundation                	0x1911b9d28 __CFRunLoopRun + 1996 (CFRunLoop.c:3149)
18  CoreFoundation                	0x1911b9478 CFRunLoopRunSpecific + 608 (CFRunLoop.c:3420)
19  GraphicsServices              	0x1d47124f8 GSEventRunModal + 164 (GSEvent.c:2196)
20  UIKitCore                     	0x1935dd62c -[UIApplication _run] + 888 (UIApplication.m:3685)
21  UIKitCore                     	0x1935dcc68 UIApplicationMain + 340 (UIApplication.m:5270)
22  Runner                        	0x1007e4694 main + 80 (main.m:7)
23  dyld                          	0x1b3ededcc start + 2240 (dyldMain.cpp:1269)

We were able to fix the crash by editing the SwiftSfmcPlugin.swift located in sfmc_plugin > ios > Classes.
We had to comment the line 184:
// To override the Keycahin accessibility attribute //        SFMCSdk.setKeychainAccessibleAttribute(accessibleAttribute: kSecAttrAccessibleWhenUnlockedThisDeviceOnly)

Thanks for the report
I will update after my investigation results.

Hi @ifelsebreak
I could not reproduce this reported issue but I can bring items below into your attention:
1- The crash log mentions SFMCKeychainItemWrapper, which suggests that the issue might be related to keychain access. iOS apps can face issues with keychain access, especially if there are changes in app entitlements, app groups, or if the app is being run in a different environment (like TestFlight). Review your app's keychain access settings and ensure they align with what the SFMC SDK expects.
2- This kind of issue often arises from incorrect or incomplete initialization parameters being passed to the SDK, or it might be due to a race condition where the SDK is being accessed before it's fully initialized or in a state that it doesn't expect. or it might be due to a race condition where the SDK is being accessed before it's fully initialized or in a state that it doesn't expect.
Make sure that all the parameters required for initializing the Salesforce Marketing Cloud SDK via sfmc_plugin are correct and non-nil. This includes appId, accessToken, mid, sfmcURL, and any other configuration parameters you're passing during the setup.
Also it's good to learn more from SFMC iOS SDK: Keychain crash
Regards,
Farshid


Make TransactionType a str enum instead of int

Fixes #36
Currently, when the detailed summary is exported, the transaction type with key "type" consists of the string version of the TransactionType Enum.
https://github.com/codereverser/casparser/blob/e2f14a09a1e9986951cbb91c75af75fd14e4a1e4/casparser/process/cas_detailed.py#L200
While this is the right design, if someone wants to reuse the TransactionType Enum elsewhere (like I am) on the exported data, this becomes a slight nuisance, as Json parsers like pydantic will not automatically parse the string into the Enum (as TransactionType is an Enum of ints).
To allow for this, I am proposing changing TransactionType into a str based enum. By overriding _generate_next_value_ method, we can make auto return the name of the Enum field as value instead of int. This way the value auto returns will be equal to the name it is being assigned and will be a string. This will allow those using TransactionType to recreate the overall object to directly import it without writing custom code to port the string to corresponding int in Enum.
This does not affect any other code as the enum can be accessed as is.
Additionally, the above mentioned line can be changed to "type": txn_type.name as now both  txn_type and txn_type.name will point to the same value.

Looks good. merging it.
Many thanks!

Thanks for the quick merge! When are planning on the next release? I can then do a pip upgrade and get these changes on my project,

I was planning for releasing v0.5.0 tomorrow with #33 , but that doesn't look plausible. shall make another release (v0.4.8) tomorrow with some other minor bug fixes.


Add October CMS

[ ] verified that the CMS I'm adding is still maintained.
[ ] read CONTRIBUTING.md.
[ ] did not generate README.md.

Thanks, @loongmxbt!


is there any sliding cnn + LSTM + CTC implement ?

the network should like this :

thx!

Perhaps you'd be interested in this: https://github.com/fchollet/keras/pull/3906


Adding implementation of moving windows to other workspaces

I thought that we could add this in to the WM as it's slightly annoying having workspaces but not being able to move windows to them. I already implemented this feature in my repo but per your request to discuss new features before we make a pull req or add them into this repo I thought I should ask you first.
In my repo, I made it use Alt+Shift+[1-9]. It's a little buggy still with as it doesn't always move windows to the other workspace but it works well on my main machine running Windows 11.
You click the window you want to move, hit Alt+Shift+2 for example and it moves the window to that workspace and retiles the one you are in. After that you can hit Alt+2 and see the window in that workspace.
The release exe and dll that I compiled with these changes is 14.5KB combined and debug is 36KB. (minus ilk and pdb files which brings it to 1MB >)
This is my commit in my repo if you want to check my implementation out. I wanted to wait for your approval before I made a pull request.
EDIT: new commit, last one needed a clean up and I needed to set something back, I apologise. it now no longer shows the code I changed and added but I'll send the link to the files.
https://github.com/AFellowSpeedrunner/lightwm/blob/master/config.h
https://github.com/AFellowSpeedrunner/lightwm/blob/master/keyboard.c
https://github.com/AFellowSpeedrunner/lightwm/blob/master/tiling.h
https://github.com/AFellowSpeedrunner/lightwm/blob/master/tiling.c
(PS. I also fixed a function spelling error too. TOGGLE_FULLSCREEN_MODE_HOYKEY_ID -> TOGGLE_FULLSCREEN_MODE_HOTKEY_ID)
Thanks!

Looks good for PR, thanks!

Looks good for PR, thanks!

Nice, pushing as a PR now!


reaction damage melt/vape multiplier not applied

Also check to see if aoe reaction damage is using correct stats?

Did some digging on this, and it appears to be related to the 4VV 60% swirl bonus not proccing on the AoE damage instances. Since you're reworking the reactions system I'm not bothering to make any changes here just FYI, so I guess just keep that interaction in mind as you're doing the rework.

not sure if this one is still bugged. @Tsym-or-Tysm you wouldn't happen to remember the config that caused this?

Just checked Sukokomon and this one looks fixed to me with the refactor. Closed.


maintenance and new release

Is this project still actively maintained?
Also a new release would be great: https://github.com/jakubpawlowicz/clean-css/compare/v4.2.1...master
And this project should probably live under an org.

@jakubpawlowicz it seems there's a new npm version published without ant git tag/release. Can you push any changes please?

^ oops, did it yesterday.


VSCode Version: Insiders
OS Version: Windows 19582.1001

Steps to Reproduce:

Launch a program (better have an extremely long run time) in a terminal using PowerShell (Core, Don't know if required to reproduce)
Kill it before it ends.
VScode didn't respond until reloading the page.

Does this issue occur when all extensions are disabled?: No, don't know which caused this problem, most likely to be related to terminal.

Sorry, I've checked this issue and it is fixed after an update. Closing now.


Fix unknown build error when reconciling HelmChart from an OCI Registry

HelmCharts reconciliation sometime result in unknown build error and Unknown reason with auth failure It happens when HelmChart build is attempted when the associated HelmRepository isn't ready because of the auth failure.
It then returns *conditions.TrueCondition(sourcev1.FetchFailedCondition, "Unknown", "unknown build error: failed to login to OCI registry").
Around source-controller/controllers/helmchart_controller.go we have a check for non-OCI charts to ensures that the HelmRepo has artifact before proceeding further. We can add a similar check for OCI where we make sure that the HelmRepo is ready before proceeding to build it. That way, we can avoid such Unknown errors and show better status conditions.

Thanks for working on this @Santosh1176
If you create a helmrepository of type oci with wrong credentials, it should fail reconciliation. If you then use it as source in a helmrelease, you should be able to reproduce. You will have to look for the helmChart custom resource declared by the helm-controller.

hello @Santosh1176, did you have the chance to look at this?

@souleb Sorry, I tried but could not reproduce the issue, I'll take a closer look this weekend. if this task is of importance, please go ahead.
Thank you!

I think we can close this? cc @darkowlzz


Simple Echobot gives serviceURL null error, when called from slack, after update to v3

Hi,
I upgraded to v3 of the bot framework and emulator and have created a simple echo bot as in the samples and successfully ran it using the emulator. However, when I integrated it with slack, it gives Activity.serviceURL null error when messaging the bot. I get the http://localhost:9000 as serviceURL in the Activity when I run it in emulator but can't get any url when running it from slack. It worked perfectly fine before the upgrade on both emulator and slack. Any suggestions on how to get my bot running in slack after upgrade?
Thanks
Niyati

Is this the Node or C# SDK?  There is an issue where the ServiceUrl is not copied to the new message when CreateReply is used.

@niyatia what is your bot's Microsoft App Id? Incoming activities from channels should have valid ServiceUrl. Are you seeing the same behavior with other channels?

@msft-shahins  I did not try with other channels. I can try integrating with skype. My appId is Oktabot. Also I am using C# sdk @willportnoy .

Never mind. I realized I did not update the settings for my bot under botframework->MyBots->EchoBot->Edit and change the version from 1.0 to 3.0. I had created this bot in 1.0 and published to slack. Later I updated the framework and made required changes in code and published again but missed this configuration.  After that update, I am able to connect the bot via slack. Thanks for the help. Closing the issue but I would say documentation about the upgrade is not very clear or helpful.


