test: false negative for volume leak check

After aborting a test run on a local cluster, I must have had one local volume left over from that aborted test. The next test then failed like this:
ESC[91mESC[1m• Failure in Spec Teardown (AfterEach) [47.146 seconds]ESC[0m
lvm-testing
ESC[90m/nvme/gopath/src/github.com/intel/pmem-csi/test/e2e/deploy/deploy.go:501ESC[0m
  sanity
  ESC[90m/nvme/gopath/src/github.com/intel/pmem-csi/test/e2e/deploy/deploy.go:488ESC[0m
    ESC[91mESC[1mPMEM-CSI [AfterEach]ESC[0m
    ESC[90m/nvme/gopath/src/github.com/intel/pmem-csi/test/e2e/storage/sanity.go:212ESC[0m
      stores state across reboots for single volume
      ESC[90m/nvme/gopath/src/github.com/intel/pmem-csi/test/e2e/storage/sanity.go:258ESC[0m

      ESC[91msame volumes before and after the test
      Expected
          <map[string][]string | len:3>: {
              "LVM Volumes on Node 1": [
                  "unset--c80a6509c9e210ba16c4943ae726af8a69c5a144be48e5fdeebcc9ac ndbus0region0fsdax -wi-a----- 36.00m",
              ],
              "LVM Volumes on Node 2": [""],
              "LVM Volumes on Node 3": [""],
          }
      to equal
          <map[string][]string | len:3>: {
              "LVM Volumes on Node 1": [
                  "unset--c80a6509c9e210ba16c4943ae726af8a69c5a144be48e5fdeebcc9ac ndbus0region0fsdax -wi-ao---- 36.00m",
              ],
              "LVM Volumes on Node 2": [""],
              "LVM Volumes on Node 3": [""],
          }ESC[0m

      /nvme/gopath/src/github.com/intel/pmem-csi/test/e2e/storage/sanity.go:1066

Note that the volumes are the same, i.e. no new volume has leaked. What has changed are the attributes (-wi-a----- ->-wi-ao----).
This happened during the reboot test. Either those attributes must not change (and we need to investigate this situation), or they may change and then shouldn't be included in the comparison.

attribute o means "device open". Typically, this is set when Logical Volume for example is mounted, and unset if volume is not mounted.

So it was open before and now no longer is? I guess that can happen if the volume was mounted before without Kubernetes knowing about it, so rebooting the node led to an umounted volume.
That's good enough for me, let's consider this a non-issue.


Refactor codebase into a python package

This PR lays the groundwork for being able to install neural-lam as a package, thereby making it possible to run from anywhere once the package has been installed. This means that it would be possible (in theory) to train neural-lam on a .npy-file based dataset (once https://github.com/mllam/neural-lam/pull/31 has been merged so that the training configuration is moved from neural_lam/constants.py to a yaml-config file) with the neural-lam package installed into a user's site-packages (i.e. in their virtualenv).
I appreciate that currently most of us will be checking out the codebase to make modifications, and then train a model with neural-lam and so these changes might seem superfluous. But making these later will be a lot harder than doing them now.
The primary changes are:

move all *.py that are currently outside of neural_lam/ into that folder, but keep the files the same
change all examples of running the neural-lam "scripts", e.g. python create_mesh.py by python -m neural_lam.create_mesh in the README
change all absolute imports to package-relative imports, i.e. from . import utils rather than from neural_lam import utils
add tests that all the CLI entrypoints to neural_lam can be imported and add ci/cd action to run these tests

I still need to resolve the issue around depending on cpu or gpu versions of pytorch. I know @sadamov found a work-around where the cpu or gpu package was automatically picked based on whether CUDA is detected. I haven't had time to look into this yet, but once I have done that I will mark this PR as complete from my side and ask for your thoughts on it :)
This PR is definitely a work-in-progress and is meant to serve as a discussion point.
(also this PR includes changes that PR #29 adds, so this PR definitely shouldn't be merged or even reviewed probably before #29 is in)

With #29 merged in I have been able to finish this off and I think it is in a pretty good shape now :) I'm curious to here what you think

If you are happy with this I would like to include this PR in the v0.2.0 release. I am realising now that I don't think I have explicitly stated that turning the codebase into a package should be part of v0.2.0, so I just want to double-check that we were all thinking it would be.

This looks great! Should the changelog also be updated for your changes?

Thanks! Yes, I thought I would complete the changelog once the reviews were done, but maybe I should just do it now :) I will do that

Great stuff! Happy to give this a full review later this week, but likely I won't have much to add.

I agree that being able to install with pip install -e seems important (necessary for us working with developing the package?). Moving most code to src/neural-lam/... seems like annoyingly many sub-directories. Is there some other way to do achieve this?
I have also implicitly viewed this as part of the 0.2.0 release.

I think with this we should also adjust the installation instructions in the Readme to install as a package.

I couldn't get the linting all green on package_inits branch. Looks like flake8 does not use the options we have specified in pyproject.toml (https://flake8.pycqa.org/en/latest/user/configuration.html#configuration-locations). Maybe we can use https://github.com/csachs/pyproject-flake8 to fix that?

change all absolute imports to package-relative imports, i.e. from . import utils rather than from neural_lam import utils

I learnt yesterday from @khintz that I have for years been wrong about relative vs absolute imports. I have for as long as I can remember I thought that relative imports were recommended over absolute imports. But I realise now this is incorrect. I do still prefer them, but I was wondering what people think. I would be ok with sticking with absolute imports if that is the consensus. Maybe a thumbs-up for relative imports and thumbs-down for relative imports on this comment could serve as a vote? @joeloskarsson, @sadamov, @SimonKamuk, @khintz

Moving most code to src/neural-lam/... seems like annoyingly many sub-directories. Is there some other way to do achieve this?

We don't have to add the src/ path prefix, but neural-lam/ would be enough. How about we stick with that?

I don't have very strong feelings about relative vs absolute imports, but if I have to choose one, I would go for absolute. That is easier to read (IMO) and there is general consensus towards that.

3. Add __init__.py files to neural_lam and neural_lam/models that exposes submodules and classes directly in the imported neural_lam module.

I see the merit of this @joeloskarsson, but it does mean that the time it takes to import neural-lam is increased. I think this why people generally don't do this by default, but I am ok with doing this. I don't see it as necessary though to make the codebase into a package.

I couldn't get the linting all green on package_inits branch. Looks like flake8 does not use the options we have specified in pyproject.toml (https://flake8.pycqa.org/en/latest/user/configuration.html#configuration-locations). Maybe we can use https://github.com/csachs/pyproject-flake8 to fix that?

Thanks @joeloskarsson, you're right. I tried adding pyproject-flake8 to the pre-commit config, but that didn't work. On the other hand it looks like https://github.com/john-hen/Flake8-pyproject did 😄

I think all changes are good 😄 I would propose to add a couple more things with this (summary of the same points I commented above, but here in a proper review):

Thanks @joeloskarsson! It was really helpful to have this list. I have merged in your changes and found a way around the flake8 issues (as you highlighted too). I have simply changed the install instructions to reflect that once torch is installed then the rest can be installed with pip install -e ., that was what you were intending too, right? I think this ready for a last review from you if you have time.

All looks good to me.

Great! Thank you. I will update the CHANGELOG and then merge


Fix branch name not showing up

Found an issue where the variable was not correct. For example, the git status command returns:
# On branch fix-git-status
# Changes to be committed:
#   (use "git reset HEAD <file>..." to unstage)
#
#	modified:   segments/git.fish

The code needs to select the 4th column, not the third. I believe the HEAD detached state would also need that. I checked the ahead/behind and that functionality already worked correctly.

Hi there,
Thank you for the fix, I am currently working on version 2 of fishline with a complete rewrite of the git segment. Will test your fix right now and merge it to master so this issue is fixed in v1 of this software.
Thanks for your contribution.

Thanks, I did realize afterwards when I had another problem with something else that my git version was ancient.

If you still want to try fishline out, I fixed the small issue that were still happening in the GIT segment in the dev branch. This version is compatible with git < 1.8.5 and should not cause any more compatibility problem.


Possible Regression: resolveJsonModule Projects must list all files or use an 'include' pattern.

TypeScript Version:  3.7.0-dev.20190912

Search Terms:
resolveJsonModule Projects must list all files or use an 'include' pattern
Code
tsconfig.json
{
  "extends": "../../tsconfig.base.json",
  "compilerOptions": {
    "outDir": "./dist",
    "rootDir": "./src",
    "strictNullChecks": true,
    "resolveJsonModule": true
  },
  "include": ["src/**/*.ts", "src/**/*.json"],
  "references": []
}

tsconfig.base.json
{
  "compilerOptions": {
    "module": "commonjs",
    "target": "es2017",
    "lib": ["es2017"],
    "composite": true,
    "esModuleInterop": true,
    "moduleResolution": "node",
    "emitDecoratorMetadata": true,
    "experimentalDecorators": true,
    "noUnusedLocals": true,
    "noUnusedParameters": true,
    "sourceMap": true,
    "skipLibCheck": true
  }
}

Expected behavior:
When resolveJsonModule is turned on, and json files have been included in the includes array, I should be able to import json files with no trouble.
Actual behavior:
When I use the above tsconfig options, and include a json file, I get the following error:

Related Issues:
https://github.com/microsoft/TypeScript/issues/31181

I'm seeing the same with this branch

@Razzeee https://github.com/elm-tooling/elm-language-server/blob/rank/tsconfig.json#L17 is not sufficient. You have to explicitly list json files to be included. Changing that to "include": ["src", "src/**/*.json"] fixes the issue.

Thank you, that fixes compilation, after restarting VSCode


Remove OS Validating WARNING in 4.4.x

If a wso2 product starts on an OS other than following

Linux
Unix
Mac OS
Windows Server 2003
Windows XP
Windows Vista
Windows 7
Mac OS X
Windows Server 2008
Windows Server 2008 R2
AIX

Following warning get shown in the startup logs.
[2017-05-10 17:53:53,526]  WARN - ValidationResultPrinter The running OS : Windows Server 2012 R2 is not a tested Operating System for running WSO2 Carbon

Closing the issue since this PR has been merged.


Create ciara

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t-sql hacks that shouldn't break mainline behavior

This is a:

[x] bug fix PR with no breaking changes — please ensure the base branch is master
[ ] new functionality — please ensure the base branch is the latest dev/ branch
[ ] a breaking change — please ensure the base branch is the latest dev/ branch

Description & motivation
MSFT's T-SQL has some weird quirks to it such as:

subquery aggregate columns need aliases; and
there is no boolean type, so there's no such thing as true and false. So in a few schema tests, I've replaced true with 1=1; and,
limit 0 at the end of the query translates in TSQL to SELECT TOP (0). If the purpose is to return zero rows, WHERE 0=1 will accomplish the same more universally.

