init adopters commit

Test name
Status

scaling
passed

upgrade-haproxy
passed

scheduled-backup
passed

storage
passed

recreate
passed

big-data
passed

haproxy
passed

init-deploy
passed

scaling-proxysql
passed

self-healing
passed

security-context
passed

restore-to-encrypted-cluster
passed

upgrade-proxysql
passed

limits
passed

self-healing-advanced
passed

monitoring-2-0
passed

demand-backup
passed

smart-update
passed

affinity
passed

operator-self-healing
passed

one-pod
passed

auto-tuning
passed

proxysql-sidecar-res-limits
passed

pitr
passed

users
failed

upgrade-consistency
passed

demand-backup-encrypted-with-tls
passed

tls-issue-self
failed

commit: https://github.com/percona/percona-xtradb-cluster-operator/pull/854/commits/8bce1e0b1de9f2da44f55b33c38332a28e099eab
image: perconalab/percona-xtradb-cluster-operator:PR-854-8bce1e0b

Test name
Status

upgrade-haproxy
passed

scaling
passed

scheduled-backup
passed

big-data
passed

storage
passed

haproxy
passed

scaling-proxysql
passed

self-healing
passed

init-deploy
failed

security-context
passed

upgrade-proxysql
passed

self-healing-advanced
passed

smart-update
passed

operator-self-healing
passed

upgrade-consistency
passed

limits
passed

monitoring-2-0
passed

affinity
passed

one-pod
passed

auto-tuning
passed

proxysql-sidecar-res-limits
failed

commit: https://github.com/percona/percona-xtradb-cluster-operator/pull/854/commits/9e604ba071c99726da31e027b2f66dae98327fd6
image: perconalab/percona-xtradb-cluster-operator:PR-854-9e604ba0

Test name
Status

upgrade-haproxy
passed

scheduled-backup
passed

scaling
passed

scaling-proxysql
passed

big-data
passed

security-context
passed

haproxy
passed

storage
passed

init-deploy
passed

self-healing
passed

self-healing-advanced
passed

operator-self-healing
passed

recreate
passed

upgrade-proxysql
passed

smart-update
passed

upgrade-consistency
passed

limits
passed

monitoring-2-0
passed

affinity
passed

one-pod
passed

auto-tuning
passed

proxysql-sidecar-res-limits
passed

restore-to-encrypted-cluster
passed

users
passed

demand-backup
passed

tls-issue-self
passed

tls-issue-cert-manager
passed

tls-issue-cert-manager-ref
passed

pitr
failed

validation-hook
passed

demand-backup-encrypted-with-tls
failed

commit: https://github.com/percona/percona-xtradb-cluster-operator/pull/854/commits/9e604ba071c99726da31e027b2f66dae98327fd6
image: perconalab/percona-xtradb-cluster-operator:PR-854-9e604ba0

Test name
Status

haproxy
passed

init-deploy
passed

scaling
passed

limits
passed

scaling-proxysql
passed

big-data
passed

scheduled-backup
passed

monitoring-2-0
passed

security-context
passed

recreate
passed

affinity
passed

restore-to-encrypted-cluster
passed

one-pod
passed

auto-tuning
passed

demand-backup
passed

upgrade-haproxy
passed

proxysql-sidecar-res-limits
passed

storage
passed

pitr
failed

users
passed

self-healing
passed

tls-issue-self
passed

self-healing-advanced
passed

tls-issue-cert-manager
passed

operator-self-healing
passed

tls-issue-cert-manager-ref
passed

validation-hook
passed

upgrade-proxysql
passed

smart-update
passed

upgrade-consistency
passed

demand-backup-encrypted-with-tls
failed

commit: https://github.com/percona/percona-xtradb-cluster-operator/pull/854/commits/9e604ba071c99726da31e027b2f66dae98327fd6
image: perconalab/percona-xtradb-cluster-operator:PR-854-9e604ba0

Test name
Status

big-data
passed

haproxy
passed

storage
passed

init-deploy
passed

self-healing
passed

limits
passed

self-healing-advanced
passed

scaling
passed

monitoring-2-0
passed

operator-self-healing
passed

scaling-proxysql
passed

affinity
passed

security-context
passed

one-pod
passed

auto-tuning
passed

recreate
passed

proxysql-sidecar-res-limits
passed

restore-to-encrypted-cluster
passed

users
passed

demand-backup
passed

tls-issue-self
passed

pitr
failed

tls-issue-cert-manager
passed

tls-issue-cert-manager-ref
passed

validation-hook
passed

upgrade-haproxy
passed

scheduled-backup
passed

upgrade-proxysql
passed

smart-update
passed

upgrade-consistency
passed

demand-backup-encrypted-with-tls
failed

commit: https://github.com/percona/percona-xtradb-cluster-operator/pull/854/commits/9e604ba071c99726da31e027b2f66dae98327fd6
image: perconalab/percona-xtradb-cluster-operator:PR-854-9e604ba0


Change base image from alpine to distroless

What this PR does / why we need it:
This PR changes the base container image from alpine to distroless. This will reduce the attack surface of the image used to run the extension.
Which issue(s) this PR fixes:
Fixes #
Special notes for your reviewer:
Release note:

The extension now uses `distroless` instead of `alpine` as a base image.

/approve


Added templates to easy parse NSDictionaries to Entites

Added new template to allow mapping an NSDictionary directly to a Entity (attributes and relationships).
With this template Parse a JSON to a NSDictionary and save it to Core Data is completely transparent to the user

We've made the decision to not accept new Contributed Templates in the main project going forward. We're going to work on getting a Wiki page set up that people can link to third-party templates in the near future.


Aborted (core dumped)

Got the same error

do you sovle it now?
i have other problem about model . like this:
RuntimeError: Error(s) in loading state_dict for UNet3DConditionModel:
size mismatch for conv_in.weight: copying a param with shape torch.Size([320, 4, 3, 3]) from checkpoint, the shape in current model is torch.Size([320, 8, 3, 3]).
size mismatch for down_blocks.0.attentions.0.proj_in.weight: copying a param with shape torch.Size([320, 320, 1, 1]) from checkpoint, the shape in current model is torch.Size([320, 320]).


ERR max number of clients reached

Are there any instructions required in the README to listen for and close redis connections for jobs as they finish?
var jobs = kue.createQueue({redis:{...}});
Or perhaps there is a way to dictate an upperbound to Kue itself for the max # of redis connections that we want it to make?

Hi,
Maybe is it the time to make RedisConnection a singleton ?
Does Kue really need multiple connection it ?

@zazapeta no you can't do that, cause of how "subscribed" connections are working in redis. When you are publishing or subscribing to redis, you can't do anything else except that. More info: https://github.com/NodeRedis/node_redis#publish--subscribe

Each worker uses a single separate connection to do the BLPOP, and that is because BLPOP is a blocking operation.
One optimization could be sharing multiple job types BLPOP's in a single connection, with a timeout value: BLPOP queueList 1
One should implement that to see the effect


Create and update ko.ini

What got changed here :

Add src/locales/ko.ini
Update 'min-width' and 'min-height' to 'minWidth' and 'minHeight'
Add label Korean in metadata.json
Update label Korean in Korean

Something is wrong. What Kaku is wrong? I can't see web pages.

thasnk !


SDL-0305 Homogenize TextFieldName

Fixes #253
This PR is ready for review.
Summary
Implements proposal SDL 0305 which Homogenizes the Enum TextFieldName in HMI_API.xml with MOBILE_API.xml in the Rpc spec. Adding timeToDestination, turnText and navigationText to enum TextFieldName

@JulianKast please regenerate the README.md file as well

@iCollin I have generated the README.md file


Is it possible to extend the default annotation processor for spring data jdbc to additionally generate q classes for  classes  with other annotation rather than only for classes that use the Id annotation?

Consider the example from your Mapped collections section of your README: https://github.com/infobip/infobip-spring-data-querydsl?tab=readme-ov-file#mappedcollection-support
public record Student(
        @Id
        Long id,

        String name,

        @MappedCollection(idColumn = "StudentId", keyColumn = "CourseId")
        Set<StudentCourse> courses
) {

   void addItem(Course course) {
      var studentCourse = new StudentCourse(null, AggregateReference.to(course.id()), null);
      courses.add(studentCourse);
   }

}

public record Course(
        @Id
        Long id,
        String name
) {

}

public record StudentCourse(
        AggregateReference<Course, Long> courseId,

        Long studentId
) {

}

Note that StudentCourse does not have a  separate id column(Let's say the real primary key on the database level is a composite key with courseId and studentId.
The annotation processor would generate q classes for each entity that has the @Id annotation.
Is it possible to easily extend the annotation processor to support this behavior :  generate q classes for classes that use @Id annotation or for classes that use other annotation such as the querydsl's @QueryEntity?
@QueryEntity
public record StudentCourse(
        AggregateReference<Course, Long> courseId,

        Long studentId
) {

}

It's doable, but this code path needs to be coded in in the annotation processor. All I can suggest is write a test and start from there. I don't think it would be too difficult - you have tests for each part of the flow - Q class generation and separate tests for interactions with a specific data module.

Are you suggesting that I should modify the annotation processor itself(located in this repo) or I am able to extend it with a code that I would write in my own project?

Both should be possible.

Can you give me pointers for the option to write custom code that extends the annotation processor to take into account custom annotations too and not only the @ID annotation?
I've tried this
`java
@AutoService(Processor.class)
public class CustomSpringDataJdbcAnnotationProcessor extends SpringDataJdbcAnnotationProcessorBase {
public CustomSpringDataJdbcAnnotationProcessor() {
super(CustomNamingStrategy.class);
}
@Override
public Set getSupportedAnnotationTypes() {
Set annotationTypes = new HashSet<>(super.getSupportedAnnotationTypes());
annotationTypes.add(Table.class.getName());
return Set.copyOf(annotationTypes);
}
}
But it seems that the annotation processor still only takes into an account only the@ID` annotation.
What am I missing?

