table-row-id in mdt-custom-cell

Is it possible to get the table-row-id in a mdt-custom-cell?
The reason for this is, that I have an id for every line. Now i want to implement a click event for this line to open details for this element in a new window.

You can find the closed tr and get the id for it.
just create a custom attribute directive in your mdt-custom-cell and do something like this element.closest('tr').attr('id')
Let me know if it doesn't work.

Now you can get it by referencing rowId in mdt-custom-cell.

this is not working for me.
when i get rowId - assigning table-row-id-key = my record id and do ajax pagination on search this rowId does not change and it gives me the first Id in the initial list

same as traasfat

+1

Have the same issue, right now have a workaround where I define the column key as '_id' and then pass it as a reference in a function attached to ClientScope. Will be good to get the rowId reference on pagination/filter/sort.


feat: Update the captcha in every 20 to 30 sec automatically

Update the captcha in every 20 to 30 seconds automatically.

Hey @AdityaSinha2305 can you assign this for me


How can a sandboxed Wolfram Language evaluator be used by default?

Having read Stephen Wolfram's release blog for v13.3 I was intrigued to see that one now obviously can have the same experience as someone working in OpenAI's web interface with the Wolfram PlugIn:

Unfortunately, using a Chat-Enabled Notebook in v13.3 does not give the same result:

How can a "sandboxed" Wolfram Language Evaluator be employed in the way it was done for the release blog?
Note: I crossposted this issue on Mathematica Stack Exchange (287083).

The tools are currently only enabled by default for GPT-4, since GPT-3.5 almost always fails to use them correctly. If you have access to GPT-4 with your API key, you should be able to see outputs like these when using that model. Note that for that particular example, GPT-4 makes a call to the WL evaluator approximately 60% of the time, so it's not guaranteed that you'll see similar results.
If you want to force GPT-3.5 to use tools, you can enable it via the "ToolsEnabled" option when creating the notebook:
CreateNotebook["ChatEnabled", "ToolsEnabled" -> True]

However, GPT-3.5 simply does not understand the tool-calling instructions, so you will not get useful results.

Hey @rhennigan
I have the same issue. Wolfram Desktop <IP_ADDRESS>.
However, I have confirmed that my OpenAI API key has GPT-4 access by using: curl https://api.openai.com/v1/models -H "Authorization: Bearer $OPENAI_API_KEY", and seeing the GPT-4 models listed. However, asking the LLM ChatInput in a Chat-Enabled Notebook always yields a code snippet and no use of the Evaluator, even after repeated tries in multiple sessions.
I've even tried using CreateNotebook["ChatEnabled", "ToolsEnabled" -> True], but that appeared to have no effect.
I've tried to prompt the Assistant to reveal which GPT model it is based on, but it swears it is a Assistant developed by Wolfram Research and in no way related to the GPT models from OpenAI. Har har :)

Hmm, perhaps a permission is missing?
{
      "id": "gpt-4",
      "object": "model",
      "created":<PHONE_NUMBER>,
      "owned_by": "openai",
      "permission": [
        {
          "id": "modelperm-cOSx57rPrEzolEUD2QSSACPS",
          "object": "model_permission",
          "created":<PHONE_NUMBER>,
          "allow_create_engine": false,
          "allow_sampling": false,
          "allow_logprobs": false,
          "allow_search_indices": false,
          "allow_view": false,
          "allow_fine_tuning": false,
          "organization": "*",
          "group": null,
          "is_blocking": false
        }
      ],
      "root": "gpt-4",
      "parent": null
    }

Hey @rhennigan
I have the same issue. Wolfram Desktop <IP_ADDRESS>.
However, I have confirmed that my OpenAI API key has GPT-4 access by using: curl https://api.openai.com/v1/models -H "Authorization: Bearer $OPENAI_API_KEY", and seeing the GPT-4 models listed (namely gpt-4, gpt-4-0613, and gpt-0314). However, asking the LLM ChatInput in a Chat-Enabled Notebook always yields a code snippet and no use of the Evaluator, even after repeated tries in multiple sessions. I've just been trying simple prompts like asking it to plot sin(x).
I've even tried using CreateNotebook["ChatEnabled", "ToolsEnabled" -> True], but that appeared to have no effect.
I've tried to prompt the Assistant to reveal which GPT model it is based on, but it swears it is a Assistant developed by Wolfram Research and in no way related to the GPT models from OpenAI. Har har :)

CreateNotebook["ChatEnabled", "Model" -> "gpt-4"] Open a new Chat Notebook with gpt-4 as the selected model

Aha, that's done it. Thanks @rhennigan .
I presume this will be fixed in a future Wolfram release?

@rhennigan ah I'm afraid there's another issue: the evaluator seems to display no results, regardless of the request.
Should I open another GitHub ticket for this issue, or should I file it somewhere else?

@orospakr Does it happen every time or was this a one-off instance? It's possible the LLM didn't format the text correctly to invoke a tool call

I am also having the same problem: This error is displayed alongside the above result shown by @orospakr in messages:

I am also having the same problem: This error is displayed alongside the above result shown by @orospakr in messages:

This bug should be fixed with recent versions of the LLMFunctions and Chatbook paclet in version 13.3. Running
PacletInstall["Wolfram/Chatbook", UpdatePacletSites -> True]
PacletInstall["Wolfram/LLMFunctions", UpdatePacletSites -> True]

should fix that - if not please let us know what Wolfram version you're using and what version those paclets return

I am also having the same problem: This error is displayed alongside the above result shown by @orospakr in messages:

This bug should be fixed with recent versions of the LLMFunctions and Chatbook paclet in version 13.3. Running
PacletInstall["Wolfram/Chatbook", UpdatePacletSites -> True]
PacletInstall["Wolfram/LLMFunctions", UpdatePacletSites -> True]

should fix that - if not please let us know what Wolfram version you're using and what version those paclets return

Ah perfect, thank you! That does solve the problem.


[swagger-jaxrs:1.5.3-M1] Operation without parameters is serialized as a YAML empty array

Hi,
When using swagger-jaxrs:1.5.3-M1 ApiListingResource, it generates an empty YAML array entry for an operation without parameters. The Swagger editor then complains about this:
/users:
    get:
      tags:
      - "users"
      summary: "List all users"
      description: ""
      operationId: "listUser"
      consumes:
      - "application/json"
      produces:
      - "application/json"
      parameters: []
      responses:
        200:
          description: "successful operation"
          schema:
            type: "array"
            items:
              $ref: "#/definitions/UserVo"

I think it's because the Yaml.mapper(), defined in com.wordnik.swagger.util.Yaml`, is configured with:
 mapper.setSerializationInclusion(JsonInclude.Include.NON_NULL);

Maybe setting it to JsonInclude.Include.NON_EMPTY would fix this issue.
Thanks,
BQ.

