Add programmatic API for customization NO_PROXY

Resolves #11
Removes unnecessary files from production distribution (10af9ff)
Bumps all development dependencies

Coverage decreased (-2.3%) to 96.078% when pulling ec679d4794887483fa272d46c0ba713db1871225 on Zekfad:master into 96d01f8fcfdccfb776735751132930bbf79c4a3a on Rob--W:master.

@Rob--W because I used ES2015 syntax for feature implementation this will limit support version for at least >6, in cfd5566c6f8c7c600ea77f41db0409e7758c0f37 you drop non LTS versions in CI, so untested environment could cause unknown behavior. As result I bumped minimum engine to minimum LTS version (v10 as for today), which is also satisfies dev dependencies.
Also, because this is breaking change, I bumped minimum version to 2.0.0 to avoid possible errors on really outdated environments.
As another possible solution I can add rollup and babel tolling in order to transpile it into ES5 and ECMA duo packed module.
What's your thoughts on it?

The primary purpose of this library is to offer a "standard" way to determine the proxy URL. Offering options to customize the behavior encourages library consumers to behave in "non-standard" ways, which goes against the goal of this project.
While I can see that some may benefit from the functionality offered in this PR, I'm not going to merge it because it's outside the scope of this library. Thanks for your willingness to make a contribution though!


Workflows

Workflows and communities were incorporated
Fixed global search endpoint issue by using the appropriate permission

fixes #260


Enable options around dask nodes in Terraform

Description
Our terraform config is currently configured such that if dask nodepools are not explicitly defined, they are automatically derived from the definition of the notebook nodepools instead. This works great when we're deploying a cluster we know will have dask hubs deployed to it, but makes it very difficult to turn off dask nodepools if we know we don't need them (e.g., Justice Innovation Lab). Since terraform is not a procedural language, this level of flexibility may be beyond it's capabilities, but none of us would be in this job if we didn't enjoy pushing the boundaries of what our tools could do 😉
I see three possible solutions:

Introduce a new boolean variable, like dasknodes_mimic_notebooks and use this in our for_each statements to decide whether or not to create dask nodepools alongside notebook nodepools.

When true, we would see the behaviour that we have currently
When false, you could EITHER define a set of dask_nodes that differ from the notebook nodes to create those specific nodepools OR fallback onto the default value dask_nodes = {} were no dask nodepools are created.

Break the link between the notebook nodepools and the dask nodepools. Any cluster that requires dask nodepools must have them explicitly defined in their tfvars file.
Do nothing. Our nodepools are created with 0 nodes, so if no dask processes exist to create a dask node, then they effectively cost nothing.

Value / benefit

I think if option 1 works, this will give us the flexibility in cluster deployment that we're looking for, but does increase complexity by adding an extra variable.
Option 2 would make our tfvars files longer but arguably clearer since everything is then explicit
Option 3 feels sub-optimal, though I can't really put into words why

Implementation details
Whichever option we take, we should implement this across both GCP and Azure terraform configurations. It's bad enough to be maintaining separate config files without having them do different things.
Tasks to complete

[ ] Decide which option to try first
[ ] Implement it!

Updates
No response

Thanks for writing this up in detail, @sgibson91. I think (2) is a good bet to keep overall complexity down, by just repeating ourselves a little bit. As you mentioned elsewhere, Terraform is maybe not the best language to do anything complex in - although I had wished that setting defaults was not too complex for it. Oh well.
(2) will also help if we end up sizing the dask nodes different from any notebook nodes, which we might as well do after observing how they are used.

@yuvipanda Gave that a go in https://github.com/2i2c-org/infrastructure/pull/850

I also think option 2 is a good compromise.


Incorporate all minerals from the GSO source

This process ended up being fairly messy, and so the code may be a little hard to follow or work through. I tried to capture at least some notes through the pipeline as to major decisions made. The important part is to take a look at the results in the GeoKB and make sure we've not made any egregious errors or introduced anything too weird.
One of the most challenging parts was dealing with the Nickel-Strunz classification, which we decided we wanted to have in play at some level. Strictly speaking, this is a classification scheme, though it is one that defies some of the conventions for OWL classification. The GSO dealt with it by simply including the lowest level code (which includes higher level codes), making up a label from parts of the description, and incorporating it as a property. Mindat includes the Strunz classification but has purportedly made a few tweaks here and there, which are not understood (to me). I opted to find the intersection between the two and incoporated it under "mineral material" as a classification scheme. I also included an "unclassifified mineral material" area to gather everything else.

Along with the documentation in the code, worked up a more thorough discussion on decisions made in representing mineral materials from the two sources (Mindat and the Geoscience Ontology). This is in the discussion page for the upper level of the classification - mineral material. (Note: I'll be reworking all of the other entity types in this way, with a short description and pointer from the main wiki page in the Wikibase instance to a more robust description at the top level of the class hierarchy in the GeoKB ontology.)
The mineral material page and the queries laid out there should form the substance of reviewing this pull request. The queries should expose everything that resulted from the work documented and executed in the two code notebooks that were committed as part of this pull request. If I've made any mistakes they should be readily exposed by taking a look through the items this work introduced.

The following set of named entities from MRDS could not be immediately connected to anything we can readily source from the Mindat/GSO source material. We'll have to determine where these concepts fit and how to deal with them (if at all). Some are other types of geologic material that we might organize under rock materials or some other part of the GeoKB ontology.
Cinders
Telluride
Manganese Ox-Hydrous
Titaniferous Magnetitite
Sand and Gravel
Brine
Polycrase
Stetefeldite
Iron Oxides-Hydrated
Amalgam
Tripoli
Geodes
Potash
Oyster Shells
Schorlite
Hausmanite
Carnalite
Thalenite
Lapis-Lazuli
Vanadiferous Magnetite
Mineral Paint
Fullers Earth
Tyunjamunite
Mineral Pigments
Yttrialite
Vordisite
Fersmannite
Verde Antique

I added a couple of network visualizations to the notebooks with this pull request to help explain how I went about classifying minerals. The graph visualization in the query service (linked to from the Try it links where I've documented SPARQL syntax) can also be used in this regard.


Consider support Azure OpenAI API?

I love how this tool provides a better prompt engineer environment for people building on APIs, who might appreciate the access to Azure OpenAI API as the backend.

Hi Chuanqi! Thank you for raising this Issue and yes we will consider Azure support. It’s hard to test —none of us has an Azure OpenAI key —and it’s admittedly lower on the priority list atm, but we will keep this Issue open and get around to it.

I can test when you have a merge request for Azure stuff.

Thanks Levi!
I think basic support could be added quickly if we just check the Python environment variables and add a guide for that, for this use case. I assume this document shows the setup required to make an Azure OpenAI call: https://learn.microsoft.com/en-us/azure/cognitive-services/openai/quickstart?pivots=programming-language-python&tabs=command-line

Hi @ianarawjo i also am interested in azure support and can test it for you.
Below is code from the link you posted.  the 4 variables identified are needed to support azure openai endpoints.

openai.api_key = os.getenv("AZURE_OPENAI_KEY")
openai.api_base = os.getenv("AZURE_OPENAI_ENDPOINT") # your endpoint should look like the following https://YOUR_RESOURCE_NAME.openai.azure.com/
openai.api_type = 'azure'
openai.api_version = '2023-05-15' # this may change in the future

deployment_name='REPLACE_WITH_YOUR_DEPLOYMENT_NAME' #This will correspond to the custom name you chose for your deployment when you deployed a model.

Ah ok, I will code this up first thing tomorrow! Sorry for the delay! Will ping everyone with a PR —whoever can test it, would be a big help!

I tried setting the api base and key using the settings menu, and later, using the OS environment variable. But in both cases I get auth error:

Could someone else verify?

Awesome. I'll bundle this into the next release (which, because of the circumstances, will happen in a couple minutes).

FYI, I showed this to my collogues, they were all amazed by the power and flexibility of the tool. Brilliant work!


Support backfill workflows

Inspired by this HN comment:

In my experience, my pain hasn't been from managing the migration files themselves.My pain comes from needing to modify the schema in such a way it requires a data migration, reprocessing data to some degree, and managing the migration of that data in a sane way.You can't just run a simple INSERT INTO table SELECT * FROM old_table, or anything like that because if the data is large, it takes forever and a failure in the middle could be fairly difficult to recover from.So what I do is I split the migration into time-based chunks, because nearly every table has a time component that is immutable after writing, but I really want a migration tool that can figure out what that column is, what those chunks are, and incrementally apply a data migration in batches so that if one batch fails I can go in there to investigate and know exactly how much progress on the data migration has been made.

This would make a ton of sense!


Compiled Library

Hello,
could you maybe provide me with the compiled library to llink against?
I'm not managing setting it up with mingw on windows 10, neither with visual studio 2017.
I would be very grateful.
Nice greetings,
Tobias

Ok, I managed to compile the library with visual studio but I still didn't manage to compile a simple test file with mingw and the library.

I'd like to support compiled binaries but my time is very limited.
Please update to 081cce2903f25e5e9e2727cbdc1b38fb4eed342e in the devel branch and re-attempt compilation with mingw.
Note that this change adds mingw configuration instructions in build.md.


remove react-native dependency

fixes https://github.com/umhan35/react-native-search-bar/issues/53

Guess I should've just done this :laughing:

No problem. Can you merge this and bump the version?

Oh, I'm not the maintainer. I also forked it and made the change, but never submitted a PR.

ah! sorry my bad. cc @umhan35


Update description of operator with information about installing/unin…

…stalling
Signed-off-by: Josh Pinkney<EMAIL_ADDRESS>What does this PR do?
This PR adds more information about installing and uninstalling the operator to the description
What issues does this PR fix or reference?
Fixes https://issues.redhat.com/browse/WTO-11
Is it tested? How?

make build
make register_catalogsource
then viewed on the console
New description looks like:

I think it may be a good idea to make make uninstall duplicate commands from that doc, then:

make uninstall properly uninstall operator
We test the documentation we have when we do make uninstall

@JPinkney please review my the latest changes and feel free to merge if you're OK and it works for you.


Implement path validation when inserting new questions

Describe the solution you'd like
The QuestionService states that create "will fail if he path provided already resolves to a QuestionDefinition or Scalar." We need to implement this logic in the service implementation
Done when
A new question insertion fails when the path already exists

M1 Review - Not worked on


Fixes ScrollView not calling onScroll handler

This is simple fix, that looks like it was an unintentional typo or merge conflict.

Thanks!


Deploy stuff

Fixes for deploy to maven central as part of merge to master

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Merge web client classes for different Android versions

Otherwise, code copy-paste is really ugly.

Done


Added advanced boilerplate example.

Not 100% sure if such an opinionated example should be a part of this project, an example like that would have at least helped me quite a lot to understand what kind of environment this project requires and how a ProtectedRoute implementation could look like :)
Please let me know what your opinion is and / or if you have any wishes what the code should look like.

Thank you!
I have a number of comments, I will write them in my next free time.


CSS draft

CSS draft

The name of a branch should all lowercase

Please fix. The rest looks good.


Lots of bars get squished

When I have a ton of bars on a bar graph, it looks like this. It's bearable but is there any way I can fix this visually?

@jbhatab what version are you using?

1.0.1

@jbhatab Try using v2.0-dev (there is a 2.0-alpha3 release that is already pretty stable).  If you're still having issues, let us know.


