Bucket - using grant (acl conflict)

apiVersion: storage.yandex-cloud.jet.crossplane.io/v1alpha1
kind: Bucket
metadata:
  name: bucket-test
  annotations:
    argocd.argoproj.io/sync-wave: '-1'
spec:
  forProvider:
    accessKeyRef:
      name: upjet-sa-s3-static-key
    secretKeySecretRef:
      name: upjet-bucket-creds
      namespace: crossplane-system
      key: attribute.secret_key
    # acl: private
    grant:
      - id: ajerahbbona80b3knndo
        permissions:
          - FULL_CONTROL
        type: CanonicalUser
    bucket: p-bucket-upjet
  providerConfigRef:
    name: p-upjet-yc

Name:         bucket-test
Namespace:
Labels:       <none>
Annotations:  argocd.argoproj.io/sync-wave: -1
              crossplane.io/external-create-pending: 2024-01-21T15:38:26Z
              crossplane.io/external-create-succeeded: 2024-01-21T15:38:31Z
              crossplane.io/external-name: p-bucket-upjet
              terrajet.crossplane.io/provider-meta: null
API Version:  storage.yandex-cloud.jet.crossplane.io/v1alpha1
Kind:         Bucket
Metadata:
  Creation Timestamp:  2024-01-21T15:38:26Z
  Finalizers:
    finalizer.managedresource.crossplane.io
  Generation:        3
  Resource Version:  614795105
  UID:               892b392a-12b0-4261-a2dd-42ca29f583c4
Spec:
  Deletion Policy:  Delete
  For Provider:
    Access Key:  YCAJEkF1c1AiCDXf7Ppk4ldM6
    Access Key Ref:
      Name:  upjet-sa-s3-static-key
    Acl:     private
    Anonymous Access Flags:
    Bucket:                 p-bucket-upjet
    Default Storage Class:  STANDARD
    Folder Id:              b1guugseh3jmtmgukcs5
    Grant:
      Id:  ajerahbbona80b3knndo
      Permissions:
        FULL_CONTROL
      Type:  CanonicalUser
    Secret Key Secret Ref:
      Key:        attribute.secret_key
      Name:       upjet-bucket-creds
      Namespace:  crossplane-system
    Versioning:
  Provider Config Ref:
    Name:  provider-upjet-yc
Status:
  At Provider:
    Bucket Domain Name: p-bucket-upjet.storage.yandexcloud.net
    Id:                  p-bucket-upjet
  Conditions:
    Last Transition Time:  2024-01-21T15:38:32Z
    Reason:                Available
    Status:                True
    Type:                  Ready
    Last Transition Time:  2024-01-21T15:38:33Z
    Message:               observe failed: cannot run refresh: refresh failed: Conflicting configuration arguments: "acl": conflicts with grant: File name: main.tf.json
Conflicting configuration arguments: "grant": conflicts with acl: File name: main.tf.json
    Reason:  ReconcileError
    Status:  False
    Type:    Synced
Events:
  Type     Reason                         Age              From                                                                  Message
  ----     ------                         ----             ----                                                                  -------
  Normal   CreatedExternalResource        5s               managed/storage.yandex-cloud.jet.crossplane.io/v1alpha1, kind=bucket  Successfully requested creation of external resource
  Warning  CannotObserveExternalResource  2s (x3 over 3s)  managed/storage.yandex-cloud.jet.crossplane.io/v1alpha1, kind=bucket  cannot run refresh: refresh failed: Conflicting configuration arguments: "acl": conflicts with grant: File name: main.tf.json
Conflicting configuration arguments: "grant": conflicts with acl: File name: main.tf.json

As you can see, I don’t specify the acl parameter, but after sync he sets it himself and talks about a conflict. As a result, the resource hangs.

Hi, I tried to reproduce this bug in all possible test cases, but I was not successful.

Crossplane versions:

Chart Version: 1.14.5
Chart Application Version: 1.14.5

Provider:

Version: v0.1.5
Image: xpkg.upbound.io/tages/provider-upjet-yc:v0.1.5

I noticed a different apiVersion. I assume you have confused it with the official unsupported YandexCloud provider, which most likely has this bug. Our provider uses: storage.yandex-cloud.upjet.crossplane.io
The difference in apiVersion was made for a smooth transition between the official and our own provider.
You can see the API reference: Upbound Marketplace and DocCrd.
If you manage to reproduce this bug with our provider, please post a message in this thread!
Thanks for your issue, it helps us to be better!


Please stop using “French” as the answer!

Please I downloaded this app and made an account just to leave this comment please make it not be French any more I love this game I look forward to it every day pleek

Today it was French baguette 😔

See https://github.com/flackr/crosswordle/issues/38 . There are only 2 more.


Connect-PNPOnline using local certificate without PFX?!

Hi there,
When deploying to azure, we are using the app-only pattern where we basically connect to Azure AD using an AppID and a local non-exportable certificate. When we initially created the certificate, we stored the private key of the certificate (pfx) in the AAD application key credentials and leaved the public key in the local certificates store.
This pattern is perfect because you don't have to carry around a PFX file or hash, the only way you can connect to Azure AD is by being on the actual server which has the local certificate installed and you can't export the certificate, hence why this is the pattern privileged by our organization in terms of security standards.
With Connect-PNPOnline, I tried all the different implementations using certificates but the problem is that they are all requiring the user to provide the certificate's private key (pfx), which seems odd because that's pretty much the equivalent of carrying a password isn't it?
It would be really useful to have an implementation of Connect-PNPOnline using only an AppId and the path to the local certificate, without having to pass the private key. If we could have something like the Connect-AzureAD command which supports this it would be great :
Connect-AzureAD -TenantId "..." -CertificateThumbprint "[local certificate thumb print]" -AppId "..."

I am sure I am not the only one struggling with this

@officedocsbot assign @yogkumgit

Hello @spplante as you may know, this issue resource is to report specifically issues in documentation, and I see, based on the description that you give, that might be more suitable for a feedback or user voice resource, as you may know also, Sharepoint PnP is community driven, I encourage you look here for that kind of resources https://docs.microsoft.com/en-us/sharepoint/dev/community/community
Hope this information helps you.

Thanks @get-itips, no problem!


ServerStyleSheet typescript definition

Hi,
Styled-components v2 is awesome, thanks! I'm using styled on a Typescript project and noticed that the SSR functionality, more specifically ServerStyleSheet, is missing Typescript definitions. I wrote the following definition to get around this. If it looks ok to you guys, I'd be happy to pull request.
export class ServerStyleSheet {
  collectStyles(children: ReactElement<any>): ReactElement<any>
  getStyleTags(): string
  getStyleElement(): ReactElement<any>
  static create(): StyleSheet
}

I'd also add the following tests to styled-component-test.tsx
// server-side rendering styles
const sheet = new ServerStyleSheet();
sheet.collectStyles(<ThemedButton name={name} />)
const styleString = sheet.getStyleTags();
const styleElement = sheet.getStyleElement();

Looks good to me :) PR would be cool 👍


Feature Request: add option to hide tooltips in diff menu

The tooltips can show lots of text and cover a lot of menu items.

