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{"level":"debug","ts":"2023-07-05T09:25:18.586Z","msg":"wrote Metadata v9","source":"kafka_client","broker":"4","bytes_written":36,"write_wait":0.00004431,"time_to_write":0.00001828,"err":null}
{"level":"debug","ts":"2023-07-05T09:25:18.586Z","msg":"kafka broker disconnected","source":"kafka_client_hooks","host":"cluster-kafka-2.cluster-kafka-brokers.kafka.svc"}
{"level":"debug","ts":"2023-07-05T09:25:18.586Z","msg":"kafka broker disconnected","source":"kafka_client_hooks","host":"cluster-kafka-3.cluster-kafka-brokers.kafka.svc"}
{"level":"debug","ts":"2023-07-05T09:25:18.586Z","msg":"kafka broker disconnected","source":"kafka_client_hooks","host":"cluster-kafka-5.cluster-kafka-brokers.kafka.svc"}
{"level":"debug","ts":"2023-07-05T09:25:18.586Z","msg":"kafka broker disconnected","source":"kafka_client_hooks","host":"cluster-kafka-6.cluster-kafka-brokers.kafka.svc"}
{"level":"debug","ts":"2023-07-05T09:25:18.586Z","msg":"kafka broker disconnected","source":"kafka_client_hooks","host":"cluster-kafka-7.cluster-kafka-brokers.kafka.svc"}
{"level":"debug","ts":"2023-07-05T09:25:18.586Z","msg":"kafka broker disconnected","source":"kafka_client_hooks","host":"cluster-kafka-8.cluster-kafka-brokers.kafka.svc"}
{"level":"debug","ts":"2023-07-05T09:25:18.586Z","msg":"kafka broker disconnected","source":"kafka_client_hooks","host":"cluster-kafka-9.cluster-kafka-brokers.kafka.svc"}
{"level":"debug","ts":"2023-07-05T09:25:18.586Z","msg":"kafka broker disconnected","source":"kafka_client_hooks","host":"cluster-kafka-10.cluster-kafka-brokers.kafka.svc"}
{"level":"debug","ts":"2023-07-05T09:25:18.586Z","msg":"kafka broker disconnected","source":"kafka_client_hooks","host":"cluster-kafka-11.cluster-kafka-brokers.kafka.svc"}
{"level":"debug","ts":"2023-07-05T09:25:18.586Z","msg":"reaped connections","source":"kafka_client","time_since_last_reap":19.999950378,"reap_dur":0.00033032,"num_reaped":11}
{"level":"debug","ts":"2023-07-05T09:25:18.586Z","msg":"read Metadata v9","source":"kafka_client","broker":"4","bytes_read":745,"read_wait":0.00009711,"time_to_read":0.00022577,"err":null}
{"level":"debug","ts":"2023-07-05T09:25:18.606Z","msg":"opening connection to broker","source":"kafka_client","addr":"cluster-kafka-4.cluster-kafka-brokers.kafka.svc:9092","broker":"4"}
{"level":"debug","ts":"2023-07-05T09:25:18.609Z","msg":"kafka connection succeeded","source":"kafka_client_hooks","host":"cluster-kafka-4.cluster-kafka-brokers.kafka.svc","dial_duration":0.00308375}
{"level":"debug","ts":"2023-07-05T09:25:18.609Z","msg":"connection opened to broker","source":"kafka_client","addr":"cluster-kafka-4.cluster-kafka-brokers.kafka.svc:9092","broker":"4"}
{"level":"debug","ts":"2023-07-05T09:25:18.609Z","msg":"connection initialized successfully","source":"kafka_client","addr":"cluster-kafka-4.cluster-kafka-brokers.kafka.svc:9092","broker":"4"}
{"level":"debug","ts":"2023-07-05T09:25:18.609Z","msg":"wrote Metadata v9","source":"kafka_client","broker":"4","bytes_written":36,"write_wait":0.00317289,"time_to_write":0.00001226,"err":null}
{"level":"debug","ts":"2023-07-05T09:25:18.610Z","msg":"read Metadata v9","source":"kafka_client","broker":"4","bytes_read":745,"read_wait":0.00005341,"time_to_read":0.00025876,"err":null}
{"level":"debug","ts":"2023-07-05T09:25:38.586Z","msg":"kafka broker disconnected","source":"kafka_client_hooks","host":"cluster-kafka-4.cluster-kafka-brokers.kafka.svc"}
{"level":"debug","ts":"2023-07-05T09:25:38.586Z","msg":"reaped connections","source":"kafka_client","time_since_last_reap":20.000648218,"reap_dur":0.00007139,"num_reaped":1}
{"level":"debug","ts":"2023-07-05T09:25:58.606Z","msg":"kafka broker disconnected","source":"kafka_client_hooks","host":"cluster-kafka-4.cluster-kafka-brokers.kafka.svc"}
{"level":"debug","ts":"2023-07-05T09:25:58.606Z","msg":"reaped connections","source":"kafka_client","time_since_last_reap":19.999645198,"reap_dur":0.0000842,"num_reaped":1}
{"level":"debug","ts":"2023-07-05T09:30:18.586Z","msg":"opening connection to broker","source":"kafka_client","addr":"cluster-kafka-5.cluster-kafka-brokers.kafka.svc:9092","broker":"5"}
{"level":"debug","ts":"2023-07-05T09:30:18.590Z","msg":"kafka connection succeeded","source":"kafka_client_hooks","host":"cluster-kafka-5.cluster-kafka-brokers.kafka.svc","dial_duration":0.00366454}
{"level":"debug","ts":"2023-07-05T09:30:18.590Z","msg":"connection opened to broker","source":"kafka_client","addr":"cluster-kafka-5.cluster-kafka-brokers.kafka.svc:9092","broker":"5"}
{"level":"debug","ts":"2023-07-05T09:30:18.590Z","msg":"connection initialized successfully","source":"kafka_client","addr":"cluster-kafka-5.cluster-kafka-brokers.kafka.svc:9092","broker":"5"}
{"level":"debug","ts":"2023-07-05T09:30:18.590Z","msg":"wrote Metadata v9","source":"kafka_client","broker":"5","bytes_written":36,"write_wait":0.0038123,"time_to_write":0.000014049,"err":null}
{"level":"debug","ts":"2023-07-05T09:30:18.590Z","msg":"read Metadata v9","source":"kafka_client","broker":"5","bytes_read":745,"read_wait":0.00005544,"time_to_read":0.0002142,"err":null}
{"level":"debug","ts":"2023-07-05T09:30:18.607Z","msg":"opening connection to broker","source":"kafka_client","addr":"cluster-kafka-5.cluster-kafka-brokers.kafka.svc:9092","broker":"5"}
{"level":"debug","ts":"2023-07-05T09:30:18.608Z","msg":"kafka connection succeeded","source":"kafka_client_hooks","host":"cluster-kafka-5.cluster-kafka-brokers.kafka.svc","dial_duration":0.0015683}
{"level":"debug","ts":"2023-07-05T09:30:18.608Z","msg":"connection opened to broker","source":"kafka_client","addr":"cluster-kafka-5.cluster-kafka-brokers.kafka.svc:9092","broker":"5"}
{"level":"debug","ts":"2023-07-05T09:30:18.608Z","msg":"connection initialized successfully","source":"kafka_client","addr":"cluster-kafka-5.cluster-kafka-brokers.kafka.svc:9092","broker":"5"}
{"level":"debug","ts":"2023-07-05T09:30:18.608Z","msg":"wrote Metadata v9","source":"kafka_client","broker":"5","bytes_written":36,"write_wait":0.00168397,"time_to_write":0.00001394,"err":null}
{"level":"debug","ts":"2023-07-05T09:30:18.609Z","msg":"read Metadata v9","source":"kafka_client","broker":"5","bytes_read":745,"read_wait":0.00005205,"time_to_read":0.00029508,"err":null}
{"level":"debug","ts":"2023-07-05T09:30:58.586Z","msg":"kafka broker disconnected","source":"kafka_client_hooks","host":"cluster-kafka-5.cluster-kafka-brokers.kafka.svc"}
{"level":"debug","ts":"2023-07-05T09:30:58.586Z","msg":"reaped connections","source":"kafka_client","time_since_last_reap":19.999763572,"reap_dur":0.00010995,"num_reaped":1}
{"level":"debug","ts":"2023-07-05T09:30:58.606Z","msg":"kafka broker disconnected","source":"kafka_client_hooks","host":"cluster-kafka-5.cluster-kafka-brokers.kafka.svc"}
{"level":"debug","ts":"2023-07-05T09:30:58.606Z","msg":"reaped connections","source":"kafka_client","time_since_last_reap":19.999749223,"reap_dur":0.00005374,"num_reaped":1}
{"level":"debug","ts":"2023-07-05T09:35:18.586Z","msg":"opening connection to broker","source":"kafka_client","addr":"cluster-kafka-6.cluster-kafka-brokers.kafka.svc:9092","broker":"6"}
{"level":"debug","ts":"2023-07-05T09:35:18.590Z","msg":"kafka connection succeeded","source":"kafka_client_hooks","host":"cluster-kafka-6.cluster-kafka-brokers.kafka.svc","dial_duration":0.003697969}
{"level":"debug","ts":"2023-07-05T09:35:18.590Z","msg":"connection opened to broker","source":"kafka_client","addr":"cluster-kafka-6.cluster-kafka-brokers.kafka.svc:9092","broker":"6"}
{"level":"debug","ts":"2023-07-05T09:35:18.590Z","msg":"connection initialized successfully","source":"kafka_client","addr":"cluster-kafka-6.cluster-kafka-brokers.kafka.svc:9092","broker":"6"}
{"level":"debug","ts":"2023-07-05T09:35:18.590Z","msg":"wrote Metadata v9","source":"kafka_client","broker":"6","bytes_written":36,"write_wait":0.003863529,"time_to_write":0.00001166,"err":null}
