Travel Time sensor error: ValueError: invalid literal for int() with base 10

Beta 14b
Got the error and warning below in my HA Logs for the travel time sensor:
2023-03-15 20:47:34.821 ERROR (SyncWorker_14) [icloud3] invalid literal for int() with base 10: '1d 1.7h'
Traceback (most recent call last):
  File "/config/custom_components/icloud3/sensor.py", line 1024, in native_value
    if sensor_value == int(sensor_value):
ValueError: invalid literal for int() with base 10: '1d 1.7h'
2023-03-15 20:47:34.851 WARNING (SyncWorker_14) [homeassistant.components.sensor] Sensor sensor.rxxxs_iphone_travel_time has device class None, state class None and unit min thus indicating it has a numeric value; however, it has the non-numeric value: 1d 1.7h (<class 'str'>); Please update your configuration if your entity is manually configured, otherwise report it to the custom integration author.

icloud3 Debug logs from the same time
2023-03-15 20:47:30 [icloud3_main:0531]  PREFETCH > rxxxx_iphone > iCloud Location Requested (prefetch)
2023-03-15 20:47:30 [pyicloud_ic3:1566]  PREFETCH >  FmF iCloudData Update RequestedBy-rxxxx_iphone
													    • axxxx_iphone, 8:47:03p/8m
													    • kxxxx_iphone, 8:47:03p/5m
													    • rxxxx_iphone, 8:47:03p/35m *
													    • gxxxx_iphone, 8:47:03p/7m
													    • qxxxx_iphone, 8:47:03p/5m
													    • txxxx_iphone, 8:47:03p/36m
2023-03-15 20:47:30 [icloud_data_:0423]  LOCDATA > jxxxx_iphone > Old Loc FamShr-8:41:27p/±6m (iOSApp-7:29:54p), UpdateIn-5 secs
2023-03-15 20:47:30 [icloud_data_:0423]  LOCDATA > kxxxx_iphone > Updated FmF-8:47:03p/±5m (FamShr-8:41:30p, iOSApp-7:18:37p)
2023-03-15 20:47:30 [icloud_data_:0423]  LOCDATA > qxxxx_iphone > Updated FmF-8:47:03p/±5m (FamShr-8:41:27p, iOSApp-6:41:20p)
2023-03-15 20:47:30 [icloud_data_:0423]  LOCDATA > axxxx_iphone > Updated FmF-8:47:03p/±8m (FamShr-8:41:23p, iOSApp-4:06:46p)
2023-03-15 20:47:30 [icloud_data_:0423]  LOCDATA > gxxxx_iphone > Updated FmF-8:47:03p/±7m
2023-03-15 20:47:30 [device......:1711]  LOCDATA > rxxxx_iphone > Updated (FmF) > 7:12:17p/±8m → 8:47:03p/±35m, Located-28 sec ago, Moved-47 m
2023-03-15 20:47:30 [icloud_data_:0423]  LOCDATA > rxxxx_iphone > Updated FmF-8:47:03p/±35m
2023-03-15 20:47:30 [icloud_data_:0423]  LOCDATA > txxxx_iphone > Updated FmF-8:47:03p/±36m
2023-03-15 20:47:30 [icloud3_main:0537]  PREFETCH > rxxxx_iphone > ────▲─△─▲──── ▷▷ ICLOUD PREFETCH ◁◁ RXXXX_IPHONE, CURRZONE-RXXX_STATIONARY,  ────▲─△─▲────
2023-03-15 20:47:30 [icloud_data_:0421]  LOCDATA > rxxxx_iphone > Updated FmF-8:47:03p/±35m
2023-03-15 20:47:30 [icloud3_main:0431]  ICLOUD > rxxxx_iphone > Trigger > Initial Locate@20:47:30
2023-03-15 20:47:30 [icloud_data_:0421]  LOCDATA > rxxxx_iphone > Updated FmF-8:47:03p/±35m
2023-03-15 20:47:30 [icloud3_main:0710]  ICLOUD > rxxxx_iphone > ────▼─▽─▼──── ▷▷ ICLOUD ◁◁ RXXXX_IPHONE, CURRZONE-RXXX_STATIONARY, INITIAL LOCATE@20:47:30 ────▼─▽─▼────
2023-03-15 20:47:30 [icloud_data_:0421]  LOCDATA > rxxxx_iphone > Updated FmF-8:47:03p/±35m
2023-03-15 20:47:30 [icloud3_main:0861]  ICLOUD > rxxxx_iphone > iCloud update started > Initial Locate@20:47:30
2023-03-15 20:47:30 [icloud3_main:1059]  ICLOUD > rxxxx_iphone > Zone > Home-18 m/r100m, GPS-(41.02347, -74.14121/±35m)
2023-03-15 20:47:30 [icloud3_main:1065]  ICLOUD > rxxxx_iphone > Zone > Home >  Home-18 m,  Axxxx Library-759 m,  Bxxxx School-800 m,  Hxxxx School-868 m,  Cxxxx Lake-1.27 km,  Northern Hxxxx School-1.38 km,  OTF Wxxxx-2.59 km,  OTF Rxxxx-4.80 km,  Rxxxx College Pool-7.17 km,  OTF Mxxxx / Wegmans-7.42 km,  Hxxxx Hospital-28.8 km,  Txxxx Home-139.4 km,  Gxxxx Home-140.6 km,  Cxxxx Home-142.5 km,  Sxxxx Gxxxx Home-150.4 km,  Mxxxx Home-363.0 km
2023-03-15 20:47:30 [determine_in:1240]  ICLOUD > rxxxx_iphone > Check Nearby Device-jxxxx_iphone, CanUse, Cnt-1, Timer-1 secs, jxxxx_iphone → txxxx_iphone → None
2023-03-15 20:47:30 [determine_in:1240]  ICLOUD > rxxxx_iphone > Check Nearby Device-kxxxx_iphone, CanUse, Cnt-1, Timer-1 secs, kxxxx_iphone → txxxx_iphone → None
2023-03-15 20:47:30 [determine_in:1231]  ICLOUD > rxxxx_iphone > Check Nearby Device-qxxxx_iphone, Checking, Cnt-1, Timer-1 secs, qxxxx_iphone → jxxxx_iphone → txxxx_iphone
2023-03-15 20:47:30 [determine_in:1240]  ICLOUD > rxxxx_iphone > Check Nearby Device-qxxxx_iphone, CanNotUse, Looped back to itself, Cnt-2, Timer-1 secs, qxxxx_iphone → jxxxx_iphone → txxxx_iphone → qxxxx_iphone
2023-03-15 20:47:30 [determine_in:1231]  ICLOUD > rxxxx_iphone > Check Nearby Device-axxxx_iphone, Checking, Cnt-1, Timer-1 secs, axxxx_iphone → jxxxx_iphone → txxxx_iphone
2023-03-15 20:47:30 [determine_in:1240]  ICLOUD > rxxxx_iphone > Check Nearby Device-axxxx_iphone, CanNotUse, Looped back to itself, Cnt-2, Timer-1 secs, axxxx_iphone → jxxxx_iphone → txxxx_iphone → axxxx_iphone
2023-03-15 20:47:30 [determine_in:1168]  ICLOUD > rxxxx_iphone > Nearby Devices (<15m) > ⊗Jxxxx/iPhone-20.95m/±6m, ⊗Kxxxx/iPhone-24.037m/±5m, ⊗Qxxxx/iPhone-32.003m/±5m⌘, ⊗Axxxx/iPhone-25.134m/±8m⌘, ⊗Gxxxx/iPhone-140.6km/±7m±, ⊗Txxxx/iPhone-31.785m/±35m±
2023-03-15 20:47:30 [icloud3_main:0933]  ICLOUD > rxxxx_iphone > ────▼─▽─▼──── ▷▷ FMF ◁◁ RXXXX_IPHONE, CURRZONE-RXXX_STATIONARY, HOME ────▼─▽─▼────
2023-03-15 20:47:30 [determine_in:0161]  ICLOUD > rxxxx_iphone > DistFmZome-0, Moved-0, Waze-0, Calc-0.017758, TravTime-1542.806735, Dir-InZone
2023-03-15 20:47:30 [determine_in:0549]  ICLOUD > rxxxx_iphone > Results: From-Home > NextUpdate-10:47:30p, Interval-2 hrs, Method-1.EnterZone
2023-03-15 20:47:30 [determine_in:0570]  [rxxxx_iphone] Tracking Results: From-Home > iOSAppZone-not_set, iC3Zone-home, Interval-2 hrs, TravTime-, Dist-0 mi, NextUpdt-10:47:30p, MaxDist-0 mi, Dir-InZone, Moved-0 m, Battery-0%, LastDataUpdate-8:47:30p, GPSAccuracy-35m, LocAge-28 sec ago, OldThreshold-2 min, LastEvLogMsg-8:47:30p, Method-1.EnterZone
2023-03-15 20:47:30 [icloud3_main:0933]  ICLOUD > rxxxx_iphone > ────▼─▽─▼──── ▷▷ FMF ◁◁ RXXXX_IPHONE, CURRZONE-RXXX_STATIONARY, CXXXXHOME ────▼─▽─▼────
2023-03-15 20:47:30 [waze_history:0354]  ICLOUD > rxxxx_iphone > WazeHistDB > Get All Records, Table-locations, Criteria- lat_long_key='41.0235:-74.1412', RecdCnt-0
2023-03-15 20:47:32 [waze_route_c:0167]  Waze Results > GPS-(74.02347, -41.14121) --> (74.83878, -39.78558), Time-108.80min, Distance-170.44km
2023-03-15 20:47:34 [waze_route_c:0167]  Waze Results > GPS-(81.42655, -28.31541) --> (74.02347, -41.14121), Time-969.55min, Distance-1776.64km
2023-03-15 20:47:34 [waze........:0228]  ICLOUD > rxxxx_iphone > Waze Route Info > TravTime-1.8 hrs, Dist-170.4 km, WazeMoved-1776.6 km, CalcMoved-1542.8 km, Took-3.94 secs
2023-03-15 20:47:34 [determine_in:0161]  ICLOUD > rxxxx_iphone > DistFmZome-170.442, Moved-1776.643, Waze-170.442, Calc-142.460174, TravTime-108.8, Dir-InZone
2023-03-15 20:47:34 [determine_in:0549]  ICLOUD > rxxxx_iphone > Results: From-Cxxxx Home > NextUpdate-10:47:30p, Interval-2 hrs, TravTime-1.8 hrs, Distance-105.9 mi, Method-1.EnterZone
2023-03-15 20:47:34 [determine_in:0570]  [rxxxx_iphone] Tracking Results: From-Cxxxx Home > iOSAppZone-not_set, iC3Zone-home, Interval-2 hrs, TravTime-1.8 hrs, Dist-105.9 mi, NextUpdt-10:47:30p, MaxDist-105.9 mi, Dir-InZone, Moved-0 m, Battery-0%, LastDataUpdate-8:47:30p, GPSAccuracy-35m, LocAge-32 sec ago, OldThreshold-2 min, LastEvLogMsg-8:47:34p, Method-1.EnterZone
2023-03-15 20:47:34 [icloud3_main:0939]  ICLOUD > rxxxx_iphone > ────▲─△─▲──── ▷▷ FMF ◁◁ RXXXX_IPHONE, CURRZONE-RXXX_STATIONARY, CXXXXHOME ────▲─△─▲────
2023-03-15 20:47:34 [icloud3_main:0870]  ICLOUD > rxxxx_iphone > iCloud update completed > Date source - FmF
2023-03-15 20:47:34 [sensor......:1027]  Traceback (most recent call last):
  File "/config/custom_components/icloud3/sensor.py", line 1024, in native_value
    if sensor_value == int(sensor_value):
ValueError: invalid literal for int() with base 10: '1d 1.7h'
2023-03-15 20:47:34 [icloud3_main:1367]  ICLOUD > rxxxx_iphone > Device Monitor > fmf, Initial Locate@20:47:30, AttrsZone-home, LocDataZone-home, Located-8:47:03p (32 sec ago), iOSAppGPS-(0.00000, 0.00000), iOSAppState-not_set), GPS-(41.02347, -74.14121/±35m)
2023-03-15 20:47:34 [icloud3_main:0827]  ICLOUD > rxxxx_iphone > ────▲─△─▲──── ▷▷ FMF/FMF ◁◁ RXXXX_IPHONE, CURRZONE-HOME, GEN UPDATE ────▲─△─▲────

Changed the initial value of some sensors to 0 instead of —-. They used to be fields with a um on them. In b15.


