dnx run command fails when user home directory contains space.

From @retk on February 3, 2016 14:40
Hi!
Unfortunately my home directory contains a space character: 'C:\Users\Ersek Attila'
When I try to run any asp.net core application with the dnx run web command, i get the following error in the command window:
'C:\Users\Ersek' is not recognized as an internal or external command,...
vscode version: 0.10.6
Copied from original issue: Microsoft/vscode#2674

There's already a pull request for the run-in-terminal package that fixes this issue on a lower level:
https://github.com/Microsoft/run-in-terminal/pull/2

Any idea when a fix will be made so it get through to VS Code? Using dnx commands are impossible through VS Code until this is corrected, making VSCode useless for targeting .NET Core on Windows.

@retk the PR has been merged.
Ping @DustinCampbell @davidfowl

And what is necessary to get this to actually be in VS Code?

the<EMAIL_ADDRESS>contains the fix and omnisharp ships with that version of the extension. There might be another issue somewhere along the way.

Omnisharp doesn't ship with run-in-terminal -- did you mean the C# Extension?

Yeah, I meant the extension

Either way... it's not in VS Code :)

run-in-terminal is in package dependencies
  "dependencies": {
   ...
    "run-in-terminal": "*",
   ...
  },

although as @jrieken mentioned there might be another issues too

Well, then I've opened a new issue on VS Code. If the dependencies have been fixed, then something in Code is broken.

And it was closed and moved back here.
Is anyone going to take this up and make sure it gets to VS Code??? I don't even know where the problem is anymore... VS Code? OmniSharp? run-in-terminal? Everyone is pointing to the other...

This is the right place for the bug.

i'll try to reproduce the issue later today with vscode 1.0 and update the issue with details.

Note that "dnx run" is unlikley to get fixed. We're focused on supporting ASP .NET Core RC2. When that is released, DNX will go away.

@DustinCampbell : yes, i'm aware of that, although the csharp package 0.3.7 still depends on run-in-terminal package. it might use this dependency for other reasons too.
I've managed to reproduce the issue with vscode 1.0. It installs csharp package 0.3.7 as extension into:
~/.vscode\extensions\ms-vscode.csharp-0.3.7
run-in-terminal package version 0.0.3 is installed as lates version of the dependency (see package.json under dependencies)
Although this version has the commited pull request the problem still exists. To fix the issue the following changes are needed:
diff --git a/lib/index.ts b/lib/index.ts
index 09c5128..e802873 100644
--- a/lib/index.ts
+++ b/lib/index.ts
@@ -60,7 +60,7 @@ function runInTerminalWin(file: string, args: string[], options: Options): Promi
'/wait',
'cmd.exe',
'/c',

   `"${file} ${args.map(escapeWinArg).join(' ') } & pause"`

   `""${file}" ${args.map(escapeWinArg).join(' ') } & pause"`

];

and it must be recompiled with tsc.
Should I open a new issue in run-in-terminal and create a pull request?
I'm not sure if this issue must remain open here until a new version is released, or someone should open a new one somewhere else? (for eg the cshap package is affected as well)

FWIW, I'm planning to fix this by not using run-in-terminal at all. I think it's a bit awkward that a new terminal window is created for this operation. Instead, I'll just launch "dotnet" as a child process and capture the output and display it within VS Code.

This should be fixed with #238. While the "dnx run" command is no longer relevant, we have removed run-in-terminal so other commands should not exhibit this problem.


[FEATURE] MS Fabric support

Hello greetings,
I would like to evaluate dbtvault in MS Fabric environment. I was wondering if you already have a some idea of what is necessary to get dbtvault to run on MS Fabric or maybe already started working on this.
Depending on the effort, glad be able to help reach the support of Ms Fabric.
Best regards
AB#5347

One idea could be to simply copy the dispatch logic from "sqlserver" and call it "fabric" and your good to go.
Here is one example "type_timestamp.sql"
{%- macro type_timestamp() -%}
{{- return(adapter.dispatch('type_timestamp', 'automate_dv')()) -}}
{%- endmacro -%}
{%- macro default__type_timestamp() -%}
TIMESTAMP_NTZ
{%- endmacro -%}
{%- macro sqlserver__type_timestamp() -%}
DATETIME2
{%- endmacro -%}
{%- macro fabric__type_timestamp() -%}
DATETIME2
{%- endmacro -%}
If you use this approach everything works fine - except the dbt model versions ( https://docs.getdbt.com/reference/resource-properties/versions )

Thank you for this request and sorry for the delayed response. MS Fabric and AWS Redshift are next on the roadmap for support.

Are there any updates regarding AutomateDV supporting MS Fabric?


fix: isDefEq handling of delayed-assigned mvars when withAssignableSyntheticOpaque is true

(In progress; fully experimental.)

WIP


Blazor Razor syntax remark improvements

Fixes #17119

Nits on how we lead into binding and event handling.
There's an out-of-place remark in the Components topic. I move it to the Binding topic.
I kill off 🔪 the "to both components and DOM elements" phrase in the opening sentence of the Binding topic. There are only components and DOM elements under discussion, so it's not really explaining anything. Also, the concept is well covered down in the topic.
We've been instructed to use the word scenario over feature. I don't fully understand it, but that's the word.

That came down via docs management quite a while ago, and there's been a low-level background effort to factor out "feature" for quite a while now. Current counts put use at about 50-50 these days ...

feature – 707
scenario – 617

@Rick-Anderson can provide more color on that guidance. We spoke about it in detail when it came down, and the guidance was (and still is AFAIK) to mostly avoid "feature" and speak about "scenarios." I even went as far as to ask if I should avoid using the word in issues, and the answer was yes generally.

avoid "feature" and speak about "scenarios."

That's no longer stressed. Feature is fine.


Is there a way to flatten custom claims in the resulging JSON?

I have a case when some function will return me a map of claims and their values to add to the list of Claims. Unfortunately i have no way to tell upfront what those claims would be, i just know that it would be a string:string map.
I've tried doing something like this:
type CustomClaims map[string]interface{}
...
type AuthTokenClaim struct {
	*jwt.StandardClaims
	*CustomClaims
}
...
m := MapClaims{}
m["key"] = "value"

claims := &AuthTokenClaim{
	StandardClaims: &jwt.StandardClaims{
		Audience:  "",
		ExpiresAt: expAt,
		Id:        fmt.Sprint(uuid.NewRandom()),
		IssuedAt:  issAt,
		Issuer:    "http://issuer.org",
		NotBefore: nbf,
		Subject:   "subj",
	},
	CustomClaims : &m,
}

However, the resulting JSON makes the "key":"value" pair nested, like that and i can't find a way to unmap it into a flat json:
  "iss": "http://issuer.org",
  "sub": "subj",
  "MapClaims": {
    "key": "value"
  }

Is there something i can do short of rewriting SigningString to hack marshalling?
I suppose i can do something like:
type MapClaims map[string]interface{}

func (MapClaims) Valid() error {
	log.Println("Not implemented!")
	return nil
}

m := MapClaims{}
m["aud"] = "aud"
m["exp"] = exp
uuids, err := uuid.NewRandom()
var id string = fmt.Sprint(uuids)
m["id"] = id
m["iat"] = iat
m["iss"] = "http://issuer.org"
m["nbf"] = nbf
m["sub"] = "subject"
m["typ"] = "Bearer"
token.Claims = m

But it looks a bit uglier that i'd like.

Dealing with unknown keys is a bit tricky. This library depends on the standard library's encoding/json package, so you should be able to rely on any knowledge about its behavior. Also, MapClaims is already provided by the library and is an alias of map[string]interface{}.
I think your implementation looks just fine. You could clean it up by using a literal for your known values:
var claims := jwt.MapClaims{
  "foo": "bar", // This value can be overwritten by otherClaims
}

for key, value := otherClaims {
    claims[key] = value
}

var importantClaims := map[string]interface{}
   "exp": exp,
   "iat": iat,
   ... etc ...
}

// Do these last so they can't be overwritten by otherClaims
for key, value := importantClaims {
    claims[key] = value
}

token.Claims = claims

One concern here would be about the otherClaims overwriting important values you want to control (such as exp).
There are a lot of valid solutions, but I don't know if any of them will look as tidy as you hope. I would advise you against being overly clever in this context. It's more important that your code is correct and easy to debug than tidy, especially where security is concerned.

I'm very much new to Go, so i was not sure whether my solution looked like something experienced people would actually implement.
That idea with ensuring important claims are not overwritten by incoming token values is a rather good one, thank you!
I guess that solves my issue so i'll go ahead and close this issue.


How to trace all the call stack in application?

EntryMethod ---call--> Method 1 ---call--> Method 2 ---call--> Method 3 ---call--> Method 4
How to get the call tree like exception call stack as follow:
EntryMethod
........|
........|---- Mehtod 1
................|
................|---- Method 2
........................|
........................|---- Method 3
................................|
................................|---- Method 4
Now I just get some call info form external jars， none is my self-defined method.

Hi peterxuxin,
To get custom methods traced, you need to specify them in pinpoint.config.
There are 2 options related to this:

profiler.include - You can specify classes, or packages here. Every method in the specified classes (or packages) are traced. Does not work if the method is an EntryMethod.
profiler.entrypoint - You can specify methods here. Works for both EntryMethods and normal Methods. However, you can only specify 1 method from a single class currently (this is fixed in 1.5.2)

Let me know if you've more question.
Thanks

I got it. Thanks
Is there a maximum limit of profiler.include and  profiler.entrypoint  ?
And, when 1.5.2 will be released?

I don't think there is a maximum limit currently.
1.5.2 will probably be release in the next few weeks, but in the mean time you could try out 1.5.2-SNAPSHOT (if you can build from source).

No maximum limit is a good news, my application have lots of classes.
My proxy has some problems, I will try to build it again.

hi @Xylus
what is  " the method is an EntryMethod" mean?

Hi @wangshihui
"EntryMethod" is the method that starts off the span for a node. For example, a method that accepts a request in a servlet container would be an entry method.

I got that,thanks # @Xylus


Create a default settings.json file if does not exist

When starting suez if settings.json isn't found in the config folder a skeleton file should be created. Something like:
{
  "service": {
    "port": 3000
  },
  "apiTargets": [
    {
      "name": "CHANGEME",
      "proxyTarget": "https://CHANGE.ME/"
    }
  ]
}

Was this taken care of in your recent PR or is this new?

No. It's something I think should be done. You can probably create the above sample file as settings.default.json and then if settings.json isn't found it'll move copy the file over in app.js. I like doing this in my applications so developers can get up and running right away.
For Docker users this will be very useful since they won't be required to even download anything from SUEZ.

No. It's something I think should be done. You can probably create the above sample file as settings.default.json and then if settings.json isn't found it'll move copy the file over in app.js. I like doing this in my applications so developers can get up and running right away.
For Docker users this will be very useful since they won't be required to even download anything from SUEZ.

There is a sample file included. :-)

Please hold since #8 may make this a moot point as we could get rid of settings.json


SAA-1244: 🔧 Move to new dev prisoner search

Codecov Report
All modified lines are covered by tests :white_check_mark:
see 1 file with indirect coverage changes
:loudspeaker: Thoughts on this report? Let us know!.


Transition to 2018 edition

related with: https://github.com/rust-lang/rust/issues/58099

r? @Centril

r? @Centril


[WIP] Position-Independent Code

This is a very sketchy start to supporting PIC in Cretonne, working with @tyler.
I have added a setting to base for PIC, used an isa predicate to make intel encodings for function addresses and global syms that we can't use under PIC illegal, and added a filetest to track progress on finding encodings.
Here's the code gcc generates for calling an extern function pointer, compiled with -fPIC, objdump -d -r:
  29:	48 8b 05 00 00 00 00 	mov    0x0(%rip),%rax        # 30 <bat+0xb>
			2c: R_X86_64_REX_GOTPCRELX	fnptr-0x4
  30:	48 8b 00             	mov    (%rax),%rax
  33:	ff d0                	callq  *%rax

I sketched, with comments, how I think we need to transform func_addr in the intel legalizer:
https://github.com/stoklund/cretonne/compare/master...pchickey:pch/pic?expand=1#diff-578c3cce3b96d79aeee8ca5f36c447a1R57
It seems like we need a rip-relative mov instruction in the intel instructions. It seems like it should have a REX.W + 8b /r encoding, plus a reloc - we think this would be a variation of urm in intel/recipes.py with just adding a relocation to emit?
We also aren't clear on exactly how to create a new instruction to apply that recipe to, create the encoding for, and use from the legalizer.

