Error with installation of Uni-Mol tools

Dear developers,
I am trying to install Uni-Mol tools on WSL from the source using command python setup.py install. But it always fails with the following message:

Searching for [numpy>=1.25.0]
Reading https://pypi.org/simple/numpy/
Downloading https://files.pythonhosted.org/packages/40/9f/a4a4e07c40dd4c4771266a2e786a3e7fb91429d93a5a8a0538753598cac3/numpy-2.0.0rc1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=070a8b1c93b0bf21c1a3c51514145acbba612e9f3fd86870c1ca37a36cebbfce
Best match: numpy 2.0.0rc1
Processing numpy-2.0.0rc1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Installing numpy-2.0.0rc1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl to /home/jingheng/venv/lib/python3.9/site-packages
Adding numpy 2.0.0rc1 to easy-install.pth file
Installing f2py script to /home/jingheng/venv/bin
Installing numpy-config script to /home/jingheng/venv/bin
Installed /home/jingheng/venv/lib/python3.9/site-packages/numpy-2.0.0rc1-py3.9-linux-x86_64.egg
error: numpy 2.0.0rc1 is installed but numpy<1.23.0,>=1.16.5 is required by {'scipy'}

After I reinstall numpy 1.22, it still goes wrong with the same message.
Please let me know how to fix it, thank you.

It looks likes there is a version incompativility between scipy and numpy and your installation of scipy might be 1.7.
Installing numpy with 1.22 should fix it, make sure it's installed correctly.
If that still doesn't work, try reinstalling both scipy and numpy.

It looks likes there is a version incompativility between scipy and numpy and your installation of scipy might be 1.7. Installing numpy with 1.22 should fix it, make sure it's installed correctly. If that still doesn't work, try reinstalling both scipy and numpy.

Thank you for your previous help with the installation. Now I am trying to do an example job with the csv file in Uni-Mol/unimol/notebooks. Here is my code:
from unimol_tools import MolTrain, MolPredict

root = '~/Projects/uni-molgen/'
clf = MolTrain(task='classification',
                data_type='molecule',
                epochs=20,
                learning_rate=0.0001,
                batch_size=16,
                early_stopping=5,
                metrics='auc',
                split='random',
                save_path=root+'exp',
                remove_hs=True,
              )

clf.fit(data=root+'data/mol_property_demo.csv')

However, I always receive the following error:
(venv_torch) (base) jingheng@Bai-Group-1:~/Uni-Mol/unimol_tools$ python ~/Projects/uni-molgen/test/test1.py
fused_multi_tensor is not installed corrected
fused_rounding is not installed corrected
fused_layer_norm is not installed corrected
fused_softmax is not installed corrected
2024-05-22 16:04:41 | unimol_tools/data/conformer.py | 90 | INFO | Uni-Mol(QSAR) | Start generating conformers...
20it [00:00, 67.45it/s]
2024-05-22 16:04:42 | unimol_tools/data/conformer.py | 94 | INFO | Uni-Mol(QSAR) | Failed to generate conformers for 0.00% of molecules.
2024-05-22 16:04:42 | unimol_tools/data/conformer.py | 96 | INFO | Uni-Mol(QSAR) | Failed to generate 3d conformers for 0.00% of molecules.
2024-05-22 16:04:42 | unimol_tools/train.py | 169 | INFO | Uni-Mol(QSAR) | Create output directory: ~/Projects/uni-molgen/exp
2024-05-22 16:04:42 | unimol_tools/models/unimol.py | 146 | INFO | Uni-Mol(QSAR) | Loading pretrained weights from /home/jingheng/Uni-Mol/unimol_tools/unimol_tools/weights/mol_pre_no_h_220816.pt
2024-05-22 16:04:43 | unimol_tools/models/nnmodel.py | 144 | INFO | Uni-Mol(QSAR) | start training Uni-Mol:unimolv1
val: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  6.79it/s, Epoch=Epoch 1/20, loss=1.6434]Traceback (most recent call last):
  File "/home/jingheng/Projects/uni-molgen/test/test1.py", line 16, in <module>
    clf.fit(data=root+'data/mol_property_demo.csv')
  File "/home/jingheng/Uni-Mol/unimol_tools/unimol_tools/train.py", line 141, in fit
    self.model.run()
  File "/home/jingheng/Uni-Mol/unimol_tools/unimol_tools/models/nnmodel.py", line 161, in run
    _y_pred = self.trainer.fit_predict(
              ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/jingheng/Uni-Mol/unimol_tools/unimol_tools/tasks/trainer.py", line 204, in fit_predict
    y_preds, val_loss, metric_score = self.predict(
                                      ^^^^^^^^^^^^^
  File "/home/jingheng/Uni-Mol/unimol_tools/unimol_tools/tasks/trainer.py", line 347, in predict
    metric_score = self.metrics.cal_metric(
                   ^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/jingheng/Uni-Mol/unimol_tools/unimol_tools/utils/metrics.py", line 207, in cal_metric
    return self.cal_classification_metric(label, predict, nan_value)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/jingheng/Uni-Mol/unimol_tools/unimol_tools/utils/metrics.py", line 162, in cal_classification_metric
    res_dict[metric_type] = nan_metric(
                            ^^^^^^^^^^^
  File "/home/jingheng/Uni-Mol/unimol_tools/unimol_tools/utils/metrics.py", line 159, in nan_metric
    def nan_metric(label, predict): return cal_nan_metric(
                                           ^^^^^^^^^^^^^^^
  File "/home/jingheng/Uni-Mol/unimol_tools/unimol_tools/utils/metrics.py", line 49, in cal_nan_metric
    result.append(metric_func(
                  ^^^^^^^^^^^^
  File "/home/jingheng/anaconda3/lib/python3.11/site-packages/sklearn/metrics/_ranking.py", line 572, in roc_auc_score
    return _average_binary_score(
           ^^^^^^^^^^^^^^^^^^^^^^
  File "/home/jingheng/anaconda3/lib/python3.11/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score
    return binary_metric(y_true, y_score, sample_weight=sample_weight)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/jingheng/anaconda3/lib/python3.11/site-packages/sklearn/metrics/_ranking.py", line 339, in _binary_roc_auc_score
    raise ValueError(
ValueError: Only one class present in y_true. ROC AUC score is not defined in that case.

I am not sure what might be wrong causing this error. Can you please provide any suggestion or give more detailed instruction with examples on the Docs site?

Hi, ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. This error is due to imbalance data, with only one class present in the labels (y_true). The ROC AUC score requires both classes to be present in order to compute a meaningful metric.
you can use other metric or increase your batch size.


Metrics 4.0 log4j2 xml configuration

The same changeset as PR #900, ported to the metrics master branch.

Thanks!

Happy to help.
Six minutes. That must be some kind of PR-acceptance record for me. :)

I think thats a PR-acceptance record for me on this project!


Organize snapshots into directories

This PR organizes snapshots into a few thematic directories, which we can continue to fill with more snapshots. I have also added a few new snapshots to see how some more things perform.

@natefaubion I've rebased this over main, but it doesn't appear that #35 has changed anything about the operator formatting.

Well, this has gotten funky. I'm going to close this, and refer back to it in the future when adding directories with new snapshots. We can reopen it if it's fixable.


'overlayEntry != null': You should call EasyLoading.init() in your MaterialApp

flutter_boost中使用，初始化不成功

For me what worked was:
MaterialApp(
    debugShowCheckedModeBanner: false,
    ....
    builder: (context, child) {
        return EasyLoading.init()(context, child);
    },
)

Since the init method returns a function, it needs to be called, in order for it to be started.

I don't resolve problem

For me what worked was:
MaterialApp(
    debugShowCheckedModeBanner: false,
    ....
    builder: (context, child) {
        return EasyLoading.init()(context, child);
    },
)

Since the init method returns a function, it needs to be called, in order for it to be started.

我试了，还是不行

我也是

I don't resolve problem

我也是

Same issue here

help

see #83

help me ! i try #83 but still not work

https://github.com/nslogx/flutter_easyloading/issues/215#issuecomment-1662461919
This solution solved my problem. Your custom builder have to be wrapped with EasyLoading.init .
builder: EasyLoading.init(
    builder: (BuildContext context, Widget child) {
      return MediaQuery(
        data: MediaQuery.of(context).copyWith(textScaleFactor: 1.0),
        child: child!,
      );
    },
  ),


Duplicated domains

Hi,
I'm using this plugin with the VVV Project on Mac OS 10.15.6
Vagrant Version: 2.2.10
VirtualBox Version: 6.1.12
Plugin Version: 1.0.4
On vagrant halt I'm getting this:
==> default: [vagrant-goodhosts] Removing hosts
==> default: [vagrant-goodhosts] - found entry for: <IP_ADDRESS> vvv
==> default: [vagrant-goodhosts] - found entry for: <IP_ADDRESS> vvv.test

But once the task is complete these entries are still in my /etc/hosts file.
On vagrant up I'm getting this:
==> default: [vagrant-goodhosts] Checking for host entries
==> default: [vagrant-goodhosts] - found entry for: <IP_ADDRESS> vvv
==> default: [vagrant-goodhosts] - found entry for: <IP_ADDRESS> vvv.test

Once the task is complete these entries are added on top of the one that weren't removed, so each domain are effictively duplicated.
So if I manually remove these entries and then cycle through vagrant up / halt 3 times, I'll have each domain appearing 3 times each on the line of my VVV instance IP.
I noticed this issue because Safari wasn't working with my local domains, I suspect it's because the host file entry line got too long. It works fine even when there are a couple of duplicates but once the line gets too long Safari returns a server not found error.
Hope that makes sense

I think that depends on https://github.com/goodhosts/cli/issues/12
I am waiting a release of the cli tool so I can release a new version

sorry @Mte90 ill do it this weekend

https://github.com/goodhosts/cli/releases/tag/v.1.0.5
you can add --clean to the add/remove commands now with the new release.


feat: add a shortcode for 'video'

This shortcode will add a video element embedded in a <figure> with an optional <figcaption>.
This fixes #1.

And...the final PR is being merged! Completely forgot this was in the queue, thanks for adding this :)


CAST-32117 Typos in loftsman ship parameters

Description
Fix typos at https://cray-hpe.github.io/docs-csm/en-12/operations/csm_product_management/post_install_customizations/:
loftsman ship charts-path ${PATH_TO_RELEASE}/helm --manifest-path ${PWD}/manifest.yaml

vs
loftsman ship --charts-path ${PATH_TO_RELEASE}/helm --manifest-path ${PWD}/manifest.yaml

Checklist

[x] If I added any command snippets, the steps they belong to follow the prompt conventions (see [example][1]).
[x] If I added a new directory, I also updated .github/CODEOWNERS with the corresponding team in [Cray-HPE][2].
[x] My commits or Pull-Request Title contain my JIRA information, or I do not have a JIRA.

/backport release/1.4

/backport release/1.3

/backport release/1.2


[Feature] Add Amortization Calculator

Description
financial tool used to determine the repayment schedule of a loan, typically a mortgage or car loan. It calculates the periodic payments and the distribution of those payments between principal and interest over the life of the loan.
Screenshots
No response
Checklist

[X] I have checked the existing issues.
[X] I follow Contributing Guidelines of this project.
[X] I am a GSSOC'24 contributor.
[ ] I am a IWOC'24 contributor.
[ ] I am a JWOC'24 contributor.
[ ] I am a SWOC'24 contributor.

Code of Conduct

[X] I agree to follow this project's "Code of Conduct".

@Rakesh9100
please remove it from standby i have done my previous isue checkout PR an assign me please

Proceed @Yashgabani845


FEATURE: change the width of the top menu bar

Top menu bar is wide, may limit visibility.
The option to set the height of a bar or adjust it to the menu items could be added so that it doesn’t take up more space than needed.

If that's from audit it probably should be height? @AgaFra am I right?

True, height, the screenshot below, and I corrected the description.


UpdateDockerBuild

I can't understand the DCO requirements. Can you please guide me how to sign my commits correctly every time.

Hi @thirdknife , when you commit add "-s" every time, which will facilitate the signoff. If you need to go back and add signoff to old commits in the branch, click the "Details" link beside the failed DCO check and it will provide instructions that should work most of the time.

BTW:

Any commits in a branch must now be done like git commit -sam "my explanation" to signoff them.
The boost part is not anymore needed by OpenColorIO library but still required by OpenImageIO. As the dockerfile directives download & compile OIIO,  we cannot remove it from the docker files.

 As the changes are minimal, you could also start from scratch your new branch (instead of picking the existing commit).

