Lnd not starting after updating to v1.6

Hey i just updated to V1.6 and Lnd is not starting. Here is the debug log if anyone might be able to help me figure out what i messed up. please and thank you.
blitzversion: 1.6.0
chainnetwork: bitcoin / main
23:26:29 up 12 min,  3 users,  load average: 1.03, 1.40, 0.95
*** BLOCKCHAIN SYSTEMD STATUS ***
● bitcoind.service - Bitcoin daemon
Loaded: loaded (/etc/systemd/system/bitcoind.service; enabled; vendor preset: enabled)
Active: active (running) since Mon 2020-08-17 23:23:03 BST; 3min 26s ago
Process: 11192 ExecStartPre=/home/admin/config.scripts/blitz.systemd.sh log blockchain STARTED (code=exited, status=0/SUCCESS)
Process: 11194 ExecStart=/usr/local/bin/bitcoind -daemon -conf=/home/bitcoin/.bitcoin/bitcoin.conf -pid=/home/bitcoin/.bitcoin/bitcoind.pid (code=exited, status=0/SUCCESS)
Main PID: 11195 (bitcoind)
Tasks: 18 (limit: 4915)
Memory: 256.3M
CGroup: /system.slice/bitcoind.service
└─11195 /usr/local/bin/bitcoind -daemon -conf=/home/bitcoin/.bitco…
Aug 17 23:23:03 raspberrypi systemd[1]: bitcoind.service: Can't open PID …tory
Aug 17 23:23:03 raspberrypi systemd[1]: Started Bitcoin daemon.
Hint: Some lines were ellipsized, use -l to show in full.
*** LAST BLOCKCHAIN ERROR LOGS ***
sudo journalctl -u bitcoind -b --no-pager -n8
-- Logs begin at Mon 2020-08-17 23:13:40 BST, end at Mon 2020-08-17 23:26:29 BST. --
Aug 17 23:18:11 raspberrypi systemd[1]: Starting Bitcoin daemon...
Aug 17 23:18:11 raspberrypi systemd[1]: Started Bitcoin daemon.
Aug 17 23:22:46 raspberrypi systemd[1]: Stopping Bitcoin daemon...
Aug 17 23:22:48 raspberrypi systemd[1]: bitcoind.service: Succeeded.
Aug 17 23:22:48 raspberrypi systemd[1]: Stopped Bitcoin daemon.
Aug 17 23:23:02 raspberrypi systemd[1]: Starting Bitcoin daemon...
Aug 17 23:23:03 raspberrypi systemd[1]: bitcoind.service: Can't open PID file /home/bitcoin/.bitcoin/bitcoind.pid (yet?) after start: No such file or directory
Aug 17 23:23:03 raspberrypi systemd[1]: Started Bitcoin daemon.
 --
Aug 17 23:25:03 raspberrypi systemd[1]: Started LND Lightning Daemon.
Aug 17 23:25:04 raspberrypi lnd[11910]: dial tcp <IP_ADDRESS>:9050: connect: connection refused
Aug 17 23:25:04 raspberrypi systemd[1]: lnd.service: Main process exited, code=exited, status=1/FAILURE
Aug 17 23:25:04 raspberrypi systemd[1]: lnd.service: Failed with result 'exit-code'.
Aug 17 23:26:04 raspberrypi systemd[1]: lnd.service: Service RestartSec=1min expired, scheduling restart.
Aug 17 23:26:04 raspberrypi systemd[1]: lnd.service: Scheduled restart job, restart counter is at 9.
Aug 17 23:26:04 raspberrypi systemd[1]: Stopped LND Lightning Daemon.
Aug 17 23:26:04 raspberrypi systemd[1]: Starting LND Lightning Daemon...
Aug 17 23:26:04 raspberrypi systemd[1]: Started LND Lightning Daemon.
Aug 17 23:26:04 raspberrypi lnd[12931]: dial tcp <IP_ADDRESS>:9050: connect: connection refused
Aug 17 23:26:04 raspberrypi systemd[1]: lnd.service: Main process exited, code=exited, status=1/FAILURE
Aug 17 23:26:04 raspberrypi systemd[1]: lnd.service: Failed with result 'exit-code'.
*** LAST 30 LND INFO LOGS ***
sudo tail -n 30 /mnt/hdd/lnd/logs/bitcoin/mainnet/lnd.log
*** LAST NGINX LOGS ***
sudo journalctl -u nginx -b --no-pager -n20
-- Logs begin at Mon 2020-08-17 23:13:40 BST, end at Mon 2020-08-17 23:26:29 BST. --
-- No entries --
--> CHECK CONFIG: sudo nginx -t
sudo: nginx: command not found
*** LAST 20 TOUCHSCREEN LOGS ***
sudo tail -n 20 /home/pi/.cache/lxsession/LXDE-pi/run.log
** Message: 23:14:33.580: app.vala:76: Launching /usr/bin/ssh-agent
SSH_AUTH_SOCK=/tmp/ssh-yBR5kUsWloJY/agent.883; export SSH_AUTH_SOCK;
SSH_AGENT_PID=887; export SSH_AGENT_PID;
echo Agent pid 887;
** Message: 23:14:33.656: app.vala:130: xset exit with this type of exit: 0
** Message: 23:14:33.657: app.vala:139: Exit normal, don't reload
** Message: 23:14:33.657: app.vala:130: xset exit with this type of exit: 0
** Message: 23:14:33.657: app.vala:139: Exit normal, don't reload
** Message: 23:14:33.657: app.vala:130: xset exit with this type of exit: 0
** Message: 23:14:33.657: app.vala:139: Exit normal, don't reload
** Message: 23:14:33.657: app.vala:130: /usr/bin/ssh-agent exit with this type of exit: 0
Traceback (most recent call last):
File "/home/admin/python3-env-lnd/bin/blitz-tui", line 6, in
from blitztui.main import main
File "/home/admin/python3-env-lnd/lib/python3.7/site-packages/blitztui/main.py", line 17, in
from PyQt5.QtCore import Qt, QProcess, QThread, pyqtSignal, QCoreApplication, QTimer, QEventLoop
ModuleNotFoundError: No module named 'PyQt5'
** Message: 23:14:33.758: app.vala:130: sh exit with this type of exit: 256
** Message: 23:14:33.758: app.vala:145: Exit normal, don't reload
Openbox-Message: Unable to find a valid menu file "/usr/share/lxde/openbox/menu.xml"
*** LAST 20 LOOP LOGS ***
sudo journalctl -u loopd -b --no-pager -n20
-- Logs begin at Mon 2020-08-17 23:13:40 BST, end at Mon 2020-08-17 23:26:29 BST. --
Aug 17 23:23:32 raspberrypi systemd[11375]: loopd.service: Changing to the requested working directory failed: No such file or directory
Aug 17 23:23:32 raspberrypi systemd[11375]: loopd.service: Failed at step CHDIR spawning /usr/local/gocode/bin/loopd: No such file or directory
Aug 17 23:23:32 raspberrypi systemd[1]: loopd.service: Main process exited, code=exited, status=200/CHDIR
Aug 17 23:23:32 raspberrypi systemd[1]: loopd.service: Failed with result 'exit-code'.
Aug 17 23:24:33 raspberrypi systemd[1]: loopd.service: Service RestartSec=1min expired, scheduling restart.
Aug 17 23:24:33 raspberrypi systemd[1]: loopd.service: Scheduled restart job, restart counter is at 8.
Aug 17 23:24:33 raspberrypi systemd[1]: Stopped Loopd Service.
Aug 17 23:24:33 raspberrypi systemd[1]: Started Loopd Service.
Aug 17 23:24:33 raspberrypi systemd[11718]: loopd.service: Changing to the requested working directory failed: No such file or directory
Aug 17 23:24:33 raspberrypi systemd[11718]: loopd.service: Failed at step CHDIR spawning /usr/local/gocode/bin/loopd: No such file or directory
Aug 17 23:24:33 raspberrypi systemd[1]: loopd.service: Main process exited, code=exited, status=200/CHDIR
Aug 17 23:24:33 raspberrypi systemd[1]: loopd.service: Failed with result 'exit-code'.
Aug 17 23:25:33 raspberrypi systemd[1]: loopd.service: Service RestartSec=1min expired, scheduling restart.
Aug 17 23:25:33 raspberrypi systemd[1]: loopd.service: Scheduled restart job, restart counter is at 9.
Aug 17 23:25:33 raspberrypi systemd[1]: Stopped Loopd Service.
Aug 17 23:25:33 raspberrypi systemd[1]: Started Loopd Service.
Aug 17 23:25:33 raspberrypi systemd[12064]: loopd.service: Changing to the requested working directory failed: No such file or directory
Aug 17 23:25:33 raspberrypi systemd[12064]: loopd.service: Failed at step CHDIR spawning /usr/local/gocode/bin/loopd: No such file or directory
Aug 17 23:25:33 raspberrypi systemd[1]: loopd.service: Main process exited, code=exited, status=200/CHDIR
Aug 17 23:25:33 raspberrypi systemd[1]: loopd.service: Failed with result 'exit-code'.
*** LAST 20 RTL LOGS ***
sudo journalctl -u RTL -b --no-pager -n20
-- Logs begin at Mon 2020-08-17 23:13:40 BST, end at Mon 2020-08-17 23:26:29 BST. --
-- No entries --
*** LAST 20 ElectRS LOGS ***
sudo journalctl -u electrs -b --no-pager -n20
-- Logs begin at Mon 2020-08-17 23:13:40 BST, end at Mon 2020-08-17 23:26:29 BST. --
Aug 17 23:23:32 raspberrypi systemd[11374]: electrs.service: Changing to the requested working directory failed: No such file or directory
Aug 17 23:23:32 raspberrypi systemd[11374]: electrs.service: Failed at step CHDIR spawning /home/electrs/electrs/target/release/electrs: No such file or directory
Aug 17 23:23:32 raspberrypi systemd[1]: electrs.service: Main process exited, code=exited, status=200/CHDIR
Aug 17 23:23:32 raspberrypi systemd[1]: electrs.service: Failed with result 'exit-code'.
Aug 17 23:24:33 raspberrypi systemd[1]: electrs.service: Service RestartSec=1min expired, scheduling restart.
Aug 17 23:24:33 raspberrypi systemd[1]: electrs.service: Scheduled restart job, restart counter is at 8.