This is due to an issue with the json schema we had for the swagger specification which was fixed a few minutes ago. @whitlockjc needs to release a new version of swagger-tools with the new schema, and then @mohsen1 can apply it to the editor and it will valid there as well.

Ok, I'll wait for the new release then. Thanks for the prompt answer!


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🏈🔴Full HD:👉 NFL Football 2024 Live Free
⬇⬆
📲Mobile friendly 1080p/720p 👇👇
🏈🔴Mobile Free:👉 https://tinyurl.com/NFL-Week-9-Live
NFL live stream: How to watch every 2024 NFL game online
💻Best NFL Streams for PC/MAC 1080p/720p 👇👇
🏈🔴Full HD:👉 NFL Football 2024 Live Free
⬇⬆
📲Mobile friendly 1080p/720p 👇👇
🏈🔴Mobile Free:👉 https://tinyurl.com/NFL-Week-9-Live
NFL live stream: How to watch every 2024 NFL game online
💻Best NFL Streams for PC/MAC 1080p/720p 👇👇
🏈🔴Full HD:👉 NFL Football 2024 Live Free
⬇⬆
📲Mobile friendly 1080p/720p 👇👇
🏈🔴Mobile Free:👉 https://tinyurl.com/NFL-Week-9-Live
NFL live stream: How to watch every 2024 NFL game online
💻Best NFL Streams for PC/MAC 1080p/720p 👇👇
🏈🔴Full HD:👉 NFL Football 2024 Live Free
⬇⬆
📲Mobile friendly 1080p/720p 👇👇
🏈🔴Mobile Free:👉 https://tinyurl.com/NFL-Week-9-Live
NFL live stream: How to watch every 2024 NFL game online
💻Best NFL Streams for PC/MAC 1080p/720p 👇👇
🏈🔴Full HD:👉 NFL Football 2024 Live Free
⬇⬆
📲Mobile friendly 1080p/720p 👇👇
🏈🔴Mobile Free:👉 https://tinyurl.com/NFL-Week-9-Live
NFL live stream: How to watch every 2024 NFL game online
💻Best NFL Streams for PC/MAC 1080p/720p 👇👇
🏈🔴Full HD:👉 NFL Football 2024 Live Free
⬇⬆
📲Mobile friendly 1080p/720p 👇👇
🏈🔴Mobile Free:👉 https://tinyurl.com/NFL-Week-9-Live


Option to configure how many lines down the @generated tag can be for format_generated_files?

In Relay we have a large number of generated files which are required by our business to being with a specific copyright header that is slightly more than five lines. This means we can't take advantage of the format_generated_files feature.
Example file: https://github.com/facebook/relay/blob/603bb4bc2927bda8b7f0dac1d86cbc5ecbf50202/compiler/crates/relay-lsp/tests/hover_test.rs
Would it be possible to make the 5 lines limit configurable?

Thanks for reaching out. Seems like a reasonable enough request, and this should be a simple change.

create the new config option that defaults to 5. Note all new configs are marked as unstable.
Update the definition of is_generated_file
Update the call site to pass in the new config

It looks like this can be closed as https://github.com/rust-lang/rustfmt/pull/5918 solves this?

@IVIURRAY the PR that you're referring to was closed but never merged. This issue should stay open.

@ytmimi whoops, thanks! - I've opened an MR to address this #5993 :)


[Question] what to do with all the python keys not starting with python-/python3-?

There are still lot of python keys not starting with hte python-/python3- prefix. Should we migrate them and use  anchors & aliases in the same way as we did with the pip packages?
The only difference is the order of the PKG and python3-PKG depends on the name of the PKG. Therefore sometime the anchor is placed at the PKG and sometime at python3-PKG.

In my opinion, we don't do anything with them.  It is annoying that they are different, but fixing it would be a lot of work for little gain.
That said, I wouldn't at all be opposed to reviewing PRs adding anchors, I just don't think we should make a concerted effort to do so.


Fix up error level for failed branch protection application

@decyjphr mind merging this?

Sure, I'll look at today


Detected as a virus by MCAfee

Describe the bug
While installing with cargo install tauri-cli it was detected as a virus and blocked. You should fix this
Reproduction
No response
Expected behavior
No response
Platform and versions
Rust 1.70.0

Stack trace
No response
Additional context
No response

Honestly, there is not much we can do. Many anti-virus programs can't handle compiled programming languages, after all they do stuff that you normally don't wanna see programs doing. In the case of rust it's creating multiple executables and executing them.
Probably the best bet is to report false positives to mcaffee which we can't do ourselves.
p.s. for issues like this please include logs or any kind of info so it's easier to search and depending on the issue potentially solvable in case of more widely known causes.


New version: LabelledArrays v1.0.0

Registering package: LabelledArrays
Repository: https://github.com/JuliaDiffEq/LabelledArrays.jl
Created by: @ChrisRackauckas
Version: v1.0.0
Commit: c407916e1daa98496322e6b8352cb4b9289cd146
Reviewed by: @ChrisRackauckas
Reference: https://github.com/JuliaDiffEq/LabelledArrays.jl/issues/64#issuecomment-559912351

@ChrisRackauckas Can you retrigger Registrator to fix the merge conflict?
[noblock]


License

I would like to use your grammar for BrightScript but I don't know what licensing terms you're distributing it under. Any chance you could pick one from here: http://choosealicense.com?

@cmink Did you get a chance to look into this? I'm considering using your grammar at github/linguist to highlight BrightScript code on GitHub.

We've opted for the MIT License. Hope this works for you.

Great! Thanks!

@nbrew Your grammar is now used to highlight BrightScript code on GitHub (files and code snippets in Markdown documents)! (See github/linguist#2119 for more information.)


Display error codes instead of messages

We're using FV with ASP.NET Core. Is it possible to customise the messages that are added to ModelState to display the ErrorCode in addition to the message? I do not really want to combine the two in my error messages and we rely on the codes for reliable testing.

No not really. FV just hands the results over to MVC to display the message....MVC has no concept of error codes so it wouldn't know what to do with them. I think you'd be looking at having to go down the completely custom route if you wanted to do that.

Ah, I was hoping there was a message formatter hook similar to what we have with the property name resolvers. Thanks anyway.

I think you'd have to create your own version of the model validator that changes how the messages are written to ModelState. Having a hook isn't a bad idea though, although it wouldn't work for clientside messages. I'll re-open for now to look at for the next version.

Hello JeremySkinner. How exactly can we use the IValidatorInterceptor interface to send the statusCode to the modelState without adding it to the errorMessage? I didn`t find a solution for this.
I construct my validationresult like this for the moment:
public static ValidationResult ToValidationResult(
this FluentValidation.Results.ValidationResult validationResult)
{
var results =
validationResult.Errors.Select(
item => new ValidationResult(item.ErrorMessage, new List { item.PropertyName }));
return results;
}
How can I provide the ErrorCode in this situation?