Clarify how "min vcore per db" affects max db per pool

As noted in incident 251999193, there was some confusion on how the min vcore amount affected max db/pool, especially in SLOs like GP_DC_2 whose lowest supported vcore choice is 2. This change adds a comment clarifying scenarios that don't have a min-vcore/db of 0.

@chriszure : Thanks for your contribution! The author(s) have been notified to review your proposed change.

Hi @chriszure - this detail is already documented in the "Important" note in the same article. Unless you see a strong reason to do this, our preference would be not to duplicate this elsewhere in documentation. Please let us know your thoughts.

That's a fair point. However this isn't the first time we've had an incident regarding factors affecting "Max number DBs per pool". The "Important" note is all the way at the bottom, below eight dense tables. At each row, subnote 1 references "Resource management in dense elastic pools for additional considerations." However, that article fails to mention vcore/dtu choices that affect resources that are pre-allocated, and instead focuses on managing active resource consumption.
It's a tad verbose to add the detail at each subheading, but since this setting is different from more 'expected' active resource limits on the pool, I think it warrants a more explicit reference.

@chriszure thanks for the feedback. We've modified the Resource management in dense elastic pools article to explicitly mention the impact of setting min vCore/DTU on the maximum number of databases in the pool. This change will be visible in the public article in a few hours. We believe this is a reasonable compromise that helps customers understand this detail without making the resource limits article too verbose.

Hi @chriszure , thanks so much for helping improve our documentation! Since a version of  your change has been published, we're going to close out this PR for the time being.
Please let us know if there's anything else we can help with.
Thanks again for reaching out and hope you have a wonderful rest of your day!
Masha from the SQL Docs team
#please-close


Tweens keep tweening after new transition is fired or .interrupt called

I've noticed that the .tween function keeps on going even after a new transition is fired. I've seen people suggest that to stop a transition in its tracks simply fire a new transition, however I've noticed that the new transition fires and the tween function still counts up the time when console logged and it just runs those tweens concurrently.
I have also just tried using .interrupt() and it doesn't seem to stop the tween either. I'm just wondering if there is possibly something I'm missing, or if stopping these tweens from firing multiple times is just impossible at the moment.

Do you have an example you can link to that demonstrates the issue?

Hmm that's interesting. I just built this quick demo and the functionality I am seeing in my full project is not happening and it seems to be cancelling the tween with the transition.
Here's the demo. Open the console and you can see that if you click the button multiple times it seems to override the tween's time output and starts counting from 0 again.
https://glitch.com/edit/#!/tween-demo https://tween-demo.glitch.me/
But back in my app it is not cancelling out the previous tween and you end up get multiple number 1's

Looks like this is some kind of issue with my code then... so feel free to disregard and close this. Thanks so much for responding so quickly too! Good work on D3 I love it!

Okay, closing as I have no means of investigating. My guess is that perhaps your transitions are being scheduled on different elements, and so are not exclusive with respect to each other.


Added tasks 21-25

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[App] Unification of scroll bar styles all over the page

Environment:
Windows 10 Home (Version 22H2)
Chrome version 108.0.5359.71
MacOS version 12.6.1 (21G217)
Chromе version 107.0.5304.110
Reproducible: always
Build found: 13/02/2023
Preconditions
Go to https://www.testgreencity.ga/GreenCityClient/#/ubs
Sign in as Super Admin/User (does not matter)
Pay attention we have different scroll bar styles in our app
Actual result: We have different scroll bar styles in our app

Expected result: scroll bar style are unified all over the app

The bug is still reproduced on Mozilla Firefox

Can't reproduce

Scroll bar is missing


Merge latest changes to live

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Missing entity descriptions for leak sensors and leaf wetness

Hi All -
First, awesome work!!!!  I am not smart enough  yet to fix this issue but in old releases (around April) the leaf wetness and leak sensors worked fine.  In the most recent release and the dev release they seem to have issues:
2022-06-14 23:54:55,144 | ecowitt2mqtt | WARNING | Skipping "leak_ch1" due to error: No entity description
2022-06-14 23:54:55,144 | ecowitt2mqtt | WARNING | Skipping "leak_ch2" due to error: No entity description
2022-06-14 23:54:55,145 | ecowitt2mqtt | WARNING | Skipping "leak_ch3" due to error: No entity description
2022-06-14 23:54:55,145 | ecowitt2mqtt | WARNING | Skipping "leak_ch4" due to error: No entity description
2022-06-14 23:54:55,148 | ecowitt2mqtt | WARNING | Skipping "leafwetness_ch1" due to error: No entity description
Hoping this is an easy fix but since it used to work.  Thanks for all the updates on this and especially for the recent fix to the lightning counts and today's fix for the multiple tf temperature probes.  I really appreciate this a ton!

Thanks for the info! Can you send me a diagnostics dump as mentioned in the readme? That'll help me get this addressed quickly. Appreciate it!

I really wish I knew more.  I am running it in docker and can't seem to figure out how to generate this.  I'm really sorry.  I do know that it worked in the 2022.5.0 release and broke when updating to the 2022.6.0 one.

No problem! Do this for me: set the ECOWITT2MQTT_VERBOSE environment variable to true. Then, after you restart your container, the logs will contain the info I need.

(FYI, #178 will "fix" things in that you'll be able to see those sensors again – but if you can get me those verbose Docker logs, we can ensure that they get units, etc.)

This is for the current dev version commit from today.  Let me know if you need more.
verbose_log_v1.txt

Perfect. Thanks!


BZ1193484: Roles added to Projects are ignored by Business Central

See https://bugzilla.redhat.com/show_bug.cgi?id=1193484
The changes to kie-wb-commons filter search results by "authorized Projects".

See https://github.com/droolsjbpm/guvnor/pull/189
Also, https://github.com/droolsjbpm/kie-wb-common/pull/132 may need either extending - or I write a new SocialSecurityConstraint to handle Projects.. @ederign Could you please give me some pointers for writing a Project constraint?

@manstis  you mean to restrict project events on social right?
I've already implement part of this:
https://github.com/ederign/kie-wb-common/blob/8f071c1b9da2e42138456c02fad11b748be7d313/kie-wb-common-screens/kie-wb-common-social-home-page/kie-wb-common-social-home-page-backend/src/main/java/org/kie/workbench/common/screens/social/hp/security/SocialEventRepositoryConstraint.java#L47
If a project is from a repository that user don't have permission, the event is not displayed on timelines.

@ederign Thanks.. I've now completed(?) support for Project level authorization - Project Explorer nor Search results show files in Projects to which the User lacks permission.. The only loose end will be to add the same to Social. Should I write a new Constraint or change SocialEventRepositoryConstraint?

@ederign Want to review (and I'll do yours!)

@csadilek This PR requires an additional change once https://github.com/droolsjbpm/kie-wb-common/pull/132 has been merged. Please therefore leave as "pending" until I give the go ahead. Thanks.

My concerns are about the old code and not the new implementations. so + 1.

https://github.com/droolsjbpm/kie-wb-common/commit/e1f1afc7882ca4e43049272308c9c6fffb0ed009


LDAP Config Options?

Thank you for providing this great program!
The official documentation (https://bayang.github.io/jelu-web/configuration/) provides basic yaml config examples, which is great.  Can more options be added here?  For example, I am attempting to implement the LDAP feature via yaml, but the only documented switch is enabled.  If enabled, errors are thrown up upon restart that more options need to be configured, but these options are not documented.
Thanks in advance!

Hi, glad you like it !
You're right, I have not documented the whole yaml, you can have a look at the sample config file here :
https://github.com/bayang/jelu/blob/66b38c1bd66503037602b5e08f5317ad38d0a8ea/src/main/resources/application-dev.yml#L20-L27
If I recall correctly you don't need all the lines.
The exhaustive list of params is here : https://github.com/bayang/jelu/blob/66b38c1bd66503037602b5e08f5317ad38d0a8ea/src/main/kotlin/io/github/bayang/jelu/config/JeluProperties.kt#L65-L73
Each property on the Ldap class maps to a yaml param in the config file.

Thanks for responding!  I am attempting to get a config file working for LLDAP right now.
However, I am getting a weird error:
INFO 1 --- [           main] o.s.l.c.support.AbstractContextSource    : Property 'userDn' not set - anonymous context will be used for read-write operations0
However, userDn is set with the correct password.
The application.yml in its entirety is:
jelu:   auth:     proxy:       enabled: true       adminName: "adminuser"       header: X-Personal-User     ldap:       enabled: true       url: "ldap://url:port/dc=home,dc=lab"       userSearchFilter: "(cn={0})"       userSearchBase: "ou=people,dc=home,dc=lab"       userDn: "cn=admin,ou=people,dc=home,dc=lab"       password: "sweet_password"
Any suggestions?

okay, first of all, I think you can disable proxy auth (above you have jelu: auth: proxy: enabled: true)
Concerning userDn + password, this has been added recently and is not needed for every ldap.
see addition here : https://github.com/bayang/jelu/pull/74
For basic config + additional logging if needed, I found the config that worked for initial development in the discord archives :
jelu:
  auth:
    ldap:
      enabled: true
      url: "ldap://<IP_ADDRESS>:389/dc=home,dc=lab"
      userDnPatterns:
        - "uid={0}"
        - "cn={0},ou=users"
      userSearchFilter: "(cn={0})"
logging:
  level:
    io.github.bayang.jelu.config.JeluLdapUserDetailsContextMapper: TRACE
    org.springframework.security.ldap: TRACE


Incorrectly raised Exception regarding number of time points

The NotImplementedError (Time course must specify an integer number of time points) in core.py#L197 is raised for specific integer numbers of steps in combination with certain time values.
Example (core.py#L191):
# sim.number_of_points = 500
# sim.initial_time = 0.0
# sim.output_start_time = 0.0
# sim.output_end_time = 1.1
step_number = (
    sim.number_of_points
    * (sim.output_end_time - sim.initial_time)
    / (sim.output_end_time - sim.output_start_time)
)

Even though the number of points is an integer, the step_number now has a value of<PHONE_NUMBER>9999994, which raises the exception.
One way to fix this would be to add more parentheses:
step_number = (
    sim.number_of_points
    * (
        (sim.output_end_time - sim.initial_time)
        / (sim.output_end_time - sim.output_start_time)
    )
)

I can confirm that this works. Fix to come!