Add new example APIs

The changes recommended in
https://github.com/apache/singa-doc/pull/14
to simply the APIs of the examples
I have reduced the number of arguments in the train_mnist_cnn

After testing, I will remove WIP

I have tested, ready for merge

Just now I have added two examples specially for the dist-train.md documentation, the API for the train fuction becomes the same as that used in the docs
MPI train: doc_dist_train.py
Multiprocess train: doc_dist_multiprocess.py

I suggest to merge the examples under examples/autograd into the following scheme
autograd
    train.py
    train_mpi.py
    train_multiprocess.py
    data   # define the data loading and preprocessing
        cifar10.py
        mnist.py
    model   # define the model
        cnn.py
        resnet.py
        xception.py

# train.py
def run(max_epoch, rank, num_workers, model, data, sgd):
    if model == 'resnet':
       from model import resnet
         model = resnet.create_model()
    elif model == 'cnn':
         model = cnn.create_model()
    ....
    if data == 'cifar10':
        from data import cifar10:
           train_x, train_y, val_x, val_y = cifar10.load()
    elif data == 'mnist':
         ....

    train_x, train_y, val_x, val_y = partition(rank, num_workers, train_x, train_y, val_x, val_y)

    # bp and sgd

if __name__ == '__main__':
   # use argparse to get command config: max_epoch, model, data, etc. for single gpu training
    sgd = # create sgd
    run(0, 1, ..., sgd)

# train_mpi.py
if __name__ == '__main__':
   # use argparse to get command args: max_epoch, model, data, etc. for multi-gpu training
    sgd = # create sgd
    dist_sgd = DistOpt(sgd...)
    run(dist_sgd.rank, dist_sgd.world_size, ... dist_sgd)

# train_multiprocess.py
def run(rank, num_gpu, ...):
    sgd = ...
    dist_sgd = DistOpt(sgd)
    train.run(rank, num_gpu, ... dist_sgd)

if __name__ == '__main__':
   # use argparse to get command args: max_epoch, model, data, etc. for multi-gpu training
    nccl_id = ...
    process = []
    for gpu_num in range(0, gpu_per_node):
        process.append(multiprocessing.Process(target=run, args=(gpu_num, ... nccl_id)))

    for p in process:
        p.start()

I suggest to merge the examples under examples/autograd into the follow structure.
autograd
    train.py
    train_mpi.py
    train_multiprocess.py
    data   # define the data loading and preprocessing
        cifar10.py
        mnist.py
    model   # define the model
        cnn.py
        resnet.py
        xception.py

The pseudo code of each file:
# train.py
def run(max_epoch, worker_id, num_workers, model, data, sgd):
    if model == 'resnet':
       from model import resnet
         model = resnet.create_model()
    elif model == 'cnn':
         model = cnn.create_model()
    ....
    if data == 'cifar10':
        from data import cifar10:
           train_x, train_y, val_x, val_y = cifar10.load()
    elif data == 'mnist':
         ....

    train_x, train_y, val_x, val_y = partition(worker_id, num_workers, train_x, train_y, val_x, val_y)

    # bp and sgd

if __name__ == '__main__':
   # use argparse to get command config: max_epoch, model, data, etc. for single gpu training
    sgd = # create sgd
    run(0, 1, ..., sgd)

# train_mpi.py
if __name__ == '__main__':
   # use argparse to get command args: max_epoch, model, data, etc. for multi-gpu training
    sgd = # create sgd
    dist_sgd = DistOpt(sgd...)
    run(dist_sgd.rank, dist_sgd.world_size, ... dist_sgd)

# train_multiprocess.py
def run(rank, num_gpu, ...):
    sgd = ...
    dist_sgd = DistOpt(sgd)
    train.run(rank, num_gpu, ... dist_sgd)

if __name__ == '__main__':
   # use argparse to get command args: max_epoch, model, data, etc. for multi-gpu training
    nccl_id = ...
    process = []
    for worker_id in range(0, gpu_per_node):
        process.append(multiprocessing.Process(target=run, args=(worker_id, ... nccl_id)))

    for p in process:
        p.start()

thanks! I will work on it. Since seems a lot of work, to speed up may need Rulin to help

I am restructing all the examples and APIs in autograd example folder,
for the first commit I have created the model and data folders

where is resnet_module.py? can we use the Module API (to replace Layer model) added by rulin for these examples and add a switch to turn on/off operation buffering at the command line?
i can handle the documentation.

where is resnet_module.py? can we use the Module API (to replace Layer model) added by rulin for these examples and add a switch to turn on/off operation buffering at the command line?
i can handle the documentation.

The graph module is here.
https://github.com/apache/singa/blob/dev/examples/autograd/resnet_module.py
https://github.com/apache/singa/blob/dev/examples/autograd/cnn_module.py
https://github.com/apache/singa/blob/dev/examples/autograd/mlp_module.py
The on/off switch of buffering function is embedded in python/singa/module.py
https://github.com/apache/singa/blob/dev/python/singa/module.py

we can use resnet_module.py to replace resnet.py?

we can use resnet_module.py to replace resnet.py?

yes, should be okay, in this case maybe run everything with graph

we can use resnet_module.py to replace resnet.py?

yes, should be okay, in this case maybe run everything with graph

note that in this setting we give up the non-graph mode, but I guess it will be okay to run all the example

since graph version is more efficient, I think it is fine to run the examples using the graph.
we can still keep the current version in another folder

imperative (for the current)
declarative (for graph based)

we can do the graph based first and if no time, then just ignore the imperative version.

since graph version is more efficient, I think it is fine to run the examples using the graph.
we can still keep the current version in another folder

imperative (for the current)
declarative (for graph based)

we can do the graph based first and if no time, then just ignore the imperative version.

OK, we can provide both, but I focus on graph based first

I followed the new design of API, now can run (i) cnn+mnist, (ii) resnet+cifar10
root@877a3759b148:~/dcsysh/singa/examples/autograd# python3 train_multiprocess.py cnn mnist --lr 0.01
Starting Epoch 0:
Training loss = 625.124268, training accuracy = 0.777227
Evaluation accuracy = 0.941206, Elapsed Time = 1.721721s
Starting Epoch 1:
Training loss = 235.566132, training accuracy = 0.920757
Evaluation accuracy = 0.945413, Elapsed Time = 1.685381s
Starting Epoch 2:
Training loss = 171.600082, training accuracy = 0.942258
Evaluation accuracy = 0.969852, Elapsed Time = 1.703893s
Starting Epoch 3:
Training loss = 139.339203, training accuracy = 0.953476
Evaluation accuracy = 0.972857, Elapsed Time = 1.695866s
Starting Epoch 4:
Training loss = 120.622467, training accuracy = 0.959118
Evaluation accuracy = 0.971254, Elapsed Time = 1.703533s
Starting Epoch 5:
Training loss = 105.304459, training accuracy = 0.964777
Evaluation accuracy = 0.978466, Elapsed Time = 1.702236s
Starting Epoch 6:
Training loss = 99.502411, training accuracy = 0.966964
Evaluation accuracy = 0.975761, Elapsed Time = 1.694606s
Starting Epoch 7:
Training loss = 88.000076, training accuracy = 0.969985
Evaluation accuracy = 0.977364, Elapsed Time = 1.700657s
Starting Epoch 8:
Training loss = 85.234161, training accuracy = 0.971004
Evaluation accuracy = 0.976262, Elapsed Time = 1.690182s
Starting Epoch 9:
Training loss = 79.724716, training accuracy = 0.973591
Evaluation accuracy = 0.983073, Elapsed Time = 1.701022s

root@877a3759b148:~/dcsysh/singa/examples/autograd# python3 train_multiprocess.py resnet cifar10 --lr 0.01 --bs 32
Check the shape of dataset:
Starting Epoch 0:
Training loss = 3112.784912, training accuracy = 0.317702
Evaluation accuracy = 0.464543, Elapsed Time = 151.415575s
Starting Epoch 1:
Training loss = 2140.442383, training accuracy = 0.500840
Evaluation accuracy = 0.568710, Elapsed Time = 156.812310s
Starting Epoch 2:
Training loss = 1680.163208, training accuracy = 0.616997
Evaluation accuracy = 0.681891, Elapsed Time = 157.775971s
Starting Epoch 3:
Training loss = 1395.033936, training accuracy = 0.688500
Evaluation accuracy = 0.711939, Elapsed Time = 157.391661s
Starting Epoch 4:
Training loss = 1174.264160, training accuracy = 0.738916
Evaluation accuracy = 0.756711, Elapsed Time = 157.423720s
Starting Epoch 5:
Training loss = 1028.127197, training accuracy = 0.773007
Evaluation accuracy = 0.779948, Elapsed Time = 157.615884s
Starting Epoch 6:
Training loss = 910.041748, training accuracy = 0.796935
Evaluation accuracy = 0.803886, Elapsed Time = 157.282300s
Starting Epoch 7:
Training loss = 829.032227, training accuracy = 0.817882
Evaluation accuracy = 0.810296, Elapsed Time = 157.427150s
Starting Epoch 8:
Training loss = 761.830078, training accuracy = 0.832686
Evaluation accuracy = 0.825621, Elapsed Time = 157.431677s
Starting Epoch 9:
Training loss = 703.944153, training accuracy = 0.846791
Evaluation accuracy = 0.825421, Elapsed Time = 157.413234s

can we run (cnn|resnet|xceptionnet) + (cifar10|mnist) ?
I think we can also add the mlp model?
then we can remove resnet_module.py, mlp_module.py

can we run (cnn|resnet|xceptionnet) + (cifar10|mnist) ?
I think we can also add the mlp model?
then we can remove resnet_module.py, mlp_module.py

There are 4 points, I guess the issue most difficult to solve is the no.1 below

xceptionnet has some problem, the running stopped after 1 epoch, maybe the model has some issues. And this model is very large I can only run batch size=16 (not 32)

The current mlp is 2d input, which is not CNN problem which has 4d input (batch, channel/RGB, height, width). Maybe need to change to 4d

The CNN has only one input channel, in order to support cifar10, maybe I need to adjust the number of input channel of CNN as variable

mnist is not RGB, so I has only one colour channel, if I used it for resnet I can see if:
(i) change the input channel of resnet to a variable so it support 1 channel
(ii) replicate one channel of mnist dataset to three

can we run (cnn|resnet|xceptionnet) + (cifar10|mnist) ?
I think we can also add the mlp model?
then we can remove resnet_module.py, mlp_module.py

There are 4 points, I guess the issue most difficult to solve is the no.1 below
1. xceptionnet has some problem, the running stopped after 1 epoch, maybe the model has some issues. And this model is very large I can only run batch size=16 (not 32)

what is the error? if you put it on colab, we can debug together.

2. The current mlp is 2d input, which is not CNN problem which has 4d input (batch, channel/RGB, height, width). Maybe need to change to 4d

when we load the data, pass an argument to specify the dimension (is_2d=True/False).
then reshape the data according to the argument inside the load_data function.