Update: I managed to write custom annotation processor that extends  SpringDataJdbcAnnotationProcessorBase and generates entity Q classes for classes annotated with some other annotation(I've chosen querydsl's QueryEntity annotation).
@AutoService(Processor.class)
public class CustomSpringDataJdbcAnnotationProcessor extends SpringDataJdbcAnnotationProcessorBase {

  private RoundEnvironment roundEnv;

  public CustomSpringDataJdbcAnnotationProcessor() {
    super(CustomSpringDataJdbcQuerydslNamingStrategy.class);
  }

  @Override
  public Set<String> getSupportedAnnotationTypes() {
    Set<String> annotationTypes = new HashSet<>(super.getSupportedAnnotationTypes());
    annotationTypes.add(QueryEntity.class.getName());
    return Set.copyOf(annotationTypes);
  }

  @Override
  protected Configuration createConfiguration(RoundEnvironment roundEnvironment) {
    this.roundEnv = roundEnvironment;
    DefaultConfiguration configuration = (DefaultConfiguration) super.createConfiguration(roundEnvironment);
    configuration.setAlternativeEntityAnnotation(QueryEntity.class);
    return configuration;
  }

  @Override
  protected Set<TypeElement> collectElements() {
    var entityElements = roundEnv.getElementsAnnotatedWith(QueryEntity.class)
        .stream()
        .filter(element -> element instanceof TypeElement)
        .map(element -> (TypeElement) element)
        .collect(Collectors.toSet());

    Stream<TypeElement> entityElementsIncludingEmbeddedEntities = entityElements.stream()
        .flatMap(this::getEntityElementWithEmbeddedEntities);

    return Stream.concat(super.collectElements().stream(), entityElementsIncludingEmbeddedEntities).collect(Collectors.toSet());
  }

  private Stream<TypeElement> getEntityElementWithEmbeddedEntities(TypeElement entityElement) {
    var types = processingEnv.getTypeUtils();
    var embeddedElements = ElementFilter.fieldsIn(entityElement.getEnclosedElements())
        .stream()
        .filter(enclosedElement -> Objects.nonNull(
            enclosedElement.getAnnotation(Embedded.class)))
        .map(element -> types.asElement(element.asType()))
        .filter(element -> element instanceof TypeElement)
        .map(element -> (TypeElement) element)
        .collect(Collectors.toSet());
    return Stream.concat(Stream.of(entityElement), embeddedElements.stream());
  }
}

And it seems to be working.
But I have some issues with this implementation of the solution:

I copied the implementation of collectElements from the base class. I probably shouldn't do this
I also copied the private method for collecting the embedded elements. Shouldn't do this too
I overrided  getSupportedAnnotationTypes  and  I am not sure what is the purpose of  that method.(I tested even without overriding this method and seems it's still working)

But I think we can do better to make the process of extending annotation processor easier and cleaner.
Essentially, we should be able to change SpringDataJdbcAnnotationProcessorBase  so we can add additionally entity annotations.
I have some ideas for changes:

Add a protected method getAdditionalEntityAnnotations that can be ovveridden by custom annotation processor
Change createConfiguration  in  SpringDataJdbcAnnotationProcessorBase to add the additional entity annotations so we don't need to override this method like I did above.
Change method collectElements in  SpringDataJdbcAnnotationProcessorBase to use  roundEnv.getElementsAnnotatedAny(conf.getEntityAnnotations())

After these changes, one should write this code in order to extend the annotation processor to support additional annotations for generating entity q classes:
@AutoService(Processor.class)
public class CustomSpringDataJdbcAnnotationProcessor extends SpringDataJdbcAnnotationProcessorBase {

  private RoundEnvironment roundEnv;

  public CustomSpringDataJdbcAnnotationProcessor() {
    super(CustomSpringDataJdbcQuerydslNamingStrategy.class);
  }

  @Overrider
  public Set<Class<? extends Annotation>> getAdditionalEntityAnnotations() {
    return Set.of(QueryEntity.class);
  }
}

Let me know what you think?

That sounds reasonable, feel free to open up a PR.

I'm closing this one as stale - if you still need the functionality feel free to reopen and please provide a PR.


UserAvatar component errors

Steps
I was able to successfully create an account after my setting up Firebase config.
My partner tried to create an account and it resulted in a blank screen with the following errors.
Expected Result

Actual Result
Version
x.y.z
Testcase

I'm not sure which version of repository you are using, because when user register I fill the data. By the way I solved this problem which the data can be null. I hope it helps 5477197.

Thank you as always.


Specification of output path

With checkpointing and VTK output, SEAS models potentially generate a lot of heavy data which should not be written into the directory where the model is run.
It would make sense to introduce a new toml parameter which specifies the output path. All output generated should be written into this location.
For checkpointed data, it would make sense to write these files in special location.
Something like this
{OUTPUT_PATH}/checkpoint/{SCENARIO_NAME}/

This would make sense as I believe that now all SEAS models need to define a scenario name and different scenarios would generally imply different model parameters resulting in different time histories / trajectories for the time solve (TS) and different green's functions.

For the vtk output you can already select different folders. Just add the
prefix="/path/to/output/scenario"

Nice. Does the path have to already exist or will it be created (recursively if required) by tandem?

No it must exist.

I guess for the probes you change this
prefix = "fltst_"

to this
prefix = "/path/to/output/fltst_"

?

Yes

Okay. So I see in SeasConfig.cpp there is support to parse prefix. That is great.
As a side note: For the end-user, waiting in a queue for days, only to have the job abort because you forgot to create the directory is annoying and frustrating. We should add support to create the directories.


settings: Make MAX_MESSAGE_LENGTH a server-level setting.

This will offer users who are self-hosting to adjust this value. Moreover, this will help to reduce the overall time taken to test test_markdown.py (since this can be now overridden with override_settings Django decorator).
This is done as a prep commit for #18641.

Merged, thanks @akshatdalton!


Render both main and "the most recent release".

What is the problem?
It may be useful for people to refer to a "stable release" of the mdbook text, such as if they are linking from another site in a discussion where updates to the rendering would confuse the discussion.
Do you recommend a solution?
IMO, an ideal solution would have rendering tree like this:
<BASE URL>/<VERSION_OR_BRANCH>/<BOOK PATH>

Some examples:

https://electric-coin-company.github.io/tfl-book/v0.1.0/index.html
https://electric-coin-company.github.io/tfl-book/main/terminology.html#definition-crosslink

Then, we can redirect the base url to either the latest release or the latest main.
I believe this is possible in the rendering automation by committing all of the version outputs into different build/html/<version> directories, then adding a redirect hack in build/html/index.html.
The <version> should either be the tag, if the revision corresponds exactly to a single well-known-formatted tag, or else the branch name.
Related, but distinct #126

This ideal solution would enable stable links to older versions, which may be useful for external discussions especially to identify important changes between versions.


Installation error

I've tried to install (executing the .m) but i have this error message
Error in setup_MbedTarget (line 36)
mbed.Prefs.setMbedTarget('DISCO_F407VG');

Did you use the release zip file download? Or did you clone/download the master branch?
The release zip version is the preferable way, because the mbed subproject is included, mex files are compiled and also the help system is compiled.
The mentioned line can be removed. It is not longer necessary and in the current working branch master1 already removed.

I download the master branch, will try again with the release zip, thanks for pointing out.

I'll change the first part of the readme soon.
Something like 'first steps' should be at the first beginning of the readme.

Yes installations steps for nebiews (like me) would be very usefould, i had to comment the lines from 36 to 47, now i don't have the arm compiler in path....

Some installation documentation exists.
But in the master branch it is included only as uncompiled markup ascii text.
In the release zip this documentation is compiled into html files. You can find installtion, first steps and so on in the htmlhelp subfolder after unzipping release 1 zip file.

Thanks will have a look.

We did some CAN staff under mbed with F446, F746 and F767.
We realized one project with F767. CAN is used very intensivly there. But it is programmed based on the Cube libraries.
There exists CAN Simulink block in the MbedTarget. But they are not used very intensivly/in only a few student projects. So, I'm sure they can be improved.

Cube Libraries for Simulink ?

No, plain C.

I have sucefoully run the .m script (it was just a path issue in matlab), i've tryed to compile one of the examples (the can receive) but i have one error
`### Processing Template Makefile: C:\mbed_target\mbed\mbed.tmf
Creating CANreceive.mk from C:\mbed_target\mbed\mbed.tmf
Building CANreceive: .\CANreceive.bat
C:\mbed_target\CANreceive_slprj>set MATLAB=C:\Program Files\MATLAB\R2017b
1 file copiati.
"make.exe" non è riconosciuto come comando interno o esterno,
un programma eseguibile o un file batch.
Build procedure for model: 'CANreceive' aborted due to an error.(error)
Error(s) encountered while building "CANreceive"`
Translating from italian it does not find the make.exe
but i have gnu make in path
C:\Program Files (x86)\GnuWin32\bin

In the targets subfolder under mbed_target you can find a subfolder, with a name combined of the model name, the target name and '_slprj'. In this subfolder you can find a batch file: 'model name.bat'. Among others there is the command to call make.exe
Because the Eclipse binutils are integrated you should not need an external make. But please check the paths in the batch file.

This is the batch file:
set MATLAB=C:\Program Files\MATLAB\R2017b @echo off & copy "C:\mbed_target\CANreceive_slprj\CANreceive.mk" "C:\mbed_target\CANreceive_slprj\Makefile" & make.exe

Sorry, I mixed it with the current version.
For v1 you need to have make.exe in the PATH. To check if everything is OK, call the setup m-file. It will give you an error message, that make.exe could not be found. Or call the above batch file. It is generated and called during the Simulink model build process resulting in the bin file to flash the microcrontroller.

Many thanks, it just just a path problem, just needed to reboot ! I have a further question, were are set the mapping between the hw pins of the micro and the various functions ?

In the Simulnik blocks you can choose a pin name like PA_4. Currently only this naming scheme is supported, all other are in preparation.
Some blocks like LED or user button are using the Mbed defines e.g. LED1 or USER_BUTTON. When you go to the Mbed platform page of one of the STM targets they put links to peripheralpins.c and pinnames.h files. These are the STM files for all pin mappings.

Got it, thanks !

That is great.
Could you close this issue, please?

Yes, of course can close


Premature version check in CLI when installing

When installing Keptn, the version check for supported Kubernetes versions is done before the actual cluster is selected.
keptn install --platform=aks
Used Installer version: docker.io/keptn/installer:0.6.2
Error: Keptn requires Kubernetes Server Version: >= 1.13, <= 1.15

Executing this command checks if the cluster that is currently configured in the kubeconfig is conforming to the supported kubernetes version. the check fails if either the cluster is unavailable or the version is not supported.
the desired solution is that the installer asks first for the cluster credentials to make sure the right cluster is checkd and second checks the cluster version.
right now the workaround is to configure kubectl manually to point to the correct cluster and then execute the installer again. unfortunately this is not documented.
Environment

Keptn Version (keptn version): 0.6.2

Additional info in the Slack thread: https://keptn.slack.com/archives/CNRCGFU3U/p1590629823308900

This is the relevant section of the CLI code:
https://github.com/keptn/keptn/blob/35dc8240b3988bc31b2beb63427591556f55d09e/cli/cmd/install.go#L220-L227
and I believe it just needs to be moved after this section:
https://github.com/keptn/keptn/blob/35dc8240b3988bc31b2beb63427591556f55d09e/cli/cmd/install.go#L230-L244


Metadata and Solution interfaces - Multiple order of magnitude faster solution retrieval

Adds metadata and solution interfaces

@ChrisRackauckas Everything passing except invalidations, is this ok to merge?