We hacked up an encoding:
https://github.com/stoklund/cretonne/pull/206/commits/e778cee616c3562b15bed72879454c03107d4037#diff-a2fd9639f31da67c58bb418a70792ea4R580
To get the rip-relative stuff to encode we put some "magic constants" into the modrm_rm and rex2 functions, there's probably a cleaner way to do that.
With that, we have the filetest passing for just the func_addr portions, which is a start.

This direction looks good to me.
FWIW, I just fixed a bug in gen_encoding.py so you can now use isap= predicates on recipes as well as encodings, should you need that.

Hey there, @stoklund. This appears to be working in our tests.
I started looking into how I would implement this for 32-bit mode and it appears to be a major pain. I'm wondering whether PIC for 32-bit is a necessity. We can do it, if necessary, but I'd definitely prefer not to.
We need to clean up the abuses of modrm_rm and rex2 in the recipes. As well as some kind of documentation for the PIC setting. But otherwise, I'm wondering what else you'd like to see in this PR to make it mergeable.
Thanks!

Also, I'm not sure if we've missed any instructions that are not PIC-safe. As far as I could see, it was only the gvaddr and fnaddr, but there might be others that I don't know about.

Ah, I see that we need to adjust the call instruction as well. Although I believe the only difference is the relocation type.

Thanks for working on this! This week is Mozilla's all-hands meeting in Austin, so @sunfishcode and I may be a bit less responsive than usual.
I don't think you need to worry about 32-bit PIC code. It is indeed a pain, and there are other more serious things missing before we can compile for 32-bit targets. We will get to it eventually, but you shouldn't let it block you.
One comment for this PR: I am not so happy about exposing the isa::{arm32,arm64,intel,riscv} sub-modules as public API because they contain a lot of details are are likely to change, and they can be enabled and disabled individually with feature flags. OTOH, I agree that the Reloc type exposed in the CodeSink trait is not very user-friendly, and we should fix it by exposing an enum to clients.
I think that instead of having a per-ISA enum with relocations, we should just have one big enum containing enough relocations for all ISAs. I think that all of the relocations we have defined for the Intel ISA could be useful for other ISAs too, and even if we have relocations that only make sense for one ISA, they can still live in one big enum.
So instead of exposing the intel::RelocKind, we should:

Replace the global binemit::Reloc struct type with an enum that contains enough relocations for everybody. Since the other ISAs are not fully developed yet, I think this amounts to adding a Call variant to the variants that are currently defined for Intel.
Drop the TargetIsa::reloc_names() method and simply have a Display implementation for the new Reloc enum. Compare to impl Display for IntCC for example.

Does that make sense? It might be better to open a new PR with this change, to keep things separate.

Your suggestion about the relocation names makes sense and is pretty straightforward. I guess I hit friction decoding Reloc because I'm the first user to give it a try :). I will make that change as a separate PR.

Thanks for merging the reloc refactor so quickly!
I did a big cleanup of our branch, squashing it down to a small number of independent commits. I also rewrote the PR description.
I believe the only thing left to do is fixup the magic constants in the recipes, which we'll work on this afternoon.

I think this is ready to go now


Support reading contract data and events from blockchain

Basic SDK with blockchain read api's
Add blockchain read functions to daemon using Web3
Demo app reading Onyx staking data from ethereum
Some refactoring of Launcher ipc request handling

@avclarke Changes look great, nice to have this app in!
I'm mostly wondering about how we can go around the hardcoded app path in the launcher, and make the app available without preventing other apps from being launched?

@avclarke I think it's OK to comment out the failing integration test for now, I'll add it back in my branch as I'll need to change how the launcher handles the manifest file anyways.


Support nodes with existing docker installation

Kismatic should be able to detect if docker is already installed and skip the package installation.
Supporting machines with running docker will allow users to configure docker in custom ways and possibly use different versions of docker.
The only requirement for the machine will be to have the docker command line and docker be managed by systemd with a service docker. (this is the default configuration when installing docker packages)

What are the upgrade implications here? This needs to be thought through and considered.


Powershell gallery

You may want to consider publishing this to the powershell gallery. https://www.powershellgallery.com basically apt for powershell modules and what not.

+1, would be really good for specifying sumo-powershell-sdk as a requirement for other modules, as this now seems to be possible with PowerShellGet (without having to use psget)

https://github.com/SumoLogic/sumo-powershell-sdk/tree/ps_gallery


Check regexp on pattern attribute in Annotation file

Check if can be added regexp on the pattern attribute, be able to match with more complex regexp than a single *.
Also, change examples (or add new ones) with regexp.

Added to be able to make any kind of regexp on the pattern parameter in annotation file.


github.io documentation restructuring

Description: This PR restructures the current github.io. Contents are intact and migrated from the old documentation with minor updates and additions
Objective:

Easier for a user to navigate (get started guide, feature list, etc.)
Easier for developer team to maintain and expand upon
Better aligns with other Intel AI software documentation while drawing inspiration from ecosystem FL documentation

Added:

get started guide (using existing examples and installation instructions)
release notes (keep users in the loop with changes in a more self-contained way)
blogs & publications (for better promotion to dev community)
features (collated existing docs into one place to better highlight features)

This is a fantastic PR! The reorganization of the documentation is a massive improvement and it's clear that a lot of effort has been put into this. The structure is much more intuitive and user-friendly, which will be appreciated by all the users!. I really like the sections:

Features page, the addition of the Features/ aggregation algorithms <- liked!
Contributing to OpenFL <- especially this
Releases
Blog and Publications

I only request changes on minor comments or typos. This is a significant contribution. Well done Kevin!

I realized I forgot to mention something in my previous comment. I've been thinking that perhaps we should consider adding a [+] icon to indicate where sections can be expanded. However, I'm a bit torn on this. On one hand, it could enhance user navigation and ensure that no sections are overlooked. On the other hand, it might detract from the minimalist aesthetic that I really appreciate in the current design. It's a minor point, but I thought it was worth bringing up for discussion. Anyway, great job on this PR!

@kta-intel once the conflicts with docs/workflow_interface.rst are resolved, I will merge this

I think it should be good now. I'm not sure the exact cause of the merge conflict, but pulling the original /docs/workflow_interface.rst, merging the main dev branch, then deleting it seemed to resolve it.
Ran a final check to make sure everything ran as it did before.
Thanks again everyone for the review!


[Swipe to refresh]  swipe will recompose whole content when item has NavController

Description
swipe will recompose whole content when item has NavController
Steps to reproduce
create empty compose project
in build.gradle
implementation "com.google.accompanist:accompanist-swiperefresh:0.24.3-alpha"
implementation "androidx.navigation:navigation-compose:2.4.1"

copy code
class MainActivity : ComponentActivity() {
    override fun onCreate(savedInstanceState: Bundle?) {
        super.onCreate(savedInstanceState)
        setContent {
            MyApplicationTheme {
                Surface(
                    modifier = Modifier.fillMaxSize(),
                    color = MaterialTheme.colors.background
                ) {
                    Content()
                }
            }
        }
    }
}

@Composable
fun Content() {
    var refreshing by remember {
        mutableStateOf(false)
    }
    val swipeRefreshState = rememberSwipeRefreshState(isRefreshing = refreshing)
    val navController = rememberNavController()
    LaunchedEffect(refreshing) {
        if (refreshing) {
            delay(2000L)
            refreshing = false
        }
    }
    SwipeRefresh(
        swipeRefreshState,
        onRefresh = {
            refreshing = true
        },
        modifier = Modifier.fillMaxSize()
    ) {
        LazyColumn(modifier = Modifier
            .fillMaxSize()
        ) {

            items((1..100).map { "text ${it}" }) {
                Item(it , navController)          /* <<< ---- recompose whole content */
//                Item(it , null)         /* it's fine , no recompose happened */
            }

        }
    }

}

@Composable
fun Item(data: String, navController: NavController?) {

    Log.d(BuildConfig.APPLICATION_ID, "Item: ${data} recompose")
    Box(
        modifier = Modifier
            .fillMaxWidth()
            .height(80.dp)
            .clickable(enabled = false) { navController?.navigate("route...") } /* bad happened.. */
            .padding(horizontal = 10.dp, vertical = 10.dp)
            .background(Color.Yellow), contentAlignment = Alignment.Center
    )
    {
        Text(text = data)
    }
}

after Build , try to trigger refresh. You will see
Item: text 1 recompose
Item: text 2 recompose
Item: text 3 recompose
Item: text 4 recompose
Item: text 5 recompose
Item: text 6 recompose
Item: text 7 recompose
Item: text 8 recompose
Item: text 9 recompose
Item: text 10 recompose

using
Item(it , null)

no recompose happened
Expected behavior
no recompose happened  when item has navcontroller
Additional context

(1..100).map { "text ${it}" } this line probably needs to be remembered. It will recreate all your items on recompose otherwise, which i guess is happening because your navcontroller is at the highest scope and so is causing the recomposition lower.
If you change it to
val navController = rememberNavController()
val myItems = remember { (1..100).map { "text ${it}" }) }

...

LazyColumn {
   ...
   items(myItems) { Item(it , navController) }
}

does it still happen?
Another thing I would suggest would be to pass in an event handler rather than the nav holder to your item. This will make it more reusable and might also solve your issue.
@Composable
fun Item(data: String, onItemClick: ()->Unit)

I've tried it before, and yes, it still happens.
I think is related to NavController is not @Stable , when refresh happened  compose runtime think navcontroller maybe changed  , so trigger recompose .

That does make sense, but if that's the case I don't think there is much we can do about that from SwipeRefresh?

But that doesn't happen with lazycolum alone , I think SwipeRefresh trigger some size measurement (not sure)?    🤔
the recompose flow is :
SwipeRefresh trigger some size measurement  ->  navcontroller changed -> recompose item
(please correct me if I'm wrong)

I think it is probably this line then
val swipeRefreshState = rememberSwipeRefreshState(isRefreshing = refreshing)
The read of refreshing would cause the composition scope to be invalidated when refreshing changes. I'm not sure there is anything we can do about it but I will keep this issue open for now

I can explore a bit more but NavController not being @Stable makes sense as an explanation here. @ianhanniballake, what do you think?
That being said, recomposition isn't inherently bad. While we don't want to recompose more often than necessary, as long as the result of the composition stays the same (which it would in this example), we don't need to re-measure or re-draw. But maybe we can do something here anyway, I'd love to hear Ian's thoughts first.

Closing as it sounds like this is just working as intended from the additional feedback.


Update DM workflow to pull input from output of phosim workflow

Using the data catalog

Tom is putting his output here:
http://srs.slac.stanford.edu/DataCatalog/?folder=14603345

How do I know which and_files to use when processing these?

We should settle on some metadata (e.g., from the instance catalog, override file, etc.) to register these files with.

Workflow updated for multiple filters.

Sweet! :-)
On Thu, Mar 3, 2016 at 6:44 PM, Tony Johnson<EMAIL_ADDRESS>wrote:

[image: twinklesdm]
https://cloud.githubusercontent.com/assets/11215747/13516518/d3ce9602-e16f-11e5-9357-c9d2b8a541a4.gif
Workflow updated for multiple filters.
—
Reply to this email directly or view it on GitHub
https://github.com/DarkEnergyScienceCollaboration/Twinkles/issues/113#issuecomment-192069682
.