Hi @thirdknife . It looks like only one of your commits didn't get a signoff (b17ff14). You could go back and amend it. Since the change is small (keeping Boost as @hodoulp suggested), it might be easier to close this PR, start a new branch with just the OpenImageIO version bump, and open a new PR. When you do so, you will likely also be prompted by the EasyCLA system to sign a ICLA or CCLA. Thanks for contributing.

Opened 872 instead


Updated my portfolio

https://github.com/vinitshahdeo/SimpleBio/issues/4

@lreddy563 Thanks for the PR.

You may look into a few good first issues which are up for grabs.


Vector normalization could easily be faster

When digging into #80, I decided to see how GLM implemented vector normalization. Turns out their approach is pretty clever:
v * inversesqrt(dot(v, v));

This avoids both an actual square root and a division by exploiting the fact that inverse square roots are cheap and that dot(v, v) gives you the magnitude of the vector squared.
I ran some quickbench benchmarks with the following results:
No optimization

-O1

-O3

This is a pretty nice speedup for unoptimized builds (about 25%) and it could really help performance on processor architectures that hate square roots and divisions. (See concerns in #80.) So, we should probably do this.

I would be a little worried about the error margins. Many times vectors are normalized in an attempt to ensure some kind of numeric drift does not occur. Personally I wouldn't like to see an inverse square root inside my math libraries, whether at work or in my personal codebase, at least not as the default option. In codebases where an "inverse sqrt option" for functions involving a sqrt are never used, and don't ever really justify their own existence.
It is sort of a neat concept to think about, but not really practical in my opinion.

Given this library's focus on graphics math, and the fact that GLM actually uses this for vector normalization, do you think it's really that big a deal?
My normal advice would be to write your own normalize function, but of course changing how vectors are normalized has far-reaching implications for many other functions. We could do another #define (like #define HMM_NO_INVERSE_SQUARE_ROOT), but that adds a bit of complexity. At least to one place.

The inverse square root implementation (if used at all) should IMO definitely be _mm_rsqrt_ss/_mm_rsqrt_ps, which are built-in and fast.

Randomly came back to this issue. Any objections to me implementing this as a FastNormalize function and leaving the existing normalize behavior the same?

Im cool with it, id just explain what the case should be used for like show the differences between the two

Fast normalization has been added. Regular normalization has been left in place. If you look at the tests, you'll see that the precision is definitely worse, as you'd expect, so good call by @RandyGaul.
It's actually worth noting that we don't use a particularly fast inverse square root on non-SSE builds, so I've opened #95 to address this.

Reopening for Handmade Math 2.0. I would rather make fast normalize the default and remove slow normalize. If inverse square root is good enough for GLM, it's good enough for us. Those who need more precision can make their own routine. (This decision will be documented, as with everything else in Handmade Math 2.0.)

Done in HMM 2.0


Update Posts “welcome”

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Documentation -> WrapperTag: "isaaclab" is wrong, write "isaac-orbit" instead

Description
In the Documentation at Wrapping-API both for pytorch and jax in the documentation file is indicated the wrapper tag: "isaaclab"
instead looking in the code it is used "isaac-orbit" (it works)
here a snap of the error occurred

What skrl version are you using?
1.1.0
What ML framework/library version are you using?
Pytorch Version: 2.2.2+cu118, Pip 24.0,  conda 24.5.0, Isaaclab 4.0.0
Additional system information
No response

Hi @PietroDrd
Starting with skrl-v1.2.0 the isaac-orbit wrapper type was changed to isaaclab.
This change was reflected in Isaac Lab last 2024-06-26.
Also, please use the docs version 1.1.0 (https://skrl.readthedocs.io/en/1.1.0) for skrl-v1.1.0:

https://skrl.readthedocs.io/en/1.1.0/api/envs/wrapping.html#skrl.envs.wrappers.torch.wrap_env


fix: revert SWC update

Proposed change

Related issues

:bug: Fixes #(issue)
:rocket: Feature #(issue)

What is the issue?


team drive not working?

I think I've followed the directions carefully (and tried multiple times).
I authorize the label with the appropriate account, set the team_drive_id in the config file...
Everything seems to work with no errors, until I try to access the folder.
ls GoogleDriveTeam
ls: cannot access 'GoogleDriveTeam':  Input/output error
Notes:

Curiously, I noticed that I was able to create a shortcut in my personal drive, mount my personal drive, then access team files through the shortcut (though it shows up with a (8 character id), it seems to work like a symlink/folder...
for team_drive_id I tried both the google folder id from the team root level, and for the desired folder, same error.
I also tried removing auth and then re-authorizing, and clearing cache between steps

I'm also having issues with my team drive folders.
Initially, I didn't set up Team Drives, so I simply mounted the drive via
google-drive-ocamlfuse ~/GDrive

and when I run ls -l ~/GDrive, it listed the few files I have there.
In that same folder, I have some shortcuts to team folders, but for each one of them, ls emits
ls: cannot access 'GDrive/folder-name': Input/output error

and I cannot list their contents via ls -l ~/GDrive/folder-name because of the same.
After fusermount -u ~/GDrive I followed the Team Drives setup instructions for one of these shortcuts and mounted with
google-drive-ocamlfuse -label MyTeam ~/GDrive

but even using ls ~/GDrive then leads to the ls: cannot access 'GDrive': Input/output error.
Some system info:
Installed google-drive-ocamlfuse.0.7.32 via the installing from source instructions on an up-to date Debian Bookworm system.


zopen build error: unexpected text from xlclang -qversion

Just did a pull from utils...now getting this error when trying to compile autoconf:
>ZOPEN_CHECK=skip ZOPEN_INSTALL=skip ../utils/bin/zopen build
Checking environment configuration...
***ERROR: xlclang compiler specified, but it is not a 'IBM C/C++ for Open Enterprise Languages on z/OS' compiler

.xlclang -qversion doesn't return what we are expecting in zopen-build:
12:05:15:zopen:/u/jshimod/zopen/dev/autoconfport>
>xlclang -qversion
        z/OS V2.3.1 XL C/C++

but zopen-build expects: IBM C/C++ for Open Enterprise Languages on z/OS

@jshimoda9323 is this fixed? if so can you close it?


Not getting value set expansions (IG creation)

Snomed should be supported, but I'm getting no codes from the terminology services. Not sure why. Here's an example of the error message from a value set page:
Expansion
Error expanding ValueSet "null: Error unmarshalling resource: Unable to provide support for code system http://snomed.info/sct version http://snomed.info/sct/731000124108
Could it possibly be that http://snomed.info/sct/731000124108 is not a valid version name?

Can you provide more detail regarding what value set this pertains to (in what branch)?  I can't find any references to<PHONE_NUMBER>08 in the shimi or may-2018-ballot branches.
That said, I did find this guidance regarding SNOMED versions:
http://hl7.org/fhir/STU3/snomedct.html#<IP_ADDRESS>.2
And I found this value set in the FHIR spec that seems to use the version you referenced:
http://www.hl7.org/fhir/valueset-consistency-type.xml.html
But without see how you were using it in the SHR spec, it's hard to determine what's going on.

It's not in the spec at all. The error message is in the IG, on the value sets page. Sorry I didn't make that more clear.

We haven't had this problem in a while. Closing.


Show scan status bar when one of the receipt is scanning in expense report

Details

We want to show the scanning status bar below the header when there is a scanning receipt.
Fixed Issues

$ https://github.com/Expensify/App/issues/40828
PROPOSAL: https://github.com/Expensify/App/issues/40828#issuecomment-2074347525
Tests

Same as QA Steps

[x] Verify that no errors appear in the JS console

Offline tests

Same as QA Steps
QA Steps

Open a chat
Create a new scan request
Press the money request preview to open the expense/IOU report
Verify there is a scanning status bar below the header
Verify there is no next step status bar

[x] Verify that no errors appear in the JS console

PR Author Checklist

[x] I linked the correct issue in the ### Fixed Issues section above
[x] I wrote clear testing steps that cover the changes made in this PR

[x] I added steps for local testing in the Tests section
[x] I added steps for the expected offline behavior in the Offline steps section
[x] I added steps for Staging and/or Production testing in the QA steps section
[x] I added steps to cover failure scenarios (i.e. verify an input displays the correct error message if the entered data is not correct)
[x] I turned off my network connection and tested it while offline to ensure it matches the expected behavior (i.e. verify the default avatar icon is displayed if app is offline)
[x] I tested this PR with a High Traffic account against the staging or production API to ensure there are no regressions (e.g. long loading states that impact usability).

[x] I included screenshots or videos for tests on all platforms
[x] I ran the tests on all platforms & verified they passed on:

[x] Android: Native
[x] Android: mWeb Chrome
[x] iOS: Native
[x] iOS: mWeb Safari
[x] MacOS: Chrome / Safari
[x] MacOS: Desktop

[x] I verified there are no console errors (if there's a console error not related to the PR, report it or open an issue for it to be fixed)
[x] I followed proper code patterns (see Reviewing the code)

[x] I verified that any callback methods that were added or modified are named for what the method does and never what callback they handle (i.e. toggleReport and not onIconClick)
[x] I verified that the left part of a conditional rendering a React component is a boolean and NOT a string, e.g. myBool && <MyComponent />.
[x] I verified that comments were added to code that is not self explanatory
[x] I verified that any new or modified comments were clear, correct English, and explained "why" the code was doing something instead of only explaining "what" the code was doing.
[x] I verified any copy / text shown in the product is localized by adding it to src/languages/* files and using the translation method

[x] If any non-english text was added/modified, I verified the translation was requested/reviewed in #expensify-open-source and it was approved by an internal Expensify engineer. Link to Slack message:

[x] I verified all numbers, amounts, dates and phone numbers shown in the product are using the localization methods
[x] I verified any copy / text that was added to the app is grammatically correct in English. It adheres to proper capitalization guidelines (note: only the first word of header/labels should be capitalized), and is either coming verbatim from figma or has been approved by marketing (in order to get marketing approval, ask the Bug Zero team member to add the Waiting for copy label to the issue)
[x] I verified proper file naming conventions were followed for any new files or renamed files. All non-platform specific files are named after what they export and are not named "index.js". All platform-specific files are named for the platform the code supports as outlined in the README.
[x] I verified the JSDocs style guidelines (in STYLE.md) were followed

[x] If a new code pattern is added I verified it was agreed to be used by multiple Expensify engineers
[x] I followed the guidelines as stated in the Review Guidelines
[x] I tested other components that can be impacted by my changes (i.e. if the PR modifies a shared library or component like Avatar, I verified the components using Avatar are working as expected)
[x] I verified all code is DRY (the PR doesn't include any logic written more than once, with the exception of tests)
[x] I verified any variables that can be defined as constants (ie. in CONST.js or at the top of the file that uses the constant) are defined as such
[x] I verified that if a function's arguments changed that all usages have also been updated correctly
[x] If any new file was added I verified that:

[x] The file has a description of what it does and/or why is needed at the top of the file if the code is not self explanatory

[x] If a new CSS style is added I verified that:

[x] A similar style doesn't already exist
[x] The style can't be created with an existing StyleUtils function (i.e. StyleUtils.getBackgroundAndBorderStyle(theme.componentBG))

[x] If the PR modifies code that runs when editing or sending messages, I tested and verified there is no unexpected behavior for all supported markdown - URLs, single line code, code blocks, quotes, headings, bold, strikethrough, and italic.
[x] If the PR modifies a generic component, I tested and verified that those changes do not break usages of that component in the rest of the App (i.e. if a shared library or component like Avatar is modified, I verified that Avatar is working as expected in all cases)
[x] If the PR modifies a component related to any of the existing Storybook stories, I tested and verified all stories for that component are still working as expected.
[x] If the PR modifies a component or page that can be accessed by a direct deeplink, I verified that the code functions as expected when the deeplink is used - from a logged in and logged out account.
[x] If the PR modifies the UI (e.g. new buttons, new UI components, changing the padding/spacing/sizing, moving components, etc) or modifies the form input styles:

[x] I verified that all the inputs inside a form are aligned with each other.
[x] I added Design label and/or tagged @Expensify/design so the design team can review the changes.

[x] If a new page is added, I verified it's using the ScrollView component to make it scrollable when more elements are added to the page.
[x] If the main branch was merged into this PR after a review, I tested again and verified the outcome was still expected according to the Test steps.