Update addons

How can i update addons, in my case i have Helm v3.9.1+unreleased (jul 2022), i updated microk8s to v1.29 but helm didn't update,

Hi @marwenbk
Workloads running in the cluster, unless specified otherwise in the documentation, will NOT be upgraded as part of a MicroK8s upgrade. This includes both enabled add-ons and the CNI. Currently, the most effective way to upgrade add-ons is to use microk8s disable <add-on> and then re-enable them.
See https://microk8s.io/docs/upgrading.
In-place upgrades are not supported.
See https://github.com/canonical/microk8s/issues/3678


MUL doesn't work on gpu delegate with error: Dimension can not be reduced to linear

System information
Have I written custom code (as opposed to using a stock example script provided in TensorFlow): Yes
OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Mac OS 10.14.6
Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device: iPhone XR, iPhone Xs
TensorFlow installed from (source or binary): installed from source.
TensorFlow version (use command below): 1.14.0
Python version: 3.6
Bazel version (if compiling from source): 1.2.1
GCC/Compiler version (if compiling from source): 4.2.1
Describe the current behavior
Mul doesn't work with multiple axis while using gpu delegate. (no issue while using cpu).
Tried to create a dummy tflite graph with simply a multiply operation on input of shape of [1, 1, n, m] and variable with the same shape. While verifying with TFLite iOS benchmark app with GPU delegate, the benchmark app reports "failed to apply GPU delegate" on the mul operation. Note that the model runs fine without gpu delegate.
Describe the expected behavior
Mul operation works on gpu delegate and supports multiple axis.
Code to reproduce the issue
Here's the dummy model that should reproduce the issue in the TFLite ios benchmark app:
dummy_model.tflite
Other info / logs
Graph: [/private/var/containers/Bundle/Application/DD8D26F4-6BC1-4EFB-A444-EA2FB7F411D9/TFLiteBenchmark.app/dummy_model_2.tflite]
Input layers: [input_feature_placeholder]
Input shapes: [1,1,2,80]
Input value ranges: []
Allow fp16 : [0]
Require full delegation : [0]
Enable op profiling: [0]
Max profiling buffer entries: [1024]
CSV File to export profiling data to: []
Use gpu : [1]
Allow lower precision in gpu : [1]
GPU delegate wait type : [aggressive]
Loaded model /private/var/containers/Bundle/Application/DD8D26F4-6BC1-4EFB-A444-EA2FB7F411D9/TFLiteBenchmark.app/dummy_model_2.tflite
2020-02-26 15:16:14.857526-0500 TFLiteBenchmark[444:628154] Initialized TensorFlow Lite runtime.
2020-02-26 15:16:14.857843-0500 TFLiteBenchmark[444:628154] Created TensorFlow Lite delegate for Metal.
2020-02-26 15:16:14.858337-0500 TFLiteBenchmark[444:628154] Metal GPU Frame Capture Enabled
2020-02-26 15:16:14.859181-0500 TFLiteBenchmark[444:628154] Metal API Validation Enabled
2020-02-26 15:16:14.932455-0500 TFLiteBenchmark[444:628154] TfLiteGpuDelegate Prepare: MUL: Dimension can not be reduced to linear.
2020-02-26 15:16:14.932670-0500 TFLiteBenchmark[444:628154] Node number 2 (TfLiteMetalDelegate) failed to prepare.
2020-02-26 15:16:14.932800-0500 TFLiteBenchmark[444:628154] tensorflow/lite/kernels/mul.cc:74 input1->type != input2->type (1 != 10)
2020-02-26 15:16:14.932891-0500 TFLiteBenchmark[444:628154] Node number 1 (MUL) failed to prepare.
Failed to apply GPU delegate.

@Richard-Yang-Bose
please note i am unable to access the file shared by you, please share a simple stand alone code so i could replicate the issue faced by you in my local.

Thanks for the reply, here's the code for generating the dummy model, after the tflite dummy model is generated, I try to run it with the TFLite ios benchmark app, with the param: "use_gpu" : "1",     "gpu_wait_type" : "aggressive",
import os
import numpy as np
import tensorflow as tf

with tf.Graph().as_default():
    x = tf.placeholder(tf.float32, shape=(1, 1, 2, 80), name='array_input_feature')
    y = tf.Variable(initial_value=tf.ones([1, 1, 2, 80]))
    x = tf.math.multiply(x, y * 2)
    x = tf.identity(x, name='output')

    with tf.Session() as sess:

        tf.contrib.quantize.create_training_graph()
        sess.run(tf.global_variables_initializer())
        tf.contrib.quantize.create_eval_graph()
        saver = tf.train.Saver()
        saver.save(sess, 'dummy_model')

def prepare_graph():
    with tf.Session() as sess:
        new_saver = tf.train.import_meta_graph('dummy_model.meta')
        new_saver.restore(sess, tf.train.latest_checkpoint('./'))
        graph = tf.get_default_graph()
        output_names = 'output:0'
        output_tensor = graph.get_tensor_by_name(output_names)
        output_node_names = ["output"]
        frozen_graph_def = tf.graph_util.convert_variables_to_constants(sess, sess.graph_def, output_node_names)
        return frozen_graph_def, output_tensor

def tfliteConvert(graph, outputs, tflite_model_file, quantize=True):
    tf.reset_default_graph()
    # Turn the input into placeholder
    tflite_input = tf.placeholder(
    "float32", [1, 1, 2, 80], name="array_input_feature")
    tf.import_graph_def(graph, name="", input_map={"array_input_feature": tflite_input})
    # prepare the graph for conversion, convert op_hint wrapper
    with tf.Session() as sess:
        curr = sess.graph_def
    converter = tf.lite.TFLiteConverter(curr, [tflite_input], [outputs])
    converter.experimental_new_converter = True
    converter.target_spec.supported_types = [tf.lite.constants.FLOAT16]
    converter.optimizations = [tf.lite.Optimize.DEFAULT]

    tflite = converter.convert()

    # save TFLite model
    open(tflite_model_file, "wb").write(tflite)

def main():
    frozen_graph_def, outputs = prepare_graph()
    tfliteConvert(frozen_graph_def, outputs, 'dummy_model.tflite')

if __name__ == "__main__":
    main()

@Richard-Yang-Bose in the log you pasted, there is 2020-02-26 15:16:14.932800-0500 TFLiteBenchmark[444:628154] tensorflow/lite/kernels/mul.cc:74 input1->type != input2->type (1 != 10), the type mismatch is between kTfLiteFloat32 = 1 and TfLiteFloat16 = 10

@Richard-Yang-Bose in the log you pasted, there is 2020-02-26 15:16:14.932800-0500 TFLiteBenchmark[444:628154] tensorflow/lite/kernels/mul.cc:74 input1->type != input2->type (1 != 10), the type mismatch is between kTfLiteFloat32 = 1 and TfLiteFloat16 = 10

@freedomtan Thanks for pointing this out, I'm aware of this and have also tried the following setups:

Set allow_fp16 as 1 in the benchmark too: still got the same error log.
Use float32 model (without setting float16 and optimization during the tflite model conversion): still failed to apply GPU delegate, but without the type mismatch error.
Also note that the the benchmark app is able to run mobilenet provided here on gpu delegate.

2020-03-02 10:21:08.174624-0500 TFLiteBenchmark[10532:2225388] Initialized TensorFlow Lite runtime.
2020-03-02 10:21:08.175107-0500 TFLiteBenchmark[10532:2225388] Created TensorFlow Lite delegate for Metal.
2020-03-02 10:21:08.176624-0500 TFLiteBenchmark[10532:2225388] Metal GPU Frame Capture Enabled
2020-03-02 10:21:08.177811-0500 TFLiteBenchmark[10532:2225388] Metal API Validation Enabled
2020-03-02 10:21:08.294492-0500 TFLiteBenchmark[10532:2225388] TfLiteGpuDelegate Prepare: MUL: Dimension can not be reduced to linear.
2020-03-02 10:21:08.294760-0500 TFLiteBenchmark[10532:2225388] Node number 1 (TfLiteMetalDelegate) failed to prepare.
2020-03-02 10:21:08.294908-0500 TFLiteBenchmark[10532:2225388] Restored previous execution plan after delegate application failure.
Failed to apply GPU delegate.

@Richard-Yang-Bose interesting. It seems you hit a limitation of GPU delegate. Maybe @impjdi might have the right answer.

@freedomtan Thanks again for the insight and appreciate all the great work!

That error message Dimension can not be reduced to linear. is printed at //tensorflow/lite/delegates/gpu/common/model_builder.cc.  Not sure why it's trying to use SetAllDimensions<Linear>.  Can you attach the TFLite model?

That error message Dimension can not be reduced to linear. is printed at //tensorflow/lite/delegates/gpu/common/model_builder.cc. Not sure why it's trying to use SetAllDimensions<Linear>. Can you attach the TFLite model?

here's the model (fp16):
dummy_model.tflite

Thank you for attaching the model.  Found the issue.  I'll prepare a fix and will let you know.

Are you okay with a fix that requires you to do

y = tf.Variable(initial_value=tf.ones([1, 2, 80]))  # without the batch dimension

instead of

y = tf.Variable(initial_value=tf.ones([1, 1, 2, 80]))

?

@impjdi  Thanks for finding the issue!
Yeah that could be a work around, however it's not ideal since I believe some network structure has MUL with 4D tensor required inside the implementation (e.g. convlstm).
Do you think it's possible to have MUL operation compatible with 4D tensor in the future fix?
Thanks again for helping out!

While it's not technically impossible (extending it to the batch dimension is trivial), I think you will have a much bigger problem as it won't be just MUL that doesn't support your particular use case; the majority, if not all, of TFLite GPU ops are not designed in a batch-friendly fashion.  We will have a lot more plumbing to do to accommodate the batch size we will investigate more in the coming months.

Yeah, that make sense, especially inference use cases usually have the batch-dimension fixed to 1 or a constant.
I'm not familiar with how the GPU ops are implemented, but I guess as a first cut, it would be nice to have MUL to allow 4D input, while the first dimension only supports to be set as 1 ?

Any progress here? How should I use MUL for multidim tensors, lets say [1,257,257,3] so I won't get Init: MUL: 1x257x257x3  cannot be reduced to linear?

@stalex91 I think I don't understand your question.  Shouldn't [1, 257, 257, 3] x [1, 257, 257, 3] work fine as elementwise multiplication?

@impjdi Have met the same problem listed in description but actual reason was before it. So yes, works for me now, thank you.

@impjdi could you help me to check this model: yolov5s.tflite?
I am using TF 2.3.0 and TFLite 2.3.0 with GPU delegate.
It has 3 element-wise multiplications: [1, 3, 400, 2] x [1, 3, 400, 2], [1, 3, 1600, 2] x [1, 3, 1600, 2], and [1, 3, 6400, 2] x [1, 3, 6400, 2].
When I run the model with GPU delegate, it complains:
E/AndroidRuntime: FATAL EXCEPTION: inference
    Process: org.tensorflow.lite.examples.detection, PID: 10947
    java.lang.IllegalArgumentException: Internal error: Failed to apply delegate: TfLiteGpuDelegate Init: MUL: 1x3x400x2  cannot be reduced to linear.
    TfLiteGpuDelegate Prepare: delegate is not initialized
    Node number 227 (TfLiteGpuDelegateV2) failed to prepare.

    Restored original execution plan after delegate application failure.
        at org.tensorflow.lite.NativeInterpreterWrapper.applyDelegate(Native Method)
        at org.tensorflow.lite.NativeInterpreterWrapper.applyDelegates(NativeInterpreterWrapper.java:351)
        at org.tensorflow.lite.NativeInterpreterWrapper.init(NativeInterpreterWrapper.java:82)
        at org.tensorflow.lite.NativeInterpreterWrapper.<init>(NativeInterpreterWrapper.java:63)
        at org.tensorflow.lite.Interpreter.<init>(Interpreter.java:266)
        at org.tensorflow.lite.examples.detection.tflite.YoloV5Classifier.recreateInterpreter(YoloV5Classifier.java:174)
        at org.tensorflow.lite.examples.detection.tflite.YoloV5Classifier.useGpu(YoloV5Classifier.java:182)
        at org.tensorflow.lite.examples.detection.DetectorActivity.lambda$updateActiveModel$0$DetectorActivity(DetectorActivity.java:213)
        at org.tensorflow.lite.examples.detection.-$$Lambda$DetectorActivity$lqWWUAbeU1gjsHY5oEBIKbdpe60.run(Unknown Source:8)
        at android.os.Handler.handleCallback(Handler.java:793)
        at android.os.Handler.dispatchMessage(Handler.java:98)
        at android.os.Looper.loop(Looper.java:173)
        at android.os.HandlerThread.run(HandlerThread.java:65)

I think it's coming from the fact that the 2nd tensor is of shape [1, h, w, c].  Can you make a modification to the model and make the 2nd tensor take the shape of [h, w, c] (without the 1st dimension of size 1)?