Add instruction on how to compile on cluster with muparser

Description of the problem
The new parsing class prevented compilation on the Digital Alliance clusters.
Description of the solution
An instruction on which module to load to allow compilation is added.
How Has This Been Tested?
Compilation of deal.II and Lethe on the cluster works, and all tests pass.
Documentation

[doc/source/installation/digital_alliance.rst] The required module is added.

Thanks man, good job finding a nice fix for this


controller: Handle lack of transaction in DeploymentRepo.Add

How hard is it to fix the TODO rather than add this workaround?

@josephglanville good point, DeploymentRepo.Add has access to the pgx.ConnPool, so I will use that to create the transaction for the rest of the method.

@josephglanville turns out switching to *pgx.Tx isn't a straight forward switch, getting the following error:
Cannot encode *string into oid 2950 - *string must implement Encoder or be converted to a string

I'm sure we could fix this up, but I am tempted to merge this and switch to pgx separately.

Yeah, it definitely looks that way for now. Was worth a shot though. :)

@titanous comments addressed

LGTM pending comment, but we should really get a transaction in here, this is bad.


Custom Swagger UI

Swagger has the ability to have custom layouts https://swagger.io/docs/open-source-tools/swagger-ui/customization/custom-layout/
How would I go about implementing that with javalin?

You probably have to write your own HTML for Swagger UI and point that to your OpenAPI JSON.

Specifically, if you aren't familiar with how it works
OpenApiOptions(applicationInfo).path("/swagger-docs")

When you do what is shown above, that hosts the OpenApi Json spec, not the UI at /swagger-docs (which might be a little misleading?). You can then point anything you like at it that can read that spec to display a UI. For example we use a static file served with that endpoint configured that reads the "/swagger-docs" endpoint and displays our UI


Document how to generate SVGs in Jupyter notebooks

The majority of our authors use VSCode, which will by default generate PNGs rather than the superior SVGs. We should tell them how to set up VSCode to use SVGs instead.
This is probably a global config you set for Jupyter?
This change would help avoid the need for https://github.com/Qiskit/documentation/pull/1226.

I'm happy to add the documentation, but I'm not sure how effective it is to keep adding things like this to our READMEs. They already contain a lot of information, only some of which is relevant to each contributor, and I doubt contributors review them regularly for updates.
I think ideally we'd have a unified workflow which everyone follows, then we could do things like switch to SVGs, update packages, etc. without needing contributors to set their config or remember to run another script. This is kinda what I'd hoped for with tox, but it doesn't seem to have stuck. Maybe we should chat to the contributors and work out if there's a better way to integrate it.
A potential solution is to incorporate something like the "auto execute" function from #601. We know for a fact all contributors go through PRs, and it makes running tox a one-step process with no local installation.
I do still think it's worth adding processes such as how to deploy tutorials; contributors will seek those out when they need them. I just think it's kinda futile adding rules and guidelines.

In the backlog sweep, we agreed with you, Frank. Rather than telling people how to better configure VSCode, we should be directing them to use tox for the final run. It's fine to use VSCode/Jupyter when iterating, but we want tox for the final execution so that we are all using a consistent environment.
So this issue is now revisiting the README to see if we can make this expectation more clear.


add flag gt_multiplier to evaluator

Users are allowed to fully configure the time limit. If gt_multiplier=K, then the time limit would be max(min_time_limit, K * gt_time).
Partly resolve #5.

Can you add timeout in the variable naming so that ppl will know it is related to time. Thanks.


Support sysconfig for init.d configuration, relates to #51

The sysconfig is not actually created but that is fine.

@traylenator we will merge this and shortly add the sysconfig file


How to call translation helpers on js?

I use  simple-schema with auto-form to generate form like this:
Branch = new SimpleSchema({
    khName: {
        type: String,
        label: "Kh Name", // {{_ "khName"}
        //unique: true,
        max: 200
    },
    khShortName: {
        type: String,
        label: "Kh Short Name",
        //unique: true,
        max: 200
    },
........................

So i want to call translation on js of simple-schema.

Hi,
Telescope project had the same issue take a look on what they did:
https://github.com/TelescopeJS/Telescope/search?utf8=✓&q=label
-Daniel
Meteor Developer, MeteorSpark

Thanks for your reply, but I don't understand.

Basically, whenever they want to print the label in the UI they pass it as a parameter to the {{_ }} helper.
See for example: https://github.com/TelescopeJS/Telescope/blob/bdc3e4eba4ae521b8490fb871a32654c7555bb63/client/views/nav/menu_item.html#L4
In their lang files they use the labels they used in the schema definitions as the key for translation.
See for example the daily label and its translations :
https://github.com/TelescopeJS/Telescope/blob/bdc3e4eba4ae521b8490fb871a32654c7555bb63/packages/telescope-daily/lib/daily.js#L5
https://github.com/TelescopeJS/Telescope/search?utf8=✓&q=daily
Hope that helps!
-Daniel

Excuse me, have any js function to do this like i18n.get('file.obj.key') of ostrio:i18n ?
because I try see your link, but don't understand.

I managed to do it with the following call. Note the double underscore.
`TAPi18n.__('my_string_to_translate');
or optionally
`TAPi18n.__('my_string_to_translate', my.argument);

Thanks


Project creation does not happen on first chef-client

Projects are currently not created on the first chef-client run (in test-kitchen at least, Im not sure about with a real node). I think there is an issue with apache or rundeckd restarting too close to right before the rd-project -p #{project} -a create command is run in the server_install recipe.
also, it doesnt help that the project creation is guarded by
    # will return 0 if grep matches
    # only run if project does not exist
    only_if "rd-jobs -p #{project} list 2>&1 | grep -q '^ERROR .*project does not exist'"

I assume rd-jobs is returning some other error (rundeckd not running perhaps) and project creation is being skipped.
I identified this bug by adding a localhost port forward from my mac into the vm (8080 on host => 80 on guest), logging in as the admin, and seeing no projects. After converging the node again, I login and see the localhost project. You can also see in the chef-client output from the initial run:
         * bash[check-project-localhost] action run (skipped due to only_if)

and from the second run
         * bash[check-project-localhost] action run
           - execute "bash"  "/tmp/chef-script20170128-5488-f89mr3"

When projects are managed via the api client, this should be resolved I think. If not, the issue will become more obvious (the api will respond with a 500).

I've added the following into the server_install recipe just before the project create command would be run:
  execute "rd-jobs -p #{project} list 2>&1" do
    live_stream true
  end

And the first chef-client results in
         * execute[rd-jobs -p localhost list 2>&1] action run
           [execute] Error: Error making server request to http://localhost:4440: Error occurred while trying to authenticate to server: Connection refused (Connection refused)

           ================================================================================
           Error executing action `run` on resource 'execute[rd-jobs -p localhost list 2>&1]'
           ================================================================================

           Mixlib::ShellOut::ShellCommandFailed
           ------------------------------------
           Expected process to exit with [0], but received '1'
           ---- Begin output of rd-jobs -p localhost list 2>&1 ----
           STDOUT: Error: Error making server request to http://localhost:4440: Error occurred while trying to authenticate to server: Connection refused (Connection refused)
           STDERR:
           ---- End output of rd-jobs -p localhost list 2>&1 ----
           Ran rd-jobs -p localhost list 2>&1 returned 1

           Resource Declaration:
           ---------------------
           # In /tmp/kitchen/cache/cookbooks/rundeck/recipes/server_install.rb

           279:   execute "rd-jobs -p #{project} list 2>&1" do
           280:     live_stream true
           281:   end
           282:

           Compiled Resource:
           ------------------
           # Declared in /tmp/kitchen/cache/cookbooks/rundeck/recipes/server_install.rb:279:in `block in from_file'

           execute("rd-jobs -p localhost list 2>&1") do
             action [:run]
             retries 0
             retry_delay 2
             default_guard_interpreter :execute
             command "rd-jobs -p localhost list 2>&1"
             backup 5
             returns 0
             live_stream true
             declared_type :execute
             cookbook_name "rundeck"
             recipe_name "server_install"
           end

           Platform:
           ---------
           x86_64-linux

         * service[rundeck] action nothing (skipped due to action :nothing)

       Running handlers:
       [2017-01-29T00:35:24+00:00] ERROR: Running exception handlers
       Running handlers complete
       [2017-01-29T00:35:24+00:00] ERROR: Exception handlers complete
       Chef Client failed. 37 resources updated in 01 minutes 46 seconds
       [2017-01-29T00:35:24+00:00] FATAL: Stacktrace dumped to /tmp/kitchen/cache/chef-stacktrace.out
       [2017-01-29T00:35:24+00:00] FATAL: Please provide the contents of the stacktrace.out file if you file a bug report
       [2017-01-29T00:35:24+00:00] ERROR: execute[rd-jobs -p localhost list 2>&1] (rundeck::server_install line 279) had an error: Mixlib::ShellOut::ShellCommandFailed: Expected process to exit with [0], but received '1'
       ---- Begin output of rd-jobs -p localhost list 2>&1 ----
       STDOUT: Error: Error making server request to http://localhost:4440: Error occurred while trying to authenticate to server: Connection refused (Connection refused)
       STDERR:
       ---- End output of rd-jobs -p localhost list 2>&1 ----
       Ran rd-jobs -p localhost list 2>&1 returned 1
       [2017-01-29T00:35:25+00:00] FATAL: Chef::Exceptions::ChildConvergeError: Chef run process exited unsuccessfully (exit code 1)

Running chef-client minutes later resulted in the same. Turns out rundeck isnt running yet when projects are trying to be created. this project logic needs to be moved out of rundeck::server and into a new recipe after rundeck::apache is run.


DS Priority Queue

DS: Priority queue
Languages Supported: C++

I want to work on this. Please assign this to me.

I would like to work on this issue in C++.

I would like to work on this issue in Python and C.

I would like to contribute to this issue in java

I would like to contribute to this issue in java

I would like to contribute to this issue in c.

I can contribute on this in both C and CPP.

I would like to work on this in java

I can work on this for Java or Python (or both). I am in CH20 and I am new to open source.

i would like to work on this in javascript.


Memory Limits

Feature Request
Proposal:
The server should have a memory configuration limit for operators to minimize the risk of internal processes triggering OOMs.
Current behavior:
There are several places in the system where large allocations are possible which could OOM the processe.
Generally, these have occurred around:

Queries - Usually using too broad of filter criteria causing too many series to be loaded at once.
In-Memory Index - High cardinality series cause the index to grow and OOM the process
Writes - Many in-flight writes with large batches and slow disks can cause lots of internal buffers to build while waiting for disks to write/sync.
Compactions - In some cases, large series could be loaded in order to deduplicate and chunk the points more optimally.

All of areas have had optimizations to reduce allocations or added limits to short-circuit expensive operations.  We've looked at adding memory limits for the in-memory index as well as queries, but have not found a good way to implement them.
Desired behavior:
The system should have a configuration setting for the max memory the process should be allowed to use before it aborts queries, writes, compactions, etc..  This setting would be for the process Heap as reported by runtime.ReadMemStats.
In critical sections of code that could potentially allocate a lot of memory, we would check if our Heap (or Sys/RSS) is above the limit, if so, the operation would return an error until memory is released by GC.  By default, the limit could be set to 90% of available memory.

@jwilder have we satisfied this by now?

@e-dard No.  We've reduced memory usage substantially, but this issue is about adding an explicit limit that when enabled would abort tasks that exceed the limit.

I think this feature will be most usefull if Influx only started such large jobs when it had enough available resources. This means that a compaction is only started if it can run it without having to abort it when 50 % is done due to lack of resources. Of course this can be hard to do when multiple go processes are working concurrently.