Is that the tooltip from the body of the commit?

Those tooltips show when I hover over a commit on the left or right sidebar.

The only reason for such a long tooltip I can see is for it to be the body of the commit message. I’d say changed file names don’t belong in there, that data is already known as part of the commit itself.


scipy.sparse.rand raises OverflowError instead of ValueError on win-amd64

See also http://stackoverflow.com/questions/28693545/creating-a-large-sparse-matrix-in-scipy-sparse.
Using scipy‑0.15.1 and numpy‑1.8.2+mkl on win-amd64 scipy.sparse.rand(100000, 100000, 0.0001) raises OverflowError: Python int too large to convert to C long instead of ValueError: Trying to generate a random sparse matrix such as the product of dimensions is greater than<PHONE_NUMBER> - this is not supported on this machine
Numpy's mtrand and randomkit use C long data types. Hence the following patch raises the expected ValueError on Windows:
diff --git a/scipy/sparse/construct.py b/scipy/sparse/construct.py
index 58a993c..2a2dc3e 100644
--- a/scipy/sparse/construct.py
+++ b/scipy/sparse/construct.py
@@ -694,7 +694,7 @@ def rand(m, n, density=0.01, format="coo", dtype=None, random_state=None):

     tp = np.intc
     if mn > np.iinfo(tp).max:
-        tp = np.int64
+        tp = np.int_

     if mn > np.iinfo(tp).max:
         msg = """\

I don't understand, is generating a random sparse matrix such that the product of dimensions is greater than<PHONE_NUMBER> really not supported on that machine?

OK after reading
https://github.com/numpy/numpy/search?q="too+large+to+convert+to+c+long"&type=Issues
I think the root of the problem is that numpy has no way to sample a random integer from a range of more than a few billion on platforms where a C long is only 32 bits.  Even on some 64 bit platforms, a C long apparently is only 32 bits.

@argriffing I had a look at your two PRs; they don't include this suggested fix right? Shall we just apply the diff above in a separate PR?


25984

https://github.com/rancher/rancher/issues/25984
https://github.com/rancher/rancher/issues/25993

LGTM


LOTR - Return of The Search Filters

This PR reimplements filtering - this time in Algolia. Filtering on job_role, education phase and working patterns is possible using the new filters service. Legacy job alerts with newly_qualified_teacher: 'true' are also respected.
Jira ticket URL
https://dfedigital.atlassian.net/browse/TEVA-900
Changes in this PR

Add algolia filters service
Refactor algolia search into separate services
Add facet count attribute

Next steps

[ ]  Reindex live vacancies so job roles attribute matches array_enum

in a similar vein to @cesidio comment, i see a lot of repetition of stuff like publication_date_timestamp and expires_timestamp greater than or equal to/less than this that and the other. and i wonder if this warrants some kind of filter utility or helper?


Drop bad crypto, fix 245

Really simplifies our crypto library and fixes #245.

Updated again, please check. I guess I should stop scratching it.


JIT compilation speed between MIR and LLVM

Hi,
I'm greatly attracted by the 100x faster compilation speed shown in your post as we need a fast and lightweight JIT compiler in our wasm-micro-runtime. Seems the speed comparison is for the entire compilation period (including start up and many other phases)? Do you have any comparison data about the time consumption from IR to machine code between MIR(MIR --> machine code) and LLVM(LLVMIR --> machine code) ?
Recently I have done some work to integrate MIR into wasm-micro-runtime. I use llvm2mir to convert llvmir to MIR, and most of my cases work well. However, I find that the compilation speed of MIR is not noticeably improved:

compiler framework
compilation time (ms)

LLVM
23339

LLVM (with FastISel)
13040

MIR
21979

The MIR is only 1.06x faster than LLVM and even slower than LLVM with FastISel option.

The llvmir->mir convertion time is not calculated in this data

So I've got a few questions:

The data I get from my case is far away from "100x faster than GCC", I wonder if my data is possible, or is it possibly that I did something wrong to cause this? I think the most time-consuming part during the compilation is the optimization passes, so I was expecting MIR codegen (including optimization) can run nearly 100x faster than LLVM.
As I know the wasmtime, which use Cranelift as its JIT compiler, can finish compilation for this case in 200ms, is there any chances for us to get such high compilation speed in MIR?

Thanks !

So I've got a few questions:
1. The data I get from my case is far away from `"100x faster than GCC"`,

I wonder if my data is possible, or is it possibly that I did something wrong to cause this?

Thanks for reporting this and starting this issue.
No I don't think you did something wrong.
It would be interesting to see your test case, then  I can say more.  It would be also useful to have this test (with mir and LLVM IR) to optimize MIR-generator algorithms.
My major motivation for MIR project is to implement Ruby tier1 JIT compiler.  Most Ruby methods (including Ruby blocks) are small, just a few lines.  It is translated into 10-20 lines of C.  The current Ruby JIT is implemented by translation of Ruby method bytecode into C and then calling GCC/LLVM to get a machine code.  It is very slow because of very big start up time of GCC/LLVM.
So my case I focus on is small code.  I use sieve program to compare its compilation speed by GCC and MIR-generator and that is where I get 100x speed.
According to your data your code is probably 300 times more than sieve I use. On this size code startup time is not important.  So you are comparing raw compilation speed.  For raw compilation speed I think MIR will have worse data.  Why?  Because MIR-generator was designed for compilation of small functions (most common case) and to have very simple optimization algorithms (small source code vs speed).  The same LLVM/GCC optimization should be always faster because their developers (including me) are spending huge amount of time to make it faster (on small and huge programs), to work for any edge case, etc.  For example, GCC RA is 40K C lines vs 500 lines in MIR-generator.  GCC RA uses so many different structures (e.g. different complicated presentations of conflict graph) to make it scalable and work fast on huge functions.

I think the most time-consuming part during the compilation is the optimization passes, so I was expecting MIR codegen (including optimization) can run nearly 100x faster than LLVM.

For compilation of small code it is not optimizations.  I'd recommend to read my blog post:
https://developers.redhat.com/blog/2020/01/20/mir-a-lightweight-jit-compiler-project/
or see my presentation on RubyKaigi2019.

2. As I know the `wasmtime`, which use `Cranelift` as its JIT compiler, can finish compilation

for this case in 200ms, is there any chances for us to get such high compilation speed in MIR?

I think to achieve this speed on your test case, some MIR optimizations should be changed and be more scalable and to be fast on big functions (although it will increase the MIR-generator source code significantly).    In any case having your testcase (what optimizations you used for LLVM) would help to analyze what should be done to improve MIR-generator speed on bigger code.
To be honest, I did not work seriously on improving MIR-generator speed and generated code performance yet.  It is a serious work and I am just at the stage of writing initial version of working code.

2\. As I know the `wasmtime`, which use `Cranelift` as its JIT compiler, can finish compilation for this case in 200ms, is there any chances for us to get such high compilation speed in MIR?