Screenshots/Videos

Android: Native

Android: mWeb Chrome

iOS: Native

iOS: mWeb Safari

https://github.com/Expensify/App/assets/50919443/40ec1eff-413e-474b-9cd8-0c7096df98cd

MacOS: Chrome / Safari

MacOS: Desktop

https://github.com/Expensify/App/assets/50919443/431a3a8b-1548-470f-bc83-e6d46e6b4e2c

Reviewer Checklist

[x] I have verified the author checklist is complete (all boxes are checked off).
[x] I verified the correct issue is linked in the ### Fixed Issues section above
[x] I verified testing steps are clear and they cover the changes made in this PR

[x] I verified the steps for local testing are in the Tests section
[x] I verified the steps for Staging and/or Production testing are in the QA steps section
[x] I verified the steps cover any possible failure scenarios (i.e. verify an input displays the correct error message if the entered data is not correct)
[x] I turned off my network connection and tested it while offline to ensure it matches the expected behavior (i.e. verify the default avatar icon is displayed if app is offline)

[x] I checked that screenshots or videos are included for tests on all platforms
[x] I included screenshots or videos for tests on all platforms
[x] I verified tests pass on all platforms & I tested again on:

[x] Android: Native
[x] Android: mWeb Chrome
[x] iOS: Native
[x] iOS: mWeb Safari
[x] MacOS: Chrome / Safari
[x] MacOS: Desktop

[x] If there are any errors in the console that are unrelated to this PR, I either fixed them (preferred) or linked to where I reported them in Slack
[x] I verified proper code patterns were followed (see Reviewing the code)

[x] I verified that any callback methods that were added or modified are named for what the method does and never what callback they handle (i.e. toggleReport and not onIconClick).
[x] I verified that the left part of a conditional rendering a React component is a boolean and NOT a string, e.g. myBool && <MyComponent />.
[x] I verified that comments were added to code that is not self explanatory
[x] I verified that any new or modified comments were clear, correct English, and explained "why" the code was doing something instead of only explaining "what" the code was doing.
[x] I verified any copy / text shown in the product is localized by adding it to src/languages/* files and using the translation method
[x] I verified all numbers, amounts, dates and phone numbers shown in the product are using the localization methods
[x] I verified any copy / text that was added to the app is grammatically correct in English. It adheres to proper capitalization guidelines (note: only the first word of header/labels should be capitalized), and is either coming verbatim from figma or has been approved by marketing (in order to get marketing approval, ask the Bug Zero team member to add the Waiting for copy label to the issue)
[x] I verified proper file naming conventions were followed for any new files or renamed files. All non-platform specific files are named after what they export and are not named "index.js". All platform-specific files are named for the platform the code supports as outlined in the README.
[x] I verified the JSDocs style guidelines (in STYLE.md) were followed

[x] If a new code pattern is added I verified it was agreed to be used by multiple Expensify engineers
[x] I verified that this PR follows the guidelines as stated in the Review Guidelines
[x] I verified other components that can be impacted by these changes have been tested, and I retested again (i.e. if the PR modifies a shared library or component like Avatar, I verified the components using Avatar have been tested & I retested again)
[x] I verified all code is DRY (the PR doesn't include any logic written more than once, with the exception of tests)
[x] I verified any variables that can be defined as constants (ie. in CONST.js or at the top of the file that uses the constant) are defined as such
[x] If a new component is created I verified that:

[x] A similar component doesn't exist in the codebase
[x] All props are defined accurately and each prop has a /** comment above it */
[x] The file is named correctly
[x] The component has a clear name that is non-ambiguous and the purpose of the component can be inferred from the name alone
[x] The only data being stored in the state is data necessary for rendering and nothing else
[x] For Class Components, any internal methods passed to components event handlers are bound to this properly so there are no scoping issues (i.e. for onClick={this.submit} the method this.submit should be bound to this in the constructor)
[x] Any internal methods bound to this are necessary to be bound (i.e. avoid this.submit = this.submit.bind(this); if this.submit is never passed to a component event handler like onClick)
[x] All JSX used for rendering exists in the render method
[x] The component has the minimum amount of code necessary for its purpose, and it is broken down into smaller components in order to separate concerns and functions

[x] If any new file was added I verified that:

[x] The file has a description of what it does and/or why is needed at the top of the file if the code is not self explanatory

[x] If a new CSS style is added I verified that:

[x] A similar style doesn't already exist
[x] The style can't be created with an existing StyleUtils function (i.e. StyleUtils.getBackgroundAndBorderStyle(theme.componentBG)

[x] If the PR modifies code that runs when editing or sending messages, I tested and verified there is no unexpected behavior for all supported markdown - URLs, single line code, code blocks, quotes, headings, bold, strikethrough, and italic.
[x] If the PR modifies a generic component, I tested and verified that those changes do not break usages of that component in the rest of the App (i.e. if a shared library or component like Avatar is modified, I verified that Avatar is working as expected in all cases)
[x] If the PR modifies a component related to any of the existing Storybook stories, I tested and verified all stories for that component are still working as expected.
[x] If the PR modifies a component or page that can be accessed by a direct deeplink, I verified that the code functions as expected when the deeplink is used - from a logged in and logged out account.
[x] If the PR modifies the UI (e.g. new buttons, new UI components, changing the padding/spacing/sizing, moving components, etc) or modifies the form input styles:

[x] I verified that all the inputs inside a form are aligned with each other.
[x] I added Design label and/or tagged @Expensify/design so the design team can review the changes.

[x] If a new page is added, I verified it's using the ScrollView component to make it scrollable when more elements are added to the page.
[x] If the main branch was merged into this PR after a review, I tested again and verified the outcome was still expected according to the Test steps.
[x] I have checked off every checkbox in the PR reviewer checklist, including those that don't apply to this PR.

Screenshots/Videos

MacOS: Chrome / Safari
https://github.com/Expensify/App/assets/3004659/730a91ec-50b8-4796-9c38-a6f5b372089b
https://github.com/Expensify/App/assets/3004659/f6e355b7-05a9-4cee-96e9-ec950260a385

Android: Native
https://github.com/Expensify/App/assets/3004659/f2a66554-2275-44e9-a2a9-40798d8b8502

Android: mWeb Chrome
https://github.com/Expensify/App/assets/3004659/ad94c4be-31b4-46e6-acc7-861c356888ed

iOS: Native
https://github.com/Expensify/App/assets/3004659/eef7f45a-090a-4172-8460-fd969487124e

iOS: mWeb Safari
https://github.com/Expensify/App/assets/3004659/64ffc71d-b458-4674-84df-ec1746fd0f0f

MacOS: Desktop
https://github.com/Expensify/App/assets/3004659/cca6e52a-ae1d-421a-8278-a9ac046f44ae

@bernhardoj There are conflicts here. Can you please resolve them?

Conflicts solved

@thienlnam All yours for review.

:hand: This PR was not deployed to staging yet because QA is ongoing. It will be automatically deployed to staging after the next production release.

🚀 Deployed to staging by https://github.com/thienlnam in version: 1.4.77-0 🚀

platform
result

🤖 android 🤖
success ✅

🖥 desktop 🖥
success ✅

🍎 iOS 🍎
success ✅

🕸 web 🕸
success ✅

🚀 Deployed to production by https://github.com/puneetlath in version: 1.4.77-11 🚀

platform
result

🤖 android 🤖
failure ❌

🖥 desktop 🖥
success ✅

🍎 iOS 🍎
success ✅

🕸 web 🕸
success ✅

🚀 Deployed to production by https://github.com/puneetlath in version: 1.4.77-11 🚀

platform
result

🤖 android 🤖
success ✅

🖥 desktop 🖥
success ✅

🍎 iOS 🍎
success ✅

🕸 web 🕸
success ✅


fix: Add file locking around accessing the state file

I found that vac has a race condition if you run multiple instances in parallel; each instance reads in the state file, adds the refreshed credentials to the state, then writes out the updated state file. The last instance to do that will revert any other updated credentials and only store the ones it updated.
I have around 28 AWS accounts and was using Steampipe to query them, which if I query across all accounts, launches multiple queries in parallel which demonstrates this problem. I was confused why vac status only showed some credentials as being refreshed.
This PR adds some file locking around the state file so only one vac process can read (and write) the state file at any one time. With this change applied, vac status now shows all account credentials refreshing correctly. The lock file used is the same path as the state file itself, with a .lock extension.

@mvisonneau any feedback? I've just added a simple test so the coverage doesn't decrease 😉

Thanks for this change @bodgit!


Centos8 requires EPEL repo for libsodium

Changes proposed in this pull request:

btw, did you try to build ?
I recall that Oracle linux is shipped with some missing features in libsodium
is EPEL fully working one ?

here are some details
https://github.com/SoftEtherVPN/SoftEtherVPN/issues/1338
I took Oracle Linux 7 (in Oracle cloud) and rebuilt libsodium from EPEL8

@chipitsine thx for this. i'm trying to build now but having issues w/ the github symlink to the libhamcore, so need to rewrite my Dockerfile to git clone instead. working on it now... this fixes the EPEL/libsodium issue at least w/ (adding it in two stages, first adding epel, then it rebuilds the package list)

can you please verify that updated guide is good for both CentOS7 and CentOS8 ?
if it will not work for CentOS7, doc should be updated

Hm having issues now finding the vpnserver binary. Post build what directory is it in? I'll tidy up this PR but as of now can't build the docker image forking the siomiz one that's on stable and quite old.

epel is needed on both CentOS 7 and 8

Ok


about tensorflow-native-cu113 probably not  using gpu to inference

Description
(A clear and concise description of what the feature is.)
Will this change the current api? How?
Who will benefit from this enhancement?
References

list reference and related literature

list known implementations
  <dependency>
      <groupId>ai.djl.tensorflow</groupId>
      <artifactId>tensorflow-native-cu113</artifactId>
      <classifier>linux-x86_64</classifier>
    <version>2.7.1</version>
  </dependency>

if i set predict device is gpu ，the preprocess output ndList's device indeed gpu 。
but continues inference output ndList‘s device is always cpu.
and i compare DJL inference speed to the docker iamge of tensorflow/serving  , DJL  spend time is double for tensorflow/serving
i want to asking what do i do  can help me certain i indeed used gpu to inference?

DJL by default will use GPU if it detected the cuda version is compatible with the engine.
Can you try run djl-bench on your system and see if DJL can pick up GPU: https://github.com/deepjavalibrary/djl-serving/tree/master/benchmark

DJL by default will use GPU if it detected the cuda version is compatible with the engine.
Can you try run djl-bench on your system and see if DJL can pick up GPU: https://github.com/deepjavalibrary/djl-serving/tree/master/benchmark

unfortunatelly，the link of you gave me have nothing.

DJL by default will use GPU if it detected the cuda version is compatible with the engine.
Can you try run djl-bench on your system and see if DJL can pick up GPU: https://github.com/deepjavalibrary/djl-serving/tree/master/benchmark

@frankfliu
unfortunatelly，the link of you gave me has nothing.

@guojian0614
The link works find for me. Are able to see anything in djl-serving github repo?

@frankfliu thank you , it is github official problem

@frankfliu
now ,i already download djl-serving project and run it accord to you suggest
the example used engine is PyTorch ， the dependencies is
runtimeOnly("ai.djl.onnxruntime:onnxruntime-engine") {
exclude group: "com.microsoft.onnxruntime", module: "onnxruntime"
}
runtimeOnly "com.microsoft.onnxruntime:onnxruntime_gpu:${onnxruntime_version}"
the following code don't detected gpu
“ Device device = Engine.getEngine(arguments.getEngine()).defaultDevice();”

if i force used gpu ，it will alert "No matching cuda flavor for win-x86_64 found: cu113"
What should I do on next？

I'm confused:

Are you running on Windows or Linux? in your earlier dependency, you use linux-x86_64
Which engine are you using? you add onnxruntime to your dependency
Which CUDA version is installed on your system?
How you force to use GPU?