@DenisaJurca I don't really understand what you're asking. Are you saying you want to store the error code in ModelState instead of the message, or as well as the message?
If the former, then use the code I pasted above to replace the message with the error code. If the latter, then that's not doable as ModelState has no concept of error codes.
If you require further help, please open a new issue rather than commenting on this old one. Thanks.


Store created uid, save it and pass to the docker sandbox

Store created uid, save it and pass to the docker sandbox

Done.


cut tag

@maximt, sorry, didn't have time for the proper review yet. Why did you close it?

@Aivean, just refactoring to "expand" tag, it will be later with the new version.


Updated runtime object from API page

Added rawTemperature and showIconMode which was recently added to the API.

can this "fix" merge into the main branch and request a patch release.

1.3.0 released with lots of changes including the above.


Modify test transpiler

Summary
Details and comments

This PR is part of QAMP Spring 23 https://github.com/qiskit-advocate/qamp-spring-23/issues/26


cross check that ovn-meta- ns exists for all vm guest networks

On a compute host running neutron-ovn-metadata-agent, all networks used by running vms should have an associated ovn-meta- namespace since if they do not the vms will not be able to reach their metadata, dhcp etc.

Info not available on sosreport. Closing this issue as discussed, @dosaboy.

well actually the info just isn't available at all. the issue was opened on the basis that we could get neutron network info from libvirt but actually it isnt there so there is nothing to cross-reference with namespace uuids.


Generate air particles randomly

Currently, they're generated in a sort of slanted line. We'll need to make it so that they are generated randomly inside the air container.

kinda hotfixed?? satisfactory for now tho.


Export active viewport as image

Export active viewport as png image

 Thank you for your submission! We really appreciate it. Like many open source projects, we ask that you sign our Contributor License Agreement before we can accept your contribution.You have signed the CLA already but the status is still pending? Let us recheck it.

I'm not really loving the thumbnail. It gets the point across, but it is currently specific to Chrome. Maybe you could request Jon for some feedback, or to create a better thumbnail?

I'm not really loving the thumbnail. It gets the point across, but it is currently specific to Chrome. Maybe you could request Jon for some feedback, or to create a better thumbnail?

Redesigned the thumbnail to be more generic, though it's hard to illustate this "feature"


Memory Leak in Android

I seem to have found a memory leak in unmounting the video component on Android, which I think is related to this, although they might be two separate problems I'm running into at once.
I ran into it running my app overnight, which switched through images and videos every 5-30 seconds. I was able to replicate this within a few hours by changing it to every second it switches.
I avoided it by never unmounting the Video component, but updating it (so I don't rebuild the component, ONLY update it).
Overtime, the CPU slowly builds up. Depending on the device, it would max out and make the app run horribly (but with no crash, maybe leveling out at about 50% CPU).
Running a systrace, I could see the process that was taking up the CPU usage was the UI thread.
TLDR: It seems unmounting the Video Component on Android doesn't totally get rid of all processes.
Here's the example I have that took me about 30 min of running to run into this error, while hitting about 30% CPU usage in dev mode. https://github.com/xvalentino/react-native-video-leak/blob/master/index.android.js

I'm running into a similar issue. I have a ListView of video components, when a video scrolls off view I unmount the component. However I am encountering an edge case where sometimes the react component is Unmounted but the video is still playing in the background and not properly being flushed from memory.
Let me know if you've come up with any solutions

@arilitan I remember running into something similar to that before. If you console.log onProgress does it continue to log after unmounting? I remember I also did something like setting a ref to the video, and calling something like React.unmountComponentAtNode in the componentWillUnmount function. I'd try that to see if that works for your purposes, but as far as I know this was never resolved, and the cleanup seems to not flush everything of the video from memory.

@xvalentino thanks so much for this tip! I was able to debug this with a console.log in the onProgress function. The players indeed were not getting deallocated like I assumed they were. I refactored the component and now able to better flush everything manually

Perhaps this is something we should document?

Closing this due to @nharlow89's PR. Issue now solved

@xvalentino thanks so much for this tip! I was able to debug this with a console.log in the onProgress function. The players indeed were not getting deallocated like I assumed they were. I refactored the component and now able to better flush everything manually

How did you fix this issue? I'm having the same problem on the latest version. Memory just eventually runs out after you render a large number of videos consecutively.


feat: trie insert, search, searchPrefix, searchMaxXor

Pull Request Test Coverage Report for Build<PHONE_NUMBER>

42 of 43 (97.67%)  changed or added relevant lines in 1 file are covered.
No unchanged relevant lines lost coverage.
Overall coverage increased (+0.4%) to 86.585%

Changes Missing Coverage
Covered Lines
Changed/Added Lines
%

trie.ts
42
43
97.67%

Totals

Change from base Build 997731210:
0.4%

Covered Lines:
673

Relevant Lines:
764

💛  - Coveralls


Additional testcases

#1 is old and not super well organized, so I'm copying things that haven't yet been done into this new issue.
RFC 5280

[ ] implementations should reject EC keys not in namedCurve format (see #173)
[ ] implementations should reject v1 certificates that contain v3 extensions

CABF

[ ] <IP_ADDRESS>: If present, Subject.commonName MUST contain exactly one entry that is one of the values contained in the subjectAltName extension, and MUST be encoded as follows

For IPv4 addresses, must be an IPv4Address per RFC 3986 S. 3.2.2
For IPv6 addresses, must be be encoded in the text representation specified in RFC 5952 S. 4.
For FQDNs or wildcard domain names, must be a char-for-char copy of the dNSName entry from subjectAltName; P-labels must not be converted to their Unicode representation.

[ ] <IP_ADDRESS>.6 and <IP_ADDRESS>.10: extKeyUsage is required in subscriber certificates, and MUST contain id-kp-serverAuth (MAY contain id-kp-clientAuth), and MUST NOT contain any other id-kp-*, anyExtendedKeyUsage, or the Precertificate Signing Certificate OID (<IP_ADDRESS>.4.1.11<IP_ADDRESS>)
[ ] <IP_ADDRESS>.6: CA EKUs are similar to subscriber cert EKUs

Regressions

[ ] CVE-2024-0567 from gnutls (https://gitlab.com/gnutls/gnutls/-/issues/1521)

General

[ ] Implementations should (generally) not be permissive around times close to expiries (e.g. a cert that expired 5 seconds before validation should generally not be accepted)

Other test suites

[ ] NIST PKITS (RFC 3280)
[ ] Chrome's test suite: https://github.com/chromium/chromium/tree/main/net/data/ssl/certificates
[ ] PITTv3: https://github.com/carl-wallace/rust-pki/tree/main/pittv3

We need a test case for DoS in name constraint handling: https://github.com/openssl/openssl/pull/4393

We need a test case for DoS in name constraint handling: openssl/openssl#4393

Opened #204 for these. Will investigate why OpenSSL appears to pass its own DoS test.