Checklist

[ ] I have verified that these changes work locally on the following warehouses (Note: it's okay if you do not have access to all warehouses, this helps us understand what has been covered)

[ ] BigQuery
[ ] Postgres
[ ] Redshift
[ ] Snowflake

[ ] I have updated the README.md (if applicable)
[ ] I have added tests & descriptions to my models (and macros if applicable)
[ ] I have added an entry to CHANGELOG.md

@jtcohen6 — what do you think of this PR? IIRC we incorporated some similar logic into dbt core since it's non-breaking, but I also know we say "create a shim package" for other adapter functionality

@swanderz As for the current error, I think you just need to add the following line here:
vars:
  dbt_utils_dispatch_list: ['dbt_utils_integration_tests']

@swanderz As for the current error, I think you just need to add the following line here:
vars:
  dbt_utils_dispatch_list: ['dbt_utils_integration_tests']

looks good! could you add a changelog entry to this one as well?

done! stoked to have both these merged! game changer!

looks good! could you add a changelog entry to this one as well?

done! stoked to have both these merged! game changer!


Tiers: Comma decimal separator fails validation in currency fields.

Describe the bug
Using a comma as a decimal separator in currency fields doesn't work properly. Depending on browser typing a comma into a currency field either removes the whole value being typed or outright doesn't print the comma into the input.
To Reproduce

Go to the Collective page, click on the gear icon and switch to the Tiers section.
Add a new Tier or edit existing Tier.
Click on any field on with a currency symbol and try inputting an amount with a decimal using a comma as a decimal separator.
See error behaviour like in the screenshots below.

The value being edited completely removed from the form

Comma not being printed into the form

Expected behaviour
Comma accepted/saved as decimal separator
Additional context
In many regions, commas are used as decimal separators in money values.

spotted by @Betree while reviewing https://github.com/opencollective/opencollective-frontend/pull/3613

Initially, I had a look around and I came across this blog post on how <input type="number"> validation is implemented differently across browser vendors and also based on the locale.
For example, on my default browser (macOS, Chrome 80),  I can't seem to be able to get a comma into the field.

However, I was able to reproduce the error behaviour you pointed out in Firefox 73 and Safari 13.
And when I added a lang attribute (Norwegian for example) to the input element in components/InputField.js lang=nb commas were accepted in the input in Firefox 73 as well as periods.

But the error behaviour still persisted in the other browsers I had tried previously.
We could try adding lang attributes that would allow commas&periods to the input fields based on the currency set, however, behaviour across browsers may not be consistent.

@znarf also shared something on this topic the other day: https://technology.blog.gov.uk/2020/02/24/why-the-gov-uk-design-system-team-changed-the-input-type-for-numbers/.
There are two things here:

The question about localization, using , vs .. I've made some changes in our StyledInputAmount, when used as a controlled component with parseNumbers I believe the behavior is pretty comprehensive (but I haven't tested it on a lot of browsers):

It probably won't be perfect, but even just relying on the native input and warning if the number is not valid is, in my opinion, an acceptable solution.

Another problem, that is to me the really problematic part here, is that all the options after the invalid one gets removed. Let's say you have [ 10, 20, 30, 40, 50 ] and you try to add a comma or a dot on 30 then it should never remove the options after (40 and 50).

So for me the two things that we need to do here are:

Have a clear error message if a number is not formatted properly
If a number is invalid, don't remove the entries after it

I'll get on this and submit a PR asap.

Another problem, that is to me the really problematic part here, is that all the options after the invalid one gets removed. Let's say you have [ 10, 20, 30, 40, 50 ] and you try to add a comma or a dot on 30 then it should never remove the options after (40 and 50).

However, for this, I haven't been able to recreate this behaviour unless I'm missing something/misunderstanding you. Like in the example you shared originally it only appears to remove the invalid value being edited and doesn't affect the values after it. (like in your example, out of [ 5, 25, 66] when you edit the 25 that makes it invalid, the 66 seems to remain intact.)

@brymut Yes you're right, I was not remembering the behavior correctly. So to edit my second point:

If a number is invalid, don't remove it but eventually show a warning/an error message

This also happens when submitting an expense.


update: set kserve as Managed by default DSC + fix nilpointer

set OSSM and serving both as default Managaed
set kserver as default Managed too
keep modelmesh for new installation as Removed but if it is upgrade from old verison, keep it as-was
sync fix upstream issue 755

live build quay.io/wenzhou/rhods-operator-catalog:v2.5.1
test:
installed 2 dependent operators
install rhods operator
DSCI created, DSC created, 4 FT created

kserve is managed, modelmesh is removed

@zdtsw what is the reasoning on switching both kserve and modelmesh to managed? I think we need to be careful here as it can have an unexpected impact to user's clusters. Lets talk before merging this.

@zdtsw what is the reasoning on switching both kserve and modelmesh to managed? I think we need to be careful here as it can have an unexpected impact to user's clusters. Lets talk before merging this.

from Adam's confirmation, i reset back modelmesh to Removed for any new clean installation

test with upgrade v2.4 to v2.5
FROM live build (rhods 2.4 branch): quay.io/wenzhou/rhods-operator-catalog:v2.4.999
DSCI

change DSC to  make only dashboard Managed

TO live build from this PR: quay.io/wenzhou/opendatahub-operator-catalog:v2.5.130
upgrade

check DSCI

check DSC


0.12.0 not buildable on arm64, ppc64el

Hi,
https://buildd.debian.org/status/package.php?p=grpc&suite=unstable shows that version 0.12.0-1 of grpc isn't building successfully on arm64 and ppc64el, two architectures where the previous version did build successfully. Due to this, it won't automatically transition to testing, and I won't be able to provide a backport to jessie.
Please figure out why it isn't building and make a new point release that fixes it, and I'll upload that to unstable and we can try again.

Hey all,
I'm on a CentOS 6.3 x86_64 (RHEL) system using GCC 4.4.6 20120305 (Red Hat 4.4.6-4) compiler and I having compile issues on the boringssl build.
I did a clone of master branch,then got submodules, then make.
The crypto/bio/connect.c file had shadow Warnings about the use of close at line 127, et al.
First question is do I need boringssl on linux cant I just use openssl ?  What do I do to build just delete boringssl directory?
Is master a stable branch?
Are sandbox branches peer tested before merged  back to master ?
Is 0.12.0 branch stable?

Nicolas, could you advise on how to proceed with this issue?

Hm, we shouldn't be compiling boringssl for the Debian build. There's something wrong here. The Makefile should detect we have a working system OpenSSL. Another solution might be to avoid recursively cloning the repository in order to build the Debian package.
I am unfamiliar with the way the Debian build system works. How can I poke at it ?

You can see the build log for these architectures here:
https://buildd.debian.org/status/fetch.php?pkg=grpc&arch=arm64&ver=0.12.0-1&stamp=1453766491
https://buildd.debian.org/status/fetch.php?pkg=grpc&arch=ppc64el&ver=0.12.0-1&stamp=1453768208
At a quick glance they both look like they're failing in the same way.
Do you have access to an arm64 or ppc64el system to reproduce on? If you do, I can provide more details, otherwise these build logs is probably your most useful thing.

Oh these are beautiful, thank you.
Turns out that this got fixed at some point by this commit: 069384da5e82b9e4274a967589bdbd58bff44507. We can backport it to 0.12 if needed. But it turns out this is also in the 0.13 release we've tagged, so maybe we could skip 0.12 as a debian package and go straight to 0.13 ?

Is 0.13 ready for release? I can package it.

0.13.1 just got released...


Training and Inference

@pqhieu thanks for open sourcing the wonderfull work , i had few queries
Q1 have you trained the architecture on the available other dataset like semanttic Kitti and 3D dataset
Q2 If not trained can we follow the same training pipeline , if trained can you please share the pre-trained model
Q3 can we use the currently pre-trained model to test on custom dataset which less number of point cloud density
Thanks in advance

Hi @abhigoku10,
Thanks for your interest in the paper. To answer your questions:

No I haven't trained the architecture on other datasets except from S3DIS and SceneNN.
You can absolutely use the same training pipeline for SemanticKITTI or other dataset.
I would advise you to test on your custom dataset first, and then to finetune the model to see if there is any improvement.


allow underscores in tag ids

We often write our HTML with underscores used in the tag identifiers. Unless you had a specific reason not to allow this functionality I would like to have it.

+1
It currently breaks on a mobile framework I am using. Probably several more.


reduction behavior is different from numpy for non-contiguous arrays

Currently the following unit test is failing (note: the unit test is annotated with testing.slow).
https://github.com/cupy/cupy/blob/fed8d1375a0a5bdff5f52ef1a2dd6ae8a1b1aa89/tests/cupy_tests/math_tests/test_sumprod.py#L51-L57
======================================================================
FAIL: test_sum_axis_huge (test_sumprod.TestSumprod)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/work/cupy/cupy/testing/helper.py", line 125, in test_func
    check_func(cupy_result, numpy_result)
  File "/work/cupy/cupy/testing/helper.py", line 194, in check_func
    c.flags.f_contiguous, n.flags.f_contiguous))
AssertionError: The state of f_contiguous flag is false. (cupy_result:False numpy_result:True)

It seems that numpy's iterator and cupy's reduction routine behaves differently for noncontiguous arrays.
The minimal code to reproduce this issue:
import numpy as np
import cupy as cp

def f(xp):
  a = xp.broadcast_to(xp.ndarray((3, 1, 2)), (3, 2, 2))
  return a, a.sum(axis=0)

print(f(np)[1].flags)
print(f(cp)[1].flags)

f(np) / f(cp) return F-contiguous / C-contiguous array, resp.

Closing as we do not guarantee compatibility of strides:  #2717


Change build badge for Jenkins CI

Change build badge for Jenkins CI to display master branch build status only
Signed-off-by: Gong Su<EMAIL_ADDRESS>

Jenkins Linux amd64 Build #1545 [push] Change build badge for J... started at 11:34

Jenkins Linux s390x Build #1543 [push] Change build badge for J... started at 12:34

Jenkins Linux ppc64le Build #683 [push] Change build badge for J... started at 12:38

Jenkins Linux s390x Build #1543 [push] Change build badge for J... passed after 47 min

Jenkins Linux amd64 Build #1545 [push] Change build badge for J... passed after 48 min

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kubectl create job --image can't create job from private image

tl;dr I want to run a 1 off job from a cronjob or an image that allows me to override the command (specifically a parameter in the command specified in the cronjob)
the two ways to create a job are

from an image using the --image flag but this doesn't allow for using private images.
from a cronjob using the --from flag but this doesn't allow for overriding the command.