2. using
@frankfliu

i run it on the win10-x86_64 system
we runed code is your serving project benchmark module provide

the gpu environment sure no problem，that's adjusted according to you before suggest ，in my custom DJL code preprocess indeed used gpu。
4.

just replace read line point code line to “  Device devic= Device.gpu();”

A few things you can try:

get latest djl-serving source code (using DJL 0.23.0-SANPSHOT), 0.21.0 doesn't recognize CUDA 12.1
or set env var: export PYTORCH_FLAVOR=cu117 and export PYTORCH_VERSION=1.13.1

@frankfliu
hello，Sorry to trouble you again！
I run it according to your suggest ,threw new exception follow
Caused by: java.lang.UnsatisfiedLinkError: C:\Users\lenovo.djl.ai\pytorch\1.13.1-cu117-win-x86_64\torch_cuda_cpp.dll: Caused by: java.lang.UnsatisfiedLinkError: C:\Users\lenovo.djl.ai\pytorch\1.13.1-cu117-win-x86_64\torch_cuda_cpp.dll: �Ҳ���ָ���ĳ���
at java.lang.ClassLoader$NativeLibrary.load0(Native Method) ~[?:?]
at java.lang.ClassLoader$NativeLibrary.load(ClassLoader.java:2442) ~[?:?]
at java.lang.ClassLoader$NativeLibrary.loadLibrary(ClassLoader.java:2498) ~[?:?]
at java.lang.ClassLoader.loadLibrary0(ClassLoader.java:2694) ~[?:?]
at java.lang.ClassLoader.loadLibrary(ClassLoader.java:2627) ~[?:?]
at java.lang.Runtime.load0(Runtime.java:768) ~[?:?]
at java.lang.System.load(System.java:1837) ~[?:?]
at ai.djl.pytorch.jni.LibUtils.loadNativeLibrary(LibUtils.java:347) ~[pytorch-engine-0.22.1-SNAPSHOT.jar:?]
at ai.djl.pytorch.jni.LibUtils.loadLibTorch(LibUtils.java:174) ~[pytorch-engine-0.22.1-SNAPSHOT.jar:?]
at ai.djl.pytorch.jni.LibUtils.loadLibrary(LibUtils.java:78) ~[pytorch-engine-0.22.1-SNAPSHOT.jar:?]
at ai.djl.pytorch.engine.PtEngine.newInstance(PtEngine.java:53) ~[pytorch-engine-0.22.1-SNAPSHOT.jar:?]
... 5 more

If you see UnsatisfiedLinkError, most like you you are missing some dependencies in your system (or not in PATH).
See: https://docs.djl.ai/docs/development/troubleshooting.html#unsatisfiedlinkerror-issue

If you see UnsatisfiedLinkError, most like you you are missing some dependencies in your system (or not in PATH).
See: https://docs.djl.ai/docs/development/troubleshooting.html#unsatisfiedlinkerror-issue

thanks，indeed torch_cuda.cpp.dll lack dependency

您可以尝试以下几件事：

获取最新的 djl-serving 源代码（使用 DJL 0.23.0-SANPSHOT），0.21.0 无法识别 CUDA 12.1
或设置环境变量：export PYTORCH_FLAVOR=cu117和export PYTORCH_VERSION=1.13.1

I have similar problem，my environment is as follows：
linux,gpu t4,tensorflow
CUDA: 113
ARCH: 75
DJL version: 0.23.0
ai.djl.util.Platform - Found placeholder platform from: cu113-linux-x86_64:2.10.1
Default Engine: TensorFlow:2.10.1, capabilities: [
MKL,
]
TensorFlow Library: /usr/local/app/.djl.ai/tensorflow/2.10.1-cu113-linux-x86_64/libjnitensorflow.so
engine.hasCapability(StandardCapabilities.CUDA) is always false


added index checks

Signed-off-by: Xavier Dharmaiyan<EMAIL_ADDRESS>Related Issue:  stolostron/backlog#<ISSUE_NUMBER>
Description of changes

/retest

/override ci/prow/sonar-pre-submit


Health check fails

Trying to add a readiness and liveness probes to the metrics-server Helm chart. I can't get the /healthz endpoint to return 200. I also tried increasing the initialDelaySeconds to 90 since it looks like it's dependent upon the metrics relist interval but this did not change anything. Pod Spec:
      - command:
        - /metrics-server
        - --logtostderr
        - --v=10
        - --kubelet-insecure-tls
        - --kubelet-preferred-address-types=InternalIP
        image: gcr.io/google_containers/metrics-server-amd64:v0.3.0
        imagePullPolicy: IfNotPresent
        livenessProbe:
          failureThreshold: 3
          httpGet:
            path: /healthz
            port: 443
            scheme: HTTPS
          initialDelaySeconds: 20
          periodSeconds: 10
          successThreshold: 1
          timeoutSeconds: 1
        name: metrics-server
        ports:
        - containerPort: 443
          name: https
          protocol: TCP
        readinessProbe:
          failureThreshold: 3
          httpGet:
            path: /healthz
            port: 443
            scheme: HTTPS
          initialDelaySeconds: 20
          periodSeconds: 10
          successThreshold: 1
          timeoutSeconds: 1

$ curl https://<IP_ADDRESS>:443/healthz -k -v
* TLSv1.2 (OUT), TLS header, Certificate Status (22):
* TLSv1.2 (OUT), TLS handshake, Client hello (1):
* TLSv1.2 (IN), TLS handshake, Server hello (2):
* TLSv1.2 (IN), TLS handshake, Certificate (11):
* TLSv1.2 (IN), TLS handshake, Server key exchange (12):
* TLSv1.2 (IN), TLS handshake, Request CERT (13):
* TLSv1.2 (IN), TLS handshake, Server finished (14):
* TLSv1.2 (OUT), TLS handshake, Certificate (11):
* TLSv1.2 (OUT), TLS handshake, Client key exchange (16):
* TLSv1.2 (OUT), TLS change cipher, Client hello (1):
* TLSv1.2 (OUT), TLS handshake, Finished (20):
* TLSv1.2 (IN), TLS change cipher, Client hello (1):
* TLSv1.2 (IN), TLS handshake, Finished (20):
> GET /healthz HTTP/1.1
> Host: <IP_ADDRESS>
> User-Agent: curl/7.60.0
> Accept: */*
>
< HTTP/1.1 500 Internal Server Error
< Content-Type: text/plain; charset=utf-8
< X-Content-Type-Options: nosniff
< Date: Fri, 07 Sep 2018 20:20:38 GMT
< Content-Length: 121
<
[+]ping ok
[+]poststarthook/generic-apiserver-start-informers ok
[-]healthz failed: reason withheld
healthz check failed

I0907 20:20:37.999109       1 request.go:897] Request Body: {"kind":"SubjectAccessReview","apiVersion":"authorization.k8s.io/v1beta1","metadata":{"creationTimestamp":null},"spec":{"nonResourceAttributes":{"path":"/healthz","verb":"get"},"user":"system:anonymous","group":["system:unauthenticated"]},"status":{"allowed":false}}
I0907 20:20:37.999399       1 round_trippers.go:386] curl -k -v -XPOST  -H "Authorization: Bearer eyJhbGciOiJSUzI1NiIsImtpZCI6IiJ9.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.jOh48CMuP-qi-NTS-QwQFEwSDBDwVBym8d_K-4GkN61ckplo7OKTztzDqYILWz-HQzMbMpG6yEMNANzK-SeXxGKG3jn6EOYlpAVpZkjy1w_2UZgoRoZyMkYKlBBV5p_eJ1wO4yHfp4YX2_yaBC5UQH1ZupxNhjKbC_1kzwjYYxO6XHf6AomyP11Id9W2QLY6hVs3e8058HILvzJzclXKvcmBGnEBp_RvLx2JTwP5S_BQXilixBITb_W7hw1jm5alunmd8HCs8RU0uLK2xVGFtfJm4IlAwB6GsIm98Cj9B-yBgRE0jsj_DAiOfX50Qn6-hgbZRVeVKW4QylW-bojquA" -H "User-Agent: metrics-server/v0.0.0 (linux/amd64) kubernetes/$Format" -H "Accept: application/json, */*" -H "Content-Type: application/json" 'https://<IP_ADDRESS>:443/apis/authorization.k8s.io/v1beta1/subjectaccessreviews'
I0907 20:20:38.011071       1 round_trippers.go:405] POST https://<IP_ADDRESS>:443/apis/authorization.k8s.io/v1beta1/subjectaccessreviews 201 Created in 11 milliseconds
I0907 20:20:38.011321       1 round_trippers.go:411] Response Headers:
I0907 20:20:38.011400       1 round_trippers.go:414]     Content-Type: application/json
I0907 20:20:38.011446       1 round_trippers.go:414]     Content-Length: 406
I0907 20:20:38.011486       1 round_trippers.go:414]     Date: Fri, 07 Sep 2018 20:20:38 GMT
I0907 20:20:38.014567       1 request.go:897] Response Body: {"kind":"SubjectAccessReview","apiVersion":"authorization.k8s.io/v1beta1","metadata":{"creationTimestamp":null},"spec":{"nonResourceAttributes":{"path":"/healthz","verb":"get"},"user":"system:anonymous","group":["system:unauthenticated"]},"status":{"allowed":true,"reason":"RBAC: allowed by ClusterRoleBinding \"system:discovery\" of ClusterRole \"system:discovery\" to Group \"system:unauthenticated\""}}
I0907 20:20:38.032803       1 handler.go:153] metrics-server: GET "/healthz" satisfied by nonGoRestful
I0907 20:20:38.032849       1 pathrecorder.go:240] metrics-server: "/healthz" satisfied by exact match
I0907 20:20:38.032873       1 healthz.go:121] healthz check healthz failed: there was an error collecting or saving metrics in the last collection tick
I0907 20:20:38.034124       1 wrap.go:42] GET /healthz: (35.345473ms) 500
goroutine 130 [running]:
github.com/kubernetes-incubator/metrics-server/vendor/k8s.io/apiserver/pkg/server/httplog.(*respLogger).recordStatus(0xc42028d570, 0x1f4)
	/go/src/github.com/kubernetes-incubator/metrics-server/vendor/k8s.io/apiserver/pkg/server/httplog/httplog.go:207 +0xd2
github.com/kubernetes-incubator/metrics-server/vendor/k8s.io/apiserver/pkg/server/httplog.(*respLogger).WriteHeader(0xc42028d570, 0x1f4)
	/go/src/github.com/kubernetes-incubator/metrics-server/vendor/k8s.io/apiserver/pkg/server/httplog/httplog.go:186 +0x35
github.com/kubernetes-incubator/metrics-server/vendor/k8s.io/apiserver/pkg/server/filters.(*baseTimeoutWriter).WriteHeader(0xc42094cc00, 0x1f4)
	/go/src/github.com/kubernetes-incubator/metrics-server/vendor/k8s.io/apiserver/pkg/server/filters/timeout.go:192 +0xac
net/http.Error(0x7ff9902d81e0, 0xc42000d390, 0xc420092580, 0x78, 0x1f4)
	/usr/local/go/src/net/http/server.go:1959 +0xda
github.com/kubernetes-incubator/metrics-server/vendor/k8s.io/apiserver/pkg/server/healthz.handleRootHealthz.func1(0x7ff9902d81e0, 0xc42000d390, 0xc4200bcd00)
	/go/src/github.com/kubernetes-incubator/metrics-server/vendor/k8s.io/apiserver/pkg/server/healthz/healthz.go:130 +0x4c4
net/http.HandlerFunc.ServeHTTP(0xc4207aed00, 0x7ff9902d81e0, 0xc42000d390, 0xc4200bcd00)
	/usr/local/go/src/net/http/server.go:1947 +0x44
github.com/kubernetes-incubator/metrics-server/vendor/k8s.io/apiserver/pkg/server/mux.(*pathHandler).ServeHTTP(0xc420684780, 0x7ff9902d81e0, 0xc42000d390, 0xc4200bcd00)
	/go/src/github.com/kubernetes-incubator/metrics-server/vendor/k8s.io/apiserver/pkg/server/mux/pathrecorder.go:241 +0x514
github.com/kubernetes-incubator/metrics-server/vendor/k8s.io/apiserver/pkg/server/mux.(*PathRecorderMux).ServeHTTP(0xc4207e0d90, 0x7ff9902d81e0, 0xc42000d390, 0xc4200bcd00)
	/go/src/github.com/kubernetes-incubator/metrics-server/vendor/k8s.io/apiserver/pkg/server/mux/pathrecorder.go:234 +0x8a
github.com/kubernetes-incubator/metrics-server/vendor/k8s.io/apiserver/pkg/server.director.ServeHTTP(0x174202f, 0xe, 0xc4203646c0, 0xc4207e0d90, 0x7ff9902d81e0, 0xc42000d390, 0xc4200bcd00)
	/go/src/github.com/kubernetes-incubator/metrics-server/vendor/k8s.io/apiserver/pkg/server/handler.go:154 +0x6a8
github.com/kubernetes-incubator/metrics-server/vendor/k8s.io/apiserver/pkg/endpoints/filters.WithAuthorization.func1(0x7ff9902d81e0, 0xc42000d390, 0xc4200bcd00)
	/go/src/github.com/kubernetes-incubator/metrics-server/vendor/k8s.io/apiserver/pkg/endpoints/filters/authorization.go:64 +0x4bd
net/http.HandlerFunc.ServeHTTP(0xc4203048c0, 0x7ff9902d81e0, 0xc42000d390, 0xc4200bcd00)
	/usr/local/go/src/net/http/server.go:1947 +0x44
github.com/kubernetes-incubator/metrics-server/vendor/k8s.io/apiserver/pkg/server/filters.WithMaxInFlightLimit.func1(0x7ff9902d81e0, 0xc42000d390, 0xc4200bcd00)
	/go/src/github.com/kubernetes-incubator/metrics-server/vendor/k8s.io/apiserver/pkg/server/filters/maxinflight.go:160 +0x3fc
net/http.HandlerFunc.ServeHTTP(0xc420771b60, 0x7ff9902d81e0, 0xc42000d390, 0xc4200bcd00)
	/usr/local/go/src/net/http/server.go:1947 +0x44
github.com/kubernetes-incubator/metrics-server/vendor/k8s.io/apiserver/pkg/endpoints/filters.WithImpersonation.func1(0x7ff9902d81e0, 0xc42000d390, 0xc4200bcd00)
	/go/src/github.com/kubernetes-incubator/metrics-server/vendor/k8s.io/apiserver/pkg/endpoints/filters/impersonation.go:50 +0x1ce4
net/http.HandlerFunc.ServeHTTP(0xc420304900, 0x7ff9902d81e0, 0xc42000d390, 0xc4200bcd00)
	/usr/local/go/src/net/http/server.go:1947 +0x44
github.com/kubernetes-incubator/metrics-server/vendor/k8s.io/apiserver/pkg/endpoints/filters.WithAuthentication.func1(0x7ff9902d81e0, 0xc42000d390, 0xc4200bcd00)
	/go/src/github.com/kubernetes-incubator/metrics-server/vendor/k8s.io/apiserver/pkg/endpoints/filters/authentication.go:75 +0x2b3
net/http.HandlerFunc.ServeHTTP(0xc420304940, 0x7ff9902d81e0, 0xc42000d390, 0xc4200bcc00)
	/usr/local/go/src/net/http/server.go:1947 +0x44
github.com/kubernetes-incubator/metrics-server/vendor/k8s.io/apiserver/pkg/server/filters.(*timeoutHandler).ServeHTTP.func1(0xc42077d520, 0x18e29c0, 0xc42000d390, 0xc4200bcc00, 0xc4208d5ce0)
	/go/src/github.com/kubernetes-incubator/metrics-server/vendor/k8s.io/apiserver/pkg/server/filters/timeout.go:97 +0x8d
created by github.com/kubernetes-incubator/metrics-server/vendor/k8s.io/apiserver/pkg/server/filters.(*timeoutHandler).ServeHTTP
	/go/src/github.com/kubernetes-incubator/metrics-server/vendor/k8s.io/apiserver/pkg/server/filters/timeout.go:96 +0x1b5