JIO tv showing/playing """UPDATE APP"""

UNABLE TO FIND WHERE TO UPDATE APP
KODI APP UPDATED LOGO CHANGE BUT PROBLEM IS SAME ""UPDATE THE APP VIDEO IS PLAYING ON ANY CHANNEL "
**continuously   FACING PROBLEM ON JIO TV LAST 3 MONTH
NOW NEW ISSUE FOUND WHEN PLAY CHANNEL IT IS SHOWING UPDATE CHANNEL
ONLY SOME CHANNEL PLAYING (3 OR 4)
@iMro0t/TEAM  KINDLY SOLVE
OR PROVIDE A NEW PLATFORM FOR JIO TV
THANKS

check now

check now

try aos tv or rts tv working fine in both mobile and tv

Pls check for jio tv....only add is showing how to update jio tv...

try aos tv or rts tv working fine in both mobile and tv

What are those? And how to get those?

https://drive.google.com/drive/folders/1wGMkB5dzQuOm2IbycSjoUa-FAu71QNLE
Use this zip to install (no need to uninstall previous version)

Bro thanks It worked.

not able to install it in firestick kodi

Addon

https://drive.google.com/drive/folders/1wGMkB5dzQuOm2IbycSjoUa-FAu71QNLE
Use this zip to install (no need to uninstall previous version)

Addon not for Android TV, kindly provide for Android TV


getting python error

hey,I tried to upgrade to your latest version of phoenixd-lnurl but with new version running from container ( in Portainer) i always get the error: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: ForwardRef._evaluate() missing 1 required keyword-only argument: 'recursive_guard'
seems to be some python version error, any idea?
_Phoenixd-lnurl_logs(1).txt

Hey, just to check, are you using the image ghcr.io/angusp/phoenixd-lnurl:latest?
But yeah that does look like some issue with a package has appeared, I'll see if I can replicate the issue and resolve it quickly for you -- should hopefully just need some package updates

Yes I tried that package and also so other packages since last month fail.The old  packages still works. So went back.

FYI running the container in portainer app on umbrel home server( portainer version 2.19.1) if that info herzlos helps to reproduce...

Hrm, seems like it might be an issue specifically with pydantic (a dependency) and Pyhton 3.12 https://github.com/pydantic/pydantic-core/pull/1299
"Just" downgrading to Python 3.11 for now seems to fix it, I'm stuck on an older version of Pydantic for now to maintain compatibility with the lnurl package.

@Funman2 thanks for the report, hopefully the :latest resolves for you (did for me!) but feel free to reopen if not 🤝

Yes it works now, thanks very much for the really quick fix!
Does this packet version have some sort of nostr zap support already included?

Does this packet version have some sort of nostr zap support already included?

Not yet, working on it in #11 -- it was blocked for a bit on an issue n phoenixd that's now been resolved (it would crash/hang on really big invoice descriptions, which is kinda what zaps are) so I just need to get around to finishing that pull request then we'll have proper zaps.


Update concept-components.md

Wrong namespace

Q
A

Is bugfix?
yes/no

New feature?
yes/no

Breaks BC?
yes/no

Tests pass?
yes/no

Fixed issues
comma-separated list of tickets # fixed by the PR, if any

Merged. Thanks!


The command "node ..." failed

I launched a project and set in my .env
NODE_PATH=node
APP_ENV=production
But I have this error:
The command "node W:\OSPanel\domains\example\storage\app/ssr\342a336ef63a6e8f23f78828aec4b37a.js" failed. Exit Code: 1(General error) Working directory: W:\OSPanel\domains\example\public Output: ================ Error Output: ================ "node" �� ���� ����७��� ��� ���譥� ��������, �ᯮ��塞�� �ணࠬ��� ��� ������ 䠩���. (View: W:\OSPanel\domains\example\resources\views\vue.blade.php)
Tell me please what can I do. Thank in advance

Same error here. I think it maybe path problem in window. So i changed path like "C:\Program file\nodejs" or "D:nodejs", but neither work. I also try "D\:nodejs" but it`s same... anyone help?

@ohdoski The reason of my problem was using serve command to run laravel. This way windows was unable to lock and terminate nodejs processes. But it works fine without using serve command.

I don't understand why I have same issue. I use Laragon with PHP + V8js extension enabled on Windows 10.
Made everything fine in configurations (as you noted) and got this error:
The command "node C:\proj\newsportal\storage\app/ssr\684c278c7f337480b7bd18d61d41ff85.js" failed.

Exit Code: 1(General error)

Working directory: C:\proj\newsportal\public

Output:
================

Error Output:
================
C:\proj\newsportal\storage\app\ssr\684c278c7f337480b7bd18d61d41ff85.js:2055
  if (next._normalized) {
           ^

TypeError: Cannot read property '_normalized' of undefined
    at normalizeLocation (C:\proj\newsportal\storage\app\ssr\684c278c7f337480b7bd18d61d41ff85.js:2055:12)
    at Object.match (C:\proj\newsportal\storage\app\ssr\684c278c7f337480b7bd18d61d41ff85.js:2125:20)
    at VueRouter.match (C:\proj\newsportal\storage\app\ssr\684c278c7f337480b7bd18d61d41ff85.js:3261:23)
    at AbstractHistory.transitionTo (C:\proj\newsportal\storage\app\ssr\684c278c7f337480b7bd18d61d41ff85.js:2649:27)
    at AbstractHistory.push (C:\proj\newsportal\storage\app\ssr\684c278c7f337480b7bd18d61d41ff85.js:3170:10)
    at VueRouter.push (C:\proj\newsportal\storage\app\ssr\684c278c7f337480b7bd18d61d41ff85.js:3341:16)
    at Module../resources/js/entry-server.js (C:\proj\newsportal\storage\app\ssr\684c278c7f337480b7bd18d61d41ff85.js:24572:54)
    at __webpack_require__ (C:\proj\newsportal\storage\app\ssr\684c278c7f337480b7bd18d61d41ff85.js:22:30)
    at Object.1 (C:\proj\newsportal\storage\app\ssr\684c278c7f337480b7bd18d61d41ff85.js:24615:18)
    at __webpack_require__ (C:\proj\newsportal\storage\app\ssr\684c278c7f337480b7bd18d61d41ff85.js:22:30)
 (View: C:\proj\newsportal\resources\views\layouts\app.blade.php) (View: C:\proj\newsportal\resources\views\layouts\app.blade.php)

When I remove $this->app->bind('path.public', function() { ... } from App\Providers\AppServiceProvider I still get same error:
C:\proj\newsportal\storage\app\ssr\4593066437a128745c5ebda80bc0ec0f.js:2055
  if (next._normalized) {
           ^

TypeError: Cannot read property '_normalized' of undefined

I just don't understand.

it took me almost 5 hours to figure this, and i think people who have figured it choose not to share a solution..

set ur NODE_PATH=node, and enable debug,
Do not use PHP artisan serve, i don't know what way yo break the grammar but please not use it , use virtual Host instead.

it took me almost 5 hours to figure this, and i think people who have figured it choose not to share a solution..

set ur NODE_PATH=node, and enable debug,
Do not use PHP artisan serve, i don't know what way yo break the grammar but please not use it , use virtual Host instead.

kindly tell how to use virtual host. ???

I couldn't solve the problem
I use invoiceninja version 5.0.17

This is the error message
The command "node ^"H:^\xampp^\htdocs^\invoiceninja5017-2^\vendor^\spatie^\browsershot^\src/../bin/browser.js^" ^"^{^^"url^^":^^"file:^/^/C:^^\Users^^\AYMEN^^\AppData^^\Local^^\Temp^^\1471831584-0559500001601456126^^\index.html^^",^^"action^^":^^"pdf^^",^^"options^^":^{^^"args^^":^[^^"--no-sandbox^^"^],^^"viewport^^":^{^^"width^^":800,^^"height^^":600,^^"deviceScaleFactor^^":1^},^^"displayHeaderFooter^^":false,^^"waitUntil^^":^^"networkidle0^^",^^"printBackground^^":true^}^}^"" failed.
Exit Code: 1(General error)
Working directory: H:\xampp\htdocs\invoiceninja5017-2\public
Output:
Error Output:
'node' is not recognized as an internal or external command,
operable program or batch file.
(View: H:\xampp\htdocs\invoiceninja5017-2\resources\views\portal\ninja2020\invoices\show.blade.php)

Also still struggling with this.
I tried some debugging and if I try the command that is generated on the command line, it works.
But not from a default XAMPP installation running under the same user :|

after suffering with this the solution was actually using a virtual host , just like @shoutershub said , and i even used the php artisan serve command BUT YOU HAVE TO USE VIRTUAL HOST
here is a useful link to configure it for your project
https://www.scmgalaxy.com/tutorials/how-to-create-virtual-host-in-laravel/
note: i think people who use laragon have virtual host by default


Added another Hori Real Arcade Pro 3 controller

The Hori Real Arcade Pro 3 I have has a Linux GUID different than the one that was already in the database.

Thank you


Add new Inception-v4 and related structure

Add new Inception-v4 and related structure

Would be awesome if we also have pre-trained models available.

hi@Trangle Thank you for your work!
But I pulled code from origin/master just now , I didn't see the inception v4 code .
where did you put it?

@albertyou2 example/image-classification

@Trangle ok i see.

@Trangle HI .I have tried your code of inception-v4 network and it works fine with 6464 images.
But why  it cost so much memory?
I use a geforce gtx 1070 display card and which has 8G memory in it. When i feed  299299 images into my model , mxnet crashed because of Out Of Memory. Then I resized the images to a small size which was (150,150),it also crashed because OOM .
Then I tried inception-v3 with 299*299 images ,it worked fine.
What 's your memory consume when training with inception-v4 ? Thank you

@albertyou2 inception-v4 takes 4000w+ parameters totally，and was designed for shape 299, in my machine with 980ti， i could only set batch-size at most 24 for running it.

@albertyou2

Layer (type)                                        Output Shape            Param #     Previous Layer
data(null)                                          3x299x299               0

conv_conv2d(Convolution)                            32x149x149              896         data

conv_batchnorm(BatchNorm)                           32x149x149              64          conv_conv2d

conv_relu(Activation)                               32x149x149              0           conv_batchnorm

conv_1_conv2d(Convolution)                          32x147x147              9248        conv_relu

conv_1_batchnorm(BatchNorm)                         32x147x147              64          conv_1_conv2d

conv_1_relu(Activation)                             32x147x147              0           conv_1_batchnorm

conv_2_conv2d(Convolution)                          64x147x147              18496       conv_1_relu

conv_2_batchnorm(BatchNorm)                         64x147x147              128         conv_2_conv2d

conv_2_relu(Activation)                             64x147x147              0           conv_2_batchnorm

pool(Pooling)                                       64x73x73                0           conv_2_relu

conv_3_conv2d(Convolution)                          80x73x73                5200        pool

conv_3_batchnorm(BatchNorm)                         80x73x73                160         conv_3_conv2d

conv_3_relu(Activation)                             80x73x73                0           conv_3_batchnorm

conv_4_conv2d(Convolution)                          192x71x71               138432      conv_3_relu

conv_4_batchnorm(BatchNorm)                         192x71x71               384         conv_4_conv2d

conv_4_relu(Activation)                             192x71x71               0           conv_4_batchnorm

pool1(Pooling)                                      192x35x35               0           conv_4_relu

mixed_conv_conv2d(Convolution)                      64x35x35                12352       pool1

mixed_conv_batchnorm(BatchNorm)                     64x35x35                128         mixed_conv_conv2d

mixed_conv_relu(Activation)                         64x35x35                0           mixed_conv_batchnorm

mixed_tower_conv_conv2d(Convolution)                48x35x35                9264        pool1

mixed_tower_conv_batchnorm(BatchNorm)               48x35x35                96          mixed_tower_conv_conv2d

mixed_tower_conv_relu(Activation)                   48x35x35                0           mixed_tower_conv_batchnorm

mixed_tower_conv_1_conv2d(Convolution)              64x35x35                76864       mixed_tower_conv_relu

mixed_tower_conv_1_batchnorm(BatchNorm)             64x35x35                128         mixed_tower_conv_1_conv2d

mixed_tower_conv_1_relu(Activation)                 64x35x35                0           mixed_tower_conv_1_batchnorm

mixed_tower_1_conv_conv2d(Convolution)              64x35x35                12352       pool1

mixed_tower_1_conv_batchnorm(BatchNorm)             64x35x35                128         mixed_tower_1_conv_conv2d