Aug 17 23:24:33 raspberrypi systemd[1]: Stopped Electrs.
Aug 17 23:24:33 raspberrypi systemd[1]: Started Electrs.
Aug 17 23:24:33 raspberrypi systemd[11719]: electrs.service: Changing to the requested working directory failed: No such file or directory
Aug 17 23:24:33 raspberrypi systemd[11719]: electrs.service: Failed at step CHDIR spawning /home/electrs/electrs/target/release/electrs: No such file or directory
Aug 17 23:24:33 raspberrypi systemd[1]: electrs.service: Main process exited, code=exited, status=200/CHDIR
Aug 17 23:24:33 raspberrypi systemd[1]: electrs.service: Failed with result 'exit-code'.
Aug 17 23:25:33 raspberrypi systemd[1]: electrs.service: Service RestartSec=1min expired, scheduling restart.
Aug 17 23:25:33 raspberrypi systemd[1]: electrs.service: Scheduled restart job, restart counter is at 9.
Aug 17 23:25:33 raspberrypi systemd[1]: Stopped Electrs.
Aug 17 23:25:33 raspberrypi systemd[1]: Started Electrs.
Aug 17 23:25:33 raspberrypi systemd[12063]: electrs.service: Changing to the requested working directory failed: No such file or directory
Aug 17 23:25:33 raspberrypi systemd[12063]: electrs.service: Failed at step CHDIR spawning /home/electrs/electrs/target/release/electrs: No such file or directory
Aug 17 23:25:33 raspberrypi systemd[1]: electrs.service: Main process exited, code=exited, status=200/CHDIR
Aug 17 23:25:33 raspberrypi systemd[1]: electrs.service: Failed with result 'exit-code'.
*** ElectRS Status ***
STATUS ELECTRS SERVICE
configured=1
serviceInstalled=1
serviceRunning=0
tipSynced=0
initialSynced=0
electrumResponding=0
infoSync='Not running - check: sudo journalctl -u electrs'
*** LAST 20 BTCPayServer LOGS ***
sudo journalctl -u btcpayserver -b --no-pager -n20
-- Logs begin at Mon 2020-08-17 23:13:40 BST, end at Mon 2020-08-17 23:26:30 BST. --
-- No entries --
*** LAST 20 LNbits LOGS ***
sudo journalctl -u lnbits -b --no-pager -n20
-- Logs begin at Mon 2020-08-17 23:13:40 BST, end at Mon 2020-08-17 23:26:30 BST. --
Aug 17 23:24:03 raspberrypi systemd[11558]: lnbits.service: Changing to the requested working directory failed: No such file or directory
Aug 17 23:24:03 raspberrypi systemd[11558]: lnbits.service: Failed at step CHDIR spawning /bin/sh: No such file or directory
Aug 17 23:24:03 raspberrypi systemd[1]: lnbits.service: Main process exited, code=exited, status=200/CHDIR
Aug 17 23:24:03 raspberrypi systemd[1]: lnbits.service: Failed with result 'exit-code'.
Aug 17 23:24:33 raspberrypi systemd[1]: lnbits.service: Service RestartSec=30s expired, scheduling restart.
Aug 17 23:24:33 raspberrypi systemd[1]: lnbits.service: Scheduled restart job, restart counter is at 9.
Aug 17 23:24:33 raspberrypi systemd[1]: Stopped lnbits.
Aug 17 23:25:03 raspberrypi systemd[1]: Started lnbits.
Aug 17 23:25:03 raspberrypi systemd[11911]: lnbits.service: Changing to the requested working directory failed: No such file or directory
Aug 17 23:25:03 raspberrypi systemd[11911]: lnbits.service: Failed at step CHDIR spawning /bin/sh: No such file or directory
Aug 17 23:25:03 raspberrypi systemd[1]: lnbits.service: Main process exited, code=exited, status=200/CHDIR
Aug 17 23:25:03 raspberrypi systemd[1]: lnbits.service: Failed with result 'exit-code'.
Aug 17 23:25:33 raspberrypi systemd[1]: lnbits.service: Service RestartSec=30s expired, scheduling restart.
Aug 17 23:25:33 raspberrypi systemd[1]: lnbits.service: Scheduled restart job, restart counter is at 10.
Aug 17 23:25:33 raspberrypi systemd[1]: Stopped lnbits.
Aug 17 23:26:04 raspberrypi systemd[1]: Started lnbits.
Aug 17 23:26:04 raspberrypi systemd[12932]: lnbits.service: Changing to the requested working directory failed: No such file or directory
Aug 17 23:26:04 raspberrypi systemd[12932]: lnbits.service: Failed at step CHDIR spawning /bin/sh: No such file or directory
Aug 17 23:26:04 raspberrypi systemd[1]: lnbits.service: Main process exited, code=exited, status=200/CHDIR
Aug 17 23:26:04 raspberrypi systemd[1]: lnbits.service: Failed with result 'exit-code'.
*** LAST 20 Thunderhub LOGS ***
sudo journalctl -u thunderhub -b --no-pager -n20
-- Logs begin at Mon 2020-08-17 23:13:40 BST, end at Mon 2020-08-17 23:26:30 BST. --
Aug 17 23:24:03 raspberrypi systemd[11563]: thunderhub.service: Changing to the requested working directory failed: No such file or directory
Aug 17 23:24:03 raspberrypi systemd[11563]: thunderhub.service: Failed at step CHDIR spawning /usr/bin/npm: No such file or directory
Aug 17 23:24:03 raspberrypi systemd[1]: thunderhub.service: Main process exited, code=exited, status=200/CHDIR
Aug 17 23:24:03 raspberrypi systemd[1]: thunderhub.service: Failed with result 'exit-code'.
Aug 17 23:24:33 raspberrypi systemd[1]: thunderhub.service: Service RestartSec=30s expired, scheduling restart.
Aug 17 23:24:33 raspberrypi systemd[1]: thunderhub.service: Scheduled restart job, restart counter is at 9.
Aug 17 23:24:33 raspberrypi systemd[1]: Stopped ThunderHub daemon.
Aug 17 23:25:03 raspberrypi systemd[1]: Started ThunderHub daemon.
Aug 17 23:25:03 raspberrypi systemd[11912]: thunderhub.service: Changing to the requested working directory failed: No such file or directory
Aug 17 23:25:03 raspberrypi systemd[11912]: thunderhub.service: Failed at step CHDIR spawning /usr/bin/npm: No such file or directory
Aug 17 23:25:03 raspberrypi systemd[1]: thunderhub.service: Main process exited, code=exited, status=200/CHDIR
Aug 17 23:25:03 raspberrypi systemd[1]: thunderhub.service: Failed with result 'exit-code'.
Aug 17 23:25:33 raspberrypi systemd[1]: thunderhub.service: Service RestartSec=30s expired, scheduling restart.
Aug 17 23:25:33 raspberrypi systemd[1]: thunderhub.service: Scheduled restart job, restart counter is at 10.
Aug 17 23:25:33 raspberrypi systemd[1]: Stopped ThunderHub daemon.
Aug 17 23:26:04 raspberrypi systemd[1]: Started ThunderHub daemon.
Aug 17 23:26:04 raspberrypi systemd[12933]: thunderhub.service: Changing to the requested working directory failed: No such file or directory
Aug 17 23:26:04 raspberrypi systemd[12933]: thunderhub.service: Failed at step CHDIR spawning /usr/bin/npm: No such file or directory
Aug 17 23:26:04 raspberrypi systemd[1]: thunderhub.service: Main process exited, code=exited, status=200/CHDIR
Aug 17 23:26:04 raspberrypi systemd[1]: thunderhub.service: Failed with result 'exit-code'.
*** LAST 20 SPECTER LOGS ***
sudo journalctl -u cryptoadvance-specter -b --no-pager -n20
-- Logs begin at Mon 2020-08-17 23:13:40 BST, end at Mon 2020-08-17 23:26:30 BST. --
-- No entries --
*** MOUNTED DRIVES ***
Filesystem     Type     1K-blocks      Used Available Use% Mounted on
/dev/root      ext4      30450024   3483680  25679288  12% /
devtmpfs       devtmpfs   1966332         0   1966332   0% /dev
tmpfs          tmpfs      2000124         0   2000124   0% /dev/shm
tmpfs          tmpfs      2000124      8692   1991432   1% /run
tmpfs          tmpfs         5120         4      5116   1% /run/lock
tmpfs          tmpfs      2000124         0   2000124   0% /sys/fs/cgroup
tmpfs          tmpfs        32768         0     32768   0% /var/cache/raspiblitz
/dev/mmcblk0p1 vfat        258095     52246    205849  21% /boot
/dev/sda1      ext4     960322016 442473136 468997456  49% /mnt/hdd
tmpfs          tmpfs       400024         0    400024   0% /run/user/1000
tmpfs          tmpfs       400024         0    400024   0% /run/user/1001
*** WIFI Info ***
activated=0
connected=0
localip=''
*** HARDWARE TEST RESULTS ***
UndervoltageReports in Logs: 0
raspberryPi=4
powerFAIL=0
powerWARN=0
powerMIN='862500 microVolt'
tempFAIL=11
tempWARN=15
tempMAX='8000 centiGrad'
IMPORTANT: There are some hardware issues with your setup.
'Run Hardwaretest' in main menu or: sudo /home/admin/05hardwareTest.sh
*** SYSTEM STATUS (can take some seconds to gather) ***
localIP='<IP_ADDRESS>'
tempCelsius='72.0'
uptime=761
upsStatus='OFF'
startcountBlockchain=3
bitcoinActive=1
blockchainHeight=644184
initialSync=0
syncProgress=100.00
startcountLightning=11
lndActive=0
blitzTUIActive=1
blitzTUIRestarts=0
scriptRuntime=1