logging error output: "[+]ping ok\n[+]poststarthook/generic-apiserver-start-informers ok\n[-]healthz failed: reason withheld\nhealthz check failed\n"
 [[curl/7.60.0] <IP_ADDRESS>:58138]

I take it back. Increasing the initialDelaySeconds to be greater than metrics relist interval made it finally mark the pod as ready. This is pretty sub-optimal: what if the metrics relist interval is 10 minutes? We have to manually adjust the probe to be greater than 10 minutes, causing the pod to not be marked as ready for 10+ minutes. This is not something I can do generically in a helm chart.
It seems like the metrics aren't fetched for the first time until the metrics relist interval has elapsed, so be default 60 seconds after startup. Can this be changed so that they are fetched as soon as possible on startup?

what if the metrics relist interval is 10 minutes

That would be basically unusable for the HPA.

It seems like the metrics aren't fetched for the first time until the metrics relist interval has elapsed, so by default 60 seconds after startup. Can this be changed so that they are fetched as soon as possible on startup?

Yes, please file a bug.  We should be attempting to list as soon as the caches are synced.

Yes, please file a bug. We should be attempting to list as soon as the caches are synced.

I thought this was that issue. But okay, I opened #136.


yosys-smtbmc: Problems with "$past()" function in "cover" statements

I'm trying to generate witnesses with yosys-smtbmc and I'm not having much luck with $past() functions in cover statements.
Yosys-smtbmc reports these cover statements as unreachable, even though I can prove that they are.
For example, I would like to make sure that a signal ("wb_itr_mon_fsm_busy") can be asserted for two consecutive clock cycles (see line 408 in the example below):
   402     //Cover state transitions in monitor
   403     //==================================
   404     always @(posedge clk_i)
   405         begin
   406  //        cover (wb_itr_mon_fsm_busy & $past(wb_itr_mon_fsm_idle));
   407  //        cover (wb_itr_mon_fsm_idle & $past(wb_itr_mon_fsm_idle));
   408            cover (wb_itr_mon_fsm_busy & $past(wb_itr_mon_fsm_busy));
   409  //        cover (wb_itr_mon_fsm_idle & $past(wb_itr_mon_fsm_busy));
   410
   411            //Enforce witness
   412            cover (wb_itr_mon_fsm_busy & ~rst & req & ack);
   413         end // always @ (posedge clk_i)

When I run yosys-smtbmc (through SymbiYosys), it reports that the cover statement in line 408 is unreachable.
However, the cover statement in line 412 is reachable and generates a witness, which satisfies the cover statement in line 408:
SBY 21:50:55 [./tools/SymbiYosis/run/cover.WbXbc_address_decoder.default] engine_0: starting process "cd ./tools/SymbiYosis/run/cover.WbXbc_address_decoder.default; yosys-smtbmc --presat --unroll -c --noprogress -t 10 --append 8 --dump-vcd engine_0/trace%.vcd --dump-vlogtb engine_0/trace%_tb.v --dump-smtc engine_0/trace%.smtc model/design_smt2.smt2"
SBY 21:50:55 [./tools/SymbiYosis/run/cover.WbXbc_address_decoder.default] engine_0: ##   0:00:00  Solver: yices
SBY 21:50:56 [./tools/SymbiYosis/run/cover.WbXbc_address_decoder.default] engine_0: ##   0:00:00  Checking cover reachability in step 0..
SBY 21:50:56 [./tools/SymbiYosis/run/cover.WbXbc_address_decoder.default] engine_0: ##   0:00:00  Checking cover reachability in step 1..
SBY 21:50:56 [./tools/SymbiYosis/run/cover.WbXbc_address_decoder.default] engine_0: ##   0:00:00  Checking cover reachability in step 2..
SBY 21:50:56 [./tools/SymbiYosis/run/cover.WbXbc_address_decoder.default] engine_0: ##   0:00:00  Checking cover reachability in step 3..
SBY 21:50:57 [./tools/SymbiYosis/run/cover.WbXbc_address_decoder.default] engine_0: ##   0:00:01  Checking cover reachability in step 4..
SBY 21:50:57 [./tools/SymbiYosis/run/cover.WbXbc_address_decoder.default] engine_0: ##   0:00:01  Checking cover reachability in step 5..
SBY 21:50:57 [./tools/SymbiYosis/run/cover.WbXbc_address_decoder.default] engine_0: ##   0:00:01  Checking cover reachability in step 6..
SBY 21:50:57 [./tools/SymbiYosis/run/cover.WbXbc_address_decoder.default] engine_0: ##   0:00:01  Checking cover reachability in step 7..
SBY 21:50:57 [./tools/SymbiYosis/run/cover.WbXbc_address_decoder.default] engine_0: ##   0:00:01  Checking cover reachability in step 8..
SBY 21:50:58 [./tools/SymbiYosis/run/cover.WbXbc_address_decoder.default] engine_0: ##   0:00:02  Checking cover reachability in step 9..
SBY 21:50:58 [./tools/SymbiYosis/run/cover.WbXbc_address_decoder.default] engine_0: ##   0:00:02  Appending additional step 10.
SBY 21:50:58 [./tools/SymbiYosis/run/cover.WbXbc_address_decoder.default] engine_0: ##   0:00:02  Appending additional step 11.
SBY 21:50:58 [./tools/SymbiYosis/run/cover.WbXbc_address_decoder.default] engine_0: ##   0:00:02  Appending additional step 12.
SBY 21:50:58 [./tools/SymbiYosis/run/cover.WbXbc_address_decoder.default] engine_0: ##   0:00:02  Appending additional step 13.
SBY 21:50:58 [./tools/SymbiYosis/run/cover.WbXbc_address_decoder.default] engine_0: ##   0:00:02  Appending additional step 14.
SBY 21:50:58 [./tools/SymbiYosis/run/cover.WbXbc_address_decoder.default] engine_0: ##   0:00:02  Appending additional step 15.
SBY 21:50:59 [./tools/SymbiYosis/run/cover.WbXbc_address_decoder.default] engine_0: ##   0:00:03  Appending additional step 16.
SBY 21:50:59 [./tools/SymbiYosis/run/cover.WbXbc_address_decoder.default] engine_0: ##   0:00:03  Appending additional step 17.
SBY 21:50:59 [./tools/SymbiYosis/run/cover.WbXbc_address_decoder.default] engine_0: ##   0:00:03  Re-solving with appended steps..
SBY 21:50:59 [./tools/SymbiYosis/run/cover.WbXbc_address_decoder.default] engine_0: ##   0:00:03  Reached cover statement at ftb_WbXbc_address_decoder.sv:412 in step 9.
SBY 21:50:59 [./tools/SymbiYosis/run/cover.WbXbc_address_decoder.default] engine_0: ##   0:00:03  Writing trace to VCD file: engine_0/trace0.vcd
SBY 21:51:00 [./tools/SymbiYosis/run/cover.WbXbc_address_decoder.default] engine_0: ##   0:00:04  Writing trace to Verilog testbench: engine_0/trace0_tb.v
SBY 21:51:00 [./tools/SymbiYosis/run/cover.WbXbc_address_decoder.default] engine_0: ##   0:00:04  Writing trace to constraints file: engine_0/trace0.smtc
SBY 21:51:00 [./tools/SymbiYosis/run/cover.WbXbc_address_decoder.default] engine_0: ##   0:00:04  Checking cover reachability in step 9..
SBY 21:51:00 [./tools/SymbiYosis/run/cover.WbXbc_address_decoder.default] engine_0: ##   0:00:04  Unreached cover statement at ftb_WbXbc_address_decoder.sv:408.
SBY 21:51:00 [./tools/SymbiYosis/run/cover.WbXbc_address_decoder.default] engine_0: ##   0:00:04  Status: FAILED (!)
SBY 21:51:00 [./tools/SymbiYosis/run/cover.WbXbc_address_decoder.default] engine_0: finished (returncode=1)
SBY 21:51:00 [./tools/SymbiYosis/run/cover.WbXbc_address_decoder.default] engine_0: Status returned by engine: FAIL
SBY 21:51:00 [./tools/SymbiYosis/run/cover.WbXbc_address_decoder.default] summary: Elapsed clock time [H:MM:SS (secs)]: 0:00:06 (6)
SBY 21:51:00 [./tools/SymbiYosis/run/cover.WbXbc_address_decoder.default] summary: Elapsed process time [H:MM:SS (secs)]: 0:00:07 (7)
SBY 21:51:00 [./tools/SymbiYosis/run/cover.WbXbc_address_decoder.default] summary: engine_0 (smtbmc) returned FAIL
SBY 21:51:00 [./tools/SymbiYosis/run/cover.WbXbc_address_decoder.default] DONE (FAIL, rc=2)

I see the same problem when I try to to implement the $past() function manually with registern.
If you'd like to replay my example, please do the following:
git clone https://github.com/hotwolf/WbXbc.git
cd WbXbc
make cover.WbXbc_address_decoder
make debug

(Note: "make debug" requires the installation of Perl and the Verilog-Perl library.  Alternatively, run "make debug.list" to see the location of the VCD file and run gtkwave manually)

How do you know this is actually reachable within the configured bounds of 10 cycles?
Adding some code that allows wb_itr_mon_fsm_busy to be high at any time (and disabling the assertion that would fail as consequence of that) makes the cover statement pass:
$ git diff
diff --git a/bench/verilog/ftb_WbXbc_address_decoder.sv b/bench/verilog/ftb_WbXbc_address_decoder.sv
index 1d27c24..0e48f53 100644
--- a/bench/verilog/ftb_WbXbc_address_decoder.sv
+++ b/bench/verilog/ftb_WbXbc_address_decoder.sv
@@ -385,8 +385,8 @@ module ftb_WbXbc_address_decoder
               ~|{wb_itr_mon_fsm_idle, wb_tgt_mon_fsm_idle, wb_pass_through_fsm_idle});

         //Busy states must be aligned
-       assert(&{wb_itr_mon_fsm_busy, wb_tgt_mon_fsm_busy, wb_pass_through_fsm_busy} |
-              ~|{wb_itr_mon_fsm_busy, wb_tgt_mon_fsm_busy, wb_pass_through_fsm_busy});
+       // assert(&{wb_itr_mon_fsm_busy, wb_tgt_mon_fsm_busy, wb_pass_through_fsm_busy} |
+       //        ~|{wb_itr_mon_fsm_busy, wb_tgt_mon_fsm_busy, wb_pass_through_fsm_busy});
      end // always @ *

    //Cover all target selects
diff --git a/bench/verilog/wb_itr_mon.sv b/bench/verilog/wb_itr_mon.sv
index b2d32fd..70b5e15 100644
--- a/bench/verilog/wb_itr_mon.sv
+++ b/bench/verilog/wb_itr_mon.sv
@@ -208,6 +208,6 @@ module wb_itr_mon
    //==================
    assign tb_fsm_reset = (state_reg === STATE_RESET) ? 1'b1 : 1'b0;
    assign tb_fsm_idle  = (state_reg === STATE_IDLE)  ? 1'b1 : 1'b0;
-   assign tb_fsm_busy  = (state_reg === STATE_BUSY)  ? 1'b1 : 1'b0;
+   assign tb_fsm_busy  = ((state_reg === STATE_BUSY)  ? 1'b1 : 1'b0) || $anyseq(1);

 endmodule // wb_itr_mon

So I don't think there's anything wrong with $past. If you disagree please create an issue with a MCVE that clearly demonstrates that there is a problem. With a large design this is not the case because it isn't obvious at all if the cover statement is reachable within the configured bounds.