@impjdi I updated my model  according to your suggestion.
The modified MUL op can be inspected in netron:

TFLite still complains
E/AndroidRuntime: FATAL EXCEPTION: inference
    Process: org.tensorflow.lite.examples.detection, PID: 14431
    java.lang.IllegalArgumentException: Internal error: Failed to apply delegate: TfLiteGpuDelegate Init: MUL: 3x400x2  cannot be reduced to linear.
    TfLiteGpuDelegate Prepare: delegate is not initialized
    Node number 227 (TfLiteGpuDelegateV2) failed to prepare.

    Restored original execution plan after delegate application failure.
        at org.tensorflow.lite.NativeInterpreterWrapper.applyDelegate(Native Method)
        at org.tensorflow.lite.NativeInterpreterWrapper.applyDelegates(NativeInterpreterWrapper.java:351)
        at org.tensorflow.lite.NativeInterpreterWrapper.init(NativeInterpreterWrapper.java:82)
        at org.tensorflow.lite.NativeInterpreterWrapper.<init>(NativeInterpreterWrapper.java:63)
        at org.tensorflow.lite.Interpreter.<init>(Interpreter.java:266)
        at org.tensorflow.lite.examples.detection.tflite.YoloV5Classifier.recreateInterpreter(YoloV5Classifier.java:174)
        at org.tensorflow.lite.examples.detection.tflite.YoloV5Classifier.useGpu(YoloV5Classifier.java:182)
        at org.tensorflow.lite.examples.detection.DetectorActivity.lambda$updateActiveModel$0$DetectorActivity(DetectorActivity.java:208)
        at org.tensorflow.lite.examples.detection.-$$Lambda$DetectorActivity$lqWWUAbeU1gjsHY5oEBIKbdpe60.run(Unknown Source:8)
        at android.os.Handler.handleCallback(Handler.java:793)
        at android.os.Handler.dispatchMessage(Handler.java:98)
        at android.os.Looper.loop(Looper.java:173)
        at android.os.HandlerThread.run(HandlerThread.java:65)

Thanks for trying and attaching the updated model!  Will took a deeper look.

@impjdi  could you help me to use the multiply op in tflite model with metal delegate？ i am using tf1.15.4 and tflite 1.15.4 with  metal metal delegate
when there is a scalar multiplier it works well,otherwise, the error occurs. i also use resize_bilinear to ensure both inputs have the same shape, it dosen't work.
When I run the model with GPU delegate, it complains:
2020-12-11 10:41:56.881639+0800 tflite_camera_example[34564:5652810] Initialized TensorFlow Lite runtime. 2020-12-11 10:41:56.881729+0800 tflite_camera_example[34564:5652810] Created TensorFlow Lite delegate for Metal. 2020-12-11 10:41:56.881924+0800 tflite_camera_example[34564:5652810] Metal GPU Frame Capture Enabled 2020-12-11 10:41:56.882223+0800 tflite_camera_example[34564:5652810] Metal API Validation Enabled 2020-12-11 10:41:56.949605+0800 tflite_camera_example[34564:5652810] TfLiteGpuDelegate Prepare: Unsupported op type: apply_mask; custom registry error: Unsupported op: apply_mask; primary registry error: Unsupported op: apply_mask;
The modified MUL op can be inspected in netron:

@impjdi @impjdi could you help me to use the multiply op in tflite model with metal delegate？ i am using tf1.15.4 and tflite 1.15.4 with metal metal delegate
when there is a scalar multiplier it works well,otherwise, the error occurs. i also use resize_bilinear to ensure both inputs have the same shape, it dosen't work.
When I run the model with GPU delegate, it complains:
2020-12-11 10:41:56.881639+0800 tflite_camera_example[34564:5652810] Initialized TensorFlow Lite runtime. 2020-12-11 10:41:56.881729+0800 tflite_camera_example[34564:5652810] Created TensorFlow Lite delegate for Metal. 2020-12-11 10:41:56.881924+0800 tflite_camera_example[34564:5652810] Metal GPU Frame Capture Enabled 2020-12-11 10:41:56.882223+0800 tflite_camera_example[34564:5652810] Metal API Validation Enabled 2020-12-11 10:41:56.949605+0800 tflite_camera_example[34564:5652810] TfLiteGpuDelegate Prepare: Unsupported op type: apply_mask; custom registry error: Unsupported op: apply_mask; primary registry error: Unsupported op: apply_mask; 2020-12-11 10:41:56.949691+0800 tflite_camera_example[34564:5652810] Node number 12 (TfLiteMetalDelegate) failed to prepare. 2020-12-11 10:41:56.949741+0800 tflite_camera_example[34564:5652810] Restored previous execution plan after delegate application failure.
The modified MUL op can be inspected in netron:

@lei522 I'm not familiar with the versions, but we removed apply_mask quite some time back.  Is it possible for you to try the latest code?

@impjdi  Hey:) I have tried the latest code, it works well. thanks for your suggestion! Have a good day!

Excuse me, how can this problem be solved?
TfLiteGpuDelegate Init: MUL: 1x1088x1x1  cannot be reduced to linear.
TfLiteGpuDelegate Prepare: delegate is not initialized
Node number 23608 (TfLiteGpuDelegateV2) failed to prepare.

@uchihaltachi for whatever reason, MUL is expecting the input tensor to be a linear tensor.  maybe insert a RESHAPE to match the other dimension?  In GPU, 1x1088x1x1 is not the same as 1x1x1x1088; each dimension has to match.

@uchihaltachi for whatever reason, MUL is expecting the input tensor to be a linear tensor.  maybe insert a RESHAPE to match the other dimension?  In GPU, 1x1088x1x1 is not the same as 1x1x1x1088; each dimension has to match.

Hi There,
We are checking to see if you still need help on this, as you are using an older version of tensorflow which is officially considered end of life . We recommend that you upgrade to the latest 2.x version and let us know if the issue still persists in newer versions. Please open a new issue for any help you need against 2.x, and we will get you the right help.
This issue will be closed automatically 7 days from now. If you still need help with this issue, please provide us with more information.


Modify type of role

Problem
terraform plan detects differences on the list just showing item order, which makes no effect actually.
Example
  # module.developer.onelogin_roles.role will be updated in-place
  ~ resource "onelogin_roles" "role" {
      ~ admins = [
          - 34873631,
            34873695,
          + 34873631,
        ]
        apps   = [
            698140,

Solution
According to official reference,  schema.TypeList represents  an ordered collection of items.
On the other hand, TypeSet is for used to represent an unordered collection of items,. It seems better for Role.
Is there any problem to change types of them?

Hey @at-k Thank you for the tip! I see the tests passing. Im going to merge this and release a new version. Thanks again 😄


Wasm 2.0 Update

This PR is created for visibility of the in-progress update to Wasm 2.0, the current version of spec displayed on the spec webpage.
Release 2.0
This release for Wasm 2.0 implemented the following changes in the official spec release 2.0:

Multiple-value blocks;
Reference types;
Table instructions;
Multiple tables;
Bulk memory and table instructions.

The new sign extension, non-trapping float-to-int conversion, and vector types are added but without any concrete implementation. Vector instructions are currently not added.
Updated Components:

[x] Base opsem/typing definitions;
[x] Type preservation theorems;
[x] Interpreter and progress theorem;
[x] Instantiation;
[ ] Instantiation soundness theorems;
[x] Type checker;
[ ] Type checker correctness theorem;
[x] Binary printer/parser;
[x] Code pretty printer.

Major Structural Changes
Values vs Instructions
Due to the introduction of reference values, values are no longer necessarily basic instructions; function references and external references are expressed as administrative instructions due to their direct usage of store addresses instead of module indices. This change has broken some assumptions that many original proofs and definitions based on -- mostly those related to value typing (see below).
Total and partial conversion operations are now provided for conversion between values and their corresponding instructions:

v_to_e/e_to_v for total conversions;
e_to_v_opt for partial operations.

Value typing validity depends on store
Due to the use of store addresses, the new reference values can only be typed given a store. This necessitated the introduction
of a separate value_typing relation with respect to a store. In addition, value typing relation now has to be done at the
e_typing level (for administrative instructions) as they can no longer be converted to basic instructions and typed using the const rule in be_typing. New value typing inversion lemmas were added to help reasoning with this change; search for terms involving value_typing and values_typing.
Threads
Threads are now properly spelt out as a separate type that constitutes the configuration tuple. The old thread-related definitions (e.g. s_typing) are renamed to the names used in the standard (e.g. thread_typing).
Store extensions
Note that in Wasm 2.0, the table and memory instances carry around their full types instead of just a maximum size. This enrichment to the type signature causes the store extension definition to need a relaxation, as table and memory grow operations could modify their types by increasing the length of the corresponding components (therefore, increasing the minimum bound). The spec soundness theorem also needs a fix for this (this was first discovered during a Master student's project back in 2023).
Note that this change has the implication that contexts are no longer invariant during execution. As a result, many assumptions that the old soundness theorems based on no longer hold and need to be revamped.
Refactorings and Feature Improvements
Host Formulation
The parametric host language is now defined using typeclasses.
The main major benefit is the automatic filling of implicit host parameter, instead of needing to redefine all operations involving anything downstream from function instances and stores. The proof context is also greatly simplified since all these redefinitions no longer exist to occupy a major chunk of the buffer window.
Numerics

Refactored the old collection of conversion operations cvtop to be split up by their individual constructors to better match the spec.

Name Changes

Changed the name of some types, instructions, and constructors to better match the official spec.
Instance indices are now simplified to the base u32 type without additional constructors.

Pretty Printer

Implemented pretty printing for conversion operations.

Typing

Massively improved the scope and automation of the typing inversion lemmas.
Provided a new tactic resolve_e_typing that automatically tries to resolve e_typing goals, dealing mostly with the operands.

Miscellaneous

Introduced many additional excerpts in comments from the official spec for various definitions.

Bug Fixes

Fixed a bug where the binary printer incorrectly prints all types of reinterpret conversions to 0xBC.
Fixed a bug where the binary printer sometimes prints indices via a conversion to nat first.

Thanks for the detailed description of the upcoming changes! This is very helpful for us.

I'm currently working on the proof that the Wasm module we generate instantiates.
Since you made some changes to the instantiation_spec, I'd like to update to a version of your 2.0 branch as soon as possible. (it's ok if it doesn't have tail calls)
How stable is the current instantiation_spec file, do you expect significant changes?
(Also, the current version doesn't seem to compile.)

I'm currently working on the proof that the Wasm module we generate instantiates. Since you made some changes to the instantiation_spec, I'd like to update to a version of your 2.0 branch as soon as possible. (it's ok if it doesn't have tail calls)
How stable is the current instantiation_spec file, do you expect significant changes? (Also, the current version doesn't seem to compile.)
thanks!

I'm hoping the definitions should be pretty stable, although proofs down that line haven't been updated, so there might be some mistakes. I'm guessing your line of work doesn't depend on the instantiation soundness proofs, so at least you could try to incorporate the current definition and see if it works with your modules (and potentially find some mistakes before I find them during the proofs)!
Note that in 2.0 the table/memory initialisations are now separated to execution of a series of the new table/memory operations, while it was initially hardcoded in the mechanised 1.0, so the 'real' final instantiation store is the store post-execution of those instructions.