this system lost a lot of stuff in this upgrade.

sudo: nginx: command not found

Someone who got some backup experience should check this out

@openoms and @frennkie I start to see a pattern here ... a lot of bug reports lately have todo with "installs not running correctly" lets talk next week how we can improve this maybe thru pre-packaging apt-get, pip and npm depencendies on the sd card - this would also make the RaspiBlitz better suitable for areas with flaky, costly or low-bandwidth internet.
@syracusebitcoin if possible send me a copy of your /home/admin/raspiblitz.recover.log to<EMAIL_ADDRESS>and then try an update again with a fresh sd card that hopefully this time all dependecies get loaded and installed correctly.

Hello everyone. Thanks for the help. @rootzoll the problem was indeed a bad SD card. I recently got a new one, re-flashed the img and it seems to be working fine for the most part. There seem to be a few kinks here and there but I think with a little bit of research I can fix these issues. Again thanks everyone for the help.


pr2_robot: 1.6.10-4 in 'indigo/distribution.yaml' [bloom]

Increasing version of package(s) in repository pr2_robot to 1.6.10-4:

upstream repository: https://github.com/pr2/pr2_robot.git
release repository: https://github.com/pr2-gbp/pr2_robot-release.git
distro file: indigo/distribution.yaml
bloom version: 0.5.20
previous version for package: 1.6.10-3

Your python-rosdistro version is out of date - you need 0.4.4 or higher. Please upgrade and rerun bloom-release with --pull-request-only or -p or edit the commit to fix the REP version.


release artifacts for release v0.0.1

release artifacts for release v0.0.1

/lgtm

/test

/test applicationautoscaling-kind-e2e

/trickortreat


Handeling null value in querystringwidget

@tisto @iFlameing Which is the use case? I mean, for all indexes, it should be a matching row.i, right? if not, that means that the value that comes from the widget is not valid/registered in Plone.


Update LAB[PL-200]_M12L06_Duplicate.md

Module: 00
Lab/Demo: 00
Fixes # .
Changes proposed in this pull request:

Submitted by ParamRamki on behalf of MOC Labs Review

I applied this change to the Jan_2022_scrub branch and will merge this evening. Thanks!