Closed curve class

Manim already has more specific classes for geometry such as Polygon. One of my current in-progress PRs adds ArcPolygon which is a more general and versatile version of a polygon allowing for arcs. The completely generalized version is a closed curve, for which we should have a ClosedCurve eventually, allowing properly filled and animated shapes of arbitrary complexity by taking in functions to draw the boundary.

This would definitely be a great addition!
E.g. in the use case of intersecting graphs this could be very useful.
At the moment this is done by rectangles, which is not the perfect solution:
https://docs.manim.community/en/latest/examples.html#graphareaplot

Indeed, that would exactly be the kind of situation ClosedCurve could be used for. I take it that GraphScene does an internal rectangle schtick? Yeah... that's not ideal at all. I get it for as long as you specifically want to show the rough bars for derivative reasons, but otherwise a proper ClosedCurve class could be better used for this and so much more.


Networkpolicies not working in v3

Details
What steps did you take and what happened:
In Version 2 it was possible to define multiple networkpolicies via a map, after upgrading to the v3 chart there is now only one networkpolicy created.
What did you expect to happen:
a networkpolicy per map entry should be created
Anything else you would like to add:
The networkpolicy is created without the suffix.

Thanks for pointing this out! I've added a fix to 3.2.0

Find some time now to test it, it's working again!
Thanks for the quick fix, and for this great helm chart :-)


Release 0.33.2

👍

Coverage increased (+0.0007%) to 26.267% when pulling ce0294e6cbcf95a117bac525ce1454299ba8ae5f on release-0.33.2 into 37e0e76c236cf4a66780cd9f2126d10b0aca25e5 on develop.


ATS as a S3 proxy configuration

Hi,
This is configuration help/explanation issue rather than bug. I am considering using ATS as a S3 proxy running as a container, however looking on https://docs.trafficserver.apache.org/en/latest/admin-guide/plugins/s3_auth.en.html
I don't know what is session_token=<token> and how can I get it. Is it a token taken from EC2 meta data?
Could anyone put more light on this please?

I think the session token is from
https://docs.aws.amazon.com/AmazonS3/latest/dev/RESTAuthentication.html#UsingTemporarySecurityCredentials
in practice, we don't use it for v4 authentication.

Looks like this has been fixed.  Please reopen if there is still an issue.


Add K8s namespace/context to Wave Prompt

Wave should track the current k8s namespace/context in the prompt.

Implemented this in v0.7.3, but I had to remove it after we had reports of a big performance issue querying the k8s context/namespace (up to 5s where Wave was hanging).  Will try to make another pass at getting this in -- potentially in an asynchronous way.


FilePicker 8.0 Multi-tenant support

Category

[x] Question
[x] Documentation issue
[ ] Bug

In File Picker 8.0 Documentation (https://learn.microsoft.com/en-us/onedrive/developer/controls/file-pickers/?view=odsp-graph-online), it mentioned support for multitenant:

c. You may optionally configure this application for multitenant but this is outside the scope of this article

I could not find any additional documentation on how to use multitenant with FilePicker 8.0
I could not find any example on how to use File Picker 8.0 with multitenant. Its not in sample code (https://github.com/OneDrive/samples/tree/master/samples/file-picking).

The AAD application defining the permissions used for authentication can be multi-tenant. The picker itself supports calling a single tenant per-launch.
Closing this issue as answered. If you have additional questions or we did not answer your question, please open a new issue, ref this issue, and provide any additional details available. Thank you!


Create 0000-01-02-matajanov.md

created a branch, created a file and made a commit, and opened a pull request

ok, i approve


add command for workaround for issue 10836

I needed to use the the following command "kubectl get svc nginx" to obtain the cluster ip for the service.
Could be useful for others also as the default command didn't display an IP. #10836

LGTM

@colmm99 Please sort out the CLA with the CLA bot :)

I signed it!
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Reply to this email directly or view it on GitHub
https://github.com/GoogleCloudPlatform/kubernetes/pull/11714#issuecomment-123887380
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Space Explorer - Ship Design tutorial

Step 4 - should add the Arrow directional keys on keyboard as this is what most kids use to move their sprites

Step 5 - The mySprite Stay in Screen block isn't showing in the Toolbox

Fixed this and more with
https://github.com/microsoft/pxt-skillmap-sample/pull/47

Thanks Kiki! :-)


[SPARK-10612][SQL] Add prepare to LocalNode.

The idea is that we should separate the function call that does memory reservation (i.e. prepare) from the function call that consumes the input (e.g. open()), so all operators can be a chance to reserve memory before they are all consumed.

Merged build triggered.

Merged build started.

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Test build #42475 has finished for   PR 8761 at commit f2d4a1d.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

Merged build finished. Test PASSed.

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OK updated - I put everything in LocalNode.
I was initially thinking it might be better to be more explicit - but it seems vast majority of the operators don't need to do anything.

Merged build triggered.

Merged build started.

Test build #42493 has started for   PR 8761 at commit 90f7943.

Test build #42493 has finished for   PR 8761 at commit 90f7943.

This patch fails Spark unit tests.
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This patch adds no public classes.

Merged build finished. Test FAILed.

Test FAILed.
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retest this please

Merged build triggered.

Merged build started.

Test build #42512 has started for   PR 8761 at commit 90f7943.

Actually this patch is essentially a no-op so I'm just going to merge it.

Test build #42512 has finished for   PR 8761 at commit 90f7943.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

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Save repositories info Core Data

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Resolve in pull request: https://github.com/EstefaniaGuardado/gitAppSwift/pull/22


Feat/add hpu support

This pull request allows users to run the Whisper code on HPUs (Intel® Gaudi®):

Created Dockerfile_hpu for HPU operations
Updated the transcribe.py script to accommodate HPU-related changes
Added HPU support to the README.md

Could you please add instruction for building Gaudi images or install steps.
Probably an hpu Dockerfile or simple install steps?

 Could you provide the steps?

Built a new image and launched it.
Ran the cmd from the readme:
python3 -m whisper.transcribe audio.flac audio.mp3 audio.wav --model turbo --device hpu
Partial crash dump below:
libpostproc    58.  1.100 / 58.  1.100
[in#0 @ 0x8669d00] Error opening input: No such file or directory
Error opening input file audio.flac.
Error opening input files: No such file or directory
Skipping audio.flac due to RuntimeError: Failed to load audio: ffmpeg version 7.0.2-static https://johnvansickle.com/ffmpeg/  Copyright (c) 2000-2024 the FFmpeg developers
built with gcc 8 (Debian 8.3.0-6)
configuration: --enable-gpl --enable-version3 --enable-static --disable-debug --disable-ffplay --disable-indev=sndio --disable-outdev=sndio --cc=gcc --enable-fontconfig --enable-frei0r --enable-gnutls --enable-gmp --enable-libgme --enable-gray --enable-libaom --enable-libfribidi --enable-libass --enable-libvmaf --enable-libfreetype --enable-libmp3lame --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenjpeg --enable-librubberband --enable-libsoxr --enable-libspeex --enable-libsrt --enable-libvorbis --enable-libopus --enable-libtheora --enable-libvidstab --enable-libvo-amrwbenc --enable-libvpx --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxml2 --enable-libdav1d --enable-libxvid --enable-libzvbi --enable-libzimg
libavutil      59.  8.100 / 59.  8.100
libavcodec     61.  3.100 / 61.  3.100
libavformat    61.  1.100 / 61.  1.100
libavdevice    61.  1.100 / 61.  1.100
libavfilter    10.  1.100 / 10.  1.100
libswscale      8.  1.100 /  8.  1.100
libswresample   5.  1.100 /  5.  1.100
libpostproc    58.  1.100 / 58.  1.100
[in#0 @ 0x8669d00] Error opening input: No such file or directory
Error opening input file audio.flac.
Error opening input files: No such file or directory
/root/whisper/transcribe.py:126: UserWarning: FP16 is not supported on CPU; using FP32 instead
warnings.warn("FP16 is not supported on CPU; using FP32 instead")
Traceback (most recent call last):
File "/root/whisper/audio.py", line 58, in load_audio
out = run(cmd, capture_output=True, check=True).stdout
File "/usr/lib/python3.10/subprocess.py", line 526, in run
raise CalledProcessError(retcode, process.args,
subprocess.CalledProcessError: Command '['ffmpeg', '-nostdin', '-threads', '0', '-i', 'audio.mp3', '-f', 's16le', '-ac', '1', '-acodec', 'pcm_s16le', '-ar', '16000', '-']' returned non-zero exit status 254.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/root/whisper/transcribe.py", line 613, in cli
result = transcribe(model, audio_path, temperature=temperature, **args)
File "/root/whisper/transcribe.py", line 135, in transcribe
mel = log_mel_spectrogram(audio, model.dims.n_mels, padding=N_SAMPLES)
File "/root/whisper/audio.py", line 140, in log_mel_spectrogram
audio = load_audio(audio)
File "/root/whisper/audio.py", line 60, in load_audio
raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
RuntimeError: Failed to load audio: ffmpeg version 7.0.2-static https://johnvansickle.com/ffmpeg/  Copyright (c) 2000-2024 the FFmpeg developers
built with gcc 8 (Debian 8.3.0-6)
configuration: --enable-gpl --enable-version3 --enable-static --disable-debug --disable-ffplay --disable-indev=sndio --disable-outdev=sndio --cc=gcc --enable-fontconfig --enable-frei0r --enable-gnutls --enable-gmp --enable-libgme --enable-gray --enable-libaom --enable-libfribidi --enable-libass --enable-libvmaf --enable-libfreetype --enable-libmp3lame --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenjpeg --enable-librubberband --enable-libsoxr --enable-libspeex --enable-libsrt --enable-libvorbis --enable-libopus --enable-libtheora --enable-libvidstab --enable-libvo-amrwbenc --enable-libvpx --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxml2 --enable-libdav1d --enable-libxvid --enable-libzvbi --enable-libzimg
libavutil      59.  8.100 / 59.  8.100
libavcodec     61.  3.100 / 61.  3.100
libavformat    61.  1.100 / 61.  1.100
libavdevice    61.  1.100 / 61.  1.100
libavfilter    10.  1.100 / 10.  1.100
libswscale      8.  1.100 /  8.  1.100
libswresample   5.  1.100 /  5.  1.100
libpostproc    58.  1.100 / 58.  1.100
[in#0 @ 0x7f6cd00] Error opening input: No such file or directory
Error opening input file audio.mp3.
Error opening input files: No such file or directory
Skipping audio.mp3 due to RuntimeError: Failed to load audio: ffmpeg version 7.0.2-static https://johnvansickle.com/ffmpeg/  Copyright (c) 2000-2024 the FFmpeg developers

