Collect doesn't display server messages

We're using ODK Collect for a project and have our own custom server (i.e, not Aggregate), and we return server messages in the <message> element of the OpenRosa document returned to ODK Collect. However, I've noticed that the messages we return to the client aren't displayed on the mobile devices.
By way of explanation, when our forms are updated, they're assigned a new UUID value as a version marker of sorts. When someone sends in a submission and the version marker is different between the submitted form and the server form, we want the sender to know that there's a version mismatch, and so we send a message, but it's not displayed on the device.
If there's any other way to get ODK Collect to display server responses, I'll be happy to find out.
Thanks for all the great work.

This would require a code modification.
If you provide a patch with the change, we can integrate it into the main
code tree.
Any patch should use a General Setting to show (the behavior you are
requesting) or hide (current behavior) the message after submitting data.
Note that submissions can be split across multiple HTTP requests (when
there are more than 10MB of image/audio/video attachments), so this change
would need to handle that submission-splitting behavior properly.
In general, we have found that keeping the interaction simple for the field
workers is best; for that reason, we decided to not display any
informational messages, but to simply report success or failure.
On Tue, Mar 15, 2016 at 6:32 AM, dodumosu<EMAIL_ADDRESS>wrote:

We're using ODK Collect for a project and have our own custom server (i.e,
not Aggregate), and we return server messages in the  element of
the OpenRosa document returned to ODK Collect. However, I've noticed that
the messages we return to the client aren't displayed on the mobile devices.
By way of explanation, when our forms are updated, they're assigned a new
UUID value as a version marker of sorts. When someone sends in a submission
and the version marker is different between the submitted form and the
server form, we want the sender to know that there's a version mismatch,
and so we send a message, but it's not displayed on the device.
If there's any other way to get ODK Collect to display server responses,
I'll be happy to find out.
Thanks for all the great work.
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--
Mitch Sundt
Software Engineer
University of Washington
<EMAIL_ADDRESS>


[StopWatch] add elapsed function

Description
Add elapsed function ot StopWatch. The function allows to return the time elapsed from first call to start()

LGTM. One suggestion, I would add a simply ascii schematic about what each function does to the class header. That will clarify more than any documentation of the individual functions can
|     Lapse      |       Lapse     |
|-----------------|------=-----------|
| accumulatedLapsDuration |

etc. WDYT?


Mixer report to mixer will not work with V2

With v1, no matter control plane auth enabled or not, all check and report calls goes through mixer sidecars (port 15004) and mixer internal metrics will be available.
With v2, behavior changes to that traffic will only go through sidecar if auth is enabled. Non-auth traffic goes directly to mixer port (9091), which causes mixer metrics unavailable and MixerReportsToMixer e2e test failure.
cc @douglas-reid @kyessenov @ostromart @costinm @rshriram

Yes, thanks for confirming. Can we please not route through port 9091? Non-auth traffic needs to go through a proxy enabled port. Why can't we use the same port 15004?

I have the same question as @kyessenov : why can't we use the same port any more? This seems like a regression and, naively, an arbitrary one as well.
If we can't use the same port, then we need to add the right listeners for the non-auth port.
@hklai why is this a 1.0 target and not a 0.8 target? this was working. we shouldn't turn features off for other features.

@douglas-reid as discussed in the code orange meeting this does not block 0.8.0, so I just added this to 1.0.
If this is intended for 0.8.1, we can change it to 0.8.

We can't use port 15004 because there are no certificates - that's the port
with TLS enabled, and if auth is off
there are no certs.
Open another port that is proxied and plain text - and we can use it.
On Fri, May 4, 2018 at 10:58 AM, Kuat<EMAIL_ADDRESS>wrote:

Yes, thanks for confirming. Can we please not route through port 9091?
Non-auth traffic needs to go through a proxy enabled port. Why can't we use
the same port 15004?
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And the change is due to using regular EDS and CDS for mixer - which follow
the global auth
setting, so all clusters (unless explicitly set) will follow the data plane
auth policy. So when
MTLS is disabled - we need a non-TLS port to connect to.
On Fri, May 4, 2018 at 6:04 PM, Costin Manolache<EMAIL_ADDRESS>wrote:

We can't use port 15004 because there are no certificates - that's the
port with TLS enabled, and if auth is off
there are no certs.
Open another port that is proxied and plain text - and we can use it.
On Fri, May 4, 2018 at 10:58 AM, Kuat<EMAIL_ADDRESS>wrote:

Yes, thanks for confirming. Can we please not route through port 9091?
Non-auth traffic needs to go through a proxy enabled port. Why can't we use
the same port 15004?
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It seems that if this is a bug fix, it should not have been forced by pilot config, but verified end-to-end. I also don't see how this is going to play nice with CDS generating a single mixer cluster.

We need to communicate better about formal deprecation of control plane auth setting. It sounds like that got removed in favor of data plane auth setting, but we only discover it post-factum, intentionally causing a regression.

What are you talking about ? CDS generates clusters the same for all
services and destinations - one cluster
for each service+port, based on settings from destinationpolicy and
defaulting to the global auth policy.
If mixer doesn't want to use the normal clusters - it can define a custom
cluster in the bootstrap,
like it had before. Whoever implemented/reviewed the mixer in v2 believed
it's cleaner to not do
one-offs - and I think it was the right decision. But if the mixer team
feels more complexity is needed
or need special code - send a PR.
I don't really see anything wrong here - the mixer plugin can pick any
cluster, and you can use
destination policy if you believe mixer should only be accessible via TLS.
The use case of gradual adoption ('a la carte') has been communicated for a
long time - and
everyone should expect and support a mixed environment.
On Fri, May 4, 2018 at 6:43 PM, Kuat<EMAIL_ADDRESS>wrote:

It seems that if this is a bug fix, it should not have been forced by
pilot config, but verified end-to-end. I also don't see how this is going
to play nice with CDS generating a single mixer cluster.
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@hklai @costinm I obviously have not been able to attend Code Orange meetings this week. However, the ability for Mixer to report to Mixer was an existing feature for over a month. We've talked about publicly and coordinated with other teams on this ability.  Whatever conversion to v2 happened should have included support for existing features.
I feel quite strongly that it should be included in the 0.8 release.

@bianpengyuan / @kyessenov : it sounds like the fix is simple-ish.  Is it just a matter of duplicating the filter stuff in an additional listener?
Can we do that first, and then figure out how we want things to work properly subsequently?

I think we need to reconcile these two streams of thought. Does mixer require an envoy in front for proper functioning or not.
If mtls is implemented in mixer then envoy is not needed.
That said we can add an envoy listener on 9091 and ensure that mixer only binds to local host and then we are good.
So fix is easy.

@mandarjog let's please not decide to start reimplementing envoy in mixer right now. I think that is too big an architectural change and will be controversial at best.
I think the easy fix of 9091 listener (or whatever) is the correct approach.
@hklai et. al: I want to stress that Mixer telemetry collection (which Mixer reporting to Mixer provides) is P0 for 1.0 and was considered "Beta" status for May. Turning it off entirely and waiting for 1.0 is not acceptable.

@hklai - one other point to consider: this feature is already mentioned prominently in the 0.8 release notes. See here: 0.8 Release Notes - Telemetry.
I know that the decision to not have it in 0.8 was made with the best of intentions. However, from the outside, it feels that it was made without full information, or was made motivated by expediency rather than from principles.
I considered this feature to be in the Beta stage. This carries the following promise according to: https://github.com/istio/community/blob/master/FEATURE-LIFECYCLE.md

The overall feature will not be dropped, though details may change.

I'd rather we not play semantic games around details changing and dropped, or even what is considered Beta. This should be in 0.8.
The fix should be straightforward and easily testable.
Please reconsider its inclusion.

@costinm let me understand what you are saying, and correct me if I'm wrong.
We have two features that we want to ship. Feature 1 is istio-on-istio telemetry -- meaning mixer adapters can take advantage of new data coming to mixer from pilot/mixer. Feature 2 is "gradual adoption", which I presume means ability to have a mixed deployment in which half of the mesh is using mTLS, but half is not.
Feature 1 needs to remain throughout any changes necessary to pilot or mixer such as moving to another envoy protocol. Otherwise, it's a clear regression, and whatever introduced the regression needs to be fixed or rolled back.
Feature 2 is also important, but it cannot come at the expense of feature 1. It's actually not very relevant to this discussion I think, since it can be implemented in parallel to feature 1. The implementation for feature 2 needs to provide a solution for 1) what to do with control plane auth enabled flag; 2) what to do with check_report_server setting since it is a single global value for both halves of the mesh. Using hard-coded ports or port-names is again, a regression, since the port is not fixed in the deployment.
Please do not fix the two discussions. It's not helping to gain clarity.

We should not be doing mTLS in mixer

@douglas-reid Do you think this is blocking 0.8.0, or this should be in 0.8.x eventually? I believe we concluded this was not blocking 0.8.0 as nobody needed/relied on this for 0.8.0. If you think otherwise, I can add the blocking release label and discuss this in the code orange meeting.
@bianpengyuan do you have ETA when this can be resolved?

I still don't understand the big storm in the teacup...
Mixer was completely broken with v2 just 2 weeks ago (since the old v1
params
no longer worked - I'm still not sure how this happened ). Auth finally
started
to work late last week. We spent a hellish week dealing with the flaky
tests.
Is the biggest problem adding a new port in mixer service with envoy in
front of it ???
From networking perspective the requirement was to use CDS/EDS for
connecting
to mixer instead of the old bootstrap config and cluster IP. This allows
any service
to do whatever it wants with its ports - and use the standard config to
enable
TLS or use any other routing feature. Pilot doesn't really care how the
service
is implemented or if it collects metrics or not.
Using envoy in front of mixer or not is primarily between the perf and
mixer teams.
As long as Laurent (or mixer team) have some data confirming policy check
latency
are not regressing - or there is some way for users to chose latency or
extra metrics

I don't see a problem with adding envoy or not.

On Sun, May 6, 2018 at 10:23 PM, Pengyuan Bian<EMAIL_ADDRESS>wrote:

The fix should be easy, either change mixer's sidecar bootstrap config to
listen on two ports: one for auth and one for non-auth or make both kinds
of traffic goes through one port. ETA would be 1~2 days.
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@costinm , why we don't have cert for mixer? The cert is generated by istio citadel and should be there

We have a cert for mixer, and it has both Spiffe and DNS SANs.
I think there was some confusion about the 'authority' - it's not a cert
issue.
On Mon, May 7, 2018 at 11:26 AM, Tao Li<EMAIL_ADDRESS>wrote:

@costinm https://github.com/costinm , why we don't have cert for mixer?
The cert is generated by istio citadel and should be there
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This should be resolved by #5370

@ostromart where is this test running ?

@mandarjog any update? did you get a chance to verify?

@hklai I am verifying this.

MixerReportingToMixer passed with v2. Created a PR to remove bypass logic.


No change in acceptance fraction when changing the 'a' parameter (emcee 3.0)

General information:

emcee version:  3.0rc2
platform:  Ubuntu 18.10
installation method (pip/conda/source/other?):  source

Problem description:
Expected behavior:
The acceptance fraction should change when using different 'a' parameters.
Actual behavior:
The acceptance fraction stays the same no matter what value is used for 'a'.
What have you tried so far?:
In version 3.0rc2, a range of values for 'a', from 1.0 to 50.0, none of which change the acceptance fraction. In version 2.2.1, this does work, so I have reverted back to that version for now.
Minimal example:

# Slightly modifying example code from the emcee website (https://emcee.readthedocs.io/en/latest/tutorials/quickstart/)

import numpy as np
import emcee
print(emcee.__version__)

def log_prob(x, mu, cov):
    diff = x - mu
    return -0.5*np.dot(diff, np.linalg.solve(cov,diff))

a_parameter=2.0

ndim = 5

np.random.seed(42)
means = np.random.rand(ndim)

cov = 0.5 - np.random.rand(ndim ** 2).reshape((ndim, ndim))
cov = np.triu(cov)
cov += cov.T - np.diag(cov.diagonal())
cov = np.dot(cov,cov)

nwalkers = 32
p0 = np.random.rand(nwalkers, ndim)

sampler = emcee.EnsembleSampler(nwalkers, ndim, log_prob, args=[means, cov], a=a_parameter)

log_prob(p0[0], means, cov)

state = sampler.run_mcmc(p0, 100)

af = np.mean(sampler.acceptance_fraction)
print(af)

# emcee 3.0rc2 output
# 3.0rc2
# 0.5484375 <-- Does not change when using different a_parameter, or when rerunning code

#emcee 2.2.1 output
# 2.2.1
# 0.5440625 <-- Changes when using different a_parameter, and changes by statistical fluctuations when rerunning

In version 3, you need to use something like the following:
move = emcee.moves.StretchMove(a=a_parameter)
sampler = emcee.EnsembleSampler(..., moves=[move])

See the docs.

This fixes my issue. Thanks for your quick response.


Nan::Null() in v8::Object in v8::Array results in array?

Hi,
I'm pretty new to nan so apologies if I'm missing something straightforward.
I'm trying to create an array of objects where the value of the keys of the objects are null. The problem is, the resulting objects in JS have values where null is in an array: [null].
Expected:
[{ foo: null }]

Actual:
[{foo: [null]}]

For context, my code more or less looks like this. It is within the HandleOKCallback callback of an Nan::AsyncWorker:
v8::Local<v8::Array> arr = Nan::New<v8::Array>(1);
v8::Local<v8::Object> obj = Nan::New<v8::Object>();
obj->Set(Nan::New("foo").ToLocalChecked(), Nan::Null());
arr->Set(0, obj);

v8::Local<v8::Value> argv[] = {
  Nan::Null(),
  arr
}

callback->Call(2, argv);

Am I missing something obvious? I noticed that if I return the object instead of the object in an array, the value is null as expected.

arr->Set(0, Nan::New(obj));

What happens when you change that to arr->Set(0, obj);?

What happens when you change that to arr->Set(0, obj);?

Ah this was a typo on my part - it should be arr->Set(0, obj);

@kkoopa This is on 6.11.1 - just tried with 8.9.0 and 4.8.4 with the same results

It looks like something is happening to the object when it is set in the array. If I create a nested object with null values, all of them are cast to arrays of nulls:
[
  {
    foo: [null]
    bar: {
      baz: [null]
    }
  }
]

Does not happen to me with node 9.8.
v8::Local<v8::Array> arr = Nan::New<v8::Array>(1);
v8::Local<v8::Object> obj = Nan::New<v8::Object>();
Nan::Set(obj, Nan::New("foo").ToLocalChecked(), Nan::Null());
Nan::Set(arr, 0, obj);
info.GetReturnValue().Set(arr);

output:
[ { foo: null } ]


CSS :host & :host context styles not applied properly for NS angular

Environment
$ tns info
✔ Getting NativeScript components versions information...
✔ Component nativescript has 5.1.0 version and is up to date.
✔ Component tns-core-modules has 5.1.1 version and is up to date.
✔ Component tns-android has 5.1.0 version and is up to date.
✔ Component tns-ios has 5.1.0 version and is up to date.

This issue occurs on both iOS and Android.
Describe the bug
Looks like CSS selectors :host and :host-context are not applied properly in ListView (or for multiple invocations of the same element). See the sample project, where the ListView template consists of a ItemCard component. This component contains a style like below, which should paint the background of all items to yellow.
:host-context(.ios) .list-group-item {
  background-color: yellow;
}

As far as I know the css selector reads as:

if the context of the host-element ItemsComponent contains a class ios, then apply this CSS to all instances of class list-group-item.

In reality however, only the first ListView element and the first element that is out of the viewable area are painted.
Sample project
https://github.com/abhayastudios/css-context

Hi @abhayastudios,
Thank you for the attached sample project.
I tested it and was able to recreate the case, however currently the :host-context pseudo selector seems that is not supported. You can check the supported ones here. Regarding that, I will mark this issue as a feature request.
Let me know if I am missing something.

Issue moved to NativeScript/NativeScript #6745 via ZenHub


SIGKILL on code that doesn't compile and doesn't crash the compiler

Right now, https://play.rust-lang.org/?version=stable&mode=debug&edition=2018&gist=0c742b14419b5ebe0396bcbd1d72c886 gives me
/playground/tools/entrypoint.sh: line 11:     7 Killed                  timeout --signal=KILL ${timeout} "$@"

when I try to run it.
I tried again just now and it works again. Maybe the server was overloaded or something?

I frequently have this same issue too. It's seemingly random.

Ditto, I have this frequently and it's been getting worse over time.

See also #712, which I expect to be the same underlying issue (server overloaded)


feat(MI-86): updating uploads/creatives API with personalized QR codes in campaigns

Fixes MI-86
Functionality already exists, just updating copy to clarify how to use personalized QR codes per recipient in a campaign.
Checklist

[x] Up to date with main
[x] All the tests are passing

[x] Delete all resources created in tests

[x] Prettier
[x] Spectral Lint
[x] npm run bundle outputs nothing suspect
[x] npm run postman outputs nothing suspect

Changes

clarify how to specify unique redirect URLs per recipient when using QR codes in campaigns
update request samples to include qr_code_redirect_url

Areas of Concern (optional)

you will need to upgrade package.json too

Hey @juanfriss, do we need to include output files from the npm run bundle and npm run postman commands since I see they are executed in the Deploy GHA?

I think you don't as "The bundled version is generated as part of CI/CD"

I think you'll need npm run bump-version, as it updates version in package.json as well as the spec yaml


typeahead_helper: Improve activity_score function for stream typeahead in composebox textarea.

This PR fixes #20618
Improve activity_score function in typeahead_helper.js -
Changing stream typeahead in the compose box from -
( pinned streams > subscribed streams > other streams, with message volume serving as the tie breaker)
To improve the likelihood that the desired streams are sorted at the top, ordering changed to-
(stream that the message is addressed to (if any) > unmuted pinned streams > unmuted subscribed streams > muted streams > other streams)
Link to zulip chat regarding this issue - https://chat.zulip.org/#narrow/stream/6-frontend/topic/stream.20typeahead.20sorting/near/1310349
Testing plan:
Lint test.
GIFs or screenshots:
Please checkout screen recording in zulip chat regarding this issue - https://chat.zulip.org/#narrow/stream/6-frontend/topic/stream.20typeahead.20sorting/near/1310349

Hey @srdeotarse, can I work on this, if you are not continuing?

@yogesh-sirsat I am still working on this issue.

I think that my recent commit should resolve this issue. So, I am abandoning this issue to work on other issues.
@timabbott Please review my recent commit and if any changes are required, I will continue working on them until this PR is merged.

@srdeotarse can you rebase this?  We just merged another PR touching the same code.

@timabbott I have rebased my commits.

@timabbott looks like this is ready for another round of review from you. Or does the failing test need to be fixed first?

@srdeotarse Would you be up for rebasing your PR so that we can get it integrated?

@alya Sure, I will rebase the PR.

Great, thanks!

Just a quick bump on getting this rebased -- thanks!

Closing in favor of https://github.com/zulip/zulip/pull/29246 for this issue.


Add field to schema

What this PR does / why we need it
When validating helm values against the schema, this field seems to be required.

Not needed when using our tooling, apparently. I'm closing this.

Ok, also fine. Just one note: When closing a PR, the branch is not getting deleted automatically. I deleted it for you. 🙂

Thanks!


Syntax Highlighting has stopped working across all files and extensions

Issue Type: Bug
This happened after I switched the python linter to pylint using the Python extension, VS Code asked me to install it, and I clicked the button to install it (after which it still complained in the output window that pylint was not installed). I noticed that the syntax highlighting had stopped working on my .py files, and after opening other types of files in different workspaces, all of my syntax highlighting has stopped.
The Log (Windows) view has this to say:
[2021-09-14 09:46:40.154] [renderer1] [error] Invalid or unexpected token: C:\Users\bleac\AppData\Local\Programs\Microsoft VS Code\resources\app\node_modules.asar\vscode-textmate\release\main.js:2
����
^

SyntaxError: Invalid or unexpected token
    at new Script (vm.js:101:7)
    at createScript (vm.js:262:10)
    at Object.runInThisContext (vm.js:310:10)
    at wrapSafe (internal/modules/cjs/loader.js:1021:15)
    at Module._compile (internal/modules/cjs/loader.js:1081:27)
    at Object.Module._extensions..js (internal/modules/cjs/loader.js:1155:10)
    at Module.load (internal/modules/cjs/loader.js:982:32)
    at Module._load (internal/modules/cjs/loader.js:823:14)
    at Function.f._load (electron/js2c/asar_bundle.js:5:12913)
    at Function.t._load (electron/js2c/renderer_init.js:69:307)
    at Module.require (internal/modules/cjs/loader.js:1006:19)
    at require (internal/modules/cjs/helpers.js:88:18)
    at t (vscode-file://vscode-app/c:/Users/bleac/AppData/Local/Programs/Microsoft%20VS%20Code/resources/app/out/vs/loader.js:5:101)
    at r.load (vscode-file://vscode-app/c:/Users/bleac/AppData/Local/Programs/Microsoft%20VS%20Code/resources/app/out/vs/loader.js:3:11082)
    at r.load (vscode-file://vscode-app/c:/Users/bleac/AppData/Local/Programs/Microsoft%20VS%20Code/resources/app/out/vs/loader.js:3:10249)
    at l (vscode-file://vscode-app/c:/Users/bleac/AppData/Local/Programs/Microsoft%20VS%20Code/resources/app/out/vs/loader.js:5:10308)
    at Object.errorback (vscode-file://vscode-app/c:/Users/bleac/AppData/Local/Programs/Microsoft%20VS%20Code/resources/app/out/vs/loader.js:5:10429)
    at r.triggerErrorback (vscode-file://vscode-app/c:/Users/bleac/AppData/Local/Programs/Microsoft%20VS%20Code/resources/app/out/vs/loader.js:3:10613)
    at vscode-file://vscode-app/c:/Users/bleac/AppData/Local/Programs/Microsoft%20VS%20Code/resources/app/out/vs/loader.js:3:10319
    at r.load (vscode-file://vscode-app/c:/Users/bleac/AppData/Local/Programs/Microsoft%20VS%20Code/resources/app/out/vs/loader.js:3:11099)
    at r.load (vscode-file://vscode-app/c:/Users/bleac/AppData/Local/Programs/Microsoft%20VS%20Code/resources/app/out/vs/loader.js:3:10249)
    at l (vscode-file://vscode-app/c:/Users/bleac/AppData/Local/Programs/Microsoft%20VS%20Code/resources/app/out/vs/loader.js:5:10308)
    at r._loadModule (vscode-file://vscode-app/c:/Users/bleac/AppData/Local/Programs/Microsoft%20VS%20Code/resources/app/out/vs/loader.js:5:10438)
    at r._resolve (vscode-file://vscode-app/c:/Users/bleac/AppData/Local/Programs/Microsoft%20VS%20Code/resources/app/out/vs/loader.js:6:452)
    at r.defineModule (vscode-file://vscode-app/c:/Users/bleac/AppData/Local/Programs/Microsoft%20VS%20Code/resources/app/out/vs/loader.js:5:6142)
    at r._relativeRequire (vscode-file://vscode-app/c:/Users/bleac/AppData/Local/Programs/Microsoft%20VS%20Code/resources/app/out/vs/loader.js:5:6828)
    at n (vscode-file://vscode-app/c:/Users/bleac/AppData/Local/Programs/Microsoft%20VS%20Code/resources/app/out/vs/loader.js:5:9414)
    at vscode-file://vscode-app/c:/Users/bleac/AppData/Local/Programs/Microsoft%20VS%20Code/resources/app/out/vs/workbench/workbench.desktop.main.js:1916:28461
    at new Promise (<anonymous>)
    at i._getOrCreateGrammarFactory (vscode-file://vscode-app/c:/Users/bleac/AppData/Local/Programs/Microsoft%20VS%20Code/resources/app/out/vs/workbench/workbench.desktop.main.js:1916:28441)
    at vscode-file://vscode-app/c:/Users/bleac/AppData/Local/Programs/Microsoft%20VS%20Code/resources/app/out/vs/workbench/workbench.desktop.main.js:1916:29329
    at i._registerDefinitionIfAvailable (vscode-file://vscode-app/c:/Users/bleac/AppData/Local/Programs/Microsoft%20VS%20Code/resources/app/out/vs/workbench/workbench.desktop.main.js:1916:29749)
    at l._handler (vscode-file://vscode-app/c:/Users/bleac/AppData/Local/Programs/Microsoft%20VS%20Code/resources/app/out/vs/workbench/workbench.desktop.main.js:1916:27052)
    at l._handle (vscode-file://vscode-app/c:/Users/bleac/AppData/Local/Programs/Microsoft%20VS%20Code/resources/app/out/vs/workbench/workbench.desktop.main.js:1476:16692)
    at l.acceptUsers (vscode-file://vscode-app/c:/Users/bleac/AppData/Local/Programs/Microsoft%20VS%20Code/resources/app/out/vs/workbench/workbench.desktop.main.js:1476:16596)
    at Function._handleExtensionPoint (vscode-file://vscode-app/c:/Users/bleac/AppData/Local/Programs/Microsoft%20VS%20Code/resources/app/out/vs/workbench/workbench.desktop.main.js:2872:151628)
    at cl._doHandleExtensionPoints (vscode-file://vscode-app/c:/Users/bleac/AppData/Local/Programs/Microsoft%20VS%20Code/resources/app/out/vs/workbench/workbench.desktop.main.js:2872:150747)
    at cl._startLocalExtensionHost (vscode-file://vscode-app/c:/Users/bleac/AppData/Local/Programs/Microsoft%20VS%20Code/resources/app/out/vs/workbench/workbench.desktop.main.js:2878:9281)
    at async cl._scanAndHandleExtensions (vscode-file://vscode-app/c:/Users/bleac/AppData/Local/Programs/Microsoft%20VS%20Code/resources/app/out/vs/workbench/workbench.desktop.main.js:2878:8243)
    at async cl._initialize (vscode-file://vscode-app/c:/Users/bleac/AppData/Local/Programs/Microsoft%20VS%20Code/resources/app/out/vs/workbench/workbench.desktop.main.js:2872:145188)

After opening up the main.js file in question, I find a bunch of broken characters at the bottom:
...
function n(r){if(t[r])return t[r].exports;var i=t[r]={exports:{}};return e[r].call(i.exports,i,i.exports,n),i.exports}(787)})()}));//# sourceMappin
����
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��	�	�
��	�	�	�
�	����	�	�	�
�	�	�	�
�
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�	�	�	�

VS Code version: Code 1.60.0 (e7d7e9a9348e6a8cc8c03f877d39cb72e5dfb1ff, 2021-09-01T10:41:52.311Z)
OS version: Windows_NT x64 10.0.19043
Restricted Mode: Yes

System Info

Item
Value

CPUs
Intel(R) Core(TM) i9-9980HK CPU @ 2.40GHz (16 x 2400)

GPU Status
2d_canvas: enabledgpu_compositing: enabledmultiple_raster_threads: enabled_onoop_rasterization: enabledopengl: enabled_onrasterization: enabledskia_renderer: enabled_onvideo_decode: enabledvulkan: disabled_offwebgl: enabledwebgl2: enabled

Load (avg)
undefined

Memory (System)
63.85GB (35.64GB free)

Process Argv
C:\Users\bleac\Documents\School\CS 422\Project1 --crash-reporter-id 0439b5de-74b6-4f22-9fa6-ddb61441b3d8

Screen Reader
no

VM
0%

Extensions (11)

Extension
Author (truncated)
Version

vscode-cudacpp
kri
0.1.1

gnuplot
mam
1.0.5

jupyter
ms-
2021.8.2041215044

jupyter-keymap
ms-
1.0.0

remote-ssh
ms-
0.65.8

remote-ssh-edit
ms-
0.65.8

remote-wsl
ms-
0.58.2

hexeditor
ms-
1.8.2

java
red
0.81.0

vscodeintellicode
Vis
1.2.14

vscode-maven
vsc
0.33.0

A/B Experiments
vsliv368cf:30146710
vsreu685:30147344
python383:30185418
pythonvspyt602:30300191
vspor879:30202332
vspor708:30202333
vspor363:30204092
vswsl492:30256859
pythonvspyt639:30300192
pythontb:30283811
pythonvspyt551cf:30345471
pythonptprofiler:30281270
vsdfh931cf:30280410
vshan820:30294714
vstes263cf:30335440
vscoreces:30358480
pythondataviewer:30285071
pythonvsuse255:30340121
vscod805cf:30301675
pythonvspyt200:30340761
vscextlangct:30333562
binariesv615:30325510
vsccppwtct:30364498
pythonvssor306:30344512
bridge0708:30335490
pygetstartedt2:30360495
bridge0723:30353136
pythonrunftest32:30365366
pythonf5test824:30361777
javagetstartedt:30364666
pythonvspyt187cf:30365362
pydsgsc2:30361791
vscus224cf:30358040
vssid140:30363603
vssur157:30365996

I ended up running the installer to try and fix it. The installer detected multiple VS Code processes that were open, despite no windows being open on my desktop. After closing the processes through task manager and re-installing, the issue was fixed.

It looks like the .asar file became corrupted on disk.

Does this reproduce if you reinstall VS Code?

@joaomoreno I think it did:

After closing the processes through task manager and re-installing, the issue was fixed.

To be clear, the issue did not reproduce after re-installing.

Interesting. Unsure why that .asar file got corrupted in your system, but it could literally be any piece of software on your system causing the issue. It could even be hardware failure. Closing this for now as you seem to be unblocked.


rmf_ros2: 2.2.2-1 in 'iron/distribution.yaml' [bloom]

Increasing version of package(s) in repository rmf_ros2 to 2.2.2-1:

upstream repository: https://github.com/open-rmf/rmf_ros2.git
release repository: https://github.com/ros2-gbp/rmf_ros2-release.git
distro file: iron/distribution.yaml
bloom version: 0.11.2
previous version for package: 2.2.1-1

rmf_fleet_adapter
* Improve linking time (#300 <https://github.com/open-rmf/rmf_ros2/pull/300>)
* Contributors: Grey, Luca Della Vedova, Yadunund

rmf_fleet_adapter_python

No changes

rmf_task_ros2
* Improve linking time (#300 <https://github.com/open-rmf/rmf_ros2/pull/300>)
* Contributors: Grey, Luca Della Vedova, Yadunund

rmf_traffic_ros2
* Improve linking time (#300 <https://github.com/open-rmf/rmf_ros2/pull/300>)
* Contributors: Grey, Luca Della Vedova, Yadunund

rmf_websocket

No changes

Required in addition to #38503 to resolve regression https://build.ros2.org/job/Ibin_uJ64__rmf_visualization_schedule__ubuntu_jammy_amd64__binary/


Sorting a Stack | JAVA

Title - Sorting a Stack such that greatest element is at the top of Stack
what will change - A new file StackSorting.java will be created in the Stack folder of the repository. The file will contains proper description , time complexity and Test cases for reference.
Type of Issue -
Please add/delete options that are not relevant.

[x] Adding New Code
[ ] Improving Code
[ ] Improving Documentation
[ ] Bug Fix

Programming Language
Please add/delete options that are not relevant.

[ ] Python
[ ] C++
[x] Java
[ ] C
[ ] Go
[ ] Other language

Self Check

Ask for issue assignment before making Pull Request.
Add your file in the proper folder
Clean Code and Documentation for better readability
Add Title and Description of the program in the file

:star2: Star it :fork_and_knife:Fork it :handshake: Contribute to it!
Happy Coding!

Please assign me the issue so that I can make the PR
Here the Test Cases screenshots :

Added the code
Please review the PR
Thank you


Using old library version due on 5.5 compability

The most of our customers are using  PHP 5.5 and above.
Is there possible to use in production mode older library version compatible with
Where can I find last travis build and library version compatible and tested with this php version?

Version 1.x is compatible with PHP 5.5.
I strongly recommend upgrading to PHP 5.6, as 5.5 is no longer supported.

The 2.x series is when we bumped the PHP requirement to >= 5.6. To support customers using PHP 5.5, use the version constraint ~1.4:
composer require league/oauth2-client=~1.4

You may find all our releases here: https://github.com/thephpleague/oauth2-client/releases. Our build history is available on Travis here: https://travis-ci.org/thephpleague/oauth2-client.

I strongly recommend upgrading to PHP 5.6, as 5.5 is no longer supported.

I should say it to our customers. But not all of them can it.


feat: use corda-cert-gen helm chart for certificate creation

Description
As a developer I want to use corda-cert-gen helm chart for certificate creation instead of ansible roles so that r3 corda os certificates can be created using the helm chart only
Acceptance Criteria

create/certificate roles are removed
corda deployment used corda-cert-gen helm chart for certificate creation
network is deployed successfully

looking on this.

@sailajakommineni This is done as part of the Chart redesign, so you do not need to work on this.

ok, thanks.


Exclude in Generic from_filesystem

Description: add in exclude parameter to GenericLoader classmethod from_filesystem
Issue: Didn't get added in https://github.com/langchain-ai/langchain/pull/9064
Dependencies: None

Fix merged https://github.com/langchain-ai/langchain/pull/9539


[Bug] Grafana doesn't load data when setting max-select-buckets to non-zero value

System info:
InfluxDB: 1.3.1
OS: Mac OS
Steps to reproduce:
influxdb.conf
[cluster]
  shard-writer-timeout = "5s"
  write-timeout = "10s"
  max-concurrent-queries = 0
  query-timeout = "0s"
  max-select-point = 0
  max-select-series = 0
  max-select-buckets = 1000000

Expected behavior:
Grafana shall display data
Actual behavior:
Grafana shows no data points
max-select-buckets = 0

max-select-buckets = 1000000

we felt down in same problem.
It seems that range provided from kibana is not closed.
If you add and time < now() it will works
https://github.com/influxdata/influxdb/blob/253b8c2b35f8529fe597d065143c0c2e287a5b15/query/compile.go#L823
max range in such situation could be solved on influx side (by using now as default for max) or on kibana side (add time< now())

We have users that write future (forecasted) data into the database and then overwrite it with current data as current data arrives. I think implying now() as a max time would break that use case. I'd prefer the query engine not guess the user's intentions and imply a max.

This should likely treat now() as the implicit end. We do that somewhere in the query engine when there is a GROUP BY time(...) in the query. If there's no interval, then the implicit end time is the maximum possible timestamp.
The bucket check must happen before that time gets reset. I'll take a look.

I took a look. This appears to be fixed in master, but the bug exists in 1.3. The fix for 1.3 is different than the one in master because it got fixed in master by accident during a refactor of the code.


How to enable alexa wake word on macOS?

IMPORTANT: Before you create an issue, please take a look at our Issue Reporting Guide.
Briefly summarize your issue:
How do I enable alexa wake word in macOS, what do i need to put in cmake?

Hold A to talk
Provide the steps to reproduce the issue, if applicable:

Tell us about your environment:

What version of the AVS Device SDK are you using?
  <x.y.z>

Tell us what hardware you're using:

[ .] Desktop / Laptop
[ ] Raspberry Pi
[ ] Other - tell us more:

Tell us about your OS (Type & version):

[ ] Linux
[ .] MacOS
[ ] Raspbian Stretch
[ ] Raspbian Jessy
[ ] Other - tell us more:

You have to enable a wake word engine to do this hands free.  For the Mac I use Kitt.AI.
Here are the WW settings required:
https://developer.amazon.com/en-US/docs/alexa/avs-device-sdk/cmake-parameters.html#wake-word

Hi @natchapol2347, thanks for reaching out. Let us now if your question is resolved and we can close the issue.
P.S. Thanks @ereich1217

Closing the issue due to inactivity, please reopen the issue if unresolved. Thank you for your contribution to the Alexa Smart Screen SDK!
P.S. Here is the link to the WWE: https://github.com/Kitt-AI/snowboy


[L3] Introduce l3_log_init() as a shell to call l3_init().

This commit lays down some boilerplate code to add support for different logging schemes under L3. Introduce enum l3_log_t to specify different logging interfaces, mainly intended for use by micro-benchmarking utility programs. l3_log_init() becomes a shell around l3_init(), which remains the default interface to init a L3-logging system, on mmap()-ed files.
There are no other functional changes introduced with this commit.

FYI, @gregthelaw - Another renaming change we spoke about. I'm checking this in, but you may wish to give this a quick check and sign-off.


Add support for SCRAM when using SASL authN

When using SASL authentication, currently only Kerberos mechanism is supported.
It should be possible to also use SCRAM (e.g. "SCRAM-SHA-256" as "sasl.mechanism") without incurring an exception.
In general, SASL should support both Kerberos and SCRAM as strong authentication mechanisms.

It could still be useful to have some constraints to avoid incomplete configurations (SASL without specifying Kerberos or SCRAM) or weak configurations (SASL + PLAIN).
What do you think?

It could still be useful to have some constraints to avoid incomplete configurations (SASL without specifying Kerberos or SCRAM) or weak configurations (SASL + PLAIN).
What do you think?
Actually I'm thinking about providing some error notification interface, or throw an exception for rejected properties, -- there's already config-verification in librdkafka, thus we could keep it simple in modern-cpp-kafka.

@eloparco I raised a PR https://github.com/morganstanley/modern-cpp-kafka/pull/86 to remove the SASL configuration checking, since the check would not help too much.
A better way would be registering an error callback (i.e. setErrorCallback), thus we would be able to get detailed error info.
E.g,
Local: Authentication failure [-169] | sasl_plaintext://.../bootstrap: Failed to initialize SASL authentication: SASL handshake failed (start (-1)): SASL(-1): generic failure: GSSAPI Error: Unspecified GSS failure.  Minor code may provide more information (Server .... not found in Kerberos database) ...

Close the PR since #86 has been merged.


feat(words): add repr, noncopyable

Note: This PR is necessary to pass CI for https://github.com/autowarefoundation/autoware_common/pull/221


New release?

Hi!
When are you planning a new release? I keep getting a Firefox version error on Windows.
Thanks

I am a student so I don't have time and am also finding a solution for this as I cannot find one. I will fix this as soon as I can Thanks.

@Bllacky no problem, I'll fix it ASAP. I completely forgot that it was pending.

If you can release a new build, that would be amazing. I have to say your project is really helpful since I can easily get into Google Sheets and everything runs sandboxed.

Do you know how I can build your project?

No, I don't think so I know it. So, are you trying to say that you will help?

I see the last release was in November 2022. How did you build it then ?


refactor: Remove call to getWorkflowRun

Fixes #1643
Signed-off-by: Ian Lewis<EMAIL_ADDRESS>

This is going to conflict with #1644...

@laurentsimon @asraa See also #1660. That PR includes this PR + fixes for the comments made here.

I think we can close this in deference to #1660 since that includes the changes in this PR as well. Maintaining both PRs is pretty cumbersome.


integer columns not created in schema

was trying this with a maria DB.
all integer columns were not created in the schema modules.
also note that all foreign keys and primary keys did not get create properly but maybe only because they are integer based.

@DEvil0000 sorry for the delay but this has been fixed with version 2.4.0. Please let me know if there are any other issues


Gridlines now support ordinal scales

Fixes #850
Long-term it would probably make sense to add a base component that contains the tick logic, so that it can be shared between the axis and gridline component. This might also help resolve https://github.com/ScottLogic/d3fc/issues/653
However, for now this fix stops gridlines barfing when used with ordinal scales :-)

LGTM, worth raising an issue for the refactor?


CFFL

CFFL

Linter check found the following problems:
The following problems have been found:
ERROR: /tmp/abs_e290i2qvns/clone/recipe/meta.yaml:14: pip_install_args: pip install should be run with --no-deps.
ERROR: /tmp/abs_e290i2qvns/clone/recipe/meta.yaml:21: missing_wheel: For pypi packages, wheel should be present in the host section
WARNING: /tmp/abs_e290i2qvns/clone/recipe/meta.yaml:46: missing_description: The recipe is missing a description
Errors were found

Linter check found the following problems:
The following problems have been found:
ERROR: /tmp/abs_76_pa93dri/clone/recipe/meta.yaml:14: pip_install_args: pip install should be run with --no-deps.
ERROR: /tmp/abs_76_pa93dri/clone/recipe/meta.yaml:21: missing_wheel: For pypi packages, wheel should be present in the host section
WARNING: /tmp/abs_76_pa93dri/clone/recipe/meta.yaml:46: missing_description: The recipe is missing a description
Errors were found


unixODBC: 2.3.4 -> 2.3.5

Semi-automatic update. These checks were performed:

built on NixOS
ran /nix/store/vs2i1mz87r31icar1kp880wq81bk2wy5-unixODBC-2.3.5/bin/isql --help got 0 exit code
ran /nix/store/vs2i1mz87r31icar1kp880wq81bk2wy5-unixODBC-2.3.5/bin/isql --version and found version 2.3.5
ran /nix/store/vs2i1mz87r31icar1kp880wq81bk2wy5-unixODBC-2.3.5/bin/isql --help and found version 2.3.5
ran /nix/store/vs2i1mz87r31icar1kp880wq81bk2wy5-unixODBC-2.3.5/bin/odbcinst --help got 0 exit code
ran /nix/store/vs2i1mz87r31icar1kp880wq81bk2wy5-unixODBC-2.3.5/bin/odbcinst --version and found version 2.3.5
ran /nix/store/vs2i1mz87r31icar1kp880wq81bk2wy5-unixODBC-2.3.5/bin/odbcinst --help and found version 2.3.5
ran /nix/store/vs2i1mz87r31icar1kp880wq81bk2wy5-unixODBC-2.3.5/bin/iusql --help got 0 exit code
ran /nix/store/vs2i1mz87r31icar1kp880wq81bk2wy5-unixODBC-2.3.5/bin/iusql --version and found version 2.3.5
ran /nix/store/vs2i1mz87r31icar1kp880wq81bk2wy5-unixODBC-2.3.5/bin/iusql --help and found version 2.3.5
ran /nix/store/vs2i1mz87r31icar1kp880wq81bk2wy5-unixODBC-2.3.5/bin/odbc_config --version and found version 2.3.5
found 2.3.5 with grep in /nix/store/vs2i1mz87r31icar1kp880wq81bk2wy5-unixODBC-2.3.5
found 2.3.5 in filename of file in /nix/store/vs2i1mz87r31icar1kp880wq81bk2wy5-unixODBC-2.3.5

Success on x86_64-darwin (full log)
Partial log (click to expand)
mkdir: cannot create directory '/etc/ODBCDataSources': Permission denied
make[2]: [Makefile:891: install-data-am] Error 1 (ignored)
cp unixodbc_conf.h /nix/store/wcknlmplfbx2xk4v4s8wj4jp026y4ji9-unixODBC-2.3.5/include/unixodbc_conf.h
make[2]: Leaving directory '/private/tmp/nix-build-unixODBC-2.3.5.drv-0/unixODBC-2.3.5'
make[1]: Leaving directory '/private/tmp/nix-build-unixODBC-2.3.5.drv-0/unixODBC-2.3.5'
post-installation fixup
gzipping man pages under /nix/store/wcknlmplfbx2xk4v4s8wj4jp026y4ji9-unixODBC-2.3.5/share/man/
strip is /nix/store/4sdh09gmvl15cy0zb6i7mbvxh5syz206-cctools-binutils-darwin/bin/strip
stripping (with command strip and flags -S) in /nix/store/wcknlmplfbx2xk4v4s8wj4jp026y4ji9-unixODBC-2.3.5/lib  /nix/store/wcknlmplfbx2xk4v4s8wj4jp026y4ji9-unixODBC-2.3.5/bin
patching script interpreter paths in /nix/store/wcknlmplfbx2xk4v4s8wj4jp026y4ji9-unixODBC-2.3.5

Success on x86_64-linux (full log)
Partial log (click to expand)
shrinking /nix/store/vs2i1mz87r31icar1kp880wq81bk2wy5-unixODBC-2.3.5/bin/isql
shrinking /nix/store/vs2i1mz87r31icar1kp880wq81bk2wy5-unixODBC-2.3.5/bin/dltest
shrinking /nix/store/vs2i1mz87r31icar1kp880wq81bk2wy5-unixODBC-2.3.5/lib/libodbcinst.so.2.0.0
shrinking /nix/store/vs2i1mz87r31icar1kp880wq81bk2wy5-unixODBC-2.3.5/lib/libodbc.so.2.0.0
shrinking /nix/store/vs2i1mz87r31icar1kp880wq81bk2wy5-unixODBC-2.3.5/lib/libodbccr.so.2.0.0
gzipping man pages under /nix/store/vs2i1mz87r31icar1kp880wq81bk2wy5-unixODBC-2.3.5/share/man/
strip is /nix/store/b0zlxla7dmy1iwc3g459rjznx59797xy-binutils-2.28.1/bin/strip
stripping (with command strip and flags -S) in /nix/store/vs2i1mz87r31icar1kp880wq81bk2wy5-unixODBC-2.3.5/lib  /nix/store/vs2i1mz87r31icar1kp880wq81bk2wy5-unixODBC-2.3.5/bin
patching script interpreter paths in /nix/store/vs2i1mz87r31icar1kp880wq81bk2wy5-unixODBC-2.3.5
checking for references to /tmp/nix-build-unixODBC-2.3.5.drv-0 in /nix/store/vs2i1mz87r31icar1kp880wq81bk2wy5-unixODBC-2.3.5...

Success on aarch64-linux (full log)
Partial log (click to expand)
shrinking /nix/store/vrdn3abwiv016pgjx95x5fs3scvwgc9n-unixODBC-2.3.5/bin/isql
shrinking /nix/store/vrdn3abwiv016pgjx95x5fs3scvwgc9n-unixODBC-2.3.5/lib/libodbccr.so.2.0.0
shrinking /nix/store/vrdn3abwiv016pgjx95x5fs3scvwgc9n-unixODBC-2.3.5/lib/libodbc.so.2.0.0
shrinking /nix/store/vrdn3abwiv016pgjx95x5fs3scvwgc9n-unixODBC-2.3.5/lib/libodbcinst.so.2.0.0
gzipping man pages under /nix/store/vrdn3abwiv016pgjx95x5fs3scvwgc9n-unixODBC-2.3.5/share/man/
strip is /nix/store/lvx1acn1ig1j2km8jds5x3ggh3f2wa8v-binutils-2.28.1/bin/strip
stripping (with command strip and flags -S) in /nix/store/vrdn3abwiv016pgjx95x5fs3scvwgc9n-unixODBC-2.3.5/lib  /nix/store/vrdn3abwiv016pgjx95x5fs3scvwgc9n-unixODBC-2.3.5/bin
patching script interpreter paths in /nix/store/vrdn3abwiv016pgjx95x5fs3scvwgc9n-unixODBC-2.3.5
checking for references to /build in /nix/store/vrdn3abwiv016pgjx95x5fs3scvwgc9n-unixODBC-2.3.5...
/nix/store/vrdn3abwiv016pgjx95x5fs3scvwgc9n-unixODBC-2.3.5


fix: Properly include RivetToken and ApiEndpoint with game build

#8
#7  👈
main

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Join @AngelOnFira and the rest of your teammates on  Graphite


Training for model based on SD1.5 got bad result

I have tried training on several SD1.5 base model (Ex: ChillOutMix, NED, ...) for person, but all get bad result.
If I use the same dataset to train on SD1.5, the result is good, even use the textual inversion on other SD1.5 base model the gen image also good.
I am using SD WebUI to train.
Image train on SD 1.5:

Image train on other SD 1.5 base model:

1 of dataset:

Are the finetuned models you are training on also realistic models? Or are they more restricted (e.g. to artistic styles)?

Are the finetuned models you are training on also realistic models? Or are they more restricted (e.g. to artistic styles)?

The model I training on is also photo realistic model, but A model itself will produce a face with A characteristics, B model will produce B face, as far as I understand, textual inversion should get the face weights better on their own model, isn't it?
I can train on SD1.5 with no issue, but I just want to understand why it get worse result, thanks for your reply~
(I edit the first post for clearer descript the train and generate model that I use)

I found if I change the vectors to >= 16, the result a lot better, I see some YouTube guide says vector 2~4 for character training is enough, may be he only trained on SD basic model, thanks you~


Handle the require call in javascript file for getSymbolAtLocation

This helps in getting the alias symbol so that it can go to the definition of external module
Fixes #9251

@sheetalkamat can you port this to release-2.0.5 as well.


Spark settings

We're wanting to use SparkFlow for source detection on radio astronomy images, but even on the MNINST example jobs with larger iteration counts are failing. Given the 60,000 images in the MNINST dataset, if we split that data into train and test sets (60% 40%) we want to train on ~ 36021 images;  mini batch size of 200 over 5 epochs means we need ~ 900 iterations. Job starts, executors begin processing until all but one executor fails with the error message below
19/05/06 09:26:13 INFO python.PythonRunner: Times: total = 1796882, boot = -1772, init = 1794, finish = 1796860
19/05/06 09:26:13 INFO executor.Executor: Finished task 13.0 in stage 4.0 (TID 45). 2409 bytes result sent to driver
19/05/06 09:36:16 ERROR executor.CoarseGrainedExecutorBackend: RECEIVED SIGNAL TERM
19/05/06 09:36:16 INFO storage.DiskBlockManager: Shutdown hook called
19/05/06 09:36:16 INFO util.ShutdownHookManager: Shutdown hook called
Spark session configuration for the job
spark2-submit
--master yarn
--deploy-mode client
--executor-memory 6g
--executor-cores 1
--driver-memory 12g
--driver-cores 5
--conf spark.yarn.appMasterEnv.RGZ_ENV=true
--conf spark.shuffle.service.enabled=true
--conf spark.dynamicAllocation.enabled=true
--conf spark.dynamicAllocation.executorIdleTimeout=600
--conf spark.executor.memoryOverhead=4096
--conf spark.driver.memoryOverhead=4096
--conf spark.default.parallelism=100
--conf spark.pyspark.python=/home/hduser/.virtualenvs/ClaRAN/bin/python
--conf spark.pyspark.driver.python=/home/hduser/.virtualenvs/ClaRAN/bin/python
RunSparkflowDemo.py >spark_submitPara10execs1Cpu.log 2>&1 &
The MNINST data has been loaded into a partitioned PARQUET table on the underlying HDFS data store. In this example, the data frame has 14 partitions.
SparkAsyncDL configuration - documentation on the partitions parameter is a little sparse, assume for best performance we match the number of partitions that the data frame has?
spark_model = SparkAsyncDL(
inputCol='features',
tensorflowGraph=mg,
tfInput='x:0',
tfLabel='y:0',
tfOutput='out:0',
tfLearningRate=.001,
partitions=14,
miniBatchSize=200,
miniStochasticIters=1,
shufflePerIter=False,
iters=900,
predictionCol='predicted',
labelCol='labels',
verbose=1
)
Cluster is running Spark 2.4.1 and the Python version for this job is 2.7.
Long term, we want to be able to use tensorflow for source finding and object classification on very large astronomical datasets (certainly in the tens or hundreds of terabytes range, and it's entirely possible possibly larger data products will be approaching petabyte size) on appropriately sized clusters.
Current cluster size is

three management servers 8 vCPUs    and 24GB Ram
ten worker nodes, running 16 vCPUs  and 48GB Ram (no GPU cores).

Assuming our understanding of the required Spark parameters (particularly the requirement for 1 cpu per executor) and SparkAsyncDL parameters (particularly the relationship between epochs, mini batches and iterations) is correct, we're not sure why the jobs keep failing; Obviously we are missing some point and if we can;t get the MNINST example running properly it'd be counter productive to try fitting an existing tensorflow application. Any assistance you could give us would be greatly appreciated.
Regards
Geoff

I'll take a look, but we haven't tested on Spark 2.4 yet. I don't know what specifically wouldn't work, but I have personally had some issues with other Spark jobs I have managed with the bump to 2.4 (stalling jobs with some YARN issues). I am actually in the process in moving support to the new barrier executor mode, which will help even more with 2.4 support. It will just take some time, since we also want backwards compatibility.

thanks for the quick reply - in the meantime, worthwhile trying on Spark 1.6?

I think Spark 2.0-3 should be fine (which is what we use, depending on the job on aws). 2.4 is really nice, so hopefully I can get the transition over soon.

Brilliant, thanks - I'll keep an eye out.

No problem at all. Thanks for filing the issue. This feedback is great, since we mostly work on AWS EMR, so it is good to see how it is working on other setups.

Hi Derek,
Ran up the tests on Spark 2.3.0, still getting the same issues, even on small numbers of iterations. FYI, the infrastructure we're running is Cloudea CDH 5.16.1 on 64 bit Ubuntu 16.04, JVM version is 1.7. Same behaviour being exhibited,  all executors bar one complete with this error
Partition Id: f3088d84b545445b8e70e51a874d85bc, Iteration: 19, Loss: 0.204772
19/05/08 04:51:03 INFO python.PythonRunner: Times: total = 2572365, boot = 851, init = 2847, finish = 2568667
19/05/08 04:51:03 INFO executor.Executor: Finished task 4.0 in stage 4.0 (TID 63). 2347 bytes result sent to driver
19/05/08 04:56:05 ERROR executor.CoarseGrainedExecutorBackend: RECEIVED SIGNAL TERM
19/05/08 04:56:05 INFO storage.DiskBlockManager: Shutdown hook called
19/05/08 04:56:05 INFO util.ShutdownHookManager: Shutdown hook called
and the remaining executor hangs. Not sure if it's a YARN issue, the logs are surprising sparse on detail but the CoarseGrainedError seems to be memory related?
we've tried a number of combinations of partitioned dataframe data, mini batches and partitions but this issue keeps cropping up. Any suggestions?
Thanks again for your help.
Cheers
Geoff

Ah. that's a bummer. I have a few suggestions that might help.

Tensorflow 1.8 and greater had issues in our ecosystem, due to a change on their end where graphs were handled with the C api. Even though we made a change to help solve this, internally, we still use Tensorflow 1.7 to avoid any weirdness.

Try adding the Lock for SparkAsyncDL. By default it runs on Hogwild, and there could be a deadlock somewhere that we missed. The lock option is still asynchronous but ensures weights won't overwrite each other.

Check to make sure the partitions are even. It may be trying to write images to disk and loading them in map partitions. Preferably, all data could fit into memory.

You could try increasing the executor memory, which should hopefully avoid writing to disk.

Hi Derek,
Thanks for the quick response - we were running tensorflow 1.13 so we've downgraded to tensorflow 1.7; looks like that may have sorted the issue. I'll be testing all morning, but at the moment it's looking promising.
Quick question on partitions, if I may - what's the relationship between the dataframe partitions and the partitions parameter we pass into SparkAsyncDL? As an example, if we load the csv file from HDFS the dataframe as 27 partitions (and we can repartiton the dataframe to any number of partitions necessary using either coalesce or repartition). So, should we be passing in the same number of partition to SparkAsyncDL? And should the partitions have record counts that are multiples of the mini batch size?
Again, thanks very much for the help, really appreciate it.
Cheers
Geoff

Hi Geoff,
Did you eventually succeed running a large job with the change to Tensorflow 1.7? I was also having trouble running the examples, and just saw this post. I’m also at Spark 2.4.1 and Tensorflow 1.13.1. I’m hoping to use sparkflow for a GAN on about 100M rows of transactional data, but downgrading to Tensorflow 1.7 may be difficult and I’d like have some confidence that it will pan out before starting down that road.
I wish I could help with you partition questions, but I have the same questions myself! I'll post back when I figure it out.
Much appreciated,
Howanu

I was able to reproduce the issue in 1.13. I am trying to work through the recent changes in TensorFlow, other than the C compiled graph issue to see if anything pops out. I think right now that 1.7 is the safest bet. I also found an issue spark 2.4.3 (not sure if it effects 2.4.1), where a job stalled. That one I am bit more complexed on, but I am tracking down recent changes from Spark to see if I can spot the issue.
The partitions is there for to match executors from the currently running cluster, which can be important for GPU training. We will basically coalesce the partitions to the number of clusters, if and only if the specified number of partitions is more than the current number number of partitions.

Specifically, this line: https://github.com/lifeomic/sparkflow/blob/master/sparkflow/tensorflow_async.py#L283

Thank you, Derek, the code clears that up! I'm investigating how hard the Tensorflow downgrade would be given my shared environment. Hopefully I'll be able to downgrade to 1.7.

Hi Derek and Howanu,
Derek, I'll echo what Howanu said, that code clears it up nicely. I've gone through the YARN and Spark settings, turns out that the yarn.nodemanager.resource settings for memory and cpu allocation hadn't been optimised, as well as the node manager settings java heap size- my bad, please accept my apologies for that. We're still fine tuning but it seems to be running well at the moment.
Howanu, we'll certainly be looking at using SparkFlow for source detection in radio images in larger and larger datasets; we currently have a RNN that we're running against 80,000 images in the training set that we're going to convert to Spark and SparkFlow. At the moment, downgrading to tensorflow 1.7 isn't an issue for us, but we'll certainly be interested in upgrading as soon as we can. Long term, some of the data products we'll need to analyse will be going hundreds of TB. (Theoretically, some data products may well go to PB scale) hence our interest in Spark and distributed tensorflow.
We're also be running tests against raw CSV data and storing the images as a Parquet table on HDFS with Snappy compression. I have the MNIST dataset loaded as a partitioned table on Parquet and the compression looks excellent - the ~107 mb csv file compresses down to ~ 23.4m (which equates to 70.3m total storage with block replication). Whether or not that translates to improved read performance remains to be seen, but I'll certainly let you know.
Derek, if you need us to help out with any of the testing, please do let us know. We'll be looking at upgrading our cluster to GPU enabled worker nodes in the next couple of months and there are a number of classification and source finding use cases we will be implementing with tensorflow, so SparkFlow is of considerable interest to us.
Thanks again
Geoff

Howanu, just a thought - we're running multiple virtual environments against Spark (Python 2.7 and 3.6 against Spark 1.6 and 2.3) and that seems to work quite well? We're running a Cloudera cluster, and going in through Jupyterhub; would a virtualenv be an option for you?

Thanks Geoff, I will definitely take you up on that. I am planning some sweeping changes to fix the c Tensorflow Graph issue once and forall. It may take me some time though with testing and everything.

So good news/bad news. I think I found the underlying cause for some of the stalling issues. The good news is that there is a fix for python 3 in using a spawn process. The bad news is that it won't work for python 2. In newer versions of SparkFlow, we may drop support of python 2.7, especially considering that TensorFlow 2.0 will definitely need that spawn process.

Hi Derek,
Great news - any idea when the fix is available? We are currently using Python 2.7 for some of our analysis but frankly I don't see that as being an issue as we're migrating to Python 3 anyway.

We released a new version over the weekend (specifically 0.7.0). Let me know if some of the same issues persist.

Will do - just finishing up some testing and we'll be installing the new version later today. I'll keep you posted.
Thanks again :)
Geoff

Hi Derek,
Ugraded to 0.7.0, but the logs are showing this error -
py4j.protocol.Py4JJavaError: An error occurred while calling None.org.apache.spark.api.java.JavaSparkContext.
: org.apache.spark.SparkException: Only one SparkContext may be running in this JVM (see SPARK-2243). To ignore this error, set spark.driver.allowMultipleContexts = true. The currently running SparkContext was created at:
org.apache.spark.api.java.JavaSparkContext.(JavaSparkContext.scala:58)
Not sure if you've seen this - I reverted back to 0.6.0 and job ran fine. Tried setting the allowMultipleContexts parameter to true, but no joy. Any ideas?
Cheers
Geoff.

That error typically happens when the spark context or session is called multiple times in the same job. Are you using a singleton for the session? Or probably a better question is do you see the error in the dnn example?

Hi Derek,
The DNN process seems to be throwing the error- monitoring the job on YARN, runs fine up until the process runs the fit statement, and then the process throws the error - I've attached the program script and the logs below. Not sure what you mean by singleton but I'm assuming that's the container/executor setup with 1 core per executor?
Here's the spark session set up we've been using. FYI, the conf statements for spark.yarn.appMasterEnv, spark.pyspark.python and spark.pyspark.driver.python are there to make sure the spark jobs pick up the libraries from the virtual environments we're using - we have identical environments across the worker and gateway nodes. In this case, RGZ3_ENV is Python 3.6, Spark 2.3, and the associated libraries for sparkflow etc installed.
spark2-submit
--master yarn
--deploy-mode client  \ # Same result if we submit cluster mode
--num-executors [varies]
--executor-cores 1
--executor-memory 2642m
--conf spark.yarn.appMasterEnv.RGZ3_ENV='True' \
--conf spark.default.parallelism=80
--conf spark.kryoserializer.buffer.max=512
--conf spark.app.name='Sparkflow testing'
--conf spark.pyspark.python=/home/hduser/.virtualenvs/ClaRAN3/bin/python
--conf spark.pyspark.driver.python=/home/hduser/.virtualenvs/ClaRAN3/bin/python \
Source code for the test script
RunSparkflowDemo.txt
Log file from last run -
Sparkflow0.7.0-Test.log_01_58_22_05_2019.log
Run log, error message at line 502
subTest.log
HTH
Cheers
Geoff

Ah, I see. This is helpful, thanks. One thing to try if this is a blocker is to turn on fair scheduling with this command: spark.scheduler.mode                    FAIR. I'll see if I can reproduce in aws today.

I tried a few different tests with Spark 2.4.0 and TensorFlow 1.12.0 on AWS EMR, and I have been unable to reproduce the issue. GPU seems to work as well. I'll keep trying to dig in. One thing maybe the sparkContext instead of SparkSession. We utilize the session over the context, and maybe another context is being created during the spawn when we ask for the session?

Hi Derek,
Changed SparkContext to SparkSession, see
RunSparkflowDemo.txt
But still getting the same error. However, running the same program in  a JupyterNotebook, runs fine.
Here's the spark-submit command
spark2-submit --master yarn --deploy-mode client
--conf spark.pyspark.python=/home/hduser/.virtualenvs/ClaRAN3/bin/python
--conf spark.pyspark.driver.python=/home/hduser/.virtualenvs/ClaRAN3/bin/python RunSparkflowDemo.py true true 1 64 80 0.4 sf-Lck-E1-B64-P80-Mclient > submit.log 2>&1 &
Run log
submit.log
Curious that this issue didn't raise it's ugly head with Sparkflow 0.6.0?

There are a couple of things that surprise me. It clearly shows that the spawn is being called and is apart of the exception. Some of these are:

I think the calling this appId=sc._jsc.sc().applicationId() would raise the same error, since I think that creates another spark context with the session.
The source of the error is pointing to the appName:   File "/home/hduser/pySparkNotebooks/SparkFlow/RunSparkflowDemo.py", line 29, in <module>  .appName(sys.argv[7])\. I am not really sure what to make of that, as I am not sure how that would throw an error.

I wonder if removing all javaspark context calls will fix the issue?

Regardless, I am going to be doing a bit of cleaning up, just not sure it will fix this issue.

Hi Derek,
OK, removed all the extraneous code, see
RunSparkflowSimple.txt
Still throwing the error
submit.log
Quick Q - how are you submitting your jobs, you using spark-submit?

yeah, this is using spark-submit. I am going to have another release soon that might address it. I tried running it on Google cloud finally, and I still wasn't able to reproduce. Here was the simple config that I used for submission with aws:
[
  {
    "Name": "ML Local",
    "Args": [
      "spark-submit",
      "--master", "yarn",
      "--deploy-mode", "cluster",
      "s3://s3-bucket-here/dnn_yarn.py"
    ],
    "Jar": "command-runner.jar",
    "Type": "CUSTOM_JAR"
  }
]

With this configuration:
[
  {
    "Classification": "spark-defaults",
    "Properties": {
      "spark.authenticate": "true",
      "spark.authenticate.enableSaslEncryption": "true",
      "spark.network.sasl.serverAlwaysEncrypt": "true",
      "spark.sql.pivotMaxValues": "100000"
    }
  },
  {
    "Classification": "spark-env",
    "Properties": {
      "SPAKR_JAVA_OPTS": "-XX:+UseCompressedOops"
    },
    "Configurations":[
      {
        "Classification":"export",
        "Properties":{
          "PYSPARK_PYTHON":"/usr/bin/python3"
        }
      }
    ]
  },
  {
    "Classification": "emrfs-site",
    "Properties": {
      "fs.s3.connection.maximum": "2000",
      "fs.s3.consistent": "false",
      "fs.s3.consistent.retryCount": "10",
      "fs.s3.consistent.retryPeriodSeconds": "10",
      "fs.s3.enableServerSideEncryption":"true",
      "fs.s3.serverSideEncryptionAlgorithm": "AES256"
    }
  },
  {
    "Classification": "spark",
    "Properties": {
      "maximizeResourceAllocation": "true"
    }
  },
  {
    "Classification": "core-site",
    "Properties": {
      "spark.authenticate": "true"
    }
  }

]

I ran the simple_dnn.py script with a simple modification in removing master and memory constraints. The really weird thing to me is that it points to the appName. I may open up an issue with Spark because that part doesn't make any sense.

Have you tried a 2.4.0 cluster yet? Not sure that it would make a difference, but I was just curious.

I finally was able to reproduce. It has to do where the TensorFlow function is put. I got around this by first adding the if __name__ == '__main__': condition. Then I put the tensorflow function outside of that if condition. So in short:
def small_model():
    x = tf.placeholder(tf.float32, shape=[None, 784], name='x')
    y = tf.placeholder(tf.float32, shape=[None, 10], name='y')
    layer1 = tf.layers.dense(x, 256, activation=tf.nn.relu, kernel_initializer=tf.glorot_uniform_initializer())
    layer2 = tf.layers.dense(layer1, 256, activation=tf.nn.relu, kernel_initializer=tf.glorot_uniform_initializer())
    out = tf.layers.dense(layer2, 10, kernel_initializer=tf.glorot_uniform_initializer())
    z = tf.argmax(out, 1, name='out')
    loss = tf.losses.softmax_cross_entropy(y, out)
    return loss

if __name__ == '__main__':
    print("Do things here")

I am working on a way to get around this, but that is the current solution that I found that works.

Hi Derek,
That works - looks like global spark session was causing the problem; creating the spark session within main and defining the tensorflow graph as a function is working. Thanks very much for the assist.
FYI, I've attached the simplified code that we got running - may help someone else getting started.
RunSparkflowSimpleCon.txt

Hi Derek,
Just need a quick clarification on some of the documentation - the iters parameter in the SparkAsyncDL class - that denotes a full pass through the training data? (ie an epoch?) reason we ask is that if we're using the calculation (iterations = trainingSetSize / batchSize * epochs) -  it takes ~ 57 hours for 10 epochs with a batch size of 32 (36000 / 32 * 10 = 11250 iterations) which sounds excessive? As a comparison, training the same dataset with a similar graph in standard tensorflow for 40 epochs on a batch size of 50 takes no more than a couple of minutes; so obviously we're doing something wrong?
Other than that, it's all running fine.
Thanks again,
Geoff.

Sorry I was out of country. Is this for the MNIST example? I am cleaning up some code, and after that I will add some larger examples. While epochs represent the number of iterations, than can be several sub batch iterations through the miniStochasticIters parameter which defaults to -1.
This parameter does:
miniStochasticIters: If using a mini batch, you can choose number of iters you would like to do with the batch size above per epoch. A value of -1 means that you would like to run mini-batches on all data in the partition.

hi Derek,
Thanks for that and no problem - we suspected that may have been the case; I did have a look through the source code (probably should have done that first!) and worked it out from there.
We came across an interesting phenomenon - we seem to be getting different results depending on whether we run the program in client or cluster mode in Spark. All other parameters being equal the accuracy results vary quite significantly. - running in client mode we get an accuracy figure of 0.8915 (20 iters, batch size if 128, training against 35769 images) but we run exactly the same set of parameters in the same pyspark program on Cluster mode, the result is 0.5695. Not sure why we would be seeing that discrepancy, any ideas?

We predominantly use client mode for now, because that puts the main network on the driver node and is easier to manage. It kind of makes sense that the cluster mode might perform less than client mode, but probably not that bad. I have never used that mode for training at work though.
The move to barrier executors should solve all of this though, as they the tasks will be running at the same time. I have a local branch started on this, but I have had split priorities. I hope to have something up soon.

Hi Derek, I am new to Spark and to TenserFlow. I am trying to run an LSTM model on spark with the help of your library. I wonder if you would not mind elaborate a bit on the options you have for SparkAsyncDL. Most of them seem straightforward but what about this ones:
tfInput='x:0',
tfLabel='y:0',
tfOutput='out:0',
tfOptimizer='adam'
Also in my LSTM model I will optimes for Mean Squared Error, would that affect the OptimizerOptions = ??


Define set of fundamental stdlib primitives and types

Types:
Clk, Reset, Signed, Unsigned, Fixed Point, Floating Point
Primitives:
Logical: not, and, nand, or, nor, xor, xnor, concat, slice
Arithmetic: add,sub,mult,div, constants, terminators
Stateful : FF, Register, Ram, Rom

These are mostly implemented. Closing for now.


Github actions follow-up: try to fix deploy-cloud on master, dev-release

Follow-up to #526 (https://github.com/ansible/ansible-hub-ui/pull/526#issuecomment-860753116)
dev-release: errors on GitHub Releases requires a tag
because we're just adding the tag in a previous step, it can't figure out which tag to use,
providing via input_tag_name
deploy-cloud: errors on npm ERR! cb() never called! (on master, but works on prod-beta)
this is a generic "we don't know what's wrong" npm error,
the hive mind suggests a local caching issue, or a http issue, trying to add npm cache verify and see what happens.
Cc @newswangerd @ZitaNemeckova

Also, I just disabled "Build pushed branches" and "Build pushed pull requests" on https://travis-ci.com/github/ansible/ansible-hub-ui/settings .. closing/reopening to see if it helps the status check


Added the prefix option

- What is it good for
To deploy multiple apps to a single bucket you can make use of the --prefix
option. This comes in handy when your CI system deploys to a staging system
with each branch as a pathname. Eg. the master branch should go to bucket
root (/), so you do not set the prefix. The feature/fancy-stuff branch
should go to the bucket path feature/fancy-stuff so just add this as the
prefix. Here comes a command line example:
# deploy your master branch build to bucket root
scotty --source ./build --bucket some-bucket-name
# deploy your branch build to the branch name on the bucket
scotty --source_ ./build --bucket some-bucket-name --prefix your/branch

- What I did

Updated the readme
Added an input tolerant prefix option (allows (non)-trailing slashes)

- What I did not
Due to the tight coupling of the lib/scotty module to the lib/upload module was
not able to test the prefix input sanitization. I tried it and found myself in the middle
of a mock-the-whole-world situation and stopped it. If you can provide me a clever
solution how to test this I will do so. :)
- A picture of a cute animal (not mandatory but encouraged)

Thanks! It will be published to NPM in an hour or so :)

Awesome <3


Fix DragonFly: clock_gettime() expects CLOCK_* constants as ulong.

While there add all CLOCK_ constants as found on DragonFly.

@bors: r+ 378bb73bcc960da500dd89a431f29eaf1810ee56
Let's see if this works...

:hourglass: Testing commit 378bb73 with merge ef979ef...

:sunny: Test successful - status-appveyor, travis


Update Salt 2016.11.5 support for pycrypto flavors on Redhat family

Updating python-impacket to have a requirement for python-crypto. Debian / Ubuntu and GitHub state this dependency but the Redhat family do not have it so.  Hence an updated version of python-impacket from Redhat family repos will not have this requirement and the user will have to install python-crypto manually.
Solution for https://github.com/saltstack/salt/issues/40900
Also patch pycryptodome to handle MD5 issues on a Redhat FIPS installed environment
Solution for https://github.com/saltstack/salt/issues/40890

This moving to Salt 2016.11.5 which will contains two hot-fixes.
Expect to tag Monday

https://github.com/saltstack/salt/issues/40890

The patch for pycryptodomex is not working and in addition
root@localhost:~# /usr/bin/salt-master -l trace
[DEBUG   ] Reading configuration from /etc/salt/master
[ERROR   ] local variable 'sig' referenced before assignment
Traceback (most recent call last):
File "/usr/lib/python2.7/site-packages/salt/utils/parsers.py", line 201, in parse_args
process_option_func()
File "/usr/lib/python2.7/site-packages/salt/utils/parsers.py", line 521, in process_config_dir
self.config.update(self.setup_config())
File "/usr/lib/python2.7/site-packages/salt/utils/parsers.py", line 1715, in setup_config
return config.master_config(self.get_config_file_path())
File "/usr/lib/python2.7/site-packages/salt/config/init.py", line 3310, in master_config
opts = apply_master_config(overrides, defaults)
File "/usr/lib/python2.7/site-packages/salt/config/init.py", line 3329, in apply_master_config
import salt.crypt
File "/usr/lib/python2.7/site-packages/salt/crypt.py", line 27, in
from Cryptodome.Cipher import AES, PKCS1_OAEP
File "/usr/lib64/python2.7/site-packages/Cryptodome/Cipher/init.py", line 78, in
from Cryptodome.Cipher._mode_ecb import _create_ecb_cipher
File "/usr/lib64/python2.7/site-packages/Cryptodome/Cipher/_mode_ecb.py", line 29, in
from Cryptodome.Util._raw_api import (load_pycryptodome_raw_lib,
File "/usr/lib64/python2.7/site-packages/Cryptodome/Util/_raw_api.py", line 89, in
Array = ffi.new("uint8_t[1]").class.bases
File "/usr/lib64/python2.7/site-packages/cffi/api.py", line 248, in new
cdecl = self._typeof(cdecl)
File "/usr/lib64/python2.7/site-packages/cffi/api.py", line 168, in _typeof
result = self._typeof_locked(cdecl)
File "/usr/lib64/python2.7/site-packages/cffi/api.py", line 153, in _typeof_locked
type = self._parser.parse_type(cdecl)
File "/usr/lib64/python2.7/site-packages/cffi/cparser.py", line 448, in parse_type
return self.parse_type_and_quals(cdecl)[0]
File "/usr/lib64/python2.7/site-packages/cffi/cparser.py", line 451, in parse_type_and_quals
ast, macros = self._parse('void __dummy(\n%s\n);' % cdecl)[:2]
File "/usr/lib64/python2.7/site-packages/cffi/cparser.py", line 260, in _parse
ast = _get_parser().parse(csource)
File "/usr/lib64/python2.7/site-packages/cffi/cparser.py", line 40, in _get_parser
_parser_cache = pycparser.CParser()
File "/usr/lib/python2.7/site-packages/pycparser/c_parser.py", line 111, in init
outputdir=taboutputdir)
File "/usr/lib/python2.7/site-packages/ply/yacc.py", line 3065, in yacc
signature = pinfo.signature()
File "/usr/lib/python2.7/site-packages/ply/yacc.py", line 2814, in signature
return sig.digest()
UnboundLocalError: local variable 'sig' referenced before assignment
2795     # Compute a signature over the grammar
2796     def signature(self):
2797         try:
2798             from hashlib import md5
2799         except ImportError:
2800             from md5 import md5
2801         try:
2802             sig = md5()
2803             if self.start:
2804                 sig.update(self.start.encode('latin-1'))
2805             if self.prec:
2806                 sig.update("".join(["".join(p) for p in self.prec]).encode('latin-1'))
2807             if self.tokens:
2808                 sig.update(" ".join(self.tokens).encode('latin-1'))
2809             for f in self.pfuncs:
2810                 if f[3]:
2811                     sig.update(f[3].encode('latin-1'))
2812         except (TypeError,ValueError):
2813             pass
2814         return sig.digest()
The problem is not limited to pycryptodomex since the code from yacc.py also similarly uses hashlib and md5 similar to pycryptodomex.

root@localhost:~# find /usr/lib/python2.7 -name ".py" | xargs grep "hashlib import md5"
/usr/lib/python2.7/site-packages/ply/yacc.py:            from hashlib import md5
/usr/lib/python2.7/site-packages/setuptools/command/upload.py:    from hashlib import md5
grep: /usr/lib/python2.7/site-packages/setuptools/script: No such file or directory
grep: template: No such file or directory
grep: (dev).py: No such file or directory
grep: /usr/lib/python2.7/site-packages/setuptools/script: No such file or directory
grep: template.py: No such file or directory
/usr/lib/python2.7/site-packages/ipapython/ssh.py:from hashlib import md5, sha1
/usr/lib/python2.7/site-packages/urllib3/util/ssl_.py:from hashlib import md5, sha1, sha256
root@localhost:~# find /usr/lib64/python2.7 -name ".py" | xargs grep "hashlib import md5"
/usr/lib64/python2.7/distutils/command/upload.py:from hashlib import md5
/usr/lib64/python2.7/md5.py:from hashlib import md5
/usr/lib64/python2.7/site-packages/tornado/test/curl_httpclient_test.py:from hashlib import md5
/usr/lib64/python2.7/site-packages/Cryptodome/Hash/MD5.py:        from hashlib import md5 as _hash_new
/usr/lib64/python2.7/uuid.py:    from hashlib import md5
root@localhost:~#

The same issues are occurring with salt-minion.
It would appear that pycryptodomex cannot be used in a FIPS environment.
Logging upstream issue against pycryptodomex
For now FIPS environments on 64-bit Redhat 6 & 7 should do the following:
rpm -e --nodeps python2-pycryptodomex
yum install python-crypto
restart salt-master and salt-minion


E2E Functional Test

Write an E2E Functional Test to test the functionality of each and every step E2E in a real k8s clusters. For this we would need to get access to a running cluster, we could use IM or UM test clusters. I think we can implement the test by writing a functional test where we call all the steps in a *.feature file and use minimal required resource files in a template directory.

We should make this part of the CI


Could not open this file

Hello!
Double click on php file Joomla prompts me this alert "Could not open this file".
However txt files opens normal
What could be the reason

May be ajax got error. Could you send me the result of ajax?


isolate-000000233DCD5DB0-v8.log - what file is this?

Everytime I run a mocha test, such a log file is created in my app root directory.
$ npm test test/file.test.js

What are they? What do they do? How to set up a config so they will be saved in other directory?

Sounds like you've got a --prof-related flag somewhere: https://stackoverflow.com/questions/30599246/starting-node-with-forever-and-prof-option-v8-log-file-not-created/30600851#30600851

hmm, ref'd wrong issue


Histogram plotting

In a 1D plot, if the coordinate represents bin edges, data should be plotted as a histogram (see https://plot.ly/python/histograms/?).

you can already do this with
plot(d, bars=True)

This was fixed in #139


Refactor ISetupHandler.Errors to List

Description

In order to properly report error messages to the engine we should communicate the entire exception object instead of just a string. This will ensure stack traces and other potentially useful information embedded in the original exception isn't lost
Checklist

[x] I have completely filled out this template
[x] I have confirmed that this issue exists on the current master branch
[x] I have confirmed that this is not a duplicate issue by searching issues

Refactored in #1629


HUE-9680 [Django Upgrade] upgrading Django 1.11.29 to 2.0.13

5 tests are failing as of now, https://app.circleci.com/pipelines/github/cloudera/hue/4253/workflows/d72a0c80-6329-404a-94c5-1d60adaa1a0a/jobs/5795 .  In these tests there are some small-small mistakes are there like decoding or converting into str etc. Please take a look and guide me. @romainr

5 tests are failing as of now, https://app.circleci.com/pipelines/github/cloudera/hue/4253/workflows/d72a0c80-6329-404a-94c5-1d60adaa1a0a/jobs/5795 .  In these tests there are some small-small mistakes are there like decoding or converting into str etc. Please take a look and guide me. @romainr

#1
FAIL: desktop.models_tests.TestDocument2ImportExport.test_import_owned_document should be the easiest as the loaddata command changed.
#2
The other ones not obvious. It feels like a Py2 vs Py3 conversion of the query handles HiveServerQueryHandle https://github.com/cloudera/hue/blob/master/apps/beeswax/src/beeswax/models.py#L540 but here we should only have changed Django. Would recommend to debug one step by step and see why the upgrade leaked there. Maybe fixing 1. could help too.

#1
FAIL: desktop.models_tests.TestDocument2ImportExport.test_import_owned_document should be the easiest as the loaddata command changed.
#2
The other ones not obvious. It feels like a Py2 vs Py3 conversion of the query handles HiveServerQueryHandle https://github.com/cloudera/hue/blob/master/apps/beeswax/src/beeswax/models.py#L540 but here we should only have changed Django. Would recommend to debug one step by step and see why the upgrade leaked there. Maybe fixing 1. could help too.

How about better name for upgrading django-nose for handling commit option to loaddata in Djang… … and pushing it first?

How about better name for upgrading django-nose for handling commit option to loaddata in Djang… … and pushing it first?


通过RPC创建的下载任务，能不能可以查看创建任务时指定的参数？

目前任务信息里好像就是几个固定的信息
其他的一些信息能不能显示呢？
比如我是115网盘里通过下载助手调用了aria2的rpc 可以指定checksum 以及header信息
但是目前页面里任务信息没有显示这个
还有
自己手动创建任务时貌似目前没法指定这个checksum参数？

在任务详情页里设置页面，会显示这个任务设置的 header 信息
checksum在今天的daily build及下一个release版本会在创建任务、任务详情页的设置页面里增加

May I ask how this works? I added a new file to download and set it to "download later".
Then I went to the waiting list, found the file, clicked the arrow on the far right, clicked the gear icon and on the checksum field I entered
sha1=8c206a1a87599f532ce68675536f0b1546900d7a
I pressed the play button, the download started, it completed and nothing new happened. Was ariang or aria supposed to show something upon completion?

After download completed, aria2 will automatically verify this file according the checksum option you filled. At the same time, AriaNg will show the verification process in the download speed column. Then, the status of task will be changed to completed.

May I ask how this works? I added a new file to download and set it to "download later". Then I went to the waiting list, found the file, clicked the arrow on the far right, clicked the gear icon and on the checksum field I entered sha-1=8c206a1a87599f532ce68675536f0b1546900d7a  I pressed the play button, the download started, it completed and nothing new happened. Was ariang or aria2c supposed to show something upon completion?
---edit Aria2c v1.36.0 on debian unstable and on aria2c -v says that these hash algorithms are supported Hash Algorithms: sha-1, sha-224, sha-256, sha-384, sha-512, md5, adler32

After download completed, aria2 will automatically verify this file according the checksum option you filled. At the same time, AriaNg will show the verification process in the download speed column. Then, the status of task will be changed to completed.

Thank you for showing me! I did not notice the verifying part at all. Plus, when the checksum fails, because I changed one random number on the sum to test it, the download is listed as failed under finished downloads!


Random fixes

This PR has a bunch of minor fixes, including some low-priority Coverity warnings in the recently-added freegeoip module, using the new JSON number parsing routines in the oicgen.py script, and using sol_util_strtodn() where strtod() was used before.

Looking into the failing tests.

Tests are passing locally. Force-pushed the branch since nobody had reviewed the series yet.

Rebased and force-pushed. This should force semaphore to recheck.

Merged, thanks.


Multi-arch docker images

Is your feature request related to a problem? Please describe.
I am unable to use the standard docker hub image on a raspberr pi cluster.
Describe the solution you'd like
I'm not sure if travis supports it, but buildx can be used to build multi-arch images than can then be pushed to docker hub.
Describe alternatives you've considered
I have forked the repo and added my own github action to buidl the multi arch image. It would be nice to have the official image support this though.

It looks like that PR is created separate images for amd64 and arch, there's a little bit more involved in creating actual multi arch images. I'll look up what's involved in doing that with travis and submit a PR then.

I'd like to pick this up and make a PR for multi-arch build but I had two questions.

I need this for arm64 & amd64. Are there any other architectures or OS that you'd like me to add at the same time?
I was basically thinking of following Multi-arch build, what about Travis? from Docker's blog to do this, is that acceptable to you?

I think that arm64 and amd64 is all that we need right now.
That said, I've been meaning to move off of Travis onto Github actions. I'll hopefully get to that in the next week or so, so perhaps hold off until then.
Thanks for volunteering for that @mtougeron - much appreciated! 👍

Awesome, thanks. Doing it with GH actions is even easier! :)

@seeker89 FWIW, I created a draft PR with the multi-arch changes + Github action for publishing. https://github.com/bloomberg/goldpinger/pull/109 I figured it might save you some time with the move from Travis.


[FLINK-12403][doc-zh]Translate 'How to use logging' page into Chinese

What is the purpose of the change
This pull request mainly translate doc "How to use logging" into Chinese
Brief change log

Translate "How to use logging" doc into Chinese

Verifying this change
This change is to add a new translated document.
Does this pull request potentially affect one of the following parts:

Dependencies (does it add or upgrade a dependency): no
The public API, i.e., is any changed class annotated with @Public(Evolving): no
The serializers: no
The runtime per-record code paths (performance sensitive): no
Anything that affects deployment or recovery: JobManager (and its components), Checkpointing, Yarn/Mesos, ZooKeeper: no
The S3 file system connector: no

Documentation

Does this pull request introduce a new feature? no
If yes, how is the feature documented? (not applicable / docs / JavaDocs / not documented)

@Armstrongya what's the status of this pr, do you still working on this PR? if yes, could you please rebase the new master to resolve the conflict.


Easier subclassing of Parameter

Hello! Many thanks for param, I'm new to it and I'm loving it.
I have an application that I'm experimenting with migrating by adding Parameters to the key classes. I've run into something that I'm trying to do that doesn't work, and I'd be interested to understand why it doesn't—perhaps there's a good reason—and also to suggest that a fix might be considered.
Let's say I have a class with the following signature before parameterizing:

class Pizza:

    def __init__(self, diameter: float, thickness: float, description: str, topping: Topping, style: PizzaStyle, ):
        pass

diameter and thickness are straightforward, but for the custom classes one needs to subclass Parameter. This is working fine for me, following the instructions in the documentation here:
https://param.holoviz.org/user_guide/Parameters.html#specialized-parameter-types
For example:

class PizzaStyle:
"""Class with attributes and methods, could be an enum"""

class PizzaStyleParam(Parameter):
"""PizzaStyle Parameter value must be PizzaStyle object"""

    def _validate_value(self, val, allow_None):
        super(MortgageParameter, self)._validate_value(val, allow_None)
        if not isinstance(val, PizzaStyle):
            raise ValueError("type of PizzaStyle parameter %r must be a PizzaStyle, "
                             "not %r." % (self.name, val))

Then Pizza can be parameterised like this:

class Pizza(Parameterized):
    diameter = param.Number(12, bounds=(6, 18), doc="pizza diameter in inches")
    pizzaStyle = PizzaStyleParam(default=PizzaStyle(**some_kw), doc="PizzaStyle object representing style of pizza")
    …

This allows Pizza to be fully parameterised and I can benefit from the documentation feature of the param. Of course, the validation is really delegated to PizzaStyle class, which may or may not be parameterised, the only validation that the PizzaStyleParam does is to type check to make sure a PizzaStyle is supplied.
So far so good. However, in my real application, I have many custom classes that are properties of a few facing classes.  Since the custom Parameter subclass above is very simple, rather than writing one for each of my classes, I would like to write a standard Parameter subclass that will take a type at the level of the Parameterized class and will validate by checking that any parameter values correspond to that type.
I have tried doing that in various ways something like this:

class TypedParameter(Parameter):
    """Subclass of param.Parameter to allow for easy use of custom objects in Parameters"""

    def __init__(self, object_class, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.object_class = object_class

    def _validate_value(self, val, allow_None):
        super(MortgageParameter, self)._validate_value(val, allow_None)
        if not isinstance(val, self.object_class):
            raise ValueError("TypedParameter value %r must be an instance of the type supplied"
                             "not %r." % (self.name, val))

class Pizza(Parameterized):
    diameter = param.Number(12, bounds=(6, 18), doc="pizza diameter in inches")
    pizzaStyle = TypedParamter(
        object_class=PizzaStyle,
        default=PizzaStyle(**some_kw),
        doc="PizzaStyle object representing style of pizza"
        )
   toppings = TypedParamter(
        object_class=Topping,
        default=PizzaStyle(**some_kw),
        doc="PizzaStyle object representing style of pizza"
        )
    …

This doesn't work — I get AttributeError: 'TypedParameter' object has no attribute 'obj_class' . I have tried various workarounds, including trying to use slots and nothing works for me.
So briefly, my questions are:

If I want to implement a TypedParameter subclass of Parameter along these lines, is there a way I can do it? Perhaps I'm just missing a simple way to pass arguments from the constructor.
Is there another way to achieve what I'm trying to do?
If the answer to 2 is 'no', could I suggest that a TypedParameter subclass could be added to param?

Many thanks

Hi...a follow up...
OK, I think I've understood how this is supposed to be done: use a Selector or ListSelector and provide a list of possible classes or instances and set the value from them.
This works to an extent but means that it means that there can't be type checking in the usual param way.

I think what you are looking for is param.ClassSelector.

Hi — yes indeed, many thanks! I should have read the docs better, I think I glossed over that thinking from the name it was the class that is actually called something like SelectorBase.

@JonathanMair , assuming that ClassSelector met your needs here (as it sounds like it does), I'll close this issue, but if you see a good way to improve the docs so that other people will be able to have a smoother experience, please contribute a PR for the docs or else open an issue with text that we can make into a PR. Thanks!

@jbednar thanks—yes I'll have a go at implementing the thing I've been working on so I have it clear in my head and then think about suggesting an improvement. Possibly just my fault for skimming though! Thanks again.


LSC: Django template IntelliSense based on code analysis

Replace requests to old analyzer extension:
getTags
getVariables
getFilters
getUrls
getMembers

with calls to language server.
https://github.com/microsoft/python-language-server/issues/1657

Current status: completions for tags and filters works using a hard-coded list of tags/filters.
Everything else requires code analysis.

We are now tracking this task in our internal Pylance Azure DevOps.


Default db name

It is sometimes useful to have a default db name from the stack name.

Hey @aarcro - again, sorry for the delay.  I've made a small change - can you take a look and let me know what you think?  Thanks!

Took me 10 read throughs, but I got it. Yeah that looks good.

Thanks @aarcro!


fix the comment for self are swallowed

related: https://github.com/rust-lang/rustfmt/issues/3198
This is a bug fix, so I don't need to add Version Gate. Did I get that right?

Thank you for the review. I fixed it.

I think I should fix other place, so add wip to title.

I improved the implements

Thank you fot the review.
I added the comment and some tests.

Looking good! @topecongiro are you OK with the changes ?

build failure is unrelated, it's about clippy failing to compile (before running rustfmt).

LGTM, thanks!


I want to be able to enable/disable via command line argument

Hi,
I was previously using version 0.10, and I was able to only enable the tracy client if my program received some CLI argument (enable-tracy, for instance).
On version 0.11, it seems to be only possible to enable/disable via feature, which would require my software to be rebuilt.
Is there any approach to that on 0.11?
Just to mention, I was doing (on 0.10), something like:
    // Redirects all gstreamer logs to tracing.
    gst::log::remove_default_log_function();
    // This fundamentally changes the CPU usage of our streams, so we are only enabling
    // this integration if absolutely necessary.
    if cli::manager::is_tracy() {
        tracing_gstreamer::integrate_events(); // This must be called before any gst::init()
        gst::init().unwrap();
        tracing_gstreamer::integrate_spans(); // This must be called after gst::init(), this is necessary to have GStreamer on tracy
    }

and
tracing-tracy = { version = "0.10.4", features = ["ondemand", "broadcast"] }

You still can have the previous behaviour Check out the manual-lifetime feature.
Note though that unconditionally enabling Tracy may have other effects even if the traces are not being collected due to manual lifetimes. But you will have to read the Tracy code to double check.
24 Feb 2024 01:58:34 João Antônio Cardoso @.***>:

Hi,
I was previously using version 0.10, and I was able to only enable the tracy client if my program received some CLI argument (/enable-tracy/, for instance).
On version 0.11, it seems to be only possible to enable/disable via feature, which would require my software to be rebuilt.
Is there any approach to that on 0.11?
Just to mention, I was doing (on 0.10), something like:
// Redirects all gstreamer logs to tracing.
gst::log::remove_default_log_function();
// This fundamentally changes the CPU usage of our streams, so we are only enabling
// this integration if absolutely necessary.
if cli::manager::is_tracy() {
    tracing_gstreamer::integrate_events(); // This must be called before any gst::init()
    gst::init().unwrap();
    tracing_gstreamer::integrate_spans(), // This must be called after gst::init(), this is necessary to have GStreamer on tracy
}

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