I can see that option2 would not be the correct solution, so option 1 must be the way to go for specifying a command. BUT there's no way to tell the k create job command to use the image pull secret present in the namespace. When i try to use the --overrides flag, I get an error that --overrides is unknown.
What happened:
When trying to create a job from an image using kubectl I get an error that the image can't be pulled.
> kl -f -n namespace podname
Error from server (BadRequest): container "jobname" in pod "podname" is waiting to start: image can't be pulled

What you expected to happen:
The image to be pulled and the job to be executed.
How to reproduce it (as minimally and precisely as possible):
using a private image:
❯ k create job jobname -n namespace --image=myprivate/imagename:tag -- some command --with-parameters

Anything else we need to know?:
My other option is to create a job from an existing cronjob, but when you specify the --from parameter in the create job command you are unable to override the command.
Environment:

Kubernetes version (use kubectl version):

Client Version: version.Info{Major:"1", Minor:"21", GitVersion:"v1.21.4", GitCommit:"3cce4a82b44f032d0cd1a1790e6d2f5a55d20aae", GitTreeState:"clean", BuildDate:"2021-08-13T15:45:10Z", GoVersion:"go1.16.6", Compiler:"gc", Platform:"linux/amd64"}
Server Version: version.Info{Major:"1", Minor:"18+", GitVersion:"v1.18.20-gke.901", GitCommit:"db8463aecdfe0eb5d9067effa2fd5ab3ff7a988e", GitTreeState:"clean", BuildDate:"2021-08-04T23:27:42Z", GoVersion:"go1.13.15b4", Compiler:"gc", Platform:"linux/amd64"}
WARNING: version difference between client (1.21) and server (1.18) exceeds the supported minor version skew of +/-1

Cloud provider or hardware configuration: GKE
OS (e.g: cat /etc/os-release): PRETTY_NAME="Ubuntu 21.04"
Kernel (e.g. uname -a): Linux hihi 5.11.0-31-generic kubernetes/kubernetes#33-Ubuntu SMP Wed Aug 11 13:19:04 UTC 2021 x86_64 x86_64 x86_64 GNU/Linux

as a workaround I realized I could add an imagePullSecret to the default serviceAccount in my namespace. Since the image pull secret exists in the namespace already, this is a viable workaround for me, but ideally we should be able to specify.

ok while that's working to run the image, I still need to be able to pass env vars into it :/
the same env vars that are defined in the cronjob.
maybe at this point, the "best" solution would be to "create job --from=cronjob" but allow for overriding the command?

maybe at this point, the "best" solution would be to "create job --from=cronjob" but allow for overriding the command?

We discussed this and using a cronjob as a cli templating engine (via overrides) isn't something we want to support.
re: flags for imagePullSecret we're really trying to avoid adding more flags to commands. Structured data is hard to represent via cli flags and arguments without adding to maintainer burden. The declarative approach is often better than the imperative.
You could possibly use a yaml file for the job and envsubst to plug in your env vars if you don't want to edit it every time.
You could also build a controller (it's easier than you may think) to create these jobs.
/close


[Components] Checkbox integration

In this ticket, we handle the basis, groupment and alignment: https://zeroheight.com/1186e1705/v/latest/p/668155-checkbox/b/700a17
The specificites will be done in another ticket: https://github.com/orgs/adevinta/projects/4/views/10?pane=issue&itemId=24374373


Make tests discoverable automatically

Automatic recursive search from current directory. Find shell scripts whose names start with test_ and end in .sh or .bash or .ksh.
If first non-flag item is a directory, search from that directory down.
If the first non-flag item is a script, just run that script.

Done in commit https://github.com/ColemanTom/shellcov/commit/4df4b423c0aa0ad256b4b3e4f4471dbcdb0c95ae


Fix up the order of the fingers joints in YARP

We spotted that the thumb and index abductions (handled by a dedicate board) come at the end of the joint set. This goes a bit against the order we have on iCub and, at least, segregates the two joints of the thumb apart.
We need to harmonize the sequence both in the real robot and in the simulated one, most likely using the remapper.

Right now the joints' order for the hands in gazebo is:
l_thumb_add l_thumb_prox l_thumb_dist l_index_add l_index_prox l_index_dist l_middle_prox l_middle_dist l_ring_prox l_ring_dist l_pinkie_prox l_pinkie_dist

I don't know if this is the desired order, but we should have the same joint order on the simulated and real robot.
First of all, I noticed that the names on the real robot do not match the names in simulation that follows this table:
See https://github.com/robotology/robots-configuration/blob/b31e860ece0e0ad539a6f2d74b19cf1f4cf60931/ergoCubSN000/hardware/mechanicals/right_arm-eb24-j11_12-mec.xml#L12.
r_hand_thumbmetacarpus -> r_thumb_add ?
r_hand_indexadduction -> r_index_add ?

l_thumb_add l_thumb_prox l_thumb_dist l_index_add l_index_prox l_index_dist l_middle_prox l_middle_dist l_ring_prox l_ring_dist l_pinkie_prox l_pinkie_dist

This list refers to the physical joints but what we control is instead:

l_thumb_add l_thumb_oc l_index_add l_index_oc l_middle_oc l_ring_pinky_oc

I took the freedom to use the suffix oc (standing for open-close) since we cannot talk about either proximal or distal joints in this case. We can even think of removing the suffix oc, if it's not self-explaining.
I deem that the order you reported, @Nicogene, is ok and best resembles what we have on iCub.
Actually, on iCub l_index_add  should come as the first on, but in that case the adduction includes also the ring and the pinky, which is not our case with ergoCub. After all, the order looks fine to me.

r_hand_thumbmetacarpus -> r_thumb_add ?
r_hand_indexadduction -> r_index_add ?

This is correct 👍🏻

cc @fiorisi @marcoaccame @davidetome @maggia80

For me it's ok; let's wait for others to chime in and agree on this.
cc @fiorisi @traversaro
Another point is related to the pinky. It's not just pinky but rather ring_pinky.
I'm in favor of the latter convention, which looks more explicit to me.

I agree with the _oc suffix and with ring_pinky, thanks for handling this!

For having this joint list:

l_thumb_add l_thumb_oc l_index_add l_index_oc l_middle_oc l_ring_pinky_oc

I tried to configure the remapper like this
<device xmlns:xi="http://www.w3.org/2001/XInclude" name="left_arm-mc_remapper" type="controlboardremapper">
    <paramlist name="networks">
        <elem name="left_arm_joints1">(  0  1 0 1 )  </elem>
        <elem name="left_arm_joints2">(  2  3 0 1 )  </elem>
        <elem name="left_arm_joints3">(  4  6 0 2 )  </elem>
        <elem name="left_arm_joints5">(  7  7 0 0 ) </elem>
        <elem name="left_arm_joints4">(  8  8 0 0 ) </elem>
        <elem name="left_arm_joints5">(  9  9 1 1 ) </elem>
        <elem name="left_arm_joints4">(  10 12 1 3 ) </elem>
    </paramlist>
    <param name="joints"> 13 </param>
    <action phase="startup" level="5" type="attach">
        <paramlist name="networks">
            <elem name="left_arm_joints1"> left_arm-eb2-j0_1-mc   </elem>
            <elem name="left_arm_joints2"> left_arm-eb4-j2_3-mc   </elem>
            <elem name="left_arm_joints3"> left_arm-eb31-j4_6-mc  </elem>
            <elem name="left_arm_joints4"> left_arm-eb23-j7_10-mc </elem>
            <elem name="left_arm_joints5"> left_arm-eb25-j11_12-mc </elem>
        </paramlist>
    </action>
    <action phase="shutdown" level="20" type="detach" />
</device>

But the yarprobotinterface parser complains about duplication of parameters (e.g. left_arm_joints5).
Is there an alternative to the configuration I wrote?
Otherwise, if we put thum_add and index_add at the beginning of the list we should not have issues.
cc @traversaro @davidetome

But the yarprobotinterface parser complains about duplication of parameters (e.g. left_arm_joints5).
Is there an alternative to the configuration I wrote?

I would use the axesNames way of configuring the controlboardremapper instead of the networks way, see https://yarp.it/latest/classControlBoardRemapper.html#details . As a side effect I personally find axesNames  more mantainable and readable, but that may be just my taste.

I would use the axesNames way of configuring the controlboardremapper instead of the networks way, see yarp.it/latest/classControlBoardRemapper.html#details . As a side effect I personally find axesNames more mantainable and readable, but that may be just my taste.

I never used axesNames for remapping, do you have an example of configuration file?
In the meanwhile I found a workaround to attach multiple times the same device:
https://github.com/robotology/robots-configuration/pull/435
I tested on the robot and we have the names/order we want, it is a little bit dirty but it works

I never used axesNames for remapping, do you have an example of configuration file?

Ok 👍🏻
I'll wait for any feedback on this before merging.

I would use the axesNames way of configuring the controlboardremapper instead of the networks way, see yarp.it/latest/classControlBoardRemapper.html#details . As a side effect I personally find axesNames more mantainable and readable, but that may be just my taste.

I never used axesNames for remapping, do you have an example of configuration file?

It is not in the controlboardremapper case, but the syntax is the same for all devices that use this parameter: https://github.com/robotology/robots-configuration/blob/b31e860ece0e0ad539a6f2d74b19cf1f4cf60931/iCubMoscow01/estimators/wholebodydynamics.xml#L6 . You just to list the name of the axis you want to consider in the order you want to consider them.

I committed here https://github.com/Nicogene/robots-configuration/tree/fix/ergocubHands2 the version proposed by @traversaro, I will test it on the robot.

I committed here https://github.com/Nicogene/robots-configuration/tree/fix/ergocubHands2 the version proposed by @traversaro, I will test it on the robot.

It worked!
Job merged via:

https://github.com/robotology/robots-configuration/pull/436

@Nicogene, I would continue here the work to update the Gazebo side as well.
Later, we can harmonize the naming convention also on the FW with a dedicated issue.

Later, we can harmonize the naming convention also on the FW with a dedicated issue.

Closing in favor of #56.


Feat/zeus/2020.9

Not sure why gitlab isn't listing the test results here, but they passed: https://main.gitlab.in.here.com/olp/edge/ota/connect/client/meta-updater/-/pipelines/1048658


presence: Switch webapp to use server-provided params.

Preparatory PR for https://github.com/zulip/zulip/pull/16381 - implementing the plan discussed around https://chat.zulip.org/#narrow/stream/3-backend/topic/UserPresence.20system.20rewrite/near/1506240

I pushed back here with a bunch of updates to comments/documentation and also going with 60/200 as our numbers.
I've marked this to merge once CI finishes, thanks so much for doing this quickly @mateuszmandera!


[Feature] - Support for Cql Proxy

Check that docker is installed
docker --version

Check that docker is running
docker ps

Commands to start and stop cqlproxy

astra db proxy beam_sdk_integration_test
astra db stop-proxy beam_sdk_integration_test

Start (-d) for detach
docker run -d -p 9042:9042   datastax/cql-proxy:v0.1.4   --astra-token TTT

cql-proxy --astra-token TTTT

cql-proxy is not really in use with improvements in clients


Bump pnpm version to 5.18.10

to have security fix for<EMAIL_ADDRESS>(see https://github.com/pnpm/pnpm/releases/tag/v5.18.10 for details)

I am going to override check-enforcer. The failing build timed out while the tests are running fine. We may want to increase build timeout limit or look into reducing build time as we have more libraries to build now.

