merging two notebooks and displaying multiple variables

Notebook description
Hi Mattia,
is it possible to merge the two notebooks for timeseries (ocean_color_timeseries) and maps (ocean_color_maps) into one?
Otherwise, we should display two variables together in each notebook.
Thanks,
Chiara
Notebook link or upload
No response
Anything else we need to know?
No response
Environment

These are just templates. As those diagnostics are completely independent from each other and you have a lot of figures, it's easier to maintain them separately (we are doing something similar form satellite_esacci_gmpe_*.ipynb)
But for your deliverable/evaluation, you can just copy and paste everything in a single notebook. Does it make sense?

I figured.
Let me think about it.
Meantime, you the can close this ticket. Thanks

Hi Mattia,
I need to modify the notebook to plot all variables included in the collection at the same time. The style should the same as for the chlor_a variable: three plots for each variable specifying the length of satellite missions used.
Could you please help?
Thanks
ocean_color_timeseries-Copy1.ipynb.zip

Basically, you want to replace the parameter variable = "chlor_a" with a list of variables e.g. variables = ("chlor_a", "Rrs_443", ...)? Did I get it right?

Hi, I've updated the template.
Now you can choose multiple variables in the same notebook.
Here is the template: https://github.com/bopen/c3s-eqc-toolbox-template/blob/main/notebooks/wp5/ocean_color_timeseries.ipynb
Here is the notebook executed: https://gist.github.com/malmans2/37373f0909e39e7e4a3c074bf6bac8c2
Unless you did it before, only ["chlor_a", "Rrs_443", "Rrs_560"] are currently cached.
Let me know if the new template does what you are looking for and if you need me to cache more variables.

Thanks.
Could you please remove the region: "15-30N 40-55W": {"lon_slice": slice(-55, -40), "lat_slice": slice(30, 15)}
and plots showing monthly and running means.
I need only daily data and running means on the same plot.

Yes, daily data and running means on the same plot

Just to clarify, with variables I meant: "chlor_a", "Rrs_443", "Rrs_560".
Do you need all of them in the sample plot? (looks like RRSs have quite different values, they're going to be quite flat)
If you just want to keep the plots as they are but remove monthly and "15-30N 40-55W", you can easily customize the template to suit your needs.
All you have to do is this:
regions = {
    "Global": {"lon_slice": slice(-180, 180), "lat_slice": slice(90, -90)},
    "50S-50N": {"lon_slice": slice(-180, 180), "lat_slice": slice(50, -50)},
-    "15-30N 40-55W": {"lon_slice": slice(-55, -40), "lat_slice": slice(30, 15)},
    "NASTG": {"lon_slice": slice(-80, 0), "lat_slice": slice(50, 0)},
}

for variable in variables:
    for ds in [ds_global, ds_regional]:
-        for plot_func in plot_daily, plot_monthly:
-            plot_func(ds[variable])
-            plt.show()
+        plot_daily(ds[variable])
+        plt.show()

Ok. Actually I needed to plot daily data and the 48-month running mean on the same plot for each variable. I've solved increasing the window for the daily running mean.
Now, I'd like to add a regression line, calculated from annual averages and performed using the mk.original_test function.
I tried to implement it in the code, but I have this error message: plot_pk() missing 1 required positional argument: 'labels'
Could you please help?
Thanks
ocean_color_timeseries.ipynb.zip

Done. You should find it easier to customize it now.
As your timeseries do not start from january, I've used yearly resample rather than annual means.
Let me know if that's OK.

Here is the template: https://github.com/bopen/c3s-eqc-toolbox-template/blob/main/notebooks/wp5/ocean_color_timeseries.ipynb
Here is the notebook executed: https://gist.github.com/malmans2/37373f0909e39e7e4a3c074bf6bac8c2

As my dataset analysis should span the January 1998 - December 2022 period, I guess it's better to use the annual_weighted_average, right?
Moreover, I have a couple of points for each variable that are outliers. How can I access the x and y plot limits for each variable? I'd like to keep these data cached while removing them from the plot.

My bad, I double-checked and annual or yearly resample do the same. Let's use yearly resample that makes it easier to plot data (time coord is already in good shape).
I added the ylim argument. I'm setting it using percentiles, but you can tune it or parametrize differently.
Same URLs as before.
Let me know.

Ok.
I'd also like to represent in each plot the period over which all satellite missions operated. I've implemented the code and it's working, but I have now a couple of issues:

missions overlap sometime. As a consequence, I have some overlapping arrows and it's confusing. I tried to implement the position of each arrow for each mission using percentiles, but it's not working.
No idea why, but satellite missions do not show up on the 50N-50S plots.
As all missions should be shown on the plots, but the size of the regional timeseries is too small. Thus, I'd like to plot them big as done for the global ocean.
Could you help? Thanks

Here the notebook I'm working on:
ocean_color_timeseries-working.ipynb.zip

OK, 3 actually simplifies everything. We implemented if because you originally wanted to plot global separate, and the others together. It also fixes 2, you need to apply your custom function to each subplot, you are applying to the last one only.
Finally, for 1. I can fix it, but I'm wondering whether it would be more clear to use different background shades for each mission.

Yes, we can give it a try. I will have a total of 6 satellite missions to show

Could you please send me the mission dictionary of all missions?

All set. Same urls.

Hi Mattia, sorry or my late reply. It looks very good.
How can I print the p_value for each linear regression?

Also, the global ocean timeseries should be shown first for each variable. How can I switch the 50S-50N and the global ocean plots?

All done (p is now printed in the legend).
Same urls.
I'm going to close this if that's OK with you.

Ok, thanks. I guess we are ready to cache all other variables, right?

Sure. All of them? Same period?

Yes, we have to analyze and show all ocean color variables

OK, I'll let you know when it's ready. In the meantime, if you send me all satellite missions I'll add those to the template.

Oh, I added them by my self. I share the notebook with you anyway. You are maybe able to make it better. Plotting all the shaded areas was kind of confusing, but I modified some colors and it seems acceptable now.
ocean_color_timeseries.ipynb.zip

Hi Mattia,
Did you finish to cache all the ocean color variables?

Not yet, hopefully tonight.

All set. Looks like there's something odd with the data or you need to tune the plots for 510 and 665.

Here is the template: https://github.com/bopen/c3s-eqc-toolbox-template/blob/main/notebooks/wp5/ocean_color_timeseries.ipynb
Here is the notebook executed: https://gist.github.com/malmans2/37373f0909e39e7e4a3c074bf6bac8c2

Let me know.

I think the vm didn't work well for 665 as all values are 0 but that's impossible.
Could you try to cache them again?
Timeseries for 510 are also weird. I will check them in the next days.
Cheers

I tried a couple of months, and I get a the same results. I think there's an issue with the methodology applied.
I don't know much about this dataset, but maybe the problem is that you need to mask out zeros?
510 and 665 are the only wl with 0s.
from c3s_eqc_automatic_quality_control import download

# Time period
start = "1997-09"
stop = "1997-09"

collection_id = "satellite-ocean-colour"

request = {
    "projection": "regular_latitude_longitude_grid",
    "version": "6_0",
    "format": "zip",
    "variable": "remote_sensing_reflectance",
}

requests = download.update_request_date(
    request,
    start=start,
    stop=stop,
    stringify_dates=True,
)
ds = download.download_and_transform(
    collection_id, requests, chunks={"year": 1, "month": 1, "variable": 1}
)
ds = ds[[var for var in ds.data_vars if var.startswith("Rrs")]]

(ds).min().compute().values

/data/common/miniforge3/envs/wp5/lib/python3.10/site-packages/dask/array/reductions.py:620: RuntimeWarning: All-NaN slice encountered
  return np.nanmin(x_chunk, axis=axis, keepdims=keepdims)

<bound method Mapping.values of <xarray.Dataset>
Dimensions:  ()
Data variables:
    Rrs_412  float32 2.955e-10
    Rrs_443  float32 2.794e-06
    Rrs_490  float32 1.694e-05
    Rrs_510  float32 0.0
    Rrs_560  float32 0.0002508
    Rrs_665  float32 0.0>