No getting new errors:
PT_HPU_LAZY_MODE = 1
PT_RECIPE_CACHE_PATH =
PT_CACHE_FOLDER_DELETE = 0
PT_HPU_RECIPE_CACHE_CONFIG =
PT_HPU_MAX_COMPOUND_OP_SIZE =<PHONE_NUMBER>854775807
PT_HPU_LAZY_ACC_PAR_MODE = 0
PT_HPU_ENABLE_REFINE_DYNAMIC_SHAPES = 0
---------------------------: System Configuration :---------------------------
Num CPU Cores : 152
CPU RAM       :<PHONE_NUMBER> KB
/root/whisper/transcribe.py:126: UserWarning: FP16 is not supported on CPU; using FP32 instead
warnings.warn("FP16 is not supported on CPU; using FP32 instead")
Traceback (most recent call last):
File "/root/whisper/audio.py", line 58, in load_audio
out = run(cmd, capture_output=True, check=True).stdout
File "/usr/lib/python3.10/subprocess.py", line 526, in run
raise CalledProcessError(retcode, process.args,
subprocess.CalledProcessError: Command '['ffmpeg', '-nostdin', '-threads', '0', '-i', 'audio.flac', '-f', 's16le', '-ac', '1', '-acodec', 'pcm_s16le', '-ar', '16000', '-']' returned non-zero exit status 254.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/root/whisper/transcribe.py", line 613, in cli
result = transcribe(model, audio_path, temperature=temperature, **args)
File "/root/whisper/transcribe.py", line 135, in transcribe
mel = log_mel_spectrogram(audio, model.dims.n_mels, padding=N_SAMPLES)
File "/root/whisper/audio.py", line 140, in log_mel_spectrogram
audio = load_audio(audio)
File "/root/whisper/audio.py", line 60, in load_audio
raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
RuntimeError: Failed to load audio: ffmpeg version 7.0.2-static https://johnvansickle.com/ffmpeg/  Copyright (c) 2000-2024 the FFmpeg developers
built with gcc 8 (Debian 8.3.0-6)
configuration: --enable-gpl --enable-version3 --enable-static --disable-debug --disable-ffplay --disable-indev=sndio --disable-outdev=sndio --cc=gcc --enable-fontconfig --enable-frei0r --enable-gnutls --enable-gmp --enable-libgme --enable-gray --enable-libaom --enable-libfribidi --enable-libass --enable-libvmaf --enable-libfreetype --enable-libmp3lame --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenjpeg --enable-librubberband --enable-libsoxr --enable-libspeex --enable-libsrt --enable-libvorbis --enable-libopus --enable-libtheora --enable-libvidstab --enable-libvo-amrwbenc --enable-libvpx --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxml2 --enable-libdav1d --enable-libxvid --enable-libzvbi --enable-libzimg
libavutil      59.  8.100 / 59.  8.100
libavcodec     61.  3.100 / 61.  3.100
libavformat    61.  1.100 / 61.  1.100
libavdevice    61.  1.100 / 61.  1.100
libavfilter    10.  1.100 / 10.  1.100
libswscale      8.  1.100 /  8.  1.100
libswresample   5.  1.100 /  5.  1.100
libpostproc    58.  1.100 / 58.  1.100
[in#0 @ 0x7723d00] Error opening input: No such file or directory
Error opening input file audio.flac.
Error opening input files: No such file or directory

Here are some comments:
I do not see pip install command for whisper in the docker file?
after
ADD . /root/whisper
I was expecting something like:
pip install .
In the README launch command, you are mounting the local whisper dir to /root/whisper which you just copied!!
Finally mounting the local dir is casing issue when rebuilding the image:
docker build --no-cache  -t sapdai/refd:whisper_gaudi-1.17.0 -f Dockerfile.hpu .
[+] Building 0.2s (6/11)                                                                                docker:default
=> [internal] load build definition from Dockerfile.hpu                                                                                0.0s
=> => transferring dockerfile: 1.62kB                                                                                0.0s
=> [internal] load metadata for vault.habana.ai/gaudi-docker/1.18.0/ubuntu22.04/habanalabs/pytorch-installer-2.4.0:latest                                                                               0.0s
=> [internal] load .dockerignore                                                                                0.0s
=> => transferring context: 53B                                                                                0.0s
=> CACHED [1/7] FROM vault.habana.ai/gaudi-docker/1.18.0/ubuntu22.04/habanalabs/pytorch-installer-2.4.0:latest                                                                                0.0s
=> ERROR [internal] load build context                                                                                0.0s
=> => transferring context: 69.98kB                                                                                0.0s
=> CANCELED [2/7] RUN apt-get update && apt-get install -y     tzdata     bash-completion     python3-pip     openssh-server     vim     iputils-ping     net-tools     protobuf-compiler     curl      0.2s

[internal] load build context:

ERROR: failed to solve: error from sender: open whisper/.graph_dumps: permission denied
I had to remove whisper/.graph_dumps to build again!
also please create new requirements_hpu.txt and remove torch from it!
Is the optimum-habana already in the container
FROM vault.habana.ai/gaudi-docker/1.18.0/ubuntu22.04/habanalabs/pytorch-installer-2.4.0:latest

Finally here is the issue with your latest update:
root@0f213d1cffaf:~/whisper# python3 -m whisper.transcribe audio.flac audio.mp3 audio.wav --model turbo --device hpu
/usr/lib/python3.10/runpy.py:126: RuntimeWarning: 'whisper.transcribe' found in sys.modules after import of package 'whisper', but prior to execution of 'whisper.transcribe'; this may result in unpredictable behaviour
warn(RuntimeWarning(msg))
/usr/lib/python3.10/inspect.py:288: FutureWarning: torch.distributed.reduce_op is deprecated, please use torch.distributed.ReduceOp instead
return isinstance(object, types.FunctionType)
Loading Habana modules from /usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/lib
100%|█████████████████████████████████████| 1.51G/1.51G [00:19<00:00, 83.2MiB/s]
/root/whisper/init.py:154: FutureWarning: You are using torch.load with weights_only=False (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for weights_only will be flipped to True. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via torch.serialization.add_safe_globals. We recommend you start setting weights_only=True for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
checkpoint = torch.load(fp, map_location="cpu")
Loading Habana modules from /usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/lib
============================= HABANA PT BRIDGE CONFIGURATION ===========================
PT_HPU_LAZY_MODE = 1
PT_RECIPE_CACHE_PATH =
PT_CACHE_FOLDER_DELETE = 0
PT_HPU_RECIPE_CACHE_CONFIG =
PT_HPU_MAX_COMPOUND_OP_SIZE =<PHONE_NUMBER>854775807
PT_HPU_LAZY_ACC_PAR_MODE = 0
PT_HPU_ENABLE_REFINE_DYNAMIC_SHAPES = 0
PT_HPU_EAGER_PIPELINE_ENABLE = 1
PT_HPU_EAGER_COLLECTIVE_PIPELINE_ENABLE = 1
---------------------------: System Configuration :---------------------------
Num CPU Cores : 152
CPU RAM       :<PHONE_NUMBER> KB
/root/whisper/transcribe.py:132: UserWarning: FP16 is not supported on HPU; using FP32 instead
warnings.warn("FP16 is not supported on HPU; using FP32 instead")
Traceback (most recent call last):
File "/root/whisper/audio.py", line 58, in load_audio
out = run(cmd, capture_output=True, check=True).stdout
File "/usr/lib/python3.10/subprocess.py", line 526, in run
raise CalledProcessError(retcode, process.args,
subprocess.CalledProcessError: Command '['ffmpeg', '-nostdin', '-threads', '0', '-i', 'audio.flac', '-f', 's16le', '-ac', '1', '-acodec', 'pcm_s16le', '-ar', '16000', '-']' returned non-zero exit status 254.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/root/whisper/transcribe.py", line 617, in cli
result = transcribe(model, audio_path, temperature=temperature, **args)
File "/root/whisper/transcribe.py", line 139, in transcribe
mel = log_mel_spectrogram(audio, model.dims.n_mels, padding=N_SAMPLES)
File "/root/whisper/audio.py", line 140, in log_mel_spectrogram
audio = load_audio(audio)
File "/root/whisper/audio.py", line 60, in load_audio
raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
RuntimeError: Failed to load audio: ffmpeg version 7.0.2-static https://johnvansickle.com/ffmpeg/  Copyright (c) 2000-2024 the FFmpeg developers
built with gcc 8 (Debian 8.3.0-6)

@orionsBeltWest
We have added a new requirements_hpu.txt with key packages:
optimum-habana==1.14.1
transformers==4.45.2
huggingface-hub==0.26.2
tiktoken==0.8.0
torch-geometric==2.6.1
numba==0.60.0

Note: optimum-habana is essential, and torch should not be overwritten.
A new section in Dockerfile.hpu installs these packages, ensuring compatibility for HPU setup:
# Copy HPU requirements
COPY requirements_hpu.txt /root/whisper/requirements.txt

# Install Python packages
RUN pip install --upgrade pip \
    && pip install -r requirements.txt

Please follow the README.md instructions under the section “Intel® Gaudi® HPU usage,” especially for:
Building the Docker Image:

Ensure the correct base image is used or updated to match your environment.
Important: Before rebuilding, delete any existing Docker images with docker image rm to start fresh.

Running the Container:

Set the volume path correctly (-v /path/to/your/whisper:/root/whisper).
Run with --device hpu for HPU compatibility in command-line usage.

Example:
python3 -m whisper.transcribe audi_file.wav --model turbo --device hpu

The results should be similar to:
Loading Habana modules from /usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/lib
============================= HABANA PT BRIDGE CONFIGURATION ===========================
 PT_HPU_LAZY_MODE = 1
 PT_RECIPE_CACHE_PATH =
 PT_CACHE_FOLDER_DELETE = 0
 PT_HPU_RECIPE_CACHE_CONFIG =
 PT_HPU_MAX_COMPOUND_OP_SIZE =<PHONE_NUMBER>854775807
 PT_HPU_LAZY_ACC_PAR_MODE = 0
 PT_HPU_ENABLE_REFINE_DYNAMIC_SHAPES = 0
 PT_HPU_EAGER_PIPELINE_ENABLE = 1
 PT_HPU_EAGER_COLLECTIVE_PIPELINE_ENABLE = 1
---------------------------: System Configuration :---------------------------
Num CPU Cores : 96
CPU RAM       : 784291348 KB
------------------------------------------------------------------------------
/root/whisper/whisper/transcribe.py:132: UserWarning: FP16 is not supported on HPU; using FP32 instead
  warnings.warn("FP16 is not supported on HPU; using FP32 instead")
Detecting language using up to the first 30 seconds. Use `--language` to specify the language
Detected language: English
[00:00.800 --> 00:03.620]  Translated text...

Let me know if you have questions or run into issues during new setup!

Also in the command:
python3 -m whisper.transcribe audi_file.wav --model turbo --device hpu
I do not see the audi_file.wav?  Is this messing or some other steps required?