mixed_tower_1_conv_relu(Activation)                 64x35x35                0           mixed_tower_1_conv_batchnorm

mixed_tower_1_conv_1_conv2d(Convolution)            96x35x35                55392       mixed_tower_1_conv_relu

mixed_tower_1_conv_1_batchnorm(BatchNorm)           96x35x35                192         mixed_tower_1_conv_1_conv2d

mixed_tower_1_conv_1_relu(Activation)               96x35x35                0           mixed_tower_1_conv_1_batchnorm

mixed_tower_1_conv_2_conv2d(Convolution)            96x35x35                83040       mixed_tower_1_conv_1_relu

mixed_tower_1_conv_2_batchnorm(BatchNorm)           96x35x35                192         mixed_tower_1_conv_2_conv2d

mixed_tower_1_conv_2_relu(Activation)               96x35x35                0           mixed_tower_1_conv_2_batchnorm

avg_pool_mixed_pool(Pooling)                        192x35x35               0           pool1

mixed_tower_2_conv_conv2d(Convolution)              32x35x35                6176        avg_pool_mixed_pool

mixed_tower_2_conv_batchnorm(BatchNorm)             32x35x35                64          mixed_tower_2_conv_conv2d

mixed_tower_2_conv_relu(Activation)                 32x35x35                0           mixed_tower_2_conv_batchnorm

ch_concat_mixed_chconcat(Concat)                    256x35x35               0           mixed_conv_relu
mixed_tower_conv_1_relu
mixed_tower_1_conv_2_relu
mixed_tower_2_conv_relu

mixed_1_conv_conv2d(Convolution)                    64x35x35                16448       ch_concat_mixed_chconcat

mixed_1_conv_batchnorm(BatchNorm)                   64x35x35                128         mixed_1_conv_conv2d

mixed_1_conv_relu(Activation)                       64x35x35                0           mixed_1_conv_batchnorm

mixed_1_tower_conv_conv2d(Convolution)              48x35x35                12336       ch_concat_mixed_chconcat

mixed_1_tower_conv_batchnorm(BatchNorm)             48x35x35                96          mixed_1_tower_conv_conv2d

mixed_1_tower_conv_relu(Activation)                 48x35x35                0           mixed_1_tower_conv_batchnorm

mixed_1_tower_conv_1_conv2d(Convolution)            64x35x35                76864       mixed_1_tower_conv_relu

mixed_1_tower_conv_1_batchnorm(BatchNorm)           64x35x35                128         mixed_1_tower_conv_1_conv2d

mixed_1_tower_conv_1_relu(Activation)               64x35x35                0           mixed_1_tower_conv_1_batchnorm

mixed_1_tower_1_conv_conv2d(Convolution)            64x35x35                16448       ch_concat_mixed_chconcat

mixed_1_tower_1_conv_batchnorm(BatchNorm)           64x35x35                128         mixed_1_tower_1_conv_conv2d

mixed_1_tower_1_conv_relu(Activation)               64x35x35                0           mixed_1_tower_1_conv_batchnorm

mixed_1_tower_1_conv_1_conv2d(Convolution)          96x35x35                55392       mixed_1_tower_1_conv_relu

mixed_1_tower_1_conv_1_batchnorm(BatchNorm)         96x35x35                192         mixed_1_tower_1_conv_1_conv2d

mixed_1_tower_1_conv_1_relu(Activation)             96x35x35                0           mixed_1_tower_1_conv_1_batchnorm

mixed_1_tower_1_conv_2_conv2d(Convolution)          96x35x35                83040       mixed_1_tower_1_conv_1_relu

mixed_1_tower_1_conv_2_batchnorm(BatchNorm)         96x35x35                192         mixed_1_tower_1_conv_2_conv2d

mixed_1_tower_1_conv_2_relu(Activation)             96x35x35                0           mixed_1_tower_1_conv_2_batchnorm

avg_pool_mixed_1_pool(Pooling)                      256x35x35               0           ch_concat_mixed_chconcat

mixed_1_tower_2_conv_conv2d(Convolution)            64x35x35                16448       avg_pool_mixed_1_pool

mixed_1_tower_2_conv_batchnorm(BatchNorm)           64x35x35                128         mixed_1_tower_2_conv_conv2d

mixed_1_tower_2_conv_relu(Activation)               64x35x35                0           mixed_1_tower_2_conv_batchnorm

ch_concat_mixed_1_chconcat(Concat)                  288x35x35               0           mixed_1_conv_relu
mixed_1_tower_conv_1_relu
mixed_1_tower_1_conv_2_relu
mixed_1_tower_2_conv_relu

mixed_2_conv_conv2d(Convolution)                    64x35x35                18496       ch_concat_mixed_1_chconcat

mixed_2_conv_batchnorm(BatchNorm)                   64x35x35                128         mixed_2_conv_conv2d

mixed_2_conv_relu(Activation)                       64x35x35                0           mixed_2_conv_batchnorm

mixed_2_tower_conv_conv2d(Convolution)              48x35x35                13872       ch_concat_mixed_1_chconcat

mixed_2_tower_conv_batchnorm(BatchNorm)             48x35x35                96          mixed_2_tower_conv_conv2d

mixed_2_tower_conv_relu(Activation)                 48x35x35                0           mixed_2_tower_conv_batchnorm

mixed_2_tower_conv_1_conv2d(Convolution)            64x35x35                76864       mixed_2_tower_conv_relu

mixed_2_tower_conv_1_batchnorm(BatchNorm)           64x35x35                128         mixed_2_tower_conv_1_conv2d

mixed_2_tower_conv_1_relu(Activation)               64x35x35                0           mixed_2_tower_conv_1_batchnorm

mixed_2_tower_1_conv_conv2d(Convolution)            64x35x35                18496       ch_concat_mixed_1_chconcat

mixed_2_tower_1_conv_batchnorm(BatchNorm)           64x35x35                128         mixed_2_tower_1_conv_conv2d

mixed_2_tower_1_conv_relu(Activation)               64x35x35                0           mixed_2_tower_1_conv_batchnorm

mixed_2_tower_1_conv_1_conv2d(Convolution)          96x35x35                55392       mixed_2_tower_1_conv_relu

mixed_2_tower_1_conv_1_batchnorm(BatchNorm)         96x35x35                192         mixed_2_tower_1_conv_1_conv2d

mixed_2_tower_1_conv_1_relu(Activation)             96x35x35                0           mixed_2_tower_1_conv_1_batchnorm

mixed_2_tower_1_conv_2_conv2d(Convolution)          96x35x35                83040       mixed_2_tower_1_conv_1_relu

mixed_2_tower_1_conv_2_batchnorm(BatchNorm)         96x35x35                192         mixed_2_tower_1_conv_2_conv2d

mixed_2_tower_1_conv_2_relu(Activation)             96x35x35                0           mixed_2_tower_1_conv_2_batchnorm

avg_pool_mixed_2_pool(Pooling)                      288x35x35               0           ch_concat_mixed_1_chconcat

mixed_2_tower_2_conv_conv2d(Convolution)            64x35x35                18496       avg_pool_mixed_2_pool

mixed_2_tower_2_conv_batchnorm(BatchNorm)           64x35x35                128         mixed_2_tower_2_conv_conv2d

mixed_2_tower_2_conv_relu(Activation)               64x35x35                0           mixed_2_tower_2_conv_batchnorm

ch_concat_mixed_2_chconcat(Concat)                  288x35x35               0           mixed_2_conv_relu
mixed_2_tower_conv_1_relu
mixed_2_tower_1_conv_2_relu
mixed_2_tower_2_conv_relu

mixed_3_conv_conv2d(Convolution)                    384x17x17               995712      ch_concat_mixed_2_chconcat

mixed_3_conv_batchnorm(BatchNorm)                   384x17x17               768         mixed_3_conv_conv2d

mixed_3_conv_relu(Activation)                       384x17x17               0           mixed_3_conv_batchnorm

mixed_3_tower_conv_conv2d(Convolution)              64x35x35                18496       ch_concat_mixed_2_chconcat

mixed_3_tower_conv_batchnorm(BatchNorm)             64x35x35                128         mixed_3_tower_conv_conv2d

mixed_3_tower_conv_relu(Activation)                 64x35x35                0           mixed_3_tower_conv_batchnorm

mixed_3_tower_conv_1_conv2d(Convolution)            96x35x35                55392       mixed_3_tower_conv_relu

mixed_3_tower_conv_1_batchnorm(BatchNorm)           96x35x35                192         mixed_3_tower_conv_1_conv2d

mixed_3_tower_conv_1_relu(Activation)               96x35x35                0           mixed_3_tower_conv_1_batchnorm

mixed_3_tower_conv_2_conv2d(Convolution)            96x17x17                83040       mixed_3_tower_conv_1_relu

mixed_3_tower_conv_2_batchnorm(BatchNorm)           96x17x17                192         mixed_3_tower_conv_2_conv2d

mixed_3_tower_conv_2_relu(Activation)               96x17x17                0           mixed_3_tower_conv_2_batchnorm

max_pool_mixed_3_pool(Pooling)                      288x17x17               0           ch_concat_mixed_2_chconcat

ch_concat_mixed_3_chconcat(Concat)                  768x17x17               0           mixed_3_conv_relu
mixed_3_tower_conv_2_relu
max_pool_mixed_3_pool

mixed_4_conv_conv2d(Convolution)                    192x17x17               147648      ch_concat_mixed_3_chconcat

mixed_4_conv_batchnorm(BatchNorm)                   192x17x17               384         mixed_4_conv_conv2d

mixed_4_conv_relu(Activation)                       192x17x17               0           mixed_4_conv_batchnorm

mixed_4_tower_conv_conv2d(Convolution)              128x17x17               98432       ch_concat_mixed_3_chconcat

mixed_4_tower_conv_batchnorm(BatchNorm)             128x17x17               256         mixed_4_tower_conv_conv2d

mixed_4_tower_conv_relu(Activation)                 128x17x17               0           mixed_4_tower_conv_batchnorm

mixed_4_tower_conv_1_conv2d(Convolution)            128x17x17               114816      mixed_4_tower_conv_relu

mixed_4_tower_conv_1_batchnorm(BatchNorm)           128x17x17               256         mixed_4_tower_conv_1_conv2d

mixed_4_tower_conv_1_relu(Activation)               128x17x17               0           mixed_4_tower_conv_1_batchnorm

mixed_4_tower_conv_2_conv2d(Convolution)            192x17x17               172224      mixed_4_tower_conv_1_relu

mixed_4_tower_conv_2_batchnorm(BatchNorm)           192x17x17               384         mixed_4_tower_conv_2_conv2d

mixed_4_tower_conv_2_relu(Activation)               192x17x17               0           mixed_4_tower_conv_2_batchnorm

mixed_4_tower_1_conv_conv2d(Convolution)            128x17x17               98432       ch_concat_mixed_3_chconcat

mixed_4_tower_1_conv_batchnorm(BatchNorm)           128x17x17               256         mixed_4_tower_1_conv_conv2d

mixed_4_tower_1_conv_relu(Activation)               128x17x17               0           mixed_4_tower_1_conv_batchnorm

mixed_4_tower_1_conv_1_conv2d(Convolution)          128x17x17               114816      mixed_4_tower_1_conv_relu

mixed_4_tower_1_conv_1_batchnorm(BatchNorm)         128x17x17               256         mixed_4_tower_1_conv_1_conv2d

mixed_4_tower_1_conv_1_relu(Activation)             128x17x17               0           mixed_4_tower_1_conv_1_batchnorm

mixed_4_tower_1_conv_2_conv2d(Convolution)          128x17x17               114816      mixed_4_tower_1_conv_1_relu

mixed_4_tower_1_conv_2_batchnorm(BatchNorm)         128x17x17               256         mixed_4_tower_1_conv_2_conv2d

mixed_4_tower_1_conv_2_relu(Activation)             128x17x17               0           mixed_4_tower_1_conv_2_batchnorm

mixed_4_tower_1_conv_3_conv2d(Convolution)          128x17x17               114816      mixed_4_tower_1_conv_2_relu