200ms means that MIR should be sped up in 100 times.  It is hard for me to say how you measure this and what is your test case actually. Unless I do something stupid, I don't think MIR can be sped up in 100 times.  Also I don't thin cranelift can be so faster than MIR and LLVM (w/o optimizations).  I probably can speed up MIR in 3-4 times (2 times by using only RA and code selection and by speeding up code selection 2 times).
I am also interesting what optimizations you used for LLVM? Is it full set of optimizations (LLVM can switch on hundreds optimization passes)?  My answer in the previous post assumes that you used LLVM w/o optimizations only with different code selection algorithms.
By the way did you use MIR-textual representation as input or started MIR-generator to work from MIR in memory?

Many thanks for your detailed and wonderful explanation.

It would be interesting to see your test case, then I can say more. It would be also useful to have this test (with mir and LLVM IR) to optimize MIR-generator algorithms.

I've tried to save the textual llvm-ir and MIR, but they are too huge to upload, so I find a smaller one:
test_case.zip

According to your data your code is probably 300 times more than sieve I use. On this size code startup time is not important. So you are comparing raw compilation speed. For raw compilation speed I think MIR will have worse data.

Yes, our IR is converted from wasm, so the size of the functions is determined by the source level language. Besides, to keep the sandbox mechanism for wasm, we need to insert some blocks in the IR to do some checks. I tried to output the llvm-ir bitcode of my case, it's more than 11MB which should be much bigger than sieve.
So, as per my understanding, the reason that MIR isn't much faster than LLVM on my case is:

The function in my IR is too big so that the start up time is only a small part of the total compilation time, which weakening the advantage of MIR in start-up time
Although there are fewer passes in MIR, the passes in LLVM runs much faster than MIR especially for big functions, so the speed increment of MIR compared to LLVM is not as high as expected

Is my understanding correct and complete?

200ms means that MIR should be sped up in 100 times. It is hard for me to say how you measure this and what is your test case actually. Unless I do something stupid, I don't think MIR can be sped up in 100 times.

This speed is really amazing. Maybe I can check this later.

I am also interesting what optimizations you used for LLVM? Is it full set of optimizations (LLVM can switch on hundreds optimization passes)? My answer in the previous post assumes that you used LLVM w/o optimizations only with different code selection algorithms.

This is my approach:
LLVM JIT:  wasm bytecode  -->  llvmir  -->  llvm codegen
MIR JIT:   wasm bytecode  -->  llvmir  -->  mir  -->  mir codegen

As for the LLVM-IR, we use un-optimized LLVM-IR in both LLVM and MIR codegen, because the optimized one will cause the register conflict problem in MIR's codegen.
And for the codegen, we use LLVMCodeGenLevelAggressive for LLVM, which should be the highest level.

By the way did you use MIR-textual representation as input or started MIR-generator to work from MIR in memory?

I use the MIR in memory:
    mir_ctx = MIR_init ();
    mir_module = llvm2mir (mir_ctx, comp_ctx->module /* LLVM Module */);
    MIR_load_module (mir_ctx, mir_module);
    // ...

Thanks again !

I've tried to save the textual llvm-ir and MIR, but they are too huge to upload, so I find a smaller one:
test_case.zip

Thank you for your test case.  I really appreciate it. It is interesting.  I did not expect that MIR will be used for such big code.  It is 500K lines module with about 2-3K lines per function.

Yes, our IR is converted from wasm, so the size of the functions is determined by the source level language. Besides, to keep the sandbox mechanism for wasm, we need to insert some blocks in the IR to do some checks. I tried to output the llvm-ir bitcode of my case, it's more than 11MB which should be much bigger than sieve.
So, as per my understanding, the reason that MIR isn't much faster than LLVM on my case is:
1. The function in my IR is too big so that the start up time is only a small part of the total compilation time, which weakening the advantage of MIR in start-up time

Yes.  That is the reason why MIR is not faster LLVM.

2. Although there are fewer passes in MIR, the passes in LLVM runs much faster than MIR especially for big functions, so the speed increment of MIR compared to LLVM is not as high as expected

I would say that the compilation speed of MIR should be somewhere between LLVM -O0 and LLVM -O1.  To be more detailed, the same set of optimizations in LLVM should be a bit faster than MIR ones because LLVM is more refined.  Although I did not work on improving MIR generation speed yet.  May be I can be closer to speed of LLVM optimization after that.
Also probably some of my optimizations are not well scaled for bigger functions and I should work on their algorithmic improvements. A lot of optimizations in compilers are not O(n) sometimes they are O(n^2) or even more.  The testcase you sent me could help me with this work.

Is my understanding correct and complete?

I believe so.

200ms means that MIR should be sped up in 100 times. It is hard for me to say how you measure this and what is your test case actually. Unless I do something stupid, I don't think MIR can be sped up in 100 times.

This speed is really amazing. Maybe I can check this later.

I don't know internals of cranelift.  But I guess such speed can be achieved by direct translation  (no CFG,no optimizations, only local RA, and no code selection) like Gnu Lighting works.  Only in this case the generated code performance will be about 1/3 of GCC/LLVM with -O2.

Thank you for your test case. I really appreciate it. It is interesting. I did not expect that MIR will be used for such big code. It is 500K lines module with about 2-3K lines per function.

I'm glad to know that my case is useful to you.

I don't know internals of cranelift. But I guess such speed can be achieved by direct translation (no CFG,no optimizations, only local RA, and no code selection) like Gnu Lighting works. Only in this case the generated code performance will be about 1/3 of GCC/LLVM with -O2.

In fact, I don’t know too much about the backend of the compiler, but I agree with you because such a fast compilation speed must be at the expense of performance.
Thanks again for your detailed answer and for such a great project !

I'm glad to know that my case is useful to you.

I did some profiling on your test case.  There is definitely room for improvements.  For example, for simplicity I use simple bitmaps.  For bigger functions they can be big.  Usually compilers (e.g. GCC) use some sort of sparse bitmaps to deal effectively with big functions and they rarely use simple bitmaps.
I am also thinking about implementing much faster code generation (no building CFG, no any global analysis, only local RA).  It is an interesting task.
I don't know when it will be done, may be even not in the 1st release.  But speeding MIR generator is an interesting work and I'll definitely work on it.

I don't know when it will be done, may be even not in the 1st release. But speeding MIR generator is an interesting work and I'll definitely work on it.

Nice to hear this, looking forward to future release of MIR !
By the way, I've tested wasmtime(cranelist) on my case, I find that it use multi-threading to speed up the compilation. On my PC with 16-core CPU, its total time is ~200ms, but when I limit the
CPU core to 1, the total time is 1.507s, nearly 8x slower than the 16 core run. I wonder will it be possible to speed up MIR by using threads?
Of course, the 1.507s on single core is still much faster than MIR and LLVM approach.
And I'm a little confused about the slow compilation speed of my case. Although MIR passes may run slower on big functions, its total number of optimization passes should be much fewer than LLVM's, so there should still be a lot of improvement on MIR, do you think there may be possibly other factors that affect the compilation speed ?
Thanks

Hi,
I think that many projects that use LLVM have it linked in as a library and there is no startup time. LLVM codegen is quite fast as it has been tuned endlessly. I don't have comparable figures as in Ravi LLVM IR is generated directly but MIR IR is generated via intermediate C code, so MIR has an extra step. However in Ravi I use LLVM -O2 optimizations - which is setup via the PassManager. This means almost the same passes as C++/C code would go through. Even so LLVM is not slow.
Personally I think MIR should be at least as fast as LLVM in linked in mode (i.e. not via Clang invocation). I think there is no point trying to be faster. Instead MIR should focus on:
a) Being very small library as it is today
b) Achieving 70% of -O2 performance in the generated code
Just my thoughts.
Regards
Dibyendu

I should add that most projects have background compilation so compilation speed is usually not an issue anyway. LLVM now has inbuilt multi-thread lazy compilation on demand

By the way, I've tested wasmtime(cranelist) on my case, I find that it use multi-threading to speed up the compilation. On my PC with 16-core CPU, its total time is ~200ms, but when I limit the
CPU core to 1, the total time is 1.507s, nearly 8x slower than the 16 core run. I wonder will it be possible to speed up MIR by using threads?