Hi @orionsBeltWest ,
We udated the Dockerfile.hpu (https://github.com/BlueLabelLabs/whisper_fork/pull/2/files#diff-8e8c9c8166fa53e2eb5a4fa3ea94131bf7e45e608f8161bbcee88c735fe171fb) following vllm (https://github.com/vllm-project/vllm/blob/main/Dockerfile.hpu) and also made an update in section Intel® Gaudi® hpu usage of README.md - https://github.com/BlueLabelLabs/whisper_fork/pull/2/files#diff-b335630551682c19a781afebcf4d07bf978fb1f8ac04c6bf87428ed5106870f5R147. Added an info about optional mapping using docker run command and also added an info about running the HPU tests inside the docker container - https://github.com/BlueLabelLabs/whisper_fork/pull/2/files#diff-b335630551682c19a781afebcf4d07bf978fb1f8ac04c6bf87428ed5106870f5R178

Hi @orionsBeltWest ,
We udated the Dockerfile.hpu (https://github.com/BlueLabelLabs/whisper_fork/pull/2/files#diff-8e8c9c8166fa53e2eb5a4fa3ea94131bf7e45e608f8161bbcee88c735fe171fb) following vllm (https://github.com/vllm-project/vllm/blob/main/Dockerfile.hpu) and also made an update in section Intel® Gaudi® hpu usage of README.md - https://github.com/BlueLabelLabs/whisper_fork/pull/2/files#diff-b335630551682c19a781afebcf4d07bf978fb1f8ac04c6bf87428ed5106870f5R147. Added an info about optional mapping using docker run command and also added an info about running the HPU tests inside the docker container - https://github.com/BlueLabelLabs/whisper_fork/pull/2/files#diff-b335630551682c19a781afebcf4d07bf978fb1f8ac04c6bf87428ed5106870f5R178

No I am not able to run this build due to RuntimeError: synStatus=26 [Generic failure] Device acquire failed.
I ran it both in baremetal and k8 same issue.
root@98ff692c3ac2:/workspace/whisper# python3 -m whisper.transcribe keynote.wav --model turbo --device hpu
/usr/lib/python3.10/runpy.py:126: RuntimeWarning: 'whisper.transcribe' found in sys.modules after import of package 'whisper', but prior to execution of 'whisper.transcribe'; this may result in unpredictable behaviour
warn(RuntimeWarning(msg))
/usr/lib/python3.10/inspect.py:288: FutureWarning: torch.distributed.reduce_op is deprecated, please use torch.distributed.ReduceOp instead
return isinstance(object, types.FunctionType)
Loading Habana modules from /usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/lib
/workspace/whisper/whisper/init.py:154: FutureWarning: You are using torch.load with weights_only=False (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for weights_only will be flipped to True. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via torch.serialization.add_safe_globals. We recommend you start setting weights_only=True for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
checkpoint = torch.load(fp, map_location="cpu")
Loading Habana modules from /usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/lib
/home/jenkins/workspace/cdsoftwarebuilder/create-binaries-from-sw-sources---bp-dt/repos/hcl/src/platform/gaudi_common/hcl_device_control_factory.cpp::84(initDevice): The condition [ g_ibv.init(deviceConfig) == hcclSuccess ] failed. ibv initialization failed
Traceback (most recent call last):
File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/usr/lib/python3.10/runpy.py", line 86, in _run_code
exec(code, run_globals)
File "/workspace/whisper/whisper/transcribe.py", line 625, in
cli()
File "/workspace/whisper/whisper/transcribe.py", line 597, in cli
model = load_model(model_name, device=device, download_root=model_dir)
File "/workspace/whisper/whisper/init.py", line 173, in load_model
model = wrap_in_hpu_graph(model)
File "/usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/hpu/graphs.py", line 720, in wrap_in_hpu_graph
stream = htorch.hpu.default_stream()
File "/usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/hpu/streams.py", line 284, in default_stream
streamdata = _hpu_C._hpu_getDefaultStream(_get_device_index(device, optional=True))
File "/usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/hpu/_utils.py", line 42, in _get_device_index
device_idx = hpu.current_device()
File "/usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/hpu/init.py", line 132, in current_device
init()
File "/usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/hpu/init.py", line 71, in init
_hpu_C.init()
RuntimeError: synStatus=26 [Generic failure] Device acquire failed.
Exception ignored in atexit callback: <built-in method cleanup of PyCapsule object at 0x7efb62607c30>
RuntimeError: Habana device not initialized
Exception ignored in atexit callback: <built-in method cleanup of PyCapsule object at 0x7efb62607c30>
RuntimeError: Habana device not initialized
root@98ff692c3ac2:/workspace/whisper# hl-smi
+-----------------------------------------------------------------------------+
| HL-SMI Version:                              hl-1.18.0-fw-<IP_ADDRESS>          |
| Driver Version:                                     1.18.0-ee698fb          |
|-------------------------------+----------------------+----------------------+
| AIP  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | AIP-Util  Compute M. |
|===============================+======================+======================|
|   0  HL-225              N/A  | 0000:0e:00.0     N/A |                   0  |
| N/A   28C   N/A    72W / 600W |    768MiB / 98304MiB |     0%           N/A |
|-------------------------------+----------------------+----------------------+
| Compute Processes:                                               AIP Memory |
|  AIP       PID   Type   Process name                             Usage      |
|=============================================================================|
|   0        N/A   N/A    N/A                                      N/A        |
+=============================================================================+
root@98ff692c3ac2:/workspace/whisper#


Send run task activation even whnen task open handler is used.

Title says it all.

@dbaeumer Steps to verify?

Not easy. You need to write an extension that contributes tasks and ensure that the extension is activated when typing F1 > Backspace > tasks<space> or you verify in code that in taskQuickOpen.ts I call extensionService.activateByEvent('onCommand:workbench.action.tasks.runTask');

@aeschli

@dbaeumer How do you contribute a task? :)

@dbaeumer I can't get this to work. My extension is like this:
export function activate(context) {
    console.log(`activated`);
    vscode.workspace.registerTaskProvider(<vscode.TaskProvider>{
        provideTasks(token) {
            console.log('tasks');

            return { tasks: [] };
        }
    })
}

and I have:
"activationEvents": [
    "onCommand:workbench.action.tasks.runTask"
],

When I type task  in quickopen as instructed, 'activated' is printed. But 'tasks' is never printed.

@roblourens task contribution is only available in the new terminal task runner. If you test it on a workspace you need to add a tasks.json with the following content to switch to the new runner: { "version": "2.0.0" }. This is to not break exist old output runner configuration since the terminal runner has more features.


in doc 0039 we have updated the markup

Please take a look at this new adaptation: https://github.com/Auden-Musulin-Papers/amp-data/blob/745905d962435f568f852a8d7b985a464a7f60d3/data/editions/amp-transcript__0039.xml#L317
let us know if it can be done something regarding the website presentation :)

please check the latest commit if thats how it should look

it works now :)


[5.0] [ABI] [Mangling] Disambiguate protocol-conformance-ref better

(full description forthcoming)
Cherry-pick of #21450. Reviewed by @rjmccall and @DougGregor.
rdar://problem/46735592

@swift-ci Please test

Build failed
Swift Test Linux Platform
Git Sha - a0a2ad43d9ec3ad8a2a4149176e9ba9b730f6d9f

Build failed
Swift Test OS X Platform
Git Sha - a0a2ad43d9ec3ad8a2a4149176e9ba9b730f6d9f

I don't see an "ABI-breaking" label but I'll be sure to mention that.
…Hm, what's up with the bots?

@swift-ci Please test


feat: generic params for checkpoints

Experimental generic parameters for checkpoints with sizes similar to solidity implementation but without code duplication.
Resolves #185
PR Checklist

[ ] Tests
[ ] Documentation

@qalisander is it ready for review or not yet?

It is ready for review but changes are highly debatable. That is why they're in draft


Feature Request: Migrate container images

Community Note

Please vote on this issue by adding a 👍 reaction to the original issue to help the community and maintainers prioritize this request
Please do not leave "+1" or "me too" comments, they generate extra noise for issue followers and do not help prioritize the request
If you are interested in working on this issue or have submitted a pull request, please leave a comment

Description
Is your feature request related to a problem?

The self-hosted agents use container images hosted in the jaredfholgate reoo and docker hub.
Describe the solution you'd like

Migrate these images to a Microsoft repo and container registry.

Support for self hosted container registry could be a future feature, but will most likely be built into the AVM module for agents.
Additional context

This is partially complete, the images have been moved to a new repo and docker registry.


Prepare SLE micro properly

What does this PR change?
Most of SLE Micro setup was disabled because of some problems with package updates. This resulted for example in no hostname for SLE Micro 5.1 and 5.2. We can't disable all initialization at once.
The idea with SLE Micro is to prepare all packages in the image and having no package to update in sumaform - merely to check everything is there. However, pkg.install does not do the job because it is impossible to acquire a rpm lock  on a read-only filesystem.
Fixes https://github.com/SUSE/spacewalk/issues/21792

LGTM, and you already mentioned you tested the code.

i still need to check the bug with avahi-lang you reported does not reappear :-) .


Builds of the same PR on the same Jenkins slave interfere with each other

If two builds of the same PR run on the same Jenkins slave at the same time, the cleaning up of one build seems to interfere with another (in terms of destroying the VMs).
Example: https://jenkins.cilium.io/job/cilium/job/cilium/job/PR-1674/5/consoleFull terminated prematurely as https://jenkins.cilium.io/job/cilium/job/cilium/job/PR-1674/4/consoleFull finished.
RCA this and figure out why the workspaces are interfering with one another.

Closing this because the build links now 404.


Formula "normalization"

Currently, fnormalize! is not always capable of collapsing two or more formulas into one unique representation.
E.g "(b∧a)∨(d∧c)" and "(d∧c)∨(a∧b)" are rearranged into, respectively, "(a∧b)∨(c∧d)" and "(c∧d)∨(a∧b)"; instead, both should collapse to the former string representation.

Is this done?

Yep, but now it is renamed to normalize.
I already added the test proposed in the initial comment, this can be closed.


Add ESLint

Here's what's included:

ESLint linter

Configuration extends prettier & typescript configuration
Also includes sonarjs and a regex optimization plugin

A GitHub action for ESLint

My only major concern is that the github action has a cron job in it. It'll currently run at 16:43 every Saturday, but I could also just remove it.

The ESLint workflow is just based on what GitHub suggested. It included a step to specifically install ESLint and the formatter for github actions, but I felt like that was weird so I just included them as dev dependencies (well the formatter specifically, I already included ESLint as one)


[Bug] Linux Ubuntu There are two title bars by default in the Linux system！

Check the following items

[ ] I have looked up relevant Issue

Description of the issue

Package Version
6.0.0-rc
Environment
Linux Ubuntu
Expected Behavior
Fix this bug.
Reproduction
Using the default configuration, under Linux, the title bar of Avalonia is not automatically hidden, but appears above the title bar of Sukiui.
Additional Information
No response

SystemDecorations="BorderOnly"


Ease engines requirement

First of all: thank you for your work!
This PR ever so slightly eases the latest VS Code version requirement as the code does not use any of the newer APIs the change is supposed to be fully backward compatible.
The reason I am creating this PR is due to some companies not allowing to update to the most recent VS Code version thus the VS Code marketplace for plugins does not allow to install the most recent version of this plugin and these users miss out on months of new functionality. I hope it is okay to make this change as it does not seem to cause any regression in functionality or add an additional maintenance burden.
The proposed minimum required VS Code version 1.89 is fairly recent April 2024.
I have ran all precommit and npx tsc which exits with a 0 exit code. Thus I think this is a safe change.
Thank you

Reverting this will require also reverting https://github.com/vscode-neovim/vscode-neovim/pull/2087
How quickly does your internal corporate cycle allow for updates? I am hesitant to speak on behalf of the project with a firm yes or no, but some data would help inform the decision.

I am not aware of consistent update cycle so I might be wrong. I presume we are usually 2 to 10 versions behind the most recent once.

What happens if you install with VSIX? I am hesitant to stop tracking the latest vscode version on behalf of a tiny minority of users.

What happens if you install with VSIX? I am hesitant to stop tracking the latest vscode version on behalf of a tiny minority of users.

Fair point. Without going into details, unfortunately this is not allowed.

This might be quite an ask. As I mentioned the PR description as I do not want to add to the maintenance burden and my goal is not to for this project maintain a number of VS Code versions but instead to have one version that includes the vscode/runtime/*.lua files. Would it be possible to make a single plugin release with support for 1.89.x?
From my understanding the current master branch does not use any of a newer VS Code API and from the technical standpoint it should be possible. I understand that this can be a big ask and can take no for an answer.

Building and running seems to work fine for me, not sure about the potential node conflict here.

@theol0403 Sorry for the delay in getting back to this. If we ease the version requirement to 1.89, while the extension itself will not have any problems necessarily, the TSConfig stating we support ES2023 will be incorrect, as we will be rolling back from Node 20 (introduced in 1.90) to Node 18. There are a couple of Array functions for ES2023 that are not implemented in Node 18 https://node.green/#ES2023
As I see it, we have three options (listed in order of preference)

Reject this PR, keeping things as they are today.
Accept this PR, but add a polyfill for the missing Array functions (e.g. ones from https://github.com/es-shims). They're generally quite small.
Accept this PR as-is, but revert https://github.com/vscode-neovim/vscode-neovim/pull/2087

I have a great deal of sympathy for the reporter's issue, as I've been on the other end of needlessly strict corporate requirements that break my workflow.  On the other hand, I do think that our current policy of tracking VSCode updates as soon as we can is a good one, and helps prevent some bitrot.