3. The CNN has only one input channel, in order to support cifar10, maybe I need to adjust the number of input channel of CNN as variable

change the CNN model to accept 4d input

4. mnist is not RGB, so I has only one colour channel, if I used it for resnet I can see if:
   (i) change the input channel of resnet to a variable so it support 1 channel
   (ii) replicate one channel of mnist dataset to three

reshape it into 4d inside the load_data function.

xceptionnet error

I have already unified terms to <local_rank, global_rank, world_size> in the singa code (last commit)

It is now able to support (cnn|resnet) + (cifar10|mnist)
root@877a3759b148:~/dcsysh/singa/examples/autograd# python3 train.py cnn cifar10
3
Starting Epoch 0:
Training loss = 1334.210571, training accuracy = 0.383543
Evaluation accuracy = 0.497997, Elapsed Time = 17.269876s
Starting Epoch 1:
Training loss = 1080.154541, training accuracy = 0.507542
Evaluation accuracy = 0.568810, Elapsed Time = 17.324370s
Starting Epoch 2:
Training loss = 974.208618, training accuracy = 0.555598
Evaluation accuracy = 0.612680, Elapsed Time = 17.230355s
Starting Epoch 3:
Training loss = 907.781128, training accuracy = 0.585147
Evaluation accuracy = 0.625901, Elapsed Time = 17.264113s
Starting Epoch 4:
Training loss = 858.995056, training accuracy = 0.609435
Evaluation accuracy = 0.650841, Elapsed Time = 17.296929s
Starting Epoch 5:
Training loss = 819.569641, training accuracy = 0.630642
Evaluation accuracy = 0.663862, Elapsed Time = 17.185191s
Starting Epoch 6:
Training loss = 791.844910, training accuracy = 0.643606
Evaluation accuracy = 0.681991, Elapsed Time = 17.057992s
Starting Epoch 7:
Training loss = 763.051758, training accuracy = 0.657690
Evaluation accuracy = 0.688301, Elapsed Time = 16.876048s
Starting Epoch 8:
Training loss = 735.888611, training accuracy = 0.671195
Evaluation accuracy = 0.686999, Elapsed Time = 16.930443s
Starting Epoch 9:
Training loss = 715.715027, training accuracy = 0.680778
Evaluation accuracy = 0.704928, Elapsed Time = 16.901450s

root@877a3759b148:~/dcsysh/singa/examples/autograd# python3 train.py resnet mnist --bs 32
Starting Epoch 0:
Training loss = 682.914490, training accuracy = 0.892883
Evaluation accuracy = 0.958233, Elapsed Time = 302.701322s

xceptionnet error

what's the size of the input to the model?

xceptionnet error

what's the size of the input to the model?

Input size of the network is 299. The first epoch can train so there may be other problem

can the original model run (not using the Module API)?

can the original model run (not using the Module API)?

Yes, I have turned the graph off (GRAPH=FALSE) and running, seems is okay I have just finished 2 rpochs, I will wait for more result. I think the different of resnet and xceptionnet is that it has multiple branch
(singa) dcsysh@panda7:~/singa/examples/autograd$ python3 train.py xceptionnet ci

Starting Epoch 0:
Training loss = 11198.645508, training accuracy = 0.214420
Evaluation accuracy = 0.309000, Elapsed Time = 606.547117s
Starting Epoch 1:
Training loss = 6354.611328, training accuracy = 0.381020
Evaluation accuracy = 0.457300, Elapsed Time = 612.817129s
Starting Epoch 2:

python3 train.py xceptionnet ci

I think it might be that the memory addresses of some tensors were changed during the evaluation phase, which caused the calculation graph to not be executed correctly.

can the original model run (not using the Module API)?

Yes, I have turned the graph off (GRAPH=FALSE) and running, seems is okay I have just finished 2 rpochs, I will wait for more result. I think the different of resnet and xceptionnet is that it has multiple branch
(singa) dcsysh@panda7:~/singa/examples/autograd$ python3 train.py xceptionnet ci

Starting Epoch 0:
Training loss = 11198.645508, training accuracy = 0.214420
Evaluation accuracy = 0.309000, Elapsed Time = 606.547117s
Starting Epoch 1:
Training loss = 6354.611328, training accuracy = 0.381020
Evaluation accuracy = 0.457300, Elapsed Time = 612.817129s
Starting Epoch 2:

I know the problem. It's because some tensors were deallocated during the evaluation phase. I will fix it.

I added two args in train_mpi,py and train_multiprocess.py to support all the distributed training options, but train.py don't need the argument because it doesn't have DistOpt
Below are two examples for the use of arguments:
root@71ac539cda77:~/dcsysh/singa/examples/autograd# mpiexec -np 2 python3 train_mpi.py cnn mnist --op fp16 --lr 0.01
Starting Epoch 0:
Training loss = 625.136963, training accuracy = 0.776893
Evaluation accuracy = 0.939704, Elapsed Time = 1.619492s
Starting Epoch 1:
Training loss = 235.280151, training accuracy = 0.920339
Evaluation accuracy = 0.947115, Elapsed Time = 1.517764s
Starting Epoch 2:
Training loss = 171.311310, training accuracy = 0.942508
Evaluation accuracy = 0.970252, Elapsed Time = 1.557606s
Starting Epoch 3:
Training loss = 139.594086, training accuracy = 0.953342
Evaluation accuracy = 0.972356, Elapsed Time = 1.541372s
Starting Epoch 4:
Training loss = 120.380058, training accuracy = 0.959852
Evaluation accuracy = 0.971655, Elapsed Time = 1.560016s
Starting Epoch 5:
Training loss = 104.767105, training accuracy = 0.965345
Evaluation accuracy = 0.979467, Elapsed Time = 1.525147s
Starting Epoch 6:
Training loss = 98.995010, training accuracy = 0.966730
Evaluation accuracy = 0.976963, Elapsed Time = 1.528173s
Starting Epoch 7:
Training loss = 88.012024, training accuracy = 0.970202
Evaluation accuracy = 0.977865, Elapsed Time = 1.515204s
Starting Epoch 8:
Training loss = 86.540741, training accuracy = 0.970519
Evaluation accuracy = 0.975361, Elapsed Time = 1.604774s
Starting Epoch 9:
Training loss = 80.653885, training accuracy = 0.973024
Evaluation accuracy = 0.982071, Elapsed Time = 1.633480s
root@71ac539cda77:~/dcsysh/singa/examples/autograd# mpiexec -np 2 python3 train_mpi.py cnn mnist --op partialUpdate --lr 0.01
Starting Epoch 0:
Training loss = 623.692139, training accuracy = 0.777694
Evaluation accuracy = 0.939804, Elapsed Time = 1.678683s
Starting Epoch 1:
Training loss = 235.369232, training accuracy = 0.920690
Evaluation accuracy = 0.948017, Elapsed Time = 1.567066s
Starting Epoch 2:
Training loss = 171.335693, training accuracy = 0.942157
Evaluation accuracy = 0.971855, Elapsed Time = 1.595140s
Starting Epoch 3:
Training loss = 139.396088, training accuracy = 0.953592
Evaluation accuracy = 0.971755, Elapsed Time = 1.661490s
Starting Epoch 4:
Training loss = 120.173752, training accuracy = 0.960019
Evaluation accuracy = 0.970753, Elapsed Time = 1.571710s
Starting Epoch 5:
Training loss = 105.472672, training accuracy = 0.965061
Evaluation accuracy = 0.978065, Elapsed Time = 1.570710s
Starting Epoch 6:
Training loss = 99.055389, training accuracy = 0.966930
Evaluation accuracy = 0.977163, Elapsed Time = 1.561047s
Starting Epoch 7:
Training loss = 88.166275, training accuracy = 0.970002
Evaluation accuracy = 0.976663, Elapsed Time = 1.665272s
Starting Epoch 8:
Training loss = 85.920563, training accuracy = 0.970469
Evaluation accuracy = 0.976162, Elapsed Time = 1.579652s
Starting Epoch 9:
Training loss = 79.568375, training accuracy = 0.973040
Evaluation accuracy = 0.982472, Elapsed Time = 1.605414s

adding mlp

now it support mlp also:
root@71ac539cda77:~/dcsysh/singa/examples/autograd# python3 train.py mlp mnist
Starting Epoch 0:
Training loss = 485.473358, training accuracy = 0.851771
Evaluation accuracy = 0.911358, Elapsed Time = 0.615843s
Starting Epoch 1:
Training loss = 267.414551, training accuracy = 0.918657
Evaluation accuracy = 0.928986, Elapsed Time = 0.623670s
Starting Epoch 2:
Training loss = 219.956863, training accuracy = 0.932998
Evaluation accuracy = 0.937400, Elapsed Time = 0.618422s
Starting Epoch 3:
Training loss = 188.373474, training accuracy = 0.942519
Evaluation accuracy = 0.946715, Elapsed Time = 0.617576s
Starting Epoch 4:
Training loss = 164.958145, training accuracy = 0.949223
Evaluation accuracy = 0.951723, Elapsed Time = 0.618342s
Starting Epoch 5:
Training loss = 146.759750, training accuracy = 0.954442
Evaluation accuracy = 0.956330, Elapsed Time = 0.619707s
Starting Epoch 6:
Training loss = 132.402222, training accuracy = 0.959178
Evaluation accuracy = 0.958534, Elapsed Time = 0.620100s
Starting Epoch 7:
Training loss = 120.471268, training accuracy = 0.963114
Evaluation accuracy = 0.961639, Elapsed Time = 0.620941s
Starting Epoch 8:
Training loss = 110.338516, training accuracy = 0.966832
Evaluation accuracy = 0.963642, Elapsed Time = 0.619157s
Starting Epoch 9:
Training loss = 101.753929, training accuracy = 0.968450
Evaluation accuracy = 0.966947, Elapsed Time = 0.619542s
root@71ac539cda77:~/dcsysh/singa/examples/autograd# python3 train.py mlp cifar10
Starting Epoch 0:
Training loss = 1560.984497, training accuracy = 0.323504
Evaluation accuracy = 0.354868, Elapsed Time = 0.689808s
Starting Epoch 1:
Training loss = 1337.684814, training accuracy = 0.396327
Evaluation accuracy = 0.398037, Elapsed Time = 0.693844s
Starting Epoch 2:
Training loss = 1263.678955, training accuracy = 0.430498
Evaluation accuracy = 0.412760, Elapsed Time = 0.688408s
Starting Epoch 3:
Training loss = 1214.546997, training accuracy = 0.453585
Evaluation accuracy = 0.427885, Elapsed Time = 0.686060s
Starting Epoch 4:
Training loss = 1174.774658, training accuracy = 0.469310
Evaluation accuracy = 0.441106, Elapsed Time = 0.688051s
Starting Epoch 5:
Training loss = 1142.419312, training accuracy = 0.483395
Evaluation accuracy = 0.437200, Elapsed Time = 0.688847s
Starting Epoch 6:
Training loss = 1112.198486, training accuracy = 0.497759
Evaluation accuracy = 0.430889, Elapsed Time = 0.685957s
Starting Epoch 7:
Training loss = 1082.584961, training accuracy = 0.510563
Evaluation accuracy = 0.447516, Elapsed Time = 0.685680s
Starting Epoch 8:
Training loss = 1060.778320, training accuracy = 0.521627
Evaluation accuracy = 0.453125, Elapsed Time = 0.686706s
Starting Epoch 9:
Training loss = 1039.045410, training accuracy = 0.529289
Evaluation accuracy = 0.449920, Elapsed Time = 0.687589s