mixed_4_tower_1_conv_3_batchnorm(BatchNorm)         128x17x17               256         mixed_4_tower_1_conv_3_conv2d

mixed_4_tower_1_conv_3_relu(Activation)             128x17x17               0           mixed_4_tower_1_conv_3_batchnorm

mixed_4_tower_1_conv_4_conv2d(Convolution)          192x17x17               172224      mixed_4_tower_1_conv_3_relu

mixed_4_tower_1_conv_4_batchnorm(BatchNorm)         192x17x17               384         mixed_4_tower_1_conv_4_conv2d

mixed_4_tower_1_conv_4_relu(Activation)             192x17x17               0           mixed_4_tower_1_conv_4_batchnorm

avg_pool_mixed_4_pool(Pooling)                      768x17x17               0           ch_concat_mixed_3_chconcat

mixed_4_tower_2_conv_conv2d(Convolution)            192x17x17               147648      avg_pool_mixed_4_pool

mixed_4_tower_2_conv_batchnorm(BatchNorm)           192x17x17               384         mixed_4_tower_2_conv_conv2d

mixed_4_tower_2_conv_relu(Activation)               192x17x17               0           mixed_4_tower_2_conv_batchnorm

ch_concat_mixed_4_chconcat(Concat)                  768x17x17               0           mixed_4_conv_relu
mixed_4_tower_conv_2_relu
mixed_4_tower_1_conv_4_relu
mixed_4_tower_2_conv_relu

mixed_5_conv_conv2d(Convolution)                    192x17x17               147648      ch_concat_mixed_4_chconcat

mixed_5_conv_batchnorm(BatchNorm)                   192x17x17               384         mixed_5_conv_conv2d

mixed_5_conv_relu(Activation)                       192x17x17               0           mixed_5_conv_batchnorm

mixed_5_tower_conv_conv2d(Convolution)              160x17x17               123040      ch_concat_mixed_4_chconcat

mixed_5_tower_conv_batchnorm(BatchNorm)             160x17x17               320         mixed_5_tower_conv_conv2d

mixed_5_tower_conv_relu(Activation)                 160x17x17               0           mixed_5_tower_conv_batchnorm

mixed_5_tower_conv_1_conv2d(Convolution)            160x17x17               179360      mixed_5_tower_conv_relu

mixed_5_tower_conv_1_batchnorm(BatchNorm)           160x17x17               320         mixed_5_tower_conv_1_conv2d

mixed_5_tower_conv_1_relu(Activation)               160x17x17               0           mixed_5_tower_conv_1_batchnorm

mixed_5_tower_conv_2_conv2d(Convolution)            192x17x17               215232      mixed_5_tower_conv_1_relu

mixed_5_tower_conv_2_batchnorm(BatchNorm)           192x17x17               384         mixed_5_tower_conv_2_conv2d

mixed_5_tower_conv_2_relu(Activation)               192x17x17               0           mixed_5_tower_conv_2_batchnorm

mixed_5_tower_1_conv_conv2d(Convolution)            160x17x17               123040      ch_concat_mixed_4_chconcat

mixed_5_tower_1_conv_batchnorm(BatchNorm)           160x17x17               320         mixed_5_tower_1_conv_conv2d

mixed_5_tower_1_conv_relu(Activation)               160x17x17               0           mixed_5_tower_1_conv_batchnorm

mixed_5_tower_1_conv_1_conv2d(Convolution)          160x17x17               179360      mixed_5_tower_1_conv_relu

mixed_5_tower_1_conv_1_batchnorm(BatchNorm)         160x17x17               320         mixed_5_tower_1_conv_1_conv2d

mixed_5_tower_1_conv_1_relu(Activation)             160x17x17               0           mixed_5_tower_1_conv_1_batchnorm

mixed_5_tower_1_conv_2_conv2d(Convolution)          160x17x17               179360      mixed_5_tower_1_conv_1_relu

mixed_5_tower_1_conv_2_batchnorm(BatchNorm)         160x17x17               320         mixed_5_tower_1_conv_2_conv2d

mixed_5_tower_1_conv_2_relu(Activation)             160x17x17               0           mixed_5_tower_1_conv_2_batchnorm

mixed_5_tower_1_conv_3_conv2d(Convolution)          160x17x17               179360      mixed_5_tower_1_conv_2_relu

mixed_5_tower_1_conv_3_batchnorm(BatchNorm)         160x17x17               320         mixed_5_tower_1_conv_3_conv2d

mixed_5_tower_1_conv_3_relu(Activation)             160x17x17               0           mixed_5_tower_1_conv_3_batchnorm

mixed_5_tower_1_conv_4_conv2d(Convolution)          192x17x17               215232      mixed_5_tower_1_conv_3_relu

mixed_5_tower_1_conv_4_batchnorm(BatchNorm)         192x17x17               384         mixed_5_tower_1_conv_4_conv2d

mixed_5_tower_1_conv_4_relu(Activation)             192x17x17               0           mixed_5_tower_1_conv_4_batchnorm

avg_pool_mixed_5_pool(Pooling)                      768x17x17               0           ch_concat_mixed_4_chconcat

mixed_5_tower_2_conv_conv2d(Convolution)            192x17x17               147648      avg_pool_mixed_5_pool

mixed_5_tower_2_conv_batchnorm(BatchNorm)           192x17x17               384         mixed_5_tower_2_conv_conv2d

mixed_5_tower_2_conv_relu(Activation)               192x17x17               0           mixed_5_tower_2_conv_batchnorm

ch_concat_mixed_5_chconcat(Concat)                  768x17x17               0           mixed_5_conv_relu
mixed_5_tower_conv_2_relu
mixed_5_tower_1_conv_4_relu
mixed_5_tower_2_conv_relu

mixed_6_conv_conv2d(Convolution)                    192x17x17               147648      ch_concat_mixed_5_chconcat

mixed_6_conv_batchnorm(BatchNorm)                   192x17x17               384         mixed_6_conv_conv2d

mixed_6_conv_relu(Activation)                       192x17x17               0           mixed_6_conv_batchnorm

mixed_6_tower_conv_conv2d(Convolution)              160x17x17               123040      ch_concat_mixed_5_chconcat

mixed_6_tower_conv_batchnorm(BatchNorm)             160x17x17               320         mixed_6_tower_conv_conv2d

mixed_6_tower_conv_relu(Activation)                 160x17x17               0           mixed_6_tower_conv_batchnorm

mixed_6_tower_conv_1_conv2d(Convolution)            160x17x17               179360      mixed_6_tower_conv_relu

mixed_6_tower_conv_1_batchnorm(BatchNorm)           160x17x17               320         mixed_6_tower_conv_1_conv2d

mixed_6_tower_conv_1_relu(Activation)               160x17x17               0           mixed_6_tower_conv_1_batchnorm

mixed_6_tower_conv_2_conv2d(Convolution)            192x17x17               215232      mixed_6_tower_conv_1_relu

mixed_6_tower_conv_2_batchnorm(BatchNorm)           192x17x17               384         mixed_6_tower_conv_2_conv2d

mixed_6_tower_conv_2_relu(Activation)               192x17x17               0           mixed_6_tower_conv_2_batchnorm

mixed_6_tower_1_conv_conv2d(Convolution)            160x17x17               123040      ch_concat_mixed_5_chconcat

mixed_6_tower_1_conv_batchnorm(BatchNorm)           160x17x17               320         mixed_6_tower_1_conv_conv2d

mixed_6_tower_1_conv_relu(Activation)               160x17x17               0           mixed_6_tower_1_conv_batchnorm

mixed_6_tower_1_conv_1_conv2d(Convolution)          160x17x17               179360      mixed_6_tower_1_conv_relu

mixed_6_tower_1_conv_1_batchnorm(BatchNorm)         160x17x17               320         mixed_6_tower_1_conv_1_conv2d

mixed_6_tower_1_conv_1_relu(Activation)             160x17x17               0           mixed_6_tower_1_conv_1_batchnorm

mixed_6_tower_1_conv_2_conv2d(Convolution)          160x17x17               179360      mixed_6_tower_1_conv_1_relu

mixed_6_tower_1_conv_2_batchnorm(BatchNorm)         160x17x17               320         mixed_6_tower_1_conv_2_conv2d

mixed_6_tower_1_conv_2_relu(Activation)             160x17x17               0           mixed_6_tower_1_conv_2_batchnorm

mixed_6_tower_1_conv_3_conv2d(Convolution)          160x17x17               179360      mixed_6_tower_1_conv_2_relu

mixed_6_tower_1_conv_3_batchnorm(BatchNorm)         160x17x17               320         mixed_6_tower_1_conv_3_conv2d

mixed_6_tower_1_conv_3_relu(Activation)             160x17x17               0           mixed_6_tower_1_conv_3_batchnorm

mixed_6_tower_1_conv_4_conv2d(Convolution)          192x17x17               215232      mixed_6_tower_1_conv_3_relu

mixed_6_tower_1_conv_4_batchnorm(BatchNorm)         192x17x17               384         mixed_6_tower_1_conv_4_conv2d

mixed_6_tower_1_conv_4_relu(Activation)             192x17x17               0           mixed_6_tower_1_conv_4_batchnorm

avg_pool_mixed_6_pool(Pooling)                      768x17x17               0           ch_concat_mixed_5_chconcat

mixed_6_tower_2_conv_conv2d(Convolution)            192x17x17               147648      avg_pool_mixed_6_pool

mixed_6_tower_2_conv_batchnorm(BatchNorm)           192x17x17               384         mixed_6_tower_2_conv_conv2d

mixed_6_tower_2_conv_relu(Activation)               192x17x17               0           mixed_6_tower_2_conv_batchnorm

ch_concat_mixed_6_chconcat(Concat)                  768x17x17               0           mixed_6_conv_relu
mixed_6_tower_conv_2_relu
mixed_6_tower_1_conv_4_relu
mixed_6_tower_2_conv_relu

mixed_7_conv_conv2d(Convolution)                    192x17x17               147648      ch_concat_mixed_6_chconcat

mixed_7_conv_batchnorm(BatchNorm)                   192x17x17               384         mixed_7_conv_conv2d

mixed_7_conv_relu(Activation)                       192x17x17               0           mixed_7_conv_batchnorm

mixed_7_tower_conv_conv2d(Convolution)              192x17x17               147648      ch_concat_mixed_6_chconcat

mixed_7_tower_conv_batchnorm(BatchNorm)             192x17x17               384         mixed_7_tower_conv_conv2d

mixed_7_tower_conv_relu(Activation)                 192x17x17               0           mixed_7_tower_conv_batchnorm

mixed_7_tower_conv_1_conv2d(Convolution)            192x17x17               258240      mixed_7_tower_conv_relu

mixed_7_tower_conv_1_batchnorm(BatchNorm)           192x17x17               384         mixed_7_tower_conv_1_conv2d

mixed_7_tower_conv_1_relu(Activation)               192x17x17               0           mixed_7_tower_conv_1_batchnorm

mixed_7_tower_conv_2_conv2d(Convolution)            192x17x17               258240      mixed_7_tower_conv_1_relu

mixed_7_tower_conv_2_batchnorm(BatchNorm)           192x17x17               384         mixed_7_tower_conv_2_conv2d

mixed_7_tower_conv_2_relu(Activation)               192x17x17               0           mixed_7_tower_conv_2_batchnorm

mixed_7_tower_1_conv_conv2d(Convolution)            192x17x17               147648      ch_concat_mixed_6_chconcat

mixed_7_tower_1_conv_batchnorm(BatchNorm)           192x17x17               384         mixed_7_tower_1_conv_conv2d

mixed_7_tower_1_conv_relu(Activation)               192x17x17               0           mixed_7_tower_1_conv_batchnorm

mixed_7_tower_1_conv_1_conv2d(Convolution)          192x17x17               258240      mixed_7_tower_1_conv_relu

mixed_7_tower_1_conv_1_batchnorm(BatchNorm)         192x17x17               384         mixed_7_tower_1_conv_1_conv2d