I'm hoping the definitions should be pretty stable, although proofs down that line haven't been updated, so there might be some mistakes. I'm guessing your line of work doesn't depend on the instantiation soundness proofs, so at least you could try to incorporate the current definition and see if it works with your modules (and potentially find some mistakes before I find them during the proofs)!

Yes, I'll do that, thanks! I'll report if I notice anything.

Note that in 2.0 the table/memory initialisations are now separated to execution of a series of the new table/memory operations, while it was initially hardcoded in the mechanised 1.0, so the 'real' final instantiation store is the store post-execution of those instructions.
I'm also hoping that all files compile except the type checker and instantiation proofs now -- let me know if that is not what you see on your side.

It works, the project compiles after deleting (for now) the said files and the ones depending on them.

I'm hoping the definitions should be pretty stable, although proofs down that line haven't been updated, so there might be some mistakes. I'm guessing your line of work doesn't depend on the instantiation soundness proofs, so at least you could try to incorporate the current definition and see if it works with your modules (and potentially find some mistakes before I find them during the proofs)!

Yes, I'll do that, thanks! I'll report if I notice anything.

Note that in 2.0 the table/memory initialisations are now separated to execution of a series of the new table/memory operations, while it was initially hardcoded in the mechanised 1.0, so the 'real' final instantiation store is the store post-execution of those instructions.
I'm also hoping that all files compile except the type checker and instantiation proofs now -- let me know if that is not what you see on your side.

It works, the project compiles after deleting (for now) the said files and the ones depending on them.

A quick status update -- it seems that there's a new introduction of a real bottom type in the 2.0 spec which has been missed in the update so far. I've only discovered this earlier when I find that there was seemingly a problem with typing certain polymorphic instructions (select, in particular). After talking to Andreas, it seems that the best approach is to implement the real bottom type with the correct subtyping, (quote from Andreas: ...which essentially is what the funcref/GC proposal does).
I may take some time to see what the GC proposal does and implement this part faithfully, but I'll need to talk to Conrad first to have a discussion on the best approach. On the upside, a correct decision here should make it easier when you implement the full GC proposal for your work, if you're to try it at some point.
This is quite a large change in the fundamental type system as it adds a new constructor to the base value type, but I don't think it will have too much impact on you, since most of the impact of this change should have been absorbed along the way in various definitions. But I thought it's useful for you to be aware of this change ahead.

Just another status update on subtyping -- I went through some lengthy email discussions with Andreas and Conrad regarding certain subtyping rules throughout the last week; in the end we decided that it is the best if I directly implement the genuine subtyping system in the GC proposal (but without introducing those new types), instead of the temporary method in 2.0. We've discussed about fixing some rules in the proposal as well during the discussion.
The subtyping implementation proved to require a huge restructuring of the type system and how certain typing rules work, due to types of many more items no longer being unique (although a principal type/typing scheme does exist for all objects), and many type signatures that originally need to be equal are now only required to be related by subtyping (sensible). I now almost have all the proofs recovered. Hopefully this proves to be the last obstacle in this update!
I'll update the changelog (and the introduction here) at some point tomorrow. In the mean time, if you want to get a rough idea of what to expect before my changelog, you can browse the subtyping branch (https://github.com/raoxiaojia/WasmCert-Coq/tree/subtyping).

thanks for the update! CC @opqrs @spitters

Update: This is finally done! Subtyping proved to be require a lot of overhauls in some existing proof structures (together with the basically reimplemented version of type checker).
More clean-ups may come in the next few days, but otherwise this should be merged at the end of this week unless anything else comes up.


Please invite me to the community

Name
EarthMaster
Additional Context
Hi everyone

Invitation sent to join the GitHub Organization. Welcome to Infraform 🎉Please check your mail to accept the invitation sent by GitHub! 🤖About usInfraform is a remarkable open-source solution tailored for serverless platforms, aimed at empowering creators to craft their infrastructure solutions. 💻 We're thrilled to welcome you into our vibrant community! ✨ We eagerly anticipate your valuable contributions! 🤓 Don't forget to explore our community on Github and connect with us on LinkedIn 💜 Feel free to spread the word about our community to like-minded individuals interested in development, DevOps, technology, and collaborative contributions. 👉💜👈


Create the nav bar to be able to search for movies (HTML)

WE NEED the nav bar created so that any user can search for a movie they want to find a review on.

Kyle K working on


Regarding datetime management for minute level data

❓ Questions and Help
Hi, I was trying to implement my own backtesting function to handle minute-level data. Can you tell me what this part does in qlib.contrib.backtest.__init__.py:
dates = np.append(dates, get_date_range(dates[-1], left_shift=1, right_shift=shift))
As in why we need to do this? AFAIU this will just add the last date to the list of dates again. Can you help me in converting this to minute level? Thanks.

The earliest time to use the prediction at timestamp T for trading is  T + 1.
So one more trading date is appended to evaluate the signal produced at T.
We are developing a new framework that could better support minute-level data.
Please pay attention to this PR
Thanks .

Thanks for the prompt reply. Is there a timeline as to when this is supposed to get merged? Thanks!

I think it will be merged in 1~2 months

Thanks for the info!


Bug: Exception when embeded local video

Thanks for writing such a great plugin. But I met a error when embeded local video, it throw a Exception:
Uncaught Exception:
TypeError [ERR_INVALID_URL]: Invalid URL
at new NodeError (node:internal/errors:370:5)
at onParseError (node:internal/url:536:9)
at new URL (node:internal/url:612:5)
at $electron$window$open_default_app_BANG_$$ (/Applications/Logseq.app/Contents/Resources/app/electron.js:12643:23)
at Object.<anonymous> (/Applications/Logseq.app/Contents/Resources/app/electron.js:12699:207)
at Object.emit (node:events:394:28)
at emitDeprecatedNewWindowEvent (node:electron/js2c/browser_init:189:1218)
at Object.openGuestWindow (node:electron/js2c/browser_init:189:1465)
at Object.<anonymous> (node:electron/js2c/browser_init:165:12226)
at Object.emit (node:events:394:28)

Do you mind sharing what you actually typed in the block itself? It should look like {{renderer ...}}

Hi, please try the latest version and re-open this issue if it still persists.


Enhance first step setup

Add details for the "first hour" first step setup.  Need to give first time users an easy to follow steps on how to use Ortelius.
The documentation repo ortelius/ortelius-docs and documentation section is where the updates are needed.

Done with the Ortelius CLI


Issue displaying svg icon on the frontend

I've added the plugin and changed the path of my icon folder to be theme-folder/assets/images/icons/ using the acf_svg_icon_picker_folder filter. I can see icons in admin no problem. I have a repeater field setup inside flexible content. The name of the sub field I'm using is called 'icon'.
Now in my theme php file I'm grabbing the subfield $icon = get_sub_field('icon'); and then echoing it echo $icon; But for some reason it's not displaying the SVG it's just showing the text name of the SVG file. I can't work out from your documentation how to display the SVG. I've tried $icon_svg = get_svg_icon($icon); but this doesn't work. Anything to point me in the right direction would be of great help. Thanks

Hi @alex-kodr , its correct that you receive only the name of the icon currently when using get_field or get_sub_field. We could change that in a future update where get_field or get_sub_field will hold the SVG directly.
You can use the function as follows:
$icon = get_sub_field('icon');
SmithfieldStudio\AcfSvgIconPicker\get_svg_icon($icon);

Be mindful of the namespaced function.
Please let us know if that helps. And if you want to help to improve upon the documentation, feel free to do so.

Hi @Levdbas, thanks for the reply. I wasn't sure if I'd missed something from the documentation. Thanks for the above function. I created an if else statement based off the icon name value, because I could get this to display. This solved the issue. It's a great little plugin 👍

Hi @alex-kodr , I now have a PR in the works to directly output an icon with get_field as a setting on the field level. You can track its process here #31 .
And thanks!


Provide API for faster incremental compilers

In https://github.com/Microsoft/TypeScript/issues/9017#issuecomment-225751262
@mhegazy suggested the possibility that TS could include an API that makes it easy to build incremental compilers, similar to the --watch behavior of TSC.
Given a ts.Program object, and a string[] of files that were modified, re-read and parse only the changed files, getPreEmitDiagnostics only for the changed files, emit only the changed files, and give the updated Program.
Right now it's too much work for us to write our own tsc that has the --watch behavior.
cc @IgorMinar

+1 😏

Tagging @ahejlsberg per conversation we had today about this issue. This is the number one problem we have reported from those who are going from Babel to TypeScript and looking for fast incremental builds. Related issue #9017 for more context Anders.


Update compile instructions

@davetcoleman and @mlautman, now the master branch compiles as well, we're going to merge this PR and remove master_compile branch.
Thanks for your feedback!

LGTM! I will remove master_compile


Update the documentation for the latest version of the plugin and the latest version of react native

Feature request
Update the documentation.
Why it is needed
After two days of struggling to setup the Google map on iOS I found out that the documentation link for installation in the README.md file is much older(from 2016). For faster setup and no missunderstanding of the documentation and the guidelines it will be a great idea to update the README.md of the latest version with the links to the docks to the latest version.
Possible implementation
This is not related to the code base, rather to the documentation. Any links to it, could be updated to the latest version for them.
Code sample
Not a code feature.

What specifically needs to be updated? Could you submit a pull request with those changes?


Fun references of operators have the wrong arity and module name

From #57 by @michallepicki:
This leads to another related issue, currently:
Lists.foldl ( + ) 1 [1; 2; 3]

generates
lists:foldl(fun '+'/0, 1, [1 | [2 | [3 | []]]])

where it lacks the erlang: prefix and the arity is wrong. Not sure if this is something that should be supported right now or not.

May be related to https://github.com/AbstractMachinesLab/caramel/issues/10 fixed in https://github.com/AbstractMachinesLab/caramel/commit/1b4e6b80083920a748b9e4ebda88cb48271bd740 ? At least it may be a good starting point to dive in

This may or may not be related, fun references of regular functions (not operators) cannot be used this way:
let print thing = Io.format "~0tp~n" [ thing ]
let main _ =
  let transforms = [ Binary.first; Binary.last ] in
     print (Lists.map (fun g -> g "Hello World") transforms)

results in
% Source code generated with Caramel.
-module(main).

-export([main/1]).
-export([print/1]).

-spec print(_) -> ok.
print(Thing) -> io:format(<<"~0tp~n">>, [Thing | []]).

-spec main(_) -> ok.
main(_) ->
  Transforms = [binary:first | [binary:last | []]],
  print(lists:map(fun
  (G) -> G(<<"Hello World">>)
end, Transforms)).

fun and the /arity is missing


fix(Rest): remove DOM reference types

Please describe the changes this PR makes and why it should be merged:
This reverts #55 and instead augments the global namespace with the missing URL and URLSearchParams (see https://github.com/DefinitelyTyped/DefinitelyTyped/issues/34960). This means that projects that use @discordjs/rest won't need to have the global namespace polluted with DOM typings (and also things like document.querySelector('.oh-no') will compile even in a Node.js environment!), even if "dom" isn't included in the user's own TSConfig.
Status and versioning classification:

Code changes have been tested against the Discord API, or there are no code changes
I know how to update typings and have done so, or typings don't need updating

I second this change. I unexpectedly discovered DOM types polluting the global namespace, meaning that TypeScript will happily compile stuff like fetch and atob, only to fail at runtime with a ReferenceError. This shouldn't happen as this is a Node package.