(ds).max().compute().values

<bound method Mapping.values of <xarray.Dataset>
Dimensions:  ()
Data variables:
    Rrs_412  float32 0.1108
    Rrs_443  float32 0.1413
    Rrs_490  float32 0.2301
    Rrs_510  float32 0.2026
    Rrs_560  float32 0.2345
    Rrs_665  float32 0.09922>

I don't know these variables very much neither…I need to discuss about that with my colleagues. Let’s put a hold on that for now.
Thanks
On Oct 30, 2023, at 12:00, Mattia Almansi<EMAIL_ADDRESS>wrote:
I tried a couple of months, and I get a the same results. I think there's an issue with the methodology applied.
I don't know much about this dataset, but maybe the problem is that you need to mask out zeros?
510 and 665 are the only wl with 0s.
from c3s_eqc_automatic_quality_control import download
Time period
start = "1997-09"
stop = "1997-09"
collection_id = "satellite-ocean-colour"
request = {
"projection": "regular_latitude_longitude_grid",
"version": "6_0",
"format": "zip",
"variable": "remote_sensing_reflectance",
}
requests = download.update_request_date(
request,
start=start,
stop=stop,
stringify_dates=True,
)
ds = download.download_and_transform(
collection_id, requests, chunks={"year": 1, "month": 1, "variable": 1}
)
ds = ds[[var for var in ds.data_vars if var.startswith("Rrs")]]
(ds).min().compute().values
/data/common/miniforge3/envs/wp5/lib/python3.10/site-packages/dask/array/reductions.py:620: RuntimeWarning: All-NaN slice encountered
return np.nanmin(x_chunk, axis=axis, keepdims=keepdims)
<bound method Mapping.values of <xarray.Dataset>
Dimensions:  ()
Data variables:
Rrs_412  float32 2.955e-10
Rrs_443  float32 2.794e-06
Rrs_490  float32 1.694e-05
Rrs_510  float32 0.0
Rrs_560  float32 0.0002508
Rrs_665  float32 0.0>
(ds).max().compute().values
<bound method Mapping.values of <xarray.Dataset>
Dimensions:  ()
Data variables:
Rrs_412  float32 0.1108
Rrs_443  float32 0.1413
Rrs_490  float32 0.2301
Rrs_510  float32 0.2026
Rrs_560  float32 0.2345
Rrs_665  float32 0.09922>
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Hi @chiaravol,
I'm going through the backlog.
Do we need to keep this issue open or can we close it?

We can close it.
Thanks


Improve Intern integration

Create "tests/support/var" dir for Selenium JAR and logs
Download Selenium if not found
Create Page Object for handling the main page
Add first dummy functional test

!!! it passed
Removing the WIP tag

@darkwing would you mind taking a look at how I've set up Intern to work with Platatus? Let me know if anything looks out of place and if it seems like this is a robust way to test the project

Can you r? @darkwing ?

Getting test failure:  https://gist.github.com/darkwing/34ecb9695a2ff5c1f483
Likely due to pre-merge-conflict resolution  commits

When I manually start a selenium JAR file I've downloaded in the past (java -jar selenium-server-standalone-2.47.1.jar), everything works.  When I don't manually start the selenium JAR, I receive a fatal error:
https://gist.github.com/darkwing/2372d53b25dbbca723a7
Please confirm that if a local selenium is found, it's up to the developer to manually start it.
Also, should we rebase and squash all these commits?

Seems like it works in travis, is that enough confirmation @darkwing ?

Yep, works for me @digitarald

It should work without any intervention on your part (like starting the JAR manually). What platform are you on? Could you check the tests/var/selenium.log file for anything descriptive?This group has had discussions about squashing commits in the past and has always decided against it. mozilla/platatus wrote:When I manually start a selenium JAR file I've downloaded in the past (java -jar selenium-server-standalone-2.47.1.jar), everything works.  When I don't manually start the selenium JAR, I receive a fatal error:
https://gist.github.com/darkwing/2372d53b25dbbca723a7
Please confirm that if a local selenium is found, it's up to the developer to manually start it.
Also, should we rebase and squash all these commits?
—Reply to this email directly or view it on GitHub.
Sent Using Firefox OS


Active livestream shows in Upcoming + Followed

We used to remove from upcoming if it's in following, not sure when this broke.  Think it only happens for 15 minutes (the buffer)

One thing i can think of is this change, worth reverting and trying: 6d40b2752488dcde9ca59779fa3ac5ae9c50287985d2ee35eaeacc9eeffb0a4bR112
It was meant to fix an issue where a stream that was started early would not load on the file page. I'd retest without that when the revert is ready, maybe there's a better fix for it. Though i also think i ran into this with an old scheduled stream + going live...will retest too.


Pin lookup

@mmoskal These are the only changes I require to be merged for jacdac.

@mmoskal we should merge this at some point

@mmoskal shall we merge this into outside-py for consistency? And perhaps we should rename that branch to something consistent...?

Maybe we can move to Lancaster org and just use master? I guess we won't be merging upstream anytime soon?


Update README.md

Had to learn this behavior the hard way, hopefully this helps someone save their time.

Thank you!


v2 label textSize property has unexpected type

interactive_control_get_property_int(MySession, "mylabel", "textSize", &outTextSize) returns MIXER_ERROR_INVALID_PROPERTY_TYPE.  The property appears to be treated as a string internally, despite holding an integer value (36 in my case).

This is by design, the textSize property supports all CSS font-size value types, e.g. "5px", "150%", "large", etc.


How to add data directory?

Could not figure out how to add subdirectories because my authentication from terminal is not working.
For now if you fork and recommit, please move my csv to a data folder if possible, thanks.

Done with creating data folder. You can move the file to that folder now. Just in case, I think I better not touch the data file for now. lol


[device_info] key systemFeatures does not exits

The crash happens in AndroidDeviceInfo class in method _fromList because null is passed to those method. The problem is in row 171 of file AndroidDeviceInfo.dart because
...
systemFeatures: _fromList(map['systemFeatures']) // key `systemFeatures` does not exits in the map!
``

Hi @bambinoua
could you provide your flutter doctor -v?
are you using the last version device_info 0.4.2+1?
please provide a minimal complete code sample to reproduce the error

Yes. I used latest version 0.4.2+1. I don't see the reason to create a sample because I located the error. I wrote the class name, function and row number. In method _fromList you try to create List from null
static List<String> _fromList(dynamic message) {
  final List<dynamic> list = message;
  return List<String>.fromList(list);
}

In this method:
static AndroidDeviceInfo _fromMap(Map<String,dynamic> map) {}

map does not contains systemFeatures key (at least for Pixel 2 emulator) and the null passed to _fromList without null checking.

@bambinoua
I run code sample from https://pub.dev/packages/device_info#-example-tab-
with Android 10 emulator with flutter dev 1.18.0 without any error.
This may be an issue with your code.
This platform is not meant for assistance on personal code.
Please see https://flutter.dev/community for resources and asking questions,
you may also get some help if you post it on Stack Overflow.
Closing, as this isn't an issue with device_info plugin,
if you disagree please write in the comments and I will reopen it
Thank you

Did you check the line of the code which I provide?!

This may be an issue with your code.
The isssue is in your code! Because at least code must check if input parameter is not null

This platform is not meant for assistance on personal code.
Thank you for explanation.

If your emulator returns all data including key systemFeatures why do you decide that this must happends for all?
Below simple test class:
class AndroidDeviceInfo {
  final List<String> systemFeatures;

  AndroidDeviceInfo._({this.systemFeatures});

  static void runTest() {
    //! empty Map is passed specially to emulate absense of `systemFeatures` key
    print(AndroidDeviceInfo._fromMap({}));
  }

  static AndroidDeviceInfo _fromMap(Map<String, dynamic> map) {
    return AndroidDeviceInfo._(
        //! all other properties are removed specially!
        systemFeatures: _fromList(map['systemFeatures']));
  }

  /// Deserializes message as List<String>
  static List<String> _fromList(dynamic message) {
    final List<dynamic> list = message;
    return List<String>.from(list); // In this row I get an exception: NoSuchMethodError: The getter 'iterator' was called on null
  }
}

// Run test
AndroidDeviceInfo.runTest();

Closing as device_info is deprecated in favor of device_info_plus.