/check-enforcer override


Honestly not really sure, raymarching.cu

It seems that ninja tries to run this file and then although it says two errors I have a few more variables than that, that it says are referenced but not declared. near, rdz, rdy, rdx seem to be the main ones, and because those ones failed, it starts going off about unexpected tokens and cuda errors having to be overridden. If anyone has any ideas how to fix that, I'd really appreciate it. I've pasted a small part of it but can provide the error in its entirety if needed.
F:\Desktop\game_files\ai_for_game\Image_Generator\stable-dreamfusion\raymarching\src\raymarching.cu(737): warning #177-D: variable "near" was declared but never referenced
detected during instantiation of "void kernel_march_rays(uint32_t, uint32_t, const int *, const scalar_t *, const scalar_t *, const scalar_t *, float, float, uint32_t, uint32_t, uint32_t, const uint8_t *, const scalar_t *, const scalar_t *, scalar_t *, scalar_t *, scalar_t *, const scalar_t *) [with scalar_t=float]"
(811): here
2 errors detected in the compilation of "F:/Desktop/game_files/ai_for_game/Image_Generator/stable-dreamfusion/raymarching/src/raymarching.cu".
raymarching.cu
ninja: build stopped: subcommand failed.

It seems that ninja tries to run this file and then although it says two errors I have a few more variables than that, that it says are referenced but not declared. near, rdz, rdy, rdx seem to be the main ones, and because those ones failed, it starts going off about unexpected tokens and cuda errors having to be overridden. If anyone has any ideas how to fix that, I'd really appreciate it. I've pasted a small part of it but can provide the error in its entirety if needed.
F:\Desktop\game_files\ai_for_game\Image_Generator\stable-dreamfusion\raymarching\src\raymarching.cu(737): warning #177-D: variable "near" was declared but never referenced
      detected during instantiation of "void kernel_march_rays(uint32_t, uint32_t, const int *, const scalar_t *, const scalar_t *, const scalar_t *, float, float, uint32_t, uint32_t, uint32_t, const uint8_t *, const scalar_t *, const scalar_t *, scalar_t *, scalar_t *, scalar_t *, const scalar_t *) [with scalar_t=float]"

(811): here
2 errors detected in the compilation of "F:/Desktop/game_files/ai_for_game/Image_Generator/stable-dreamfusion/raymarching/src/raymarching.cu".
raymarching.cu
ninja: build stopped: subcommand failed.

Sure thanks, but it still doesn't solve the issue... any clues?


Upgrade to Vite5

Resolves #989

Thanks!

Had to revert this one. Broke things on production:

That's weird!

@faissaloux managed to merge https://github.com/laravelio/laravel.io/pull/1024 which fixes this


Don't issue a use-before-declared error for a property that exists in a superclass

Fixes #14822
@sandersn What's the proper way to get a superclass symbol from a class symbol?

@sandersn Any more comments?


git repository lack tags corresponding with CPAN releases.

Hi,
Please consider taging CPAN releases as git tags.  That helps those wanting correlate CPAN releases with atomic git commits, e.g. those (like me, for Debian) redistributing your nice project.

I've added tags at what I believe are the commits corresponding to the CPAN releases.  Let me know if anything seems off.

Quoting Anthony Mirabella (2018-12-23 07:35:26)

I've added tags at what I believe are the commits corresponding to the CPAN releases.  Let me know if anything seems off.

Thanks!
--

Jonas Smedegaard - idealist & Internet-arkitekt
Tlf.: +45 40843136  Website: http://dr.jones.dk/

[x] quote me freely  [ ] ask before reusing  [ ] keep private


set correct links to wikipedia

one link went to the german version
another went to a 404-cat.

ack


Similar() to find similar pages by tags is broken.

Created missing script and added "top count" parameter.


Png tweaks

Port of https://github.com/ianshmean/ImageIO.jl/pull/3 where I also describe what is going on.
This should work with https://github.com/JuliaIO/FileIO.jl/pull/257, at least locally it seems to work fine. I'd certainly welcome some code review here:)
I ran the benchmarks from https://github.com/JuliaIO/PNG.jl/issues/1, but I'm not sure how make FileIO actually use QuartzImageIO (guess it's only used on Mac?), so here are my local results:
[ Info: Testing with rand(RGB{N0f8}, 244, 244) image -----------------------------
[ Info: Testing FileIO with ImageMagick (dedicated environment, new instance)
Time to first save (including save): 2.2 seconds
Save @btime:  23.942 ms (1424 allocations: 597.88 KiB)
Load @btime:  4.331 ms (1584 allocations: 605.14 KiB)

[ Info: Testing FileIO with PNG (dedicated environment, new instance)
Time to first save (including save): 4.708 seconds
Save @btime:  2.700 ms (46 allocations: 178.64 KiB)
Load @btime:  3.009 ms (182 allocations: 359.53 KiB)

[ Info: Testing with rand(RGB{N0f8}, 20000, 4000) image -----------------------------
[ Info: Testing FileIO with ImageMagick (dedicated environment, new instance)
Time to first save (including save): 30.831 seconds
Save @btime:  27.494 s (1424 allocations: 686.72 MiB)
Load @btime:  5.206 s (1591 allocations: 686.73 MiB)

[ Info: Testing FileIO with PNG (dedicated environment, new instance)
Time to first save (including save): 6.485 seconds
Save @btime:  4.703 s (49 allocations: 229.04 MiB)
Load @btime:  4.599 s (185 allocations: 457.92 MiB)

Note that integrating this MR into FileIO might be technically breaking as the rules that determine the color_type and bit_depth might not always be the same as before (we'll need to verify that).

If you want to run a specific package's FileIO-based load and save, you can do this:
using FileIO
f = File(format"PNG", "/tmp/img.png")
ImageMagick.save(f, img)
PNG.save(f, img)

Wonderful. Thanks so much @Drvi. Indeed, QuartzImageIO is mac-only.
@timholy I probably should've just benchmarked like that, but I wanted to double-check load time for each from a fresh session. Results look great!
[ Info: Testing with rand(RGB{N0f8}, 244, 244) image -----------------------------
[ Info: Testing FileIO with ImageMagick (dedicated environment, new instance)
Time to first save (including save): 2.303 seconds
Save @btime:  27.416 ms (3562 allocations: 710.64 KiB)
Load @btime:  5.335 ms (3722 allocations: 717.97 KiB)

[ Info: Testing FileIO with QuartzImageIO (dedicated environment, new instance)
Time to first save (including save): 2.37 seconds
Save @btime:  8.847 ms (1837 allocations: 794.13 KiB)
Load @btime:  3.456 ms (2062 allocations: 574.75 KiB)

[ Info: Testing FileIO with PNG (dedicated environment, new instance)
Time to first save (including save): 2.05 seconds
Save @btime:  4.608 ms (46 allocations: 178.64 KiB)
Load @btime:  3.145 ms (182 allocations: 359.59 KiB)

[ Info: Testing with rand(RGB{N0f8}, 20000, 4000) image -----------------------------
[ Info: Testing FileIO with ImageMagick (dedicated environment, new instance)
Time to first save (including save): 33.791 seconds
Save @btime:  29.370 s (3562 allocations: 686.83 MiB)
Load @btime:  5.350 s (3729 allocations: 686.84 MiB)

[ Info: Testing FileIO with QuartzImageIO (dedicated environment, new instance)
Time to first save (including save): 15.481 seconds
Save @btime:  13.516 s (1837 allocations: 915.62 MiB)
Load @btime:  4.120 s (2064 allocations: 610.46 MiB)

[ Info: Testing FileIO with PNG (dedicated environment, new instance)
Time to first save (including save): 9.059 seconds
Save @btime:  6.418 s (49 allocations: 229.04 MiB)
Load @btime:  4.638 s (185 allocations: 457.92 MiB)


Allow redis server and redis sentinel to be installed on the same server

Do you think we should merge this patch branch with master?

Do you think we should merge this patch branch with master?

We could. I guess it doesn't matter. The code to pull it pulls it via the branch but it's our fork so we might as well.


[FEATURE] Implement Xpos for inference

Is your feature request related to a problem? Please describe.
Please implement Xpos Rotary Embedding for inference, as models can now be trained in or utilize extended context lengths using it, and without gptq inference supporting it, we can't use huggingface inference + extended ctx's (due to only implementing decent fp16 inference) without running out of vram
Describe alternatives you've considered
Using inference of hf transformers with patched in xpos, but it is limited due to higher vram costs in fp16/fp8, and more vram is needed for larger ctx lengths

For context this is a way someone has done it with hf transformers:
https://github.com/kaiokendev/cutoff-len-is-context-len/blob/main/util/xpos_rope_llama_monkey_patch.py

@PanQiWei Where did you gone?


1h & 60m time interval

hi all,
I get data from 1h & 60m time intervals in  0830-0930 format. Is it possible to get data  in 0800-0900 format.
I hope question is meaninful
best regards,

Hi All,
I'm not sure if this a related issue, but when I try to pull anything other than daily data, the dataframe index values returned are not unique and all still in the date format, not in datetime with the hour or minute granularity.  Also I am unable to readjust them to the appropriate datetime with hour format.
For example:
df_test = yf.download(tickers='NFLX', period='100d', interval='1h')
df_test.tail(20)

If I run the comparable syntax for the pandas_datareader, the '1h' still has issues, but the '1m' yields expected data:
df_test = pdf.get_data_yahoo(tickers='NFLX', period='100d', interval='1m')

@KennSmithDS  please  check issue #125.
my problem is different

as you see above picture time interval  like this
2020-10-26 09:30:00-04:00  -  2020-10-26 10:30:00-04:00
is it possible get data like
2020-10-26 09:00:00-04:00  -  2020-10-26 10:00:00-04:00
thanks for all replies.

as you see above picture time interval like this
2020-10-26 09:30:00-04:00 - 2020-10-26 10:30:00-04:00
is it possible get data like
2020-10-26 09:00:00-04:00 - 2020-10-26 10:00:00-04:00

This is how the yahoo api returns it. Try https://query1.finance.yahoo.com/v8/finance/chart/AAPL?range=1mo&interval=1h and convert the integer timestamp using datetime.datetime.fromtimestamp(1602599400). Then you get the 30 minute time datetime.datetime(2020, 10, 13, 16, 30).
If you figure out why the api returns it like that, please let me know.

Market opens at 9:30 so that's when first interval begins.


Showing this Error Cannot read property 'getAll' of undefined

Closing issue due to inactivity

@dileepbolisetti I'm facing the same problem. May I ask how you solved it?


Dynamic Paths

Code for handling extended authentication paths. The part beyond the normal path will be passed on to the setup, callback and failure phases.
This is very useful in cases where you're using the same provider for different authorization scenarios. As an  example, you might log normal users in via /auth/facebook, while allowing admins to authorize restricted actions using /auth/facebook/projects/:id. In this example, rails routes would be set up as:
match '/auth/facebook/setup' => 'sessions#setup'
match '/auth/facebook/callback' => 'sessions#create'
match '/auth/failure' => 'sessions#failure'
match '/auth/facebook/admin/projects/:id/setup' => 'admin/facebook_accounts#setup'
match '/auth/facebook/admin/projects/:id/callback' => 'admin/facebook_accounts#callback'
match '/auth/facebook/admin/projects/:id/failure' => 'admin/facebook_accounts#failure'
Tests are included. I'm using this in a rails app with Facebook and Twitter providers, but haven't tested it with other providers.