{"level":"debug","ts":"2023-07-05T09:35:18.590Z","msg":"read Metadata v9","source":"kafka_client","broker":"6","bytes_read":745,"read_wait":0.00006391,"time_to_read":0.00019073,"err":null}
{"level":"debug","ts":"2023-07-05T09:35:18.606Z","msg":"opening connection to broker","source":"kafka_client","addr":"cluster-kafka-6.cluster-kafka-brokers.kafka.svc:9092","broker":"6"}
{"level":"debug","ts":"2023-07-05T09:35:18.608Z","msg":"kafka connection succeeded","source":"kafka_client_hooks","host":"cluster-kafka-6.cluster-kafka-brokers.kafka.svc","dial_duration":0.00149353}
{"level":"debug","ts":"2023-07-05T09:35:18.608Z","msg":"connection opened to broker","source":"kafka_client","addr":"cluster-kafka-6.cluster-kafka-brokers.kafka.svc:9092","broker":"6"}
{"level":"debug","ts":"2023-07-05T09:35:18.608Z","msg":"connection initialized successfully","source":"kafka_client","addr":"cluster-kafka-6.cluster-kafka-brokers.kafka.svc:9092","broker":"6"}
{"level":"debug","ts":"2023-07-05T09:35:18.608Z","msg":"wrote Metadata v9","source":"kafka_client","broker":"6","bytes_written":36,"write_wait":0.0015819,"time_to_write":0.00001283,"err":null}
{"level":"debug","ts":"2023-07-05T09:35:18.608Z","msg":"read Metadata v9","source":"kafka_client","broker":"6","bytes_read":745,"read_wait":0.00003956,"time_to_read":0.00023211,"err":null}
{"level":"debug","ts":"2023-07-05T09:35:58.586Z","msg":"kafka broker disconnected","source":"kafka_client_hooks","host":"cluster-kafka-6.cluster-kafka-brokers.kafka.svc"}
{"level":"debug","ts":"2023-07-05T09:35:58.586Z","msg":"reaped connections","source":"kafka_client","time_since_last_reap":19.999632056,"reap_dur":0.00010912,"num_reaped":1}
{"level":"debug","ts":"2023-07-05T09:35:58.606Z","msg":"kafka broker disconnected","source":"kafka_client_hooks","host":"cluster-kafka-6.cluster-kafka-brokers.kafka.svc"}
{"level":"debug","ts":"2023-07-05T09:35:58.606Z","msg":"reaped connections","source":"kafka_client","time_since_last_reap":19.999620797,"reap_dur":0.0000847,"num_reaped":1}
{"level":"debug","ts":"2023-07-05T09:40:18.586Z","msg":"opening connection to broker","source":"kafka_client","addr":"cluster-kafka-7.cluster-kafka-brokers.kafka.svc:9092","broker":"7"}
{"level":"debug","ts":"2023-07-05T09:40:18.589Z","msg":"kafka connection succeeded","source":"kafka_client_hooks","host":"cluster-kafka-7.cluster-kafka-brokers.kafka.svc","dial_duration":0.0035577}
{"level":"debug","ts":"2023-07-05T09:40:18.589Z","msg":"connection opened to broker","source":"kafka_client","addr":"cluster-kafka-7.cluster-kafka-brokers.kafka.svc:9092","broker":"7"}
{"level":"debug","ts":"2023-07-05T09:40:18.589Z","msg":"connection initialized successfully","source":"kafka_client","addr":"cluster-kafka-7.cluster-kafka-brokers.kafka.svc:9092","broker":"7"}
{"level":"debug","ts":"2023-07-05T09:40:18.589Z","msg":"wrote Metadata v9","source":"kafka_client","broker":"7","bytes_written":36,"write_wait":0.00369631,"time_to_write":0.00001164,"err":null}
{"level":"debug","ts":"2023-07-05T09:40:18.590Z","msg":"read Metadata v9","source":"kafka_client","broker":"7","bytes_read":745,"read_wait":0.00005822,"time_to_read":0.00022295,"err":null}
{"level":"debug","ts":"2023-07-05T09:40:18.606Z","msg":"opening connection to broker","source":"kafka_client","addr":"cluster-kafka-7.cluster-kafka-brokers.kafka.svc:9092","broker":"7"}
{"level":"debug","ts":"2023-07-05T09:40:18.607Z","msg":"kafka connection succeeded","source":"kafka_client_hooks","host":"cluster-kafka-7.cluster-kafka-brokers.kafka.svc","dial_duration":0.0014933}
{"level":"debug","ts":"2023-07-05T09:40:18.607Z","msg":"connection opened to broker","source":"kafka_client","addr":"cluster-kafka-7.cluster-kafka-brokers.kafka.svc:9092","broker":"7"}
{"level":"debug","ts":"2023-07-05T09:40:18.607Z","msg":"connection initialized successfully","source":"kafka_client","addr":"cluster-kafka-7.cluster-kafka-brokers.kafka.svc:9092","broker":"7"}
{"level":"debug","ts":"2023-07-05T09:40:18.607Z","msg":"wrote Metadata v9","source":"kafka_client","broker":"7","bytes_written":36,"write_wait":0.00156652,"time_to_write":0.00001381,"err":null}
{"level":"debug","ts":"2023-07-05T09:40:18.608Z","msg":"read Metadata v9","source":"kafka_client","broker":"7","bytes_read":745,"read_wait":0.0000492,"time_to_read":0.000215,"err":null}
{"level":"debug","ts":"2023-07-05T09:40:58.585Z","msg":"kafka broker disconnected","source":"kafka_client_hooks","host":"cluster-kafka-7.cluster-kafka-brokers.kafka.svc"}
{"level":"debug","ts":"2023-07-05T09:40:58.586Z","msg":"reaped connections","source":"kafka_client","time_since_last_reap":19.999883998,"reap_dur":0.0000894,"num_reaped":1}
{"level":"debug","ts":"2023-07-05T09:40:58.606Z","msg":"kafka broker disconnected","source":"kafka_client_hooks","host":"cluster-kafka-7.cluster-kafka-brokers.kafka.svc"}
{"level":"debug","ts":"2023-07-05T09:40:58.606Z","msg":"reaped connections","source":"kafka_client","time_since_last_reap":19.999592909,"reap_dur":0.00008638,"num_reaped":1}
{"level":"debug","ts":"2023-07-05T09:45:18.586Z","msg":"opening connection to broker","source":"kafka_client","addr":"cluster-kafka-8.cluster-kafka-brokers.kafka.svc:9092","broker":"8"}
{"level":"debug","ts":"2023-07-05T09:45:18.589Z","msg":"kafka connection succeeded","source":"kafka_client_hooks","host":"cluster-kafka-8.cluster-kafka-brokers.kafka.svc","dial_duration":0.003128859}
{"level":"debug","ts":"2023-07-05T09:45:18.589Z","msg":"connection opened to broker","source":"kafka_client","addr":"cluster-kafka-8.cluster-kafka-brokers.kafka.svc:9092","broker":"8"}
{"level":"debug","ts":"2023-07-05T09:45:18.589Z","msg":"connection initialized successfully","source":"kafka_client","addr":"cluster-kafka-8.cluster-kafka-brokers.kafka.svc:9092","broker":"8"}
{"level":"debug","ts":"2023-07-05T09:45:18.590Z","msg":"wrote Metadata v9","source":"kafka_client","broker":"8","bytes_written":36,"write_wait":0.003253939,"time_to_write":0.00001415,"err":null}
{"level":"debug","ts":"2023-07-05T09:45:18.590Z","msg":"read Metadata v9","source":"kafka_client","broker":"8","bytes_read":745,"read_wait":0.00005456,"time_to_read":0.00025707,"err":null}
{"level":"debug","ts":"2023-07-05T09:45:18.606Z","msg":"opening connection to broker","source":"kafka_client","addr":"cluster-kafka-8.cluster-kafka-brokers.kafka.svc:9092","broker":"8"}
{"level":"debug","ts":"2023-07-05T09:45:18.607Z","msg":"kafka connection succeeded","source":"kafka_client_hooks","host":"cluster-kafka-8.cluster-kafka-brokers.kafka.svc","dial_duration":0.00146027}
{"level":"debug","ts":"2023-07-05T09:45:18.607Z","msg":"connection opened to broker","source":"kafka_client","addr":"cluster-kafka-8.cluster-kafka-brokers.kafka.svc:9092","broker":"8"}
{"level":"debug","ts":"2023-07-05T09:45:18.607Z","msg":"connection initialized successfully","source":"kafka_client","addr":"cluster-kafka-8.cluster-kafka-brokers.kafka.svc:9092","broker":"8"}
{"level":"debug","ts":"2023-07-05T09:45:18.608Z","msg":"wrote Metadata v9","source":"kafka_client","broker":"8","bytes_written":36,"write_wait":0.00154272,"time_to_write":0.00001079,"err":null}
{"level":"debug","ts":"2023-07-05T09:45:18.608Z","msg":"read Metadata v9","source":"kafka_client","broker":"8","bytes_read":745,"read_wait":0.00003821,"time_to_read":0.00023257,"err":null}
{"level":"debug","ts":"2023-07-05T09:45:58.586Z","msg":"kafka broker disconnected","source":"kafka_client_hooks","host":"cluster-kafka-8.cluster-kafka-brokers.kafka.svc"}
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{"level":"debug","ts":"2023-07-05T09:45:58.606Z","msg":"kafka broker disconnected","source":"kafka_client_hooks","host":"cluster-kafka-8.cluster-kafka-brokers.kafka.svc"}
{"level":"debug","ts":"2023-07-05T09:45:58.606Z","msg":"reaped connections","source":"kafka_client","time_since_last_reap":20.000236906,"reap_dur":0.00008832,"num_reaped":1}