Result-image-size option for resizing only larger pictures

Hello.
Is there any possibility to use result-image-size, or to create a new functionality result-image-min-size that resizes only the larger files / cropped areas?
I have to make sure that there aren't any files or cropped images that have a resolution smaller than 1024 and also I have to resize the bigger Images to 1024.
This new functionality would be very helpful.
Thank you

Agree, you are welcome to contribute, sounds useful but i don't currently have time to implement it.
If you decide to do so, feel free to ask any question here.


Secret token not retained post module uninstall

The current version of the module generates an MD5# security token to ensure secure communication with external APIs. However, when the module is uninstalled, the previously generated security token is not retained by the module.
This causes issues when reinstalling the module, as the previously generated security token is no longer valid. I have checked the module's documentation but it does not specify how to retrieve security token after uninstallation.
Would you mind looking into this issue ?

This issue requires triage. Added to queue


训练yolox-s出现RuntimeError: CUDA error: device-side assert triggered问题

尝试减少batch和input_size,没有效果，之前可以正常训练，现在报错：
/pytorch/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [10,0,0], thread: [26,0,0] Assertion input_val >= zero && input_val <= one failed.
/pytorch/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [10,0,0], thread: [27,0,0] Assertion input_val >= zero && input_val <= one failed.
/pytorch/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [10,0,0], thread: [28,0,0] Assertion input_val >= zero && input_val <= one failed.
/pytorch/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [10,0,0], thread: [29,0,0] Assertion input_val >= zero && input_val <= one failed.
/pytorch/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [10,0,0], thread: [30,0,0] Assertion input_val >= zero && input_val <= one failed.
/pytorch/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [10,0,0], thread: [31,0,0] Assertion input_val >= zero && input_val <= one failed.
2022-03-03 14:35:41 | ERROR    | yolox.models.yolo_head:328 - OOM RuntimeError is raised due to the huge memory cost during label assignment.                            CPU mode is applied in this batch. If you want to avoid this issue,                            try to reduce the batch size or image size.
2022-03-03 14:35:41 | INFO     | yolox.core.trainer:196 - Training of experiment is done and the best AP is 0.00
2022-03-03 14:35:41 | ERROR    | yolox.core.launch:98 - An error has been caught in function 'launch', process 'MainProcess' (32), thread 'MainThread'<PHONE_NUMBER>25728):
Traceback (most recent call last):
File "/project/train/src_repo/YOLOX/tools/../yolox/models/yolo_head.py", line 322, in get_losses
imgs,
└
File "/usr/local/lib/python3.6/dist-packages/torch/autograd/grad_mode.py", line 26, in decorate_context
return func(*args, **kwargs)
│     │       └ {}
│     └
└ <function YOLOXHead.get_assignments at 0x7f731c7157b8>
File "/project/train/src_repo/YOLOX/tools/../yolox/models/yolo_head.py", line 505, in get_assignments
) = self.dynamic_k_matching(cost, pair_wise_ious, gt_classes, num_gt, fg_mask)
│    │                  │     │               │           │       └
│    │                  │     │               │           └ 2
│    │                  │     │               └
│    │                  │     └
│    │                  └
│    └ <function YOLOXHead.dynamic_k_matching at 0x7f731c715950>
└ YOLOXHead(
(cls_convs): ModuleList(
(0): Sequential(
(0): BaseConv(
(conv): Conv2d(128, 128, kernel_size=...
File "/project/train/src_repo/YOLOX/tools/../yolox/models/yolo_head.py", line 616, in dynamic_k_matching
dynamic_ks = dynamic_ks.tolist()
│          └ <method 'tolist' of 'torch._C._TensorBase' objects>
└
RuntimeError: CUDA error: device-side assert triggered
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "train.py", line 146, in
args=(exp, args),
│    └ Namespace(batch_size=8, cache=False, ckpt='/project/train/models/weight/yolox_s.pth', devices=0, dist_backend='nccl', dist_ur...
└ ╒═══════════════════╤════════════════════════════════════════════════════════════════════════════════════════════════════════...

File "/project/train/src_repo/YOLOX/tools/../yolox/core/launch.py", line 98, in launch
main_func(*args)
│          └ (╒═══════════════════╤═══════════════════════════════════════════════════════════════════════════════════════════════════════...
└ <function main at 0x7f731c747ae8>

File "train.py", line 124, in main
trainer.train()
│       └ <function Trainer.train at 0x7f72d48b2bf8>
└ <yolox.core.trainer.Trainer object at 0x7f731c7589e8>
File "/project/train/src_repo/YOLOX/tools/../yolox/core/trainer.py", line 74, in train
self.train_in_epoch()
│    └ <function Trainer.train_in_epoch at 0x7f72d48d3f28>
└ <yolox.core.trainer.Trainer object at 0x7f731c7589e8>
File "/project/train/src_repo/YOLOX/tools/../yolox/core/trainer.py", line 83, in train_in_epoch
self.train_in_iter()
│    └ <function Trainer.train_in_iter at 0x7f731c745950>
└ <yolox.core.trainer.Trainer object at 0x7f731c7589e8>
File "/project/train/src_repo/YOLOX/tools/../yolox/core/trainer.py", line 89, in train_in_iter
self.train_one_iter()
│    └ <function Trainer.train_one_iter at 0x7f731c7459d8>
└ <yolox.core.trainer.Trainer object at 0x7f731c7589e8>
File "/project/train/src_repo/YOLOX/tools/../yolox/core/trainer.py", line 103, in train_one_iter
outputs = self.model(inps, targets)
│    │     │     └
│    │     └
│    └ YOLOX(
│        (backbone): YOLOPAFPN(
│          (backbone): CSPDarknet(
│            (stem): Focus(
│              (conv): BaseConv(
│                (conv): ...
└ <yolox.core.trainer.Trainer object at 0x7f731c7589e8>
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
│    │        │        └ {}
│    │        └
│    └ <function YOLOX.forward at 0x7f731c715c80>
└ YOLOX(
(backbone): YOLOPAFPN(
(backbone): CSPDarknet(
(stem): Focus(
(conv): BaseConv(
(conv): ...
File "/project/train/src_repo/YOLOX/tools/../yolox/models/yolox.py", line 35, in forward
fpn_outs, targets, x
│         │        └
│         └
└
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
│    │        │        └ {}
│    │        └
│    └ <function YOLOXHead.forward at 0x7f731c715510>
└ YOLOXHead(
(cls_convs): ModuleList(
(0): Sequential(
(0): BaseConv(
(conv): Conv2d(128, 128, kernel_size=...
File "/project/train/src_repo/YOLOX/tools/../yolox/models/yolo_head.py", line 203, in forward
dtype=xin[0].dtype,
└
File "/project/train/src_repo/YOLOX/tools/../yolox/models/yolo_head.py", line 352, in get_losses
"cpu",
File "/usr/local/lib/python3.6/dist-packages/torch/autograd/grad_mode.py", line 26, in decorate_context
return func(*args, **kwargs)
│     │       └ {}
│     └
└ <function YOLOXHead.get_assignments at 0x7f731c7157b8>
File "/project/train/src_repo/YOLOX/tools/../yolox/models/yolo_head.py", line 446, in get_assignments
gt_bboxes_per_image = gt_bboxes_per_image.cpu().float()
│                   └ <method 'cpu' of 'torch._C._TensorBase' objects>
└
RuntimeError: CUDA error: device-side assert triggered
terminate called after throwing an instance of 'c10::Error'
what():  CUDA error: device-side assert triggered

Why did this error occur55555

I have the same issue when training ByteTrack with YOLOX-X

Did you modify any code?