Closing this out since it applied to 0.3 branch and the code has changed quite a bit. I'd be willing to entertain a new approach to this on master.

@mbeligh ok, thanks for the info. i might look into a 1.0 version if you find it worthwhile. i haven't yet looked into what changed since 0.3. but i gather that the functionality is not availably out of the box.


[stable/redis] support for setting up ClusterIP

hello. i like to specify ClusterIP for pods.
I beginning to be familiar with helm , please review my PR

/ok-to-test

Please document also the new option in the README file. You should add it to the table.

looks like all is done, pls review

Hi @cronolio,
It seems you still need to do two things before this PR can be merged:

You need to fix the conflicts in the stable/redis/Chart.yaml file
You need to sign your commits. Check this link to know what you have to do

2. You need to sign your commits. Check [this link](https://github.com/helm/charts/pull/15470/checks?check_run_id=167787690) to know what you have to do

Hello, how can i do this via github website interface?


No Wire.beginTransmission and no Wire.endTransmission needed with Wire.requestFrom

A Wire.requestFrom is encapsulated by Wire.beginTransmission and Wire.endTransmission. But those should only be used when writing data, they can be removed.
The Wire.requestFrom is a complete I2C transaction on its own.

Sorry. This article is about 6 years old and probably won't be updated unless it is broken. Because I personally verified every thing posted I won't make changes without verifying the changes as well - But I don't have the hardware anymore to do so.


Webhook for email verification

When a user clicks the email verification link sent by parse there should be some way to create an event in response. I tried implementing this by checking if the emailVerified field became true in the beforesave and aftersave functions but apparently these don't run on verification either! If anyone has a workaround for this please let me know but there should be some way to capture this event.

Hey, thanks for reporting this issue.
Can you please add all the info specified in the template? This is necessary for people to be able to understand and reproduce the issue being reported.


Null pointer exception when using model

Running this application with EF 7.0.0-rc1-final (i.e. after running Install-Package EntityFramework.Commands –Pre and Install-Package EntityFramework.MicrosoftSqlServer –Pre)
using Microsoft.Data.Entity;
using Microsoft.Data.Entity.Infrastructure;
using System.Collections.Generic;
using System.ComponentModel.DataAnnotations;
using System.ComponentModel.DataAnnotations.Schema;
using System.Linq;
class Program
{
    private static void Main(string[] args)
    {
        var options = new DbContextOptionsBuilder();
        options.UseSqlServer(@"Server=.;Database=Test;Integrated Security=True;");
        using (var dc = new DatabaseContext(options.Options))
            dc.Bars.Where(u => u.Id == 1).SingleOrDefault();
    }
}
public partial class DatabaseContext : DbContext
{
    public DatabaseContext(DbContextOptions options) : base(options) { }
    public DbSet<Bar> Bars { get; set; }
}
public partial class Foo
{
    [NotMapped]
    public string[] ListDisplay { get { return new string[0]; } }
    [Key]
    public int Id { get; set; }
    public ICollection<Bar> Bars { get; set; }
    public ICollection<Baz> Bazs { get; set; }
}
public partial class Bar
{
    [NotMapped]
    public string[] ListDisplay { get { return new string[0]; } }
    [Key]
    public int Id { get; set; }
    public int FooId { get; set; }
    [ForeignKey("FooId")]
    [InverseProperty("Bars")]
    public Foo Foo { get; set; }
}
public partial class Baz
{
    [Key]
    public int Id { get; set; }
    public int FooId { get; set; }
    [ForeignKey("FooId")]
    [InverseProperty("Bazs")]
    public Foo Foo { get; set; }
}

Results in this exception in the call to "Where(...)":

An unhandled exception of type 'System.ArgumentNullException' occurred in EntityFramework.Core.dll
ParamName: navigation

I tried using the "aspnetcidev" package source, but using that currently results in a "missing method" error related to logging.

Make sure you are using Microsoft.EntityFrameworkCore.* packages, we had a rename/rebranding and there are two sets of packages available currently on myget, the new one that works and the old one that doesnt

So what you are saying is that I should use the "aspnetvnext" myget repository(?), as the nuget only have an alpha-version, and issue Install-Package Microsoft.EntityFrameworkCore.Commands –Pre and Install-Package Microsoft.EntityFrameworkCore.SqlServer –Pre? It does seem to fix it.
Unfortunately the database provider used in the real app does not yet support this version. But thanks for the note.

@andershol correct, you should install from https://www.myget.org/F/aspnetvnext/... or wait til we ship RC2. Depending on the provider in use, they may have nightly builds that work with out nightly feed (though things are often broken at the moment).


Inovelli LZW36 - Fan turning off after 2h10m (just like #2159)

Is your problem within Home Assistant (Core or Z-Wave JS Integration)?
NO, my problem is NOT within Home Assistant or the ZWave JS integration
Is your problem within ZWaveJS2MQTT?
NO, my problem is NOT within ZWaveJS2MQTT
Checklist

[X] I have checked the troubleshooting section and my problem is not described there.

[X] I have read the changelog and my problem was not mentioned there.

Describe the bug
What causes the bug?
I have an Inovelli LZW36 Red Series Fan + Light Switch that was previously working correctly with Hubitat. I excluded the device and included it via the Z-Wave JS addon in HA. I have also since tried excluding the device, resetting it, and then reincluding it.
What do you observe?
The fan turns off by itself after 2 hours and 10 minutes.
What did you expect to happen?
The fan to remain on after being turned on until turned off.
Steps to reproduce the behavior:

Turn on the fan via HA.
Wait for 2 hours and 10 minutes.
The fan turns off.

Device information
Manufacturer: Inovelli
Model name: LZW36
Node ID in your network: 41
How are you using node-zwave-js?

[ ] zwavejs2mqtt Docker image (latest)
[ ] zwavejs2mqtt Docker image (dev)
[ ] zwavejs2mqtt Docker manually built (please specify branches)
[ ] ioBroker.zwave2 adapter (please specify version)
[X] HomeAssistant zwave_js integration  (please specify version)
[ ] pkg
[ ] node-red-contrib-zwave-js (please specify version, double click node to find out)
[ ] Manually built from GitHub (please specify branch)
[ ] Other (please describe)

Which branches or versions?
Home Assistant Z-Wave JS addon version: 0.1.45
Z-Wave JS Server: 1.10.7
Z-Wave JS: 8.4.1
Did you change anything?
no
If yes, what did you change?
No response
Did this work before?
Don't know, this is a new device
If yes, where did it work?
No response
Attach Driver Logfile
zwave_js.log

Thanks @AlCalzone + @kpine. After updating today and re-interviewing the device, everything is working perfectly again.


Incorrect root type on mixing prismaNode and prismaField and select

Sorry for my lazy report. I don't know how to explain this issue in simple way because I am still not really clear what's happening but somethings should be wrong but everything works.
Check this image

the root is missing articleTags returned.
but in runtime, it does return and everything is working fine except the type error. I believe there should be something wrong with the type.

Thanks for reporting this.  Just pushed a fix, should be released in the next few minutes.


Version bumps

I am able to run our demo on PyTorch 1.1, and I am sure we are Python 3.7 compatible. Can we bump our documented versions?

Yeah we might as well test under 1.2 and 1.3 while we're at it. I can do that in the next day or two unless you already have

I haven't tested on anything else. I guess the biggest culprit would probably be the sequence-to-sequence model
P.S. It cracks me up that we didn't even make this installable hahaha.

执行python scripts/extract_features.py \ --input_image_dir
/CLEVR_v1.0/images/train  \ --output_h5_file data/train_features.h5 \  --model_stage 2
的时候，会报python: can't open file 'scripts/extract_features.py': [Errno 2] No such file or directory
这个错


Transfer query does not support LendToken, LpToken, or StableLpToken

Context
For the testnet competition, we need to filter transfers made by users to see if they were trying to cheat.
Problem
The current query only supports NativeToken and ForeignAsset. The new token types LendToken, LpToken, and StbleLpToken are not yet supported. Hence, if users send these tokens to their own accounts, we cannot detect it.
Steps to reproduce

Go to https://api-dev-kintsugi.interlay.io/graphql/graphql
Enter query

query MyQuery {
  transfers(limit: 10) {
    amount
    from
    to
    timestamp
    token {
      ... on NativeToken {
        token
      }
      ... on ForeignAsset {
        asset
      }
    }
  }
}

See that adding other token types is not supported

This has been fixed with the redeployment of squid recently.
ie. The types were there, but our deployment used outdated images at the time.
Right now, I am able to query for all new currency types:
query MyQuery {
  transfers(limit: 10) {
    amount
    from
    to
    timestamp
    token {
      ... on NativeToken {
        token
      }
      ... on ForeignAsset {
        asset
      }
      ... on LendToken {
        __typename
        lendTokenId
      }
      ... on StableLpToken {
        __typename
        poolId
      }
      ... on LpToken {
        __typename
        token0 {
          ... on NativeToken {
            __typename
            token
          }
          ... on ForeignAsset {
            __typename
            asset
          }
          ... on StableLpToken {
            __typename
            poolId
          }
        }
        token1 {
          ... on NativeToken {
            __typename
            token
          }
          ... on ForeignAsset {
            __typename
            asset
          }
          ... on StableLpToken {
            __typename
            poolId
          }
        }
      }
    }
  }
}

Can confirm it works now. Thanks for updating and closing the issue!


Allow for setting target

When setting the href of a nav bar item allow it to be possible to set the target to something like _self for when needing the ui-router to no longer listen to the change as a state.
TP: https://rockabox.tpondemand.com/entity/9073

I might be wrong and this work might be needed. But when I worked on this before just setting an href and not a ui-sref was enough for the link to be ignored by ui-router I'm not sure _self is required?

@nwhite89  explained this is needed.


Create docker-compose to run TAMU/Scholars locally

The goal would be to create a docker environment for running, developing and exploring the TAMULib/Scholars instance.

[x] Create a docker-compose.yml for running the TAMU/Scholars application in local development.
[x] This should start a SOLR and middleware instance
[ ] Document how to install

Stretch Goal

Get angular front-end included in the docker-compose.yml

Testing
@awoods offered to give it a try once we have it working.

It would be good to clarify which individuals need to have this running locally (with sample data) and by when.


INFO    | Failed to load SSL library: <class 'OSError'> (no library called "ssl" found)

What is that ?
How can i resolve ?
21:04:41.206 INFO    | Failed to load SSL library: <class 'OSError'> (no library called "ssl" found)

Enable Usb Debugging on your phone. İf you use xiaomi, Enable debugging security also.

debugging is enable. Phone is huawei p30 lite

Same issue for me on a P30 lite too

RAB will still work despite that error.
If it doesn't, report the error AFTER that.