My colleague suggested that websocket errors might come from Loadbalancer that serves console backend traffic. Could you do port-forward to the console pod?
How console is exposed to your machine (loadbalancer)?

Indeed port forwarding works.
This is how the console is exposed:
GCP TCP LB -> HaProxy VM -> Spring Cloud gateway -> K8s service ->  k8s pod

If you could do the same test but port forward from Spring Cloud Gateway and then from HA proxy maybe we could narrow down which proxy break websockets

tip of the day: you can attach logs as files to comments. See the grey text at the bottom of the comment entry field.


Add "Mushoku Tensei: Isekai Ittara Honki Dasu Part 2/2nd Season"

Sorry, doesn't appear to be working.

This seems fine, what's the issue?

I got it to correctly recognize the [SubsPlease] release and some others by adding just "Mushoku Tensei" to the alternative titles field of both seasons, in conjunction with lukas' commit.

I got it to correctly recognize the [SubsPlease] release and some others by adding just "Mushoku Tensei" to the alternative titles field of both seasons, in conjunction with lukas' commit.

Yeah, I checked the following combinations of the alternative title (Mushoku Tensei) with the [SubsPlease] release:
on neither season: detected as episode 12 of S2
on just S1: detected as episode 1 of S2
on just S2: detected as episode 12 of S2
on both S1 and S2: detected as episode 1 of S2
@paulo27ms
Either way, without adding an alternative title, it wouldn't work correctly, hence why I removed my pull request.

It's not meant to work if the titles aren't set correctly so the issue was not this change as that has nothing to do with it.

I just merged #88 because it had episode counts too. But still, thanks, @lukas-berger.


⚠ Bofrost.be has degraded performance

In cbde70c, Bofrost.be (https://www.bofrost.be) experienced degraded performance:

HTTP code: 200
Response time: 941 ms

Resolved: Bofrost.be performance has improved in 54bbf04 after 1 hour, 1 minute.


Dictionaries refactoring

As an Architect
I want the dictionaries architecture to be reviewed and maybe changed
So that it can address current requirements and challenges.

so what?


SSZ zig enhancement

There is a zig ssz lib here developed by the member of Verkle/ZEAM team, but we found some issues when used it in consensus spec types. We can enhance this lib and further do optimization to make it available for both beacon and beam chain.
There is a talk at Devcon 7 which introduced high perf ssz lib implementation here which can be inspired.

What are some of the issues found?

@eightfilms  Our code is not exact the same with the ssz lib , because we use the latest version of zig, the std.builtin.Type has changed.
In fact our encode and decode methods are from the zabi, the origin code of zabi is also from the ssz lib , but with some updates;  the hashRootTree method are from the ssz lib .
In our ssz spec tests, we find some bugs(according to the current ssz lib)

when serialize struct, int, bool, array, struct with fixed length should serialize directly, the same with deserialize method
there is bug in merkleize method, you can in this pr
in hashTreeRoot method, when the type is Slice,  we must pass the limit, after that, it should call the  mixInLength method. Now, we doesn't find a solution to pass the limit of Slice type.


Feature request nimless nim

Love the tool and I am a big fan of using the thread pool injection method.  I do however see that the common theme of using NIM it's being detected by AV at the nim runtime.  A really good talk about this was done at BSIDESKC and would be cool to implement this as it does circumvent the severity and in some cases all AV detection.  I firmly believe all the other functions you have will prove to be evasive against top tier EDRs.
https://www.youtube.com/watch?v=EXX3HmCG3pw
https://github.com/m4ul3r/writing_nimless

Glad to hear that you like the tool and find it useful.
I did not know the tool/technique you shared, I'll check this out and maybe will implement here and in my other projects.
Thanks for your feedback and for letting me know 🤟

@shrek3n @itaymigdal I just noticed this.
I recently ported all of the variants over to Nim on my repo: https://github.com/m4ul3r/malware/tree/main/nim/thread_pool_injection
I can get around to porting it over to nimless - should be straight forward for me

Will have to clean up quite a few things, but it should be possible

@m4ul3r  Really cool work 🙌.
BTW, I tried to implement remote stomping here (using the poolparty technique), still  no success.