Did you modify any code?

The code was not modified，I found the problem in reg_conv module through debug

Did you modify any code?

Did you modify any code?

Is there something wrong with my graphics card？Or float16？It can still run on the server before。

tesla v100, face the same problems even after trying to change batchsize, suddenly encountered it and there was no such error reported before, besides find the reg_feat as NaN tensor like the former one

tesla v100, face the same problems even after trying to change batchsize, suddenly encountered it and there was no such error reported before, besides find the reg_feat as NaN tensor like the former one

i met the same problem on v100,but i don't know the reason,can you provide some solutions,i am at a loss

Did you modify any code?

i modify my code and met this problem,can you provide some solutions,i really don't know the reason

I face the same question , anyone can help me ?
log:
/pytorch/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [85,0,0], thread: [50,0,0] Assertion input_val >= zero && input_val <= one failed.
/pytorch/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [85,0,0], thread: [51,0,0] Assertion input_val >= zero && input_val <= one failed.
/pytorch/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [85,0,0], thread: [52,0,0] Assertion input_val >= zero && input_val <= one failed.
/pytorch/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [85,0,0], thread: [53,0,0] Assertion input_val >= zero && input_val <= one failed.
/pytorch/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [85,0,0], thread: [54,0,0] Assertion input_val >= zero && input_val <= one failed.
/pytorch/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [85,0,0], thread: [55,0,0] Assertion input_val >= zero && input_val <= one failed.
/pytorch/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [85,0,0], thread: [56,0,0] Assertion input_val >= zero && input_val <= one failed.
/pytorch/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [85,0,0], thread: [57,0,0] Assertion input_val >= zero && input_val <= one failed.
/pytorch/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [85,0,0], thread: [58,0,0] Assertion input_val >= zero && input_val <= one failed.
/pytorch/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [85,0,0], thread: [59,0,0] Assertion input_val >= zero && input_val <= one failed.
/pytorch/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [85,0,0], thread: [60,0,0] Assertion input_val >= zero && input_val <= one failed.
/pytorch/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [85,0,0], thread: [61,0,0] Assertion input_val >= zero && input_val <= one failed.
/pytorch/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [85,0,0], thread: [62,0,0] Assertion input_val >= zero && input_val <= one failed.
/pytorch/aten/src/ATen/native/cuda/Loss.cu:102: operator(): block: [85,0,0], thread: [63,0,0] Assertion input_val >= zero && input_val <= one failed.
THCudaCheck FAIL file=/pytorch/aten/src/THC/THCCachingHostAllocator.cpp line=278 error=710 : device-side assert triggered
2022-04-13 16:20:52 | ERROR    | yolox.models.yolo_head:330 - OOM RuntimeError is raised due to the huge memory cost during label assignment.                            CPU mode is applied in this batch. If you want to avoid this issue,                            try to reduce the batch size or image size.
2022-04-13 16:20:52 | INFO     | yolox.core.trainer:189 - Training of experiment is done and the best AP is 0.00
2022-04-13 16:20:52 | ERROR    | yolox.core.launch:98 - An error has been caught in function 'launch', process 'MainProcess' (24745), thread 'MainThread'<PHONE_NUMBER>80032):
Traceback (most recent call last):
File "/home/zyz/Documents/YOLOX/yolox/models/yolo_head.py", line 324, in get_losses
imgs,
└
File "/home/zyz/.conda/envs/py38/lib/python3.7/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
│     │       └ {}
│     └
└ <function YOLOXHead.get_assignments at 0x7f4d655dc2f0>
File "/home/zyz/Documents/YOLOX/yolox/models/yolo_head.py", line 519, in get_assignments
cls_preds_.sqrt_(), gt_cls_per_image, reduction="none"
│          │        └
│          └ <method 'sqrt_' of 'torch._C._TensorBase' objects>
└
File "/home/zyz/.conda/envs/py38/lib/python3.7/site-packages/torch/nn/functional.py", line 2759, in binary_cross_entropy
return torch._C._nn.binary_cross_entropy(input, target, weight, reduction_enum)
│     │  │   │                    │      │       │       └ 0
│     │  │   │                    │      │       └ None
│     │  │   │                    │      └
│     │  │   │                    └
│     │  │   └
│     │  └ <module 'torch._C._nn'>
│     └ <module 'torch._C' from '/home/zyz/.conda/envs/py38/lib/python3.7/site-packages/torch/_C.cpython-37m-x86_64-linux-gnu.so'>
└ <module 'torch' from '/home/zyz/.conda/envs/py38/lib/python3.7/site-packages/torch/init.py'>
RuntimeError: CUDA error: device-side assert triggered
@FateScript

maybe you can check you environment,and is the version of python 3.8?

I'm confused about this problem too

我开始也遇到同样的问题，是Tesla V100。后来把Batch size从48一路尝试降到16后就没出现过这个问题了。GPU内存应该是足够的，不知道为什么batch size会导致这个错误。不知道会不会因为大batch在做mixed precision的时候导致gradient有问题？因为我观测到报错前有些iter会出现nan的loss

Setting the learning rate a little smaller can solve this problem. In fact, I succeeded.

Setting the learning rate a little smaller can solve this problem. In fact, I succeeded.

So why large lr may cause this problem?

About this problem, I finally found that the gpu is broken. You need to change the CPU to run the yolox code to determine whether there is a problem with your code

Had there anyone fixed it? I met the same problem。。。Any I have tried to reduce the lr and batch-size，still useless......
And that's my GPU device shown below:

I run my code in docker container. And I have run others code using the GPU without any problem...
Also, I have run the same code with same setting of batch-size and lr  before in similar docker container successfully.

有人修过吗？我遇到了同样的问题。。。任何我尝试过减少lr和batch-size，仍然没用...... 这是我的GPU设备，如下所示： 我在docker容器中运行我的代码。而且我已经使用 GPU 运行其他代码没有任何问题...... 此外，我之前在类似的 docker 容器中成功地运行了具有相同批处理大小和 lr 设置的相同代码。

You first switch to CPU to run YOLOX, to confirm that it is not a problem with the code

how to switch to CPU? by any simple parameter setting?

i also met this question, after i add siou loss and uesed it...