NotebookRendererMessaging unit test flake

Build: https://dev.azure.com/monacotools/a6d41577-0fa3-498e-af22-257312ff0545/_build/results?buildId=230723
Changes: https://github.com/Microsoft/vscode/compare/a4e1870...dc8730c
  1) NotebookRendererMessaging
       "after each" hook for "buffers and then plays events":

Not sure how do I read the errors
Error: These disposables were not disposed:
  Error:
  	at $5S.trackDisposable (file:///mnt/vss/_work/1/s/out-build/vs/base/test/common/utils.js:55:21)
  	at trackDisposable (file:///mnt/vss/_work/1/s/out-build/vs/base/common/lifecycle.js:58:28)
  	at new $lb (file:///mnt/vss/_work/1/s/out-build/vs/base/common/lifecycle.js:261:13)
  	at new $e6 (file:///mnt/vss/_work/1/s/out-build/vs/editor/contrib/suggest/browser/suggestWidget.js:74:22)
  	at $59b.j (file:///mnt/vss/_work/1/s/out-build/vs/platform/instantiation/common/instantiationService.js:91:28)
  	at $59b.createInstance (file:///mnt/vss/_work/1/s/out-build/vs/platform/instantiation/common/instantiationService.js:62:31)
  	at file:///mnt/vss/_work/1/s/out-build/vs/editor/contrib/suggest/browser/suggestController.js:98:39
  	at $Tg.a (file:///mnt/vss/_work/1/s/out-build/vs/base/common/async.js:964:30)
  	at file:///mnt/vss/_work/1/s/out-build/vs/base/common/async.js:973:50
  --------------------

  Error:
  	at $5S.trackDisposable (file:///mnt/vss/_work/1/s/out-build/vs/base/test/common/utils.js:55:21)
  	at trackDisposable (file:///mnt/vss/_work/1/s/out-build/vs/base/common/lifecycle.js:58:28)
  	at new $lb (file:///mnt/vss/_work/1/s/out-build/vs/base/common/lifecycle.js:261:13)
  	at new $e6 (file:///mnt/vss/_work/1/s/out-build/vs/editor/contrib/suggest/browser/suggestWidget.js:75:22)
  	at $59b.j (file:///mnt/vss/_work/1/s/out-build/vs/platform/instantiation/common/instantiationService.js:91:28)
  	at $59b.createInstance (file:///mnt/vss/_work/1/s/out-build/vs/platform/instantiation/common/instantiationService.js:62:31)
  	at file:///mnt/vss/_work/1/s/out-build/vs/editor/contrib/suggest/browser/suggestController.js:98:39
  	at $Tg.a (file:///mnt/vss/_work/1/s/out-build/vs/base/common/async.js:964:30)
  	at file:///mnt/vss/_work/1/s/out-build/vs/base/common/async.js:973:50

It's complaining about the SuggestWidget which is not used in this test, are these carried over from previous tests, especially there are hundreds of them @connor4312 ?

This should be fixed one everyone's abort the disposable checker train: https://github.com/microsoft/vscode/issues/200091


Additional SEO enhancements

Some additional SEO fixes
Since I removed all ".html" from links adding canonical link to all pages, because Jekyll's auto-generated sitemap includes them (can't verify locally that it works though).
Added microdata for SoftwareApplication (and included one line at the bottom of the page to provided supported Operating systems, which is required for SoftwareApplication).
Separated menu into it's own include
Added JSX support to the homepage.

Lgtm


Update time_series_flood.md

Added analysis link for JKinsler to analysis/time_series_flood.md

Hey Johanna,
I am able to see the updates that include your second analysis! It looks good to me. You may want to include a data source and citation on your page.

I apologize for my lateness in getting to this... I was in the field last week and it looks like me trying to get caught up didn't quite work.
Very cool to see the surge of water this past spring.  It truly was an amazing winter as far as rain and snow goes.


Convert script not running

It is not currently possible to run the MT->T5X convertion script directly like this:
python -m t5x.scripts.convert_tf_checkpoint \
 --gin_file=t5x/examples/t5/t5_1_0/small.gin\
 --gin.convert_checkpoint.model=%MODEL\
 --gin.convert_checkpoint.tf_checkpoint_path=\
\"gs://t5-data/pretrained_models/small/model.ckpt-1000000\"\
 --gin.convert_checkpoint.output_dir=\"/tmp/t5x_checkpoints/t5_small\"

You are getting the following error:
 raise ValueError(fmt.format(binding_key))
ValueError: DROPOUT_RATE/macro.value set to `%gin.REQUIRED` but not subsequently overridden.

Setting DROPOUT_RATE manually, for instance at the end of the small.gin-file, is a workaround. Not sure what is a clean way of fixing the issue.

Since there now is a direct conversion-script from T5X to Transformers, this is no longer an issue. Closing.

Hi @peregilk , do you remember what is the parsing value for %MODEL ?


❓ [Question] Examples not working in nvcr.io/nvidia/pytorch:23.09-py3.

❓ Question
I am within the nvcr.io/nvidia/pytorch:23.09-py3 container. Trying out some snippets from:
https://youtu.be/eGDMJ3MY4zk?si=MhkbgwAPVQSFZEha.
Both JIT and AoT examples failed. For JIT, it complained that "tensorrt" backend isn't available, for AoT, it complained that "The user code is using a feature we don't support. Please try torchdynamo.explain() to get possible the reasons".
I am on an A100. What's going on?

Hello - for the backend="tensorrt" issue, please ensure the line import torch_tensorrt is in the script (even though it may not be used directly). This is important because the import itself registers the backend, as here:
https://github.com/pytorch/TensorRT/blob/0112bb4996d464f46ae8027f7d7146eb045d6364/py/torch_tensorrt/dynamo/backend/backends.py#L27
If this does not address the issue, could you please also share the version of Torch-TensorRT being used?
Regarding the AoT approach, @peri044 may be able to help further, but based on the error it sounds like the model may not be natively trace-able. Which specific example from the presentation are you referencing for the AoT sample?

This one:

Hello @sayakpaul , I would suggest you to try our latest main branch. 23.09 is old now. Let us know if you see any issues with the main branch.
Alternatively, you can try our nightly container if you don't want to build from source : https://github.com/pytorch/TensorRT/pkgs/container/tensorrt%2Ftorch_tensorrt

With the latest container (https://github.com/pytorch/TensorRT/pkgs/container/tensorrt%2Ftorch_tensorrt), it works. Have a few questions. So, let me use this thread to ask them. Maybe the community might find them to be useful.

I used the following code snippet I am using to benchmark the JIT workflow:
import torch
import torch_tensorrt
import torch.utils.benchmark as benchmark

class MyModel(torch.nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv = torch.nn.Conv2d(64, 128, 3)
    def forward(self, a):
        if a.sum() > 3:
            b = torch.nn.functional.elu(a)
        else:
            b = a + 1
        return self.conv(b)

# Taken from
# https://pytorch.org/tutorials/intermediate/scaled_dot_product_attention_tutorial.html
def benchmark_fn(f, *args, **kwargs):
    t0 = benchmark.Timer(
        stmt="f(*args, **kwargs)", globals={"args": args, "kwargs": kwargs, "f": f}
    )
    return t0.blocked_autorange().mean * 1e6

model = MyModel().eval().cuda()
inputs = torch.nn.randn(32, 64, 64, 64, device="cuda")

print("Using regular model:")
print(benchmark_fn(model, inputs))

optimized_model = torch.compile(
    model,
    backend="tensorrt",
    dynamic=False,
        options={
            "debug": True,
            "enabled_precisions": {torch.half},
            "min_block_size": 1,
        }
)
_ = optimized_model(inputs)
print("Using optimized model:")
print(benchmark_fn(optimized_model, inputs))

I am getting:
620.6526302793078 microseconds (for the non-compiled model)
2095.269737765193 (for the compiled model)

I am on an 80 GB A100. I can understand the compute load might not be enough for me to see evident speedups here, but is this expected? Wanted to know your opinion.