@itaymigdal  I can give a shot at remote stomping for poolparty technique when i get a chance. I'm sure you saw my port of it; I think it's pretty robust in the usage and pretty portable (imo)
For an example of how portable it might be..
Check out this branch - It's a work in progress at the moment:
https://github.com/m4ul3r/writing_nimless/tree/nimless_dll/src/0x12 - nimless_DLL
I've got a working DLL (tested with rundll32), and Pool Party Worker Factory Start Routine working in an executable.

@m4ul3r port of what? not sure.
You have a lot of stuff going on there, I'm following :)

@itaymigdal port of pool party here: https://github.com/m4ul3r/malware/tree/main/nim/thread_pool_injection
I followed Uriens code and it's very abstracted to just import nimpool and the type of variant to call.
When you mentioned remote stomping, do you mean function or module?
Feel free to message me on twitter and we can talk more or on discord.

I tried to reach you already at twitter and couldn't (so we discussed in comments of your post).
Can you email me<EMAIL_ADDRESS>Linkedin is also an option (look at my Github profile).
Regarding your questions, yes I saw briefly your implementation, looks really cool, havn't played with that yet.
I tried to do remote function stomping, I played around a bit, and maybe I was close, but it was not successful. I have very short time to play around at the late evenings, so I haven't progressed a lot.


Show CMS content in sidebar of shuttles page

Summary of changes
Asana Ticket: Shuttles | Show BBT boilerplate in sidebar

Each of the three lines (red, orange, green) share 1-2 common paragraphs, and are output on each line's shuttle tab regardless of line.
Add a switch to load certain, other paragraphs based on line name
Fully render these paragraphs and show in sidebar if there is at least one

Testing: Connect your DRUPAL_ROOT env var to https://live-mbta.pantheonsite.io

@ianwestcott could our broken Codecov have to do with your changes too?

Looks like we're going to 1 column a little too soon.

2 column with 1 column gutter for XL, LG
2 column with 0 column gutter for MD
1 column layout on SM, and XS,

👍 looks good to me!

@phildarnowsky i'm seeing this error but I don't know how to resolve it. I'm not seeing whitespace in either the shuttles-loader nor the shuttlespage.
react-dom.development.js:506 Warning: render(): Target node has markup rendered by React, but there are unrelated nodes as well. This is most commonly caused by white-space inserted around server-rendered markup.

@amaisano you don't need to worry about that warning, we get that on quite a few pages and it's harmless.

@phildarnowsky thanks. I did adjust the scaffolding/layout a bit. I re-requested your review, as you may want to look at those most recent commits.


[window] Disable GPU for COUNT(exp) queries

Mitigates #218.
GpuWindowExec currently counts null-rows when running COUNT(col)
(or generally COUNT(expr)) window queries, owing to a bug in CUDF/Java.
Left unchecked, this will produce incorrect results for said queries.
This commit disables GPU acceleration for COUNT(expr) queries, while
retaining support for COUNT(1) and COUNT(*).
This may be reverted once we have a fix in CUDF/Java.
Please note that there is already an XFAIL test that checks this condition, in window_function_test.py
Signed-off-by: Mithun RK<EMAIL_ADDRESS>

Can we link the cudf bug issue to this PR? Approving as changes look good to me.

Linking the bug will close it when this PR is merged. I'd like to keep it open, and actually fix it in 0.3, after pushing a fix to cudf/java.

Oh, I didn't know that cudf issues would close automatically as well if a PR in this repo went in! Thought it was only the rapids plugin issues. I stand corrected.

Oh, I didn't know that cudf issues would close automatically as well if a PR in this repo went in! Thought it was only the rapids plugin issues. I stand corrected.

D'oh! Wait, maybe not. I'll raise the CUDF bug and leave a link here.

Can we link the cudf bug issue to this PR? Approving as changes look good to me.

rapidsai/cudf#6156 seems to be the cause. The libcudf implementation seems fine. The COUNT operation is lost in translation, in the Java layer. #218 has been updated to reflect this.

For it to be closed you have to say something like fixes hash-bug-number or closes hash-bug-number

build

For it to be closed you have to say something like fixes hash-bug-number or closes hash-bug-number

The text under "Linked issues" on the right pane seems to suggest that the issues listed there "may" be closed.
In any case, I can't seem to manually link CUDF issues or indeed anything outside of spark-rapids there. :shrug: Oh, well.

Thanks for the reviews, all.


Update data when rows or columns are changed

When the user sets rows or columns data has to be updated. Use the id to determine which row/column has been present before, preserve data from those. Previously missing ids are added as empty row/column (all 0). Previously existing, now missing ids are deleted. So this can also be used to reorder the data matrix. All actions have to be performed independent of sparse or dense data representation.

Related to #1


Atom 1.13 Shadow DOM removal

Starting from Atom version 1.13, the contents of atom-text-editor elements are no longer encapsulated within a shadow DOM boundary.
This means the :host and ::shadow pseudo-selectors should be completely removed and all syntax selectors should be prepended with syntax--.
To prevent breakage with existing style sheets, Atom will automatically upgrade selectors for versions less or equal to 1.12.
Tasks

[x] Remove all :host and ::shadow pseudo-selectors
[x] Prepend all syntax selectors with syntax--
[x] Update package.json minimal Atom engine version to >=1.13.0

🚢 Shipped in apm package release version 🏷 2.1.0!


How to test on a single image?

Hi,
I downloaded the regular hourglass (80 conv layers) pre-trained model and tried to test on a single image. However, the result turned out to be incorrect. I am not sure whether I have used API correctly, so could anyone take a look?
import os
from inference import Inference
from train_launcher import process_config
from hourglass_tiny import HourglassModel
from datagen import DataGenerator
import numpy as np
import tensorflow as tf
import cv2

RED = (0, 0, 255)

def show_prections(img, predictions):
	i = 0
	for coord in predictions:
		i += 1
		keypt = (int(coord[0]), int(coord[1]))
		text_loc = (keypt[0]+5, keypt[1]+7)
		cv2.circle(img, keypt, 3, RED, -1)
		cv2.putText(img, str(i), text_loc, cv2.FONT_HERSHEY_DUPLEX, 0.5, RED, 1, cv2.LINE_AA)
	cv2.imshow('img', img)
	cv2.waitKey(0)

if __name__ == '__main__':
	print('--Parsing Config File')
	params = process_config('config.cfg')

	img = cv2.imread(os.path.join(params['img_directory'], '005808361.jpg'))
        # the bounding boxes used below are ground truth from dataset.txt
	img1 = np.copy(img)[80:711, 798:1167]
	img2 = np.copy(img)[66:651, 310:721]

	test_img = cv2.resize(img2, (256, 256))

	model = Inference()
	predictions = model.predictJoints(test_img, mode='gpu')
	show_prections(test_img, predictions)

In what way are the results incorrect? Do you have an image or some results you can share?