Specify requirements for DGX environment

If the current environment is installed on DGX in a clean virtual environment
https://github.com/mihirkatare/DeepMEM/blob/61e5d7ef9e9f097f13ee4e98d55a6611d76cd4c4/requirements.txt#L5
then the user will get torch v1.8.1 — that's fine by itself. However, if the user checks the compatibility with the GPUs available
$ curl -sL https://raw.githubusercontent.com/matthewfeickert/nvidia-gpu-ml-library-test/main/torch_detect_GPU.py | python
PyTorch build CUDA version: 10.2
PyTorch build cuDNN version: 7605
PyTorch build NCCL version: 2708

Number of GPUs found on system: 8
PyTorch has active GPU: True
/raid/projects/feickert/.pyenv/versions/DeepMEM-dev/lib/python3.9/site-packages/torch/cuda/__init__.py:106: UserWarning:
A100-SXM4-40GB with CUDA capability sm_80 is not compatible with the current PyTorch installation.
The current PyTorch install supports CUDA capabilities sm_37 sm_50 sm_60 sm_70.
If you want to use the A100-SXM4-40GB GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/

  warnings.warn(incompatible_device_warn.format(device_name, capability, " ".join(arch_list), device_name))

Active GPU index: 0
Active GPU name: A100-SXM4-40GB

To get a PyTorch release that is compatible with the A100 the user needs to get one of the custom wheels that PyTorch hosts built with CUDA 11
$ nvidia-smi | grep "CUDA Version"
| NVIDIA-SMI 450.142.00   Driver Version: 450.142.00   CUDA Version: 11.0     |
$ python -m pip install --upgrade torch==1.9.0+cu111 --find-links https://download.pytorch.org/whl/torch_stable.html
Looking in links: https://download.pytorch.org/whl/torch_stable.html
Collecting torch==1.9.0+cu111
  Downloading https://download.pytorch.org/whl/cu111/torch-1.9.0%2Bcu111-cp39-cp39-linux_x86_64.whl (2041.4 MB)
     |████████████████████████████████| 2041.4 MB 20 kB/s
Requirement already satisfied: typing-extensions in /raid/projects/feickert/.pyenv/versions/3.9.6/envs/DeepMEM-dev/lib/python3.9/site-packages (from torch==1.9.0+cu111) (<IP_ADDRESS>)
Installing collected packages: torch
  Attempting uninstall: torch
    Found existing installation: torch 1.9.0
    Uninstalling torch-1.9.0:
      Successfully uninstalled torch-1.9.0
Successfully installed torch-1.9.0+cu111
$ curl -sL https://raw.githubusercontent.com/matthewfeickert/nvidia-gpu-ml-library-test/main/torch_detect_GPU.py | python
PyTorch build CUDA version: 11.1
PyTorch build cuDNN version: 8005
PyTorch build NCCL version: 2708

Number of GPUs found on system: 8
PyTorch has active GPU: True

Active GPU index: 0
Active GPU name: A100-SXM4-40GB

None of that is a big problem, but it might require either a dgx-requirements.txt or some instructions for the user to manually resolve things.

Yep! I had to manually install the correct version of pyTorch and CUDA to fix this but it slipped my mind to change the requirements.txt. Since the project is stable in the newer version, I can just update the requirements.txt itself.

Since the project is stable in the newer version, I can just update the requirements.txt itself.

So the tricky part here though is that we should avoid locking in general requirements.txt to a specific machine. As torch==1.9.0+cu111 is making assumptions about the CUDA libraries that are available it will probably be required to either have machine specific environment files (e.g. dgx-requirements.txt or dgx-env.yml). Optionally we could create some setup scripts that do some looking for users in the environments they have, but in my personal experience (https://github.com/matthewfeickert/nvidia-gpu-ml-library-test) this is gets tedious and hard to do robustly.
This will eventually become more apparent as we make a deepmem library and then we'll have library dependencies vs. runtime application dependencies (c.f. https://caremad.io/posts/2013/07/setup-vs-requirement/). The library dependencies will give us minimum required APIs for things to work as expected (e.g. torch>=1.8.0) and then our runtime dependencies will define the actual "application" environment (e.g. torch==1.9.0+cu111).


cannot install cudnn file to Kohya ss

when I try to install the cudnn folder i get the text "The system cannot find the path specified."

I can confirm that cudnn is installed with pytorch.


Fix #103: support parallel databases and protect from HttpContext interference

@sergeyshushlyapin, fair, I will take a look. I didn't like that one either. The reason I needed to convert FakeDataProvider's DataStorage property into a method was to be able to stub it out on a partial mock. Let me find a better way to handle FakeStandardValuesProvider. Hold on... :)

@sergeyshushlyapin, cleaned it up. No more leaky abstraction with internal Database property. Also both DataProvider() methods look a lot more alike after a small refactoring inside the FakeStandardValuesProvider. It is still a lot more convenient to use the this.Database in the FakeDataProvider because it is naturally there. StandardValuesProvider is by design global and can work with any database.
Anyway. Let me know what you think know. I personally like it better. Thanks for the comment!

Just wondering why did I decide to create a Fixture in the FakeDataProviderTest class and keep the dataProvider and dataStorageSwitcher fields... All of them must go away, only tiny test methods should stay (but that is not the case for this PR ;)).
I'll merge it right away! Thanks, @pveller!


Power Apps: Download and Unpack Solution Error

Hi,
sorry to bother you, but I have the following problem.
When I want to download and unpack a Solution, I get the following error message:
Command 'Power Apps: Download and Unpack Solution' resulted in an error (command 'mme2k-powerapps-helper.solution.downloadAndUnpack' not found).
Do you know how I can solve this problem?
Thank you and kind regards

Hi, how did you start action 'Power Apps: Download and Unpack Solution'?

Hi,
my OS: macOS Catalina Ver. 10.15.7
VScode Ver. 1.57.1
I start the action with 'command+shift+p' and choose 'Power ....'

I guess the OS is the Problem. I have changed the tooling libraries to the latest Microsoft Power Platform CLI, which is probably not compatible with MacOS.
Can you please try to install the Power Platform CLI from https://docs.microsoft.com/en-us/powerapps/developer/data-platform/powerapps-cli
Afterwards configure the PAC.exe or similar (😁 I have no Mac and don't know what is installed) in the vscode settings.

😄ok, thx, I try it

Did you try? If yes, did it work 😅 However, there might be another issue on MacOS with storing secret information for OAuth Update. This is because I use keytar as dependency:
https://github.com/megel/powerapps-helper/blob/ae418823c764839d2d207cd620d8098b0f8be222/package.json#L354
... and this is - I guess - not supported on MacOS ... maybe you have an idea to handle this

@LegionDevOps I have wrapped the usage of keytar to avoid errors during usage. Furthermore contains the current release 0.7.1 some improvements for MacOS.
You should now be able to:

Pack / Unpack PowerApps using Power Platform Cli
Pack / Unpack Dataverse solutions using Power Platform Cli

I have an issue with unpacking as well. I was able to get the command to run manually, grabbing the command from the output window.
RUN: "c:\Users\User\AppData\Roaming\Code\User\globalStorage\microsoft-isvexptools.powerplatform-vscode\pac\tools\pac.exe" canvas unpack --msapp "c:\Options\PowerApps/[redacted].msapp" --sources "c:\Options\PowerApps/src/[redacted]/" Error: Command failed: "c:\Users\User\AppData\Roaming\Code\User\globalStorage\microsoft-isvexptools.powerplatform-vscode\pac\tools\pac.exe" canvas unpack --msapp "c:\Options\PowerApps/[redeacted].msapp" --sources "c:\Options\PowerApps/src/[redeacted]/"
I changed the direction of the slashes to follow the Windows format and added the required "." prior to the executables filepath.
The follow-on issue, is that my .msapp name had spaces in it. The unpacking process wouldn't complete until I renamed the file to something with no spaces.

@pwshdevops spaces shouldn't be the problem, because the path is masked by ". Which OS do you use?

@pwshdevops I assume, you have found a solution. I'll close this issue.


How to quit menu bar app?

Is there a button/keyboard command to quit the app that doesn't involve going to Activity Monitor?

On a Mac? CMD + Q

Ha, sure enough that works -- thanks.  I guess the hidden request here would be to include some sort of menu that explicitly indicates how to quit.  The patterns I typically see are a native menu or a graphical menu, both of which include the ability to quit.
Example of a native menu-only app with a quit option:

Example of a UI app with an an overflow menu containing a quit option:

Gitify which provides neither a native menu nor an overflow menu:

If you click the cogwheel, there’s a Quit button.

But from the screenshot you posted, it looks like the cogwheel menu isn’t available before you’re signed in…

We now have a quit button on the sidebar for logged out users 😄
Will go out with the next release - #467


v3 only shows metric and not Fahrenheit

when you run v3 no matter where you are or even if you put the ?u at the end of the url it still shows metric temperature

I also noticed this. I use one-liners

Is there a good one liner to replace the map with the temperature

Accepted.

to clarify, I have this issue in V1 one-liners as well.

I am still having no luck with v3

Celsius again today ☹


[App]: Unnormal Behavior when writing a report using Chinese input methods

Current Behavior
When writing one experiment report using Chinese input methods, the behavior of the whole text input UI will behave unnormally.
To be precise, the unnormal behavior of the report editor page using Chinese input methods is:

When using English input methods, everything is good.
When using Chinese input methods (provided by macOS), after selecting the correct character with Chinese input methods, the location of the cursor will change automatically to difference places and two replicate characters or words will appear on this report editer even though I only input one character or word.
Problem also exists when using Japanese input methods.
Problem do not exist when editing the headline or the abstract for the report, only exists for the editing of the main part of the report.

Expected Behavior
Have the save behavior as English input methods
Steps To Reproduce

Go to create a new report
Type in the main part of the report using English input methods

Screenshots
No response
Environment
OS: macOS
Browsers: Chrome
Version:
Additional Context
No response

Hello @lwaekfjlk,
I apologize for the problems you're facing in writing your reports in Chinese and Japanese. Would you be able to provide a link to the report or a gif showing this behavior? I will be inputting a ticket for our engineers to fix as we value the literature provided by all of our users.
Thank you!

Thanks a lot for your reply. Here is one simple example for this possible web error.

Hello @lwaekfjlk,
If possible can you check the settings for your input sources on your keyboard? What browser are you using?
I have tested this behavior on Safari and found I could not recreate the issue shown in your video. Linked is my video of an identical recreation.
https://user-images.githubusercontent.com/93145909/163839600-62c7101a-0023-4e67-ab78-fa37eaf39f01.mov

Hi, you can see that after each time you confirm the Chinese word, the location of the cursor is moved to the beginning of the line. It should not be the normal behaviour.
In Safari, I do found the same behaviour like yours. And the GIF is related to Chrome 100.0.4896.88.

@lwaekfjlk,
I escalated this to our engineers to look at. Thank you for bringing this to our attention. We really appreciate this

Hello @lwaekfjlk! We appreciate your patience. My awesome colleague @altay has been investigating this and we believe the issue to be resolved. Can you please test this and confirm on your side when you have a chance?

Awesome! Yes, this bug has been fixed. Thank you for that!


Twitter link not working

Twitter link for Chelsea Banke not working

I don't think so

Send me the link you opened

No.

Send the link of the mentor match site you are opening.

https://mentormatch-ng-plknlkqrf-kanyelings.vercel.app/

You were using the wrong link. I sent an updated link with the deployed build that already rectified that error.