The AOT workflow still fails:
import torch
import torch_tensorrt
import torch.utils.benchmark as benchmark

class MyModel(torch.nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv = torch.nn.Conv2d(64, 128, 3)
    def forward(self, a):
        if a.sum() > 3:
            b = torch.nn.functional.elu(a)
        else:
            b = a + 1
        return self.conv(b)

# Taken from
# https://pytorch.org/tutorials/intermediate/scaled_dot_product_attention_tutorial.html
def benchmark_fn(f, *args, **kwargs):
    t0 = benchmark.Timer(
        stmt="f(*args, **kwargs)", globals={"args": args, "kwargs": kwargs, "f": f}
    )
    return t0.blocked_autorange().mean * 1e6

inputs = torch.randn((256, 64, 32, 32)).cuda()
model = MyModel().eval().cuda()

print("Using regular model:")
print(benchmark_fn(model, inputs))

exp_program = torch_tensorrt.dynamo.trace(model, [inputs])
trt_model = torch_tensorrt.dynamo.compile(exp_program, inputs=[inputs])

_ = trt_model(input)
print("Using optimized model:")
print(benchmark_fn(optimized_model, inputs))

print("Serializing...")
trt_ser_model = torch_tensorrt.dynamo.serialize(trt_model, *inputs)
torch.save(trt_ser_model, "trt_model.pt")

Trace:
Traceback (most recent call last):
  File "/opt/torch_tensorrt/trt_aot.py", line 33, in <module>
    exp_program = torch_tensorrt.dynamo.trace(model, [inputs])
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch_tensorrt/dynamo/aten_tracer.py", line 51, in trace
    if input.shape_mode == Input._ShapeMode.DYNAMIC:
AttributeError: 'Tensor' object has no attribute 'shape_mode'

When I swapped the inputs with the following:
inputs = [torch_tensorrt.Input(
    min_shape=(1, 512, 1, 1),
    opt_shape=(4, 512, 1, 1),
    max_shape=(8, 512, 1, 1)
)]

it led to the following:
Traceback (most recent call last):
  File "/opt/torch_tensorrt/trt_aot.py", line 37, in <module>
    exp_program = torch_tensorrt.dynamo.trace(model, inputs)
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch_tensorrt/dynamo/aten_tracer.py", line 86, in trace
    exp_program = export(model, tuple(trace_inputs), constraints=constraints)
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/_export/__init__.py", line 556, in export
    return _export(
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/_export/__init__.py", line 596, in _export
    gm_torch_level = _export_to_torch_ir(
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/_export/__init__.py", line 517, in _export_to_torch_ir
    gm_torch_level, _ = torch._dynamo.export(
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 1226, in inner
    result_traced = opt_f(*args, **kwargs)
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1519, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1528, in _call_impl
    return forward_call(*args, **kwargs)
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 410, in _fn
    return fn(*args, **kwargs)
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1519, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1528, in _call_impl
    return forward_call(*args, **kwargs)
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 558, in catch_errors
    return callback(frame, cache_entry, hooks, frame_state)
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 148, in _fn
    return fn(*args, **kwargs)
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 402, in _convert_frame_assert
    return _compile(
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 610, in _compile
    guarded_code = compile_inner(code, one_graph, hooks, transform)
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/_dynamo/utils.py", line 221, in time_wrapper
    r = func(*args, **kwargs)
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 527, in compile_inner
    out_code = transform_code_object(code, transform)
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/_dynamo/bytecode_transformation.py", line 1028, in transform_code_object
    transformations(instructions, code_options)
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 497, in transform
    tracer.run()
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 2127, in run
    super().run()
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 751, in run
    and self.step()
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 714, in step
    getattr(self, inst.opname)(inst)
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 382, in inner
    raise exc.UserError(
torch._dynamo.exc.UserError: Dynamic control flow is not supported at the moment. Please use functorch.experimental.control_flow.cond to explicitly capture the control flow. For more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#cond-operands

from user code:
   File "/opt/torch_tensorrt/trt_aot.py", line 10, in forward
    if a.sum() > 3:

Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information

I changed the code accordingly as well:
exp_program = torch_tensorrt.dynamo.trace(model, inputs)
trt_model = torch_tensorrt.dynamo.compile(exp_program, inputs=inputs)

Hi @sayakpaul - thank you for the comments. Regarding the JIT workflow, this specific example was intended to illustrate how graph breaks are handled in torch.compile with the Torch-TRT backend. As you suggested, the model itself does not have sufficient operations to make the overhead of using a TRT engine worthwhile here. Additionally, this graph has an intentionally-added control-flow break, which means that a minimum of two TRT engines will be generated, further increasing the overhead for this small model. Since this model only has one computational operator per TRT block, the default options would not have converted these operators to TRT at all ("min_block_size": 1 overrides this default). As a result, I believe the metrics are roughly as expected here.
The AOT workflow is not expected to work for this specific model (the one with the conditional). It would only work for the model without the conditional, as here. This is because the Torch ATen tracer cannot handle this sort of Python control flow. In order to use the AOT workflow on a model like this, the code would need to be modified to either remove the conditional or use tracer-allowed conditionals, like torch.cond

Will try and get back. Thanks for your inputs!
Could you maybe provide an example for JIT where the improvements are evident? FWIW, we did try something with SD but the benefits were not there: https://github.com/huggingface/diffusers/issues/5564.
Probably we are missing something out?

Thanks for the follow-up! I will look into that sample you provided a bit more - it looks like the TRT conversion errored out for some reason, which would cause compilation to fall back to Torch eager. One other to try might be the Stable Diffusion version highlighted here.

Also, regarding the error cited in https://github.com/huggingface/diffusers/issues/5564, I retried your sample on the latest nightly version of the repository, with the following small code modifications, and it is working:
    elif run_compile and with_tensorrt:
        print("Run torch compile with TensorRT backend")
        pipe.unet = torch.compile(
            pipe.unet, fullgraph=True, backend="tensorrt", dynamic=False,
            options={"min_block_size": 1,
                     "debug": True,
                     "truncate_long_and_double": True,
                     "enabled_precisions": {torch.half}}
        )

Please let me know if this resolves the issue for you as well.

I retried your sample on the latest nightly version of the repository

Don't want to build from source. Will https://github.com/pytorch/TensorRT/pkgs/container/tensorrt%2Ftorch_tensorrt work here? (I will prune the previous container before mounting)

Yes, I believe the Docker container should also work. You could alternatively also use the following for installing the latest nightly distribution of Torch-TRT:
pip install --pre torch torch-tensorrt --extra-index-url --extra-index-url https://download.pytorch.org/whl/nightly/cu121

Hello @gs-olive! Apologies for the delay on my end.
I did run the SDXL sample script on the latest nightly container (ensuring that the previous one was pruned from the system before), but I am still not seeing the expected speedups:
With compilation: False, and TensorRT: False in 23988496.810 microseconds

With compilation: True, and TensorRT: False in 20895331.856 microseconds

With compilation: True, and TensorRT: True in 32650460.551 microseconds

Here is the trace:
Traceback (most recent call last):
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch_tensorrt/dynamo/backend/backends.py", line 95, in _pretraced_backend
    trt_compiled = compile_module(
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch_tensorrt/dynamo/_compiler.py", line 304, in compile_module
    trt_module = convert_module(
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch_tensorrt/dynamo/conversion/_conversion.py", line 34, in convert_module
    module_outputs = module(*torch_inputs)
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/fx/graph_module.py", line 736, in call_wrapped
    return self._wrapped_call(self, *args, **kwargs)
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/fx/graph_module.py", line 315, in __call__
    raise e
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/fx/graph_module.py", line 302, in __call__
    return super(self.cls, obj).__call__(*args, **kwargs)  # type: ignore[misc]
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1519, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1528, in _call_impl
    return forward_call(*args, **kwargs)
  File "<eval_with_key>.14", line 6, in forward
    view_14 = torch.ops.aten.view.default(permute_15, [8, -1, 640]);  permute_15 = None
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/_ops.py", line 513, in __call__
    return self._op(*args, **kwargs or {})
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/utils/_stats.py", line 20, in wrapper
    return fn(*args, **kwargs)
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/_subclasses/fake_tensor.py", line 1378, in __torch_dispatch__
    return self.dispatch(func, types, args, kwargs)
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/_subclasses/fake_tensor.py", line 1676, in dispatch
    r = func(*args, **kwargs)
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/_ops.py", line 513, in __call__
    return self._op(*args, **kwargs or {})
RuntimeError: view size is not compatible with input tensor's size and stride (at least one dimension spans across two contiguous subspaces). Use .reshape(...) instead.