Two weeks have passed. Does this PR still matter?

Two weeks have passed. Does this PR still matter?

Thanks for checking. Yes, this PR still matters.
Thank you for a detailed comment @ollien . One thing I can add is while the reason for this issue is still a niche of users of this extension. We have a few hundred potential users amongst my colleagues. While I cannot share an exact number I hope this shows a better ball park of the users who would appreciate a chance to use all the features as this group is still being stuck with 0.9.1 which is 50 versions behind the most recent one.
If you the maintainers are happy with either version 2 or 3, I am happy to send a follow up PR to make sure a missing Array method does not break in the runtime.

I think I would be ok with #2, but I'll let @theol0403 make the final call. The question I have is how long we'd support 1.89 for. We can't stay there forever, but I don't think we've made use of any new features.

The goal of this PR was to have a chance for a single release with the all recent features. If it sticks a longer, that would be a bonus but I definitely do not want to add any maintenance burden.

I'm happy to merge this + make a one-time release once a polyfill is added in a PR.

I've updated the PR to include an Array.prototype.findLast polyfill and tested the extension with VS Code 1.89
This PR is good to get merged


Splitting up the code into classes

Hi, I've been working on creating a PlatformIO version of the ESP firmware, would you be interested in including that in your repo?

This is all developed in PlatformIO and the project should be directly portable with no code changes, just drag the .ino into ./src, pull in your libraries, and it will build.

Thanks for quick response.
I had some problems with function declarations but that might be an error on my part renaming it to .cpp
In any case, what I had in mind (and failed to properly explain) is splitting the code in classes as it might simplify it a bit and make the main file more readable. I will probably do it in my fork anyway, and see how it turns out :)

Sounds good.  I'm keeping the codebase Arduino IDE-native because I feel like that's the lowest-friction option for new users, but personally I can't stand working in that environment :D
Splitting up the code into classes is something that I should have done from the get-go, and we are now where we are due to years of accumulated features and me being a lousy programmer.  If you want to take a swing at this, please do!

Hello.
I have forked your project to add some features I want (mostly more pages despite Nextion's limitations) and along the way I am going to try to break the .ino apart into C++ classes and use PlatformIO too because I like the environment better.
Since it is not fair to keep changes to myself, I have tried to make my code less ugly than usual and started uploading it to GitHub.
So far I have a separate class for all the Nextion accesses, and a separate class for all the debugPrintln. I still want to separate MQTT, Webserver and Telnet into more classes.
Since I am walking into your project and tearing it all to pieces on my fork, I thought at the least I should say Hello. You are welcome to have a look at my fork and let me know how you feel. If I have "the right end of the stick", if there are small things I can do differently that would make the class-separating-work more useful to you; if there is anything I can usefully do to make the HASP project better.
Thanks!,

Closing as this repo is not being maintained.  Check here for the updated project!


fix: disable mutlischema mat-select when form is disabled

Issue
https://github.com/gravitee-io/issues/issues/XXXXX
Description
Disable mat-select in json-schema-form component
Additional context

🧪  Gravitee.io Automatic Prerelease @gravitee/ui-particles-angular
npm install<EMAIL_ADDRESS>
yarn add<EMAIL_ADDRESS>

🧪  Gravitee.io Automatic Prerelease @gravitee/ui-policy-studio-angular
npm install<EMAIL_ADDRESS>
yarn add<EMAIL_ADDRESS>

📚  View the storybook of this branch here

:tada: This PR is included in version 7.28.7 :tada:
The release is available on:

npm package (@latest dist-tag)
npm package (@latest dist-tag)
npm package (@latest dist-tag)
GitHub release

Your semantic-release bot :package::rocket:


EnumRequest - Table 80 Gen. Journal Template - Field - Type

EnumRequest - Table 80 Gen. Journal Template - Field - Type

Already done

Thanks for reporting this. We agree, and we’ll publish a fix asap, either in an update for the current version or in the next major release. We will update this issue with information about availability.


[TILT-3] Add instrumentation for Tilt::Template#prepare

This must be completed after TILT-1
Resources

Tilt::Template#prepare code
Playground application with Tilt (internal)

Description

This should become a trace/span within the transaction that calls #prepare
Multiverse tests should be added for the new instrumentation
Consider whether any performance tests could be added for this instrumentation

Potential Gotchas

Similar data flows through ActionView, we need to be careful about duplication if tilt is loaded within a Rails context
We need to be careful about instrumenting versions below 0.8. Before this version, the #prepare method was called #compile.

Acceptance Criteria

[ ] When an error is raised by Tilt, it is recorded by the agent as a TransactionError
[ ] When an error is raised by New Relic code, it does not escape the agent and stop the user's application from executing
[ ] When a Tilt template calls #prepare, it is saved as a trace/span
[ ] When a Tilt template calls #prepare, it does not create an overwhelming amount of noise within the UI

PR Content

[ ] Have you attached a screenshot where the data is reporting in the UI?
[ ] Do you have a blurb in the PR about why the instrumented method(s) were chosen? Consider how this instrumentation might help our customers solve problems.
[ ] Does this instrumentation create any new metrics? What are they?
[ ] Does this instrumentation create any new events? What are they?

Tilt::Template#prepare is a protected method, which prevents us from instrumenting it. The #initialize method is not protected and calls #prepare. My suggestion is to pivot to instrumenting #initialize instead, since the common pattern to render a template using Tilt is:
Tilt::HamlTemplate.new("test.haml").render

After comparing the data collected by #initialize and #render, @tannalynn  and I decided that we do not need to instrument both methods for this library. The data was very similar, and #render is the method that is part of the direct request/response cycle. Instrumenting #render only also puts the Tilt instrumentation on more of a level field with the ActionView instrumentation. This seems beneficial for customers since both libraries serve similar purposes.
The PR initially for this Issue has been converted into instrumentation for #render


Incompatible with the Selene VSCode extension

This extension uses a special language id (luau) in the package.json contributions instead of the normal "lua" language id that other extensions use, such as the VSCode extension for Selene. This make the extension incompatible with Selene for linting.

Will look into changing the language id this week. My original intent with this was to allow developers to easily switch between Lua or Luau without having to disable the extension everytime.

The incompatibilities have been fixed in version 1.0.2 @filiptibell


Is there any documentation on concurrency scenarios this library handles

Hi,
Thanks for a great product, is there any part of the documentation that deals with the concurrency aspects of the cache, like thread safety, atomicity .. both in-memory and distributed. If there is no such doc, could you please just brief here. I need to know what kind of operations I need to consider myself that the library does not provide out of box

The library is thread safe in general. Of course, this doesn't handle concurrent modifications.
If you care about concurrency with a distributed cache, you can make use of the Update method which is documented with some examples here: http://cachemanager.michaco.net/documentation/CacheManagerUpdateOperations


Upgrade graphQL v11

Closes https://github.com/TriPSs/nestjs-query/issues/109
Closes https://github.com/TriPSs/nestjs-query/pull/111
In the first commit, I followed the steps in the migration guide and regenerated the snapshots (see the extra !).
In the second commit, I just removed so unused/deprecated packages.

For the e2e tests, the http status changed from 400 to 500. I believe the error comes from this file examples/complexity/src/complexity.plugin.ts. If we wanted to keep 400, I think we could throw a BadRequestException instead.

Codecov Report

Merging #127 (727f548) into master (ddbdc6e) will not change coverage.
The diff coverage is 100.00%.

@@           Coverage Diff           @@
##           master     #127   +/-   ##
=======================================
  Coverage   77.86%   77.86%
=======================================
  Files         597      597
  Lines        8483     8483
  Branches      833      833
=======================================
  Hits         6605     6605
  Misses       1148     1148
  Partials      730      730

Impacted Files
Coverage Δ

examples/complexity/src/complexity.plugin.ts
93.75% <ø> (ø)

...query/field-comparison/field-comparison.factory.ts
70.78% <ø> (ø)

...aphql/src/decorators/filterable-field.decorator.ts
71.42% <100.00%> (ø)

...query-graphql/src/decorators/id-field.decorator.ts
86.66% <100.00%> (ø)

:mega: We’re building smart automated test selection to slash your CI/CD build times. Learn more


[Event Request] codeunit 5988 "Serv-Documents Mgt."

Could you please add a publisher in the codeunit 5988 "Serv-Documents Mgt." function PostDocumentLines
        LineCount := 0;

        // init cu for posting SLE type Usage
        ServPostingJnlsMgt.InitServiceRegister(NextServLedgerEntryNo, NextWarrantyLedgerEntryNo);
        if not ApplicationAreaMgmt.IsSalesTaxEnabled then begin
            ServLine.CalcVATAmountLines(1, ServHeader, ServLine, TempVATAmountLine, Ship);
            ServLine.CalcVATAmountLines(2, ServHeader, ServLine, TempVATAmountLineForSLE, Ship);
        end;

        ServLine.Reset;

        // New Event
        OnBeforeSortLines(ServLine);

        SortLines(ServLine);
        ServLedgEntryNo := FindFirstServLedgEntry(ServLine);

  [IntegrationEvent(false, false)]
    local procedure OnBeforeSortLines(var ServiceLine: Record "Service Line");
    begin
    end;

Thanks for reporting this. We agree, and we’ll publish a fix asap, either in an update for the current version or in the next major release. We will update this issue with information about availability.

Thanks for reporting this. We agree, and we’ll publish a fix asap, either in an update for the current version or in the next major release. We will update this issue with information about availability.


Merge main into branch

Getting up to par with main. Verified myself as it is our branch.
Need to accept this merge to debug the failed tests.


adjust next.js example to be more aligned  with apollo example from the Readme

Here is the Pull-Request https://github.com/zeit/next.js/pull/7651 to Canary release.

@kitten im glad to help


Stack overflow in equality, comparison and hashing

Equality, comparison and hashing stack overflow on deep data structures including both built-in structures like list and arrays as well as custom data structures.
Repro steps
Applying the built-in hash function to a long F# list causes a stack overflow:
hash [1..1000000] // Process is terminated due to StackOverflowException.

Assuming a function that nests n applications of f around x:
let rec nest n f x =
  if n=0 then x else
    nest (n-1) f (f x)

If we define a simple linked list type of our own:
type A = One of A | Nil

And create a one-million element list:
let x = nest 1000000 One Nil

Then equality and comparison work:
x=x // OK
compare x x // OK
nest 1000000 One Nil = nest 1000000 One Nil // OK
compare (nest 1000000 One Nil) (nest 1000000 One Nil) // OK

But hashing stack overflows:
hash x // Process is terminated due to StackOverflowException.

I find this behaviour surprising in several ways. Firstly, comparison is taking a long time (15ms) to determine a result that is trivial because I am giving it physically identical references, i.e. I'd expect it to return immediately in the first case from something like if x==y then 0 else. Secondly, equality and comparison work but hashing fails and it is not clear to me why. Thirdly, hash functions usually stop after examining ~100 data because they quickly reach the point of diminishing returns so I'd expect hash to do a shallow breadth-first search.
If we use a slightly different collection composed of two mutually-recursive types then we get stack overflows from equality, comparison and hashing:
type A = A of AList
and AList = One of A * AList | Nil

let x = nest 1000000 (fun x -> A(One(x, Nil))) (A Nil)

x = x // Process is terminated due to StackOverflowException.

compare x x // Process is terminated due to StackOverflowException.

hash x // Process is terminated due to StackOverflowException.