Okay


Move healthcheck endpoint to GET http method only

Coverage increased (+0.02%) to 90.834% when pulling 9a1024daad70e02836c9e02728d0b5b7f3655117 on SAP:healthcheck-on-get into 00a6892d24bedb7dd19be7ccc4b176ee7942e95c on cloudfoundry-incubator:master.


spago doesn't like development versions of purs

So I grabbed purs 0.12.3 and stack installed it (no windows release it seems).
spago install gives
spago: Failed to parse 'purs --version' output:
"0.12.3 [development build; commit: b35412a9ea49a46d315a0214cd2c3908442ab7a7]
"

Might be nice if there is some sort of flag/escape hatch for "It may not be compatible but I know what I'm doing"

@btrepp fix should be up in #117, could you try it?
Re having a "general escape hatch": it's hard to propagate this kind of thing all over the place, as the failure modes are different. E.g. in this case we could log a warning and return a Maybe instead of failing, and then propagate the general handling around the rest of the program that needs it (in this case in the package-set version check). Though this fix should be good enough to support all the versions we might get

Update: now we don't fail if parsing fails, but just leave a warning


Export a ClientOnly component

The children of ClientOnly will only be rendered on the client side, you can use it with defineAsyncComponent to disable SSR for specific pages.

So the difference between this and https://github.com/ream/ream/issues/13 is that ClientOnly can work on a whole page?

@nblthree ah I kinda forgot #13, that nossr function is basically a combination of defineAsyncComponent and ClientOnly, I will rename that nossr to clientOnly.

See https://ream.dev/docs/references/app#defineclientonlycomponent


Nvidia A100 GPU on Windows Server 2022 - GPU not detected

From what I can see CUDA is installed properly and it's visible from within python (testing using torch). I'm not sure why Cupscale isn't detected it..

Because you need to tweak torch to work with the A100.

Ah that makes sense.. I forgot that different GPU types need to be supported specifically.


Solve incorrect "Show on GitHub" link

Since the package isn't tagged yet, the current file prefix URL isn't valid, causing the declarations "Go to GitHub" button to lead to a 404 page.

thanks @adtrevor! can you rebase & squash so this has a single commit with the changes

@tomerd Is this different from an admin directly merging it by choosing the squash and merge option?

@adtrevor nope, but its hard to review the actual changes in this PR right now because of the noise, so its more to help the review

I understand, thank you! Will do it when back on my computer.

Le 30 avr. 2019 à 19:30, tomer doron<EMAIL_ADDRESS>a écrit :
@adtrevor nope, but its hard to review the actual changes in this PR right now because of the noise, so its more to help the review
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub, or mute the thread.

@tomerd I did a soft reset to #fdb9dc10e5ac47e378b0d4c2278dd5dca858d7c3 followed by a commit using this method
git reset --soft fdb9dc10e5ac47e378b0d4c2278dd5dca858d7c3
git commit

Yet there is still another commit and I'm not sure why the GitHub diff doesn't show the changes of this PR only.

This time there only is one commit remaining, but the diff still shows more than I would hope...

i think head is 44ddc8bae0d054bf89ec8f0958f164574254abc0

@tomerd Thank you! I had used the wrong commit indeed! Done! 🙂


Automated Package Upgrade

:tada: This PR is included in version 7.7.300 :tada:
The release is available on:

npm package (@latest dist-tag)
GitHub release

Your semantic-release bot :package::rocket:


Ease of adapting Ibex memory protocol to tilelink for non 32-bit access

Tilelink has a size field it its message payload, that the native Ibex protocol does not have.
When accessing 'standard' memory this is not a big problem. An Ibex -> Tilelink adapater could just always use 32-bit sizes and set all byte-enables for read (Get in Tilelink terms) and for writes take the byte-enables from the data_be_o from Ibex.
However this could give some difficulties around reads to devices where a read could trigger some action. Specifically if a device triggers some action on particular byte reads and the simple adapter described above is used a single lb instruction will turn into a 4-byte Get in Tilelink. The device might then trigger an action because certain bytes have been read but from a programmer's perspective these bytes haven't been touched.
In Tilelink for a Get message you cannot partially set the byte mask, so you must use the size field to communicate what bytes you actually intend to read where this matters. Ibex (from a quick look at the RTL anyway) does generate data_be_o that indicates the precise bytes you want so it would be possible to build some logic that can work out an appropriate size from the data_be_o.
You may also find that your TileLink devices only respond to PutFullData so in order to do a byte-write to such a device you'd need to generate PutFullData with a byte size. Again the logic mentioned above could be employed to ensure the correct size field is generated along with some logic to understand if it's a PullFullData or not.
This logic would be a little fiddly, add extra area and increase the depth of logic. In some sense it's simply undoing what ibex_load_store_unit has done internally (taking the size and address and generating a be). We could introduce a data_size_o to the Ibex top-level interface allowing simpler adaption to Tilelink and other similar protocols.
We probably also want a requirement that data_be_o matches tilelink requires for its mask field but I think this is already the case, but worth documenting.
Another potential addition would a data_full_o that indicates data_be_o is set for every byte that's covered by data_size_o. This would allows a simple way to choose PutPartialData vs PutFullData on the tilelink interface. However it may be the adapter would always emit PutPartialData anyway. I will open an OpenTitan issue to discuss this point.

@davideschiavone @imphil @tomroberts-lowrisc @vogelpi @rswarbrick any thoughts?

Possibly a silly question, but is it possible to have reads with "sparse" byte enables? (The top and bottom bytes, but not the ones in the middle). It seems like this would require two TileLink transactions, each of size 1. Is that correct?

"Sparse" reads are not possible in TL-UL, and are currently not emitted by Ibex. I thought we had an assert for that in the adapter code somewhere but I can't find it any more, maybe it got removed or it was never there ...

In which case, I guess another option would be to get rid of the byte enables entirely, changing Ibex's LSU to generate size and address requests instead. Then it's not possible to encode the thing we don't support.

If at all possible, it would be great to have a memory interface that matches the one used in other PULP-derived IP, e.g. CV32E40P (RISCY), Ariane, and the debug module.
@GregAC can we have a look at a potential AXI adapter as well to see if such an adapter has the same problem?

FYI: For CV32E40P I wrote a document describing its bus interface in a bit more detail. Just to give it a name the protocol is now called OBI (Open Bus Interface). The pinout will stay identical to what it was on RI5CY, but certain combinatorial paths inside the core will (still need to) be fixed. The specification for OBI can now be found at https://github.com/openhwgroup/core-v-docs/blob/master/cores/cv32e40p/OBI-v1.0.pdf. In section 3.4.1 I tried to describe the legal byte enable values. I decided to forbid 'sparse reads' as it was currently not used and it eased the conversion to e.g. AHB.

In which case, maybe Greg's original suggestion of adding data_size_o to the LSU makes the most sense. Then synthesis will throw away the generated byte enable registers if we're connecting it to a TileLink target.

Opened OT related issue here https://github.com/lowRISC/opentitan/issues/1867

Adding data_size_o is just a matter of wiring out the internal signal. Though probably re-encoding it too as it uses the reverse of tilelink (2'b00 == word where 2'b00 == byte in tilelink). Need to understand if there's an internal advantage in Ibex to that encoding, matches RISC-V instruction encoding maybe?

I'm working on such processor based on AXI bus. I want to remove the AXI bus and replace it by an OBI interface for an ASIC implementation. My question is: is that possible to insert the OBI interface at top level and not into the load store unit and prefetch buffer for data and instruction? which means at the top, I'll have the OBI interface plus the ibex core. I'm doing that, but it is not possible to generate some inputs for this interface.


fix advance setting folding in reingestion popup

Description
fix advance setting folding in re-ingestion popup
Related Issue :

How Has This Been Tested?
Re-ingest a datasource with a file.
Fold advance setting in re-ingestion popup
Need additional checks?
Types of changes

[x] Bug fix (non-breaking change which fixes an issue)
[ ] New feature (non-breaking change which adds functionality)
[ ] Breaking change (fix or feature that would cause existing functionality to change)

Checklist:

[ ] I have read the CONTRIBUTING document.
[ ] My code follows the code style of this project.
[ ] My change requires a change to the documentation.
[ ] I have added tests to cover my changes.

Additional Context

run build
deploy to 2


Optional query parameters in webapi

I was converting a C# webapi project to F# using the F# ASP.NET templates. Everything is working great except optional query parameters. I keep getting this error
{
    "message": "The request is invalid.",
    "messageDetail": "The parameters dictionary contains an invalid entry for parameter 'start' for method 'System.Threading.Tasks.Task`1[System.Net.Http.HttpResponseMessage] GetVendorFiles(Int32, System.Nullable`1[System.DateTime])' in 'Thor.WebApi.VendorFilesController'. The dictionary contains a value of type 'System.Reflection.Missing', but the parameter requires a value of type 'System.Nullable`1[System.DateTime]'."
}

F# function signature:
        [<HttpGet; Route("")>]
        member x.GetVendorFiles( [<Optional; DefaultParameterValue(100)>] count, [<Optional; DefaultParameterValue(null)>] start : Nullable<DateTime> ) =

C# function signature:
        [HttpGet]
        [Route("")]
        public async Task<HttpResponseMessage> GetVendorFiles(int count = 100,DateTime? start = null)

Does any one know of any work arounds?

For what it's worth, I rarely use the parameter bindings. I find request.GetQueryNameValuePairs() a more reliable mechanism, especially when you want optional parameters. F# doesn't seem to map well to the MVC conventions for parameter binding. I'm sure this is not the answer for which you hoped, but I've never been able to get parameter binding to work reliably. You ma want to raise this in https://github.com/Microsoft/visualfsharp/issues.


“Dose 3/2” displayed after third dose of vaccine

I just received the third dose of my pfizer vaccine and a new green pass. Now when I create the wallet certificate it says “Dose 3/2”. Is it possible to change the value on the right to the correct number?

Duplicate of #117


3Sum

Description: Given an array nums of n integers, find all unique triplets that sum to zero.
Constraints: The solution should not return duplicate triplets.
Example:
Input: [-1, 0, 1, 2, -1, -4]
Output: [[-1, 0, 1], [-1, -1, 2]]

hello @Saloni6111 assign me the task.

hello @Saloni6111 assign me the task.

Sure @rastogi30 , it's already assigned to you. Please go ahead!

@Saloni6111, please assign me this.

Hello!
Kindly please assign me this issue.


test: check loading Nargo artifacts works in the cli

This PR adds an end-to-end test for the fix in #4352

Benchmark results
Metrics with a significant change:

note_trial_decrypting_time_in_ms (8): 110 (+338%)

Detailed results
All benchmarks are run on txs on the Benchmarking contract on the repository. Each tx consists of a batch call  to create_note and increment_balance, which guarantees that each tx has a private call, a nested private call, a public call, and a nested public call, as well as an emitted private note, an unencrypted log, and public storage read and write.
This benchmark source data is available in JSON format on S3 here.
Values are compared against data from master at commit 47a0a79f and shown if the difference exceeds 1%.
L2 block published to L1
Each column represents the number of txs on an L2 block published to L1.

Metric
8 txs
32 txs
128 txs

l1_rollup_calldata_size_in_bytes
45,412
179,524
716,068

l1_rollup_calldata_gas
221,728
866,956
3,448,192

l1_rollup_execution_gas
314,215
984,696
3,667,849

l2_block_processing_time_in_ms
1,154 (-1%)
4,351
17,205

note_successful_decrypting_time_in_ms
300 (-4%)
960 (-1%)
3,618

note_trial_decrypting_time_in_ms
:warning: 110 (+338%)
91.2 (-7%)
143 (+1%)

l2_block_building_time_in_ms
18,280
72,186
290,209

l2_block_rollup_simulation_time_in_ms
13,074
51,868
208,860

l2_block_public_tx_process_time_in_ms
5,155
20,246
80,935

L2 chain processing
Each column represents the number of blocks on the L2 chain where each block has 16 txs.