Are you seeing anything different?
I am using a 80 GB A100 FYI.

Cool, let me know know :-) Looking forward to it. I will try to fix the SHA of the Docker container so that it's reproducible for the community.

@gs-olive a gentle ping if the container is available :)

Thanks for the message and apologies for the delay - I expect it to be ready at some point today or tomorrow and I will update this issue once it is ready.

Hi @sayakpaul - the container built against PyTorch 2.1.0 can be found here:
ghcr.io/pytorch/tensorrt/torch_tensorrt:release_2.1

This is what I get now:
Traceback (most recent call last):
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch_tensorrt/dynamo/backend/backends.py", line 95, in _pretraced_backend
    trt_compiled = compile_module(
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch_tensorrt/dynamo/_compiler.py", line 312, in compile_module
    trt_module = convert_module(
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch_tensorrt/dynamo/conversion/_conversion.py", line 33, in convert_module
    module_outputs = module(*torch_inputs)
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/fx/graph_module.py", line 678, in call_wrapped
    return self._wrapped_call(self, *args, **kwargs)
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/fx/graph_module.py", line 284, in __call__
    raise e
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/fx/graph_module.py", line 274, in __call__
    return super(self.cls, obj).__call__(*args, **kwargs)  # type: ignore[misc]
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "<eval_with_key>.14", line 6, in forward
    view_14 = torch.ops.aten.view.default(permute_15, [8, -1, 640]);  permute_15 = None
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/_ops.py", line 448, in __call__
    return self._op(*args, **kwargs or {})
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/utils/_stats.py", line 20, in wrapper
    return fn(*args, **kwargs)
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/_subclasses/fake_tensor.py", line 1250, in __torch_dispatch__
    return self.dispatch(func, types, args, kwargs)
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/_subclasses/fake_tensor.py", line 1541, in dispatch
    r = func(*args, **kwargs)
  File "/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/_ops.py", line 448, in __call__
    return self._op(*args, **kwargs or {})
RuntimeError: view size is not compatible with input tensor's size and stride (at least one dimension spans across two contiguous subspaces). Use .reshape(...) instead.

Ok - thanks for the update. Could you also try with transformers==4.33.2? I am trying to determine whether the transformers version may also be related to the issue.

Sorry for the delay. It still remains the same.
On Tue, Nov 7, 2023, 14:08 George S @.***> wrote:

Ok - thanks for the update. Could you also try with transformers==4.33.2?
I am trying to determine whether the transformers version may also be
related to the issue.
—
Reply to this email directly, view it on GitHub
https://github.com/pytorch/TensorRT/issues/2415#issuecomment-1798041142,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/AFPE2TDTFP2UC4A5RWSKM23YDHXOXAVCNFSM6AAAAAA6Q446XWVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTOOJYGA2DCMJUGI
.
You are receiving this because you were mentioned.Message ID:
@.***>

@gs-olive I have a chance to do the benchmarking for Stable Diffusion. Here are the timings:
With compilation: False, and TensorRT: False in 3.767 seconds
With compilation: True, and TensorRT: False in 3.045 seconds
With compilation: True, and TensorRT: True in 1.157 seconds

Env:

ghcr.io/pytorch/tensorrt/torch_tensorrt:release_2.1
transformers==4.33.2 diffusers==0.21.4
Code: https://gist.github.com/sayakpaul/2e4534b205841ccce085400b8c42da85

Will see with SDXL too.

Also, a bit suprising that with diffusers latest stable release timings are quite off:
With compilation: False, and TensorRT: False in 1.708 seconds
With compilation: True, and TensorRT: False in 1.447 seconds
With compilation: True, and TensorRT: True in 2.379 seconds

Are you able to reproduce these numbers on a 80GB A100?

I also extended the above the script to use SDXL and the timings (updated here):
With compilation: False, and TensorRT: False in 6.713 seconds
With compilation: True, and TensorRT: False in 6.417 seconds
With compilation: True, and TensorRT: True in 5.537 seconds

I have set transformers version to 4.33.2 and I am using diffusers latest.

Thanks for the follow-up. It seems that in the newer transformers and diffusers versions, some of the operators have changed, so our converter support is different, which can affect performance. We have issues filed to support these as well, so the performance can be more uniform across transformers and diffusers versions.


chore: remove commitlint

:tada: This PR is included in version 1.0.2 :tada:
The release is available on GitHub release
Your semantic-release bot :package::rocket:


Color of connection items in database navigator does not match connection color

System information:

OS Version: Windows 10 Pro 1809.17763.379
DBeaver version: 6.0.0
Java version:1.8.0_77
Additional extensions: None

Describe the problem you're observing:
First off DBeaver is great and thanks for providing such a feature rich tool!
DBeaver allows me to color code different connections. When executing a query it is very clear which environment that query will be executed in (the entire script window is colored) however the application also offers a "Database Navigator" with access to all connections and within the database navigator there is no color coding or indication of which environment you are working in (other than the top level connection name which is often not visible hidden). In my case I had selected my sandbox connection in the "Select Data Source" and so had a visual cue that I was operating on the sandbox but had inadvertently opened a production database connection in the Database Navigator. I used the context menu in the navigator to drop an index and it was unclear to me that I was operating against the production connection.

I would like to recommend that list items in the database navigator also present the connection color in some way to serve as a visual indicator (e.g. the open/close caret is colored or change the background color of the item to match the connection). Another option would be to color the confirmation window that prompts "Are you sure...?" and / or including the connection name in the prompt.

Generally I agree. We need better indication of prod connections.

verified

Downloaded and verified as well - thanks guys you rock!


Save GIF doesn't work

Hi,
tried to make some gif / videos. Viedeo worked OK so far (would be great to have sound optional too).
Saving as .gif didn't work. Was doing something (one can not see any progressbar - just can wait if it finishes or not, or ...?).
After 6 minutes (for a 25 seconds gif it stopped finally with : "Didn't work. Check your settings ..". But which settings? Didnt find any point where I can change any settings, presets, options?
Cheers

I have the same issue, but the "Didn't work. Check your settings" message happens immediately.
Could there be a bit more of an error message?


[build-script] Don't run other products tests in "only_long" mode.

What's in this pull request?
CC: @gribozavr @shahmishal
Regarding: https://github.com/apple/swift/commit/457f2b901caad14e2085ea0fe2c9b116b5df3520#commitcomment-17319839
build-script --long-test (without -t or -T) should run just the long test.
Since long tests is only in Swift, we should --skip-test-*** on other products.
WARNING: This change may hide current CI bot failure while testing Foundation:
https://ci.swift.org/job/oss-swift-incremental-RA-linux-ubuntu-14_04-long-test/70/console
Test Suite 'TestNSAffineTransform' started at 03:57:06.975
/home/buildnode/jenkins/workspace/oss-swift-incremental-RA-linux-ubuntu-14_04-long-test/swift/utils/build-script-impl: line 2040: 40395 Segmentation fault      LD_LIBRARY_PATH="${INSTALL_DESTDIR}"/"${INSTALL_PREFIX}"/lib/swift/:"${build_dir}/Foundation":"${XCTEST_BUILD_DIR}":${LD_LIBRARY_PATH} "${build_dir}"/TestFoundation/TestFoundation
/home/buildnode/jenkins/workspace/oss-swift-incremental-RA-linux-ubuntu-14_04-long-test/swift/utils/build-script: command terminated with a non-zero exit status 139, aborting

I don't understand how that could be happen only in "Long Test" bots, because the compiled product itself must be identical to other test modes...
We might want to investigate it first.
FWIW, utils/build-script --preset="buildbot_incremental_linux,long_test" runs without any problem in my Ubuntu 15.10 with current master.