Chart Calculation Inconsistencies

When using the chart tab, the overlap time doesn't match when you interchange classes for first two fields. If you choose one class for the first section (Ex: SAA-CO2) and a different one for the second section (DVA-CO1) and choose All Lectures for the third, the total shared time is 36:34:03. when you switch those choices so that DVA-CO1 is chosen for the first section and SAA-CO2 is chosen for the second the total shared time is 40:03:33. All the other values are off by a similar percentage within the venn diagram. The "Across" does not matter either.
This is present for every option available.

Hey @minniear
Thanks for reporting the issue. I'll have a closer look when I have time and I think I know the culprit in both cases.
With regards to the large discrepancy with SAA-C02 that is attributed to lectures tagged as SAAC02SHARED. I think I can do a better job handling these as right now it seems they are counted from the perspective of the other course only.
With regards to the minor discrepancy my hunch (although I need to take a closer look) is because of the fuzzy matching logic involved to match lectures. This algorithm tries to match lectures as long as the name matches at around 95% and the duration delta is not more than 5%. Now because the duration can differ slightly although the lecture is identical the intersection duration will have a different value depending on which course was used to determine the overlap.

Fixed in https://github.com/sfanous/cantrill.io-courses-scraper/commit/1e05d174d9b601724a6c0c8df182119fdb8ced5d


DeviceSourceManager.onDeviceConnectedObservable notifies an observer with undefined event data

Repro

Bug repro on playground

The bug only repro's with touch events and not pointer events. You can still repro on a desktop browser in device emulator mode ([more info](more info)).

Expected result: Registering for onDeviceConnectedObservable should only send event data for a valid device.
Current result: If you register for onDeviceConnectedObservable after a touch event has occurred you will get an immediate callback with undefined event data.

Desktop (please complete the following information):

OS: MacOS 11.2.1
Browser Edge
Version 89.0.774.27

Smartphone (please complete the following information):

Device: Note 10+
OS: Android 10
Browser: Babylon React Native

Additional context
From debugging it looks like the issue is that after a touch event has ended DeviceSourceManager deletes the entry into the _devices[DeviceType.Touch][index] array. That causes means that the _devices[DeviceType.Touch] entry still has a value defined (albeit an Array with no values). Then when you register for the observable event it iterates over _devices and finds the non-undefined _devices[DeviceType.Touch] entry and fires the event with it's value (which is undefined at that point).

I'll take a look and see what's going on.

Fix for this is in PR: https://github.com/BabylonJS/Babylon.js/pull/9991

Closed as fixed


Fix issue with docker ports and DDL

LGTM!

Coverage remained the same at 26.307% when pulling 2f9b9382b7c1ca32298505b023731987f800e9e1 on alyiwang:master into f18ddd92b5c3e687013e746d229d5c1626b29663 on linkedin:master.


sort 'debugIdChunkFilePaths' to ensure deterministic output

The results from glob() are not guarenteed to be ordered, and prevent deterministic output.
This manifests in the assignment of artefeact names, as well as the bundle-id.

Hi, thanks for the contribution. I am struggling a bit to understand how sorting the globbing will in any way affect the determinism of the plugin since globbing doesn't affect the actual output of the plugins (apart from maybe what ends up inside an os tmp folder).
Artifact names are currently determined by the contained debug id, which are currently just random uuids. We haven't figured out a way of making this deterministic for all bundlers yet. We are open to suggestions.
Big hope lies in the bundlers themselves to emit debug IDs. Bun already does this based on our input:
https://github.com/oven-sh/bun/blob/012a8ba3736a671e914171574eea9357f4f659f3/src/sourcemap/sourcemap.zig#L1369-L1384
We are actively pushing in TC39 for this to become a standard.

Assume I have overriden the generation of random UUIDs (which I have ;)).
The order of those files affects the generated bundle-id, as without sorting, the {debug-id}-{chunkIndex} coupling is random. Sorting ensures we get predictable pairings, and stable Bundle-Ids.

The order of those files affects the generated bundle-id, as without sorting, the {debug-id}-{chunkIndex} coupling is random. Sorting ensures we get predictable pairings, and stable Bundle-Ids.

That makes sense. Thanks!

Assume I have overriden the generation of random UUIDs (which I have ;)).

Just because I am interested: What does your approach look like?

Please do not think bad of me. I'm just a guy trying to get deterministic builds whilst you sort it out proper, like. 😉
function stevesMegaHack(plugin) {
	return {
		apply(compiler) {
			console.log("Hijacking Sentry.... ***evil laugh***")
			plugin.apply({ // fake webpack compiler:
				webpack: compiler.webpack,
				hooks: compiler.hooks,
				options: {
					plugins: {
						push: (new_plugin)=>{
							if( new_plugin.constructor?.name === "BannerPlugin"
							 && typeof(new_plugin.options.banner) === "function" ) { // we want this one
								new_plugin.banner = ({chunk})=>{
									const it = (function*(s){while(1)yield* s})(chunk.hash)
									const guid = 'xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx'
											     .replace(/x/g, x=>it.next().value)

									return bundlerPluginCore.getDebugIdSnippet(guid);
								}
							}

							compiler.options.plugins.push(new_plugin)
						}
					}
				}
			})
		},
	}
}

...and then in my webpack.config.js:
	plugins: [
		stevesMegaHack(
			sentryWebpackPlugin({ ... })
		)
	]

Please do not think bad of me.

@smbroadley Nah man that is hella smart we can add that to the plugin!

That's great news - Thank you, Luca!


Inconsistent allocation of alpha characters in numbering convention

Description
The Numbering Convention described for the CDR Test Documentation allocates alpha characters to specific Suites and Assertions.  The general rule is Suite specific Test Cases and Assertions inherit three alpha characters assigned to the Scenario they belong to for grouping purposes. Additionally the Character 'C' has been reserved for Common assertions because each assertion is named based on the field name being tested.
To automate the sequential allocation of numbers to the Scenario, Test Case and Assertion hierarchy, this logic is contradicted because Scenarios for Common APIs, i.e. Admin (error as documentation suggests this character should be A) and Customer, have been allocated alpha character C.  This character naturally flows on to Test Cases and Assertions where the three alpha characters are appended with sequential numbers as each test case or assertion is added.  However, Suite specific assertions have the potential to repeat numbered Common assertions which also start with the character C.
Options
To ensure the numbering convention remains unique one of the following options needs to be considered.

Common Suites have the character C reassigned to another alpha, meaning the scenario, test case and assertion identifiers published for Suite.005 - Common Admin and Suite.006 - Common Customer will need to be reallocated.
Common Assertions have the current convention of naming based on Common plus meaningful field names reallocated to a sequentially order character set and to reallocate the identifiers.
Automated numbering routines take this anomaly into consideration by creating exceptions to the rule.
Create a Suite for Common Assertions to assign the three alphas so they can't be used by any other suite

Related Work Items
None

This issue will be resolve when DSB deploys a new version of the CDR Test Documentation (anticipated to be v2.0.0).  Details of the solution will added to this ticket when it's closed.

To address this issue the following changes have been made and will be published in v2.0.0.

Suites have had numbers replaced with the corresponding alpha characters that are already assigned to Scenarios, Test Cases and Assertions, e.g. SUITE.008 is now SUITE.EAA
Scenarios, Test Cases and Assertions have all had numeric characters increased from 3 to 4, e.g. S.EAA.001, T.EAA.001 and A.EAA.001 are all now S.EAA.0001, T.EAA.0001 and A.EAA.0001
Common Assertions are now known as Global Assertions and have had alpha characters such as CMN, CPG etc replaced with ZAA, numbers are now sequentially allocated and all test cases linked to the global assertions are now linked to the new identifier.

Full details of the changes can be found in the change log linked from the introduction text when v2.0.0 is published.
The information banner alerting site visitors of these changes should also be deleted.