mixed_7_tower_1_conv_1_relu(Activation)             192x17x17               0           mixed_7_tower_1_conv_1_batchnorm

mixed_7_tower_1_conv_2_conv2d(Convolution)          192x17x17               258240      mixed_7_tower_1_conv_1_relu

mixed_7_tower_1_conv_2_batchnorm(BatchNorm)         192x17x17               384         mixed_7_tower_1_conv_2_conv2d

mixed_7_tower_1_conv_2_relu(Activation)             192x17x17               0           mixed_7_tower_1_conv_2_batchnorm

mixed_7_tower_1_conv_3_conv2d(Convolution)          192x17x17               258240      mixed_7_tower_1_conv_2_relu

mixed_7_tower_1_conv_3_batchnorm(BatchNorm)         192x17x17               384         mixed_7_tower_1_conv_3_conv2d

mixed_7_tower_1_conv_3_relu(Activation)             192x17x17               0           mixed_7_tower_1_conv_3_batchnorm

mixed_7_tower_1_conv_4_conv2d(Convolution)          192x17x17               258240      mixed_7_tower_1_conv_3_relu

mixed_7_tower_1_conv_4_batchnorm(BatchNorm)         192x17x17               384         mixed_7_tower_1_conv_4_conv2d

mixed_7_tower_1_conv_4_relu(Activation)             192x17x17               0           mixed_7_tower_1_conv_4_batchnorm

avg_pool_mixed_7_pool(Pooling)                      768x17x17               0           ch_concat_mixed_6_chconcat

mixed_7_tower_2_conv_conv2d(Convolution)            192x17x17               147648      avg_pool_mixed_7_pool

mixed_7_tower_2_conv_batchnorm(BatchNorm)           192x17x17               384         mixed_7_tower_2_conv_conv2d

mixed_7_tower_2_conv_relu(Activation)               192x17x17               0           mixed_7_tower_2_conv_batchnorm

ch_concat_mixed_7_chconcat(Concat)                  768x17x17               0           mixed_7_conv_relu
mixed_7_tower_conv_2_relu
mixed_7_tower_1_conv_4_relu
mixed_7_tower_2_conv_relu

mixed_8_tower_conv_conv2d(Convolution)              192x17x17               147648      ch_concat_mixed_7_chconcat

mixed_8_tower_conv_batchnorm(BatchNorm)             192x17x17               384         mixed_8_tower_conv_conv2d

mixed_8_tower_conv_relu(Activation)                 192x17x17               0           mixed_8_tower_conv_batchnorm

mixed_8_tower_conv_1_conv2d(Convolution)            320x8x8                 553280      mixed_8_tower_conv_relu

mixed_8_tower_conv_1_batchnorm(BatchNorm)           320x8x8                 640         mixed_8_tower_conv_1_conv2d

mixed_8_tower_conv_1_relu(Activation)               320x8x8                 0           mixed_8_tower_conv_1_batchnorm

mixed_8_tower_1_conv_conv2d(Convolution)            192x17x17               147648      ch_concat_mixed_7_chconcat

mixed_8_tower_1_conv_batchnorm(BatchNorm)           192x17x17               384         mixed_8_tower_1_conv_conv2d

mixed_8_tower_1_conv_relu(Activation)               192x17x17               0           mixed_8_tower_1_conv_batchnorm

mixed_8_tower_1_conv_1_conv2d(Convolution)          192x17x17               258240      mixed_8_tower_1_conv_relu

mixed_8_tower_1_conv_1_batchnorm(BatchNorm)         192x17x17               384         mixed_8_tower_1_conv_1_conv2d

mixed_8_tower_1_conv_1_relu(Activation)             192x17x17               0           mixed_8_tower_1_conv_1_batchnorm

mixed_8_tower_1_conv_2_conv2d(Convolution)          192x17x17               258240      mixed_8_tower_1_conv_1_relu

mixed_8_tower_1_conv_2_batchnorm(BatchNorm)         192x17x17               384         mixed_8_tower_1_conv_2_conv2d

mixed_8_tower_1_conv_2_relu(Activation)             192x17x17               0           mixed_8_tower_1_conv_2_batchnorm

mixed_8_tower_1_conv_3_conv2d(Convolution)          192x8x8                 331968      mixed_8_tower_1_conv_2_relu

mixed_8_tower_1_conv_3_batchnorm(BatchNorm)         192x8x8                 384         mixed_8_tower_1_conv_3_conv2d

mixed_8_tower_1_conv_3_relu(Activation)             192x8x8                 0           mixed_8_tower_1_conv_3_batchnorm

max_pool_mixed_8_pool(Pooling)                      768x8x8                 0           ch_concat_mixed_7_chconcat

ch_concat_mixed_8_chconcat(Concat)                  1280x8x8                0           mixed_8_tower_conv_1_relu
mixed_8_tower_1_conv_3_relu
max_pool_mixed_8_pool

mixed_9_conv_conv2d(Convolution)                    320x8x8                 409920      ch_concat_mixed_8_chconcat

mixed_9_conv_batchnorm(BatchNorm)                   320x8x8                 640         mixed_9_conv_conv2d

mixed_9_conv_relu(Activation)                       320x8x8                 0           mixed_9_conv_batchnorm

mixed_9_tower_conv_conv2d(Convolution)              384x8x8                 491904      ch_concat_mixed_8_chconcat

mixed_9_tower_conv_batchnorm(BatchNorm)             384x8x8                 768         mixed_9_tower_conv_conv2d

mixed_9_tower_conv_relu(Activation)                 384x8x8                 0           mixed_9_tower_conv_batchnorm

mixed_9_tower_mixed_conv_conv2d(Convolution)        384x8x8                 442752      mixed_9_tower_conv_relu

mixed_9_tower_mixed_conv_batchnorm(BatchNorm)       384x8x8                 768         mixed_9_tower_mixed_conv_conv2d

mixed_9_tower_mixed_conv_relu(Activation)           384x8x8                 0           mixed_9_tower_mixed_conv_batchno

mixed_9_tower_mixed_conv_1_conv2d(Convolution)      384x8x8                 442752      mixed_9_tower_conv_relu

mixed_9_tower_mixed_conv_1_batchnorm(BatchNorm)     384x8x8                 768         mixed_9_tower_mixed_conv_1_conv2

mixed_9_tower_mixed_conv_1_relu(Activation)         384x8x8                 0           mixed_9_tower_mixed_conv_1_batch

mixed_9_tower_1_conv_conv2d(Convolution)            448x8x8                 573888      ch_concat_mixed_8_chconcat

mixed_9_tower_1_conv_batchnorm(BatchNorm)           448x8x8                 896         mixed_9_tower_1_conv_conv2d

mixed_9_tower_1_conv_relu(Activation)               448x8x8                 0           mixed_9_tower_1_conv_batchnorm

mixed_9_tower_1_conv_1_conv2d(Convolution)          384x8x8                 1548672     mixed_9_tower_1_conv_relu

mixed_9_tower_1_conv_1_batchnorm(BatchNorm)         384x8x8                 768         mixed_9_tower_1_conv_1_conv2d

mixed_9_tower_1_conv_1_relu(Activation)             384x8x8                 0           mixed_9_tower_1_conv_1_batchnorm

mixed_9_tower_1_mixed_conv_conv2d(Convolution)      384x8x8                 442752      mixed_9_tower_1_conv_1_relu

mixed_9_tower_1_mixed_conv_batchnorm(BatchNorm)     384x8x8                 768         mixed_9_tower_1_mixed_conv_conv2

mixed_9_tower_1_mixed_conv_relu(Activation)         384x8x8                 0           mixed_9_tower_1_mixed_conv_batch

mixed_9_tower_1_mixed_conv_1_conv2d(Convolution)    384x8x8                 442752      mixed_9_tower_1_conv_1_relu

mixed_9_tower_1_mixed_conv_1_batchnorm(BatchNorm)   384x8x8                 768         mixed_9_tower_1_mixed_conv_1_con

mixed_9_tower_1_mixed_conv_1_relu(Activation)       384x8x8                 0           mixed_9_tower_1_mixed_conv_1_bat

avg_pool_mixed_9_pool(Pooling)                      1280x8x8                0           ch_concat_mixed_8_chconcat

mixed_9_tower_2_conv_conv2d(Convolution)            192x8x8                 245952      avg_pool_mixed_9_pool

mixed_9_tower_2_conv_batchnorm(BatchNorm)           192x8x8                 384         mixed_9_tower_2_conv_conv2d

mixed_9_tower_2_conv_relu(Activation)               192x8x8                 0           mixed_9_tower_2_conv_batchnorm

ch_concat_mixed_9_chconcat(Concat)                  2048x8x8                0           mixed_9_conv_relu
mixed_9_tower_mixed_conv_relu
mixed_9_tower_mixed_conv_1_relu
mixed_9_tower_1_mixed_conv_relu
mixed_9_tower_1_mixed_conv_1_rel
mixed_9_tower_2_conv_relu

mixed_10_conv_conv2d(Convolution)                   320x8x8                 655680      ch_concat_mixed_9_chconcat

mixed_10_conv_batchnorm(BatchNorm)                  320x8x8                 640         mixed_10_conv_conv2d

mixed_10_conv_relu(Activation)                      320x8x8                 0           mixed_10_conv_batchnorm

mixed_10_tower_conv_conv2d(Convolution)             384x8x8                 786816      ch_concat_mixed_9_chconcat

mixed_10_tower_conv_batchnorm(BatchNorm)            384x8x8                 768         mixed_10_tower_conv_conv2d

mixed_10_tower_conv_relu(Activation)                384x8x8                 0           mixed_10_tower_conv_batchnorm

mixed_10_tower_mixed_conv_conv2d(Convolution)       384x8x8                 442752      mixed_10_tower_conv_relu

mixed_10_tower_mixed_conv_batchnorm(BatchNorm)      384x8x8                 768         mixed_10_tower_mixed_conv_conv2d

mixed_10_tower_mixed_conv_relu(Activation)          384x8x8                 0           mixed_10_tower_mixed_conv_batchn

mixed_10_tower_mixed_conv_1_conv2d(Convolution)     384x8x8                 442752      mixed_10_tower_conv_relu

mixed_10_tower_mixed_conv_1_batchnorm(BatchNorm)    384x8x8                 768         mixed_10_tower_mixed_conv_1_conv

mixed_10_tower_mixed_conv_1_relu(Activation)        384x8x8                 0           mixed_10_tower_mixed_conv_1_batc

mixed_10_tower_1_conv_conv2d(Convolution)           448x8x8                 917952      ch_concat_mixed_9_chconcat

mixed_10_tower_1_conv_batchnorm(BatchNorm)          448x8x8                 896         mixed_10_tower_1_conv_conv2d

mixed_10_tower_1_conv_relu(Activation)              448x8x8                 0           mixed_10_tower_1_conv_batchnorm

mixed_10_tower_1_conv_1_conv2d(Convolution)         384x8x8                 1548672     mixed_10_tower_1_conv_relu

mixed_10_tower_1_conv_1_batchnorm(BatchNorm)        384x8x8                 768         mixed_10_tower_1_conv_1_conv2d

mixed_10_tower_1_conv_1_relu(Activation)            384x8x8                 0           mixed_10_tower_1_conv_1_batchnor

mixed_10_tower_1_mixed_conv_conv2d(Convolution)     384x8x8                 442752      mixed_10_tower_1_conv_1_relu

mixed_10_tower_1_mixed_conv_batchnorm(BatchNorm)    384x8x8                 768         mixed_10_tower_1_mixed_conv_conv

mixed_10_tower_1_mixed_conv_relu(Activation)        384x8x8                 0           mixed_10_tower_1_mixed_conv_batc

mixed_10_tower_1_mixed_conv_1_conv2d(Convolution)   384x8x8                 442752      mixed_10_tower_1_conv_1_relu

mixed_10_tower_1_mixed_conv_1_batchnorm(BatchNorm)  384x8x8                 768         mixed_10_tower_1_mixed_conv_1_co

mixed_10_tower_1_mixed_conv_1_relu(Activation)      384x8x8                 0           mixed_10_tower_1_mixed_conv_1_ba

max_pool_mixed_10_pool(Pooling)                     2048x8x8                0           ch_concat_mixed_9_chconcat

mixed_10_tower_2_conv_conv2d(Convolution)           192x8x8                 393408      max_pool_mixed_10_pool

mixed_10_tower_2_conv_batchnorm(BatchNorm)          192x8x8                 384         mixed_10_tower_2_conv_conv2d

mixed_10_tower_2_conv_relu(Activation)              192x8x8                 0           mixed_10_tower_2_conv_batchnorm

ch_concat_mixed_10_chconcat(Concat)                 2048x8x8                0           mixed_10_conv_relu
mixed_10_tower_mixed_conv_relu
mixed_10_tower_mixed_conv_1_relu
mixed_10_tower_1_mixed_conv_relu
mixed_10_tower_1_mixed_conv_1_re
mixed_10_tower_2_conv_relu

global_pool(Pooling)                                2048x1x1                0           ch_concat_mixed_10_chconcat

flatten(Flatten)                                    2048                    0           global_pool

fc1(FullyConnected)                                 1000                    2050048     flatten

softmax(SoftmaxOutput)                              1000                    0           fc1
Total params: 23852832

as you see, the parameter of v3 is about half of v4.