Metric
5 blocks
10 blocks

node_history_sync_time_in_ms
13,889 (+1%)
26,609

note_history_successful_decrypting_time_in_ms
2,347 (-9%)
4,656 (+3%)

note_history_trial_decrypting_time_in_ms
99.6 (-37%)
229 (+12%)

node_database_size_in_bytes
17,342,544
33,169,488

pxe_database_size_in_bytes
29,923
59,478

Circuits stats
Stats on running time and I/O sizes collected for every circuit run across all benchmarks.

Circuit
circuit_simulation_time_in_ms
circuit_input_size_in_bytes
circuit_output_size_in_bytes

private-kernel-init
310
44,481
26,193

private-kernel-ordering
194
44,513
14,929

base-rollup
1,408
128,970
881

root-rollup
82.3
4,088
677

private-kernel-inner
414 (+1%)
71,640
26,193

public-kernel-private-input
243
32,615
26,193

public-kernel-non-first-iteration
242
32,657
26,193

merge-rollup
7.62
2,608
881

Tree insertion stats
The duration to insert a fixed batch of leaves into each tree type.

Metric
0 deployed contracts
1 deployed contracts

tx_size_in_bytes
15,575
37,972

Transaction processing duration by data writes.

Metric
0 new commitments
1 new commitments

tx_pxe_processing_time_ms
547
1,375

Metric
0 public data writes
1 public data writes

tx_sequencer_processing_time_ms
0.495 (+1%)
629


preserve_fp32_precision flag is accepted as true for OPs for which it is not valid

Background:
The preserve_fp32_precision flag was introduced to enable full fp32 precision for SFPU-only OPs, meaning in cases that the data is unpacked directly into Dest using copy_tile(). It does so by setting the unpack_dst_format for CBs to fp32 instead of tf32, so that the unpacker puts fp32 datums from the CB directly into Dest unchanged (skipping SrcA/B regs which are limited to 19bits).
Issue:
The preserve_fp32_precision flag is not valid for all OPs, but there is no warning/error produced when it is used incorrectly. For example reduce_tile utilizes the FPU, which works on the srcA and srcB registers, and setting preserve_fp32_precision=True will set the data type of srcA and srcB to fp32 instead of tf32 and lead to wrong results.
Also, the question was raised whether or not the flag is allowed in cases where a compute kernel may use one LLK for which the flag is supported but a second one for which it is not. One such example could be reloading fp32 partials into the Dest (flag supported) in order to perform further accumulation using matmul (flag not supported). In these cases, since the flag works by changing the unpack_dst_format for the buffer, if the buffer is only used for unpacking to Dest then the flag should be allowed. If the buffer is used to unpack to Dest but also unpack into SrcA/B, then the flag is not allowed.
To Reproduce:
This was detected in this bug https://github.com/tenstorrent/tt-metal/issues/10337 and this example can be used.
Expected behavior:
We need a mechanism to disallow this flag to be set to true for OPs for which it is not valid, and better documentation surrounding this flag.

We need a mechanism to disallow this flag to be set to true for OPs for which it is not valid.

What does the above sentence mean among the two options below?

If reduce_tile exists in the compute kernel, that compute kernel does not support the preserve_fp32 feature.
In the compute kernel, if both add_tiles and reduce_tile are present, preserve_fp32 is disabled only for reduce_tile, which doesn't support it, while add_tiles remains unaffected.

We need a mechanism to disallow this flag to be set to true for OPs for which it is not valid.

What does the above sentence mean among the two options below?

If reduce_tile exists in the compute kernel, that compute kernel does not support the preserve_fp32 feature.
In the compute kernel, if both add_tiles and reduce_tile are present, preserve_fp32 is disabled only for reduce_tile, which doesn't support it, while add_tiles remains unaffected.

add_tiles also uses FPU and is not compatible with the flag, but if you had some other function for which preserve_fp32_precision is valid, that one should work fine, at least that is what we aim for.

@razorback3 Is there a reason this needs to be P0, since it is not blocking anyone. Now that we've clarified how to use the flag properly this isn't the highest priority. (I will still get to it next after solving #11962 but can we push this to P1 for now?)

@rdjogoTT,
As we discussed in #10267, I found that there are scenarios where both cases are used in the same compute kernel in fp32 mode.
The issue is that a single preserve_fp32_precision flag cannot satisfy both cases. When the flag is set to true, the compute API produces incorrect results when using an fp32 CB as input. On the other hand, if the flag is false, only 16 bits are copied when unpacking to the destination register.

Dear @rdjogoTT,
Currently, we are working on a training workload in a single Wormhole.
We succeeded in running the whole workload without errors or problems but we are facing a model convergence issue.
For that purpose, we are trying to use the fp32 datatype for some operations, not only the fp32 accumulation but also the fp32 datatype itself.
The problem is that some of our ops are using both SFPU and FPU in a single kernel.
In order to preserve whole fp32 data bits for computation that exploits SFPU, we should turn on the preserve_fp32_precison flag.
However, FPU computations require turning off the preserve_fp32_precison flag for correct output.
This is the collision point and the reason why we are sticking this issue as p0.
We hope you understand this situation and we appreciate you for the past issues that are resolved quickly.

Due to the need to support the ability to write compute kernels that mix OPs that can and can't use the flag (eg binary FPU and unary SFPU), we will need to rework the flag. The current plan to tackle this is to make the flag apply on a per-CB basis, meaning each CB can be controlled individually. CBs that serve as input to unsupported OPs will have the flag off, without preventing the other OPs from having fp32 precision. A plan is still being worked on for how to prevent flag misuse using asserts or similar.

Due to the need to support the ability to write compute kernels that mix OPs that can and can't use the flag (eg binary FPU and unary SFPU), we will need to rework the flag. The current plan to tackle this is to make the flag apply on a per-CB basis, meaning each CB can be controlled individually. CBs that serve as input to unsupported OPs will have the flag off, without preventing the other OPs from having fp32 precision. A plan is still being worked on for how to prevent flag misuse using asserts or similar.

Do you or your team have exact timeline for this?

@razorback3
I have implemented the discussed solution on my branch rd/preserve_fp32_precision. I tested it with my own typecast op and made all necessary changes on the TT side. I also looked into where the flag was used in Moreh files and made appropriate changes to accommodate for the new vector flag. However, I need your help with the Moreh program factory files to set the correct CBs to have the flag enabled. Can someone from the Moreh side help with this?
For example in ttnn/cpp/ttnn/operations/moreh/moreh_matmul/device/moreh_matmul_program_factory.cpp I have initialized a vector vector<PreserveFP32Target> preserve_fp32_precision(NUM_CIRCULAR_BUFFERS, PreserveFP32Target::Disabled); which represents all CBs having the flag disabled. To enable the flag you can individually program which CBs you want it for by setting preserve_fp32_precision[cb_id] = PreserveFP32Target::DEST.

@razorback3
I have implemented the discussed solution on my branch rd/preserve_fp32_precision. I tested it with my own typecast op and made all necessary changes on the TT side. I also looked into where the flag was used in Moreh files and made appropriate changes to accommodate for the new vector flag. However, I need your help with the Moreh program factory files to set the correct CBs to have the flag enabled. Can someone from the Moreh side help with this?
For example in ttnn/cpp/ttnn/operations/moreh/moreh_matmul/device/moreh_matmul_program_factory.cpp I have initialized a vector vector<PreserveFP32Target> preserve_fp32_precision(NUM_CIRCULAR_BUFFERS, PreserveFP32Target::Disabled); which represents all CBs having the flag disabled. To enable the flag you can individually program which CBs you want it for by setting preserve_fp32_precision[cb_id] = PreserveFP32Target::DEST.

Nice work!
@dongjin-na will check the functionality on the given branch and give feedback soon 😀.

Dear @rdjogoTT,
I’ve reviewed the moreh_matmul case on your branch.
The test_moreh_matmul_fp32_dest_acc passed successfully after setting preserve_fp32_precision[tt::CB::c_intermed0] = PreserveFP32Target::DEST.
I’ll take a look at a few other cases on the Moreh side and will update you soon.
I do have one question, though.
Is PreserveFP32Target::SRCA_B supported yet? When I set it, the JIT build failed with the following error:
Always | FATAL | preserve_fp32_precision is only available when unpack target is DEST register

Let me know if you need further adjustments. :)

@dongjin-na
I forgot to mention that I added PreserveFP32Target::SRCA_B even though it is NOT supported. It isn't supported because if the CBs are being unpacked into SrcA/B, then fp32 precision is not possible (the best we can do if TF32). This is due to the limitations of the Src regs and FPU that we have discussed in earlier issues/threads.
I added PreserveFP32Target::SRCA_B intentionally so that programmers who are trying to use the flag can set the unpack target to SrcA/B or Dest and will hit this error if trying to use the flag incorrectly (with srca/b).

@rdjogoTT, thanks for the quick response and detailed explanation.
I have one more question.
In our softmax implementation, there is a case where both the SFPU and FPU use fp32 CBs.
    auto intermed_data_format = fp32_dest_acc_en ? tt::DataFormat::Float32 : data_format;
    // ......
    constexpr auto cb_exps = tt::CB::c_intermed0;
    constexpr auto cb_recipsumexps = tt::CB::c_intermed1;
    constexpr auto cb_add = tt::CB::c_intermed2;
    constexpr auto cb_max = tt::CB::c_intermed3;
    constexpr auto cb_tmp = tt::CB::c_intermed4;

https://github.com/tenstorrent/tt-metal/blob/0d20c99c3c8e86226e6ac70f09f5d4577cc0a25a/ttnn/cpp/ttnn/deprecated/tt_dnn/op_library/moreh_softmax/kernels/moreh_softmax_w_large.cpp#L91-L140
Creating two CBs with different PreserveFP32Target values that share the same memory seems like a good approach to reducing precision loss as much as possible. The SFPU would use a CB with PreserveFP32Target::DEST enabled, while the FPU would use a CB with PreserveFP32Target::Disabled.
Do you think this approach is appropriate?

I'm not sure what you mean when you say that two CBs can "share the same memory". Does this mean that you will refer to a single memory space by two different CB names?

Yes, as in the example with matmul, two CBs can share the same memory space. That’s what I was referring to.

Also, can you clarify what you want to accomplish for Softmax? Do you want to use a single CB as an input to both a FPU and SFPU OP?

In the softmax example mentioned above, the already implemented kernel code does have a scenario where a single CB is used as input to both an FPU and an SFPU.
To achieve this, based on your PR, we can modify the kernel code to use a CB with PreserveFP32Target::DEST enabled for SFPU operations and a CB with PreserveFP32Target::Disabled for FPU operations.
I’ve checked the PreserveFP32Target on Moreh's side.
Could you please merge these changes into the main branch?

@dongjin-na Ok makes sense. Do you want to push the missing more changes to my branch so I can merge them together with my changes, or do you want me to just push my changes and you will later fix the moreh files?