Vote Crate gives ordinary Dragon Scale instead of 1 Specific IVs Modifier

Rolled a Vote Crate in front of Ras while testing, got an IV Modifier (image attached); got a vanilla dragon scale instead.

Followup: I feel it's worth noting that the item representing the voucher isn't even a Dragon Scale, and nothing on the page is a Dragon Scale either.

Should be fixed once IV modifiers are readded to crates. Thank you for the report


Winui 3 background task not run in x64 debug

Describe the bug
When i try to start my background task from visual studio 2022 in x64 debug,  Windows 11 event viewer contain this log:
Failed to trigger background task with entry point Samples.Tasks.ExampleToastTask and name ExampleToastTask. Error code: 0x80073CFC.
(The debug working in x86 debug).
Steps to reproduce the bug

I run the application debug for x64 patform in visual studio 2022
I manually run the task from visual studio UI

Expected behavior
No response
Screenshots
No response
NuGet package version
WinUI 3 - Windows App SDK 1.5.0: 1.5.240227000
Windows version
Windows 11 (23H2): Build 22631
Additional context
Repo url: https://github.com/jhonToni/Winui3BackgroudWorkerBug

Problem solved. The task project was bad configured for the x64 architecture.

Problem solved. The task project was bad configured for the x64 architecture.

I'm trying the code from your repo? Is it in a working state?

@parko65, the code in the repo was updated with correct configuration.


CreateRemoteThread Failed

CreateRemoteThread Failed，My shellcode arch is x64

Are you using an x86 built version of SigLoader ? try using the x64 version of SigLoader and the thread should be created with no errors this time.
Let me know if that fixed the issue.

Yep，Thank You!


Web Log Viewer Plugin

It would be really nice if kdb had a plugin for viewing the irc logs from the logger plugin.
Basic features:

View index of channels
View channel logs
Link to date/time in log entries
Search logs of a channel
Search logs of any channel

We may as well make this pretty fancy and have a few bells 'n whistles :)

Live logs (via websockets)
Admin interface to manage channels


Advanced Topics

From @nzoschke on October 17, 2016 22:12
This guide covers the basics. It should also offer links to some advanced topics. Some ideas...

Picking A Base Image
Vendoring Private Dependencies
Optimizing Image Build Times
Multiple Dockerfiles
Balancer Header Injection
Balancer SSL Certificates
End-to-End Encryption
Development Database Persistence
Data Migrations
Twelve-Factor Backing Services
Graceful Handling of Linked Service Unavailability
Avoiding Service Discovery
Container File Syncing

Copied from original issue: convox/guide#4

Capacity planning: many small instances vs. several large instances and the trade offs between them.
Sizing containers: coming up with good CPU/memory limits
How to support real time (web) and async (worker) containers without impacting user performance.


Error: swift-format: the bottle needs the Apple Command Line Tools to be installed.

Hi, im not able to install tools, and getting following error on mac m1 :
Error: swift-format: the bottle needs the Apple Command Line Tools to be installed.
DO i need to do any additional setup?

Thanx, This is resolved, i needed to install the command line tools for xcode:
xcode-select --install

Yes, i think you can show command as well, in case if user have not installed xcode command line tools i think.😀


header: Fix unresponsive dropdown pill on /help pages.

My PR #18974 introduced a bug where the logged-in dropdown pill on
the /help pages stopped working and has been unresponsive ever
since. This was caused by the incorrect assumption that each
.dropdown would be inside an unordered list ul. However, that
isn't the case for the dropdown pill on the /help pages. This commit
simply removes said assumption, by widening the scope of the relevant
CSS selectors.
@timabbott :)

Testing plan:
GIFs or screenshots:

@timabbott Just a reminder that this PR is ready for review! Thanks! :)

Merged, thanks @eeshangarg!


Restore EnsureAotCompatibility test

The test is passing locally without issue. Running an ADO PR build to ensure it passes there too.
Update - test passed in ADO build with no issues, running again
Update again - passed again

I think this might be a build step. At least it would make sense to be one to me instead of a unit test.

I think this might be a build step. At least it would make sense to be one to me instead of a unit test.

https://github.com/AzureAD/azure-activedirectory-identitymodel-extensions-for-dotnet/blob/90dfd314cf39f40aae8f23a51155762d91c3416c/build/common.props#L78

LGTM Thanks @kellyyangsong
Do we understand why it was skipped in the first place?

Yes, it was skipped because it would timeout which for this test is taking longer than 3 min in the ADO build. I re-ran this many times and never saw it take longer than 50s to complete.

I think this might be a build step. At least it would make sense to be one to me instead of a unit test.

I think this might be a build step. At least it would make sense to be one to me instead of a unit test.

https://github.com/AzureAD/azure-activedirectory-identitymodel-extensions-for-dotnet/blob/90dfd314cf39f40aae8f23a51155762d91c3416c/build/common.props#L78

We have an analyzer that will cause warnings - but I still think this test is useful until we start treating warnings as errors.


ValueError: Dimensions must be equal, ... when training is started using YOLO

Hi,
I tested all models and they work very well except for YOLO.
 Devices:
2020-06-11 08:58:42.573665: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): GeForce GTX 1650, Compute Capability 7.5
2020-06-11 08:58:42.593525: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz
2020-06-11 08:58:42.593991: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x56b8550 executing computations on platform Host. Devices:
2020-06-11 08:58:42.594015: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): <undefined>, <undefined>
2020-06-11 08:58:42.594243: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-11 08:58:42.594556: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties:
name: GeForce GTX 1650 major: 7 minor: 5 memoryClockRate(GHz): 1.56
pciBusID: 0000:01:00.0
2020-06-11 08:58:42.594836: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2020-06-11 08:58:42.595891: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0
2020-06-11 08:58:42.596735: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0
2020-06-11 08:58:42.596998: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0
2020-06-11 08:58:42.598197: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0
2020-06-11 08:58:42.599063: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0
2020-06-11 08:58:42.601488: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
2020-06-11 08:58:42.601588: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-11 08:58:42.601883: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-11 08:58:42.602095: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0
2020-06-11 08:58:42.602130: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2020-06-11 08:58:42.602803: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-06-11 08:58:42.602815: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0
2020-06-11 08:58:42.602821: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N
2020-06-11 08:58:42.602904: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-11 08:58:42.603170: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-11 08:58:42.603412: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2946 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1650, pci bus id: 0000:01:00.0, compute capability: 7.5)
[ INFO  ] data augment is  False
WARNING: Logging before flag parsing goes to stderr.
W0611 08:58:42.764019<PHONE_NUMBER>45760 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/data/ops/dataset_ops.py:494: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.
Instructions for updating:
tf.py_func is deprecated in TF V2. Instead, there are two
    options available in V2.
    - tf.py_function takes a python function which manipulates tf eager
    tensors instead of numpy arrays. It's easy to convert a tf eager tensor to
    an ndarray (just call tensor.numpy()) but having access to eager tensors
    means `tf.py_function`s can use accelerators such as GPUs as well as
    being differentiable using a gradient tape.
    - tf.numpy_function maintains the semantics of the deprecated tf.py_func
    (it is not differentiable, and manipulates numpy arrays). It drops the
    stateful argument making all functions stateful.