@Trangle
SORRY  I was very busy these days.
recently I  changed the batch size to 16 , it works fine!
Nice work!

@Trangle you forgot to add the choices to train_imagenet.py:
parser.add_argument('--network', type=str, default='inception-bn',
                    choices = ['alexnet', 'vgg', 'googlenet', 'inception-bn', 'inception-bn-full', 'inception-v3', 'inception-resnet-v1', 'inception-resnet-v2', 'inception-v4'],

Awesome work!!

Any updates on this?

closed due to inactive.
feel free to rebase and reopen


creat dell_edge_gateway_5200

Thank you for contributing to the Azure IoT Plug and Play Models repository
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact<EMAIL_ADDRESS>with any additional questions or comments.
The repository pipeline will ensure minimum validation of incoming DTDL models.

PR validation steps are described in the tools Wiki.
The same validation can be run locally via the dmr-client command line tool.

Requested info template for model(s) submission
When submitting models to the repository we ask that you provide as much of the following meta information around your models and related devices as possible. This info will be used to improve Plug and Play.
:star2: Please replace the markdown comment examples with your own values.
Company Info

IoT Plug and Play Device Info

Model Submission Goals

Code Owners & Reserved Names

What is the problem that makes this unsuccessful

Sorry!
I accidentally clicked on this
Please help to repair

Hi @ADLINK-EPM
This PR still contains edits to existing interfaces.
I'd suggest to close this PR, and start a new one with the new interfaces you want to publish

... What is the problem that makes this unsuccessful

@ADLINK-EPM, in the future you are able to use the file view for the PR to see the submission issues more clearly

Or the action summary view
https://github.com/Azure/iot-plugandplay-models/actions/runs/2015028646

Hi All:
I see what's the problem
But I don't know the rules very well
I don't know where it was renamed
Can you elaborate on where to rename
or help me circle the areas that don't follow the rules

HI @rido-min
I have recovered deleted files
please help me review
thank you


Computed signals - best practise

What is best practise for creating a "computed signal" in processing pipeline?
Except from article:
"Computed signals from the processing pipeline are pushed to Azure Time Series Insights Gen2 for storing and analytics."

Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: 8e91b487-f485-0bb2-625a-f8ca191bbc9e
Version Independent ID: c4936bfb-79bb-7c96-256f-f4c5c0cf396f
Content: Gen2 use cases - Azure Time Series Insights Gen2
Content Source: articles/time-series-insights/overview-use-cases.md
Service: time-series-insights
GitHub Login: @deepakpalled
Microsoft Alias: dpalled

@gechu Thanks for the feedback! We are currently investigating and will update you shortly.

Adding @deepakpalled for further insights.
@deepakpalled Could you please check this issue and provide your insights?

Regarding computed signals, reading "Historical Data Ingestion", the article recommends:
"Do not stream live and historical data in parallel. Ingesting out of order data will result in degraded query performance."
"Ingest historical data in time-ordered fashion for best performance."
How can I calculate signals that may be the product of a range of "historical"  events, while feeding them into the same time series database as for the regular stream of events?  I.e ingesting them in parallel.
https://docs.microsoft.com/en-us/azure/time-series-insights/concepts-streaming-ingestion-event-sources

@deepakpalled This is more of a doc enhancement request to add more details. Please have a look at this issue and provide your suggetions.

@gechu, sorry for the delay. The answers depend on your computed signal needs. Some suggestions for a solution can be found in the "Operational analysis and driving process efficiency" section, like: "Perform data processing and computations. Funnel ingested data through services such as Stream Analytics, Logic Apps, and Azure Functions. The service you use depends on the specific data-processing needs."
For ingestion best practices, please read https://docs.microsoft.com/en-us/azure/time-series-insights/concepts-streaming-ingestion-event-sources#streaming-ingestion-best-practices
It's not recommended to stream Iive and historical data in parallel when it uses the same property. But as you will be streaming computed and live signals, I assume it will be using different properties. You can read more about it here https://docs.microsoft.com/en-us/azure/time-series-insights/concepts-json-flattening-escaping-rules
Let me know if that answers your questions or you need more details.

Thanks @jatinas for the reply!
Calculated signals will actually have the same properties as the "raw" values. A calculated signal may take a range of raw measurements, and produce a computed/calculated measurement point.
Due to the need to use a range of measurements as input to the calculation, I'm thinking that it would be efficient to perform that query towards a time series DB (TSDB). Then I would end up with this flow:

Retrieved a range of measurements from the TSDB (using i.e. Azure Functions),
do some calculations,
and then feed the new measurements back to the event hub ingestion point - where the data later would be consumed into the TSDB again

Would that be an anti-pattern due to the way Time-series Insights dislikes consuming similar measurements (possibly quite) out of chronological order?
Or would a better architecture make use of two different TSDBs - one for momentary/raw values,, and a second one for calculated values?
Would it be possible to connect e.g. PowerBI or Time Series Insights to query data from two different TSDBs?

Looks like the discussion is extending beyond the original request for doc edit. Can we please move it to an appropriate channel like https://docs.microsoft.com/en-us/answers/topics/azure-time-series-insights.html? And close that issue?

Thanks for the questions, @gechu, and thank you @jatinas for the answers and follow-up suggestion.
#please-close


Allow to use monoio on stable.

There were only a few nightly features left in the code-base so this code changes monoio so it can be compiled on stable too.
Also adds a unstable feature flag that preserves the nightly-only code paths for easier migration in the future.
Fixes https://github.com/bytedance/monoio/issues/129

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

I have also adjusted the README, but probably README-zh.md needs to be adjusted too.

In thread.rs the LazyLock is used. It seems break the compiling.


Documentation for static config file

Welcome!

[X] Yes, I've searched similar issues on GitHub and didn't find any.
[X] Yes, I've searched similar issues on the Traefik community forum and didn't find any.

What did you expect to see?
New user here. I'd like to understand the options in the "static configuration". There's a full traefik.yml sample here.
But it's not documented. I don't know what each option does.
Please consider adding documentation for this?

Hello,
the documentation of the static configuration is all the items of the documentation:

https://doc.traefik.io/traefik/providers/overview/
https://doc.traefik.io/traefik/routing/entrypoints/
https://doc.traefik.io/traefik/https/overview/
etc...

The page here is not a sample but a full reference of the configuration fields.
For help on your configuration, please join our Community Forum and reach out to us on the Traefik v2 section.

Hi @lonix1, thanks for your feedback!
Just to let you know, we're aware that the docs have room for improvements.
I am happy to say that we are working to improve the language and organization. It will take some time, we don't have an ETA, and we can't say how long it will take.
If you have specific suggestions to improve things in the meantime, please feel free to open a PR. We love community help!
If you are struggling with any particular questions, visit our Community Forum. It is very active, and your question might already be answered there.


Pagination component SCSS not included in production build

This is a (multiple allowed):

[x] bug

[ ] enhancement

[ ] feature-discussion (RFC)

Swiper Version: 3.1.2

Platform/Target and Browser Versions: macOS FIrefox + Chrome

What you did
Included Swiper (React) along with pagination:
import SwiperCore, { Pagination } from 'swiper';
import { Swiper, SwiperSlide } from 'swiper/react';
import 'swiper/swiper.scss';
import 'swiper/components/pagination/pagination.scss';

Expected Behavior
Swiper slides and pagination bullets should be styled in the production build.
Actual Behavior
Swiper slides have styles (since the sideEffects fix in 6.1.0) but pagination bullets do not have any style rules. Issue only appears in production build; development is fine.
Seems to be related to #3734 / #3708, but only with the optional components.
My first thought is that the sideEffects aren't matching nested directories. I tried editing the sideEffects section of swiper's package.json from e.g. *.scss to **/*.scss, but that didn't seem to work.
Possible workaround: if I remove the scss imports from my .jsx file and move them into my .scss file (e.g. @import '~swiper/components/pagination/pagination.scss'), it seems to work. But importing them in jsx should work too.

Confirmed that this is still an issue. Thanks for the workaround.
Tying this to an issue reported in Gatsby, for the benefit of others who wander.
https://github.com/gatsbyjs/gatsby/issues/19446#issuecomment-553019807

The cause of the issue is this sideEffects.

The cause of the issue is this sideEffects.

I think

If you load the CSS file in JS. CSS configuration ideEffect:false , then it won't be packaged.

If you load CSS in a CSS file, there is no limit.

So you can use js to load custom CSS files, and then css loads CSS files that have no side effects.

Using require instead of import is another quick workaround, works for me


pivccu usage of raspberry ports

I installed the pivccu3 image (Raspbian_Stretch_Lite_piVCCU3_2018-11-19.zip) successfully on my Raspberry 2, applying the HM-MOD-RPI-PCB as the RF modem.
I also have another very simple bash script application, which uses some GPIO pins (GPIO 5,6,12,14,16,19,20,26,21). Some of these pins seem also be used by pivccu, as the application can no longer read the state of some of the pins.
Does pivccu use other pins / prevent the use of other pins of the raspberry then the pins needed for the  HM-MOD-RPI-PCB? Can I avoid this somehow?
Kind regards
Rolf Breuning

GPIO 16, 20 and 21 are in use if a RPI-RF-MOD is detected, but not, if the HM-MOD-RPI-PCB or no radio module at all is detected.
I will check, if there might be a bug in this on sunday.

I can confirm that 16,20,21 seem to be used. After a reboot I did a simple test:
$ echo 5  > "/sys/class/gpio/export"
$ echo 6  > "/sys/class/gpio/export"
$ echo 12 > "/sys/class/gpio/export"
$ echo 13 > "/sys/class/gpio/export"
$ echo 16 > "/sys/class/gpio/export"
-bash: echo: write error: Device or resource busy
$ echo 19 > "/sys/class/gpio/export"
$ echo 20 > "/sys/class/gpio/export"
-bash: echo: write error: Device or resource busy
$ echo 21 > "/sys/class/gpio/export"
-bash: echo: write error: Device or resource busy
$ echo 26 > "/sys/class/gpio/export"

Ok, I found the problem. please update the package pivccu-modules-dkms using apt and reboot.

I updated pivccu-modules-dkms and pivccu3.
I checked all gpios outside the range of the HM-MOD-RPI-PCB using:
arr=( 27 22 23 24 10 9 25 8 7 11 5 6 12 13 16 19 20 21 26 ); for i in "${arr[@]}"; do echo $i; echo $i > "/sys/class/gpio/export"; done
Everything - ok
I checked my application - ok
I upgraded the overall system with apt-get upgrade, repeated the checks above - ok
Your fix was successful and you can close the topic
Thanks for this fast resolution !


Add containerized build process.

fix #949
Old build target is renamed to build-local.

/lgtm

/approve