Hi~
@wbenbihi
Could u please show me a simple example for testing a single image?
Like how to utilize your API? plt_skeleton() .etc.
: )

I think I understand why your results are incorrect. I tried your code, it works fine excepts it uses BGR images in the network whereas it was trained on RGB images.
You should add the following before running it :
test_img = cv2.cvtColor(test_img, cv2.COLOR_BGR2RGB)

Hi I have follow  @o0t1ng0o  steps. And down load the pretained model(refined and tiny). I have I convert the test_img and key point. But it seems still a wrong result. Can this model can detect all the joint?
`
import os
from inference import Inference
from train_launcher import process_config
from hourglass_tiny import HourglassModel
from datagen import DataGenerator
import numpy as np
import tensorflow as tf
import cv2
RED = (0, 0, 255)
if name == 'main':
print('--Parsing Config File')
params = process_config('config.cfg')
model = Inference()
img = cv2.imread(os.path.join('000003072.jpg'))
# the bounding boxes used below are ground truth from dataset.txt
img1 = np.copy(img)[132:538, 639:850]
img2 = np.copy(img)[82:565, 367:487]
test_img = cv2.resize(img2, (256, 256))
#test_img = cv2.cvtColor(test_img, cv2.COLOR_RGB2BGR)
def show_prections(img, predictions):
i = 0
for coord in predictions:
i += 1
keypt = (int(coord[1]), int(coord[0]))
print(keypt)
text_loc = (keypt[0]+5, keypt[1]+7)
cv2.circle(img, keypt, 3, RED, -1)
cv2.putText(img, str(i), text_loc, cv2.FONT_HERSHEY_DUPLEX, 0.5, RED, 1)
cv2.imshow('img', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
cam.release()
predictions = model.predictJoints(test_img, mode='gpu')
show_prections(test_img, predictions)
`
The joints output are (204, 184) (112, 152) (120, 204) (116, 204) (112, 136) (212, 184) (152, 192) (240, 40) (236, 40) (208, 40) (204, 184) (240, 44) (240, 40) (240, 40) (116, 100) (212, 188)

I am also trying to test the model on a single image. I tried but I was not able to do so. Can anyone help me regarding the same?

I am also trying to test the model on a single image. I tried but I was not able to do so. Can anyone help me regarding the same?
Your help will be highly appreciated.

I was able to test the model on a single image but the results I am getting are inaccurate. Can you please help me with this?
Your help will be highly appreciated.

@GajjarMihir
Hi, i have figure out that why the transform.rotate(img, r_angle, preserve_range = True) get errors. The reason is the version of skimage(sickit image). It seems only high version sickit image (v1.3) support this.
In the predictHM.(line 125 in inference.py)
Your original code is
return self.predict.pred(self, img / 255, debug = False, sess = None)
In my python. img/255 will cause data lost. So i cannot get the right prediction.
For example
1/255 = 0
so all the similar function i have changed to
return self.predict.pred((img.astype(np.float32) / 255), debug = False, sess = None)
and then get right result.
I have noticed that many people in isses6 meet this problem. Because the data lost.
You can change your data type in the predict file.
I think in some python version img/255 will cause data lost. so you have to change the ((img.astype(np.float32) / 255), for example. Hope to help u

Thank you @Crazod. I changed the things you mentioned but the results were still the same. The results are inaccurate. Thank you for helping me. Are you getting accurate results?

@Crazod
Running you code I get the following error:
[jalal@goku hourglasstensorlfow]$  /scratch/sjn/anaconda/bin/python  test.py
/scratch/sjn/anaconda/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
CREATE MODEL:
---Inputs : Done (0 sec.)
---Graph : Done (2 sec.)
---Loss : Done (0 sec.)
WARNING:tensorflow:From /scratch2/body_pose/hourglasstensorlfow/hourglass_tiny.py:625: arg_max (from tensorflow.python.ops.gen_math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `argmax` instead
---Acc : Done (1 sec.)
---LR : Done (0 sec.)
---Optim : Done (0 sec.)
---Minimizer : Done (4 sec.)
---Init : Done (0 sec.)
Model created (8 sec.)
Graph Generated in  8  sec.
Session initialization
2018-03-25 04:33:44.382615: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
Sess initialized in 0 sec.
Loading Trained Model
Traceback (most recent call last):
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1350, in _do_call
    return fn(*args)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1320, in _run_fn
    self._extend_graph()
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1381, in _extend_graph
    self._session, graph_def.SerializeToString(), status)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py", line 473, in __exit__
    c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: Cannot assign a device for operation 'model/stacks/stage_3/out/weights/RMSProp_1': Operation was explicitly assigned to /device:GPU:0 but available devices are [ /job:localhost/replica:0/task:0/device:CPU:0 ]. Make sure the device specification refers to a valid device.
	 [[Node: model/stacks/stage_3/out/weights/RMSProp_1 = VariableV2[_class=["loc:@model/stacks/stage_3/out/weights"], container="", dtype=DT_FLOAT, shape=[1,1,256,16], shared_name="", _device="/device:GPU:0"]()]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "test.py", line 14, in <module>
    model = Inference()
  File "/scratch2/body_pose/hourglasstensorlfow/inference.py", line 74, in __init__
    self.predict.load_model(load = model)
  File "/scratch2/body_pose/hourglasstensorlfow/predictClass.py", line 163, in load_model
    self.HG.restore(load)
  File "/scratch2/body_pose/hourglasstensorlfow/hourglass_tiny.py", line 215, in restore
    self.saver.restore(self.Session, load)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 1686, in restore
    {self.saver_def.filename_tensor_name: save_path})
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 895, in run
    run_metadata_ptr)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1128, in _run
    feed_dict_tensor, options, run_metadata)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1344, in _do_run
    options, run_metadata)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1363, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Cannot assign a device for operation 'model/stacks/stage_3/out/weights/RMSProp_1': Operation was explicitly assigned to /device:GPU:0 but available devices are [ /job:localhost/replica:0/task:0/device:CPU:0 ]. Make sure the device specification refers to a valid device.
	 [[Node: model/stacks/stage_3/out/weights/RMSProp_1 = VariableV2[_class=["loc:@model/stacks/stage_3/out/weights"], container="", dtype=DT_FLOAT, shape=[1,1,256,16], shared_name="", _device="/device:GPU:0"]()]]

Caused by op 'model/stacks/stage_3/out/weights/RMSProp_1', defined at:
  File "test.py", line 14, in <module>
    model = Inference()
  File "/scratch2/body_pose/hourglasstensorlfow/inference.py", line 73, in __init__
    self.predict.model_init()
  File "/scratch2/body_pose/hourglasstensorlfow/predictClass.py", line 154, in model_init
    self.HG.generate_model()
  File "/scratch2/body_pose/hourglasstensorlfow/hourglass_tiny.py", line 182, in generate_model
    self.train_rmsprop = self.rmsprop.minimize(self.loss, self.train_step)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/training/optimizer.py", line 365, in minimize
    name=name)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/training/optimizer.py", line 516, in apply_gradients
    self._create_slots([_get_variable_for(v) for v in var_list])
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/training/rmsprop.py", line 113, in _create_slots
    self._zeros_slot(v, "momentum", self._name)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/training/optimizer.py", line 883, in _zeros_slot
    named_slots[_var_key(var)] = slot_creator.create_zeros_slot(var, op_name)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/training/slot_creator.py", line 174, in create_zeros_slot
    colocate_with_primary=colocate_with_primary)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/training/slot_creator.py", line 148, in create_slot_with_initializer
    dtype)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/training/slot_creator.py", line 67, in _create_slot_var
    validate_shape=validate_shape)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/ops/variable_scope.py", line 1262, in get_variable
    constraint=constraint)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/ops/variable_scope.py", line 1097, in get_variable
    constraint=constraint)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/ops/variable_scope.py", line 435, in get_variable
    constraint=constraint)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/ops/variable_scope.py", line 404, in _true_getter
    use_resource=use_resource, constraint=constraint)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/ops/variable_scope.py", line 806, in _get_single_variable
    constraint=constraint)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/ops/variables.py", line 229, in __init__
    constraint=constraint)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/ops/variables.py", line 329, in _init_from_args
    name=name)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/ops/state_ops.py", line 133, in variable_op_v2
    shared_name=shared_name)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/ops/gen_state_ops.py", line 1061, in _variable_v2
    shared_name=shared_name, name=name)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
    op_def=op_def)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 3160, in create_op
    op_def=op_def)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1625, in __init__
    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access