I met same problem.Then, I can train normally after turning off fp16

同样出现前向NAN的问题，降低学习率也不work，初步确定是BN的weight的问题，修改BN初始化时weight的分布，其方差变小是work的，但是学不动，ap=0。建议将网络中所有BN改为GN，我这可以成功解决此问题。

I met same problem.Then, I can train normally after turning off fp16

This worked for me, thank you

Anyone solved this problems ?
 I even switch yolox back to 0.2.0, switch python version.
But nothing worked. ....

I met same problem.Then, I can train normally after turning off fp16

omg!!!!!thank   you  ！  useful！

Anyone solved this problems ?  I even switch yolox back to 0.2.0, switch python version. But nothing worked. ....

Have you ever tried to change FOCUS to a normal 3*3 convolution?

可以试一下加载预训练模型或者将bn改成gn
---原始邮件---
发件人: "Yu @.>
发送时间: 2022年11月7日(周一) 下午4:37
收件人: @.>;
抄送<EMAIL_ADDRESS>主题: Re: [Megvii-BaseDetection/YOLOX] 训练yolox-s出现RuntimeError: CUDA error: device-side assert triggered问题 (Issue #1161)
Anyone solved this problems ?  I even switch yolox back to 0.2.0, switch python version. But nothing worked. ....
Have you ever tried to change FOCUS to a normal 3*3 convolution?
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i also met this question, after i add siou loss and uesed it...

What did you do to fix it?

I resize my images and run succeded. I think It is a solution !

我认为是因为做了mosaic增强导致的，mosaic有些增强猛了会导致随机组合的图出来的gt超出了图片的范围，我现在训练都关mosaic的，就几乎没出过这个问题。

同样遇到该问题，怎么解决的哇？

Reducing lr solves but I dont know why it happens.


Ensure Queue is empty before finishing simulation

@knute-sifive I added this assert to make sure there are no pending stores and the current model fails.Do you some ideas on this

I ran the regression with your branch and the failure makes sense -- and doesn't.  😄
One of the tests terminates simulation after 1M instructions have retired:
olympia -i 1M --workload traces/core_riscv.zstf

which will leave "unretired" stores in the ROB.  In other words -- that's ok!
This test works fine:
olympia -i 1M --workload traces/core_riscv.zstf

Got it, should I change the test cases to remove the instruction limit.

Well, you can remove the instruction limit if you want, but the simulator should not die if a user wants to only generate a pipeout or report or whatever for just the first few million instructions.
In other words, I do not think this assert is required.

Got it, Is there another way to get around this issue?
Its pretty useful when developing to know that all the instructions were correctly handled in the LSU


Update README.md

Cleaned up README.md so that it is presentable for "GitHub Include" on the wiki
Also update community meeting registration / join link

Please comment in https://github.com/camaraproject/Template_Lead_Repository/pull/3 regarding the upcoming structure for the README.md. That should be agreed before we touch all repositories.

@hdamker @wrathwolf Can we merge this PR? Are we conform with the agreed format?

@akoshunyadi I have updated the README according to the template (moved the release information back into an own section). Megalinter is failing on unchanged files, I suppose that the branch need to be rebased?

Don't we need the link to the meeting minutes?

@akoshunyadi you are right, missed that line from the template. Added with https://github.com/camaraproject/DeviceStatus/pull/139/commits/efd99cb002e9a172cfdf61478e513e8b30d55022
(will leave approval and merge now to the Sub Project ... had approved before only to get my blocking comments out of the way)


Scrape nginx ingress engine

I was having the same problems reported in this issue https://github.com/coreos/prometheus-operator/issues/617 and I fixed it adding permissions to the cluster role used by prometheus-k8s.

Kube prometheus is meant you you to take and adapt, we cannot possibly apart to every use case.

I encourage you to modify the manifests to your need, but the set of manifests we provide will stay the most minimal version to get basic cluster monitoring up and running.

ok thanks =)


SVG Convertor

Added link to convert svg to VectorDrawable + MrVector scheme support

@ozodrukh awesome thanks again!


Add license attribute

https://docs.npmjs.com/files/package.json#license
http://npm1k.org/

npm1k is just shitty license resolver if it cannot find the license file in the repo.

I partially agree, but they want to standardize the package.json and it makes sense.

@pgilad that's also why I merged it. Just merely stating that this site is just spreading FUD because they didn't use a good license parser.


[Question] How can I only see errors?

Is there a way to have TroubleToggle show only errors?

This is now possible. See https://github.com/folke/trouble.nvim/pull/285


Removed duplicate icon

This project uses semver, please check the scope of this pr:

[x] #patch# - backwards-compatible bug fix
[ ] #minor# - adding functionality in a backwards-compatible manner
[ ] #major# - incompatible API change

CHANGELOG

Removed duplicate icon warn

Visual Acceptance tests:
No changes

Approved

Approved


[Bug] Fix storage issue on editor

Data is being stored in out/... directory along with its classes.

Note: I forgot to create an issue before merging the PR.
PR has been closed already


feat(ExAdmin.Register): allow changesets to be specified

The changeset functions to be used on create and update can now be
specified. If not specified then module.changeset/2 will be used by
default.

This seems to be broken, and I could not find the related test either.


headline color

Hey thanks for sharing but I am looking to edit the headline colors, because h1 with super red is too aggressive for my liking. Can you comment the css lines or tell me which ones are the headlines?

You're welcome, happy you like it! :) Yeah sure, you can change it here: https://github.com/sspaeti/obsidian_kanagawa/blob/1adf4f59b14dbc159bcf4ec052c6dafda9e8d85e/obsidian.css#L108
You see --neutral-red is used, just change it to any color you like.

Sadly I just updated my plugins an overwrite my customization. Is there a way to prevent from happening that?

Sadly I just updated my plugins an overwrite my customization. Is there a way to prevent from happening that?

Yes there is, you can copy the obsidian.css to your local .obsidian/themes/ folder. Best to rename to something else e.g. custom_kanagawa.css, and you will see it as an alternative in your Obsidian:


Feature: Align controller services.

Closes https://github.com/giantswarm/giantswarm/issues/22703.
Tests on workload clusters (not always required)
For changes in the chart, chart templates, and ingress controller container images, I executed the following tests
to verify them working in live enviromnents:

Test / Provider
AWS
Azure
KVM

Upgrade from previous version

Existing Ingress resources are reconciled correctly

Fresh install

Fresh Ingress resources are reconciled correctly

Testing was done using hello-world-app.
Hint for KVM:
kubectl port-forward -n kube-system svc/nginx-ingress-controller-app 8080:80
ingress_domain=host.configured.in.ingress; curl --connect-to "$ingress_domain:80:<IP_ADDRESS>:8080" "http://$ingress_domain" -v

Could I have some review before taking the long trip of testing?