Fix: Wrong raven/raven version

RavenHandler uses Raven_Client::user_context(). This method is available since 0.8.0.
see https://github.com/getsentry/raven-php/commit/7dd1fb3a353eb2b456336a996e5265fc5756a085

Anything wrong with this one? It just fixes a small bug inside dependency management. Build status is same as mainline.

~0.5 translates to >=0.5.0 < 1.0.0 in composer. See this

Indeed it seems just fine as it is.

I does not make sense to require raven/raven versions < 0.8.0. Monolog is using functionality which is missing there.
If there is a raven/raven 0.5.x package installed already...and you installing monolog via composer afterwards

the unsupported 0.5.x version of raven/raven will not get updated
no warning/conflict will be displayed during installation
logging to sentry will fail.

Monolog requires raven/raven version >= 0.8.0 < 1.0.0 which translates to ~0.8.

Right sorry for the misunderstanding, I checked and indeed it segfaults with 0.5 - 0.7 versions.

:+1:


Problem with SignalK server

Hi, I try to use the plugin with SignalK in Lysmarine OS (bareboat necessities).
I have downloaded the error for the parent plugin, but the result is the same also for yours.
Plugin configured with /dev/ttyUSB0 port.
In logfile I get this error:
Oct 14 09:21:34 2022-10-14T09:21:34.855Z signalk-hwt901b-imu plugin.stop
Oct 14 09:21:34 2022-10-14T09:21:34.861Z signalk-hwt901b-imu plugin.connect
Oct 14 09:21:34 connecting to /dev/ttyUSB0:0
Oct 14 09:21:34 2022-10-14T09:21:34.869Z signalk-hwt901b-imu Plugin options saved
Oct 14 09:21:34 POST /skServer/plugins/signalk-hwt901b-imu/config 200 9.690 ms - 50
Oct 14 09:21:34 2022-10-14T09:21:34.877Z signalk-hwt901b-imu plugin.connect.close
Oct 14 09:21:34 TypeError: DelimiterParser is not a constructor at SerialPort. (/home/signalk/.signalk/node_modules/.pnpm/signalk-hwt901b-imu@0.1.0/node_modules/signalk-hwt901b-imu/index.js:98:44) at SerialPort.emit (node:events:513:28) at binding.open.then.opening (/home/signalk/.signalk/node_modules/.pnpm/https://github.com/serialport+stream@9.2.4/node_modules/@serialport/stream/lib/index.js:234:12)
I already found a fix for this, but it would be great to have this already done in the origin.
It seems I have fixed my local copy by changing this row in index.js =>
const DelimiterParser = require('@serialport/parser-delimiter')
with the following =>
const { DelimiterParser } = require('@serialport/parser-delimiter')
I already opened an issue on W-Geronius plugin's page, but it seems he disagrees.
Could you please have a look too?
Thanks a lot.
Marco

Hello Marco
Thank you for the input. It works for me as you described with RPI4, bookworm, Signalk 2.8.2 (BBN/lysmarine 2024-06-20-r0235)
and WT901C-232 and CP2102  USB \ TTL \ RS232 \ RS485 adapter.
I do not know why it is not fixed yet.
Alex


feat!: DVC-8020 rename DVC to DevCycle on public interfaces

@jsalaber let me know if you want to review this.


AgoraErrorCodeNoPermission no longer getting called

rtcEngine(_ engine: AgoraRtcEngineKit, didOccurError used to be reported with AgoraErrorCodeNoPermission, but since I updated recently, that error no longer occurs.

just to check, it's no longer being called when it should for you @jonah-katz? I checked the docs and can't see anything about it being removed.
This repo is more for distribution of the SDK, rather than issue with it, to get to the SDK team the best route is hitting the support button on the console. It will lead you to here: https://agora-ticket.agora.io
Be sure to tell them all relevant information, including the SDK version you're having this issue with, as well as device OS. Cam/mic permissions on Apple devices have changed in the past with OS updates.

@jonah-katz there is a separate method, rtcEngine(_ engine:, permissionError type:), which might be what you're looking for.

That worked, thanks. I'll report the origin bug as well.


Ultima 5 NumLock bug

Update dosbox config? Did it work for both people with numpad and without.
Fix in u5 executable?

"thankfully Dosbox has that covered
[DOS]
Numlock = OFF
"

WtF is reaching out to GoG to make this part of the default install. Dunno if it'll happen though.


Changing IAM Identity Center breaks LZA

Describe the bug
We have changed the delegated account for IAM IDC and subsequent running of LZA fails.
To Reproduce
Change the delegated account from X -> Y AWS account
Expected behavior
Nothing this difficult.
Please complete the following information about the solution:

[ 1.3.0] Version: [e.g. v1.1.0]
We've observed the following failure in the operations-X-CFN stack in the Audit account

Additionally, because the stack is in a rollback failure state, we are unable to make any further updates to LZA.

Hi @farmerbean , thank you for creating this issue! We don't recommend manually changing the delegated administrator of any AWS Service used by LZA. I believe what you are seeing is occurring because the LZA attempts to create AWS Identity Center resources within the account it believes to be the delegated admin, according to the configuration.
Based on the discussion in #72 , in v1.3.0 of the LZA, LZA is using the delegatedAdminAccount specified in the security-config.yaml account as the AWS Identity Center delegated administrator. We understand that customers may wish to specify a different account to be used for the AWS Identity Center delegated administrator, and we expect to include that feature in our next minor release.
In the meantime, I would recommend manually changing the AWS Identity Center back to the Audit account. That will enable you to continue moving forward until the next minor release, at which point you will be able to specify a different account to use as the AWS Identity Center delegated administrator.

We have run the 1.3.1 upgrade as per https://docs.aws.amazon.com/solutions/latest/landing-zone-accelerator-on-aws/update-the-solution.html
and changed the Iamconfig-IdentityCentre config as per the updated documentation https://awslabs.github.io/landing-zone-accelerator-on-aws/classes/_aws_accelerator_config.IamConfig.html#identityCenter
re-running the pipeline does not move IDC delegated admin
We have manually moved the IDC delegated admin and then re-run the pipeline. This then breaks the pipeline as per previous error
@erwaxler

Hi @farmerbean,
Thanks for reaching out and following back up. Can you confirm the following:
Which account did you manually move the IDC delegated admin to?
Does this match the delegatedAdminAccount field?
For instance, if you moved it back to Audit as per @erwaxler's recommendation, does this matchup with current delegatedAdminAccount in  iam-config-yaml?

Moved the account back and fixed the cloudFormation stack ✅
Ran the 1.3.1 pipeline with the delegatedAdminAccount field set to Audit account ✅
Ran the 1.3.1 pipeline with the delegatedAdminAccount field set to sandboxIDC account ... nothing happens.

Note - we followed the upgrade instructions from the AWS doc but still seeing this in the pipeline

Fails
❌  AWSAccelerator-OrganizationsStack-111111111111-eu-west-1 failed: Error: The stack named AWSAccelerator-OrganizationsStack-111111111111-eu-west-1 failed to deploy: UPDATE_ROLLBACK_COMPLETE: Response object is too long.