InvalidArgumentError (see above for traceback): Cannot assign a device for operation 'model/stacks/stage_3/out/weights/RMSProp_1': Operation was explicitly assigned to /device:GPU:0 but available devices are [ /job:localhost/replica:0/task:0/device:CPU:0 ]. Make sure the device specification refers to a valid device.
	 [[Node: model/stacks/stage_3/out/weights/RMSProp_1 = VariableV2[_class=["loc:@model/stacks/stage_3/out/weights"], container="", dtype=DT_FLOAT, shape=[1,1,256,16], shared_name="", _device="/device:GPU:0"]()]]

[jalal@goku hourglasstensorlfow]$

Code:
import os
from inference import Inference
from train_launcher import process_config
from hourglass_tiny import HourglassModel
from datagen import DataGenerator
import numpy as np
import tensorflow as tf
import cv2

RED = (0, 0, 255)

params = process_config('config.cfg')
model = Inference()

img = cv2.imread(os.path.join('000003072.jpg'))
# the bounding boxes used below are ground truth from dataset.txt
img1 = np.copy(img)[132:538, 639:850]
img2 = np.copy(img)[82:565, 367:487]
test_img = cv2.resize(img2, (256, 256))
#test_img = cv2.cvtColor(test_img, cv2.COLOR_RGB2BGR)

def show_prections(img, predictions):
	i = 0
	for coord in predictions:
		i += 1
		keypt = (int(coord[1]), int(coord[0]))
		print(keypt)
		text_loc = (keypt[0]+5, keypt[1]+7)
		cv2.circle(img, keypt, 3, RED, -1)
	cv2.putText(img, str(i), text_loc, cv2.FONT_HERSHEY_DUPLEX, 0.5, RED, 1)
	cv2.imshow('img', img)
	cv2.waitKey(0)
	cv2.destroyAllWindows()
	cam.release()

predictions = model.predictJoints(test_img, mode='gpu')
show_prections(test_img, predictions)

How should I fix it?

@yolandawww running your code, I get this error
$ python  another_test.py
/scratch/sjn/anaconda/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
--Parsing Config File
Traceback (most recent call last):
  File "another_test.py", line 29, in <module>
    img1 = np.copy(img)[80:711, 798:1167]
IndexError: too many indices for array

Do you know how can I fix it?

@yolandawww  running your code I get this error. Do you know how can I fix it?
[jalal@goku hourglasstensorlfow]$  /scratch/sjn/anaconda/bin/python  another_test.py
/scratch/sjn/anaconda/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
--Parsing Config File
CREATE MODEL:
---Inputs : Done (0 sec.)
---Graph : Done (2 sec.)
---Loss : Done (0 sec.)
WARNING:tensorflow:From /scratch2/body_pose/hourglasstensorlfow/hourglass_tiny.py:625: arg_max (from tensorflow.python.ops.gen_math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `argmax` instead
---Acc : Done (1 sec.)
---LR : Done (0 sec.)
---Optim : Done (0 sec.)
---Minimizer : Done (4 sec.)
---Init : Done (0 sec.)
Model created (8 sec.)
Graph Generated in  8  sec.
Session initialization
2018-03-25 04:43:58.167594: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
Sess initialized in 0 sec.
Loading Trained Model
Traceback (most recent call last):
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1350, in _do_call
    return fn(*args)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1320, in _run_fn
    self._extend_graph()
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1381, in _extend_graph
    self._session, graph_def.SerializeToString(), status)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py", line 473, in __exit__
    c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: Cannot assign a device for operation 'model/stacks/stage_3/out/weights/RMSProp_1': Operation was explicitly assigned to /device:GPU:0 but available devices are [ /job:localhost/replica:0/task:0/device:CPU:0 ]. Make sure the device specification refers to a valid device.
	 [[Node: model/stacks/stage_3/out/weights/RMSProp_1 = VariableV2[_class=["loc:@model/stacks/stage_3/out/weights"], container="", dtype=DT_FLOAT, shape=[1,1,256,16], shared_name="", _device="/device:GPU:0"]()]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "another_test.py", line 33, in <module>
    model = Inference()
  File "/scratch2/body_pose/hourglasstensorlfow/inference.py", line 74, in __init__
    self.predict.load_model(load = model)
  File "/scratch2/body_pose/hourglasstensorlfow/predictClass.py", line 163, in load_model
    self.HG.restore(load)
  File "/scratch2/body_pose/hourglasstensorlfow/hourglass_tiny.py", line 215, in restore
    self.saver.restore(self.Session, load)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 1686, in restore
    {self.saver_def.filename_tensor_name: save_path})
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 895, in run
    run_metadata_ptr)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1128, in _run
    feed_dict_tensor, options, run_metadata)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1344, in _do_run
    options, run_metadata)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1363, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Cannot assign a device for operation 'model/stacks/stage_3/out/weights/RMSProp_1': Operation was explicitly assigned to /device:GPU:0 but available devices are [ /job:localhost/replica:0/task:0/device:CPU:0 ]. Make sure the device specification refers to a valid device.
	 [[Node: model/stacks/stage_3/out/weights/RMSProp_1 = VariableV2[_class=["loc:@model/stacks/stage_3/out/weights"], container="", dtype=DT_FLOAT, shape=[1,1,256,16], shared_name="", _device="/device:GPU:0"]()]]

Caused by op 'model/stacks/stage_3/out/weights/RMSProp_1', defined at:
  File "another_test.py", line 33, in <module>
    model = Inference()
  File "/scratch2/body_pose/hourglasstensorlfow/inference.py", line 73, in __init__
    self.predict.model_init()
  File "/scratch2/body_pose/hourglasstensorlfow/predictClass.py", line 154, in model_init
    self.HG.generate_model()
  File "/scratch2/body_pose/hourglasstensorlfow/hourglass_tiny.py", line 182, in generate_model
    self.train_rmsprop = self.rmsprop.minimize(self.loss, self.train_step)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/training/optimizer.py", line 365, in minimize
    name=name)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/training/optimizer.py", line 516, in apply_gradients
    self._create_slots([_get_variable_for(v) for v in var_list])
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/training/rmsprop.py", line 113, in _create_slots
    self._zeros_slot(v, "momentum", self._name)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/training/optimizer.py", line 883, in _zeros_slot
    named_slots[_var_key(var)] = slot_creator.create_zeros_slot(var, op_name)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/training/slot_creator.py", line 174, in create_zeros_slot
    colocate_with_primary=colocate_with_primary)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/training/slot_creator.py", line 148, in create_slot_with_initializer
    dtype)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/training/slot_creator.py", line 67, in _create_slot_var
    validate_shape=validate_shape)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/ops/variable_scope.py", line 1262, in get_variable
    constraint=constraint)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/ops/variable_scope.py", line 1097, in get_variable
    constraint=constraint)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/ops/variable_scope.py", line 435, in get_variable
    constraint=constraint)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/ops/variable_scope.py", line 404, in _true_getter
    use_resource=use_resource, constraint=constraint)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/ops/variable_scope.py", line 806, in _get_single_variable
    constraint=constraint)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/ops/variables.py", line 229, in __init__
    constraint=constraint)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/ops/variables.py", line 329, in _init_from_args
    name=name)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/ops/state_ops.py", line 133, in variable_op_v2
    shared_name=shared_name)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/ops/gen_state_ops.py", line 1061, in _variable_v2
    shared_name=shared_name, name=name)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
    op_def=op_def)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 3160, in create_op
    op_def=op_def)
  File "/scratch/sjn/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1625, in __init__
    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access

InvalidArgumentError (see above for traceback): Cannot assign a device for operation 'model/stacks/stage_3/out/weights/RMSProp_1': Operation was explicitly assigned to /device:GPU:0 but available devices are [ /job:localhost/replica:0/task:0/device:CPU:0 ]. Make sure the device specification refers to a valid device.
	 [[Node: model/stacks/stage_3/out/weights/RMSProp_1 = VariableV2[_class=["loc:@model/stacks/stage_3/out/weights"], container="", dtype=DT_FLOAT, shape=[1,1,256,16], shared_name="", _device="/device:GPU:0"]()]]

code is:
import os
from inference import Inference
from train_launcher import process_config
from hourglass_tiny import HourglassModel
from datagen import DataGenerator
import numpy as np
import tensorflow as tf
import cv2