Tested upgrading several deployment options with CLB on AWS. NLB next, fresh installations and Azure pending. KVM will be skipped.


Add reduced embeddings sample to azure-search-documents

API change check
API changes are not detected in this pull request.


[Enhancement] Add highlighting to kcl.mod and kcl.mod.lock files

Hey @Peefy, I'd be happy to take this one

Thank you! I'll assign it to you.


Create Vale rules for all required OCP assembly metadata

Vale should check that each of the following are in place for assemblies.

If an attribute is found in the title, the rule should flag if the _attributes/common-attributes.adoc include is placed after the assembly title.
:context: value should not include _{context}

include::_attributes/common-attributes.adoc[]
:context: <unique-context-for-assembly>
= title
toc::[]

If an attribute is found in the title, the rule should flag if the _attributes/common-attributes.adoc include is placed after the assembly title.
We can discard this one as it's not valid anymore.
Is the second bullet point an issue you are seeing?

{_context} is used in modules to resolve unique ids. If we see :context: something_{context}, we run the risk of the previous value of :context: getting set and messing up links and URLs.

Could have sworn I saw this somewhere recently.

toc::[] should be placed after the heading.

_attributes/common-attributes.adoc should be in every assembly

:shrug:

Definitely agree with the other two points. I think an assembly metadata rule could be:

check for the include attributes
check toc is under heading

Gabriel fixed a bunch of the context errors last week which is why they are no longer there.

OK I see so we should inlude it then, a rule to check the following:

check for the include attributes
check toc is under heading
check the context does not include _{context}

https://studio.vale.sh/s/691cb2f40e893adea3abf3d95071892e - something like this but perhaps should allow include to appear above/below the title

Module should not have a TOC: https://redhat-internal.slack.com/archives/C04P5S34GP2/p1696431880071879?thread_ts=1696397420.123719&cid=C04P5S34GP2


Remove nearcon graphics

Description
This PR reverts the changes added in this PR https://github.com/near/near-wallet/pull/3096 , now that the NearCon event has finished we need to revert these changes and remove the graphic from the homepage.
Changes in this PR

Hi, @hcho112 can you merge this one since I do not have access to do it?


⚠ TorrentLeech has degraded performance

In 86df5d6, TorrentLeech ($TL) experienced degraded performance:

HTTP code: 200
Response time: 5185 ms

Resolved: TorrentLeech performance has improved in 44cd59a after 8 minutes.


Remove extra flatmap

There was a flatMap call that wasn't doing anything, as the value was already an option.  Just removed it.

Hi,
FlatMap correct the error in case of a empty value in the json Map.
After "get(name)", we would have a "Some(null)" , and in the flatMap we do  "Option(null)" wich is a "None" and it avoid the error.

Oh nice, OK, closing this PR then.

Thanks for your feedback! :)


new function: apply-replacements

/cc @mengqiy
/cc @yuwenma
/cc @droot

I'd suggest making its logic improtable by other go program.
With it, we can compile it as part of porch if we want to do.

I'd suggest making its logic improtable by other go program.

@mengqiy I made ApplyReplacements(*fn.ResourceList) in main.go public. LMK if this fulfills this requirement.

Question for @mengqiy and @yuwenma, right now we have to convert to RNode because the replacements filter (like all kustomize filters) accepts and returns a list of RNodes. Do we want to add some sort of wrapper to the kpt-functions-sdk to the kustomize filters so that users do not need to do this conversion?

We may want to wait for @yuwenma to take another look


Fixed indexing

Search engine bots were indexing the admin panel, so I fixed it. It is no necessary to index this part of Mofhy.

Nice work


Where is secret.sh?

what is secret.sh, and where is it?

It is gitignored. As the Readme says, you need to create that file defining some env variables.
My secrets.sh looks like this:
export API_TOKEN='xxxxxxxxxx'
export REACT_APP_API_TOKEN=$API_TOKEN
export SERVER_URL="xxxxxxxxx" #this is the IP address or the domain where your backend is

Thank you so much. I already generated JWT token, and appplyed to code.
Thanks kindly,
-Lucas
2020년 6월 18일 (목) 오전 10:51, Carlos Andrés Álvarez Restrepo <
<EMAIL_ADDRESS>작성:

It is gitignored. As the Readme says, you need to create that file
defining some env variables.
My secrets.sh looks like this:
export API_TOKEN='xxxxxxxxxx'export REACT_APP_API_TOKEN=$API_TOKENexport SERVER_URL="xxxxxxxxx" #this is the IP address or the domain where your backend is
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MGMT-8890: Support -multi stream for OCP releases

Contributes-to: MGMT-8890
Closes: MGMT-11553
/cc @osherdp

Sample run gave me the following
INFO       bump_ocp_releases.py:290 New latest ocp release available for 4.11, 4.11.0 -> 4.11.1
INFO       bump_ocp_releases.py:200 Getting all releases for version 4.8 and architecture x86_64
INFO       bump_ocp_releases.py:200 Getting all releases for version 4.9 and architecture x86_64
INFO       bump_ocp_releases.py:200 Getting all releases for version 4.10 and architecture x86_64
INFO       bump_ocp_releases.py:200 Getting all releases for version 4.10 and architecture arm64
INFO       bump_ocp_releases.py:200 Getting all releases for version 4.11 and architecture x86_64
INFO       bump_ocp_releases.py:200 Getting all releases for version 4.11 and architecture arm64
INFO       sh.py:670 <Command '/usr/bin/make generate-configuration', pid 2962796>: process started
INFO       sh.py:670 <Command '/usr/bin/make generate-bundle', pid 2962893>: process started

[...]

-  value: '[{"openshift_version":"4.8","cpu_architecture":"x86_64","cpu_architectures":["x86_64"],"url":"quay.io/openshift-release-dev/ocp-release:4.8.47-x86_64","version":"4.8.47"},{"openshift_version":"4.9","cpu_architecture":"x86_64","cpu_architectures":["x86_64"],"url":"quay.io/openshift-release-dev/ocp-release:4.9.46-x86_64","version":"4.9.46"},{"openshift_version":"4.10","cpu_architecture":"x86_64","cpu_architectures":["x86_64"],"url":"quay.io/openshift-release-dev/ocp-release:4.10.26-x86_64","version":"4.10.26"},{"openshift_version":"4.10","cpu_architecture":"arm64","cpu_architectures":["arm64"],"url":"quay.io/openshift-release-dev/ocp-release:4.10.26-aarch64","version":"4.10.26"},{"openshift_version":"4.11","cpu_architecture":"multi","cpu_architectures":["x86_64","arm64","ppc64le","s390x"],"url":"quay.io/openshift-release-dev/ocp-release:4.11.0-multi","version":"4.11.0","default":true}]' # release images
+  value: '[{"openshift_version":"4.8","cpu_architecture":"x86_64","cpu_architectures":["x86_64"],"url":"quay.io/openshift-release-dev/ocp-release:4.8.47-x86_64","version":"4.8.47"},{"openshift_version":"4.9","cpu_architecture":"x86_64","cpu_architectures":["x86_64"],"url":"quay.io/openshift-release-dev/ocp-release:4.9.46-x86_64","version":"4.9.46"},{"openshift_version":"4.10","cpu_architecture":"x86_64","cpu_architectures":["x86_64"],"url":"quay.io/openshift-release-dev/ocp-release:4.10.26-x86_64","version":"4.10.26"},{"openshift_version":"4.10","cpu_architecture":"arm64","cpu_architectures":["arm64"],"url":"quay.io/openshift-release-dev/ocp-release:4.10.26-aarch64","version":"4.10.26"},{"openshift_version":"4.11","cpu_architecture":"multi","cpu_architectures":["x86_64","arm64","ppc64le","s390x"],"url":"quay.io/openshift-release-dev/ocp-release:4.11.1-multi","version":"4.11.1","default":true}]' # release images