W0611 08:58:42.782631<PHONE_NUMBER>45760 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/data/util/random_seed.py:58: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
2020-06-11 08:58:42.786745: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-11 08:58:42.787202: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties:
name: GeForce GTX 1650 major: 7 minor: 5 memoryClockRate(GHz): 1.56
pciBusID: 0000:01:00.0
2020-06-11 08:58:42.787283: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2020-06-11 08:58:42.787312: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0
2020-06-11 08:58:42.787349: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0
2020-06-11 08:58:42.787398: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0
2020-06-11 08:58:42.787451: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0
2020-06-11 08:58:42.787504: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0
2020-06-11 08:58:42.787537: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
2020-06-11 08:58:42.787690: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-11 08:58:42.788133: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-11 08:58:42.788488: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0
2020-06-11 08:58:42.788899: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-11 08:58:42.789267: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties:
name: GeForce GTX 1650 major: 7 minor: 5 memoryClockRate(GHz): 1.56
pciBusID: 0000:01:00.0
2020-06-11 08:58:42.789312: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2020-06-11 08:58:42.789348: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0
2020-06-11 08:58:42.789362: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0
2020-06-11 08:58:42.789388: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0
2020-06-11 08:58:42.789429: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0
2020-06-11 08:58:42.789455: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0
2020-06-11 08:58:42.789468: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
2020-06-11 08:58:42.789565: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-11 08:58:42.789932: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-11 08:58:42.790293: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0
2020-06-11 08:58:42.790313: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-06-11 08:58:42.790318: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0
2020-06-11 08:58:42.790322: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N
2020-06-11 08:58:42.790438: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-11 08:58:42.790889: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-11 08:58:42.791276: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2946 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1650, pci bus id: 0000:01:00.0, compute capability: 7.5)
[ INFO  ] data augment is  False
W0611 08:58:42.951060<PHONE_NUMBER>45760 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
W0611 08:58:46.494499<PHONE_NUMBER>45760 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:97: calling GlorotUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
W0611 08:58:46.494857<PHONE_NUMBER>45760 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:97: calling Zeros.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
W0611 08:58:46.495954<PHONE_NUMBER>45760 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:97: calling Ones.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
W0611 08:58:54.212770<PHONE_NUMBER>45760 hdf5_format.py:221] No training configuration found in save file: the model was *not* compiled. Compile it manually.
Traceback (most recent call last):
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py", line 1864, in _create_c_op
    c_op = c_api.TF_FinishOperation(op_desc)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Dimensions must be equal, but are 13 and 7 for 'loss/l1_loss/calc_mask_0/xywh_to_all_0/add' (op: 'Add') with input shapes: [?,13,13,3,2], [7,10,1,2].

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "./keras_train.py", line 163, in <module>
    args.prune_frequency)
  File "./keras_train.py", line 88, in main
    metrics=[Yolo_Precision(obj_thresh, name='p'), Yolo_Recall(obj_thresh, name='r')])
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/base.py", line 457, in _method_wrapper
    result = method(self, *args, **kwargs)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py", line 337, in compile
    self._compile_weights_loss_and_weighted_metrics()
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/base.py", line 457, in _method_wrapper
    result = method(self, *args, **kwargs)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py", line 1710, in _compile_weights_loss_and_weighted_metrics
    self.total_loss = self._prepare_total_loss(masks)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py", line 1770, in _prepare_total_loss
    per_sample_losses = loss_fn.call(y_true, y_pred)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py", line 215, in call
    return self.fn(y_true, y_pred, **self._fn_kwargs)
  File "/app/K210_Yolo_framework/tools/utils.py", line 760, in loss_fn
    iou_thresh, layer, h)
  File "/app/K210_Yolo_framework/tools/utils.py", line 689, in calc_ignore_mask
    pred_xy, pred_wh = tf_xywh_to_all(p_xy, p_wh, layer, helper)
  File "/app/K210_Yolo_framework/tools/utils.py", line 545, in tf_xywh_to_all
    all_pred_xy = (tf.sigmoid(grid_pred_xy[..., :]) + h.xy_offset[layer]) / h.out_hw[layer][::-1]
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py", line 897, in binary_op_wrapper
    return func(x, y, name=name)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_math_ops.py", line 387, in add
    "Add", x=x, y=y, name=name)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py", line 788, in _apply_op_helper
    op_def=op_def)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/deprecation.py", line 507, in new_func
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py", line 3616, in create_op
    op_def=op_def)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py", line 2027, in __init__
    control_input_ops)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py", line 1867, in _create_c_op
    raise ValueError(str(e))
ValueError: Dimensions must be equal, but are 13 and 7 for 'loss/l1_loss/calc_mask_0/xywh_to_all_0/add' (op: 'Add') with input shapes: [?,13,13,3,2], [7,10,1,2].
Makefile:35: recipe for target 'train' failed
make: *** [train] Error 1

I'm using TensorFlow docker with GPU support. But I think it's not problem because training with all other models works very well in the same environment.
I downloaded pretrained weights yolo_weights.h5 from your Google Drive.
Please, help me.

original yolo_weights.h5 using 416*416 inputs, k210 yolo using 224*320 inputs.  I think your anchors are not equal to 416*416 inputs. You need re-generate anchors.

Thank you for advice.
I re-generated anchors like below:
tf-docker /app/K210_Yolo_framework > make anchors DATASET=voc ANCNUM=3 LOW='.0 .0' HIGH='1. 1.' IMGSIZE='416 416'
python3 ./make_anchor_list.py \
		voc \
		--max_iters 10 \
		--is_random True \
		--in_hw 416 416 \
		--out_hw 7 10 14 20 \
		--anchor_num 3 \
		--low .0 .0 \
		--high 1. 1.
WARNING: Logging before flag parsing goes to stderr.
W0612 03:30:25.575221<PHONE_NUMBER>45184 deprecation_wrapper.py:119] From ./make_anchor_list.py:135: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.

W0612 03:30:25.607122<PHONE_NUMBER>45184 deprecation_wrapper.py:119] From ./make_anchor_list.py:161: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.

W0612 03:30:25.607288<PHONE_NUMBER>45184 deprecation_wrapper.py:119] From ./make_anchor_list.py:163: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.

2020-06-12 03:30:25.607824: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2020-06-12 03:30:25.613279: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcuda.so.1
2020-06-12 03:30:25.752058: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-12 03:30:25.752791: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x3f61b10 executing computations on platform CUDA. Devices:
2020-06-12 03:30:25.752807: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): GeForce GTX 1650, Compute Capability 7.5
2020-06-12 03:30:25.780437: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz
2020-06-12 03:30:25.780873: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x3d81d60 executing computations on platform Host. Devices:
2020-06-12 03:30:25.780889: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): <undefined>, <undefined>
2020-06-12 03:30:25.781096: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-12 03:30:25.781390: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties:
name: GeForce GTX 1650 major: 7 minor: 5 memoryClockRate(GHz): 1.56
pciBusID: 0000:01:00.0
2020-06-12 03:30:25.783050: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2020-06-12 03:30:25.801779: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0
2020-06-12 03:30:25.810966: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0
2020-06-12 03:30:25.813934: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0
2020-06-12 03:30:25.837980: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0
2020-06-12 03:30:25.854329: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0
2020-06-12 03:30:25.901036: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
2020-06-12 03:30:25.901356: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-12 03:30:25.902317: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-12 03:30:25.903073: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0
2020-06-12 03:30:25.903542: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2020-06-12 03:30:25.906046: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-06-12 03:30:25.906092: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0
2020-06-12 03:30:25.906110: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N
2020-06-12 03:30:25.906810: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-12 03:30:25.907603: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-12 03:30:25.908312: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3056 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1650, pci bus id: 0000:01:00.0, compute capability: 7.5)
[ NOTE ] Now anchors are :
[[[0.75866947 0.46098343]
  [0.71398164 0.73741943]
  [0.51004807 0.26167539]]

 [[0.3257576  0.52905172]
  [0.18880768 0.26815896]
  [0.08822226 0.11132833]]]