I think this PR is ready for merge, Rulin can submit another PR to fix xceptionnet

now it supports cifar 100 as well, we can train (mlp|cnn|resnet) + (cifar10|cifar100|mnist)
root@71ac539cda77:~/dcsysh/singa/examples/autograd# mpiexec -np 2 python3 train_mpi.py resnet cifar100 --lr 0.1
Starting Epoch 0:
Training loss = 3123.622803, training accuracy = 0.094952
Evaluation accuracy = 0.151743, Elapsed Time = 71.773229s
Starting Epoch 1:
Training loss = 2569.560547, training accuracy = 0.197817
Evaluation accuracy = 0.273538, Elapsed Time = 72.339331s
Starting Epoch 2:
Training loss = 2142.290039, training accuracy = 0.299519
Evaluation accuracy = 0.367688, Elapsed Time = 72.516621s
Starting Epoch 3:
Training loss = 1766.168091, training accuracy = 0.395593
Evaluation accuracy = 0.455929, Elapsed Time = 72.540777s
Starting Epoch 4:
Training loss = 1488.601074, training accuracy = 0.478446
Evaluation accuracy = 0.503806, Elapsed Time = 72.693376s
Starting Epoch 5:
Training loss = 1302.042725, training accuracy = 0.535337
Evaluation accuracy = 0.536859, Elapsed Time = 72.012540s
Starting Epoch 6:
Training loss = 1148.359131, training accuracy = 0.583213
Evaluation accuracy = 0.583133, Elapsed Time = 72.194807s
Starting Epoch 7:
Training loss = 1035.075928, training accuracy = 0.620353
Evaluation accuracy = 0.606370, Elapsed Time = 72.698941s
Starting Epoch 8:
Training loss = 924.950745, training accuracy = 0.654067
Evaluation accuracy = 0.621194, Elapsed Time = 72.009050s
Starting Epoch 9:
Training loss = 834.806763, training accuracy = 0.685857
Evaluation accuracy = 0.643129, Elapsed Time = 72.860034s


Create dialog flow for adding a New Item

This ticket will be used to create the dialog that will popup in case the user wants to add a new item to a Tab.
The ticket should meet the following requirements:

The dialog should have a title New Item or New Entry.
Initially, there should be a Title: text field to enter a entry title. Also there should be another option Movie or Series: with two radio buttons to enable the user to choose between a movie or a series. It should be empty at first (no default selection). Then, there should be another item Column: with a dropdown list for the user to choose where the new entry should be added. At the bottom right of the dialog, there should be two buttons Cancel as a OutlinedButton and a Add ElevatedButton. The Add button should be greyed out in case not all fields are filled in.
In case the user selected a Movie option, nothing else should happen and in case all fields are entered, the Add button should be pressed. This dismisses the dialog and the new entry should be seen in an column.
In case the user selects a Series option, two new options/tiles should pop up. Number of seasons: and Number of episodes:. Both should only be allowed to enter numbers. In case all values has been entered, the user can press Add and the dialog should be dismissed, with the new entry present in the selected column.
In case the cancel button is pressed at any stage of the dialog, it should be dismissed and taken back to the home screen.

Update:
This ticket should add functionality to add new tabs as well as new entries.
When tapping the + button for a new tab:

A dialog with form fields and two button Cancel and Ok should pop up
Dialog should have a Title as Add new tab
There should be a Title form field
There should be a Subtitle/Description form field
A new Bloc should be created

When tapping the + button for a new entry, the following should be found:

A dialog should pop up
The dialog should have a title as Add new entry to <insert tab name>
A Title form field to enter a title
A Subtitle/Description form field to enter a subtitle or short description
A Type dropdown field with two entries, Movie and Series. Initially, neither should be selected
Confirm: Use the same Bloc used with Tabs


run error in benchmark_pipelines.ipynb on Win 10

when I run onnx_transformers\notebooks\benchmark_pipelines.ipynb，I got error below:
onnxruntime.capi.onnxruntime_pybind11_state.InvalidArgument: [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Unexpected input data type.
only use nlp_torch = pipeline("feature-extraction", onnx=False)，get the inference time figure.

hi @baiziyuandyufei
did you get this error after running nlp_torch = pipeline("feature-extraction", onnx=False) ?

if only run nlp_torch = pipeline("feature-extraction", onnx=False),  no problem.
but run nlp_onnx = pipeline("feature-extraction",  onnx=True), will get error.

Could you post your env info (transformers version, onnruntime version, torch version etc) ?
I ran the notebook again and couldn't reproduce the issue.

My operating system environment is Win10 Professional.
My env info:
>pip list
Package           Version
----------------- ---------
certifi           2020.6.20
chardet           3.0.4
click             7.1.2
coloredlogs       14.0
cycler            0.10.0
dataclasses       0.7
filelock          3.0.12
future            0.18.2
humanfriendly     8.2
idna              2.10
joblib            0.16.0
kiwisolver        1.2.0
matplotlib        3.3.1
numpy             1.19.1
onnx              1.7.0
onnx-transformers 0.1.0
onnxruntime       1.4.0
onnxruntime-tools 1.4.2
packaging         20.4
pandas            1.1.2
Pillow            7.2.0
pip               20.2.3
protobuf          3.13.0
psutil            5.7.2
py-cpuinfo        7.0.0
py3nvml           0.2.6
pyparsing         2.4.7
pyreadline        2.1
python-dateutil   2.8.1
pytz              2020.1
regex             2020.7.14
requests          2.24.0
sacremoses        0.0.43
scipy             1.5.2
seaborn           0.11.0
sentencepiece     0.1.91
setuptools        36.4.0
six               1.15.0
tokenizers        0.8.1rc2
torch             1.6.0+cpu
torchvision       0.7.0+cpu
tqdm              4.48.2
transformers      3.1.0
typing-extensions <IP_ADDRESS>
urllib3           1.25.10
wheel             0.29.0
wincertstore      0.2
xmltodict         0.12.0

I estimate the reason is that the distilbert-base-cased model has not been download.
Under MacOS, It will automatically download the distilbert-base-cased file, (I dowload the model manually, and set the pipline's parameter model='./distilbert-base-cased')
run it again, there's no problem.
the distilbert-base-cased model file include:
config.json
modelcard.json
pytorch_model.bin
tf_model.h5
these file can be downlaoded from https://huggingface.co/distilbert-base-cased#list-files
and need download the vocab.txt
the vocab.txt can be downloaded from https://huggingface.co/bert-base-cased#list-files

With Win10, errors occur even when the local model is used.

I haven't tested on windows not sure about onnx support on windows, so can't much here

The problem is in the numpy array type, which defaults to INT32 in Windows, modify the function _forward_onnx
in onnx_transformers\pipelines.py  file.
modified detail:
   def _forward_onnx(self, inputs, return_tensors=False):
        # inputs_onnx = {k: v.cpu().detach().numpy() for k, v in inputs.items() if k in self.input_names}
        # inputs_onnx = {k: v for k, v in inputs.items() if k in self.input_names}
        inputs_onnx = {k: v.astype(np.int64) for k, v in inputs.items() if k in self.input_names}
        predictions = self.onnx_model.run(None, inputs_onnx)
        return predictions

Thank you very much @baiziyuandyufei ! I'll run tests again with this change and update the code.


make large file error

When doing make to generate the large file on macOS 13.1 I get...
% make
seq  -f "x" -s ''<PHONE_NUMBER> > root/files/large
seq: invalid format string: `x'
make: *** [root/files/large] Error 1
on ubuntu 22.04.1 LTS..
% make
seq  -f "x" -s ''<PHONE_NUMBER> > root/files/large
seq: format ‘x’ has no % directive
make: *** [Makefile:6: root/files/large] Error 1
both of which generate a large file with size 0
maybe something simpler?
dd if=/dev/urandom of=root/files/large bs=64M count=128

@randall  - wanna take a look at this?


Keyring implementation for Near protocol chain account management

Implement Keyring functionality to manage multiple near protocol account keypairs.
This package should include methods to generate accounts, export keys, sign transactions, broadcast transactions and get the network transaction fees

NEAR protocol is not EVM compatible. ETH like addresses does not work on NEAR and nor does the web3 integration works. They have their own package and APIs to deploy smart contracts and blockchain integration.
AURORA is an EVM compatible chain built on NEAR and thus we need to update the near-controller to aurora controller.