Some feedback

Disclaimer: I'm not an AI researcher so I could've done something wrong.
So I got it working after some minor changes and import fixes. Now when I run it with 512x768 resolution the issue is: RuntimeError: cuFFT only supports dimensions whose sizes are powers of two when computing in half precision, but got a signal size of[12, 8] I suppose that's the corresponding layer size which becomes rectangular because of the base resolution being like that. 512x512 should result in a [8, 8] array here and it all works fine.
As expected, running A1111 with --no-half makes it work but the speed is much worse.
I used the parameters for SD1.4 and simply hardcoded them to quickly test if it works at all. On a fine tuned model epiCRealism naturalSin FreeU makes the images worse: they become more saturated, the skin texture turns into plastic (maybe because we suppress the high frequency features exactly?). It starts looking more like the base models or the early fine tuned models:
Original:

FreeU:

AnimateDiff doesn't seem to work in --no-half mode, throws a CUDA error. So we're limited by 512x512. Same symptoms of oversaturation, the skin quality doesn't apply due to grain and artifacts. However, FreeU added a third hand. I tried two slightly different prompts.
Original:

FreeU:

The faces are garbled in all cases but to be honest I much prefer the results without FreeU. The colors are better, the anatomy is better, the skirt is much more detailed and moves more naturally.
My patch, applied to stable-diffusion-webui/repositories/stable-diffusion-stability-ai:
diff --git a/ldm/modules/diffusionmodules/openaimodel.py b/ldm/modules/diffusionmodules/openaimodel.py
index cc3875c..ede0b5a 100644
--- a/ldm/modules/diffusionmodules/openaimodel.py
+++ b/ldm/modules/diffusionmodules/openaimodel.py
@@ -4,6 +4,7 @@ import math
 import numpy as np
 import torch as th
 import torch.nn as nn
+import torch.fft as fft
 import torch.nn.functional as F

 from ldm.modules.diffusionmodules.util import (
@@ -418,6 +419,24 @@ class Timestep(nn.Module):
         return timestep_embedding(t, self.dim)

+def Fourier_filter(x, threshold, scale):
+    # FFT
+    x_freq = fft.fftn(x, dim=(-2, -1))
+    x_freq = fft.fftshift(x_freq, dim=(-2, -1))
+
+    B, C, H, W = x_freq.shape
+    mask = th.ones((B, C, H, W)).cuda()
+
+    crow, ccol = H // 2, W //2
+    mask[..., crow - threshold:crow + threshold, ccol - threshold:ccol + threshold] = scale
+    x_freq = x_freq * mask
+
+    # IFFT
+    x_freq = fft.ifftshift(x_freq, dim=(-2, -1))
+    x_filtered = fft.ifftn(x_freq, dim=(-2, -1)).real
+
+    return x_filtered
+
 class UNetModel(nn.Module):
     """
     The full UNet model with attention and timestep embedding.
@@ -798,8 +817,24 @@ class UNetModel(nn.Module):
             hs.append(h)
         h = self.middle_block(h, emb, context)
         for module in self.output_blocks:
-            h = th.cat([h, hs.pop()], dim=1)
-            h = module(h, emb, context)
+            if True:
+                hs_ = hs.pop()
+
+                # --------------- FreeU code -----------------------
+                # Only operate on the first two stages
+                if h.shape[1] == 1280:
+                    h[:,:640] = h[:,:640] * 1.2
+                    hs_ = Fourier_filter(hs_, threshold=1, scale=0.9)
+                if h.shape[1] == 640:
+                    h[:,:320] = h[:,:320] * 1.4
+                    hs_ = Fourier_filter(hs_, threshold=1, scale=0.2)
+                # ---------------------------------------------------------
+
+                h = th.cat([h, hs_], dim=1)
+                h = module(h, emb, context)
+            else:
+                h = th.cat([h, hs.pop()], dim=1)
+                h = module(h, emb, context)
         h = h.type(x.dtype)
         if self.predict_codebook_ids:
             return self.id_predictor(h)

The simplest way to switch between FreeU and vanilla is to change if True: to if False:. Again, it's just a hack to test if it works.
In conclusion, if everything is correct on my end, it's probably not worth it for the best fine tuned models. On the opposite, to make it work in all cases you have to run it in full 32 bit resolution at ≈3x slowdown and get images that look worse than without it. The base models sure benefit from it but honestly, who uses them except the researchers and LoRA trainers?
I hope I did a mistake somewhere so these results are all wrong. After all, I just copied the part that differs from the original code and fixed the errors to make it work, but who knows.

Thanks for your feedback.  I tried AnimateDiff according to https://github.com/guoyww/AnimateDiff.
The following results without pick：
Original:

FreeU (different factors):

Maybe it works better for cartoon-style images and animations because they naturally lack high frequencies... What were the factors that you used in the above? I can try with anime models.

This is just a simple attempt according to the readme of AnimateDiff. We will be providing more results on the FreeU page and paper. We appreciate your continued interest.

Maybe it works better for cartoon-style images and animations because they naturally lack high frequencies... What were the factors that you used in the above? I can try with anime models.

@rkfg I also had poor results with FreeU, and then I started switching s1/s2/b1/b2 back to 1.0 at some point during the image render. The global features seem to be mostly settled early in the diffusion process so that you can transition back to normal values between about 30% and 75% of the way through your steps.
And the results are greatly improved. I've tested it thoroughly, and most of the FreeU "fixes" are kept while still letting the fine details shine through at the end.

This is basically what I'm doing:
    steps = 30
    unet.freeu.sd21()

    def cb(step, _, __):
        if step == int(steps * 0.5):
            unet.freeu.ones()

    output = pipe(prompt, num_inference_steps=steps, callback=cb)

Mario is noticeably improved, yes, but I prefer the vanilla waterfall, it's more detailed and interesting even though the contrast is a bit lower. SD 2.1 isn't that good in general, even with finetunes. Can you try on the best 1.5 models? Both cartoon and realistic? Would be interesting to see if this method can improve the output over what we can get without it.

Maybe it works better for cartoon-style images and animations because they naturally lack high frequencies... What were the factors that you used in the above? I can try with anime models.

@rkfg I also had poor results with FreeU, and then I started switching s1/s2/b1/b2 back to 1.0 at some point during the denoising process. The global features seem to be mostly settled early so that you can transition back to normal values between about 30% and 75% of the way through your steps.
And the results are greatly improved. I've tested it thoroughly, and most of the FreeU "fixes" are kept while still letting the fine details shine through at the end.

This is basically what I'm doing:

    steps = 30

    unet.freeu.sd21()

    def cb(step, _, __):

        if step == int(steps * 0.5):

            unet.freeu.ones()

    output = pipe(prompt, num_inference_steps=steps, callback=cb)

Can you share full code snippet?

Can you share full code snippet?

I describe my changes in the diffusers repo: https://github.com/huggingface/diffusers/pull/5164#issuecomment-1732638706

RuntimeError: cuFFT only supports dimensions whose sizes are powers of two when computing in half precision, but got a signal size of[12, 8]

To fix this you can just cast x to float for this operation in the first line of the Fourier_filter function
x_freq = fft.fftn(x.float(), dim=(-2, -1))
And cast back at the last line
return x_filtered.to(x.dtype)

Thanks for your feedback. I tried AnimateDiff according to https://github.com/guoyww/AnimateDiff.
The following results without pick：
Original:
FreeU (different factors):

Thanks for your feedback. I tried AnimateDiff according to https://github.com/guoyww/AnimateDiff.
The following results without pick：
Original:
FreeU (different factors):

Hi,how to add FreeU code in T2V like AniamteDiff?

Thanks for your feedback. I tried AnimateDiff according to https://github.com/guoyww/AnimateDiff.
The following results without pick：
Original:
FreeU (different factors):

Hi, can you share how to add FreeU code in T2V, like AnimateDiff?

@ChenyangSi Hi, can you share how to add FreeU code in T2V, like AnimateDiff?

https://github.com/ykk648/AnimateDiff-I2V/commit/0842585aa4fd41e25542979b5110701dca923870
@YisuiTT  you can refer to my codes

@ykk648 Thank u for your codes

having tested visually the first value (q) affects the overall picture (reduction gives a departure from the foreground clue) the second (k) value affects the overall midground the third value affects the details of the midground in the clue (v), (it seems that the reduction value helps to reduce sometimes artefacts) and the fourth (out) changes the attention at the clues (noticed a departure from the character canons with the reduction)
потестировав визуально впечаитление первое значение (q) влияет на общюю картину (уменьшение дает отход от подсказки переднего плана)

второе (k) значение влияет на общий средний план

третье значение на детали среднего плана в подсказке (v), (кажется что значение уменьшения помогает снизить иногда артефакты)

и четвертое (out) изменяет внимание у подсказок (замечен отход от канонов персонажа с уменьшением)


chore: classify was decide! porting notes

Classifies by adding issue number #11043 to porting notes claiming:

was decide!

Thanks :tada:
bors merge


Failed to import pytorch_quantization

I have used to following code for testing (available in documentation):
import super_gradients

yolo_nas = super_gradients.training.models.get("yolo_nas_l", pretrained_weights="coco").cuda()
yolo_nas.predict("https://deci-pretrained-models.s3.amazonaws.com/sample_images/beatles-abbeyroad.jpg").show()

but I received the following error:
The console stream is logged into /home/ethan/sg_logs/console.log
[2023-05-14 13:39:15] INFO - crash_tips_setup.py - Crash tips is enabled. You can set your environment variable to CRASH_HANDLER=FALSE to disable it
[2023-05-14 13:39:32] WARNING - __init__.py - Failed to import pytorch_quantization
[2023-05-14 13:39:32] WARNING - calibrator.py - Failed to import pytorch_quantization
[2023-05-14 13:39:32] WARNING - export.py - Failed to import pytorch_quantization
[2023-05-14 13:39:32] WARNING - selective_quantization_utils.py - Failed to import pytorch_quantization
[2023-05-14 13:39:33] INFO - checkpoint_utils.py - License Notification: YOLO-NAS pre-trained weights are subjected to the specific license terms and conditions detailed in
https://github.com/Deci-AI/super-gradients/blob/master/LICENSE.YOLONAS.md
By downloading the pre-trained weight files you agree to comply with these terms.
Could not load library libcudnn_cnn_infer.so.8. Error: libcuda.so: cannot open shared object file: No such file or directory
Aborted

Environment:

OS

Linux <IP_ADDRESS>-microsoft-standard-WSL2 x86_64

Nvidia 3090
Super Gradients version: 3.1.1
Python environment: 3.10.6

absl-py==1.4.0
alabaster==0.7.13
antlr4-python3-runtime==4.9.3
attrs==23.1.0
Babel==2.12.1
boto3==1.26.133
botocore==1.29.133
build==0.10.0
cachetools==5.3.0
certifi==2023.5.7
charset-normalizer==3.1.0
click==8.1.3
coloredlogs==15.0.1
contourpy==1.0.7
coverage==5.3.1
cycler==0.11.0
Deprecated==1.2.13
docutils==0.17.1
einops==0.3.2
flatbuffers==23.5.9
fonttools==4.39.4
future==0.18.3
google-auth==2.18.0
google-auth-oauthlib==1.0.0
grpcio==1.54.2
humanfriendly==10.0
hydra-core==1.3.2
idna==3.4
imagesize==1.4.1
Jinja2==3.1.2
jmespath==1.0.1
json-tricks==3.16.1
jsonschema==4.17.3
kiwisolver==1.4.4
Markdown==3.4.3
markdown-it-py==2.2.0
MarkupSafe==2.1.2
matplotlib==3.7.1
mdurl==0.1.2
mpmath==1.3.0
numpy==1.23.0
nvidia-cublas-cu11==<IP_ADDRESS>
nvidia-cuda-nvrtc-cu11==11.7.99
nvidia-cuda-runtime-cu11==11.7.99
nvidia-cudnn-cu11==<IP_ADDRESS>
oauthlib==3.2.2
omegaconf==2.3.0
onnx==1.13.0
onnx-simplifier==0.4.28
onnxruntime==1.13.1
opencv-python==<IP_ADDRESS>
packaging==23.1
Pillow==9.5.0
pip-tools==6.13.0
protobuf==3.20.3
psutil==5.9.5
pyasn1==0.5.0
pyasn1-modules==0.3.0
pycocotools==2.0.4
pyDeprecate==0.3.2
Pygments==2.15.1
pyparsing==2.4.5
pyproject_hooks==1.0.0
pyrsistent==0.19.3
python-dateutil==2.8.2
PyYAML==6.0
rapidfuzz==3.0.0
requests==2.30.0
requests-oauthlib==1.3.1
rich==13.3.5
rsa==4.9
s3transfer==0.6.1
scipy==1.10.1
six==1.16.0
snowballstemmer==2.2.0
Sphinx==4.0.3
sphinx-rtd-theme==1.2.0
sphinxcontrib-applehelp==1.0.4
sphinxcontrib-devhelp==1.0.2
sphinxcontrib-htmlhelp==2.0.1
sphinxcontrib-jquery==4.1
sphinxcontrib-jsmath==1.0.1
sphinxcontrib-qthelp==1.0.3
sphinxcontrib-serializinghtml==1.1.5
stringcase==1.2.0
super-gradients==3.1.1
sympy==1.12
tensorboard==2.13.0
tensorboard-data-server==0.7.0
termcolor==1.1.0
tomli==2.0.1
torch==1.13.1+cu117
torchaudio==0.13.1+cu117
torchmetrics==0.8.0
torchvision==0.14.1+cu117
tqdm==4.65.0
treelib==1.6.1
typing_extensions==4.5.0
urllib3==1.26.15
Werkzeug==2.3.4
wrapt==1.15.0

Hi @mriamnobody
It seems to be related to cudnn and not SuperGradients.
You can check out the following resources, it might help you;

https://stackoverflow.com/questions/66977227/could-not-load-dynamic-library-libcudnn-so-8-when-running-tensorflow-on-ubun
https://discuss.pytorch.org/t/libcudnn-cnn-infer-so-8-library-can-not-found/164661

Thank you @Louis-Dupont for providing the links/source to resolve an issue which is not related to super-gradients. I tried to perform the steps mentioned in the StackOverflow link and it got rid of the error Could not load library libcudnn_cnn_infer.so.8. Error: libcuda.so: cannot open shared object file: No such file or directory Aborted the following errors were still present:
[2023-05-15 06:43:25] WARNING - __init__.py - Failed to import pytorch_quantization
[2023-05-15 06:43:25] WARNING - calibrator.py - Failed to import pytorch_quantization
[2023-05-15 06:43:25] WARNING - export.py - Failed to import pytorch_quantization
[2023-05-15 06:43:25] WARNING - selective_quantization_utils.py - Failed to import pytorch_quantization

So did some searching on internet and found that two more modules need to be installed:
nvidia-pyindex and pytorch-quantization. For anyone in future gets this issue, you first need to install the nvidia-pyindex and then pytorch-quantization else if you try to install pytorch-quantization first and then nvidia-pyindex you will the following error:
Collecting pytorch-quantization
  Downloading pytorch-quantization-0.0.1.dev5.tar.gz (7.9 kB)
  Preparing metadata (setup.py) ... error
  error: subprocess-exited-with-error

  × python setup.py egg_info did not run successfully.
  │ exit code: 1
  ╰─> [17 lines of output]
      Traceback (most recent call last):
        File "<string>", line 2, in <module>
        File "<pip-setuptools-caller>", line 34, in <module>
        File "/tmp/pip-install-t9i6zekc/pytorch-quantization_9cb4035ffda345a7959b12c1917f5e0c/setup.py", line 150, in <module>
          raise RuntimeError(open("ERROR.txt", "r").read())
      RuntimeError:
      ###########################################################################################
      The package you are trying to install is only a placeholder project on PyPI.org repository.
      This package is hosted on NVIDIA Python Package Index.

      This package can be installed as:
      ```
      $ pip install nvidia-pyindex
      $ pip install pytorch-quantization
      ```
      ###########################################################################################

      [end of output]

  note: This error originates from a subprocess, and is likely not a problem with pip.
error: metadata-generation-failed

× Encountered error while generating package metadata.
╰─> See above for output.

note: This is an issue with the package mentioned above, not pip.
hint: See above for details.

@Louis-Dupont
I have still one query related to this problem after performing the steps unnecessary to resolve the issue I'm facing what shall be the output of this code:
import super_gradients

yolo_nas = super_gradients.training.models.get("yolo_nas_l", pretrained_weights="coco").cuda()
yolo_nas.predict("https://deci-pretrained-models.s3.amazonaws.com/sample_images/beatles-abbeyroad.jpg").show()

When the above code is run I only get the following on terminal:
The console stream is logged into /home/ethan/sg_logs/console.log
[2023-05-15 06:49:10] INFO - crash_tips_setup.py - Crash tips is enabled. You can set your environment variable to CRASH_HANDLER=FALSE to disable it

and it takes close to 25 seconds for the script to complete. Also, the load on GPU fluctuates from 10% to 100% while the script is running. Am I missing something?

Is this all the logs you had in the terminal? Because if its the first time you run, it makes sense it would take more time because it downloads the model, but you should have logs like:

Try rerunning it again after the model is downloaded it should be way faster.

Thanks for your reply @Louis-Dupont. Nothing is being displayed on my terminal except the text which I shared in my last comment. No model download progress bar nothing. I have attached a screenshot for your reference.

Let's see what takes time.
import time
import torch

start_predict = time.perf_counter()

from super_gradients.common.object_names import Models
from super_gradients.training import models

post_import_time = time.perf_counter()
print("Loading SG: ", post_import_time - start_predict)

# Note that currently only YoloX, PPYoloE and YOLO-NAS are supported.
model = models.get(Models.YOLO_NAS_L, pretrained_weights="coco")

# We want to use cuda if available to speed up inference.
model = model.to("cuda" if torch.cuda.is_available() else "cpu")

post_model_time = time.perf_counter()
print("Loading Model: ", post_model_time - post_import_time)

images_predictions = model.predict(
    "../../../../documentation/source/images/examples/pose_elephant_flip.gif",
)

post_predict_time = time.perf_counter()
print("Predict: ", post_predict_time - post_model_time)

Below are the results of three consecutive runs. The first run took a bit longer than the succeeding two. The script took close to below 45 seconds to complete its execution. I have attached the console image for your reference.


Reduce managed SDK install path length

About #595.
Reduces SDK home path length. With 20 more characters, I think we can practically get away from #595.
New: Users\<user name>\AppData\Local\google\ct4j-cloud-sdk\LATEST\google-cloud-sdk
Old: Users\<user name>\AppData\Local\google-cloud-tools-java\managed-cloud-sdk\LATEST\google-cloud-sdk
I also looked into if I could remove the last segment google-cloud-sdk too, but it is a bit complicated. Maybe next time if it still matters.
This will cause existing users to re-install the SDK on the new path. Because all our Cloud tools cannot be upgrade simultaneously (and even users may run multiple instances of Eclipses with different CT4E versions), we should not delete the old SDK install for some time. (For deleting in old SDK, I actually have a working implementation. To be submitted later.)

Alright, the gcloud installation seems really large (617M on my machine). I originally sought consistency, but given the impact, it's wise to not touch Linux and Mac.

Updated to shorten the path only on Windows. I've tested installing SDK on both Linux and Windows, and it works fine.


unable to access the ui

Note: this is the technical bug tracker, please use other platforms for getting support and starting a (non technical) discussion. See the getting help page for details.
I'm submitting a ...
[ x] bug report
[ ] question about the decisions made in the repository
Action taken (what you did)
i had started gekko with "node gekko --ui"
on my digital ocean droplet (ssh)
Expected result (what you hoped would happen)
navigate on the ui of gekko
Actual result (unexpected outcome)
when i open my browser on my pc, i go to the public ip of my vps but
my browser(chrome) give me the "ERR_CONNECTION_REFUSED"
error.
Other information (e.g. detailed explanation, stacktraces, related issues, suggestions how to fix, links for us to have context, eg. stackoverflow, etc)
in the console everything seems ok.
i have already opened the 3000 and 443 ports.

have you change the "localhost" and ip "<IP_ADDRESS>"?
if not change that in gekko/web/vue/dist/UIconfig.js
and read here for setup : https://gekko.wizb.it/docs/installation/configuring_gekko_on_a_server.html

/root/gekko/web/server.js:117
server.timeout = config.api.timeout || 120000;
^
TypeError: Cannot read property 'timeout' of undefined
at Object. (/root/gekko/web/server.js:117:29)
at Module._compile (module.js:653:30)
at Object.Module._extensions..js (module.js:664:10)
at Module.load (module.js:566:32)
at tryModuleLoad (module.js:506:12)
at Function.Module._load (module.js:498:3)
at Module.require (module.js:597:17)
at require (internal/module.js:11:18)
at Object. (/root/gekko/gekko.js:43:10)
at Module._compile (module.js:653:30)
at Object.Module._extensions..js (module.js:664:10)
at Module.load (module.js:566:32)
at tryModuleLoad (module.js:506:12)
at Function.Module._load (module.js:498:3)
at Function.Module.runMain (module.js:694:10)
at startup (bootstrap_node.js:204:16)
now i have this error

You are missing export statements!
On Thu, 4 Oct 2018, 03:23 smatiush<EMAIL_ADDRESS>wrote:

const CONFIG = {
headless: true,
api: {
host: '<IP_ADDRESS>',
port: 3000,
timeout: 120000
},
ui: {
ssl: false,
host: '<IP_ADDRESS>', // Set this to the IP of the machine that will r$
port: 3000,
path: '/'
},
adapter: 'sqlite'
}
this is my "UIconfig.js" file
—
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.

i have always this:
/root/gekko/web/server.js:117
server.timeout = config.api.timeout || 120000;
^
TypeError: Cannot read property 'timeout' of undefined
at Object. (/root/gekko/web/server.js:117:29)
at Module._compile (module.js:653:30)
at Object.Module._extensions..js (module.js:664:10)
at Module.load (module.js:566:32)
at tryModuleLoad (module.js:506:12)
at Function.Module._load (module.js:498:3)
at Module.require (module.js:597:17)
at require (internal/module.js:11:18)
at Object. (/root/gekko/gekko.js:43:10)
at Module._compile (module.js:653:30)
at Object.Module._extensions..js (module.js:664:10)
at Module.load (module.js:566:32)
at tryModuleLoad (module.js:506:12)
at Function.Module._load (module.js:498:3)
at Function.Module.runMain (module.js:694:10)
at startup (bootstrap_node.js:204:16)

See my comment, you changed your UI config so it's not exporting any data
anymore.
On Thu, 4 Oct 2018, 14:36 smatiush<EMAIL_ADDRESS>wrote:

i have always this:
/root/gekko/web/server.js:117
server.timeout = config.api.timeout || 120000;
^
TypeError: Cannot read property 'timeout' of undefined
at Object. (/root/gekko/web/server.js:117:29)
at Module._compile (module.js:653:30)
at Object.Module._extensions..js (module.js:664:10)
at Module.load (module.js:566:32)
at tryModuleLoad (module.js:506:12)
at Function.Module._load (module.js:498:3)
at Module.require (module.js:597:17)
at require (internal/module.js:11:18)
at Object. (/root/gekko/gekko.js:43:10)
at Module._compile (module.js:653:30)
at Object.Module._extensions..js (module.js:664:10)
at Module.load (module.js:566:32)
at tryModuleLoad (module.js:506:12)
at Function.Module._load (module.js:498:3)
at Function.Module.runMain (module.js:694:10)
at startup (bootstrap_node.js:204:16)
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what can i do to have gekko running?