Then, I tried to train with IMGSIZE='416 416' like below:
tf-docker /app/K210_Yolo_framework > make train MODEL=yolo MAXEP=1 ILR=0.001 DATASET=voc CLSNUM=20 IAA=False BATCH=16 IMGSIZE='416 416'
python3 ./keras_train.py \
		--train_set voc \
		--class_num 20 \
		--pre_ckpt "" \
		--model_def yolo \
		--depth_multiplier 0.75 \
		--augmenter False \
		--image_size 416 416 \
		--output_size 7 10 14 20 \
		--batch_size 16 \
		--rand_seed 3 \
		--max_nrof_epochs 1 \
		--init_learning_rate 0.001 \
		--learning_rate_decay_factor 0 \
		--obj_weight 1 \
		--noobj_weight 1 \
		--wh_weight 1 \
		--obj_thresh 0.7 \
		--iou_thresh 0.5 \
		--vaildation_split 0.05 \
		--log_dir log \
		--is_prune False \
		--prune_initial_sparsity 0.5 \
		--prune_final_sparsity 0.9 \
		--prune_end_epoch 5 \
		--prune_frequency 100
2020-06-12 03:32:17.062105: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2020-06-12 03:32:17.066694: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcuda.so.1
2020-06-12 03:32:17.213333: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-12 03:32:17.213670: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x46616d0 executing computations on platform CUDA. Devices:
2020-06-12 03:32:17.213706: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): GeForce GTX 1650, Compute Capability 7.5
2020-06-12 03:32:17.232653: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz
2020-06-12 03:32:17.233263: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x461ce20 executing computations on platform Host. Devices:
2020-06-12 03:32:17.233290: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): <undefined>, <undefined>
2020-06-12 03:32:17.233465: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-12 03:32:17.233766: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties:
name: GeForce GTX 1650 major: 7 minor: 5 memoryClockRate(GHz): 1.56
pciBusID: 0000:01:00.0
2020-06-12 03:32:17.233973: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2020-06-12 03:32:17.235006: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0
2020-06-12 03:32:17.235805: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0
2020-06-12 03:32:17.236119: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0
2020-06-12 03:32:17.237214: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0
2020-06-12 03:32:17.238073: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0
2020-06-12 03:32:17.240547: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
2020-06-12 03:32:17.240645: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-12 03:32:17.240991: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-12 03:32:17.241235: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0
2020-06-12 03:32:17.241272: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2020-06-12 03:32:17.241980: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-06-12 03:32:17.241992: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0
2020-06-12 03:32:17.241997: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N
2020-06-12 03:32:17.242076: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-12 03:32:17.242416: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-12 03:32:17.242695: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2992 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1650, pci bus id: 0000:01:00.0, compute capability: 7.5)
[ INFO  ] data augment is  False
WARNING: Logging before flag parsing goes to stderr.
W0612 03:32:17.406857<PHONE_NUMBER>49184 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/data/ops/dataset_ops.py:494: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.
Instructions for updating:
tf.py_func is deprecated in TF V2. Instead, there are two
    options available in V2.
    - tf.py_function takes a python function which manipulates tf eager
    tensors instead of numpy arrays. It's easy to convert a tf eager tensor to
    an ndarray (just call tensor.numpy()) but having access to eager tensors
    means `tf.py_function`s can use accelerators such as GPUs as well as
    being differentiable using a gradient tape.
    - tf.numpy_function maintains the semantics of the deprecated tf.py_func
    (it is not differentiable, and manipulates numpy arrays). It drops the
    stateful argument making all functions stateful.

W0612 03:32:17.425872<PHONE_NUMBER>49184 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/data/util/random_seed.py:58: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
2020-06-12 03:32:17.429657: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-12 03:32:17.430020: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties:
name: GeForce GTX 1650 major: 7 minor: 5 memoryClockRate(GHz): 1.56
pciBusID: 0000:01:00.0
2020-06-12 03:32:17.430062: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2020-06-12 03:32:17.430075: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0
2020-06-12 03:32:17.430086: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0
2020-06-12 03:32:17.430097: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0
2020-06-12 03:32:17.430108: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0
2020-06-12 03:32:17.430122: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0
2020-06-12 03:32:17.430133: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
2020-06-12 03:32:17.430249: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-12 03:32:17.430577: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-12 03:32:17.430964: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0
2020-06-12 03:32:17.431456: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-12 03:32:17.431754: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties:
name: GeForce GTX 1650 major: 7 minor: 5 memoryClockRate(GHz): 1.56
pciBusID: 0000:01:00.0
2020-06-12 03:32:17.431775: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2020-06-12 03:32:17.431786: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0
2020-06-12 03:32:17.431797: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0
2020-06-12 03:32:17.431808: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0
2020-06-12 03:32:17.431819: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0
2020-06-12 03:32:17.431829: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0
2020-06-12 03:32:17.431840: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
2020-06-12 03:32:17.431922: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-12 03:32:17.432331: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-12 03:32:17.432646: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0
2020-06-12 03:32:17.432678: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-06-12 03:32:17.432687: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0
2020-06-12 03:32:17.432695: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N
2020-06-12 03:32:17.432853: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-12 03:32:17.433265: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-12 03:32:17.433640: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2992 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1650, pci bus id: 0000:01:00.0, compute capability: 7.5)
[ INFO  ] data augment is  False
W0612 03:32:17.591526<PHONE_NUMBER>49184 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
W0612 03:32:21.196579<PHONE_NUMBER>49184 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:97: calling GlorotUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
W0612 03:32:21.197006<PHONE_NUMBER>49184 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:97: calling Zeros.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
W0612 03:32:21.198018<PHONE_NUMBER>49184 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:97: calling Ones.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
W0612 03:32:29.135478<PHONE_NUMBER>49184 hdf5_format.py:221] No training configuration found in save file: the model was *not* compiled. Compile it manually.
Traceback (most recent call last):
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py", line 1864, in _create_c_op
    c_op = c_api.TF_FinishOperation(op_desc)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Dimensions must be equal, but are 13 and 7 for 'loss/l1_loss/calc_mask_0/xywh_to_all_0/add' (op: 'Add') with input shapes: [?,13,13,3,2], [7,10,1,2].

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "./keras_train.py", line 163, in <module>
    args.prune_frequency)
  File "./keras_train.py", line 88, in main
    metrics=[Yolo_Precision(obj_thresh, name='p'), Yolo_Recall(obj_thresh, name='r')])
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/base.py", line 457, in _method_wrapper
    result = method(self, *args, **kwargs)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py", line 337, in compile
    self._compile_weights_loss_and_weighted_metrics()
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/base.py", line 457, in _method_wrapper
    result = method(self, *args, **kwargs)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py", line 1710, in _compile_weights_loss_and_weighted_metrics
    self.total_loss = self._prepare_total_loss(masks)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py", line 1770, in _prepare_total_loss
    per_sample_losses = loss_fn.call(y_true, y_pred)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py", line 215, in call
    return self.fn(y_true, y_pred, **self._fn_kwargs)
  File "/app/K210_Yolo_framework/tools/utils.py", line 760, in loss_fn
    iou_thresh, layer, h)
  File "/app/K210_Yolo_framework/tools/utils.py", line 689, in calc_ignore_mask
    pred_xy, pred_wh = tf_xywh_to_all(p_xy, p_wh, layer, helper)
  File "/app/K210_Yolo_framework/tools/utils.py", line 545, in tf_xywh_to_all
    all_pred_xy = (tf.sigmoid(grid_pred_xy[..., :]) + h.xy_offset[layer]) / h.out_hw[layer][::-1]
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py", line 897, in binary_op_wrapper
    return func(x, y, name=name)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_math_ops.py", line 387, in add
    "Add", x=x, y=y, name=name)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py", line 788, in _apply_op_helper
    op_def=op_def)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/deprecation.py", line 507, in new_func
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py", line 3616, in create_op
    op_def=op_def)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py", line 2027, in __init__
    control_input_ops)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py", line 1867, in _create_c_op
    raise ValueError(str(e))
ValueError: Dimensions must be equal, but are 13 and 7 for 'loss/l1_loss/calc_mask_0/xywh_to_all_0/add' (op: 'Add') with input shapes: [?,13,13,3,2], [7,10,1,2].
Makefile:35: recipe for target 'train' failed
make: *** [train] Error 1

Is there any parameters should I change to fit in original YOLO?