Thanks for looking into this issue.  I guess the test case I've prepared appears to be more complex, than it really is. All the design  does, is pass through a set of wishbone signals. It doesn't contain any sequential logic. The test bench contains two cover statements:

The first one "cover (wb_itr_mon_fsm_busy & $past(wb_itr_mon_fsm_busy));" is reported as unsatisfiable.

The second one "cover (wb_itr_mon_fsm_busy & ~rst & req & ack);" produces a witness which contradicts the first result. (wb_itr_mon_fsm_busy is asserted for two consecutive clock cycles). Here is a screenshot.

I don't know how to simplify the test case.  The make file I've provided let's you reproduce the problem with a single command  ("make cover.WbXbc_address_decoder").
I'll re-post the issue one more time, in case you do want to follow up on why Yosys is reporting these contradictory results.
PS: The assertions you've removed are required for running proofs with k-induction.

See the answer given for #853 .  The tool is working as intended, and it is the "append" statement that is confusing you.  One more clock is necessary to successfully find the missing trace.

You are right, I did not consider that part of the trace consists appended cycles. Thanks for pointing this out to me.


Make 'system' mode persist in storage

Describe the feature in detail (code, mocks, or screenshots encouraged)
When you setMode('system') though ModeWatcher tracks the system preferences, and the theme changes accordingly, it does not get stored in localStorage and will stop tracking system preferences after a refresh.
What type of pull request would this be?
Enhancement
Provide relevant links or additional information.


[Bug]? Not CFG latent_model_input Instead of latents?

hi @LinB203 @qqingzheng @clownrat6 et al.,
may i check if there is a bug here, where else latents should be else latent_model_input for I2V?
https://github.com/PKU-YuanGroup/Open-Sora-Plan/blob/294993ca78bf65dec1c3b6fb25541432c545eda9/opensora/sample/pipeline_inpaint.py#L823
thx & rgds,

Thanks for your issue, we will fix this bug.


Status:         Running
IP:             <IP_ADDRESS>
Created By:     ReplicaSet/tiller-deploy-2003435939
Controlled By:  ReplicaSet/tiller-deploy-2003435939
Containers:
  tiller:
    Container ID:   docker://2d7d532ec6822d53b3df0badcbb5c672250cc00190725f6f4db22dce2cc66a58
    Image:          gcrio.azureedge.net/kubernetes-helm/tiller:v2.6.0
    Image ID:       docker-pullable://gcrio.azureedge.net/kubernetes-helm/tiller@sha256:6f1aef0a851de5bd0abecc8dcd9870c7590f7f995874d9e6010ae9e82e5e0f49
    Port:           44134/TCP
    State:          Waiting
      Reason:       CrashLoopBackOff
    Last State:     Terminated
      Reason:       Error
      Exit Code:    2
      Started:      Fri, 10 Nov 2017 19:36:58 +0000
      Finished:     Fri, 10 Nov 2017 19:37:17 +0000
    Ready:          False
    Restart Count:  45
    Limits:
      cpu:     50m
      memory:  150Mi
    Requests:
      cpu:      50m
      memory:   150Mi
    Liveness:   http-get http://:44135/liveness delay=1s timeout=1s period=10s #success=1 #failure=3
    Readiness:  http-get http://:44135/readiness delay=1s timeout=1s period=10s #success=1 #failure=3
    Environment:
      TILLER_NAMESPACE:  kube-system
    Mounts:
      /var/run/secrets/kubernetes.io/serviceaccount from tiller-token-k839f (ro)
Conditions:
  Type           Status
  Initialized    True
  Ready          False
  PodScheduled   True
Volumes:
  tiller-token-k839f:
    Type:        Secret (a volume populated by a Secret)
    SecretName:  tiller-token-k839f
    Optional:    false
QoS Class:       Guaranteed
Node-Selectors:  beta.kubernetes.io/os=linux
Tolerations:     <none>
Events:
  Type     Reason      Age                 From                               Message
  ----     ------      ----                ----                               -------
  Normal   Pulled      1m (x44 over 1h)    kubelet, k8s-agentpool-41044328-2  Container image "gcrio.azureedge.net/kubernetes-helm/tiller:v2.6.0" already present on machine
  Normal   Created     1m (x44 over 1h)    kubelet, k8s-agentpool-41044328-2  Created container
  Normal   Started     1m (x44 over 1h)    kubelet, k8s-agentpool-41044328-2  Started container
  Warning  Unhealthy   1m (x89 over 1h)    kubelet, k8s-agentpool-41044328-2  Readiness probe failed: Get http://<IP_ADDRESS>:44135/readiness: dial tcp <IP_ADDRESS>:44135: getsockopt: connection refused
  Normal   Killing     1m (x45 over 1h)    kubelet, k8s-agentpool-41044328-2  Killing container with id docker://tiller:pod "tiller-deploy-2003435939-qstjt_kube-system(6e4c2507-b372-11e7-8aa0-000d3ab1ed58)" container "tiller" is unhealthy, it will be killed and re-created.
  Warning  Unhealthy   1m (x89 over 1h)    kubelet, k8s-agentpool-41044328-2  Liveness probe failed: Get http://<IP_ADDRESS>:44135/liveness: dial tcp <IP_ADDRESS>:44135: getsockopt: connection refused
  Warning  FailedSync  13s (x490 over 1d)  kubelet, k8s-agentpool-41044328-2  Error syncing pod
  Warning  BackOff     13s (x488 over 1h)  kubelet, k8s-agentpool-41044328-2  Back-off restarting failed container

kubectl describe no k8s-agentpool-41044328-2
Name:               k8s-agentpool-41044328-2
Roles:              <none>
Labels:             agentpool=agentpool
                    beta.kubernetes.io/arch=amd64
                    beta.kubernetes.io/instance-type=Standard_D3_v2
                    beta.kubernetes.io/os=linux
                    failure-domain.beta.kubernetes.io/region=northeurope
                    failure-domain.beta.kubernetes.io/zone=0
                    kubernetes.io/hostname=k8s-agentpool-41044328-2
                    msi-enable=true
                    role=agent
                    storageprofile=managed
                    storagetier=Standard_LRS
Annotations:        node.alpha.kubernetes.io/ttl=0
                    volumes.kubernetes.io/controller-managed-attach-detach=true
Taints:             <none>
CreationTimestamp:  Wed, 20 Sep 2017 20:02:22 +0000
Conditions:
  Type                 Status  LastHeartbeatTime                 LastTransitionTime                Reason                       Message
  ----                 ------  -----------------                 ------------------                ------                       -------
  NetworkUnavailable   False   Wed, 20 Sep 2017 20:02:37 +0000   Wed, 20 Sep 2017 20:02:37 +0000   RouteCreated                 RouteController created a route
  OutOfDisk            False   Fri, 10 Nov 2017 19:39:19 +0000   Sat, 04 Nov 2017 23:46:49 +0000   KubeletHasSufficientDisk     kubelet has sufficient disk space available
  MemoryPressure       False   Fri, 10 Nov 2017 19:39:19 +0000   Sat, 04 Nov 2017 23:46:49 +0000   KubeletHasSufficientMemory   kubelet has sufficient memory available
  DiskPressure         False   Fri, 10 Nov 2017 19:39:19 +0000   Sat, 04 Nov 2017 23:46:49 +0000   KubeletHasNoDiskPressure     kubelet has no disk pressure
  Ready                True    Fri, 10 Nov 2017 19:39:19 +0000   Thu, 09 Nov 2017 10:15:53 +0000   KubeletReady                 kubelet is posting ready status
Addresses:
  InternalIP:  <IP_ADDRESS>
  Hostname:    k8s-agentpool-41044328-2
Capacity:
 alpha.kubernetes.io/nvidia-gpu:  0
 cpu:                             4
 memory:                          14359432Ki
 pods:                            110
Allocatable:
 alpha.kubernetes.io/nvidia-gpu:  0
 cpu:                             4
 memory:                          14257032Ki
 pods:                            110
System Info:
 Machine ID:                 833e0926ee21aed71ec075d726cbcfe0
 System UUID:                FF54E8A4-320D-6540-9F50-8FF6F18911DD
 Boot ID:                    1e7574cd-b863-4d74-9c4a-a58e8278db13
 Kernel Version:             4.4.0-98-generic
 OS Image:                   Debian GNU/Linux 8 (jessie)
 Operating System:           linux
 Architecture:               amd64
 Container Runtime Version:  docker://1.12.6
 Kubelet Version:            v1.7.5
 Kube-Proxy Version:         v1.7.5
PodCIDR:                     <IP_ADDRESS>/24
ExternalID:                  /subscriptions/a6cec963-c286-421a-b449-e842b26cee9a/resourceGroups/acs-prod-europesector-northeurope/providers/Microsoft.Compute/virtualMachines/k8s-agentpool-41044328-2
Non-terminated Pods:         (9 in total)
  Namespace                  Name                                      CPU Requests  CPU Limits  Memory Requests  Memory Limits
  ---------                  ----                                      ------------  ----------  ---------------  -------------
  containerservice           containerservice-1945594285-m6vs3         0 (0%)        0 (0%)      0 (0%)           0 (0%)
  containerservice-async     containerserviceasync-1900242997-hn4g1    0 (0%)        0 (0%)      0 (0%)           0 (0%)
  default                    mdm-2968816999-rn8qt                      0 (0%)        0 (0%)      0 (0%)           0 (0%)
  default                    mdsd-p8hk6                                0 (0%)        0 (0%)      0 (0%)           0 (0%)
  default                    msi-proxy-3096262957-t5msm                0 (0%)        0 (0%)      0 (0%)           0 (0%)
  default                    omsagent-0bbzc                            0 (0%)        0 (0%)      0 (0%)           0 (0%)
  default                    td-agent-78ncd                            0 (0%)        0 (0%)      0 (0%)           0 (0%)
  kube-system                kube-proxy-rqwlc                          100m (2%)     0 (0%)      0 (0%)           0 (0%)
  kube-system                tiller-deploy-2003435939-qstjt            50m (1%)      50m (1%)    150Mi (1%)       150Mi (1%)
Allocated resources:
  (Total limits may be over 100 percent, i.e., overcommitted.)
  CPU Requests  CPU Limits  Memory Requests  Memory Limits
  ------------  ----------  ---------------  -------------
  150m (3%)     50m (1%)    150Mi (1%)       150Mi (1%)
Events:         <none>

kubectl logs -n kube-system tiller-deploy-2003435939-qstjt
[main] 2017/11/10 19:37:00 Starting Tiller v2.6.0 (tls=false)
[main] 2017/11/10 19:37:00 GRPC listening on :44134
[main] 2017/11/10 19:37:00 Probes listening on :44135
[main] 2017/11/10 19:37:00 Storage driver is ConfigMap

If I delete the pod, the new one seems to work...

Everything you posted makes it seem like it is ok. Were there any other pods running in the cluster that were exhibiting similar issues?

@thomastaylor312 sorry, my github notifications is wonky.
Nope, it doesn't show anything in particular. No error messages.

I had exactly same issues in GCP k8s cluster. Nothing in logs, tiller-deploy restarting from time to time. Scaling VMs up for more RAM solved it. Now it's stable as rock.

The error and behaviour (tiller being evicted from the node) seems like it's the same as what we're seeing in https://github.com/kubernetes/helm/issues/3409. Can anyone take a look and see if we should consolidate these two issues together?