You deleted some parts of the UIconfig. Easiest is to copy in the complete
UIconfig as found in GitHub and edit that one again (but don't remove the
export statements)
On Thu, 4 Oct 2018, 16:04 smatiush<EMAIL_ADDRESS>wrote:

what can i do to have gekko running?
—
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https://github.com/askmike/gekko/issues/2576#issuecomment-426923343, or mute
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.

could someone help me out with the same error codes as shown above? I checked the config over and over again but can't seem to find whats going on? I got gekko installed on ubuntu 16.04 in the folder /gekko. Am I missing something?
const CONFIG = {
headless: true,
api: {
host: '<IP_ADDRESS>',
port: 3000,
},
ui: {
ssl: true,
host: 'my.domain.de',
port: 443,
path: '/gekko' // change this if you are serving from something like example.com/gekko
},
adapter: 'sqlite'

maybe edit documentation to include the export statement in the config posted for users are copy and pasting it seems. https://gekko.wizb.it/docs/installation/configuring_gekko_on_a_server.html

I don't think anything is wrong with your installation. The program maybe throwing these error because you tampered the it's modularity. Try rechecking the configuration files and ensure its the same with the one in the GitHub uiconfig. You can edit but dont delete the export statement as the code won't be available to other modules.


TypeError: 'encoding' is an invalid keyword argument for this function

When I try to load the model I get the following error. Am I missing any dependency?
File "visualize.py", line 97, in
model = BigGAN.from_pretrained(model_name)
File "/home/ricardo/.local/lib/python2.7/site-packages/pytorch_pretrained_biggan/model.py", line 274, in from_pretrained
config = BigGANConfig.from_json_file(resolved_config_file)
File "/home/ricardo/.local/lib/python2.7/site-packages/pytorch_pretrained_biggan/config.py", line 56, in from_json_file
with open(json_file, "r", encoding='utf-8') as reader:
TypeError: 'encoding' is an invalid keyword argument for this function

solution: run with python3 command


fix OVModelForCausalLM for auto device

What does this PR do?
Fix loading OVModelForCausalLM if AUTO specified as device (AUTO device does not have property INFERENCE_PRECISION_HINT among supported)

@AlexKoff88 @helena-intel @echarlaix  could you please take a look?

The docs for this PR live here. All of your documentation changes will be reflected on that endpoint.

Thanks @eaidova ! It is a shame it is not possible to query this for AUTO device. It is possible to do that once the model has been loaded on the "final" device:
device = compiled_model.get_property("EXECUTION_DEVICES")[0]
compiled_model.get_property("DEVICE_PROPERTIES")[device]["INFERENCE_PRECISION_HINT"]

but we can't predict when the model will have been loaded to that device. I really wish we could find a way to get the optimal PKV precision with AUTO too though, even if it's a bit hacky for now. Should we for now have a warning about potentially slower performance on some devices when using AUTO with LLMs?
And should we have a separate method to allow users to set this PKV precision manually without having to set ov_config's INFERENCE_PRECISION_HINT?

Thanks @eaidova ! It is a shame it is not possible to query this for AUTO device. It is possible to do that once the model has been loaded on the "final" device:
device = compiled_model.get_property("EXECUTION_DEVICES")[0]
compiled_model.get_property("DEVICE_PROPERTIES")[device]["INFERENCE_PRECISION_HINT"]

but we can't predict when the model will have been loaded to that device. I really wish we could find a way to get the optimal PKV precision with AUTO too though, even if it's a bit hacky for now. Should we for now have a warning about potentially slower performance on some devices when using AUTO with LLMs?
And should we have a separate method to allow users to set this PKV precision manually without having to set ov_config's INFERENCE_PRECISION_HINT?

@helena-intel let's discuss it internally what we can suggest for users who are interested in using auto (probably we also need some advice from @peterchen-intel here). My opinion, we can postpone applying of PKV transformation until target device is unknown and move it on compilation step (only if AUTO selected, because reloading model several times maybe time consuming for some devices). Also I need to note that default device for model class now is CPU, AUTO used only if user explicitly specified it, so impact on users probably not so big.
Current solution is also nonuniversal, because it does not take into account that model precision and device can be changed in runtime, now it applicable only at the moment when model initialized, so probably move this logic inside compile will be better place (as recompilation will be triggered also by half() and to() methods)


Can't create test if there's a space in the table name

Affected module
Backend?
Describe the bug
I'm trying to create a test for a table called Fire Incidents.  It fails.  Looking at logs it seems that sometimes the space is being turned into + and sometimes into %20%.
Version:

OpenMetadata version: 0.12.2

Additional context
I'm ingesting MSSQL if that matters.

Possibly related to https://github.com/open-metadata/OpenMetadata/issues/7623 although that dealt with column names not table names.

@Sachin-chaurasiya @TeddyCr ^

This should be fixed in 0.13.2. @sfirke let's know if you are still experiencing this issue.


Android renderer throws uncatcheable exception with Teams dark Host Config

Platform

Android

Version of SDK
1.2 (latest master)
Issue
When using the Teams dark host config (or Teams light) in the Android renderer, it throws an exception that cannot be caught.

terminating with uncaught exception of type AdaptiveCards::AdaptiveCardParseException: Enum type was invalid. Expected type string.

@almedina-ms could you take a stab at this ? Thanks!

@andrewleader is this a regression wtih 1.2?

@shalinijoshi19 no clue, I don't have Android 1.1 to test against, and only discovered this when playing with 1.2 in our new app.

I have no repro on this bug using the latest master

Thanks for confirming @almedina-ms ; @andrewleader  closing this one out then


Fails to wrap suspend functions which return Unit

Should be easy fix, working on it.

Fixed in 0.1.2

Fixed in 0.1.2


#41 disable initial magnification

By adjusting the initial scale, loaded image will fit the crop box instead of the screen.

This fix is extremely helpful, thank you.
I hope that @lykhonis will continue maintaining this package. It's the most useful image cropper I found yet.


Fix cache path

The PR fixes the issue of missing folder with cache files when starting a validation using the CLI.
The operation: cache_path: str = f"{os.path.dirname(__file__)}/../{args.cache}" returns a path like /Users/Aleksei_Furmenkov/Desktop/core/scripts/../resources/cache when executed in scripts/run_validation.py.
But the issue is that there is no folder scripts when building the CLI version (at least on MacOS):

As a solution, I suggest concatenating given cache path and current directory in core.py file, not in scripts/run_validation.py.
@nhaydel can you please run the CLI using main branch and check if the issue exists on your machine? Me and Denis have Macs, sorry.

I downloaded the windows zip file from the last release. I did not have any issue running it

@alexfurmenkov if you want to do a mini release (4.9.1?) from this branch I can test it on windows

@nhaydel okay, will do. Good idea

Sorry for the late response. This new cache path worked for me. Tested on version: <IP_ADDRESS>


Could the s390x be added to the architecture supported by the velero plugin?

Hello,
Currently, Openshift is being configured and used in IBM Z(s390x) equipment. I want to use velero to backup and restore my Openshift cluster, but I don't have an s390x container image.
I am wondering if there are plans to be added or if there is a way to create a container image.
Thank you.

@jwyoon-darum not sure I understand. We ship the plugin as a container that is based on UBI, but you can run adn deploy this in any OCP cluster. Why do you need the image built on s390x?


add unittest for getLogURLMergePatch

Signed-off-by: Chmouel Boudjnah<EMAIL_ADDRESS>

should be fine just a unittest


Enable Travis

@Gankro I think you have to take care of this.

Will do tonight; key is sitting in a text editor at home.

This appears to have been done.


Power off all devices (CEC)

Hi there,
I don‘t know why but when powering off my yamah with this plugin only the receiver went off not the connected CEC devices (ATV + TV). The power settings in the avr are set correct.
Any ideas?
Kind regards,
Tim

Ok, same behaviour in the yamaha app so not a problem of this plugin.


Mitigate redis timeout errors

Context
We are receiving Redis::TimeoutError on AKS very frequently so we decide to increase the timeout and the attemps to help investigate and to mitigate any issues with Redis.

 Investigate redis timeout errors


Remove reference to Travis

I think there is still .travis.yml file in the repository root, on both 2.4.x and 2.5.x branches. They should be deleted too.

Oh right, I grep-ped, but did not find

Thanks @greg0ire!


Fix bug in restore SDB endpoint

Fix bug where the SDB id cannot be found before the metadata is restored.

Coverage remained the same at 54.99% when pulling 7ccbc5462cce4c8f8c385ec770d321255b4e1d47 on bugfix/restore-sdb-endpoint into d05992f39862086e718f2bddeb47e7c02aab9035 on highlander.


Understanding input dimensions for LSTM

For a long time I cannot figure out how to set input dimensions for RNN.
My data is 5000 examples of 60 time series. For example 5000 records of 60 successive times. If they are regular it is ok, if not, we classify it as error. Shape=(5000, 60)
I would like to use a simple model
    model = Sequential()
    model.add(LSTM(256, activation='relu', input_shape=(60,1), return_sequences=True))
    model.add(Dense(num_classes, activation='softmax'))

It does not work.
How should I reshape features and change the input_shape?
Is it possible to classify data with not equal number of recodred  successive times (some records have 60, but other 20?)

@smolendawid .  I'm not sure I understand your question, but the input array should be 3d -> (5000,60,1) and your target array should be 2d  -> (5000,1), if I understand your basic configuration.  I hope that helps.  Thanks.


Release notes for <IP_ADDRESS> (Edge) cc @gbarr01

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@gtardif - I've made some formatting and style edits. PTAL.