@rdjogoTT

@rdjogoTT
Please push your changes to the main first. Thanks for your work 👍

@razorback3 @dongjin-na can I get a review for the PR: https://github.com/tenstorrent/tt-metal/pull/12794

@rdjogoTT
Seems like all works as intended. Salute to the nice work!
I think you can close this issue after the PR is merged to the main branch.

@razorback3 @dongjin-na I've updated the naming of the flag based on feedback from others within TT. The flag still works the exact same way as the preserve_fp32_precision vector I created, it just had a different name. The new enum is:
enum class UnpackToDestMode : uint8_t
{
    UnpackToDestFp32,
    Default
};

Default is the new value that corresponds to regular functionality, and UnpackToDestFp32 needs to be set for CBs that you want to use to unpack fp32 data to Dest in full precision.


Example returns empty object.

Version:
1.2.1
Platform:
node.js v18.17.1
Subsystem:
tcp,muxers,connectionEncryption,peerDiscovery

Severity:
Severity: High
Description:
`
import { createLibp2p } from 'libp2p'
import { tcp } from '@libp2p/tcp'
import { mplex } from '@libp2p/mplex'
import { noise } from '@chainsafe/libp2p-noise'
import { yamux } from '@chainsafe/libp2p-yamux'
import{ mdns } from '@libp2p/mdns';
const createNode = () => {
return createLibp2p({
addresses: {
listen: ['/ip4/<IP_ADDRESS>/tcp/0']
},
transports: [
tcp()
],
streamMuxers: [
yamux(),mplex()
],
connectionEncryption: [
noise()
],
peerDiscovery: [
mdns()
]
})
}
export const main = async () => {
   const [node1, node2] = await Promise.all([
    createNode(),
    createNode()
  ])

  await node1.start();
  await node2.start();
  console.log('node1: ', node1);
  console.log('node2: ', node2);

}
main()
`
console outputs the following empty objects
node1:  Libp2pNode {}
node2:  Libp2pNode {}

which example is this for? I can't find that code anywhere via https://github.com/search?q=org%3Alibp2p+'const+[node1%2C+node2]+%3D+await+Promise.all(['&type=code or searching locally

Slightly modified from this example which is outdated it seems?
https://github.com/libp2p/js-libp2p/blob/v0.28.4/examples/pubsub/1.js

I'm not sure this is a problem, this is just how console.log prints objects instantiated from ES6 classes.
You may find it more useful to log the peer id instead?
console.log('node1', node1.peerId)
// node1 PeerId(12D3Foo...)


Fix deprecation warnings due to invalid escape sequences.

Fixes #55

Codecov Report

Merging #56 into master will decrease coverage by 0.33%.
The diff coverage is 100%.

@@            Coverage Diff             @@
##           master      #56      +/-   ##
==========================================
- Coverage    74.1%   73.77%   -0.34%
==========================================
  Files          32       32
  Lines        2379     2379
==========================================
- Hits         1763     1755       -8
- Misses        616      624       +8

Impacted Files
Coverage Δ

pysmi/codegen/intermediate.py
85.47% <100%> (ø)
:arrow_up:

pysmi/error.py
80.55% <0%> (-11.12%)
:arrow_down:

pysmi/reader/zipreader.py
78.37% <0%> (-3.61%)
:arrow_down:

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update 2721cba...e098a56. Read the comment docs.

Indeed, thank you!


Add wheels for python 3.8

Can you please add wheels for python3.8?

Please find the link to the Jetson Orin installation wheel in the README.md


Php auto load faild

Can't run " php artisan key:generate " because of the missing vendor folder

Oh yeah you are right you have to composer install before I will add this in the readme !

when I request for installing composer I got these errors!!
Problem 1
- Installation request for pusher/pusher-php-server 3.0.0 -> satisfiable by pusher/pusher-php-server[3.0.0].
- pusher/pusher-php-server 3.0.0 requires ext-curl * -> the requested PHP extension curl is missing from your system.
Problem 2
- Installation request for stripe/stripe-php v5.1.2 -> satisfiable by stripe/stripe-php[v5.1.2].
- stripe/stripe-php v5.1.2 requires ext-curl * -> the requested PHP extension curl is missing from your system.

You need to enable curl in your php installation

Yeah, you are right!! after enabling curl in php.ini my problem been solved!! thanks!


Add CI testing of unittests and linting

Initially, add whitespace and pretty-print formatters.

@crangelsmith - I think that this is now ready to merge.
This reproduces in GitHub Actions some of the tests that are run in Travis-CI in monitoring-ecosystem-resilience.

Only tests relevant to pyveg are reproduced. None of the tests relevant to rveg are run.
Due to issue #19, one of the tests fails in CI (pytest pyveg/tests/test_combiner_modules.py::test_combine). In my opinion, this is dues to a bug in the production code, not the CI nor the tests. For now the relevant test is skipped on CI, but still runs locally.

Additionally, this PR adds linting rules via pre-commit (black, isort, autopep8, various whitespace formatters). These rules are repeated on the CI. As a result, this PR touches a lot of files - though most of these changes are minor. (With the benefit of hindsight I think that this should have been two separate PRs).
If/when merged, this PR would close #1.

I see the pre-commit hooks checks are failing after updating main with PR #14 , anything I should do on that front?

Not for now thanks - I'm working on this and will let you know if there is anything.

I have disabled a second test on CI, due to issue #20. In this case, I suspect (though haven't confirmed) that the problem is with the test code, not the CI or the production code.
In order to complete this PR, I've opted to disable the test on CI, rather than delay implementing the rest of the CI/liniting checks.


gold linker is deprecated now

Description
ITK defaults to using the "gold" linker on linux if present.  Word on the street is that this linker is deprecated - at least as of Fedora 33 - and in particular does not support LTO (link time optimizations) that Fedora is starting to enable by default.  The build fails with:
/usr/bin/ld.gold: fatal error: lto-wrapper failed
collect2: error: ld returned 1 exit status
gmake[2]: ***
[Modules/Filtering/ImageIntensity/test/CMakeFiles/ITKImageIntensityTestDriver.dir/build.make:1307:
bin/ITKImageIntensityTestDriver] Error 1

When compiled with -flto=auto -ffat-lto-objects
A test of compiles on Fedora rawhide saw no overall speed different between using "gold" and the default linker.  Time to change the default?
Versions
4.13.3

References
https://fedoraproject.org/wiki/Changes/BINUTILS_GOLD
https://www.phoronix.com/scan.php?page=news_item&px=GNU-Gold-Stagnate-F31

I will not however that reverting to the standard linker does not resolve the LTO build failure, so it's less of a concern than I initially thought it was.  At least for now.

When performing a fresh, build compile times dominate over link times, so the linker speed benefits only really shine with incremental (development builds).
But, if the gold linker is not well maintained, we could remove support for it.


Docker container prompting to install after updating

Gitea version (or commit ref): docker:latest
Git version:
Operating system: Arch Linux
Database (use [x]):

[x] PostgreSQL
[ ] MySQL
[ ] MSSQL
[ ] SQLite

Can you reproduce the bug at https://try.gitea.io:

[ ] Yes (provide example URL)
[ ] No
[x] Not relevant

Log gist:

Description
After recently updating my gitea docker container, the container is now leading to /install like when it was first installed. I have run into this previously when moving the location of my docker data, however, after fixing the mistake the container started working. I have 2 containers in this case (gitea and db_gitea - which is a postgres container). I have destroyed these containers before and re-created them to test that the data is persistent across updates. No configuration has been changed. I have an ansible playbook that updates the containers and nothing has been changed in it recently so the setup should be identical. I tried removing the containers and re-creating them but the issue persists.
I checked the data of the postgres container and the gitea container and everything seems to be there. Is there a specific configuration file that is setup after the initial install that it is looking for and failing to find? If so I can check on that file or files.
Screenshots
In this case I can only reach the generic install page so I am not sure a screenshot would help.

Could you provide us more niformation like what mount point you us (docker run cmd or docker-come.yml file) and what version of gitea. If you can the startup log or any information that could be usefull.
For the insigth, after install gitea write the configuation to a file /data/gitea/conf/app.ini that should among others set INSTALL_LOCK   = true (https://github.com/go-gitea/gitea/blob/master/conf/app.ini#L194). If still set to false the install page is displayed.
Maybe not related but there is a current issue with saving app.ini changes inside docker: go-gitea/gitea/issues/2197

I have an issue opened with zfsonlinux in regards to issues mounting zfs datasets. Since my gitea data is stored inside one of these datasets I am retracting this issue since I can't be sure gitea is to blame. I will re-open if needed.


Node js Live server module not working

I am learning JavaScript now and don’t know much about node.js till now, but I want to use the live server to create a live server.
I use these commands:
npm install -g live-server
live-server
but I got this error:

live-server --no-browser


Toggle Column drop take full width

Package
filament/tables
Package Version
v2.15.38
Laravel Version
v9.27.0
Livewire Version
v2.3
PHP Version
PHP 8.1.4
Problem description
when I open toggle column drop down it opened in full width like this

Expected behavior
toggle column drop down opened in small with like this

Steps to reproduce
make some table toggleable then open toggle column drop down
Reproduction repository
https://github.com/filamentphp/filament
Relevant log output
No response

Please run artisan filament:upgrade, delete any published views, and recompile your theme.


Remove extension services

XRTK - Mixed Reality Toolkit Pull Request
Overview

Removed extension services
Changes

Removed Extension Definitions / Interfaces / Implementatons
Renamed "RegisteredServiceProviders" to "ActiveSystemsDataProviders"
Removed "RegisteredServiceProviders" Profiles and definitions against the "MixedRealityToolkit" service
Removed Extensions Tests
General cleanup and tidy
Updated https://github.com/XRTK/SDK/pull/242
Updated https://github.com/XRTK/Examples/pull/45

Breaking Changes

Breaks Any implementation using RegisteredServices, should be converted to Systems

Any final comments @StephenHodgson  / @FejZa would love to get this cleared up asap.

I'll give it a once over today

Updated as requested.
However, note.  The CUstom Service example will need to be updated accordingly, as it does not currently work.  The editor reports a "Constuctor Error" when trying to instantiate a "Custom Service" that has no profile or constructor.

However, note. The Custom Service example will need to be updated accordingly, as it does not currently work. The editor reports a "Constructor Error" when trying to instantiate a "Custom Service" that has no profile or constructor.

I'll take a look when I get a chance.

Any last comments @StephenHodgson  / @FejZa ?

Sorry haven't had time to check this out

Do you guys think we could push this off into 0.4.x release?

I am fine with that. Let's let @SimonDarksideJ have the last word on this one.


515 gpu token external liquidity not found

Hotfix support for new token GPU
Allow the external liquidity to be found by adding the event mappings

closing, replaced by another PR


fix: Update README.md badges

:tada: This PR is included in version 1.3.3 :tada:
The release is available on:

npm package (@latest dist-tag)
GitHub release

Your semantic-release bot :package::rocket:


[BEAM-13052] Increment pubsub python version and fix breakages.

This is needed to unblock adding a dependency on google-cloud-pubsublite.
This is a breaking API change in the Pub/Sub client library, but does not affect the python pubsubio API surface.