@bacongobbler #3409 is nothing to do with Tiller being evicted. In that issue, Tiller is running throughout.

Some users say otherwise. Sorry! Just trying to triangulate between the different symptoms of this error.

I had the same problem , but I solved it.
In my case , FluentD daemonset also crash many times.
FluentD sends log to Elasticsearch(AWS ES service) , and ES's FreeStorageSpace had been zero.
This had caused FluentD to crash .
And it also had influenced tiller.
So, I removed some indices in ES.
After that , FluentD and tiller-deploy have not been restarting any more.
My configurations are the followings.
Cloud：AWS
$ kops version
Version 1.8.1

$ kubectl version

Client Version: version.Info{Major:"1", Minor:"9", GitVersion:"v1.9.3", GitCommit:"d2835416544f298c919e2ead3be3d0864b52323b", GitTreeState:"clean", BuildDate:"2018-02-09T21:50:44Z", GoVersion:"go1.9.4", Compiler:"gc", Platform:"darwin/amd64"}
Server Version: version.Info{Major:"1", Minor:"8", GitVersion:"v1.8.6", GitCommit:"6260bb08c46c31eea6cb538b34a9ceb3e406689c", GitTreeState:"clean", BuildDate:"2017-12-21T06:23:29Z", GoVersion:"go1.8.3", Compiler:"gc", Platform:"linux/amd64"}

$ helm version

Client: &version.Version{SemVer:"v2.8.1", GitCommit:"6af75a8fd72e2aa18a2b278cfe5c7a1c5feca7f2", GitTreeState:"clean"}
Server: &version.Version{SemVer:"v2.8.1", GitCommit:"6af75a8fd72e2aa18a2b278cfe5c7a1c5feca7f2", GitTreeState:"clean"}

We are seeing the same behaviour as @davidchua just with a newer version of Helm and Tiller.
Client: &version.Version{SemVer:"v2.10.0", GitCommit:"9ad53aac42165a5fadc6c87be0dea6b115f93090", GitTreeState:"clean"}
Server: &version.Version{SemVer:"v2.10.0", GitCommit:"9ad53aac42165a5fadc6c87be0dea6b115f93090", GitTreeState:"clean"}

It might happened when i upgrade --wait fails because of the timeout, this is yet to be confirmed though. But all evidence points in that direction

After experiencing this in the wild myself, I've been able to narrow down an easy way to reliably reproduce this. It looks like tiller crashes when trying to --force upgrade a release that is stuck with the status PENDING_INSTALL.

helm upgrade --install foo {anychart} (assumes a release named foo does not already exist)
kubectl patch configmap foo.v1 -p '{"metadata":{"labels":{"STATUS": "PENDING_INSTALL"}}}' --namespace kube-system
helm upgrade --install foo {samechart} --force

The last command results in:
Error: UPGRADE FAILED: transport is closing
tiller-deploy-67d8b477f7-brsvb              0/1       CrashLoopBackOff   13         20h

My tiller-deploy is having a CrashLoopBackOff loop due to Liveness probe failed: getsockopt: connection refused with 2.11.0
What can I do to figure out whats what?

can you check kubectl describe po <failing tiller pod>?

Name:           tiller-deploy-758ccd65c6-xjqrl
Namespace:      kube-system
Node:           gke-develop-v2-default-pool-ef607479-t3sj
Start Time:     Tue, 06 Nov 2018 20:58:51
Labels:         app=helm
                name=tiller
                pod-template-hash=3147782172
Annotations:    <none>
Status:         Running
IP:             <IP_ADDRESS>
Controlled By:  ReplicaSet/tiller-deploy-758ccd65c6
Containers:
  tiller:
    Container ID:   docker://76dcb6bb124ecd11fcb9bfe152f3be773c530e5ad47e581bbbf0007078de503d
    Image:          gcr.io/kubernetes-helm/tiller:v2.11.0
    Image ID:       docker-pullable://gcr.io/kubernetes-helm/tiller@sha256:f6d8f4ab9ba993b5f5b60a6edafe86352eabe474ffeb84cb6c79b8866dce45d1
    Ports:          44134/TCP, 44135/TCP
    Host Ports:     0/TCP, 0/TCP
    State:          Waiting
      Reason:       CrashLoopBackOff
    Last State:     Terminated
      Reason:       Error
      Exit Code:    2
      Started:      Wed, 07 Nov 2018 11:53:57
      Finished:     Wed, 07 Nov 2018 11:54:17
    Ready:          False
    Restart Count:  281
    Liveness:       http-get http://:44135/liveness delay=1s timeout=1s period=10s #success=1 #failure=3
    Readiness:      http-get http://:44135/readiness delay=1s timeout=1s period=10s #success=1 #failure=3
    Environment:
      TILLER_NAMESPACE:    kube-system
      TILLER_HISTORY_MAX:  0
    Mounts:
      /var/run/secrets/kubernetes.io/serviceaccount from tiller-token-9qtg5 (ro)
Conditions:
  Type           Status
  Initialized    True
  Ready          False
  PodScheduled   True
Volumes:
  tiller-token-9qtg5:
    Type:        Secret (a volume populated by a Secret)
    SecretName:  tiller-token-9qtg5
    Optional:    false
QoS Class:       BestEffort
Node-Selectors:  <none>
Tolerations:     node.kubernetes.io/not-ready:NoExecute for 300s
                 node.kubernetes.io/unreachable:NoExecute for 300s
Events:
  Type     Reason     Age                  From                                                     Message
  ----     ------     ----                 ----                                                     -------
  Normal   Pulled     26m (x272 over 13h)  kubelet, gke-develop-v2-default-pool-ef607479-t3sj  Container image "gcr.io/kubernetes-helm/tiller:v2.11.0" already present on machine
  Warning  BackOff    6m (x3299 over 13h)  kubelet, gke-develop-v2-default-pool-ef607479-t3sj  Back-off restarting failed container
  Warning  Unhealthy  1m (x495 over 13h)   kubelet, gke-develop-v2-default-pool-ef607479-t3sj  Readiness probe failed: Get http://<IP_ADDRESS>:44135/readiness: dial tcp <IP_ADDRESS>:44135: getsockopt: connection refused

Extra info: I tried adding 1 more k8s node, the problem goes away; if I remove the extra node, it start crashing again. It seems that it is not being evicted, but it just crashes itself without any log. @bacongobbler

So if adding a new node to the cluster removes the problem, then perhaps the issue is with the node?

Turns out the problem was other containers in a node burning all CPU resources (some bugged istio mixers in my case), making tiller-deploy always not ready for liveness probe, nor commands.
sorry for the dumb question


[Backport 2.2] Failed conformance tests due to kube-apiserver node subresources permissions

rancher/rancher#22081

rancher/rancher:v2.2.8

Created a cluster w/ Rancher

k8s 1.15.3, 1.14.6
1 worker, etcd node
1 worker, controlplane node
1 worker node

Executed Sonobuoy conformance tests
The 2 failing tests are now passing, with 0 failing tests:

- name: '[sig-network] Proxy version v1 should proxy logs on node with explicit
      kubelet port using proxy subresource  [Conformance]'
    status: passed
- name: '[sig-network] Proxy version v1 should proxy logs on node using proxy subresource  [Conformance]'
    status: passed


Update lang version to 2201.2.0-rc1.5

Purpose
Examples
Checklist

[ ] Linked to an issue
[ ] Updated the changelog
[ ] Added tests
[ ] Updated the spec

Closing this PR since random module does not dependent on Update 2


some problem of dem.m

Hello, I use demo.m to try to get some results, but I find there is no result in figure of recall@N . Here is my code.
1. netID='vd16_tokyoTM_conv5_3_vlad_preL2_intra_white';%%%%%按照github上更换成这个网络
2.
3. paths= localPaths();
4. load( sprintf('%s%s.mat', paths.ourCNNs, netID), 'net' );
5. net= relja_simplenn_tidy(net);
6. im= vl_imreadjpeg({which('football.jpg')}); im= im{1};
7. feats= computeRepresentation(net, im);
8.  dbTest= dbTokyo247();
9. paths= localPaths();
10. dbFeatFn= sprintf('%s%s_%s_db.bin', paths.outPrefix, netID, dbTest.name);
11.
12. qFeatFn = sprintf('%s%s_%s_q.bin', paths.outPrefix, netID, dbTest.name);
13. serialAllFeats(net, dbTest.dbPath, dbTest.dbImageFns, dbFeatFn, 'batchSize', 10);
14. serialAllFeats(net, dbTest.qPath, dbTest.qImageFns, qFeatFn, 'batchSize', 1);
15. [recall, ~, ~, opts]= testFromFn(dbTest, dbFeatFn, qFeatFn);
16. plot(opts.recallNs, recall, 'ro-'); grid on; xlabel('N'); ylabel('Recall@N');

And I have acquired two .bin files of query picture and database picture by running code. But the result of retrieval doesn't look so good.

Did the serialAllFeats print any messages like not finding files? Are the sizes of the .bin files reasonable, i.e. num_images x dimensionality x 4 bytes?
I think if you don't have the images (to get them you should email me) or paths are not properly set in localPaths.m, then the feature extraction will silently fail and performance will be 0. Or maybe it failed the first time, then you set up the paths correctly, but the features were not recomputed (it looks if the .bin file is there and doesn't recompute it - this is bad if the existing file is corrupt) so the performance is still 0.
So - delete the .bin files, make sure dataset images are there and the paths are correct, rerun serialAllFeats, make sure the .bin sizes are correct, and see if this fixes it.

I inspect and run the code again, the information that is printed is shown at the end I think there is no problem about localPath.m.
And we have acquired two bin files.

The only place I haven't configured is paths.pretraineedCNNs, I don't know if it cause the wrong result:

the information printed:
``
lujing =
'E:\学术和工作\netvlad\code_run\netvlad_v103\netvlad_v103\Databases\Tokyo247\'