Again, I'd expect comparison to return zero immediately when given two references to the same data structure. In general, the NaN value of floating point numbers can mess up equality on non-identical data but in this case the compiler knows that there are no such values in the data structure and, therefore, could potentially add this optimisation safely for equality too. And I'm surprised hash goes so deep.
Expected behavior
I'd expect equality, comparison and hashing to never stack overflow when applied to values of F# types (numbers, strings, tuples, records, unions, lists, arrays, Set and Map).
Known workarounds
When I hit this problem with a stack overflow in equality this week it took me a long time to diagnose because Visual Studio cannot debug stack overflows. Once I had identified the problem it took me a long time to write a custom equality function over pair of mutually recursive types (one union and one record referring to each other via F# lists).
Related information

Windows 7
Visual Studio 2015
F# Interactive version 14.0.23413.0 in 64-bit
.NET 4.6

When I hit this problem with a stack overflow in equality this week it took me a long time to diagnose because Visual Studio cannot debug stack overflows.

use
devenv /debugexe foo.exe args

from an admin command prompt and tick "CLR exceptions" in the "Debug --> Exceptions" dialog.  This will stop at the SO and give you a stack trace

Once I had identified the problem it took me a long time to write a custom equality function over pair of mutually recursive types (one union and one record referring to each other via F# lists).

Yes, for such data structures I think its best to take control of your destiny and write the recursive functions as you want them.  F# automatic hash/equality/comparison is what it is, for better or worse. I can see us removing the SO for lists but there are a lot of tradeoffs  in the other behaviors (much discussed over the years in the context of SML and OCaml), and some performance gotchas too - for very large data structures it's better just to put yourself in charge.

Closing this as a duplicate of https://github.com/Microsoft/visualfsharp/issues/1838


Update wishlist.php

Fixed price JSON data by subbing in correct classes.

The price is updated now


Repo sync for protected CLA branch

The pull request is created from master637738075522478115 to master to fix git push error for protected CLA branch

@opbld16 : Thanks for your contribution! The author(s) have been notified to review your proposed change.


Support Lazy loading of Offline Segments

https://apache-pinot.slack.com/archives/CDRCA57FC/p1603720037246100
Some discussion already here.
This would involve modifying the pinot server to include a lazy mode that would set it to lazily pull segments as they are requested using an LRU cache. It should just take some modification to the SegmentDataManager and maybe the table manager.
This would allow using s3 as the primary storage, with pinot as the query/caching layer for long term historical tiers of data. Similar to the tiering example, you’d have a third set of lazy servers for reading data older than 2 weeks. This is explicitly to avoid large EBS volume costs for very large data sets.
My main concern is this — a moderately sized dataset for us is 130GB a day. We have some that can be in the terra range per day. Using 500MB segments, you’re looking at ~260 segments a day. Maybe ~80k segments a year. In this case, broker pruning is very important because any segment query sent to the lazy server means materializing data from s3. This data is mainly time series, which means segments would be in time-bound chunks. Does Pinot broker prune segments by time? How is the broker managing segments? Does it just have an in-memory list of all segments for all tables? If so, metadata pruning will become a bottleneck for us on most queries. I’d like to see query time scale logarithmically with the size of the data.

Thanks @noahprince22 for doing the analysis and creating this issue. This is an amazing feature to have.
Adding broker pruning by time should be easy. Yes, routing table should be enhanced to return the list of segments for a time efficiently.
The biggest piece of work will be around tablemanager and segmentmanager. Let's create separate issues for time based pruning and datatypes and link it to his issue.

https://github.com/apache/incubator-pinot/issues/6189
Created an issue for the segment pruning.

As a potential future optimization, after we lazy load entire segments, we should look into separately caching the metadata and the columns.psf. creation.meta, index.map, metadata.properties are all small and could either be eagerly loaded or have much looser LRU requirements.

https://eng.uber.com/operating-apache-pinot/
Reading this blog:

As the scale of data grew, we also experienced several issues caused by too many segments. Pinot leverages Apache Helix over Apache Zookeeper for cluster management. For example, when a server transitioned from offline to online, Pinot will propagate state transition messages via Helix to notify other instances. The number of such state transition messages are proportional to the number of the segments on the server. When a server hosts too many segments, there could be a spike of state transition messages on Helix, resulting in lots of zookeeper nodes. If the number of zookeeper nodes is beyond the buffer threshold, the Pinot server and controller will crash. To solve this issue, we added message throttling to Pinot controllers to flatten the state transition surge.

At large scale of data that requires this kind of lazy loading, you're going to have a lot of segments. Do we see this causing an issue with helix state management messages?

https://eng.uber.com/operating-apache-pinot/
Reading this blog:

As the scale of data grew, we also experienced several issues caused by too many segments. Pinot leverages Apache Helix over Apache Zookeeper for cluster management. For example, when a server transitioned from offline to online, Pinot will propagate state transition messages via Helix to notify other instances. The number of such state transition messages are proportional to the number of the segments on the server. When a server hosts too many segments, there could be a spike of state transition messages on Helix, resulting in lots of zookeeper nodes. If the number of zookeeper nodes is beyond the buffer threshold, the Pinot server and controller will crash. To solve this issue, we added message throttling to Pinot controllers to flatten the state transition surge.

At large scale of data that requires this kind of lazy loading, you're going to have a lot of segments. Do we see this causing an issue with helix state management messages?

If you have millions of segments per table, this can be a problem. We already faced this issue at LinkedIn, and took a few actions:

Increased the network speed in zookeeper.
Moved to allocate a limited number of instances for any table (i.e. cluster the segments of a table within as few hosts as possible).
We tried enabling helix batching with mixed results. It seemed to help, but other helix bugs took over and prevented us from moving further on that. Some of those bugs may have been fixed, so we may be able to enable batching again.

I believe we need to also explore segments in cold storage that DO NOT make it into the idealstate. I don't have any specific ideas along these lines, though :)


library's pub types interoperability

guideline

https://rust-lang.github.io/api-guidelines/interoperability.html
https://doc.rust-lang.org/reference/attributes/type_system.html

has been done together when updating docs and test


What is the OB v2 signature placeholder?

What is the placeholder to store the JWS signature value, per OB v2 specification? Is it always separately stored from the certificate JSON? Can you clarify please.

Would you describe what you mean by "placeholder"?

@amiller-ims, sure. Let me use an example. The spec prescribes that a Profile has a property verification, that in turn includes a property type. The type captures one of "hosted", "signed" values. Likewise, where should the signature of the certificate be stored? In simple terms, which class and which attribute is the placeholder for the signature? I find the Open Badges Vocabulary mentioning the following classes and am not sure which one could hold a signature value?
Assertion, BadgeClass, Profile (Issuer), IdentityObject, VerificationObject, Evidence, Image, Criteria, AlignmentObject, RevocationList, CryptographicKey
To give you some context, I'd like to create a certificate that has both the plain certificate data as well as its signature. JWS may not be the only algorithm, I'd like to support.

I think I understand. And I'm pretty sure that you are using "certificate" as is a synonym for "badge", which is also a synonym for the Open Badges Assertion class.
If I got that right, then the answer is simple: it is always stored separately.
The Assertion class is the top level JSON object for a badge. When you issue a badge, you can bake the assertion JSON or the JWS string into an image. For example,
var chunk = new iTXt({
  keyword: 'openbadges',
  compression: 0,
  compressionMethod: 0,
  languageTag: '',
  translatedKeyword: '',
  text: signature || JSON.stringify(assertion)
})

Please see the Baking Specification for more examples. And note that the specification says:

There MUST be only one openbadges:assertion tag in an SVG.

If you issue a badge image with both the plain text JSON and the JWS string, it is unlikely that a displayer or host would know what to do with it.

Thank you @amiller-ims. Yeah, I realized I used "certificate" instead of "assertion". Special thanks for reading my thoughts on that terminology. Closing this.


Enable per environment

Hi there,
Very nice extension, I have been using it for quite a long time!
I was wondering if it would be possible to add some granularity to enabling the extension. Specifically, I would like to keep the extension active in the editor view, but disable it in the terminal view as it is mostly just annoying there.

Thank you for your feedback and for using the extension for such a long time!
I apologize for the inconvenience caused. I will work on resolving this issue as soon as possible.

No need to apologise, I don't pay you after all :P thank you for your work!

I have addressed this issue and added a new option excludedSchemes. You can now customize which URIs you want to exclude!

Thanks!


[ko] "Installing kubeadm" page references deprecated legacy repositories

This is a Bug Report

Problem:

when i firstly install kubeadm by docs, the apt repository expired(https://packages.cloud.google.com/apt/doc/apt-key.gpg)

and i found this issue https://github.com/kubernetes/release/issues/2862
summarize upon issue,

in recently, the google hosting repository support ends see this
the community-owned repository introduced in here

but documentation do not updated apt repositories into community-owned
Proposed Solution:

the kubeadm guide should be updated

Page to Update:
https://kubernetes.io/ko/docs/setup/production-environment/tools/kubeadm/install-kubeadm/
Optional Information
used ubuntu-server 22.04

i think this maybe 'korean language specific' right?

i think this maybe 'korean language page specific' right?

Certainly! The mentioned page is in Korean language.
/language ko

I see the Korean version of the page is not up to date compared to the English version, and this could be the reason why the page still contains references to outdated legacy package repositories.

/retitle [ko] "Installing kubeadm" page references deprecated legacy repositories

/priority important-longterm
/triage accepted

maybe, i can do this.
/assign

[ ]  1. fix pagination bug in https://kubernetes.io/ko/docs/setup/learning-environment/
[ ]  2. fix instllation guide for kubeadm https://kubernetes.io/ko/docs/setup/production-environment/tools/kubeadm/install-kubeadm/
[ ] 3. update https://kubernetes.io/ko/docs/setup/production-environment/tools/kubeadm/


uint64_t conflict in cygwin

While compiling on cygwin in AppVeyor (I use the latest and greatest cygwin), I ran across this issue this morning:
/home/appveyor/bfprefix/include/catch/catch.hpp:2380:28: error: conflicting declaration ‘typedef long long unsigned int uint64_t’
 typedef unsigned long long uint64_t;
                            ^
In file included from /usr/include/stdint.h:14:0,
                 from /usr/lib/gcc/x86_64-pc-cygwin/5.4.0/include/stdint.h:9,
                 from /usr/lib/gcc/x86_64-pc-cygwin/5.4.0/include/c++/cstdint:41,
                 from /usr/lib/gcc/x86_64-pc-cygwin/5.4.0/include/c++/bits/char_traits.h:420,
                 from /usr/lib/gcc/x86_64-pc-cygwin/5.4.0/include/c++/ios:40,
                 from /usr/lib/gcc/x86_64-pc-cygwin/5.4.0/include/c++/istream:38,
                 from /usr/lib/gcc/x86_64-pc-cygwin/5.4.0/include/c++/sstream:38,
                 from /home/appveyor/bfprefix/include/catch/catch.hpp:377,
                 from /cygdrive/c/projects/bfsdk/tests/test_errorcodes.cpp:23:
/usr/include/sys/_stdint.h:60:20: note: previous declaration as ‘typedef __uint64_t uint64_t’
 typedef __uint64_t uint64_t ;
                    ^

It appears that under cygwin, uint64_t does not need to be defined.

Fixing this might be problematic, because I don't think we have had this problem before, so it might still be needed for older cygwin versions and I would really rather not go checking various cygwin versions for compatibility.

Why is Catch defining a type in the global namespace, anyway? Should define / alias uint64_t in its own namespace.

I agree... you should not overwrite an existing type. Probably the best solution here is to create your own type or place this into a private namespace as suggested by @lightmare

stdint.h is correct as of at least Visual Studio 2015 update 3 and should be used.
It doesn't need it anyway; on Windows it should just use unsigned long long instead of uint64_t  in getCurrentTicks(). (Catch uses long long elsewhere, but guards them with CATCH_CONFIG_CPP11_LONG_LONG, but not here.)

I've changed it so that (a) it doesn't define uint64_t itself - but rather introduces a new typedef (in the Catch namespace) and (b) only uses long long for _MSC_VER, rather than CATCH_WINDOWS - so should avoid the Cygwin issue.
It's not a complete solution as it doesn't account for potential older non-MS compilers that don't support uint64_t - but that's no worse than it was.
It also still doesn't respect CATCH_CONFIG_CPP11_LONG_LONG (which seems misnamed).
But I think this change should be enough to address this issue?