MIR does not use threads.  But its code can be adapted for parallel compiling in multiple threads.  You just need to use different contexts for threads.

Of course, the 1.507s on single core is still much faster than MIR and LLVM approach.

I see.  So cranelift is 14 times faster MIR right now on your test.  I guess that implementing the faster MIR code generation I mentioned could achieve the same speed.

And I'm a little confused about the slow compilation speed of my case. Although MIR passes may run slower on big functions, its total number of optimization passes should be much fewer than LLVM's, so there should still be a lot of improvement on MIR, do you think there may be possibly other factors that affect the compilation speed ?

Yes, LLVM has a lot of optimizations (I guess about hundred ones) but the big part of time is spent in few optimizations (e.g. RA and code selection).  For example, RA in GCC consumes >20% on a test I frequently use (500K fortran lines program containing 7K subroutines).

I should add that most projects have background compilation so compilation speed is usually not an issue anyway. LLVM now has inbuilt multi-thread lazy compilation on demand

Thank you for the info about LLVM, Dibyendu.

MIR does not use threads. But its code can be adapted for parallel compiling in multiple threads. You just need to use different contexts for threads.

Yes, we can use multiple threads to compile multiple contexts, but each context should have its own MIR_module, in this way I have to divide all my functions in MIR into different modules, if func1 in module1 call a func2 in module2, how does the codegen know the actual address of func2 as module2 is in another context?

I should add that most projects have background compilation so compilation speed is usually not an issue anyway. LLVM now has inbuilt multi-thread lazy compilation on demand

Thanks @dibyendumajumdar for the information. In our runtime, we use LLVM to compile all the wasm functions before execution, so the compilation speed of JIT is important as it may influence the start up time of the application.

Hi re LLVM I think you need to use ORC v2 apis to get multi-threading and lazy compilation.
https://www.youtube.com/watch?v=MOQG5vkh9J8

Yes, we can use multiple threads to compile multiple contexts, but each context should have its own MIR_module, in this way I have to divide all my functions in MIR into different modules, if func1 in module1 call a func2 in module2, how does the codegen know the actual address of func2 as module2 is in another context?

Currently there is no possibility to use functions generated from different context.  In JIT environment it means that JITted code should only calls JITted functions from the same context, already existing run-time functions or call an interpreter.
In the future I am planning to permit to call JITted code from a different context.  It requires to implement some thread-safe data structures.  I am planning to implement some MIR speculative insns (and some support of this in c2mir) which will collect run-time data.  Depending on the data, a different JITted code will be generated.  It also requires to implement some thread-safe data collection as JITted code can be executed by different threads.  I was planning to implement this at the same time.  Speculative insns will be necessary for better performance implementation of dynamic languages.

I'd like to give some update for this issue.  Some details here are mostly for myself to keep these notes.
First of all MIR-generator can be used in different optimization levels.  The current default optimization level is -O2.  Probably it is better to make -O1 optimization level as default as for my benchmarks on x86-64 going from -O1 to -O2 improves code performance by about 3% (geomean) slowing down the generator > 2 times.
For -O1 generator runs only RA, combiner (code selection) and live analysis (3 times) and dead code elimination several times. Most time (70%) is spent in live-analysis and RA (approximately equally).
So I removed one pass of live analysis unnecessary for -O1.
I also sped up RA almost by 2 times by improving bitmap code, major loop in assign and by adding live-range compression.  I implemented sparse bitmaps which should work better for big functions (2K MIR insns in the test case) but the result was the opposite as the code is more complicated and this complication probably overwhelms bitmap sparseness.  May be for much bigger functions they will be benefitial.  In any case I threw sparse bitmaps code (it can still be found in git repository).
I've tried couple simple RA implementations w/o life analysis (one based on approximation of life analysis computation and another one based on local RA) but the resulted code performance was so bad (sometimes 3 times worse) so I don't think it is worth to have it even if the generator speed improves in 3 times by speeding RA by 70% (local RA takes 1.6B insns vs 2.7B insns for current RA for given test case) and removing life analysis completely. If I can find/design a good RA w/o life analysis, this approach can be viable.
So currently the generator speed (in executed billions of insns) for given test case for different optimization levels and geomean performance improvements from previous level on my benchmarks (make c2mir-bench) looks like
-O0  - 8.738B
-O1 - 10.864B  +27%
-O2 - 25.178B  +3%
-O3 - 43.673B  +1%

Probably the benchmarks (mostly from old computer language shooutout) I use are not good enough and I need better ones.
I should also look at improving life analysis.  I've checked that the data-flow solver processes BB about 1.3 times for the test case which means it works ok (the right BB traversing order is used).
Probably I can improve life-analysis by excluding local vars from it.  I don't know what the results will be as I need a pass to identify local vars and what bitmap sizes I have on the test case.  In any case, I think it is worth to try.

Hi, I thought I would share following timings.
These results show the time taken to compile and execute thousands of Ravi/Lua functions in the Ravi test suite.
For MIR the C2MIR front-end is used.
Both MIR and LLVM backends are run with -O2. I am using the LLVM PassManager to setup the optimization passes - believe these are the same as that used by front-ends such as Clang.

MIR

real	1m58.523s
user	1m57.161s
sys	0m1.340s

LLVM 10 ORC v2 api

real	2m26.194s
user	2m25.308s
sys	0m0.852s

There is a mix of compilation and execution performance times here as the overall result depends on both these factors.

Dibyendu, thank you for the numbers.
Actually I did not expect that C2MIR will be faster LLVM.  As I wrote C2MIR design was not about the compilation speed but more about the simplicity.
As for the MIR-code generator, I've just added a fast generator.  The generator speed on test sent by Xu is 4-5 times faster than generation with -O1/-O2.  The generated code performance is about the same as Clang/GCC with -O0 (sometimes better, sometimes worse).

As for the MIR-code generator, I've just added a fast generator. The generator speed on test sent by Xu is 4-5 times faster than generation with -O1/-O2. The generated code performance is about the same as Clang/GCC with -O0 (sometimes better, sometimes worse).

Yes I noticed that you now have a fast generator. I have not tried it yet but I suspect that the generated code performance will be suboptimal?

Yes I noticed that you now have a fast generator. I have not tried it yet but I suspect that the generated code performance will be suboptimal?