Don't Merge! PR real time test for webhook case

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vue-meta script is not loaded before mounted

I'm using this to autocomplete addresses through google map API. It's not ssr compatible so it's loaded through a ssr: false plugin.
I'm loading maps script from the page head:
head () {
  return {
	script: [
	  {src: 'https://maps.googleapis.com/maps/api/js?key=API_KEY&libraries=places'}
	]
  }
}

Now when the component is mounted, google is not defined meaning that the script is not loaded.
Moving the script to the layout helps when the related page is not the first loaded, but fails the same way otherwise.
Any suggestion to work around this?

This question is available on Nuxt.js community (#c2700)

I do have a workaround, but I find it rather ugly:
data () {
  return {
	googleReady: false,
	googleCheckingInterval: null,
  }
},
created () {
  this.googleCheckingInterval = setInterval(() => {
	if (window.google) {
	  this.googleReady = true
	}
  }, 100)
},
watch: {
  googleReady: (data) => {
	if (data) {
	  clearInterval(this.checkingInterval)
	}
  }
},

then on the component: v-if="googleReady"

Download complete is different from the execution complete.

For reference:
function asyncInjectScript(url, globalName) {
  return new Promise((resolve, reject) => {
    if (!process.browser) {
      reject(new Error('This Fun only works in browser'))
      return
    }
    if (url == null) {
      reject(new Error("'url' params is required"))
      return
    }
    if (globalName == null) {
      reject(new Error("'globalName' params is required"))
      return
    }
    if (window[globalName]) {
      resolve(window[globalName])
      return
    }
    const script = document.createElement('script')
    script.setAttribute('src', url)
    script.setAttribute('charset', 'utf-8')
    script.setAttribute('async', '')
    const head = document.getElementsByTagName('head')[0]
    const start = moment()
    head.appendChild(script)

    const fn = function() {
      if (window[globalName]) {
        resolve(window[globalName])
      } else {
        const diff = moment().diff(start, 'milliseconds')
        if (diff > 1000 * 15) {
          reject(new Error('Inject failed'))
        } else {
          setTimeout(fn, 50)
        }
      }
    }
    setTimeout(fn, 0)
  })
}

Or use globel script https://nuxtjs.org/api/configuration-head#the-head-property
Or see #3137

@yoyo837  thanks for response! I'd rather not load google API globally when it's only used in 1% of my project.
In your opinion, which hook should call asyncInjectScript?

In any where in your code, not just a hook. It is a loader.

nuxt-edge handles this way better, so closing...

@syffs I'm wondering how nuxt-edge handles this better and how you managed to fix your original problem. I'm having this issue currently: https://cmty.app/nuxt/nuxt.js/issues/c7300
Do you have any tips?
Many thanks!

I have this problem too. @syffs How you fix? Explain better please

I have this problem too ...
Please :( ..
@syffs


[postprocessor/ffmpeg] Allow embedding subtitles in m4a

Please follow the guide below

You will be asked some questions, please read them carefully and answer honestly
Put an x into all the boxes [ ] relevant to your pull request (like that [x])
Use Preview tab to see how your pull request will actually look like

Before submitting a pull request make sure you have:

[x] Searched the bugtracker for similar pull requests
[x] Read adding new extractor tutorial
[x] Read youtube-dl coding conventions and adjusted the code to meet them
[x] Covered the code with tests (note that PRs without tests will be REJECTED)
[x] Checked the code with flake8

In order to be accepted and merged into youtube-dl each piece of code must be in public domain or released under Unlicense. Check one of the following options:

[x] I am the original author of this code and I am willing to release it under Unlicense
[ ] I am not the original author of this code but it is in public domain or released under Unlicense (provide reliable evidence)

What is the purpose of your pull request?

[ ] Bug fix
[x] Improvement
[ ] New extractor
[ ] New feature

Description of your pull request and other information
Subtitles can be embedded in m4a same as mp4.
I imagine the reason m4a is not allowed now is because no one ever needed it or it is not video. However, given that webm may only contain audio, I think m4a should be allowed as well.

You can't embed subtitles into an audio codec, you need to create a separate vid to show the subtitles, at which point you no longer have an audio file but a vid file without anything but the text to see.

mpv shows subtitles for audio only files on console, or if thumbnail is embedded (or with some options) on gui.

@rautamiekka Just like this one, audio files can have subtitles, and ffmpeg supports this. Formats for subtitles are very limited for m4a (which is same as mp4 except that m4a lacks video), but this should work.


Paket is not telling the truth: "The package can't be found" in local nuget source

Description
I think we have a parsing bug when using a local nuget source and some long package names (or maybe we cannot parse this particular package name). Paket tells me:
  Trying to resolve runtime.osx.10.10-x64.runtime.native.System.Security.Cryptography.Apple >= 4.3 (from runtime.native.System.Security.Cryptography.Apple 4.3.0)
 - runtime.osx.10.10-x64.runtime.native.System.Security.Cryptography.Apple 4.3.0
Source 'C:\\temp\\offline_nuget' yielded (0): []
 - Request 'C:\temp\offline_nuget' finished with: []
Source 'C:\\PROGRA~1\\dotnet\\sdk\\NUGETF~1' yielded (0): [4.0.1 ; 4.3.0]
 - Request 'C:\Program Files\dotnet\sdk\NuGetFallbackFolder' finished with: [4.0.1 ; 4.3.0]

Source 'C:\\temp\\offline_nuget' yielded (0): []
 - Request 'C:\temp\offline_nuget' finished with: []
Source 'C:\\PROGRA~1\\dotnet\\sdk\\NUGETF~1' yielded (0): [4.1.0 ; 4.3.0]
 - Request 'C:\Program Files\dotnet\sdk\NuGetFallbackFolder' finished with: [4.1.0 ; 4.3.0]

Source 'C:\\PROGRA~1\\dotnet\\sdk\\NUGETF~1' exception: System.Exception: The package runtime.osx.10.10-x64.runtime.native.System.Security.Cryptography.Apple 4.3.0 can't be found in C:\Program Files\dotnet\sdk\NuGetFallbackFolder.
Please check the feed definition in your paket.dependencies file.
   at Microsoft.FSharp.Core.PrintfModule.PrintFormatToStringThenFail@1379.Invoke(String message)
   at Paket.NuGetLocal.getDetailsFromLocalNuGetPackage@86.Invoke(Unit unitVar)
   at Microsoft.FSharp.Control.AsyncBuilderImpl.callA@839.Invoke(AsyncParams`1 args)
GetPackageDetails failed: System.Exception: Couldn't get package details for package runtime.osx.10.10-x64.runtime.native.System.Security.Cryptography.Apple 4.3.0 on C:\\PROGRA~1\\dotnet\\sdk\\NUGETF~1. ---> System.Exception: The package runtime.osx.10.10-x64.runtime.native.System.Security.Cryptography.Apple 4.3.0 can't be found in C:\Program Files\dotnet\sdk\NuGetFallbackFolder.
Please check the feed definition in your paket.dependencies file.
   at Microsoft.FSharp.Core.PrintfModule.PrintFormatToStringThenFail@1379.Invoke(String message)
   at Paket.NuGetLocal.getDetailsFromLocalNuGetPackage@86.Invoke(Unit unitVar)
   at Microsoft.FSharp.Control.AsyncBuilderImpl.callA@839.Invoke(AsyncParams`1 args)
   --- End of inner exception stack trace ---
   at Paket.Utils.rethrowf@26.Invoke(String msg)
   at Paket.NuGet.getPackageDetails@337-20.Invoke(Unit unitVar)
   at Microsoft.FSharp.Control.AsyncBuilderImpl.callA@839.Invoke(AsyncParams`1 args)
Source 'C:\\temp\\offline_nuget' yielded (0): []
 - Request 'C:\temp\offline_nuget' finished with: []
Source 'C:\\PROGRA~1\\dotnet\\sdk\\NUGETF~1' yielded (0): [4.0.0 ; 4.3.0 ; 4.4.0]
 - Request 'C:\Program Files\dotnet\sdk\NuGetFallbackFolder' finished with: [4.0.0 ; 4.3.0 ; 4.4.0]

Source 'C:\\temp\\offline_nuget' yielded (0): []
 - Request 'C:\temp\offline_nuget' finished with: []
Source 'C:\\PROGRA~1\\dotnet\\sdk\\NUGETF~1' yielded (0): [1.2.0 ; 1.3.0 ; 1.3.1 ; 1.4.0]
 - Request 'C:\Program Files\dotnet\sdk\NuGetFallbackFolder' finished with: [1.2.0 ; 1.3.0 ; 1.3.1 ; 1.4.0]

Source 'C:\\PROGRA~1\\dotnet\\sdk\\NUGETF~1' exception: System.Exception: The package runtime.osx.10.10-x64.runtime.native.System.Security.Cryptography.Apple 4.3.0 can't be found in C:\Program Files\dotnet\sdk\NuGetFallbackFolder.
Please check the feed definition in your paket.dependencies file.
   at Microsoft.FSharp.Core.PrintfModule.PrintFormatToStringThenFail@1379.Invoke(String message)
   at Paket.NuGetLocal.getDetailsFromLocalNuGetPackage@86.Invoke(Unit unitVar)
   at Microsoft.FSharp.Control.AsyncBuilderImpl.callA@839.Invoke(AsyncParams`1 args)
    Package not available.
      Message: Unable to retrieve package details for 'runtime.osx.10.10-x64.runtime.native.System.Security.Cryptography.Apple'-4.3.0
  Conflict detected:
   - runtime.native.System.Security.Cryptography.Apple 4.3.0 requested package runtime.osx.10.10-x64.runtime.native.System.Security.Cryptography.Apple: >= 4.3
   - Available versions:
     - (4.3.0, [C:\\PROGRA~1\\dotnet\\sdk\\NUGETF~1])

But my local nuget source looks like this:

Therefore paket is straight up lying to me and doesn't tell me the root cause.
Repro steps
Probably (not tested)
source C:\\PROGRA~1\\dotnet\\sdk\\NUGETF~1
nuget runtime.osx.10.10-x64.runtime.native.system.security.cryptography.apple

paket install
Expected behavior
Do not print the above error (captured with -v)
Actual behavior
The message is wrong (but paket recovers itself for some reason):
   Retrieved Explored Package  runtime.ubuntu.16.04-x64.runtime.native.System.Security.Cryptography.OpenSsl 4.3.2
Resolution finished, but some errors were encountered:
-> One or more errors occurred. (Unable to retrieve package details for 'runtime.osx.10.10-x64.runtime.native.System.Security.Cryptography.Apple'-4.3.0)
-> Unable to retrieve package details for 'runtime.osx.10.10-x64.runtime.native.System.Security.Cryptography.Apple'-4.3.0
   StackTrace:
        at Paket.PackageResolver.getPackageDetailsBlock@1034.Invoke(GetPackageDetailsParameters details)
        at Paket.PackageResolver.explorePackageConfig(FSharpFunc`2 getPackageDetailsBlock, PackageConfig pkgConfig)
-> Couldn't get package details for package runtime.osx.10.10-x64.runtime.native.System.Security.Cryptography.Apple 4.3.0 on C:\\PROGRA~1\\dotnet\\sdk\\NUGETF~1.
-> The package runtime.osx.10.10-x64.runtime.native.System.Security.Cryptography.Apple 4.3.0 can't be found in C:\Program Files\dotnet\sdk\NuGetFallbackFolder.
   Please check the feed definition in your paket.dependencies file.