Since there has been no followup, I am going to assume that this is fixed.


access device name in snapfile?

I'm using the suggested script to copy files to the device for testing - however, I'd like to copy different files based on whether it is an iPhone or iPad
the device value passed into setup_for_device_change seems to be some form of simulator id (like AB1EAC90-CD32-4C9E-B7E8-A8358E86AB24 )
is there a way to get the device name from the devices array?
e.g. 'iPhone 6'
thanks.

It's probably easier to copy files for all devices and do the logic within your app. In your app's code, you copy over the files you need for the current device type. Does that make sense?

actually - I just spotted that the setup_for_device_change in the readme has the wrong params:
in the example in the readme, it uses:
setup_for_device_change do |lang, device|
where the correct input is
setup_for_device_change do |device, udid, language|
once this is fixed, it is really easy:
setup_for_device_change do |device, udid, language|
# This will make sure, all example documents are installed on the simulator
deviceType=device.split[0]

device_example_files=example_files+"/"+deviceType

puts "Copying example files to .app from : "+device_example_files

so, I'm sorted, but the readme could use a fix!

thanks.

Thanks, I updated the README.


update findup-sync & glob to remove deprecation warnings

findup-sync: v0.2 -> v0.4 (no breaking changes)
glob: v5 -> v7 (small breaking change, described below)

This gets rid of the DDoS deprecation warning on install of Polymer CLI & WCT by updating two outdated uses of glob v5.
The only change to behavior is that !xxx glob patterns in loadSuites() now won't do anything, instead of incorrectly matching all files. Since this behavior was already broken, this shouldn't constitute a breaking change for WCT.

Nice, glad to see these warnings going away


Error: Cannot find module 'svg-to-vue-component/nuxt'

Hi :wave: ,
I'm trying to deploy to Now 2 with @nuxtjs/now-builder and get this error:
λ Error while initializing nuxt: { /var/task/node_modules/@nuxt/core/dist/core.js:1
Error: Cannot find module 'svg-to-vue-component/nuxt'
Require stack:
-<EMAIL_ADDRESS>- /var/task/now__launcher.js
- /var/runtime/node_modules/awslambda/index.js
    at Resolver.requireModule (/var/task/node_modules/@nuxt/core/dist/core.js:627:31)
    at ModuleContainer.addModule (/var/task/node_modules/@nuxt/core/dist/core.js:163:36)
    at promise.then (/var/task/node_modules/@nuxt/utils/dist/utils.js:1796:43)
    at <anonymous>
    at process._tickDomainCallback (internal/process/next_tick.js:228:7)
  code: 'MODULE_NOT_FOUND',

now.json is basically copy-pasted from here.
svg-to-vue component is cofigured as suggested.
No issues locally or deploying to netlify with nuxt generate.
I assume this could be an issue with the 'nested' module naming?  🤔
Or is it actually the fault of svg-to-vue-component?

Hi. I think you can simply put it in devModules (instead of modules) in config

Hi @pi0
Whoa, that was a quick solution, thanks!
nuxt.config.js
  devModules: [
    'svg-to-vue-component/nuxt'
  ],

I can't find any documentation about devModules, but I guess it will follow soon.
Keep up the awesomeness team nuxt! 🤘


Better Today Extension

Closes #53
Created Today Extension with simplified LiteEatery and LiteEvent models to minimize cost of the extension. I had to reset to master and reimplement my work from the mark/today-widget branch because after rebasing with master on that branch, the branch got corrupted.
There's an issue with app groups that prevented me from fully implementing favorites (due to problems sharing favorites data from the main app to the extension via NSUserDefaults). However, the functionality for it is commented out in my changes on this branch so it can be easily integrated once this is fixed.
Currently, instead of listing favorites, the table view adds as many eateries (in order of fetching) and their hours descriptions until it reaches the max height for a table view in Notification Center.

This will have to wait until the DiningStack repo accepts the PR I have in to change the APIKey enum to be public. Until then this will just not compile.


Could there be a memory leak in the conformer_lstm model?

❓ Questions & Help
I am training using the random sampler and using ddp_sharded strategy, however after some training steps I get a CUDA out-of-memory error.
Details
I am training on a SLURM-managed cluster using 2 nodes with 2 Tesla M60 (8GB) GPUs each. As I understand if the model doesnt fit in a single GPU, pytorch-lightning would automatically scatter it across different ones.
As expected, the larger I make the batch size the sooner the error occurs. What I find strange is that it takes a few iterations to crash, if the model and the batch didn't fit wouldn't it crash directly?
Here I attach a picture of the memory usage:

And these are the parameters I'm using:
audio:
  name: fbank
  sample_rate: 16000
  frame_length: 20.0
  frame_shift: 10.0
  del_silence: false
  num_mels: 80
  apply_spec_augment: true
  apply_noise_augment: false
  apply_time_stretch_augment: false
  apply_joining_augment: false
augment:
  apply_spec_augment: false
  apply_noise_augment: false
  apply_joining_augment: false
  apply_time_stretch_augment: false
  freq_mask_para: 27
  freq_mask_num: 2
  time_mask_num: 4
  noise_dataset_dir: None
  noise_level: 0.7
  time_stretch_min_rate: 0.7
  time_stretch_max_rate: 1.4
dataset:
  dataset: librispeech
  dataset_path: /home/ubuntu/data/librispeech
  dataset_download: false
  manifest_file_path: /home/ubuntu/data/librispeech/libri_subword_manifest.txt
criterion:
  criterion_name: cross_entropy
  reduction: mean
lr_scheduler:
  lr: 0.0001
  scheduler_name: warmup_reduce_lr_on_plateau
  lr_patience: 1
  lr_factor: 0.3
  peak_lr: 0.0001
  init_lr: 1.0e-10
  warmup_steps: 4000
model:
  model_name: conformer_lstm
  encoder_dim: 256
  num_encoder_layers: 6
  num_attention_heads: 4
  feed_forward_expansion_factor: 4
  conv_expansion_factor: 2
  input_dropout_p: 0.1
  feed_forward_dropout_p: 0.1
  attention_dropout_p: 0.1
  conv_dropout_p: 0.1
  conv_kernel_size: 31
    half_step_residual: true
  num_decoder_layers: 2
  decoder_dropout_p: 0.1
  max_length: 128
  teacher_forcing_ratio: 1.0
  rnn_type: lstm
  decoder_attn_mechanism: loc
  optimizer: adam
trainer:
  seed: 1
  accelerator: ddp_sharded  # This I have hardcoded the necessary parts, I basically say use 2 nodes with 2 gpus each
  accumulate_grad_batches: 1
  num_workers: 4
  batch_size: 16
  check_val_every_n_epoch: 1
  gradient_clip_val: 5.0
  logger: wandb
  max_epochs: 20
  save_checkpoint_n_steps: 10000
  auto_scale_batch_size: binsearch
  sampler: random
  name: gpu
  device: gpu
  use_cuda: true
  auto_select_gpus: true
tokenizer:
  sos_token: <s>
  eos_token: </s>
  pad_token: <pad>
  blank_token: <blank>
  encoding: utf-8

Thank you a lot guys!

When the audio input length is long, the memory seems to explode.

@sooftware so is it designed in such way that the input length increases within an epoch?

That's not true. Perhaps every time a memory is held in a GPU, the memory held in a cache increases and seems to be increasing. Then the memory explodes. (I think)

Any ideas on how to solve it?

same problem encountered? any ideas to slove?

Hi,
Not sure if it's really a memory leak, as the audio batches can have different length during training.
In my case I had the GPU mem % increasing then decreasing during the training.
Have you tried to decrease the batch size in order to have some room left for longer sequence batches ?

I think @virgile-blg is right, although it is a bit weird that it keeps increasing over the first epochs and then it becomes more stable

On option is to disable Pytorch Lightning's auto_scale_batch_size. When set to False there is not OOM error during the 1st epoch.
I guess that it is scaling the batch size not using the biggest sequence in the training set.


add ExpressionMap for core functions

add a map that approximates core functions like sin, cos, tan, etc.

no longer relevant


Javascript Error: unable to find registry key or value

On Windows 10 and latest version of cerebro right after installing the app startup fails due to this error

@KELiON this looks like a Windows version of #41 / #124, isn't it?

Yeah seems to be. So what's the solution.Update the machine-id dependency or something ? Or just checkout master and build ?

I think the workaround would be to manually create the registry key in place where the app is trying to find it.
Also, have a look: https://github.com/automation-stack/node-machine-id/issues/9

Well most of them are saying that this arises because app is 64 bit but electron is 32 bit. So i guess installing 64 bit electron will work ? Or as you said manually creating registry key is the best solution ?

@saketh-bobby after 3rd issue about node-machine-id I see that it is not stable and we have to eliminate errors of this package. I've fixed this issue in #176.
You can download test build with this fix here

Even the test build is not working for me !! Same error

I think the workaround would be to manually create the registry key in place where the app is trying to find it.

The key is there, the reg query just fails from unknown reason. The command (%windir%\System32\REG QUERY ....) works fine, when executed in console.

Yup the test build is working !!


Ng/post tac

This PR addresses the following three modeling issues:

Make supply-demand balance constraints strictly equality (and associated changes with dual formulation).
Consider aggregated DPV generation to be part of RPS.
Add DER aggregator revenues under VIU as part of rate-making.

I was able to run Haley's plotting script on before and after test runs, and that looked okay to me.


Panic in pool: invalid memory address or nil pointer dereference

Hello. Experimenting with go-redis in Centrifugo - got panic after testing work when simulating Redis server hanging:
redis-cli DEBUG sleep 30