serialAllFeats: Start
serialAllFeats 19-Jan-2023 10:44:32 1 / 7599
serialAllFeats 19-Jan-2023 10:45:26 2 / 7599; time 00:00:54; left 114:39:41; avg 54.3276 s
serialAllFeats 19-Jan-2023 10:45:27 3 / 7599; time 00:00:55; left 58:03:11; avg 27.5098 s
serialAllFeats 19-Jan-2023 10:45:27 4 / 7599; time 00:00:55; left 38:58:44; avg 18.4735 s
serialAllFeats 19-Jan-2023 10:45:29 8 / 7599; time 00:00:57; left 17:10:43; avg 8.1459 s
serialAllFeats 19-Jan-2023 10:45:32 16 / 7599; time 00:01:00; left 08:27:16; avg 4.0133 s
serialAllFeats 19-Jan-2023 10:45:38 32 / 7599; time 00:01:06; left 04:30:54; avg 2.1478 s
serialAllFeats 19-Jan-2023 10:45:51 64 / 7599; time 00:01:19; left 02:38:00; avg 1.2580 s
serialAllFeats 19-Jan-2023 10:46:16 128 / 7599; time 00:01:44; left 01:42:41; avg 0.8246 s
serialAllFeats 19-Jan-2023 10:47:08 256 / 7599; time 00:02:35; left 01:14:49; avg 0.6113 s
serialAllFeats 19-Jan-2023 10:48:50 512 / 7599; time 00:04:18; left 00:59:43; avg 0.5056 s
serialAllFeats 19-Jan-2023 10:50:29 759 / 7599; time 00:05:57; left 00:53:46; avg 0.4716 s
serialAllFeats 19-Jan-2023 10:52:16 1024 / 7599; time 00:07:44; left 00:49:43; avg 0.4537 s
serialAllFeats 19-Jan-2023 10:55:35 1518 / 7599; time 00:11:03; left 00:44:18; avg 0.4371 s
serialAllFeats 19-Jan-2023 10:59:11 2048 / 7599; time 00:14:39; left 00:39:44; avg 0.4294 s
serialAllFeats 19-Jan-2023 11:00:44 2277 / 7599; time 00:16:12; left 00:37:54; avg 0.4274 s
serialAllFeats 19-Jan-2023 11:05:54 3036 / 7599; time 00:21:22; left 00:32:08; avg 0.4225 s
serialAllFeats 19-Jan-2023 11:11:02 3795 / 7599; time 00:26:30; left 00:26:35; avg 0.4193 s
serialAllFeats 19-Jan-2023 11:13:04 4096 / 7599; time 00:28:32; left 00:24:25; avg 0.4182 s
serialAllFeats 19-Jan-2023 11:16:11 4554 / 7599; time 00:31:39; left 00:21:10; avg 0.4172 s
serialAllFeats 19-Jan-2023 11:21:19 5313 / 7599; time 00:36:47; left 00:15:50; avg 0.4155 s
serialAllFeats 19-Jan-2023 11:26:29 6072 / 7599; time 00:41:57; left 00:10:33; avg 0.4147 s
serialAllFeats 19-Jan-2023 11:31:40 6831 / 7599; time 00:47:08; left 00:05:18; avg 0.4141 s
serialAllFeats 19-Jan-2023 11:36:51 7590 / 7599; time 00:52:19; left 00:00:04; avg 0.4137 s
serialAllFeats 19-Jan-2023 11:36:55 7599 / 7599; time 00:52:23; left 00:00:00; avg 0.4137 s
serialAllFeats: Done
serialAllFeats: Start
serialAllFeats 19-Jan-2023 11:36:55 1 / 315
serialAllFeats 19-Jan-2023 11:36:57 2 / 315; time 00:00:01; left 00:09:22; avg 1.7901 s
serialAllFeats 19-Jan-2023 11:36:59 3 / 315; time 00:00:03; left 00:09:20; avg 1.7917 s
serialAllFeats 19-Jan-2023 11:37:01 4 / 315; time 00:00:05; left 00:09:14; avg 1.7758 s
serialAllFeats 19-Jan-2023 11:37:08 8 / 315; time 00:00:12; left 00:09:04; avg 1.7681 s
serialAllFeats 19-Jan-2023 11:37:21 16 / 315; time 00:00:26; left 00:08:41; avg 1.7370 s
serialAllFeats 19-Jan-2023 11:37:47 31 / 315; time 00:00:51; left 00:08:06; avg 1.7076 s
serialAllFeats 19-Jan-2023 11:37:48 32 / 315; time 00:00:52; left 00:08:02; avg 1.6988 s
serialAllFeats 19-Jan-2023 11:38:37 62 / 315; time 00:01:41; left 00:07:03; avg 1.6654 s
serialAllFeats 19-Jan-2023 11:38:40 64 / 315; time 00:01:45; left 00:07:00; avg 1.6668 s
serialAllFeats 19-Jan-2023 11:39:29 93 / 315; time 00:02:33; left 00:06:12; avg 1.6726 s
serialAllFeats 19-Jan-2023 11:40:18 124 / 315; time 00:03:22; left 00:05:15; avg 1.6443 s
serialAllFeats 19-Jan-2023 11:40:24 128 / 315; time 00:03:28; left 00:05:09; avg 1.6452 s
serialAllFeats 19-Jan-2023 11:41:08 155 / 315; time 00:04:12; left 00:04:23; avg 1.6395 s
serialAllFeats 19-Jan-2023 11:41:59 186 / 315; time 00:05:04; left 00:03:33; avg 1.6435 s
serialAllFeats 19-Jan-2023 11:42:51 217 / 315; time 00:05:55; left 00:02:42; avg 1.6442 s
serialAllFeats 19-Jan-2023 11:43:43 248 / 315; time 00:06:47; left 00:01:52; avg 1.6495 s
serialAllFeats 19-Jan-2023 11:43:56 256 / 315; time 00:07:00; left 00:01:38; avg 1.6496 s
serialAllFeats 19-Jan-2023 11:44:34 279 / 315; time 00:07:38; left 00:01:00; avg 1.6482 s
serialAllFeats 19-Jan-2023 11:45:28 310 / 315; time 00:08:32; left 00:00:09; avg 1.6600 s
serialAllFeats 19-Jan-2023 11:45:37 315 / 315; time 00:08:41; left 00:00:01; avg 1.6604 s
serialAllFeats: Done
testFromFn:
.\Data\netvlad\output\vd16_tokyoTM_conv5_3_vlad_preL2_intra_white_tokyo247_db.bin
.\Data\netvlad\output\vd16_tokyoTM_conv5_3_vlad_preL2_intra_white_tokyo247_q.bin
NaN 19-Jan-2023 11:45:39 1 / 315
0.0000 19-Jan-2023 11:45:39 2 / 315; time 00:00:00; left 00:02:19; avg 0.4454 s
0.0000 19-Jan-2023 11:45:40 3 / 315; time 00:00:00; left 00:02:15; avg 0.4315 s
0.0000 19-Jan-2023 11:45:40 4 / 315; time 00:00:01; left 00:02:12; avg 0.4245 s
0.0000 19-Jan-2023 11:45:42 8 / 315; time 00:00:02; left 00:02:06; avg 0.4105 s
0.0000 19-Jan-2023 11:45:45 16 / 315; time 00:00:06; left 00:02:00; avg 0.4030 s
0.0000 19-Jan-2023 11:45:51 31 / 315; time 00:00:11; left 00:01:53; avg 0.3993 s
0.0000 19-Jan-2023 11:45:51 32 / 315; time 00:00:12; left 00:01:53; avg 0.3990 s
0.0000 19-Jan-2023 11:46:03 62 / 315; time 00:00:24; left 00:01:40; avg 0.3969 s
0.0000 19-Jan-2023 11:46:04 64 / 315; time 00:00:25; left 00:01:40; avg 0.3969 s
0.0000 19-Jan-2023 11:46:16 93 / 315; time 00:00:36; left 00:01:28; avg 0.3979 s
0.0000 19-Jan-2023 11:46:28 124 / 315; time 00:00:49; left 00:01:16; avg 0.3986 s
0.0000 19-Jan-2023 11:46:30 128 / 315; time 00:00:50; left 00:01:14; avg 0.3987 s
0.0000 19-Jan-2023 11:46:40 155 / 315; time 00:01:01; left 00:01:04; avg 0.3997 s
0.0000 19-Jan-2023 11:46:53 186 / 315; time 00:01:13; left 00:00:51; avg 0.3991 s
0.0000 19-Jan-2023 11:47:05 217 / 315; time 00:01:26; left 00:00:39; avg 0.3986 s
0.0000 19-Jan-2023 11:47:17 248 / 315; time 00:01:38; left 00:00:27; avg 0.3986 s
0.0000 19-Jan-2023 11:47:21 256 / 315; time 00:01:41; left 00:00:23; avg 0.3991 s
0.0000 19-Jan-2023 11:47:30 279 / 315; time 00:01:50; left 00:00:14; avg 0.3990 s
0.0000 19-Jan-2023 11:47:42 310 / 315; time 00:02:03; left 00:00:02; avg 0.3986 s
0.0000 19-Jan-2023 11:47:44 315 / 315; time 00:02:05; left 00:00:00; avg 0.3985 s
rec@10= 0.0000, time= 125.5254 s, avgTime= 398.4932 ms

001 0.0000
002 0.0000
003 0.0000
004 0.0000
005 0.0000
010 0.0000
015 0.0000
020 0.0000
025 0.0000
030 0.0000
035 0.0000
040 0.0000
045 0.0000
050 0.0000
055 0.0000
060 0.0000
065 0.0000
070 0.0000
075 0.0000
080 0.0000
085 0.0000
090 0.0000
095 0.0000
100 0.0000
``

Hmm, that all looks fine really so I'm not sure what to say - I never had such a complaint. My best guess is that there is some small annoying mistake.
I'm a bit confused by the logs and screenshots as they don't seem consistent. The folder shows modified time as 18:45 and 19:05 while the logs show computations finishing at 11:36 and 11:45 - the times are differnt and  the time needed to create the 2nd file is 2x faster for some reason. So I just wonder if everything is consistent, e.g. by accident you switched networks used to compute the files etc. What are the actual sizes of the .bin files? E.g. if I'm not mistaken for Tokyo 24/7 the db file should be around 240x larger than the q file.
If you don't find an error here - can you query with a database image and see if you get a reasonable result?

I don't solve the problem, but I find the picture  of images is "png", but in the file of tokyo247.mat ,the dbImageFns describes the path which use "jpg", and the picture of query is "jpg", so I convert the picture of images from png to jpg, then to run the code.
so I want to ask whether the format of picture have influenced the result of code?
Thanks！
the dbImageFns of the file of tokyo247.mat：

Hi,
Oh sorry about that, I don't remember that the data contains png's and nobody mentioned this to me. I certainly converted to jpg and it's probably best if you do that too - the code uses vl_imreadjpeg which if I recall correctly is more optimized than matlab's generic imread. I don't remember the exact command I used to convert to jpeg but I believe it was just the default parameters of ImageMagick's convert tool. I never measured the difference between running it on png or jpeg - I would be surprised if it mattered for any normal quality jpeg, and the network was anyway trained on jpegs so all should be good.
You wrote "I don't solve the problem" - does this not solve it for you? Or did you not convert to jpeg yet and are yet to try?

Sorry, I just check the notifications of github.
I don‘t solve the problem of there is no result of recall rate.The Tokyo 24/7 the db file is actual 240x larger than the q file, but I can't get the any result of recall rate.

That's really strange.. Sorry it's very hard for me to check it myself because I don't have a computer with a GPU that can run MATLAB (nor a MATLAB licence).
Since the files exist and look to be the correct size, then it seems feature extraction is not a problem (presumably you ran feature extraction after fixing png->jpg because otherwise I don't see how it would work), unless the features are somehow nonsense. Can you try visually inspecting the process, for example take a few queries, do the retrieval and see what are the retrieved images. Do they look reasonable, are they positives for the query? So are features the problem, or is retrieval for some reason wrong, or the evaluation metric computation.

Thank for you help，whether use the demoRetrieval.m this code to get the best matching result? If so, I will try to modify it in the next few days to inspect the process.

You can just follow what testFn does - it's simple, loads the query and database features and calls testCore.
Then testCore uses recallAtN to compute the recall based on the lambda functions that do the search and tell you if it's a positive. You instead just do the rawNnSearch yourself. Then look at top 10 retrieved database IDs for a few queres - do they make sense visually (so are features bad or is there something broken in the search itself), are they positives according to the ground truth (db.isPosQ(iQuery, iDb), is this broken?), or if all that looks fine then is recallAtN broken?

sorry，I went to prepare for the exam a while ago
I found that there might be a problem with the extraction of features, when I did the feature query matching I found that many of the features were exactly the same, for example, in the Tokyo247 dataset, there are 75981 images in total and 315 images in the query set, but I found that in the deFeat and qFeat arrays, they are mostly the same.

I think it should be in the function vl_simplenn; if I understand correctly, setting the batchsize to 10 will extract the features of 10 images at once, but I return the same res result many times after vl_simplenn.
They will first subtract the mean value of these 10 images in the three primary colors, and afterwards accumulate to the fourth dimension to input vl_simpenn for feature extraction, after which the output variable res is the same, after which he will reshape to the corresponding 10 columns, which is also the same a vector
I don't know what caused this phenomenon.
Thanks!

Ok so feature extraction is broken somehow if it produces the same features for everything. I'm not sure what would be the reason but this should be easy to experiment with as at least you don't have to extract all features, do the evaluation etc. Just take a few images and extract features, and see why are they all given the same descriptor.
E.g. readme shows you how to do it for individual images:
im= vl_imreadjpeg({which('football.jpg')}); im= im{1}; % slightly convoluted because we need the full image path for `vl_imreadjpeg`, while `imread` is not appropriate - see `help computeRepresentation`
feats= computeRepresentation(net, im); % add `'useGPU', false` if you want to use the CPU

Hi, did you get this to work?

Hi, did you get this to work?

Yes, I have run this work successful on Linux. Thanks for your direction.
Good luck with your research, sir.

Glad to hear that. Thanks, likewise!


pkg/cmd/pr/update/update.go

          No change in consensus, so **[accepted](https://go.dev/s/proposal-status#accepted)**. 🎉

This issue now tracks the work of implementing the proposal.
— rsc for the proposal review group
Following GOARM, GOAMD64, and so on, we define a GORISCV64 variable to specify the architecture level that can be assumed by the compiler and (using //go:build tags) in Go packages.
The three levels are:

GORISCV64=rva20u64: build tags riscv64.rva20u64
GORISCV64=rva22u64: build tags riscv64.rva20u64, riscv64.rva22u64
GORISCV64=rva23u64: build tags riscv64.rva20u64, riscv64.rva22u64, riscv64.rva23u64

There is uncertainty about the future of compression in RISC-V. For now, even if we are using one of these later architecture levels, we will continue not to emit compressed instructions.
Perhaps at some point in the future we would add a “,compress” suffix to ask the toolchain to generate compressed instructions, like in GORISCV64=rva20u64,compress. Because every compressed instruction has an uncompressed instruction with identical semantics, “compress or not” can be handled entirely inside the toolchain, without exposing that detail to assembly files or in build tags.
Originally posted by @rsc in https://github.com/golang/go/issues/61476#issuecomment-1791156741

Related Issues and Documentation

all: add GORISCV64 environment variable #61476 (closed)

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