The code performance generated by fast gen will be 2-3 times worse (as it for GCC -O0 vs GCC -O1/-O2).

As for the MIR-code generator, I've just added a fast generator. The generator speed on test sent by Xu is 4-5 times faster than generation with -O1/-O2. The generated code performance is about the same as Clang/GCC with -O0 (sometimes better, sometimes worse).
This is awesome! We will test it for WAMR in our environment. Thanks for the great project ! @vnmakarov

@vnmakarov Is it possible to support parallel fast gen with multi-threading?


Add test case interpolating stack

This PR contains:

[x] a new test-case for the benchmark

[x] I hereby confirm that NO LLM-based technology (such as github copilot) was used while writing this benchmark

[ ] new dependencies in requirements.txt

[ ] The environment.yml file was updated using the command conda env export > environment.yml

[ ] new generator-functions allowing to sample from other LLMs
[ ] new samples (sample_....jsonl files)
[ ] new benchmarking results (..._results.jsonl files)
[ ] documentation update
[ ] bug fixes

Related github issue (if relevant): closes #6
Short description:

adds a test case for interpolating 2 2d slices into a stack (inspired by this discussion)

How do you think will this influence the benchmark results?

should be a relatively simple problem

Why do you think it makes sense to merge this PR?

interpolation is frequently used in BiA

Hi @haesleinhuepf ! Thank you for your quick reply. Yes I think it makes sense and I also wanted to add it, but thought it would be cool to have your opinion first: My first idea would be to add another input to the function called num_slices and then in the function:
If num_slices = 1,
Do what is already there
Else:
Take somehow num_slices into
account and interpolate linearly over
them.
Does this make sense or should I do it differently?
Best,
Mara

If would not write the if-statement. Consider using np.linspace (sorry, I should have mentioned earlier).

Oki thanks, I will do that

I modified the test case using np.linspace, if there is anything else that needs modification I can change it

Yes, thank you for the hint! I agreed to this change

Thanks again Mara!


Reject sequence definitions for Active Record primary keys

It might be useful to define sequence(:id) attributes for models representing objects in external systems. It might be tempting to apply that same pattern to Active Record-backed models that are internal to the system under test.
However, a factory that generates its own sequence of id (or any other primary key attribute) risks collision or desynchronization with the underlying datastore (most likely an SQL database with its own primary key generation strategy and "next" value).
This commit introduces a check in the FactoryBot::Definition#compile method to ensure that descendants of ActiveRecord::Base cannot declare a sequence to generate their own primary key.

I love this idea.
In terms of execution, what do you think of adding it as a lint instead of completely forbidden?
That's a direction I want to go as well: use the linting mechanism to push people into use FB correctly.

@mike-burns We spent hours troubleshooting bugs caused by this issue on a client project, and while I discovered FactoryBot.lint as an option as part of figuring out what was happening, I think of linting (in general) as something that you do to make your code more consistent in terms of readability, not something that is required for it to function correctly.
While I agree that we should push people to use FB correctly via lint, this particular instance seems worthy of being more restrictive and not just a "hey, you might want to fix this" kind of warning.

OK, I cannot think of a valid use of a sequence for an AR primary key. I'm fine with this.
That said, I think this needs to go into factory_bot_rails. AR is not a runtime dependency of factory_bot.

What if we added a new notification instrument in the compile method, and then add a subscription for that in f_b_r?
In lib/factory_bot/factory_runner.rb we notify on factory_bot.run_factory, so maybe we could notify on factory_bot.compile_factory too.
The #compile method is a bit of a mess -- it mostly warms memoization caches, I suppose -- but maybe we could do something just before the end:
instrument_data = {
  attributes: declarations.attributes,
  traits: base_traits + additional_traits,
  klass: klass,
}

ActiveSupport::Notifications.instrument('factory_bot.compile_factory', instrument_data) do
  @compiled = true
end

ActiveSupport::Notifications.subscribe('factory_bot.compiled_factory') do |name, start, finish, id, payload|
  payload[:attributes].each do |attribute|
    ensure_attribute_not_generating_primary_key! attribute, for_class: payload[:klass]
  end
end

I haven't tried this or played with instrumentation in a very long time, so maybe this won't work at all.
What do you think?

I haven't tried this or played with instrumentation in a very long time, so maybe this won't work at all.
What do you think?

Thank you for sketching out that pseudo-code. I've opened https://github.com/thoughtbot/factory_bot/pull/1587 to explore what it'd take to publish that notification.

I've opened https://github.com/thoughtbot/factory_bot_rails/pull/419 as an alternative.


Add a test for artifact ID size

We really care that the Artifact ID type is as small as possible, which should only be the size of the buffer to store the underlying hash. There should be a test which validates this to protect against any future regressions.

Done!


ci: make releases create a new PR instead of committing in main.

Pull Request Template
Please include a summary of the change and which issue is fixed.
Motivation and Context
After seeing today the proposed release flow we decided to improve it:

Manually trigger the Prepare release action. This will create a new PR bumping the needed packages versions, and updating its change-logs
Review the changes in that PR. If necessary, fix changelogs, version, whatever is needed.
When merging the PR (I suggest using the squash technique) the Publish release action will be automatically invoked.

Type of change

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

What is the destination branch of this PR?
The main branch
How Has This Been Tested?
This flow has been tested at the Lerna Repo Playground
Checklist:

[ x ] My code follows the style guidelines of this project
[ x ] I have performed a self-review of my own code
[ ] I have commented my code, particularly in hard-to-understand areas
[ ] I have made corresponding changes to the documentation
[ x ] My changes generate no new warnings
[ ] I have added tests that prove my fix is effective or that my feature works
[ ] New and existing unit tests pass locally with my changes

One day we'll have fully automated alpha releases... Sadly, today is not that day :(

It would be easy to implement, but I decided not to do it in this PR for several reasons:

We might run into race conditions when merging. If we merge 2 PRs fast (which is something common), both release process would run at the same time, with different results, but trying to publish the same version.
Making an alpha release should be more strange. Only when we need it for some client.
I wanted to keep this PR focused on the small change that we discussed.

We might publish "nightlies" at the end of the day with planned jobs, or create a job that allows to immediately publish an alpha, but manually or re-discuss this in the future.


Upgrade to pytorch 0.2

When is the upgrade to pytorch 0.2 planned? (#9) Thanks @dasguptar for the amazing work done

where to download pytorch 0.12 ? the new version of pytorch is 0.2. I don't see any old version in the home page of pytorch. so I meet some problem when using the code.

@wq343580510 you can build it from source or use those links:
linux python 2.7 cuda 8.0
linux python 2.7 cuda 7.5 / no cuda
linux python 3.6 cuda 8.0
linux python 3.6 cuda 7.5 / no cuda
macosx python 2.7
macosx python 3.6

thanks very much。 @federicoFerri

@federicoFerri @wq343580510 the codebase is now updated to support PyTorch 0.2.0
Apologies for the delay in getting around to this, I was busy with some other work, and forgot to push!

Thanks!
On Wed, Nov 8, 2017, 7:32 AM Riddhiman Dasgupta<EMAIL_ADDRESS>wrote:

@federicoFerri https://github.com/federicoferri @wq343580510
https://github.com/wq343580510 the codebase is now updated to support
PyTorch 0.2.0
Apologies for the delay in getting around to this, I was busy with some
other work, and forgot to push!
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Improve the documentation of Select2TagWidget

As already discussed, the documentation of this widget is lacking. This PR adds a description of the functionality provided by the widget.

🎉


AssertionError in column_hook

sqlachemy version: 1.2.17
mypy version: 0.670
<path to project>/.venv/lib/python3.6/site-packages/sqlalchemy/dialects/mssql/information_schema.py:59: error: INTERNAL ERROR -- please report a bug at https://github.com/python/mypy/issues version: 0.670
Traceback (most recent call last):
  File "<path to project>/build.py", line 46, in <module>
    lint()
  File "<path to project>/build.py", line 36, in lint
    "--show-traceback"
  File "<path to project>/.venv/lib/python3.6/site-packages/mypy/main.py", line 91, in main
    res = build.build(sources, options, None, flush_errors, fscache)
  File "<path to project>/.venv/lib/python3.6/site-packages/mypy/build.py", line 162, in build
    result = _build(sources, options, alt_lib_path, flush_errors, fscache)
  File "<path to project>/.venv/lib/python3.6/site-packages/mypy/build.py", line 217, in _build
    graph = dispatch(sources, manager)
  File "<path to project>/.venv/lib/python3.6/site-packages/mypy/build.py", line 2360, in dispatch
    process_graph(graph, manager)
  File "<path to project>/.venv/lib/python3.6/site-packages/mypy/build.py", line 2660, in process_graph
    process_stale_scc(graph, scc, manager)
  File "<path to project>/.venv/lib/python3.6/site-packages/mypy/build.py", line 2767, in process_stale_scc
    graph[id].type_check_first_pass()
  File "<path to project>/.venv/lib/python3.6/site-packages/mypy/build.py", line 1919, in type_check_first_pass
    self.type_checker().check_first_pass()
  File "<path to project>/.venv/lib/python3.6/site-packages/mypy/checker.py", line 282, in check_first_pass
    self.accept(d)
  File "<path to project>/.venv/lib/python3.6/site-packages/mypy/checker.py", line 393, in accept
    stmt.accept(self)
  File "<path to project>/.venv/lib/python3.6/site-packages/mypy/nodes.py", line 969, in accept
    return visitor.visit_assignment_stmt(self)
  File "<path to project>/.venv/lib/python3.6/site-packages/mypy/checker.py", line 1709, in visit_assignment_stmt
    self.check_assignment(s.lvalues[-1], s.rvalue, s.type is None, s.new_syntax)
  File "<path to project>/.venv/lib/python3.6/site-packages/mypy/checker.py", line 1828, in check_assignment
    in_final_declaration=inferred.is_final,
  File "<path to project>/.venv/lib/python3.6/site-packages/mypy/checkexpr.py", line 3188, in accept
    typ = node.accept(self)
  File "<path to project>/.venv/lib/python3.6/site-packages/mypy/nodes.py", line 1446, in accept
    return visitor.visit_call_expr(self)
  File "<path to project>/.venv/lib/python3.6/site-packages/mypy/checkexpr.py", line 244, in visit_call_expr
    return self.visit_call_expr_inner(e, allow_none_return=allow_none_return)
  File "<path to project>/.venv/lib/python3.6/site-packages/mypy/checkexpr.py", line 314, in visit_call_expr_inner
    ret_type = self.check_call_expr_with_callee_type(callee_type, e, fullname, object_type)
  File "<path to project>/.venv/lib/python3.6/site-packages/mypy/checkexpr.py", line 658, in check_call_expr_with_callee_type
    object_type=object_type)[0]
  File "<path to project>/.venv/lib/python3.6/site-packages/mypy/checkexpr.py", line 695, in check_call
    object_type)
  File "<path to project>/.venv/lib/python3.6/site-packages/mypy/checkexpr.py", line 780, in check_callable_call
    callee, args, arg_kinds, formal_to_actual)
  File "<path to project>/.venv/lib/python3.6/site-packages/mypy/checkexpr.py", line 873, in infer_arg_types_in_context
    res[ai] = self.accept(args[ai], callee.arg_types[i])
  File "<path to project>/.venv/lib/python3.6/site-packages/mypy/checkexpr.py", line 3188, in accept
    typ = node.accept(self)
  File "<path to project>/.venv/lib/python3.6/site-packages/mypy/nodes.py", line 1446, in accept
    return visitor.visit_call_expr(self)
  File "<path to project>/.venv/lib/python3.6/site-packages/mypy/checkexpr.py", line 244, in visit_call_expr
    return self.visit_call_expr_inner(e, allow_none_return=allow_none_return)
  File "<path to project>/.venv/lib/python3.6/site-packages/mypy/checkexpr.py", line 314, in visit_call_expr_inner
    ret_type = self.check_call_expr_with_callee_type(callee_type, e, fullname, object_type)
  File "<path to project>/.venv/lib/python3.6/site-packages/mypy/checkexpr.py", line 658, in check_call_expr_with_callee_type
    object_type=object_type)[0]
  File "<path to project>/.venv/lib/python3.6/site-packages/mypy/checkexpr.py", line 695, in check_call
    object_type)
  File "<path to project>/.venv/lib/python3.6/site-packages/mypy/checkexpr.py", line 802, in check_callable_call
    callable_name, object_type, context)
  File "<path to project>/.venv/lib/python3.6/site-packages/mypy/checkexpr.py", line 574, in apply_function_plugin
    callee.ret_type, formal_arg_exprs, context, self.chk))
  File "<path to project>/.venv/lib/python3.6/site-packages/sqlmypy.py", line 257, in column_hook
    assert len(ctx.default_return_type.args) == 1
AssertionError:

Thanks for reporting and sorry for the crash! Do you have a way to reproduce this? Maybe you can show some simplified code that triggers this?

I have also ran into this issue, here is a minimal case to reproduce the issue.
Setup:
To keep pathing simple, this was done in /temp

create venv, python -m venv .venv
activate venv, source .venv/bin/activate
install packages, pip install sqlalchemy mypy sqlalchemy-stubs
create my_file.py:

import sqlalchemy

create mypy.ini:

[mypy]
plugins = sqlmypy
mypy_path = /temp/.venv/lib/python3.7/site-packages/

run test, mypy my_file.py

Produces:
/temp/.venv/lib/python3.7/site-packages/sqlalchemy/dialects/mssql/information_schema.py:60: error: INTERNAL ERROR -- Please try using mypy master on Github:
https://mypy.rtfd.io/en/latest/common_issues.html#using-a-development-mypy-build
Please report a bug at https://github.com/python/mypy/issues
version: 0.720
Traceback (most recent call last):
  File "mypy/checkexpr.py", line 3404, in accept
  File "mypy/nodes.py", line 1527, in accept
  File "mypy/checkexpr.py", line 234, in visit_call_expr__ExpressionVisitor_glue
  File "mypy/checkexpr.py", line 243, in visit_call_expr
  File "mypy/checkexpr.py", line 315, in visit_call_expr_inner
  File "mypy/checkexpr.py", line 670, in check_call_expr_with_callee_type
  File "mypy/checkexpr.py", line 728, in check_call
  File "mypy/checkexpr.py", line 835, in check_callable_call
  File "mypy/checkexpr.py", line 573, in apply_function_plugin
  File "/temp/.venv/lib/python3.7/site-packages/sqlmypy.py", line 296, in column_hook
    assert len(ctx.default_return_type.args) == 1
AssertionError:
/temp/.venv/lib/python3.7/site-packages/sqlalchemy/dialects/mssql/information_schema.py:60: : note: use --pdb to drop into pdb

Env:
OS: Ubuntu 18.04.3
python: 3.7.4
pip list
mypy              0.720
mypy-extensions   0.4.1
pip               19.0.3
setuptools        40.8.0
SQLAlchemy        1.3.8
sqlalchemy-stubs  0.2
typed-ast         1.4.0
typing-extensions 3.7.4

Notes:
If you leave out the mypy_path setting in mypy.ini, it passes just fine.
If you leave out the plugins setting in mypy.ini, it passes just fine.
If you leave out the mypy.ini, it passes just fine.
The reason for pointing at site-packages is to solve issues with modules not being found.
Errors like: error: Cannot find module named 'example'

Interestingly, if you leave off the trailing / in mypy_path setting, mypy complains and exits with error code 1.
Using setup from above:

mypy.ini:

[mypy]
plugins = sqlmypy
mypy_path = /temp/.venv/lib/python3.7/site-packages

run mypy my_file.py

Produces:
/temp/.venv/lib/python3.7/site-packages is in the MYPYPATH. Please remove it.

I don't think its actually relevant, but there you go.

Ouch, adding site-packages to MYPYPATH is actually a bad idea, it can break many things, that error doesn't explain why, you can read more details here. I am going to make a PR for mypy to make tighten this.

As an FYI to those adding site-packages due to missing import errors, I had to make the package I was developing PEP 561 compatible, as documented by mypy.


chore: support new ts module resolution(bundler)

This pr support new ts module resolution option (node16, nodenext, bundler)

fix this import error

Hi @Su-Yong. This looks good 👍. Would you mind resolving the conflicts and I'll get it merged?

Hi @Su-Yong. This looks good 👍. Would you mind resolving the conflicts and I'll get it merged?

Thanks @johnwalley. I resolved the conflicts

@johnwalley Seems like this didn't make it into a new release yet, any updates on this?


Openshift-ci needs OWNERS at the root of the repo

Creating OWNERS file since openshift-ci needs such at the root of the repo

/lgtm
/approve


video recording

The sensors need to be able to record videos by using the Raspberry Pi camera module.

closed by #15


Reduce memory consumption in Cinder services

This patch reduces memory usage on the Cinder Volume and Backup services by tuning glibc.
The specific tuning consist on disabling the per thread arenas and disabling dynamic thresholds.
The Cinder Backup service suffers from high water mark memory usage and uses excessive memory.  As an example just after 10 restore operations the service uses almost 1GB of RAM and does not ever free it afterwards. With this patch the memory consumption of the service is reduced down to almost 130MB and memory consumption during runtime is also halved.
This patch is the equivalent to OpenStack patches:

DevStack: https://review.opendev.org/c/openstack/devstack/+/845805
TripleO: https://review.opendev.org/c/openstack/tripleo-common/+/845807

Information on glibc tuning is available at:
https://www.gnu.org/software/libc/manual/html_node/Memory-Allocation-Tunables.html

patch looks good (lgtm). @Akrog wondering if we need to do similar for other openstack services? Also, should we be setting a pod request/limit alongside of these to further fine tune the statefulsets?

patch looks good (lgtm). @Akrog wondering if we need to do similar for other openstack services? Also, should we be setting a pod request/limit alongside of these to further fine tune the statefulsets?

Originally I did a good amount of testing for Cinder services and I clearly saw how it fixed the watermark memory issue and how it reduce memory consumption.
My original devstack patch was changing all devstack services, but we observed that it didn't seem to help other services so we left it only for Cinder.
We may want to set resource limits at some point, but I'd rather not do it now since it's not that easy to determine. For example for Cinder-Backup it depends on the configured max concurrency and size of the chunks, and for Cinder-Volume I don't really know what are reasonable values.


Added a testing/debugging section

aah yes, now there is one.
On Thu, May 18, 2023 at 12:07 PM Alan de Freitas @.***>
wrote:

@.**** commented on this pull request.
In user-guide/modules/ROOT/pages/testing-debugging.adoc
https://github.com/cppalliance/site-docs/pull/92#discussion_r1198202612:

@@ -0,0 +1,162 @@
+= Testing and Debugging
+:navtitle: Testing and Debugging

+Debugging code that uses Boost libraries follows the same general principles as debugging any other pass:[C++] code.
There are still two commits in the PR. Maybe you didn't force push to your
develop branch.
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Low Condition sort grouping by owner.

Low Condition sort that would sort by number of properties, preferably with the landlords with the greatest number of properties first.  This would only catch the sloppiest landlords due to the fact that many of them set up multiple corporations - one for each property - which would mean more digging to create a thorough list.  But that goes beyond programming into deep research.  But it would still be interesting.

I took a quick gander at the numbers, and there are 89 with more than one property, and 26 with more than 2 properties

✗ head lowPoorWornOutConditionParcels.json | jq | grep OWNER_NAME | sort | uniq -c | sort -nr | head
  10     "OWNER_NAME": "KOCH JOHN R",
   8     "OWNER_NAME": "OMAHA MUNICIPAL LAND BANK",
   7     "OWNER_NAME": "D&L REAL ESTATE GROUP LLC",
   6     "OWNER_NAME": "OMAHA ECONOMIC DEVELOPMENT",
   6     "OWNER_NAME": "AMP ENTERPRISES LLC",
   5     "OWNER_NAME": "SINCLAIR GROUP LLC",
   4     "OWNER_NAME": "SRE TAB BRANDEIS LLC",
...

A put this together and threw it on the homepage, with similar tables for the other groupings (out of state, out of city)


info.today and info.tomorrow are writable

Hi, first I want to thank you for this adapter! Great work.
I was confused about info.today and info.tomorrow, which you call "switches". I wondered what functions I can control by setting those to true or false. Then I looked into the code and found out that those are indicators showing if today or tomorrow are free. Wouldn't it be better to make them read only?

thx for your Feedback... Fix with v1.1.7


Issue running basic_motion.ipynb directly after software setup

Forgive my ignorance, as I'm sure the answer to this issue is incredibly simple.
I have attempted to run basic_motion.ipynb after finishing the software setup. However, there are apparently some erroneous values in the code, and I am not experienced enough to figure out what to do to fix it.
I figured posting this as an issue would help others who get stuck right after the software setup as well.
Thanks for any advice you can give, and happy holidays!

Recently,
I made another tamiya build using the list of materials shared in this page..,
While executing the program (basic_motion), right after the software setup
i got this error.
Any suggestions to overcome this error

Thanks

I had this problem.
Fixed it by running this command once on a terminal, then restart the notebook.

sudo pip3 install Adafruit-PureIO --upgrade

It'll upgrade Adafruit-PureIO package to 1.0.4 from 0.2.3 which resolve this problem.
credit to tsteinle at https://devtalk.nvidia.com/default/topic/1068733/jet-racer-basic-motion-error/?offset=2

Thanks for the suggestion krissdap, I'll give it a shot when I can and report back!

Coworker tried that out on our car at work and it fixed everything. Thanks again for the advice!

Glad it helps!
Thanks for reporting back.


An error during inference: AttributeError: 'Generator' object has no attribute 'remove_weight_norm'

I am getting an error during inference:
Initializing Inference Process..
{'num_gpus': 0, 'batch_size': 32, 'learning_rate': 0.0002, 'adam_b1': 0.8, 'adam_b2': 0.99, 'lr_decay': 0.999, 'seed': 1234, 'n_blocks': 11, 'latent_dim': 256, 'latent_dropout': 0.1, 'dec_istft_input': 'cartesian', 'segment_size': 8192, 'num_mels': 80, 'n_fft': 1024, 'hop_size': 256, 'win_size': 1024, 'sampling_rate': 22050, 'fmin': 0, 'fmax': 8000, 'fmax_for_loss': None, 'num_workers': 4, 'dist_config': {'dist_backend': 'nccl', 'dist_url': 'tcp://localhost:54321', 'world_size': 1}}
Loading 'cp_autovocoder/g_00010000'
Complete.
Traceback (most recent call last):
  File "inference_file.py", line 88, in <module>
    main()
  File "inference_file.py", line 84, in main
    inference(a)
  File "inference_file.py", line 44, in inference
    generator.remove_weight_norm()
  File "/home/yehor/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1265, in __getattr__
    raise AttributeError("'{}' object has no attribute '{}'".format(
AttributeError: 'Generator' object has no attribute 'remove_weight_norm'

I figured out how to make inference after reading the code in train.py and its part where validation is going. I can now do inference over a file using an encoder and generator checkpoints. But it would be better to have the offiial inference file.

@hcy71o can I close the issue?

Sorry for the late response. I'll add the inference code for future work :)

Thanks Sent from my iPhoneOn 19 Dec 2022, at 21:29, Hyungchan Yoon @.***> wrote:
Sorry for the late response. I'll add the inference code for future work :)
—Reply to this email directly, view it on GitHub, or unsubscribe.You are receiving this because you authored the thread.Message ID: @.***>


Karma always exit with exitcode 1 in windows

Expected behaviour
After successful test in windows it should exit with exitcode 0
Actual behaviour
In windows I always get exitcode 1
Environment Details
This is a problem in Windows i'm running it in Windows 10. It works fine in unix environments

Karma version (output of karma --version): tested in 1.3.0 and also 2.0.0
Relevant part of your karma.config.js file

Steps to reproduce the behaviour

create a karma.Server(config, callbackfunc);
Run some tests with the karma.runner.
stopping with karma.Stopper(config, callbackStop);

because in middleware stopper process.kill(process.pid, "SIGINT") get called and then windows is shutting down the process with errorcode 1 and not gracefully as in a unix environments. I tried to shut it down with kill.process(process.pid, 0) and that works fine, if I also in my callback calls process.exit(0).
Can I write my own stopper middleware as a plugin? Or should I try to fix it with a pull request?

According the current source:
https://github.com/karma-runner/karma/blob/39d378d20dee61a88572225db9e1855da3adb346/lib/middleware/stopper.js#L9
SIGINT is called only if the server url says 'stop':
if (request.url !== urlRoot + 'stop') return next()

Also: why step 1,

create a karma.Server(config, callbackfunc);
?

This is what I have used.
https://karma-runner.github.io/2.0/dev/public-api.html
I need to stop the server otherwise my browser will hang and I need to finish without errors.

I gather you are writing some nodejs code not using the normal command line?  If so you are responsible for the exit code.  Don't call the stopper, just exit with the code you want.

Thanks for the support, I stopped using the stopper in windows and it worked fine, but in unix it was needed so i hade to fix it with a check on process.plattform. But now it works in both windows and unix.


added failover_split parameter to configure loadbalancing for DHCPD failover

While the hardcoded default split of 128 is a perfectly reasonable value there are scenarios where it may be preferrable to only have one peer (eg the primary) give out leases as long as both peers are running. The split-parameter allows to configure what percentage of clients should be handled by either peer in normal mode. It would probably also be an option to implement this as a choice and only allow values of 0, 128 and 256.
there is still an issue with handling of 0 as non-default in this code, I am not sure how to achieve this.
If someone finds the time to review this that would be great, its my first pull-request so I hope I did not screw something up...

@clystron thanks, this https://github.com/opnsense/core/commit/7a15556bad9925cf24e31f9ae885da6a919080cf should fix the 0 handling.

Great, thanks!
I should be able to test this using opnsense-patch, right?

should work if O'm not mistaken, the master branch is pretty aligned for these files.


[Bug] [Dropdown] Values are not resetting on form submit.

Dropdown widgets are not resetting when 'Reset form on success' is set to true.

@jsartisan seems to be working for me. Can you please add the steps to reproduce?

@Nikhil-Nandagopal Closing it as the value get resets to the default value which is expected behavior. Sorry for the confusion.


Equinor fonts load every time viewport resizes

Step 1: open network console
Step 2: resize browser window
Step 3: see lots of requests for /fonts/equinor/EquinorVariable-VF.woff2
Step 4: ??
Step 5: profit

This seems to happen at exactly 1285px. But whyyyyyyyyyyy

Using: Edge version 95.0.1020.40 (Official build) (64-bit) on Windows 10 with a 1920x1080p monitor.
Seems to happen at these sizes for me:

1282px
733px
683px
583px

Which makes it even weirder.
I cannot reproduce this on Firefox, which indicates it might be a Chromium related issue?
We can live with the font loading several times when people resize their browser, it's really not that common a scenario and the font file isn't particularly large - but it might be indicative of a larger problem with global styles from styled-components 🤔

Cannot figure out what is causing this at the moment. It's a small enough issue to ignore for the time being, and we cannot waste more time on it. Moving to backlog so we have it in case we have more time to look at it later.

I can't reproduce this anymore so this somehow got fixed along the way. Not sure why it happened or how it got fixed which bothers me, but at least it's fixed 🤷♂


Mentioned "conference" explicitly in intro text and removed a typo

I've added "conference" below the title to make it clear to anyone that this is what we are:


Support Image-less Notification Message

Is your feature request related to a problem? Please describe.
SwiftEntryKit should offer a notification message without an image.
Describe the solution you'd like
Make the image optional and if nil, do not initialize an imageView at all.
Describe alternatives you've considered
Add separate preset model objects and views for notification and alert, but it creates reuse issue and redundancy.

Resolved in v0.3.1


[release-1.15] Expose OIDC audience of KafkaSink in its status

Cherry-pick of #4067
Same as #4071 but fixes build issues
/cherry-pick release-1.14

/test reconciler-tests-namespaced-broker