Panic:
[E]: 2016/03/02 01:21:32 read tcp <IP_ADDRESS>:49650-><IP_ADDRESS>:6379: i/o timeout
redis: 2016/03/02 01:21:33 Unwatch failed: read tcp <IP_ADDRESS>:49646-><IP_ADDRESS>:6379: i/o timeout
[C]: 2016/03/02 01:21:33 read tcp <IP_ADDRESS>:49651-><IP_ADDRESS>:6379: i/o timeout
2016/03/02 01:21:33 http: panic serving [::1]:64495: runtime error: invalid memory address or nil pointer dereference
goroutine 6 [running]:
net/http.(*conn).serve.func1(0xc8200c3760, 0xb97888, 0xc82002e180)
	/Users/fz/projects/go15/src/net/http/server.go:1287 +0xb5
gopkg.in/redis%2ev3.(*connPool).Remove(0xc8201087d0, 0xc820372ae0, 0x0, 0x0, 0x0, 0x0)
	/private/var/www/different/go/gopath/src/gopkg.in/redis.v3/pool.go:295 +0x34
gopkg.in/redis%2ev3.(*baseClient).putConn(0xc82010faa0, 0xc820372ae0, 0x0, 0x0)
	/private/var/www/different/go/gopath/src/gopkg.in/redis.v3/redis.go:28 +0xb5
gopkg.in/redis%2ev3.(*baseClient).process(0xc82010faa0, 0x1b055c0, 0xc8202ea780)
	/private/var/www/different/go/gopath/src/gopkg.in/redis.v3/redis.go:71 +0x375
gopkg.in/redis%2ev3.(*baseClient).(gopkg.in/redis%2ev3.process)-fm(0x1b055c0, 0xc8202ea780)
	/private/var/www/different/go/gopath/src/gopkg.in/redis.v3/redis.go:189 +0x34
gopkg.in/redis%2ev3.(*commandable).Process(0xc82015c338, 0x1b055c0, 0xc8202ea780)
	/private/var/www/different/go/gopath/src/gopkg.in/redis.v3/commands.go:57 +0x34
gopkg.in/redis%2ev3.(*commandable).EvalSha(0xc82015c338, 0xc82015a480, 0x28, 0xc8201fd950, 0x1, 0x1, 0xc820373620, 0x6, 0x6, 0x0)
	/private/var/www/different/go/gopath/src/gopkg.in/redis.v3/commands.go:1582 +0x52e
gopkg.in/redis%2ev3.(*Script).EvalSha(0xc82010fae0, 0x1b05580, 0xc82015c330, 0xc8201fd950, 0x1, 0x1, 0xc820373620, 0x6, 0x6, 0x18c)
	/private/var/www/different/go/gopath/src/gopkg.in/redis.v3/script.go:43 +0x99
gopkg.in/redis%2ev3.(*Script).Run(0xc82010fae0, 0x1b05580, 0xc82015c330, 0xc8201fd950, 0x1, 0x1, 0xc820373620, 0x6, 0x6, 0x23f36)
	/private/var/www/different/go/gopath/src/gopkg.in/redis.v3/script.go:47 +0x98
github.com/centrifugal/centrifugo/libcentrifugo.(*RedisEngine).publish(0xc820108820, 0xc8204f0a00, 0x1f, 0xc820442c00, 0x1a8, 0x2e5, 0xc82020a540, 0x0, 0x0)
	/private/var/www/different/go/gopath/src/github.com/centrifugal/centrifugo/libcentrifugo/engineredis.go:489 +0x650
github.com/centrifugal/centrifugo/libcentrifugo.(*Application).pubClient(0xc820156140, 0xc8201fd400, 0xc, 0x101010101, 0xa, 0x1e, 0x1, 0xc8201fd420, 0xd, 0x10, ...)
	/private/var/www/different/go/gopath/src/github.com/centrifugal/centrifugo/libcentrifugo/application.go:456 +0x5a4
github.com/centrifugal/centrifugo/libcentrifugo.(*Application).publish(0xc820156140, 0xc8201fd400, 0xc, 0xc8201fd420, 0xd, 0x10, 0xc8201f4540, 0x24, 0xc8204ac660, 0xc820222501, ...)
	/private/var/www/different/go/gopath/src/github.com/centrifugal/centrifugo/libcentrifugo/application.go:408 +0x2ac
github.com/centrifugal/centrifugo/libcentrifugo.(*client).publishCmd(0xc8200f4c30, 0xc8204ac630, 0x30, 0x0, 0x0)
	/private/var/www/different/go/gopath/src/github.com/centrifugal/centrifugo/libcentrifugo/client.go:924 +0x3b9
github.com/centrifugal/centrifugo/libcentrifugo.(*client).handleCmd(0xc8200f4c30, 0xc8201fd3d0, 0x2, 0xc8201fd3a0, 0x7, 0xc8204ac600, 0x2f, 0x30, 0x449a00, 0x0, ...)
	/private/var/www/different/go/gopath/src/github.com/centrifugal/centrifugo/libcentrifugo/client.go:456 +0x846
github.com/centrifugal/centrifugo/libcentrifugo.(*client).handleCommands(0xc8200f4c30, 0xc8201182a0, 0x1, 0x4, 0x0, 0x0)
	/private/var/www/different/go/gopath/src/github.com/centrifugal/centrifugo/libcentrifugo/client.go:393 +0x206
github.com/centrifugal/centrifugo/libcentrifugo.(*client).message(0xc8200f4c30, 0xc8204e8c00, 0x5a, 0x600, 0x0, 0x0)
	/private/var/www/different/go/gopath/src/github.com/centrifugal/centrifugo/libcentrifugo/client.go:358 +0x74a
github.com/centrifugal/centrifugo/libcentrifugo.(*Application).RawWebsocketHandler(0xc820156140, 0xb97ac8, 0xc8200c3810, 0xc8200e81c0)
	/private/var/www/different/go/gopath/src/github.com/centrifugal/centrifugo/libcentrifugo/handlers.go:275 +0x5c0
github.com/centrifugal/centrifugo/libcentrifugo.(*Application).RawWebsocketHandler-fm(0xb97ac8, 0xc8200c3810, 0xc8200e81c0)
	/private/var/www/different/go/gopath/src/github.com/centrifugal/centrifugo/libcentrifugo/handlers.go:92 +0x3e
net/http.HandlerFunc.ServeHTTP(0xc820193380, 0xb97ac8, 0xc8200c3810, 0xc8200e81c0)
	/Users/fz/projects/go15/src/net/http/server.go:1422 +0x3a
github.com/centrifugal/centrifugo/libcentrifugo.(*Application).WrapShutdown.func1(0xb97ac8, 0xc8200c3810, 0xc8200e81c0)
	/private/var/www/different/go/gopath/src/github.com/centrifugal/centrifugo/libcentrifugo/handlers.go:470 +0xb8
net/http.HandlerFunc.ServeHTTP(0xc820190480, 0xb97ac8, 0xc8200c3810, 0xc8200e81c0)
	/Users/fz/projects/go15/src/net/http/server.go:1422 +0x3a
github.com/centrifugal/centrifugo/libcentrifugo.(*Application).Logged.func1(0xb97ac8, 0xc8200c3810, 0xc8200e81c0)
	/private/var/www/different/go/gopath/src/github.com/centrifugal/centrifugo/libcentrifugo/handlers.go:486 +0x13f
net/http.HandlerFunc.ServeHTTP(0xc8201904a0, 0xb97ac8, 0xc8200c3810, 0xc8200e81c0)
	/Users/fz/projects/go15/src/net/http/server.go:1422 +0x3a
net/http.(*ServeMux).ServeHTTP(0xc82015b8f0, 0xb97ac8, 0xc8200c3810, 0xc8200e81c0)
	/Users/fz/projects/go15/src/net/http/server.go:1699 +0x17d
net/http.serverHandler.ServeHTTP(0xc820110f00, 0xb97ac8, 0xc8200c3810, 0xc8200e81c0)
	/Users/fz/projects/go15/src/net/http/server.go:1862 +0x19e
net/http.(*conn).serve(0xc8200c3760)
	/Users/fz/projects/go15/src/net/http/server.go:1361 +0xbee
created by net/http.(*Server).Serve
	/Users/fz/projects/go15/src/net/http/server.go:1910 +0x3f6

This could be my mistake in the end, but anyway it should not result in panic.

Hello. I am 99% sure this panic was fixed yesterday in https://github.com/go-redis/redis/pull/261. So please try to upgrade.
But overall it looks like there can be another issue, because there is unread data left in the connection. Is there any way I can try to reproduce this issue? Alternatively please look in the log for connection has unread data: %q message. It can reveal something interesting.

@vmihailenco I was able to reproduce this only once since then - with the same looking panic. But I have no ideas how to reproduce this reliably - it just happens occasionally. I am using version without yesterday's fix. So hopefully this was fixed. I'll try to find a way to reproduce this so we can be sure it was fixed - no ideas at moment though. Or will try to use version with fix and maybe panic will be there too.
After Redis hanging goes away I usually get lots of log messages containing connection has unread data... - but when panic happened there were no such messages around. Is this ok to have such messages? Or such log messages should never appear in normal workflow?

Or will try to use version with fix and maybe panic will be there too.

I am sure I understand what caused that panic. So you should not see the same panic again :)

Or such log messages should never appear in normal workflow?

I think they should not appear in normal workflow. At least I don't see them in production logs.
But now I understand what is happening. When you use redis-cli DEBUG sleep 30 Redis Server does not answer immediately, but instead buffers commands and after 30 seconds sends replies for all buffered commands. But go-redis expects answer for only one command so it closes the connection and opens new one. That is okay, but probably go-redis should always close connection on timeout? I am not sure, because timeout is temporal error in Go (net.Error.Temporary()) and it did not caused any problems in practise.

So you should not see the same panic again :)

Ok thanks:) you can close this then to not confuse other users - I'll reopen it if I get the same panic with new version.
I also have some other questions:

I see that method that allowed turning connection into PubSub connection is now deprecated. I don't know the history behind this - but it seems to be useful shortcut when we don't need to subscribe immediately and just want to start subscribing later.
Seems that when I publish message into channel during Redis hanging and I have MaxRetries option set to something > 0 then this results in publishing message several times when Redis becomes available.

but it seems to be useful shortcut when we don't need to subscribe immediately and just want to start subscribing later.

Why not to just Subscribe later? :) We can allow Subscribe with zero arguments if there is a use case.

results in publishing message several times when Redis becomes available.

If you use DEBUG sleep then Redis buffers published messages and then makes them available all at once. But that is true that go-redis retries publish on timeout and MaxRetries > 0. So seems to work as expected.

Why not to just Subscribe later?

Because my code looks like this:
func (e *RedisEngine) runPubSub() {
	pubsub := e.client.PubSub()
	defer pubsub.Close()

	done := make(chan struct{})
	defer close(done)

	// Run subscriber goroutine
	go func() {
		for {
			select {
			case <-done:
				return
			case chan := <-e.sub:
				err := pubsub.Subscribe(chan)
			}
            ...
		}
	}()
    ...
}

So it's just a bit more simple to initialise pubsub connection outside goroutine in upper scope and then just use that pubsub connection in goroutine.

So I have a similar issue with a cluster pipeline:
panic: runtime error: invalid memory address or nil pointer dereference
[signal 0xb code=0x1 addr=0x20 pc=0x5288fd]

goroutine 783769 [running]:
gopkg.in/redis%2ev3.(*connPool).Remove(0xc21a94be50, 0xc21bd70e40, 0x0, 0x0, 0x0, 0x0)
        /go/src/gopkg.in/redis.v3/pool.go:295 +0x3d
gopkg.in/redis%2ev3.(*baseClient).putConn(0xc216213860, 0xc21bd70e40, 0x0, 0x0)
        /go/src/gopkg.in/redis.v3/redis.go:28 +0xaa
gopkg.in/redis%2ev3.(*ClusterPipeline).Exec(0xc212935560, 0xc208075500, 0x130, 0x150, 0x0, 0x0)
        /go/src/gopkg.in/redis.v3/cluster_pipeline.go:82 +0x784

Please try to upgrade. It panicked on p.storeLastErr(reason.Error()), because reason was nil in one of the code paths...


ng serve does not respect ng build --env

To simply the details below:
Is it expected behavior where ng serve rebuilds the app and overwrites the prior ng build?
I am seeing an issue where a custom environment configuration file is initially generated as expected but then reverts when the app is served.
Here are the relevant details:
OS: Windows 10:
ng --version: angular-cli: 1.0.0-beta.10, node: 4.4.7, os: win32 x64
App generated via CLI
A new environment file was created to establish a 'local' development environment.
environment.local.ts
export const environment = {   production: false,   local :true };
Command executed for build:
ng build --env=local
The dist/app/environment.js file is properly built with the configuration set as expected.
Command executed for serve:
'ng serve'
It appears as if ng serve is actually rebuilding the app. Adding '--env=local' appears to respect the settings.
Prior to requesting the application via a browser, the dist/app/environment.js file is okay.
After connecting to the application via the browser, the environment.js reverts to the environment.dev.ts settings.
environment.dev.ts file:
export const environment = {   production: false,   local :false };
Thanks in advance.

Yes this is the expected behaviour:

ng build: Builds your app.
ng serve: Builds your app and also starts a live reload server.

This means that when you were running ng build --env=local your bundle was built correctly but when after that you run ng serve the bundle was built again but this time with the default environment which is 'dev'.
So you just need to run ng serve --env=local.

@DavidRSchaller You can close the issue yourself if you think its solved. Thanks!!


Replace this with window

When requiring ionic via browserify, this parameters in IIFE causes undefined errors.
I have replaced this with window.
Created release directory via
gulp build --release --dist=release

which resulted in more changes.
I don't know that is the correct way doing it. If not, let me know and I will update the pull request.

Thanks! Landed the first two of your commits here: e899ad59b96e3ed5eded6453560f94853a83405d 8fc6e57a426eaa6b6f437ed0c65793b19e83515f.
For the last one, generally we don't do the release for code changes like this, we do that internally, so it's best to not include it in the PR. No worries, I was able to cherry-pick those two commits.

Ok, thanks. That's great.