CWL output for the Lambda
2023-03-01T23:51:04.187Z	09009961-e0a6-4139-a0ca-e065b35f68fa	INFO	[provider-framework] submit response to cloudformation {     "Status": "FAILED",     "Reason": "Delegated Admin Identity Center cannot be updated due to existing Permission Sets or Assignments. Please remove the following Permission Sets and Assignments: \n      Permission Sets: [{\"Name\":\"StormPermissionSetViewOnly\",\"PermissionSetArn\":\"arn:aws:sso:::permissionSet/ssoins-6804cbf0e09ea22e/ps-c7897568e3cee05f\",\"CreatedDate\":\"2023-02-24T09:44:23.119Z\",\"SessionDuration\":\"PT1H\"},{\"Name\":\"AdministratorAccess\",\"PermissionSetArn\":\"arn:aws:sso:::permissionSet/ssoins-6804cbf0e09ea22e/ps-afe45648145dffd8\",\"CreatedDate\":\"2023-02-13T20:50:28.714Z\",\"SessionDuration\":\"PT4H\"},{\"Name\":\"Billing\",\"PermissionSetArn\":\"arn:aws:sso:::permissionSet/ssoins-6804cbf0e09ea22e/ps-347b18bb75d7777f\",\"CreatedDate\":\"2023-02-14T12:17:01.833Z\",\"SessionDuration\":\"PT1H\"}]\n      Assignments: [\"[{\\\"AccountId\\\":\\\"941111174898\\\",\\\"PermissionSetArn\\\":\\\"arn:aws:sso:::permissionSet/ssoins-6804cbf0e09ea22e/ps-c354510bf0f3199b\\\",\\\"PrincipalType\\\":\\\"GROUP\\\",\\\"PrincipalId\\\":\\\"725594e4-0061-70d6-6f08-7ac3d5fafbc4\\\"},{\\\"AccountId\\\":\\\"941111174898\\\",\\\"PermissionSetArn\\\":\\\"arn:aws:sso:::permissionSet/ssoins-6804cbf0e09ea22e/ps-c354510bf0f3199b\\\",\\\"PrincipalType\\\":\\\"GROUP\\\",\\\"PrincipalId\\\":\\\"82b53444-6051-7037-dda5-663a916ece5c\\\"}]\",\"[{\\\"AccountId\\\":\\\"941111174898\\\",\\\"PermissionSetArn\\\":\\\"arn:aws:sso:::permissionSet/ssoins-6804cbf0e09ea22e/ps-bc3f9582bc1e85c5\\\",\\\"PrincipalType\\\":\\\"GROUP\\\",\\\"PrincipalId\\\":\\\"0295a414-a0a1-7025-4116-93670adcc419\\\"}]\",\"[{\\\"AccountId\\\":\\\"941111174898\\\",\\\"PermissionSetArn\\\":\\\"arn:aws:sso:::permissionSet/ssoins-6804cbf0e09ea22e/ps-dff164425c200c77\\\",\\\"PrincipalType\\\":\\\"GROUP\\\",\\\"PrincipalId\\\":\\\"92b51484-d011-7024-6a04-94bf0335b8ba\\\"}]\",\"[{\\\"AccountId\\\":\\\"941111174898\\\",\\\"PermissionSetArn\\\":\\\"arn:aws:sso:::permissionSet/ssoins-6804cbf0e09ea22e/ps-208ec9669212112a\\\",\\\"PrincipalType\\\":\\\"GROUP\\\",\\\"PrincipalId\\\":\\\"421504d4-2021-7089-1ec5-752bc4282af0\\\"},{\\\"AccountId\\\":\\\"941111174898\\\",\\\"PermissionSetArn\\\":\\\"arn:aws:sso:::permissionSet/ssoins-6804cbf0e09ea22e/ps-208ec9669212112a\\\",\\\"PrincipalType\\\":\\\"GROUP\\\",\\\"PrincipalId\\\":\\\"82b53444-6051-7037-dda5-663a916ece5c\\\"},{\\\"AccountId\\\":\\\"941111174898\\\",\\\"PermissionSetArn\\\":\\\"arn:aws:sso:::permissionSet/ssoins-6804cbf0e09ea22e/ps-208ec9669212112a\\\",\\\"PrincipalType\\\":\\\"GROUP\\\",\\\"PrincipalId\\\":\\\"725594e4-0061-70d6-6f08-7ac3d5fafbc4\\\"}]\"]\n\nLogs: /aws/lambda/AWSAccelerator-Organizati-IdentityCenterAdminIdent-rUPwWhXr4TRJ\n\n      Permission Sets: [{\"Name\":\"StormPermissionSetViewOnly\",\"PermissionSetArn\":\"arn:aws:sso:::permissionSet/ssoins-6804cbf0e09ea22e/ps-c7897568e3cee05f\",\"CreatedDate\":\"2023-02-24T09:44:23.119Z\",\"SessionDuration\":\"PT1H\"},{\"Name\":\"AdministratorAccess\",\"PermissionSetArn\":\"arn:aws:sso:::permissionSet/ssoins-6804cbf0e09ea22e/ps-afe45648145dffd8\",\"CreatedDate\":\"2023-02-13T20:50:28.714Z\",\"SessionDuration\":\"PT4H\"},{\"Name\":\"Billing\",\"PermissionSetArn\":\"arn:aws:sso:::permissionSet/ssoins-6804cbf0e09ea22e/ps-347b18bb75d7777f\",\"CreatedDate\":\"2023-02-14T12:17:01.833Z\",\"SessionDuration\":\"PT1H\"}]\n      Assignments: [\"[{\\\"AccountId\\\":\\\"941111174898\\\",\\\"PermissionSetArn\\\":\\\"arn:aws:sso:::permissionSet/ssoins-6804cbf0e09ea22e/ps-c354510bf0f3199b\\\",\\\"PrincipalType\\\":\\\"GROUP\\\",\\\"PrincipalId\\\":\\\"725594e4-0061-70d6-6f08-7ac3d5fafbc4\\\"},{\\\"AccountId\\\":\\\"941111174898\\\",\\\"PermissionSetArn\\\":\\\"arn:aws:sso:::permissionSet/ssoins-6804cbf0e09ea22e/ps-c354510bf0f3199b\\\",\\\"PrincipalType\\\":\\\"GROUP\\\",\\\"PrincipalId\\\":\\\"82b53444-6051-7037-dda5-663a916ece5c\\\"}]\",\"[{\\\"AccountId\\\":\\\"941111174898\\\",\\\"PermissionSetArn\\\":\\\"arn:aws:sso:::permissionSet/ssoins-6804cbf0e09ea22e/ps-bc3f9582bc1e85c5\\\",\\\"PrincipalType\\\":\\\"GROUP\\\",\\\"PrincipalId\\\":\\\"0295a414-a0a1-7025-4116-93670adcc419\\\"}]\",\"[{\\\"AccountId\\\":\\\"941111174898\\\",\\\"PermissionSetArn\\\":\\\"arn:aws:sso:::permissionSet/ssoins-6804cbf0e09ea22e/ps-dff164425c200c77\\\",\\\"PrincipalType\\\":\\\"GROUP\\\",\\\"PrincipalId\\\":\\\"92b51484-d011-7024-6a04-94bf0335b8ba\\\"}]\",\"[{\\\"AccountId\\\":\\\"941111174898\\\",\\\"PermissionSetArn\\\":\\\"arn:aws:sso:::permissionSet/ssoins-6804cbf0e09ea22e/ps-208ec9669212112a\\\",\\\"PrincipalType\\\":\\\"GROUP\\\",\\\"PrincipalId\\\":\\\"421504d4-2021-7089-1ec5-752bc4282af0\\\"},{\\\"AccountId\\\":\\\"941111174898\\\",\\\"PermissionSetArn\\\":\\\"arn:aws:sso:::permissionSet/ssoins-6804cbf0e09ea22e/ps-208ec9669212112a\\\",\\\"PrincipalType\\\":\\\"GROUP\\\",\\\"PrincipalId\\\":\\\"82b53444-6051-7037-dda5-663a916ece5c\\\"},{\\\"AccountId\\\":\\\"941111174898\\\",\\\"PermissionSetArn\\\":\\\"arn:aws:sso:::permissionSet/ssoins-6804cbf0e09ea22e/ps-208ec9669212112a\\\",\\\"PrincipalType\\\":\\\"GROUP\\\",\\\"PrincipalId\\\":\\\"725594e4-0061-70d6-6f08-7ac3d5fafbc4\\\"}]\"]\n    at lm (/var/task/index.js:32:17746)\n    at processTicksAndRejections (internal/process/task_queues.js:95:5)\n    at async um (/var/task/index.js:32:17338)\n    at async Runtime.sm [as handler] (/var/task/index.js:32:15969)",     "StackId": "arn:aws:cloudformation:eu-west-1:885614340325:stack/AWSAccelerator-OrganizationsStack-885614340325-eu-west-1/52d95f20-aee3-11ed-bb42-0a9840e9e66b",     "RequestId": "45bc73c3-8c11-44a5-b93b-7fa1b7091a07",     "PhysicalResourceId": "0898c874-a5db-49c6-a5e0-f875016e9e22",     "LogicalResourceId": "IdentityCenterAdminidentityCenterAdminEB714AB1" }

We've now gotten this working for you:
1.3.0 - LZA automatically delegates Audit account for IDC :exploding_head: :white_check_mark:
1.3.0 upgrade to 1.3.1 :white_check_mark:
Run pipeline on 1.3.1 with no changes to identity centre :white_check_mark:
Manually UN-DELEGATE Audit account as IDC from Main Billing Account :tired_face: :white_check_mark:
1.3.1 update iam-config for new-target IDC delegated account :white_check_mark:
1.3.1 run pipeline. This makes no changes, but completes successfully :checkered_flag:

Hi @farmerbean,
Looks like there is an issue with returning the error response which includes all of the PermissionSets and Assignments (I'll open up a bug request to reduce verbosity on this). However, if you manually remove these Permission Sets and Assignments, this should fix the issue. This behavior is intentional as we want to ensure that dangling Permission Sets and Assignments are not left in the previous Delegated Admin Account after it has been updated.
Please let me know if you continue running into issues.
Cheers,
Ryan

Hi ryan - I have removed the permission set we created with LZA in audit account.
The other permission sets were created in the main account and not in Audit account.
To be clear: I simply need to remove all permission sets from the source account (Audit) and this will "definitely fix" the issue?

Both AdministratorAccess and Billing permissionSets only exist in the Main Billing Account ..

I'm having the same issue - brown field where I already have identity center set up in the Management account and I'm trying to move the permissionsets and assignments to LZA.  After setting delegatedAdminAccount under the identityCenter configs, I get:
AWSAccelerator-OrganizationsStack-854Management-us-east-2 failed: Error: The stack named AWSAccelerator-OrganizationsStack-854661928400-us-east-2 failed to deploy: UPDATE_ROLLBACK_COMPLETE: Response object is too long.

I'm using a config:
identityCenter:
  name: identityCenter1
  delegatedAdminAccount: Management
  identityCenterPermissionSets:
    - name: Admin
      policies:
        awsManaged:
          - arn:aws:iam::aws:policy/AdministratorAccess
      sessionDuration: 720
  identityCenterAssignments:
    - name: Admin
      permissionSetName: Admin
      principalId: 'ad admin group'
      principalType: GROUP
      deploymentTargets:
        organizationalUnits:
          - Root

This is a new permissionset that doesn't conflict with existing ones.
Prior to setting delegatedAdminAccount, I was getting:
AWSAccelerator-OperationsStack-582Audit-us-east-2 failed: Error: The stack named AWSAccelerator-OperationsStack-582Audit-us-east-2 failed to deploy: UPDATE_ROLLBACK_COMPLETE: Resource handler returned message: "User: arn:aws:sts::582Audit:assumed-role/cdk-accel-cfn-exec-role-582Audit-us-east-2/AWSCloudFormation is not authorized to perform: sso:CreateAccountAssignment on resource: arn:aws:sso:::account/854Master (Service: SsoAdmin, Status Code: 400, Request ID: b61da444-...)" (RequestToken: df02454e-..., HandlerErrorCode: GeneralServiceException)

My centralSecurityServices delegatedAdminAccount is Audit.  There is no identitycenter in my audit account.

I'm getting the same permissions error even when I set the delegatedAdminAccount to Audit.  The permission sets are created successfully, but the sso:CreateAccountAssignment fails when the audit account tries to create an assignment on the management account.

I'm getting the same permissions error even when I set the delegatedAdminAccount to Audit. The permission sets are created successfully, but the sso:CreateAccountAssignment fails when the audit account tries to create an assignment on the management account.

Just in case it helps anyone else, this is actually a different issue and a limitation of the delegated admin as it can't manage permission sets for the management account (presumably, to avoid escalating permissions). Full details: https://docs.aws.amazon.com/singlesignon/latest/userguide/delegated-admin.html

I'm getting the same permissions error even when I set the delegatedAdminAccount to Audit. The permission sets are created successfully, but the sso:CreateAccountAssignment fails when the audit account tries to create an assignment on the management account.

@sigfigsteve as @zakbatinica-reply stated there are limitations to deploying permission sets from the delegated account will not allow you to put them on the Main account that SSSO resides from, there are 3 ways around this 1 you can deploy at Root OU and exclude the Management account 2. you can deploy to a specific account only or 3. you can deploy to a specific OU above root IF you have nested OU you must list them as well it will not matriculate down the OU structure  SEE example code :
`identityCenter:
name: MYSSO
delegatedAdminAccount: Audit
identityCenterPermissionSets:

name: AWS-GBL-Sec-Admin
policies:
awsManaged:
- arn:aws:iam::aws:policy/SecurityAudit
- arn:aws:iam::aws:policy/job-function/SystemAdministrator
customerManaged: []
sessionDuration: 60
name: AWS-GLB-Contributor
policies:
awsManaged:
- arn:aws:iam::aws:policy/PowerUserAccess
- arn:aws:iam::aws:policy/job-function/SystemAdministrator
customerManaged: []
sessionDuration: 60
name: AWS-GLB-ReadOnly
policies:
awsManaged:
- arn:aws:iam::aws:policy/ReadOnlyAccess
customerManaged: []
sessionDuration: 60
name: AWS-Audit-ReadOnly
policies:
awsManaged:
- arn:aws:iam::aws:policy/ReadOnlyAccess
customerManaged: []
sessionDuration: 60
name: AWS-EpicProd-Contributor
policies:
awsManaged:
- arn:aws:iam::aws:policy/PowerUserAccess
- arn:aws:iam::aws:policy/job-function/SystemAdministrator
customerManaged: []
sessionDuration: 60

identityCenterAssignments:

name: AWS-GBL-Sec-Admin
permissionSetName: AWS-GBL-Sec-Admin
principalId: 85c1d77340f4-10c1-70e0-75db-a2e41a9ac6e6
principalType: GROUP
deploymentTargets:
organizationalUnits:
- Root
excludedAccounts:
- Management
name: AWS-GLB-Contributor
permissionSetName: AWS-GLB-Contributor
principalId: 85c1d77340f4-a0b1-7039-7980-85c1d77340f4
principalType: GROUP
deploymentTargets:
organizationalUnits:
- Root
excludedAccounts:
- Management
name: AWS-GLB-ReadOnly
permissionSetName: AWS-GLB-ReadOnly
principalId: 85c1d77340f4-90e1-70e6-7c89-0b179d9fc29e
principalType: GROUP
deploymentTargets:
organizationalUnits:
- Root
excludedAccounts:
- Management
name: AWS-Audit-ReadOnly
permissionSetName: AWS-Audit-ReadOnly
principalId: 85c1d77340f4-c031-70e3-c6e1-6f880fc55a73
principalType: GROUP
deploymentTargets:
accounts:
- Audit
name: AWS-EpicProd-Contributor
permissionSetName: AWS-EpicProd-Contributor
principalId: e4c8a448-00b1-70ec-c3a9-85c1d77340f4
principalType: GROUP
deploymentTargets:
organizationalUnits:
- HIS`

Thank you @frankscalzo - excluding the Management account was the missing piece to make this work for me.  I ended up using customizations-config to add a few permissions to the management account and identityCenterAssignments for everything else.  Now if I add new workload accounts, they're automatically configured with the appropriate permissionSets.