Thank you for your contribution! Follow this checklist to help us incorporate your contribution quickly and easily:

[ ] Choose reviewer(s) and mention them in a comment (R: @username).
[ ] Format the pull request title like [BEAM-XXX] Fixes bug in ApproximateQuantiles, where you replace BEAM-XXX with the appropriate JIRA issue, if applicable. This will automatically link the pull request to the issue.
[ ] Update CHANGES.md with noteworthy changes.
[ ] If this contribution is large, please file an Apache Individual Contributor License Agreement.

See the Contributor Guide for more tips on how to make review process smoother.

GitHub Actions Tests Status (on master branch)

See CI.md for more information about GitHub Actions CI.

R: @chamikaramj

Run Python PreCommit

Run Python PreCommit

Run Python PostCommit

Can you document the breaking change for the Pub/Sub client in the CL description ?
LGTM other than that.

Run Python 3.8 PostCommit

Run Python Dataflow ValidatesRunner

Can you document the breaking change for the Pub/Sub client in the CL description ?
LGTM other than that.

Done

There are some existing test failures in Python 3.8 PostCommit but seems like the run on this PR caused more tests to fail. Trying again.

Adding @TheNeuralBit as a reviewer since I'll be OOO for the rest of the year.

Run Python 3.8 PostCommit

Run Python 3.8 PostCommit

Run Python 3.8 PostCommit

Run Python 3.8 PostCommit

Run Python 3.8 PostCommit

are the test failures related?

Run Python 3.8 PostCommit

Run Python 3.8 PostCommit

Run Python 3.8 PostCommit

Run Python 3.7 PostCommit

Run Python 3.8 PostCommit

Run Python 3.7 PostCommit

Hi, apologies if this is the wrong place for the discussion, but this is a breaking change for the Pubsub library but was not mentioned in the release blog post:
https://beam.apache.org/blog/beam-2.36.0/
https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12319527&version=12350407

Sorry about that @cozos, could you clarify how this is a breaking change? Is it because you had to install a new pubsub client library to satiisfy beam requirements, or  did you actually have to change Python code?

Not sure if this is the same person, but I just responded in effectively the same way on the JIRA https://issues.apache.org/jira/browse/BEAM-13052?page=com.atlassian.jira.plugin.system.issuetabpanels%3Acomment-tabpanel&focusedCommentId=17511793#comment-17511793

I did not have to change any Beam pipeline code, but I did have to install the 2.x pubsub client, which did have breaking changes from the 1.x pubsub client. So a transitive breakage, I guess.
I was under the impression that this was something that was meant to be documented in the changelog from this comment:

Can you document the breaking change for the Pub/Sub client in the CL description ?
LGTM other than that.

If not, then that's fine I guess. But I think it would be helpful to changelog readers ("if you're on pubsub < 2.1, you'll have to rewrite some pubsub code")

I sent https://github.com/apache/beam/pull/17188 to add a note about this


Issue importing keras-rl on tensorflow-macos

The tensorflow-macos branch is currently archived so I was hoping someone could help me find a fix for this Import error. Im trying to import keras-rl packages and its throwing errors because TensorFlow does not have a function that keras uses during import. Idk if there's a previous version of keras-rl that would fix this or if I'm kinda just boned until TensorFlow has proper support for the m1 chip. logs below, package versions listed from pip also below. (run on a jupyter notebook and a minforge/conda venv)
jupyter-client                7.0.2
jupyter-core                  4.7.1
jupyterlab-pygments           0.1.2
tensorflow-addons-macos       0.1a3
tensorflow-estimator          2.5.0
tensorflow-macos              0.1a3
keras                         2.6.0
keras-rl                      0.4.2
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten
from tensorflow.keras.optimizers import Adam
from rl.agents.dqn import DQNAgent
from rl.policy import BoltzmannQPolicy
from rl.memory import SequentialMemory
ImportError                               Traceback (most recent call last)
/var/folders/mt/kt4lp3rx2p37sfswrnyhlkjr0000gn/T/ipykernel_2558/438834739.py in
----> 1 from rl.agents.dqn import DQNAgent
2 from rl.policy import BoltzmannQPolicy
3 from rl.memory import SequentialMemory
~/miniforge3/envs/tensorflow/lib/python3.8/site-packages/rl/agents/init.py in
1 from future import absolute_import
----> 2 from .dqn import DQNAgent, NAFAgent, ContinuousDQNAgent
3 from .ddpg import DDPGAgent
4 from .cem import CEMAgent
5 from .sarsa import SarsaAgent, SARSAAgent
~/miniforge3/envs/tensorflow/lib/python3.8/site-packages/rl/agents/dqn.py in
2 import warnings
3
----> 4 import keras.backend as K
5 from keras.models import Model
6 from keras.layers import Lambda, Input, Layer, Dense
~/miniforge3/envs/tensorflow/lib/python3.8/site-packages/keras/init.py in
23
24 # See b/110718070#comment18 for more details about this import.
---> 25 from keras import models
26
27 from keras.engine.input_layer import Input
~/miniforge3/envs/tensorflow/lib/python3.8/site-packages/keras/models.py in
17
18 import tensorflow.compat.v2 as tf
---> 19 from keras import backend
20 from keras import metrics as metrics_module
21 from keras import optimizer_v1
~/miniforge3/envs/tensorflow/lib/python3.8/site-packages/keras/backend.py in
34 from tensorflow.core.protobuf import config_pb2
35 from tensorflow.python.eager import context
---> 36 from tensorflow.python.eager.context import get_config
37 from tensorflow.python.framework import config
38 from keras import backend_config
ImportError: cannot import name 'get_config' from 'tensorflow.python.eager.context' (/Users/sebastian/miniforge3/envs/tensorflow/lib/python3.8/site-packages/tensorflow/python/eager/context.py)

Hi, I've had some problems with keras-rl and tensorflow and I managed to find a solution to this problem, after hours of trying to figure it out. Hopefully it works for you too.
keras-rl works with tensorflow 1.X, from my understanding, and keras-rl2 is supposed to fix that by requiring tensorflow>=2.1.0. Unfortunately, keras-rl2 is archived, so there can't be anymore fixes. My solution was this:

Download the keras-rl2 source code from here
Go into setup.py and remove the install_requirements=['tensorflow'], line OR you can even change it to tensorflow-macos
Follow the install instructions from source code from here (the keras-rl2 instructions are incomplete)

Once you install keras-rl2 your problems should dissapear.
My issues here these:

ERROR: Could not find a version that satisfies the requirement tensorflow (from keras-rl2) (from versions: none) when trying to install keras-rl2
ERROR: when trying to do pip install keras-rl2==1.0.5
Keras symbolic inputs/outputs do not implement __len__. You may be trying to pass Keras symbolic inputs/outputs to a TF API that does not register dispatching, preventing Keras from automatically converting the API call to a lambda layer in the Functional Model. This error will also get raised if you try asserting a symbolic input/output directly.

Hopefully this helps.

hello everyone, can you help me to solve this issue
ImportError                               Traceback (most recent call last)
Cell In[11], line 5
3 from keras import version
4 tf.keras.version = version
----> 5 from rl.agents import DQNAgent
6 from rl.policy import BoltzmannQPolicy
7 from rl.memory import SequentialMemory
File /opt/anaconda3/envs/py311/lib/python3.11/site-packages/rl/agents/init.py:1
----> 1 from .dqn import DQNAgent, NAFAgent, ContinuousDQNAgent
2 from .ddpg import DDPGAgent
3 from .cem import CEMAgent
File /opt/anaconda3/envs/py311/lib/python3.11/site-packages/rl/agents/dqn.py:8
5 from tensorflow.keras.layers import Lambda, Input, Layer, Dense
7 from rl.core import Agent
----> 8 from rl.policy import EpsGreedyQPolicy, GreedyQPolicy
9 from rl.util import *
12 def mean_q(y_true, y_pred):
File /opt/anaconda3/envs/py311/lib/python3.11/site-packages/rl/policy.py:3
1 import numpy as np
----> 3 from rl.util import *
6 class Policy:
...
----> 3 from tensorflow.keras.models import model_from_config, Sequential, Model, model_from_config
4 import tensorflow.keras.optimizers as optimizers
5 import tensorflow.keras.backend as K
ImportError: cannot import name 'model_from_config' from 'tensorflow.keras.models' (/opt/anaconda3/envs/py311/lib/python3.11/site-packages/keras/_tf_keras/keras/models/init.py)
I've tried to solve this issue in python version 3.9, 3.8, 3.11, 3.10

hello everyone, can you help me to solve this issue ImportError Traceback (most recent call last) Cell In[11], line 5 3 from keras import version 4 tf.keras.version = version ----> 5 from rl.agents import DQNAgent 6 from rl.policy import BoltzmannQPolicy 7 from rl.memory import SequentialMemory
File /opt/anaconda3/envs/py311/lib/python3.11/site-packages/rl/agents/init.py:1 ----> 1 from .dqn import DQNAgent, NAFAgent, ContinuousDQNAgent 2 from .ddpg import DDPGAgent 3 from .cem import CEMAgent
File /opt/anaconda3/envs/py311/lib/python3.11/site-packages/rl/agents/dqn.py:8 5 from tensorflow.keras.layers import Lambda, Input, Layer, Dense 7 from rl.core import Agent ----> 8 from rl.policy import EpsGreedyQPolicy, GreedyQPolicy 9 from rl.util import * 12 def mean_q(y_true, y_pred):
File /opt/anaconda3/envs/py311/lib/python3.11/site-packages/rl/policy.py:3 1 import numpy as np ----> 3 from rl.util import * 6 class Policy: ... ----> 3 from tensorflow.keras.models import model_from_config, Sequential, Model, model_from_config 4 import tensorflow.keras.optimizers as optimizers 5 import tensorflow.keras.backend as K
ImportError: cannot import name 'model_from_config' from 'tensorflow.keras.models' (/opt/anaconda3/envs/py311/lib/python3.11/site-packages/keras/_tf_keras/keras/models/init.py) I've tried to solve this issue in python version 3.9, 3.8, 3.11, 3.10

do you got it?

I met the same probleｍ．
ｅｎｖ：　ｔｅｎｓｏｒｆｌｏｗ＝＝２．１７．０　ｋｅｒａｓ＝＝　３．４．１　ｋｅｒａｓ－ｒｌ２＝＝１．０．５
Traceback (most recent call last):
File "d:\Drone\Drone-RL-share\Drone-RL\models\ddpg_tf_vel.py", line 30, in
from rl.util import *
File "E:\anaconda\envs\drone\lib\site-packages\rl\util.py", line 3, in
from tensorflow.keras.models import model_from_config, Sequential, Model, model_from_config
ImportError: cannot import name 'model_from_config' from 'tensorflow.keras.models' (E:\anaconda\envs\drone\lib\site-packages\keras_tf_keras\keras\models_init_.py)


Make add support the quick options

This is a feature request to go along with the quick option for adding. The interactive part of regular edit is not always ideal, especially when I know that I'm only editing the title. Could either a quickedit option be added or an alternative to regular edit that takes arguments in the same format as add? The option could raise an assertion in case there is more than one event that exists matching the criteria.