Before merging this pull request to apple/swift repository:

[ ] Test pull request on Swift continuous integration.

@shahmishal has looked into the Foundation failure, and clearing the build directory has fixed it.
LGTM, but I'd like someone else to take another look -- this is a critical part of the code.
@shahmishal @modocache Could you take a look?

@shahmishal @modocache Ping?

@swift-ci Please test and merge

@shahmishal reviewed offline.

LGTM


Dashboard multiple linting error

Expected result:

No linting errors in all project files

Expected delivery time:
3 days
Comment: "Assign this to me", if you wish to pick this up

Assign this to me


Update dependency js-yaml version to >=3.13.1

Hi.
Pursuant to two advisories (https://npmjs.com/advisories/788 and https://npmjs.com/advisories/813 ), I would like to respectfully request that you update a dependency found in svgo as it relates to js-yaml. Apparently, if the version of js-yaml is updated to a version that is greater than or equal to 3.13.1 ( >=3.13.1 ), in an ideal world both of the aforementioned advisories should be cured.
Thanks in advance.
p.s. I ran an npm audit and the path field indicated that svgo is the vulnerable package:
gulp-cssnano > cssnano > postcss-svgo > svgo > js-yaml

The dependency has been updated in the package.json, however there has not been a new release created. As soon as a new release is made to npm, then it should be fixed.

The vulnerability seems to have been fixed in the latest versions of this library and all the way up through cssnano. I suspect the issue is that you're using gulp-cssnano in the first place, which is no longer maintained (https://github.com/ben-eb/gulp-cssnano), and will likely never be updated to use newer versions of cssnano.

Hey, @flakeparadigm! I wanted to say thank you for the advice. It was golden! My project(s) required a few changes in workflow and in the end the project(s) worked just fine, too. Ta!

The vulnerability seems to have been fixed in the latest versions of this library and all the way up through cssnano. I suspect the issue is that you're using gulp-cssnano in the first place, which is no longer maintained (https://github.com/ben-eb/gulp-cssnano), and will likely never be updated to use newer versions of cssnano.

@flakeparadigm do you have a recommendation on an alternative tool to replace gulp-cssnano?

@paoga87 No, I don't really have any recommendations as I've moved away from using gulp. Sorry.

@paoga87 No, I don't really have any recommendations as I've moved away from using gulp. Sorry.

No problem. I used gulp-clean-css instead and that solved my issues. Do you mind sharing what you're using now, instead of gulp?


ROX-17702: proto, storage and service interfaces

Description
Service template and mocks:

proto files: storage and API;
csv file HTTP handle;
sensor pipeline instrumentation.

Checklist

[x] Investigated and inspected CI test results
[x] Unit test and regression tests added
[x] Evaluated and added CHANGELOG entry if required
[ ] ~Determined and documented upgrade steps~
[ ] ~Documented user facing changes (create PR based on openshift/openshift-docs and merge into rhacs-docs)~

Testing Performed
Unit tests.

Current dependencies on/for this PR:

master

PR #7136   👈

This comment was auto-generated by Graphite.

/retest

I think that we should consider changing the name from usage to something more descriptive. What usage is this indicative of?

This name has been agreed in tough discussions, as an extensible wording and a better alternative to billing metrics.

/restart


How to get the loss for each sample ?

I want to write a custom loss function that weights each sample differently according the loss w.r.t each of them in the last epoch. How can I get the loss for each sample? I have tried the official example:
class LossHistory(keras.callbacks.Callback):
def on_train_begin(self, logs={}):
self.losses = []
def on_batch_end(self, batch, logs={}):
    self.losses.append(logs.get('loss'))

, but logs.get('loss') only returns an average loss of that batch. How can I get the loss for each sample?
Please make sure that the boxes below are checked before you submit your issue.
If your issue is an implementation question, please ask your question on StackOverflow or on the Keras Slack channel instead of opening a GitHub issue.
Thank you!

[ √] Check that you are up-to-date with the master branch of Keras. You can update with:
pip install git+git://github.com/keras-team/keras.git --upgrade --no-deps

[ √] Check that your version of TensorFlow is up-to-date. The installation instructions can be found here.

[ ×] Provide a link to a GitHub Gist of a Python script that can reproduce your issue (or just copy the script here if it is short).

I have same question. anyone knows?

I have same question, is it possible at all?


possible logic bug in diagrams_common

https://github.com/useblocks/sphinxcontrib-needs/blob/95e51277393ff32081db6c3b8195b1a7565b7995/sphinxcontrib/needs/diagrams_common.py#L94
just want to query the logic here. if self.options["show_legend"] is present and false, this returns False. If the value is present and True, then the expression also returns false because the is None assertion fails. If the value isn't present, then it defaults to False, and the expression returns False due the is None comparison. It returns False no matter what, right?
what are the intended semantics?

by the way, all of these dictionaries would be better as concrete classes...

show_legend is a flag, so it has no value and the key is only present, if set in rst-code.
So the logic is fine, but ugly:
.. needflow::
   :show_legend:

--> leads to -->

self.options['show_legend'] = None  #  That's how it is set by Sphinx.

In this case, the result should be True.
But for sure, it would be much easier to check for the key:
show_leggend = "show_legend'"in self.optins.keys()

Ah thanks for the explanation
Actually I think this could be just
show_legend = "show_legend" in self.options

Will open a PR to test


Cabal can't find source for Graphics/Rendering/Ombra/Backend/WebGL

Hello, I'm trying to build Ombra using ghcjs 0.2.1 with ghc 8.0.2. It builds fine up to commit 98b70899e250203c7a3873d9df06c18c17aee85d, but after that it complains about not having a cabal file for a handful of commits, then when the cabel file is added in a later commit the build fails with the following error:
    Cabal-simple_mPHDZzAJ_<IP_ADDRESS>_ghcjs-<IP_ADDRESS>08011_ghc-8.0.2: can't find
    source for Graphics/Rendering/Ombra/Backend/WebGL in .,
    .stack-work/dist/x86_64-linux/Cabal-<IP_ADDRESS>_ghcjs/build/autogen

I readded the WebGL backend with 6044ba8, did you try to build it after that?
By the way I just realized that two modules were missing from this repo (not the WebGL ones), with the last commit it should work fine.

It looks like those two missing modules were the culprits. It's building fine with the latest commit.
Thanks!


V0.5.0

[x] Make build process of Caddy deterministic (#158)
[ ] Ensure Stream Enrich resolver uses cacheTtl of one (#137)
[ ] Update base Ubuntu AMI being used (#147)
[ ] Bump Iglu Server to 0.3.0 (#152)
[ ] Add support for external Postgres for Iglu Server (#154)
[ ] Add gitter badge (#157)
[ ] Add publication of image for Google Compute Engine (#36)
[ ] Run each app as a separate Docker container (#22)
[ ] Assemble the apps using Docker Compose (#23)
[ ] Add CI/CD (#142)
[ ] Add ami-factory credentials to .travis.yml (#143)
[ ] Expose nsqadmin UI (#144)
[ ] Add log rotation for internal services to prevent out of disk issues (#151)
[ ] Fix typo in the readme (#146)
[ ] Update example user_data.sh (#148)

release process TO-DO

[ ] blog post
[ ] wiki update
[ ] VERSION
[ ] CHANGELOG
[ ] README update (copyright years & release badge)

Ben will be reviewer as Snowplow Mini falls under his purview.

yes, feel free to ping me once you get more done @oguzhanunlu


sel fails confusingly or silently when a dimension name matches an optional argument

Given that many Xarray methods accept dict or keyword arguments, this may effect other methods. That said, here is a minimal example with sel.
Minimal example
If I want to select based on a dimension named method, I cannot because Xarray thinks method is the method argument to sel:
>>> da1 = xr.DataArray(range(3), dims=['method'], coords={'method': range(3)})
>>> da1
<xarray.DataArray (method: 3)>
array([0, 1, 2])
Coordinates:
  * method   (method) int64 0 1 2
>>> da1.sel(method=0)
...
TypeError: ``method`` must be a string

And if the method dimension has string labels, this fails silently:
>>> da2 = xr.DataArray(range(3), dims=['method'], coords={'method': list('abc')})
>>> da2.sel(method='a')
<xarray.DataArray (method: 3)>
array([0, 1, 2])
Coordinates:
  * method   (method) <U1 'a' 'b' 'c'

Expected Output
I think raising a ValueError and providing a clarifying error message is the right call, but maybe the maintainers have a different opinion. At the very least, it seems like Xarray could note that the DataArray instance has a dimension that matches one of the function arguments and ask the user to use dict-like arguments if required.
I imagine a general error handling function could be written to check this for any function and DataArray pair.