Why is paket even trying to resolve that package when it doesn't actually need it?
Known workarounds
Please provide a description of any known workarounds.

are you going to fix it?


Refine UA and balance point calculations through outlier detection

fixes #13
fixes #49
fixes #50
To do:

[x] add outlier removal functionality
[x] make sure requirements.txt reflects the addition of numpy to pyproject.toml
[x] raise some kind of error if balance point goes as high as thermostat set point (meaning we can't produce a valid estimate for that home)
[x] add a parameter for the minimum reduction in stdev percentage that makes the removal of an outlier worth it
[x] consider making a BillingPeriod class which bundles together start date, end date, usage, etc
[x] add some more tests

Made 0.10 into a parameter


Update vade-evan-bbs dependency for revocation fix

Description
Update vade-evan dependency for new vade-evan-bbs revocation size fix.
Details

Update dependency of vade-evan-bbs github branch

Lgtm. Please update back branches, when original branch is megred to develop


Can you indicate the correspondence between jaeger-operator version and k8s version?

I installed jaeger-operator on master on k8s version 1.14 and got the following error when deploying jaegertracing.io_jaegers_crd.yaml
error: error validating "jaegertracing.io_jaegers_crd.yaml": error validating data: [ValidationError(CustomResourceDefinition.spec.validation.openAPIV3Schema.properties.spec.properties.agent.properties.imagePullSecrets): unknown field "x-kubernetes-list-type" in io.k8s.apiextensions-apiserver.pkg.apis.apiextensions.v1beta1.JSONSchemaProps, ValidationError(CustomResourceDefinition.spec.validation.openAPIV3Schema.properties.spec.properties.agent.properties.tolerations): unknown field "x-kubernetes-list-type" in io.k8s.apiextensions-apiserver.pkg.apis.apiextensions.v1beta1.JSONSchemaProps, ValidationError(CustomResourceDefinition.spec.validation.openAPIV3Schema.properties.spec.properties.agent.properties.volumeMounts): unknown field "x-kubernetes-list-type".........

We strive to support as many versions as possible, but we test and support up to N-2 versions, where N is the latest.

+1
I'm having the same problem, I'm using operator version 1.8 trying to deploy in the K8S 1.4 cluster, I'm having the same problem.
what is the corresponding kubernetes version of jaeger-operator 1.8?
@dylanyht excuse me, what version of K8S did you finally use？

A good first step is to send a PR documenting the current support to the readme file, plus a change to the release document, so that we document this for every future release.

A good first step is to send a PR documenting the current support to the readme file, plus a change to the release document, so that we document this for every future release.

@xichengliudui  I used version 1.13, but I ended up using the new version deployed in the new cluster

@xichengliudui  I used version 1.13, but I ended up using the new version deployed in the new cluster

+1
me too,I'm used operator version 1.21.2 trying to deploy in the Kubernetes 1.14 cluster.

Unfortunately, I think it's clear by now that Kubernetes 1.14 isn't supported with the most recent version of the operator.

I suggest describing the version requirements of Kubernetes in the documentation

Agree. Would you like to contribute by starting such a doc?

Thank you  very much for your invitation, I'm also happy to  contribute a doc, but due to my current work arrangements, i am sorry that i am not qualified for this great thing. However, I will try my best to discuss some issues.


Add ksp_version_max field to mods which lack it

Don't merge until discussed in https://github.com/KSP-CKAN/NetKAN/issues/1002
After a potential merge, update the list of hardcoded mods in the above discussion.

Removed ksp_version fields from 8 mods that are active and correctly indexed on KerbalStuff so having version fields isn't needed

Removed some $vref that overwrote KS metadata and forced a ksp_version_min rather than ksp_version field.
Removed Tantares and Tantares LV due to their inability to be re-indexed for now.
This build is now failing due to mods within colliding with eachother and since the only thing changed was version fields I think it should be ready for merging now.

1.0 comes out tomorrow. Lots of revision of metadata is still needed and I want netkan-bot to have time to request and fix metadata from KS before authors go ham and start updating stuff. I'm merging this and if everything explodes I'll revert it. Fingers crossed!


Math equations are being y-scaled during transitions

Hi
When adding transitions after a math equation, which has either subscripts or superscripts, the equations are scaled in the y-direction.
<script lang="ts">
	import { Presentation, Slide, Transition } from '@animotion/core'
</script>

<Presentation options={{ history: false, transition: 'slide', controls: false, progress: true }}>
	<Slide class="h-full place-content-center place-items-center">
		<Transition>
			{`\\[ A_1 \\]`}
		</Transition>
		<Transition>
			{`\\[ B \\]`}
		</Transition>
		<Transition>
			{`\\[ C^2 \\]`}
		</Transition>
		<Transition>
			{`\\[ D \\]`}
		</Transition>
		<Transition>
			{`\\[ \\dot{E} \\]`}
		</Transition>
		<Transition>
			{`\\[ F \\]`}
		</Transition>
	</Slide>
</Presentation>

The presentation above will render A_1, C^2 and \dot{E} incorrectly during the transitions, where they are squeezed in the y-direction.
Great work on the tool nevertheless.
Kind regards Anders

I looked into it, but I have no idea what's causing it — I should probably remove the math plugin and find something better that also lets you animate the formula.

All right, thanks :)


Federation in for django type

Feature Request Type

[X] Core functionality
[ ] Alteration (enhancement/optimization) of existing feature(s)
[ ] New behavior

Description
Skimming through the code, I did not notice anything related to federation? is this feature available for django?

hi @firaskafri, there's no built-in @strawberry.federation.django.type, but you can add the directives manually (that's what the strawberry decorator does after all :))
See: https://github.com/strawberry-graphql/strawberry/blob/main/strawberry/federation/object_type.py#L53-L68

hi @firaskafri, there's no built-in @strawberry.federation.django.type, but you can add the directives manually (that's what the strawberry decorator does after all :))
See: https://github.com/strawberry-graphql/strawberry/blob/main/strawberry/federation/object_type.py#L53-L68

neat!


vehicle_id versus id

In the apiary documentation, almost all the methods require a "vehicle_id" parameter, however it actually seems this must be the value labeled "id" in the list of all vehicles.

Could you submit a PR with changes?

In the vehicles list API there is a vehicle_id param and an id/id_s param and they are different values. Does anyone know what the difference is?

The vehicle_id is a GUID of some sort for the vehicle. There are a few different APIs (info/command JSON API, the streaming telematics, the autopark system), so it's used as a unique identifier for the vehicle everywhere. It may come from some internal ERP system.
The id is because the JSON API runs on a Rails application. Rails' ORM (ActiveRecord) always adds an auto-incrementing id field to every model. It also makes it easy to look up a model by this id (Vehicle.find(params[:id])), so it's passed around as a convenience.
When you need to hop systems, the vehicle_id is used. When you are sticking to just the JSON API, then you can use id.

Great info. Thanks!

I agree there are a few starved areas that could be improved in the api.  I have not written any down yet, but from what I remember:

vehicle_id vs vehicles' id (this issue)

request timeout time

some responses are not full / up to date? (climate state the one that I noticed)

wake up car lacks detail of what it does / who needs it

Love the progress so far, I'll PR when I get the time

Finally added a blurb on this! https://tesla-api.timdorr.com/api-basics/vehicles
I'm cleaning up the other usages as I go too.


hack-fixed circular dependency

Pull request summary
Brief description:
When using creep-tasks with Typescript (and webpack), coppied /src/creep-tasks to my project (as per instructions), but was unable to solve this particular circular-dependency. Project compiles just fine, but in-game it throws error.
Tried to modify creep-tasks with Overmind version, because Overmind works, but nothing successful.
When using your rollup config instead of webpack, it doesn't work ingame either. And it prints warning
(!) Circular dependency: src\creep-tasks\utilities\initializer.ts -> src\creep-tasks\TaskInstances\task_attack.ts -> src\creep-tasks\Task.ts -> src\creep-tasks\utilities\initializer.ts
Basically, its only first circular dependency, theese cycles are present for each task type. And its breaking, when there is more than 1 task.
(for webpack, i used https://github.com/aackerman/circular-dependency-plugin which printed one hell of cycles)
Added features:
Thic PR included somewhat hacky solution for the problem, as i was unable to solve it on the design level.
Changes to existing features:

None

Bugfixes:

None

Testing checklist:

[ ] Codebase compiles with current tsconfig configuration
[x] Deployed and tested changes

My config is there, if you want https://github.com/ArcanisCz/screeps

Interesting. I've seen these types of circular import errors in Overmind before I switched to using rollup instead of webpack. I'd rather not clutter up the global namespace by moving the task instantiator there (although that was my previous solution in Overmind). I'm repackaging this as a typed npm module; hopefully that should fix the issue, and if not we can reconsider this.