Sample run gave me the following
INFO       bump_ocp_releases.py:290 New latest ocp release available for 4.11, 4.11.0 -> 4.11.1
INFO       bump_ocp_releases.py:200 Getting all releases for version 4.8 and architecture x86_64
INFO       bump_ocp_releases.py:200 Getting all releases for version 4.9 and architecture x86_64
INFO       bump_ocp_releases.py:200 Getting all releases for version 4.10 and architecture x86_64
INFO       bump_ocp_releases.py:200 Getting all releases for version 4.10 and architecture arm64
INFO       bump_ocp_releases.py:200 Getting all releases for version 4.11 and architecture x86_64
INFO       bump_ocp_releases.py:200 Getting all releases for version 4.11 and architecture arm64
INFO       sh.py:670 <Command '/usr/bin/make generate-configuration', pid 2962796>: process started
INFO       sh.py:670 <Command '/usr/bin/make generate-bundle', pid 2962893>: process started

[...]

-  value: '[{"openshift_version":"4.8","cpu_architecture":"x86_64","cpu_architectures":["x86_64"],"url":"quay.io/openshift-release-dev/ocp-release:4.8.47-x86_64","version":"4.8.47"},{"openshift_version":"4.9","cpu_architecture":"x86_64","cpu_architectures":["x86_64"],"url":"quay.io/openshift-release-dev/ocp-release:4.9.46-x86_64","version":"4.9.46"},{"openshift_version":"4.10","cpu_architecture":"x86_64","cpu_architectures":["x86_64"],"url":"quay.io/openshift-release-dev/ocp-release:4.10.26-x86_64","version":"4.10.26"},{"openshift_version":"4.10","cpu_architecture":"arm64","cpu_architectures":["arm64"],"url":"quay.io/openshift-release-dev/ocp-release:4.10.26-aarch64","version":"4.10.26"},{"openshift_version":"4.11","cpu_architecture":"multi","cpu_architectures":["x86_64","arm64","ppc64le","s390x"],"url":"quay.io/openshift-release-dev/ocp-release:4.11.0-multi","version":"4.11.0","default":true}]' # release images
+  value: '[{"openshift_version":"4.8","cpu_architecture":"x86_64","cpu_architectures":["x86_64"],"url":"quay.io/openshift-release-dev/ocp-release:4.8.47-x86_64","version":"4.8.47"},{"openshift_version":"4.9","cpu_architecture":"x86_64","cpu_architectures":["x86_64"],"url":"quay.io/openshift-release-dev/ocp-release:4.9.46-x86_64","version":"4.9.46"},{"openshift_version":"4.10","cpu_architecture":"x86_64","cpu_architectures":["x86_64"],"url":"quay.io/openshift-release-dev/ocp-release:4.10.26-x86_64","version":"4.10.26"},{"openshift_version":"4.10","cpu_architecture":"arm64","cpu_architectures":["arm64"],"url":"quay.io/openshift-release-dev/ocp-release:4.10.26-aarch64","version":"4.10.26"},{"openshift_version":"4.11","cpu_architecture":"multi","cpu_architectures":["x86_64","arm64","ppc64le","s390x"],"url":"quay.io/openshift-release-dev/ocp-release:4.11.1-multi","version":"4.11.1","default":true}]' # release images

You can also see the result in the dry-run job: https://gcsweb-ci.apps.ci.l2s4.p1.openshiftapps.com/gcs/origin-ci-test/pr-logs/pull/openshift-assisted_assisted-installer-deployment/293/pull-ci-openshift-assisted-assisted-installer-deployment-master-bump-ocp-dry-run/1562109633315737600/artifacts/bump-ocp-dry-run/assisted-tools-bump-ocp/build-log.txt

/hold
In 4.12 there will be the same upgrade graph for single- and multi-arch release image. Not sure this here will be ever needed because we roll on 4.11-multi only as a temporary solution

/close


Fix when Session, Authentication or Message middlewares are not present

Hello!
In this PR I try fix the issue https://github.com/jazzband/django-silk/issues/347
Doesn't fail when one of these middlewares are not present:
'django.contrib.sessions.middleware.SessionMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware'

and if SILKY_AUTHENTICATION is True, an exception is raised: SILKY_AUTHENTICATION can not be enabled without Session, Authentication or Message Django's middlewares
Let me know any change required!
Thank you!

profiling.py, requests.py, and summary.py look good to me.
The tests look good at a glance.
Appears to resolve the issue on the ticket.
Hopefully Seb or someone else comes in and checks it too.

@SebCorbin First, thank you for your quick responses and the pacience 🙏
Regarding the review:

Thanks for your work, it's a great beginning, but if I'm not mistaking, the filters are not working when selecting them. They should work if you fall back to request.GET or request.POST here

I followed the session middleware variable approach and defined a new one for the silk's filters: silk_filters as a fallback.
With that and the custom filter manager we can get filters from session or silk_filters if the first one is not defined.
What do you think?
Other question: would it be worth adding selenium tests?

Hi @albertyw! Sorry to bother you, just wondering if there is something I can do to push forward the pull request. As I mentioned in my last comment, perhaps add selenium tests could give more confident to the change? Thank you!

Hi!
Sorry to bother, but any progress on the review? We would really appreciate this feature, since it would allow us to use silk without the middleware needed. Thank you very much

Hi @MattyCZ!
This pull request was marked as part of the last release but never merged. I think we should wait.
I'm available in case of changes required!

Hi @albertyw! I just want to let you know that this PR was marked as part of the release 5.1.0 but was never merged! https://github.com/jazzband/django-silk/releases/tag/5.1.0


Add PostComment/PostStackKeyComment to EventsApiClient

:tada: This issue has been resolved in version 5.0.0 :tada:
The release is available on:

npm package (@latest dist-tag)
GitHub release

Your semantic-release bot :package::rocket: