fix(deps): update terraform ibm to latest for deployable modules / architectures

This PR contains the following updates:

Package
Type
Update
Change

ibm (source)
required_provider
minor
1.65.1 -> 1.68.0

Release Notes

IBM-Cloud/terraform-provider-ibm (ibm)
v1.68.0
Compare Source
Features

Support CBR

Datasources

ibm_cbr_zone_addresses

Resources

ibm_cbr_zone_addresses

Support CIS

Datasources

ibm_cis_origin_certificates

Resources

ibm_cis_advanced_certificate_pack_order
ibm_cis_origin_certificate_order

Support VPC

Resources

ibm_is_bare_metal_server_initialization

Enhancements

Add dhcp network support for stratos (5503)
IAM Policy Assignment: S2S Policy Assignments (5499)
Code Engine Provider and Documentation Update (5347)
move wait_till logic into function, integrate it into vpc_cluster datasource (5476)
Remove hardcoded values for private and direct cos config endpoint (5484)
feat(bm-dynamic-bandwidth): Support for bandwidth in bare metal (5493)
Doc update for ODF (5454)
feat(fs-cross-account): Support for file share cross account access (5510)
feat Bm firmware update (5519)
Changing the documentation for SCC (5456)
feat(lb-parameterized-redirect): Update doc to specify parameterized url redirect (5521)
chore(Cloud-Databases): Remove Datastax (5511)
feat(ResourceController): Added onetime_credentials to ResourceInstance and ResourceKey read schemas (5532)
Add PhysicalAddress and CapabilitiesManagedBySatellite to Terraform SatelliteLocation (5530)
Added support for bm reinitialization (5520)

BugFixes

fix(CIS): updating managed ruleset documents (5488)
fix(ins-keys): Make VSI keys optional (5518)
fix(is-volume): Set catalogoffering computed attribute empty list (5514)
add import_on_create param to ibm_container_vpc_worker_pool doc (5506)
[Doc]Update Doc for E1080 Support (5536)
Fix(iam-identity):trusted profile templates (5440)
S2S documentation note for ibm_kms_key and minor linting fixes (5529)

v1.67.1
Compare Source
BugFixes

Error: The terraform-provider-ibm_v1.67.0 plugin crashed! (5485)
Fix for adding retention rule issue for existing bucket (5481)

v1.67.0
Compare Source
Features

Support MQCloud

Datasources

ibm_mqcloud_queue_manager_options

Support VPC

Datasources

ibm_is_vpn_gateway_connection_local_cidrs
ibm_is_vpn_gateway_connection_peer_cidrs

Support Cloud Logs

Datasources

ibm_logs_dashboard_folders
ibm_logs_data_usage_metrics
ibm_logs_enrichment
ibm_logs_data_access_rule

Resources

ibm_logs_dashboard_folders
ibm_logs_data_usage_metrics
ibm_logs_enrichment
ibm_logs_data_access_rule

Support Transite Gateway

Resources

ibm_tg_connection_rgre_tunnel

Support Power VS

Datasources

ibm_pi_available_hosts
ibm_pi_host_group
ibm_pi_host_groups
ibm_pi_host
ibm_pi_hosts

Resources

ibm_pi_host_group

Enhancements

Adding default prefixfiletr for TGW connection dependency (5406)
Add ams channels config support to keystore resource (5411)
fix(CatalogManagement): remove deprecated arguments not supported in latest sdk (5405)
support for terraform v1.6 in schematics workspace (5405)
support Management Data Events in COS (5277)
Support for vpn advanced configurations (5407)
support for snapshot crn (5399)
enhancement(sg): added filters on sg data source (5390)
Billing and Metring for images- BM3PI (5419)
Support for protocol state filtering mode in VNI protocol (5428)
Support for confidential compute profiles in instance and instance template (5430)
Project service: support for ca-tor region, support for auto_deploy feature (5430)
Transit Gateway: Adding new connection type Redundant GRE to the transite gateway (5433)
Support deploymentTarget for Power Instance (5446)
Datasource ibm_catalog_cm by Private Catalog Name (5434)
Add host_id to Power SPP (5447)
Add initialize fault attribute to nil on first read (5420)
Allow resizing kubernetes service worker-pools to 0 (5460)
Fix managed ruleset apis (5417)
Image import for Power Image (5448)
UI 35145 os image user data format (5467)

BugFixes

store_value argument ignored when creating a service api key (4615)
Adding PAG changes (5421
Fix logs resources and support dashboard_folder resource (5426)
Fix policy references bug in AG templates (5427)
ibm_cos_bucket doesn't have the right value for s3_endpoint_public when endpoint_type is set to private (5404)
doc update for vni in instance and shares (5429)
Update tagging logic in volume attachments in is_instance (5029)
fix(MQ on Cloud): options data source (5438)
Update doc for vm no storage (5445)
IAM user invite always shows force new due to case sensitive of email address (5462)
fix(lb): Lb pool member target_id update fix (5457)
Added an empty check on protocol_state_filtering_mode (5468)
fix(vpngwc): bug fix for local and peer (5458)
Updated docs for confidential compute mode in is_instance (5477)
fix admin_state_up default issue on vpn_gateway_connection (5479)

v1.66.0
Compare Source
Features

Support Cloud Logs

Datasources

ibm_logs_alert
ibm_logs_alerts
ibm_logs_rule_group
ibm_logs_rule_groups
ibm_logs_policy
ibm_logs_policies
ibm_logs_dashboard
ibm_logs_e2m
ibm_logs_e2ms
ibm_logs_outgoing_webhook
ibm_logs_outgoing_webhooks
ibm_logs_view_folder
ibm_logs_view_folders
ibm_logs_view
ibm_logs_views

Resources

ibm_logs_alert
ibm_logs_rule_group
ibm_logs_policy
ibm_logs_dashboard
ibm_logs_e2m
ibm_logs_outgoing_webhook
ibm_logs_view_folder
ibm_logs_view

Support Event Notification

Datasources

ibm_en_smtp_configuration
ibm_en_smtp_configurations
ibm_en_smtp_user
ibm_en_smtp_users
ibm_en_slack_template

Resources

ibm_en_smtp_configuration
ibm_en_smtp_user
ibm_en_slack_template
ibm_en_smtp_setting

Support CIS

Datasources

ibm_cis_rulesets
ibm_cis_ruleset_versions
ibm_cis_ruleset_rules_by_tag
ibm_cis_ruleset_entrypoint_versions

Resources

ibm_cis_ruleset
ibm_cis_ruleset_version_detach
ibm_cis_ruleset_rule
ibm_cis_ruleset_entrypoint_version

Support KMS

Datasources

ibm_kms_kmip_adapter
ibm_kms_kmip_adapters
ibm_kms_kmip_client_cert
ibm_kms_kmip_client_certs
ibm_kms_kmip_object
ibm_kms_kmip_objects

Resources

ibm_kms_kmip_adapter
ibm_kms_kmip_client_cert

Support IAM

Resources

ibm_iam_policy_assignment

Enhancements

Add empty validation for console languages (5336)
Lower timeout for placement-group add/delete to 10 minutes (5335)
align Projects terraform provider to latest API spec (5293)
feat(Catalog Management): support plan_id on offering instances (5337)
remove workerpool related fields from vpc cluster Read (5287)
Add Fault Attribute to Pi_Instance (5344)
Support outbound-traffic-protection (5268)
scc fix: ability to add profile_version during create (5332)
update documentation with instructions to provide public access to bucket (1268)
Docfix: name change Activity Tracker to Activity Tracker Event Routing (5312)
wait for kms apply for cluster provisioning (5383)
ContinuousTargetOccurence updates (5393)
Restrict to old multitenant (5395)
Update Usage Reports terraform code to support new error handling (5365)
regenerate code for projects (5375)
ICD: Add switchover warning for Old Multitenant (5395)
Add support for role 'NONE' (5400)

BugFixes

Fix snapshot markdown (5331)
Resource ibm_scc_rule creation issue (5327)
Enterprise: Added IAM attributes for reference in enterprise markdown (5334)
fix(security-group-rule): Wrong error message when remote value wrong (5348)
updating docs for SCC profile attachment notifications (5357)
updated the docs for vpc security group rule (5360)
description property of cis_filter and cis_firewall_rule bugged (5378)
SM imported cert bug fix (5392)
fix(tg): fixed global change issue (5366)
fix instance policies to set endpoint type when api url specifies so (5394)

Configuration
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♻ Rebasing: Whenever PR becomes conflicted, or you tick the rebase/retry checkbox.
👻 Immortal: This PR will be recreated if closed unmerged. Get config help if that's undesired.

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This PR has been generated by Renovate Bot.

/run pipeline

/run pipeline

/run pipeline

/run pipeline

/run pipeline

/run pipeline

/run pipeline

/run pipeline

:tada: This issue has been resolved in version 1.0.3 :tada:
The release is available on GitHub release
Your semantic-release bot :package::rocket:


⚠ Samsung has degraded performance

In 619fcfd, Samsung (https://samsung.com) experienced degraded performance:

HTTP code: 200
Response time: 2683 ms

Resolved: Samsung performance has improved in 0b35797.


Which one performs better compared to FAN in 3d landmark detection?

We didn't directly compare with FAN for 3d landmark detection, if your are interested, you can use our face recon model or landmark model to have a comparison.


Fully automate import

Use file browser to add the content pack from file, automatically group it, name the group, and then act on the group according to dialog options

this level of UI improvement is a pipe dream, not even worth leaving it open, will open a new issue when we reach the stage to consider massive UI overhaul and automation


CUDA V2

I dramatically rewrite the CUDA part to make the code runs more efficient, with cleaner code structure, and some extra functionalities.
New features:

Support batch rendering in one pass: N cameras -> images with shape (N, H, W, C). This allows for sampling evenly distributed cameras during every training iteration, thus could potentially lead to better convergence. With this, we probably don't need gradient accumulation anymore. Also, for rendering GS, if some latency can be allowed, we can use batched rendering to further boost FPS.
Supports fully PyTorch Auto-Grad: Provides a version of the rasterization & projection implementation that the gradients are fully managed by PyTorch Auto-Grad (see _rendering in experimental/cuda/__init__.py). This allows for easier try-out for the new ideas as the modifications can probably done purely on the python side. It could also serves as a gradient checker for the CUDA implementation.
Fused implementation with verified Gradients: Every gradient in this PR is carefully checked against PyTorch Auto-Grad. On the other hand, the current GSplat implementation has flaws (errors) in the gradients including
-- 1. FoV clip is applied in the forward pass but not the backward pass. As a result, the gradients are incorrect on some GSs.
-- 2. The backward suffers from a numerical issue caused by using the remained transmittance output from the forward pass because the remained transmittance can be very small (~1e-4). Double precision instead of float should be used for it.
Faster FWD and BWD (esp. in ND case):  See table below for details.

Gradient Diff results again PyTorch Auto Grad (code in experimental/test.py):

viewmats
scales
quats
means

GSplat
42.8834
2e-4
5e-4
4e-3

This PR
4e-3
2e-7
5e-6
3e-6

Runtime Performance (in ms) for Forward (code in experimental/profile.py):

Forward, Channel=1
Batch=1
Batch=8
Batch=64

GSplat
1.492
9.921
76.295

This PR
1.146
3.593
28.948

Forward, Channel=32
Batch=1
Batch=8
Batch=64

GSplat
5.078
32.824
264.146

This PR
1.930
7.773
64.461

Runtime Performance for Backward (disable viewmats gradients):

Backward, Channel=1
Batch=1
Batch=8
Batch=64

GSplat
1.594
10.600
84.395

This PR
1.562
7.409
60.402

Backward, Channel=32
Batch=1
Batch=8
Batch=64

GSplat
10.242
80.935
653.627

This PR
7.850
43.392
353.733

Runtime Performance for Backward (enable viewmats gradients):

Backward, Channel=3
Batch=1
Batch=8
Batch=64

GSplat
2.721
16.629
130.977

This PR
2.183
10.697
85.817

It is not fully compatible with the master yet, including:

anti-aliasing compensation term is not yet implemented.
AbsGrad is to yet implemented.
Fused background is not yet implemented.

But they should be easy to add and should not affect performance too much.

Borrowing the splatfacto model from nerfstudio into experimental/example.py, I was able to do a quick test on how this PR affects splatfacto on the case of single batch size, RGB rendering:

Training time stays the same (I'm not even sure if gsplat is the main time cost in the training. Probably not)
Evaluation on 24 of the image in the Garden scene is 35% faster (0.2185s -> 0.1619s). This indicates the viewer FPS can be better.
Evaluation results are also marginally better: LPIPS 0.15073 -> 0.14789

Thanks! Looking forward to trying out some faster rendering 🚀 . I'm surprised about the no difference to splatfacto training times, I can do some profiling on the models I'm training but I thought it was a large part of the time used. Perhaps a naive question - wouldn't we need to have a .get_outputs_batch method where we feed in batches of camera poses and intrinsics to really see a speedup?

Thanks! Looking forward to trying out some faster rendering 🚀 . I'm surprised about the no difference to splatfacto training times, I can do some profiling on the models I'm training but I thought it was a large part of the time used. Perhaps a naive question - wouldn't we need to have a .get_outputs_batch method where we feed in batches of camera poses and intrinsics to really see a speedup?

Yeah if you train with batches then I would expect a noticeable speedup. The current example that I took from nerfstudio does not trivially support batch though. More upgrade is needed on that.

This is very exciting @liruilong940607! One quick question - am I correct in assuming that the batch rendering supports multiple cameras against the same scene (ie: against the same set of gaussians), and not a potentially different set of gaussians per camera? I can think of some hacky ways of doing the latter (ie: move sets of gaussians very far apart), if it's it not a huge lift would be nice to have less hacky API support.

This is very exciting @liruilong940607! One quick question - am I correct in assuming that the batch rendering supports multiple cameras against the same scene (ie: against the same set of gaussians), and not a potentially different set of gaussians per camera? I can think of some hacky ways of doing the latter (ie: move sets of gaussians very far apart), if it's it not a huge lift would be nice to have less hacky API support.

Yeah currently it is designed for the same scene. To support multiple scenes, I guess we would have to assume each scene contains different number of gaussians, which means GSs can not be trivially batched like cameras. This would make it a little bit more painful to implement than support batched cameras but not impossible!

Makes sense - FWIW I think it would be reasonable to expect that each scene has the same number of gaussians (worse-case one could find a way of "zero-padding" gaussians). It would still be better than the status quo.

This is very exciting @liruilong940607! One quick question - am I correct in assuming that the batch rendering supports multiple cameras against the same scene (ie: against the same set of gaussians), and not a potentially different set of gaussians per camera? I can think of some hacky ways of doing the latter (ie: move sets of gaussians very far apart), if it's it not a huge lift would be nice to have less hacky API support.

Yeah currently it is designed for the same scene. To support multiple scenes, I guess we would have to assume each scene contains different number of gaussians, which means GSs can not be trivially batched like cameras. This would make it a little bit more painful to implement than support batched cameras but not impossible! Though it probably is not in our near future roadmap.

What about supporting the same scene (or at least the same number of gaussians) but with possibly moved gaussian centres? E.g. if the means were allowed to move through time? Currently the projection function only allows unbatched means and covariances as far as I can tell

Closing this PR in favor of #183


Update: alibaba.adrive version 2.2.0

[X] Have you signed the Contributor License Agreement?
[ ] Have you checked that there aren't other open pull requests for the same manifest update/change?

[ ] Have you tested your manifest locally with winget install --manifest <path>?
[X] Does your manifest conform to the 1.0 schema?

Resolves #20946

Microsoft Reviewers: Open in CodeFlow

This is a issue with the URL.. it times out at times..


Error of zGetSurfaceData

I tried to run following code and got error.
ValueError: could not convert string to float: 'BAD COMMAND'

I want to know how to avoid this error.
I use Python 3.6 and Zemax 150624.
import numpy as np
import matplotlib.pyplot as plt
import pyzdde.zdde as pyz
import os

ln = pyz.createLink()
print("Hello Zemax version: ", ln.zGetVersion())
ln.zSetSurfaceData (0, 1, "0.0")
ln.zSetSurfaceData (0, 3, 0.0)
ln.zSetSurfaceData (1, 3, 0.0)
ln.zSetSurfaceData (2, 3, 0.0)
print(ln.zGetSurfaceParameter (3, 3))
ln.zSetSurfaceData (3, 3, 0.0)

ln.close()

The function zSetSurfaceData expects integers as input but you have provided a float ln.zSetSurfaceData (0, 1, "0.0")


When creating a new session, after having created or attempting to create another, "Select Sprint" drop-down does not populate/activate.

WHEN: User creates a session, then creates another session.
OR: User is configuring a new session, cancels, then opens window to re-try creating a new session.
THEN: After the Team drop-down is filled, the "Select Sprint" drop-down does not activate nor populate.

I am trying to upload a screenshot but I cannot

Hi, Please drag and drop the screenshot here. Could you describe your environment? Also, could you check if there are any errors in the browser console or network log?

I’ve been waiting on a screenshot and description of your environment. If you're still experiencing the same issue, please open a new ticket so we can address it accordingly.


Hold-up on ex2

Follow the instruction with exercise 2. While I try to verify the contract(ex2b_test_declare_animal()) after submitting it, it will prompt a msg to reject my request
Error in the called contract (0x2d15a378e131b0a9dc323d0eae882bfe8ecc59de0eb206266ca236f823e0a15):
Error message: Token 1 doesn't belong to the evaluator
Error at pc=0:2600:
An ASSERT_EQ instruction failed:<PHONE_NUMBER>635697963204543407605438159849675014318520616686163352039693617685 != 183906.....
Cairo traceback (most recent call last):
Unknown location (pc=0:1883)
Unknown location (pc=0:1872)

127451... is the Evaluator contract and 183906... is my test account on Argent X.
Is there anything I make a mistake with?

Looks like you have to assign that NFT to the Evaluator smart contract before running the test as it's currently assigned to your Argent X account

Looks like you have to assign that NFT to the Evaluator smart contract before running the test as it's currently assigned to your Argent X account

So you're saying that function 'declare _animal'?

solution: https://github.com/surajsharma/starknet-erc721/blob/main/contracts/token/ERC721/ERC721_ex2.cairo


[comp:upload] upload组件在移动端上传文件名称比较长的时候宽度没有自适应

[x] I have searched the issues of this repository and believe that this is not a duplicate.

What problem does this feature solve?
upload组件在移动端上传文件名称比较长的样式问题
What does the proposed API look like?
在upload的包裹层处理文件名超长的情况

1


Grammar update

removed second word 'and and'

[x] I have reviewed my changes in staging (look for "Automatically generated comment" and click Modified to view your latest changes).
[x] For content changes, I have completed the self-review checklist.

Needs review and update


Fix localtime in legacy timestamp semantics

Fix localtime in legacy timestamp semantics when session zone has
different zone offset on current date compared to offset on 1970-01-01.
Fixes https://github.com/prestosql/presto/issues/3846
Fixes https://github.com/prestosql/presto/issues/3850

CI failed -- https://github.com/prestosql/presto/issues/3161


Question on auto-refreshing of JWTs

First off, thanks for putting together this POC! Was exactly what I was looking to put together, and this saved me a bunch of time.
Everything's working nicely except by default Directus JWTs expire after 20 minutes. I had thought that next-auth would auto-refresh the JWT but that seems to not be happening in some basic server usage like:
export default async function Home() {
  const session = await getServerSession(options)
  const user = session?.user as UserAuthenticated

  let teams
  if (session && session.user) {
    const { accessToken } = session.user as any
    teams = await directus(accessToken).request(readItems("siftie_teams"))
    console.log('server teams', teams)
  }

I'll keep digging into next-auth so I can see if I can figure it out, but thought I'd drop a note here as well.
Thanks again!

Actually it looks like the session itself is not expiring, but the access_token is. 🤔

Hi, I'm glad it was helpful for you.
To expire the session if the access token expires you can try the following at the client level
import { signIn, useSession } from "next-auth/react";
import { useEffect } from "react";

const HomePage() {
  const { data: session } = useSession();

  useEffect(() => {
    if (session?.error === "RefreshAccessTokenError") {
      signIn(); // Force sign in to hopefully resolve error
    }
  }, [session]);

return (...)
}

If you want to extend the duration of your token directly, edit the ACCESS_TOKEN_TTL variable in your environment "Directus".

@pintoderian yeah I did end up trying that but that just forces a login. I went through about a dozen GH issues and threads on expiring Directus access tokens and have mostly given up hope of trying to maintain a shared server and client auth session. I haven't found a strategy that has worked, unfortunately.

Going to close this out as I mostly just wanted to say thanks for the example!


Person Uuid field should not be NULL when a new person assigned to position

When a new person assigned to a position, a row with a NULL personUuid field is added to peoplePositions table in database.
Closes AB#609
User changes

None

Super User changes

None

Admin changes

None

System admin changes

[ ] anet.yml or anet-dictionary.yml needs change
[ ] db needs migration
[ ] documentation has changed
[ ] graphql schema has changed

Checklist

[x] Described the user behavior in PR body
[x] Referenced/updated all related issues
[x] commits follow a repo#issue: Title title format and these 7 rules
[x] commits have a clean history, otherwise PR may be squash-merged
[ ] Added and/or updated unit tests
[ ] Added and/or updated e2e tests
[ ] Added and/or updated data migrations
[ ] Updated documentation
[ ] Resolved all build errors and warnings
[ ] Opened debt issues for anything not resolved here

Obsoleted by #4125


redis-py-cluster support part  pipeline ?

see title, thank U.

see also #140

@rfyiamcool I do not really get what you are after. I do not know the concept of "part pipeline". You need to explain what you want further before i can give you a good answer yes or no.

@rfyiamcool Yes please see #140 as it might be what you are looking for. But becuase you have not updated your question in a while i will now close this issue and if you have some other question with more details then we can handle that in a new issue becuase this one is now stale imo.


Vietnamese translation

Finish Vietnamese translation base on pr #34. This is a word by word translation. This book should be improved in the future, to make a fluently translation.

Hi @thuvh! Sorry for the belated response and thank you for taking the time to translate these pages.
We love to have one reviewer per translation if we can - do you happen to have a colleague that can proof-read your PR?

@joneslee85 could you please help to review?

LGTM 👍
Few nitpicks and typos to address. Then it is good to merge

@thuvh literal translation of technical terms could lead to confusion because many would have been used to English word. I reckon you should have both English term and translated term next to each other, for example Gán cổng (Port binding)


Section Changer - deleting sections issue

Steps to reproduce

Use https://github.com/Samsung/TAU-Design-Editor/pull/414
Create a new project in Design Editor in WATT
Sample --> Mobile --> TAU Basic
D&D Section Changer widget
Click + sign to ad section
Press delete button

Expected behavior
One section is removed but section changer is still visible
OR
whole section changer is removed
Actual behavior
one section is removed but section changer is no longer visible

Able to reproduce it.
I prefer removing a particular section (currently visible) to removing whole section changer.
If there is only section, we should remove whole section changer.

Out of scope.


Update orientation Hook

Update the orientation hook in RollPass to also accept strings, therefore one could just pass vertical, horizontal, Y or AntiY. This would be more convenient for the human eye.

Good idea, but there is only the type of the hook updated. No implementation for this logic?

Just saw that core handles rotating therefore will need to implement the logic.

Ready for merging

Should we consider to make Y configuration the default for 3-roll-passes?

Yes, let's make Y our default.


[v1.0.0 windows] opening app twice at once freezes second instance

on Windows opening a second instance of the app caused the second instance to freeze.

https://sentry.io/codeforscience/sciencefair/issues/300277240/


add option to connectionDefinitions for forward-only paging

The connectionDefinitions function has quite a few options for customization. However, it always has the same output fields for the pageInfo. When my connections only support forward pagination, I would like to omit hasPreviousPage from the pageInfo, since hasPreviousPage is always false for forward pagination. Something like this would be nice:
const { connectionType: PostConnection } = connectionDefinitions({ nodeType: Post, forwardOnly: true })

As an alternative, maybe just an option to replace the entire pageInfo object for the connection, so that way I only have to manually create my pageInfo and pass it into the helper instead of having to create the whole connection definition manually.
const pageInfo = new GraphQLObjectType({ /*...*/ })
const { connectionType: PostConnection } = connectionDefinitions({ nodeType: Post, pageInfo })

Sorry for the late reply.
Sadly having a single global PageInfo type is mandated by relay connection spec.


Can't Synthesize sound - Matrix Missmatch ?

Hi there , im training a Polish speech model , spectrograms looks quite good, even the sound in /Log category is not so bad like for 35k steps. Even thoo I can't synthesize any audio , from text file or in live mode. Here is the error ps.

and here is the screen from the training begining .

Maybe it's becouse there's something wrong in my hparams.py ? Im running on M-AILABS model.
In Windows system I can't set parameters to synthesize.py from command promt, only thing I could do to make it works, is to set default values inside script.
`
#Audio
num_mels = 80, #Number of mel-spectrogram channels and local conditioning dimensionality
num_freq = 513 , # (= n_fft / 2 + 1) only used when adding linear spectrograms post processing network
rescale = True, #Whether to rescale audio prior to preprocessing
rescaling_max = 0.999, #Rescaling value
trim_silence = True, #Whether to clip silence in Audio (at beginning and end of audio only, not the middle)
clip_mels_length = True, #For cases of OOM (Not really recommended, working on a workaround)
max_mel_frames = 1300,  #Only relevant when clip_mels_length = True
# Use LWS (https://github.com/Jonathan-LeRoux/lws) for STFT and phase reconstruction
# It's preferred to set True to use with https://github.com/r9y9/wavenet_vocoder
# Does not work if n_ffit is not multiple of hop_size!!
use_lws=False,
silence_threshold=2, #silence threshold used for sound trimming for wavenet preprocessing

#Mel spectrogram
n_fft = 1024, #Extra window size is filled with 0 paddings to match this parameter
hop_size = 256, #For 22050Hz, 275 ~= 12.5 ms
win_size = 1024, #For 22050Hz, 1100 ~= 50 ms (If None, win_size = n_fft)
sample_rate = 16000, #22050 Hz (corresponding to ljspeech dataset)
frame_shift_ms = None,

#M-AILABS (and other datasets) trim params
trim_fft_size = 512,
trim_hop_size = 128,
trim_top_db = 23,

#Mel and Linear spectrograms normalization/scaling and clipping
signal_normalization = True,
allow_clipping_in_normalization = True, #Only relevant if mel_normalization = True
symmetric_mels = False, #Whether to scale the data to be symmetric around 0
max_abs_value = 4., #max absolute value of data. If symmetric, data will be [-max, max] else [0, max]
normalize_for_wavenet = True, #whether to rescale to [0, 1] for wavenet.

#Limits
min_level_db = -100,
ref_level_db = 20,
fmin = 0, #Set this to 75 if your speaker is male! if female, 125 should help taking off noise. (To test depending on dataset)
fmax = 7600,

#Griffin Lim
power = 1.5,
griffin_lim_iters = 60,
###########################################################################################################################################

#Tacotron
outputs_per_step = 3, #number of frames to generate at each decoding step (speeds up computation and allows for higher batch size)
stop_at_any = True, #Determines whether the decoder should stop when predicting <stop> to any frame or to all of them

embedding_dim = 512, #dimension of embedding space

enc_conv_num_layers = 3, #number of encoder convolutional layers
enc_conv_kernel_size = (5, ), #size of encoder convolution filters for each layer
enc_conv_channels = 512, #number of encoder convolutions filters for each layer
encoder_lstm_units = 256, #number of lstm units for each direction (forward and backward)

smoothing = False, #Whether to smooth the attention normalization function
attention_dim = 128, #dimension of attention space
attention_filters = 32, #number of attention convolution filters
attention_kernel = (31, ), #kernel size of attention convolution
cumulative_weights = True, #Whether to cumulate (sum) all previous attention weights or simply feed previous weights (Recommended: True)

prenet_layers = [256, 256], #number of layers and number of units of prenet
decoder_layers = 2, #number of decoder lstm layers
decoder_lstm_units = 1024, #number of decoder lstm units on each layer
max_iters = 1000, #Max decoder steps during inference (Just for safety from infinite loop cases)

postnet_num_layers = 5, #number of postnet convolutional layers
postnet_kernel_size = (5, ), #size of postnet convolution filters for each layer
postnet_channels = 512, #number of postnet convolution filters for each layer

mask_encoder = False, #whether to mask encoder padding while computing attention
mask_decoder = False, #Whether to use loss mask for padded sequences (if False, <stop_token> loss function will not be weighted, else recommended pos_weight = 20)

cross_entropy_pos_weight = 1, #Use class weights to reduce the stop token classes imbalance (by adding more penalty on False Negatives (FN)) (1 = disabled)
predict_linear = True, #Whether to add a post-processing network to the Tacotron to predict linear spectrograms (True mode Not tested!!)
###########################################################################################################################################

#Wavenet
# Input type:
# 1. raw [-1, 1]
# 2. mulaw [-1, 1]
# 3. mulaw-quantize [0, mu]
# If input_type is raw or mulaw, network assumes scalar input and
# discretized mixture of logistic distributions output, otherwise one-hot
# input and softmax output are assumed.
input_type="raw",
quantize_channels=2 ** 16,  # 65536 (16-bit) (raw) or 256 (8-bit) (mulaw or mulaw-quantize) // number of classes = 256 <=> mu = 255

log_scale_min=float(np.log(1e-14)), #Mixture of logistic distributions minimal log scale
log_scale_min_gauss = float(np.log(1e-7)), #Gaussian distribution minimal allowed log scale

#To use Gaussian distribution as output distribution instead of mixture of logistics, set "out_channels = 2" instead of "out_channels = 10 * 3". (UNDER TEST)
out_channels = 2, #This should be equal to quantize channels when input type is 'mulaw-quantize' else: num_distributions * 3 (prob, mean, log_scale).
layers = 30, #Number of dilated convolutions (Default: Simplified Wavenet of Tacotron-2 paper)
stacks = 3, #Number of dilated convolution stacks (Default: Simplified Wavenet of Tacotron-2 paper)
residual_channels = 512,
gate_channels = 512, #split in 2 in gated convolutions
skip_out_channels = 256,
kernel_size = 3,

cin_channels = 80, #Set this to -1 to disable local conditioning, else it must be equal to num_mels!!
upsample_conditional_features = True, #Whether to repeat conditional features or upsample them (The latter is recommended)
upsample_scales = [15, 20], #prod(upsample_scales) should be equal to hop_size
freq_axis_kernel_size = 3,
leaky_alpha = 0.4,

gin_channels = -1, #Set this to -1 to disable global conditioning, Only used for multi speaker dataset. It defines the depth of the embeddings (Recommended: 16)
use_speaker_embedding = True, #whether to make a speaker embedding
n_speakers = 5, #number of speakers (rows of the embedding)

use_bias = True, #Whether to use bias in convolutional layers of the Wavenet

max_time_sec = None,
max_time_steps = 13000, #Max time steps in audio used to train wavenet (decrease to save memory) (Recommend: 8000 on modest GPUs, 13000 on stronger ones)
###########################################################################################################################################

#Tacotron Training
tacotron_random_seed = 5339, #Determines initial graph and operations (i.e: model) random state for reproducibility
tacotron_swap_with_cpu = False, #Whether to use cpu as support to gpu for decoder computation (Not recommended: may cause major slowdowns! Only use when critical!)

tacotron_batch_size = 6, #number of training samples on each training steps
tacotron_reg_weight = 1e-6, #regularization weight (for L2 regularization)
tacotron_scale_regularization = True, #Whether to rescale regularization weight to adapt for outputs range (used when reg_weight is high and biasing the model)

tacotron_test_size = None, #% of data to keep as test data, if None, tacotron_test_batches must be not None
tacotron_test_batches = 48, #number of test batches (For Ljspeech: 10% ~= 41 batches of 32 samples)
tacotron_data_random_state=1234, #random state for train test split repeatability

#Usually your GPU can handle 16x tacotron_batch_size during synthesis for the same memory amount during training (because no gradients to keep and ops to register for backprop)
tacotron_synthesis_batch_size = 32 * 16, #This ensures GTA synthesis goes up to 40x faster than one sample at a time and uses 100% of your GPU computation power.

tacotron_decay_learning_rate = True, #boolean, determines if the learning rate will follow an exponential decay
tacotron_start_decay = 50000, #Step at which learning decay starts
tacotron_decay_steps = 50000, #Determines the learning rate decay slope (UNDER TEST)
tacotron_decay_rate = 0.4, #learning rate decay rate (UNDER TEST)
tacotron_initial_learning_rate = 1e-3, #starting learning rate
tacotron_final_learning_rate = 1e-5, #minimal learning rate

tacotron_adam_beta1 = 0.9, #AdamOptimizer beta1 parameter
tacotron_adam_beta2 = 0.999, #AdamOptimizer beta2 parameter
tacotron_adam_epsilon = 1e-6, #AdamOptimizer beta3 parameter

tacotron_zoneout_rate = 0.1, #zoneout rate for all LSTM cells in the network
tacotron_dropout_rate = 0.5, #dropout rate for all convolutional layers + prenet

tacotron_clip_gradients = False, #whether to clip gradients
natural_eval = False, #Whether to use 100% natural eval (to evaluate Curriculum Learning performance) or with same teacher-forcing ratio as in training (just for overfit)

#Decoder RNN learning can take be done in one of two ways:
#	Teacher Forcing: vanilla teacher forcing (usually with ratio = 1). mode='constant'
#	Curriculum Learning Scheme: From Teacher-Forcing to sampling from previous outputs is function of global step. (teacher forcing ratio decay) mode='scheduled'
#The second approach is inspired by:
#Bengio et al. 2015: Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks.
#Can be found under: https://arxiv.org/pdf/1506.03099.pdf
tacotron_teacher_forcing_mode = 'constant', #Can be ('constant' or 'scheduled'). 'scheduled' mode applies a cosine teacher forcing ratio decay. (Preference: scheduled)
tacotron_teacher_forcing_ratio = 1., #Value from [0., 1.], 0.=0%, 1.=100%, determines the % of times we force next decoder inputs, Only relevant if mode='constant'
tacotron_teacher_forcing_init_ratio = 1., #initial teacher forcing ratio. Relevant if mode='scheduled'
tacotron_teacher_forcing_final_ratio = 0., #final teacher forcing ratio. Relevant if mode='scheduled'
tacotron_teacher_forcing_start_decay = 10000, #starting point of teacher forcing ratio decay. Relevant if mode='scheduled'
tacotron_teacher_forcing_decay_steps = 280000, #Determines the teacher forcing ratio decay slope. Relevant if mode='scheduled'
tacotron_teacher_forcing_decay_alpha = 0., #teacher forcing ratio decay rate. Relevant if mode='scheduled'
###########################################################################################################################################

#Wavenet Training
wavenet_random_seed = 5339, # S=5, E=3, D=9 :)
wavenet_swap_with_cpu = False, #Whether to use cpu as support to gpu for decoder computation (Not recommended: may cause major slowdowns! Only use when critical!)

wavenet_batch_size = 4, #batch size used to train wavenet.
wavenet_test_size = 0.0441, #% of data to keep as test data, if None, wavenet_test_batches must be not None
wavenet_test_batches = None, #number of test batches.
wavenet_data_random_state = 1234, #random state for train test split repeatability

#During synthesis, there is no max_time_steps limitation so the model can sample much longer audio than 8k(or 13k) steps. (Audio can go up to 500k steps, equivalent to ~21sec on 24kHz)
#Usually your GPU can handle 1x~2x wavenet_batch_size during synthesis for the same memory amount during training (because no gradients to keep and ops to register for backprop)
wavenet_synthesis_batch_size = 4 * 2, #This ensure that wavenet synthesis goes up to 4x~8x faster when synthesizing multiple sentences. Watch out for OOM with long audios.

wavenet_learning_rate = 1e-3,
wavenet_adam_beta1 = 0.9,
wavenet_adam_beta2 = 0.999,
wavenet_adam_epsilon = 1e-8,

wavenet_ema_decay = 0.9999, #decay rate of exponential moving average

wavenet_dropout = 0.05, #drop rate of wavenet layers
train_with_GTA = True, #Whether to use GTA mels to train WaveNet instead of ground truth mels.
###########################################################################################################################################`

Please , take a look on my issue, Thank you.

This looks like a duplicate of #194


[FLINK-6543] [table] Deprecate toDataStream

This deprecates toDataStream in Scala and Java Table API.

I forgot to update the documentation. Will do this later.

Will fix the remaining issues and merge this for RC2 of Flink 1.3.0.


About HydroDyn

Dear guys,
I would like to know how to call HydroDyn's dynamic link library in matlab. Or how to use HydroDyn alone in matlab.
Thanks！

Dear @b100mmm,
I'm not aware that NREL has compiled the standalone HydroDyn driver as a dynamic link library, although I'm sure that would be possible, similar to how we've compiled OpenFAST as a dynamic link library.  I'm not sure what application you have in mind.
You can always call an executable like OpenFAST or the compiled standalone HydroDyn driver from MATLAB, e.g., using the runFAST.m script from the MATLAB Toolbox: https://github.com/OpenFAST/matlab-toolbox/blob/main/Utilities/runFAST.m.
Best regards,

There is a HydroDyn interface library that can be compiled as a DLL (HydroDyn_C_Binding).  However, this is not a full HydroDyn driver as it is designed to be interfaced with and called by some other code.  There are some small Python examples of how this interface works (module level regression test examples which use the hydrodyn_library.py interface), but these are not complete codes.
Since the HydroDyn_C_Binding interface DLL uses a standard C bindings interface, it should in theory be possible to call directly from MATLAB (through a mex funtion interface on the MATLAB side).  However, we do not provide any guidance or examples on how to do this as we do not have the resources to do so.
So your options for using HydroDyn directly with MATLAB are:

develop the appropriate mex function interface and calling code in MATLAB (our apologies that we are unable to provide direct support for such an endeavor).  If you do develop such a mex function and would like to contribute it to the OpenFAST community, we would be more than happy to include it as an example for other users (however, we may not be able to maintain it when interfaces are updated).
Call the HydroDyn executable from MATLAB as mentioned by @jjonkman in the above comment.

Regards,

Thank you for your reply！
Is the compiled standalone HydroDyn driver  ‘HydroDynDriver_x64.exe’  in Releases? And I want to use HydroDyn module alone in matlab for hydrodynamic calculations. What should I do with this compiled standalone HydroDyn driver （if the compiled standalone HydroDyn driver is  ‘HydroDynDriver_x64.exe’） in matlab?


Pre-commit config

Hit some weirdness where one of the checks it installed didn't exist on Github, so manually deleted that from the autogenerated config file.

Fixing problems on M1: if you get a 'mach-o but unsupported architecture' issue, activate the virtual environment and
pip install --no-binary :all: regex``` (I think)


Update my résumé

Add a working cv for the site.

[ ] Introduction
[ ] Summary
[ ] Skills
[ ] Experience
[ ] Education
[ ] Contact

One day you'll finish this.
I hope you like kittens.
The rescue, look how tiny


Issues running TensorFlow module in a Python sub-interpreter

I'm using the tensorflow Python module in a custom C++ application, by instantiating the Python interpreter and then running Python code that imports tensorflow.
The Python code is run in a sub-interpreter (created using Py_NewInterpreter). This works fine the first time around, but when I end the sub-interpreter and create a second one, the call to 'import tensorflow' fails with the exception shown below. Looks like the TensorFlow module is either not being cleaned up properly when it de-initializes, and/or it has some global state that is shared between multiple interpreters? In particular it looks it tries to load a shared object that has already been loaded.
Is there a way to fix this and to make TensorFlow compatible with Python sub-interpreters, or is this just the tip of the iceberg and more issues would pop up once this particular one is resolved?
Thanks!
Peter
--- C++ code to reproduce the problem:
#include <cassert>
#include <python/Python.h>

// set this to the folder containing a virtual env with TensorFlow:
static char g_pythonHome[] = "/Users/peter/tf101";

void create_sub_interpreter_import_tensorflow()
{
    // acquire global interpreter lock and create new sub-interpreter:
    PyEval_AcquireLock();
    PyThreadState* pThreadState = Py_NewInterpreter();
    assert(pThreadState);

    // set sys.argv because TensorFlow needs it:
    int ret = PyRun_SimpleString("import sys\n"
                                 "sys.argv = ['']\n");
    assert(ret == 0);

    // import TensorFlow, say hello.
    // !!! this fails when it is called the second time:
    ret = PyRun_SimpleString("import tensorflow\n"
                             "print 'hello world'\n");
    assert(ret == 0);

    // end sub-interpreter and release global interpreter lock:
    Py_EndInterpreter(pThreadState);
    PyEval_ReleaseLock();
}

int main(int argc, const char * argv[]) {
    // set Python home to a virtual env containing TensorFlow:
    Py_SetPythonHome(g_pythonHome);

    // initialise Python and threads:
    Py_Initialize();
    PyEval_InitThreads();

    // store current thread state, release global interpreter lock:
    PyThreadState* pMainThreadState = PyEval_SaveThread();
    assert(pMainThreadState);

    create_sub_interpreter_import_tensorflow();
    create_sub_interpreter_import_tensorflow();

    // acquire global interpreter lock, restore main thread state, finalize Python:
    PyEval_RestoreThread(pMainThreadState);
    Py_Finalize();

    return 0;
}

--- Python error message:
Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "/Users/peter/tf101/lib/python2.7/site-packages/tensorflow/__init__.py", line 23, in <module>
    from tensorflow.python import *
  File "/Users/peter/tf101/lib/python2.7/site-packages/tensorflow/python/__init__.py", line 65, in <module>
    import tensorflow.contrib as contrib
  File "/Users/peter/tf101/lib/python2.7/site-packages/tensorflow/contrib/__init__.py", line 26, in <module>
    from tensorflow.contrib import grid_rnn
  File "/Users/peter/tf101/lib/python2.7/site-packages/tensorflow/contrib/grid_rnn/__init__.py", line 27, in <module>
    from tensorflow.contrib.grid_rnn.python.ops.grid_rnn_cell import *
  File "/Users/peter/tf101/lib/python2.7/site-packages/tensorflow/contrib/grid_rnn/python/ops/grid_rnn_cell.py", line 28, in <module>
    from tensorflow.contrib import layers
  File "/Users/peter/tf101/lib/python2.7/site-packages/tensorflow/contrib/layers/__init__.py", line 77, in <module>
    from tensorflow.contrib.layers.python.layers import *
  File "/Users/peter/tf101/lib/python2.7/site-packages/tensorflow/contrib/layers/python/layers/__init__.py", line 22, in <module>
    from tensorflow.contrib.layers.python.layers.feature_column import *
  File "/Users/peter/tf101/lib/python2.7/site-packages/tensorflow/contrib/layers/python/layers/feature_column.py", line 79, in <module>
    from tensorflow.contrib.layers.python.ops import bucketization_op
  File "/Users/peter/tf101/lib/python2.7/site-packages/tensorflow/contrib/layers/python/ops/bucketization_op.py", line 25, in <module>
    resource_loader.get_path_to_datafile("_bucketization_op.so"))
  File "/Users/peter/tf101/lib/python2.7/site-packages/tensorflow/python/framework/load_library.py", line 71, in load_op_library
    raise errors._make_specific_exception(None, None, error_msg, error_code)
tensorflow.python.framework.errors.AlreadyExistsError: /Users/peter/tf101/lib/python2.7/site-packages/tensorflow/contrib/layers/python/ops/_bucketization_op.so has already been loaded

--- Environment:
OS: OSX El Capitan (10.11.5)
tensorflow version: 0.9.0
installed using: pip install --upgrade https://storage.googleapis.com/tensorflow/mac/tensorflow-0.9.0-py2-none-any.whl

@keveman - this seems to be tf.layers loading a custom bucketization_op via load_op_library for the second time.
The dynamic loader presumably returns a status indicating the shared library is already loaded - but this causes TF to fail rather then use the existing version.

Closing due to inactivity. Feel free to open a new github issue if the problem still persists in recent versions.


[edn/dv] fix broken private CI tests

This fixes broken DV tests run in private CI that was introduced in #22602.

Gah, I'm really sorry for breaking this. Thanks for the fix.

No problem! :)


fix for https://github.com/splunk/splunk-sdk-ruby/issues/28

This should raise exceptions when error / fatal messages are sent by the API.

Hi @fornellas,
Thanks for doing this work, and submitting the contributor agreement!
Can you make a new PR against the develop branch?
If possible, can you write a test to reproduce the issue from #28?

Did you review the code? Would this be the best solution? If so, I'll try to write some tests.

@fornellas The changes look reasonable, tests would be even better! We won't merge directly into the master branch though, so you will need to make a new PR.

@fornellas thanks for submitting this. Would you mind:

Rebasing your branch against develop
Adding tests?

Any progress on this?

I have not had time to learn Test::Unit and build a proper test, I solved my issue here by monkey patching the solution in my application code for now.


Added example for setting up virtual environment

Wired. The real commit is "Added example for creating virtual environment". The other commits were from Master branch I think, somehow showing up here...


Research - what are the other sources we can add?

Public sector
Air Aberdeen
FOI reports?

We have currently these organisation types:
https://github.com/OpenDataScotland/jkan/blob/gh-pages/_data/org_types.yml
These cover these organisations:
https://opendata.scot/organizations/
More sources here?
https://github.com/watty62/SOD

FOIs can be pulled through for organizations with a WhatDoTheyKnow ID as a JSON feed. Should be able to display them on the page using some JavaScript: https://www.whatdotheyknow.com/feed/body/aberdeen_city_council.json

@KarenJewell Do you reckon we can close this one? Any relevant data portal we've identified has its own individual GitHub issue at this point.

Agree to close.


Update connect-akamai-security-events.md

Hi @nayef997 - Thank you for your contribution. Would you take a moment to sign the Contributor License Agreement (CLA)? After the CLA is signed, someone can review your pull request. Thanks!

@nayef997 : Thanks for your contribution! The author(s) have been notified to review your proposed change.

Hi @yelevin Could you review the proposed update to your article?
Thanks.

Hi - it looks like this change has already been passed to the live docs in a different PR - it's currently live at:
https://github.com/MicrosoftDocs/azure-docs/blob/master/articles/sentinel/connect-akamai-security-events.md#find-your-data
Let's close this one. @nayef-yassin please be in touch if you have any questions about this.
#please-close


proxy or additional gateway support

In the context of linux machines that do not have access to the internet (think of hackthebox challenge boxes - where you, the user, vpn into a lab environment, and YOU have internet access, but the boxes dont - all the stuff where a tool like this tries to contact the internet would fall over) some parts of this tool (the exploit suggester for example) don't work.
It would be super neat to be able to specify a proxy (like the machine you're vpnining into a lab from!) so that orc can reach the internet somehow

We could do this with the HTTP_PROXY/HTTPS_PROXY/FTP_PROXY environmental variables, would just have to double check that all the downloaders we support will consistently respect these.
Might be a good idea to have a "connectivity check" function that alerts the user that they should set these.

Those are both excellent ideas!
Both would work for my immediate use case scenario.
 Shell variable setting is sufficent because the exports to environment are included in the functions.
Now the two script load and run functions (getsuspect and getexploit) checks the TCP connection to github if no https_proxy is defined.

@Viss - for the reverse port forward, running something like 3proxy or another SOCKS proxy on localhost (on the "attacking host"), and doing something like "ssh -R 1337:<IP_ADDRESS>:1337 -l root -i key.rsa victim.tld" would forward the local port 1337 (assuming that is where your 3proxy/SOCKS proxy is listening) to the remote hosts port 1337. You could then (on the victim side) use that as your SOCKS proxy.
Given a lot of the things require the env vars to be HTTP proxies though, maybe using Squid or something and forwarding that is the way to go. You can certainly forward multiple ports using "-R" arguments too.
Given we have added proxy awareness, I guess this will have to go in the wiki for the tool as a usage case/instruction once I've done some testing on it. Will update this issue once that is done :)

Just going to add this here, but an OPSEC note for if you are forwarding remote ports back to local ports to do proxying (and acting as a gateway). If your proxy is exiting via your attack-boxes IP, you could end up with a catastrophic OPSEC failure, where someone else on the pwned box does a request for some IP checker over your proxy, leaking your IP. So it will be heavily suggested to use a local Tor instance for the SOCKS proxy you forward (meaning, "exit IP's" of the tunnel are Tor exits), and a Privoxy -> Tor setup for HTTP/HTTPS.
Other OPSEC considerations/risks are also being looked at here, will note them down as stuff gets tested and all that.

So there are several really awesome projects on github that are either go or python proxies that are "one script with no dependencies that run on python 2.7", so you don't have to try to install stuff on the box you've gained access to. One of those is called "almondrocks", and I've had decent success with it in the past: https://github.com/klustic/AlmondRocks
but it turns out there's somewhat of a market for this sort of thing (shady, one-file, zero-deps proxies for socks or http) and there are a few of them floating around.
Ideally, this project shouldnt have to rely too much on the specifics of the proxy, just that "the operator understands the requirements, and that this tool is part of a larger toolset". :D

also as far as opsec is concerned, what I have noticed lately is that blueteamers are becoming lazy and complacent with the BILLIONS AND BILLIONS OF DOLLARS IN TOOLS they have available to them, so they tend not to go hunting anymore, and only investigate leads that bloop their siem or logging tool or dashboard. So basically unless you hit a tripwire or something, they wont come looking - and GENERALLY that boils down to "is a host trying to talk to the internet that doesnt normally talk to the internet".

I'd like to imagine that orc won't bloop too much, but then I have seen carbon black on *nix hosts before. That said, opsec should still be a concern, I think - even if they don't care right now (and nobody seems to care too much about *nix from either colour), they might in the future. Should get things right/add warnings from the start.
that said, vpn/proxyery is nice to have, and we do have options for pulling down other tools...


Document clarity: IReliableQueue API concurrency

https://github.com/Azure/azure-docs-sdk-dotnet/blob/99dbe1fc35599241d91e3409af4fa2a75f86da64/xml/Microsoft.ServiceFabric.Data.Collections/IReliableQueue`1.xml#L30
Transaction is the unit of concurrency: Users can have multiple transactions in-flight at any given point of time but for a given transaction each API must be called one at a time.
So all Reliable Collection APIs that take in a transaction and return a Task, must be awaited one at a time.

I don't see the causality here. The first sentence states that if we inspect each transaction individually (regardless of the other txs), we call the APIs sequentially.
The second sentence states that for all the APIs that are ITransaction -> Task, they should be awaited sequentially.
If we wait for tasks from different txs concurrently, rule 1 holds but rule 2 doesn't. This means rule 2 is not an implication of rule 1.
Can someone elaborate on this? We should either remove So, or remove rule 2 altogether.

This is a reference doc issue that requires a change to the underlying /// comments.
Reopened as: https://github.com/Azure/service-fabric-issues/issues/913


[FLINK-14970]Doomed test for equality to NaN

What is the purpose of the change
scala pattern matching can't handle "NaN","NegativeInfinity " etc.
in Flink planner module( GenerateUtils, CodeGenerator), some code logic fall into this issue.
the PR target to fix this.
Brief change log
fix pattern matching issue in below classes:
org.apache.flink.table.planner.codegen.GenerateUtils
org.apache.flink.table.codegen.CodeGenerator
Verifying this change
This change is  covered by UT
org.apache.flink.table.planner.codegen.GenerateUtilsTest
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)

Hi @caidezhi , thanks for fixing this. Could you add some tests to this bug?

@KurtYoung I have add some UT cases as you suggested. Would you please help to review? Thanks.

There are some test failures after you changed this, could you check that?

hi @KurtYoung the latest build failed("misc" module ), but it is not related to the PR. Do you think it is required  to trigger another build?(the travis-ci seems quite busy, a request may stay in the queue for a whole day :( )

Hi @KurtYoung , i also aware of that.  Calcite uses BigDecimal for handling DOUBLE, FLOAT. when we pass unnormal numerical value((eg: INFINITY, NaN) literalType=DoubleType, literalValue=null are passed to method "GenerateUtils.generateLiteral",  the program will go to another branch, the pattern matching which has bug will not cause any impact.
can we just remove the pattern matching logic to make it clean? if it is ok, i will make another commit for this PR. Thanks.

Only  Double.NaN  does't work.  I update the Jira issue description and the type as "Improvement-Trivial".  Deleting the NaN test seems not worth a PR.
Sorry for causing any inconvenience, will be more careful in future.
(Thanks for your patience :) )

I think it's still worthy to clean up some wrongly written codes. If you wish you could continue to refine the PR

Thanks Kurt. refine PR as you suggested.

Merging...


WIP: Move legoify into a python module

Okay, so there is A LOT of stuff going on in here. First, I've made the code pep8 compliant. This includes:

Moving from tabs to spaces (W191)
Removing excessive spaces (E201)
Fixing > 80 chars (E501)
Few other small fixes

I also moved all the procedural code into methods. They should probably be broken down more but, eh. Finally, I made a cli entry point so anyone can python setup.py install or (if you upload to pypi, which I recommend) pip install legoify. This should also help with the distribution model for Windows.
There's a few more things I want to clean up, but I decided to put this up here for your opinion. To reproduce (on Ubuntu from root directory):
sudo apt-get install python-pip python-virtualenv imagemagick libjpeg-dev zlib1g-dev libopenjpeg-dev
virtualenv .venv
. .venv/bin/activate
python setup.py develop

At this point you should have a legoify command that you can run feeding it an image as the parameter. You can also dynamically switch out brick images by supplying a --brick flag. The output parameter doesn't work yet, but that shouldn't take long to wire up.

Wow, a lot is going on. This looks really nice, thanks. Merging


Add Python Package To New options

PyCharm has an option under the "New" menu for creating a Python Package. All it does is add a init.py file in a new directory but it saves that extra minute of having to remember to manually create it. It would be awesome if this extension added that.

Thanks for the feature request! We are going to give the community 60 days from when this issue was created to provide 7 👍 upvotes on the opening comment to gauge general interest in this idea. If there's enough upvotes then we will consider this feature request in our future planning. If there's unfortunately not enough upvotes then we will close this issue.

Thank you for submitting your feature request and everyone who considered it! Unfortunately, this issue did not receive enough votes over the allotted time, and so we are closing the issue.


unable to extract OpenGraph title - Facebook video

Error while getting the Title of the Facebook videos.

Output logs below:
[debug] System config: []
[debug] User config: []
[debug] Custom config: []
[debug] Command-line args: ['--verbose', '--ignore-errors', '--encoding', 'utf-8', '--get-title', 'https://www.facebook.com/satyaktar/videos/750364805160973/']
[debug] Encodings: locale cp65001, fs mbcs, out cp65001, pref utf-8
[debug] youtube-dl version 2019.10.22
[debug] Python version 3.4.4 (CPython) - Windows-10-10.0.17763
[debug] exe versions: ffmpeg N-94014-g80e68ce116
[debug] Proxy map: {}
WARNING: unable to extract OpenGraph title; please report this issue on https://yt-dl.org/bug . Make sure you are using the latest version; type  youtube-dl -U  to update. Be sure to call youtube-dl with the --verbose flag and include its complete output.
[debug] Default format spec: bestvideo+bestaudio/best
Mufti Kannada Dubbed Hindi Full Movie 2017 - ShivaRajkumar  SriiMurali -201  ...

Wrong method of submitting bug. Will create a new one.

i have the same problem with you,  have you found the  method  to solve the problem ?


Library Not Found

Hey Guys
i have this really annoying issue that the IDE throws a fatal error, File not Found exception when i include the library. I wasted two weeks of searching the solution but i was not found any help.
I Tried different firebase libraries but every one gives me the same exception. (Yes, the ArduinoJson library is installed..)

It will solve your issue.

Reinstall the Library and set ArduinoJson library to 5.13.2.
It will solve your issue.

it didn't solve mine either...please help :(

Reinstall the Library and set ArduinoJson library to 5.13.2.
It will solve your issue.

Documents\Arduino\libraries\firebase-arduino-master\src/Firebase.h:24:10: fatal error: memory: No such file or directory
#include
      ^~~~~~~~

compilation terminated.
exit status 1
Error compiling for board Arduino Mega or Mega 2560.

I'm getting the same error, first I got "No such file or directory: #include " which I managed to solve by adding ".h" to the library name; but this doesn't seem to work with the "memory" library. Any ideas?? I'm using ATMega2560 as the main board and ESP8266 as the WiFi module.


Release/0.15.0

 Thank you for your submission! We really appreciate it. Like many open source projects, we ask that you all sign our Contributor License Agreement before we can accept your contribution.1 out of 5 committers have signed the CLA.:white_check_mark: avaakash:x: bot-harness:x: akhilcheruvalat:x: bot2-harness:x: bharatgoelharnessYou have signed the CLA already but the status is still pending? Let us recheck it.


[Messaging] Add delay messages block integration test

Motivation
Currently, in the docker environment, if the consumer enables the retry feature and sets the retry topic in DeadLetterPolicy, the consumer will be blocked after receive retry messages many times.
I'll fix this problem in the next PR.
Modifications
Add an integration test for reproducing this problem.
Does this pull request potentially affect one of the following parts:
If yes was chosen, please highlight the changes

Dependencies (does it add or upgrade a dependency): (no)
The public API: (no)
The schema: (no)
The default values of configurations: (no)
The wire protocol: (no)
The rest endpoints: (no)
The admin cli options: (no)
Anything that affects deployment: (no)

/pulsarbot run-failure-checks

It seems that I couldn't reproduce the problem with CI, so strange.

/pulsarbot run-failure-checks

The PR https://github.com/apache/pulsar/pull/10078 include this test, I closed this PR.


TechDocs: Change metadata fields to camel case

Context
#3716 suggested to add an explicit type to handle TechDocs metadata. These changes have been introduced in #3968.
Based on the PR feedback (1), (2) a further improvement could be made to the field names; more specifically to change them from snake_case to camelCase.
The advantage here is stylistic. Changing these fields would promote consistency across the codebase and adhere to JS naming standards. At first I thought I could make these changes as part of #3968 but I think this would have added quite a bit of work and would have stalled the review process. It would be great to have it done now as a follow-up.
Possible Implementation

I haven't looked much at possible implementations but I have snooped around the TechDocs codebase in the past week.  I can think of two approaches to this.

Based on @OrkoHunter's helpful comment:

The site_name inside mkdocs.yml has to be snake case because of mkdocs. However, we do have a choice to change them in techdocs_metadata.json file. I'm 50/50 about this.

We can change the mkdocs techdocs script to add the fields as camelCase to techdocs_metadata.json -- this would happen here. We can then go through techdocs-common, techdocs, techdocs-backend and change all occurrences of the fields to their camelCase counterpart. I don't expect there to be (many) changes to the publishers and any of the parsing logic, but I also haven't checked very thoroughly.

Without any changes to mkdocs-techdocs-core we can change the publishers and add a bit more logic to each when we parse techdocs_metadata. Off the top of my head something like this should work for all three of them:
e.g  in stages/publisher/local.ts

fetchTechDocsMetadata():
...
fetch(metadataURL)
          .then(response =>
            response
              .json()
              .then(resp => {
               const techdocsMetadata = {
                     siteName: resp.site_name,
                     siteDescription: resp.site_description,
               }
               resolve(techdocsMetadata))
}

Both of these would do the trick, it's likely I'm missing something and I get a hunch tests will have to be changed either way :). In terms of trade-offs I think something like option 1 would be a bit cleaner since there won't have to be any explanations in the future when a new publisher is added (or a publisher is changed); less rules and constraints to keep track off and less chance for errors. The downside of it is that it will require two PRs, I am also not sure what the community guidelines are or what the rules of thumb are when dealing with the mkdocs-techdocs-core repo (if there are any).
I'm more than happy to look into this myself.
@OrkoHunter @adamdmharvey tagging you both here as you've given me the feedback to begin with. Lmk what you think!

Hey @mateiidavid, I like option 2 over 1. All the publishers can use a single helper function which maps the snake_cake techdocs_metadata.json values to camelCase TechDocsMetadata instance.

Hey @mateiidavid, I like option 2 over 1. All the publishers can use a single helper function which maps the snake_cake techdocs_metadata.json values to camelCase TechDocsMetadata instance.


Dependabot should not add google/android-fhir group as reviewers to its PRs

This is a follow-up to #2320:
On e.g. #2582, Dependabot should not automatically add google/android-fhir group as reviewers to its PRs.

---------- Forwarded message ---------
From: Michael Vorburger <>
Date: Mon, Jun 24, 2024 at 2:02 PM
Subject: Unknown owner on line 11: make sure @santosh-pingle exists and has write access to the repository
To<EMAIL_ADDRESS>Hello GitHub Support,
https://github.com/google/android-fhir/blob/master/CODEOWNERS (currently) shows:
Unknown owner on line 11: make sure @santosh-pingle exists and has write access to the repository
But this appears to be a bug at GitHub, because https://github.com/santosh-pingle does exist...
https://github.com/google/android-fhir/pull/2582 initially got assigned to the 3 other OWNERS, and then afterwards got assigned to santosh-pingle and @google/android-fhir - which we don't want; how does Dependabot determine those additional reviewers? How can we make it not add @google/android-fhir anymore? It's not configured in https://github.com/google/android-fhir/blob/master/.github/dependabot.yaml, so I don't understand what makes it do that.
Tracked as https://github.com/google/android-fhir/issues/2585.
Tx,
M.


Reolink Doorbell WiFi: 2 way audio not working

I can't find a way to make two-way audio work.
I have read all the past issues, questions and FAQs, but nothing worked. Would appreciate some guidance
My setup:

HASS 2024.6.3 on Raspberry PI 4
External access via Duck DNS and NGINX (public dynamic IP)
WebRTC 3.5.2 installed as HASS custom integration (via HACS)
Reolink Doorbell Wifi - firmware <IP_ADDRESS>15. UPnP, RSTP and ONVIP enabled
Port forwarding enabled for port 8555 on my router

This is my go2rtc yaml:
doorbell_2way:
    - rtsp://USER:PWD@IP:554/Preview_01_sub
    - "ffmpeg:doorbell_2way#audio=opus#audio=volume#async"

ffmpeg:
  bin: ffmpeg
  volume: "-af \"volume=25dB\""

webrtc:
  candidates:
    - stun:8555

This is my HA UI card:
type: custom:webrtc-camera
streams:
  - url: doorbell_2way
    mode: webrtc
    media: video,audio
    name: 🔇
  - url: doorbell_2way
    mode: webrtc
    media: video,audio,microphone
    name: 🔈
ui: true
muted: false

I have tried both desktop (chrome) and mobile (android chrome and HA companion app) and both from local access (LAN IP) and remote (HTTPS).
When I switch to the mic-on profile, I can see the browser either asking or displaying access to the microphone, but no sound is reproduced at the doorbell end.
I have tried multiple configs and navigated all the previous issues and threads on the topics.
I have seen a reference to reverse proxy (here) but even if I needed it, I am surprised it does not even work in local.
Thanks

https://github.com/AlexxIT/go2rtc/issues/331

thanks @AlexxIT - the issue was the doorbell firmware, had to downgrade.
FYI - I have found a workaround to make two way audio working also on non HTTPS addresses (see here).
Tested and it works on both Chrome and Chrome mobile.
Unfortunately I can't seem to find a way to access the flags page from the Android companion app.

You can find this link on go2rtc main readme page :)


Jenkins library to generate buildInfo.yml

Signed-off-by: Zelin Hao<EMAIL_ADDRESS>Description
Add the new jenkins library to generate buildInfo.yml used for our self-service dashboards.
Issues Resolved
https://github.com/opensearch-project/opensearch-build/issues/2410
By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.
For more information on following Developer Certificate of Origin and signing off your commits, please check here.

I also believe things like "current stage" can be obtained from ENV and may not need to be passed around, which should cut the logic here by half

The stage parameter passing into the function will also be used as the name variable in the buildInfo.yml file. That why I wish to name it instead of just using the stage name from the distribution workflow since a lot of them are more like a sentence.

Had some offline discussion with @dblock , I will be make following changes:

job.yml will be more related to what this final result yaml file contains and will replace my current name BuildInfo.yml.
Current I checkout the commits for each components when I initialized the yaml file, this will be wrong if any plugin commits anything into their repo. We should be getting these commit IDs from the assemble manifest (e.g. this sample assemble manifest).
Current buildInfoYaml jenkins method contains too much hardcoded structure and could be improved.
Future enhancements could be made to these entire workflow similar to https://github.com/opensearch-project/opensearch-build/blob/main/src/jenkins/Messages.groovy

Codecov Report

Merging #2473 (82bb12e) into main (856b691) will increase coverage by 5.36%.
The diff coverage is n/a.

@@              Coverage Diff              @@
##               main     #2473      +/-   ##
=============================================
+ Coverage     94.63%   100.00%   +5.36%
=============================================
  Files           211         6     -205
  Lines          4322       105    -4217
  Branches         29        19      -10
=============================================
- Hits           4090       105    -3985
+ Misses          226         0     -226
+ Partials          6         0       -6

Impacted Files
Coverage Δ

src/paths/assemble_output_dir.py

src/assemble_workflow/bundle_url_location.py

src/ci_workflow/ci_target.py

src/assemble_workflow/bundle_recorder.py

...orkflow/integ_test/integ_test_runner_opensearch.py

src/test_workflow/test_kwargs.py

...est_workflow/perf_test/perf_test_cluster_config.py

tests/jenkins/jobs/DetectDockerImage_Jenkinsfile

tests/jenkins/jobs/RunGradleCheck_Jenkinsfile

src/git/git_repository.py

... and 194 more

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