I've resolved these problems in the latest release of this module. It should also now work with your bot: I downloaded your screeps repository and removed the local creep-tasks directory, then ran npm install creep-tasks, modifying the imports to point to the npm module. It compiles and runs on the sim fine even without the CircularDependencyPlugin you added; however, you'll need to add '.js' to your resolve extensions in your webpack config to get it to work.

Great! But i am not sure about use-case with creep-tasks as a dependency. For it being dependency, its not extensible by custom tasks (they are hardcoded in initializeTask)

Using the module with npm ensures that it correctly compiles and integrates into your code, but you are correct that it doesn't allow you to modify the module. If you'd like to extend or modify it, you should install the module from source like you did in your codebase previously. (Also, I've added almost all creep actions to this module as tasks; what functionality were you planning on implementing? Feel free to make a pull request if you'd like to get it integrated into this module!)
The circular dependency issues that you addressed are specific to the way your codebase (using webpack) bundles the compiled output. (For example, I use rollup and I don't experience this issue.) Given this, I don't think it's necessary to add a global fix for this issue to the module, but I will add a link to this issue in an FAQs page that I'll add to the wiki shortly.

Interesting fact, with your current tsconfig.json, it works on webpack without that hack! (coppied to my project with creep-tasks source)
I dont intend to add anything at the moment, since i am fresh new in screeps. But at some point, i will probably do. And some my own modifications and experimens, you know :D
By the way, many thanks for your articles, very much enjoyed the story. And for sharing creep-tasks. Copying whole AI is boring, but theese block are excellent, to use and to learn from them :)


Regression in 3.1.200 for dotnet test -t

From @mareklinka on Friday, March 20, 2020 1:00:41 AM
Hello all. I seem to have stumbled upon a rather weird possible regression after I migrated from SDK 3.1.102 to 3.1.200.
Context
A few days ago I forked the VSCode extension formulahendry/vscode-dotnet-test-explorer and started playing around with bringing it up to speed for Core SDK 3.1 and doing some other changes. For testing my changes, I was using the solution I work on at my job.
The extension first discovers possible test projects in a VSCode workspace and then executes dotnet test -t -v=q [project] against one project at a time. It waits until the execution terminates and reads the stdout in order to retrieve the list of tests. This list is further processed and displayed etc.
While I was using the SDK 3.1.102, everything was working perfectly here. The command produced the list of tests every time without issues. However, after I updated to SDK 3.1.200, this started failing in more than 50% of executions (but not always).
The issue
In SDK 3.1.200, the dotnet test -t -v=q [project path] terminates but it looks like the stdout is not properly flushed. The extension reads the stdout and only  gets as far as
Test run for [pathToDll](.NETCoreApp,Version=v3.1)
Microsoft (R) Test Execution Command Line Tool Version 16.5.0
Copyright (c) Microsoft Corporation.  All rights reserved.

The following Tests are available:

And that's it. No tests are listed in the stdout (even though they are there, of course). If I run the same command from the command line, the output is complete (with tests), but there is an observable pause between outputting The following Tests are available: and the actual list of tests.
To verify the regression theory, I introduced a global.json and used it to fix a specific SDK version for the solution in question. The results are consistent - 3.1.102 works every time, 3.1.200 fails more often then not.
Since the problem does not manifest every time, it feels like a possible race condition to me. If the dotnet process terminates too quickly, the stdout isn't flushed completely and whatever is in the buffer gets lost. But it's really just a theory.
The problematic code
function executeDotnetTest(testDirectoryPath: string, dotnetTestOptions: string): Promise<string> {
    return new Promise((resolve, reject) => {
        Logger.Log(`Executing dotnet test -t -v=q in ${testDirectoryPath}`);

        let buffer = "";

        const p = cp.spawn("dotnet", ["test", "-t", "-v=q" ], { cwd: testDirectoryPath });
        p.stdout.on("data", (data) => buffer += data.toString());
        p.on("close", (code) => {
            if (code !== 0) {
                Logger.LogError(`Error while executing dotnet test -t -v=q`, code);

                reject();
            }

            Logger.Log(buffer);

            resolve(buffer);
        });
    });
}

Now I'm not the greatest with Node.js but this should work and does work when using the older SDK. It spawns a new process with the necessary parameters, hooks to the stdout to build a buffer, then, when the process closes all its IO streams, logs the buffer and resolves the promise.
More details: the projects in question use xUnit for testing framework, but it also reproduces with MSTest.
Question
Can someone please try and clarify what's going on here? Or possibly offer a workaround?
Copied from original issue: dotnet/sdk#10926

From @mareklinka on Friday, March 20, 2020 5:56:08 AM
Ok, after digging more deeply into what is happening here, I managed to dig up this. Each of the failed dotnet executions end up with the following error:
Error while executing dotnet test -t -v=q - Microsoft.VisualStudio.TestPlatform.ObjectModel.TestPlatformException: Testhost process exited with error: Failed to load the dll from [c:\Users\marek\AppData\Roaming\Code\User\globalStorage\ms-vsliveshare.vsliveshare\dotnet-3.1.1\host\fxr\3.1.1\hostfxr.dll], HRESULT: 0x800700C1
The library hostfxr.dll was found, but loading it from c:\Users\marek\AppData\Roaming\Code\User\globalStorage\ms-vsliveshare.vsliveshare\dotnet-3.1.1\host\fxr\3.1.1\hostfxr.dll failed
  - Installing .NET Core prerequisites might help resolve this problem.
     http://go.microsoft.com/fwlink/?LinkID=798306&clcid=0x409
. Please check the diagnostic logs for more information.
   at Microsoft.VisualStudio.TestPlatform.CrossPlatEngine.Client.ProxyOperationManager.ThrowExceptionOnConnectionFailure(Int32 connTimeout)
   at Microsoft.VisualStudio.TestPlatform.CrossPlatEngine.Client.ProxyOperationManager.SetupChannel(IEnumerable`1 sources, String runSettings)
   at Microsoft.VisualStudio.TestPlatform.CrossPlatEngine.Client.ProxyDiscoveryManager.DiscoverTests(DiscoveryCriteria discoveryCriteria, ITestDiscoveryEventsHandler2 eventHandler)

This looks like VS Code's Live Share extension is getting in the way for some reason. It packs it's own runtime? And that runtime is still 3.1.1xx, which is causing issues. Would there be a way to bypass this interference?

From @franck-schmidlin on Monday, March 30, 2020 2:39:01 PM
I've hit a similar issue which I was only able to resolve by removing the live share extension :-(

@mareklinka this looks like the live share added something to your PATH, and possibly the installation in that path is broken. So now testhost / console is trying to find and run the dotnet in liveshare.
Please post result of dotnet --info.

I checked the path and live share doesn't seem to be affecting it. It would also be rather strange as the extension installs without admin privileges so it shouldn't be possible to modify the path from there.
dotnet --info looks normal:
OS Version:  10.0.18363
 OS Platform: Windows
 RID:         win10-x64
 Base Path:   C:\Program Files\dotnet\sdk\3.1.201\

Host (useful for support):
  Version: 3.1.3
  Commit:  4a9f85e9f8

.NET Core SDKs installed:
  3.1.201 [C:\Program Files\dotnet\sdk]

.NET Core runtimes installed:
  Microsoft.AspNetCore.All 2.1.17 [C:\Program Files\dotnet\shared\Microsoft.AspNetCore.All]
  Microsoft.AspNetCore.All 2.2.7 [C:\Program Files\dotnet\shared\Microsoft.AspNetCore.All]
  Microsoft.AspNetCore.All 2.2.8 [C:\Program Files\dotnet\shared\Microsoft.AspNetCore.All]
  Microsoft.AspNetCore.App 2.1.17 [C:\Program Files\dotnet\shared\Microsoft.AspNetCore.App]
  Microsoft.AspNetCore.App 2.2.7 [C:\Program Files\dotnet\shared\Microsoft.AspNetCore.App]
  Microsoft.AspNetCore.App 2.2.8 [C:\Program Files\dotnet\shared\Microsoft.AspNetCore.App]
  Microsoft.AspNetCore.App 3.1.3 [C:\Program Files\dotnet\shared\Microsoft.AspNetCore.App]
  Microsoft.NETCore.App 2.1.17 [C:\Program Files\dotnet\shared\Microsoft.NETCore.App]
  Microsoft.NETCore.App 2.2.7 [C:\Program Files\dotnet\shared\Microsoft.NETCore.App]
  Microsoft.NETCore.App 2.2.8 [C:\Program Files\dotnet\shared\Microsoft.NETCore.App]
  Microsoft.NETCore.App 3.1.3 [C:\Program Files\dotnet\shared\Microsoft.NETCore.App]
  Microsoft.WindowsDesktop.App 3.1.3 [C:\Program Files\dotnet\shared\Microsoft.WindowsDesktop.App]

To install additional .NET Core runtimes or SDKs:
  https://aka.ms/dotnet-download

However, I managed to catch a glimpse of the culprit, I think. Under certain circumstances (I ahven't been able to repro this reliably so far), this is what VSCode's child process running the dotnet commands ends up with:

The value in that variable points to the runtime that comes with Live Share.This environment variable is not system-wide so I assume this only gets injected sometimes by the live share extension.

To clarify, the DOTNET_ROOT variable is not there most of the time and those times everything works without issues. If the variable is present, it points to the wrong runtime and causes the observed error.

. It would also be rather strange as the extension installs without admin privileges so it shouldn't be possible to modify the path from there.

This assumption is not correct. 🙂 PATH is set on system, user and process level. Non-elevated processes can set the path on the user and process level. But I don't think it matters much.
Additionally processes inherit env variables from their parent, so that is what you see. As for where the variable comes from is the question. Could you start a test that will wait for a while (say 20s) and then in Process Explorer find the process running the test, and walk up the tree of processes until you DON'T see the variable being defined.

. It would also be rather strange as the extension installs without admin privileges so it shouldn't be possible to modify the path from there.

This assumption is not correct. 🙂 PATH is set on system, user and process level. Non-elevated processes can set the path on the user and process level. But I don't think it matters much.

Right, of course, I assumed you originally meant the system-persisted PATH.
At any rate, I have a process tree like this:

The first process with the DOTNET_ROOT variable seems to be VSCode's extension host process:

And then of course all the processes launched from the extension host inherit the variable.