I also tried to train using tiny_yolo with IMGSIZE='416 416' to check what error messages are generate when the model's input size is different from anchors.
tf-docker /app/K210_Yolo_framework > make train MODEL=tiny_yolo MAXEP=1 ILR=0.001 DATASET=voc CLSNUM=20 IAA=False BATCH=16 IMGSIZE='416 416'
python3 ./keras_train.py \
		--train_set voc \
		--class_num 20 \
		--pre_ckpt "" \
		--model_def tiny_yolo \
		--depth_multiplier 0.75 \
		--augmenter False \
		--image_size 416 416 \
		--output_size 7 10 14 20 \
		--batch_size 16 \
		--rand_seed 3 \
		--max_nrof_epochs 1 \
		--init_learning_rate 0.001 \
		--learning_rate_decay_factor 0 \
		--obj_weight 1 \
		--noobj_weight 1 \
		--wh_weight 1 \
		--obj_thresh 0.7 \
		--iou_thresh 0.5 \
		--vaildation_split 0.05 \
		--log_dir log \
		--is_prune False \
		--prune_initial_sparsity 0.5 \
		--prune_final_sparsity 0.9 \
		--prune_end_epoch 5 \
		--prune_frequency 100
2020-06-12 03:43:10.244032: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2020-06-12 03:43:10.248613: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcuda.so.1
2020-06-12 03:43:10.384543: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-12 03:43:10.384907: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x4acba80 executing computations on platform CUDA. Devices:
2020-06-12 03:43:10.384922: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): GeForce GTX 1650, Compute Capability 7.5
2020-06-12 03:43:10.408814: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz
2020-06-12 03:43:10.409339: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x4a87770 executing computations on platform Host. Devices:
2020-06-12 03:43:10.409362: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): <undefined>, <undefined>
2020-06-12 03:43:10.409537: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-12 03:43:10.409887: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties:
name: GeForce GTX 1650 major: 7 minor: 5 memoryClockRate(GHz): 1.56
pciBusID: 0000:01:00.0
2020-06-12 03:43:10.410160: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2020-06-12 03:43:10.411018: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0
2020-06-12 03:43:10.411760: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0
2020-06-12 03:43:10.411984: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0
2020-06-12 03:43:10.413051: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0
2020-06-12 03:43:10.413852: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0
2020-06-12 03:43:10.416065: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
2020-06-12 03:43:10.416168: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-12 03:43:10.416866: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-12 03:43:10.417095: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0
2020-06-12 03:43:10.417134: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2020-06-12 03:43:10.417830: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-06-12 03:43:10.417842: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0
2020-06-12 03:43:10.417848: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N
2020-06-12 03:43:10.417930: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-12 03:43:10.418196: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-12 03:43:10.418436: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2980 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1650, pci bus id: 0000:01:00.0, compute capability: 7.5)
[ INFO  ] data augment is  False
WARNING: Logging before flag parsing goes to stderr.
W0612 03:43:10.583317<PHONE_NUMBER>96896 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/data/ops/dataset_ops.py:494: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.
Instructions for updating:
tf.py_func is deprecated in TF V2. Instead, there are two
    options available in V2.
    - tf.py_function takes a python function which manipulates tf eager
    tensors instead of numpy arrays. It's easy to convert a tf eager tensor to
    an ndarray (just call tensor.numpy()) but having access to eager tensors
    means `tf.py_function`s can use accelerators such as GPUs as well as
    being differentiable using a gradient tape.
    - tf.numpy_function maintains the semantics of the deprecated tf.py_func
    (it is not differentiable, and manipulates numpy arrays). It drops the
    stateful argument making all functions stateful.

W0612 03:43:10.601178<PHONE_NUMBER>96896 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/data/util/random_seed.py:58: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
2020-06-12 03:43:10.604792: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-12 03:43:10.605172: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties:
name: GeForce GTX 1650 major: 7 minor: 5 memoryClockRate(GHz): 1.56
pciBusID: 0000:01:00.0
2020-06-12 03:43:10.605216: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2020-06-12 03:43:10.605254: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0
2020-06-12 03:43:10.605304: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0
2020-06-12 03:43:10.605332: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0
2020-06-12 03:43:10.605366: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0
2020-06-12 03:43:10.605399: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0
2020-06-12 03:43:10.605433: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
2020-06-12 03:43:10.605534: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-12 03:43:10.605862: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-12 03:43:10.606121: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0
2020-06-12 03:43:10.606591: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-12 03:43:10.606968: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties:
name: GeForce GTX 1650 major: 7 minor: 5 memoryClockRate(GHz): 1.56
pciBusID: 0000:01:00.0
2020-06-12 03:43:10.606990: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2020-06-12 03:43:10.607004: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0
2020-06-12 03:43:10.607018: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0
2020-06-12 03:43:10.607031: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0
2020-06-12 03:43:10.607044: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0
2020-06-12 03:43:10.607057: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0
2020-06-12 03:43:10.607071: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
2020-06-12 03:43:10.607180: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-12 03:43:10.607519: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-12 03:43:10.607881: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0
2020-06-12 03:43:10.607899: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-06-12 03:43:10.607904: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0
2020-06-12 03:43:10.607908: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N
2020-06-12 03:43:10.608069: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-12 03:43:10.608401: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-06-12 03:43:10.608625: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2980 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1650, pci bus id: 0000:01:00.0, compute capability: 7.5)
[ INFO  ] data augment is  False
W0612 03:43:10.766265<PHONE_NUMBER>96896 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
W0612 03:43:11.322644<PHONE_NUMBER>96896 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:97: calling GlorotUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
W0612 03:43:11.323169<PHONE_NUMBER>96896 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:97: calling Zeros.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
W0612 03:43:11.324368<PHONE_NUMBER>96896 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:97: calling Ones.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
W0612 03:43:12.716792<PHONE_NUMBER>96896 hdf5_format.py:221] No training configuration found in save file: the model was *not* compiled. Compile it manually.
2020-06-12 03:43:16.079521: W tensorflow/compiler/jit/mark_for_compilation_pass.cc:1412] (One-time warning): Not using XLA:CPU for cluster because envvar TF_XLA_FLAGS=--tf_xla_cpu_global_jit was not set.  If you want XLA:CPU, either set that envvar, or use experimental_jit_scope to enable XLA:CPU.  To confirm that XLA is active, pass --vmodule=xla_compilation_cache=1 (as a proper command-line flag, not via TF_XLA_FLAGS) or set the envvar XLA_FLAGS=--xla_hlo_profile.
W0612 03:43:23.013009<PHONE_NUMBER>38176 backprop.py:820] The dtype of the watched tensor must be floating (e.g. tf.float32), got tf.string
W0612 03:43:23.013548<PHONE_NUMBER>55168 backprop.py:820] The dtype of the watched tensor must be floating (e.g. tf.float32), got tf.string
W0612 03:43:23.014048<PHONE_NUMBER>47872 backprop.py:820] The dtype of the watched tensor must be floating (e.g. tf.float32), got tf.string
W0612 03:43:23.014739<PHONE_NUMBER>30880 backprop.py:820] The dtype of the watched tensor must be floating (e.g. tf.float32), got tf.string
W0612 03:43:23.015295<PHONE_NUMBER>71232 backprop.py:820] The dtype of the watched tensor must be floating (e.g. tf.float32), got tf.string
2020-06-12 03:43:23.331411: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
2020-06-12 03:43:24.856483: E tensorflow/stream_executor/cuda/cuda_driver.cc:828] failed to allocate 915.19M (959643648 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2020-06-12 03:43:24.974073: W tensorflow/core/common_runtime/bfc_allocator.cc:237] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.16GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2020-06-12 03:43:24.975073: W tensorflow/core/common_runtime/bfc_allocator.cc:237] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.16GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2020-06-12 03:43:24.995769: W tensorflow/core/common_runtime/bfc_allocator.cc:237] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.22GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2020-06-12 03:43:24.995791: W tensorflow/core/common_runtime/bfc_allocator.cc:237] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.22GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2020-06-12 03:43:25.120383: W tensorflow/core/common_runtime/bfc_allocator.cc:237] Allocator (GPU_0_bfc) ran out of memory trying to allocate 870.38MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2020-06-12 03:43:25.120415: W tensorflow/core/common_runtime/bfc_allocator.cc:237] Allocator (GPU_0_bfc) ran out of memory trying to allocate 870.38MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
Traceback (most recent call last):
  File "./keras_train.py", line 163, in <module>
    args.prune_frequency)
  File "./keras_train.py", line 107, in main
    validation_data=h.test_dataset, validation_steps=int(h.test_epoch_step * h.validation_split))
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py", line 780, in fit
    steps_name='steps_per_epoch')
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training_arrays.py", line 274, in model_iteration
    batch_outs = f(actual_inputs)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py", line 3292, in __call__
    run_metadata=self.run_metadata)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py", line 1458, in __call__
    run_metadata_ptr)
tensorflow.python.framework.errors_impl.InvalidArgumentError: 2 root error(s) found.
  (0) Invalid argument: Input to reshape is a tensor with 202800 values, but the requested shape has 84000
	 [[{{node l1/Reshape}}]]
  (1) Invalid argument: Input to reshape is a tensor with 202800 values, but the requested shape has 84000
	 [[{{node l1/Reshape}}]]
	 [[loss/Identity/_1549]]
0 successful operations.
0 derived errors ignored.
Makefile:35: recipe for target 'train' failed
make: *** [train] Error 1