RED = (0, 0, 255)

def show_prections(img, predictions):
        i = 0
        for coord in predictions:
                i += 1
                keypt = (int(coord[0]), int(coord[1]))
                text_loc = (keypt[0]+5, keypt[1]+7)
                cv2.circle(img, keypt, 3, RED, -1)
                cv2.putText(img, str(i), text_loc, cv2.FONT_HERSHEY_DUPLEX, 0.5, RED, 1, cv2.LINE_AA)
        cv2.imshow('img', img)
        cv2.waitKey(0)

print('--Parsing Config File')
params = process_config('config.cfg')

img = cv2.imread('005808361.jpg')
# the bounding boxes used below are ground truth from dataset.txt
img1 = np.copy(img)[80:711, 798:1167]
img2 = np.copy(img)[66:651, 310:721]

test_img = cv2.resize(img2, (256, 256))

model = Inference()
predictions = model.predictJoints(test_img, mode='gpu')
show_prections(test_img, predictions)

you can just change the predictClass.py hpeWebcam function,you give an image param ,you don't need to
process the image.then you can test on a single image.

I get this error. Do you know how can I fix it?
NotFoundError (see above for traceback): Key BatchNorm_100/moving_mean not found in checkpoint

same issue , did you fin any solution @Mashiandmaro ?


Queue::push Unable to find Class

Laravel Version: 5.1.46 (LTS)
PHP Version: PHP 7.0.15-0ubuntu<IP_ADDRESS> (cli) ( NTS )

Description:
Using Queue::push is causing:
$data = [
    'server' => $server,
    'week' => '201427'
];

Queue::push('\Cleanse\MyPluginName\Classes\Jobs\MyQueueClass', $data);

exception 'ReflectionException'
with message 'Class \Cleanse\MyPluginName\Classes\Jobs\MyQueueClass does not exist'
in /october/vendor/laravel/framework/src/Illuminate/Container/Container.php:741

It works fine on my local Mac server I develop on, but after uploading to my live server, I am getting the error above.
I have also tried using a closure, but I get:
Queue::push(function($job) use ($data) {
    $crawl = new RankingsUpdate();
    $crawl->updateWeek($data);

    $job->delete();
});

[2017-05-30 02:41:33] production.ERROR:
Symfony\Component\Debug\Exception\FatalThrowableError:
Class 'Cleanse\MyPluginName\Classes\CategoryDir\CategoryClass' not found in
/var/www/html/vendor/jeremeamia/SuperClosure/src/SerializableClosure.php(210) : eval()'d code:8

Could it be a PHP version issue?

Sorry but better ask such questions on the forums in order to get more responses, here we only discuss framework core bugs :)


hubble-relay: Return underlying connection errors when connecting to peer manager

When connecting to the hubble peer manager ensure we return the underlying connection error so it's easier to diagnose connection related problems.
Currently the only error returned is the context timeout:
time="2024-09-18T21:13:13Z" level=warning msg="Failed to create peer client for peers synchronization; will try again after the timeout has expired" error="context deadline exceeded" subsys=hubble-relay target="hubble-peer.kube-system.svc.cluster.local.:443"

This will ensure the underlying connection level error is returned when the context is cancelled.
In the future we should switch to not using grpc.WithBlock at all which avoids many of these problems, but that requires more testing. In the short-term, lets set these dial options to improve things now, in-case the switch to non-blocking dials is delayed.
hubble-relay: Return underlying connection errors when connecting to peer manager

I'm marking for backport to v1.15 and 1.16 because the change is small and will be a huge improvement in errors that users see returned in Hubble Relay logs when they have an issue with Hubble Relay connecting to cilium agent.

/test


Add two files in Fundamentals

📥 Pull Request
Description
Add Loops and Conditional Statements to the Fundamental Section
Fixes #1537
Type of change

[ ] Bug fix (non-breaking change which fixes an issue)
[ ] New feature (non-breaking change which adds functionality)
[ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
[x] Documentation update

Checklist:

[x] My code follows the style guidelines of this project
[x] I have performed a self-review of my code
[x] I have commented my code, particularly in hard-to-understand areas
[x] I have added tests that prove my fix is effective or that my feature works
[x] New and existing unit tests pass locally with my changes
[x] Any dependent changes have been merged and published in downstream modules

@ajay-dhangar Done


terraform: migrate to Homebrew

Deleting terraform from cask as part of deduplication process . See context here.
Please note that this PR is created to continue case-by-case discussions from thread linked above and if decision is made not to remove the cask, PR will be closed.
cc @MikeMcQuaid

Good to see you here, @phinze! The convention so far has been to keep open-source CLI tools in Homebrew, but if HashiCorp would prefer to keep the Cask around instead I'm fine with it.
@MikeMcQuaid, any objections?

I think if there is a way to make a Homebrew formula behave like the original cask - i.e. just download the binary and place it to the default path that would be also 👌  (correct me @phinze if I'm wrong).
While I understand why recompilation from scratch may in many cases be useful, the formula upgrade is now blocked purely because a single test is failing while the official binary is available and works just fine.

@jawshooah I vaguely object but not strongly.

While I understand why recompilation from scratch may in many cases be useful, the formula upgrade is now blocked purely because a single test is failing while the official binary is available and works just fine.

Does it definitely work fine on 10.9? If the installation process is failing there I think that's a valid reason for it to be blocked. It's unclear to me whether that's an indication of a legitimate runtime bug on 10.9.

Hi @MikeMcQuaid,
I did a local bottle build under 10.9 using https://github.com/Homebrew/homebrew-core/pull/4237, and terraform builds just fine and creates the bottle. It looks like https://github.com/hashicorp/terraform/pull/8481 was the needed fix, and is patched in to https://github.com/Homebrew/homebrew-core/pull/4237.
That being said, all that might be moot if terraform was moved into homebrew-cask and the pre-compiled and signed binaries are installed into place (instead of compiling locally or installing via bottle).

@cblecker It's not unreasonable for open-source software to be compiled by packagers rather than the software creators. As mentioned, I'm completely OK with upstream maintaining their own tap, too.

@MikeMcQuaid I wasn't suggesting that the current process was unreasonable.. I was only referring to @phinze's above comment that suggested maintaining the cask version over the main homebrew version.

FYI: I have just created https://github.com/unofficial-cask/hashicorp-tap in case anyone's interested.

It's not unreasonable for open-source software to be compiled by packagers rather than the software creators.

Totally agreed! The only reason I suggested the binary-download strategy supersede the compile-from-source strategy is because HashiCorp doesn't have the bandwidth to provide consistent support to the community members maintaining the build in the Formula. If there is sufficient community interest to successfully maintain the Formula across Terraform releases, then that's perfectly okay. 👌


Test new issue from PR

Short description:
More details:
What this PR does / why we need it:
Which issue(s) this PR fixes:
Special notes for reviewer:
Bug:

!verified


Self Bots against Discord TOS :/

Just a tip, Discord Self Bots are completely forbidden by the Discord TOS :/

It's not a valid issue.
I've already mentioned it in the Readme. Please read the readme completely before opening up an issue.
As per the disclaimer, the users are solely responsible for the actions. This repo itself isn't a violation of Discord ToS.
Discord usually flags selfbot accounts that put stress on their systems through spamming, etc.
The code makes sure that such a limit is not hit. I've rarely had any users report about bans unless they didn't follow the instructions or made their own modifications.


Update Country Files functions modify QSO data of other users

From reading the code while coding https://github.com/wavelog/wavelog/pull/767 it seems that not only the distance function but the others ass well also modify QSO data of other users. This is potentially not intended. Needs some investigation and probably a fix.

Bug/problem is confirmed. A QSO logged by another user is updated on calling these functions. Needs fixes.