Please see https://awslabs.github.io/landing-zone-accelerator-on-aws/classes/_aws_accelerator_config.IdentityCenterConfig.html#delegatedAdminAccount for more information regarding Delegated Admin Accounts and Identity Center configuration with LZA.
Closing this issue as the thread topic has changed and workarounds / usage has been also discussed.
We are internally tracking the request for configuring permission sets for the management account


Current S3 file system code path doesn't use Aws SigV4

Related to #2278.
The s3 region can be set in the sigv4 auth body to find the right IAM to do the auth. We set s3 region by TURI_S3_REGION but the underlying code path doesn't pass the region info to s3.

repro,
import turicreate as tc
tc.config.set_runtime_config("TURI_S3_REGION", "blobby")
tc.config.set_runtime_config("TURI_S3_ENDPOINT", "https://blob.mr3.simcloud.apple.com")
sf = tc.SFrame('s3://model-ideation-dev/image-classification/data/transfer-learning/sframes/v2_train_food.sframe')

Traceback (most recent call last):
  File "call.py", line 7, in <module>
    sf = tc.SFrame('s3://model-ideation-dev/image-classification/data/transfer-learning/sframes/v2_train_food.sframe')
  File "/Users/guihaoliang/Work/gui-2-36/debug/src/python/turicreate/data_structures/sframe.py", line 809, in __init__
    self.__proxy__.load_from_sframe_index(url)
  File "cy_sframe.pyx", line 71, in turicreate._cython.cy_sframe.UnitySFrameProxy.load_from_sframe_index
  File "cy_sframe.pyx", line 74, in turicreate._cython.cy_sframe.UnitySFrameProxy.load_from_sframe_index
OSError: s3://model-ideation-dev/image-classification/data/transfer-learning/sframes/v2_train_food.sframe not found. ErrMsg: ('bucket: 'model-ideation-dev', object_name: 'image-classification/data/transfer-learning/sframes/v2_train_food.sframe', endpoint: '', proxy: '', region: 'blobby') Error while performing ListObjects. Error Name: XML_ERROR_PARSING_TEXT. Error Message: Unable to parse ExceptionName: XML_ERROR_PARSING_TEXT Message: Unable to generate a proper httpResponse from the response stream.   Response code: 401 in ../src/core/storage/fileio/s3_api.cpp at 392
. operator() from ../src/core/storage/sframe_interface/unity_sframe.cpp at 120


fix(types): fix Input value property type #2

The Input "value" property type ends up being narrower because of the intersection with HTMLTextareaAttributes, my earlier change did not really fix it, omit is the way to go, sorry for wasting your time.

Never wasting my time, and I was actually making the omit change, but I'll just take this instead. Thanks for taking the time to contribute 👍🏻


Ensure scheduled jobs have the appropriate scheduled_task decorator

Untested. Having trouble starting celery beat. It just hangs. I've had this problem before; I don't think it's due to my changes.

Hello @CodeRhymesLife! Thanks for submitting the PR.

In the file portal/celery_worker.py, following                are the PEP8 issues :

Line 44:80: E501 line too long (82 > 79 characters)


how to use GPU？

how to use GPU？  i tried but failed！

any plan to rebuilt the deep learning model ?


Numbered List Style

In  GitHub flavored markdown ,there two differentways to write a link in a para.
the one Which To-MarkDown choosen is
[linktitle](linkurl)

But,For a Long Article,The format
[linktitle][linknumber]
.......
[1]:linkurl

is good.
can i use the second format in ToMarkDown?

to-markdown does not currently support the numbered link style, but I’ll keep this open as a feature request.

This has been implemented in https://github.com/domchristie/turndown:
var turndownService = new TurndownService({ linkStyle: 'referenced' })
turndownService.turndown('Hello world!')

You can also specify the linkReferenceStyle: full (default), collapsed, or shortcut.


Issue with solving for 4x^4-3x^3+2x^2=1

I am having problem getting any result coming back for the equation of 4x^4-3x^3+2x^2=1.  It seems that AlgebraJS is parsing it well, but it is throwing an exception when it is trying to solve, as shown below.
Equation: 4x^4-3x^3+2x^2=1

Result: An exception at this moment

The answers should be -0.479, 0.7219.  Is there something I can do on my side, so that eq.solveFor("x") function would provide results?

It doesn't solve quartics yet. See https://github.com/nicolewhite/algebra.js/issues/10.


Aggregation by products fails in Category Repository when EnterpriseSearch plugin is enabled

Description
We used the code below to count products inside a category:
        $criteria = new Criteria();
        $criteria->addAggregation(new CountAggregation('products-count', 'products.id'));
        $criteria->addFilter(new EqualsFilter('id', $categoryEntity->getId()));
        $result = $this->categoryRepo->search($criteria, $context);
        /** @var CountResult $aggregation */
        $aggregation = $result->getAggregations()->get('products-count');
        return $aggregation->getCount();

On plain Shopware 6 (version <IP_ADDRESS>), it works - ElasticsearchEntityAggregator falls back on DbalEntityReader, as there's no index for the Category entity by default. However, after enabling SwagEnterpriseSearch plugin and reindexing all the data, the same code produces the following error:
TypeError: Noticed exception 'TypeError' with message 'Argument 2 passed to Shopware\Core\Framework\DataAbstractionLayer\Search\AggregationResult\Metric\CountResult::__construct() must be of the type int, null given, called in /app/vendor/shopware/elasticsearch/Framework/DataAbstractionLayer/ElasticsearchEntityAggregatorHydrator.php on line 86'

Possible Solution
Add null check in ElasticsearchEntityAggregatorHydrator when instantiating CountResult, or better yet, index all fields from Category entity necessary to preserve the functionality of the DB implementation of the aggregator, in SwagEnterpriseSearch plugin.
Additional context
Stack trace from the origin until our file (line 29 is $result = $this->categoryRepo->search($criteria, $context) from the pasted snippet):
in /app/vendor/shopware/core/Framework/DataAbstractionLayer/Search/AggregationResult/Metric/CountResult.php:14
in Shopware\Core\Framework\DataAbstractionLayer\Search\AggregationResult\Metric\CountResult::__construct called at /app/vendor/shopware/elasticsearch/Framework/DataAbstractionLayer/ElasticsearchEntityAggregatorHydrator.php (86)
in Shopware\Elasticsearch\Framework\DataAbstractionLayer\ElasticsearchEntityAggregatorHydrator::hydrateAggregation called at /app/vendor/shopware/elasticsearch/Framework/DataAbstractionLayer/ElasticsearchEntityAggregatorHydrator.php (69)
in Shopware\Elasticsearch\Framework\DataAbstractionLayer\ElasticsearchEntityAggregatorHydrator::hydrate called at /app/vendor/shopware/elasticsearch/Framework/DataAbstractionLayer/ElasticsearchEntityAggregator.php (86)
in Shopware\Elasticsearch\Framework\DataAbstractionLayer\ElasticsearchEntityAggregator::aggregate called at /app/vendor/shopware/core/Framework/DataAbstractionLayer/Cache/CachedEntityAggregator.php (79)
in Shopware\Core\Framework\DataAbstractionLayer\Cache\CachedEntityAggregator::aggregate called at /app/vendor/shopware/core/Framework/DataAbstractionLayer/EntityRepository.php (132)
in Shopware\Core\Framework\DataAbstractionLayer\EntityRepository::aggregate called at /app/vendor/shopware/core/Framework/DataAbstractionLayer/EntityRepository.php (84)
in Shopware\Core\Framework\DataAbstractionLayer\EntityRepository::search called at /app/custom/plugins/OurPlugin/src/Repository/CategoryRepository.php (29)

Hey,
Elasticsearch implementation has been rewritten in 6.4. Can you test it there again?


Replace our GPTQ implementation with something better

The original implementation that we use is slow and hardcodes the list of datasets to use https://github.com/Lightning-AI/lit-gpt/blob/e095ed300dd9ffbca89c6416eeb056b08869721f/quantize/gptq.py#L474-L481 and layers to quantize: https://github.com/Lightning-AI/lit-gpt/blob/e095ed300dd9ffbca89c6416eeb056b08869721f/quantize/gptq.py#L498-L516
One example could be https://github.com/PanQiWei/AutoGPTQ. The library is specific to huggingface/transformers modules but it could be forked to support regular torch.nn.Modules.
Any other suggestions are appreciated.

At the moment of writing, there are two general-purpose post-training quantization methods: AutoGPTQ and AutoAWQ.
It's a bit tricky to choose to what approach to use, since different models might show different results.
For instance, in this comparison with LlaMa2-7b (on 3090) GPTQ shows much lower VRAM usage and faster token generation, albeit with slightly higher perplexity.
But in this test by Huggingface (on A100), AWQ with Zyphyr-7B shows slightly lower VRAM usage (at small batch sizes) and higher throughput (on larger batch sizes), but higher latency.
I did a quick sanity check on A10G with facebook/opt-125m and AWQ was around 30% slower to generate 1k tokens.
So, in overall, I'd say it's better to stick to AutoGPTQ for now.


feat: fixes button for newsletter page

Preview Newsletter Page

@xmulligan Hello! Could you please review the button fix and merge it if everything looks good?


Recursive option to test imported children

Clear and concise description of the problem
As a developer using Vitest I want to recursively test my own imported files when I run vitest watch SpecificFileOrComponent (or any equivalant command), so it is easier to test specific parts of my project (see "Additional context" for an example).
I would like to make a PR for this potential feature
Suggested solution
An additional option we can add to the vitest command, like --recursive or --re to allow a recursive search.
I apologies if this is an unwanted feature, but for my use-case it would be quite nice-to-have
Alternative
I could write something locally where I check all the import statements of my files and generate the vitest watch command with all my file names. But I feel like I am just overcomplicating at that point.
Additional context
I have a lot of test files and when im working on part A of my application I do not care about B-to-Z.
I only care about what I use to make part A work.
Example
There already exists an option to filter on name. However, this does not meet my use case.
When I have a file ParentComponent with the following import statements:
import Child1 from '...'
import Child2 from '...'
import useFunctionality from '...'

...

Child1:
import someFunction from '...'
...

(also run SomeFunction its tests)
I want to be able to test the ParentComponent and also the Child1, Child2, useFunctionality, someFunction and their imported files when running vitest watch ParentComponent.
Validations

[X] Follow our Code of Conduct
[X] Read the Contributing Guidelines.
[X] Read the docs.
[X] Check that there isn't already an issue that request the same feature to avoid creating a duplicate.

From the description it looks like you are looking for https://vitest.dev/guide/cli.html#vitest-related

Ah, yes your right. Thanks mate!