I hope this to be helpful for you.
Thank you.

the out_hw  must be related in_hw.
when in_hw is 224*320, detect layers is 2, out_hw must be 224/32 320/32 224/16 320/16 = 7 10 14 20.
when in_hw is 416*416, detect layers is 3, out_hw must be 416/32 416/32 416/16 416/16 416/8 416/8 = 13 13 26 26 52 52.
Therefore, you need to consider the number of detection layers and the input size when setting  in_hw and out_hw

I tried as you commented.
The ValueError seems to be resolved.
But the result is Resource exhausted: OOM.... So I reduced batch size to 8. Then it works!
Thank you very much.


Update "Delete remote tag" to match delete remote branch

The --delete flag can be used for deleting both remote branches and tags. This updates "Delete remote tag" to do that. It also adds a deletion syntax without the refs/tags so it's known that it's usually not necessary.

git push origin --delete refs/tags/<tag-name> feels a bit redundant. It can be changed to the following, which will be more concise.

git push origin --delete <tagname>

Alternatives:
git push origin :refs/tags/<tag-name>

@baooab Agreed! Made those edits


MSSQL columnInfo() had hard coded schema name

columnInfo() for MSSQL has the default schema (dbo) hard coded.  It should take the database and schema as parameters like the Postgres version does.

/home/travis/build/tgriesser/knex/src/dialects/mssql/query/compiler.js
162:9  error  'bindings' is never modified, use 'const' instead  prefer-const
✖ 1 problem (1 error, 0 warnings)

Not knowing the dialect, changes looks fine I suppose :)

The eslint error looks incorrect as the variable 'bindings' can be modified under the if condition.  Does anyone know if this can be forced through or is it "better" to use const/var to get eslint to pass?

@nickwhiteley It's odd, I know. Short answer is change it to const.
Longer answer: An Object of any kind when defined as a constant simply means it cannot be re-assigned to anything else. It can still have properties assigned to itself, but cannot be re-assigned. For instance:
const bindings = [1,2,3];
bindings.push(4); //Ok, bindings [1,2,3,4]
bindings = 'String'; //Error thrown.

const bindings = {col1: 'val1'};
bindings.col2 = 'val2'; //Ok, bindings = {col1: 'val1', col2: 'val2'}
bindings = 'String'; //Error thrown.

In this case the bindings is originally assigned as an Array, and is not re-assigned later on, this is why it should be constant instead. ESLint picks up on this.

I'll change it.
It's counter intuitive (to me) so thanks for the explanation.  Always good to learn.


Subscription Meal Plans.

Subscription Meal Plans
Is your feature request related to a problem? Please describe.
Users who enjoy the convenience of regular homemade meals currently need to place individual orders each time. This process can be time-consuming and inconvenient, especially for busy individuals who rely on consistent meal delivery.
Describe the solution you'd implement
Implement a Subscription Meal Plans feature that allows users to subscribe to weekly or monthly meal plans. Users can:
Select their preferred meal plan (weekly or monthly).
Customize their meal plan by choosing from a variety of dishes.
Enjoy automatic, recurring deliveries without the need to place individual orders.
Benefit from special discounts for subscribers.
Describe alternatives you've considered
Manual Order Scheduling: Users could manually schedule orders for the week/month. However, this approach still requires repetitive actions from the user and lacks the seamless experience of a true subscription model.
Prepaid Meal Packages: Offering a set number of pre-paid meals at a discount. While this can be beneficial, it doesn't provide the recurring convenience and predictability of a subscription model.
Additional context
Adding a Subscription Meal Plans feature will enhance user satisfaction by providing a hassle-free experience for regular meal deliveries. This feature can boost user engagement and loyalty, as users will appreciate the convenience and potential cost savings.

Hey @sv410, What is your approach to implement this feature?


undefined method `users_url' for #Devise::RegistrationsController:0x00000005018470

I get this error with my app:
https://github.com/gwuix2/anabol
On user registration with devise.
With omniauth facebook login everything works.
I opened a stackoverflow question on it:
http://stackoverflow.com/questions/16971357/devise-undefined-method-users-url-for-deviseregistrationscontroller0x0000

how did you fix it? I am having the exact same problem

I'm running into this again, with a fresh project started with rails-7.0.0.rc1, and devise installed and set up exactly following https://stucklucky.medium.com/how-to-set-up-rails-devise-5bd39732629f .

NoMethodError (undefined method `user_url' for #Devise::RegistrationsController:0x0000000001b3c8)

hey @martindemello @zaouch-bahae-eddine , looks like this issue (present in rails 7) is being tackled here: https://github.com/heartcombo/devise/issues/5439

In config/devise.rb include line:
config.navigational_formats = ['/', :html, :turbo_stream]
before  rails s

great @SalomaoMiguel thank u!!!

thank you, @SalomaoMiguel.

Thanks @SalomaoMiguel this works for rails 7

if you have devise.rb is in initializer folder, you can add this:
config.navigational_formats = ['/', :html, :turbo_stream]


Feat/docs config header

https://alpha-docs-git-feat-docsconfigheader-kadena-js.vercel.app/contribute
Under the Run A Node left menu clicking Start Mining going to 404 page and also Docs menu is missing from left side menu.


cocomac: Blacklist (?-i:lLx_R4Y0v7c)(?#YouTube)

cocomac requests the blacklist of the keyword (?-i:lLx_R4Y0v7c)(?#YouTube). See the MS search here and the Stack Exchange search in text, in URLs, and in code.

(?-i:lLx_R4Y0v7c)(?#YouTube) has been seen in 12 true positives, 1 false positive, and 0 NAAs.

Approved by Spevacus in Charcoal HQ


cmd/kubectl-gadget/top: Use BaseParser to uniform and simplify gadgets

Use BaseParser to uniform and simplify top category gadgets
This PR modifies the top category gadgets to use BaseParser so that they don't have to implement the logic to print the header and transform stats to columns.
This PR doesn't modify the logic or current behaviour of the gadgets. This PR aims to uniform this category with the rest of the categories so we can manage the two mentioned logics from a single place for all the gadgets. For instance, combine the OutputModeColumns and OutputModeCustomColumns, and printing the columns that can be used with the custom-columns flag.
How to use
These changes don't have any impact on the user.

I like it. I'm wondering if you are planning to create a TopParser to reduce the duplicated logic in those gadgets?

@mauriciovasquezbernal I would like to, but as we agreed here, I was waiting for the outcomes of the discussion about the possible change of the GlobalTracer/API, which may change the implementation of the CLI for those gadgets #673. Given that such a task is taking more time than expected, do we want to unblock the refactoring of this category?

I rebased the PR to solve conflicts created after merging https://github.com/kinvolk/inspektor-gadget/pull/825.


Conflict using Mac/Chrome

I am including captures of errors that I'm getting when trying to use PBot Workbench in Chrome. I am also including my Chrome version info and Mac IOS version info. This is NOT happening for Andrew (PC) or Dori (Mac). Seems like it's a browser thing, but not sure. Can we identify issue? Hopefully it's just a training/settings thing we need to resolve.

@NoisyFlowers Doug, I am not having this issue anymore. Should we close this and I'll let you know if there are circumstances it shows up again? Sidenote, I'm working from a Starbucks now so if it happens again because I'm at home, I'll re-enter it a trouble shooting issue for users.

@aazaff @NoisyFlowers I just tested this at home and everything appears to be working. Wish I had a reason why it's suddenly working since I haven't changed anything, but I think we can close this issue.