I'm not sure if this is a bug. In fact it's an admin function and the description and usage intends to affect all QSO within Wavelog. tbh: I don't see an issue here but I'm may wrong.
It's not that bad to have some "sanitize" functions which clean the database from missing or wrong data.

Basic question is: Do we want to modify QSO data for any user here?

Basic question is: Do we want to modify QSO data for any user here?

if admin: Yes
if available for user: only Users QSOs


feat: implement dialById

Need this in preparation for dht

see https://github.com/libp2p/js-libp2p/pull/64 instead

Done in master now :)


Add new generic elastic cache entities

Relevant information
Create 2 new entities that englobes the elasticache (redis | memcached) cluster and node.
This is needed because in some scenarios (Metric Streams) there is no way to distinguish between redis or memcached, then we need a way to somehow assign an entity to those metrics
Checklist

[x] I've read the guidelines and understand the acceptance criteria.
[x] The value of the attribute marked as identifier will be unique and valid.
[x] I've confirmed that my entity type wasn't already defined. If it is I'm providing an
explanation above.

and this discussion: https://github.com/newrelic/entity-definitions/pull/765#issuecomment-1253341796


[HUDI-5861] fix table can not read data after overwrite table with bu…

a table with bucket index, if insert overwrite the same partition twice, can not get any data will query sql
reason: insert overwrite in bucket index will tagLocation, the new file version will use the same fgId, so the lastest file slice will be filtered by replacecommit file with the same fileGroupId.
Change Logs
if op is insert_overwrite, ignore tagLocation.
Impact
none
Risk level (write none, low medium or high below)
none
Documentation Update
none
Contributor's checklist

[ ] Read through contributor's guide
[ ] Change Logs and Impact were stated clearly
[ ] Adequate tests were added if applicable
[ ] CI passed

#8072 look like meet the same question, close it

#8072 look like meet the same question, close it

I prefer this solution

#8072 look like meet the same question, close it

I prefer the solution that remove tagLocation


Backport Node library JS changes to the release branch

I'm doing this so that I can publish an update to the Node library that is compatible with the released C core.

ping


Get images and tags, etc in batch requests instead of spamming dockerhub/quay/etc.

Closing as duplicate of #208.


snippets test doesn't work

@rultor release, tag is 0.0.7

@rultor release, tag is 0.0.7

@yegor256 OK, I will release it now. Please check the progress here

@rultor release, tag is 0.0.7

@yegor256 Done! FYI, the full log is here (took me 4min)


Why model averaging does not work for multi-gpu parallism?

I'm trying multi-GPU parallism by averaging models returned from each GPU after each training batch, same as @zhangtemplar @https://github.com/fchollet/keras/issues/106.
Say I have 4 GPU named as ['gpu0', 'gpu1', 'gpu2', 'gpu3'], at the beginning, each model is initialized by set_weights to be the same with model at 'gpu0'. Then during the training stage, each model is fed with the exactly same train data. After a train batch, these 4 models are averaged by get_weights and set_weights.
In theory, these 4 models should be equal since they're initialized and trained by the same data. However, after mode averaging, the train loss keeps going larger, and finally NaN.
So, my question is, what's wrong about above model averaging mechanism? I'm using Keras 1.0.4 and Theano 0.9.0.
Please make sure that the boxes below are checked before you submit your issue. Thank you!

[ ] Check that you are up-to-date with the master branch of Keras. You can update with:
pip install git+git://github.com/fchollet/keras.git --upgrade --no-deps

[ ] If running on Theano, check that you are up-to-date with the master branch of Theano. You can update with:
pip install git+git://github.com/Theano/Theano.git --upgrade --no-deps

[ ] Provide a link to a GitHub Gist of a Python script that can reproduce your issue (or just copy the script here if it is short).

I think you will have more luck after reading and implemening this paper:
http://arxiv.org/abs/1412.6651

Well, not all processes are deterministic. Dropout and other noise layers are stochastic, as well as by default, your training data gets shuffled for training.
Unless you'll have the same seed, even with the same data you can get different results.

@daweileng Hi David, did you finally achieve data parallelism with keras+tensorflow? If yes, could you please share more details? Thank you.


Small docs issue under Batch Policy

The first paragraph here starts with the following:

When an input component has a config field batching that means it supports a batch policy.

However, the example below shows an output instead of an input.

Thanks, keen eyes @mihaitodor. I'm not actually sure why it was ever specified as an input thing.

Clarified some other stuff while I was at it: https://github.com/Jeffail/benthos/commit/231bdfe7310f1e285bd204425793493d81f82f12

That clarifies things, thanks @Jeffail! ❤


🚀 Feature: Support the '--dirtyreload' option in 'techdocs-cli serve'

🔖 Feature description
Enhance the functionality of 'techdocs-cli serve' to accommodate the 'mkdocs --dirtyreload' option. See https://github.com/mkdocs/mkdocs/pull/997.
🎤 Context
Currently, the process of building ~20 PlantUML diagrams with plantuml-markdown takes approximately 35 seconds on my M1 Max. With the integration of the '--dirtyreload' option, the update time for modified diagrams reduces significantly to around 3 seconds.
✌ Possible Implementation
--- a/dist/cjs/mkdocsServer-107b8efd.cjs.js
+++ b/dist/cjs/mkdocsServer-107b8efd.cjs.js
@@ -76,6 +76,7 @@ const runMkdocsServer = async (options) => {
         ...options.dockerOptions || [],
         dockerImage,
         "serve",
+        "--dirtyreload",
         "--dev-addr",
         `<IP_ADDRESS>:${port}`,
         ...options.mkdocsConfigFileName ? ["--config-file", options.mkdocsConfigFileName] : []

👀 Have you spent some time to check if this feature request has been raised before?

[X] I checked and didn't find similar issue

🏢 Have you read the Code of Conduct?

[X] I have read the Code of Conduct

Are you willing to submit PR?
Yes I am willing to submit a PR!

Hi @wtchnm, would this PR provide what you asking for? https://github.com/backstage/backstage/pull/21403

Hey @awanlin, yes it does!


docs(workshop): final fixes

Fixes #
Description
Doing a final round of testing, and fixes for the Azure track of the KubeCon workshop
How was this change tested?
This was created based on testing the workshop
*
Does this change impact docs?

[x] Yes, PR includes docs updates
[ ] Yes, issue opened: #
[ ] No

Release Note

NONE

Pull Request Test Coverage Report for Build<PHONE_NUMBER>6
Details

0 of 0   changed or added relevant lines in 0 files are covered.
No unchanged relevant lines lost coverage.
Overall coverage remained the same at 94.229%

Totals

Change from base Build<PHONE_NUMBER>8:
0.0%

Covered Lines:
37147

Relevant Lines:
39422

💛  - Coveralls


fix: set SMCP autoinject to false

Simple change, most time was spent testing. Tested by ensure basic dashboard functionality still works, and that created notebooks are still injected with istio-proxy. Please let me know if you can think of other scenarios to test.

@cam-garrison There are a few other things that come to mind:

What about datascience projects created in the dedicated namespace?
How does it work now on the odh-project-controller side? Do we need to change something there?

Thanks for the suggestions:

What do you mean dsp created in dedicated namespace? Do you mean notebooks spawned in data science project namespaces? I have tested this and this works.
Will TAL at this change and see if any changes are necessary.

Looked thru all pieces of project controller as of right now and see no changes needed - no new namespace annotations or labels are needed since the change this introduces is just new pods that require injection now require the istio.io/inject label/anno.


Add a Bias parameter to a Matmul op

Matmul's operation should support bias as an optional argument.

Duplicate of:
#925


Ability to set Working Directory of Child Process

It would be nice if it was possible to set the working directory of the child process like is possible with the --directory parameter of flatpak-spawn.

What is the use case?

Spawning git in a specific repository on the host system. I’m working on flatpak support for Zed

Merged the new flag "-cwd". Will be part of the v1.6.0 release