Could you post the whole command line of this process and also the one above it (the one that does not have the dotnet_root) please?

The extension host (with variable):
"C:\Program Files\Microsoft VS Code\Code.exe" --inspect-port=0 "c:\Program Files\Microsoft VS Code\resources\app\out\bootstrap-fork" --type=extensionHost
Parent:
"C:\Program Files\Microsoft VS Code\Code.exe" --type=renderer --disable-color-correct-rendering --field-trial-handle=1700,11397253229512069791,10254540765277873455,131072 --disable-features=LayoutNG,PictureInPicture,SpareRendererForSitePerProcess --lang=en-US --standard-schemes --secure-schemes=vscode-resource --bypasscsp-schemes --cors-schemes=vscode-resource --fetch-schemes=vscode-resource --service-worker-schemes --app-user-model-id=Microsoft.VisualStudioCode --app-path="C:\Program Files\Microsoft VS Code\resources\app" --node-integration --webview-tag --no-sandbox --no-zygote --background-color=#1f2430 --device-scale-factor=1.25 --num-raster-threads=4 --enable-main-frame-before-activation --service-request-channel-token=15901080703558785554 --renderer-client-id=5 --no-v8-untrusted-code-mitigations --mojo-platform-channel-handle=2464 /prefetch:1

So it looks like the Live Share extension, when booting, is setting the DOTNET_ROOT variable on the extension host process. I guess this makes sense - it needs dotnet runtime to function and there is no guarantee that the local system has the runtime installed globally.
The unfortunate side-effect of this is that is affects all the down-stream processes.
The upside here is that I can work around this in my own extension - I'll just remove the variables before starting my dotnet test.

Rather than removing it, I would copy it from the user context if defined and if possible. That way it stays set to the value the user defined.

Imho this is work as designed and I can close this? 🙂

Yeah, I'll need to explore the options available, worst case I'll expose this for the user to control.. Thanks for pointing me in the right direction with this.

Thanks for reaching out. And good luck :)


View order details

Story
As a user, I want to view my order details so that I can see all of the information regarding that trade. If the order I am viewing is active, I want to be able to cancel the order from the details page.
Design Considerations
Straightforward data display with the option to cancel if active, however I'm not sure which / how much data to show. Orders have the following information:
Order ID
User ID
Ticker Symbol
Order Type
Trigger Price
Quantity
Fulfilled Status
Cancelled Status
Order Date
We may want to include:
Company name
Current price
Total order value
Portfolio snapshot of that time
Link to ticker page
...
Technical Scope
No new tables needed. Data already loaded from order history (most likely, not implemented yet). May need to use or create more routes/requests depending on the data we choose to display.

[ ] Page/component
[ ] Re-use ticker data request

The Transaction History and Transaction Details pages are being removed from the diagram. Transaction information will remain separate in the DB, however it will only be accessed through orders by the user.


Update passwords

added endpoint for updating password (spee.ch/api/password)
cleaned up the use of passport.js

changed base to development to follow GitFlow branching model

LGTM though possibly shouldn't be at the root level in the project.


chore(build): add additional metadata for mapping to improve map paths

Description
After adding @spectrum-css/typography imports to Storybook, we started seeing:
Error: An error occurred while trying to read the map file at components/typography/index.css.map
Error: ENOENT: no such file or directory, open '/Users/carobert/Projects/spectrum/spectrum-css/components/typography/dist/components/typography/index.css.map'

in the console which reflects an invalid path being used in the sourcemap files generated by the component-builder. This fix improves the path generated and removes the error messaging from our Storybook build.
How and where has this been tested?
Please tag yourself on the tests you've marked complete to confirm the tests have been run by someone other than the author.
Validation steps

Do a clean local build (storybook symlinks to local dist); yarn build.
Kick off the storybook server: yarn start

[ ] Expect to see no logging in the output related to invalid local *.css maps.

To-do list

[x] I have read the contribution guidelines.
[x] ✨ This pull request is ready to merge. ✨

Great work! This resolve the source map errors you mentioned. 🎉
I don't think this is on our end, but I do see these warnings as well (on main and this branch):
Sourcemap for<EMAIL_ADDRESS>points to missing source files
Sourcemap for<EMAIL_ADDRESS>points to missing source files

Is this anything we'd need to address in the future? Either way, I don't think they are blocking this from merging, so approved!

Yes!  I looked into that earlier and found this is a known bug: https://github.com/storybookjs/storybook/issues/28567.  It doesn't impact the rendering so I thought we were okay to stay at this version of Vite instead of rolling back.


Fix jassimp import of light intensities

GearVRF assumes these are 0-1, in jassimp they can be 0 to anything. Jassimp now scales the light intensity  by the maximum R,G,B so the light intensity won't be > 1.
This should improve the look of assets which use lights with very large intensities like the models in the VRBenchmark app.
GearVRF DCO signed off by: Nola Donato<EMAIL_ADDRESS>

Demos run fine. Code looks good except that for lights imported from assimp the attenuation_constant, attenuation_linear and attenuation_quadratic do not seem to be set anymore. Maybe this is intended but it is unclear from the code.
@roshanch any suggestions for models with lights we can test?

I also rebased the pull request. There was a merge conflict in GVRTexture.

@liaxim we have the cube models with lights in it. but i doubt it will be good test for the attenuation. Papiya has reported the issue with benchmark app with problem with light intensity for direct light, she will test this change with that app.

Ok then. If you are ok with merging this then I am ok too.

let me talk with Papiya about VRBenchmark app Nola has mentioned. I will check whether it works with that

Per Nola, I uncommented the attenuation settings.

so, what's the latest status on this?

We can merge it.


Docs update

add blog tab
add blog: KubeVela: The Extensible App Platform Based on Open Application Model and Kubernetes
update footer
update install docs: add brew install kubevela
fix some issues

/pushed #679


i don't know this is project issue or my pc issue plz help

i got this error after i import Client from nepse

import asyncio
import httpx
from nepse import Client
Traceback (most recent call last):
File "", line 1, in
File "/usr/local/lib/python3.9/dist-packages/nepse/init.py", line 1, in
from .core import Client
File "/usr/local/lib/python3.9/dist-packages/nepse/core.py", line 5, in
from nepse.broker.core import BrokerClient
File "/usr/local/lib/python3.9/dist-packages/nepse/broker/init.py", line 1, in
from .core import BrokerClient
File "/usr/local/lib/python3.9/dist-packages/nepse/broker/core.py", line 9, in
from nepse.utils import _ClientWrapperHTTPX
File "/usr/local/lib/python3.9/dist-packages/nepse/utils.py", line 40, in
@attr.frozen
AttributeError: module 'attr' has no attribute 'frozen'

and i try https://newweb.nepalstock.com/api/ api from react directly its shows cross origin error how can i use this api directly in react without any issue?
thanks in advance

If its a library issue, I will try to fix it by releasing a patch as fast as possible but for now, you can do:
pip uninstall attr
pip install attrs

Thank you for reporting this issue

@prabhatghimire Tell me if it works or not

I just released new version of nepse-api you can update it using pip as well, and it will also fix the issue. Please update me wheather the issue still persists or is fixed.

Closing this issue as the author hasn't replied.

it worked :+1:  after installing attrs


update Instagram selectors

Motivation
ARI-5460
Description
Update selectors and click to dismiss new sign-in pop-up

Ran local tests and all looked good. Thanks, @galgeek!


How to perform lookup in parent chart when object is created from subchart?

Output of helm version:
version.BuildInfo{Version:"v3.11.0", GitCommit:"472c5736ab01133de504a826bd9ee12cbe4e7904", GitTreeState:"clean", GoVersion:"go1.19.5"}

Output of kubectl version:
Client Version: version.Info{Major:"1", Minor:"26", GitVersion:"v1.26.1", GitCommit:"8f94681cd294aa8cfd3407b8191f6c70214973a4", GitTreeState:"clean", BuildDate:"2023-01-18T15:51:24Z", GoVersion:"go1.19.5", Compiler:"gc", Platform:"darwin/amd64"}
Kustomize Version: v4.5.7
Server Version: version.Info{Major:"1", Minor:"25", GitVersion:"v1.25.2", GitCommit:"5835544ca568b757a8ecae5c153f317e5736700e", GitTreeState:"clean", BuildDate:"2022-09-21T14:27:13Z", GoVersion:"go1.19.1", Compiler:"gc", Platform:"linux/amd64"}

Cloud Provider/Platform (AKS, GKE, Minikube etc.):  Minikube
Hello Community,
I am creating an umbrella chart our application which can install all the components in one go. The umbrella chart looks like this (simplified version)
parent-chart
├── Chart.yaml
├── charts
│   ├── subchart1
│   │   ├── Chart.yaml
│   │   ├── templates
│   │   │   └── configmap.yaml
│   │   └── values.yaml
│   └── subchart2
│       ├── Chart.yaml
│       ├── templates
│       │   └── configmap.yaml
│       └── values.yaml
├── templates
│   └── configmap.yaml
└── values.yaml

In subchart1, I am creating a kubernetes secret. I am reading this secret in my parent-chart using lookup function of helm.
This works fine if I install the subchart1 first and then the parent chart as independent charts (secret object will be created by the subchart1).
The secret value created by the subchart1 is dynamic. I cannot control / set these values.
My requirement is that the subcharts should to be installed first and then the parent chart. Is this possible with an umbrella chart?
I am also open to suggestions if I need to change my approach. Please provide your thoughts.

The short answer is that you can't use lookup for this. There's nothing to look up during the templating phase. You can, however, exercise the template of the subchart configmap (in your example above).
{{ $subchart1Configmap := fromYaml (include (
    print
        .Subcharts.subchart1.Template.BasePath
        "/configmap.yaml"
    )
    .Subcharts.subchart1
)}}

Then you can just parse $subchart1Configmap for whatever's in the CM that you need.

Thanks a ton @joejulian 🦸🏻 . Your suggestion works very well for my use case and I am able get this working.

@joejulian IME this is causing a new secret to be generated and isn't an actual solution to this problem.