Yes, agree this isn't ideal. One of the great qualities of xarray is the code we write in exploration also works in production, and this behavior violates some of that.
I'd vote for a warning as getting some of the way to a solution
FWIW passing a dict to these methods will work, if you want complete dependability

What if I create a PR with a function in utils that takes an array and a list of keyword arguments, and warns the user if the array has any of the keyword arguments as dimensions. The warning can suggest using a dict.

+1. Though I prefer raising a more informative over raising a warning.

+1. Though I prefer raising a more informative over raising a warning.

Raising an error?

Yes. fat fingers on mobile :)

Yes I agree, on reflection

What if I create a PR with a function in utils that takes an array and a list of keyword arguments, and warns the user if the array has any of the keyword arguments as dimensions. The warning can suggest using a dict.

This sounds great to me!
I have also been bitten by this issue, particularly with the same dimension name method.

This could be an addition to the either_dict_or_kwargs helper function:
https://github.com/pydata/xarray/blob/df259331c5b66088f67738338e0b1b3f940e09c2/xarray/core/utils.py#L248

This could be an addition to the either_dict_or_kwargs helper function.

Currently, either_dict_or_kwargs doesn't have access to the DataArray instance's dims or the function's keyword arguments; are you suggesting that either_dict_or_kwargs be modified to take these? I do like this approach. Otherwise, I'd have to add a second utility function check everywhere either_dict_or_kwargs is used, and it'd be easy to forget going forward.

Yes add another kwarg


Add multiple Fields in Fieldgroups between two fields

Hello All,
I have table 27 and for that fieldgroups
DropDown : No.,Description,Base Unit of Measure,Unit Price
I need to create tableextension with below field groups in which "No. 2" and "Shelf No." are added after the No. field. How can we do that ?
No.,"No. 2","Shelf No.",Description,Base Unit of Measure,Unit Price

Hi @jigneshdhandhanav,
Thanks for your feedback.
That is currently not possible, but we will add it to backlog of features

Please we need this. Implement "addafter" for fieldgroups, not just "addlast".

I would also be very happy about the implementation of this feature.
Is there an implementation planned for this in the near future?
I assume that there is no WorkAround for this problem at the current time either, right?
Best regards

I would also be very happy about the implementation of this feature.
Is there an implementation planned for this in the near future?
I assume that there is no WorkAround for this problem at the current time either, right?
Best regards

Please go ahead and post this to our Ideas forum at https://aka.ms/BusinessCentralideas, or vote up the idea if its already there. We're constantly monitoring top Ideas and will consider them for a future release.

Please go ahead and post this to our Ideas forum at https://aka.ms/BusinessCentralideas, or vote up the idea if its already there. We're constantly monitoring top Ideas and will consider them for a future release.


HAWQ-1131. Fix error message difference between Orca and Planner

In ORCA, due to the way exception handled previously we do warning first
and then later print error referring that message. In this commit, we
enhanced the exception handling so we just print a single error message.
Removed 'PQO unable to generate a plan' or 'Aborting PQO plan
generation' message and make the error message as close as the planner.
Updated error message with filename and line number from where the exception
is raised.
Updated regression test expected output files

Patches are ported the following commits from GPDB:
https://github.com/greenplum-db/gpdb/commit/4eb5db7a6bc8ac3faeca761193dc37e2c70e1ea3
https://github.com/greenplum-db/gpdb/commit/d41ebde141104dd3db6fa29ac46b4096ee7d4825
Author: Karthikeyan Jambu Rajaraman<EMAIL_ADDRESS>

+1

+1

Pushed to master:
https://github.com/apache/incubator-hawq/commit/56b44f8595b4ba1ac9c49b775d5110e9be1275b2
https://github.com/apache/incubator-hawq/commit/31ea7d08ede7d5bc3108454731ba11bdcf1092d1
https://github.com/apache/incubator-hawq/commit/fed12f1a57ac64a8c7cab867f31b2930dddc2744


Bug - Rviz GLSL

Ubuntu 22.04
Ros2 Humble
Hi, Many thanks for the framework very clear, it helped me alot understanding SLAM.
unfortently i keep getting a error once i change the Slam Toolbox to work with localization, a saved map is not loading, i am keeping getting a new map visualization, moreover, after i changed RViz to work with map i get the follow error, please any idea, i did not find hint in Google
"
Vertex Program:rviz/glsl120/indexed_8bit_image.vert Fragment Program:rviz/glsl120/indexed_8bit_image.frag GLSL link result :
[rviz2-1] active samplers with a different type refer to the same texture image unit
"

Please don't open up the same issue on multiple repositories; it causes us to have to do more work to track and close them.  Closing this one.

Ok, many thanks!!
Sorry for the trouble..


The custom card requires an entity but I cant figure out what it should be

I tried to use this card but it (apparently) requires an entity to be defined, but which one?

By accident I've omitted the entity setting from the readme. Just add
entity: climate.ca350_climate

in your case this should probably be climate.flexit_nordic.

Adding an entity like this gave me an error "unknown entity". However in the latest version it does not, so this issue can be closed. I have other issues though, see additional reports.


Added watchr to fix noseglid/atom-build#277

Added watchr to fix https://github.com/noseglid/atom-build/issues/277

Sidenote: Funny how Travis passes these checks, while it clearly fails?

Now it works for Linux and Mac, but it doesn't for Windows. This looks like a problem with watchr...
@noseglid Should I try fixing watchr, add an os-check before using watchr, or search for another library?

There's definitely something weird going on with windows.
Maybe it'd be better to just do the OS check and use watchFile on windows and try to work out the caching issue?

Watchr fixes the caching issue, so that is not the matter anymore. I'll add an OS check

One could question the use of watchr if we do OS corrections ourselves. After all it's an additional dependency which increases size and load times.
Maybe we could scavange the cache clearing solution from watchr and copy that?

To me, watchr looks a bit complicated. If someone else would like to search for the responsible code, please. In the meantime, I'll try to get this to work. Maybe later someone will be able to speed up the loading progress, but for now, I think fixing this bug is more important.

I'm sorry for taking so long to respond to this. I don't feel comfortable merging this with all these unknowns:

We don't know why watchr doesn't work on windows.
We don't know why watchr doesn't crash on linux.

I really feel like we need to sort this out so we can hopefully avoid the watchr dependency, or use only watchr on all platforms.
I'll help you look into this as soon as I have some time if you don't find anything. I'm very eager to find a fix for this issue - so it's not like I don't want to fix this.

Hey! Sorry I was inactive this week. I will see which functions watchr uses here, and try to mimic those in build. Still don't have much time today, but I'll keep it in my mind.

Reading all other commits. Doesn't https://github.com/noseglid/atom-build/pull/372 fix this exact issue?

Exactly. That's my alternative solution so we don't need watchr. Question is, does it solve the crash for you?

As stated on that commit, it indeed does. Closing this issue