Should be possible for edit to take the same parameters as add.  quickedit isn't likely to happen as that's not an API endpoint Google offers.

So IIUC you're asking for something like gcalcli edit --description=xyz "some title" that would prompt you whether to save changes to each matching event, still with the same prompts but with a prepopulated descr=xyz change to be applied to each one you saved? So you'd still get interactive prompting but would only have to "[s]ave, [s]ave, [s]ave" on each one to apply changes?
Or you might be asking for a --noprompt variant of that to batch save edits without any interactivity? That seems in some ways more useful but also kind of a footgun... Personally I'd at least want an overall edit summary and interactive prompt like editing to apply descr=xyz on the 99 calendar events listed below. Apply changes? [S]ave [q]uit: .
FWIW thinking of ergonomics for this kind of batch editing I'd also personally want a TUI variant of edit where you could arrow around through a list of all matching events and interactively pick which edits to apply to which events, using arrow keys to explore the list (think vi vs ed).

(still interested in improving this case, but now that I understand some of the complexities involved I think we should come back to it after finalizing other work for the 4.5 release)


MAHOUT-1493 driver cleanup

This removes parseIndexedDatasetFormatOptions() and some other option parsers not used by spark-trainnb and spark-testnb.  There are still a couple of options that I need to add but first want to figure out how to handle the  required -o option.
Currently spark-testnb doesn't have any actual output.  I was thinking about writing a confusion matrix as Text out to a file.

Seems clean to me.

cool- thanks for looking at it @dlyubimov.

@andrewpalumbo see comments about the "required" change. I wouldn't push this as-is. It will allow illegal params for other drivers.

@pferrel thanks- I'll change it back and probably just add in a -i option and remove parseIOOptions() from testNBDriver as you suggested.

Sounds good

BTW @andrewpalumbo could you please change the header to show the issue like this: MAHOUT-1493,  then close and repopen it. Once re-opened, @asfgit bot will start synching jira to reviews and backwards.

thx - was gonna do this under a new issue, but since the branch is for M-1493 will finish this up there.

well if you decide to get another issue, just edit the header again and re-open again. since current issue seems to be closed. or just re-open the current jira issue.

fixing in a different branch


Tab bar buttons should have a minimum size when adding lots of tabs

n tabs:

n+1 tabs:

n+2 tabs:

Yeah, the tab bar needs more work... In addition to handling a large number of tabs, it also needs drag-and-drop for reordering them.


Update link to Commands documentation

The previous link pointed to the Markdown file, but the links are broken on that page. This pull request points to the actual documentation.

Fixes #4406

@srp33 our ci requires that submits are signed.    The change that @srp33 is proposing is to change the commands link on the readme from our commands.md page to the podman.readthedocs.io.  The only downside to that is that the commands.md not only had links to the docs, but also links to demos of the commands.  Granted we're lagging way behind in the demo's bit of that on that page.
Is there a way to add demo links to the readthedocs?  I'm guessing not.  I'm thinking we perhaps keep this change and move commands.md to command_demos.md or something (in a separate pr) and work those from there?

Yes lets setup a different page for demos.

@srp33 you need to run make validate ... it will identify some issue that needs correction for the tests to pass.

I apologize, but I'm not well-versed in doing pull requests. I cloned the repository, but I'm not sure what commands to run after that. (Or if it's easier for you, you can delete this pull request and do it on your own.)

@srp33 i took a look, it just needs a signature ... do this:
git commit --amend -s and then repush your pr.

I cloned the repository and ran that command. Then I tried git push, but it
said I don't have access. Sorry for these dumb questions.

Stephen Piccolo, PhD
Assistant Professor
Department of Biology
Brigham Young University
Office: 4056 LSB
Email<EMAIL_ADDRESS>Phone:<PHONE_NUMBER>
Web: https://piccolo.byu.edu
Twitter: @stevepiccolo
On Fri, Nov 1, 2019 at 11:10 AM Brent Baude<EMAIL_ADDRESS>wrote:

@srp33 https://github.com/srp33 i took a look, it just needs a
signature ... do this:
git commit --amend -s and then repush your pr.
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You should have cloned the REPO on the github web site, Then clone your own REPO, and push the change up there.
Once you have pushed to your own REPO you can go to the web site and open a PR to the upstream to make the change.

Replacing this PR with my own, to make life easier on @srp33.  Thanks alot for the PR.

I'm going to close this PR givein @rhatdan is moving forward with #4424


Add plain list style

I've added a plain '.list' style and example. This is primarily for lists of links where we want the extra margins but we don't want bullets (for example, lists of related links in the right hand column).

Hi @timpaul, I've made these changes over in #114. Let me know if you're happy with those. I've made the default margin for lists 15px for smaller screens and 30px for larger screens and removed the elements page override, so the bottom margin is the same everywhere.

Hi @timpaul, these changes are now in https://github.com/alphagov/govuk_elements/pull/135.
Once #135 is merged, I'll close this PR.


twitter x logo added

Hi @arghadipmanna101
I have changed twitter logo in the web page.

Hi,
Will you please add GSSOC label and level.

Hi @arghadipmanna101 ,
Could you please assign my name to this merged PR
otherwise it won't be counted into leaderboard


Custom template files

Hi,
I have tried to customize the template files but what kind of path I must insert in Options?
I have these paths:
/home/stefano/.netbeans/my/DrupalDevel/files/module/7
/home/stefano/.netbeans/my/DrupalDevel/files/theme/7
with, respectively, these files:
drupal_safe_name.inc   drupal_safe_name.install  README.txt
drupal_safe_name.info  drupal_safe_name.module
and
drupal_safe_name.info  print.css  style.css
I have setted version 7 as default in Options.
I have tried to set the "Custom Template Files" path to:
/home/stefano/.netbeans/my/DrupalDevel/files
or
/home/stefano/.netbeans/my/DrupalDevel
but I still have the original templates when I create a new module.
Where I wrong?
Thanks
Stefano

@stefanocallegari Have you solved this yet? Can we please close this issue then?

Not yet.

@stefanocallegari I just tried this out and it works just fine. Like you I set the custom templates folder in the global options and created a module that now gets created from the custom templates.
Have you checked your project properties? There is also a Drupal section where you can set the Drupal version for that project. If this points to a different version, then NDDT will use the templates for that version instead.

@stefanocallegari ping


Ruff Incorrectly Flags Quoted Class used as Generic Parameter as Unused

To give some context, I have from __future__ import annotations required in every-single file, and the TCH rule-set enabled across my codebase, along with the full Pyflakes rule-set.
I'm running into a situation like this:
from __future__ import annotations

from typing import TypeVar, Generic
from my_module import ModelB

T = TypeVar("T")

class BaseModel(Generic[T]):
   ...

class ModelA(BaseClass[ModelB]]
   ...

Here, even though ModelB is only being passed in as the generic parameter, TCH moves it out of the type-checking block. If I try and quote ModelB.
from __future__ import annotations

from typing import TypeVar, Generic, TYPE_CHECKING

if TYPE_CHECKING:
    from my_module import ModelB  # Ruff flags `ModelB` as unused.

T = TypeVar("T")

class BaseModel(Generic[T]):
   ...

class ModelA(BaseClass["ModelB"]]
   ...

In this case my type-checker (Pyright) still works as expected, however F401 flags  ModelB as unused and removes it. Unfortunately in my situtation, importing ModelB causes a circular import, so it would need it to go under the TYPE_CHECKING block . As a note I also have UP037 enabled, and it doesn't attempt to remove the quotes in this case.
Lot of intertwining rules here, so happy to clarify anything about my configuration. Also, I'm not sure if TCH moving ModelB when non-quoted out of the TYPE_CHECKING block is also a bug.
Ruff Version: 0.1.9
Ruff Command: ruff check .

The general problem here is that, when looking at BaseClass[ModelB], we don't have a great way to know whether [Model] is a generic annotation or an actual subscript access (like BASE_CLASSES[0], as a counterexample). We could come up with some better heuristics, but right now, we effectively scope generics to those that are known in the standard library (like Sequence).
We do have a mechanism for marking classes as generics: extend-generics. Maybe that can help here?

The general problem here is that, when looking at BaseClass[ModelB], we don't have a great way to know whether [Model] is a generic annotation or an actual subscript access (like BASE_CLASSES[0], as a counterexample). We could come up with some better heuristics, but right now, we effectively scope generics to those that are known in the standard library (like Sequence).
We do have a mechanism for marking classes as generics: extend-generics. Maybe that can help here?

Makes sense on why this is then!
Yes, that actually solves my problem, I didn't know there was a configuration setting for this. Really appreciate it!

I'm assuming not, but does it support wildcards? For example, if you wanted to treat all of your Django models as valid generics you might want to do extend-generics = ["src.my_app.models.*"]

I don't believe so... but we could extend it to support wildcards.

Hi!
I have the same problem with generic annotations.
I read suggestions above and I think the "store all unused imports in settings" way is a bit odd

extend-generics is a good solution for general case. But... Would the heuristic "subscript expression in parent classes list" work? I agree that it can cause a false positive (below), but such case would be marginally less popular, I guess (I do not remember ever inheriting from dict lookup example), and can be easily overcome without linter silencing (below-2).
So, I suggest to treat any string that is a valid identifier as a name when used in a subscript in class parents list.
FP example:
BASES = {"foo": int, "bar": str}
class A(Bases["foo"]): ...  # Name "foo" not found

To overcome:
BASES = {"foo": int, "bar": str}
A_BASE = BASES["foo"]
class A(A_BASE): ...

We may also want to extend this a bit, so that "name" should be interpreted as a name:

class X(Foo["name"])
X: TypeAlias = Foo["name"] and right-hand side of new 3.12 type expression
Anything else I'm missing?

Alternatively, this may be introduced as some kind of "soft name" that may be absent from namespace, but marks the name as "used" in scope. With such approach the FP example above is no longer failing, and I do not see any backwards-incompatible consequences from doing that.


[FEA] Update GeneratedInternalRowToCudfRowIterator for string transitions

Is your feature request related to a problem? Please describe.
Update GeneratedInternalRowToCudfRowIterator. Be careful though this is the hard part because it does code generation to speed thing up. Also you might want to rename it because it is not going to CudfRow, it is going to a ColumnarBatch.

This means we are going to need to have a version of copyData for the String that puts in the offset and length at the fixed length location and then writes the string data to the offset after the fixed width portion. Then updates a new offset value so later Strings can use it.

Ref: https://github.com/rapidsai/cudf/issues/10033#issuecomment-1132121915

reopened by https://github.com/NVIDIA/spark-rapids/pull/6367


Add measurement name to type conflict error messages

Makes these errors easier to find when writing large batches of points.
Fixes #2948

+1


Height calculation when resizing up the browser

Bug Description
On load, it works perfectly, but when resizing the browser up, the iframe body takes extra white space underneath.
In addition, on mouse click inside the iframe body, the height decreases by 16px per click.
@davidjbradshaw
Iframe styles are:
iframe {
width: 1px;
min-width: 100%;
}
Script:
iFrameResize({ log: true }, '#myiframe')
This behavior is present on all browsers except for Firefox.

@MohamadElfar Were you able to fix the issue? I am experiencing this too.

Check the CSS in your iframe. That is always the issue when this happens.